Datasets:

debajyotidasgupta

/

dblp_2022_2023

like 0

Title: A Survey on Rural Internet Connectivity in India. Abstract: Rural connectivity is widely research topic for several years. In India, around 70% of the population have poor or no connectivity to access digital services. Different solutions are being tested and trialled around the world, especially in India. They key driving factor for reducing digital divide is exploring different solutions both technologically and economically to lower the cost for the network deployments and improving service adoption rate. In this survey, we aim to study the rural connectivity use-cases, state of art projects and initiatives, challenges, and technologies to improve digital connectivity in rural parts of India. The strengths and weakness of different technologies which are being tested for rural connectivity is analyzed. We also explore the rural use-case of 6G communication system which would be suitable for rural Indian scenario.

Title: "There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making. Abstract: Automated decision systems (ADS) are increasingly used for consequential decision-making. These systems often rely on sophisticated yet opaque machine learning models, which do not allow for understanding how a given decision was arrived at. In this work, we conduct a human subject study to assess people's perceptions of informational fairness (i.e., whether people think they are given adequate information on and explanation of the process and its outcomes) and trustworthiness of an underlying ADS when provided with varying types of information about the system. More specifically, we instantiate an ADS in the area of automated loan approval and generate different explanations that are commonly used in the literature. We randomize the amount of information that study participants get to see by providing certain groups of people with the same explanations as others plus additional explanations. From our quantitative analyses, we observe that different amounts of information as well as people's (self-assessed) AI literacy significantly influence the perceived informational fairness, which, in turn, positively relates to perceived trustworthiness of the ADS. A comprehensive analysis of qualitative feedback sheds light on people's desiderata for explanations, among which are (i) consistency (both with people's expectations and across different explanations), (ii) disclosure of monotonic relationships between features and outcome, and (iii) actionability of recommendations.

Title: Recent Advances in Computational Optimization: Results of the Workshop on Computational Optimization WCO 2015 Abstract: This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization 2015. It presents recent advances in computational optimization. The volume includes important real life problems like parameter settings for controlling processes in bioreactor, control of ethanol production, minimal convex hill with application in routing algorithms, graph coloring, flow design in photonic data transport system, predicting indoor temperature, crisis control center monitoring, fuel consumption of helicopters, portfolio selection, GPS surveying and so on. It shows how to develop algorithms for them based on new metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming and others. This research demonstrates how some real-world problems arising in engineering, economics, medicine and other domains can be formulated as optimization problems.

Title: Reduction from Non-Unique Games to Boolean Unique Games. Abstract: We reduce the problem of proving a "Boolean Unique Games Conjecture" (with gap 1-delta vs. 1-C*delta, for any C> 1, and sufficiently small delta>0) to the problem of proving a PCP Theorem for a certain non-unique game. In a previous work, Khot and Moshkovitz suggested an inefficient candidate reduction (i.e., without a proof of soundness). The current work is the first to provide an efficient reduction along with a proof of soundness. The non-unique game we reduce from is similar to non-unique games for which PCP theorems are known. Our proof relies on a new concentration theorem for functions in Gaussian space that are restricted to a random hyperplane. We bound the typical Euclidean distance between the low degree part of the restriction of the function to the hyperplane and the restriction to the hyperplane of the low degree part of the function.

Title: Correlation Detection in Trees for Planted Graph Alignment. Abstract: We consider alignment of sparse graphs, which consists in finding a mapping between the nodes of two graphs which preserves most of the edges. Our approach is to compare local structures in the two graphs, matching two nodes if their neighborhoods are 'close enough': for correlated Erd\H{o}s-R\'enyi random graphs, this problem can be locally rephrased in terms of testing whether a pair of branching trees is drawn from either a product distribution, or a correlated distribution. We design an optimal test for this problem which gives rise to a message-passing algorithm for graph alignment, which provably returns in polynomial time a positive fraction of correctly matched vertices, and a vanishing fraction of mismatches. With an average degree $\lambda = O(1)$ in the graphs, and a correlation parameter $s \in [0,1]$, this result holds with $\lambda s$ large enough, and $1-s$ small enough, completing the recent state-of-the-art diagram. Tighter conditions for determining whether partial graph alignment (or correlation detection in trees) is feasible in polynomial time are given in terms of Kullback-Leibler divergences.

Title: eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference Abstract: As the most significant data source in smart mobility systems, GPS trajectories can help identify user travel mode. However, these GPS datasets may contain users' private information (e.g., home location), preventing many users from sharing their private information with a third party. Hence, identifying travel modes while protecting users' privacy is a significant issue. To address this challenge, we use federated learning (FL), a privacy-preserving machine learning technique that aims at collaboratively training a robust global model by accessing users' locally trained models but not their raw data. Specifically, we designed a novel ensemble-based Federated Deep Neural Network (eFedDNN). The ensemble method combines the outputs of the different models learned via FL by the users and shows an accuracy that surpasses comparable models reported in the literature. Extensive experimental studies on a real-world open-access dataset from Montréal demonstrate that the proposed inference model can achieve accurate identification of users' mode of travel without compromising privacy.

Title: Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control. Abstract: This paper considers over-the-air federated learning (OTA-FL). OTA-FL exploits the superposition property of the wireless medium, and performs model aggregation over the air for free. Thus, it can greatly reduce the communication cost incurred in communicating model updates from the edge devices. In order to fully utilize this advantage while providing comparable learning performance to conventional federated learning that presumes model aggregation via noiseless channels, we consider the joint design of transmission scaling and the number of local iterations at each round, given the power constraint at each edge device. We first characterize the training error due to such channel noise in OTA-FL by establishing a fundamental lower bound for general functions with Lipschitz-continuous gradients. Then, by introducing an adaptive transceiver power scaling scheme, we propose an over-the-air federated learning algorithm with joint adaptive computation and power control (ACPC-OTA-FL). We provide the convergence analysis for ACPC-OTA-FL in training with non-convex objective functions and heterogeneous data. We show that the convergence rate of ACPC-OTA-FL matches that of FL with noise-free communications.

Title: On Integrating the Number of Synthetic Data Sets m into the a priori Synthesis Approach Abstract: The synthesis mechanism given in [4] uses saturated models, along with overdispersed count distributions, to generate synthetic categorical data. The mechanism is controlled by tuning parameters, which can be tuned according to a specific risk or utility metric. Thus expected properties of synthetic data sets can be determined analytically a priori, that is, before they are generated. While [4] considered the case of generating m = 1 data set, this paper considers generating m > 1 data sets. In effect, m becomes a tuning parameter and the role of m in relation to the risk-utility trade-off can be shown analytically. The paper introduces a pair of risk metrics, tau(3)(k, d) and tau(4)(k, d), that are suited to m > 1 data sets; and also considers the more general issue of how best to analyse m > 1 categorical data sets: average the data sets pre-analysis or average results post-analysis. Finally, the methods are demonstrated empirically with the synthesis of a constructed data set which is used to represent the English School Census.

Title: What Kinds of Connectives Cause the Difference Between Intuitionistic Predicate Logic and the Logic of Constant Domains? Abstract: It is known that intuitionistic Kripke semantics can be generalized so that it can treat arbitrary propositional connectives characterized by truth functions. Our previous work studied how the choice of propositional connectives changes the relation between classical and intuitionistic propositional logics, and showed that the set of valid sequents in intuitionistic propositional logic coincides with the set of valid sequents in classical propositional logic if and only if the connectives we choose are all monotonic. In this paper, we extend the generalized Kripke semantics to first-order logic and study how the choice of connectives changes the relation between intuitionistic predicate logic and the logic of constant domains. In the case of the usual connectives $$\lnot $$ , $$\wedge $$ , $$\vee $$ , and $$\rightarrow $$ , it is well-known that the presence of disjunction causes the difference between these two logics. Generalizing this result to general propositional connectives, we give a simple necessary and sufficient condition for the set of valid sequents in intuitionistic predicate logic to coincide with the set of valid sequents in the logic of constant domains.

Title: Evolutionary Optimization of Spiking Neural P Systems for Remaining Useful Life Prediction Abstract: Remaining useful life (RUL) prediction is a key enabler for predictive maintenance. In fact, the possibility of accurately and reliably predicting the RUL of a system, based on a record of its monitoring data, can allow users to schedule maintenance interventions before faults occur. In the recent literature, several data-driven methods for RUL prediction have been proposed. However, most of them are based on traditional (connectivist) neural networks, such as convolutional neural networks, and alternative mechanisms have barely been explored. Here, we tackle the RUL prediction problem for the first time by using a membrane computing paradigm, namely that of Spiking Neural P (in short, SN P) systems. First, we show how SN P systems can be adapted to handle the RUL prediction problem. Then, we propose the use of a neuro-evolutionary algorithm to optimize the structure and parameters of the SN P systems. Our results on two datasets, namely the CMAPSS and new CMAPSS benchmarks from NASA, are fairly comparable with those obtained by much more complex deep networks, showing a reasonable compromise between performance and number of trainable parameters, which in turn correlates with memory consumption and computing time.

Title: Numerical analysis of DDFV schemes for semiconductors energy-transport models Abstract: This article addresses the construction and the numerical analysis of implicit Discrete Duality Finite Volume schemes for a semiconductors' energy-transport model. The considered energy-transport model is presented in its scaled version as well as in a symmetrized form which involves entropy variables. We propose implicit in time numerical schemes for both the original system and its symmetrized form. As in the continuous framework, the numerical analysis is based on the reformulation of the PDE system using the set of entropic variables. The equivalence of both schemes allows to establish a discrete entropy inequality and consequently an priori estimates. As a by-product, existence of solutions to the schemes is proved by means of a Leray-Schauder argument. Numerical evidences allow to compare the performances of both schemes on the test case of a 2D ballistic diode.

Title: MMGET: a Markov model for generalized evidence theory Abstract: In real life, lots of information merge from time to time. To appropriately describe actual situations in open world, a generalized evidence theory based on Dempster–Shafer evidence theory is designed. However, everything occurs in sequence and owns some underlying relationships with each other which are missing in this theory. To further embody the details of information and better conform to situations of real world, a Markov model is introduced into the generalized evidence theory which helps extract complete information volume from evidence provided. More specially, the Markov model investigates influences on properties of information given which are brought by dynamic process of transitions among different incidents and provides new solutions in evidence combination, distance measure, reliability measure, and certainty measure. Besides, some numerical examples are offered to verify the correctness and rationality of the proposed method in these relevant aspects.

Title: Numerical Analysis & No Regrets. Special Issue Dedicated to the Memory of Francisco Javier Sayas (1968-2019) Abstract: This is the preface of a special issue dedicated to the memory of Francisco Javier Sayas who passed away on April 2, 2019. The articles reflect Sayas' main research interests in the numerical analysis of partial differential equations, containing contributions on the scattering and propagation of acoustic and electromagnetic waves, and the analysis of discontinuous Galerkin schemes, boundary element methods, and coupled schemes. We discuss the main contributions of Sayas and give an overview of the results covered by this special issue.

Title: Probing Classifiers: Promises, Shortcomings, and Advances. Abstract: Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. The basic idea is simple -- a classifier is trained to predict some linguistic property from a model's representations -- and has been used to examine a wide variety of models and properties. However, recent studies have demonstrated various methodological weaknesses of this approach. This article critically reviews the probing classifiers framework, highlighting shortcomings, improvements, and alternative approaches.

