Reviewerly/exHarmony huggingface dataset:
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress (Extended Abstract)

Time series anomaly detection has been a perennially important topic in data science, with papers dating back to the 1950s. However, in recent years there has been an explosion of interest in this topic, much of it driven by the success of deep learning in other domains and for other time series tasks. Most of these papers test on one or more of a handful of popular benchmark datasets, created by Yahoo, Numenta, NASA, etc. In this work we make a surprising claim. The majority of the individual exemplars in these datasets suffer from one or more of four flaws. Because of these four flaws, we believe that many published comparisons of anomaly detection algorithms may be unreliable, and more importantly, much of the apparent progress in recent years may be illusionary. In addition to demonstrating these claims, with this paper we introduce the UCR Time Series Anomaly Archive. We believe that this resource will perform a similar role as the UCR Time Series Classification Archive, by providing the community with a benchmark that allows meaningful comparisons between approaches and a meaningful gauge of overall progress.
Communication-Efficient Vertical Federated Learning

Federated learning (FL) is a privacy-preserving distributed learning approach that allows multiple parties to jointly build machine learning models without disclosing sensitive data. Although FL has solved the problem of collaboration without compromising privacy, it has a significant communication overhead due to the repetitive updating of models during training. Several studies have proposed communication-efficient FL approaches to address this issue, but adequate solutions are still lacking in cases where parties must deal with different data features, also referred to as vertical federated learning (VFL). In this paper, we propose a communication-efficient approach for VFL that compresses the local data of clients, and then aggregates the compressed data from all clients to build an ML model. Since local data are shared in compressed form, the privacy of these data is preserved. Experiments on publicly available benchmark datasets using our proposed method show that the final model obtained by aggregation of compressed data from clients outperforms the performance of the local models of the clients.
Bitcoin is so Last Decade – How Decentralized Finance (DeFi) could Shape the Digital Economy

Ever since the Global Financial Crisis, the financial system has seen an accelerated level of innovation. Bitcoin offered a decentralized alternative to money, with thousands of other cryptoassets quickly emerging. Most recently, Decentralized Finance (DeFi) promises to offer a new digital economy, with smart contracts replacing the need for financial intermediaries. This new market aims to reinvent traditional financial products in a more transparent and interoperable way. In this paper we aim to undertake an extensive literature review of the financial stability risks posed by the fast-growing DeFi market and assess these using the Financial Stability Board’s fintech framework. We find that DeFi could pose several challenges to traditional financial infrastructure, primarily by increasing interlinkages between cryptoassets and traditional financial markets. However, if DeFi evolves in a safe way, it could increase competition and financial inclusion with overall positive implications for financial stability.
The basics of quantum computing for chemists

Abstract The rapid and successful strides in quantum chemistry in the past decades can be largely credited to a conspicuous synergy between theoretical and computational advancements. However, the architectural computer archetype that enabled such a progress is approaching a state of more stagnant development. One of the most promising technological avenues for the continuing progress of quantum chemistry is the emerging quantum computing paradigm. This revolutionary proposal comes with several challenges, which span a wide array of disciplines. In chemistry, it implies, among other things, a need to reformulate some of its long established cornerstones in order to adjust to the operational demands and constraints of quantum computers. Due to its relatively recent emergence, much of quantum computing may still seem fairly nebulous and largely unknown to most chemists. It is in this context that here we review and illustrate the basic aspects of quantum information and their relation to quantum computing insofar as enabling simulations of quantum chemistry. We consider some of the most relevant developments in light of these aspects and discuss the current landscape when of relevance to quantum chemical simulations in quantum computers.
1st Symposium on Advances in Educational Technology: Outlook

This is an overview of the Proceedings of the 1st Symposium on Advances in Educational Technology (AET 2020), which was held in Kyiv, Ukraine, on November 12-13, 2020. It includes an introduction, a summary of the papers, and some reflections on the event and its future.
Communicating and Collaborating with Others through Digital Competence: A Self-Perception Study Based on Teacher Trainees’ Gender

Digital competence in teaching can be understood as the set of skills, attitudes and abilities to use technologies critically and creatively, both in the personal and professional environment. Likewise, it is one of the eight key competencies for lifelong learning. In this paper, in line with the Common Digital Competence Framework (DigComp), we analyze the self-perception of teacher trainees’ digital competence to communicate and collaborate with other people. Additionally, we state the existence of statistically significant differences from a gender perspective (women/men). In this sense, we have carried out non-experimental quantitative research that has a descriptive nature. To this end, we used a questionnaire as an instrument for collecting information, with a total sample of 698 pre-service teachers in Andalusia (Spain). The results show that teacher trainees have an intermediate level in terms of their abilities to communicate and collaborate with other people through digital technologies. At the same time, significant differences are highlighted regarding participants’ gender, which implies that gender can still be considered a limitation in the use of ICTs, thereby decreasing participants’ digital competence. Finally, this study sheds light on the need to improve future teachers’ digital competence.
Software Refactoring Prediction Using SVM and Optimization Algorithms

