Reviewerly/exHarmony huggingface dataset:
Library Carpentry: Towards a New Professional Dimension (Part I – Concepts and Case Studies)

The domain of library and information science is always on the move and LIS professionals are ardent users of emerging technologies. This research work discusses an emerging possibility in the LIS domain, which applies data science principles and techniques in the bibliographic world. The concept is known as library carpentry and involves different data wrangling techniques to get insight of bibliographic datasets. The discussion starts with the basic concepts of library carpentry and systematically reveals the components and methods of library carpentry with the help of three case studies. The case studies represent a variety of actual problem solving projects by using open datasets and open source data wrangling software called Openrefine. The case study (I) deals with the application of library carpentry in e-book selection by taking into consideration socio-academic web space data, the case study (II) shows how is it possible to quickly get an overview of institutional contributions to open access domain by applying library carpentry methods and the case study (III) demonstrates the process of gender analysis with the help of a name-to-gender inference service and by applying data wrangling techniques. Each case study is supported by a comprehensive and representative dataset to support and promote real-life problem solving in processional sphere by applying library carpentry methods.
Adelfa: A System for Reasoning about LF Specifications

We present a system called Adelfa that provides mechanized support for reasoning about specifications developed in the Edinburgh Logical Framework or LF. Underlying Adelfa is a new logic named L_LF. Typing judgements in LF are represented by atomic formulas in L_LF and quantification is permitted over contexts and terms that appear in such formulas. Contexts, which constitute type assignments to uniquely named variables that are modelled using the technical device of nominal constants, are characterized in L_LF by context schemas that describe their inductive structure. We present these formulas and an associated semantics before sketching a proof system for constructing arguments that are sound with respect to the semantics. We then outline the realization of this proof system in Adelfa and illustrate its use through a few example proof developments. We conclude the paper by relating Adelfa to existing systems for reasoning about LF specifications.
Mfagan: A Compression Framework for Memory-Efficient On-Device Super-Resolution Gan

Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory consumption of GAN-based SR (usually generators) causes performance degradation and more energy consumption, hindering the deployment of GAN-based SR into resource-constricted mobile devices. In this paper, we propose a novel compression framework Multi-scale Feature Aggregation Net based GAN (MFAGAN) for reducing the memory access cost of the generator. First, to overcome the memory explosion of dense connections, we utilize a memory-efficient multi-scale feature aggregation net as the generator. Second, for faster and more stable training, our method introduces the PatchGAN discriminator. Third, to balance the student discriminator and the compressed generator, we distill both the generator and the discriminator. Finally, we perform a hardware-aware neural architecture search (NAS) to find a specialized SubGenerator for the target mobile phone. Benefiting from these improvements, the proposed MFAGAN achieves up to 8.3× memory saving and 42.9× computation reduction, with only minor visual quality degradation, compared with ESRGAN. Empirical studies also show ∼70 milliseconds latency on Qualcomm Snapdragon 865 chipset.
<i>SIMBox</i> Bypass Frauds in Cellular Networks: Strategies, Evolution, Detection, and Future Directions

Due to their complexity and opaqueness, cellular networks have been subject to numerous attacks over the past few decades. These attacks are a real problem to telecom operators and cost them about USD 28.3 Billion annually, as reported by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Communications Fraud Control Association</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIMBox</i> fraud is one of the most prevalent of these telephone frauds. It consists of diverting international calls on the VoIP network and terminating them as local calls using an off-the-shelf device, referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIMBox</i> . This paper surveys both the existing literature and the major <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIMBox</i> manufacturers to provide comprehensive and analytical knowledge on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIMBox</i> fraud, fraud strategies, fraud evolution, and fraud detection methods. We provide the necessary background on the telephone ecosystem while extensively exploring the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIMBox</i> architecture required to understand fraud strategies. Our goal is to provide a complete introductory guide for research on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIMBox</i> fraud and stimulate interest for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIMBox</i> fraud detection, which remains little investigated. In this vein, we conclude the paper by presenting insights into tomorrow’s <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIMBox</i> fraud detection challenges and research directions.
AI-Enabled Large-Scale Entanglement Distribution Quantum Networks

We propose an entanglement distribution switching architecture to support large-scale dynamic quantum networking. Deep neural networks are further developed for predicting the performance of a dynamic entanglement distribution network utilizing the proposed architecture.
SCRUM Framework Extended with Clean Architecture Practices for Software Maintainability

