Reviewerly/exHarmony huggingface dataset:
Lifetime Aware Secure Data Aggregation through Integrated Incentive-based Mechanism in IoT-based WSN Environment

Internet of things grabbed fine attention by researchers due to wide range of applicability in daily human life-based application like healthcare, agriculture, and so on. WSN possesses a restricted environment and also generates a huge amount of data and further cause’s data redundancy. Although data redundancy is efficiently solved through the various data aggregation mechanism, security remains a primary concern for adaptability in the real-time environment. Integrated incentive-based mechanism (IIBM) follows three parts, i.e., first, this research work designs the optimal and secure data aggregation; second part follows the formulation of correctly identification of deceptive data packets, and third part includes discarding deceptive node through conditional approach. Integrated incentive mechanism is evaluated considering the different security parameters like identification of malicious node and misidentified malicious or dishonest node; further, comparison is carried out with the existing model to prove the model efficiency. Furthermore, another parameter like energy utilization and several node functioning is considered for the optimality evaluation of the model. Performance evaluation shows enhancement of nearly 7%, 14%, and 15% considering the three distinctive deceptive nodes, i.e., 5, 10, and 15 (in percentage), respectively.
New Fractal Features for Textural Morphologic Analysis

Significantly increased resolution of image formation systems (down to a few centimetres) causes a possibility of more effective using of objects textural features and signs in case of thematic processing of radar and optical images. The existing methods of image fractal features measurement allows to evaluate numerically the following topological characteristics of image texture: fractal dimension (FD); directional FD in the analysis directions (DFD); multifractal dimension (MFD) (a widespread case — the spectrum of Renyi dimensions (SRD)); morphological multifractal exponent (MME); fractal signature (FS) and directional FS (DFS); morphological MFS (MMFS) and lacunarity. However today there are no complex methods allowing to measure at the same time parameters of the scaling, multifractal and anisotropic properties of a texture possessing reciprocal relationships. In this work the specificities of new Directional Multifractal Blanket method (morphological) (DMBMM) for fractal features measurement of an image textures synthesized on the basis of two best ABRG and MBMM methods in the groups, are considered. Simultaneous accounting of multifractal, singular and anisotropic properties of the image texture with limited scaling character allowed to increase measuring accuracy both FD, and FS at each analysis scale. This feature is the most representative on comparing with all features considered in this work as the functional correlation of the derived features. The increased informativeness of the developed feature in case of image processing is caused by additional determination, along with multifractal and singular properties, anisotropic properties and their joint account and implied the possibility of its using for the properties description of different images textures and also in images clustering and segmentation tasks.
On the Notion of Naturalness in Formal Modeling

We investigate what it means for a formal model to be natural using theories from cognitive science and linguistics. Intuitively, naturalness describes that the formal model fits the domain it is modeling – it is not an intrinsic property of the formal model, but a property that is assigned to it by some human interpreter who is making sense of it. Our main observation is that for each formal model, two sense-making processes are possible: First, the process that interprets the formal model as a symbol in the application domain and assigns it a domain concept. Second, the process that interprets the formal model as a symbol in the engineering domain and assigns it a concept describing an engineering view. Naturalness is described as the similarity of these two mental concepts, i.e., the cognitive complexity to map the domain concept to the engineering concept. We discuss these ideas and formalize then using conceptual spaces, a similarity-based concept representation theory based on cognitive semantics.
Understanding SARS-CoV-2 Transmission: Linking Traditional Contact Tracing with Genomic Surveillance of a SARS-CoV-2 Student Cluster on a Large Urban Campus