Title: CepGen - A generic central exclusive processes event generator for hadron-hadron collisions Abstract: We present an event generator for the simulation of central exclusive processes in hadron-hadron reactions. Among others, it implements the two-photon production of lepton pairs previously introduced in LPAIR. As a proof of principle, we show that the two approaches are numerically consistent. The k(T)-factorized description of this process is also handled, along with the two-photon production of a quark, or a W-+/- gauge boson pair. This toolbox may be used as a common framework for the definition of many other processes following this approach. Additionally, photoproduction and other photon induced processes are also considered, or being implemented. Program summary Program title: CepGen CPC Library link to program files: https://doi.org/10.17632/24jg665g65.1 Developer's repository link: https://github.com/cepgen/cepgen Licensing provisions: GNU General Public License 3 Programming language: C++/Python External routines/libraries: GSL [1] for MC integration and histogramming, optional wrappers for LHAPDF [2] for the partonic proton structure functions evaluation, or ROOT [3], Delphes [4] for the output treatment. Nature of problem: The simulation of central exclusive, and in particular two-photon induced processes is becoming increasingly topical given its potential source of contamination for electroweak studies and resonance searches at LHC and future colliders. However, most of simulation tools available are only accounting for the production of photons collinear to the incoming proton beams. Legacy codes such as LPAIR, have however shown their effectiveness in predicting such processes at LHC energies. Unfortunately, they are barely maintained nor maintainable with modern computing infrastructures. Solution method: CepGen provides a modern implementation of legacy photon-induced matrix elements (gamma gamma -> l(+)l(-), and W+ W-, with more to be added), including standard e(+) e(-), or ppbeams (both elastic and dissociative final beam states for the latter). For the modern implementation of LPAIR, it inherits from the former fine treatment of the low-|t| region accounting for a large fraction of the cross section. It also introduces a general wrapping framework to define new photon-induced and diffractive processes, either in C++ or in Fortran. This wrapper provides the k(T) factorization procedure for 2 -> 4 process computation, and a highly flexible 2 -> N process placeholder. A user-defined taming of the matrix element is also included to study the effect of kinematic variables-dependent survival factors observed experimentally. Additional comments including restrictions and unusual features: Depending on the complexity of the central process, memory and CPU time. Currently event generation runs only in single-threaded mode, development ongoing to support multi-threading. (C) 2021 The Author. Published by Elsevier B.V.

Title: Fostering growth orientations in students’ identities as knowledge builders Abstract: Fast-moving changes to society as part of the digital age are posing new educational challenges that require students to be flexible, adaptive, and growth-oriented. Humanistic knowledge building communities (HKBCs) are a growth promoting pedagogy, suitable to address these challenges. Yet, the way that students’ identities as knowledge builders are transformed remains undertheorized. In this study, we rise above existing frameworks of fixedness versus fluidity to elucidate how students develop growth orientations. Using a grounded approach, we examined a graduate course, coding 322 relevant utterances that were expressed by the course participants over the semester. This resulted in a five dimensional framework of fixedness versus growth that was used to describe the personal transformation of students within the HKBC. The changes that students made over time were shown to occur at statistically significant levels. This study suggests that learning communities should focus on the complementary nature of collective idea-advancement and personal growth promotion if they are to address the challenges of preparing students for life in a rapidly changing world.

Title: Implementing learning analytics in wiki-supported collaborative learning in secondary education: A framework-motivated empirical study Abstract: Learning analytics (LA) and group awareness tools are regarded as top priorities for research in the field of computer-supported collaborative learning. As such, this study investigated whether LA-enabled group awareness information facilitates wiki-supported collaborative learning in secondary education. We proposed an analytic framework of measures for assessing collaboration quality in a wiki-based collaborative learning environment, covering student contribution, participation, transactivity, and social dynamics. Based on this framework, we designed an LA-enabled group awareness tool, Wikiglass, for use by both teachers and students in K-12 schools for visualizing statistics of students’ input and interactions on wikis at the class, group, and individual levels. Adopting a naturalistic design, this study allowed teachers and students to decide whether and how often to use the tool. System logs from wikis and Wikiglass and interview data were collected from 440 students and six teachers involved in semester-long wiki-supported group inquiry projects in a secondary school. Regression analyses of quantitative data and thematic content analysis of interview responses showed relationships between the frequencies of teachers’ and students’ use of Wikiglass and measures of students’ collaboration quality at both the individual and group levels. These results indicate that teachers’ scaffolding, students’ collaboration styles, and ethical issues must all be considered when implementing collaborative learning approaches for secondary education. We also discuss the implications of our results for research and practice in the application of LA and group awareness tools for enhancing wiki-supported collaborative learning in K-12 education.

Title: iTalk–iSee: A participatory visual learning analytical tool for productive peer talk Abstract: Productive peer talk moves have a fundamental role in structuring group discussions and promoting peer interactions. However, there is a lack of comprehensive technical support for developing young learners’ skills in using productive peer talk moves. To address this, we designed iTalk–iSee, a participatory visual learning analytical tool that supports students’ learning and their use of productive peer talk moves in dialogic collaborative problem-solving (DCPS). This paper discusses aspects of the design of iTalk–iSee, including its underlying theoretical framework, visualization, and the learner agency it affords. Informed by the theory of Bakhtinian dialogism, iTalk–iSee maps productive peer talk moves onto learning goals in DCPS. It applies well-established visualization design principles to connect with students, hold and direct their attention, and enhance their understanding. It also follows a three-step (code → visualize → reflect) macro-script to strengthen students’ agency in analyzing and interpreting their talk. This paper also discusses the progressive modifications of iTalk–iSee and evaluates its usability in a field study. We present the implications of essential design features of iTalk–iSee and the challenges of using it (relating to, for example, teacher guidance, data collection, transcription, and coding). We also provide suggestions and directions for future research.

Title: Shared meaning-making in online intergroup discussions around sensitive topics Abstract: Shared meaning-making across differences in today’s polarized society requires a socio-political perspective toward conceptualizing and operationalizing collaborative competence. Thus, there is a pressing need for socio-political pedagogies and designs in CSCL to empower students as cultural-historical agents who can communicate and work effectively across different communities. As the initial steps of our larger efforts to conceptualize and operationalize a model of multicultural collaborative competence (MCC), we explore communication patterns associated with productive and dysfunctional shared meaning-making around difficult topics related to identity (e.g., race, gender) during intergroup dialogues in a CSCL context. We also examine how our preexisting, general model of collaborative competence (GCC) aligns with these communication patterns to explore (1) whether GCC is robust enough to capture the socio-political dynamics of difficult dialogues and (2) the ways in which we could modify it to better address the tensions between GCC and MCC goals. We collected the discussion transcripts of four three-person teams over two-time points from an undergraduate Multicultural Psychology course. We first conducted thematic and cross-case analyses to identify the communication patterns and behaviors associated with productive and dysfunctional shared meaning-making processes in the context of difficult dialogues (i.e., MCC). We then employed another set of cross-case analyses to examine the relationship between the multicultural collaborative competencies (MCC) and general collaborative competencies (GCC). We found four main communication patterns associated with MCC: (1) grounding with narratives and aims, (2) exploring differences and commonalities of narratives/perspectives, (3) critical reflection of diverse narratives/perspectives, and (4) providing emotional support to team members. We also found that although the GCC does not cover these communication patterns and associated behaviors, there were some overlaps between the sophistication of multiculturally competent communication patterns and collaboration quality as defined by the GCC.

Title: The general goodness-of-fit tests for correlated data Abstract: Analyzing correlated data by goodness-of-fit type tests is a critical statistical problem in many applications. A unified framework is provided through a general family of goodness of-fit tests (GGOF) to address this problem. The GGOF family covers many classic and newly developed tests, such as the minimal p-value test, Simes test, the GATES, onesided Kolmogorov-Smirnov type tests, one-sided phi-divergence tests, the generalized Higher Criticism, the generalized Berk-Jones, etc. It is shown that the omnibus test that automatically adapts among GGOF statistics for given data, i.e., the GGOF-O, is still contained in the GGOF family and is computationally efficient. For analytically controlling the type I error rate of any GGOF tests, exact calculation is deduced under the Gaussian model with positive equal correlations. Based on that, the effective correlation coefficient (ECC) algorithm is proposed to address arbitrary correlations. Simulations are used to explore how signal and correlation patterns jointly influence typical GGOF tests' statistical power. The GGOF-O is shown robustly powerful across various signal and correlation patterns. As demonstrated by a study of bone mineral density, the GGOF framework has good potential for detecting novel disease genes in genetic summary data analysis. Computational tools are available in the R package SetTest on the CRAN. (C) 2021 Elsevier B.V. All rights reserved.

Title: Robust Regression With Compositional Covariates Abstract: Many biological high-throughput datasets, such as targeted amplicon-based and metagenomic sequencing data, are compositional. A common exploratory data analysis task is to infer robust statistical associations between high-dimensional microbial compositions and habitat- or host-related covariates. To address this, a general robust statistical regression framework RobRegCC (Robust Regression with Compositional Covariates) is proposed, which extends the linear log-contrast model by a mean shift formulation for capturing outliers. RobRegCC includes sparsity-promoting convex and non-convex penalties for parsimonious model estimation, a data-driven robust initialization procedure, and a novel robust cross-validation model selection scheme. The procedure is implemented in the R package robregcc. Extensive simulation studies show the RobRegCC's ability to perform simultaneous sparse log-contrast regression and outlier detection over a wide range of settings. To demonstrate the seamless applicability of the workflow to real data, the gut microbiome dataset from HIV patients are analyzed and robust associations between a sparse set of microbial species and host immune response from soluble CD14 measurements are inferred. (C) 2021 The Author(s). Published by Elsevier B.V.

Title: Model-Based Sparse Coding Beyond Gaussian Independent Model Abstract: Sparse coding aims to model data vectors as sparse linear combinations of basis elements, but a majority of related studies are restricted to continuous data without spatial or temporal structure. A new model-based sparse coding (MSC) method is proposed to provide an effective and flexible framework for learning features from different data types: continuous, discrete, or categorical, and modeling different types of correlations: spatial or temporal. The specification of the sparsity level and how to adapt the estimation method to large-scale studies are also addressed. A fast EM algorithm is proposed for estimation, and its superior performance is demonstrated in simulation and multiple real applications such as image denoising, brain connectivity study, and spatial transcriptomic imaging. (C) 2021 Elsevier B.V. All rights reserved.

Title: Power Analysis And Type I And Type Ii Error Rates Of Bayesian Nonparametric Two-Sample Tests For Location-Shifts Based On The Bayes Factor Under Cauchy Priors Abstract: Hypothesis testing is a central statistical method in the biomedical sciences. The ongoing debate about the concept of statistical significance and the reliability of null hypothesis significance tests (NHST) and p-values has brought the advent of various Bayesian hypothesis tests as possible alternatives, which often employ the Bayes factor. However, careful calibration of the prior parameters is necessary for the type I error rates or power of these alternatives to be any better. Also, the availability of various Bayesian tests for the same statistical problem leads to the question which test to choose based on which criteria. Recently proposed Bayesian nonparametric two-sample tests are analyzed with regard to their type I error rates and power to detect an effect. Results show that approaches vary substantially in their ability to control the type I and II errors, and it is shown how to select the prior parameters to balance power and type I error control. This allows for prior elicitation and power analyses based on objective criteria like type I and II error rates even when conducting a Bayesian nonparametric two-sample test. Also, it is shown that existing nonparametric Bayesian two-sample tests are adequate only to test for location-shifts. Together, the results provide guidance how to perform a nonparametric Bayesian two-sample test while simultaneously improving the reliability of research. (C) 2021 Elsevier B.V. All rights reserved.

Title: Model Averaging For Linear Mixed Models Via Augmented Lagrangian Abstract: Model selection for linear mixed models has been a focus of recent research in statistics. Yet, the method of model averaging has been sparsely explored in this context. A weight finding criterion for model averaging of linear mixed models is introduced, as well as its implementation for the programming language R. Since the optimization of the underlying criterion is non-trivial, a fast and robust implementation of the augmented Lagrangian optimization technique is employed. Furthermore, the influence of the weight finding criterion on the resulting model averaging estimator is illustrated through simulation studies and two applications based on real data. (C) 2021 Elsevier B.V. All rights reserved.

