Joint Edge Server Selection and Data Set Management for Federated-Learning-Enabled Mobile Traffic Prediction

Abstract: To realize intelligent network management for future 6G-mobile edge computing (MEC) systems, mobile traffic prediction is crucial. Most of the previous machine learning-driven prediction approaches adopt traditional centralized training paradigm wherein mobile traffic data should be transferred to a central server. To exploit the distributed and parallel processing nature of MEC servers for training mobile traffic prediction models in a fast and secure manner, we propose a novel federated learning (FL) framework wherein locally trained prediction models over MEC servers are aggregated into a global model with joint optimization of MEC server selection and dataset management for FL participation. From mathematical investigations of the influence of MEC server participation and dataset utilization on the global model accuracy and training costs, including both training latency and energy consumption in the FL process, we first formulate an optimization problem for balancing the accuracy-cost trade-off by considering a linear accuracy estimation model. Here, the optimization problem is designed using mixed-integer nonlinear programming, which is generally known as NP-hard. We then leverage a number of relaxation techniques to develop near-optimal yet plausible algorithm based on linear programming. Furthermore, for practical concern, the proposed problem is extended by considering a concave accuracy estimation model; a genetic-based heuristic approach to the extension is proposed for determining the suboptimal solution. The numerical and simulation results suggest that our proposed framework can be effective for building mobile traffic prediction models in a more cost-efficient manner while maintaining competitive prediction accuracy.


Enhanced Syntactic and Semantic Graph Convolutional Network With Contrastive Learning for Aspect-Based Sentiment Analysis

Abstract: Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity of a given specific aspect in the sentence. Recent studies focus on leveraging graph convolutional neural networks to encode both syntactic and semantic information. However, current syntactic parsers, which are not specifically for ABSA, introduce noise to the syntactic information. Besides, ongoing studies ignore the distinctiveness of semantics and syntax. To address these issues, we proposed an enhanced syntactic and semantic graph convolutional network (GCN) with contrastive learning in this article. An aspect-oriented syntactic graph is constructed with aspect-specific perturbed masking for reducing the syntactic noise, and a semantic graph is established with self-attention weights from bidirectional encoder representation from transformers (BERT). The semantic and syntactic representations are further enhanced by both sentiment polarity-based supervised contrastive learning and syntactic reliability-based unsupervised contrastive learning. Furthermore, label embeddings of syntactic reliability are learned to determine the weights of syntactic and semantic information. Extensive experiments on four publicly available datasets demonstrate that our model is more competitive than the state-of-the-arts.


E3-UAV: An Edge-Based Energy-Efficient Object Detection System for Unmanned Aerial Vehicles

Abstract: Motivated by the advances in deep learning techniques, the application of Unmanned Aerial Vehicle (UAV)-based object detection has proliferated across a range of fields, including vehicle counting, fire detection, and city monitoring. While most existing research studies only a subset of the challenges inherent to UAV-based object detection, there are few studies that balance various aspects to design a practical system for energy consumption reduction. In response, we present the E$^3$-UAV, an edge-based energy-efficient object detection system for UAVs. The system is designed to dynamically support various UAV devices, edge devices, and detection algorithms, with the aim of minimizing energy consumption by deciding the most energy-efficient flight parameters (including flight altitude, flight speed, detection algorithm, and sampling rate) required to fulfill the detection requirements of the task. We first present an effective evaluation metric for actual tasks and construct a transparent energy consumption model based on hundreds of actual flight data to formalize the relationship between energy consumption and flight parameters. Then we present a lightweight energy-efficient priority decision algorithm based on a large quantity of actual flight data to assist the system in deciding flight parameters. Finally, we evaluate the performance of the system, and our experimental results demonstrate that it can significantly decrease energy consumption in real-world scenarios. Additionally, we provide four insights that can assist researchers and engineers in their efforts to study UAV-based object detection further.


Improving Adversarial Robustness With Adversarial Augmentations

Abstract: Deep neural networks (DNNs)-based applications are extensively being researched and applied in the Internet of Things (IoT) devices in daily lives due to impressive performance. Recently, adversarial attacks pose a significant threat to the security of DNNs, adversarial training has emerged as a promising and effective defense approach for defending against such attacks. However, existing adversarial training methods have shown limited success in defending against attacks unseen during training, thereby undermining their effectiveness. Besides, generating adversarial perturbations for adversarial training requires massive expensive labeled data, which is a critical obstacle in the IOT applications of robust DNNs. In this paper, we first explore the effective data augmentations by implementing adversarial attacks with self-supervised in latent space. Then, we propose new loss metric functions that can avoid collapse phenomenon of contrastive learning (CL) by measuring the distances between adversarial augmented pairs. Based on extracted adversarial features in self-supervised CL, we propose a novel Adversarial Robust Learning (ARL) method, which implements adversarial training without any labels and obtains more general robust encoder network. Our approach is validated on commonly used benchmark datasets and models, where it achieves comparable adversarial robustness against different adversarial attacks when compared to supervised adversarial training methods. Additionally, ARL outperforms state-of-the-art self-supervised adversarial learning techniques in terms of achieving higher robustness and clean prediction accuracy for the downstream classification task.


Secure and Efficient Bloom-Filter-Based Image Search in Cloud-Based Internet of Things

Abstract: Image search is a hot topic, which has played a significant role in various Internet of Things (IoT) applications, such as disease diagnosis, face recognition, and fingerprint recognition. Meanwhile, the proliferation of images has led image owners to outsource images to the cloud for reducing local storage and computation burdens. Therefore, image search without compromising privacy over cloud has received considerable attention and extensively explored in the literature. Many Bloom filter-based schemes have been put forth in past years, however most of them suffer from high storage overhead, low false positive rate, and even expose the values in Bloom filter. To solve these challenges, in this paper, we first design a Merged and Repeated Indistinguishable Bloom Filter (MRIBF) index structure, which can reduce the storage overhead and achieve adaptive security with a low false positive rate. Then, with the MRIBF, we propose a secure and efficient Bloom Filter-based Image Search scheme (BFIS) to achieve a faster-than-linear and more accurate search. Detailed theoretical analysis shows that our scheme is really accurate and secure. Extensive experiments demonstrate that our scheme is indeed efficient and feasible.


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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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 ., 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 -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 . 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 .


Adaptive Block-Wise Regularization and Knowledge Distillation for Enhancing Federated Learning

Abstract: 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 , 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 , 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 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 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

Abstract: 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

Abstract: 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 -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 . 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 +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 -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 ; therefore, we obtain <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r -plateaued functions for every <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r > <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s . 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 -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 -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 as far as we know.


Adaptive Indoor People-Counting System Based on Edge AI Computing

Abstract: 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.


A Surrogate-Assisted Evolutionary Algorithm for Seeking Multiple Solutions of Expensive Multimodal Optimization Problems

Abstract: Surrogate-assisted evolutionary algorithms for expensive optimization problems have gained considerable attention in recent years. In many real-world optimization problems, we may face expensive optimization problems with multiple optimal solutions. Locating multiple optima for such expensive problems is qualitatively challenging. This study proposes a surrogate-assisted differential evolution based on region decomposition to seek multiple optima for expensive multimodal optimization problems. In this study, we have designed three major components: 1) the adaptive region decomposition, 2) the multilayer perceptron-based global surrogate, and 3) the self-adaptive gradient descent-based local search. First, the improved adaptive region decomposition detects promising subregions at the beginning phase, and continuously discards inferior subregions successively. Second, the multilayer perceptron-based surrogate and self-adaptive gradient-based mutation work in a collaborative manner on distinct sub-populations to seek multiple optimal solutions. Overall, an attempt has been made to solve expensive multimodal optimization problems. Systematic experiments on 20 test functions show the encouraging and promising performance of our proposed approach.


