Reviewerly/exHarmony huggingface dataset:
Smart Home Infrastructure with Blockchain-Based Cloud IoT for Secure and Scalable User Access

Human beings in the modern world are greatly attracted towards using the smart gadgets that simplifies their daily routine-based activities, which is very essential after a tiresome day at work. Various devices including electronic and non-electronics like lockers are all controlled through a remote system that can be analysed in future iterations to trace the log of all operations. However, if this data is accessed by unauthorized users, then there is a great loss to be incurred by the house owners. On the other side, integration of IoT with blockchain will cater the need to overcome centralized behaviour in a distributed fashion. Also, security aspects need to be strengthened to overcome unprecedented loss of confidential information. Towards this end, the proposed work focuses on integration of the decentralized property of blockchain with data capturing aspects of IoT across multi-tenant environment in a secure way. In the proposed framework, multiple user access to multiple keys stored in secure private cloud is presented.
Psychological analysis of the means and conditions for the formation of elementary mathematical representations in preschoolers

The article aims to identify and describe the conditions as well as means of teaching that form the zone of proximal development when mastering elementary mathematical concepts (EMC) at an older preschool age (first of all, ideas about quantities: length, area, mass, volume, etc.). Also, the work analyzes the representation of these conditions and means of teaching in modern Russian preschool education programs developed on the basis of the Federal State Educational Standard for Preschool Education. The selection of programs was carried out on the basis of the PRISMA procedure, as a result of which 8 programs were selected out of 43 for analysis according to the selected criteria. Three types of the most common teaching tools were identified in situations when introducing new EMC: samples (visual or verbal instructions, rules), models (schematic representation of essential relations between objects) and symbols (symbolic representation of essential relations between objects). For the main learning conditions, the content and characteristics of those actions in which EMC data are assimilated were analyzed. It was shown that when organizing the formation of ideas about values in older preschoolers, it is psychologically more appropriate to select conditions that require the implementation of actions of selection by magnitude, indirect comparison, as well as measurement using conditional measures that is necessary in situations of representation of modeling or symbolization tools. The obtained results of the analysis of modern Russian preschool education programs indicate that most often they use only one tool — a visual sample or instruction, and the formation of ideas about the values is carried out solely on the basis of the action of direct comparison. The conducted research will make it possible to provide psychologically sound recommendations to teachers, educators and the administration of preschool educational institutions on the choice and implementation of EMC training programs.
Wait Long and Prosper! Delaying Production During Training Boosts Word Learning

A growing number of studies show that producing novel words while learning to recognize them can disrupt their full incorporation into the mental lexicon. One interpretation of this finding is that production distracts listeners from processing perceptual input. If that is the case, delaying production until after perceptual processing is completed should minimize (or even reverse) its detrimental effect. To test this hypothesis, we examined how the detrimental effect of production on word learning is modulated by the delay between hearing and producing a new word. Each participant learned 12 new words in one of four training conditions: Perception-Only , Immediate-Production , 2-seconds-Delayed-Production , and 4-seconds-Delayed-Production . At test, we assessed how well new words were embedded into the mental lexicon by measuring the degree to which they could drive phonemic recalibration (also called “perceptual learning”; Norris et al., 2003; Samuel & Kraljic, 2009). Training and testing were repeated on the next day along with a separate task assessing lexical configuration. Replicating previous findings, our Day 1 results showed that repeating a new word immediately after hearing it disrupted learning compared to just hearing the word. Critically, in line with our prediction, this negative effect disappeared when a 4-second pause was inserted between hearing and producing each word. In addition to advancing our understanding of word learning mechanisms, this finding also has implications for language teaching practices.
Analysis of 3D virtual avatars for video game experience

Avatar personalization may contribute to the engagement in a video game. This exploratory study investigated which features a 3D virtual avatar should have for the user to experience Body Ownership. 10 participants (aged 20 to 30) participated in this study. For each participant three avatars were created based on a single picture of them from the shoulders up. The three software that created the avatars are Avatar SDK, Wolf 3D and Character Creator. After the avatars were created every participant filled out a questionnaire and rated each avatar according to the features. The features that the participants were asked to rate were skin color, hair color, hair style, shape of the face, personal details (e.g. freckles and moles), body shape, height and clothes. The results show that the participants had the highest level of Body Ownership with the avatar from the Avatar SDK, which was rated the highest on the features skin color and shape of the face. Most of the participants would choose the avatar created by the Wolf 3D software to play a video game with, because it has a more cartoon-like look. When the participants were asked what they thought would be the most important features the highest rated answers were skin color, shape of the face and hair color.
Operational Resilience in Digital Payments: Experiences and Issues