Test suite code coverage is often used as an indicator for test suite capability in detecting faults. However, earlier studies that have explored the correlation between code coverage and test suite effectiveness have not addressed this correlation evolutionally. Moreover, some of these works have only addressed small sized systems, or systems from the same domain, which makes the result generalization process unclear for other domain systems. Software refactoring promotes a positive consequence in terms of software maintainability and understandability. It aims to enhance the software quality by modifying the internal structure of systems without affecting their external behavior. However, identifying the refactoring needs and which level should be executed is still a big challenge to software developers. In this paper, the authors explore the effectiveness of employing a support vector machine along with two optimization algorithms to predict software refactoring at the class level. In particular, the SVM was trained in genetic and whale algorithms. A well-known dataset belonging to open-source software systems (i.e., ANTLR4, JUnit, MapDB, and McMMO) was used in this study. All experiments achieved a promising accuracy rate range of between 84% for the SVM–Junit system and 93% for McMMO − GA + Whale + SVM. It was clear that added value was gained from merging the SVM with two optimization algorithms. All experiments achieved a promising F-measure range between the SVM–Antlr4 system’s result of 86% and that of the McMMO − GA + Whale + SVM system at 96%. Moreover, the results of the proposed approach were compared with the results from four well known ML algorithms (NB-Naïve, IBK-Instance, RT-Random Tree, and RF-Random Forest). The results from the proposed approach outperformed the prediction performances of the studied MLs.
Federated Learning: Collaborative Machine Learning without Centralized Training Data

Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm without transferring data samples across numerous decentralized edge devices or servers. This strategy differs from standard centralized machine learning techniques in which all local datasets are uploaded to a single server, as well as more traditional decentralized alternatives, which frequently presume that local data samples are uniformly distributed. Federated learning allows several actors to collaborate on the development of a single, robust machine learning model without sharing data, allowing crucial issues such as data privacy, data security, data access rights, and access to heterogeneous data to be addressed. Defence, telecommunications, internet of things, and pharmaceutical industries are just a few of the sectors where it has applications.
A Robust Variable Selection Method for Sparse Online Regression via the Elastic Net Penalty

Variable selection has been a hot topic, with various popular methods including lasso, SCAD, and elastic net. These penalized regression algorithms remain sensitive to noisy data. Furthermore, “concept drift” fundamentally distinguishes streaming data learning from batch learning. This article presents a method for noise-resistant regularization and variable selection in noisy data streams with multicollinearity, dubbed canal-adaptive elastic net, which is similar to elastic net and encourages grouping effects. In comparison to lasso, the canal adaptive elastic net is especially advantageous when the number of predictions (p) is significantly larger than the number of observations (n), and the data are multi-collinear. Numerous simulation experiments have confirmed that canal-adaptive elastic net has higher prediction accuracy than lasso, ridge regression, and elastic net in data with multicollinearity and noise.
Light-SDNet: A Lightweight CNN Architecture for Ship Detection

Ship detection plays a vital role in monitoring and managing maritime safety.Most recently proposed learning-based object detection methods have achieved marked progress in detection accuracy, but the size of these models is too large to be applied to mobile devices with limited resources.Although some compact models have been presented in the previous study, they achieve unsatisfactory results in ship detection, especially under extreme weather conditions.To address these challenges, this article presents a lightweight convolutional neural network (CNN) called Light-SDNet to perform an end-to-end ship detection under different weather conditions.In the proposed model, we introduce the improved CA-Ghost, C3Ghost, and DepthWise Convolution (DWConv) into the You Only Look Once version 5 (YOLOv5) to reduce the number of model parameters, while remaining its powerful feature expression ability.We use parallel attention to highlight the features that contribute to the ship detection in the marine surveillance.To enhance the adaptability of the proposed model, a hybrid training strategy with generating synthetically-degraded images is proposed to augment the volume and diversity of the original datasets.The proposed strategy enables Light-SDNet to improve the ship detection results under severe weather conditions such as haze, rain, and low illumination.We compare Light-SDNet with other competitive approaches on a large-scaled ship dataset called SeaShips.We show that Light-SDNet achieves a better balance between the detection accuracy and the model complexity.The ship detection results on degraded marine images have proven the superior performance of the proposed model in terms of detection accuracy, robustness and efficiency.
A Case of PCOS (Polycystic Ovarian Syndrome) Treated Successfully by the Application of Yoga Prana Vidya Healing System Through Sustained Selfpractice, Self-healing and Self-belief