On the Geometry of Fixed Points and Discontinuity

Abstract: Recently, there has been a considerable effort to obtain new solutions to the Rhoades' open problem on the existence of contractive mappings that admit discontinuity at the fixed point. An extended version of this problem is also stated using a geometric approach. In this paper, we obtain new solutions to this extended version of the Rhoades' open problem. A related problem, the fixed-circle problem (resp. fixed-disc problem) is also studied. Both of these problems are related to the geometric properties of the fixed point set of a self-mapping on a metric space. Furthermore, a new result about metric completeness and a short discussion on the activation functions used in the study of neural networks are given. By providing necessary examples, we show that our obtained results are effective.


Enabling Efficient Privacy-Preserving Spatiotemporal Location-Based Services for Smart Cities

Abstract: GPS-enabled Internet of Things devices, which can obtain the location and temporal information of installed objects to promote Location Based Service, are completely changing our lives. In recent years, the confidentiality and privacy of personal data have attracted widespread attention, especially when outsourcing to third-party providers. To achieve both the confidentiality and availability of outsourced data, various Dynamic Searchable Symmetric Encryption (DSSE) schemes have been proposed. However, existing solutions are limited either in terms of security or efficiency. To address this challenge, we propose a Secure and Efficient Search over Encrypted Spatio-Temporal Data (SES-ESTD) scheme that utilizes constrained pseudo-random function and enhanced asymmetric scalar-product preserving encryption. Our scheme not only achieves high retrieval efficiency but also ensures forward security and content privacy. We provide a formal security analysis to prove SES-ESTD is forward secure and content private. Furthermore, extensive experiments indicate that SES-ESTD incurs lower computation and storage overheads compared to other schemes. Most importantly, SES-ESTD achieves millisecond-level retrieval for millions of data points and provides a retrieval speed that is 2.85 times faster than existing forward secure spatio-temporal DSSE schemes.


ASO-DKELM: Alpine skiing optimization based deep kernel extreme learning machine for elderly stroke detection from EEG signal

Abstract: Stroke is considered the third leading cause of mortality followed by cancer and heart disease worldwide. Stroke detection in advance is the only procedure to avoid health risks while it necessitates continuous monitoring and observation of subjects. Many research works already exist in the medical field to detect diseases in advance using machine learning techniques. However, their detection results are limited by several biasing factors such as more training time and imprecise detection. So a novel stroke detection system using the Deep Kernel Extreme Learning Machine based Alpine Skiing (DKELM-AS) approach is proposed to predict the probability of subjects being affected by stroke disease. The electroencephalogram (EEG) signals of normal and stroke patients are collected and utilized to investigate the proposed approach. The raw data are preprocessed and the features are extracted based on frequency ranges using the Fast Hartley Transform (FHT) technique. Finally, the proposed DKELM-AS approach classifies them as stroke-affected EEG or normal EEG based on the precursor symptoms obtained from EEG signals. The experimental results reveal that the proposed DKELM-AS approach achieves an overall accuracy rate of about 95.2% in stroke detection based on the precursor symptoms of EEG signals.


Novel Robust Parcel-Size Classification Using mmWave Radar

Abstract: A novel robust package-height estimation and parcel-size classification scheme is proposed in this work. A pertinent prototype system consisting of a millimeter-wave radar, a shipment track, and a stepping motor has been built for proof of concept. Three new schemes using the range-profile data produced by the mmWave radar, namely, the detection based-on cumulative distribution (DBCD) scheme, the detection based-on peak-value clustering (DBPC) scheme, and the DBCD–DBPC hybrid scheme are introduced here. For benchmark evaluation of the proposed new system, two practical scenarios, namely, the undisturbed scenario (no people is close to the system) and the disturbed scenario (some people is close to the system), are tested. This new system can overcome the serious drawback of the existing camera-assisted systems, which have to rely on sufficient lighting conditions, and operate normally even in complete darkness. Meanwhile, the proposed new system can accommodate parcels/packages in arbitrary orientations, which cannot be allowed by the current camera-assisted systems, during the parcel-size measurements. According to the test experiments, the proposed DBCD–DBPC hybrid scheme can reach up to a very high average classification accuracy of 96.4% for both undisturbed and disturbed scenarios. The proposed novel package-height estimation and parcel-size classification technique in this work can be very useful for intelligent inventory management and smart logistics in the future.


Anchor-Free Multi-UAV Detection and Classification Using Spectrogram

Abstract: The advancements in unmanned aerial vehicle (UAV) technology have brought immense convenience to society. However, unauthorized UAVs pose a serious threat to personal privacy, public safety, and aviation security. Therefore, accurate UAV detection and classification are crucial. Moreover, with the increased popularity of UAVs, the likelihood of multiple UAVs appearing in the same area simultaneously has also dramatically increased. Recent studies demonstrate that object detectors, such as FasterRCNN and YOLO, can be used to detect and classify multiple UAVs based on spectrograms. To our best knowledge, the object detectors are directly used to classify UAV without considering the characteristics of the UAV signal spectrogram, which results in a decrease in recognition performance. In this paper, we analyze the characteristics of the UAV signal spectrogram in detail and conclude two problems, i.e., prior anchor mismatch and cross-domain detection, hindering the implementation of object detector for UAV recognition. To solve prior anchor mismatch, we propose an anchor-free detector based on keypoint and design a novel keypoints matching algorithm to improve recognition performance. To solve cross-domain detection, we propose an adversarial learning based data adaptation method, which can generate domain-independent and domain-aligned features. Finally, the experiments adopt practical spectrogram and synthetic spectrogram to verify the superiority of the proposed anchor-free detector and the effectiveness of the proposed data adaptation method.


Recommendations with minimum exposure guarantees: A post-processing framework

Abstract: Relevance-based ranking is a popular ingredient in recommenders, but it frequently struggles to meet fairness criteria because social and cultural norms may favor some item groups over others. For instance, some items might receive lower ratings due to some sort of bias (e.g. gender bias). A fair ranking should balance the exposure of items from advantaged and disadvantaged groups. To this end, we propose a novel post-processing framework to produce fair, exposure-aware recommendations. Our approach is based on an integer linear programming model maximizing the expected utility while satisfying a minimum exposure constraint. The model has fewer variables than previous work and thus can be deployed to larger datasets and allows the organization to define a minimum level of exposure for groups of items. We conduct an extensive empirical evaluation indicating that our new framework can increase the exposure of items from disadvantaged groups at a small cost of recommendation accuracy.


