Reviewerly/exHarmony huggingface dataset:
A nonlinear kernel SVM classifier via <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e5125" altimg="si83.svg"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn><mml:mo>/</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math> soft-margin loss with classification performance

Recent advance in linear support vector machine with the 0-1 soft-margin loss (L0/1-SVM) shows the ability to solve the 0-1 loss problem directly. However, its theoretical and algorithmic requirements restrict us from directly extending the linear solving framework to its nonlinear kernel form. The lack of an explicit expression of the Lagrangian dual function of L0/1-SVM is a major shortcoming among them. By applying the nonparametric Representation Theorem, we propose a nonlinear model for support vector machine with 0-1 soft-margin loss, called L0/1-KSVM, which skillfully incorporates the kernel technique, and more importantly, follows the success in systematically solving its linear problem. The optimal condition is theoretically explored and a working set selection alternating direction method of multipliers (ADMM) algorithm is introduced to obtain its numerical solution. Furthermore, we first introduce a closed-form definition to the support vector (SV) of L0/1-KSVM. Theoretically, we prove that all SVs of L0/1-KSVM are only located on the parallel decision surfaces. The experimental results show that L0/1-KSVM has much fewer SVs compared to its linear counterpart and the other six nonlinear benchmark SVM classifiers, while maintaining good prediction accuracy.
Self-Adaptive Deep Asymmetric Network for Imbalanced Recommendation

Recently, rating prediction based on deep models has been a promising way to the personalized recommendation for the problem of information overloaded, which is a crucial problem to evaluate users' preferences for different items. In reality, the rating distribution in training data being mostly imbalanced in that the number of low ratings is often much less than that of high ratings, which is the biased prediction or imbalanced dataset problem in the recommendation. However, due to the degree of imbalance always varying greatly in different scenarios, most of the existing imbalanced recommendation methods fix the model parameters and cannot adapt to the change of the ratio between frequent and rare ratings in different datasets. In this article, we propose a novel Self-adaptive Deep Asymmetric Network (SDAN) to address the problem of imbalanced recommendation, which can automatically adjust the model based on the degree of imbalance in the recommendation dataset and accurately predict users' frequent and rare ratings of items. Specifically, two different autoencoders are first introduced to learn the user-based and item-based feature representations. Then the self-adaptive deep network is designed to automatically emphasize more on rare ratings and compensate for the bias prediction problem. Finally, the reconstructed approximated matrix with the features of users and items is utilized for predicting the ratings of user-item pairs. Through comprehensive experiments on several public recommendation datasets with varying imbalanced degrees, our SDAN consistently outperforms state-of-the-art methods.
BAKA: Biometric Authentication and Key Agreement Scheme Based on Fuzzy Extractor for Wireless Body Area Networks

Biometric and password-based two-factor authentication has received attention from the community over the past decades because of its simplicity, portability, and robustness. In Wireless Body Area Networks (WBAN), dozens of authentication and key agreement schemes have been proposed. Despite well-studied security issues, preserving user privacy in these schemes is still challenging. In this work, we propose Biometric-based Authentication and Key Agreement (BAKA), a scheme based on a fuzzy extractor for WBAN, where a novel biometric and password-based authentication algorithm is proposed by using a fuzzy extractor to achieve anonymous identity authentication, a privacy-preserving key agreement algorithm for session key security, and lastly, we deploy blockchain to record biometric information using its non-comparability and distributed storage to protect users’ privacy to a large extent. BAKA is secure as per formal security proof and informal security analysis, symmetric encryption is utilized to reduce computation overhead to improve the efficiency of BAKA, where security is not compromised. Extensive experiments to validate the performance of BAKA are performed, and the results demonstrate the security efficacy proposed.
PSSC: Practical and Secure Sidechains Construction for Heterogeneous Blockchains Orienting IoT

