Reviewerly/exHarmony huggingface dataset:
Performance Analysis of the Classification of Breast Cancer

In recent decades, breast cancer has increased to become the world's second leading cause of death among women. Chronic pain, genetic abnormalities, skin issues, texture of the skin, and color (redness) all appear to be indications of BC. Benign and malignant cancer is the most common binary classifications. Clinicians may discover a method of treatment that is both comprehensive and reliable. Machine Learning (ML) approaches are increasingly being employed in the classification of breast cancer. It supports with highaccuracy classifications and fast calculation skills. The proposed research work examines a supervised learning technique for classifying breast cancer that uses four different classifiers: Boosted Tree, Bagged Tree, Logistic Regression (LR) and Artificial Neural Network (ANN). Also, this research work will compare and contrast the four classifiers, as well as assess the performance. Based on the performance metrics, the above classifiers are analyzed, in which the Artificial Neural Network results with the accuracy of 97.56 % when compared to other classifiers.
A Prostate Cancer Game Based on Multiple Decision-Making and Planning Skills

The scientific literature on games and health applications aimed at helping decision-making for prostate cancer is relatively modest, therefore, still requiring more significant contribution and exploration. This work presents a prostate cancer game based on decision-making and planning skills designed to replicate classic real-world prostate symptoms, treatment patterns, and care pathways. Players on the role of medical professionals have to manage the patients' health and cope with the care of multiple prostate cancer patients concurrently. Which patient to help first and which medical tests to request to decide the diagnosis and correct medical action are tasks that the player has to manage. Since every decision has its consequences, the ultimate goal is to make decisions without losing patients' lives.
FAIR Forever? Accountabilities and Responsibilities in the Preservation of Research Data

Digital preservation is a fast-moving and growing community of practice of ubiquitous relevance, but in which capability is unevenly distributed. Within the open science and research data communities, digital preservation has a close alignment to the FAIR principles and is delivered through a complex specialist infrastructure comprising technology, staff and policy. However, capacity erodes quickly, establishing a need for ongoing examination and review to ensure that skills, technology, and policy remain fit for changing purpose. To address this challenge, the Digital Preservation Coalition (DPC) conducted the FAIR Forever study, commissioned by the European Open Science Cloud (EOSC) Sustainability Working Group and funded by the EOSC Secretariat Project in 2020, to assess the current strengths, weaknesses, opportunities and threats to the preservation of research data across EOSC, and the feasibility of establishing shared approaches, workflows and services that would benefit EOSC stakeholders.&#x0D; This paper draws from the FAIR Forever study to document and explore its key findings on the identified strengths, weaknesses, opportunities, and threats to the preservation of FAIR data in EOSC, and to the preservation of research data more broadly. It begins with background of the study and an overview of the methodology employed, which involved a desk-based assessment of the emerging EOSC vision, interviews with representatives of EOSC stakeholders, and focus groups with digital preservation specialists and data managers in research organizations. It summarizes key findings on the need for clarity on digital preservation in the EOSC vision and for elucidation of roles, responsibilities, and accountabilities to mitigate risks of data loss, reputation, and sustainability. It then outlines the recommendations provided in the final report presented to the EOSC Sustainability Working Group.&#x0D; To better ensure that research data can be FAIRer for longer, the recommendations of the study are presented with discussion on how they can be extended and applied to various research data stakeholders in and outside of EOSC, and suggest ways to bring together research data curation, management, and preservation communities to better ensure FAIRness now and in the long term.
CRH*: A Deadlock Free Framework for Scalable Prioritised Path Planning in Multi-robot Systems

Multi-robot system is an ever growing tool which is able to be applied to a wide range of industries to improve productivity and robustness, especially when tasks are distributed in space, time and functionality. Recent works have shown the benefits of multi-robot systems in fields such as warehouse automation, entertainment and agriculture. The work presented in this paper tackles the deadlock problem in multi-robot navigation, in which robots within a common work-space, are caught in situations where they are unable to navigate to their targets, being blocked by one another. This problem can be mitigated by efficient multi-robot path planning. Our work focused around the development of a scalable rescheduling algorithm named Conflict Resolution Heuristic A* (CRH*) (https://github.com/iranaphor/crh_star) for decoupled prioritised planning. Extensive experimental evaluation of CRH* was carried out in discrete event simulations of a fleet of autonomous agricultural robots. The results from these experiments proved that the algorithm was both scalable and deadlock-free. Additionally, novel customisation options were included to test further optimisations in system performance. Continuous Assignment and Dynamic Scoring showed to reduce the make-span of the routing whilst Combinatorial Heuristics showed to reduce the impact of outliers on priority orderings.
Blockchain-Based Medical Insurance Storage Systems

