Reviewerly/exHarmony huggingface dataset:
On Information Technology Disaster Recovery and Its Relevance to Business Continuity

A technology disaster can result in a long-term interruption, a loss of reputation, monetary losses, and the incapacity to continue doing business. Although there is much information regarding disaster recovery and business continuity, there is little research on how firms might use other technology frameworks to help with information technology disaster recovery, given the maturity level of existing disaster recovery processes. There is also an inadequate understanding of organizational recovery from unforeseen events, with little or no interruption to their business continuity. Disruptions in the business world can happen anywhere and at any moment. It is hard to predict what will happen and when. It is now a legal requirement for businesses to be prepared for disasters and recovery scenarios. With an ever-increasing reliance on business processes using electronics channels, it has practically become a requirement for any company build a framework that suit them to plan for Business Continuity in case of IT catastrophe, and thus the article seeks to address.
A Novel Hybrid Approach Based on Filters to Ensure Cloud Storage Data Security

The next-generation architecture of IT organization has been planned for cloud storage. We have proposed a new Hybrid-Secure Cloud Storage (HSCS) approach to ensure data protection during auditing in the cloud. The hybrid approach consists of integrating the Schnorr Elliptical Curve (SEC) framework with the Bloom Filter (BF). Finally, the BF is added to delete redundant keys in table entries to avoid duplication. The performance metrics for SCS and HSCS compared, in this HSCS would provide greater security than SCS, including Auditing-Time, Encryption-Time, Decryption Time, and Storage-Overhead.
Adaptive Classifier Using Extreme Learning Machine for Classifying Twitter Data Streams

As the online business is being exponentially advanced from time to time, analyzing the data on the fly is the need of the hour and getting high attention from the researchers. The proliferation of a variety of data that has been generated from different kinds of devices needs to have a practically efficient algorithm for the data stream analysis. However, most of the existing data mining algorithms are widely used for the analysis of the stream of data as well which are better for static data mining and not as efficient for the data stream. Thus, to overcome this limitation, this work has proposed an adaptive classifier (algorithm) for the analysis of data stream instantly. The proposed adaptive classifier uses extreme learning machine along with the product’s attributes-based rule database to identify the popularity of the product.
Register-Like Storage Block Used as Histograms, Cluster Buffers, and Hough Transform Accumulators for HEP Trigger Systems

In high-energy physics (HEP) experiment trigger systems, block memories are utilized for various purposes, especially in binned searching algorithms. In these algorithms, the storage is demanded to perform like a large set of registers. The writing and reading operation must be performed in single clock cycle, and once an event is processed, the memory must be globally reset. These demands can be fulfilled with registers, but the cost of using registers for large memory is unaffordable. Another common requirement is the boundary coverage feature during the reading process. When a memory bin is addressed, the stored contents in the addressed bin and its neighboring bin must be output simultaneously. In this article, a register-like block storage design scheme fulfilling these requirements is described. The implementation and test results are presented. Practical applications of the register-like block storage as cluster buffers, histograms, and Hough transform accumulators are discussed.
American sign language hand gesture interpreter

For many disabled people who are dumb or deaf sign language is a way of communication, to do communication both people need to know sign language or need a translator between them, but everywhere they are not getting a translator. This project is a prototype that will work as a translator for them so that they can communicate with anyone irrespective of knowledge of sign language. With the help of gestures, we can perform many actions like human-computer interaction, gesture-controlled home appliances, and many applications that use gestures as the trigger input. In this prototype, an image is obtained using a webcam. OpenCV helps in detecting gestures from images. And on a later stage gesture is converted to text. OpenCV is a python package tool that provides support with image processing techniques.
Exploring User-Preferred Gestures for Interaction Tasks of Data Visualization on the Large Display

Interaction with graphical visualization is an important part of visual analytics. The purpose of this study is to explore how analysts will understand the using of gestures for interacting with data displayed on large screen. For this purpose, this study aims at investigating user-preferred interactions for the tasks of data visualization. To elicit gestures for 20 visualization tasks which cover all categories of interaction for visual analysis, the researcher designed a quick-response experiment based on the Wizard-of-Oz (WoZ) method. Finally, the gestures with high frequency were selected. Findings show the distance between user and the display has an impact on the elicited gestures for specific tasks. The consensus on users’ gesture proposals is also discussed. The conclusion of this research has some implications for natural interaction design for visualization.
Estimating Blockchain Using Time-Series Forecasting ARIMA