Contact tracing and genomic data have both been important tools in understanding the nature of SARS-CoV-2 transmission during the current pandemic. Often, these approaches are separate processes not linked in any systematic manner. Building on a university SARS-CoV-2 surveillance platform, we instituted a quick-turnaround SARS-CoV-2 whole genome sequencing pipeline. Linked analysis of contact tracing and sequence relatedness of SARS-CoV-2 genomes from a regularly sampled university environment were then used to build a multilevel transmission tracing and confirmation system to monitor and understand transmission on campus. This integrated, iterative approach allowed the quantification of previously difficult to understand potential transmission scenarios such as intra-classroom transmission, school-sanctioned social events, and athletic practice environments. Specifically, our investigation of an 18-person cluster stemming from an athletic team highlighted the importance of linking contact tracing and genomic analysis. Contact tracing rapidly identified the cluster, while genomic sequencing ruled out unrelated, coincident infection within the practice environment and identified previously unrecognized transmission. Together, the data suggested that SARS-CoV-2 transmission within the practice environment was unlikely and unmasked social gatherings were the most plausible site of transmission. These findings help better define the sources of and environments in which transmission occurs and, perhaps more importantly, does not occur. Through these findings, it is suggestive that certain safety protocols in the athletic practice setting reduced transmission. The linking of traditional contact tracing with rapid-return genomic information is an effective approach for differentiating between multiple plausible transmission scenarios and informing subsequent public health protocols to limit disease spread in a university environment.Funding Information: The BU Charles River Campus Institutional Review Board reviewed and approved the protocol for sequencing to determine variant distribution and emergence on the BU campus (protocol # 5693E).Conflict of Interests: The authors have no competing interests.Ethical Approval: Boston University provided financial support for the testing program described in this study and supported sequencing efforts. The BUMC Genome Sciences Institute provided financial support for sequencing. JHC acknowledges support from R21AI135517.
Designing XAI from policy perspectives

In the recent decade, deep neural networks have contributed to the rapid advance of artificial intelligence (AI). AI became able to learn and predict complex patterns in big data and solve nonlinear problems effectively. However, because deep neural networks were literally too deep, people could only understand some parts of the prediction process. The issue has led people to be concerned about the unknown part of AI systems. Focusing on the limitation, researchers suggested the concept of explainable AI and provided several techniques and policies. This chapter reviews two major issues in AI and how techniques and policies of explainable AI have addressed the issues. Based on the review, I suggest some policy implications and remaining issues together.
IRIS Image Encryption and Decryption Based Application Using Chaos System and Confusion Technique

In today’s era, Digital Image Security is of primary importance and is receiving a huge amount of consideration. Because digital photographs are used in almost all technological fields, confidential data may be present in these images. To maintain the security of these images, several image encryption and decryption techniques have been presented by various authors and academics. We propose a novel approach for IRIS image-based algorithm to perform encryption and decryption in this paper. The image is compressed using the SVD technique, then shuffled based on the confusion pattern. Encryption and decryption are performed using a logistic mapping-based chaos technique. The proposed algorithm is compared to several existing techniques in terms of compression ratio, computation time, PSNR, SSIM, and NPCR values.
DESIGNING ESP READING COMPREHENSION: THE PORTRAYING OF STUDENTS’ NEEDS IN LEARNING ENGLISH FOR ISLAMIC RELIGIOUS EDUCATION STUDY PROGRAM

The objective of this study is to find out what kind of textbooks and teaching materials are needed by students of Islamic Education Program (PAI). This is done so that students get the material they really need to support their learning process at this time or in the future. This research conducted at students of PAI IAIN Sultan Amai Gorontalo. The data were collected by observing the PAI Study Program documents and distributing questionnaires for the needs of English learning materials. This study involved 50 students who were taken randomly and spread out from 3 batches or semesters. The involvement of students who have already taken part in English learning is of course to get more comprehensive data. Data were analyzed with need analysis for students’ need. The research findings indicated that the needs of non-English study programs for ESP English courses are very diverse, so it can be concluded that they need English not only for their current needs such as reading English literature, but also for future work. Therefore, it is recommended that ESP English teachers or interested parties can accommodate the needs of students, and always carry out a need analysis because the needs of ESP students are always changing.
A Survey of Optimization Big Data Analytical Tools

Today their billions of people are share information on social media like Facebook, twitter, News, YouTube, articles etc. through by using mobile phone, laptop and PDAs, so that our traditional databases system is not capable to handle unstructured data or large amount of data. In large data context, traditional data techniques and platforms are less efficient. Because it shows a slow response and lack of data, scalable ness, performance and accuracy. The Big data Analytical tools are help to store, manage, process, analyze, visualize, and extract the large amount data and unstructured data. The main objectives of this paper to find out that how many types of latest big data tools or interface which is used for data processing, data integration and data management easily etc. This paper is also help to select or acquire the right combination of diverse Big Data tools according to their needs and specific platform requirements. Bigdata Analytics Tools have become the key to achieve a meaningful analysis of data. This paper helps to provide a detailed analysis and relative assessment of latest big data tools. we shall also discuss the key features of all latest Big Data Tools. In the end, we present the keywords for relevant points for opportunities and opportunities according to the current trend of large data tools / interfaces and also check the latest equipment for large data with recent development. We also discuss how these tools differ from each other and these interfaces help solve real-life applications.
Technology and Accommodations at University Centers for Students with Disabilities

