Reviewerly/exHarmony huggingface dataset:
Skilling digital technologies in the labour sphere: experience of empirical research in Minsk and Saint Petersburg

Article is written on the basis of an analysis of empirical data obtained in 2021 from sociological studies conducted in Saint Petersburg and Minsk – cities with a population of one million, which served as the object of research of the current digital transformation. A feature of both samples was the large percentage of people with higher education among the employed population. In fact, we studied a group of urban professionals. The authors identify trends in the world of work that detail digital transformation processes. The purpose of the article is to describe the trends in the digitalisation process in the world of work in a large city and to reveal how relevant these trends are for the Belarusian and Russian professionals. The authors show the level of digital acquisition of the employed urban population today, as well as the impact on this process of the previous year associated with the pandemic and the inevitable transition of a part of the employed population to remote work. The article presents the factors that determine the labour motivation of urban professionals of different levels in their mastering of information and communication technologies, reveals the development trends of labour digitalisation processes and their impact on certain groups of professionals employed in both production and non-production spheres. It is concluded that the motivation of the employed population to master new digital knowledge directly depends on how much a person needs it in the workplace, contributes to his career advancement or helps to keep the workplace.
Speak Much, Remember Little: Cryptography in the Bounded Storage Model, Revisited

The goal of the bounded storage model (BSM) is to construct unconditionally secure cryptographic protocols, by only restricting the storage capacity of the adversary, but otherwise giving it unbounded computational power. Here, we consider a streaming variant of the BSM, where honest parties can stream huge amounts of data to each other so as to overwhelm the adversary’s storage, even while their own storage capacity is significantly smaller than that of the adversary. Prior works showed several impressive results in this model, including key agreement and oblivious transfer, but only as long as adversary’s storage $$m = O(n^2)$$ is at most quadratically larger than the honest user storage n. Moreover, the work of Dziembowski and Maurer (DM) also gave a seemingly matching lower bound, showing that key agreement in the BSM is impossible when $$m > n^2$$ . In this work, we observe that the DM lower bound only applies to a significantly more restricted version of the BSM, and does not apply to the streaming variant. Surprisingly, we show that it is possible to construct key agreement and oblivious transfer protocols in the streaming BSM, where the adversary’s storage can be significantly larger, and even exponential $$m = 2^{O(n)}$$ . The only price of accommodating larger values of m is that the round and communication complexities of our protocols grow accordingly, and we provide lower bounds to show that an increase in rounds and communication is necessary. As an added benefit of our work, we also show that our oblivious transfer (OT) protocol in the BSM satisfies a simulation-based notion of security. In contrast, even for the restricted case of $$m = O(n^2)$$ , prior solutions only satisfied a weaker indistinguishability based definition. As an application of our OT protocol, we get general multiparty computation (MPC) in the BSM that allows for up to exponentially large gaps between m and n, while also achieving simulation-based security.
Evaluation of scientific research capability based on neural network

Through the investigation and evaluation of the enterprise's scientific research capability, the important indexes affecting the enterprise's scientific research capability are analyzed and summarized. Combined with the associative memory capability of the discrete Hopfield neural network, the evaluation model of the enterprise's scientific research capability based on discrete Hopfield neural network is established, and the ideal level evaluation index and code are designed. The discrete Hopfield neural network was created by simulation software. By taking the code of the enterprise level evaluation index’s score to be classified as the input of discrete Hopfield neural network, the scientific research capability of the enterprises can be evaluated objectively, fairly, quickly and accurately after training.
The Survey and Proposal on Machine Learning Based Phishing Detection Techniques

Rapidly growing cyber infrastructure has reduced the cost of communication and internettherefore,normal peopleand attackers usages it much frequently. Additionally cheaters are changing their faces therefore secure communication isessential.Among a number of security challenges phishing is one of the issues where a fixed solution is not available.Therefore identification of changing faces of phishing is needed to be investigated. In this work the current techniques basedon ML (machine learning)are investigated and categorize methods according to the features used and classifiers. Using theobserved techniques a model based ML is proposed. The basic design of the required phishing detection model is reported.Additionally its functional aspects are discussed. Finally the summary is provided and future work is proposed.
RGen: Data Generator for Benchmarking Big Data Workloads

