Reviewerly/exHarmony huggingface dataset:
Detection of Illegal Transactions of Cryptocurrency Based on Mutual Information

In recent times, there has been a swift advancement in the field of cryptocurrency. The advent of cryptocurrency has provided us with convenience and prosperity, but has also given rise to certain illicit and unlawful activities. Unlike classical currency, cryptocurrency conceals the activities of criminals and exposes their behavioral patterns, allowing us to determine whether present cryptocurrency transactions are legitimate by analyzing their behavioral patterns. There are two issues to consider when determining whether cryptocurrency transactions are legitimate. One is that most cryptocurrency transactions comply with laws and regulations, but only a small portion of them are used for illegal activities, which is related to the sample imbalance problem. The other issue concerns the excessive volume of data, and there are some unknown illegal transactions, so the data set contains an abundance of unlabeled data. As a result, it is critical to accurately distinguish between which transactions among the plethora of cryptocurrency transactions are legitimate and which are illegal. This presents quite a difficult challenge. Consequently, this paper combines mutual information and self-supervised learning to create a self-supervised model on the basis of mutual information that is used to improve the massive amount of untagged data that exist in the data set. Simultaneously, by merging the conventional cross-entropy loss function with mutual information, a novel loss function is created. It is employed to address the issue of sample imbalance in data sets. The F1-Score results obtained from our experimentation demonstrate that the novel loss function in the GCN method improves the performance of cryptocurrency illegal behavior detection by four points compared with the traditional loss function of cross-entropy; use of the self-supervised network that relies on mutual information improves the performance by three points compared with the original GCN method; using both together improves the performance by six points.
PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain

Privacy and verifiability are crucial security requirements in e-voting systems and combining them is considered to be a challenge given that they seem to be contradictory. On one hand, privacy means that cast votes cannot be traced to the corresponding voters. On the other hand, linkability of voters and their votes is a requirement of verifiability which has the consequence that a voter is able to check their vote in the election result. These two contradictory features can be addressed by adopting privacy-preserving cryptographic primitives, which at the same time as achieving privacy, achieve verifiability. Many end-to-end schemes that support verifiability and privacy have the need for some voter action. This makes ballot casting more complex for voters. We propose the PVPBC voting system, which is an e-voting system that preserves privacy and verifiability without affecting voter usability. The PVPBC voting system uses an effective and distributed method of authorization, which is based on revocable anonymity, by making use of a permissioned distributed ledger and smart contract. In addition, the underlying PVPBC voting system satisfies election verifiability using the Selene voting scheme. The Selene protocol is a verifiable e-voting protocol. It publishes votes in plaintext accompanied by tracking numbers. This enables voters to confirm that their votes have been captured correctly by the system. Numerical experiments support the claim that PVPBC scales well as a function of the number of voters and candidates. In particular, PVPBC’s authorization time increases linearly as a function of the population size. The average latency associated with accessing the system also increases linearly with the voter population size. The latency incurred when a valid authentication transaction is created and sent on the DLT network is 6.275 ms. Empirical results suggest that the cost in GBP for casting and storing an encrypted ballot alongside a tracker commitment is a linear function of the number of candidates, which is an attractive aspect of PVPBC.
Cybercrime as a Sustained Business

While cybercrime continues to challenge governments, businesses, and people, cybercriminals are harvesting massive payoffs by engaging in malicious operations. A significant impediment to countering cybercrime is the lack of a standardized definition of cybercrime that transcends international boundaries and hegemonic societies. The extensive taxing on law enforcement organizations to pursue cybercrime across international borders contributes to normalizing cybercrime as a sustained business. As cybercrime evolves and challenges traditional defenses, new types of cybercrime are engineered as digitalization and internet-based activity increase. Unfortunately, policies, policing, legal authorities, and prosecution of cybercrime trail the escalating cybercriminal activity. Cybercriminals are mimicking traditional business models to offer nefarious services such as cybercrime-as-a-service and customer service support. Until progressive action with legal authorization is manifested by international and national organizations, cybercrime will be a sustained business for cybercriminals.
A Systematic Approach for Benchmarking of Container Orchestration Frameworks