Major operational incidents in payment systems suggest the need to improve their resiliency.Meanwhile, as payment infrastructures become more digitalized, integrated, and interdependent, they require an even higher degree of resilience.Moreover, risks that could trigger major disruptions have become more acute given the rise in power outages, cyber incidents, and natural disasters.International experiences suggest the need to strengthen reliability objectives, redundancies, assessment of critical service providers, endpoint security, and alternative arrangements.
Taxonomy of Object Detection methods: A Survey

Object detection (OD) within a video is one of the relevant and critical research areas in the computer vision field. Due to the widespread of Artificial Intelligence, the basic principle in real life nowadays and its exponential growth predicted in the epochs to come, it will transmute the public. Object Detection has been extensively implemented in several areas, including human-machine Interaction, autonomous vehicles, security with video surveillance, and various fields that will be mentioned further. However, this augmentation of OD tackles different challenges such as occlusion, illumination variation, object motion, without ignoring the real-time aspect that can be quite problematic. This paper also includes some methods of application to take into account these issues. These techniques are divided into five subcategories: Point Detection, segmentation, supervised classifier, optical flow, a background modeling. This survey decorticates various methods and techniques used in object detection, as well as application domains and the problems faced. Our study discusses the cruciality of deep learning algorithms and their efficiency on future improvement in object detection topics within video sequences.
Analyzing the Wehe Network Neutrality Monitoring Tool

Network Neutrality is protected by law in many countries over the world, but monitoring has to be performed to ensure operators conform to the rules. The Wehe application, jointly developed by Northeastern University and the French regulator ARCEP, allows users to take measurements and analyze them to detect possible traffic differentiation.In this paper, we investigate the playroom left for ISPs to still differentiate traffic, when tested with the Wehe detection tool. Our contributions include the design of a test bed to evaluate the detection capacities of Wehe, and its use to provide some elements of response: By computing the detection probabilities and estimating the potential benefit of a revenue-interested operator, we fine-tune and compare the main differentiation types (throughput, packet loss and delay) that an operator could implement.
Image Of Trickster As Communication Model Of Modern Blogger Network Representation

This article is devoted to the features of modeling the network image of a blogger who is gaining the maximum number of positive mentions among student youth according to the results of our sociological survey. Carnival, feast, theatricality are becoming the most important part of the modern socio-cultural practice. And a rogue, a mischievous person or a trickster is becoming its main figure. On the one hand, the trickster is a universal image embodying timeless and supranational features. On the other hand, being an archetypal creature (formation), this image is filled with specific content due to the historical, cultural and social experience. Contemporary youth culture also reveals an active appeal to this archetypal image. The digital society provides (is providing) new technological tools (mainly social networks), with the help of which it becomes possible to release your own inner trickster, providing it with an almost unlimited field of action. At the same time, it generates additional capabilities of the “trickster approach”, actively using it as a certain basis for communicative interaction with its social audience. The works of C. Jung, P. Radin, K. Blaeser, V. N. Toporov became the theoretical basis of our research. To identify the main features (characteristics) of the blogger network modeling image and its correlating with the trickster archetype are the expected results of the research.
CONTOPT-JS: Metaheuristic Algorithms based JavaScript Software Library for Continuous Optimization Problems

In this study, a software library called CONTOPT-JS has been developed for solving continuous optimization problems. By using this JavaScript language based library, fully client-side web applications can be developed. In the library, Artificial Bee Colony, Differential Evolution, Particle Swarm Optimization and Evolution Strategies metaheuristics exist and new algorithms and new problems can be added because of its modular design. Using the CONTOPT-JS library, experimental works have been conducted on some standard optimization benchmark functions and Sensor Deployment application area and the obtained results have been presented.
Research on Reliability of Mine Data Based on Blockchain and Edge Computing

In view of the reliability of the comprehensive utilization of mine data resulting from the rapid development of smart mine research, it is proposed that the fusion of blockchain and edge computing technology be applied to the study of mine data transmission, which brings new ideas for the digital construction of mine safety. Edge computing shortens the data processing distance, improves real-time performance, and increases the redundant broadband space in the central cloud. At the same time, the security and privacy features provided by the blockchain technology bring solutions to the information security problems in the mine's digital networking. This article conducts a comprehensive analysis of these two technologies, first describing the definition and architectural organization of the two technologies; at the same time, analyzing the differences in data transmission protocols and giving corresponding solutions; in addition, it discusses its relationship with smart mines. In this way, the system architecture of smart mines is proposed, and at the same time, feasibility suggestions are provided for the problems existing in the construction of smart mines, which will provide guidance for the future development of smart mines.
A Simple Theory of Argument Schemes