This paper presents RGen, a parallel data generator for benchmarking Big Data workloads, which integrates existing features and new functionalities in a standalone tool. The main functionalities developed in this work were the generation of text and graphs that meet the characteristics defined by the 4 Vs of Big Data. On the one hand, the LDA model has been used for text generation, which extracts topics or themes covered in a series of documents. On the other hand, graph generation is based on the Kronecker model. The experimental evaluation carried out on a 16-node cluster has shown that RGen provides very good weak and strong scalability results. RGen is publicly available to download at https://github.com/rubenperez98/RGen, accessed on 30 September 2021.
A Deep Reinforcement Learning Method for Lion and Man Problem

David Gale’s lion and man problem, which is a fundamental pursuit-evasion game, attracts the interest of many scholars. We propose a deep reinforcement learning method based on Deep Deterministic Policy Gradient (DDPG) to solve this problem. We first improve the exploration strategy by adding guided exploration and dynamic spaces exploration strategies to the greedy algorithm. Then we introduce a learning reset mechanism to help the agents escape the traps in the learning process. With these improvements, our method achieves better performance than the classic DDPG algorithm in the lion and man problem. The simulation result shows that deep reinforcement learning method may be promising to solve this problem.
Head Movements of 3D Virtual Head in HMI Systems using Rigid Elements

This paper focuses on rigid head motion synthesis for a virtual person in human–machine interaction systems. Head gestures transmit additional, nonverbal information in human communication and can be used to increase the emotional context of the facial animation authentically simulating human behaviour. The presented method includes the head motion of the expressive face. The proposed motion model was described using Lie algebra for four rigid objects, which reflect the character of the neck and head skeleton movement and jaw bone. The obtained dependencies were used to generate three-dimensional motion of the head model, which was made considering the anatomical structure of the human head. The description presented in this article has the main advantage that it can be extended with additionally rigid elements. It means that we can get a description of the movement of the entire human skeleton, which reflects human anatomy. The proposed motion description eliminates the requirement to use motion capture whenever we need avatar movement in a particular situation, and we use it only for measurement data acquisition.
A Method of Hand Gestures Recognition using Convolutional Neural Network

The progress in the realm of machine learning has caused the field of image processing to undergo a significant advancement. Image processing is a vast subject due to the diverse algorithms and techniques that can be implemented. Sign Language is the hearing disabled community’s way of conveying information and this dissertation was drafted to understand and provide them with what little assistance we can. Visual cues and signs are used to convey messages and intentions of the speaker. It is a well-developed language that has its own vocabulary and grammar. In this paper, we analyze the various methods used in converting Sign Language into text which can be read or to audio that can be heard. This treatise also includes our very own methodology, which makes use of a CNN architecture called AlexNet and discusses the results of the same.
Analysis of Foreign Versions of the Websites of Russian Higher Educational Institutions as a Tool of Applicants Attraction

Today all higher educational institutions use Internet marketing technologies to attract applicants. Websites serve as a starting point of this work. They become particularly topical in the context of the «Export of education» project aimed at the attraction of foreign applicants to national higher educational institutions. The study analyzes foreign versions of the websites of Russian higher educational institutions by several parameters: technical parameters, content structure, user friendliness and user behavior on the website. 34 websites formed the sample. The analysis of user behavior at the website made it possible to estimate its efficiency. Following the results of the study it was found that foreign versions of the websites of Russian higher educational institutions need to be optimized with respect to several indicators. Recommendations were given to improve the websites regarding each parameter.
Interaction Techniques for 3D-positioning Objects in Mobile Augmented Reality

This paper explores interaction techniques for positioning objects in 3D-space during instantiation and movement interactions in mobile augmented reality. We designed the methods for 3D-objects positioning (no rotation or scaling) based on camera position and orientation, touch interaction, and combinations of these modalities. We consider four interaction techniques for creation: three new techniques and an existing one, and four techniques for moving: two new techniques and another two from previous work. We implemented all interaction methods within a smartphone application and used it as a basis for the experimental evaluation. We evaluated the interaction methods in a comparative user study (N=12): The touch-based methods outperform the camera-based techniques in perceived workload and accuracy. Both are comparable regarding the task completion time. The multimodal methods performed worse than the methods based on individual modalities both in terms of performance and workload. We discuss the implications of these findings to the HCI research and provide corresponding design recommendations. For example, we recommend avoiding the combination of camera and touch-based methods for a simultaneous interaction, as they interfere with each other and introduce jitter and inaccuracies in the user input.
Survey of Various Techniques for Voice Encryption

