Reviewerly/exHarmony huggingface dataset:
Secure and Real-Time Traceable Data Sharing in Cloud-Assisted IoT

Authors:
raw_author_name: Jintian Lu
raw_affiliation_string: College of Computer Science and Engineering, Jishou University, Jishou, China
raw_author_name: Jiakun Sun
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
raw_author_name: Ruizhi Xiao
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
raw_author_name: Bolin Liao
raw_affiliation_string: College of Computer Science and Engineering, Jishou University, Jishou, China

Abstract: Cloud-assisted Internet of Things (IoT) has become an increasingly popular paradigm to greatly improve the performance of IoT applications by delegating the cloud to manage the massive IoT data. How to achieve secure and real time traceable data sharing is crucial in this paradigm, especially, a large amount of sensitive data produced by IoT devices need to be stored or accessed to/from the clouds. This paper proposes a secure and traceable data sharing (STDS) scheme, which leverages the decentralized information flow control model to allow data owners to not only securely and efficiently share their data produced by IoT devices with data users, but also have the capability of tracking the data users’ identity with non-repudiation based on the hash chain technique. Subsequently, the high-level Petri net, satisfiability modulo theories library, and Z3 solver are used to formally analyze and verify STDS based on bounded model checking technique to prove the correctness and security STDS. The formal analysis results show that STDS fulfills its intended security goals. Finally, the performance evaluation results have demonstrated the efficiency of STDS.
SNPF: Sensitiveness-Based Network Pruning Framework for Efficient Edge Computing

Authors:
raw_author_name: Yiheng Lu
raw_affiliation_string: Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi&#x2019;an, China
raw_author_name: Ziyu Guan
raw_affiliation_string: Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi&#x2019;an, China
raw_author_name: Wei Zhao
raw_affiliation_string: Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi&#x2019;an, China
raw_author_name: Maoguo Gong
raw_affiliation_string: Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi&#x2019;an, China
raw_author_name: Wenxiao Wang
raw_affiliation_string: State Key Laboratory of CAD&#x0026;CG, Zhejiang University, Hangzhou, China
raw_author_name: Kai Sheng
raw_affiliation_string: Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi&#x2019;an, China

Abstract: Convolutional neural networks (CNNs) are used comprehensively in the field of the Internet of Things (IoTs), such as mobile phones, surveillance, and satellite. However, the deployment of CNNs is difficult because the structure of hand-designed networks is complicated. Therefore, we propose a sensitiveness based network pruning framework (SNPF) to reduce the size of original networks to save computation resources. SNPF will evaluate the importance of each convolutional layer by the reconstruction of inference accuracy when we add extra noise to the original model, and then remove filters in terms of the degree of sensitiveness for each layer. Compared with previous weight-norm based pruning methods such as “l1-norm”“, BatchNorm-Pruning”, and “Taylor-Pruning”, SNPF is robust to the update of parameters, which can avoid the inconsistency of evaluation for filters if the parameters of the pre-trained model are not fully optimized. Namely, SNPF can prune the network at the early training stage to save computation resources. We test our method on three prevalent models of VGG-16, ResNet-18, ResNet-50 and a customized Conv-4 with 4 convolutional layers. They are then tested on CIFAR-10, CIFAR-100, ImageNet, and MNIST, respectively. Impressively, we observe that even when the VGG-16 is only trained with 50 epochs, we can get the same evaluation of layer importance as the results when the model is fully trained. Additionally, we can also achieve comparable pruning results to previous weight-oriented methods on the other three models.
AttAuth: An Implicit Authentication Framework for Smartphone Users Using Multimodality Data

Authors:
raw_author_name: Chengmei Zhao
raw_affiliation_string: School of Electronic and Information Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China
raw_author_name: Feng Gao
raw_affiliation_string: School of Electronic and Information Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China

Abstract: Smartphones have become the most important devices for users to communicate and interact with different forms of media, and at the same time stored a large amount of sensitive and private data. The security and protection of such data has become increasingly critical. As the sensor technology rapid developed, the diversity of sensors on smartphones has greatly increased (e.g., motion sensor, touchscreen sensor), empowering the smartphones to provide continuous and implicit user authentication by capturing behavioral biometrics. Unfortunately, it remains a challenge to make full use of the data from the multi-modality sensors to provide accurate authentication. Towards this end, in this paper, we develop an implicit authentication framework AttAuth which explores organic integration of such multi-modality data to authenticate smartphone users through the usage session. Specifically, AttAuth first develops a series of data processing techniques to process the multi-channel motion sensor data and the discrete touchscreen data. Then, a temporal and channel-wise attention based recurrent neural network (TCA-RNN) is developed to build authentication model, which allows to jointly model continuously-monitored motion sensor data and irregularly-recorded discrete touchscreen data effectively. By generating a guidance vector based on touch events, TCA-RNN guides the temporal-wise attention mechanism on the processed multi-channel motion sensor data and outputs authentication results. We evaluate AttAuth on a real-world multi-modality smartphone usage dataset of 100 users. Extensive experiments demonstrate that AttAuth achieves the state-of-the-art authentication accuracy. Additional experiments are provided to examine the applicability of AttAuth in terms of running overheads and sensitivity to various application scenarios.
A Novel IoT-Enabled System for Real-Time Face Mask Recognition Based on Petri Nets

