Reviewerly/exHarmony huggingface dataset:
Towards Prediction of Academic School of Students for Real-Time

This study presented the five predictive models to predict the student's study school based on their demographics, personal, social, and grade features. Using the Orange stable version 3.23.0 machine learning tool, the authors performed numerous experiments to identify the study place (school) of students. The dataset stored the records and attributes of two senior secondary schools in Portugal. A binary classification problem has solved using the five machine learning algorithms. Also, the training time and testing time of each classifier have been compared. After the preprocessing, 875 instances and 31 attributes were practically analyzed in the Orange workflow environment. This experiential study's findings presented five predicted models and proved that Artificial Neural Network outperformed others with providing the most accuracy of 84.7%. The second winner classifier Random Forest attained an accuracy of 83.7% with 10-folds. Increasing holdout ratios and folds significantly improved the prediction accuracies of each classifier. Also, full cross-validation (leave one out) is recommended for this dataset compared to others. Based on the testing time of 8.2 seconds, the Random Forest classifier has been suggested, and the Neural Network is recommended owing to the highest prediction accuracy. The school predictive models supported identifying the school to be aware of student situations in their academic, personal, and social life.
Comparative analysis of feature extraction and fusion for blind authentication of digital images using chroma channels

Blind authentication is one of the challenging techniques which has attracted considerable attention with the increasing security hacks on digital images. This paper evaluates different models based on feature extraction to detect digitally-altered images. Various feature-extraction methods have been investigated and compared including LBP, MSLBP, CSLBP, SLBP, WLD, LPQ, MSLPQ and DCT. Moreover, we propose a number of ways to combine a variety of features utilizing single and multi-scale representations of images. For classification, SVM is employed with different kernels to identify forged and authentic images. To evaluate the effectiveness of the investigated models, several experiments have been conducted using k-fold cross-validation and computed performance measures for two benchmark image tampering datasets (CASIA v1.0 and CASIA v2.0). Additionally, we have conducted statistical analysis for the top-ten models and the results confirmed that the best models for CASIA v1.0 and CASIA v2.0 are MSLBP-DCT and MSLPQ, respectively. Further improvements have been achieved by integrating features from the three color channels (Y, Cb and Cr) with and without feature reduction using PCA and LLP. In this case, the results show that MSLPQ-DCT achieved a better accuracy of 98.56% on CASIA v1.0 with 1020 features and MSLPQ achieved a slightly better accuracy of 97.4% on CASIA v2.0 with 1536 features.
Constructions of Binary Sequence Pairs of Length 5&lt;i&gt;q&lt;/i&gt; with Optimal Three-Level Correlation

Binary sequence pairs with optimal periodic correlation have important applications in many fields of communication systems. In this letter, four new families of binary sequence pairs are presented based on the generalized cyclotomy over Z5q, where q ≠ 5 is an odd prime. All these binary sequence pairs have optimal three-level correlation values {-1, 3}.
Prediction of Breast Cancer Using Different Machine Learning Algorithms

Breast cancer is one of the most critical medical problems in our society. It is caused by numerous factors which are ignored in our daily life. The diagnosis process is tedious and lengthy. It mainly classifies cancer into malignant or benign. Malignant cancer spreads in our body at an exponential rate and its early detection can save the life of the victim person. When several tests are conducted on the body, the mean area, clump thickness, cell shape, mean radius, all measures are evaluated for the detection of cancer. Artificial intelligence is used as the diagnostic tool for the detection of cancer by evaluating the features extracted from the MRI scan of the breast area. This paper mainly focuses on comparing the accuracy, precision, recall, and other measures of different classification algorithms in the prediction of breast cancer. The UCI repository data set is used in comparing all the algorithms. Thus, machine learning comes into the picture to solve this problem. In machine learning, previous reports and data sets are used to predict. With the help of data sets, the system is trained and then it tells whether there is any tumor growth or not in the patient. With the help of various algorithms, we are able to predict whether someone is affected or not. In this paper, we are using dynamic prediction. Dynamic prediction helps future proofs the algorithm, since if in the future, the data sets change and new features are introduced, the algorithm would be able to adjust itself with the changes. The algorithms used in this paper are decision tree, k-nearest neighbor, Naives bayes, and support vector machine. After applying the above algorithm in dynamic prediction, we found that support vector machine is the best algorithm for our data set to predict the correct result. On applying support vector machine, it predicted roundly correct result.
Analyzing Performance of Classification Algorithms in Detection of Depression from Twitter

