Reviewerly/exHarmony huggingface dataset:
Intersectionality and Incremental Value: What Combination(s) of Student Attributes Lead to the Most Effective Adaptations of the Learning Environment?

Students can be placed in more than one category at the start, middle, and end of their educational journey. These categories can be based on demographics (age, gender, sex, minority, disability, ethnicity), on behavior (procrastination, struggle, frustrated guessing, pathological re-reading), on individual attributes (help-seeking, locus of control, time management, optimism), on community (internet access, setting, average education and income), and on academic factors (previous grades and degrees). These categories are frequently used by faculty, designers, and leadership to seek a better understanding of students and their needs with the goal to personalize or adapt the learning environment in the hopes of leading to more effective learning and more successful student outcomes. In these analyses we seek to determine the relative value of different student categories – and how these can be combined to result in the most effective educational process. We find ourselves asking what attributes matter the most – and which interact with each other to increase or reduce the amount of relative value. It is worth noting that several of the categories – while they play a large role in our students’ holistic selves – are both highly sensitive (frequently protected) and static. If we can approach or match their value (educationally) in other categories which are less sensitive or more changeable, that will be a positive result. Because, while these attributes play a role in who the students are, they need not play a role in how the students are taught.
Questioning activity of informatics teachers and authors of school textbooks

The article examines the questioning activity, firstly, of teachers of informatics, and secondly, the authors of school textbooks on informatics. For the first case, some methodological techniques are presented that contribute to an increase in the degree of variety of questions that sound in a school or student audience. For the second case, the analysis of the question-task (QT) subsystem of the educational book containing the sections “Questions and Tasks” and “Tasks and Exercises” is carried out. The purpose of this part of the work is to introduce a quantitative characteristic of the question-task subsystem, which reflects the stylistic features of the work of the authors of textbooks and, therefore, can be attributed to the digital trail of the work of the group of authors. With regard to the organization of the work of students, the formation of their individual educational trajectories, a similar digital trail can play the role of one of the characteristics of the digital profile of the student. In the work, B. Bloom’s goal-setting technology is used to consider the questioning activity, and, in particular, the widespread in the literature misconception about the strong verbal predetermination of the types of educational tasks is criticized. This remark is true for Russian-language educational texts.
Secure Multi-tenant Design for Cloud Computing Environment

Multi-tenancy is one of the most important elements of cloud computing environment due to salability and economic benefit it offers for both cloud service provider (CSP) and end user. As resources are shared, it induces security and privacy issues and tenant dependent on cloud service provider to assign trustworthy co-tenants. However, CSP maximize its resource utilization by allowing maximum co-tenancy irrespective of the behaviors of tenants. Subsequently techniques like Reputation management based design aid in identifying good and malicious tenants. However, the state-of-art model is not efficient when behavior of malicious tenant changes rapidly. Addressing the said research issue, a secure multi-tenant (SMT) design using a modified reputation management model is proposed which handles dynamic behavior of tenants efficiently for cloud computing environment. Experiments are conducted using TPCC benchmark shows significant performance in terms of latency and throughput by SMT over state-of-art model.
Blockchain

Blockchain is an emerging technology of the new generation. Safety and protection of the data have been the prime concern of people. Digitalization has paved the way for the generation of trillions of data every second. With these developing lives of people, blockchain is the solution. The authors discuss the applications of blockchain in various aspects of life along with the introduction of digital currency. The means and norms are considered for digital money transfer and generation of end-to-end encrypted code for the sake of high-end security. They discuss the framework that can be used by the organizations to develop a new form of the internal network. Finally, the suggestion for future work and development along with all the cons is shown.
Introduction to Intellectual Property: Cases and Questions (Fall 2021)

This open access textbook for a survey course on intellectual property law contains 28 chapters, each with introductory material, learning outcomes, and one or two judicial opinions. Each opinion is accompanied by a set of questions to focus the reading and guide further discussion. This set of materials is designed for instructors who seek to have a discussion on one or two cases per class session, supplemented by lectures and active-learning activities.
Analysis of Resource Management and Security Management in Cloud Computing Environment

This paper elaborates the concept of Cloud Computing towards the Resource Management and Security issues. In current scenario most of the people tend towards the cloud computing because of its enormous usage and many features. This paper gives descriptive information. Cloud Computing gives and access to the applications. It enables users to setup, design and personalize business usance. The network or Internet refers the Cloud Computing. The Cloud means the information which is present at remote place where users data is saved. This paper gives an information about the management of the cloud computing. The cloud brokerage suppliers manage the solution and their fulfilment. The main resources such as load balance, storage, production, backups, capacity and utilization etc. The main concern in cloud computing is security. Data should store the encrypted form so others can’t read or modify data. The cloud computing service provider should give the protection of the user’s sensitive data.
Committee of NAS-based models

