Reviewerly/exHarmony huggingface dataset:
A Dynamic Cloud Bayes Network-Based Cleaning Method of Multi-Source Unstructured Data

Abstract Aiming at the problems of data redundancy and data abnormality of multi-source unstructured data such as video, picture, and text in the process of processing quality inspection and equipment status monitoring of discrete intelligent production line, a multi-source unstructured data cleaning method based on dynamic cloud Bayesian network is proposed. We analyze the characteristics of multi-source unstructured data in the processing operation of the discrete intelligent production line and construct a multi-source unstructured data description model. combine dynamic Bayesian network and cloud model theory to design a multi-source unstructured data cleaning framework and processing flow based on dynamic cloud Bayesian network. finally, the feasibility of the proposed method is demonstrated by simulation analysis of arithmetic cases.
PHILOSOPHY OF EDUCATION IN THE ASPECT OF MODERN REFORMS

This article examines the modern problems development of education in Russia. Devoted to the interaction of personal and culture in the education. Presents an analysis of the phenomenon morals values in the context of modern education morals values to the context of modern education in secondary and high school.
Storyboard Visualization for Gamification Design for Deaf Children's Education Using Octalysis Approach

Deafness is a condition of deafness that makes it impossible to perceive various sound stimuli. Therefore, for the education of hearing-impaired students, it is necessary to develop special methods for the learning process, especially with regard to personality learning. In this digital age, the use of digital media and technology is a very suitable solution, especially in the field of education. The purpose of this study is digital by prioritizing visual elements in a way that hearing-impaired students can see and understand, that is, by creating gaming for hearing-impaired students. It's about developing learning media. Gamification aims to support the process of education and learning for hearing-impaired students. Gamification has a framework called octalysis. Octalysis is a tool for strategic and visual analysis of gamification. The way to design a gamification is to create a visualization in the form of a storyboard based on the gamification framework. Storyboards can be used as the basis for developing gamification, allowing you to focus more and convey the material presented. As a result, by creating a good storyboard, you can create the right gamification visualizations for hearing-impaired students to motivate them and capture the power of learning offered at school. Keywords: Deaf Student, Gamification, Octalysis, Storyboard, Education
DEFINITION AND LEGISLATIVE CONSTRUCTION OF OBJECTIVE SIGNS OF EXTREMIST CRIMES

Within the framework of the article, crimes of an extremist ori- entation are analyzed from the standpoint of a definitive analysis, as well as the specifics in constructing the object and the objective side of those crimes are in- vestigated. At the same time, the authors, proceeding from their own scientific interests, limited the subject of our research to the method of committing an extremist crime - on the Internet and, accordingly, an analysis of the objective signs of those crimes of an extremist orientation will be carried out, in the main or qualified composition of which such a method is provided - in particular Ar- ticles 280, 280.1, 282 of the Criminal Code of the Russian Federation.
Development of AI models to predict mortality and long-term hospitalization in pneumonia

<b>Background:</b> Recently, the achievement of personalized medicine is expected. We plan to develop AI algorithms to predict clinically important outcomes using inpatient data aggregated through CITA, the integrated medical care support platform. As proof of concept, we aimed to develop AI algorithms to predict mortality and long-term hospitalization in pneumonia. <b>Method:</b> We analyzed patients with unplanned hospitalization admitted to Nagoya University Hospital from November 2018 to December 2020, using all electrical health record (EHR) through CITA. Patients with pneumonia assessed using A-DROP were used as test data (Set T). Those without A-DROP (Set A) and non-pneumonia (Set B) were used as training data. GBDT, one of AI algorithm, was used to predict 30-day mortality and 15-day or longer hospitalization, using data within 2 days of admission. Free text data were analyzed using vectorization. The accuracy of algorithm was evaluated by AUC for Set T. <b>Results:</b> Set A and Set B had 3,861 and 440 cases. Set T had 214 cases, with 8 deaths and 83 long-term hospitalizations. Training with Set B showed almost the same good mortality prediction as with Set A (93.5 vs 85.3, p= 0.23). GBDT showed higher accuracy than A-DROP in mortality prediction (93.5 vs 74.0, p= 0.03) and long-term hospitalization (71.7 vs 65.1, p= 0.06). The feature importance analysis showed medical staff records via free text, lab results, and diet information were more associated with outcomes. <b>Conclusion:</b> We developed AI algorithms to predict mortality and long-term hospitalization using integrated EHR in patient with pneumonia. Using this pipeline, we could automatically develop AI to predict important outcomes in many diseases.
AI Enabled Educational Bot to Improve Learning Outcomes using Bag of Words Algorithm