EnsMulHateCyb: Multilingual hate speech and cyberbully detection in online social media

Abstract: Nowadays, users across the globe interact with one another for information exchange, communication, and association on various online social media. However, some individuals exploit these venues for malicious practices like hate speech and cyberbully. In this paper, we present an improved multilingual hate speech and cyberbully detection model using bagging-stacking based hybrid ensemble deep learning techniques. The proposed model utilizes Bi-directional Long Short-Term Memory (BiLSTM), Bi-directional Gated Recurrent Unit (Bi-GRU), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) techniques to enhance the overall performance. We first preprocess the multilingual data streams followed by adoption of Global vectors for word Representation (GloVe) embeddings to convert words to a vector representation in parallel enabling the data streams for binary classification task. In order to construct an architecture for the detection of hate speech and cyberbully, we introduce a heterogeneous fusion of multiple effective models in a unique approach such that CNN-LSTM utilizes a stacking approach with stochastic gradient descent to achieve optimal weights, whereas all the base learners used bagging ensemble approach with cross-validation to reach optimal weights. The final output layer of the proposed ensemble deep learning architecture is achieved using a super learner approach on base learners. To show the efficacy of the proposed model, we conduct the simulation on a total of nine real-world social media datasets in different languages and compared the results with other contemporary hate speech and cyberbully detection methods. The collected findings show that the proposed model outperforms other models on considered datasets and shows an improvement of at least 4.44% in F1 scores.


Enabling secure image transmission in unmanned aerial vehicle using digital image watermarking with H-Grey optimization

Abstract: Drone technology, also known as Unmanned Aerial Vehicles (UAVs), has advanced rapidly in the last decade owing to the huge number of users. This approach has an immense opportunity in areas like healthcare, agriculture, and forestry. However, the transmission of digital images via drone technology is associated with vulnerabilities and risks, due to a lack of efficient security solutions. Digital Image Watermarking (DIW) has emerged as a viable solution for digital image transmission in UAV applications. However, achieving robustness, security, and imperceptibility in a watermarking system at the same time is a difficult task. To address the aforementioned concerns, this paper proposes a secure digital image watermarking scheme in the hybrid DWT-SVD domain. The scaling factor is chosen adaptively from the hybrid Harmony-Grey Wolf (H-Grey) optimization algorithm to maintain a balance between imperceptibility and robustness. In addition, a novel symmetric cryptographic-based four-level encryption approach PSMD (Partitioning, Substituting, Merging, Division) is proposed to address UAV image security concerns by using Fibonacci and prime number series to generate two random keys. The proposed encryption scheme provides high security at low computational cost. The experimental results show that the visual quality and robustness are both high (Avg. PSNR = 41.27 dB and Avg. SSIM = 0.99, NC = 0.99, and BER = 0.0008 calculated for 100 images). The subjective and objective experimental analysis indicates that the proposed encryption scheme is highly secure and the computational cost is also low. The average embedding and extraction time is 0.25 s and 0.09 s It is resistant to various image processing attacks. A comparison with some of the most recent popular schemes confirms the scheme's effectiveness. The presented DIW can be used for copyright protection, UAV image transmission applications, military applications, and other purposes.


Galaxy: A Scalable BFT and Privacy-Preserving Pub/Sub IoT Data Sharing Framework Based on Blockchain

Abstract: The emergence of the Internet of Things (IoT) technology in recent years has led to a considerable amount of data to be shared across different organizations. The publish and subscribe (Pub/Sub) paradigm, with its asynchronous, one-to-many and decoupling characteristics, is considered to be a promising communication model in IoT. However, designing a Pub/Sub framework for IoT data sharing confronts two challenges: Byzantine faults and privacy concerns. Byzantine nodes that are subjectively malicious or hacked by attackers may discard or forge data in the broker network composed of untrusted IoT organizations. Unauthorized brokers or clients may try to obtain the content of publications or subscriptions, thus violating the IoT data privacy. Existing works have limitations in terms of relatively low scalability and high overhead in tackling these two challenges. In this paper, we propose Galaxy, a blockchain based Pub/Sub IoT data sharing framework. To achieve Byzantine fault-tolerant (BFT) Pub/Sub, Galaxy adopts sharding to improve scalability and achieve efficient BFT Pub/Sub workflow within each shard with a novel leader rotation scheme. In attaining privacy-preserving Pub/Sub, a secret key sharing and encrypted Pub/Sub scheme is designed in Galaxy to achieve low overhead without breaking the decoupling of the system. We implemented a prototype of Galaxy and deployed it on Alibaba Cloud for experimental evaluation. The experiment results show the feasibility and efficiency of Galaxy.


Multi-Label Feature Selection via Positive or Negative Correlation

Abstract: Feature selection, a meaningful preprocessing technique in machine learning, plays a key role in multi-label learning to select more discriminative features. Recently, multi-label feature selection algorithms based on sparse regression model have received extensive attention. However, in the existing sparse regression model, feature-label correlations are obtained from a single viewpoint via a loss function. With the aim of learning richer information, in this article, the sparse regression model is integrated with a new feature-label correlation, which allows different viewpoints to be considered simultaneously. For this reason, a new correlation, called positive or negative correlation, is proposed. Furthermore, in many existing multi-label feature selection algorithms, each feature is associated with all of the labels, while in many scenarios, each feature is associated with only some labels. Aiming at the above two shortcomings, a new multi-label feature selection algorithm is proposed in four stages. First, a pseudo-label space is built and the global label correlation is embedded into this space. Second, the positive or negative correlation between features and labels is calculated. Third, a new objective function is established. The weight matrix and feature ranking results are obtained through the optimization process. Final, using the weight matrix as prior knowledge, the proposed algorithm exploits label-specific and label-group-specific features techniques to further improve performance. Extensive experiments on over than 20 multi-label datasets show its superior performance.


Revisiting Nakamoto Consensus in Asynchronous Networks: A Comprehensive Analysis of Bitcoin Safety and Chain Quality

Abstract: The Bitcoin blockchain safety relies on strong network synchrony. Therefore, violating the blockchain safety requires strong adversaries that control a mining pool with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx$</tex-math> </inline-formula> 51% hash rate. In this paper, we show that the network synchrony does not hold in the real world Bitcoin network which can be exploited to feasibly violate the blockchain safety and chain quality. Towards that, first we construct the Bitcoin ideal functionality to formally specify its ideal execution model in a synchronous network. We then develop a large-scale data collection system through which we connect with more than 103K IP addresses of the Bitcoin nodes and identify 871 mining nodes. We contrast the ideal functionality against the real world measurements to expose the network anomalies that can be exploited to optimize the existing attacks. Particularly, we observe a non-uniform block propagation pattern among the mining nodes showing that the Bitcoin network is asynchronous in practice. To realize the threat of an asynchronous network, we present the attack that allows an adversary to orchestrate concurrent mining on multiple branches of the blockchain to violate common prefix and chain quality properties. We also propose the attack countermeasures by tweaking Bitcoin Core to model the Bitcoin ideal functionality. Our measurements, theoretical modeling, proposed attack, and countermeasures open new directions in the security evaluation of Bitcoin and similar blockchain systems.