This study consisted of the extension of the Scrum framework with Clean Architecture practices (Scrum-CA) to improve software maintainability. Methodologically, in this study: i) the requirements that Scrum-CA had to satisfy were identified; ii) identification of Scrum aspects (artifacts and/or activities) to be extended, iii) specify the extended framework; and, iv) validate the proposal through a pilot project. At the extended framework level, this proposal considers the formulation of a business context as an input to the Product Backlog. The specification of tasks in the Sprint backlog diversifies them into 4 spaces (infrastructure, controllers, application and domain) so that in the development of the Sprint the achievement of a maintainable architecture is allowed, an issue that Scrum teams must define empirically a priori. SOLID principles are added in the implementation of the product within the Sprints and coherence is maintained between the planning of the requirements and the achievement of the product architecture. Scrum-CA was validated through an implementation project in a Peruvian State entity nationwide. The results show an improvement in the maintainability of the project software, from Sprint1 to Sprint2, reducing code duplication from 4.8% to 3.5%, from 31 duplicated blocks to 9, a technical debt from 6.3% to 1.3%. Also, it is necessary to repeat Scrum-CA in other projects to generate a standard.
Axiological Aspect of Professional Training of Future Pedagogues

This scientific research examines the issues related to the axiological aspect of the professional training of future teachers. The relevance of the stated research topic is determined by the urgent necessity to increase the importance of high-quality professional training of future representatives of pedagogical professions, in the context of changes taking place in the modern education system. The purpose of this research work is to identify and state the main components of the axiological aspect of the professional training of future workers in the pedagogical industry, which is able to impart integrity to the entire process of their training. The leading approach in this scientific study is a combination of quantitative and qualitative methodological approaches to the study of issues related to the axiological aspect of the professional training of future workers in the pedagogical industry, in order to determine the main components of the factors affecting the quality and effectiveness of this training and further professional activities of teachers. The main results of this research work were: a statement of the necessity to develop and take a set of measures to clarify the existing value criteria of the education system to future teachers at the stage of their university preparation, as well as the acceptance of the fact of the need for the constant practice of future teachers in instilling in students respect for existing values in the context modern education system. The applied value of this study is determined by the possibility and urgent need to apply in practice the recommendations obtained as a result in order to get practical outcomes expressed in a real increase in the quality of professional training of future teachers, primarily in terms of increasing the value of their profession and their awareness of the value orientation of their activities. Further scientific research within the framework of the stated topic will help to fully reveal the essence of the axiological approach to the system of professional training of future teachers and supplement the existing ideas about the value of teacher education, in the context of the real state of affairs in the modern education system in Ukraine.
Blockchain Equity System Transaction Method and System Research Based on Machine Learning and Big Data Algorithm

With the development of machine learning and big data, traditional equity trading system methods can no longer meet the current trading needs, and there are still problems such as low operating efficiency and serious homogeneity. Blockchain technology has the characteristics of decentralization and can also complete transactions through smart contracts, innovating the way of equity system transactions. The purpose of this paper is to build an equity trading system in combination with blockchain in the context of machine learning and big data and provide innovative trading methods, so as to provide reference and reference significance for the construction of my country’s equity market. This article uses literature data method, comparative analysis method, factor analysis method, and other methods to carry out research, in-depth study of machine learning and big data, blockchain-related concepts, system composition, application situation, etc., and discusses the allocation of equity trading market The functions of resources, risk diversification, risk transfer, price determination, etc., have built a blockchain equity trading system, designed a consensus mechanism, block generation protocol, block verification, decentralization, and smart contract platform, and finally conducted a national equity transaction the background of the market is analyzed, and the experimental results, simulation indicators, transaction time, transmission consumption, and other content of the system constructed in this article are analyzed. In the single-node test, the CPU usage of the PoW consensus mechanism algorithm reached 100%, but the improved PBFT consensus mechanism was only 16%, which saved a lot of computing power and improved computing performance.
AR-enhanced Widgets for Smartphone-centric Interaction

We contribute a detailed investigation of AR-enhanced widgets for smartphones, where AR technology is not only used to offload widgets from the phone to the air around it, but to give users more control on input precision as well. Such widgets have the obvious benefit of freeing up screen real-estate on the phone, but their other potential benefits remain largely theoretical. Their limitations are not well understood, most particularly in terms of input performance. We compare different AR-enhanced widget designs against their state-of-the-art touch-only counterparts with a series of exploratory studies in which participants had to perform three tasks: command trigger, parameter value adjustment, and precise 2D selection. We then derive guidelines from our empirical observations.
Digital Image Forensic based on Machine Learning approach for Forgery Detection and Localization