Blockchain carries the key to a new era for the transparent, decentralized, and immutable handling of digital assets. Supply chain management, voting, the banking sector, and healthcare have already started to make use of some of its benefits. Undoubtedly, the field of healthcare faces a lot of difficulties with the existing system of storing medical transaction data. Today, computerization has helped to store data in a systemic manner to a larger extent, but manipulation still seems to be a solemn concern. Health insurance is one of the major sectors vulnerable to fraudulent activities like false claims, claim denials, forgery, etc. Some people claim false amounts from insurance by showing the wrong amounts. Conversely, some insurance companies may deny genuine claims.
Detecting Text Semantic Similarity by Siamese Neural Networks with MaLSTM in Thai Language

This paper proposes to develop a model to detect the text semantic similarity in Thai by using a Siamese neural network with MaLSTM. As the text's intent is varied and hard to analyze, thus making the comparison between two sentences for the semantics similarity challenging. Our project wants to find similar intent of two questions by comparing the question between frequently asked questions (FAQs) and input questions from customers via Facebook Messenger of computer engineering at the King Mongkut's University of Technology Thonburi (CPE KMUTT). The data gather manually from the Pantip, CPE KMUTT FAQ, and Wikipedia, which create a dataset and data corpus. Although, the overall score performance of the model is not as we expected. Our model shows promising results, as it can detect between two questions' intent similarity. Results suggest that with the increase of the data, the model should have great potential for finding semantic similarity.
Compressed Domain Consistent Motion Based Frame Scoring for IoT Edge Surveillance Videos

IoT Edge is a major active technical front. Among others, video surveillance is one of the most common use cases for IoT Edge. However, there is a need to analyze the surveillance stream, due to the sheer size of the generated data. The stream can be analyzed in either the Edge environment itself or send to a cloud for analysis. There are two main constraints to be considered. First is the associated bandwidth cost to send the data to a cloud server for processing. It underlines the need to reduce the data sent to a cloud. Second is the computational and memory constraints of the devices in the IoT Edge Environment. It implies that only computationally cheap and fast algorithms can be allowed to run in the Edge Environment. However, generally highly effective algorithms require more computational resources and memory. Pruning of uninterested frames is a viable methodology that can potentially reduce the bandwidth cost and the resources utilized. We have developed a fast, computationally cheap, and effective frame scoring algorithm that scores frames based on the consistent motion. The algorithm works in the compressed domain using H.264 encoded motion vectors, by which it saves on the resources spent to decode the video stream. The algorithm can be used to prune uninteresting frames, while the interesting frames can be send either to the cloud or processed further in the edge itself.
An acceleration method for correlation-based high-speed object tracking

We propose an acceleration method for correlation-based object tracking. Correlation-based tracking methods involve an inverse Fourier transform, which is a bottleneck in acceleration. We exploit a trait of high-speed vision to accelerate this computation: namely, we assume that displacements between consecutive frames do not change dramatically within a short frame acquisition interval. By limiting a small region where the displacement is assumed to be, we propose to reduce computational cost in the inverse Fourier transform. We implemented the proposed method in phase-only correlation and conducted experiments using both simulated and real data. We achieved a computation speed around five-times faster than the conventional method without sacrificing accuracy. The proposed method provides as a useful building block in accelerating correlation-based object tracking.
A Novel Geometric Calibration Method for Active Stereovision System

In this paper, a novel active stereovision calibration method is proposed to determine the time-varying extrinsic parameters of the cameras. This method mainly focuses on the rotation of cameras around the corresponding spatial axes with deviation. In the offline calibration, the installation matrices and baseline matrix are determined by introducing geometrical characteristics that represents the relationship of rotation axis and the transformation of camera rotation. On this basis, the extrinsic parameters can be updated online. The proposed method solves the problem where the spatial relationship between each camera and its rotation axis can be arbitrary, which has been verified by the simulations as well as tracking and measurement experiments on the developed active stereovision platform.
Bounding Box Propagation for Semi-automatic Video Annotation of Nighttime Driving Scenes

Ground-truth annotations are a fundamental requirement for the development of computer vision and deep learning algorithms targeting autonomous driving. Available public datasets have for the most part been recorded in urban settings, while scenes showing countryside roads and nighttime driving conditions are underrepresented in current datasets. In this paper, we present a semi-automated approach for bounding box annotation which was developed in the context of nighttime driving videos. In our three-step approach, we (a) generate trajectory proposals through a tracking-by-detection method, (b) extend and verify object trajectories through single object tracking, and (c) propose a pipeline for efficient semiautomatic annotation of object bounding boxes in videos. We evaluate our approach on the CVL dataset, which focuses on nighttime driving conditions on European countryside roads. We demonstrate the improvements achieved by each processing step, and observe an increase of 23% in recall while precision remains almost constant when compared to the initial tracking-by-detection approach.
Students and Parents’ Perceptions toward English Online Learning during Corona Virus Pandemic

