Reviewerly/exHarmony huggingface dataset:
Implementation of Naïve Bayes Classifier-based Machine Learning to Predict and Classify New Students at Matana University

Abstract Data is growing fast, triggered by the wider use of information technology (IT) by the public or organizations. In line with data growth, data processing is also developing. One of them is data mining. Data mining is very necessary in business to obtain the information that needed to make business strategy such as marketing strategy. At Matana University, the Marketing Department needs to utilize data mining to optimize the achievement of its targets, particularly to predict and classify the prospective student data. Current data mining processing machines already have the high performances with large storage capacities. To improve the performance of data mining processing machines, machine learning is applied. The machine becomes intelligent and able to learn from the provided data. The application of machine learning to predict and classify new students is based on supervised learning by applying the Naïve Bayes Classifier (NBC) algorithm. The data used is the data of prospective students who have registered at Matana University. Machine learning is built, using the Python programming language. The result of the application has an accuracy of 0.73 (73%) and very helpful to the head of marketing in making marketing strategies. In the future, several developments can be done such as using other algorithms, accessed by smartphone, using dashboards in visualization, or adding data attributes such as parental income, religion, hobby, future goal and so on.
InstaVis: Visualizing Clusters of Instagram Message Feeds

We provide a method for visualizing the information associated with the clusters used for topic modeling of Instagram Message Feeds. For this purpose, a series of interactive dashboards are used to determine the right number of clusters and a suitable interpretation of each cluster. These extend previous approaches for regular text documents and focus on including specific information in Instagram feeds such as hashtags and linking structure.
Novel Initialization Strategy for K-modes Clustering Algorithm

K-modes is a method that is computationally efficient toZafar, Aizan cluster categorical data. To perform a better clustering, an appropriate selection of initial modes is required by K-modes clustering algorithm. Hence, selecting an initial mode for K-mode clustering algorithmSwarupa Rani, K. plays a crucial role in the performance of clustering. Currently, for numerical data, different initialization methods exist, but we cannot use those initialization methods for categorical data due to lack of geometry. In this paper, we proposed and adopted density parameter-based method for selecting an initial cluster mode integrated with the traditional K-modes clustering algorithm, leading to more accurate cluster results. Our proposed method selects an initial cluster mode so that it will be closer to the final cluster mode. The experiment has been carried out on different benchmark datasets and compared with the traditional methods.
Combination of Thermal and sRGB imaging Techniques for Advanced Surveillance System

Surveillance is described as close observation; this term is used mostly when it comes to security observations and recording. Nowadays in our daily life we see the need of security rising up with the constant increase in the world of technology and features. On the other hand, the industries and firms continuously face threats and are pushed to a situation of seeking help for their safety. Also, that there are various blind spots in the regular security cam even the operator is not in a position to identify an emergency or any such need. This work is focused on providing clarity to such issues to the respective personal. It might be an industry, an office workspace, a supermarket or even a house. This concept deals with the enhancement of the surveillance system by infusing two different frames of Thermal and sRGB obtained input and give the output of noise reduced, enhanced and more visible image.
Motives for the New Disruptive Technologies

This literature review presents a number of motives and opportunities for Supply Chain 4.0 transition with underlying pillars in focus that are IoT, AI, big data, and blockchain technology. All technologies and systems are described briefly concerning the existing literature, with an extensive focus on blockchain technology. It becomes apparent that we are standing in front of a massive supply chain transition, where connectivity and digital networks will create competitive advantages. Aspects like efficiency, traceability, visibility, transparency, auditability, and flexibility will play significant roles for organisations and their ability to successfully adapt to future supply chain transformations.
Blockchain technology in Germany: An excerpt of real use cases in logistics industry

Purpose: Due to the large number of intermediaries in logistics networks there is avariety of possible failures, frictions and waste of time and money among the logisticsprocess chain. With the ability to provide data securely in near real-time, liketrack and tracing data of goods to every participant, the Blockchain-Technology(BCT) can help to solve these problems. Methodology: A structured literature analysis has been executed by using the databasesWeb of Science and Science Direct to find out current intermediary's functionsand real use cases. In addition, available other sources, like manufacturer websites,blog entries or whitepapers are searched for specific blockchain-based applicationsinvented and used by German companies. The identified use cases are then qualitativelyanalyzed. Findings: The first results showed that most of the business cases are still in the conceptphase or are merely ideas how the BCT could solve existing problems. Additionally,we got results on the distribution of applications and economic benefits alongthe logistics chain. Furthermore, important conclusions on implementation problemscan be derived from this. Originality: In order to maintain Germany's top economic position, it is necessaryto push ahead with the adaption of the BCT. Our analysis contains first results in thearea of real blockchain use cases of German companies. Initial comparisons betweencurrently used and blockchain-based logistics networks are also possible.
RIVAChain: Blockchain-based Integrity Verification for File Transfers