The vast majority of U.S. universities and colleges have specialized centers to support students with disabilities. These centers have various names (Student Accommodation Centers, Student Access Services, Disability Resources) but the goal of these centers is to support students with disabilities who are obtaining post-secondary education. This study extends the findings and outcomes of our previous research completed in 2020 investigating university student accommodation centers. This research is an outcome of surveying these centers at public 4-year institutions in the US. The survey asked questions about the demographics of the students including number of students and type of disabilities served and they type of technology available for these students. This presentation will discuss the technology, personnel, and types of students the centers support at their institutions.
A Survey of Real-Time ETL Process Applied to Data Warehousing Environments

ETL (Extract, Transform, and Load) is an essential process required to perform data extraction from data sources, transforming the extracted data into an integrated format and loading the integrated data into a data warehouse. Moreover, the ETL process can be performed in a real-time way. This paper presents a survey of real-time ETL process applied to data warehousing environments. The related studies were collected by means of a systematic review conducted by a methodological protocol. Moreover, we grouped the studies by categories, which had not been made before in the literature. The results showed that we have a wide range of opening opportunities of original research, as there are a lot of initial ideas and in turn they have not yet been tested, validated, and compared to related work.
Anthropomorphism in human-centered AI: Determinants and consequences of applying human knowledge to AI agents

Anthropomorphism is defined as a tendency to apply human knowledge to a nonhuman target. Under this process, individuals perceive social characteristics from human-like cues in a nonhuman target, make sense of the situation in which they interact with the target, and behave in a way appropriate to the inferred situation. Acknowledging that anthropomorphism is the key mechanism in users' responses to human–AI interaction, this chapter reviewed literature on user experience in interacting with AI agents and reinterpreted their findings through the theoretical lens of anthropomorphism. The underlying mechanism of user experience reported in the literature was articulated, and the determinants of anthropomorphism were identified. Positive and negative consequences of anthropomorphism in the context of using a recommendation system were discussed. Potential solutions for the negative outcomes of anthropomorphism were also presented.
Convolutional Neural Networks Applied to Data Organized as Olap Cubes

This paper presents a Convolutional Neural Network (CNN) architecture named \textit{OlapNet}, which incorporates implicit operations of OLAP cubes (or data cubes). OLAP cubes are produced from database tables or spreadsheets and they allow particular operations that support performing complex queries efficiently. OlapNet permits evaluating various combinations of these OLAP operations in its search space and thus, it enables, in part, to automate the data transformation step in the knowledge discovery process. A sample of data from an actual database containing anonymized data of the debt history of customers of a financial institution has been used to evaluate our proposal. A predictive classification problem to estimate the probability of any given customer contracting new credits in the next three months has been modeled from these data. Then, traditional methods of Machine Learning and CNN were applied. The results showed that CNN, using the OlapNet architecture, outperforms traditional methods in almost all cases, indicating that the proposed architecture is quite promising.
Selfish Mining and Defending Strategies in the Bitcoin

As a kind of distributed, decentralized and peer-to-peer transmitted technology, blockchain technology has gradually changed people’s lifestyle. However, blockchain technology also faces many problems including selfish mining attack, which causes serious effects to the development of blockchain technology. Selfish mining is a kind of mining strategy where selfish miners increase their profit by selectively publishing hidden blocks. This paper builds the selfish mining model from the perspective of node state conversion and utilize the function extremum method to figure out the optimal profit of this model. Meanwhile, based on the experimental data of honest mining, the author conducts the simulation of selfish mining and discovers that selfish miners are able to acquire more revenue than honest miners when they account for more than 1/3 computing power of the whole system. Lastly, to defend the selfish mining attack, the author also summarizes the existing defending strategies and evaluates every kind of strategy briefly.
Real Time Automatic Gesture Controlled Based Wireless Wheelchair for disabled people

According to research, around six million people worldwide are disabled and require the use of a wheelchair for mobility. Before anything else, the wheel directors must be relocated and remotely maintained. To help with this, "joy stick controlled wheelchairs" were developed. However, in normal use, these joystick-controlled wheelchairs were difficult to operate. Because of the firm fastens and unidirectional use of the joysticks, the use of joysticks became more sensitive, especially in the case of deadened individuals. To address these challenges, we developed a "signal-controlled wheelchair" that can be maneuvered with a small motion of the hand. This can be used with two hands and controlled to approach the stoner in a positive manner. The ongoing effort is supported with Arduino grounded predispositions similar to the Arduino NANO and UNO processors, which are tweaked using the Arduino IDE.
Action Recognition of Simulated Workplace with Occlusion Based on Interpolated Skeleton Data Using Openpose