Introduction: Polycystic ovary syndrome (PCOS) is a disorder involving infrequent, irregular or prolonged menstrual periods, caused often by excess male hormone (androgen) levels. The ovaries develop numerous small collections of fluid — called follicles — and may fail to regularly release eggs. PCOS is a complex disorder of uncertain etiology, having strong evidence that it is a genetic disease. This paper presents a case of PCOS treated successfully by Yoga Prana Vidya healing protocols. Method: This report uses case study method of investigating the case of a 22-year-old female diagnosed with having PCOS (polycystic ovarian syndrome) during 2015. First symptoms were heavy menstrual flow accompanied by weakness. A Gynaecologist advised Ultrasound, full body test that revealed multiple cysts. The doctor prescribed steroids for treatment of this condition without any improvement, and instead deterioration was taking place. Her medical history before PCOS showed that she had sinusitis, low Iron count in blood and irregular periods. Results: The patient joined YPV from January 2017. She started taking healings from a senior YPV trainer from March, 2017, when she stopped taking medications. The senior YPV trainer helped the patient greatly through healings and constant nurturing. Slowly and gradually through healings, meditations, physical exercises, rhythmic breathing, forgiveness and learning lessons over the course of three years, the patient began to recover from march 2017 onwards. Conclusion: PCOS is one of the most important endocrine female disorders affecting in the reproductive age and may lead to serious complications, if not given due care and attention. Scientific studies are needed to determine the exact etiology of PCOS, and methods of proper management. Further research using YPV with appropriate number of samples of cases similar to this case is recommended to throw more light on treatment and normalization of this condition to give renewed life to patients in the society at large.
Multi-party quantum private size comparison protocol with d-dimensional Bell states

A feasible multi-party quantum private comparison (MQPC) protocol based on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mrow><mml:mi>d</mml:mi></mml:mrow></mml:math> -dimensional Bell states was proposed. In the protocol, all participants can independently encrypt their privacies and send them to a semi-honest quantum third party (TP) through authenticated channels. Then, the TP can determine the size relationship among all participants’ privacies without gaining access to the private information. We verified correctness and effectiveness of the proposed protocol with some examples. In addition, compared with other similar protocols, it is not necessary to perform unitary operation on particles and only single-particle measurement is required. Furthermore, the relatively high qubit efficiency is promised. The security analysis verifies that the proposed protocol can counteract external and internal attacks in theory.
HR-STAN: High-Resolution Spatio-Temporal Attention Network for 3D Human Motion Prediction

3D human motion prediction requires making sense of the complex spatio-temporal dynamics which underpin human motion to make highly accurate predictions. Part of this complexity is due to the trade-off between long-term (>400ms) and short-term predictions (<400ms) which require different levels of granularity to observe patterns. Several works have explored methods of improving long-term prediction performance by utilizing longer motion histories but this typically comes at the cost of very short-term (<200ms) performance. Inspired by high-resolution network architectures, we propose a novel high-resolution spatio-temporal attention network (HR-STAN) which leverages parallel feature branches and dilated convolutions to observe human motion at different scales. Furthermore, we augment this architecture with split spatial and temporal attention mechanisms to efficiently capture spatio-temporal dependencies within a given motion. We evaluate the ability of our HR-STAN architecture at incorporating long-term motion histories while producing short-term predictions and show that it improves over several state-of-the-art methods on both the AMASS and Human3.6M benchmarks.
Deep Learning-Based Application of Image Style Transfer

Generative adversarial network (GAN) is a deep learning model that is widely applied to image generation, semantic segmentation, superresolution tasks, and so on. CycleGAN is a new model architecture that is used for various applications in image translation. This paper mainly focuses on the CycleGAN algorithm model. To improve the network model’s capacity of extracting image features, the generator model uses the neural network of Unet, which consists of eight down-sampling and eight up-sampling layers, to extract image features. We use the Markov discriminator of PatchGAN since it has high-resolution and high-detail characteristics in the image style transfer. In order to improve the running efficiency, the depthwise separable convolution and standard convolution are combined in the Markov discriminator. The experimental results show that it can effectively shorten the running time. Then, we compare the image with the generative image that uses the L1 loss function, the L2 loss function, and the smooth L1 loss function. The experimental results show that the CycleGAN neural network can effectively complete the image style transfer. The L1 loss model can well retain the details of the original image. The L2 loss model is clear in the distant part of the natural photo generated by Monet painting, and the color tone is more similar to the original image. The image generated by the smoothL1 loss model is smoother. The L1 loss model and the smooth L1 loss model have some miscoloring in the natural photos generated by the Monet painting. In general, the L2 loss model is more stable, and the generative image is better.
Compact Hybrid Multi-Color Space Descriptor Using Clustering-Based Feature Selection for Texture Classification