Title: Dtmr: An Adaptive Distributed Tree-Based Multicast Routing Protocol For Vehicular Networks Abstract: The modern driver-assistance and autonomous technologies in vehicles increase the ease of driving vehicles. The increased vehicle usage increases the requirement of efficient group communication between the vehicles to support the safety as well as the non-safety applications in Vehicular Ad-hoc NETworks (VANET). In VANET, multicast communication is mostly preferred for the effective utilization of computational resources and the attainment of the Quality of Service (QoS). To implement an efficient multicast communication, we need to build and retain a multicast tree. Due to the high mobility and rapid topology changes, the formation and maintenance of a multicast tree are challenging in VANET. Existing multicast communication techniques are either centralized or location-based flooding and suffer from tree reconfiguration overhead. In this paper, a novel Distributed Treebased Multicast Routing (DTMR) algorithm is proposed. During the link failure, the connection is switched to a predetermined and highly stable guardian node. Further, the tree fragmentation and the rejoining delay is reduced in VANET using the proposed algorithm, DTMR. From the simulation results, it is evident that the DTMR achieves a significant performance improvement against the existing multicast techniques such as Distributed Time-limited Reliable Broadcast Incremental Power Strategy (DTRBIP) and Energy Efficient Multicast routing protocol based on Software Defined Networks and Fog computing for Vehicular networks (EEMSFV) in terms of delay, packet loss ratio, scalability, and reliability. In terms of end-to-end delay, packet loss rate, and reliability, the DTMR outperforms the existing DTRBIP and EEMSFV protocols. DTMR achieves 94-96% reliability in highway scenarios and 96-99% reliability in urban environments. Further, as a proof of concept, the proposed, DTMR is implemented and analyzed in real-time. It is found that the real-time experimental results are on par with the simulation results. Hence, the proposed DTMR achieves network stability and reliability in real-time.

Title: Optimized Routing Technique For Iot Enabled Software-Defined Heterogeneous Wsns Using Genetic Mutation Based Pso Abstract: Now a days, emerging trends in the field of wireless sensor networks (WSNs) tend to work on more complex scenarios and flexible network models as the conventional WSN systems that are based on a classical arrangement of sensors. Generally, these networks have different limitations such as control node election, data aggregation, load balancing during data collection etc. The load balancing depends on the effective routing techniques which provide an optimum path to transmit the data such that the minimum amount of energy should be consumed. The control nodes are responsible for assigning the task and data transmission in the cluster-based routing techniques and the selection of the control node is an NP-hard problem. To resolve this problem, an adaptive particle swarm optimization (PSO) ensemble with genetic mutation-based routing is proposed to select control nodes for IoT based software-defined WSN. The proposed algorithm plays a significant role in selecting the control nodes by considering energy and distance parameters. The proposed work is implemented for the heterogeneous networks having different computing power accompanied by single and multiple sinks. The experiment was carried out on the scale of the performance matrix such as fitness value, stability period, average residual energy, etc. The simulation result of the proposed algorithm outperforms over other algorithms under the different arrangements of the network.

Title: An Iot-Based Approach For Optimal Relative Positioning Of Solar Panel Arrays During Backtracking Abstract: There are several studies in the literature and common solutions in practice about solar tracking systems (STSs) and their effective usage in solar power plants. However, they all mainly lack of a serious consideration to eliminate the undesirable row-to-row shading effect of nearby solar panels in instant positioning of a solar panel. The proposed approach presented here smoothly tackles this problem through use of a developed IoT-based STS solution deployed in solar panels in order to increase solar energy harvesting efficiency. A special IoT-STS component designed and implemented with hardware and software, developed for this purpose, effectively transfers the relative panel position information to the other panel IoT-STSs with the help of wireless and internet communication features in real-time. Depending on the array geometry and the steepness of the cross-axis grade, using the proposed backtracking strategy can improve the annual yield up to 5%.

Title: A New Secure Arrangement For Privacy-Preserving Data Collection Abstract: A big number of users' healthy data are necessary for the Internet of Things (IoT) healthcare. Therefore, the institutions, which have access to more data can provide better medical services such as more accurate diagnosis. However, privacy is often a bottleneck for IoT healthcare. Users often refuse to provide their health data based on privacy considerations. To balance the requirement of data collection and personal privacy, a lot of privacy preserving data collection schemes are provided. A very important work of these schemes is to produce a secret position for every user to store her/his data, which is named secure arrangement. A novel secure arrangement method is proposed in this paper, which is based on matrix eigenvalue calculation. Compared with the current secure arrangement methods, the proposed method is more robust and efficient, which drives the proposed scheme to be more suitable for repeated aggregation. Then we use an example to illustrate how to use the proposed arrangement method to construct a privacy data collection protocol. We prove the proposed scheme is secure and efficient in security analysis and efficiency analysis.

Title: Platonic solids, Archimedean solids and semi-equivelar maps on the sphere Abstract: A map X on a surface is called vertex-transitive if the automorphism group of X acts transitively on the set of vertices of X. A map is called semi-equivelar if the cyclic arrangement of faces around each vertex is same. In general, semi-equivelar maps on a surface form a bigger class than vertex-transitive maps. There are semi-equivelar maps on the torus, the Klein bottle and other surfaces which are not vertex-transitive. It is known that the boundaries of Platonic solids, Archimedean solids, regular prisms and anti-prisms are vertex-transitive maps on S-2. Here we show that there is exactly one semi-equivelar map on S-2 which is not vertex-transitive. As a consequence, we show that all the semi-equivelar maps on RP2 are vertex-transitive. Moreover, every semi-equivelar map on S-2 can be geometrized, i.e., every semi-equivelar map on S-2 is isomorphic to a semi-regular tiling of S-2. In the course of the proof of our main result, we present a combinatorial characterisation in terms of an inequality of all the types of semi-equivelar maps on S-2. Here we present combinatorial proofs of all the results. (c) 2021 Elsevier B.V. All rights reserved.

Title: A Short Note On Graphs With Long Thomason Chains Abstract: We present a family of 3-connected cubic planar Hamiltonian graphs with an exponential number of steps required by Thomason's algorithm. The base of the exponent is approximately 1.1812..., which exceeds previous results in the area by approximately 0.003. (C) 2021 Elsevier B.V. All rights reserved.

Title: Kruskal-Katona function and variants of cross-intersecting antichains Abstract: We prove some properties of the Kruskal-Katona function and apply to the following variant of cross-intersecting antichains. Let n >= 4 be an even integer and A and B be two cross-intersecting antichains on [n] with at most k disjoint pairs, i.e., for all A(i) is an element of A, B-j is an element of B, A(i) boolean AND B-j = empty set only if i = j <= k. We prove a best possible upper bound on vertical bar A vertical bar + vertical bar B vertical bar and show that the extremal families contain only n/2 and (n/2 + 1)-sets. The main tools are Sperner operations and Kruskal-Katona Theorem. (C) 2021 Elsevier B.V. All rights reserved.

Title: Packing A-Paths Of Length Zero Modulo Four Abstract: We show that A-paths of length 0 modulo 4 have the Erdos-Posa property. Modulus m = 4 is the only composite number for which A-paths of length 0 modulo m have the property. (C) 2021 Elsevier Ltd. All rights reserved.

Title: Affine transitions for involution Stanley symmetric functions Abstract: We study a family of symmetric functions volutions z in the affine symmetric group. These power series are analogues of Lam's affine Stanley symmetric functions and generalizations of the involution Stanley symmetric functions introduced by Hamaker, Pawlowski, and the first author. Our main result is to prove a transition formula for F circumflex accent z which can be used to define an affine involution analogue of the Lascoux- Schutzenberger tree. Our proof of this formula relies on Lam and Shimozono's transition formula for affine Stanley symmetric functions and some new technical properties of the strong Bruhat order on affine permutations. (C) 2021 Elsevier Ltd. All rights reserved.

Title: A short proof of Talbot's theorem for intersecting separated sets Abstract: A subset A of [n] = {1, . . . , n} is k -separated if, when the elements of [n] are considered on a circle, between every two elements of A there are at least k elements of [n] that are not in A. A family A of sets is intersecting if every two sets in A intersect. We give a short proof of a remarkable result of Talbot (2003), stating that if n >= (k+1)r and A is an intersecting family of k-separated r-element subsets of [n], then |A| <= ((n-kr-1) (r-1)). This bound is best possible. (C) 2021 Elsevier Ltd. All rights reserved.

Title: Hierarchy Depth in Directed Networks Abstract: In this study, we explore the depth measures for flow hierarchy in directed networks. Two simple measures are defined-rooted depth and relative depth-and their properties are discussed. The method of loop collapse is introduced, allowing investigation of networks containing directed cycles. The behavior of the two depth measures is investigated in Erdos-Renyi random graphs, directed Barabasi-Albert networks, and in Gnutella p2p share network. A clear distinction in the behavior between non-hierarchical and hierarchical networks is found, with random graphs featuring unimodal distribution of depths dependent on arc density, while for hierarchical systems the distributions are similar for different network densities. Relative depth shows the same behavior as existing trophic level measure for tree-like networks, but is only statistically correlated for more complex topologies, including acyclic directed graphs.

Title: The Consensus Problem in Polities of Agents with Dissimilar Cognitive Architectures Abstract: Agents interacting with their environments, machine or otherwise, arrive at decisions based on their incomplete access to data and their particular cognitive architecture, including data sampling frequency and memory storage limitations. In particular, the same data streams, sampled and stored differently, may cause agents to arrive at different conclusions and to take different actions. This phenomenon has a drastic impact on polities-populations of agents predicated on the sharing of information. We show that, even under ideal conditions, polities consisting of epistemic agents with heterogeneous cognitive architectures might not achieve consensus concerning what conclusions to draw from datastreams. Transfer entropy applied to a toy model of a polity is analyzed to showcase this effect when the dynamics of the environment is known. As an illustration where the dynamics is not known, we examine empirical data streams relevant to climate and show the consensus problem manifest.

Title: Does United Kingdom parliamentary attention follow social media posts? Abstract: News and social media play an important role in public political discourse. It is not clear what quantifiable relationships public discussions of politics have with official discourse within legislative bodies. In this study we present an analysis of how language used by Members of Parliament (MPs) in the United Kingdom (UK) changes after social media posts and online reactions to those posts. We consider three domains: news articles posted on Facebook in the UK, speeches in the questions-debates in the UK House of Commons, and Tweets by UK MPs. Our method works by quantifying how the words used in one domain become more common in another domain after an event such as a social media post. Our results show that words used in one domain later appear more commonly in other domains. For instance after each article on Facebook, we estimate that on average 4 in 100,000 words in Commons speeches had changed, becoming more similar to the language in the article. We also find that the extent of this language change positively correlates with the number of comments and emotional interactions on Facebook. The observed language change differs between political parties; in particular, changes in word use by Labour MPs are more strongly related to social media content than that of Conservative MPs. We argue that the magnitude of this word flow is quite substantial given the large volume of news articles shared on Facebook. Our method and results quantify how parliamentary attention follows public interest as expressed on Facebook and also indicate how this effect may be stronger for posts which evoke reactions on Facebook associated with laughter or anger.

Title: Diameter of Io-Decomposable Riordan Graphs of the Bell Type Abstract: Recently, in the paper (Cheon et al. in Linear Algebra Appl 579:89-135, 2019) we suggested the two conjectures about the diameter of io-decomposable Riordan graphs of the Bell type. In this paper, we give a counterexample for the first conjecture. Then we prove that the first conjecture is true for the graphs of some particular size and propose a new conjecture. Finally, we show that the second conjecture is true for some special io-decomposable Riordan graphs.

Title: Evolutionary approximation and neural architecture search Abstract: Automated neural architecture search (NAS) methods are now employed to routinely deliver high-quality neural network architectures for various challenging data sets and reduce the designer’s effort. The NAS methods utilizing multi-objective evolutionary algorithms are especially useful when the objective is not only to minimize the network error but also to reduce the number of parameters (weights) or power consumption of the inference phase. We propose a multi-objective NAS method based on Cartesian genetic programming for evolving convolutional neural networks (CNN). The method allows approximate operations to be used in CNNs to reduce the power consumption of a target hardware implementation. During the NAS process, a suitable CNN architecture is evolved together with selecting approximate multipliers to deliver the best trade-offs between accuracy, network size, and power consumption. The most suitable 8 × N-bit approximate multipliers are automatically selected from a library of approximate multipliers. Evolved CNNs are compared with CNNs developed by other NAS methods on the CIFAR-10 and SVHN benchmark problems.