With the application of blockchain in IoT, various IoT scenario-oriented blockchains have been proposed, meaning that multi-chain future within IoT ecosystem is growing. Cross-chain interoperability thus is essential. Sidechains is one of the crucial technologies for cross-chain interoperability, which allows blockchains to interoperate with each other. However, sidechains encounters practicability challenges impeding adoption, such as poor generality and inefficiency. In this paper, we propose PSSC in the form of parent-child chains, a practical and secure sidechains construction for heterogeneous blockchains orienting IoT scenarios. To achieve practicability, we first utilize SNARK, which is succinct non-interactive proof (arguments) of knowledge, to reproduce the state transition of blockchains, so that PSSC can get rid of the constraints on consensus mechanisms of blockchains; we then design an impawn-punish mechanism based on smart contract, which transfers the work verifying SNARK proof from mainchain to sidechain, preventing fork threat to mainchain. Due to employing SNARK based on recursive proof composition to generate cross-chain proof, our PSSC is more efficient with succinct cross-chain proof, reducing the overhead of storage of nodes. Besides, we formally prove our PSSC satisfies the three fundamental security properties of sidechains: persistence, liveness and firewall. Finally, we develop a proof-of-concept implementation of PSSC, and the experimental results show that the proof size of PSSC is about 28.8 KB, which is roughly 2094×, 9.36×, 6.56×, 4.23× and 3.27× smaller than BTCRelay proof, PoW sidechains proof, PoS sidechains, zkRelay proof and Fast sidechains, respectively.
Generating and validating cluster sampling matrices for model-free factor screening

Morris' elementary effect-based screening (MM) has been widely used in a variety of domains to identify a few important factors among many possible ones. In MM, cluster sampling offers substantial computational savings over non-cluster sampling, but it remains a challenge to construct a cluster sampling matrix that generates any particular number of elementary effects for each factor. In this paper, we thoroughly address this issue. We uncover the mathematical association between distinct block sampling matrices within the complete cluster sampling matrix, by introducing a "dummy" sub-block matrix. By leveraging this property, we propose an easy-to-implement adaptive cluster sampling (ACS) method that is capable of identifying the appropriate sub-block sampling matrix to use. Its advantage over existing brute-force methods is that it can provide easy-to-obtain cluster sampling matrices, and it can be applied to computational model with any number of factors given a prior. We demonstrate the attractive properties of ACS using analytical proofs and simulation experiments. We show the robustness of ACS via a real-world case study. Our code for the algorithm is available online.
Self-Learning Modeling in Possibilistic Model Checking

Generalized possibility Kripke structure (GPKS) plays a key role in modeling fuzzy systems for possibilistic model checking. However, it is unrealistic, in practice, to produce manually a large number of fuzzy states of GPKSs and transition relations over states space. In this article, we develop an online supervised learning algorithm for GPKS to learn its fuzzy states and possibilistic transition matrix. We assume that true fuzzy states of GPKSs are unknown, only available knowledge is the external (sensor's) variables that have ambiguous connections with the states. We connect the sensor's variables to the atomic propositions used to describe the fuzzy states through a family of (Gaussian) fuzzy functions. The first GPKS's learning model is called the GPKS with Fuzzifier Sets (GPKS-FS) that consists of a standard GPKS and a group of fuzzy functions that connect model's states to different sensor variables. The learning algorithm based on stochastic gradient descent is derived to learn all parameters of the (Gaussian) fuzzy functions and all elements of atomic propositions evolution matrix used to explain mechanism of transition between system's states. Based on the evolution matrix, we propose a method of constructing the similarity-based possibilistic transition matrix. This produces the possibilistic transition matrix which is critical for model checking over GPKSs. The learning algorithm do not rely on any subjective information from humans and remove a significant bottleneck for modeling of possibilistic model checking. Computer simulation results showing learning performance.
Privacy-Preserving and Poisoning-Defending Federated Learning in Fog Computing

Federated learning (FL) has been widely applied in Internet of Things (IoT). However, two security problems hinder the proliferation of FL in practical IoT, i.e., privacy leakage and poisoning attacks. To address these problems, various approaches have been proposed from different perspectives. Nevertheless, there remain two critical challenges: i) how to establish a unified framework for protecting privacy and defending against poisoning attacks, and ii) how to implement such methods in the flexible computing architecture of fog computing. In this paper, we propose Crossbeam, a comprehensive scheme that provides both defense against poisoning attacks and privacy protection for federated learning in fog computing. Specifically, we construct frameworks to defend against poisoning attacks under both independent and identically distributed (IID) and non-IID settings. Meanwhile, we establish an actively secure framework to protect users’ privacy, building a bridge between privacy protection and poisoning defense. Our Crossbeam allows multiple fog nodes and users to collaboratively achieve the FL training. Besides, it can effectively alleviate the negative impact caused by poisoning attacks, meanwhile, users’ data confidentiality can still be guaranteed, even if multiple active fog nodes collude with each other to infer users’ privacy. Additionally, our scheme is of robustness to participants (fog nodes and users) being off-line during the training process. Moreover, benefited from the superiorities of our hierarchical mechanism and secure framework, our scheme can perform with high efficiency. We present rigorous security proof and extensive performance analysis for our Crossbeam.
Learning Scene-Pedestrian Graph for End-to-End Person Search