SIFDriveNet: Speed and Image Fusion for Driving Behavior Classification Network

Abstract: Driving behavior classification is an important direction in the field of social transportation systems and advanced driving assistance system (ADAS), which has attracted more and more attention in recent years. An accurate driving behavior classification algorithm plays a great role in traffic safety, energy saving, and other fields. In this article, we propose a novel vehicle speed and image fusion for driving behavior classification network (SIFDriveNet), which classifies driver behaviors into normal driving, aggressive driving, and drowsy driving. Our method has the following key advantages. First, in the research of driving behavior classification, we are the first to introduce a 2-D image with rich roadside information and convert speeds into a 2-D spectrogram expressing the time–frequency characteristics of speeds through short-time Fourier transform (STFT) while unifying the data space of image information and speed information. Second, we propose a tensor fusion method based on weight decomposition to fully fuse the vectors of the two modalities. This method maps the tensor outer product results to the low-dimensional space through weight decomposition and has a low computational cost while maintaining the fusion effect of the tensor outer product. In addition, we evaluated our model on the public UAH-DriveSet and compared it with the most advanced model. Experimental results show that our model has a better performance, and F1-score is 97.9% on all roads. Especially on the secondary road, our F1-score is 99.4%. Also, our model has strong generalization, and we have reached 99.3% F1 in distracted driving multimodal dataset. In addition, the inference speed reaches 411 FPS, enabling real-time needs. The code is available on https://github.com/alu222/SIFDriveNet.


Modeling Long- and Short-Term Service Recommendations with a Deep Multi-Interest Network for Edge Computing

Abstract: Edge computing platforms enable application developers and content providers to provide context-aware services (such as service recommendations) using real-time wireless access network information. How to recommend the most suitable candidate from these numerous available services is an urgent task. Click-through rate (CTR) prediction is a core task of traditional service recommendation. However, many existing service recommender systems do not exploit user mobility for prediction, particularly in an edge computing environment. In this paper, we propose a model named long and short-term user preferences modeling with a multi-interest network based on user behavior. It uses a logarithmic network to capture multiple interests in different fields, enriching the representations of user short-term preferences. In terms of long-term preferences, users' comprehensive preferences are extracted in different periods and are fused using a nonlocal network. Extensive experiments on three datasets demonstrate that our model relying on user mobility can substantially improve the accuracy of service recommendation in edge computing compared with the state-of-the-art models.


A Server Placement Algorithm for Reducing Risk and Improving Power Utilization in Data Centers

Abstract: As the power demand in data centers is increasing, the power capacity of the power supply system has become an essential resource to be optimized. Although many data centers use power oversubscription to make full use of the power capacity, there are unavoidable power supply risks associated with it. Therefore, how to improve the data center power capacity utilization while ensuring power supply security has become an important issue. To solve this problem, we first define it and propose a risk evaluation metric called Weighted Power Supply Risk (WPSRisk). Then, a method, named Hybrid Genetic Algorithm with Ant Colony System (HGAACS), is proposed to improve power capacity utilization and reduce power supply risks by optimizing the server placement in the power supply system. HGAACS uses historical power data of each server to find a better placement solution by population iteration. HGAACS possesses not only the remarkable local search ability of Ant Colony System (ACS), but also enhances the global search capability by incorporating genetic operators from Genetic Algorithm (GA). To verify the performance of HGAACS, we experimentally compare it with five other placement algorithms. The experimental results show that HGAACS can perform better than other algorithms in both improving power utilization and reducing the risk of power supply system.


Lesson Learned from COVID-19 Retrospective Study: An Entropy-Based Clinical-Interpretable Scorecard for Mortality Risk Control at ICU Admission

Abstract: With severe acute respiratory syndrome coronavirus 2 spreading globally and causing 2019 coronavirus disease (COVID-19), a challenge that we unprepared for was about how to optimally plan and distribute limited top-medical resources for patients in need of urgent care. To address this challenge, physicians desperately needed a scientific tool to methodically differentiate between cases with varying severity. In this study, the unique data of COVID-19 intensive care unit (ICU) patients provided by the national medical team in Wuhan were classified into discrete and continuous variable types. All continuous data were discretized using an entropy-based method and transformed into serial information margins, in which each information margin is related to a specific symptom or clinical meaning. Finally, all these native and processed discrete data were used to configure a readable scorecard through logistic regression, which is the desired scientific tool aforementioned. A total of 322 ICU patients (age: [median: 64, interquartile range: 54–75], males: 178 [55.28%], and death: 72 [22.36%]) were included in the study. Probabilities of mortality in COVID-19 patients can be evaluated using a scorecard model (calibration slope: 1.343, Brier: 0.048, Dxy = 0.972, and population stability index = 0.071), with desired model performances (accuracy = 0.948, area under curve = 0.99, sensitivity = 1, and specificity = 0.939). This new model can interpret clinical meanings from complex data, and compare it with existing machine learning methods through a black-box mechanism. This new data-information model answers a critical question of how a computing algorithm produces clinically meaningful results that will help physicians logically allocate medical resources for COVID-19 patients. Notably, this tool has limitations, giving that this research is a retrospective study. Hopefully, this tool will be tested further and optimized for adaptation to similar clinical cases in the future.


ISFB-GAN: Interpretable semantic face beautification with generative adversarial network

Abstract: Face beautification is an emerging application widely used in the entertainment industry. Previous studies have proved that Generative Adversarial Networks (GANs) can successfully build the mapping from a general face to a target face. However, their facial style code in the latent space is taken as a black box with a lack of semantics interpretation. They usually fail to consider the geometric semantic information, and cannot focus on the facial beauty-related regions. Moreover, most of them change the background information. To overcome these problems, we propose a novel Interpretable Semantic Generative Adversarial Network (ISFB-GAN) for face beautification. First, we design a new dual-branch facial style encoder to factorize the abundant semantic information of the reference face into the facial geometry style codes and the appearance style codes in the latent space. Then, a novelty facial attention generator is proposed to preserve more background information in the generated images. In the attention generator, the facial style attention is designed to focus on the most discriminative facial region; the background attention is designed to retain the background information. Finally, we propose a hybrid loss function including geometric loss, orthogonality loss, segmentation loss, style reconstruction loss, perceptual loss, etc., to enhance the learning capacity and improve the visual quality. The combination of the geometric loss and the orthogonality loss is deployed to guarantee the disentanglement between the semantic information. Extensive experiments conducted on the CelebAMask-HQ dataset and the SCUT-FBP5500 dataset, demonstrate that ISFB-GAN performs better than the state-of-the-art methods in facial attractiveness enhancement.


Adaptive stepsize estimation based accelerated gradient descent algorithm for fully complex-valued neural networks

Abstract: Nesterov accelerated gradient (NAG) method is an efficient first-order algorithm for optimization problems. To ensure the convergence, it usually takes a relatively conservative constant as the stepsize. However, the choice of stepsize has a great impact on the optimization process. In this paper, two adaptive stepsize estimation methods are proposed for complex-valued NAG algorithm for efficient training of fully complex-valued neural networks. The basic idea of the first one is to adaptively determine suitable stepsize by estimating the local smoothness constant of the loss function with the norm of approximate complex Hessian matrix. Its validity is theoretically analyzed by means of the decomposition of complex matrix. Furthermore, by introducing a new parameter design method for multi-step quasi-Newton condition, an improved stepsize estimation is presented. Experimental results on pattern recognition, channel equalization, wind forecasting and synthetic aperture radar (SAR) target classification demonstrate the effectiveness of the proposed methods.