Title: Applying genetic programming to PSB2: the next generation program synthesis benchmark suite Abstract: For the past seven years, researchers in genetic programming and other program synthesis disciplines have used the General Program Synthesis Benchmark Suite (PSB1) to benchmark many aspects of systems that conduct programming by example, where the specifications of the desired program are given as input/output pairs. PSB1 has been used to make notable progress toward the goal of general program synthesis: automatically creating the types of software that human programmers code. Many of the systems that have attempted the problems in PSB1 have used it to demonstrate performance improvements granted through new techniques. Over time, the suite has gradually become outdated, hindering the accurate measurement of further improvements. The field needs a new set of more difficult benchmark problems to move beyond what was previously possible and ensure that systems do not overfit to one benchmark suite. In this paper, we describe the 25 new general program synthesis benchmark problems that make up PSB2, a new benchmark suite. These problems are curated from a variety of sources, including programming katas and college courses. We selected these problems to be more difficult than those in the original suite, and give results using PushGP showing this increase in difficulty. We additionally give an example of benchmarking using a state-of-the-art parent selection method, showing improved performance on PSB2 while still leaving plenty of room for improvement. These new problems will help guide program synthesis research for years to come.

Title: Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set Abstract: In some situations, the interpretability of the machine learning models plays a role as important as the model accuracy. Interpretability comes from the need to trust the prediction model, verify some of its properties, or even enforce them to improve fairness. Many model-agnostic explanatory methods exists to provide explanations for black-box models. In the regression task, the practitioner can use white-boxes or gray-boxes models to achieve more interpretable results, which is the case of symbolic regression. When using an explanatory method, and since interpretability lacks a rigorous definition, there is a need to evaluate and compare the quality and different explainers. This paper proposes a benchmark scheme to evaluate explanatory methods to explain regression models, mainly symbolic regression models. Experiments were performed using 100 physics equations with different interpretable and non-interpretable regression methods and popular explanation methods, evaluating the performance of the explainers performance with several explanation measures. In addition, we further analyzed four benchmarks from the GP community. The results have shown that Symbolic Regression models can be an interesting alternative to white-box and black-box models that is capable of returning accurate models with appropriate explanations. Regarding the explainers, we observed that Partial Effects and SHAP were the most robust explanation models, with Integrated Gradients being unstable only with tree-based models. This benchmark is publicly available for further experiments.

Title: A grammar-based GP approach applied to the design of deep neural networks Abstract: Deep Learning has been very successful in automating the feature engineering process, widely applied for various tasks, such as speech recognition, classification, segmentation of images, time-series forecasting, among others. Deep neural networks (DNNs) incorporate the power to learn patterns through data, following an end-to-end fashion and expand the applicability in real world problems, since less pre-processing is necessary. With the fast growth in both scale and complexity, a new challenge has emerged regarding the design and configuration of DNNs. In this work, we present a study on applying an evolutionary grammar-based genetic programming algorithm (GP) as a unified approach to the design of DNNs. Evolutionary approaches have been growing in popularity for this subject as Neuroevolution is studied more. We validate our approach in three different applications: the design of Convolutional Neural Networks for image classification, Graph Neural Networks for text classification, and U-Nets for image segmentation. The results show that evolutionary grammar-based GP can efficiently generate different DNN architectures, adapted to each problem, employing choices that differ from what is usually seen in networks designed by hand. This approach has shown a lot of promise regarding the design of architectures, reaching competitive results with their counterparts.

Title: Robustness in the Optimization of Risk Measures Abstract: We study issues of robustness in the context of Quantitative Risk Management and Optimization. We develop a general methodology for determining whether a given risk-measurement-related optimization problem is robust, which we call "robustness against optimization." The new notion is studied for various classes of risk measures and expected utility and loss functions. Motivated by practical issues from financial regulation, special attention is given to the two most widely used risk measures in the industry, Value-at-Risk (VaR) and Expected Shortfall (ES). We establish that for a class of general optimization problems, VaR leads to nonrobust optimizers, whereas convex risk measures generally lead to robust ones. Our results offer extra insight on the ongoing discussion about the comparative advantages of VaR and ES in banking and insurance regulation. Our notion of robustness is conceptually different from the field of robust optimization, to which some interesting links are derived.

Title: Improving A State-Of-The-Art Heuristic For The Minimum Latency Problem With Data Mining Abstract: Recently, hybrid metaheuristics have become a trend in operations research. A successful example combines the Greedy Randomized Adaptive Search Procedures (GRASP) and data mining techniques, where frequent patterns found in high-quality solutions can lead to an efficient exploration of the search space, along with a significant reduction of computational time. In this paper, a GRASP-based state-of-the-art heuristic for the minimum latency problem is improved by means of data mining techniques. Computational experiments showed that the hybrid heuristic with data mining was able to match or improve the solution quality for a large number of instances, together with a substantial reduction of running time. Besides, 32 new best-known solutions are introduced to the literature. To support our results, statistical significance tests, analyses over the impact of mined patterns, comparisons based on running time as stopping criterion, and time-to-target plots are provided.

Title: A Review of Consensus-based Multi-agent UAV Implementations Abstract: In this paper, a survey on distributed control applications for multi Unmanned Aerial Vehicles (UAVs) systems is proposed. The focus is on consensus-based control, and both rotary-wing and fixed-wing UAVs are considered. On one side, the latest experimental configurations for the implementation of formation flight are analysed and compared for multirotor UAVs. On the other hand, the control frameworks taking into account the mobility of the fixed-wing UAVs performing target tracking are considered. This approach can be helpful to assess and compare the solutions for practical applications of consensus in UAV swarms.

Title: Modeling and Flight Control of Small UAV with Active Morphing Wings Abstract: In recent research works, morphing wings were studied as an interesting field for a small unmanned aerial vehicle (UAV). The previous studies either focused on selecting suitable material for the morphing wings or performing experimental tests on UAVs with morphing wings. Though, the dynamic modeling of active flexible morphing wings and their involved interactions with the aerodynamics of the UAV body are challenging subjects. Using such a model to control a small UAV to perform specific maneuvering is not investigated yet. The dynamic model of UAV with active morphing wings generates a multi-input multi-output (MIMO) system which rises the difficulty of the control system design. In this paper, the aeroelastic dynamic model of morphing wing activated by piezocomposite actuators is established using the finite element method and modal decomposition technique. Then, the dynamic model of the UAV is developed taking into consideration the coupling between the wing and piezocomposite actuators, as well as the dynamic properties of the morphing actuators with the aerodynamic wind disturbances. A model predictive control (MPC) is designed for the MIMO control system to perform specific flight maneuvering by tracking desired trajectories of UAV altitude and yaw angle. Additionally, the MPC achieves constrained behavior of pitch and roll angles to get satisfactory UAV motion. Also, the behaviors of the UAV control system using MPC are evaluated after adding Dryden wind turbulence to the UAV outputs. Finally, a UAV flight simulation is conducted which shows that the control system successfully rejects the applied disturbances and tracks the reference trajectories with acceptable behavior of pitch and roll angles.

Title: Objects Relocation in Clutter with Robot Manipulators via Tree-based Q-Learning Algorithm: Analysis and Experiments Abstract: This work addresses the problem of retrieving a target object from cluttered environment using a robot manipulator. In the details, the proposed solution relies on a Task and Motion Planning approach based on a two-level architecture: the high-level is a Task Planner aimed at finding the optimal objects sequence to relocate, according to a metric based on the objects weight; the low-level is a Motion Planner in charge of planning the end-effector path for reaching the specific objects taking into account the robot physical constraints. The high-level task planner is a Reinforcement Learning agent, trained using the information coming from the low-level Motion Planner. In this work we consider the Q-Tree algorithm, which is based on a dynamic tree structure inspired by the Q-learning technique. Three different RL-policies with two kinds of tree exploration techniques (Breadth and Depth) are compared in simulation scenarios with different complexity. Moreover, the proposed learning methods are experimentally validated in a real scenario by adopting a KINOVA Jaco $$^{2}$$ 7-DoFs robot manipulator.

Title: Visual-based Assistive Method for UAV Power Line Inspection and Landing Abstract: Over the years, many methods and technologies have been developed to improve power lines’ visual-based inspection, such as the recent advances in computer vision and machine learning techniques and the use of Unmanned Aerial Vehicles (UAVs). Although aerial inspection of transmission lines is a well-researched topic, there is still space for research papers and solutions that focus on shared control, landing, and experimental evaluation of the solutions. This kind of system has the capabilities to acquire and process information about its surrounding. By employing automatic UAVs embedded with artificial intelligence, industries, business and researchers can significantly improve their visual-based inspection routines, bringing safety to the user and processing reliable information. Therefore, this research work proposes a strategy to both detect and track power transmission lines and a method to allow assistive control during UAV landing. Both methods were evaluated in simulated and real-world scenarios. Regarding the detection and tracking strategies, the outcomes suggested that the proposed system is capable of correctly identifying power transmission lines and navigating above them, even in the presence of cluttered backgrounds. Futhermore, the results of the assisted landing strategy showed that the method has excellent performance and is technically viable for practical deployment.

Title: Propagation of Chaos in the Nonlocal Adhesion Models for Two Cancer Cell Phenotypes Abstract: We establish a quantitative propagation of chaos for a large stochastic systems of interacting particles. We rigorously derive a mean-field system, which is a diffusive cell-to-cell nonlocal adhesion model for two different phenotypes of tumors, from that stochastic system as the number of particles tends to infinity. We estimate the error between the solutions to a N-particle Liouville equation associated with the particle system and the limiting mean-field system by employing the relative entropy argument.

Title: Area Quasi-minimizing Partitions with a Graphical Constraint: Relaxation and Two-Dimensional Partial Regularity Abstract: We consider a variational model for periodic partitions of the upper half-space into three regions, where two of them have prescribed volume and are subject to the geometric constraint that their union is the subgraph of a function, whose graph is a free surface. The energy of a configuration is given by the weighted sum of the areas of the interfaces between the different regions and a general volume-order term. We establish existence of minimizing configurations via relaxation of the energy involved, in any dimension. Moreover, we prove partial regularity results for volume-constrained minimizers in two space dimensions. Thin films of diblock copolymers are a possible application and motivation for considering this problem.

Title: A Stochastic Nesterov’s Smoothing Accelerated Method for General Nonsmooth Constrained Stochastic Composite Convex Optimization Abstract: We propose a novel stochastic Nesterov’s smoothing accelerated method for general nonsmooth, constrained, stochastic composite convex optimization, the nonsmooth component of which may be not easy to compute its proximal operator. The proposed method combines Nesterov’s smoothing accelerated method (Nesterov in Math Program 103(1):127–152, 2005) for deterministic problems and stochastic approximation for stochastic problems, which allows three variants: single sample and two different mini-batch sizes per iteration, respectively. We prove that all the three variants achieve the best-known complexity bounds in terms of stochastic oracle. Numerical results on a robust linear regression problem, as well as a support vector machine problem show that the proposed method compares favorably with other state-of-the-art first-order methods, and the variants with mini-batch sizes outperform the variant with single sample.