Color texture classification aims to recognize patterns by the analysis of their colors and their textures. This process requires using descriptors to represent and discriminate the different texture classes. In most traditional approaches, these descriptors are used with a predefined setting of their parameters and computed from images coded in a chosen color space. The prior choice of a color space, a descriptor and its setting suited to a given application is a crucial but difficult problem that strongly impacts the classification results. To overcome this problem, this paper proposes a color texture representation that simultaneously takes into account the properties of several settings from different descriptors computed from images coded in multiple color spaces. Since the number of color texture features generated from this representation is high, a dimensionality reduction scheme by clustering-based sequential feature selection is applied to provide a compact hybrid multi-color space (CHMCS) descriptor. The experimental results carried out on five benchmark color texture databases with five color spaces and manifold settings of two texture descriptors show that combining different configurations always improves the accuracy compared to a predetermined configuration. On average, the CHMCS representation achieves 94.16% accuracy and outperforms deep learning networks and handcrafted color texture descriptors by over 5%, especially when the dataset is small.
A Connection-Oriented Entanglement Distribution Design in Quantum Networks

Quantum networks create a completely new way for communication, and the most important function of a quantum network is to generate long-distance quantum entanglement to serve a number of quantum applications. As the scale of the network expands, in order to establish end-to-end entanglement between two remote nodes, entangled pairs need to be generated and distributed among multiple repeaters along the path from the source to the destination, which requires a specific protocol to negotiate resource allocations and quantum operations among the repeaters. Thus, to realize such remote entanglement distribution in a quantum network, designing a stable and reliable protocol becomes urgent and necessary. In this article, we focus on how to guarantee the generation rate of entangled pairs between any two quantum nodes to meet the requirements of various quantum applications in a large-scale quantum network. We present a connection-oriented entanglement distribution protocol inspired by the connection-oriented communication model in classical networks. Our protocol is located in the network layer of the quantum network to enable end-to-end quantum communication. Three main features are provided by the proposed protocol: 1) it is reliable and can guarantee the successful entanglement generation; 2) it can reduce the influence of quantum decoherence via reducing the latency caused by resource competition; and 3) it can guarantee the rate of generating entanglement between two quantum nodes according to the requirement of quantum applications.
Data Integrity Audit Scheme Based on Blockchain Expansion Technology

Increasing numbers of users are outsourcing data to the cloud, but data integrity is an important issue. Due to the decentralization and immutability of blockchain, more and more researchers tend to use blockchain to replace third-party auditors. This paper proposes a data integrity system based on blockchain expansion technology that aims to solve the problem of high cost for blockchain network maintenance and for user creation of new blocks caused by the rapid growth of blocks in the data integrity audit scheme of existing blockchain technology. Users and cloud service providers (CSP) deploy smart contracts on the main chain and sub-chains. Intensive and frequent computing work is transferred to the sub-chain for completion, and the computation results of the sub-chain are submitted to the main chain periodically or when needed to ensure its finality. The concept of non-interactive audit is introduced to avoid affecting user experience due to the communication with the CSP during the audit process. In order to ensure data security, a reward pool mechanism is introduced. Comprehensive analysis from aspects such as storage, batch auditing and data consistency proves the correctness of the scheme. Experiments on the Ethereum blockchain platform demonstrate that this scheme can effectively reduce storage and computational overhead.
Cybersecurity Knowledge Extraction Using XAI

Global networking, growing computer infrastructure complexity and the ongoing migration of many private and business aspects to the electronic domain commonly mandate using cutting-edge technologies based on data analysis, machine learning, and artificial intelligence to ensure high levels of network and information system security. Transparency is a major barrier to the deployment of black box intelligent systems in high-risk domains, such as the cybersecurity domain, with the problem getting worse as machine learning models increase in complexity. In this research, explainable machine learning is used to extract information from the CIC-IDS2017 dataset and to critically contrast the knowledge attained by analyzing if–then decision tree rules with the knowledge attained by the SHAP approach. The paper compares the challenges of the knowledge extraction using the SHAP method and the if–then decision tree rules, providing guidelines regarding different approaches suited to specific situations.