The purpose of the study is to forecast the bitcoin prices fluctuation for a month based on the last ten years. The data is secondary and collected from investing.com using the time series technique ARIMA. The findings state there are price fluctuations seen, it is increasing at a low rate. ARIMA model accurately predicts the direction of travel; nevertheless, the predicted values are consistently lower than the actual observations. AI is being used to estimate the value of bitcoins as a means of measuring the potential risk connected with blockchain technology’s financial use is being investigated further work that may be followed up based on this research.
Implementing Classical Logic in a Quantum Environment

This paper is meant to serve as an introductory guide on how to implement simple logic gates in a quantum environment. Uncompressed circuits for each statement can be found in the appendix.
PLT (Persona Logical Thinking): A Method to Generate User Requirements for Multidisciplinary Design Teams

In this paper, we present a new original method to generate user requirements, Persona Logical Thinking (PLT). Because this method combines several ways of reasoning (empathetic thinking and logical thinking), we hypothesized that it would be particularly efficient for multidisciplinary design teams. To test this assumption, we compared PLT to an empathetic method and to a logical method for generating user requirements for tablets in the education field. We organized working sessions of 60 minutes with 4 designers (from student population of engineers, industrial designers and ergonomists). Nine working sessions were conducted with a total of 36 participants (21 males and 15 females aged 25 years old on average). To evaluate the effectiveness of the three methods, we collected two variables: quantity and originality of user requirements, classified into three categories (User's Needs, Product Functions and Technical Solutions). Our results show that teams were more productive with PLT for the generation of product functions (examples: thinness, being powerful, long life battery…), including original functions (be recyclable, be resistant to chemical substances...). These preliminary results being promising for improving the innovation process, we discuss the future steps that would be necessary to validate our method.
Identification and Localization of Potato Bud Eye Based on Binocular Vision Technology

The solution to three dimensional coordinate of potato bud eye is of great significance to the improvement of seed potato cutting machine. In this study, checkerboard camera calibration method developed by Zhang Zhengyou was used to calibrate the binocular camera. On the basis of stereo correction, the potato bud eye was identified by image recognition technology and its pixel coordinate information was obtained. Then, the three-dimensional coordinate of the potato bud eye was calculated by binocular distance measurement method. The results showed that when the distance between the camera and the potato was 20 cm and the ambient light intensity was (351 lx–725 lx), the eye recognition accuracy was the highest. The absolute error and relative error of 3D coordinate measurement of potato bud eye are less than 3 mm and 5%. This study has a certain reference value for the future location of the bud eye and the improvement of the seed potato cutting machine.
Elementary Teachers Reaching a Quasi-complete Knowledge of Rational Numbers through an Online Course

There is evidence that most of the Elementary Teachers in Mexico have various conceptual deficiencies in their knowledge about rational numbers; however, the deficiencies were not the same in all the cases. So, we decided to design a non-traditional-personalized online course, constructed as an adaptative system, in which it was identified if the participant covered each one of the different conceptual approaches in various contexts. When it was identified that a conceptual approach was not covered, interactive materials and videos were presented to them that allowed them to understand what they had not covered. The aim of the course is to enable teachers to reach a quasicomplete conceptualization, whose meaning for us it is to understand the topic from different conceptual approaches in a deep way. This paper presents the structure of one module of the course, one detailed example, and results of the pilot test of this module.
2D and 3D Human Pose Estimation and Analysis Using Deep Learning

3D human pose estimate has recently been made, a novel pipeline and architecture for 3D human posture estimation in in-the-wild pictures is a challenge. The major goal of this paper is to reduce keypoint re-projection loss, allowing the proposed model to be trained using in-the-wild pictures with only 2D ground truth annotations. In proposed model, several networks are employed at various phases of the process. COCO, a publicly available dataset, was utilized to train and validate results. Proposed pipeline model consists of yolo v3 to recognize and segment a person from an image, scale the picture without distorting the aspect ratio and inferring 17 essential points of a human body using PoseNet. Further, to measure the performance difficulties are highlighted with a subset of pictures where 2D keypoint recognition isn’t doing well, and proposed model is showing better performance that HMR and PoseNet.
Software Tools to Support Visualising Systematic Literature Review

A systematic literature review is a sine qua non for performing significant research in the selected field. However, it is a complex, error-prone and time-consuming process. In order to reduce the probability of errors’ occurrence during the literature review process, as well as to make it easier for researchers to perform this process, tools that support systematic literature review (SLR) are developed. Visualisation is essential for the conducting of SLR. There is a lack of researches about the software tools that could support SLR process by means of visualisation. This paper focuses on identifying visualisation tools that are currently used in practice to support SLR process. In the paper, we present and com- pare four tools in order to provide valuable information on current visualisation trends in the context of SLR, best practices, and preferable characteristics of visualisation tools for SLR.
Feature Extraction and Fusion of Multiple Convolutional Neural Networks for Firearm Detection

