Reviewerly/exHarmony huggingface dataset:
Leveraging pervasive computing for ambient intelligence: A survey on recent advancements, applications and open challenges

Authors:
raw_author_name: John Violos
raw_affiliation_string: Department of Software and IT Engineering, École de technologie supérieure, 1100 Notre-Dame St W, Montreal, H3C 1K3, Quebec, Canada
raw_author_name: Aris Leivadeas
raw_affiliation_string: Department of Software and IT Engineering, École de technologie supérieure, 1100 Notre-Dame St W, Montreal, H3C 1K3, Quebec, Canada
raw_author_name: Iraklis Varlamis
raw_affiliation_string: Department of Informatics and Telematics, Harokopio University of Athens, Omirou 9, Tavros, 177 78, Attica, Greece

Abstract: The advent of pervasive computing and ambient intelligence has opened up new possibilities for the development of intelligent systems that can adapt to the needs of their users. This paper provides a comprehensive survey of recent advancements in this field, focusing on the different techniques, tools, and applications that have been developed to leverage the benefits of pervasive computing for ambient intelligence. We discuss the various challenges and opportunities that arise when designing such systems and provide a critical evaluation of the current state-of-the-art. In particular, we examine the role of machine learning, data analytics, and context awareness in the development of intelligent systems, and we investigate the impact of these technologies on the quality of life of their users. The paper also presents a discussion on the open challenges in this field, such as ensuring user privacy, security, and trust, designing effective user interfaces, and dealing with the complexity of large-scale systems. Finally, we conclude with an outlook on the future of ambient intelligence and its potential impact on society, and we highlight some of the emerging research directions that are likely to shape the field in the coming years.
Autoregressive Feature Extraction with Topic Modeling for Aspect-based Sentiment Analysis of Arabic as a Low-resource Language

Authors:
raw_author_name: Asmaa Hashem Sweidan
raw_affiliation_string: Faculty of Computers and Artificial Intelligence, Fayoum University, Fayoum, Egypt
raw_author_name: Nashwa El-Bendary
raw_affiliation_string: College of Computing and Information Technology, Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Egypt

Abstract: This paper proposes an approach for aspect-based sentiment analysis of Arabic social data, especially the considerable text corpus generated through communications on X (formerly known as Twitter) for expressing opinions in Arabic-language tweets during the COVID-19 pandemic. The proposed approach examines the performance of several pre-trained predictive and autoregressive language models; namely, Bidirectional Encoder Representations from Transformers (BERT) and XLNet, along with topic modeling algorithms; namely, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), for aspect-based sentiment analysis of online Arabic text. In addition, Bidirectional Long Short Term Memory (Bi-LSTM) deep learning model is used to classify the extracted aspects from online reviews. Obtained experimental results indicate that the combined XLNet-NMF model outperforms other implemented state-of-the-art methods through improving the feature extraction of unstructured social media text with achieving values of 0.946 and 0.938, for average sentiment classification accuracy and F-measure, respectively.
Memory backtracking strategy: An evolutionary updating mechanism for meta-heuristic algorithms

Authors:
raw_author_name: Heming Jia
raw_affiliation_string: School of Information Engineering, Sanming University, Sanming 365004, China
raw_author_name: Chenghao Lu
raw_affiliation_string: School of Information Engineering, Sanming University, Sanming 365004, China
raw_author_name: Zhikai Xing
raw_affiliation_string: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Abstract: The search domain of meta-heuristic algorithms is always constantly changing, which make it difficult to adapt the diverse optimization issues. To overcome above issue, an evolutionary updating mechanism called Memory Backtracking Strategy (MBS) is proposed, which contains thinking stage, recall stage, and memory stage. Overall, the adoption of the MBS enhances the efficiency of MHSs by incorporating group memory, clue recall, and memory forgetting mechanisms. These strategies improve the algorithm's ability to explore the search space, optimize the search process, and escape local optima. MBS will be applied to three different types of MHS algorithms: evolutionary based (LSHADE_SPACMA), physical based (Stochastic Fractal Search, SFS), and biological based (Marine Predators Algorithmnm, MPA) to demonstrate the universality of MBS. In the experimental section including 57 engineering problems, algorithm complexity analysis, CEC2020 Friedman ranking, convergence curve, Wilcoxon statistical, and box plot. Among them, 21 algorithms participated in the Friedman experiment, including MBS_LSHADE_SPACMA ranked first, LSHADE_SPACMA ranked second, MBS_MPA ranked 6th, MPA ranked 8th, MBS_SFS ranked 9th and SFS ranked 12th. Combined with the analysis of "MBS testing analysis" and the experimental results of engineering problems, it has proven that MBS has universality and good ability to improve optimization algorithm performance. The source codes of the proposed MBS (MBS_MPA) can be accessed by https://github.com/luchenghao2022/Memory-Backtracking-Strategy
The synchronization of K-valued Fuzzy cognitive maps