Educational institutes across India have closed due to COVID-19 pandemic which has jeopardized academic schedules. To maintain their academic activities, several Indian educational institutes have shifted to online learning platforms. However, there are still questions about the effectiveness, design, and readiness of e-learning. In light of this fact, E-learning still tends to be controversial. As a result, it is inevitable to design an application with greater usability. In this paper, a novel application tool is developed and the design is proposed to enhance student knowledge and facilitate their study process, so they can study in comfort at home. The proposed system allows students to receive personalized educational assistance and also allows the students to get instant responses to all their questions throughout the day via a voice-enabled chatbot. It facilitates the connection between students and tutors, as well as the awarding of mind coins and badges based on how well they do, motivating them to learn more. Data analytics is incorporated and usability is measured. The result shows that the proposed system has greater usability resulting in a progressive improvement in the student’s performance.
After 40 years of search for the promised land BICA*AI designers come to a point of disillusion and discord. Why? For what sins and misdeeds?

The ultimate goal of research in cognitive architectures is to model the human mind, eventually enabling the recreation of human-level artificial intelligence. However current wave of research in cognitive architectures is centered only on the study of human mind modeling occupied with the human behavior and the underlying cognitive mechanisms necessary for implementation of this behavior. In doing so, human mind research totally derails its own ability to study other operating mechanisms of biological brains, such as how the brain processes information, evaluates its semantics, makes decisions and interacts with the surrounding world. The prime intention of this short comment on the current trends in the human brain research is to draw people's attention to the harmful consequences of some of such trends.
Concurrency

In modular programming, large programs are usually made up of several smaller sub-programs. For example, a web server handles several requests from multiple web browsers and serves them with HTML web pages simultaneously. Each request is treated like a small program. The ability to run and make progress on multiple smaller components of a larger program simultaneously is known as concurrency. Concurrent programming often requires the use of different constructs, like threads and locks, to perform complex tasks of synchronization and deadlock prevention.
Effective Resource Utilization in Heterogeneous Hadoop Environment Through a Dynamic Inter-cluster and Intra-cluster Load Balancing

Apache Hadoop is one of the most popular distributed computing systems, used largely for big data analysis and processing. The Hadoop cluster hosts multiple parallel workloads requiring various resource usage (CPU, RAM, etc.). In practice, in heterogeneous Hadoop environments, resource-intensive tasks may be allocated to the lower performing nodes, causing load imbalance between and within clusters and high data transfer cost. These weaknesses lead to performance deterioration of the Hadoop system and delays the completion of all submitted jobs. To overcome these challenges, this paper proposes an efficient and dynamic load balancing policy in a heterogeneous Hadoop YARN cluster. This novel load balancing model is based on clustering nodes into subgroups of nodes similar in performance, and then allocating different jobs in these subgroups using a multi-criteria ranking. This policy ensures the most accurate match between resource demands and available resources in real time, which decreases the data transfer in the cluster. The experimental results show that the introduced approach allows reducing noticeably the completion time s by 42% and 11% compared with the H-fair and a load balancing approach respectively. Thus, Hadoop can rapidly release the resources for the next job which enhance the overall performance of the distributed computing systems. The obtained finding also reveal that our approach optimizes the use of the available resources and avoids cluster over-load in real time.
Emotion Detection from Multilingual Text and Multi-Emotional Sentence using Difference NLP Feature Extraction Technique and ML Classifier

