Reviewerly/exHarmony huggingface dataset:
The effect of individual stress on the signature verification system using muscle synergy

Authors:
raw_author_name: Arsalan Asemi
raw_affiliation_string: Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
raw_author_name: Keivan Maghooli
raw_affiliation_string: Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
raw_author_name: Fereidoun Nowshiravan Rahatabad
raw_affiliation_string: Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
raw_author_name: Hamid Azadeh
raw_affiliation_string: Department of Physical Therapy, School of Rehabilitation Sciences, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract: Biometric authentication systems, in terms of using the biometric characteristic, and because these indicators are resistant to the passage of time, and environmental, and physical changes of people, in most different situations from the environment or changes in emotional factors of person, perform identity verification with optimal accuracy. In the signature verification systems, people may experience stress caused by environmental or internal factors when registering a signature, this stress can affect the performance of the signature verification system in terms of accuracy and stability. We try to study the performance of a signature verification system based on a biometric characteristic (muscle synergy patterns) for people who are stressed while signing. For this purpose, electromyography (EMG) signals were recorded from hand and arm muscles of people while signing. Then, using Non-Negative Matrix Factorization (NMF) method to extracted muscle synergies from EMG signals after pre-processing. The extracted synergy patterns are classified into genuine and forgery classes by Support Vector Machine (SVM) classifier. Furthermore, the confirmation of the authentication of stressed people was studied using this method. Finally, the results obtained were compared to the results obtained from the signature verification system for unstressed people and using K-means classifier method.
Dynamic Programming Principle for Automatic Negotiations

Authors:
raw_author_name: Karmvir Singh Phogat
raw_affiliation_string: Department of Information Physics and Computing, the University of Tokyo, Tokyo, Japan

Abstract: In automatic negotiation, an automated agent is trained to negotiate on behalf of a human negotiator. We consider that the domain of negotiations is known to both the agent and its opponents. In this setting, a greedy concession algorithm (GCA) is employed to find an optimal policy for the agent when the agent's belief about the opponent is known. However, the GCA is computationally expensive to certain class of policies. In this article, we propose a reverse greedy concession algorithm that is computationally less expensive for such class of policies. We also provide an alternate proof to establish that the GCA is a dynamic programming principle.
An Ensemble Semi-Supervised Adaptive Resonance Theory Model With Explanation Capability for Pattern Classification

Authors:
raw_author_name: Pourpanah, Farhad
raw_affiliation_string: Department of Electrical and Computer Engineering, Queen&#x0027;s University, Kingston, ON, Canada
raw_author_name: Lim, Chee Peng
raw_affiliation_string: Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Victoria, Australia
raw_author_name: Etemad, Ali
raw_affiliation_string: Department of Electrical and Computer Engineering, Queen&#x0027;s University, Kingston, ON, Canada
raw_author_name: Wu, Q. M. Jonathan
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada

Abstract: Most semi-supervised learning (SSL) models entail complex structures and iterative training processes as well as face difficulties in interpreting their predictions to users. To address these issues, this paper proposes a new interpretable SSL model using the supervised and unsupervised Adaptive Resonance Theory (ART) family of networks, which is denoted as SSL-ART. Firstly, SSL-ART adopts an unsupervised fuzzy ART network to create a number of prototype nodes using unlabeled samples. Then, it leverages a supervised fuzzy ARTMAP structure to map the established prototype nodes to the target classes using labeled samples. Specifically, a one-to-many (OtM) mapping scheme is devised to associate a prototype node with more than one class label. The main advantages of SSL-ART include the capability of: (i) performing online learning, (ii) reducing the number of redundant prototype nodes through the OtM mapping scheme and minimizing the effects of noisy samples, and (iii) providing an explanation facility for users to interpret the predicted outcomes. In addition, a weighted voting strategy is introduced to form an ensemble SSL-ART model, which is denoted as WESSL-ART. Every ensemble member, i.e., SSL-ART, assigns {\color{black}a different weight} to each class based on its performance pertaining to the corresponding class. The aim is to mitigate the effects of training data sequences on all SSL-ART members and improve the overall performance of WESSL-ART. The experimental results on eighteen benchmark data sets, three artificially generated data sets, and a real-world case study indicate the benefits of the proposed SSL-ART and WESSL-ART models for tackling pattern classification problems.
Joint Dataset Reconstruction and Power Control for Distributed Training in D2D Edge Network

