Reviewerly/exHarmony huggingface dataset:
Breast Classification of Young North Vietnamese Women Using K-means Clustering

Breast data classifying is a crucial basis for forming the sizing system, the design, and the production of female bras. However, the discovery and analysis of breast data for Vietnamese women have not been fully mentioned. This article presents the results of breast classification of Young North Vietnamese Women based on 8 characteristic breast dimensions that have been extracted by Principal Component Analysis and Random Forests algorithm. Breast sizes of 460 females between 18 and 24 years old with BMI from 14.5 to 24.3 kg/m2 were determined using Geomagic Design X software on scan data using Scan3D MB2019. The K-means clustering technique is used for breast classification. Optimal K was then determined by the Elbow and Gap Statistic methods. The research results have identified requisite dimensions for classifying female breasts; The statistics characteristics of the 3 groups female breast sizes (optimal K = 3) were well clustered (SW = 0.55) from the obtained data.
Acquisition, Sampling and Coding

Image acquisition, sampling and coding are introduced. The sampling theorem is described and the notion of degrees of freedom is mentioned.
On the issue of the composition of a crime associated with illegal enterprise (Article 171 of the Criminal Code of the Russian Federation) in the light of criminalization and decriminalization theory

The research is focused on the fact that illegal enterprise is characteristic of the whole world. At the same time, the informal sector of the economy accounts for 10 to 20 % of gross domestic product in developed coun-tries and about 60 % in emerging economies. We indicate that the Criminal Code of the Russian Federation re-flects responsibility for illegal enterprise in Article 171. We emphasize that there is a discussion at the doctrinal level about the expediency of having this corpus delicti. The purpose of the study: to analyze the (un)foundedness of the criminalization of corpus delicti associated with illegal business (Article 171 of the Crim-inal Code of the Russian Federation). Tasks: to analyze the social danger of an act related to illegal enterprise; characterize the relative prevalence of this act; to study the historical continuity of the reflection of this corpus delicti in the criminal law of Russia; conduct an analysis of foreign legislation to reflect such a corpus delicti. As a result, we analyze the social danger of this crime, as well as its compliance with the principles of criminaliza-tion, such as the relative prevalence of the act, compliance with historical succession and modern foreign expe-rience.
CFFNet: Cross-scale Feature Fusion Network for Real-Time Semantic Segmentation

Despite deep learning based semantic segmentation methods have achieved significant progress, the inference speed of high-performance segmentation model is harder to meet the demand of various real-time applications. In this paper, we propose an cross-scale feature fusion network (CFFNet) to harvest the compact segmentatiHon model with high accuracy. Specifically, we design a novel lightweight residual block in backbone with increasing block depth strategy instead of inverted residual block with increasing local layer width strategy for better feature representative learning while reducing the computational cost by about 75%. Moreover, we design the cross-scale feature fusion module which contains three path to effectively fuse semantic features with different resolutions while enhancing multi-scale feature representation via cross-edge connections from inputs to last path. Experiments on Cityscapes demonstrate that CFFNet performs agreeably on accuracy and speed. For $$2048\times 1024$$ input image, our model achieves 81.2% and 79.9% mIoU on validation and test sets at 46.5 FPS on a 2080Ti GPU.
A Tri-valued Trimmed Mean Decision-Based Filter for Removal of Salt-and-Pepper Noise

This paper presents a Tri-valued Trimmed Mean Decision-based De-noising Filter for Removal of Salt-and-Pepper noise. The proposed filter estimates noisy pixel value using the weighted median of noise-free pixels in the filter window. A fixed window size $$(3\times 3)$$ is used at each iteration of the technique to maintain edge preservation and maximum correlation throughout the process. The proposed filter is evaluated on various natural and medical images with varying noise density (10–90%), where it provides high visual clarity and improved quality metrics. The simulation results show an average increase of 0.92 dB (2.6%) and 1.07 dB (2.8%) increased value of PSNR (Peak Signal-to-Noise Ratio) using grayscale and X-ray images, with varying noise density (10–90%).
Building an Internal Control System for the Purposes of Managing the Activities of the Smart University

With the complication of market competition, the aggravation of crisis situations, regular monitoring of the business processes of each organization is necessary. Modern business conditions also significantly affect the activities of a smart university. For the current and strategic management of its activities, an effective system of internal control is necessary. The conceptual foundation of internal control define it as a smart university management subsystem, which is aimed at achieving high performance results. The effectiveness of internal control is substantiated by the quality of the tools, methods, procedures used, the efficiency and qualifications of specialists. The article reveals the theoretical foundations for interpreting internal control as an important tool in the smart university management system. The basic procedures for internal control of the smart university’s business processes are presented. The result of the study is the proposed concept of building an internal control system for the purposes of managing the activities of a smart university. The results obtained were tested during the internal control of the activities of the Smart-division of the university on the example of the department.
Dependability Analysis for Transaction Processing in On-Demand Computing System