It is well established that wireless communications face significant challenges in terms of maintaining high security, owing to the ease with which attackers can compromise communication channels. As a result, there are ongoing developments toward developing a suitable approach to ensuring high security for wireless communication. Voice signals are highly vulnerable to security threats and are often influenced by network interferences such as reflection. Various methods for encrypting voice signals have already been suggested. Bit shift register schemes are commonly used in voice encryption signals. The LFSRs and FCSRs are two common techniques for voice signal encryption that are based on the bit shift register. The advantage of the LFSR is that it is simple, while that of FCSR is that it is non-linear. A variety of algorithms have been used with LFSR and FCSR to increase the unpredictability and randomness of the LFSRs and FCSRs.
Performance Comparison Of Semi-Automatic Machine For Mendong Woven Industrial

In this study, a comparative analysis of the performance of a weaving machine for energy-efficient semi-automatic mendong between the laboratory and the performance in an industrial environment has been investigated. Tests are carried out in laboratories and in production houses on a home scale industry. The analysis includes the Energy Performance Index (EPI) of the performance of the semi-automatic weaving machine. The measured variable is the electricity consumption (Wh) per 1 meter of the mat that is woven by the semi-automatic weaving machine. The test was carried out in 10 repetitions. The test results in the industrial house are compared with the test results in the laboratory. The target of the test is said to be good if the ratio is below 10%. The results of the test show that energy consumption in industrial homes is 7% higher than in the laboratory. These results can be considered reasonable and acceptable. The conclusion of this examination is that the Mendong weaving machine is suitable for use in the Mendong weaving industry.
A Design on Multilayer Perceptron (MLP) Neural Network for Digit Recognition

This paper presents a low-cost design with half-, single-, and double precision on a MultiLayer Perceptron (MLP) neural network. It contains one input layer, one hidden layer, and one output layer. And in each layer, multiple sigmoid neurons are implemented to train and recognize handwritten digits. The weights and biases of this network are trained using the Modified National Institute of Standards and Technology (MNIST) handwritten digit data in Python using the stochastic gradient descent method. By running the design network on Matlab, the accuracy of the three designs can reach over 93% accuracy. This work basically offers a preliminary result of a hardware demonstration on field-programmable gate array (FPGA).
Color Recognition Method Based on Image Segmentation

In this paper, we present a color recognition method that conforms to the color perception of human eyes in order to realize the recognition of the bleeding position of the human body through RGB image. The HSV (hue, saturation and value) color space is employed to perform color feature extraction. First, image preprocessing is performed to reduce image quality degradation caused by illumination. Then, an adaptive segmentation algorithm is designed to divide the image into homogeneous regions. Finally, the HSV color features of each homogeneous region are extracted, and the color discrimination algorithm is applied to determine the color of the region. Experiment results show that the algorithm is reliable and accurate.
Parameter Redundancy and Identifiability Diana Cole Chapman and Hall/CRC, 2020, xx + 252 pages, £140, hardcover ISBN: 978‐1‐498‐72087‐8

International Statistical ReviewVolume 89, Issue 3 p. 657-658 Book Reviews Parameter Redundancy and Identifiability Diana Cole Chapman and Hall/CRC, 2020, xx + 252 pages, £140, hardcover ISBN: 978-1-498-72087-8 Carl M. O'Brien, Corresponding Author Carl M. O'Brien carl.obrien@cefas.co.uk Centre for Environment, Fisheries & Aquaculture Science, Lowestoft Laboratory, Pakefield Road, Lowestoft, Suffolk, NR33 0HT UKSearch for more papers by this author Carl M. O'Brien, Corresponding Author Carl M. O'Brien carl.obrien@cefas.co.uk Centre for Environment, Fisheries & Aquaculture Science, Lowestoft Laboratory, Pakefield Road, Lowestoft, Suffolk, NR33 0HT UKSearch for more papers by this author First published: 14 October 2021 https://doi.org/10.1111/insr.12473Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume89, Issue3December 2021Pages 657-658 RelatedInformation
Lukewarm Starts for Computerized Adaptive Testing Based on Clustering and IRT

In the field of educational data, the usage of recommendations can considerably improve the experience of the students during the training or assessment phases. In such cases, the activities have to be well adjusted to the latent knowledge of specific students groups in order to individualize, alleviate and speed up the process. We propose a framework to avoid cold starts by recommending initial items inputting a classical Computerized Adaptive Testing. Our global approach is supported by the hybrid usage of Item Response Theory and techniques of clustering to build tailored evaluation paths. To illustrate our methodology, we use real data from the Brazilian National High School Exam.
Application of Fall Detection for the Elderly: A Case Study of Thailand