E-learning dramatically changes the learning process. As a result, both teachers and students have to work hard to adapt to the process. In response to these changes, this study aims to analyze the perceptions of junior high school, senior high school, and parents regarding online learning. This study involved 44 parents, 24 junior high school students, and 20 high school students. Data were collected using two questionnaires. The data collected were analyzed quantitatively in the form of percentages. The results of data analysis show that junior high school students use computers/devices for online learning activities; however, they consider traditional methods (live class meetings) to be more appropriate than online learning. (2) High school students also use their computers/devices for online learning and have full support from their teachers, but they prefer traditional methods. They consider traditional methods more effective than online learning, and (3) parents prefer traditional methods over online learning. Therefore, teachers are advised to be creative in teaching online; parents are expected to continue to control their students' online learning. More research is needed so that more information about online learning can be obtained.
Blockchain for Information Systems Applications

The potential of blockchain technology is increasing rapidly as the technology is more secure and immutable. IoT, the Internet of Things, is an evolving technique that adds quality to lives. IoT raises security issues because of the violations of security policies and rules. Blockchain authorizes IoT devices to make IoT strong in security-related areas. Blockchain provides a decentralized environment to IoT platforms and applications. Blockchain is an expanding technology to various fields due to its greater transparency and enhanced security. Blockchain technology works in a secured atmosphere. Without a third person, people can send money directly, where transactions are inexpensive and fast, and technology also prevents corruption and manipulation. Each cryptocurrency user can monitor their transactions and authenticate them at the same time. The blockchain framework developed by Ethereum and Hyperledger helps to store data for this application. We use many functionalities for digital health records. The computer protocol which verifies or enforces the negotiation or performance is known as the smart contracts. These have a broad spectrum of applications, like financial services, prediction markets, and the IoT. When the use of blockchain increased, issues like the scalability of major public-chain platforms came up, which directly affect the development of blockchain.
History of development of modern concepts of criminal responsibility of artificial intelligence in Spain

This research proves the failure to address theoretically fundamental issues of robots' legal capacity and cyber security and as a result crudity of issues concerning criminal liability of robots for their actions. The proposals of a number of Spanish scientists on the possibility of non-proliferation of the sphere of criminal law on robots in connection with the existing possibility of criminal prosecution of legal entities in Spain have been worked out. In retrospect, the Spanish concepts of criminal responsibility of artificial intelligence were studied: their novels, shortcomings and problems of application in modern conditions were revealed.
PREDICTION OF MONTHLY RAINFALL IN ORDAN VALLEY USING ARTIFICIAL NEURAL NETWORK

Artificial Neural Network (ANN) was applied to predict the long-term monthly rainfall for selected stations in Jordan valley.Meteorological data measured by Jordan meteorological department in the period between 1975 and 2005 were used.Three neurons which receive input signals of total monthly rainfall for three stations were used in the input layer of the network.One neuron, which produces corresponding output signals of the total monthly rainfall, to one station, is utilized in the output layer of the network.Finally, the values determined by the artificial neural network model were compared with the actual data.Errors obtained in this model are well within acceptable limits.
Rethinking Blockchain for Access Control in the Internet of Things

IoT is a network of interconnected heterogeneous devices which sense, accumulate the data and forward the same to the cloud platform for analytical purposes. There are various IoT verticals in which huge research is going on. IoT security is the most challenging research area in which researchers are investing a huge number of efforts. The challenges in IoT security include access control, trust management, authentication, authorization, privacy, and secured device to device communication. To overcome these, this paper gives an overview of proposed trust based distributed access control approach in IoT. Some of the challenges and threats can be controlled by blockchain technology. Basically, blockchain is an open and distributed ledger of records that can be verified efficiently and stored permanently. This paper checks the feasibility study of the applicability of blockchain in the IoT ecosystem to apply access control mechanism and privacy-preserving policies. This paper discusses how access control and privacy can be addressed by blockchain without compromising security. This paper consists of rigorous gap analysis which is done on the top of comprehensive literature survey. The paper also addresses the challenges and issues which can be faced while applying access control mechanism using blockchain in the context of IoT.
Data Analytics for Power Grid Control Centers Management