Container orchestration frameworks play a critical role in modern cloud computing paradigms such as cloud-native or serverless computing. They significantly impact the quality and cost of service deployment as they manage many performance-critical tasks such as container provisioning, scheduling, scaling, and networking. Consequently, a comprehensive performance assessment of container orchestration frameworks is essential. However, until now, there is no benchmarking approach that covers the many different tasks implemented in such platforms and supports evaluating different technology stacks. In this paper, we present a systematic approach that enables benchmarking of container orchestrators. Based on a definition of container orchestration, we define the core requirements and benchmarking scope for such platforms. Each requirement is then linked to metrics and measurement methods, and a benchmark architecture is proposed. With COFFEE, we introduce a benchmarking tool supporting the definition of complex test campaigns for container orchestration frameworks. We demonstrate the potential of our approach with case studies of the frameworks Kubernetes and Nomad in a self-hosted environment and on the Google Cloud Platform. The presented case studies focus on container startup times, crash recovery, rolling updates, and more.
Recovery time from VR sickness due to susceptibility: Objective and quantitative evaluation using electroencephalography

With the increasing use of virtual reality (VR) devices, interest in reducing their negative effects, such as VR sickness, is also increasing. This study used electroencephalography (EEG) to investigate participants' VR sickness recovery time after watching a VR video. We tested 40 participants in advance using a motion sickness susceptibility questionnaire (MSSQ). We classified the participants into two groups (sensitive group/non-sensitive group) depending on their MSSQ scores. We used a simulator sickness questionnaire (SSQ) and EEG to evaluate VR sickness. The SSQ score increased significantly after watching the VR sickness-inducing video (VR video) in both groups (p < 0.001). The recovery time based on the SSQ was 11.3 ± 6.6 min for the sensitive group and 9.1 ± 5.2 min for the non-sensitive group. The difference in recovery time between the two groups was not significant (p > 0.05). EEG results showed that recovery time took an average of 11.5 ± 7.1 min in both groups. The EEG data showed that the delta wave increased significantly across all brain areas (p < 0.01). There was no statistical difference between groups in recovering VR sickness depending on individual characteristics. However, we confirmed that subjective and objective VR recovery required at least 11.5 min. This finding can inform recommendations regarding the VR sickness recovery times.
Generative adversarial networks for network traffic feature generation

Generative Adversarial Networks (GANs) have remained an active area of research, particularly due to their increased and advanced evolving application capabilities. In several domains such as images, facial synthesis, character generation, language processing and multimedia, they have been implemented for advanced tasks. However, there has been more limited progress in network traffic data generation due to the complexities associated with data formats and distributions. This research implements two GAN architectures that include data transforms to simultaneously train and generate categorical and continuous network traffic features. These architectures demonstrate superior performance to the original ‘Vanilla’ GAN approach, which is included as a baseline comparator. Close matches are obtained between logarithms of the means and standard deviations of the fake data and the corresponding quantities from the real data. Moreover, similar principal components are exhibited by the fake and real data streams. Furthermore, some 85% of the features from the fake data could replace those in the real data without detection.
Model-based optimization approaches for pressure-driven membrane systems

Membrane technology is commonly used within food, bio- and pharmaceutical processes. Beside single-stage membranes, multi-stage membrane systems are become more popular to improve separation performance. In this review, we present a unified four-phase model-based optimization framework to optimize these systems, using mechanistic models, empirical models including machine learning models, or a combination of them. We begin by providing a general overview and outlining the steps to construct each phase in the framework. The importance of each stage and critical points to consider are discussed. We then provide detailed information for each phase, including the governing equations from known literature models. Finally, we explore the platform’s potential applications and outlook. Despite the great potential of an integrated approach, studies thus far focus either on extensive membrane modeling with brute-force optimization via simple comparison or on meticulous optimization using an oversimplified membrane model. We believe that the integrated framework can bridge the well justified approaches in both filtration modeling and mathematical optimization and help in designing multi-unit processes.
Transforming graph data visualisations from 2D displays into augmented reality 3D space: A quantitative study

