Reviewerly/exHarmony huggingface dataset:
Systematic analysis of digital reading studies in the digital age

Digitalization has led to significant changes in reading like it does in almost every field. When traditional print reading is considered together with digital reading, it can be argued that digital reading differs from printed reading in many respects. Given the complex nature of reading, it is predictable that an in-depth examination of digital reading is needed. In this context, this research aims to determine the trends and prominent concepts/topics by examining the studies on digital reading in a systematic and holistic manner. In the research, peer reviewed articles related to the concept of digital reading indexed in the Web of Science database were analyzed. Data analysis methods such as content analysis, descriptive analysis and bibliometric analysis were used in the research. As a result of the research, it was seen that there was a significant increase in digital reading studies especially in recent years. It is also determined that digital reading studies are concentrated in developed countries. The data on the participants were also examined in the studies. Accordingly, it was identified that the highest number of studies took place at the K-12 level, and the participants generally had a lower-to-medium socioeconomic status. The sampled papers were also analyzed in terms of reading methods and it was seen that digital shared reading was used more than other reading methods. When the most commonly used variables, the most used concepts in the abstract sections or the most used keywords are examined, it is noticeable that the concept of reading comprehension comes to the fore. When the authors/references that received the most references in the field were analyzed, it was determined that OECD reports were referenced the most. In line with the findings obtained, results were discussed mutually with the literature and various inferences were made.
Professional Consciousness as a Subjective Component of a Personal Professional Development

The study discusses the professional development of the individual, which is a multi-faceted process. The relevance of this work is defined by development trends that radically change the essence of professions on the labour market and require an understanding of the role of career guidance and professional self-determination as a single process throughout the entire working life of an individual. The study aims to investigate professional consciousness as a subjective component of professionalism and consider the algorithm for the development of professional self-consciousness in society. The study covers the main stages of professional development of the individual as a single process. The analysis of documents (monographs, websites, statistics, academic papers, and textbooks) was the objective method for the study. The study researched and systematised the leading approaches to discovering personal potential based on the collected material and identified all types of professional competence using the analysis method. As a result, the definition of factors of professionalism creates mental models of professional activity and the professional self-consciousness of the individual. The practical value of the study discussed and analysed the stages of development and establishment of a professional individual is implementing the results in the educational process and using the provided recommendations for creating a personal programme for mastering the course on the psychology of personality and professional development.
Building Blockchains with Secure and Practical Public-Key Cryptographic Algorithms: Background, Motivations and Example

Blockchain is a publicly distributed ledger used to record transactions in Bitcoin-like cryptocurrencies. In recent years, the successful integrations of Public-Key Cryptographic (PKC) algorithms with cryptocurrencies have driven researchers to pursue the study of PKC. However, it is challenging to technically integrate PKC algorithms with blockchain properly in that the studies of blockchain leverage to broad domains and each existing problem can lead to diverse solutions. For cryptographically-solvable problems, it is important to find a secure and practical integration of PKC algorithm with blockchain. We systematically review three major topics in cryptocurrencies, including security, privacy and scalability. We conduct a case analysis which demonstrates how to integrate PKC with blockchains. As an illustration, we propose mutable blockchain which incorporates multiple PKC schemes and show how to use it to remove double-spending transactions via redaction. We then give a concrete construction. As suggested by our performance evaluation, the adopted PKC algorithms can run scalably and efficiently and avoid bottlenecks in the system.
SIGCHI Research Ethics Committee Town Hall

Since 2016, the SIGCHI Research Ethics Committee has been in place to advise CSCW and other SIGCHI conferences and communities on ethical issues that arise in the course of our research. This town hall style panel provides an annual opportunity to connect with the committee, which has a remit to report back to the HCI research community on issues that arise as our methods, technologies, and best practices evolve.
Comparative Analysis of SVM, XGBoost and Neural Network on Hate Speech Classification

In social media, it is found that hate speech is conveyed in the form of text, images and videos, as a result it can provoke certain people to do things that are against the law and harm other person. Therefore, it is necessary to make early detection of hate speech by utilizing machine learning algorithms. This study is to analyze the level of accuracy, precision, recall and F1-Score of 3 kinds of algorithms (SVM, XGBoost, and Neural Network) in the classification of hate speech, using datasets sourced from public hate speech on Twitter in Indonesian. The results of the analysis show that the SVM algorithm has a level of accuracy (83.2%), precision (83%), recall (83%) and F1-score (83%), SVM occupies the highest level compared to XGBoost and Neural Network, so the SVM algorithm can be considered for use in hate speech classification
Secure Delivery Scheme of Common Data Model for Decentralized Cloud Platforms