While there has been in depth discussion of many particular argumentation schemes, some lament that there is little to no theory underpinning the notion of an argumentation scheme. Here I shall argue against the utility of argument schemes, at least as a fundamental part of a complete theory of arguments. I shall also present and defend a minimalist theory of their nature—a scheme is just a set of proposition expressions and propositional functions. While simple, the theory contravenes several typical desiderata of argumentation schemes such as (i) aiding in the identification of enthymemes and (ii) keeping arguments constrained to a manageable taxonomy. So much the worse for the desiderata. Instead, I shall recommend focusing less on schemes and more on the component propositional functions.
A Multi-Agent Motion Prediction and Tracking Method Based on Non-Cooperative Equilibrium

A Multi-Agent Motion Prediction and Tracking method based on non-cooperative equilibrium (MPT-NCE) is proposed according to the fact that some multi-agent intelligent evolution methods, like the MADDPG, lack adaptability facing unfamiliar environments, and are unable to achieve multi-agent motion prediction and tracking, although they own advantages in multi-agent intelligence. Featured by a performance discrimination module using the time difference function together with a random mutation module applying predictive learning, the MPT-NCE is capable of improving the prediction and tracking ability of the agents in the intelligent game confrontation. Two groups of multi-agent prediction and tracking experiments are conducted and the results show that compared with the MADDPG method, in the aspect of prediction ability, the MPT-NCE achieves a prediction rate at more than 90%, which is 23.52% higher and increases the whole evolution efficiency by 16.89%; in the aspect of tracking ability, the MPT-NCE promotes the convergent speed by 11.76% while facilitating the target tracking by 25.85%. The proposed MPT-NCE method shows impressive environmental adaptability and prediction and tracking ability.
Moving Object Tracking Algorithm for Complex Shape and Occlusion Handling

An object tracking increases loads of enthusiasm for dynamic research in applications such as video surveillance, vehicle navigation, highways, crowded public places, borders, forest and traffic monitoring areas. The system we develop aims to measure and analyze the application of background subtraction method and block matching algorithm to trace object movements through video-based. The making of video surveillance systems “smart” requires fast, reliable and robust algorithms for moving object detection and tracking. This research applies background subtraction method to detect moving object, assisted with block matching algorithm which aims to get good results on objects that have been detected. Performance evaluation is carried out to determine the various parameters. In this paper author design and develop a novel algorithm for moving object tracking in video surveillance also compares and analyse existing algorithms for moving object tracking. Author main aim to design and develop an algorithm for moving object tracking to handle occlusion and complex object shapes.
Encoding semantic relationships in literary texts

Encoding meaningful semantic relationships in literary texts is almost as difficult as defining and identifying them. Defining the types and the components of semantic relationships that can be extracted from literary texts is a quite challenging task because literature is full of implicit and oblique messages and references. Subsequently, identifying and encoding semantic relationships in literature is even more challenging because often relations do not have neither clear nor standard linguistic form and usually they overlap each other. This paper discusses modeling and encoding issues concerning the mapping of relationships of cultural content in literary and humanities texts, highlighted by the case of the ECARLE project annotation campaign. On handling these modeling and encoding issues the paper proposes a methodology of minimalistic and flexible annotation techniques, combined in order to generate human annotated training data for a Relation Extraction machine learning system. The proposed methodology utilizes the available TEI tagset, and, without any further customizations, allows the mapping of relations formed by named entities in a simple yet flexible way, open to reuse, interchange, conversion and visualization.
Convolutional Neural Networks Used to Date Photographs