Modern video-based head-mounted displays allow users to operate along Milgram’s entire reality-virtuality continuum. This opens up the field for novel cross-reality applications that distribute data analytics tasks along this continuum to combine benefits of established 2D information visualisation in the real environment with immersive analytics. In this publication, we explore this potential by transforming 2D graph data from a planar, large-scale display in the real environment into a spherical layout in augmented reality 3D space, letting it appear as if the graph is moving out of the display. We focus on design aspects of this transformation that potentially help users to form a joint mental model of both visualisations and to continue their tasks seamlessly in augmented reality. For this purpose, we implemented a framework of transformation parameters that can be categorised as follows: transformation methods, node transformation order (groupings) and different ways of visual interconnection. Variants in each of these areas were investigated in three quantitative user studies in which users had to solve a simple cluster search task. We confirmed that a visual transformation from 2D to 3D helps users to continue their tasks in augmented reality with less interruptions, and that node transformation order should be adjusted to data and task context. We further identified that users can perform tasks more efficiently when a user-controlled transformation is used, while a constant transformation with fixed duration can contribute to lower error rates.
Asymptotic normality for -dependent and constrained -statistics, with applications to pattern matching in random strings and permutations

Abstract We study (asymmetric) $U$ -statistics based on a stationary sequence of $m$ -dependent variables; moreover, we consider constrained $U$ -statistics, where the defining multiple sum only includes terms satisfying some restrictions on the gaps between indices. Results include a law of large numbers and a central limit theorem, together with results on rate of convergence, moment convergence, functional convergence, and a renewal theory version. Special attention is paid to degenerate cases where, after the standard normalization, the asymptotic variance vanishes; in these cases non-normal limits occur after a different normalization. The results are motivated by applications to pattern matching in random strings and permutations. We obtain both new results and new proofs of old results.
Ultra-short-term PV power prediction using optimal ELM and improved variational mode decomposition

The development of photovoltaic (PV) power forecast technology that is accurate is of utmost importance for ensuring the reliability and cost-effective functioning of the power system. However, meteorological factors make solar energy have strong intermittent and random fluctuation characteristics, which brings challenges to photovoltaic power prediction. This work proposes, a new ultra-short-term PV power prediction technology using an improved sparrow search algorithm (ISSA) to optimize the key parameters of variational mode decomposition (VMD) and extreme learning machine (ELM). ISSA’s global search capability is enhanced by levy flight and logical chaotic mapping to search the optimal number of decomposition and penalty factor of VMD, and VMD adaptively decomposes PV power into sub-sequences with different center frequencies. Then ISSA is used to optimize the initial weight and threshold of ELM to improve the prediction performance of ELM, the optimized ELM predicts each subsequence and reconstructs the prediction results of each component to obtain the final result. Furthermore, isolated forest (IF) and Spearman correlation coefficient (SCC) are respectively used in the data preprocessing stage to eliminate outliers in the original data and determine appropriate input features. The prediction results using the actual data of solar power plants show that the proposed model can effectively mine the key information in the historical data to make more accurate predictions, and has good robustness to various weather conditions.
Learning-Based Visual Servoing for High-Precision Peg-in-Hole Assembly

Visual servoing is widely used in the peg-in-hole assembly due to the uncertainty of pose. Humans can easily align the peg with the hole according to key visual points/edges. By imitating human behavior, we propose P2HNet, a learning-based neural network that can directly extract desired landmarks for visual servoing. To avoid collecting and annotating a large number of real images for training, we built a virtual assembly scene to generate many synthetic data for transfer learning. A multi-modal peg-in-hole strategy is then introduced to combine image-based search-and-force-based insertion. P2HNet-based visual servoing and spiral search are used to align the peg with the hole from coarse to fine. Force control is then used to complete the insertion. The strategy exploits the flexibility of neural networks and the stability of traditional methods. The effectiveness of the method was experimentally verified in the D-sub connector assembly with sub-millimeter clearance. The results show that the proposed method can achieve a higher success rate and efficiency than the baseline method in the high-precision peg-in-hole assembly.
Decentralized brokered enabled ecosystem for data marketplace in smart cities towards a data sharing economy

Abstract Presently data are indispensably important as cities consider data as a commodity which can be traded to earn revenues. In urban environment, data generated from internet of things devices, smart meters, smart sensors, etc. can provide a new source of income for citizens and enterprises who are data owners. These data can be traded as digital assets. To support such trading digital data marketplaces have emerged. Data marketplaces promote a data sharing economy which is crucial for provision of available data useful for cities which aims to develop data driven services. But currently existing data marketplaces are mostly inadequate due to several issues such as security, efficiency, and adherence to privacy regulations. Likewise, there is no consolidated understanding of how to achieve trust and fairness among data owners and data sellers when trading data. Therefore, this study presents the design of an ecosystem which comprises of a distributed ledger technology data marketplace enabled by message queueing telemetry transport (MQTT) to facilitate trust and fairness among data owners and data sellers. The designed ecosystem for data marketplaces is powered by IOTA technology and MQTT broker to support the trading of sdata sources by automating trade agreements, negotiations and payment settlement between data producers/sellers and data consumers/buyers. Overall, findings from this article discuss the issues associated in developing a decentralized data marketplace for smart cities suggesting recommendations to enhance the deployment of decentralized and distributed data marketplaces.
A Variational Quantum Linear Solver Application to Discrete Finite-Element Methods

