Reviewerly/exHarmony huggingface dataset:
Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary study

Authors:
raw_author_name: Younhyun Jung
raw_affiliation_string: School of Computing, Gachon University, Seongnam 13120, South Korea
raw_author_name: Michael Fulham
raw_affiliation_string: Department ofMolecular Imaging, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia; School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia
raw_author_name: Jinman Kim
raw_affiliation_string: School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia

Abstract: A medical content-based image retrieval (CBIR) system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image. CBIR is widely used in evidence- based diagnosis, teaching, and research. Although the retrieval accuracy has largely improved, there has been limited development toward visualizing important image features that indicate the similarity of retrieved images. Despite the prevalence of3D volumetric data in medical imaging such as computed tomography (CT), current CBIR systems still rely on 2D cross-sectional views for the visualization of retrieved images. Such 2D visualization requires users to browse through the image stacks to confirm the similarity of the retrieved images and often involves mental reconstruction of 3D information, including the size, shape, and spatial relations of multiple structures. This process is time-consuming and reliant on users’ experience. In this study, we proposed an importance-aware 3D volume visualization method. The rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval process. We then integrated the proposed visualization into a CBIR system, thereby complementing the 2D cross-sectional views for relevance feedback and further analyses. Our preliminary results demonstrate that 3D visualization can provide additional information using multimodal positron emission tomography and computed tomography (PET- CT) images of a non-small cell lung cancer dataset.
An improved quantum payment protocol based on group signature without entanglement

Authors:
raw_author_name: Jincheng Zhuang
raw_affiliation_string: Quan Cheng Laboratory, Jinan, P. R. China

Abstract: With the popularization of E-payment, more and more electronic payment protocols have been proposed. In 2021, Lin et al. proposed a trusted third-party E-Payment protocol based on a set of three-particle local indistinguishable orthogonal product (X-LIOP) states. In this paper, we first point out that there is a security flaw where the merchant can forge the purchase goods information to sell something that the customer does not want to buy in Lin et al. protocol. Besides, the X-LIOP states used by Lin are not orthogonal and can be discriminated by Local Operation and Classical Communication (LOCC). Second, we propose a new electronic payment protocol that combines group signature and the three-particle X-LIOP states. Compared with Lin et al. protocol, customers can purchase goods anonymously in our protocol and the unforgeability of messages and signatures is guaranteed.
Semi-supervised Federated Learning for Digital Twin 6G-enabled IIoT: A Bayesian Estimated Approach

Authors:
raw_author_name: Yuanhang Qi
raw_affiliation_string: School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China
raw_author_name: M. Shamim Hossain
raw_affiliation_string: Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia

Abstract: In recent years, the proliferation of Industrial Internet of Things (IIoT) devices has resulted in a substantial increase in data generation across various domains, including the nascent 6G networks. Digital Twins (DTs), serving as virtual replicas of physical entities, have gained popularity within the realm of IoT due to their capacity to simulate and optimize physical systems in a cost-effective manner. Nonetheless, the security of DTs and the safeguarding of the sensitive data they generate have emerged as paramount concerns. Fortunately, the Federated Fearning (FL) system has emerged as a promising solution to address the challenge of data privacy within DTs. Nonetheless, the requisite acquisition of a significant volume of labeled data for training purposes poses a formidable challenge, particularly in a DT environment that blends real and virtual data.To tackle this challenge, this study presents an innovative Semi-supervised FL (SSFL) framework designed to overcome the scarcity of labeled data through the strategic utilization of pseudo-labels.Specifically, our proposed SSFL algorithm, named SSFL-MBE, introduces a novel approach by combining Mix data augmentation and Bayesian Estimation consistency regularization loss, thereby integrating robust augmentation techniques to enhance model generalization. Furthermore, we introduce a Bayesian-estimated pseudo-label loss that leverages prior probabilistic knowledge to enhance model performance. Our investigation focuses particularly on a demanding scenario where labeled and unlabeled data are segregated across disparate locations, specifically, the server and various clients.Comprehensive evaluations conducted on CIFAR-10 and MNIST datasets conclusively demonstrate that our proposed algorithm consistently surpasses mainstream SSFL baseline models, exhibiting an enhancement in model performance ranging from 0.5% to 1.5%.Overall, this work contributes to the development of more efficient and secure approaches for model training in DT-empowered FL settings, which is crucial for the deployment of IIoTs in 6G-enabled environments.
Expanding the cloud-to-edge continuum to the IoT in serverless federated learning

