Reviewerly/exHarmony huggingface dataset:
Semi-Supervised Learning with Ensemble Deep Learning Networks for Descriptor Generation

Deep neural networks and machine learning have made many real-world tasks easier. They can analyze large data sets that humans can't. Deep neural networks can be utilized for image, video, and sound matching, making them an intriguing research area. This research focuses on semi-supervised training and proposes a "enhanced semi-supervised" learning strategy. Robots can learn from labeled and unlabeled data with semi-supervised learning. Supervised learning employs labeled data. Semi-supervised learning is used to govern the Deep neural network's output. Assigning values to input groups and accessing the output area have no interaction. This method seeks to deliver a more efficient learning approach with an equitable distribution of output throughout the output field of space, and the authors developed an ensemble strategy based on three deep learning approaches (Convolution Deep neural network, Alexnet, and MobileNetv2). The result is a more effective learning approach. The handwritten digit experiment was 90.74 percent accurate, whereas Alzheimer's detection was 99.76 percent accurate. When the proposed strategy was used on two experimental data sets, the accuracy was better than when different applications used Siamese Deep neural networks.
A Chaotification Model Based on Modulo Operator and Secant Functions for Enhancing Chaos

Many drawbacks in chaos-based applications emerge from the chaotic maps' poor dynamic properties. To address this problem, in this paper a chaotification model based on modulo operator and secant functions to augment the dynamic properties of existing chaotic maps is proposed. It is demonstrated that by selecting appropriate parameters, the resulting map can achieve a higher Lyapunov exponent than its seed map. This chaotification method is applied to several well-known maps from the literature, and it produces increased chaotic behavior in all cases, as evidenced by their bifurcation and Lyapunov exponent diagrams. Furthermore, to illustrate that the proposed chaotification model can be considered in chaos-based encryption and related applications, a voice signal encryption process is considered, and different tests are being used with respect to attacks, like brute force, entropy, correlation, and histogram analysis.
An Underwater Human–Robot Interaction Using a Visual–Textual Model for Autonomous Underwater Vehicles

The marine environment presents a unique set of challenges for human-robot interaction. Communicating with gestures is a common way for interacting between the diver and autonomous underwater vehicles (AUVs). However, underwater gesture recognition is a challenging visual task for AUVs due to light refraction and wavelength color attenuation issues. Current gesture recognition methods classify the whole image directly or locate the hand position first and then classify the hand features. Among these purely visual approaches, textual information is largely ignored. This paper proposes a visual-textual model for underwater hand gesture recognition (VT-UHGR). The VT-UHGR model encodes the underwater diver's image as visual features, the category text as textual features, and generates visual-textual features through multimodal interactions. We guide AUVs to use image-text matching for learning and inference. The proposed method achieves better performance than most existing purely visual methods on the dataset CADDY, demonstrating the effectiveness of using textual patterns for underwater gesture recognition.
Open Science: the Role of Universities and Libraries in Modern Changes in Scientific Communication

The purpose of the article is to summarize the main concepts and trends in the field of open science, as well as the main relevant policies, services, and resources that can be useful for universities and academic libraries in their research and academic strategies.&#x0D; The research methodology of the study is based on the application of analysis and synthesis methods, which made it possible to achieve the stated purpose of the article and to obtain reasonable conclusions.&#x0D; The scientific novelty of the results obtained is to clarify the role of universities and academic libraries in promoting the principles and ideas of open science, presenting numerous applications to researchers, and learning to create appropriate infrastructure and services.&#x0D; Conclusions. Attention is drawn to the radical transformation of scientific communication, the creation of new knowledge through research, and their dissemination through training and publications. The need to realize the value of research data, which provides many new opportunities not only for further research of other scientists in the global world but also for teaching, administration, partnership and strategic planning, is emphasized. The conclusion is made about the need to participate in open scientific practices, to open scientific data and scientific results, and to make them available to the scientific community and the general public.
Deep Learning Based Target Tracking Algorithm Model for Athlete Training Trajectory