Network Architecture Search (NAS) has achieved impressive results and generated models comparable with humans' classifications. Automating the definition of a neural architecture reduces the need for expert work efforts and mitigates human bias from architecture design. NAS techniques usually consist of an algorithm to search for the best architecture in a predetermined space of parameters or functions. Due to the number of deep neural architectures' parameters, this search space includes millions of parameters, which makes NAS a cost procedure and may lead the search to overfit the training set. To reduce NAS search spaces' complexity and still obtain competitive results, we propose CoNAS, a committee of NAS-based models, by restricting the search spaces to perform Differentiable ARchiTecture Search (DARTS). Our results point to improved accuracy over DARTS on CIFAR-10, training the networks from scratch. and Imagnette, using a transfer learning approach.
Observer-Based Decentralized Event-Triggered Neuro-Adaptive Controller for Complex Uncertain Affine Nonlinear Systems

In this paper, an observer-based decentralized event-triggered neuro-adaptive controller (DETNAC) is presented for complex uncertain nonlinear systems. Novelty of the study lies in the construction of the scheme and observer-based control design which uses artificial neural network (ANN), machine learning (polynomial regression), and nominal system dynamics along with Event-triggering. Event-triggering is based on actual system's performance parameter (tracking error) and although multiple uncertainties are considered but online estimation using advance techniques makes controller much robust and efficient. Proposed method not only provide estimation and tracking performance, but its two tunable gains provide filtering effect which helps to avoid transient high frequency oscillations. Lyapunov analysis is used for stability analysis and to develop event-triggering condition. Efficacy of the controller is demonstrated using a nonlinear numerical example of a chaotic complex system.
Weaklier-Supervised Semantic Segmentation with Pyramid Scene Parsing Network

Semantic image segmentation is the essential task of computer vision. It requires dividing visual input into different meaningful interpretable categories. In this work image attribution and segmentation approach is proposed. It can identify complex objects present in an image. The proposed model starts with superpixelization using Simple Linear Iterative Clustering (SLIC). A Multi Heat Map Slices Fusion model (MSF) produces an object seed heat map, and a Saliency Edge Colour Texture (SECT) model generates pixel-level annotations. Lastly, the PSPNet model for developing the final semantic segmentation of the object. The proposed model was implemented, and compared with the earlier work, it excelled the performance score.
The Effectiveness of Information Organization and Authority Control

Information organization is a process of describing information sources and access points making the easy access to materials. Practicing authority control when organizing information is vital to guide proper information searching which is needed in the modern web era. The development of information organization is discussed from the period of Panizi, Cutter, Lubetzky and development of AACR up to RDA, FRBR model. Evolution of authority control education was also presented. The main purpose of this chapter is to share best practices followed at OUSL library and to emphasize its effectiveness among LIS professionals and LIS educators who are in the field. The chapter emphasizes the effectiveness of practicing subject analysis and authority control and how it is done by the library professionals, maintaining three authority files. Finally, the author emphasizes the necessity of curriculum changes at Master's level and changes needed for recruitment procedure of academic librarians. Compilation of national subject heading list (NSAC) is also emphasized and recommended.
Cross-Media and Multilingual Image Understanding Method Based on Attention Mechanism

Different types of media data are represented by the underlying features of different dimensions and different attributes, resulting in heterogeneity and incomparability; on the other hand, in order to use the underlying feature information extracted from cross-media, additional information must be obtained from different styles. There is a compromise between the difference and the semantic ambiguity obtained only from the bottom layer. These problems lead to traditional feature learning methods not suitable for cross-media analysis. This paper proposes a neural network structure that combines feature extraction and context semantics and introduces a new attention mechanism. Tests on experimental data show that the network structure proposed in this paper can obtain a better BLEU score than traditional methods.
Sofware Quality Prediction: An Investigation Based on Artificial Intelligence Techniques for Object-Oriented Applications

