Reviewerly/exHarmony huggingface dataset:
Visual Analysis of Money Laundering in Cryptocurrency Exchange

Authors:
raw_author_name: Fangfang Zhou
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha, China
raw_author_name: Yunpeng Chen
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha, China
raw_author_name: Chunyao Zhu
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha, China
raw_author_name: Zengsheng Zhong
raw_affiliation_string: School of Computer Science and Engineering, Chongqing Technology and Business University, Chongqing, China
raw_author_name: Xiaohui Chen
raw_affiliation_string: Institute of Data and Target Engineering, Information Engineering University, Zhengzhou, China
raw_author_name: Yi Chen
raw_affiliation_string: School of Computer Science and Engineering, Beijing Technology and Business University, Beijing, China
raw_author_name: Ying Zhao
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha, China

Abstract: Blockchain-based cryptocurrencies, such as Bitcoin (BTC) and Ethereum (ETH), are newly emerging financial assets. Cryptocurrency exchanges are marketplaces for cryptocurrency circulation while becoming a new venue for money laundering. In this work, we cooperate with a cryptocurrency exchange to investigate new solutions for anti-money laundering in cryptocurrency exchanges. First, we learn the domain knowledge of cryptocurrency transactions and summarize data analytical requirements of transaction supervisors in their daily work of anti-money laundering. Then, we propose a visual analysis approach to support their daily work. The approach consists of a new algorithm that automatically detects suspicious money laundering accounts and a multiviewed user interface that visualizes the algorithm results and relevant transaction data. An abacus-inspired visualization is designed in the interface to depict transaction patterns contained in numerous cryptocurrency transactions, which can help supervisors find money laundering clues and deduce the trading tactic adopted by launderers. Finally, an algorithm performance experiment, a case study, and a field study are conducted with real-world data to demonstrate the effectiveness of our solution.
Blockchain Oriented Effective Charity Process During Pandemics and Emergencies

Authors:
raw_author_name: Mandeep Kaur
raw_affiliation_string: Department of Engineering and Technology, Guru Nanak Dev University, Regional Campus, Jalandhar, India
raw_author_name: Pankaj Deep Kaur
raw_affiliation_string: Department of Engineering and Technology, Guru Nanak Dev University, Regional Campus, Jalandhar, India
raw_author_name: Sandeep Kumar Sood
raw_affiliation_string: Department of Computer Applications, National Institute of Technology, Kurukshetra, India

Abstract: During any emergency, a donation is considered a moral responsibility all over the globe. The lack of transparency and oversight in charity donations hurts people’s enthusiasm to donate. Donors are distrustful about how their funds are utilized. The use of blockchain technology (BCT) will provide a solution to make the donation procedure more viable. It is a distributed technology that offers a secure and transparent environment by avoiding the involvement of third parties between contributors and charities. This article proposed a blockchain-based donation mechanism for the convenience of charity organizations, donors, and beneficiaries during disasters, pandemics such as Covid-19, and other emergencies. All transactions can be traced in blockchain, giving donors visibility into where and how their funds are utilized. This article contributes to improving donations’ openness to strengthen public interest in donations and encourage BCT in charity. Ethereum blockchain is used to implement the proposed framework and provides a convenient donation platform. Smart contracts are used to make donations, which build trust between contributors, beneficiaries, and charity organizations. The blockchain-based donation method saves time, lowers donation costs, and eliminates the chances of dubious campaign funds. This study will contribute to improving emergency recovery efforts.
Zooming Into Video Conferencing Privacy

Authors:
raw_author_name: Dima Kagan
raw_affiliation_string: Department of Software and Information Systems Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel
raw_author_name: Galit Fuhrmann Alpert
raw_affiliation_string: Department of Software and Information Systems Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel
raw_author_name: Michael Fire
raw_affiliation_string: Department of Software and Information Systems Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel

Abstract: The COVID-19 pandemic outbreak, with its related social distancing and shelter-in-place measures, has dramatically affected ways in which people communicate with each other, forcing people to find new ways to collaborate, study, celebrate special occasions, and meet with family and friends. One of the most popular solutions that have emerged is the use of video conferencing applications to replace face-to-face meetings with virtual meetings. This resulted in unprecedented growth in the number of video conferencing users. In this study, we explored privacy issues that may be at risk by attending virtual meetings. We extracted private information from collage images of meeting participants that are publicly posted on the Web. We used image processing, text recognition tools, as well as social network analysis to explore our web crawling curated dataset of over 15,700 collage images, and over 142,000 face images of meeting participants. We demonstrate that video conference users are facing prevalent security and privacy threats. Our results indicate that it is relatively easy to collect thousands of publicly available images of video conference meetings and extract personal information about the participants, including their face images, age, gender, usernames, and sometimes even full names. This type of extracted data can vastly and easily jeopardize people's security and privacy both in the online and real-world, affecting not only adults but also more vulnerable segments of society, such as young children and older adults. Finally, we show that cross-referencing facial image data with social network data may put participants at additional privacy risks they may not be aware of and that it is possible to identify users that appear in several video conference meetings, thus providing a potential to maliciously aggregate different sources of information about a target individual.
Hadoop-based secure storage solution for big data in cloud computing environment

Authors:
raw_author_name: Shaopeng Guan
raw_affiliation_string: School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, 264003, China
raw_author_name: Conghui Zhang
raw_affiliation_string: School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, 264003, China
raw_author_name: Yilin Wang
raw_affiliation_string: School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, 264003, China
raw_author_name: Wenqing Liu
raw_affiliation_string: School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, 264003, China

Abstract: In order to address the problems of the single encryption algorithm, such as low encryption efficiency and unreliable metadata for static data storage of big data platforms in the cloud computing environment, we propose a Hadoop based big data secure storage scheme. Firstly, in order to disperse the NameNode service from a single server to multiple servers, we combine HDFS federation and HDFS high-availability mechanisms, and use the Zookeeper distributed coordination mechanism to coordinate each node to achieve dual-channel storage. Then, we improve the ECC encryption algorithm for the encryption of ordinary data, and adopt a homomorphic encryption algorithm to encrypt data that needs to be calculated. To accelerate the encryption, we adopt the dual-thread encryption mode. Finally, the HDFS control module is designed to combine the encryption algorithm with the storage model. Experimental results show that the proposed solution solves the problem of a single point of failure of metadata, performs well in terms of metadata reliability, and can realize the fault tolerance of the server. The improved encryption algorithm integrates the dual-channel storage mode, and the encryption storage efficiency improves by 27.6% on average.
A Novel DAO-Based Parallel Enterprise Management Framework in Web3 Era

Authors:
raw_author_name: Ge Wang
raw_affiliation_string: Macao Institute of Systems Engineering, Macau University of Science and Technology, Macau, China
raw_author_name: Rui Qin
raw_affiliation_string: State Key Laboratory for Management and Control of Complex Systems and the State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
raw_author_name: Juanjuan Li
raw_affiliation_string: State Key Laboratory for Management and Control of Complex Systems and the State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
raw_author_name: Fei-Yue Wang
raw_affiliation_string: Macao Institute of Systems Engineering, Macau University of Science and Technology, Macau, China
raw_author_name: Yu Gan
raw_affiliation_string: Qingdao Huituo (Waytous) Intelligent Robotics Company Ltd, Qingdao, China
raw_author_name: Lihua Yan
raw_affiliation_string: Qingdao Huituo (Waytous) Intelligent Robotics Company Ltd, Qingdao, China

Abstract: This article proposes a novel parallel management mode based on decentralized autonomous organizations (DAOs) for enterprises by utilizing the artificial systems, computational experiments, parallel execution (ACP) approach, parallel intelligence theory, and blockchain technologies, to realize the distributed management of an enterprise. The artificial enterprise DAO (EnDAO) corresponding to the actual enterprise is constructed, and they constitute a parallel system via virtual–real interaction and parallel execution. Through the non-fungible token (NFT)-based incentive mechanism, metaverse-based virtual learning and training, as well as DAO-based distributed management and decision-making, the management and control of the actual enterprise as well as its employees can be carried out. By virtue of the virtual–real interactions of three types of employees, as well as the virtual–real feedback of three closed loops in the parallel systems, DAO-based parallel management for enterprises can realize descriptive intelligence, predictive intelligence, and prescriptive intelligence. On this basis, this article takes the recruitment-oriented key performance indicator (KPI) management of a startup technology enterprise as the case to introduce the operation processes and illustrate the superiorities of the proposed DAO-based enterprise parallel management mode.
Transformer and GAN-Based Super-Resolution Reconstruction Network for Medical Images

