Reviewerly/exHarmony huggingface dataset:
Out-of-Distribution Detection for Radar-based Gesture Recognition Using Metric-Learning

The paper addresses the question how and to what extent metric learning can be beneficial for reducing the false alarm rate in radar-based hand gesture recognition systems. To this end, we evaluate different metric learning approaches for out-of-distribution or unknown motion detection. We found that metric learning can help to significantly increase the out-of-distribution capabilities of the network. We further investigated what conditions must be met for metric learning to work well, and found that the composition of the data set for known gestures has a large influence on the out-of-distribution detection rate.
Robot Arm Movement Control by Model-based Reinforcement Learning using Machine Learning Regression Techniques and Particle Swarm Optimization

Robot arms are machines which are not only used in industrial technologies but also other applications such as medicine and agriculture. The robot arm movement control is important in the use of robot arms. This paper presents model-based reinforcement learning (MBRL) in robot arm movement control in case studies where targets are in random positions. The numerical studies of 2 and 3-DOF robots moving in planar motion are used as test problems. In 2-DOF robot control, 2 tasks - placing and reaching are employed. In 3-DOF robot control, noise is considered. Machine learning regression techniques - Gaussian process regression (GPR), artificial neural network (ANN), and support vector regression (SVR) - are used in environment modelling in MBRL while particle swarm optimization (PSO) is used as optimization tool in MBRL. The numerical studies show that MBRL with GPR has the highest performance with at least 95% success rate while MBRL with the other regression techniques such as ANN and SVR can succeed only 23-93% and 31-47%, respectively. Therefore, MBRL with GPR and PSO is suitable for robot arm movement control.
Utilization of Blockchain Technology In Human Resource Management

With the advancement of time and the advancement scientific knowledge, the blockchain technology has expanded in various applications. The security and data privacy of employee recruitment system has been compromised. Therefore, hiring managers can enhance job satisfaction by adopting blockchain technology to secure human resource data management. This paper provides current research status, issues and a deep investigation on the blockchain technology's potential use in the management of human resources.
A Framework to Reconstruct Digital Forensics Evidence via Goal-Oriented Modeling

ThDeveloping a goal-oriented model for digital forensic evidence is critical because the reconstructed scenarios help forensic analysts not only understand steps taken by threat actors, but also present digital evidence in ways that are understandable in the court of law. In this paper, we propose a goal-oriented approach to reconstruct attack scenarios based on a forensic evidence acquisition model. We first build the model, from which digital forensic examiners can trace and collect forensic evidence, then formalize the graph and evaluate the semantics based on the evidence found on digital devices and their supporting environments. Finally, we apply the model to a typical scenario based on the semantics of the model. Our preliminary results show that our model can give any practitioner or investigator formal instructions to gather and rebuild the evidence in the simulation or real-world environment.
Computational Thinking Training and Deep Learning Evaluation Model Construction Based on Scratch Modular Programming Course

To improve the algorithmic dimension, critical thinking, and problem-solving ability of computational thinking (CT) in students' programming courses, first, a programming teaching model is constructed based on the scratch modular programming course. Secondly, the design process of the teaching model and the problem-solving model of visual programming are studied. Finally, a deep learning (DL) evaluation model is constructed, and the effectiveness of the designed teaching model is analyzed and evaluated. The T-test result of paired samples of CT is t = -2.08, P < 0.05. There are significant differences in the results of the two tests, and the designed teaching model can cause changes in students' CT abilities. The results reveal that the effectiveness of the teaching model based on scratch modular programming has been verified on the basis of experiments. The post-test values of the dimensions of algorithmic thinking, critical thinking, collaborative thinking, and problem-solving thinking are all higher than the pretest values, and there are individual differences. The P values are all less than 0.05, which testifies that the CT training of the designed teaching model has the algorithm dimension, critical thinking, collaborative thinking, and problem-solving ability of students' CT. The post-test values of cognitive load are all lower than the pretest values, indicating that the model has a certain positive effect on reducing cognitive load, and there is a significant difference between the pretest and post-test. In the dimension of creative thinking, the P value is 0.218, and there is no obvious difference in the dimensions of creativity and self-efficacy. It can be found from the DL evaluation that the average value of the DL knowledge and skills dimensions is greater than 3.5, and college students can reach a certain standard level in terms of knowledge and skills. The mean value of the process and method dimensions is about 3.1, and the mean value of the emotional attitudes and values is 2.77. The process and method, as well as emotional attitude and values, need to be strengthened. The DL level of college students is relatively low, and it is necessary to improve their DL level from the perspective of knowledge and skills, processes and methods, emotional attitudes and values. This research makes up for the shortcomings of traditional programming and design software to a certain extent. It has a certain reference value for researchers and teachers to carry out programming teaching practice.
Performance Analysis of Machine Learning Methods