Authors:
raw_author_name: Davide Loconte
raw_affiliation_string: Polytechnic University of Bari, Via Orabona 4, Bari, I-70125, BA, Italy
raw_author_name: Saverio Ieva
raw_affiliation_string: Polytechnic University of Bari, Via Orabona 4, Bari, I-70125, BA, Italy
raw_author_name: Agnese Pinto
raw_affiliation_string: Polytechnic University of Bari, Via Orabona 4, Bari, I-70125, BA, Italy
raw_author_name: Giuseppe Loseto
raw_affiliation_string: University LUM “Giuseppe Degennaro”, Strada Statale 100 km 18, Casamassima, I-70010, BA, Italy
raw_author_name: Floriano Scioscia
raw_affiliation_string: Polytechnic University of Bari, Via Orabona 4, Bari, I-70125, BA, Italy

Abstract: Serverless computing enables greater flexibility and efficiency in the cloud-to-edge continuum. Artificial Intelligence and Machine Learning (AI/ML) applications benefit greatly from this paradigm, as they need to gather, preprocess, aggregate and analyze data at various scales. In such contexts, the increasing hardware/software resource availability of Internet of Things (IoT) devices provides the opportunity to exploit them not only as data sources in AI/ML infrastructures, but also as computational nodes for model training and inference; nevertheless, comprehensive frameworks are still mostly missing. This work introduces an innovative serverless computing architecture which expands the cloud-to-edge continuum toward IoT devices. The same functions can run on IoT, edge and cloud nodes with minimal to no code modification and they can be invoked through a uniform interface. A federated learning framework is defined based on the proposed architecture, exploiting an existing IoT-oriented ML algorithm in a novel way. Notably, IoT nodes are used for both federated training and local inference tasks. A full prototype implementation has been built with off-the-shelf technologies and devices. A case study on federated machine learning for activity recognition and experiments have been conducted to validate key elements of the proposal.
Multi-behavior recommendation with SVD graph neural networks

Authors:
raw_author_name: Shengxi Fu
raw_affiliation_string: Department of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
raw_author_name: Xingfeng Lv
raw_affiliation_string: Department of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
raw_author_name: Jinbao Li
raw_affiliation_string: Shandong Artificial Intelligence Institute,School of Mathematics and Statistics, Qilu University of Technology, Jinan 250014, China

Abstract: Graph Neural Networks (GNNs) have been extensively employed in the field of recommendation systems, offering users personalized recommendations and yielding remarkable outcomes. Recently, GNNs incorporating contrastive learning have demonstrated promising performance in handling the sparse data problem of recommendation systems. However, existing contrastive learning methods still have limitations in resisting noise interference, especially for multi-behavior recommendation. To mitigate the aforementioned issues, this paper proposes a GNN-based multi-behavior recommendation model called MB-SVD that utilizes Singular Value Decomposition (SVD) graphs to enhance model performance. In particular, MB-SVD considers user preferences across different behaviors, improving recommendation effectiveness. First, MB-SVD integrates the representation of users and items under different behaviors with learnable weight scores, which efficiently considers the influence of different behaviors. Then, MB-SVD generates augmented graph representation with global collaborative relations. Next, we simplify the contrastive learning framework by directly contrasting original representation with the enhanced representation using the InfoNCE loss. Through extensive experimentation, the remarkable performance of our proposed MB-SVD approach in multi-behavior recommendation endeavors across diverse real-world datasets is exhibited.
Two-step ensemble under-sampling algorithm for massive imbalanced data classification

Authors:
raw_author_name: Lin Bai
raw_affiliation_string: School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China; Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
raw_author_name: Tong Ju
raw_affiliation_string: School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
raw_author_name: Hao Wang
raw_affiliation_string: School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
raw_author_name: Mingzhu Lei
raw_affiliation_string: School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
raw_author_name: Xiaoying Pan
raw_affiliation_string: School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China; Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an 710121, China

