Reviewerly/exHarmony huggingface dataset:
Enhancement of Drone-as-a-Service Using Blockchain and AI

With the rapid technological development of robust and intelligent UAVs (Unmanned Aerial Vehicles), typically referred to as drones, much opportunities have emerged to provide DraaS (Drone-as-a-Service) to help industries such as agriculture, energy and utilities, GIS, package delivery, cinematography, industrial inspection and many more. The capability of drones to lift payload, acquire data with camera and sensors mounted on it make drones as a useful tool for various commercial applications. However, there exist great challenges for executing autonomous missions, operations, management, ensuring safety and secure communications. In this research paper, we review the latest research in the field of Artificial Intelligence and Blockchain applied for DraaS. Blockchain being a distributed ledger protects the shared data using cryptography techniques such as hash functions and public key encryption. It can also be used for assuring the truthfulness of the information stored and for improving the security and transparency of the UAVs. The integration of AI contributes more intelligence to the system enabling informed decision making and eventually converting drones into vehicles capable of executing autonomous missions in the real-world.
Selection of Expanded Data Points in Immersive Analytics

We propose a novel technique to facilitate the selection of data points, a type of data representation we often work with in immersive analytics. We designed and implemented this technique based on the expansion of data points following Fitt’s law. A user study was conducted in an headset-based augmented reality environment. The results significantly highlight the performance of our technique in helping the user select data points and their subjective appreciation in working with the expendable data points.
Classification of Electrooculography Signals Using Convolutional Neural Networks for Interaction with a Manipulator Robot

Electrooculography (EOG) has been widely applied in human–machine interfaces (HMI) because it provides a reliable communication channel to assist people with disabilities. However, signal behavior under different conditions hinders eye movement classification when algorithms based on voltage threshold detection are used. Therefore, recalibration of the system is required for the classification algorithm to work correctly. Based on the above, a classification algorithm was developed to analyze the data vector corresponding to the entire EOG waveform, instead of just one characteristic value of the signal, thus avoiding the system recalibration process. A convolutional neural network (CNN) was implemented to classify six targets corresponding to different eye movements. The proposed model was compared with a feedforward neural network (FNN) to evaluate its performance. The results were implemented in an HMI for interaction with a manipulator robot.
Damage Analysis of Hydraulic Bucket Cylinder on Excavator Komatsu CP-200 At PT. Wiratako Mitra Mulia

&#x0D; &#x0D; &#x0D; &#x0D; PT. Wiratako Mitra Mulia is a company engaged in asphalt construction and road repair. At this time PT. Wiratako Mitra Mulia uses several heavy equipment to facilitate the work process, one of the heavy equipment used by PT. Wiratako Mitra Mulia is an excavator which is used for the process of dredging soil, moving materials and leveling the ground. From this, there are several obstacles or problems that occur in the excavator, such as damage to the bucket hydraulic cylinder, in which the bucket hydraulic cylinder has an oil leak, cylinder piston damage and seal leakage, therefore proper and scheduled maintenance is needed so that the damage that occurs to the hydraulic cylinder of the bucket excavator can be minimized so that the work process can run smoothly and do not experience any problems. Therefore, daily, weekly, monthly and yearly maintenance is required. For daily maintenance carried out every day by checking lubricants and performing services, for weekly maintenance inspections are carried out every 60 working hours. For monthly maintenance it is done every 250 working hours and for annual maintenance it is done every 2500 working hours.&#x0D; &#x0D; &#x0D; &#x0D;
Browser Extension Crash Course

Chapters later in this book will cover various aspects of browser extensions in great detail. This chapter, on the other hand, will guide you through the quickest way to get up and running with browser extensions. The crash course will cover building a trivial browser extension from scratch. It will not use any third-party libraries or dependencies, just vanilla HTML, CSS, and JavaScript.
A Factor Analysis Study Affecting High School Students' Global Citizenship