The Common Data Model (CDM) is being used to deal with problems caused by the various electronic medical record structures in the distributed hospital information system. The concept of CDM is emerging as a collaborative method of exchanging data from each hospital in the same format and conducting various clinical studies based on shared data. The baseline of a CDM system is centralized with an infrastructure typically controlled by a single entity with full authority. The characteristics of this centralized system can pose serious security issues. Therefore, the proposed SC-CDM system is designed as a platform for distributed ledger and provides data with a high level of confidentiality, security, and scalability. This framework provides a reference model that supports multiple channels, using secure CDM as an encryption method. The data confidentiality of CDM is guaranteed by asymmetric and symmetric protocols. Delivering CDM is protected by a symmetric key signed by the CDM creator and maintains lightweight distributed ledger transactions on Inter Planetary File System (IPFS), which acts as a file share. To deliver an encrypted CDM on the SC-CDM platform, the CDM is encrypted with a block cipher by a random symmetric key and Initialization Vector (IV). The symmetric key protocol is used for the fast encryption of large-capacity data. The SC-CDM is implemented the repository with IPFS for storing the encrypted CDM, in which symmetric key, two hash values, and IV are shared through blockchain. Data confidentiality of SC-CDM is guaranteed by only registered users accessing the data. In conclusion, the SC-CDM is the first approach to demultiplexing with the data confidentiality proof based on asymmetric key cryptography. We analyze and verify the security of SC-CDM by comparing qualitative factors and performance with existing CDM. Moreover, we adopt a byte-level processing method with encryption to ensure efficiency while handling a large CDM.
Spam Detection in Social Media Networking Sites using Ensemble Methodology with Cross Validation

Social media networking sites are more popular over Internet. The Internet users spend more amount of time on social media sites like Twitter, Facebook, Instagram and LinkedIn etc. The social media networking users share their ideas, opinions, information and make new friends. Social networking sites provide large amount of valuable information to the users. This large amount of information in social media attracts spammers to misuse information. These spammers create fake accounts and spread irrelevant information to the genuine users. The spam message information may be advertisements, malicious links to disturb the natural users. This spam data in social media is a very serious problem. Spam detection in social media networking sites is critical process. To extract spam messages in social media various spam detection methodologies are developed by researchers. In this paper we proposed an ensemble methodology for identification spam on Twitter social media network. In this methodology we used Decision tree induction algorithm, Naïve bayes algorithm and KNN algorithm to construct a model. As part of this approach, we compare the classification results of any two classification algorithms, if both classifiers predict the same result, then we finalize the class of tweet under investigation. If the predicted classes of both classification algorithms differ, then we use the prediction of third algorithm as the final class label of tweet. To measure the performance of our model we used precision, recall and F measure.
Time Series Data Processing for Classifying Wandering Patterns in People With Dementia

As the population ages, dementia has become one of the main health issues worldwide affecting the elderly. It is a disease related to the damage of the brain cells, causing memory loss, impairing of written and spoken communication skills, difficulty in performing previously routine tasks, as well as personality and mood changes. There is no cure for dementia, but if diagnosed correctly, providing the proper treatment and support allows for a better quality of life for those affected by this disease. People with dementia tend to wander, and a relationship between the wandering pattern and the level of dementia has been established. In this paper, two-time series techniques, the autocorrelation function and the partial autocorrelation function used in conjunction with the machine learning algorithms, including linear discriminant analysis, multivariate Gaussian, adaptive boost, and k-nearest neighbors, were evaluated to classify wandering patterns in people affected by dementia. The main contribution of this work is the use of time-series data techniques and machine learning algorithms to classify wandering patterns. The use of smoothing filters and time series feature extraction techniques, used in combination with ML algorithms, showed a very good performance, with an accuracy greater than 90%.
Grouping optimization algorithm for natural image matting

Image matting is an important technology for image synthesis and video editing. The existing image matting method based on pixel pair optimization can significantly improve the accuracy of image matting, but with the increase of image resolution, the optimization difficulty increases. This paper proposes a group optimization strategy, which transforms the large-scale combinatorial optimization matting problem into multiple small-scale combinatorial optimization problems. Based on the grouping optimization strategy, we designed a grouping optimization algorithm for natural image matting according to the assumption that the alpha matting within the group is similar. In the algorithm, the color average of the intra-group is selected as the representative of the optimization target, return the alpha matte corresponding to the optimization target in place of the group alpha matte. To verify the effectiveness of the proposed algorithm, the experiments study on alpha matting benchmark data set. Experimental results reveal that the proposed algorithm with a grouping strategy can significantly improve the alpha matte of the natural images.
Geodesic Models with Convexity Shape Prior