Abstract Machine Learning has been studied worldwide for its functions in data science and artificial intelligence (AI) fields. Previous works have shown the excellent performance of machine learning methods in image classification. This paper uses various machine learning methods for fashion product classification. This paper aims to analyze the result of predictions for all classes and the first three ranked classes, and meanwhile, compare and discuss Support Vector Machine (SVM), K Nearest Neighbor (KNN), Convolution Neural Network (CNN), Contrastive Language-Image Pre-training (CLIP) methods’ performance. The results show that the F-Score is increased if just predicted for the first three ranked classes, and among SVM, KNN, and CNN models, CNN is the best in both conditions. From the performance of all four models, CLIP was the best model with better learning ability. Besides, the results suggest that an imbalanced dataset may harm predictions, and the CLIP method yields the best result. In the future, CLIP methods may be more likely recommended in an image classification problem with lots of classes, and an imbalanced dataset adjusted will provide new insights into unsolved and unimproved classification problems.
Mobile researchers, immobile data: Managing data (producers)

Scientific institutions have increasingly embraced formalized research data management strategies, which involve complex social practices of codifying the tacit dimensions of data practices. Several guidelines to facilitate these practices have been introduced in recent years, for example, the FAIR guiding principles. The aim of these practices is to foster transparency and reproducibility through 'data sharing,' the public release of data for unbounded reuse. However, a closer look suggests that many scientists' practices of data release might be better described as what I call data handovers. These practices are not rooted in the lofty ideals of good scientific practice and global data reuse but in the more mundane necessities of research continuity, which have become more urgent in light of increasing academic mobility. The Austrian scientists interviewed for this study reinterpreted defining features of research data management - such as ensuring findability - as techniques for managing the effects of researcher mobility. This suggests that the adoption of Open Science practices might be dissociated from its stated epistemic goals, and explains why many Open Science initiatives at present are administratively strong but normatively weak.
Threats Targeting Children on Online Social Networks

Today, practically everyone has access to the internet and owns some type of digital gadgets, such as a smartphone, laptop, tablet, etc. We live in a digital world where internet use is widespread. Nowadays, a growing number of children have access to the internet via portable electronic devices such as tablets, laptops, and gaming consoles. Online social networks (OSN) have become increasingly popular and are now widely used along with the rise of the internet. A large percentage of children worldwide have profiles on at least one online social networking site. Children register on these platforms so they can communicate with their peers, make new friends, share their interests and hobbies in the hopes of meeting someone who has similar ones, play games, and more. But in addition to the benefits and enjoyment that kids derive from social networks, there are also risks and dangers that specifically target children on OSN platforms. Online harassment, online predators, sexual solicitation, cyberbullying, and cyber grooming are all threats that target children on OSNs. This paper aims to bring awareness to parents and children about the potential dangers present on online social networking platforms and offers guidelines on how to better protect children in the social networking environment.
Automating the Management of 300 Years of Ocean Mapping Effort in Order to Improve the Production of Nautical Cartography and Bathymetric Products: Shom’s Téthys Workflow

With more than 300 years of existence, Shom is the oldest active hydrographic service in the world. Compiling and deconflicting this much history automatically is a real challenge. This article will present the types of data Shom has to manipulate and the different steps of the workflow that allows Shom to compile over 300 years of bathymetric knowledge. The Téthys project for Shom will be presented in detail. The implementation of this type of process is a scientific, algorithmic, and infrastructure challenge.
CDF‐net: A convolutional neural network fusing frequency domain and spatial domain features