The objective of this study is to develop a sense of global community among young people and to lay the foundation for growth with desirable qualities and capabilities as members of a global civil society. The interpersonal competency was selected to conduct the study. The subjects of this study were high school students in K city located in Gyeonggi-do and analyzed how the gender, academic performance, parents' educational background, conversation time with parents, and participation in international cooperation activities affect global citizenship. Study subjects were a total of 248 high school students. Survey was selected as the study method. Collected questionnaires were subjected to descriptive statistical analysis and correlation analysis using IBM SPSS (Statistical Package for the Social Science) 21 program, Student’s t-test, and one-way analysis of variance (ANOVA). As a result of the analysis, first, it was found that female students had more influence than male students according to gender in global competency, and conversation time with parents was also found to have a significant effect. Second, in terms of self-competence, it was found that students who had experience in international cooperation had more influence than students who did not have experience in international cooperation. Lastly, in interpersonal competency, according to gender, female students were found to have more influence than male students, and conversation time with parents was also found to have a significant effect. Based on the results of this study, it is hoped that in the future, young people will participate in activities that cooperate with the world beyond regional and national identities, thereby providing a framework for recognizing and implementing their attitudes toward responsibility at the global level.
Optimalisasi Pemanfaatan Media Pembelajaran Berbasis TIK Melalui Program Kampus Mengajar bagi Sekolah Terpencil di Kabupaten Lebak

This community service activity aims to optimize the use of information and communication technology (ICT)-based learning media through campus teaching programs to improve the quality of education in remote areas. The method of implementing this service is with training which includes the stages of preparation, implementation, and evaluation of results through filling out questionnaires by teachers and students. This service partner is SMAN 1 Malingping as one of the remote school areas in Lebak district. The results of this dedication show that partners, namely teachers and students, feel the positive impact of using ICT application-based learning media in the classroom. Teachers' skills in implementing ICT-based learning media and students' motivation to learn increases. In addition, teachers have succeeded in overcoming various challenges in the learning process by optimizing the use of ICT-based learning media.
Analisis Kebutuhan Pengembangan Media Video Pembelajaran Tata Boga bagi Murid Tunarungu

This research was conducted to identify the need for instructional media before the development of cooking video media. The results of this needs analysis will be the basis for making video materials for deaf students. Needs analysis needs to be done as part of the pre-development stage of the research and development (R&amp;D) process, using research subjects of as many as 13 classroom teachers, as well as researchers conducting observations and collecting data through a google form questionnaire distributed via Whatsapp. Based on the research findings that to involve deaf students in the culinary learning process and generate a desire to learn, innovative learning media are needed so that deaf students can achieve maximum learning goals and improve learning outcomes.
A Graph Convolution Collaborative Filtering Integrating Social Relations Recommendation Method

Traditional collaborative filtering recommendation algorithms only consider the interaction between users and items leading to low recommendation accuracy. Aiming to solve this problem, a graph convolution collaborative filtering recommendation method integrating social relations is proposed. Firstly, a social recommendation model based on graph convolution representation learning and general collaborative filtering (SRGCF) is constructed; then, based on this model, a social relationship recommendation algorithm (SRRA) is proposed; secondly, the algorithm learns the representations of users and items by linear propagation on the user–item bipartite graph; then the user representations are updated by learning the representations with social information through the neighbor aggregation operation in the social network to form the final user representations. Finally, the prediction scores are calculated, and the recommendation list is generated. The comparative experimental results on four real-world datasets show that: the proposed SRRA algorithm performs the best over existing baselines on Recall@10 and NDCG@10; specifically, SRRA improved by an average of 4.40% and 9.62% compared to DICER and GraphRec, respectively, which validates that the proposed SRGCF model and SRRA algorithm are reasonable and effective.
Outer-synchronization criterions for asymmetric recurrent time-varying neural networks described by differential-algebraic system via data-sampling principles

Asymmetric recurrent time-varying neural networks (ARTNNs) can enable realistic brain-like models to help scholars explore the mechanisms of the human brain and thus realize the applications of artificial intelligence, whose dynamical behaviors such as synchronization has attracted extensive research interest due to its superior applicability and flexibility. In this paper, we examined the outer-synchronization of ARTNNs, which are described by the differential-algebraic system (DAS). By designing appropriate centralized and decentralized data-sampling approaches which fully account for information gathering at the times tk and tki . Using the characteristics of integral inequalities and the theory of differential equations, several novel suitable outer-synchronization conditions were established. Those conditions facilitate the analysis and applications of dynamical behaviors of ARTNNs. The superiority of the theoretical results was then demonstrated by using a numerical example.
Data Fixing Algorithm for Electronic Tax System based on J2EE Architecture

Through the analysis of the current tax work requirements, the electronic tax system based on J2EE is developed, and the B/S structure is adopted in the software structure. It includes technologies such as J2EE technology, data acquisition technology, information maintenance and safe storage; The functional module scheme design and database design based on the demand analysis; the design and optimization of the login module and the declaration module; based on the four-tier system structure, the realization of various functions of the system, mainly the login and tax declaration modules. The interface-based black-box testing method is adopted to test the functions of the system to verify whether the requirements of the system can be met.
Research on university profiles about entrepreneurship and innovation orientation: Case of a developing country

