Reviewerly/exHarmony huggingface dataset:
Innovative Teaching Methodology in Engineering Education: Accepting the Challenges of 4.0. Industry

At present Russia forms a new education system focused on entering into the world’s information and educational space. The article reveals the relationship between the emergence of innovative forms of student’s education and the quality of their mastering of both professional competencies and soft skills. It also describes the mutual influence of education using a digital educational platform and interactive teaching methods (such as flipped classroom, mind maps, case studies). We also used questioning and testing of students who completed the above described course. The analysis and comparison of the results of education with the use of 3 variants of approach was done: 1. the use of digital educational platform, 2. the use of innovative teaching methods, and 3. digital educational platform and innovative methods combined together. We have concluded that the most effective education model is the combination of traditional classes with the introduction of innovative methods and digitalization of education. This method allows you to keep all the advantages of face-to-face communication between students and the lecturer, stimulates creative thinking and gives an opportunity for individual time planning due to offline digital technologies.
Research on the Innovation Mode of Scientific Research Service of University Library Based on the Operation Mode of Question-and-answer Community

The university library is one of the important organizations of the school and the external expression of the school culture. Library is the main position of school education and teaching, it not only can perfect and improve the student’s professional knowledge, it can also perfect the student’s personality, to the student’s ideological and political education, to cultivate a socialist qualified builder has an important role. In the context of the Internet, the application model of the question-and-answer community has developed rapidly .With the help of the question-and-answer community, users can fully express their views and ideas, promote the exchange and sharing of information and knowledge. At the same time, the operating mode of the question-and-answer community also provides a new way of thinking for the innovation of scientific research services in academic libraries. Based on the question-and-answer community operation model, this article analyzes the relevant content of the scientific research service innovation model of university libraries.
Sampling a Near Neighbor in High Dimensions — Who is the Fairest of Them All?

Similarity search is a fundamental algorithmic primitive, widely used in many computer science disciplines. Given a set of points S and a radius parameter r &gt; 0, the r-near neighbor ( r -NN) problem asks for a data structure that, given any query point q , returns a point p within distance at most r from q . In this paper, we study the r -NN problem in the light of individual fairness and providing equal opportunities: all points that are within distance r from the query should have the same probability to be returned. In the low-dimensional case, this problem was first studied by Hu, Qiao, and Tao (PODS 2014). Locality sensitive hashing (LSH) , the theoretically strongest approach to similarity search in high dimensions, does not provide such a fairness guarantee. In this work, we show that LSH based algorithms can be made fair, without a significant loss in efficiency. We propose several efficient data structures for the exact and approximate variants of the fair NN problem. Our approach works more generally for sampling uniformly from a sub-collection of sets of a given collection and can be used in a few other applications. We also develop a data structure for fair similarity search under inner product that requires nearly-linear space and exploits locality sensitive filters. The paper concludes with an experimental evaluation that highlights the unfairness of state-of-the-art NN data structures and shows the performance of our algorithms on real-world datasets.
The Application of Deep Learning in Wireless Communications of Maglev System

In the high-speed maglev transportation system, an efficient and reliable train ground communication system plays an important role in ensuring the safe operation of the train. And, the increasing demand for high-speed broadband services such as digital video services also puts forward higher requirements for wireless communication in high-speed transportation. The development of the fifth generation mobile communication (5G) is to meet the requirements of high reliability and super capacity of wireless communication system. However, the explosive growth of data, high-speed and low delay communication requirements have brought great challenges to the communication system. The organic integration of wireless communication and deep learning is expected to solve the technical problems of complex communication systems. Firstly, this paper describes the challenges of the 5G key technology - MIMO technology in channel estimation, beamforming and channel detection. Then it introduces a series of research on introducing deep learning into MIMO physical layer technology. The results show that deep learning is expected to solve the problems of MIMO technology.
Performance Variability in Zero-Shot Classification

Zero-shot classification (ZSC) is the task of learning predictors for classes not seen during training. Although the different methods in the literature are evaluated using the same class splits, little is known about their stability under different class partitions. In this work we show experimentally that ZSC performance exhibits strong variability under changing training setups. We propose the use ensemble learning as an attempt to mitigate this phenomena.
Research on Domain Variable Identification among Different Data Cubes

