Reviewerly/exHarmony huggingface dataset:
ATAL: Active Learning Using Adversarial Training for Data Augmentation

Authors:
raw_author_name: Xuanwei Lin
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, China
raw_author_name: Ximeng Liu
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, China
raw_author_name: Yuyang Wang
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, China
raw_author_name: Chen Dong
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, China
raw_author_name: Pengzhen Hu
raw_affiliation_string: School of Life Sciences, Northwestern Polytechnical University, Xi&#x2019;an, China

Abstract: Active learning (AL) tries to maximize the model’s performance when the labeled dataset is limited, and the annotation cost is high. Although it can be efficiently implemented in deep neural networks (DNNs), it is questionable whether the model can maintain the ability to generalize well when there are significant distributional deviations between the labeled and unlabeled datasets. In this paper, we consider introducing adversarial training and adversarial samples into active learning to mitigate the problem of degraded generalization performance due to different data distributions. In particular, our proposed adversarial training active learning (ATAL) has two advantages, and one is that adversarial training by different networks enables the network to have better prediction performance and robustness with limited labeled samples. And the other is that the adversarial samples generated by the adversarial training can effectively expand the labeled dataset so that the designed query function can efficiently select the most informative unlabeled samples based on the expanded labeled dataset. Extensive experiments have been performed to verify the feasibility and efficiency of our proposed method, i.e., CIFAR-10 demonstrates the effectiveness of our method — new state-of-the-art robustness and accuracy are achieved.
Identity-Based Multiproxy Signature With Proxy Signing Key for Internet of Drones

Authors:
raw_author_name: Young Ah Shin
raw_affiliation_string: School of Cybersecurity, Korea University, Seoul, South Korea
raw_author_name: Ik Rae Jeong
raw_affiliation_string: School of Cybersecurity, Korea University, Seoul, South Korea
raw_author_name: Jin Wook Byun
raw_affiliation_string: Department of Information and Communication, Pyeongtaek University, Pyeongtaek, South Korea

Abstract: A multi-proxy signature scheme constructs a legitimate signature by a proxy group when an original signer is absent. In this paper, we newly propose an efficient and provably secure ID-Based Multi-Proxy Signature (IBMPS) from bilinear pairings based on a random proxy signing key. We observe that a proxy signing key from a Proxy Key Generation (PKG) makes IBMPS much more efficient and secure than directly using a private key for a multi-proxy signature. We show formal security results under the CDH assumption with PKG queries in the random oracle. Our scheme not only preserves provable security but also achieves the least computation cost in the signing and verification phase than other IBMPS schemes. To show its applicability in Internet-of-Drones (IoD), which usually demands greater efficiency due to the limited resources while the drones are in flight, we also present a practical on/offline IBMPS for an IoD setting. Our on/offline IBMPS scheme just requires one point scalar addition for generating a proxy signature in terms of each proxy signer. Considering that our IBMPS schemes can be practical in urgent situations, e.g., surveillance and rescue operations, we show our implementation of IBMPS schemes on a Raspberry Pi drone platform along with the relevant schemes. We demonstrate that our schemes are considerably more efficient than others with respect to computation time and energy consumption by the number of proxy signers.
Joint Edge Server Selection and Data Set Management for Federated-Learning-Enabled Mobile Traffic Prediction

Authors:
raw_author_name: Doyeon Kim
raw_affiliation_string: School of Computing, Gachon University, Seongnam, Republic of Korea
raw_author_name: Seungjae Shin
raw_affiliation_string: Telecommunications and Media Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea
raw_author_name: Joohyung Lee
raw_affiliation_string: School of Computing, Gachon University, Seongnam, Republic of Korea

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

Authors:
raw_author_name: Fenghuan Li
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Yun Xue
raw_affiliation_string: School of Electronics and Information Engineering, South China Normal University, Foshan, China

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.
E<sup>3</sup>-UAV: An Edge-Based Energy-Efficient Object Detection System for Unmanned Aerial Vehicles