Abstract: Imbalanced data classification is a challenging problem in the field of machine learning. Class imbalance, class overlap, and large data volume significantly affect classification performance. Focusing on the impact of class overlap on classification effectiveness, we propose a two-step ensemble under-sampling algorithm based on boundary information mining (TSSE-BIM) with the goal of reducing the information loss from under-sampling methods on large-scale imbalanced data. In the first stage, the proposed method applies an improved equalization under-sampling strategy to mine sample contribution information and quickly obtains the distribution information of data relative to the decision boundary. In the second stage, based on the boundary information, a weighted boundary sampling is performed to remove noisy and highly overlapping samples. It is easy to retain samples with high contribution and effectively suppress the information loss caused by under-sampling. Then, the overall framework is designed based on a serial ensemble similar to boosting, where the weights of each base classifier are assigned to achieve a more powerful performance based on the false positive rate and false negative rate on the original data. Finally, extensive experiments indicate that TSSE-BIM outperforms state-of-the-art methods and ranks first on average under four metrics, especially F1 and MMC.
Non-iterative reversible information hiding in the secret sharing domain

Authors:
raw_author_name: Tao Liu
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
raw_author_name: Bin Yan
raw_affiliation_string: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China
raw_author_name: Jeng-Shyang Pan
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
raw_author_name: Hong-Mei Yang
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Abstract: Long running time due to multiple iterations is the main drawback of existing information hiding in the sharing domain (IHSD) algorithm. To address this problem, we propose non-iterative reversible information hiding in the secret sharing domain (NIIHSD). We calculate the coefficients of the polynomial that make the least significant bit of the shadow pixel equal to ‘0’ or ‘1’ and store them in two forms (A0 and A1). According to the information to be embedded, coefficients are randomly selected in A0 and A1 to generate qualified shares by once calculation. The comparison with IHSD demonstrates our scheme improves the running speed of the algorithm by a factor of 2 to 4 for different (k,n) thresholds and secret image with different sizes. The coefficient forms only need to be created once to be reused. Our scheme has a greater advantage when sharing a large number of images.
High-speed encrypted traffic classification by using payload features

Authors:
raw_author_name: Xinge Yan
raw_affiliation_string: Nanjing Huafei Data Technology Co., Ltd., Nanjing 210019, China; School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
raw_author_name: Liukun He
raw_affiliation_string: School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
raw_author_name: Jiuxin Cao
raw_affiliation_string: School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
raw_author_name: Liangmin Wang
raw_affiliation_string: School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
raw_author_name: Guyang Xie
raw_affiliation_string: Nanjing Huafei Data Technology Co., Ltd., Nanjing 210019, China

Abstract: Traffic encryption techniques facilitate cyberattackers to hide their presence and activities. Traffic classification is an important method to prevent network threats. However, due to the tremendous traffic volume and limitations of computing, most existing traffic classification techniques are inapplicable to the high-speed network environment. In this paper, we propose a High-speed Encrypted Traffic Classification (HETC) method containing two stages. First, to efficiently detect whether traffic is encrypted, HETC focuses on randomly sampled short flows and extracts aggregation entropies with chi-square test features to measure the different patterns of the byte composition and distribution between encrypted and unencrypted flows. Second, HETC introduces binary features upon the previous features and performs fine-grained traffic classification by combining these payload features with a Random Forest model. The experimental results show that HETC can achieve a 94% F-measure in detecting encrypted flows and a 85%–93% F-measure in classifying fine-grained flows for a 1-KB flow-length dataset, outperforming the state-of-the-art comparison methods. Meanwhile, HETC does not need to wait for the end of the flow and can extract mass computing features. The average time for HETC to process each flow is only 2 or 16 ms, which is lower than the flow duration in most cases, making it a good candidate for high-speed traffic classification.
Deepfake face discrimination based on self-attention mechanism

Authors:
raw_author_name: Donghui Zhu
raw_affiliation_string: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, China
raw_author_name: Jian Chen
raw_affiliation_string: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, China
raw_author_name: Wenyi Wang
raw_affiliation_string: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