This paper presents a data analytics model applied to the management of equipment and functions of SCADA systems in power system control centers. The model is composed of systems for the management, analysis and visualization of data in the cloud that is based on the processing of data stored in long-term repositories with a focus on assisted decision- making. This model allows evaluating the behavior of equipment and functions of a control center to manage them in a timely manner. The proposed model was implemented in five power grid control centers in Latin America (Colombia, Peru and Bolivia), in which it is observed that the management of the SCADA system from data-driven model allows recognizing trend and behavior patterns for prioritizing preventive management.
Integrating code samples

Charlotte looked through her draft and the feedback from Karthik. Most edits had been straightforward: fixing a typo here and slightly restructuring the text there. Karthik’s other comments could be grouped into two questions:
A Hybrid Recommendation System for Video Games: Combining Content-based &amp; Collaborative Filtering

Abstract: Recommending video games can be trickier than movies. When it comes to selecting a video game, many factors are involved such as its genre, platform on which it’s played, duration of main and side quests, and more. However, recommending games based on just these features won’t suffice as a person who, for example, enjoys a certain genre of game can equally enjoy a vastly different genre. Therefore, a scoring mechanism is required which takes into account both, features of a game (contentbased filtering) and also studies the buying patterns of people playing a particular game (collaborative filtering). In this paper I have proposed a way to take into account both content-based and collaborative filtering into the final recommendation. I have used cosine similarity to quantify the similarity between the features of games. Along with this, I have employed a Deep fullyconnected AutoEncoder (DAE) to generalize the implicit data representation of an user’s buying patterns. Finally, I present a novel approach to combine the scores of these filtering techniques in such a way that it gives equal weightage to both. In other words, they both have equal influence over the final list of the top 10 games recommended to the user. Keywords: Hybrid Recommender, Collaborative filtering, Content-based filtering, Cosine similarity, AutoEncoder.
Pseudo-labeling with transformers for improving Question Answering systems

Advances in neural networks contributed to the fast development of Natural Language Processing systems. As a result, Question Answering systems have evolved and can classify and answer questions in an intuitive yet communicative way. However, the lack of large volumes of labeled data prevents large-scale training and development of Question Answering systems, confirming the need for further research. This paper aims to handle this real-world problem of lack of labeled datasets by applying a pseudo-labeling technique relying on a neural network transformer model DistilBERT. In order to evaluate our contribution, we examined the performance of a text classification transformer model that was fine-tuned on the data subject to prior pseudo-labeling. Research has shown the usefulness of the applied pseudo-labeling technique on a neural network text classification transformer model DistilBERT. The results of our analysis indicated that the model with additional pseudo-labeled data achieved the best results among other compared neural network architectures. Based on that result, Question Answering systems may be directly improved by enriching their training steps with additional data acquired cost-effectively.
The Relationship between Leadership Factors and Successful Technopreneur through the Mediator Role of Digital Enabler

The business digitalization wave has changed the entrepreneurship landscape globally. The existing digital enabler practices and thinks differently through making an item or arrangement that utilizes the ability of innovation to change the way was customarily done, especially in UAE. The power of technology completely changed the economics of an industry, where entrepreneurial venture aims to meet a marketplace or a platform that require them to transform into digital enablers as a successful technopreneur. This research aims to develop a new leadership model for successful technopreneur in the UAE. Adapting the theory of planned behaviors to predict a person's goal to participate in technology behavior and the transformational leadership theory that focuses on a leader towards identifying technology needs for better performance. A survey approach as a primary data collection was applied to test concepts and reflect the attitude of 323 respondents among technopreneur from the Innovation and Entrepreneur Centre located in Abu Dhabi. The data were analyzed through SmartPLS software for Structural Equation Modelling (SEM) using the Partial Least Squares (PLS) path modeling method that indicates that leadership factors of authenticity, devolved decision making, agility, collaborative achievement, and purpose and direction have a positive relationship towards successful technopreneur in UAE. The mediation outcomes of digital enablers indicate a positive relationship between leadership factors and successful technopreneur in UAE.
Endoscopy Image Retrieval by Mixer Multi-Layer Perceptron

In Computer Vision, the Image Retrieval task is one of the interests of researchers, particularly medical image retrieval and endoscopy images.With the development of the Convolution Neural Network and Vision Transformer Technique, there are many proposals for using these techniques to make Image Retrieval Task and achieve a competitive result.In this paper, we propose a method that using Mixer Multi-Layer Perceptron architecture (Mixer-MLP) to build an Image Retrieval System with Medical images, particularly Endoscopic Images.This System base on the Classification process of Mixer-MLP architecture to generate vector representation for similarity calculation.The research result achieves competitively with efficient training time.