Authors:
raw_author_name: Jiaxin Wu
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Jigang Wu
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Long Chen
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Yifei Sun
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Yalan Wu
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

Abstract: The intrinsic nature of non-independent and identically distributed datasets on heterogeneous devices slows down the distributed model training process and reduces the training accuracy. To settle this problem, we propose a dataset reconstruction scheme to transform the data distribution of training device’s dataset into independent and identically distributed dataset via data exchange among trusted devices. For energy efficiency, we further consider power control for the devices. We then formulate an optimization problem, which is a mixed integer non-linear programming problem, to minimize the total energy consumption for each round of distributed training. Due to the NP-hardness and coupling property of the optimization problem, we decompose it into two subproblems for dataset reconstruction and power control, respectively. An approximation algorithm is designed to obtain a near-optimal auxiliary devices set for dataset reconstruction with minimum energy consumption, while meeting the variance constraint of the optimization problem. We prove that approximation algorithm has a worst-case approximation ratio of 1+ln|Ωi(t)|, where |Ωi(t)| is the required data samples for dataset reconstruction of each training device. For power control, we design a dynamic programming algorithm to further reduce the energy consumption. For comparison, we propose three benchmark schemes that adopt either one of the algorithms or neither. We also customize three baseline algorithms based on the state-of-the-arts to compare with our proposed algorithm. Numerical results show that, our proposed algorithm outperforms three benchmarks on the average energy consumption for one round for different cases. When varying the labels that each device owns, our proposed algorithm outperforms the other three baseline algorithms on training accuracy. Besides, when setting a target accuracy, our proposed algorithm always has the lowest energy consumption.
Q-DDCA: Decentralized Dynamic Congestion Avoid Routing in Large-Scale Quantum Networks

Authors:
raw_author_name: Lutong Chen
raw_affiliation_string: School of Cyber Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Kaiping Xue
raw_affiliation_string: School of Cyber Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Jian Li
raw_affiliation_string: School of Cyber Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Ruidong Li
raw_affiliation_string: Institute of Science and Engineering, Kanazawa University, Kakuma, Kanazawa, Japan
raw_author_name: Nenghai Yu
raw_affiliation_string: School of Cyber Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Qibin Sun
raw_affiliation_string: School of Cyber Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Jun Lu
raw_affiliation_string: School of Cyber Science and Technology and the Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China

Abstract: The quantum network that allows users to communicate in a quantum way will be available in the foreseeable future. The network capable of distributing Bell state entangled pairs faces many challenges due to entanglement decoherence and limited network performance, especially when the network scale is enormous. Many entanglement distribution protocols have been proposed so far, and most of them are in a centralized and synchronized manner, which may be infeasible in large-scale networks. As such, in this paper, we propose a full spontaneous version of quantum networks in which the quantum nodes autonomously manage multiple entanglement distribution requests. However, one major issue is that quantum nodes have little knowledge about the network, especially the congestion (e.g., some nodes may have no usable quantum memories). We present a routing algorithm to adaptive evaluate the congestion on the neighbor nodes to avoid potential congestion. We use SimQN, the new network layer simulation platform built by our research team, to evaluate our proposed design. The result demonstrates that it can adapt to changes in network resources and reduce the drop rate that eventually leads to a higher entanglement distribution rate but remains fair for multiple requests to use the network resources fairly and achieve a more balanced throughput.
Learning Semantic Alignment Using Global Features and Multi-Scale Confidence

Authors:
raw_author_name: Huaiyuan Xu
raw_affiliation_string: Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
raw_author_name: Jing Liao
raw_affiliation_string: Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
raw_author_name: Huaping Liu
raw_affiliation_string: Department of Computer Science and Technology, Institute for Artificial Intelligence, Tsinghua University, Beijing, China
raw_author_name: Yuxiang Sun
raw_affiliation_string: Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong

Abstract: Semantic alignment aims to establish pixel correspondences between images based on semantic consistency. It can serve as a fundamental component for various downstream computer vision tasks, such as style transfer and exemplar-based colorization, etc. Many existing methods use local features and their cosine similarities to infer semantic alignment. However, they struggle with significant intra-class variation of objects, such as appearance, size, etc. In other words, contents with the same semantics tend to be significantly different in vision. To address this issue, we propose a novel deep neural network of which the core lies in global feature enhancement and adaptive multi-scale inference. Specifically, two modules are proposed: an enhancement transformer for enhancing semantic features with global awareness; a probabilistic correlation module for adaptively fusing multi-scale information based on the learned confidence scores. We use the unified network architecture to achieve two types of semantic alignment, namely, cross-object semantic alignment and cross-domain semantic alignment. Experimental results demonstrate that our method achieves competitive performance on five standard cross-object semantic alignment benchmarks, and outperforms the state of the arts in cross-domain semantic alignment.
Privacy-Preserving and Lightweight Verification of Deep Packet Inspection in Clouds