Graph isomorphism U-Net

Abstract: Graph embedding learning is a fundamental task when dealing with diverse datasets. While encoder–decoder architectures, such as U-Nets, have shown great success in image pixel-wise prediction tasks, applying similar methods to graph data poses challenges due to the lack of natural pooling and up-sampling operations for graphs. Recent methods leverage learnable parameters to extract structural information from neural networks and extend pooling and unpooling to graphs using node features and graph structural information. This paper proposes a novel model called GIUNet (Graph Isomorphism U-Net) for the graph classification task. The proposed Graph U-Net structure is based on graph isomorphism convolution while using a comprehensive pqPooling layer. The pqPooling layer in our approach effectively combines node features and graph structure information during the graph down-sampling stage. To incorporate graph structure information, we utilize both the spectral representation and node centrality measurements. Node centrality measurements capture various structural aspects of nodes in the graph, while the spectral representation helps us focus on the informative low-frequency components of the graph structure. Through ablation studies, we have demonstrated that leveraging the GIUNet model leads to significant improvements compared to state-of-the-art methods across multiple benchmark datasets.


Collaborative Learning of Different Types of Healthcare Data From Heterogeneous IoT Devices

Abstract: In the realm of healthcare data analysis, privacy concerns have been tackled by the Federated Learning (FL) framework. However, in the situation that heterogeneous healthcare Internet of Things (IoT) devices collect different types of data, applying FL becomes difficult. To train a model leveraging diverse healthcare IoT devices, we propose an advanced collaborative learning framework to fill the gap. With the proposed collaborative learning framework, individual IoT devices project their sensed features into a carefully developed latent space, which are transmitted to a central server. For privacy preservation, the latent local features are encoded within this space, while the samples’ labels remain securely stored in the individual IoT devices. Collaboratively, the deep neural network model is trained by both the central server and the diverse IoT devices. The central server handles the computationally intensive training processes, while the individual IoT devices evaluate the model’s performance and initiate back-propagation based on their locally stored labels. Experimental results demonstrate that the proposed collaborative learning framework achieves performance similar to centralized training and significantly outperforms individual training while preserving data privacy.


FDTR: Weakening feature disparity transformer for accurate multicategory computed tomography image segmentation

Abstract: Accurately segmenting object structures in computed tomography (CT) images is crucial for computer-aided surgery, diagnosis, and other interdisciplinary applications. Most state-of-the-art CT segmentation methods employ a classical jump structure to integrate shallow and deep feature information. However, the effectiveness of the current CT image segmentation and related subtasks must still be improved due to feature disparity between different layers and information transfer loss. To alleviate the problem, we propose a novel three-dimensional multicategory segmentation model, the weakening feature disparity transformer (FDTR), based on the transformer structure for CT imaging. Firstly, to effectively capture global features, we design a vision transformer-based encoder. Secondly, to enhance the model's information representation in a lightweight manner, we devise a nested structure of dense compression connections. Lastly, to mitigate the disparity in semantic features at shallow layers, we propose supervised semantic signals. By employing the vision transformer-based encoder and the dense connection structure, we enhance the detailed information of deep features. Additionally, the semantic supervisory signal introduces deep semantic information to the shallow features, enriching the overall feature representation. We conducted extensive experiments on the KIPA2022 dataset for multiorgan segmentation and the YC2022 dataset for volumetric trait segmentation in broiler breeding. The proposed method, FDTR, effectively enhances feature transfer between shallow and deep features, achieving optimal results compared to other advanced models and demonstrating broad prospects for application.


Consistent penalizing field loss for zero-shot image retrieval

Abstract: Zero-shot image retrieval involves retrieving images of unseen classes using a query image of the same class. To determine whether a given image is of the same class as the query image, a universal threshold of similarity measures is needed, as class-specific thresholds are not feasible for unseen classes. However, existing methods for zero-shot image retrieval focus on pushing a margin between intra-class and inter-class similarities for each class during the training phase. This approach can result in varying decision boundaries between intra- and inter-class similarities across classes, which could compromise performance when a universal threshold is used in the inference stage. Additionally, for classes with low intra-class variances or inter-class correlations, the pushing force of the margin-pushing approach might be too weak to learn high-quality embeddings. To address these issues, we propose a novel Consistent Penalizing Field (CPF) Loss for zero-shot image retrieval. The proposed method has a single consistent penalizing field for all classes, resulting in similar decision boundaries across classes. By penalizing samples outside the penalizing field, CPF Loss can better utilize the information of samples with highly unbalanced intra-class and inter-class correlations, and improve the discriminative power of DML learning for zero-shot image retrieval. Extensive experiments are conducted on the challenging Shopee Product Matching dataset and other established benchmarks, and the results demonstrate that the proposed method consistently outperforms the state-of-the-art methods. The code is available at https://github.com/cloudlc/CPF.


Explainable subgradient tree boosting for prescriptive analytics in operations management

Abstract: Motivated by the success of gradient boosting approaches in machine learning and driven by the need for explainable prescriptive analytics approaches in operations management (OM), we propose subgradient tree boosting (STB) as an explainable prescriptive analytics approach to solving convex stochastic optimization problems that frequently arise in OM. The STB approach combines the well-known method of subgradient descent in function space with sample average approximation, and prescribes decisions from a problem-specific loss function, historical demand observations, and prescriptive features. The approach provides a decision-maker with detailed explanations for the prescribed decisions, such as a breakdown of individual features' impact. These explanations are particularly valuable in practice when the decision-maker has the discretion to adjust the recommendations made by a decision support system. We show how subgradients can be derived for common single-stage and two-stage stochastic optimization problems; demonstrate the STB approach's applicability to two real-world, complex capacity-planning problems in the service industry; benchmark the STB approach's performance against those of two prescriptive approaches—weighted sample average approximation (wSAA) and kernelized empirical risk minimization (kERM); and show how the STB approach's prescriptions can be explained by estimating the impact of individual features. The results suggest that the quality of the STB approach's prescriptions is comparable to that of wSAA's and kERM's prescriptions while also providing explanations.


Multi-Level Medical Image Segmentation Network Based on Multi-Scale and Context Information Fusion Strategy

Abstract: Accurate segmentation of human tissue structure from medical images is one of the critical links in medical image diagnosis. However, due to the medical image scale of different tissues varying significantly and being structurally complex, the low contrast between tissues and background in some medical imaging makes it challenging to identify. The existing models are difficult to extract representative multi-scale features of medical images that cannot accurately segment the organizational structure from the background in low-contrast medical images. To solve these problems, this study presents a scale and context information fusion network structure based on multi-type medical image segmentation (SCIF-Net), which contains three modules: feature pyramid encoder (FPE), multi-scale feature dynamic aggregation (MFDA), and adaptive spatial information fusion (ASIF). We build the FPE module to further enhance the representational ability of the network encoder output feature map at each stage. The MFDA module is used to effectively extract multi-scale information from the encoder output feature map and aggregate multi-scale features. The constructed ASIF module enables the network to selectively concentrate on the vital spatial information in the encoder feature map and merge the decoder feature map semantic information, minimizing background noise influence. Extensive experiments on the retinal segmentation task, gland segmentation task, and femur segmentation task, show that the SCIF-Net network outperforms other advanced methods.