Abstract Convolutional neural network (CNN), as a classic deep learning algorithm, has been applied to various computer vision tasks. However, most classic CNN models focus on the extraction and utilisation of spatial domain features, while ignoring the potential ability of frequency domain feature extraction. In this study, the mechanism in the backbone design is explored. Firstly, the traditional DCT formula is converted into a convolution form through mathematical derivation. On the basis of a a new type of convolution, namely the DCT Convolution is designed. It is more applicable to deep learning network architectures. Secondly, based on the DCT Convolution, a new cross‐domain fusion network named CDF‐Net is designed. The frequency domain and spatial domain features of the input sample are extracted and fused by the network. CDF‐Net is a general network framework which can be applied to most existing prevalent networks. Finally, various experiments are conducted. On image classification task, for Imagenet2012 dataset, the method proposed was applied to ResNet50, and the accuracy of Top1 was increased by 3.684%. On object detection task, for COCO2017 dataset, the method proposed in this study was applied to ResNet50 and ResNeXt50, mAP were improved by 0.5% and 1.2% respectively.
Some Insights about the Applicability of Logistic Factorisation Machines in Banking

Logistic regression is a very popular binary classification technique in many industries, particularly in the financial service industry. It has been used to build credit scorecards, estimate the probability of default or churn, identify the next best product in marketing, and many more applications. The machine learning literature has recently introduced several alternative techniques, such as deep learning neural networks, random forests, and factorisation machines. While neural networks and random forests form part of the practitioner’s model-building toolkit, factorisation machines are seldom used. In this paper, we investigate the applicability of factorisation machines to some binary classification problems in banking. To stimulate the practical application of factorisation machines, we implement the fitting routines, based on logit loss and maximum likelihood, on commercially available software that is widely used by banks and other large financial services companies. Logit loss is usually used by the machine learning fraternity while maximum likelihood is popular in statistics. Depending on the coding of the target variable, we will show that these methods yield identical parameter estimates. Often, banks are confronted with predicting events that occur with low probability. To deal with this phenomenon, we introduce weights in the above-mentioned loss functions. The accuracy of our fitting algorithms is then studied by means of a simulation study and compared with logistic regression. The separation and prediction performance of factorisation machines are then compared to logistic regression and random forests by means of three case studies covering a recommender system, credit card fraud, and a credit scoring application. We conclude that logistic factorisation machines are worthy competitors of logistic regression in most applications, but with clear advantages in recommender systems applications where the number of predictors typically outnumbers the number of observations.
Certificateless Public Key Authenticated Encryption with Keyword Search Achieving Stronger Security

Transforming data into ciphertexts and storing them in the cloud database is a secure way to simplify data management. Public key encryption with keyword search (PEKS) is an important cryptographic primitive as it provides the ability to search for the desired files among ciphertexts. As a variant of PEKS, certificateless public key authenticated encryption with keyword search (CLPAEKS) not only simplifies certificate management but also could resist keyword guessing attacks (KGA). In this paper, we analyze the security models of two recent CLPAEKS schemes and find that they ignore the threat that, upon capturing two trapdoors, the adversary could directly compare them and distinguish whether they are generated using the same keyword. To cope with this threat, we propose an improved security model and define the notion of strong trapdoor indistinguishability. We then propose a new CLPAEKS scheme and prove it to be secure under the improved security model based on the intractability of the DBDH problem and the DDH problem in the targeted bilinear group.
Use of Digital Tools by English Language Schoolteachers

The present study explored the use of digital tools by English language schoolteachers in Hong Kong. Using a qualitative-dominant sequential mixed-methods design, wherein an online mixed-methods survey of primary and secondary school English language teachers (N = 83) and follow-up in-depth interviews (N = 22) were employed, this study collectively explored which digital tools the participants used, why the participants used these tools, and how the participants used them. Fourteen categories of tools utilized by teachers in their professional practice were identified. These were further categorized into core, additional, and remote digital tools based on their mode and frequency of use. Affordances of the core digital tools for language teaching were identified, with examples of teachers’ pedagogical uses of the tools presented. By providing an overview of digital tool use, teachers, school leadership, and professional development providers can better understand how teachers should be prepared for the digital realities of schools. In addition, this study provides a model for understanding the context and subject-specific bidirectional relationship between tools, their affordances, and teachers’ professional tasks.
Cryptanalysis of an Image Encryption Algorithm Based on Two-Dimensional Hyperchaotic Map