File transfer integrity verification is used to detect silent data corruption by calculating and comparing the checksum of files using secure hash functions, such as SHA-256. However, it incurs significant performance overhead due to I/O and compute-intensive checksum calculation process. In this paper, we present blockchain-based ledger architecture to store the checksum of frequently accessed scientific datasets to minimize the overhead of integrity verification. In the proposed architecture, the checksum of files is calculated and pushed to a private blockchain when they are first created such that future transfers will not require data source to recalculate checksum. As scientific datasets are typically generated in one location (e.g., observatory) and streamed to many geographically distributed locations to enable collaboration, eliminating checksum calculation for data sources will save a significant amount of resource consumption. Moreover, we find that blockchain-based integrity verification reduces transfer time by up to 50% when data source is the bottleneck in the integrity verification process. Finally, we show that private blockchains can scale to thousands of transactions per second thus they are better fit for scientific applications in which data generation rate can easily outpace the transaction confirmation rates of public blockchains.
Temperature and Short- wave Irradiation Trends in Ikogosi Climatic Change Pattern

Abstract Every living or non-living thing has its unique temperature consequent of absorption of electromagnetic radiation, light. The source of this molecular kinetic is Solar energy incident on Earth’s surface, the sole supplier of life on the planet. This phenomenon markedly determines the climatic conditions in our habitat. Atypical of previous much-required characteristic balance in nature, myriads of surface processes, ranging from evaporation, photosynthesis and even terrestrial carbon uptake effects are becoming altered in nature. These effects on long-term timing of events defines global warming, it distinguishes the diurnal from seasonal course of surface temperatures and shortwave radiation which contains larger quantity of energy and longwave radiation which holds less amount of energy. Earth’s emitted longwave radiation, also has major practical implications on solar energy technologies, agricultural productivity, profound environmental, societal, and economic implications. There is cumulative evidence that the volume of solar radiation incident on the Earth’s surface is not constant but experiences substantial decadal variations. In view of the certainty of unpredictability of these climatic changes, a positive recourse lies in utilizing these enormous natural forces to solve Man’s energy crisis challenge. Thus, this study uses meteorological data from MERRE to investigate the prospect of shortwave radiation in Ikogosi SW Nigeria and its implications technologically and environmentally in the future of energy prospecting first locally, and on a spatial scale.
Investigating the basis of memory-based effects on common ground

Much previous research has investigated the effect of domain-general memory processes on production and comprehension in conversation. In this paper, we present a paradigm in which common ground targets are kept consistent between a participant and two different speakers, and demonstrate a speaker effect that draws participants’ attention away from the common ground target in Experiment 1. Further, we hypothesise that one important factor that promotes speaker identity as a cue for memory processes is that speakers are in conversational partners’ shared attention at the memory coding phase. In Experiments 2 and 3, we demonstrate a non-speaker cue can give rise to a memory-based interference effect when the relevant contextual information is in shared attention but not when it is not. Our results provide an important insight into the interplay of domain-general memory mechanisms and domain specific biases for shared attention.
A Scanner Jitter Test Method of CR System Based on Improved Hough Transform Algorithm

Abstract Computed radiology system (CR) test technology has been applied in the testing of industrial welding defect after the promulgation and implementation of digital radiographic imaging test standards. The performance of CR system directly affects the testing rate of welding defects. Therefore, testing and evaluating the performance of CR system becomes the premise and guarantee for the application of CR system. In this paper, based on the testing image of CR phantom and the simulation of scanner dither results, an algorithm combining fault-tolerant voting and improved point-based hoof transform is proposed to test the performance index of scanner dither in CR system. Experimental results show that the proposed algorithm can test not only the row and column dithering, but also the subtle arc dithering. Compared with standard Hough transform algorithm, this algorithm has higher accuracy and can avoid the influence of other image indicators around the T-shaped image quality indicator used in CR phantom.
Intelligent system for fault detection of phase failure and temperature