Authors:
raw_author_name: Luo Chao
raw_affiliation_string: School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China; Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan 250014, China
raw_author_name: Wang Xiaojie
raw_affiliation_string: School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China

Abstract: Fuzzy cognitive maps (FCMs) with the abilities of knowledge representation and causal reasoning have been widely applied in the control-related fields of multi-agent systems (MAS), such as the mobile robots and unmanned aerial vehicles (UAVs). From the perspective of system applications, the cooperative control of group behaviors is a meaningful topic in this community, where the synchronization of agents is one of fundamental issues. However, how to achieve the synchronization of FCM-based control mechanism is still an open problem. In this article, two finite state fuzzy cognitive map systems with unidirectional coupling in the drive-response configuration are introduced. By utilizing matrix semi-tensor product (STP), the coupling dynamical systems are transformed into algebraic forms, based on which the necessary and sufficient conditions for complete synchronization are presented. The corresponding theoretical proofs are given and quantitative analysis are implemented. Finally, several experiments are given to prove the effectiveness of the proposed methods.
GradDiff: Gradient-based membership inference attacks against federated distillation with differential comparison

Authors:
raw_author_name: Xiaodong Wang
raw_affiliation_string: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
raw_author_name: Longfei Wu
raw_affiliation_string: Department of Mathematics and Computer Science, Fayetteville State University, Fayetteville 28301, USA
raw_author_name: Zhitao Guan
raw_affiliation_string: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China

Abstract: Membership inference attacks (MIAs) has demonstrated a great threat to federated learning (FL) and its extension federated distillation (FD). However, existing research on MIAs against FD is insufficient. In this paper, we propose a novel membership inference attack named GradDiff, which is a passive gradient-based MIA employing differential comparison. Additionally, to make full use of the federated training process, we also design the gradient drift attack (GradDrift), an active version of GradDiff, in which the attacker modifies the target model by gradient tuning and is able to obtain more information about membership privacy. We conduct extensive experiments on three real-world datasets to evaluate the effectiveness of the proposed attacks. The results show that our proposed attacks can outperform the existing baseline methods in terms of precision and recall. Besides, we perform a thorough investigation of the factors that may influence the performance of MIAs against FD.
Enhancing deep feature representation in self-knowledge distillation via pyramid feature refinement

Authors:
raw_author_name: Hao Yu
raw_affiliation_string: Chongqing University of Technology, The People’s Republic of China
raw_author_name: Yunlong Wang
raw_affiliation_string: Chongqing University of Technology, The People’s Republic of China

Abstract: In recent years, various self-knowledge distillation approaches have been proposed to reduce the cost of training teacher networks. However, these methods often overlook the significance of deep features. To address this limitation and strengthen the capability of deep features while preserving the ability of shallow features, we propose performing Self-Knowledge Distillation via Pyramid Feature Refinement (PR-SKD). Inspired by the representation learning characteristics of deep neural networks, PR-SKD builds a cohort of sub-networks with a pyramid architecture to hierarchically transfer refined information to the target network. According to the different contributions and functions between deep and shallow feature maps, our PR-SKD fully utilizes feature information to improve deep feature representation ability without compromising the capability of shallow feature maps. Extensive experiments on various image classification datasets demonstrate the superiority of our proposed method over widely used state-of-the-art knowledge distillation methods. The code is available at: https://github.com/wo16pao/PR-SKD.
LiDAR video object segmentation with dynamic kernel refinement

Authors:
raw_author_name: Jianbiao Mei
raw_affiliation_string: Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China
raw_author_name: Yu Yang
raw_affiliation_string: Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China
raw_author_name: Mengmeng Wang
raw_affiliation_string: Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China
raw_author_name: Zizhang Li
raw_affiliation_string: Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China
raw_author_name: Jongwon Ra
raw_affiliation_string: Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China
raw_author_name: Yong Liu
raw_affiliation_string: Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China