Nowadays, Thailand is ranked the third most rapidly aging population in the world. The number of people aged 60 and over stands at about 13 million or accounting for 20% of the population. By 2050, Thailand’s aging population is expected to increase to 20 million, accounting for 36% of the population. Most of elderly people who are too old to work, will be live alone since most of working people have to work and have not time to take care of them. When an accident occurs, and no one know, it is very serious. An important one of the most accidents is falling. If we can suddenly know that the elderly’s fall and hurry to rescue, it will increase the elderly’s chance to survive. Therefore, we implemented the fall detection system. Our research consists of three main parts: fall detecting, communication, and application. We developed a locket with a microcontroller, accelerometer, GPS (Global Positioning System), and communication module inside. When the sensors detect fall, this system is activated. An application will display the notifications (elder identification number, map of location, and the elderly’s profile) to users such as village health volunteer, hospital, or/and family members. We developed front-end of the application with Flutter framework and back-end with Loopback as a server framework. Computer programming is Node JS. Moreover, we used Firebase Cloud Messaging, and Google Map API. Consequently, we get this valuable innovation. Hopefully, it can help to reduce the mortality rate of the elderly fall. Also, it can reduce the family burden and government cost in taking care of them.
Core Competencies to Create Effective Protection Programs

To accomplish the goals of successful management practice, what is important must be determined. This chapter looks at the evolution of security as a management activity. The growth of services, products, and systems is provided. Most important security threats are presented.
Application of Object Recognition for Plastic Waste Detection and Classification Using YOLOv3

Object recognition is a computer vision technique to detect the semantic of objects either in digital images or videos then to identify those objects into a particular class. This intelligent technique can be used for various applications. In this study, object recognition is implemented for real-time plastic waste detection and classification using YOLOv3. Six macro plastic waste classes are proposed, namely plastic bag, plastic bottle, crushed bottle, cup, cartoon, and straw. These six categories are usually among the top of our daily plastic waste. This classification of plastics waste aims to make the sorting task more efficient both at home and recycling center. Using around 1858 images and 2000 iterations during dataset training, results show that the detection achieves a good confidence value for plastic bottle and cartoon class which is 85% and 75% consecutively. Meanwhile, straw achieves 65% and the others are between 30 and 40%. This means the algorithm can detect and classify the plastic waste correctly. However, the further review of images used in the dataset in terms of item variety, angle, lighting, and image resolution, as well as increase the iteration number during the training phase, are required to gain a higher confidence value.
Moving Past Metadata: Improving Digital Libraries with <scp>Content‐Based</scp> Methods

Abstract The growth of text mining and corpus analytic scholarship over large digital libraries brings to light the issues created by text duplication and variation within collections that are not adequately addressed in metadata practices. The SaDDL project was a study examining text duplication and similarity in massive digital library collections. Using the HathiTrust Digital Library, this project aims to reduce the bias that duplication gives rise to. We present the content‐based methods of the project, employing a convolutional neural network classification approach, as well as SaDDL's outcomes. The datasets provided by the project will assist in improving cataloging practice and aid scholars in using large text corpora in research.
Multiple Demonstrations Based Trajectory Generation for Mobile Agent Using DMPs with T-S Fuzzy Model

Dynamic Movement Primitives (DMPs), which represent movement plans based on a set of nonlinear differential equations with well-defined attractor dynamics, have been adapted for movement planning. In this paper, we present a trajectory generation method for mobile agent from multiple demonstration using DMPs. The original DMPs can be only used to learn from single demonstration. In order to adapt multiple demonstrations of different tasks, we use DMPs with Takagi-Sugeno fuzzy model to realize autonomous trajectory planning. The proposed scheme is capable to regenerate trajectory in the presence of obstacles even when the goal position is altered. A simulation example is given to confirm the effectiveness of the proposed scheme.
Streaaam: A fully automated experimental audio streaming server

Streaaam is a fully automated experimental audio streaming server, built from Free/Libre Open Source Software in a higher education context. Some of its features include an automatic synthesized-speech moderator; real-time data integration via web APIs; and automatic loading and playing of generative music patches in Pd, SuperCollider, and Csound. These features are creatively combined in our stream’s program, such as in the form of a robot comedy show. Streaaam was conceived of and continues to be developed as a pedagogical vehicle in the context of an undergraduate education in audio arts, music technology, and sound design. Its goals are to inspire students to work creatively in the sonic arts, to introduce them to the development of audio applications for the web, and to showcase student art works at our department and college. This paper discusses the project’s technical implementation, our software development and program curation cycles, pedagogical experiences with the project thus far, and plans for future work.