Authors:
raw_author_name: Cheng-Ying Yang
raw_affiliation_string: Department of Computer Science, University of Taipei, Taipei, Taiwan
raw_author_name: Yi-Nan Lin
raw_affiliation_string: Department of Electronic Engineering, Ming Chi University of Technology, New Taipei City, Taiwan
raw_author_name: Victor R.L. Shen
raw_affiliation_string: Department of Information Management, Chaoyang University of Technology, Taichung City, Taiwan
raw_author_name: Frank H.C. Shen
raw_affiliation_string: Department of Electronic Engineering, Fu Jen Catholic University, New Taipei City, Taiwan
raw_author_name: Chien-Chi Wang
raw_affiliation_string: Department of Electronic Engineering, Ming Chi University of Technology, New Taipei City, Taiwan

Abstract: Due to Coronavirus Disease 2019 (COVID-19), many countries have formulated pandemic prevention regulations, requiring the masses to wear a face mask before entering public places and taking public transportation. However, if the entrances of some places are manually controlled to check whether people are wearing a face mask or not, it becomes not only labor intensive, but also time consuming. Therefore, this paper aims to develop a face mask recognition system based on an edge computing platform. The traditional manual inspection control method is replaced by artificial intelligence (AI) technology to achieve automatic recognition and control. As an edge computing platform, Jetson Nano is an embedded system equipped with an AI platform, which can be used for object detection and image classification. Developed by Ultralytics LLC, the YOLOv5 model with PyTorch framework runs on the edge computing platform, featuring high speed, high precision, and small size. According to the model training results, the average precision (AP) reaches 95.41%, while the mean average precision (mAP) reaches 94.42%. The average single-class running time is 0.016 seconds, and the file size of training model is 3.8MB. The recognition distance is up to 8m, and the maximum face rotation angle is 90. In addition, a Petri net software tool, Workflow Petri Net Designer (WoPeD), with graphical features based on mathematical theories, is used to verify the mask recognition system; and ensures the system has acceptable precision and recall values.
An Efficient FPGA-based Depthwise Separable Convolutional Neural Network Accelerator with Hardware Pruning

Authors:
raw_author_name: Zhengyan Liu
raw_affiliation_string: School of Microelectronics, Tianjin University, China
raw_author_name: Qiang Liu
raw_affiliation_string: School of Microelectronics, Tianjin University, China
raw_author_name: Shun Yan
raw_affiliation_string: School of Microelectronics, Tianjin University, China
raw_author_name: Ray C.C. Cheung
raw_affiliation_string: Department of Electrical Engineering, City University of Hong Kong

Abstract: Convolutional neural networks (CNNs) have been widely deployed in computer vision tasks. However, the computation and resource intensive characteristics of CNN bring obstacles to its application on embedded systems. This article proposes an efficient inference accelerator on Field Programmable Gate Array (FPGA) for CNNs with depthwise separable convolutions. To improve the accelerator efficiency, we make four contributions: (1) an efficient convolution engine with multiple strategies for exploiting parallelism and a configurable adder tree are designed to support three types of convolution operations; (2) a dedicated architecture combined with input buffers is designed for the bottleneck network structure to reduce data transmission time; (3) a hardware padding scheme to eliminate invalid padding operations is proposed; and (4) a hardware-assisted pruning method is developed to support online tradeoff between model accuracy and power consumption. Experimental results show that for MobileNetV2 the accelerator achieves 10× and 6× energy efficiency improvement over the CPU and GPU implementation, and 302.3 frames per second and 181.8 GOPS performance that is the best among several existing single-engine accelerators on FPGAs. The proposed hardware-assisted pruning method can effectively reduce 59.7% power consumption at the accuracy loss within 5%.
Reference Tracking Optimization With Obstacle Avoidance via Task Prioritization for Automated Driving

Authors:
raw_author_name: Francesco Vitale
raw_affiliation_string: Department of Built Environment, School of Engineering, Aalto University, Espoo, Finland
raw_author_name: Claudio Roncoli
raw_affiliation_string: Department of Built Environment, School of Engineering, Aalto University, Espoo, Finland