Depression is a mental disorder that is characterized by a general mood or feeling of low self-esteem, loss of interest towards daily activities and low energy within a particular person. It is a very serious mental condition and its automatic detection through online social media platforms like Twitter could help identifying depressed individuals remotely. This paper suggests a novel method to extract tweets indicating depression using word lists. Various classification algorithms like SVM, KNN, Naive Bayes and Random Forests have been used to classify the individual tweets as to whether they indicate depression in the subject or not. Metrics like F1-Score has been used the verify and compare the results of the models using an unseen test dataset.
Supporting of Waste Management in Indonesia Using Self Organizing Map for Clustering Analysis

Abstract The amount of waste in Indonesia is continuously increasing, along with the increasing population and welfare. The government already takes various efforts to control the amount of garbage; for example, they prohibit using disposable plastic in any form. Mapping waste generation and waste composition hopefully will help the government (or local government) make a more precise policy. Therefore, this research employed a modern clustering analysis that was Self Organizing Maps (SOM). SOM is an Artificial Neural Network (ANN) that uses unsupervised learning to reduce data dimensions into two dimensions. SOM produces low-dimensional visualizations of high-dimensional data. The data of waste generation and composition were obtained from the National Waste Management Information System in 2017-2018. The variables processed were the daily average of waste per square kilometer (ton/km2), the daily average of waste per person (kg/person), and the daily average of waste per sub-district area (tons/sub-district). The researchers built the 3x3 hexagonal topology. The districts of Brebes, Buleleng, Cilacap, and Jepara were grouped into areas with high waste generation compared to other districts. Meanwhile, based on the composition of garbage, Morowali, Sinjai, and Palangkaraya districts were the districts where the composition of food waste, plastic waste, and textile waste was high. Tableau was used to visualize the result into map.
Empirical Policy Evaluation With Supergraphs

We devise algorithms for the policy evaluation problem in reinforcement learning, assuming access to a simulator and certain side information called the supergraph. Our algorithms explore backward from high-cost states to find high-value ones, in contrast to approaches that work forward from all states. While several papers have demonstrated the utility of backward exploration empirically, we conduct rigorous analyses which show that our algorithms can reduce average-case sample complexity from O(S logS) to as low as O(logS). Analytically, we adapt tools from the network science literature to provide a new methodology for reinforcement learning problems.
An ICT-based supporting system for home-nursing services

These days, the demand for home nursing in Japan has increased significantly. Along the increase, the shortages of staff and inefficiency of operations have become serious problems. This study aims to develop a system to support the operational efficiency of home nursing. As a thing being of particular importance, we focus on producing a summarized report of a patient's presenting symptom for a medical doctor with several nurse's notes that include unstructured data. A manual process of making the report imposes excessive burdens on nurses. However, the unstructured data contains crucial information, the course of symptoms. In this paper, we demonstrated an analysis of symptom courses, which includes extracting sentences about symptoms and classifying the status of symptoms. Experimental outcomes show that status classification works well, but there is room for improvement in both symptom extraction and status classification. As a future work, to improve the accuracy of symptom name extraction by human annotation and symptom classification by learning word dependencies. The symptom analysis system will contribute to support the time-consuming paperwork in home nursing.
A Sensor Fusion Method for In-Station Articulation of Train

Under the background of the traditional method of manual carriage hooking, this paper studies and validates a sensor fusion method to provide data fusion information for the automatic articulation study based on millimeter-wave radar and monocular camera sensor hardware platform. We build the train model for joint calibration, process the trace point clustering, and optimize the traditional radar Kalman filtering method which improves the stability of radar signal. After the experimental verification, the sensor fusion system in this paper is effective in such complex environment like the train station to provide effective information fusion of the foreground. Compared with the single sensor, the method greatly enhanced target carriage in front of the train for bearing awareness, for subsequent automatic articulated method ensures the real-time performance and reliability of the data source.
The Impact of Denial-of-Service Attack for Bitcoin Miners, Lisk Forgers, and a Mitigation Strategy for Lisk Forgers