The minimal geodesic models established upon the eikonal equation framework are capable of finding suitable solutions in various image segmentation scenarios. Existing geodesic-based segmentation approaches usually exploit image features in conjunction with geometric regularization terms, such as Euclidean curve length or curvature-penalized length, for computing geodesic curves. In this paper, we take into account a more complicated problem: finding curvature-penalized geodesic paths with a convexity shape prior. We establish new geodesic models relying on the strategy of orientation-lifting, by which a planar curve can be mapped to an high-dimensional orientation-dependent space. The convexity shape prior serves as a constraint for the construction of local geodesic metrics encoding a particular curvature constraint. Then the geodesic distances and the corresponding closed geodesic paths in the orientation-lifted space can be efficiently computed through state-of-the-art Hamiltonian fast marching method. In addition, we apply the proposed geodesic models to the active contours, leading to efficient interactive image segmentation algorithms that preserve the advantages of convexity shape prior and curvature penalization.
CREATIVE THINKING OF STUDENTS AS A STRATEGIC PURPOSE OF UNIVERSITY EDUCATION

An analysis of the essence and development of creative thinking, the determination of the main structural elements of this phenomenon makes it possible to identify effective approaches to the formation and development of professional creative thinking of students. You can define a few approaches to the formation of creative personality thinking. This is a systematic, activity and cultural approaches. Within the system approach, the main components of creative thinking are considered not distinguished, but in interconnection with each other as a holistic system. The definition of creative activity of personality and its personal abilities is the basis of a personal approach. The activity approach is guided by educational and scientific activities in the educational process and is the main condition for the development of professional creative thinking of students. The meaningful basis of the culturological approach is assimilated by values. To the main elements of training that are capable of influencing the formation of creative thinking of students is problematic, that is, in order to increase mental activity it is necessary to include problematic tasks, simulated situations. This approach will contribute to the formation of creative thinking of students, therefore, in our opinion; the methodology of problem-oriented learning is the most successful. Students are given the opportunity to solve complex tasks independently, make decisions and open scientific truths. Making a new material to think, analyze the facts synthesizing new ideas, develop reflexive thinking by self-knowledge, self-observation.
Dynamic Optimal ANFIS Parameters Tuning with Particle Swarm Optimization

This paper presents dynamic modification parameters of the Adaptive Neuro-Fuzzy Inference System (ANFIS) using the Particle Swarm Optimization (PSO) algorithm.In the proposed ANFIS_PSO, each particle dynamically adjusts its weight to the optimal states of the particles using a nonlinear fuzzy model.Tests of the model were performed using the "Signal-Time Series".The methods are tested simultaneously until the best method to solve the problem is found.The proposed model takes advantage of PSO to tune ANFIS parameters by minimizing mean square error (MSE), root mean square error (RMSE), R-Squared (R2) and Mean Absolute Error (MEA) metrics.The main contribution is a strategy for dynamically finding the best result, which identifies methods for solving a given problem using different performance metrics depending on the problem.The proposed structure's results were compared with several machine learning algorithms.Simulation results show the effectiveness of the proposed algorithm.
Issues in clustering algorithm consistency in fixed dimensional spaces. Some solutions for k-means

Abstract Kleinberg introduced an axiomatic system for clustering functions. Out of three axioms, he proposed, two (scale invariance and consistency) are concerned with data transformations that should produce the same clustering under the same clustering function. The so-called consistency axiom provides the broadest range of transformations of the data set. Kleinberg claims that one of the most popular clustering algorithms, k -means does not have the property of consistency. We challenge this claim by pointing at invalid assumptions of his proof (infinite dimensionality) and show that in one dimension in Euclidean space the k -means algorithm has the consistency property. We also prove that in higher dimensional space, k -means is, in fact, inconsistent. This result is of practical importance when choosing testbeds for implementation of clustering algorithms while it tells under which circumstances clustering after consistency transformation shall return the same clusters. Two types of remedy are proposed: gravitational consistency property and dataset consistency property which both hold for k -means and hence are suitable when developing the mentioned testbeds.
Position Paper:Defending Direct Memory Access with CHERI Capabilities