Large spatio-temporal Earth observation (EO) data are organized as multidimensional data cubes, which is a new paradigm for users to interact with EO data. For the multidimensional data cubes, geospatial dimensions, denoting by latitude and longitude, are indispensable. However, time series, climate and ocean data, etc. also require time axes and non-spatio-temporal axes in the n-dimensional structure. Therefore, at this level, a major challenge in data cubes interoperability concerns the harmonization of domain variables among different data cube implementations. Harmonization of data cubes from data with significantly different spatio-temporal reference systems always requires domain variable knowledge base. We propose to identify domain variables via a standardized resolver approach. In this way, cubes interoperability of different domain variables can be referred to and interoperated across multidisciplinary applications.
Multiclassifier learning for the early prediction of dementia disease progression from MCI

Recently many machine learning and deep learning prediction models have been proposed for the early detection and classification of Alzheimer's disease (AD). AD pathology causes mild cognitive impairment (MCI). The proposed study intends to develop a machine learning model that utilises a relevant subset of predictors to diagnose the progression of the disease. The conversion from MCI to stable MCI (sMCI) or progressive MCI (pMCI) is identified at early stage of onset of symptoms. The quality of existing research works lies in more early identification of disease that greatly affects subjects' recovery. This study utilised mini-mental state exam (MMSE), clinical dementia rating (CDR), estimated total intracranial volume, normalise whole brain volume, and Atlas scaling factor for constructing randomised trees and thus predicting the progression of disease stages from MCI to Alzheimer's disease that causes Dementia. The proposed model proved to give robust classification results that are sufficient for future clinical implementation.
HierCC: Hierarchical RDMA Congestion Control

RDMA has been increasingly deployed in data centers to decrease latency and CPU utilization. However, existing RDMA congestion control schemes fail to address instantaneous large queue build-up or bandwidth under-utilization associated with frequent traffic bursty. In this paper, we argue that traffic uncertainty is the essential reason that constrains data center congestion control from simultaneously achieving high throughput and deterministic latency. Since aggregated flows within the same rack are relatively long-lived, we propose HierCC, which aggregates flows destined to the same IP in a rack and hierarchically controls the rate of flows. The rate of aggregate flows between racks is controlled by a credit-based congestion control mechanism. Then the bandwidth obtained by an aggregate flow in a rack is allocated to the corresponding individual flows from that rack promptly and accurately. We evaluate HierCC using SystemC and large-scale NS3 simulations. Results indicate that HierCC can significantly mitigate buffer usage and reduce the 99th percentile FCT by up to 20% and 40% compared with HPCC and DCQCN under a realistic workload, respectively.
THE USE OF BLENDED LEARNING TECHNOLOGIES IN THE TRAINING OF FUTURE TEACHERS OF MATHEMATICS

The article highlights the peculiarities of the blended learning technology of future teachers of mathematics and ways of its application in the process of professional training of students. Due to the availability of a wide range of learning tools, blended learning forms different learning models that differ in the relationship with three components combined, namely traditional, distance and e-learning. The generalized classification of blended learning models is represented, and also the strategic stages of its implementation into higher educational institutions are offered. Furthermore, some models of blended learning in the process of methodical training of future teachers of mathematics are presented. The authors analyze the structure of online and offline forms of education, trends in the introduction of mixed forms of education. Moreover, the pedagogical expediency of using various computer technologies as an effective means of organizing blended learning is revealed. The peculiarities of the use of online programs and their role at different stages of the lesson in the process of training future mathematics teachers are described and discussed. The article provides examples of the use of tools for simultaneous viewing of educational videos, programs for assessing the level of mastery of educational material, which will be efficient to students in their future professional activities. The authors consider the views of modern experts on the use of information technology in the training of future teachers of mathematics, as well as give their examples of the introduction of blended learning in the educational process; emphasize the role of the teacher as an individual consultant in the process of acquiring knowledge through the use of elements of blended learning. In addition, the authors outline further directions for the development and implementation of blended and e-learning.&#x0D; Keywords: blended learning; technology; future teachers of mathematics; methodical training of students; online learning; offline learning; flipped learning; software.
Design and Evaluation of a Heterogeneous Lightweight Blockchain-Based Marketplace

The proposal of this paper is to introduce a low-level blockchain marketplace, which is a blockchain where participants could share its power generation and demand. To achieve this implementation in a secure way for each actor in the network, we proposed to deploy it over efficient and generic low-performance devices. Thus, they are installed as IoT devices, registering measurements each fifteen minutes, and also acting as blockchain nodes for the marketplace. Nevertheless, it is necessary that blockchain is lightweight, so it is implemented as a specific consensus protocol that allows each node to have enough time and computer requirements to act both as an IoT device and a blockchain node. This marketplace will be ruled by Smart Contracts deployed inside the blockchain. With them, it is possible to make registers for power generation and demand. This low-level marketplace could be connected to other services to execute matching algorithms from the data stored in the blockchain. Finally, a real test-bed implementation of the marketplace was tested, to confirm that it is technically feasible.
Discovering effective requirements elicitation techniques using a multivocal ethnographic framework