On-demand computing is very popular computing technology which enables convenient, ubiquitous, and on-demand network access to shared computing resources which are on-demand. There are several areas which can be similar to on-demand computing such as grid computing, cloud computing, utility computing, adaptive management, and autonomic computing. On-demand computing can be defined as a form of distributed computing which deals with the sharing and co-ordinations of on-demand resources globally. Transaction processing in on-demand computing is a challenging operation which is used widely using on-demand sufficient resources efficiently fulfilling the users’ demands. Transaction processing in on-demand computing consists of several operations which are executed on distributed platforms in order to execute the desired functions by accessing and updating the databases. Load balancing is also an important in on-demand computing as several jobs or transactions may access the same resource at a time. In order to guarantee the trustworthy of resources, reliability, and performance of services, the system needs to be dependable. In this study, we explore the dependability analysis in on-demand computing.
Deep Learning in Solar Image Classification Tasks

The exponential increasing of data being collected in astronomy has raised a big data challenge. Mining valuable information timely and efficiently from massive raw data is highly demanded. Even simple binary classification of collected raw data is of great importance, reducing the burden of the following data processing. Inspired by the success of image classification with deep learning, we investigated solar radio spectrum classification using deep learning, including the premier deep belief network (DBN), the most popular convolutional neural network (CNN) and long short-term memory (LSTM) network. For model training, a database of solar spectrum was established and published to the public. As far as we know, it is the first one in the world. The database contains 8816 spectrums with different image patterns which represent different solar radio mechanisms. Then, each spectrum was given a label by the invited experts of solar radio astronomy.
Underwater Acoustic Image Processing for Detection of Marine Debris

Underwater acoustic images obtained from acoustic imaging equipment, either stand-alone or on-board autonomous underwater vehicles, consist of noise, mainly in multiplicative form. Enhancing and restoring acoustic images is a crucial step towards computer vision applications that require high-quality data for segmentation, detection, classification and recognition in underwater images. This paper presents a technical study of image enhancement and restoration techniques that are used to pre-process acoustic images and their application in underwater environment. The experimental results prove that VisuShrink, a wavelet transformation technique, and Unsharp Masking, a linear image processing technique, outperform the other methods at restoring and enhancing underwater acoustic images of marine debris. The efficiency of the proposed techniques is calculated and compared using Image Quality Assessment metric, the Peak Signal -to -Noise- Ratio (PSNR).
Detection of Cyber Threats in Application Platforms

Illicit behavior on application platform, the gadget’s popular social platform, promotes misuse of ‘search rankings’ and the spread of malware. The evidence that has been left by the scammers is detected by Fraud rank detector. It detects vulnerable malware and application to hunt for malware manipulation. A Pseudo Clique Finder, program is proposed which tracks the behavior of deceiver enlisted to download/rate a technology is suitably to advertise those rating with comparably less interval of time or download the app using symmetric key. The server needs to permit the key to access the secret key as referred to PCF takes the input as set of downloads of an app in and a threshold value u (u = 3). Whenever the threshold limit exceeds by downloading the application more than the u times the user is said to be fraudster.
Examining Voice Assistants in the Context of Children's Speech

An estimated 3.25 billion voice assistants (VAs) are in homes around the world, but these devices are not always able to recognize and respond to children's speech. To inform the design of VAs that support kids, we report on a lab study where 28 5-to-10-year-old participants interacted with a commercial VA to: 1) attempt to execute common VA-supported requests (such as setting an alarm), 2) recite a set of such scripts verbatim, and 3) engage in unstructured conversation. We find that transcription (aka recognition) accuracy is high, but devices only respond appropriately to the content of children's speech half of the time. Frequency of appropriate responses increased with children's age and as their discourse became more standardized. Based on themes in participants’ speech, we identify design opportunities in child-VA interaction, such as exploring a topic or responding to a conversational bid. In addition to our empirical findings, we contribute a structured corpus of children's speech.
Intellectual Property Law and Robotics

What we may be witnessing in the not so distant future is not only the continued progress of robotic technology which will increase AI capabilities, but also an evolution of copyright and patent regimes that will put at the centre of the debate the creation of “works” or the development of “inventions”, possibly diminishing the roles of “authors” and “inventors”. Are we moving from the “law of authors and inventors” to the “law of copyrightable works and patentable inventions”? The robotics and AI industry may be the innovative sectors that will provide an answer to these questions.
OntoKIQE: An Ontology Infused Approach for Knowledge Integrated Query Expansion Using Semantic Intelligence