DATA REPORT article Front. Psychol., 22 November 2022Sec. Educational Psychology https://doi.org/10.3389/fpsyg.2022.968996
Virtual Reality Serious Game Session for Rehabilitation Through Calculation of the Center of Pressure in a Stabilometric Platform

Virtual reality (VR) offers an alternative neurological rehabilitation based on the effectiveness of the gamification technique to get compromised, enjoy, and turn treatment periods less boring through an immersive task. This work presents a system composed of a VR environment through a 2-DOF robotic platform for a balance session executing a parachute game. The player controls the direction of fall through the variation of his center of pressure (CoP) and interacts with the mechanism generating his movement. These changes supply the desired position for each actuator. The system involves a gyroscope measuring pitch and roll angles. Psychophysiological responses of the autonomous nervous system record information about the experience. We propose a mechatronic system that joins virtual scenarios, robotic devices, and biosignals for the acquisition of healthcare purposes and pleasant recovery therapy. This exergame could aid with the problems of the traditional method: low attention, monotonous, and predictable sessions.
Machine Learning Based Crime Rate Analysis Using Python

Abstract: Crime is obvious that the rate of crimes were increasing day by day in all societies in world, but we personally do believe that there are a lot which can be done by both the governments and the individuals to reduce the crimes in communities. Crime analysis is a well-organized way of detecting and examining patterns and trends in crime. We should give utmost importance to study the reasons behind the crimes, so that we can prevent various crimes occurring and we can be able to find suitable solutions to prevent them. When people cannot find work, they have all the free time in the world. They think of crimes as a shortcut to obtaining and processing the riches of life, without any hardwork. To my mind, the overwhelming majority of people tend to participate in activities assisting the government to keep the society a safe place for their own families and the others and for all age groups. Our main aim of this project is to distinguish various crimes using clustering techniques based on the occurrences and regularity. In this project, the crime data is classified using the Support Vector Machine, Decision Tree, Random Forest Algorithm. This proposed system can indicate the areas which has more probability of occurring crimes so that we can easily identify the crimes based on the previous history and we can take measures to prevent the occurrences of crimes.
College English Intelligent Writing Score System Based on Big Data Analysis and Deep Learning Algorithm

With the development of technologies such as big data analysis and deep learning, various industries have begun to integrate with big data analysis and deep learning and continue to promote the development of the industry. This system is an intelligent writing scoring system for college English teaching. It uses popular big data analysis and deep learning to distinguish training algorithms. From 2015 to 2022, the number of college students taking exams has increased yearly, with an increase of more than 50%. Therefore, the system proposes a text vector calculation method that can find matching samples in the text set after the text is weighted by the weight function and uses deep learning to distinguish the algorithm evaluates the matched text, and finally can get the final score according to the content quality, semantic coherence, text readability, and other aspects of the text. Compared with traditional manual scoring, this technology is more convenient, quick, concise, and effective. This system is significant for improving the efficiency of teaching English writing in college.
Demonstration of PseudoJumpOn: Repetitive Step-up Jump in Virtual Reality

PseudoJumpOn is a novel locomotion technique that allows step-up jumping movements using actual jumping motions on a flat floor with a common virtual reality (VR) setup. In this demo, we introduce an installation utilizing PseudoJumpOn where the player repeatedly makes step-up jumps to climb up to higher platforms. The aim is to provide players with the experience of richer vertical locomotion in VR without causing them to feel unnaturally manipulated, even though they know there are no physical steps in reality. While jumping, the system applies two types of viewpoint manipulation (i.e., gain manipulation and peak shifting) based on the user’s physical jumping height and the height of the jumping target. This calculation previously assumed that the height of the jumping target was known, but we improved this by predicting it using the user’s facing direction when taking off.
Policing cryptomarkets and the digital war on drugs