Title: Dissipation-Preserving Rational Spectral-Galerkin Method for Strongly Damped Nonlinear Wave System Involving Mixed Fractional Laplacians in Unbounded Domains Abstract: This paper aims at developing a dissipation-preserving, linearized, and time-stepping-varying spectral method for strongly damped nonlinear wave system in multidimensional unbounded domains $${\mathbb {R}}^d$$ (d=1, 2, and 3), where the nonlocal nature is described by the mixed fractional Laplacians. Because the underlying solutions of the problem involving mixed fractional Laplacians decay slowly with certain power law at infinity, we employ the rational spectral-Galerkin method using rational basis (or mapped Gegenbauer functions) for the spatial approximation. To capture the intrinsic dissipative properties of the model equations, we combine the Crank-Nicolson scheme with exponential scalar auxiliary variable approach for the temporal discretization. Based on the rate of nonlocal energy dissipation, we design a novel time-stepping-varying strategy to enhance the efficiency of the scheme. We present the detailed implementation of the scheme, where the main building block of the stiffness matrices is based on the Laguerre-Gauss quadrature rule for the modified Bessel functions of the second kind. The existence, uniqueness, and nonlocal energy dissipation law of the fully discrete scheme are rigourously established. Numerical examples in 3D case are carried out to demonstrate the accuracy and efficiency of the scheme. Finally, we simulate the nonlinear behaviors of 2D/3D dissipative vector solitary waves for damped sine-Gordon system I, for damped sine-Gordon system II, and for damped Klein–Gordon system to provide a deeper understanding of nonlocal physics.

Title: High-Order ADI-FDTD Schemes for Maxwell’s Equations with Material Interfaces in Two Dimensions Abstract: In this paper, we apply the immersed interface method (IIM) and the hierarchical derivative matching (HDM) method, respectively, to restore the accuracy of the high-order alternating direction implicit finite-difference time-domain (ADI-FDTD) scheme of the 2D Maxwell’s equations with material interfaces. For the case of discontinuous permittivity $$\varepsilon $$ and continuous permeability $$\mu $$ , we propose four high-order schemes. Two of them are of second order in time and fourth order in space (ADI-IIM-FDTD(2,4) scheme and ADI-HDM-FDTD(2,4) scheme). Others are of fourth order both in time and space (ADI-IIM-FDTD(4,4) scheme and ADI-HDM-FDTD(4,4) scheme). For the case of discontinuous permittivity $$\varepsilon $$ and permeability $$\mu $$ , the (2,4) scheme and the (4,4) scheme are constructed as well (ADI-HDM-FDTD-X(2,4) scheme and ADI-HDM-FDTD-X(4,4) scheme). The proposed six schemes maintain the advantages of ADI-FDTD method such as unconditional stability and computational efficiency. Numerical examples are given to verify the performance of the proposed schemes.

Title: Nonparametric spectral methods for multivariate spatial and spatial–temporal data Abstract: We propose computationally efficient methods for estimating stationary multivariate spatial and spatial–temporal spectra from incomplete gridded data. The methods are iterative and rely on successive imputation of data and updating of model estimates. Imputations are done according to a periodic model on an expanded domain. The periodicity of the imputations is a key feature that reduces edge effects in the periodogram and is facilitated by efficient circulant embedding techniques. In addition, we describe efficient methods for decomposing the estimated cross spectral density function into a linear model of coregionalization plus a residual process. The methods are applied to two storm datasets, one of which is from Hurricane Florence, which struck the southeastern United States in September 2018. The application demonstrates how fitted models from different datasets can be compared, and how the methods are computationally feasible on datasets with more than 200,000 total observations.

Title: Ultra high-dimensional multivariate posterior contraction rate under shrinkage priors Abstract: In recent years, shrinkage priors have received much attention in high-dimensional data analysis from a Bayesian perspective. Compared with widely used spike-and-slab priors, shrinkage priors have better computational efficiency. But the theoretical properties, especially posterior contraction rate, which is important in uncertainty quantification, are not established in many cases. In this paper, we apply global–local shrinkage priors to high-dimensional multivariate linear regression with unknown covariance matrix. We show that when the prior is highly concentrated near zero and has heavy tail, the posterior contraction rates for both coefficients matrix and covariance matrix are nearly optimal. Our results hold when number of features p grows much faster than the sample size n, which is of great interest in modern data analysis. We show that a class of readily implementable scale mixture of normal priors satisfies the conditions of the main theorem.

Title: TWO-SCALE METHODS FOR CONVEX ENVELOPES Abstract: We develop two-scale methods for computing the convex envelope of a continuous function over a convex domain in any dimension. This hinges on a fully nonlinear obstacle formulation (see A. M. Oberman [Proc. Amer. Math. Soc. 135 (2007), pp. 1689-1694]). We prove convergence and error estimates in the max norm. The proof utilizes a discrete comparison principle, a discrete barrier argument to deal with Dirichlet boundary values, and the property of flatness in one direction within the non-contact set. Our error analysis extends to a modified version of the finite difference wide stencil method provided by Oberman [Math. Models Methods Appl. Sci. 18 (2008), pp. 759-780].

Title: Price-Directed Cost Sharing and Demand Allocation Among Service Providers with Multiple Demand Sources and Multiple Facilities Abstract: Problem definition: We consider capacity sharing through demand allocation among firms with multiple demand sources and multiple service facilities. Firms decide on the allocation of demand from different sources to different facilities to minimize delay costs and service-fulfillment costs possibly subject to service-level requirements. If firms decide to operate collectively as a coalition, they must also decide on a scheme for sharing the total cost. Academic/practical relevance: We study capacity sharing through demand allocation in service systems in the presence of service-fulfillment costs. Our problem is motivated by service collaboration in healthcare involving public-private partnerships. Methodology: We formulate the problem as a cooperative game and identify a cost allocation that is in the core. Results: The cost-allocation scheme we identify is price-directed, and the cost of each firm consists of three components: (1) the delay cost incurred within the firm; (2) a cost paid for the capacity used by the firm at facilities owned by other firms; and (3) a payment received for fulfilling demand of other firms at facilities owned by the firm. Interestingly, we show that the cost-allocation scheme is equivalent to a market equilibrium-that is, it can be implemented in a decentralized fashion. We extend our analysis to settings where the capacity of each facility is endogenously determined and to settings where a service-priority policy is deployed. Our results are robust to a variety of generalizations: partial sharing, partial transfer, facilities modeled as general queueing systems, and convex delay costs. Managerial implications: Our findings provide guidelines for and insights into how to carry out demand allocation and cost sharing among different firms in the presence of service-fulfillment costs to foster service collaboration. In particular, the equilibrium market prices can be viewed as the prices/subsidies for service collaboration in a public-private partnership.

Title: Dropping rational expectations Abstract: We consider a two-period pure-exchange economy, where uncertainty prevails and agents, possibly asymmetrically informed, exchange commodities and securities of all kinds. Consumers’ characteristics, anticipations, beliefs and actions are all private and typically not known nor assessed by the other agents. This setting drops rational expectations along the ‘common knowledge of rationality and market clearing’ (CKRMC) assumptions, and, in particular, it drops the Radner (1979) price model and price inference assumptions. Unaware agents are shown to face an incompressible uncertainty over future states and prices, represented by a so-called ”minimum uncertainty set”. A sequential equilibrium obtains when agents expect the ‘true’ price as a possible outcome on every spot market, and elect optimal market-clearing strategies. This so-called ”correct foresight equilibrium” (CFE) is shown to exist whenever agents’ anticipation sets include the minimum uncertainty set. When the CKRMC assumptions are restored, the CFE is shown to lead to an overarching concept of rational expectations equilibrium, which generalizes the classical concepts.

Title: Human activity recognition by combining external features with accelerometer sensor data using deep learning network model Abstract: Various Human Activities are classified through time-series data generated by the sensors of wearable devices. Many real-time scenarios such as Healthcare Surveillance, Smart Cities and Intelligent surveillance etc. are based upon Human Activity Recognition. Despite the popularity of local features-based approaches and machine learning approaches, it fails to capture adequate temporal information. In this paper, the deep convolutional neural model has been proposed by combining external features, i.e. orientation invariant (|| $$v$$ ||) and consecutive point trajectory information (|| $$\Delta v$$ ||) with tri-axis data of the accelerometer. The proposed external features based approach experimented on three different deep learning architecture, namely Long-Short Term Memory (LSTM), Convolutional Neural Networks (CNN) and Convolution Long-Short Term Memory (ConvLSTM). Accuracy of the algorithms radically improve with the additional input feature || $$v$$ || and || $$\Delta v$$ || along with tri-axis data of accelerometer. The results show that the performance of all three LSTM, CNN and ConvLSTM models is better to compare with the state of art methods on WISDOM dataset and Activity dataset also the performance of ConvLSTM is 98.41% for WISDOM dataset and 98.04 for activity dataset, which is higher than that of CNN and LSTM model used in this paper.

Title: Improved two-stage image inpainting with perceptual color loss and modified region normalization Abstract: In this work, we propose a two-stage architecture to perform image inpainting from coarse to fine. The framework extracts advantages from different designs in the literature and integrates them into the inpainting network. We apply region normalization to generate coarse blur results with the correct structure. Then, contextual attention is applied to utilize the texture information of background regions to generate the final result. Although using region normalization can improve the performance and quality of the network, it often results in visible color shifts. To solve this problem, we introduce perceptual color distance in the loss function. In quantitative comparison experiments, the proposed method is superior to the existing similar methods in Inception Score, Fréchet Inception Distance, and perceptual color distance. In qualitative comparison experiments, the proposed method can effectively resolve the problem of color shifts.

Title: Techniques for blocking the propagation of two simultaneous contagions over networks using a graph dynamical systems framework Abstract: We consider the simultaneous propagation of two contagions over a social network. We assume a threshold model for the propagation of the two contagions and use the formal framework of discrete dynamical systems. In particular, we study an optimization problem where the goal is to minimize the total number of new infections subject to a budget constraint on the total number of available vaccinations for the contagions. While this problem has been considered in the literature for a single contagion, our work considers the simultaneous propagation of two contagions. This optimization problem is NP-hard. We present two main solution approaches for the problem, namely an integer linear programming (ILP) formulation to obtain optimal solutions and a heuristic based on a generalization of the set cover problem. We carry out a comprehensive experimental evaluation of our solution approaches using many real-world networks. The experimental results show that our heuristic algorithm produces solutions that are close to the optimal solution and runs several orders of magnitude faster than the ILP-based approach for obtaining optimal solutions. We also carry out sensitivity studies of our heuristic algorithm.

Title: Improved Upper Bounds for the Rate of Separating and Completely Separating Codes Abstract: A binary code is said to be an $$(s,\ell)$$ -separating code if for any two disjoint sets of its codewords of cardinalities at most $$s$$ and $$\ell$$ respectively, there exists a coordinate in which all words of the first set have symbol 0 while all words of the second have 1. If, moreover, for any two sets there exists a second coordinate in which all words of the first set have 1 and all words of the second have 0, then such a code is called an $$(s,\ell)$$ -completely separating code. We improve upper bounds on the rate of separating and completely separating codes.

Title: On One Extremal Problem for Mutual Information Abstract: We address the problem of finding the maximum of the mutual information $$I(X;Y)$$ of two finite-valued random variables $$X$$ and $$Y$$ given only the value of their coupling, i.e., the probability $$\Pr\{X=Y\}$$ . We obtain explicit lower and upper bounds on this maximum, which in some cases are optimal.

Title: On Weight Distributions for a Class of Codes with Parameters of Reed-Muller Codes Abstract: We present a new construction method for a doubly exponential class of binary codes with the parameters of Reed–Muller codes. We investigate the weight spectrum and the distance-invariance property of the proposed codes. In the constructed class of codes with the parameters of Reed–Muller codes, we show the existence of codes with the same weight distribution as for a Reed–Muller code and of codes with weight distributions other than this. We establish that all codes with the parameters of the Reed–Muller code which are obtained by the Vasil’ev–Pulatov construction but are distinct from extended perfect codes either are equivalent to the original Reed–Muller codes or have distance distributions different from those.

Title: Partitions into Perfect Codes in the Hamming and Lee Metrics Abstract: We propose new combinatorial constructions of partitions into perfect codes in both the Hamming and Lee metrics. Also, we present a new combinatorial construction method for diameter perfect codes in the Lee metric, which is further developed to a construction of partitions into such codes. For the Lee metric, we improve previously known lower bounds on the number of perfect and diameter perfect codes proposed by Etzion in 2011.