Exploring the relationship between Object Oriented software metrics and predicting the accuracy rate in defect prediction using the Chidamber & Kemerer (CK) metrics suite are the main purposes of this study. For these purposes, eleven machine learning (ML) techniques were analyzed to predict models and estimate reliability for eight open-source projects. Therefore, evaluating the relation between CK metrics and defect proneness was determined. Moreover, the performances of eleven machine learning techniques were compared to find the best technique for determining defect prone classes in Object Oriented software. The techniques were analyzed using Weka and RapidMiner tools and were validated using a tenfold cross-validation technique with different kernel types and iterations. Also, Bayesian belief’s network forms show which metrics are being the primary estimators for reliability. Due to the imbalanced nature of the dataset, receiver operating characteristic (ROC) analysis was used. The accuracy of the projects was evaluated in terms of precision, recall, accuracy, area under the curve (AUC) and mean absolute error (MAE). Our analyses have shown that Random Forest, Bagging and Nearest Neighbors techniques are good prediction models with AUC values. The least effective model is Support Vector Machines (SVM).
Research on Multi-perception Data Analysis Model for Power Grid Emergency Services

The power grid disaster response system is an important part of the power grid, and the multi-perception data is an important basis for data perception and in-depth decision-making of the power grid disaster situation. Through the establishment of the multi-perception data analysis model, it can meet the needs of the grid emergency business to deal with various emergencies quickly, efficiently, accurately and flexibly.
A Visionary Approach to Smart Voting System

With the emergence of COVID-19 as a global pandemic, the need for an online voting system is becoming appallingly evident in India. Unfortunately, India still suffers from a flawed electoral system in today's scenario. Ballot rigging, hacking of the EVM (Electronic voting machine), election manipulation, and polling booth capturing are the significant issues in this voting system. To avert such a costly predicament in the future, many countries are currently experimenting with blockchain-based voting systems. However, there are also significant drawbacks to this method. Consequently, our paper proposes a novel online voting system based on hash graph technology. The hash graph encryption method is a superior version of blockchain encryption and eradicates a few drawbacks of blockchain. This system preserves participant's anonymity while still being open to public inspection. Voters are authenticated using their Voter Id, Aadhaar Card Number, and face recognition. Furthermore, JWT Authentication is implemented to enhance the security of the login portal. Additionally, the voters can also assure their cast vote using the highly encrypted unique ID generated by our system. Besides that, the voter data is stored in a highly secured database. Furthermore, homomorphic encryption is used to store the votes and assist in counting the vote securely. Finally, it is also equipped with a chatbot that works as a support to the voters. In conclusion, this paper presents an in-depth evaluation of the scheme that successfully demonstrates its effectiveness in achieving an end-to-end verifiable online voting system.
Online Review Content Moderation Using Natural Language Processing and Machine Learning Methods : 2021 Systems and Information Engineering Design Symposium (SIEDS)

With the ubiquity of Internet-based words-of-mouth to inform decisions on various products and services, people have become reliant on the authenticity of website reviews. These reviews may be manually evaluated for publishability onto a website, however increasing volumes of user-submitted content may strain a website’s resources for accurate content moderation. Recognizing the important for patients to receive authentic reviews of cosmetic surgery procedures, we considered a corpus of 523,564 user-submitted reviews to the RealSelf.com website spanning the dates of 2018-01-01 through 2020-05-31. Prior binary classifications of "published" or "unpublished" were applied to these reviews by the RealSelf content moderation team. Textual and behavioral machine learning models were developed in this study to predict the classification of RealSelf’s reviews. An ensemble model, constructed from the top-performing textual and behavioral models in this study, was found to have a classification accuracy of 82.9 percent.
Pattern Recognition Development in Bulgarian Academy of Sciences

The development of Pattern Recognition (PR) theory in Bulgarian Academy of Sciences (BAS) is traced out from 1970. The major part of the activity has been carried out at the Institute of Information and Communication Technologies (IICT). Due to a few administrative transformations IICT is actually a follower of the ex-institutes ITC (Institute of Technical Cybernetics, 1970–1980), ITCR (Institute of Technical Cybernetics and Robotics, 1881–1989, Institute of Information Technologies, 1990–2010) and IICT (Institute of Information and Communication Technologies, 2010–). The research included image and speech processing and recognition. Attention has been paid to the following scientific problems: noise suppression, contrast enhancement contour delineation, object segmentation, feature selection, feature space minimization, methods, algorithms and software for object classification and identification. All this aimed the solution of different practical problems. The obtained theoretical results have been published as 370 papers and conference presentations, mainly international. 11 doctoral theses have been developed [1–11], 4 monographs [12–15], two of which in English, and a Handbook [16] have been written, and an international patent was obtained [17]. These results were connected to 51 projects, 4 of which in the frame of 4th, 5th, 6th and COST European programs. 14 software products have been implemented in the country and 2 abroad. This activity is further described in more detail for each of the predecessors.
Ecologization and digitalization of preschool education for sustainable development: problems and prospects