FMAP: Learning robust and accurate local feature matching with anchor points

Abstract: Local feature matching involves the task of establishing the pixel-wise correspondences between a pair of images. As an integral component of plentiful computer vision applications (e.g., visual localization), this task has been successfully performed using Transformer-based methods. However, these methods typically extract numerous keypoints from sparse texture regions to construct a densely-connected graph neural network (GNN) for long-range feature aggregation, which inevitably triggers redundant message exchange and hampers the learning process. Furthermore, they employ transformer encoder layers that consider images as 1D sequences, leaving them incapable of extracting multiscale local structural information from the images, which is critical for establishing correspondence in image pairs with significantly scales shifts. In this study, we develop FMAP, an innovative detector-free approach that enables accurate local feature matching. For the first issue, FMAP employs an anchor points feature aggregation module (APAM) that captures representative keypoints and discards the extraneous keypoints to build a sparsified GNN for compact yet clean message exchange, with the key insight that the keypoints containing abundant visual information are distinguishable from their neighbors. For the second issue, FMAP proposes a global–local multiscale perception module (GMPM), which incorporates abundant multiscale local context information into global feature representation by employing multiple depth-wise convolutions with varying kernel sizes, thereby generating discriminative features that are robust to scale shifts. In addition, the depth-wise convolutions are utilized in the feed-forward network of the GMPM to further fuse the global context information and local feature representation. Extensive experiments on several standard benchmarks demonstrate that the proposed FMAP method significantly outperforms state-of-the-art methods. Compared to the cutting-edge methods MatchFormer, QuadTree, and TopicFM in relative pose estimation task, FMAP surpasses them by 2.27%, 0.58%, and 1.08% in terms of AUC@5°. Besides, FMAP noticeably outperforms the baseline LoFTR by (2.38%,1.89%,1.45%) in terms of AUC@(5°, 10°, 20°). Moreover, we integrate FMAP into an official visual localization framework and conduct a visual localization experiment, with the results showing that FMAP exceeds LoFTR by 2.3% in terms of AP.


SufRec, an algorithm for mining association rules: Recursivity and task parallelism

Abstract: The mining of frequent itemsets and association rules is a core problem in data mining and an essential task in data analysis. In this paper, we present SufRec, a new algorithm for finding frequent itemsets and association rules. We give two versions. In both versions, the mining of the frequent itemsets is decomposed as a sequence of tasks. The first version (the SufRecDep algorithm) proceeds the tasks successively, each one using the results of the previous ones. The second version (the SufRecInd algorithm) performs the tasks independently of each other. Both versions are recursive with respect to the items and have thus the advantage to be particularly efficient for updating the mining process when new items are added to the database or when others are excluded. Moreover, the task-independent processing of SufRecInd makes it very easy to build parallel versions of SufRec. We present two parallel SufRec algorithms. With the first one, P-FIFO-SufRec, the processors perform the tasks according to the order in which the items appear in the database, preserving the recursive nature of SufRec. For second one, P-PAST-SufRec, the set of items is fixed and the tasks are pre-assigned to each processor. In order to evaluate the performance of SufRec, we carry out a consequent experimental study. In particular, we compare its running times with those of well-known parallel and non-parallel frequent itemsets mining algorithms.


Computing recommendations from free-form text

Abstract: While searching for consumer goods, users frequently ask for suggestions from their peers by writing short free-form textual requests. For example, when searching for movies users may ask for “Drama movies with a mind-bending story and a surprise ending, such as Fight Club” in one of the many online discussion boards. Despite the recent developments in large language models (LLMs) and natural language processing (NLP), modern recommender systems still struggle to process such requests. Therefore, in this paper we evaluate several approaches for annotating structured information from such short, free-form natural language user texts to calculate recommendations. We set up this evaluation as a two phase processes including (a) identification of the best NLP approach to identify key elements of users’ requests, and (b) assessment of the quality of recommendations computed with such elements. For our evaluation, we use a gold-standard reddit movie recommendation dataset consisting of annotations, manually created by crowdworkers who extracted keywords, actor names and movie titles. Using this dataset we evaluate a collection of more than 30 NLP and five recommender approaches. In addition, we perform an ablation study to assess relative annotation importance for movie recommendations. We find that domain-specific deep learning models, trained on a subset of data as well as embedding-based recommendation approaches are able to match the recommendation performance of recommendations computed from manual annotations. These promising results warrant further investigation in automatic processing of short free-form texts for computation of recommendations. Specifically, we provide insights into which NLP models and configurations work best for automatically annotating free text to compute (movie) recommendations, hence substantially reducing the search space for combinations of NLP and recommendation algorithms in the movie and potentially other domains.


MLIBT: A multi-level improvised binarization technique for Tamizhi inscriptions

Abstract: The Tamizhi inscriptions, one of the earliest ever discovered, is predominantly found on memorial stones and caves which dates 5th century BCE to 3rd century CE. Today's generations need ways to interpret the script in order to know the historical figures and events because the Tamizhi script evolved into the modern Tamil script over time. Currently, only few epigraphists are available to manually decode the inscriptions into modern Tamil. Hence, there is a need for an alternate way to preserve this cultural heritage. Image processing is one such digital technology that enables binarization on inscription images, and the retrieved text may then be utilized to convert them to the needed target language. Nevertheless, binarization of Tamizhi inscription images are highly complex due to aging, environmental factors, handwritten, similar foreground & background and uneven size and shapes of the stones. Also, due to the small dataset, deep learning techniques are inapplicable. Furthermore, existing approaches produce poor results for Tamizhi inscriptions since they can only be used on flat stone backgrounds and require sufficient light illumination for effective binarization. This research suggests a multi-level improvised binarization solution for Tamizhi inscription images to address these challenges. It achieves this utilizing post-processing with shrink and swell filters together with an improved median filter with modified adaptive thresholding. Outperforming the current binarization techniques, which achieved a maximum accuracy of about 74%, MLIBT produced an accuracy of around 92.19%.


Temporal-Spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit

Abstract: Recent advancements in medical information technology have enabled electronic health records (EHRs) to store comprehensive clinical data which has ushered healthcare into the era of "big data". However, medical data are rather complicated, making problem-solving in healthcare be limited in scope and comprehensiveness. The rapid development of deep learning in recent years has opened up opportunities for leveraging big data in healthcare. In this article we introduce a temporal-spatial correlation attention network (TSCAN) to address various clinical characteristic prediction problems, including mortality prediction, length of stay prediction, physiologic decline detection, and phenotype classification. Leveraging the attention mechanism model's design, our approach efficiently identifies relevant items in clinical data and temporally correlated nodes based on specific tasks, resulting in improved prediction accuracy. Additionally, our method identifies crucial clinical indicators associated with significant outcomes, which can inform and enhance treatment options. Our experiments utilize data from the publicly accessible Medical Information Mart for Intensive Care (MIMIC-IV) database. Finally, our approach demonstrates notable performance improvements of 2.0% (metric) compared to other SOTA prediction methods. Specifically, we achieved an impressive 90.7% mortality rate prediction accuracy and 45.1% accuracy in length of stay prediction.


Dual-branch counting method for dense crowd based on self-attention mechanism

Abstract: A dense crowd counting method based on self-attention mechanism with dual-branch fusion network is proposed in this paper. Our method aims to address the problems of large variations in head scales and complex backgrounds in dense crowd images. This method combines the CNN and Transformer network frameworks and consists of shallow feature extraction network, dual-branch fusion network, and deep feature extraction network. The VGG16 network is employed by the shallow feature extraction network to extract low-level features. A multi-scale CNN branch and a Transformer branch built on an improved self-attention module make up the dual-branch fusion network, which collects local and global information on crowd areas, respectively. The Transformer network, which is based on a mixed attention module, is employed by the deep feature extraction network to further separate complicated backgrounds and concentrate on crowd areas. Both counting-level weakly supervised and location-level fully supervised methods are employed in the experiments. On four widely used datasets, the results demonstrate that the proposed method outperforms the most recent research. Our method has a higher counting accuracy with low parameter volumes and a counting accuracy of 89.1% under full supervision when compared to existing weakly supervised methods. The results of the experiments demonstrate that the method has excellent crowd counting performance and can accurately count in high-density and high-occlusion scenes.