Title: On Minimax Detection of Gaussian Stochastic Sequences with Imprecisely Known Means and Covariance Matrices Abstract: We consider the problem of detecting (testing) Gaussian stochastic sequences (signals) with imprecisely known means and covariance matrices. An alternative is independent identically distributed zero-mean Gaussian random variables with unit variances. For a given false alarm (1st-kind error) probability, the quality of minimax detection is given by the best miss probability (2nd-kind error probability) exponent over a growing observation horizon. We study the maximal set of means and covariance matrices (composite hypothesis) such that its minimax testing can be replaced with testing a single particular pair consisting of a mean and a covariance matrix (simple hypothesis) without degrading the detection exponent. We completely describe this maximal set.

Title: Fast Evaluation Algorithms for Elementary Algebraic and Inverse Functions Using the FEE Method Abstract: We construct new fast evaluation algorithms for elementary algebraic and inverse functions based on application of two methods: A.A. Karatsuba’s method of 1960 and the author’s FEE method of 1990. The computational complexity is close to the optimal. The algorithms admit partial parallelization.

Title: Recoverable Formal Language Abstract: We study the problem of recovering distorted arbitrarily long messages written in some dynamically specified formal language. We obtain necessary and sufficient conditions on the language definition for an admissible message to exist in a neighborhood of a distorted message provided that local perturbations occur rarely.

Title: User-Level Label Leakage from Gradients in Federated Learning. Abstract: Federated learning enables multiple users to build a joint model by sharing their model updates (gradients), while their raw data remains local on their devices. In contrast to the common belief that this provides privacy benefits, we here add to the very recent results on privacy risks when sharing gradients. Specifically, we propose Label Leakage from Gradients (LLG), a novel attack to extract the labels of the users' training data from their shared gradients. The attack exploits the direction and magnitude of gradients to determine the presence or absence of any label. LLG is simple yet effective, capable of leaking potential sensitive information represented by labels, and scales well to arbitrary batch sizes and multiple classes. We empirically and mathematically demonstrate the validity of our attack under different settings. Moreover, empirical results show that LLG successfully extracts labels with high accuracy at the early stages of model training. We also discuss different defense mechanisms against such leakage. Our findings suggest that gradient compression is a practical technique to prevent our attack.

Title: Measurement-based universal blind quantum computation with minor resources Abstract: Blind quantum computation (BQC) enables a client with less quantum computational ability to delegate her quantum computation to a server with strong quantum computational power while preserving the client's privacy. Generally, many-qubit entangled states are often used to complete BQC tasks. But for a large-scale entangled state, it is difficult to be described since its Hilbert space dimension is increasing exponentially. Furthermore, the number of entangled qubits is limited in experiment of existing works. To tackle this problem, in this paper we propose a universal BQC protocol based on measurement with minor resources, where the trap technology is adopted to verify correctness of the server's measurement outcomes during computation and testing process. In our model, there are two participants, a client who prepares initial single-qubit states and a server that performs universal quantum computation. The client is almost classical since she does not require any quantum computational power, quantum memory. To realize the client's universal BQC, we construct an m x n latticed state composed of six-qubit cluster states and eight-qubit cluster states, which needs less qubits than the brickwork state. Finally, we analyze and prove the blindness, correctness, universality and verifiability of our proposed BQC protocol.

Title: Functional equations with multiple recursive terms Abstract: In this paper, we study a functional equation for generating functions of the form $$f(z) = g(z) \sum _{i=1}^M p_i f(\alpha _i(z)) + K(z)$$ , viz. a recursion with multiple recursive terms. We derive and analyze the solution of this equation for the case that the $$\alpha _i(z)$$ are commutative contraction mappings. The results are applied to a wide range of queueing, autoregressive and branching processes.

Title: Derivative of the expected supremum of fractional Brownian motion at $$H=1$$ Abstract: The H-derivative of the expected supremum of fractional Brownian motion $$\{B_H(t),t\in {\mathbb {R}}_+\}$$ with drift $$a\in {\mathbb {R}}$$ over time interval [0, T] $$\begin{aligned} \frac{\partial }{\partial H} {\mathbb {E}}\Big (\sup _{t\in [0,T]} B_H(t) - at\Big ) \end{aligned}$$ at $$H=1$$ is found. This formula depends on the quantity $${\mathscr {I}}$$ , which has a probabilistic form. The numerical value of $${\mathscr {I}}$$ is unknown; however, Monte Carlo experiments suggest $${\mathscr {I}}\approx 0.95$$ . As a by-product we establish a weak limit theorem in C[0, 1] for the fractional Brownian bridge, as $$H\uparrow 1$$ .

Title: WCFS: a new framework for analyzing multiserver systems Abstract: Multiserver queueing systems are found at the core of a wide variety of practical systems. Many important multiserver models have a previously-unexplained similarity: identical mean response time behavior is empirically observed in the heavy traffic limit. We explain this similarity for the first time. We do so by introducing the work-conserving finite-skip (WCFS) framework, which encompasses a broad class of important models. This class includes the heterogeneous M/G/k, the Limited Processor Sharing policy for the M/G/1, the Threshold Parallelism model and the Multiserver-Job model under a novel scheduling algorithm. We prove that for all WCFS models, scaled mean response time $$E[T](1-\rho )$$ converges to the same value, $$E[S^2]/(2E[S])$$ , in the heavy-traffic limit, which is also the heavy traffic limit for the M/G/1/FCFS. Moreover, we prove additively tight bounds on mean response time for the WCFS class, which hold for all load $$\rho $$ . For each of the four models mentioned above, our bounds are the first known bounds on mean response time.

Title: State space collapse for multi-class queueing networks under SBP service policies Abstract: In Braverman et al. [3], the authors justify the steady-state diffusion approximation of a multiclass queueing network under static buffer priority policy in heavy traffic. A major assumption in [3] is the moment state space collapse (moment-SSC) property of the steady-state queue length. In this paper, we prove that moment-SSC holds under a corresponding state space collapse condition on the fluid model. Our approach is inspired by Dai and Meyn [8], which was later adopted by Budhiraja and Lee [4] to justify the diffusion approximation for generalized Jackson networks. We will verify that the fluid state space collapse holds for various networks.

Title: Queueing and risk models with dependencies Abstract: This paper analyzes various stochastic recursions that arise in queueing and insurance risk models with a ‘semi-linear’ dependence structure. For example, an interarrival time depends on the workload, or the capital, immediately after the previous arrival; or the service time of a customer depends on her waiting time. In each case, we derive and solve a fixed-point equation for the Laplace–Stieltjes transform of a key performance measure of the model, like waiting time or ruin time.

Title: Subexponential asymptotics of asymptotically block-Toeplitz and upper block-Hessenberg Markov chains Abstract: This paper studies the subexponential asymptotics of the stationary distribution vector of an asymptotically block-Toeplitz and upper block-Hessenberg (atUBH) Markov chain in discrete time. The atUBH Markov chain is a kind of the upper block-Hessenberg (UBH) one and is a generalization of the M/G/1-type one. The atUBH Markov chain typically arises from semi-Markovian retrial queues, as the queue-length process, its embedded process, or appropriately time-scaled versions of these processes. In this paper, we present subexponential and locally subexponential asymptotic formulas for the stationary distribution vector. We then extend the locally subexponential asymptotic formula to a continuous-time version of the atUBH Markov chain by uniformization and change of time scale. This extension expands the applicability of the locally subexponential asymptotic formula.

Title: Birth and death processes in interactive random environments Abstract: This paper studies birth and death processes in interactive random environments where the birth and death rates and the dynamics of the state of the environment are dependent on each other. Two models of a random environment are considered: a continuous-time Markov chain (finite or countably infinite) and a reflected (jump) diffusion process. The background is determined by a joint Markov process carrying a specific interactive mechanism, with an explicit invariant measure whose structure is similar to a product form. We discuss a number of queueing and population-growth models and establish conditions under which the above-mentioned invariant measure can be derived. Next, an analysis of the rate of convergence to stationarity is performed for the models under consideration. We consider two settings leading to either an exponential or a polynomial convergence rate. In both cases we assume that the underlying environmental Markov process has an exponential rate of convergence, but the convergence rate of the joint Markov process is determined by certain conditions on the birth and death rates. To prove these results, a coupling method turns out to be useful.

Title: Optimal staffing for ticket queues Abstract: Ticket queues are popular in many service systems. Upon arrival, each customer is issued a numbered ticket and receives service on a first-come-first-served basis according to the ticket number. There is no physical queue; customers may choose to walk away and return later (before their numbers are called) to receive service. We study the problem of optimal staffing in such a system with two capacity levels, where the staffing decision can only be based on ticket numbers, as opposed to the physical queue length in a traditional system. Using renewal reward theorem, we first derive the long-run average total cost (including customer delay and abandonment costs, operating cost and cost for changing staffing levels) and then obtain the optimal solution using fractional programming. In addition, we pursue a random-walk analysis, which leads to some highly accurate approximations.

Title: Creating RESTful APIs over SPARQL endpoints using RAMOSE Abstract: Semantic Web technologies are widely used for storing RDF data and making them available on the Web through SPARQL endpoints, queryable using the SPARQL query language. While the use of SPARQL endpoints is strongly supported by Semantic Web experts, it hinders broader use of RDF data by common Web users, engineers and developers unfamiliar with Semantic Web technologies, who normally rely on Web RESTful APIs for querying Web-available data and creating applications over them. To solve this problem, we have developed RAMOSE, a generic tool developed in Python to create REST APIs over SPARQL endpoints. Through the creation of source-specific textual configuration files, RAMOSE enables the querying of SPARQL endpoints via simple Web RESTful API calls that return either JSON or CSV-formatted data, thus hiding all the intrinsic complexities of SPARQL and RDF from common Web users. We provide evidence that the use of RAMOSE to provide REST API access to RDF data within OpenCitations triplestores is beneficial in terms of the number of queries made by external users of such RDF data using the RAMOSE API, compared with the direct access via the SPARQL endpoint. Our findings show the importance for suppliers of RDF data of having an alternative API access service, which enables its use by those with no (or little) experience in Semantic Web technologies and the SPARQL query language. RAMOSE can be used both to query any SPARQL endpoint and to query any other Web API, and thus it represents an easy generic technical solution for service providers who wish to create an API service to access Linked Data stored as RDF in a triplestore.

Title: A longitudinal analysis on Instagram characteristics of Olympic champions Abstract: This study examines Olympic champions' characteristics on Instagram to first understand how age and gender affect the characteristics and their interrelations and second to see if the future changes in those characteristics are predictable. We crawled Instagram data of individual gold medalists in the Rio2016 Olympics for four months and utilized a content analytic method to analyze their photograph posts. The cross-sectional analysis shows that as the champions age, the follower engagement rate decreases in both genders. However, men increase their pure self-presentation posts while women extend their circle of followings. In its approval, the longitudinal analysis shows that when a higher engagement rate is achieved, men and women champions lose their tendency to increase self-presenting posts and the number of followings, respectively. In the light of relative theories and the previous literature, these findings contribute to a better understanding of athletes' cyber behavior in social media. Moreover, the findings serve as a guide for sport researchers seeking to grasp the ways that aid athletes to better interact with their followers and build the personal brand, which involves sponsorship and other promotional opportunities.

Title: Quantum Period Finding against Symmetric Primitives in Practice. Abstract: We present the first complete implementation of the offline Simon's algorithm, and estimate its cost to attack the MAC Chaskey, the block cipher PRINCE and the NIST lightweight candidate AEAD scheme Elephant. These attacks require a reasonable amount of qubits, comparable to the number of qubits required to break RSA-2048. They are faster than other collision algorithms, and the attacks against PRINCE and Chaskey are the most efficient known to date. As Elephant has a key smaller than its state size, the algorithm is less efficient and ends up more expensive than exhaustive search. We also propose an optimized quantum circuit for boolean linear algebra as well as complete reversible implementations of PRINCE, Chaskey, spongent and Keccak which are of independent interest for quantum cryptanalysis. We stress that our attacks could be applied in the future against today's communications, and recommend caution when choosing symmetric constructions for cases where long-term security is expected.