Bandwidth depletion Denial-of-Service (DoS) attack can impact the propagation of a mined block in the Bitcoin blockchain network. On Bitcoin Proof-of-Work (PoW) consensus several machines try to resolve an expensive cryptographic puzzle faster than anyone else and succeed to mine a valid block. Despite a DoS attack impedes one machine to propagate its mined block allowing it to become valid for most peers, there will be several other peers to resolve the puzzle in time, hence the blockchain will continue to grow. However, from the perspective of the owner of the attacked machine, this can be critical because it will not receive a mining reward. This chapter covers such an attack in the Lisk blockchain that utilizes the Delegated Proof of Stake (DPoS) consensus mechanism. A mitigation strategy was created based on two tools that I have created allowing a delegate account to be configured in more than one node, allowing to forge a block even when one of its nodes is under DoS attack. Also, the transaction flood DoS attack is explored, and a mitigation strategy was created for a specific sidechain in the Lisk ecosystem. The mitigation strategy identifies spam transactions and rejects them to be included on the Lisk nodes transaction pool, hence they will not be propagated into the blockchain. Towards the end, I evaluated scenarios and mitigation strategies created for each attack demonstrating solutions for several scenarios.
INTRODUCTION OF THE INFORMATION AND REFERENCE SYSTEM "UNIBELL" IN THE EDUCATIONAL ENVIRONMENT INSTITUTION OF HIGHER EDUCATION

Scientific work is aimed to identifying problem areas during distance educational process and to development of services that could help of solving them. Empirical methods and methods of real-life observation were used to collect the initial data for study. Processing of the obtained data is performed on the basis of using analytical methods and methods of expert evaluations. This allowed to identify weaknesses at distance education organization and to form a list of necessary services, which development and implementation will improve the quality of distance and inpatient educational process and education situation generally. The study identified and characterized the range of internal stakeholders of the educational environment, to meet the needs of which the development of these services is aimed. The results of empirical observations are presented using of set theory. To identify priority of declared services on the range for immediate implementation were provided their detailed analysis that showed high needs of an informational reference system development for remote access to the schedule with its further integration into educational environment. The proposed system is called "UniBell". This system is the part of education project "Smart-University", the main purpose of which is to integrate Universities into modern digital environment. Also, study defined the term "Smart University" or "Smart Educational Environment" and described main functionalities, general architecture and features of separate client as well as server parts of the "UniBell" service. Furthermore, features of additional subsystems of server part management, implemented with the use of software technologies.Net, Java and the language of structured queries SQL were highlighted. The model of client-server architecture, as well as algorithms for accounting and control of the training schedule have formed the basis for the development of an existing application for the Android operating system with the ability to download it via Google Play. The analysis of educational process virtualization has identified the main factors that may affect the educational process quality in the format of distance learning. The results of the analysis have become the basis for developing the architecture and algorithm of information system "UniBell" tasked to organize remote access to the training schedule using mobile technology. Using the conceptual apparatus of set theory, the volumes of internal stakeholders are investigated have been characterized, which provides a solid basis and target audience for further research on the effectiveness of the developed system in the Lviv State University of Life Safety educational environment.
Research on human-vehicle interaction based on natural language processing

Abstract With the rapid development of intelligent networked vehicles and driverless technology, the importance of the dialogue between human and vehicle artificial intelligence has also become prominent. In the case of the driver outputting speech and converting it into text, how to correctly distinguish the driver’s intention and make the correct response is the purpose of the research. Google released the first-generation BERT (Bidirectional Encoder Representations from Transformers) training model in 2018, bringing natural language processing methods to a new way of understanding. This article analyzes the current mainstream machine learning algorithms and applies them to natural language processing (NLP) to classify text data sets. The purpose is to study various machine learning models to achieve higher accuracy of text classification. Research results not only lay the foundation for improving vehicle driver interaction, but also serve as a reference for the natural language processing part of the future car networking and driverless technology.
Secure QIM-Based Image Watermarking in JPEG Compression Domain

Previously, we proposed two secure watermarking algorithms (IM-QIM and SIM-QIM) based on the Quantization Image Modulation (QIM) technique that showed its robustness against a specific histogram attack with multiple images. Traditional QIM versions, including DM-QIM, DC-QIM, and others, are vulnerable to this attack. Our algorithms are based on a modified “correlation immune” scalar quantizer and do not introduce any correlation between the key bits and signal values. Earlier, these algorithms were implemented for grayscale images in the spatial domain. In this paper, we apply our approach in JPEG compression domain and analyze IM-QIM performance in this case. The experimental results have shown that IM-QIM maintains its security in the JPEG domain, while its robustness to typical distortions such as AGWN and JPEG compression improves, compared with spatial domain watermarking. This fact, along with the prevalence of QIM usage in the JPEG (block DCT) domain, indicates the high practical potential of the proposed algorithm.
An Optimization Method for FPGA’s Logic Gates