In the last few years, terror activities across the world have been raised drastically. Therefore, we need a framework that can detect these terror and illegal actions automatically. In spite of various state-of-the-art deep learning algorithms, weapon detection is still a serious challenge. This work focuses on the classification and detection of guns which integrates deep fusion of feature information to generate the most discriminant feature vector. Firstly, we utilize the fully connected layers of recent deep CNN models: Inception-ResNetv2 and MobileNetv2 for feature extraction, and then we fuse these features by using concatenation operation. To acquire a more compact presentation of features and reduce the complexity of computation, we have utilized NCA. After that, we classify the images by using an SVM classifier. Finally, to detect the guns in an image, a bounding box regression module is proposed by applying LSTM. Qualitative and quantitative outcomes indicate that our framework can detect guns with huge variations in size along with rigorous occlusion. In particular, our method achieves an accuracy rate of 98.62% with a false alarm rate of 1.38% on the gun dataset which is extracted from Kaggle.
Putting a Price on Sharing: Fresh Cost Data as an Essential Ingredient of Evaluating Interlibrary Loan Services

In an age when information is abundantly available online, libraries must continuously demonstrate relevance both to users and to funders. Libraries can do this by showing that essential services are being provided to users efficiently, and at a reasonable cost. This paper examines challenges that library administrators face in evaluating collection sharing services, due to the scarcity of detailed cost information and a lack of current benchmarks against which to measure their own library's data. The authors have been involved with a multiyear project to create the OCLC Interlibrary Loan Cost Calculator, a free internet-based tool that acts as a virtual real-time collection sharing cost study. Designed in collaboration with experts from the resource sharing community and built by OCLC Research, the calculator has the potential to fill in the knowledge gaps regarding current collection sharing costs, allowing for more comprehensive evaluations of this core library service.
Social and Information Space of Student Youth in a Situation of Uncertainty

This article presents the results of the author’s study of the social and information space of student youth in a situation of uncertainty as a space of personal and professional self-determination of a student, formed in the process of online learning during the Covid-19 pandemic. The authors formulate the conclusion that in the social and information space of student youth, the professionalization of the individual is carried out, determined by its position, value-motivational attitude to future professional activities, and its subject resource. As the analysis of studies on this problem has shown, in a situation of uncertainty, the development of the subject of educational activity is not only an internal state, but also the transformation of the conditions of interaction with information sources, first of all, with teachers who mediate interaction with information, and students in remote learning mode. The authors point out that under these conditions, a qualitatively new social and information space is being created. In such a space, the goals of educational activities are realized, which anticipate possible external-objective results in the online learning mode, and changes that occur in the subject and go beyond the situations, which have already existed. In other words, the acceptance of the anticipated future as an uncertain situation, but at the same time as desirable and purposeful desire to realize the opportunities that open up in it, is a significant factor in the formation of a specialist, as well as the formation of his activity. Based on theoretical analysis, the authors stated the existence of a contradiction between the accumulated scientific facts in this area and insufficient experience in building a social and information space in a situation of uncertainty. Characterizing the methodological problems of defining the concept of “information space,” the authors note that the concept of “global” information space acts as a set of various channels of information links. In general, this is a certain stable ordering of information processes, in which, in some way, the subsystem under consideration (in this context, student youth) can take part. But in reality, a certain “effective” information space is allocated for each of the subsystems, which is a systemic informational condition for its functioning and the implementation of specific acts of communication.
An Enhanced Pixel Intensity Range-Based Reversible Data Hiding Scheme for Interpolated Images

Singh, Rama Vaish, AnkitaThis paper line ups an improved interpolation-based reversible data hiding technique (IRDH). The modified neighbor mean interpolation(MNMI) method is used for upsampling of original cover media that uses weighted average method for evaluating the estimated values of interpolated image. The proposed technique works on overlapping block division by taking advantage of spatial correlation. The pixel intensity range is divided based on concept that whenever the number of bits of estimated pixels is replaced by the secret information to be embedded, they again fall within the same range with ease of extracting the secret information and recover the original cover media without any distortion. Proposed technique discusses the cases of secret information that has yet not been discussed in existing interpolation techniques. The auxiliary data of storing secret information to be less than, greater than or equal to case is embedded in the first estimated pixel only by just replacing the fourth or fifth bit according to pixel intensity division. It removes use of location map which is an overhead. Accordingly, our proposed technique preserves the visual perceptibility of stego-image with improved embedding capacity. The experimental results indicate that proposed scheme attains PSNR greater than 36dB.
Ensemble approach for fake news classification using machine learning