Effective requirements elicitation (RE) interviews are vital to the success of information systems development. Unfortunately, the factors that lead to effective RE interviews are often vaguely and contentiously delineated. In this paper, we describe our development and use of a multivocal, ethnographic framework employed to discover a rich set of critical success factors for effective RE interviews. Our interpretive framework emphasises performance ethnography along with other ethnographic techniques that are rigorously described and used to yield practical results. We then discuss and elaborate on our relevant findings that include eight critical success factors and practical lessons learned. We close with a discussion of our work and its place within the larger context of RE research in the information systems field.
A Robust Digital Speech Watermarking Based on Least Significant Bit

Watermarking is a technique to ensure the original information and to validate the digital content. Watermarking is required because of the rise in the utilization of the internet in one's everyday life. As the usage of digital content is developing quickly, there are numerous occurrences where information is uncertain. Watermarking is a procedure to conceal information for authorization reasons. Watermarking is the ideal approach to make sure about the digital content. Watermarking should be possible through different strategies. Least Significant Bit Watermarking (LSBW) strategy is one of them. Right now, pixel estimations of the image are changed over into binary. The data is covered in the bits of the pixel esteems. Watermarking consistently infers embedding of furtive signal that should be robust and imperceptible within the host data. In order to cater its robustness and imperceptibility, the energy of the watermark is accommodated to the energy of speech components.
Secure Sharing of Data using an Algorithm Namely KAN

Large amount of data is transferred through the internet, which is highly insecure. This can cause disruption of data due to attacks. To resist those attacks, the analysts are centered on the distinctive sort of systems to verify the information from assaults. Some of the techniques are AES, DES, and Digital Signatures etc. These techniques are not providing abnormal state proficient of security. So, in order to increase the efficiency level, we proposed a new method called KAN algorithm which is an extension of RSA algorithm to enhance the security of normal data by utilizing graph theory approach. This algorithm can be applicable for military basis and highly information secure system.
Cyberbullying detection on multi-modal data using pre-trained deep learning architectures

Cyberbullying is a big challenging task in the social media era. The forms of bullying are increasing with the increase of digital technologies. In the past, most of the bullying happened through text messages. Now bullies take advantage of technology, they try bullying others in different forms such as images, videos, and emojis. In this paper, we proposed an approach to identify cyberbullying on both text and image data combinations. We used RoBERTa and Xception deep learning architectures to generate word embeddings from the text data and the image respectively. LightGBM classifier is used to classify bullying and non-bullying tweets. The experiments conducted on 2100 samples of combined data of text and image. The proposed approach efficiently classifies bullying data with F1-score of 80% and outperforms as compared to existing approaches.
Face mask recognition based on MTCNN and MobileNet

Since the 2020 epidemic, wearing masks to travel has become a normal part of life. However, detecting faces with masks has been a challenging task due to the mask's obscuring of facial features. Automatic monitoring of mask wear in public places has become an important problem that needs to be addressed urgently in epidemic prevention and control. Currently, convolutional neural networks (CNNs) have been commonly used in the field of computer vision, and good results have been achieved. To this end, the paper proposes a face mask recognition method based on MTCNN and MobileNet, and experimental validation by training samples confirms the usefulness of the algorithm for recognizing whether a face is wearing a mask or not.
Effects of Virtual Reality and Music Therapy on Academic Stress Reduction Using a Mobile Application

In recent years, music therapy has proven to be an effective strategy to reduce stress. This research examines the effects of applying a social support strategy to reduce stress levels in students of higher education students, by combining music therapy with the use of immersive virtual reality, making use of a mobile application. This proposal implements a system that performs the following tasks: i) collects information on the emotions of the participants through a facial recognition module, ii) provides a relaxation experience within an animated and musicalized virtual environment, iii) measures stress levels, through the application of a psychology stress questionnaire, and iv) evaluates the effects produced on the user. Experiments were carried out with the participation of several students from Universidad de las Fuerzas Armadas-ESPE. Results show that the use of music therapy, combined with virtual reality, could be a powerful strategy to decrease stress by up to 50%.
A Comprehensive Study of AI-Based Steganalysis Techniques on Image and Text Documents