We propose new solutions that can efficiently address the problem of malicious memory access from pluggable computer peripherals and microcontrollers embedded within a system-on-chip. This problem represents a serious emerging threat to total-system computer security. Previous work has shown that existing defenses are insufficient and poorly deployed, in part due to performance concerns. In this paper we explore the threat and its implications for system architecture. We propose a range of protection techniques, from lightweight to heavyweight, across different classes of systems. We consider how emerging capability architectures (and specifically the CHERI protection model) can enhance protection and provide a convenient bridge to describe interactions among software and hardware components. Finally, we describe how new schemes may be more efficient than existing defenses.
OPTIMIZING K-MEASN ALGORITHM USING PARTICLE SWARM OPTIMIZATION TO GROUP STUDENT LEARNING PROCESSES

In the implementation of learning, there are several factors that affect the student learning process, including internal factors, external factors, and learning approach factors. Internal factors (factors within students), for example: the physical and spiritual condition of the student. Namely: physiological aspects (body, eyes and ears) and psychological aspects (student intelligence, student attitudes, student talents, student interests and student motivation). External factors (factors from outside students), for example: environmental conditions around students. Namely: social environment (family, teachers, community, friends) and non-social environment (home, school, equipment, nature). While the student learning approach factors, for example: The learning approach factor, namely the type of student effort which includes the strategies and methods used by students to carry out learning activities of subject matter, which consists of a high approach, medium approach and low approach. So the first focus of this research is to do student clustering based on their learning process using 11 parameters. Second, using the PSO algorithm to get maximum clustering results. The research data were obtained from vocational secondary education institutions in the city of Pasuruan. Where the data is data obtained from the results of school reports and questionnaires as much as 350 student data. Data attributes include environmental features, social features, and related school features to group student data for learning data processing. From the classification results using the PSO method, there are 0.97140754 silhouette values that are obtained because the distance between the data is very close. From these results indicate that the PSO method is able to improve the performance of the k-means clustering method in the classification process of student learning interest.
Design and Implementation of a Robotic Arm Assistant with Voice Interaction Using Machine Vision

It is evident that the technological growth of the last few decades has signaled the development of several application domains. One application domain that has expanded massively in recent years is robotics. The usage and spread of robotic systems in commercial and non-commercial environments resulted in increased productivity, efficiency, and higher quality of life. Many researchers have developed systems that improve many aspects of people’s lives, based on robotics. Most of the engineers use high-cost robotic arms, which are usually out of the reach of typical consumers. We fill this gap by presenting a low-cost and high-accuracy project to be used as a robotic assistant for every consumer. Our project aims to further improve people’s quality of life, and more specifically people with physical and mobility impairments. The robotic system is based on the Niryo-One robotic arm, equipped with a USB (Universal Serial Bus) HD (High Definition) camera on the end-effector. To achieve high accuracy, we modified the YOLO algorithm by adding novel features and additional computations to be used in the kinematic model. We evaluated the proposed system by conducting experiments using PhD students of our laboratory and demonstrated its effectiveness. The experimental results indicate that the robotic arm can detect and deliver the requested object in a timely manner with a 96.66% accuracy.
Blockchain Technology: Beyond Cryptocurrencies

The following sections are included:IntroductionThe Characteristics of Blockchain TechnologyThe New Applications of Blockchain TechnologyConclusionAppendix AReferences
Enhanced Differential Evolution Algorithm with Local Search Based on Hadamard Matrix

Differential evolution (DE) is a robust algorithm of global optimization which has been used for solving many of the real-world applications since it was proposed. However, binomial crossover does not allow for a sufficiently effective search in local space. DE’s local search performance is therefore relatively poor. In particular, DE is applied to solve the complex optimization problem. In this case, inefficiency in local research seriously limits its overall performance. To overcome this disadvantage, this paper introduces a new local search scheme based on Hadamard matrix (HLS). The HLS improves the probability of finding the optimal solution through producing multiple offspring in the local space built by the target individual and its descendants. The HLS has been implemented in four classical DE algorithms and jDE, a variant of DE. The experiments are carried out on a set of widely used benchmark functions. For 20 benchmark problems, the four DE schemes using HLS have better results than the corresponding DE schemes, accounting for 80%, 75%, 65%, and 65% respectively. Also, the performance of jDE with HLS is better than that of jDE on 50% test problems. The experimental results and statistical analysis have revealed that HLS could effectively improve the overall performance of DE and jDE.