Authors:
raw_author_name: Weizhi Du
raw_affiliation_string: Arts &#x0026; Sciences College, Washington University in St. Louis,St. Louis,MO,USA,63130
raw_author_name: Shihao Tian
raw_affiliation_string: Cornell University,Department of Electric and Computing Engineering,Ithaca,NY,USA,14850

Abstract: Super-resolution reconstruction in medical imaging has become more demanding due to the necessity of obtaining high-quality images with minimal radiation dose, such as in low-field magnetic resonance imaging (MRI). However, image super-resolution reconstruction remains a difficult task because of the complexity and high textual requirements for diagnosis purpose. In this paper, we offer a deep learning based strategy for reconstructing medical images from low resolutions utilizing Transformer and generative adversarial networks (T-GANs). The integrated system can extract more precise texture information and focus more on important locations through global image matching after successfully inserting Transformer into the generative adversarial network for picture reconstruction. Furthermore, we weighted the combination of content loss, adversarial loss, and adversarial feature loss as the final multi-task loss function during the training of our proposed model T-GAN. In comparison to established measures like peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), our suggested T-GAN achieves optimal performance and recovers more texture features in super-resolution reconstruction of MRI scanned images of the knees and belly.
Hybrid ultra-short-term PV power forecasting system for deterministic forecasting and uncertainty analysis

Authors:
raw_author_name: Jianzhou Wang
raw_affiliation_string: School of Statistics, Dongbei University of Finance and Economics, Dalian, 116025, China
raw_author_name: Yue Yu
raw_affiliation_string: School of Statistics, Dongbei University of Finance and Economics, Dalian, 116025, China
raw_author_name: Haiyan Lu
raw_affiliation_string: School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Australia

Abstract: The rapid development of the photovoltaic industry provides a new source of power for the continued operation of the over-consumed energy world. While providing new opportunities for global energy systems, it also poses challenges for power grids. Therefore, it is a priority to fully grasp the characteristics of photovoltaic changes and accurately forecast and analyze them. To enrich the existing research, a novel hybrid prediction system considering meteorological factors is constructed. First, a feature selection module is introduced to select features and assign weights to exogenous meteorological factors, which breaks through the limitations of single-data dimension prediction. Second, shallow and deep learning models are flexibly applied and multi-objective intelligent optimization strategies are introduced to construct deterministic combinatorial prediction models. The module can effectively increase the diversity of prediction models while fully weighing the accuracy and stability of prediction to meet the needs of different information users. Finally, an interval prediction model is constructed to further enrich the PV power prediction system from the perspective of uncertainty analysis. The empirical study is carried out with 5-minute interval data at three sites, and the results show that the hybrid system obtains superior out-of-sample forecasting performance with technical feasibility and general applicability.
Dense Text Retrieval Based on Pretrained Language Models: A Survey

Authors:
raw_author_name: Wayne Xin Zhao
raw_affiliation_string: Renmin University of China, China
raw_author_name: Jing Liu
raw_affiliation_string: Baidu Inc., China
raw_author_name: Ruiyang Ren
raw_affiliation_string: Renmin University of China, China
raw_author_name: Ji-Rong Wen
raw_affiliation_string: Renmin University of China, China

Abstract: Text retrieval is a long-standing research topic on information seeking, where a system is required to return relevant information resources to user’s queries in natural language. From heuristic-based retrieval methods to learning-based ranking functions, the underlying retrieval models have been continually evolved with the ever-lasting technical innovation. To design effective retrieval models, a key point lies in how to learn text representations and model the relevance matching. The recent success of pretrained language models (PLM) sheds light on developing more capable text-retrieval approaches by leveraging the excellent modeling capacity of PLMs. With powerful PLMs, we can effectively learn the semantic representations of queries and texts in the latent representation space, and further construct the semantic matching function between the dense vectors for relevance modeling. Such a retrieval approach is called dense retrieval , since it employs dense vectors to represent the texts. Considering the rapid progress on dense retrieval, this survey systematically reviews the recent progress on PLM-based dense retrieval. Different from previous surveys on dense retrieval, we take a new perspective to organize the related studies by four major aspects, including architecture, training, indexing and integration, and thoroughly summarize the mainstream techniques for each aspect. We extensively collect the recent advances on this topic, and include 300+ reference papers. To support our survey, we create a website for providing useful resources, and release a code repository for dense retrieval. This survey aims to provide a comprehensive, practical reference focused on the major progress for dense text retrieval.
A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme

Authors:
raw_author_name: Nianyin Zeng
raw_affiliation_string: Xiamen University,Department of Instrumental and Electrical Engineering,Xiamen,China,361005
raw_author_name: Peishu Wu
raw_affiliation_string: Xiamen University,Department of Instrumental and Electrical Engineering,Xiamen,China,361005
raw_author_name: Han Li
raw_affiliation_string: Xiamen University,Department of Instrumental and Electrical Engineering,Xiamen,China,361005
raw_author_name: Xin Luo
raw_affiliation_string: College of Computer and Information Science, Southwest University,Chongqing,China,400715

Abstract: Unmanned aerial vehicles (UAVs) have gained significant attention in practical applications, especially the low-altitude aerial (LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network (TDKD-Net) is proposed, where the TT-format TD (tensor decomposition) and equal-weighted response-based KD (knowledge distillation) methods are designed to minimize redundant parameters while ensuring comparable performance. Moreover, some robust network structures are developed, including the small object detection head and the dual-domain attention mechanism, which enable the model to leverage the learned knowledge from small-scale targets and selectively focus on salient features. Considering the imbalance of bounding box regression samples and the inaccuracy of regression geometric factors, the focal and efficient IoU (intersection of union) loss with optimal transport assignment (F-EIoU-OTA) mechanism is proposed to improve the detection accuracy. The proposed TDKD-Net is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the developed methods in comparison to other advanced detection algorithms, which also present high generalization and strong robustness. As a resource-efficient precise network, the complex detection of small and occluded LAA objects is also well addressed by TDKD-Net, which provides useful insights on handling imbalanced issues and realizing domain adaptation.
Spherical autoregressive models, with application to distributional and compositional time series

Authors:
raw_author_name: Changbo Zhu
raw_affiliation_string: Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, USA
raw_author_name: Hans-Georg Müller
raw_affiliation_string: Department of Statistics, University of California, Davis, Davis, CA 95616, USA

Abstract: We introduce a new class of autoregressive models for spherical time series. The dimension of the spheres on which the observations of the time series are situated may be finite-dimensional or infinite-dimensional, where in the latter case we consider the Hilbert sphere. Spherical time series arise in various settings. We focus here on distributional and compositional time series. Applying a square root transformation to the densities of the observations of a distributional time series maps the distributional observations to the Hilbert sphere, equipped with the Fisher–Rao metric. Likewise, applying a square root transformation to the components of the observations of a compositional time series maps the compositional observations to a finite-dimensional sphere, equipped with the geodesic metric on spheres. The challenge in modeling such time series lies in the intrinsic non-linearity of spheres and Hilbert spheres, where conventional arithmetic operations such as addition or scalar multiplication are no longer available. To address this difficulty, we consider rotation operators to map observations on the sphere. Specifically, we introduce a class of skew-symmetric operators such that the associated exponential operators are rotation operators that for each given pair of points on the sphere map the first point of the pair to the second point of the pair. We exploit the fact that the space of skew-symmetric operators is Hilbertian to develop autoregressive modeling of geometric differences that correspond to rotations of spherical and distributional time series. Differences expressed in terms of rotations can be taken between the Fréchet mean and the observations or between consecutive observations of the time series. We derive theoretical properties of the ensuing autoregressive models and showcase these approaches with several motivating data. These include a time series of yearly observations of bivariate distributions of the minimum/maximum temperatures for a period of 120 days during each summer for the years 1990-2018 at Los Angeles (LAX) and John F. Kennedy (JFK) international airports. A second data application concerns a compositional time series with annual observations of compositions of energy sources for power generation in the U.S..