This chapter explores the problems associated with the war on drugs in the pre-digital era, and the translation of approaches to the policing of organised crime into the dark web. Our analysis focuses on US and Australian legal developments, which demonstrate how permissive laws provide the basis for sanctioning contentious enforcement tactics that do little to limit the nature, extent, and scope of harms caused by illicit drug distribution on the dark web. First, we frame our argument considering key elements of the US-led war on drugs, before moving into a discussion of the moral panic that accompanies contemporary approaches to the policing of cryptomarkets. This is followed by a critical discussion of organised crime and conspiracy laws that are used to identify ‘kingpins’ in charge of administering cryptomarkets. We conclude with consideration of how expanded Australian surveillance laws (that have extraterritorial impacts) conflate drug trafficking with other harmful dark web activities. We argue that taken together these processes are the product of a constant expansion of state power that undermines human rights and civil liberties, while conferring enormous powers on the police for domestic and transnational activities that do little to disrupt drug cryptomarkets or mitigate drug-related harms.
A coarse-to-fine and automatic algorithm for breast diagnosis on multi-series MRI images

Introduction Early breast carcinomas can be effectively diagnosed and controlled. However, it demands extra work and radiologist in China often suffer from overtime working due to too many patients, even experienced ones could make mistakes after overloaded work. To improve the efficiency and reduce the rate of misdiagnosis, automatic breast diagnosis on Magnetic Resonance Imaging (MRI) images is vital yet challenging for breast disease screening and successful treatment planning. There are some obstacles that hinder the development of automatic approaches, such as class-imbalance of samples, hard mimics of lesions, etc. In this paper, we propose a coarse-to-fine algorithm to address those problems of automatic breast diagnosis on multi-series MRI images. The algorithm utilizes deep learning techniques to provide breast segmentation, tumor segmentation and tumor classification functions, thus supporting doctors' decisions in clinical practice. Methods In proposed algorithm, a DenseUNet is firstly employed to extract breast-related regions by removing irrelevant parts in the thoracic cavity. Then, by taking advantage of the attention mechanism and the focal loss, a novel network named Attention Dense UNet (ADUNet) is designed for the tumor segmentation. Particularly, the focal loss in ADUNet addresses class-imbalance and model overwhelmed problems. Finally, a customized network is developed for the tumor classification. Besides, while most approaches only consider one or two series, the proposed algorithm takes in account multiple series of MRI images. Results Extensive experiments are carried out to evaluate its performance on 435 multi-series MRI volumes from 87 patients collected from Tongji Hospital. In the dataset, all cases are with benign, malignant, or both type of tumors, the category of which covers carcinoma, fibroadenoma, cyst and abscess. The ground truths of tumors are labeled by two radiologists with 3 years of experience on breast MRI reporting by drawing contours of tumor slice by slice. ADUNet is compared with other prevalent deep-learning methods on the tumor segmentation and quantitative results, and achieves the best performance on both Case Dice Score and Global Dice Score by 0.748 and 0.801 respectively. Moreover, the customized classification network outperforms two CNN-M based models and achieves tumor-level and case-level AUC by 0.831 and 0.918 respectively. Discussion All data in this paper are collected from the same MRI device, thus it is reasonable to assume that they are from the same domain and independent identically distributed. Whether the proposed algorithm is robust enough in a multi-source case still remains an open question. Each stage of the proposed algorithm is trained separately, which makes each stage more robust and converge faster. Such training strategy considers each stage as a separate task and does not take into account the relationships between tasks.
Michael T. Turvey – Interview and reflection

In this interview conducted by Agnes Szokolszky in 1997, Michael Turvey, a leading figure of the “Connecticut school” of ecological psychology recounts his career, including his early award-winning work in the decidedly orthodox information processing perspective. The interview details how he became acquainted with Gibson's ecological approach to perception, and dedicated himself to developing this heterodox program of theory and research, especially by building a theory of action that was compatible with Gibson's approach to perception. The interview evokes the intense atmosphere of the “Storrs scene” from the time Robert Shaw arrived there, and the establishment of the Center for the Ecological Study of Perception and Action which has become a leading training and research center. Turvey assesses the state of cognitive science and the state of the ecological approach in the late 1990s and gives his thoughts on the future of psychology and Gibson's place in it. In the current reflection that follows the interview, he emphasizes the continuing contributions of generations of graduate students to development of the ecological perspective. He also argues that progress will continue to come by anchoring the explanation of perception and action not in the nervous system and not limited to human achievements but in generic principles that support a theory for all organisms. The chapter also contains a short biography and a list of ten of his most important publications.
hBag - Smart Assistant for Healthy Shopping

This paper presents a method of improving the quality of consumption of different products, through a mobile application build for Android operating system. It is a smart assistant for shopping management, because it can help a user to choose the optimal product for him. When scanning a product barcode, the user can see the details of this product, like the quality of ingredients or the brand. This app was developed using java programming language, with Spring Framework and its libraries.