Finite-element methods are industry standards for finding numerical solutions to partial differential equations. However, the application scale remains pivotal to the practical use of these methods, even for modern-day supercomputers. Large, multi-scale applications, for example, can be limited by their requirement of prohibitively large linear system solutions. It is therefore worthwhile to investigate whether near-term quantum algorithms have the potential for offering any kind of advantage over classical linear solvers. In this study, we investigate the recently proposed variational quantum linear solver (VQLS) for discrete solutions to partial differential equations. This method was found to scale polylogarithmically with the linear system size, and the method can be implemented using shallow quantum circuits on noisy intermediate-scale quantum (NISQ) computers. Herein, we utilize the hybrid VQLS to solve both the steady Poisson equation and the time-dependent heat and wave equations.
Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection

Breast cancer is a serious medical condition that requires early detection for successful treatment. Mammography is a commonly used imaging technique for breast cancer screening, but its analysis can be time-consuming and subjective. This study explores the use of deep learning-based methods for mammogram analysis, with a focus on improving the performance of the analysis process. The study is focused on applying different computer vision models, with both CNN and ViT architectures, on a publicly available dataset. The innovative approach is represented by the data augmentation technique based on synthetic images, which are generated to improve the performance of the models. The results of the study demonstrate the importance of data pre-processing and augmentation techniques for achieving high classification performance. Additionally, the study utilizes explainable AI techniques, such as class activation maps and centered bounding boxes, to better understand the models’ decision-making process.
On the value of label and semantic information in domain generalization

In this work, we tackle the domain generalization (DG) problem aiming to learn a universal predictor on several source domains and deploy it on an unseen target domain. Many existing DG approaches were mainly motivated by domain adaptation techniques to align the marginal feature distribution but ignored conditional relations and labeling information in the source domains, which are critical to ensure successful knowledge transfer. Although some recent advances started to take advantage of conditional semantic distributions, theoretical justifications were still missing. To this end, we investigate the theoretical guarantee for a successful generalization process by focusing on how to control the target domain error. Our results reveal that to control the target risk, one should jointly control the source errors that are weighted according to label information and align the semantic conditional distributions between different source domains. The theoretical analysis then leads to an efficient algorithm to control the label distributions as well as match the semantic conditional distributions. To verify the effectiveness of our method, we evaluate it against recent baseline algorithms on several benchmarks. We also conducted experiments to verify the performance under label distribution shift to demonstrate the necessity of leveraging the labeling and semantic information. Empirical results show that the proposed method outperforms most of the baseline methods and shows state-of-the-art performances.
Zero-shot stance detection via multi-perspective contrastive learning with unlabeled data

Stance detection is to distinguish whether the text's author supports, opposes, or maintains a neutral stance towards a given target. In most real-world scenarios, stance detection needs to work in a zero-shot manner, i.e., predicting stances for unseen targets without labeled data. One critical challenge of zero-shot stance detection is the absence of contextual information on the targets. Current works mostly concentrate on introducing external knowledge to supplement information about targets, but the noisy schema-linking process hinders their performance in practice. To combat this issue, we argue that previous studies have ignored the extensive target-related information inhabited in the unlabeled data during the training phase, and propose a simple yet efficient Multi-Perspective Contrastive Learning Framework for zero-shot stance detection. Our framework is capable of leveraging information not only from labeled data but also from extensive unlabeled data. To this end, we design target-oriented contrastive learning and label-oriented contrastive learning to capture more comprehensive target representation and more distinguishable stance features. We conduct extensive experiments on three widely adopted datasets (from 4870 to 33,090 instances), namely SemEval-2016, WT-WT, and VAST. Our framework achieves 53.6%, 77.1%, and 72.4% macro-average F1 scores on these three datasets, showing 2.71% and 0.25% improvements over state-of-the-art baselines on the SemEval-2016 and WT-WT datasets and comparable results on the more challenging VAST dataset.