Title: Auxiliary Learning for Relation Extraction Abstract: Relation extraction aims to predict a semantic relation between entities in a sentence, which is usually regarded as a classification problem. However, due to the limited relation set, many semantic relations are labeled as a special artificial relation type, termed $no\_relation$, if they are beyond the predefined relation set...

Title: Event-Triggered Adaptive Fuzzy Setpoint Regulation of Surface Vessels With Unmeasured Velocities Under Thruster Saturation Constraints Abstract: This article investigates the event-triggered adaptive fuzzy output feedback setpoint regulation control for the surface vessels. The vessel velocities are noisy and small in the setpoint regulation operation and the thrusters have saturation constraints. A high-gain filter is constructed to obtain the vessel velocity estimations from noisy position and heading. An auxiliary dynamic filter with control deviation as the input is adopted to reduce thruster saturation effects. The adaptive fuzzy logic systems approximate vessel's uncertain dynamics. The adaptive dynamic surface control is employed to derive the event-triggered adaptive fuzzy setpoint regulation control depending only on noisy position and heading measurements. By the virtue of the event-triggering, the vessel's thruster acting frequencies are reduced such that the thruster excessive wear is avoided. The computational burden is reduced due to the differentiation avoidance for virtual stabilizing functions required in the traditional backstepping. It is analyzed that the event-triggered adaptive fuzzy setpoint regulation control maintains position and heading at desired points and ensures the closed-loop semi-global stability. Both theoretical analyses and simulations with comparisons validate the effectiveness and the superiority of the control scheme.

Title: Spatio-Temporal Constraint-Based Low Rank Matrix Completion Approaches for Road Traffic Networks Abstract: Road traffic sensing is pivotal in all the imperative services offered by Intelligent Transportation Systems (ITS). Both static and probe based traffic sensing technologies aid in the acquisition of road traffic parameters, represented as spatio-temporal traffic data matrices. These matrices suffer from inevitable data losses, urging accurate matrix reconstruction. Many Low Rank Matrix Completion (LR-MC) approaches have been formulated in the literature, with varied optimization problems and solution methods. The subsequent approaches have exploited spatio-temporal constraints to further reduce the reconstruction error. In this paper, these formulations are modified to develop two new LR-MC approaches, the Augmented Lagrangian Sparsity Regularized Matrix Factorization (AL-SRMF) and the Constrained Low Rank (C-LR). AL-SRMF replaces the Alternating Least Squares (ALS) solution method of SRMF with an AL based method. Optimization in C-LR inherits its temporal constraint from the Temporal and Adaptive Spatial constrained Low Rank (TAS-LR) approach, while adopting its spatial constraint similar to SRMF. Spatial constraint in both C-LR and AL-SRMF is deduced by computing an adjacency matrix that leverages graph notion of the geographical road traffic network. For experimental validations of the proposed approaches, traffic matrices of Californian road network are obtained from PEMS database. Investigations reveal that for a signal integrity (SI) of 0.5, C-LR demonstrated a Normalized Mean Absolute Error (NMAE) of 3.4% and a Root Mean Square Error (RMSE) of 3.2. With a slight increase in computational time, the AL-SRMF rendered least values of NMAE and RMSE, 2.4% and 2.3 respectively, demonstrating significantly better performance than the state-of-the-art approaches.

Title: State Estimation With Heading Constraints for On-Road Vehicle Tracking Abstract: In transportation networks, vehicle motions are usually subject to the constraints arising from the roads, preset sea-routes, or preset flight routes. Taking advantage of prior known information of such constraints generally produces better surveillance performance. The problem of state estimation with heading constraints, in which only the direction of the target trajectory is prior known, is considered. When only part of the trajectory information is available, the conventional constrained estimation methods cannot be used to produce constrained estimates. The heading constraints in two typical situations are investigated, one is with a straight line, the other is a circular arc. For the straight trajectory, two augmentation approaches are proposed to formulate the constraints. One, parameter augmentation, augments the base state by the y-intercept of the constraint straight line. In the other approach, state augmentation, the states at the past time step are used to augment the base state. Two corresponding heading constraints are formulated by the augmented state's elements and the prior known information about road direction. For a circular trajectory, the heading constraint is formulated using position and velocity components in the base state. Pseudo-measurements are constructed to incorporate the heading constraint into the estimators and result in three heading constraint Kalman filters (HCKFs). They are parameter augmentation HCKF and state augmentation HCKF in case of straight trajectory, and HCKF for circular trajectory. The discrimination of road segments at junctions is also discussed. Furthermore, the proposed constraint filters are integrated into the interacting multiple model estimator to handle on-road maneuvers with possible accelerations. Numerical simulations are conducted to evaluate the performances of the three HCKFs compared with existing methods.

Title: Introducing the Effects of Road Geometry Into Microscopic Traffic Models for Automated Vehicles Abstract: Road geometry (e.g. slope and curvature) has significant impacts on driving behaviours of low-level automated vehicles (AVs), but it has been largely ignored in microscopic traffic models. To capture these effects, this study proposes a generic approach to extend any (free-flow or car-following) microscopic models characterized by acceleration functions. To this end, three model extensions are developed, each of which can use different submodels for comparison. Their effectiveness is demonstrated with a microscopic free-flow model that represents the AVs' control logic. Finally, all possible combinations of the microscopic model and the model extensions are calibrated and cross-validated against data sets, which contain empirical trajectories of commercial adaptive cruise control (ACC) systems on different test tracks. Results suggest that two submodels, i.e., the nonlinear vehicle dynamics (NVD) and the radius difference method (RDM), can extend the microscopic model to effectively capture the effects of road slope and road curvature, respectively, on automated driving. Specifically, the NVD is the dominant factor contributing to increasing (by 34.9% on average) model accuracy. In addition, when simulating reckless turning behaviours (i.e. the vehicle turns at high speeds), the inclusion of the developed RDM is significant for model performance, and the models extended with both the NVD and the RDM can achieve the largest accuracy gains (39.6%).

Title: Cooperative Perception for Estimating and Predicting Microscopic Traffic States to Manage Connected and Automated Traffic Abstract: Real-time traffic state estimation and prediction are of importance to the traffic management systems. New opportunities are enabled by the emerging sensing and automation technologies to manage connected and automated traffic, particularly in terms of controlling trajectories of automated vehicles. Traffic information from connected and automated vehicles (CAV) and roadside detectors (RSD) can be fused and has great potential for providing detailed microscopic traffic states (i.e., vehicle speeds, positions) of all vehicles. In this paper, we propose a cooperative perception framework for this purpose. The proposed framework based on particle filtering is developed to provide an accurate estimation and prediction of the microscopic states of partially observed traffic systems, while accounting for different sources of errors that intrinsically exist in the system, including those from sensor data, vehicle movement, and process models. Selected freeway and arterial vehicle trajectory datasets from the Next Generation Simulation (NGSIM) program and CAV traffic simulation are applied to test the proposed methodological framework. The accuracy of position and speed estimation is between 50% and 70% when the CAV market penetration rate (MPR) is 12.5%, and between 80% and 90% when the MPR is 50%. The incorporation of RSD data can further increase the accuracy by up to 10% under low CAV MPRs. The framework can also provide an accurate short-term prediction (i.e., 5 - 15 seconds) of position and speed with 60% to 90% accuracy. The proposed framework provides efficient and accurate estimations and predictions of detailed microscopic traffic states, even at low CAV MPRs, creating dynamic traffic environment world models to enable fine control and management of the connected and automated traffic systems.

Title: DIMA: Distributed cooperative microservice caching for internet of things in edge computing by deep reinforcement learning Abstract: The ubiquitous Internet of Things (IoTs) devices spawn growing mobile services of applications with computationally-intensive and latency-sensitive features, which increases the data traffic sharply. Driven by container technology, microservice is emerged with flexibility and scalability by decomposing one service into several independent lightweight parts. To improve the quality of service (QoS) and alleviate the burden of the core network, caching microservices at the edge of networks empowered by the mobile edge computing (MEC) paradigm is envisioned as a promising approach. However, considering the stochastic retrieval requests of IoT devices and time-varying network topology, it brings challenges for IoT devices to decide the caching node selection and microservice replacement independently without complete information of dynamic environments. In light of this, a MEC-enabled di stributed cooperative m icroservice ca ching scheme, named DIMA, is proposed in this paper. Specifically, the microservice caching problem is modeled as a Markov decision process (MDP) to optimize the fetching delay and hit ratio. Moreover, a distributed double dueling deep Q-network (D3QN) based algorithm is proposed, by integrating double DQN and dueling DQN, to solve the formulated MDP, where each IoT device performs actions independently in a decentralized mode. Finally, extensive experimental results are demonstrated that the DIMA is well-performed and more effective than existing baseline schemes.

Title: Edge-cloud-enabled matrix factorization for diversified APIs recommendation in mashup creation Abstract: A growing number of web APIs published on the Internet allows mashup developers to discover appropriate web APIs for polishing mashups. Developers often have to manually pick and choose several web APIs from extremely massive candidates, which is a laborious and cumbersome task. Fortunately, recommender system comes into existence. Some approaches perform recommendations in cloud platforms by utilizing historical records of Mashup-API interactions stored in edge nodes. However, many of these methods often pay more attention to recommendation accuracy while ignoring recommendation diversity, i.e., there are usually popular web APIs in recommendation list while most of the other novel web APIs are absent. The poor recommendation diversity may limit the usefulness of the recommendation results due to the lack of novelty. In order to implement an accurate and diversified web API recommendation, a novel MF-based recommendation approach named Div_PreAPI is put forward in this paper. Div_PreAPI integrates a weighting mechanism and neighborhood information into matrix factorization (MF) to implement diversified and personalized APIs recommendations. Finally, we conduct a series of experiments on a real-world dataset. Experimental results show the effectiveness of our proposal.

Title: Efficient resource-aware control on SIP servers in 802.11n wireless edge networks Abstract: Voice transmission over wireless edge networks is a conventional, cost-effective technology to transmit voice calls over 802.11 edge networks. In this type of service, Session Initiating Protocol (SIP) is responsible for initiating sessions. Research has shown that the call control algorithm that controls SIP sessions can also guarantee the quality of the medium during audio data transmission. In spite of decision-making in wireless access points and changing the parameters of the medium access sublayer (MAC), the existing algorithms are complicated and cannot adequately ensure service quality and efficient use of system resources. Therefore, the purpose of this study is to propose a new algorithm that enables a SIP server to decide to admit or reject an incoming call, in concise time. This algorithm considers the dynamic parameters of the network such as the location of node, channel busyness rate and real-time traffic percentage of the channel without changing the settings of MAC sublayer. The location of each system depending on the access point that transmits the call has a great impact on how the required service is provided. Implementation of this location-aware method on a real network testbed is indicative of remarkable superiority over the recently proposed methods in terms of service quality as measured by parameters, such as response time, call duration, loss-rate, and delay in real-time packets.

Title: LSH-aware multitype health data prediction with privacy preservation in edge environment Abstract: With the increasing development of electronic technology, traditional paper-driven medical systems have been converting to efficient electronic records that can be easily checked and transmitted. However, due to system updating and equipment failure, missing data problems are very common in the healthcare field. Health data can help people evaluate their health status and adjust their fitness. Therefore, predicting missing health data is a current pressing task. There are two challenges when predicting missing data: (1) people’s health data are complex. The data contain multiple data types (such as continuous data, discrete data and Boolean data) and (2) privacy issues are raised at the edge because huge amounts of health data are published while the edge devices can only provide limited computing and storage resources. Therefore, a novel multitype health data privacy-aware prediction approach based on locality-sensitive hashing is proposed in this paper. Through locality-sensitive hashing, our proposed method can realize a good tradeoff between prediction accuracy and privacy preservation. Finally, through a set of experiments deployed on the WISDM dataset, we verify the validity of our approach in dealing with multitype data and attaining user privacy.