As the Web 2.0 moves towards the Web 3.0 there is an increasing need for simple and efficient information retrieval techniques. Yielding the users’ required search intent has proved to be an exhaustive and tedious task from just using Mutual Information model (NPMI) and the Pielou’s Evenness Index (PEI), along with Domain Ontology based query term enrichment and Knowledge enrichment of the term pool with the help of Wikidata API. The user input terms, resulting in unreliable and quavering search results. To solve this issue efficiently, we deploy semantically compliant automatic query expansion techniques. The proposed OntoKIQE model is composed of three primary statistical base models namely Bose Einstein-1, Normalized Pointwise Accuracy and Recall percentages of 94.18 and 92.84 respectively have been yielded by the proposed model, being a substantial improvement over the baselined models by enriching the query terms with auxiliary background knowledge and generating ontologies.
Breaking the silence: A corpus-assisted analysis of narratives of the victims of an Egyptian sexual predator

Abstract Beginning in July 2020, Egyptian social media were flooded with stories about a young man raping and sexually harassing about 100 women and under-age girls. An Instagram account called @assaultpolice posted narratives of the man’s victims reporting the verbal and physical abuse they were subject to. The whole set of issues about the sexual activities of this man, who was dubbed the “Sexual Predator,” trended on Twitter and Instagram and was then picked up by many Egyptian talk shows. The issue received much attention because of the conservative nature of Egyptian society in which the tendency to blame victims of sexual abuse leads to their remaining silent about any abuse they have suffered. The power of social media in highlighting these narratives, and ensuing similar ones, has helped the whole community to realize the severity of the problem of sexual harassment. This resulted in a push for an amendment to Egyptian law in 2021 placing harsher penalties on crimes relating to sexual harassment and concealing the identity of victims. This article uses a corpus-assisted approach to analyse the discursive strategies used in these narratives to explore the discursive construction of the sexual aggressor and the victims. The analysis shows that the narratives are told from the perspective of the victims, with access always given to the victims’ inner feelings and perception, and that the male abuser is constructed as the active agent while the female victims as acted upon.
New Perspectives in Business Education: Key Topics, Digital Technologies, and Learning Partnerships

The article analyzes the current issues of the development of a new concept of business education, examines examples of the effective use of innovations and digital technologies in training in the context of Industry 4.0. The authors investigate the key trends in business education and the potential of learning partnership, as well as summarize the experience of online education and analyze the prospects for hybrid learning. The study used general scientific methods of research of traditional and new models of business education, which allow to identify the problems of training in the conditions of digitalization, to assess the risks and world experience of the digital transformation of the industry, to objectify the requirements for the development of innovative business education in Russia. In the scientific material, the authors identified the main trends in business education of the twenty-first century and key problems, among which one of the most urgent is the resistance of the educational system to changes, including the transition to digital services. The reference experience of world and Russian business schools is studied, the main conclusions on online training and competencies of specialists for the “knowledge economy” are made. The current situation associated with the COVID-19 pandemic was most difficult for the education sector (with the exception of medicine). It was necessary to restructure the work of educational organizations in the new conditions and move from traditional to online learning. Most business schools have discovered the huge potential of mandatory digitalization, which has strengthened the influence of key trends discussed in the global discourse: personalization and digitalization of learning, development of new skills (soft skills, digital skills), continuity of learning, private market and localization, security of the educational environment, partnerships in the context of the ecosystem. The transition to the type of mixed (hybrid) training was painful, but not critical.
Comparative Analysis of Machine Learning Algorithms for Imbalance Data Set Using Principle Component Analysis

Class imbalance is not new in the world of Machine Learning (ML). In this digital world, almost all physical systems are converted to automated systems. Digitization of systems generates a huge amount of data. Domains like healthcare, finance, security drastically improve their performance and business too. Digital data is categorized according to its domains and it is popular as data set. For effective utilization of data sets, ML plays an important role and changing the world of digitization. Class imbalance is the major issue in ML-based systems which is reduced the performance of the systems. The healthcare domain is majorly suffered due to class imbalance. To address the class imbalance issue in healthcare, the proposed study focused on the structure and feature of the data set. The data set is preprocessed using two techniques, traditional preprocessing and using feature extraction technique principle component analysis (PCA). The preprocessed data set is evaluated with various standard ML algorithms (decision tree, support vector machine, neural network analysis, etc.).The evaluation of the data set has been recording accuracy and precision values of unbalanced to balanced data set for both techniques. Finally, results are compared for both techniques and conclude that the data set preprocessed using PCA is performed better than traditional preprocessed data and also handle class imbalance for ML-based systems.
Dimensional Reduction Layer: The Simple Way to Build Lightweight Models