Authors:
raw_author_name: Xiaoli Zhang
raw_affiliation_string: Department of Computer Science, Zhejiang University of Technology, Hangzhou, China
raw_author_name: Wei Geng
raw_affiliation_string: Department of Computer Science and Technology, Institute of Network Science and Technology, Tsinghua University, Beijing, China
raw_author_name: Yiqiao Song
raw_affiliation_string: Department of Computer Science, Zhejiang University of Technology, Hangzhou, China
raw_author_name: Hongbing Cheng
raw_affiliation_string: Department of Computer Science, Zhejiang University of Technology, Hangzhou, China
raw_author_name: Ke Xu
raw_affiliation_string: Department of Computer Science and Technology, Tsinghua University, Beijing, China
raw_author_name: Qi Li
raw_affiliation_string: Institute of Network Science and Technology, Tsinghua University, Beijing, China

Abstract: In the trend of network middleboxes as a service, enterprise customers adopt in-the-cloud deep packet inspection (DPI) services to protect networks. As network misconfigurations and hardware failures notoriously exist, recent efforts envision to ensure the execution integrity of DPI services in untrusted clouds. However, they either require enterprise customers to know proprietary DPI rulesets of cloud providers or introduce forbidden overhead in the network context. In the paper, we propose a privacy-preserving and lightweight verification scheme that efficiently checks whether in-the-cloud DPI services run correctly without leaking private DPI rulesets. Particularly, our design introduces one trusted third party to perform privacy-preserving and trustworthy ruleset evaluation and DPI execution verification. Meanwhile, it devises a novel DPI ruleset authentication method that enables tamper-proof DPI operations and facilitates fast proof generation. The proofs can be verified without requiring the verifier to always maintain all rulesets. To further reduce the verification costs while resisting cloud cheating behaviors like bias treatments of packets, it employs a commitment-based delayed sampling mechanism which requires the DPI services to first demonstrate that all packets have been processed before receiving sampling decisions. Moreover, extensive experiments are conducted based on Click modules. The results show that the proposed scheme is practical and only incurs the real-time overhead of 10-20 microseconds.
Imperceptible Attacks on Fault Detection and Diagnosis Systems in Smart Buildings

Authors:
raw_author_name: Ismail R. Alkhouri
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USA
raw_author_name: Akram S. Awad
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USA
raw_author_name: Qun Z. Sun
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USA
raw_author_name: George K. Atia
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USA

Abstract: Automated Fault Detection and Diagnosis (AFDD) systems are critical to safe and efficient operation of smart buildings. A significant amount of building data can be collected and analyzed to detect building compo-nent failures. Attacks against such data that are contami-nated with small additive disturbances (i.e., adversarial per-turbation attacks) could dreadfully impact the performance of such systems while maintaining a high level of imper-ceptibility. The vulnerability studies of such data attacks is lacking. Specifically, most existing detection and clas-sification models have flat structures, regarded as Single-Stage Classifiers (SSCs), are prone to adversarial data perturbation attacks. In this paper, we present a coarse-to-fine Hierarchical Fault Detection and multi-level Diagnosis (HFDD) model, and formulate a mathematical program to derive targeted attacks on the model with respect to a pre-specified target diagnosis level. Two algorithms are developed based on convex relaxations of the formulated program for non-targeted attacks. An Alternating Direction Method of Multipliers (ADMM)-based solver is developed for the convex programs. Extensive experiments are con-ducted using two real-world datasets of measurements from air handling units and chillers, demonstrating the fea-sibility of the proposed attacks with regard to misclassifi-cation rate and imperceptibility of the attack. We also show that the HFDD is more robust to disturbances than SSC-based fault detection and multi-level diagnosis systems.
TMANomaly: Time-Series Mutual Adversarial Networks for Industrial Anomaly Detection