This paper analyzes the security of an image encryption algorithm based on a two-dimensional hyperchaotic map. This encryption algorithm generated chaotic sequences through a combination of two one-dimensional chaotic maps and used them as the permutation and diffusion key. Then, the image was encrypted by using the structure of row-column permutation, forward-diffusion, and backward-diffusion. The proposer claimed that the above algorithm was secure. However, it was found through cryptanalysis that the algorithm cannot withstand the chosen plaintext attack. Although the forward-diffusion and backward-diffusion of the original algorithm use two different diffusion keys and there is a ciphertext feedback mechanism, the analysis of the diffusion by iterative optimization showed that it can be equivalent to global diffusion. In addition, the generation of chaotic sequences in the encryption process is independent of the plaintext image, so the equivalent diffusion and permutation key stream can be obtained by adjusting the individual pixel values of the chosen plaintexts. Aiming at the security loopholes in the encryption algorithm, the theoretical and experimental results are presented to support the efficiency of the proposed attack and suggestions for improvement are given. Finally, compared with the performance analysis of the existing cracking algorithm, our cryptanalysis greatly improved the cracking efficiency without increasing the complexity of the data.
Dynamic Target Tracking of Small UAVs in Unstructured Environment

In this paper, an adaptive multi-rotor UAV system of dynamic target tracking and path planning is proposed for the problems of occlusion, lighting change, and similar target interference in an unstructured environment. A DTE-tracker module is designed, consisting of a detector, tracker, and examiner, and proposes a dynamic target capture mechanism to improve the robustness and continuity of target tracking in complex environments. A DWA local path planning algorithm based on a dynamic target tracking task is proposed to control the yaw of the UAV to accurately locate specific targets in the center of the image, and finally achieve the purpose of stable tracking. A UAV platform was built equipped with an onboard computer, laser sensor, and visual sensor, and a series of target tracking and path planning experiments were carried out to verify the effectiveness of the method and test the performance of the algorithm in a complex jungle environment.
DBFNet: A Dual-Branch Fusion Network for Underwater Image Enhancement

Due to the absorption and scattering effects of light propagating through water, underwater images inevitably suffer from severe degradation, such as color casts and losses of detail. Many existing deep learning-based methods have demonstrated superior performance for underwater image enhancement (UIE). However, accurate color correction and detail restoration still present considerable challenges for UIE. In this work, we develop a dual-branch fusion network, dubbed the DBFNet, to eliminate the degradation of underwater images. We first design a triple-color channel separation learning branch (TCSLB), which balances the color distribution of underwater images by learning the independent features of the different channels of the RGB color space. Subsequently, we develop a wavelet domain learning branch (WDLB) and design a discrete wavelet transform-based attention residual dense module to fully employ the wavelet domain information of the image to restore clear details. Finally, a dual attention-based selective fusion module (DASFM) is designed for the adaptive fusion of latent features of the two branches, in which both pleasing colors and diverse details are integrated. Extensive quantitative and qualitative evaluations of synthetic and real-world underwater datasets demonstrate that the proposed DBFNet significantly improves the visual quality and shows superior performance to the compared methods. Furthermore, the ablation experiments demonstrate the effectiveness of each component of the DBFNet.
GRNTI 2022 master version. (Part 1. Developing thematic and structural organization of subject heading nodes)

The authors introduce the findings of their research on updating and improving the existing master version of the State Rubricator of Sci-tech Information (GRNTI) version 2021 and building the General Classification Schedule of the master GRNTI 2022 version. They define the main functions of GRNTI as the standard classification of the State System of Sci-tech Information. The functional efficiency depends on the compliance of the Rubricator’s subject and structural organization to the current status and trends of scientific knowledge and technologies. The main stages of the accomplished studies are described: comparative intellectual analysis of subject scope and hierarchy of GRNTI classes and thematic description of sections within the Plan of Fundamental and Thematic Scientific Research for 2021–2030; submitting proposals for GRNTI by researchers of relevant research institutes and STI agencies; analyzing and content editing of GRNTI proposals, eliminating concept duplication, and updating links and references. The authors include the statistics of changes distribution in GRNTI 2021 by types: changed subject headings, new headings introduction, and elimination of the subject headings. Statistical indicators are analyzed to characterize GRNTI subject and structural organization, as well as its thematic classes (2nd and 3rd level headings).
Abstract Univariate Neural Network Approximation Using a q-Deformed and λ-Parametrized Hyperbolic Tangent Activation Function

In this work, we perform univariate approximation with rates, basic and fractional, of continuous functions that take values into an arbitrary Banach space with domain on a closed interval or all reals, by quasi-interpolation neural network operators. These approximations are achieved by deriving Jackson-type inequalities via the first modulus of continuity of the on hand function or its abstract integer derivative or Caputo fractional derivatives. Our operators are expressed via a density function based on a q-deformed and λ-parameterized hyperbolic tangent activation sigmoid function. The convergences are pointwise and uniform. The associated feed-forward neural networks are with one hidden layer.