Convolutional neural network (CNN) has been making great achievements in computer vision and artificial intelligence. To achieve state-of-the-art performance, the CNN models are designed to become deeper and deeper, using more feature maps to extract many features from the input image such as ResNet-152 with 60.3M parameters and 11 GFLOPs. Therefore, running these models in real-time is a big issue, especially, running real-time on PC and/or embedded devices without using GPU. In this work, we propose a simplest layer named “Dimensional Reduction Layer” (DR layer) that reduces the number of feature maps and makes models lighter and less computational. The experiments on various network backbones including basic model, mobile model, and res model show that the DR layer reduces significantly the performance of these models when we concatenate more feature maps together. Besides, we found that the DR layer works well on models using residual learning blocks such as ResNet.
One-Shot Image Learning Using Test-Time Augmentation

Modern image recognition systems require a large amount of training data. In contrast, humans can learn the concept of new classes from only one or a few image examples. A machine learning problem with only a few training samples is called few-shot learning and is a key challenge in the image recognition field. In this paper, we address one-shot learning, which is a type of few-shot learning in which there is one training sample per class. We propose a one-shot learning method based on metric learning that is characterized by data augmentation of a test target along with the training samples. Experimental results demonstrate that expanding both training samples and test target is effective in terms of improving accuracy. On a benchmark dataset, the accuracy improvement by the proposed method is 2.55% points, while the improvement by usual data augmentation which expands the training samples is 1.31% points. Although the proposed method is very simple, it achieves accuracy that is comparable or superior to some of existing methods.
Two-Factor Authentication for Safe Deposit Box Based on Embedded System

Security is one of the most significant needs for people everywhere. Likewise, people usually use a safe deposit box (SDB) to store valuables for the storage of goods. However, traditional SDB is very easy to open or steal from criminals. For this reason, an effective SDB system is needed to secure valuables. This study proposed a two-factor authentication for SDB based on fingerprint and PIN. The proposed SDB system is secure and more efficient for storage system authentication in embedded system environments. To be able to use the system, the user must register. After being registered in the system, the registered user will perform authentication. The prototype of a safe authentication system provides a basic overview of integrating a fingerprint sensor, Arduino Mega 2560, keypad, and safe deposit box with a simple application. The prototype system that has been designed can be used to solve access control security problems in a safe system. Two alternatives of fingerprint or PIN authentication provide convenience and accuracy for safe authentication systems. It is hoped that this design is an alternative to a cheap SDB and secure to use.
Unsupervised Learning for the Automatic Management of Scientific Aspect in Academic Conferences

Medakene, Abdeldjaouad Nusayr Eddoud, Abdelhadi Mohammed Ali Bouanane, KhadraAn academic conference is of great importance in the scientific community as it provides a platform for scientists and researchers to share their research findings, exchange ideas and establish new cooperative relationships with research groups around the world. Tasks related to the management of a scientific conference can be categorized according to two main aspects: the technical or scientific aspect and the administrative and logistical aspect. While tasks in both aspects are challenging and time/effort consuming, the scientific aspect of a conference requires in addition, a high level of expertise. Despite the great support that conference management systems provide to conference organizers, most of them focus mainly on the administrative and logistical aspect, while tasks of the scientific aspect are less considered. In this work, we make use of several unsupervised algorithms to automate some tasks from the scientific aspect, where expertise, time and efforts are required. Namely, the detection of out of scope papers and the creation of an accurate conference program.
Combining Holistic Source Code Representation with Siamese Neural Networks for Detecting Code Clones

Code clones can be defined as two identical pieces of code having the same or similar functionality. Code clone detection is critical to improve and sustain code quality. Current methods are unable to extract semantic and syntactic features and classify code bases satisfactorily. We propose a novel two-stage machine-learning approach towards code clone detection. Firstly, multiple intermediate representations of source code are extracted and combined to generate a holistic embedding based on a recently proposed technique. Next, we use these embeddings to train an Intermediate Merge Siamese Neural Network to detect functional code clones. Siamese Neural Networks are a state-of-the-art machine learning architecture particularly suited to code clone detection. This novel combination allows for learning subtle syntactic and semantic features and identifying previously undetectable similarities. Our solution shows a significant improvement in code clone detection, as shown by experimental evaluation over the OJClone C++ dataset.