Authors:
raw_author_name: Lianming Zhang
raw_affiliation_string: College of Information Science and Engineering, Hunan Normal University, Changsha, China
raw_author_name: Xiaowei Xie
raw_affiliation_string: College of Information Science and Engineering, Hunan Normal University, Changsha, China
raw_author_name: Pingping Dong
raw_affiliation_string: College of Information Science and Engineering, Hunan Normal University, Changsha, China

Abstract: Large-scale sewage treatment plants are one of the typical Industrial Internet of Things systems, where the presence of a large number of sensors generates massive dynamic time series data, and such multivariate time series data are usually time-dependent and random. Therefore, there is a certain risk when fitting the potential anomalies of real-world data, which will bring great challenges to anomaly detection. In this article, we propose a time-series mutual adversarial network (TMAN), a novel reconstruction model for anomaly detection on multivariate time series. It is based on the idea of adversarial learning and consists of two identical subnetworks. During the training process, two subnetworks can independently complete the learning of the time distribution of normal samples of industrial time series data for mutual adversarial. In the process of detecting, we obtain the residual values of TMAN reconstructed for different time series samples to discriminate anomalies. We combine TMAN and anomaly determination mechanisms to build a new industrial time series anomaly detection framework named TMANomaly. In addition, we select the dataset features with a grey correlation algorithm to achieve very high performance with a small number of features. Experimental results show that our proposed TMANomaly outperforms five popular anomaly detection methods and effectively improves the accuracy of industrial multivariate time series anomaly detection.
A Streaming Graph Partitioning Method to Achieve High Cohesion and Equilibrium via Multiplayer Repeated Game

Authors:
raw_author_name: Guosun Zeng
raw_affiliation_string: Department of Computer Science and Technology, Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China
raw_author_name: Chunling Ding
raw_affiliation_string: College of Chemical Science and Engineering, Tongji University, Shanghai, China

Abstract: With the advent of 5G era, Internet-of-Things will become possible, and a large amount of correlative data, namely the streaming graph (SG), is continuously generated in various application fields, which poses a great challenge to analyze and make good use of such graph data. The balanced partition of a big graph, especially a dynamic SG, has always been basic research in graph theory, and there are many classical methods. However, this article explores a novel method, that is, an SG <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> -way partitioning via a multiplayer repeated game so as to realize the equilibrium of the total number of nodes in different partitions and achieve high cohesion or low edge-cut in each partition. We regard the SG partitioning process during each time window as a multiplayer game process, treat <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> target partitions as game players, and use all possible actions that players select new nodes as the strategy set. Some effective constraints are given to reduce the strategy space and accelerate the game process. Moreover, we define equilibrium degree (ED) and modularity degree (MD), which are used to design the utility function of game players. All game players finally reach the Nash equilibrium via multiplayer repeated games. At this time, each player corresponds to a strategy. Under the guidance of these strategies, the new nodes are added to each target partition, and then the goal of optimal partitioning is achieved. The above operations are carried out in each time window. The subsequent time windows can reuse the partitioning results of the previous time window in order to realize dynamic incremental partitioning for an SG. Extensive experiments indicate that while ensuring the partition quality of an SG, our proposed algorithm also greatly improves the speed of online partitioning. Even compared with the state-of-the-art SG partitioning algorithm PLDG, our algorithm still obtains better performance.
DAG-BLOCK: A Novel Architecture for Scaling Blockchain-Enabled Cryptocurrencies

Authors:
raw_author_name: Yong Yuan
raw_affiliation_string: School of Mathematics, Renmin University of China, Beijing, China
raw_author_name: Fei-Yue Wang
raw_affiliation_string: State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Abstract: With the rapid development of blockchain technology and industries, scalability has been widely realized as one of the primary and urgent concerns for the large-scale adoption of blockchain, especially for cryptocurrencies. In this respect, directed acyclic graph (DAG) proves to be an elegant solution to scaling blockchain but suffers from weak consistency and security issues. In this article, we designed a novel DAG-BLOCK architecture for blockchain-enabled cryptocurrency markets in order to improve the scalability. In our work, DAG is used to replace the Merkel-tree-based transaction structure within the block, and a novel design of open blocks is proposed to enable user nodes to participate in verifying the transactions in blockchain systems. On this basis, we designed a new segmented market structure, in which each miner serves only a group of users instead of all users, so as to reduce miners’ workload and thus scale transaction processing capabilities. Our work can help improve the scalability of cryptocurrencies via evolving the underlying blockchain systems to graph-based distributed ledgers and is expected to shed new light on designing blockchain-based decentralized markets.