Development of an automatic grammar checker for Yorùbá word processing using Government and Binding Theory

Abstract: Grammar checkers have been developed for well-resourced languages such as French, German and English. However, there is a dearth of automatic grammar checkers for African languages including Standard Yorùbá (SY) and consequently no standard software application. This study developed a software system for Yorùbá grammar checking. Requirements for a Yorùbá grammar checking system were investigated by gathering information on Yorùbá syntax from Textbooks and Journals. Based on the information gathered, a computational model was formulated using the theoretical framework of Government and Binding (GB). The formulated system was designed using the Context Free Grammar Tool in Automata Theory. The system was implemented using the Python programming language version 3.5 and Natural Language Tool kit (NLTK). The system was tested for accuracy using a total of three hundred sentences. In detecting grammatically correct and incorrect sentences, an accuracy of 86.67%, 88.89% and 88.89% were achieved for simple, complex and compound sentences respectively. The grammar checker has been able to function as an automatic grammatical error checker of Yorùbá digital text. Its implementation in software applications has the potential to increase the quality of Yorùbá word processing. Also, the dictionary of words created can be used in other areas of human language processing.


ATFA: Adversarial Time–Frequency Attention network for sensor-based multimodal human activity recognition

Abstract: Human activity recognition (HAR) is a key component of mobile and ubiquitous computing. With the increasing number of sensors embedded in devices such as smartphones and smartwatches, models are enhanced to recognize more complex human behaviors. However, this also leads to increased data heterogeneity, caused by diverse user backgrounds, health conditions, and sensing environments, which makes it difficult to align the multimodal data distributions across individuals. This paper proposes a novel unsupervised domain adaptation framework via Adversarial Time-Frequency Attention (ATFA) to efficiently adapt models to new users. In particular, the proposed attention-based modality fusion module captures and fuses important modalities based on their context to reduce redundant information. Additionally, the network explores frequency domain features to improve performance in recognizing human activities. Extensive experiments are conducted on three publicly available HAR datasets to demonstrate the superiority of our proposed method compared to state-of-the-art baselines.


Exploring the optimality of byte-wise permutations of a piccolo-type block cipher

Abstract: Piccolo is a lightweight block cipher based on a 16-bit word 4-line generalized Feistel structure. Piccolo adopts byte-wise round permutation (RP) instead of the typical word-based RP to improve diffusion. In this paper, we explore the optimality of byte-based RP from the viewpoint of security. We evaluate the security of differential, linear, impossible differential, and integral attacks for all byte-wise RPs using mixed integer linear programming (MILP). We show that the RP of Piccolo is optimal in terms of the number of rounds required to guarantee security against such attacks. In addition, we introduce new two classes of RPs that require 7 rounds for security against impossible differential attacks, which is one round less than required by Piccolo. These new classes require 7/9 and 8/8 rounds to guarantee security against differential/linear attacks, respectively, which is more rounds than required by Piccolo.


TIEN: Temporal interest-aware evolution model for “Next Item Recommendation”

Abstract: The “next item recommendation” is used to predict the next item that a user may be interested in and its main challenges is identifying the user's interests. The current state-of-the-art methods mostly adopt time-series-based recommendation methods but ignore the role of time intervals in the interest evolution process. This motivated us to propose a new Time Interest-aware Evolutionary Network model (TIEN) to predict the next item that a user may be interested in. TIEN highlights the role of time intervals in the interest evolution process and divides interests into long-term interests and short-term interests. In TIEN: (1) We devise a time interval-based attention network TAN to capture the user's interests, which employs item embeddings, behavior sequence embeddings, and time intervals to calculate the user's interest attention; (2) We devise an ATT-GRU network based on candidate activation vectors to capture the interest evolution process, which skillfully leverages the reset gate, hidden layer state, item information, time intervals, etc. to participate in interest evolution; (3) We introduce a dual-network collaborative training mechanism, where the obtained interests of users are treated as weights to guide the interest evolution process. Experimental results on multiple benchmark datasets show that TIEN has higher performance than the current state-of-the-art methods.


Fused robust geometric nonparallel hyperplane support vector machine for pattern classification

Abstract: Recently, introducing nonconvex loss functions in support vector machine (SVM) to improve the robustness against varies noises has been drawing much attention. In this paper, we first construct a new robust capped asymmetric elastic net (CaEN) loss function. Second, we describe a novel robust Huberized kernel-based (HK) loss function and theoretically demonstrate several important properties, such as smoothness, boundness and the trade-off between the standard least squares and the truncated least squares. Finally, we apply the CaEN loss and the HK loss into elastic net nonparallel hyperplane SVM (ENNHSVM) to develop a fused robust geometric nonparallel SVM (FRGNHSVM). The proposed FRGNHSVM not only inherits the advantages of ENNHSVM but also improves the robustness of classification problems. An efficient Pegasos-based DC (difference of convex functions) algorithm is implemented to solve the FRGNHSVM optimization problem. In comparison with four famous SVMs, including Lagrangian SVM, twin SVM, pinball SVM and C-loss twin SVM, experimental results on simulations and twelve UCI datasets show that the proposed FRGNHSVM can often improve more than 5% average prediction accuracy. Moreover, the performance of the high prediction accuracy of FRGHNSVM is more significant along with the ratio of label noise increasing, indicating its superiority in dealing with label-contaminated datasets.


Context-aware Multi-level Question Embedding Fusion for visual question answering

Abstract: Question model has been widely concerned as the cornerstone of constructing Visual Question Answering (VQA) models. Existing question models attempt to exploit word context to extract multi-level concepts for modeling multi-level questions. However, they still have many defects. For example, most question models utilize simple fusion methods to fuse shallow modules and extract parameter-unshared low-level concepts, leading to poor modeling of multi-level questions; although some question models use deep bidirectional Transformer encoder in external knowledge transfer and BERT for multi-level questions, their complexity is still high. To solve these issues, we propose a novel low-complex contextual question model, termed Context-aware Multi-level Question Embedding Fusion (CMQEF). We formalize its concepts and theories, deduce its modeling process, optimization process and feature extraction process, analyze its low complexity and high expressiveness, and prove that it defines a new way to solve parameter non-sharing for extracting parameter-shared multi-level concepts and optimize the tradeoff between expressiveness and complexity in question models. Extensive experiments on VQAv2 and VQA-CPv2 validate that comparing with the state-of-the-art, our CMQEF outperforms it on SANs and UpDn, reduces the language priors of SANs and UpDn, and has preferable interpretability and applicability. Our code is available at https://github.com/lsdruc/CMQEF-for-VQA.