Nowadays, the information provided by digital photographs is very complete and very relevant in different professional fields, such as scientific or forensic photography. Taking this into account, it is possible to determine the date when they were taken, as well as the type of device that they were taken with, and thus be able to locate the photograph in a specific context. This is not the case with analog photographs, which lack any information regarding the date they were taken. Extracting this information is a complicated task, so classifying each photograph according to the date it was taken is a laborious task for a human expert. Artificial intelligence techniques make it possible to determine the characteristics and classify the images automatically. Within the field of artificial intelligence, convolutional neural networks are one of the most widely used methods today. This article describes the application of convolutional neural networks to automatically classify photographs according to the year they were taken. To do this, only the photograph is used, without any additional information. The proposed method divides each photograph into several segments that are presented to the network so that it can estimate a year for each segment. Once all the segments of a photograph have been processed, a general year for the photograph is calculated from the values generated by the network for each of its segments. In this study, images taken between 1960 and 1999 were analyzed and classified using different architectures of a convolutional neural network. The computational results obtained indicate that 44% of the images were classified with an error of less than 5 years, 20.25% with a marginal error between 5 and 10 years, and 35.75% with a higher marginal error of more than 10 years. Due to the complexity of the problem, the results obtained are considered good since 64.25% of the photographs were classified with an error of less than 10 years. Another important result of the study carried out is that it was found that the color is a very important characteristic when classifying photographs by date. The results obtained show that the approach given in this study is an important starting point for this type of task and that it allows placing a photograph in a specific temporal context, thus facilitating the work of experts dedicated to scientific and forensic photography.
IEEE Access Special Section Editorial: Big Data Learning and Discovery

We are now witnessing a dramatic growth of heterogeneous data, consisting of a complex set of cross-media content, such as texts, images, videos, audio, graphics, spatio-temporal data, and multivariate time series. The inception of modern techniques from computer science have offered very robust and hi-tech solutions for data and information analysis, collection, storage, and organization, as well as product and service delivery to customers. Recently, technological advancements, particularly in the form of big data, have resulted in the storage of enormous amounts of potentially valuable data in a wide variety of formats. This situation is creating new challenges for the development of effective algorithms and frameworks to meet the strong requirements of big data representation and analysis, knowledge understanding, and discovery.
EDUCATION AND TECHNOLOGY DURING COVID-19 TIMES

It is difficult to separate technology from education because technology is embedded in teaching. The best technology for teaching is the one that does not interfere with the communication between the teacher and the students. In other words, it should be barely “noticeable” in how it is used, and easy enough to use that it does not require special training, such as window in a room to see the flowers outside, without interfering in any way. The technology used during an in-person classes is so basic, transparent, and simple, since it consists of a classroom, chairs, blackboard and pieces of chalk. However, a piece of chalk could break. In this case, the flow of information is interrupted; which is why the plastic whiteboards and markers are preferable and cleaner. An electronic board may seem to be better with its many improvements with computers; however, its higher technology could get in the way if it is not used properly. During the pandemic, different technologies, like computers and cell phones, connected to internet were massively used. Yet, computers and cell phones with the appropriate software and apps had an additional cost; with no guarantee of antivirus applications against viruses or worms, leaving aside protection for documents, software and hardware. On top of that, a Zoom, Google or any similar online platform was necessary. Using these platforms, other technologies had to be relied on, including Ethernet or a USB adapter. On the other hand, in developing countries before the pandemic, low-tech was used by most students to store and pass information to their peers and teachers. They would use mainly pens and notebooks to write notes, or CD-Rs or USB to pass information. However, during the pandemic, with the advances in internet speed, many students have now moved on to using e-mail and cloud drives where they can transport their files. All the electronics listed to make online education or tech-based education needed electricity to work. In other words: no electricity, no class; no electric supply or stored electricity in batteries, no class. In this paper we analyze the role of technology in education, and how this technology could enhance or obstruct the communication between the teacher and the students.
APPLICATION OF K-MEANS ALGORITHM IN GROUPING OF CITY TOURISM CITY PAGAR ALAM

The purpose of this study was to analyze the application of the k-means algorithm in classifying tourist visits to the city of Pagar Alam. The k-means algorithm in grouping tourist objects begins by determining the number of clusters to be formed, determining the centroid value of each cluster, calculating the distance between the data, and calculating the minimum object distance calculated. There are 10 tourism objects that are superior from the data from the Tourism Office of the City of Pagar Alam. The research data used is the number of tourist visitors during the COVID-19 pandemic, namely 2020. The data are grouped into 4 clusters, namely C1 = high number of tourist visitors, C2 = moderate number of tourist visitors, C3 = low number of tourist visitors, C4 = number of visitors travel is very low. the centroid values ​​used are C1 = 92,494, Centroid C2 = 71,658, Centroid C3 = 26,981 and centroid C4 = 4,485. then we get the results of grouping C1=Green Paradise tourism, C2=Janang Orange Gardens,, C3=Curup Tujuh Kenangan, Curup Mangkok, Curup dew, Tegur Wangi Site, Pelang Kenidai Village, and C4= Lumai Site, Tebing Tinggi Site and Tanjung Aro Site . From the results of grouping for c4 it becomes a note for the government of the City of Pagar Alam in increasing the number of tourist visitors.