Abstract: In this paper, we formalize memory- and tracking-based methods to perform the LiDAR-based Video Object Segmentation (VOS) task, which segments points of the specific 3D target (given in the first frame) in a LiDAR sequence. LiDAR-based VOS can directly provide target-aware geometric information for practical application scenarios like behavior analysis and anticipating danger. We first construct a LiDAR-based VOS dataset named KITTI-VOS based on SemanticKITTI, which acts as a testbed and facilitates comprehensive evaluations of algorithm performance. Next, we provide two types of baselines, i.e., memory-based and tracking-based baselines, to explore this task. Specifically, the first memory-based pipeline is built on a space–time memory network equipped with the non-local spatiotemporal attention-based memory bank. We further design a more potent variant to introduce the locality into the spatiotemporal attention module by local self-attention and cross-attention modules. For the second tracking-based baseline, we modify two representative 3D object tracking methods to adapt to LiDAR-based VOS tasks. Finally, we propose a refine module that takes mask priors and generates object-aware kernels, which could boost all the baselines’ performance. We evaluate the proposed methods on the dataset and demonstrate their effectiveness.
Adaptive trajectory-constrained exploration strategy for deep reinforcement learning

Authors:
raw_author_name: Guojian Wang
raw_affiliation_string: Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing 100191, China
raw_author_name: Faguo Wu
raw_affiliation_string: Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; Zhongguancun Laboratory, Beijing 100194, China
raw_author_name: Xiao Zhang
raw_affiliation_string: Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; Zhongguancun Laboratory, Beijing 100194, China
raw_author_name: Ning Guo
raw_affiliation_string: Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing 100191, China
raw_author_name: Zhiming Zheng
raw_affiliation_string: Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; Zhongguancun Laboratory, Beijing 100194, China

Abstract: Deep reinforcement learning (DRL) faces significant challenges in addressing hard-exploration tasks with sparse or deceptive rewards and large state spaces. These challenges severely limit the practical application of DRL. Most previous exploration methods relied on complex architectures to estimate state novelty or introduced sensitive hyperparameters, resulting in instability. To mitigate these issues, we propose an efficient adaptive trajectory-constrained exploration strategy for DRL. The proposed method guides the agent’s policy away from suboptimal solutions by regarding previous offline demonstrations as references. Specifically, this approach gradually expands the exploration scope of the agent and strives for optimality in a constrained optimization manner. Additionally, we introduce a novel policy-gradient-based optimization algorithm that utilizes adaptive clipped trajectory-distance rewards for both single- and multi-agent reinforcement learning. We provide a theoretical analysis of our method, including a deduction of the worst-case approximation error bounds, highlighting the validity of our approach for enhancing exploration. To evaluate the effectiveness of the proposed method, we conducted experiments on two large 2D grid world mazes and several MuJoCo tasks. The extensive experimental results demonstrated the significant advantages of our method in achieving temporally extended exploration and avoiding myopic and suboptimal behaviors in both single- and multi-agent settings. Notably, the specific metrics and quantifiable results further support these findings. The code used in the study is available at https://github.com/buaawgj/TACE.
Unsupervised feature selection with high-order similarity learning

Authors:
raw_author_name: Yong Mi
raw_affiliation_string: Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Chengdu 611756, China; Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu 611756, China
raw_author_name: Hongmei Chen
raw_affiliation_string: Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Chengdu 611756, China; Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu 611756, China
raw_author_name: Chuan Luo
raw_affiliation_string: College of Computer Science, Sichuan University, Chengdu 610065, China
raw_author_name: Shi-Jinn Horng
raw_affiliation_string: Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan; Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 404327, Taiwan
raw_author_name: Tianrui Li
raw_affiliation_string: Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Chengdu 611756, China; Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu 611756, China

Abstract: Graph-based unsupervised feature selection methods have successfully processed high-dimensional data since they can effectively preserve data structure information. However, most existing methods are facing two challenges: (1) it is unreliable to exploit the intrinsic spatial structure from raw data because of the existence of redundant information and noise; (2) they only consider the first-order similar information but ignore the high-order similar information (i.e., samples with similar neighborhood network structure should be similar to each other). This study proposes an unsupervised feature selection with high-order similarity learning (HSL) to tackle the above problems. The projection matrix, first-order similar information, and high-order similar information are simultaneously learned in a unified framework, such that the intrinsic structure information can be exploited in the clean data that come from a latent low-dimensional embedding subspace. Specifically, we project raw data into a low-dimensional embedding subspace to effectively eliminate redundant information and noise. Moreover, we simultaneously consider the first-order similarity and the high-order similarity in the low-dimensional embedding space so that a better graph Laplacian matrix can be learned to fully preserve the intrinsic structure information of raw data and simultaneously utilize the label information. Furthermore, we design an effective optimization algorithm to tackle the proposed HSL. Comprehensive experiments on real-world datasets verify the superiority and effectiveness of HSL.
Self-attention deep ConvLSTM with sparse-learned channel dependencies for wearable sensor-based human activity recognition