During the covid 19 outbreak, fake news has grown highly, affecting people’s mental and physical health. There is a wide range of solutions for fake news classification which are machine learning-based proposed models. Research shows that the existing proposed models have less accuracy, and they are only text-based models. In our research paper, we are focused on different algorithms, and we are comparing these algorithms in our proposed model in this research paper. We are considering the title author and text in the proposed model. Based on our experiments, Logistic Regression has high accuracy, recall, and precision score values. This research paper suggests using a logistic regression model to classify fake news.
Algorithms and Human Creativity: Threats or Opportunities?A Literature Review

We explore the move from a mechanical vision of Artificial Intelligence (AI) to a systemic vision of Intelligence Augmentation (IA) (Barile et al., 2018, 2019, 2020, 2021; Navarrini, 2020; Chiriatti, 2019). AI assumes the role of empowered intelligence (IA) as it is capable of expressing a capacity for modeling integration of experiences, knowledge and emotions in conditions of strong uncertainty (Barile et al., 2021; Hagel, 2021). But in a world where the nature of machine learning is changing so rapidly, does technology empower or annihilate creativity? The aim of the paper is to draw attention to the impact that disruptive technology has on human creative processes. How might progress in AI affect Human Creativity (HC)? We propose a literature review to better understand both trends and gaps.Keywords: AI, Human Creativity, Viable Systems Approach (VSA).
A Novel Keyless Lcd-Based Method to Control the Latch

Security is the biggest concern that everyone faces when they are away from home. Satisfied with the current scenario No solution has been found for the above problem. Introduced here is an electronic locking system where Arduino Uno has a key role as processing unit Arduino microcontroller board is a member of Atmega family. A simple and important open-source tool. It is powerful to store, find, control and monitor your application. Entrance control is achieved using Arduino Uno board. This paper presents the concept of a keyless locking and unlocking system with a password. The circuit of lock consists of a PN2222A, Medium Power NPN Transistor (BD139 transistor), a 4 × 4 matrix keyboard, a solenoid lock, an LED, a SIM900D GSM module. Unauthorized access by users. Input from the 4 × 4 matrix keyboard. If the passwords you entered matched, the access is granted and the gate will open. A displaying of the false authentication message will be on Liquid Crystal Display (LCD) screen and the authorized person or the homeowner will receive an awareness message indicating that an unsolicited attempt has been made and security is not compromised. This material paper guarantees the safety of commonly available components and reduces energy consumption.
Virtual Target Penetration Test

AbstractIn the CTF offline competition, penetration test against a virtual target appears more and more frequently and is becoming more and more diversified. Compared to the CTF online competition, the introduction of penetration challenges is as simple as the web challenges, which does not require the participants to know the underlying system principles and have profound programming ability, but only requires the exploits of existing vulnerabilities, skilled use of various tools and a brain with strong learning ability. This chapter will start from how to build a smooth penetration environment, step by step explain common vulnerabilities and exploits, the basics of Windows security, combined with cases in the CTF competition, so that the reader has a clear understanding of the penetration test.
Analysis of Adversarial Jamming From a Quantum Game Theoretic Perspective

Over recent years, quantum communication systems have demarcated themselves as promising candidates for deployment in next-generation communication networks (6G and beyond). Several recent experimental demonstrations of such complex systems have been highly successful and have been instrumental in transitioning this field from the theoretical to the practical domain. In this article, we investigate the application of quantum game theory for the modeling and analysis of jamming in the context of quantum networks. We begin with a general model of jamming based on the Colonel Blotto game and generalize it to the context of quantum networks. We provide an in-depth analysis of the two-person quantum Colonel Blotto game (QCBG) in relation to classical versus classical, classical versus quantum, and quantum versus quantum strategies. We also investigate the Nash equilibria for such games via a multiagent adversarial reinforcement learning-based system. Finally, we discuss further optimizations on this model and outline several open problems for further research along these lines.
Lexicon-Based Argument Extraction from Citizen’s Petition in Arabic Language

Argument extraction is the task of identifying arguments, along with their components in text. Arguments can be usually decomposed into a petition and one or more premises justifying it. In this paper, an approach to extract the arguments has been proposed. The proposed approach based on an Arabic lexicon included the main words which play an important role in arguments extraction. Text mining classical stages have been applied with the lexicon tool. The dataset has been collected from the Citizen Affairs Department in the service departments of the capital, Baghdad, Iraq, including more than 5000 petitions. The experimental results show that the proposed approach has a (91.5%) successful ratio in arguments extraction from the collected dataset.