AEGraph: Node attribute-enhanced graph encoder method

Abstract: Graph representation learning faces challenges in node classification tasks due to the intricate interplay between node categories, topological structure, significant characteristics, node attributes, and label information. However, prevailing graph convolutional networks (GCNs) exhibit inefficiencies in information propagation and need more robustness when dealing with missing node attributes. To surmount these issues, attribute-based dynamic wandering and attribute enhancement methods are proposed. First, the Node Attribute Random Wandering (NARW) method is proposed to obtain the node sequences. The NARW method fully considers the node-to-node attribute information to determine the wandering direction of the sequences. Subsequently, the node attribute enhanced graph encoder model (AEGraph) is proposed, which aims to improve the node classification performance by combining the graph structure and node attribute information during the information propagation process. Each iteration process includes node label information aggregation and node label information update. Finally, to fully consider the local and global information of the graph, this paper reduces the noise generated by information propagation during the iteration process. The model proposed in this paper shows promising results with a low labeling rate. Experimental results show that the method achieves better results and significant improvements on different datasets.


Graphical Consensus-Based Sharding for Efficient and Secure Sharings in Blockchain-Enabled Internet of Vehicles

Abstract: Blockchain-enabled Internet of Vehicles provides a reliable collaboration environment for traffic entities and promotes road safety and traffic efficiency through real-time sharings between vehicles and infrastructure. However, a bottleneck is likely to emerge as the Internet of Vehicles scales up. In this paper, we propose a novel Graphical Consensus-based Sharding (GCS) framework, which is underpinned by four important strategies: 1) A graphical consensus is adopted as its intra-shard consensus, where the consensus group is set up according to its maximal connected subgraph, and the leader is elected by its reliability weight. The consensus group is refreshed intermittently by alternating the leader role. 2) Within GCS, the intra-shard data are stored in the local chain, while a block-based directed acyclic graph, rather than a chained structure, is employed as the main chain. The local chain is used to respond to requests within each shard, and the main chain supports the cross-shard sharings. GCS will parallelly optimize the throughput of the blockchain-enabled Internet of Vehicles. 3) GCS further introduces the shard backup and the node scheduling to handle shard failure and overheating by using new backup strategies and a temporal-spatial graph convolutional network prediction model, respectively. 4) An off-chain transmission algorithm is presented for secure sharing between the infrastructure and the vehicles. Simulation results show that the number of Transactions Per Second is 1.69 times higher than that of the non-sharding blockchain, and the pending time is dramatically reduced compared to the mainstreaming sharding approach, which is 1.02s.


Path Planning for the Gantry Welding Robot System Based on Improved RRT*

Abstract: In the shipbuilding industry, various workpieces are often produced in small batches. A new program must be written for new workpiece to be processed, which leads to the inefficiency of the traditional teaching programming method. In particular, some manufacturers have applied large gantry structures to robots to improve their handling space. Although the external positioning device enhances the robot's flexibility, it also increases the difficulty of path planning. The RRT* algorithm based on sampling is widely used in the path planning of manipulator for its efficient expansibility and probability completeness. However, in the robot system equipped with gantry structure, the increase of freedom makes its efficiency relatively low. Therefore, this article presents an improved RRT* algorithm for autonomous path planning of welding robots with a large gantry structure. This method introduces the sampling pool mechanism, and selects the node nearest to the connection line between the starting node and the target node in the sampling pool, which effectively shortens the length of the search path. In addition, it adopts the strategy of limiting the nearest node to prevent the transitional search of the configuration space. The improved RRT* algorithm proposed in this paper is verified in complex environment, and compared with improved algorithms such as IB-RRT*, the path cost and time cost are increased by 22.2% and 32.5%, respectively, and the success rate is relatively stable.


Imperceptible UAPs for Automatic Modulation Classification Based on Deep Learning

Abstract: Automatic Modulation Classification (AMC), which based on deep learning has been extensively researched and implemented in wireless communication systems. Universal adversarial perturbation refers to a single perturbation that can cause most samples to be misclassified by deep learning models. In this paper, we aim to achieve imperceptible universal adversarial attacks on AMC models, and thus an imperceptible universal adversarial perturbations (imperceptible UAPs) framework was proposed. Specifically, loss functions were separately designed for target signals and non-target signals, and then a total loss was calculated. This total loss function can be modified by hyperparameters to achieve class-specific universal adversarial attacks (CUAAs), class-discriminative universal adversarial attacks (CD-UAAs), and class-discriminative target universal adversarial attacks (CD-TUAAs). Meanwhile, wavelet reconstruction was applied to the training data, thus further improving the discriminability of the generated UAPs. After CUAAs were implemented, the class dominant in radio signals was visualized and analyzed by confusion matrices. Furthermore, with the confusion matrices of CUAAs, CD-TUAAs were efficiently implemented. The experiments were conducted on two radio signal datasets and models. In most scenarios, CD-UAAs achieved excellent performance with an average ΔAcc of 58. 20% and CD-TUAAs achieved an average ΔK of 73.78%.


CB-DSL: Communication-Efficient and Byzantine-Robust Distributed Swarm Learning on Non-i.i.d. Data

Abstract: The valuable data collected by IoT devices together with the resurgence of machine learning (ML) stimulate the latest trend of artificial intelligence (AI) at the edge. However, traditional ML and recent federated learning (FL) face major challenges including communication bottleneck, data heterogeneity, and security concerns in edge IoT. Meanwhile, the swarm nature of IoT systems is overlooked by most existing literature, which calls for new designs of distributed learning algorithms. Inspired by the success of biological intelligence (BI) of gregarious organisms, we propose a novel edge learning approach for swarm IoT, called communication-efficient and Byzantine-robust distributed swarm learning (CB-DSL), through a holistic integration of AI-enabled stochastic gradient descent and BI-enabled particle swarm optimization. To deal with the non-i.i.d. data issues and Byzantine attacks, a small amount of global data samples are introduced in CB-DSL and shared among IoT workers, which alleviates the local data heterogeneity effectively and enables to fully utilize the exploration-exploitation mechanism of swarm intelligence. Our convergence analysis theoretically demonstrates that the CB-DSL is superior to the standard FL with better convergence behavior. We also evaluate the model divergence of CB-DSL by deriving its upper bound, which measures the effectiveness of the introduction of the globally shared dataset.


Spoken Language Processing

Abstract: From the Publisher: New advances in spoken language processing: theory and practice In-depth coverage of speech processing, speech recognition, speech synthesis, spoken language understanding, and speech interface design Many case studies from state-of-the-art systems, including examples from Microsoft’s advanced research labs Spoken Language Processing draws on the latest advances and techniques from multiple fields: computer science, electrical engineering, acoustics, linguistics, mathematics, psychology, and beyond. Starting with the fundamentals, it presents all this and more: Essential background on speech production and perception, probability and information theory, and pattern recognition Extracting information from the speech signal: useful representations and practical compression solutions Modern speech recognition techniques: hidden Markov models, acoustic and language modeling, improving resistance to environmental noises, search algorithms, and large vocabulary speech recognition Text-to-speech: analyzing documents, pitch and duration controls; trainable synthesis, and more Spoken language understanding: dialog management, spoken language applications, and multimodal interfaces To illustrate the book’s methods, the authors present detailed case studies based on state-of-the-art systems, including Microsoft’s Whisper speech recognizer, Whistler text-to-speech system, Dr. Who dialog system, and the MiPad handheld device. Whether you’re planning, designing, building, or purchasing spoken language technology, this is the state of the art—fromalgorithms through business productivity.