With the rapidly increasing aging problem in Japan, the population of middle-aged and older adults is growing, especially in the construction industry. The construction industry tends to have more fall-related accidents than other industries do because of the complicated work environment. With modern employees gradually placing more emphasis on the comfort and safety aspects of their work environment, it is necessary to have a management system to establish a safe and healthy working environment for them. As part of a key technique of such systems, several prior action recognition methods have been proposed based on vision information or skeleton data. However, the occlusion problem is still a challenge in action recognition for real-world situations. In this study, we propose an action recognition approach, based on skeleton data with linear interpolation methods, which uses simulated workplace action videos as data. The experimental results demonstrated that the proposed method was able to discriminate actions with an average accuracy of 18.60% in the Hide-and-Seek occlusion situation; this accuracy was better than that exhibited using the existing approach.
DEVELOPMENT OF GIFTED PERSONALITY IN THE CONTEXT OF CULTURAL-HISTORICAL PSYCHOLOGY OF L. S. VYGOTSKY

The article presents the results of the analysis of the scientific views of L. S. Vygotsky on giftedness and abilities in the context of the theory of cultural-historical development. Giftedness is considered by the scientist as a universal prerequisite for all people for their personal development in specific cultural-historical conditions. Special giftedness (intellectual, motor, practical, emotional, musical, etc.) is the result of the purposeful development of the abilities and structures of the personality in their interconnection and mutual influence. Thus, the development of a gifted personality as a whole is a process of mastering historically established forms of activity and relationships. The main mechanism for the development of a gifted person in the educational process is the solution of the dynamic contradiction between the level of actual development and the zone of proximal development. The ideas of L. S. Vygotsky about an integral gifted personality and the directions of its development are consonant with the goals facing modern education, and are applied in the theory and practice of building a personality-oriented New Ukrainian School.
Efficient Quantum Private Comparison Using Locally Indistinguishable Orthogonal Product States

In this paper, an efficient quantum private comparison (QPC) for equality comparison using locally indistinguishable orthogonal product states is proposed. In the proposed protocol, two participants can compare the equality of three classical bits in each comparison, which could greatly reduce comparison times and improve efficiency. A semi-honest third party (TP) is involved to help two participants compare the equality of their secrets and she may misbehave on her own, but she is not allowed to conspire with any participants. Additionally, the security analysis shows that the proposed QPC protocol is secure against both outside attacks and participant ones. Compared with the existing QPC protocols, the proposed protocol needs neither swapping entanglement technology nor unitary operations, in which quantum devices are expensive and difficult to implement. Finally, it is generalized to multi-party QPC (MQPC) protocol.
Review Analysis Using Ensemble Algorithm

Today digital reviews play a vital part in influencing the customer. E-commerce companies provide a platform for consumers to share their thoughts and comments, and thus, it provides an insight into the performance of the product to the company as well as to buyers. To make it as useful, one classification of a review is needed. Opinion mining is also known as sentiment analysis which in general is a process of extracting subjective information from the data collected. Machine learning provides better insights by automatically analyzing the product review and separating them into classes and labels. Opinion mining is an artificial intelligence tool, and its research is very useful for determining the sentiment of comments. A feed-forward neural network classifier is used to determine the sentiment tendency of the comment. The proposed revamp of the sentiment analysis approach was compared with RNN and CNN approaches. The result is displayed in the form of a chart that has higher precision. Thus, this technique is helpful for comments analysis.
A GDIoU Loss Function-Based YOLOv4 Deep Learning Network for High-Performance Insulator Location in Field Images

Aiming at the slow and low accuracy of insulator positioning in the process of power line health inspection, a YOLOv4 deep learning framework based on the Gaussian Distance Intersection over Union (GDIoU) loss function is proposed in this paper. In the scheme, A GDIoU loss function is designed to accelerate the convergence speed of the YOLOv4 deep learning network, through which the performance of the YOLOv4 network is enhanced and the insulator’s location speed and accuracy are accordingly improved. A large number of field insulator images are collected as training and test samples to enhance and evaluate the performance of the program. The experimental results show that the GDIoU based YOLOv4 deep learning network combined with the adaptive tilt correction method accelerates the insulator location speed by four times compared with the other state-of-the-art schemes, and the average precision is increased by 7.37% compared with naive YOLOv4 scheme. The performance can meet the requirement of online insulator location adequately.