Abstract Machine learning for multimedia forensic is a new way of image forgery detection due to its amazing features of fast forgery detection. Compared with existing techniques of Deep Learning and Convolution Neural Network (“CNN”), machine learning improves security in the specific forged region under various test conditions. Some researchers use Support Vector Machine (“SVM”) and k-nearest neighbors (k-NN) algorithms to detect forgeries and another category uses unsupervised classification, including self-organization feature map (SOFM) and fuzzy c-means. But there occurs a need to address the detection speed improvement under the present scenario. The proposed algorithm has been developed using a machine learning approach to improve detection speed by pre-processing of feature extraction and feature reduction using “DWT” and “PCA” where data is trained by support vector machine (“SVM”) to provide quick results under various test conditions. This work specifies different image attacks like all types of geometric transformation, post-processing operations, etc., and presents efficiency in forgery detection and localization in case of multiple forgeries.
FlashByte: Improving Memory Efficiency with Lightweight Native Storage

In-memory caching of intermediate data is effective in reducing re-computation and I/O cost in distributed data-analytics frameworks, but it also generates a large amount of data in Java heap which increases the overhead of garbage collection (GC). An alternative off-heap approach caches data in native storage by transmitting the data from heap to native storage so as to reduce GC overhead. However, it incurs severe serialization and de-serialization overheads. Serialization also generates non-trivial metadata of the cached data in native storage. We propose and develop FlashByte, a lightweight native storage that efficiently caches intermediate data. FlashByte improves memory efficiency by achieving low GC overhead, low data transmission overhead, and low memory consumption. Specifically, the cached data are divided into two parts: metadata stored in Java heap and raw data stored in native storage. The metadata is generated based on the profile of workloads. Its size is trivial because it only contains a concise format of raw data, which achieves low memory consumption in the heap as well as low GC overhead. Native storage only stores the raw data to reduce its memory consumption. According to the metadata, the raw data are efficiently transmitted between the heap and native storage without serialization and de-serialization. We implement FlashByte in Spark and conduct evaluation with benchmark workloads. Experimental results show that, compared with the in-heap approach of Vanilla Spark, FlashByte achieves up to 4x speedup of the job execution time, reduces GC time by up to 96%, and reduces the memory consumption in heap by up to 36%. Compared with the alternative off-heap approach, FlashByte achieves up to 2.3x speedup of the job execution time, reduces the data transmission time by up to 84%, and reduces the cache size in native storage by up to 34%.
Face Expression Recognition Using a Combination of Local Binary Patterns and Local Phase Quantization

Facial expressions are the first communication channel in human interactions. This channel helps us to understand people thoughts. The accuracy of the knowledge that have obtained by this channel benefits us in decision-making stages. Therefore, many studies in this domain are conducted for the accurate face expression recognition (FER) systems. In order to establish a robust and reliable FER system, it is a requirement to have the informative facial image features. In this study, it is used Local Binary Patterns (LBP) and Local Phase Quantization (LPQ) methods to establish a reliable FER approach. The main goal is to determine whether combining these two methods makes a contribution or not. Experiments show that combining these two methods increases the classification success rate from 75% to 88%.
Investigating Binary Neural Networks for Traffic Sign Detection and Recognition

Traffic sign detection is crucial for enabling autonomous vehicles to navigate in real-world streets, which must be carried out with high accuracy and in real-time. CNNs have become one of the standard approaches for traffic sign detection research in recent years. The use of CNNs has allowed the development of traffic sign detectors that are capable of achieving prediction accuracies similar to those of human drivers. However, most CNNs do not run in real-time due to the high number of computational operations involved during the inference phase. This hinders the deployment of CNNs in autonomous vehicles despite their high prediction accuracy. In this paper, we explore BNNs to tackle this problem. BNNs binarize the full-precision weights and activations of a CNN, drastically reducing the complexity of the computational operations required for inference, while at the same time maintaining the architectural parameters, as well as spatial dimensions of the input image. This reduces the memory required to run the model and enables faster inference time. We carry out in-depth studies on applying BNNs for traffic sign detection using real-world datasets. We observe an improvement of 11.63 × for normalized compute complexity, while suffering only 3.93 pp in detection accuracy on GTSDB dataset.
Securing Neural Networks Using Homomorphic Encryption