Authors:
raw_author_name: Jiashun Suo
raw_affiliation_string: Engineering Research Center of Cyberspace and the School of Software, Yunnan University, Kunming, China
raw_author_name: Xingzhou Zhang
raw_affiliation_string: Research Center of Distributed Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
raw_author_name: Weisong Shi
raw_affiliation_string: Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA
raw_author_name: Wei Zhou
raw_affiliation_string: Engineering Research Center of Cyberspace and the School of Software, Yunnan University, Kunming, China

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

Authors:
raw_author_name: Chuanxi Chen
raw_affiliation_string: Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, and the School of Cyber Science and Engineering, Wuhan University, Wuhan, China
raw_author_name: Dengpan Ye
raw_affiliation_string: Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, and the School of Cyber Science and Engineering, Wuhan University, Wuhan, China
raw_author_name: Long Tang
raw_affiliation_string: Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, and the School of Cyber Science and Engineering, Wuhan University, Wuhan, China
raw_author_name: Yue Xu
raw_affiliation_string: Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, and the School of Cyber Science and Engineering, Wuhan University, Wuhan, China

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

Authors:
raw_author_name: Jianfeng Ma
raw_affiliation_string: School of Cyber Engineering and the Shaanxi Key Laboratory of Network and System Security, Xidian University, Xi&#x2019;an, China
raw_author_name: Yinbin Miao
raw_affiliation_string: School of Cyber Engineering and the Shaanxi Key Laboratory of Network and System Security, Xidian University, Xi&#x2019;an, China
raw_author_name: Xiangyu Wang
raw_affiliation_string: School of Cyber Engineering and the Shaanxi Key Laboratory of Network and System Security, Xidian University, Xi&#x2019;an, China
raw_author_name: Rongxing Lu
raw_affiliation_string: Faculty of Computer Science, University of New Brunswick, Fredericton, Canada
raw_author_name: Wei Zhang
raw_affiliation_string: School of Cyber Engineering and the Shaanxi Key Laboratory of Network and System Security, Xidian University, Xi&#x2019;an, China

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

Authors:
raw_author_name: Ju Liu
raw_affiliation_string: Management School, Hainan University, Haikou, 570228, PR China
raw_author_name: Yuan-Hai Shao
raw_affiliation_string: Management School, Hainan University, Haikou, 570228, PR China
raw_author_name: Wei-Jie Chen
raw_affiliation_string: Zhijiang College, Zhejiang University of Technology, Hangzhou, 310024, PR China
raw_author_name: Chun-Na Li
raw_affiliation_string: Management School, Hainan University, Haikou, 570228, PR China

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

Authors:
raw_author_name: Yi Zhu
raw_affiliation_string: School of Information Engineering, Yangzhou University, Yangzhou, China
raw_author_name: Yishuai Geng
raw_affiliation_string: School of Information Engineering, Yangzhou University, Yangzhou, China
raw_author_name: Yun Li
raw_affiliation_string: School of Information Engineering, Yangzhou University, Yangzhou, China
raw_author_name: Jipeng Qiang
raw_affiliation_string: School of Information Engineering, Yangzhou University, Yangzhou, China
raw_author_name: Yunhao Yuan
raw_affiliation_string: School of Information Engineering, Yangzhou University, Yangzhou, China
raw_author_name: Xindong Wu
raw_affiliation_string: Research Center for Knowledge Engineering, Zhejiang Lab, Hangzhou, China

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

Authors:
raw_author_name: Shiwen Zhang
raw_affiliation_string: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
raw_author_name: Ziwei Yan
raw_affiliation_string: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
raw_author_name: Wei Liang
raw_affiliation_string: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
raw_author_name: Kuan-Ching Li
raw_affiliation_string: Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan
raw_author_name: Ciprian Dobre
raw_affiliation_string: Department of Computer Science, University Politehnica of Bucharest, Bucharest, Romania

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

Authors:
raw_author_name: Taotao Li
raw_affiliation_string: School of Software Engineering, Sun Yat-Sen University, Zhuhai, China
raw_author_name: Chunming Tang
raw_affiliation_string: School of Mathematics and Information Science, Guangzhou University, Guangzhou, China
raw_author_name: Debiao He
raw_affiliation_string: School of Cyber Science and Engineering, Wuhan University, Wuhan, China
raw_author_name: Zibin Zheng
raw_affiliation_string: School of Mathematics and Information Science, Guangzhou University, Guangzhou, China

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.