Authors:
raw_author_name: Shan Ullah
raw_affiliation_string: Electrical and Computer Engineering Department, Inha University, Incheon, 22212, South Korea
raw_author_name: Deok-Hwan Kim
raw_affiliation_string: Electrical and Computer Engineering Department, Inha University, Incheon, 22212, South Korea

Abstract: In this study, we propose a novel deep-learning architecture with sparse learning for human activity recognition. The proposed model contains 1D CNNs and LSTM layers with a self-attention mechanism to enhance a substantial number of time points in time-series data for human activity recognition systems. Based on the recent success of squeeze-and-excite (SE) networks, the proposed deep learning model utilizes the SE module to enhance channel-wise interdependencies, which in turn leads to a boost in performance. In addition, we utilized sparse learning to retrain only weak nodes and freeze stronger nodes in a fully connected layer prior to classification layer. Furthermore, we utilized an entropy-inspired formula to find sparsely located weaker nodes and validated our model on various datasets, including Opportunity, UCI-HAR, and WISDM. Herein, we present an extensive analysis and survey of state-of-the-art studies, in addition to our proposed research. For a fair comparison, we evaluated our deep learning architecture using various performance metrics and achieved better results; the proposed model outperformed state-of-the-art algorithms for human activity recognition.
Parallel private information retrieval protocol with index anonymity for untrusted databases

Authors:
raw_author_name: Xiaopeng Yu
raw_affiliation_string: Science and Technology on Communication Security Laboratory, Chengdu 610041, China
raw_author_name: Dianhua Tang
raw_affiliation_string: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Science and Technology on Communication Security Laboratory, Chengdu 610041, China
raw_author_name: Zhen Zhao
raw_affiliation_string: State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, 710071, China
raw_author_name: Wei Zhao
raw_affiliation_string: Science and Technology on Communication Security Laboratory, Chengdu 610041, China

Abstract: Private information retrieval (PIR) enables a client to search data from a single (or multiple) untrusted server, without revealing which entry was queried. PIR can be divided into two categories: information-theoretic PIR (IT-PIR) and computational PIR (CPIR). However, there is a deployment challenge with IT-PIR because the non-collusion assumption is difficult to implement in practice. Meanwhile, due to the fact that CPIR involves performing cryptographic operations on each element, its performance significantly decreases as the size and number of database entries increase. To overcome these problems, based on homomorphic encryption, this paper presents a parallel PIR (ParPIR) with index anonymity for untrusted databases, which uses the batch encoding to compress request size. Furthermore, our ParPIR eliminates computationally expensive ciphertext multiplication operations, which improves the response time. Moreover, our ParPIR packs multiple responses through rotation column operations on ciphertext, thereby reducing the response size. Compared to previous PIR protocols, the offline time in our ParPIR has almost 1∼63× improvement; the online time in our ParPIR decreases by at least 49.9∼98.9%. Meanwhile, although the request size of our ParPIR has a 0∼31× increase, the response size of our ParPIR has a 0∼4095× improvement.
Subfield subcodes of projective Reed-Muller codes

Authors:
raw_author_name: Philippe Gimenez
raw_affiliation_string: IMUVA-Mathematics Research Institute, Universidad de Valladolid, Valladolid, 47011, Spain
raw_author_name: Diego Ruano
raw_affiliation_string: IMUVA-Mathematics Research Institute, Universidad de Valladolid, Valladolid, 47011, Spain
raw_author_name: Rodrigo San-José
raw_affiliation_string: IMUVA-Mathematics Research Institute, Universidad de Valladolid, Valladolid, 47011, Spain

Abstract: Explicit bases for the subfield subcodes of projective Reed-Muller codes over the projective plane and their duals are obtained. In particular, we provide a formula for the dimension of these codes. For the general case over the projective space, we generalize the necessary tools to deal with this case as well: we obtain a universal Gröbner basis for the vanishing ideal of the set of standard representatives of the projective space and we show how to reduce any monomial with respect to this Gröbner basis. With respect to the parameters of these codes, by considering subfield subcodes of projective Reed-Muller codes we obtain long linear codes with good parameters over a small finite field.