The main function of the athlete tracking system is to collect the real-time competition data of the athletes. Deep learning is a research hotspot in the field of image and video. With the rapid development of science and technology, it has not only made a breakthrough in theory, but also achieved excellent results in practical application. SiamRPN (Siamese Region Proposal Network) is a single target tracking network model based on deep learning, which has high accuracy and fast operation speed. However, in long-term tracking, if the target is completely obscured and out of the sight of SiamRPN, the tracking of the network will be invalid. Considering the difficulty of long-term tracking, the algorithm is improved and tested by introducing channel attention mechanism and local global search strategy into SiamRPN. Experimental results show that this algorithm has higher accuracy and prediction average overlap rate than the original SiamRPN algorithm when performing tracking tasks on long-term tracking sequences. At the same time, the improved algorithm can still achieve good results in the case of target disappearance and other challenging factors. This study provides an important reference for the coaches of deep learning to realize long-term tracking of athletes.
Improving IoT Security Using Elliptic Curve Integrated Encryption Scheme with Primary Structure-Based Block Chain Technology

Security and privacy remain as a significant challenge in the distributed Internet of Things (IoT) framework. IoT has already demonstrated that it can significantly impact our daily lives. The internet sends and receives a large volume of data from different devices present worldwide. IoT comprises of heterogeneous units present across a wide variety of networks to communicate sensitive and non-critical data, resulting in significant challenges in terms of protecting and ownership of the data collected from users. The Internet security architecture developed in a constrained location, such as healthcare, is presented in this research work. Across a range of networks and PCs, the system offers safe data sharing and authentication mechanism. The proposed design is based on the blockchain technology's underlying structure and the usage of secure hazarding. This research study has attempted to propose a technique for improving IoT security using elliptic curve integrated encryption scheme with primary structure based blockchain technology.
Informational Resources Transformation of Ukrainian Public Libraries Current Conditions

The purpose of the article is to substantiate the transformation of information resources of public libraries of Ukraine in modern conditions.&#x0D; The methodological basis of the article was based on systemic, information-service and modernist approaches and the application of the principles of objectivity and comprehensiveness of knowledge. The organic combination of structural-functional analysis, synthesis, and cognitive methods made it possible to successfully realize the set goal.&#x0D; The scientific novelty consists in considering the main aspects of the formation of electronic information resources against the background of the aggression of the Russian Federation against the sovereign state of Ukraine, starting in 2014, arguing that public libraries actively participated in the information processes of creating a regional source base on events that take place in modern history of our state. The existing ones are analyzed and the author's understanding of the term "electronic library and information resources" is proposed.&#x0D; Conclusions. Digitization stimulates the general trend of transformation of public libraries of Ukraine into hubs for comprehensive processing of all types of information on all media. The use of new media in library practice contributes to the production of relevant electronic information resources, which marks the third stage of the use of information and communication technologies in the work of libraries. The public libraries of Ukraine currently have the following types of electronic information resources: a) electronic catalogs; b) full-text databases; c) electronic collections of text analogues of printed publications; d) collections of digitized publications; e) electronic documents that do not have printed counterparts; f) electronic publications of audio and video information; g) multimedia products. Electronic information resources enrich the assortment and include the following audiovisual products: audio recordings, films, videos, slides, presentations, etc., including exhibitions and tours of the library, advertising of new arrivals, online meetings with famous people or solutions to current community issues, etc. Own accounts in social networks (Facebook, Twitter, Pinterest, Instagram), messengers (WhatsApp, Telegram, Viber), video hosting (YouTube), on the one hand, have a significant potential for popularizing the library, on the other hand, are a platform for approving the demand for created electronic information resources.
Blind Identification of Channel Codes under AWGN and Fading Conditions via Deep Learning

Blind identification of channel codes is crucial in intelligent communication and non-cooperative signal processing, and it plays a significant role in wireless physical layer security, information interception, and information confrontation. Previous researches show a high computation complexity by manual feature extractions, in addition, problems of indisposed accuracy and poor robustness are to be resolved in a low signal-to-noise ratio (SNR). For solving these difficulties, based on deep residual shrinkage network (DRSN), this paper proposes a novel recognizer by deep learning technologies to blindly distinguish the type and the parameter of channel codes without any prior knowledge or channel state, furthermore, feature extractions by the neural network from codewords can avoid intricate calculations. We evaluated the performance of this recognizer in AWGN, single-path fading, and multi-path fading channels, the results of the experiments showed that the method we proposed worked well. It could achieve over 85 % of recognition accuracy for channel codes in AWGN channels when SNR is not lower than 4dB, and provide an improvement of more than 5% over the previous research in recognition accuracy, which proves the validation of the proposed method.
Deep Metric Learning for Histopathological Image Classification