Person search aims to find specific persons from visual scenes, including two subtasks, pedestrian detection, and person reidentification. The dominant fashion in this area is end-to-end networks that focus on analyzing the foreground (i.e., pedestrian) while ignoring the background (i.e., scene) information. However, the scene information often offers useful clues for person search. For example, pedestrians normally appear on the road rather than the top of a tree, and pedestrians appearing at the same location are likely to have similar occlusions. The interplay between the pedestrians and scenes can potentially improve the performance. In this article, a novel scene-pedestrian graph (SPG) is proposed, which can explicitly model the interplay between the pedestrians and scenes. To polish the quality of pedestrian bounding boxes, we pioneer a strategy of using the high-quality pedestrian bounding box to guide the low-quality one in the same scene. In addition, we design a contextual and temporal graph matching algorithm to effectively utilize the contextual and temporal information present in the constructed SPG to improve the performance of pedestrian matching. Benefiting from the robustness on complex scenes, our model achieves promising performance over the state-of-the-art methods on two popular person search benchmarks, CUHK-SYSU and PRW.
Information geometric optimal reconfiguration of pulse-Doppler sensor parameters

The objective of this paper is to provide the mathematics needed to develop a new model for continuously adjusting the configuration parameters of a pulse-Doppler sensor so as to optimize information gain. The reconfiguration of the sensor parameters is optimal in the sense that the parameters are updated over time so as to traverse a geodesic over a parameter manifold. The main result is to compute the Fisher information metric for measuring the instantaneous information gained by a sensor regarding the target. In pursuing this goal, a method for evaluating a class of integrals comprising products of shifted sinc functions and derivatives of such functions is presented. Expressions are also found to allow estimates of the number of terms needed to evaluate the infinite series representation of the Fisher metric to within a given tolerance. Finally, steps for computing geodesics in the configuration manifold are provided.
Dynamic Event-Based Model Predictive Control of IT2 T–S Fuzzy System

For interval type-2 Takagi-Sugano (IT2 T-S) fuzzy systems, the model predictive control (MPC) synthesis is concerned under an improved dynamic event-triggered mechanism (DETM). The main point is an exponential decay term added in the error part. And then a more compact DETM-MPC algorithm is given for the MPC optimization problem with less data releases and signal updates. Moreover, the recursive feasibility of MPC optimization method is demonstrated and the asymptotic stability of the control system is proved, respectively. Finally, the effectiveness of the proposed method is verified by numerical simulations.
Analyzing Ta-Shma’s Code via the Expander Mixing Lemma

Random walks in expander graphs and their various derandomizations ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., replacement=zig-zag product) are invaluable tools from pseudorandomness. Recently, Ta-Shma used <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> -wide replacement walks in his breakthrough construction of a binary linear code almost matching the Gilbert-Varshamov bound (STOC 2017). Ta-Shma’s original analysis was entirely linear algebraic, and subsequent developments have inherited this viewpoint. In this work, we rederive Ta-Shma’s analysis from a combinatorial point of view using repeated application of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">expander mixing lemma</i> . We hope that this alternate perspective will yield a better understanding of Ta-Shma’s construction. As an additional application of our techniques, we give an alternate proof of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">expander hitting set lemma</i> .
Adaptive Block-Wise Regularization and Knowledge Distillation for Enhancing Federated Learning

Federated Learning (FL) is a distributed model training framework that allows multiple clients to collaborate on training a global model without disclosing their local data in edge computing (EC) environments. However, FL usually faces statistical heterogeneity (e.g., non-IID data) and system heterogeneity (e.g., computing and communication capabilities), resulting in poor model training performance. To deal with the above two challenges, we propose an efficient FL framework, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedBR</i> , which integrates the idea of block-wise regularization and knowledge distillation (KD) into the pioneering FL algorithm <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAvg</i> , for resource-constrained edge computing. Specifically, we first divide the model into multiple blocks according to the layer order of deep neural network (DNN). The server only sends some consecutive model blocks instead of an entire model to clients for communication efficiency. Then, the clients make use of knowledge distillation to absorb the knowledge of global model blocks to alleviate statistical heterogeneity during local training. We provide a theoretical convergence guarantee for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedBR</i> and show that the convergence bound will decrease as the increasing number of model blocks sent by the server. Besides, since the increasing number of model blocks brings more computing and communication costs, we design a heuristic algorithm (GMBS) to determine the appropriate number of model blocks for clients according to their varied data distributions, computing, and communication capabilities. Extensive experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedBR</i> can reduce the bandwidth consumption by about 31%, and achieve an average accuracy improvement of around 5.6% compared with the baselines under heterogeneous settings.
RARSMSDou: Master the Game of DouDiZhu With Deep Reinforcement Learning Algorithms