Title: Time-aware scalable recommendation with clustering-based distributed factorization for edge services Abstract: Joint edge and recommendation have recently drawn much attention, as massive candidate edge applications are available and accessed by end-users. Recommendation technology has been proved to be a significant technique to help people to find their interests. Though recent researches of collaborative recommendation models have achieved some great success, which still suffer the data spareness. With the explosive growth of edge services, real time recommendation is urgent. In addition, most of the existing collaborative recommendation algorithms make predictions with only rating information, which fail to model user preferences accurately. Based on these observations, a time-aware scalable collaborative model with trust-clustering and scalable factorization is presented. It aims to provide real-time edge recommendations. Firstly, for a better understanding of user preferences, textual information and temporal information are integrated for better preference estimates. Then a fuzzy-clustering-based neighbor searching model is presented to cluster users into trust groups. Moreover, a distributed -learning-based tensor factorization mechanism is employed to mine user preferences over time for real time predictions. Finally, experiments conducted on well-known recommendation datasets show that our proposal achieves good performance in both recommendation accuracy and efficiency.

Title: A distributed learning based sentiment analysis methods with Web applications Abstract: The main challenge of using deep learning (DL) for sentiment analysis tasks is that insufficient data leads to a decrement in classification accuracy. In addition, privacy issues are always concerned for sentiment data analysis. To tackle the above two mentioned problems, We propose a model based on the federated learning framework (Fed_BERT_MSCNN), which contains a Bidirectional Encoder Represent-ations from Transformers (BERT) module and a multi-scale convolution layer. It uses the BERT_MSCNN model for training on the data sets of multiple companies, and employs the federated learning framework to collect the model parameters of different distributed nodes. Finally, these model parameters are transmitted to the central node. The central node performs a weighted average of all model parameters, sending a set of common model parameters to the distributed nodes. According to the experimental results, the proposed model performs better than the state-of-the-art models in terms of accuracy, F1-score, and computational efficiency. In addition, we optimize the model parameters in order to practice in distributed computing models for web applications.

Title: An edge-assisted cloud framework using a residual concatenate FCN approach to beam correction in the internet of weather radars Abstract: Internet of Things (IoT) has been rapidly developed in recent years, being well applied in the fields of Environmental Surveillance, Smart Grid, Intelligent Transportation, and so on. As one of the typical earth-based meteorological observation methods, networked Doppler weather radars, i.e. the Internet of weather Radars (IoR) can detect the signals of large-area water particles in the atmosphere with high resolution, but suffer from beam blockage due to surrounded mountains, buildings, as well as other obstacles. In addition, how to establish a distributed platform for large-scale radar data analytics becomes critical and challenging, especially considering optimised strategies on the storage, processing and exchange of radar raw data, beam/echo signal, and final products etc. In this paper, an edge-assisted cloud framework is proposed to facilitate effective and proficient communication and progression, where echo signal from a single site radar can be analysed and pre-processed at the edge, and then trained in the cloud with elastic resources and distributed learning ability. A Residual Concatenate Fully Convolutional Network (RC-FCN) is presented for beam blockage correction, which is integrated into the framework to be compared with other deep learning models, including FCN, ResNet, VGG, etc. According to experiment results, better performance and efficiency have been achieved using the proposed framework and its fitted RC-FCN model.

Title: MDHandNet: a lightweight deep neural network for hand gesture/sign language recognition based on micro-doppler images Abstract: Edge computing is agreed to provide services on the network edge closer to the terminal, including all technologies on the Internet of Things (IoT). Today, various terminals running on the edge of the network, such as mobile phones, notebook computers, handheld game consoles, wearable devices, and the resurgent virtual reality devices, have developed into the main platform for accessing and interacting information. Due to the limitations of size and application environment, these edge terminals need new interaction ways that adapt to them and meet human preferences. As an interactive way, gesture interaction is intelligent, convenient, and intuitive. It can get rid of the limitations of device size and application environment. It is one of the most expressive natural interactive ways of human beings and is most suitable for portable terminals. As a way of communication, gesture is also a non-verbal way of communication for people to express their thoughts and feelings. Sign language is considered as a structured gesture and used as an information communication system. Hand gesture recognition (HGR)/sign language recognition (SLR) technology can make machines understand people’s actions and their meanings. It is the basis of various IoT applications such as gesture interaction and sign language communication services at the edge of the network and has broad application prospects. In this paper, we develop an embedded measurement system for HG/SL actions based on an ultra-wideband radar and measure the radar echo signals of 15-type sign language actions, form a micro-Doppler (MD) image dataset. According to the characteristics of MD images, we propose a novel lightweight network MDHandNet for HGR/SLR, and the recognition accuracy is 97.1%. Compared with other competitive methods, the proposed MDHandNet not only has encouraging performance advantages, and but also has less parameters and lower computational complexity.

Title: FedAda: Fast-convergent adaptive federated learning in heterogeneous mobile edge computing environment Abstract: With rapid advancement of Internet of Things (IoT) and social networking applications generating large amounts of data at or close to the network edge, Mobile Edge Computing (MEC) has naturally been proposed to bring model training closer to where data is produced. However, there still exists privacy concern since typical MEC frameworks need to transfer sensitive data resources from data collection end devices/clients to MEC server. So the concept of Federated Learning (FL) has been introduced which supports privacy-preserved collaborative machine learning involving multiple clients coordinated by the MEC server without centralizing the private data. Unfortunately, FL is prone to multiple challenges: 1) systems heterogeneity between clients causes straggler issue, and 2) statistical heterogeneity between clients brings about objective inconsistency problem, both of which may lead to a significant slow-down in the convergence speed in heterogeneous MEC environment. In this paper, we propose a novel framework, FedAda (Federated Adaptive Training), that incorporates systems capabilities and data characteristics of the clients to adaptively assign appropriate workload to each client. The key idea is that instead of running a fixed number of local training iterations as in Federated Averaging (FedAvg), our algorithm adopts an adaptive workload assignment strategy by minimizing the runtime gap between clients and maximizing convergence gain in heterogeneous MEC environment. Moreover, we design a light mechanism extending FedAda to accelerate the convergence speed by further fine-tuning the workload assignment based on the global convergence status in each communication round. We evaluate FedAda on CIFAR-10 dataset to explore the performance of the algorithm in the simulated heterogeneous MEC environment. Experimental results show that FedAda is able to assign appropriate amount of workload to each client and substantially reduces the convergence time by up to 49.5% compared to FedAvg in heterogeneous MEC environment. In addition, we demonstrate that fine-tuning the workload assignment can help FedAda improve the learning performance in heterogeneous mobile edge computing environment.

Title: Task offloading for vehicular edge computing with edge-cloud cooperation Abstract: Vehicular edge computing (VEC) is emerging as a novel computing paradigm to meet low latency demands for computation-intensive vehicular applications. However, most existing offloading schemes do not take the dynamic edge-cloud computing environment into account, resulting in high delay performance. In this paper, we propose an efficient offloading scheme based on deep reinforcement learning for VEC with edge-cloud computing cooperation, where computation-intensive tasks can be executed locally or can be offloaded to an edge server, or a cloud server. By jointly considering: i) the dynamic edge-cloud computing environment; ii) fast offloading decisions, we leverage deep reinforcement learning to minimize the average processing delay of tasks by effectively integrating the computation resources of vehicles, edge servers, and the cloud server. Specifically, a deep Q-network (DQN) is used to adaptively learn optimal offloading schemes in the dynamic environment by balancing the exploration process and the exploitation process. Furthermore, the offloading scheme can be quickly learned by speeding up the convergence of the training process of DQN, which is good for fast offloading decisions. We conduct extensive simulation experiments and the experimental results show that the proposed offloading scheme can achieve a good performance.

Title: FlowSpectrum: a concrete characterization scheme of network traffic behavior for anomaly detection Abstract: As the 5G rolls out around the world, many edge applications will be deployed by app vendors and accessed by massive end-users. Efficient detection of malicious network behavior is paid more and more attention. The current traffic detection work is still stuck on the analysis of high-dimensional data. It will restrict the improvement of threat monitoring and network governance when facing massive network flows. Characterization of network flows within simple domains is required to simplify the process of network analysis. Traffic characterization is a key task that allows service providers to detect and intercept anomalous traffic, such that high QoS (Quality of Service) and service availability are maintained and spread of malicious content is prevented. Unfortunately, there is still a lack of research on the concrete characterization of network data. Analogous to spectrum, in this paper, we proposed the concept of FlowSpectrum for the first time in order to represent the network flow, concretely. In the FlowSpectrum, network flow is represented as a spectral line rather than the raw data or a feature vector of the network flow. All flows are able to be mapped as spectral lines, and traffic identification is achieved by analyzing the positions of spectral lines. FlowSpectrum can significantly reduce the complexity of network traffic behavior analysis while enhancing the interpretability of detection and facilitating cyberspace behavior management. We designed a neural network structure based on semi-supervised AutoEncoder for decomposition and dimensionality reduction of network flows in FlowSpectrum. The characterization capability of FlowSpectrum is proved by thorough experiments. Moreover, we realized the correspondence between network behaviors and intervals of spectral lines, preliminarily. Generally speaking, FlowSpectrum can provide new ideas for the field of network traffic analysis.

Title: A survey on nature-inspired techniques for computation offloading and service placement in emerging edge technologies Abstract: Internet of Things (IoT) aims to make an environment more innovative and productive by connecting physical things to the internet. Processing generated data from IoT devices and actuation warranted in real-time requires computational infrastructure near the edge to get the outcome without delay. Emerging edge technologies such as Fog computing, Multi-Access Edge Computing, and Cloudlet provide computing resources near the edge, i.e. closer to the IoT devices, where devices can place their services/applications or offload their computational job for processing. The utilization of computing resources provided by emerging edge technologies addresses the issue of delay in the outcome and increases the battery life of IoT devices/End-user devices. Computational resources provided by the edge technologies, i.e. edge/fog nodes, can be heterogeneous, dynamic and mobile. Therefore, service placement and computation offloading on edge/fog nodes are challenging issues, and the problem to finding the best suitable fog/edge nodes is NP-Hard. Nature-inspired algorithms provide robust solutions to NP-Hard problems. Nowadays, nature-inspired algorithms have been widely applied for resource allocation for service placement and computation offloading in emerging edge technologies. In this work, we provide a detailed study on the applications of nature-inspired algorithms in emerging edge computing domains. We provide an overview of emerging edge technologies, related quality parameters and nature-inspired algorithms followed by the basic formulation of service placement and computation offloading in emerging edge computing systems. In this work, we classify the works in emerging edge computing applying nature-inspired algorithms into two categories: works related to service placement and works related to offloading. We provide a thorough review and comparison of the existing nature-inspired approaches in each category. We discuss various open issues at the end to set future research directions.

Title: Joint optimization of delay and cost for microservice composition in mobile edge computing Abstract: With the development of software technology, some complex mobile and Internet-of-Things (IoT) applications can be constituted by a set of microservices. At present, mobile edge computing (MEC) has been used for microservice provision to achieve faster response speed and less network pressure. Based on container technology, microservices can be easily deployed in the MEC environment, while multiple microservice instances in multiple locations need to be selected to provide services for a large number of users geographically distributed. How to fully consider the service response time, scheduling the startup and running strategies of microservice instances with the least resource cost for multiple mobile edge servers is the core problem of microservice composition. In this paper, we propose a multi-objective evolutionary approach (MSCMOE) based on improved NSGA-III to minimize the service access delay and network resource consumption in the process of microservice composition. In order to maintain the diversity of the population, we use the improved reference point strategy to enhance the computational efficiency of seeking elite solutions in the non-dominated layer. Experimental results based on a real data set of Shanghai Telecom demonstrate that MSCMOE can effectively reduce network resource consumption while reducing service request time.