Neural networks are becoming increasingly popular within the modern world, and they are often implemented without much consideration of their potential flaws, which makes them vulnerable and are easily being hacked by hackers. One of such vulnerabilities, namely, a backdoor attack is studied in this paper. A backdoor attacked neural network involves inducing unique misclassification rules or patterns as triggers in the neural network such that, upon encountering the trigger, the neural network will only predict the output based upon the misclassification rules, giving the attacker control over the output of the neural network. To prevent such a vulnerability, we propose to employ homomorphic encryption as a solution. Homomorphic Encrypted Data has a special property where certain operations can be performed on encrypted data to in-turn directly perform the operations on the plain-text data itself, without the need of any special mechanism. This ability of homomorphic encryption can be used in conjunction with the vulnerable neural network, to revoke the control of the attacker from the neural network. Thereby, in this paper, we will be securing a vulnerable neural network from backdoor attack using homomorphic encryption.
On Asymptotic Power of the New Test for Equality of Two Distributions

The paper introduces a new test for equality of two distributions in a class of models. We proved analytically and by stochastic simulation that the test possesses high efficiency. For the case of normal and Cauchy distributions that differ only by shift the asymptotic power of the test appears to be approximately the same as for the Wilcoxon-Mann-Whitney, the Kolmogorov-Smirnov and the Anderson-Darling tests. But if the distributions differ by scale parameters the power of the new test is considerably better.
Fast Multi-label Learning

Embedding approaches have become one of the most pervasive techniques for multi-label classification. However, the training process of embedding methods usually involves a complex quadratic or semidefinite programming problem, or the model may even involve an NP-hard problem. Thus, such methods are prohibitive on large-scale applications. More importantly, much of the literature has already shown that the binary relevance (BR) method is usually good enough for some applications. Unfortunately, BR runs slowly due to its linear dependence on the size of the input data. The goal of this paper is to provide a simple method, yet with provable guarantees, which can achieve competitive performance without a complex training process. To achieve our goal, we provide a simple stochastic sketch strategy for multi-label classification and present theoretical results from both algorithmic and statistical learning perspectives. Our comprehensive empirical studies corroborate our theoretical findings and demonstrate the superiority of the proposed methods.
Piecewise Linear map enabled Harris Hawk optimization algorithm

Abstract Chaotic maps were usually introduced to improve the original swarm-based nature-inspired algorithms. Due to their chaotic characteristics, the chaotic maps were introduced to replace the pseudo random numbers in computer engineering and consequently better performance would be achieved. In this paper, we introduce another chaotic improvement to the Harris hawk optimization (HHO) algorithm with Piecewise Linear map. Nevertheless, the chaos would be introduced to improve the randomness of the controlling parameter which was used to balance the ratio of exploration and exploitation. Monte Carlo simulation experiments were carried out and results confirmed this kind of improvements would significantly raise the capability in optimization.;
UAV detection based on rainy environment

Based on the UAV detection is under the rainy day will remove the rain to UAV for the front-end process highlighted in depth study methods. Target detection technology has made great progress in recent years. But when it comes to low-flying drones, especially in the case of environmental impacts such as rain and fog. the accuracy and robustness can not meet the real-time requirement. Based on the existing results, this paper uses deep convolutional neural network to detect UAV. First,. introduce the current image fog removal, rain removal related algorithms, using the basic DID-MDN algorithm to achieve rain removal. Second, introduce the algorithm of target detection, and YOLOv5 based on deep learning is used for target detection.
On Secure and Efficient Data Sharing for Smart Grids: An Anti-Collusion Scheme

High volumes of real-time energy consumption data are generated by smart meters each day, which may create tremendous values if shared to third parties, e.g., government agencies, real estate agents, and travel agencies, etc. However, significant security and privacy challenges are always in place in case these sensitive digital assets are directly shared outside the grid utilities without any protection. It is, therefore, vital to guarantee the data security and privacy while maintaining its values. To meet this gap, in this paper we propose a secure and efficient data sharing scheme with anti-collusion for smart grids. In this scheme, we devise a privacy-preserving data acquisition algorithm for smart meters based on a modified Paillier cryptosystem, and also an anti-collusion proxy re-encryption algorithm for the control center & cloud server to achieve secure energy consumption data sharing of its mean and variance. Importantly, the proposed scheme is also designed to support customer identity preservation while requesting data sharing services. Security analysis strictly demonstrate the security of the proposed scheme, and extensive experiments validate the high efficiency of the proposed scheme.