Neural networks demonstrated to be effective in multiple classification tasks with performances that are similar to human capabilities. Notwithstanding, the viability of the application of this kind of tool in real cases passes through the possibility to interpret the provided results and let the human operator take his decision according to the information that is provided. This aspect is much more evident when the field of application is bound to people's health as for biomed-ical image classification. We propose for the classification of histopathological images a convolutional neural network that, through metric learning, learns a representation that gathers in homogeneous clusters the labeled samples according to their characteristics. This representation, beyond improving the classification performance, also provides for the new test image sets of previously labeled samples that can be inspected to support the labeling decision. The technique has been tested on the LC25000 dataset that collects lung and colon histopathological images and with the Epistroma dataset. The source code is available at https://e:ithub.com/Calder10/EpistromaLC25000-Classification.
Freedom of Expression and Hate Speech: When Values Collide in Divided Societies

One of the thorniest issues in law, especially concerning the boundaries of what is reasonable and proportionate, is the distinction between freedom of expression and hate speech. Striking a balance between freedom of expression and hate speech is, however, not a mere exercise in theory; it goes to the core of respect of individual rights and freedoms. To one person, uttering speech pursuant to the right to free expression is essential for a free and open democratic society; whereas another person, offended by what they perceive as hatred, can experience such speech as an attack on their identity and self-worth, causing harm, fear and anxiety that deny their individual rights to equality, identity and dignity. This paper gives a brief overview of jurisprudential developments in international law concerning speech that may fall within the category of hate speech, whereafter two prominent South African judgments by the Equality Court are discussed. Those two judgments highlight the complexities in determining when speech can be regarded as hate speech; what test is applied to ascertain whether speech constitutes hate speech; what evidence is required for a finding to be made; and the effect of a declaratory order. The two judgments discussed, the Nelson Mandela Trust and Ors v. AfriForum and Ors (Old Flag case 2019) and the AfriForum and Economic Freedom Fighters and Ors (Kill the Boer Case 2022), attempted to determine the line that separates freedom of expression from hate speech. The judgments, perhaps not unexpectedly, have given rise to more questions than answers. The inconsistency in comparative jurisprudence reaffirms that the labelling of speech as hate speech should be reserved for the most extreme forms of speech; it should be proportionate to the speech, including who expressed it, where and when; and any declaration should only be directed at the specific incident and not restrict speech in general.
Single-frequency and accurate phase unwrapping method using deep learning

Phase unwrapping is an important part of fringe projection profilometry(FPP), which greatly affects the efficiency and accuracy of reconstruction. Phase unwrapping methods with deep learning achieve single-frequency phase unwrapping without additional cameras. However, existing methods have low accuracy in the real complex scene, and can not process data whose resolution is greater than the resolution of training data. This paper introduces a neural convolutional network named as VRNet which achieves accurate and single-frequency phase unwrapping without extra cameras. VRNet with encoder-decoder structure gets multi-scale feature maps through feeding the wrapped phase map into the encoder, then fuses the feature maps recursively by using the proposed feature fusion module to accomplish precise prediction. In order to further improve the accuracy of phase unwrapping, this paper presents a phase correction method based on the distribution characteristics of the absolute phase. The method divides the cross-section of the absolute phase map into several curves and identifies a misclassified pixel by comparing its absolute phase value with the value of neighboring curves. In contrast to existing methods, the method is row-independent and does not require segmentation of image. Moreover, this paper accomplishes the prediction of high-resolution data through the phase stitching strategy and fine-tuning the phase correction method. Extensive experiments show that the proposed method is able to achieve high-accuracy and single-frequency phase unwrapping in real scenes which consist of at least one complex object, and is also effective for wrapped phase maps with a resolution larger than the training data.
DAACO: adaptive dynamic quantity of ant ACO algorithm to solve the traveling salesman problem