Abstract Recently, most countries began utilizing Electronic governance (EG), which is the application of intelligence technique (IT) for delivering government services, exchange information, communicate transactions, and integrate various stand-alone systems between governments and citizen. The lack of electricity (from unstable power supply) tends to severely interrupt e-governance, as EG needs power in 24 hours per day. The phase failure, over-voltage, and frequency instability can result in the lack of electricity. This paper details the design and implementation of an intelligent fault detection system that controls the feeding power for IT equipment by calling the engineer or utilizing another power source. The system was implemented based on the Global System for Mobile communication (GSM)-sim900and Arduino microcontroller, where the Arduino code is used to implement the software. The result show that the system will try feeding the IT system the power via other power sources line until the service engineers arrive to address the problems. The intelligent system improves the reliability and stability of EG, which improves its performance and the delivery of public services to citizens.
Fuzzy C-Means Based Synthesis of Ion-Exchange Materials

The presented article is devoted to the synthesis of sulfocationites with simplified technology and with improved properties. For this purpose, polymer wastes taken in different proportions were mechanochemically modified with polyvinyl chloride. Samples of polystyrene taken from the interior walls of the household “ORSK” refrigerator were used as polymer waste. The extracted polymer samples were first cleaned, washed, crushed and then some of their parameters (molecular weight, density, solubility, swelling rate, chemical resistance) were determined. The process of mechanochemical modification of polymers was carried out at a temperature of 150–160 °C in a wide range of shear strain. The rheological properties of the polymer alloy were studied to confirm the homogeneity of the obtained polymer mixtures. Ion exchange resins were synthesized by sulfonation on the basis of a polymer mixture obtained with mechanochemical method. Using anhydrous AlCl3 as a catalyst, the sulfonation process was carried out with the concentrated sulfuric acid at different temperatures and times. Optimal conditions for this process have been determined. In order to confirm the course of mechanochemical modification and sulfonation process, IR spectrum of both the primary components and the modified composition, as well as sulfocationites were taken. Satisfactory results were obtained by studying the main properties of the obtained sulfocationites such as swelling coefficient, static and dynamic exchange capacity, mechanical resistance, and the use of ion exchange resins in desalination of water was proposed.
WITHDRAWN: Highly imperceptible data hiding technique using MSB in the grayscale image

Security of confidential information during communication in the network has been the primary concern. Therefore, data hiding techniques have become the focal point among the researchers. In this regard, to keep the communication secret, watermarking and steganography are the two most important fields of data concealing. Image steganography techniques embed the secret data bits in the image's pixels to detour attention of outsiders. In this paper, an improved data hiding technique is proposed using the XOR features of the cover image (CI) pixels to conceal the secret data bits in the least significant bit (LSB). During embedding, the most significant bits (MSBs) of the pixels are partitioned into two different segments. Next, using these segments and the embedding bits, the stego-image pixels are obtained. The comparative analysis is done with various image steganography techniques to demonstrate that the proposed work offers better imperceptibility and capacity.
Poisoning attack of the training dataset for on-line signature authentication using the perceptron

Abstract The study of the dependences of the effectiveness of a poisoning attack for the task of on-line signature authentication using a perceptron to make a decision. The dependences on the structure of the neural network, the volume of poisoning sets and the noise of the training sample were studied. Conclusions are drawn on the lack of the ability to detect such attacks by changing the overall accuracy of the system.
Student Perceptions of Augmented Reality (AR) Media in Calculus Courses

Abstract Learning media plays an important role in the learning process. The development of technology and science also has an impact on the development of learning media. One of the learning media developments that are currently still new is learning media using Augmented Reality. Augmented Reality is an application that combines the real world with the virtual world in two-dimensional or three-dimensional form which is projected in the real environment at the same time. This research is a descriptive study conducted by using a questionnaire to students. The descriptive analysis in this study examines students’ perceptions of Augmented Reality media in Calculus Learning. Augmented Reality media response in terms of aspects of Media Design, Material Content and Benefits. The results obtained by an average Augmented Reality media design is 85.7 with a decent category, the average Augmented Reality Media Material Content is 83 with a decent category and the average aspect of benefits is 83.5 with a decent category. Based on the rearata, the score for the observation aspect of AR learning media is 84.07 with a decent category. However, many improvements are needed, including a reduction in application capacity and 3-dimensional display.
Characterization of Elevated Tumor Markers in Diagnosis of HCC Using Data Mining Methods