Abstract: With the rapid progress of deepfake technology, the improper use of manipulated images and videos presenting synthetic faces has arisen as a noteworthy concern, thereby posing threats to both daily life and national security. While numerous CNN based deepfake face detection methods were proposed, most of the existing approaches encounter challenges in effectively capturing the image contents across different scales and positions. In this paper, we present a novel two-branch structural network, referred to as the Self-Attention Deepfake Face Discrimination Network (SADFFD). Specifically, a branch incorporating cascaded multi self-attention mechanism (SAM) modules, is parallelly integrated with EfficientNet-B4 (EffB4). The multi SAM branch supplies additional features that concentrate on image regions essential for discriminating between real and fake. The EffB4 network is adopted because of its efficiency by adjusting the resolution, depth, and width of the network. According to our comprehensive experiments conducted on FaceForensics++, Celeb-DF, and our self-constructed SAMGAN3 datasets, the proposed SADFFD demonstrated the highest detection accuracy, averaging 99.01% in FaceForensics++, 98.65% in Celeb-DF, and an impressive 99.99% in SAMGAN3, surpassing other state-of-the-art (SOTA) methods.
Continual learning for adaptive social network identification

Authors:
raw_author_name: Simone Magistri
raw_affiliation_string: Department of Information Engineering, University of Florence, Via di S. Marta 3, 50134, Firenze, Italy
raw_author_name: Daniele Baracchi
raw_affiliation_string: Department of Information Engineering, University of Florence, Via di S. Marta 3, 50134, Firenze, Italy
raw_author_name: Dasara Shullani
raw_affiliation_string: Department of Information Engineering, University of Florence, Via di S. Marta 3, 50134, Firenze, Italy
raw_author_name: Andrew D. Bagdanov
raw_affiliation_string: Department of Information Engineering, University of Florence, Via di S. Marta 3, 50134, Firenze, Italy
raw_author_name: Alessandro Piva
raw_affiliation_string: Department of Information Engineering, University of Florence, Via di S. Marta 3, 50134, Firenze, Italy

Abstract: The popularity of social networks as primary mediums for sharing visual content has made it crucial for forensic experts to identify the original platform of multimedia content. Various methods address this challenge, but the constant emergence of new platforms and updates to existing ones often render forensic tools ineffective shortly after release. This necessitates the regular updating of methods and models, which can be particularly cumbersome for techniques based on neural networks which cannot quickly adapt to new classes without sacrificing performance on previously learned ones – a phenomenon known as catastrophic forgetting. Recently, researchers aimed at mitigating this problem via a family of techniques known as continual learning. In this paper we study the applicability of continual learning techniques to the social network identification task by evaluating two relevant forensic scenarios: Incremental Social Platform Classification, for handling newly introduced social media platforms, and Incremental Social Version Classification, for addressing updated versions of a set of existing social networks. We perform an extensive experimental evaluation of a variety of continual learning approaches applied to these two scenarios. Experimental results demonstrate that, although Continual Social Network Identification remains a difficult problem, catastrophic forgetting can be significantly mitigated in both scenarios by retaining only a fraction of the image patches from past task training samples or by employing previous tasks prototypes.
Space Limited Linear-Time Graph Algorithms on Big Data

Authors:
raw_author_name: Zirui Chu
raw_affiliation_string: School of Computer Science, Guangzhou University, Guangzhou 510006, PR China
raw_author_name: Ying Guo
raw_affiliation_string: School of Computer Science, Guangzhou University, Guangzhou 510006, PR China
raw_author_name: Wei Yang
raw_affiliation_string: School of Computer Science, Guangzhou University, Guangzhou 510006, PR China

Abstract: We study algorithms for graph problems in which the graphs are of extremely large size N so that super-linear time ω(N) or linear space Θ(N) would become impractical. We use a parameter k to characterize the computational power of a normal computer that can provide additional time and space bounded by polynomials of k in dealing with the large graphs. In particular, we are interested in strict linear-time algorithms using space O(kO(1)). In our case studies, as examples, we present (1) a randomized greedy algorithm of time O(N) and space O(k2) for a parameterized version of the Maximal Matching problem; and (2) randomized kernelization algorithms of time O(N) and space O(kO(1)) for a number of well-known NP-hard problems. Our kernelization algorithms have their kernel sizes match the best kernel sizes by known polynomial-time kernelization algorithms with no space constraints for the problems. We also study the relationship between our proposed model and the streaming model. This study motivates a new streaming kernelization algorithm for the famous Vertex Cover problem that has an optimal update time complexity while matches the best known space complexity of streaming algorithms for the problem.