Abstract: Obstacle avoidance is a fundamental operation for automated driving and its formulation traditionally originates from robotics and decision making control fields. Given the high complexity required to compute an obstacle-free trajectory, this operation is usually demanded to a lower frequency planning layer that provides then a trajectory reference to be followed by a higher frequency control layer. As a result, whenever replanning is needed (for example, due to a new detected obstacle), the control layer must wait for a new planned trajectory to be generated. In this paper, we propose a novel methodology to approach obstacle avoidance already in the control layer, which allows a prompter response. In particular, we show how obstacle avoidance and reference tracking can be integrated, thus with no need to switch among different controllers, based on a null-space based behavioral control approach, implemented in a (possibly nonlinear) model predictive control scheme. We demonstrate practical implementation of the proposed methodology employing two different vehicle dynamic models and in four different (urban and highway) scenarios. Furthermore, we provide a sensitivity analysis to understand how parameters choice affects the automated vehicle behavior.
PBFL: Privacy-Preserving and Byzantine-Robust Federated-Learning-Empowered Industry 4.0

Authors:
raw_author_name: Wenjie Li
raw_affiliation_string: State Key Laboratory of Integrated Service Networks, Xidian University, Xi&#x2019;an, China
raw_author_name: Kai Fan
raw_affiliation_string: State Key Laboratory of Integrated Service Networks, Xidian University, Xi&#x2019;an, China
raw_author_name: Kan Yang
raw_affiliation_string: Department of Computer Science, University of Memphis, Memphis, TN, USA
raw_author_name: Yintang Yang
raw_affiliation_string: Key Laboratory of the Ministry of Education for Wide BandGap Semiconductor Materials and Devices, Xidian University, Xi&#x2019;an, China
raw_author_name: Hui Li
raw_affiliation_string: State Key Laboratory of Integrated Service Networks, Xidian University, Xi&#x2019;an, China

Abstract: In Industry 4.0, Artificial Intelligence (AI) has been successfully applied in scenarios such as fault prediction, traffic analysis, and production decision-making. However, due to the sensitivity and security of data, privacy regulations prohibit the transfer and exchange of industrial data between entities, resulting in training data being fragmented into data silos that limit the accuracy of AI models. Federated learning (FL) can effectively break the data silo effect, but naive FL (FedAvg) is vulnerable to inference attacks from aggregators and Byzantine attacks from participants. To address these issues, we propose a privacy-preserving and Byzantine-robust federated learning scheme (PBFL) for Industry 4.0. Under the setting of an benign-majority participants, PBFL can always identify benign direction and magnitude of updates. Extensive experiments demonstrate that PBFL is more robust than state-of-the-art schemes, even with extreme proportion (49%) of malicious participants. Moreover, PBFL contains a series of well-optimized 2-Party computation (2PC) protocols, causing it reduces total runtime of the unoptimized implementation by around 3×~4× and 9×~10× for 32-bit and 64-bit circuits, respectively.
Adaptive Modulation and Retransmission Scheme for Semantic Communication Systems

Authors:
raw_author_name: Huiguo Gao
raw_affiliation_string: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
raw_author_name: Guanding Yu
raw_affiliation_string: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
raw_author_name: Yunlong Cai
raw_affiliation_string: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

Abstract: Traditional adaptive modulation scheme aims to maximize the spectral efficiency by selecting the appropriate modulation scheme under the premise of perfect bit-level data transmission. However, in task-oriented semantic communication systems, imperfect transmission can still lead to a good inference performance of semantic tasks due to the error correction capability of neural networks. In this paper, we propose novel adaptive modulation and retransmission schemes to maximize the spectral efficiency while guaranteeing the performance of semantic tasks. Specifically, we first introduce the robustness verification problem in semantic communication systems to analyze the robustness of neural network inference. We then formulate and solve the modulation scheme selection problem constrained by a robustness probability threshold. Consequently, a novel adaptive modulation scheme is developed to maximize the spectral efficiency while guaranteeing the goal of semantic communication. We also develop a retransmission scheme using existing combining techniques to further increase the data rate under harsh channel conditions. Extensive simulations are performed in unencoded and encoded semantic communication systems to validate the effectiveness of the proposed schemes.
Labels Are Culprits: Defending Gradient Attack on Privacy

Authors:
raw_author_name: Zhaohua Li
raw_affiliation_string: Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Le Wang
raw_affiliation_string: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China
raw_author_name: Zhaoquan Gu
raw_affiliation_string: Peng Cheng Laboratory, Shenzhen, China
raw_author_name: Yang Lv
raw_affiliation_string: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China
raw_author_name: Zhihong Tian
raw_affiliation_string: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China