Abstract The traveling salesman problem (TSP) is an NP-hard problem. Thus far, a large number of researchers have proposed different ant colony optimization (ACO) algorithms to solve the TSP. These algorithms inevitably encounter problems such as long convergence time and the tendency to easily fall into local optima. On the basis of the ACO algorithm, this study proposes a dynamic adaptive ACO algorithm (DAACO). DAACO realizes the diversity of initialization of the ACO algorithm by dynamically determining the number of ants to be prevented from falling into local optimization. DAACO also adopts a hybrid local selection strategy to increase the quality of ant optimization and reduce the optimization time. Among the 20 instances of the TSPLIB dataset, the DAACO algorithm obtains 19 optimal values, and the solutions of 10 instances are better than those of other algorithms. The experimental results on the TSPLIB dataset show that the DAACO algorithm has obvious advantages in terms of convergence time, solution quality, and average value relative to existing state-of-the-art ACO algorithms.
Combining white box models, black box machines and human interventions for interpretable decision strategies

Abstract Granting a short-term loan is a critical decision. A great deal of research has concerned the prediction of credit default, notably through Machine Learning (ML) algorithms. However, given that their black-box nature has sometimes led to unwanted outcomes, comprehensibility in ML guided decision-making strategies has become more important. In many domains, transparency and accountability are no longer optional. In this article, instead of opposing white-box against black-box models, we use a multi-step procedure that combines the Fast and Frugal Tree (FFT) methodology of Martignon et al. (2005) and Phillips et al. (2017) with the extraction of post-hoc explainable information from ensemble ML models. New interpretable models are then built thanks to the inclusion of explainable ML outputs chosen by human intervention. Our methodology improves significantly the accuracy of the FFT predictions while preserving their explainable nature. We apply our approach to a dataset of short-term loans granted to borrowers in the UK, and show how complex machine learning can challenge simpler machines and help decision makers.
Fisher's Linear Discriminant Analysis With Space-Folding Operations

Fisher's linear discriminant analysis (LDA) is an easy-to-use supervised dimensionality reduction method. However, LDA may be ineffective against complicated class distributions. It is well-known that deep feedforward neural networks with rectified linear units as activation functions can map many input neighborhoods to similar outputs by a succession of space-folding operations. This short paper shows that the space-folding operation can reveal to LDA classification information in the subspace where LDA cannot find any. A composition of LDA with the space-folding operation can find classification information more than LDA can do. End-to-end fine-tuning can improve that composition further. Experimental results on artificial and open data sets have shown the feasibility of the proposed approach.
A meta-analysis study of the effect of the blended learning model on students’ mathematics learning achievement

Many studies have researched the effect of blended learning on mathematics learning achievement, but the results of previous research reported different results. Therefore, this research aims to determine the effect of the blended learning model on mathematics learning achievement. The design of this study used a meta-analysis approach by analyzing 20 effect sizes from 18 primary studies that were Scopus-indexed and met the inclusion criteria. The results of the analysis show that the use of the blended learning model affects mathematics achievement compared to traditional learning (d = 0.725; p &lt; 0.05). The results of the analysis according to the moderator variable are known that the effect of the blended learning model on mathematics achievement is different based on the level of education (Qb = 13.923; p &lt; 0.05) and the year of research (Qb = 16.140; p &lt; 0.05). However, no differences were found according to the sample size group (Qb = 0.039; p &gt; 0.05) and media platform (Qb = 2.861; p &gt; 0.05. The findings show the consistency of the publication of research results on the effect of using the blended learning model on students' mathematics learning achievement.
Evincing Offence: How Digital Forensics Turns Big Data into Evidence for Policing Sexual Abuse

A reader can expect the abstract, paper and keywords to discuss descriptions of evidence, classification schema, seizure rules and more generally the data frictions, constraints and limitations associated with the processing of digital forensic evidence involving children in England.&#x0D; The widespread availability and use of digital devices both enables criminal acts and helps to detect them. The production and circulation of indecent images of children has been one area of crime that has transformed in recent years because of developments in modern communication technologies. Through in-depth ethnographic observations and qualitative interviews with four police forces in England, this article examines the resources and labor required to turn digital footprints into evidence for the possession of indecent images. In doing so, our aim is twofold. One, we detail the formal and informal processes whereby large sets of data become discrete pieces of judicial evidence. A notable feature of these administrative and technical processes is that while criminal justice agencies often strive for linear investigations, such aspirations fail to acknowledge the messy interrelation of expertise and roles that underpin the transformation of digital devices into evidence. As a second aim, we seek to identify similarities and differences in the practices whereby evidence is constructed between digital and other areas of forensics. In particular, this analysis raises questions around the descriptive and normative adequacies of prevalent theories of objectivity for digital forensics.