Hepatocellular carcinoma (HCC) is an abnormal condition of human liver which is diagnosed at a very advanced stage. The disease is liver disorder which can be predicted after series of clinical and laboratory and imaging studies. Hepatocellular carcinoma is the most malignant tumors which is the major cause of death and requires to be treated at early stage. The prognosis of liver disease is the reflection of both tumor characteristics like tumor size, location and tumor biology along with the degree of underlying resection. Healthcare domain generates huge data which is very complex and vast. The data contains many hidden parameters and patterns which is useful in predicting the disease. Data mining helps in recognizing these hidden patterns and arriving at the diagnosis of the disease. In this chapter the authors are concentrating on HCC tumor makers. The aim of the study is to use data mining techniques to predict the presence of tumor markers and their contribution in HCC Progression.
Global trends in library web-archives

The need for studying and promoting web-archiving for longterm information preservation and accessibility in future is substantiated. The existing technologies of web-archiving are specified and the problems related to the web dynamic character, errors, content complexity, are revealed. Successful experience in the world libraries’ web-archiving is discussed (selection, search, description technologies, access terms, etc.). The study findings demonstrate that web-archives are selected to supplement the libraries’ digital collections on hot topics, like COVID-19, or to meet the demands of specific user groups. For the purpose of cultural heritage preservation, the national libraries often focus of acquiring web-sites by the domains in the corresponding country. The university libraries focus on acquiring web-archives that meet research and educational demands of their users; and public libraries prefer the resources of interest to their local community. The findings may be used by world libraries for developing their digital collections.
Research on Face Aging Synthesis Based on Hybrid Domain Attention Mechanism GAN Network

Facial aging synthesis refers to the rendering of young people’s facial images from an aesthetic point of view by adding wrinkles, eye bags, stains, or changing hair color, so that they can finally show the effect of natural facial aging. Facial aging synthesis needs to consider both the authenticity of the aging effect and the consistency of identity information. The authenticity of the aging effect is based on the accurate expression of aging characteristics and the clarity of aging details. The consistency of identity information means that during the aging process, the aging face needs to maintain a certain similarity with the original young face in terms of feature information. Based on the above two issues and the instability of GAN training, this paper proposes a face aging method based on the hybrid domain attention mechanism to generate an anti-network. A large number of experiments show that this method can synthesize aging human face while ensuring the aging effect and the requirements of identity information.
Methods of Detecting Image forgery using convolutional neural network

Abstract Image Forgery means manipulation of the digital image so as to hide the necessary information or to convert the image into something which can be used for illegal mo-tives.Differentiating between an original image and a tampered image is a strenuous work for a human being. Machines can do that easily for large amount of data with the help of many artificial intelligence technique and mathematical approaches that are used to develop a system which will help in recognizing whether the image is tampered or not. In this paper Various models like CNN , Hashing , DCT, DWT will be discussed.This paper also discussed about the implementation of CNN done on a data set to figure out a model which is best suited for the development of an image forgery detection system.
Novel Hybrid GWO-WOA and BAT-PSO Algorithms for Solving Design Optimization Problems

This paper aims at the design and development of two hybrid nature inspired algorithms based on Grey Wolf Optimizer and Whale Optimization Algorithm (GWOWOA) and Binary Bat Optimization Algorithm and Particle Swarm Optimization Algorithm (BATPSO). Hybridization is a useful method to enhance the performance of these algorithms. The GWO algorithm is easy to fall into local optimum especially when it is used in the high-dimensional data. WOA algorithm experiences relatively low convergence precision and poor rate of convergence when it is applied in complex optimization problems. In this paper we embed bubble-net foraging method in WOA with that of prey encircling method in GWO. It has been observed experimentally that than just updating position vectors with respect to the three best fitness solutions, we were able to achieve faster convergence and better global optimum in most cases. In BATPSO, both algorithms are integrated and run in parallel and they perform a comparison between both minimum fitness function at each iteration. According to the observation, this greedy search algorithm in BAT optimization works best in higher values; however, finds it difficult in finding the global minimum as it reaches lower values; especially fractional fitness value. PSO is based on element-wise pos[i,j] search and updating the velocity to converge to a global minimum. The proposed hybrid algorithms are bench-marked using a set of 23 classical benchmark functions employed to test different characteristics of hybrid optimizers. The paper also performs solving two classical engineering design problems - Cantilever beam design and the multiple disc clutch brake problem. The results of the fitness functions prove that the proposed hybrid algorithms are able to produce better or very competitive output with respect to improved exploration, local optima avoidance, exploitation and convergence. All these hybrid algorithms find superior optimal designs for quintessential engineering problems engaged, showcasing that these algorithms are capable of solving constrained complex problems with diverse search spaces.