Artificial Intelligence (AI) has seen several breakthroughs in some perfect- and imperfect-information games, such as Go, Texas Hold'em, and StarCraft II. However, the Chinese poker game, DouDiZhu presents new challenges for AI systems to overcome, including infering imperfect information, training with sparse rewards, and handling a large state-action space. This article describes our proposed DouDiZhu AI system, RARSMSDou, based on Deep Reinforcement Learning (DRL) algorithms that combines Proximal Policy Optimization (PPO), Relative Advantage Reward Shaping with Minimum Splits (RARSMS), and Deep Monte-Carlo (DMC) into a self-play framework. In RARSMSDou, we propose RARSMS as a novel intrinsic reward to guide the training for PPO in a sparse reward environment. We treat the imperfect information as observable information and feed it into the critic-network of PPO, and we propose abstract actions to simplify the large-action space (27,472 actions) to a low-dimensional action space (309 actions contain 189 specific actions and 120 abstract actions) which is output by the policy network of PPO. When the policy is an abstract action, DMC (DouZeroX) maps this abstract action to its specific action as a policy for training or execution. We compare the performance of RARSMSDou with its four variants (PPO, PPO+RARSMS, PPO+DMC, DMC (DouZeroX)) and five state-of-the-art DouDiZhu AI programs. The experiment results show that after 30 days of self-play and training, RARSMSDou outperforms its variants and DouZero (with a WP of 0.582 and an ADP of 0.414), which is the best DouDiZhu baseline.
Algorithms for Constructing Balanced Plateaued Functions With Maximal Algebraic Degrees

It is important to study constructions of plateaued functions with balancedness and high algebraic degrees for preventing cryptographic attacks. Our goal of this paper is to find practical construction methods for producing infinite families of balanced <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> -plateaued functions with maximal algebraic degrees for every positive integer <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> . We first present a theoretical framework for secondary constructions of plateaued functions. From this framework, we derive three practical algorithms by controlling initial input vectors. These algorithms produce ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> +1)-plateaued functions from a given bent function and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> -plateaued functions in a recursive way for any nonnegative integer <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> ; therefore, we obtain <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> -plateaued functions for every <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> > <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> . Then we obtain three concrete construction methods of balanced <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> -plateaued functions with maximal algebraic degrees from the algorithms. For implementation, in the tables, we list up some initial bent (0-plateaued) functions, which guarantee the maximality of algebraic degrees of plateaued functions. Furthermore, we discuss the complexities of the three algorithms, which shows the feasibility of our methods. We emphasize that this is the first time to give constructions of balanced <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> -plateaued functions with maximal algebraic degrees for every positive integer <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> as far as we know.
Adaptive Indoor People-Counting System Based on Edge AI Computing

Since the outbreak of COVID-19 in 2019, people counting in confined spaces has become essential for controlling the flow of people and reducing viral spread. Many people-counting systems use a fisheye lens to achieve a wide viewing range. However, the images captured with a fisheye lens can have different sizes and deformations due to different camera heights, which consequently affects the accuracy of people counting. In this study, a Raspberry Pi 4 development board attached to a fisheye lens was used to construct an edge computing platform for people counting by using artificial intelligence. A modified You Only Look Once (YOLO) model, which has three output layers and a spatial pyramid pooling module, is proposed to recognize people in images captured at various heights. For efficiently running this deep learning model on the proposed edge computing platform, a TensorFlow Lite framework was used to reduce the model size. The final experimental results indicated that the mean average error and mean squared error of the modified tiny version of the YOLO v4 model were 0.249 and 0.292, respectively, and the detection speed of the platform was 2 frames per second. The results indicate that the proposed edge computing platform can efficiently and accurately count the number of people. Because the proposed system uses edge computing, it can effectively solve personal privacy and transmission bandwidth problems.