The paper describes a method of finding optimal sets of logic gates for the development of field-programmable gate array (FPGA). The proposed method is based on the Pareto optimality search procedure. The search procedure has a number of distinctive features. At the stage of forming various combinations of technical solutions, these solutions are filtered. This filtering allows us to initially discard unnecessary sets of logic gates. Then for each combination a subset is formed. An optimal subset is found from this subset. In the set of optimal subsets, the Pareto optimality search procedure is performed. At the stage of forming a set of solutions, the proposed method takes into account the following complex characteristics: the number of transistors, the area occupied on the chip, speed, power consumption and reliability of logic gates. The developed method was programmed in the C ++ language.
Integration of ICQ New into Business Processes Implemented on the Basis of the 1C: Enterprise

In this paper, ways to integrate chat-bots into corporate processes of enterprises are investigated. The subject of the research is the ICQ New messenger's chat-bot, which is integrated into the configuration of 1C: Enterprise during the research. The paper discusses the implementation of ICQ bot in the corporate structure of the company, as well as various features when interacting with the 1C: Enterprise's platform, such as recording the message history between the chat-bot and the messenger user, notification of system actions using the bot, ability to integrate the chat-bot in other configurations, etc.
Flipping the Script: A Sociotechnical Approach to Platforms and Unanticipated Uses

Social media platforms display unique qualities that afford unanticipated uses and challenge the application of popular human-centered evaluations and interpretations. This observation, coupled with a rise in unanticipated uses, demonstrates that the design, function, and use of platforms are best treated through a sociotechnical lens.
A Study of Guide Interpretation of Haihunhou Pavilion in Nanchang in the Age of Artificial Intelligence

Since the twenty-first century, against the backdrop of fast development in natural language processing, computer science, cloud computing, big data, and artificial intelligence, technology integrates with translation practice deeply. With the further development of artificial intelligence technologies, its influence in all walks of life has gradually increased. In this paper, the author explores the ways machine interpretation, combined with artificial interpretation, is applied to guide interpretation of Haihunhou Pavilion in Nanchang.
Cyber Security Management Model for Critical Infrastructure and Improving the Security Level on Transferring Digital Data

Cyber security is the most important part of our technologically dependent lives today. Government institutions, financial firms, public and private services, nuclear power stations, power grid providers, water supplies or wastewater disposal corporations use information technology in their day-to-day activities. Anything that uses technology is focused on connectivity and computer networks, which ensures that it relies on cyber security. Every year, the public and private sectors spend millions of dollars on technology, protection tools and hardware that can improve cyber security within their businesses, but are still vulnerable. The key problem with this situation is that data defense is now generally viewed as a technological component or technology that can be quickly introduced within the enterprise, and this application would ensure cyber protection. This mentality needs to change, because cyber security is more than mere technology nowadays. This post presents the taxonomy of critical infrastructure threats, analyses attack vectors and attack tactics used to destroy critical infrastructure, as well as the most frequent cyber security failures that companies create in the area of cyber security while attempting to make themselves safer from vulnerabilities. The main purpose of this article is to include technical elements of the cyber security management model that can be used to ensure the protection of sensitive infrastructure in an enterprise or business. The network protection management model discussed in this article is examined from a management viewpoint and does not discuss the technical issues and products used to secure sensitive infrastructure from cyber security threats and vulnerabilities.
The Adaptive Compression System for Historical Data of Power Load

In view of the fact that the traditional equal interval data sampling method cannot achieve the goal of on-demand compression, this paper designs an adaptive compression system for historical data of power load, which uses the method of combining the average variation of power load data with distortion to compress the data, so as to reduce the amount of data without losing the amount of information, and reduce the burden of later data cleaning and data processing.
Performance Evaluation of Conventional and Systematic IT Services Automation

Task automation and configuration systems have been vastly used in this rapidly growing industry of cloud computing which has increased the number of instances. In the meantime, some small- and medium-scale businesses are still using conventional methods of automation, which uses scripting languages to automate their repetitive tasks and software installations. This paper explores the evaluation and resource utilization of conventional automation, namely shell scripting with a systematic automation system (Ansible). The performance parameters of both systems in terms of automation deployment time, CPU usage, Memory usage, and network status will be evaluated. The scenario has been built using a virtualized environment with the VMware workstation hypervisor. CentOS distribution of Linux has been used for both server and client nodes in the topology. Findings and scenarios in this paper are based on real-world IT tasks that are being automated to reduce the time and make the job easily done in a working environment by an IT administrator.