Since there is a large demand for internet applications in recent times, the data which is transmitted must be secure and safe. Transmitting data in public communication systems is not a secure option because of interception that can take place in between and improper manipulation. To achieve a solution to this problem a common process used is steganography, in which the messages are masked in a way that the message can be identified only by the receiver at the end of transmission. Unfortunately, cyber-criminals use steganography to target people or to communicate within themselves. Attackers are increasingly taking advantage of innocent people who casually open files thinking they are safe but these files contain malware. These files contain hidden codes which can be used to bring harm to our devices. Various researches are already done to identify these masked files/data. With more and more studies being released every day with varying techniques and effectiveness, it becomes hard to cope up and try to find the best technique available. To tackle this problem, in this paper, we have surveyed different Artificial Intelligence based steganalysis approaches used for document and image files.
Low-cost in-bus people counting system for the reordering of urban passenger traffic based on artificial vision and neural networks

Due to the high rate of passenger traffic in public transport in Paraguay, it has become indispensable for public transport companies to implement specific methods for the organization and management of the service. Transportation companies have begun to take into account passenger counting, which could optimize the deployment of the vehicle fleet, make a more appropriate budget distribution among the different companies, and improve the service in general. This work aims to provide a low-cost analysis system for bus passenger traffic, geo-referencing the GPS positions where people get on and off the bus, also adding data based on parameters such as weather, temperature and humidity, among others. The counting system consists of a Jetson Nano running a MobileNet V2 SSD network for person detection and a tracking algorithm in conjunction with a CSI camera placed in a zenithal position to obtain the images. Data acquisition and network training were performed entirely on the Jetson Nano. The results show that the developed system can process video at a resolution of 640x480 pixels at 30 fps, and can count passengers in different lighting conditions with an accuracy of 94%.
Overview on Machine Learning in Image Compression Techniques

The demand for data compression is increasing rapidly as modern communication technologies advance. This paper provides a general overview of compression principles, compression classes, and image compression algorithms. Image compression is the answer to issues related to virtual photograph transmission and Large quantities of information are saved for virtual images. Image compression is used in a variety of applications including satellite remote sensing, television broadcasting, and other long distance communications. Satellite images, medical images, documents, and photos require image storage. Image compression is critical for these applications. Machine learning principles are used in every horizon and can be used for image compression also. In another point of view some of the test images are compressed low quality images. This article attempts to help rank one of the best and most widely used image compression algorithms and analyze the relationship between image compression and machine learning algorithms.
A Secure Digital Text Watermarking Algorithm for Portable Document Format (PDF)

Nowadays, with the rapid development of advanced technologies, an illegal copy of digital documents can be easily generated. The Portable Document Format is the most common and widely used text document on the internet. The copyright protection of these documents is a challenging task. Advanced techniques have been proposed in the past but have not delivered the expected results. These techniques are either robust or imperceptible or have a high capacity, but do not maintain the balance between all parameters. Digital watermarking has been used over the past decade to detect forgery and tampering detection, maintain copyright and authentication. This study proposes a novel approach for Portable Document Format based on document page objects. The Special objects of Portable Document Format are used for watermarking without affecting the content of the original document. The proposed technique preserves the imperceptibility and resists against formatting attacks. The watermark information is extracted with high probability, the proposed technique is robust, secured, imperceptible, and embeds 0.22 KB of the watermark in the host document.
Minimization of Cable-Net Reflector Shape Error by Target-Approaching and Procedural-Learning Method

Cable length adjustment is a critical step to minimize the shape error of cable-net reflectors. The adjustment requires tedious repetitive work, but the surface accuracy is often unsatisfactory because of inevitable operating error and modeling inaccuracy. To solve this problem, this work proposes a target-approaching and procedural-learning method. At the initial stage of the adjustment process, the target-approaching method is proposed to ensure the shape error is improved and that adjustment data are generated. Then an online prediction model is built by sensitivity analysis and least-squares support vector machine. On this basis, a multidimensional forward-backward algorithm is applied to calculate the optimal adjustment amounts in order to achieve fast convergence of adjustment accuracy. Finally, the proposed method is verified by both numerical simulation and prototype experimentation. The results show that the proposed method can well reflect the adjustment characteristics of cable-net reflectors, effectively improve shape accuracy, and greatly shorten adjustment time.