Abstract: Federated learning (FL) is widely studied for local privacy protection, and it involves exchanging model parameters rather than raw data among clients. However, gradient attacks (GAs) make a malicious client or parameter server of FL infer the local data of other clients only based on the model parameters exchanged. In FL frameworks and processes, it is important to understand the features that provide heuristic information for inferring raw data, as well as how best to defend against GAs. The academic community is currently investigating this problem. In this study, we demonstrate that the labels of input samples play a key role in the success of GAs. We analyze the rank of the coefficient matrix of the non-homogeneous linear equation of gradients and input samples and propose an approach that performs special operations on the repetition and order of labels. The approach achieves a better defense effect against GAs without using a differential privacy (DP) framework. Our experimental results show that GAs fail (i.e., without leaking any valid information about local data) during the entire training process of a deep convolutional network in FL, and the accuracy of the network is less affected than that of DP. The code is available at https://github.com/zhaohuali/Label-based-Defense.
Time Estimation for a New Block Generation in Blockchain-Enabled Internet of Things

Authors:
raw_author_name: Malka N. Halgamuge
raw_affiliation_string: Department of Information Systems and Business Analytics, RMIT University, Melbourne, VIC, Australia
raw_author_name: Moshe Zukerman
raw_affiliation_string: Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, P.R. China

Abstract: The Internet of Things (IoT) has emerged with Distributed Ledger Technology (DLT) to address existing scalability challenges and improve the trustworthiness of machine-tomachine communication. Among the numerous potential benefits of combining IoT and DLT, Blockchain, a subset of DLT, is a crucial enabler to accelerate secure IoT adoption. Appending a new block to a blockchain, especially in a blockchain-based IoT ecosystem, requires more delay than expected. This delay is one of several issues limiting the broader adoption of blockchain within the IoT domain. To assess this delay, we develop a new comprehensive model to estimate the time required to generate a new block in a blockchain-enabled IoT system. To this end, we develop sub-computation models and compare time consumption associated with the block generation process by conducting an extensive analysis of the following selected IoT layers: device layer, cluster head layer, fog/edge layer, and cloud layer. Our study identifies potential time-consuming steps in adding a new block to a network. Our results demonstrate that the type of blockchain framework and data encryption algorithms could affect the block generation time and that Avalanche, Conflux, Algorand, Polkadot Hyperledger Fabric outperforms Ethereum in terms of block generation time in IoT networks. On the other hand, the blockchain framework does not play a significant role in block generation time for smaller data packets. We also observed the benefit of using 256-bit ECC (elliptic curve cryptography) encryption and the fog layer in IoT networks to enhance the scalability of the block generation process. All in all, our results indicate that the total block generation time varies depending on the selected IoT framework, data encryption algorithm, blockchain type, and key functions of the layers. However, we found that time delays associated with queuing or block size are negligible relative to the other key components of block generation time.
Conversational recommender based on graph sparsification and multi-hop attention

Authors:
raw_author_name: Yihao Zhang
raw_affiliation_string: School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
raw_author_name: Yuhao Wang
raw_affiliation_string: School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
raw_author_name: Wei Zhou
raw_affiliation_string: School of Big Data & Software Engineering, Chongqing University, Chongqing, China
raw_author_name: Pengxiang Lan
raw_affiliation_string: School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
raw_author_name: Haoran Xiang
raw_affiliation_string: School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
raw_author_name: Junlin Zhu
raw_affiliation_string: School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
raw_author_name: Meng Yuan
raw_affiliation_string: Institute of Artificial Intelligence, Beihang University, Beijing, China

Abstract: Conversational recommender systems provide users with item recommendations via interactive dialogues. Existing methods using graph neural networks have been proven to be an adequate representation of the learning framework for knowledge graphs. However, the knowledge graph involved in the dialogue context is vast and noisy, especially the noise graph nodes, which restrict the primary node’s aggregation to neighbor nodes. In addition, although the recurrent neural network can encode the local structure of word sequences in a dialogue context, it may still be challenging to remember long-term dependencies. To tackle these problems, we propose a sparse multi-hop conversational recommender model named SMCR, which accurately identifies important edges through matching items, thus reducing the computational complexity of sparse graphs. Specifically, we design a multi-hop attention network to encode dialogue context, which can quickly encode the long dialogue sequences to capture the long-term dependencies. Furthermore, we utilize a variational auto-encoder to learn topic information for capturing syntactic dependencies. Extensive experiments on the travel dialogue dataset show significant improvements in our proposed model over the state-of-the-art methods in evaluating recommendation and dialogue generation.