The paper is devoted to the problems of modernization of the preschool education in Russia. The general scientific and private scientific methods formed the methodological basis of the research. There are two directions in the development of the Russian education system over the past few years: ecologization and digitalization. The ideas of the “green economy” are actively entering the field of education. The federal state educational standards provide the opportunity of forming the basics of children’s ecological literacy, ecological culture and ecological intellection in schools, but not in preschool educational organizations. The process of digital transformation is more focused on school and colleges and less on kindergartens. These problems slow down the pace of sustainable development because preschool education is the foundation of a child’s personal development in the future. This paper presents the promising directions for improving the preschool educational system for sustainable development.
User Assessment of Webpage Usefulness

During a web search, the search engines provide relevant pages while the users decide the usefulness of them. The usefulness assessment has been found to be related to various factors. In this study, the effects of task domain and task type on webpage usefulness assessment were investigated. The two task domains involved were health and travel and the two task types were fact finding and decision making. In an experimental environment, 24 participants were asked to do internet search on 4 pre-defined tasks. In the interview after each search task, the users were asked to evaluate the usefulness of each web page they had clicked and talked about why the page was evaluated as such. According to the results of the experiment, we found that the task domain, task type, and various other factors had impacts on the usefulness assessment during web search.
Screw Slot Quality Inspection System Based on Tactile Network

The popularity of 5G networks has made smart manufacturing not limited to high-tech industries such as semiconductors due to its high speed, ultra-high reliability, and low latency. With the advance of system on chip (SoC) design and manufacturing, 5G is also suitable for data transmission in harsh manufacturing environments such as high temperatures, dust, and extreme vibration. The defect of the screw head is caused by the wear and deformation of the die forming the head after mass production. Therefore, the screw quality inspection system based on the tactile network in this article monitors the production quality of the screw; the system will send a warning signal through the router to remind the technician to solve the production problem when the machine produces a defective product. Sensors are embedded into the traditional screw heading machine, and sensing data are transmitted through a gateway to the voluntary computing node for screw slot quality inspection. The anomaly detection data set collected by the screw heading machine has a ratio of anomaly to normal data of 0.006; thus, we propose a time-series deep AutoEncoder architecture for anomaly detection of screw slots. Our experimental results show that the proposed solution outperforms existing works in terms of efficiency and that the specificity and accuracy can reach 97% through the framework proposed in this article.
HashWires: Hyperefficient Credential-Based Range Proofs

Abstract This paper presents HashWires, a hash-based range proof protocol that is applicable in settings for which there is a trusted third party (typically a credential issuer) that can generate commitments. We refer to these as “credential-based” range proofs (CBRPs). HashWires improves upon hashchain solutions that are typically restricted to micro-payments for small interval ranges, achieving an exponential speedup in proof generation and verification time. Under reasonable assumptions and performance considerations, a Hash-Wires proof can be as small as 305 bytes for 64-bit integers. Although CBRPs are not zero-knowledge and are inherently less flexible than general zero-knowledge range proofs, we provide a number of applications in which a credential issuer can leverage HashWires to provide range proofs for private values, without having to rely on heavyweight cryptographic tools and assumptions.
La introducción de la tecnología en los contextos educativos: evolución y retos

Educational technology has evolved in a constant way over the last decades, conforming an essential element in current educational contexts. This study focus its attention in conducting a review of the way the introduction and educational use of digital tools and resources have evolved from the 1970s until our current era in a global context. After structuring thearticle in different phases and detailing the transformation and central elements in each of them, it concludes with an exposition of some of the current educational tendencies and challenges.
Putting a Face on Algorithms: Personas for Modeling Artificial Intelligence

We propose a new type of personas, artificial intelligence (AI) personas, as a tool for designing systems consisting of both human and AI agents. Personas are commonly used in design practices for modelling users. We argue that the personification of AI agents can help multidisciplinary teams in understanding and designing systems that include AI agents. We propose a process for creating AI personas and the properties they should include, and report on our first experience using them. The case we selected for our exploration of AI personas was the design of a highly automated decision support tool for air traffic control. Our first results indicate that AI personas helped designers to empathise with algorithms and enabled better communication within a team of designers and AI and domain experts. We call for a research agenda on AI personas and discussions on potential benefits and pitfalls of this approach.