Machines can read, comprehend, and extrapolate meaning from human languages, thanks to natural language processing.In this paper, we have detected emotion from multilingual text and multi-emotional sentences.For our research, we have collected a dataset containing around 7000 tweets on 4 emotions (Anger, Fear, Joy, and Sadness). After pre-processing our data, we used 2 NLP feature extraction models and trained those with the help of 4 different Machine Learning classifiers. We have also developed an algorithm for detecting exact emotions from multi-emotional sentences. Also, we compared our result with a research paper using the same dataset (ISEAR). And found out our model provides relatively better resultsthan that model. We also tried to determine emotion from the Bangla text. Although there is not much data regarding emotion in Bengali. We managed to get around 600 data on Bangla.
A Toolbox for Barriers on Interactive Oracle Proofs

Interactive oracle proofs (IOPs) are a proof system model that combines features of interactive proofs (IPs) and probabilistically checkable proofs (PCPs). IOPs have prominent applications in complexity theory and cryptography, most notably to constructing succinct arguments. In this work, we study the limitations of IOPs, as well as their relation to those of PCPs. We present a versatile toolbox of IOP-to-IOP transformations containing tools for: (i) length and round reduction; (ii) improving completeness; and (iii) derandomization. We use this toolbox to establish several barriers for IOPs: We believe that our toolbox will prove useful to establish additional barriers beyond our work.
Responsive Web-Based E-Voting Information System

Technological advancement has growth rapidly impacted to bring great changes to humans. It is included for voting. By utilizing the advancement and growth of the technology, voting is conducted computer technology in the term of electronic voting or generally referred to as e-voting. The problem currently being confronted by the chairman’s election is the COVID-19 pandemic, which makes it impossible to hold a direct election. This research uses qualitative research methods through system development method in carrying out the the SDLC (System Development Lift Cycle) method as it has gone through several e-voting system development processes such as design, analysis, prototype, and implementation. In building this e-voting, the application design uses the PHP programming language, HTML tags, Mysql database as a database server, Web Hosting. The development of this e-voting application is web-based online. Thus, the e-voting applications can be accessed online. Based on the test results, it can be concluded that the application of online e-voting can be implemented to the election of the chairman of RT.01 RW.03 Kludan Village. It can be proved through the application testing to 10 random voters by filling out a questionnaire and getting the results of application acceptance of 83% which is encompassed as very good category.
A Force Sensing System and Power Assist Control for Omnidirectional Cart with Active-casters

This paper presents a force sensing system and power assist control for an omnidirectional cart with multiple active-casters. The drive wheel unit which includes differential type active-caster, 2motors, force sensing mechanisms is used to omnidirectional cart to measure the drive torque and force applied by a human operator. The force sensing system can detect force applied at any point on the cart. Based on the force information, the omnidirectional cart is controlled to assist the applied force to realize omnidirectional motion of the cart. The structure and fundamental experiments are presented on this paper.
Topic Modeling in the ENRON Dataset

E-discovery is the electronic version of identifying, collecting, reviewing, and producing Electronically Stored Information (ESI) for the pre-trial procedure in a prosecution or legal investigation in many countries. There are challenges in e-discovery, such as cost and time consumption of the information retrieval process. Hence, natural language processing (NLP) is a key component in solving this problem, and we show using our case study that it scales effortlessly. Litigation costs are increasing, and as a result, legal professionals have sought to use fast information retrieval and machine learning methods to reduce manual labor and increase accuracy. In this paper, we consider using NLP to represent documents in a topic space using Latent Dirichlet Allocation and solving the information retrieval problem via finding document similarities in the topic space rather than doing it in the corpus vocabulary space. We also used the TF-IDF method in LDA to improve its performance. We report the results of our experiments on the ENRON dataset in the electronic discovery domain.
Comparison of Convolutional Neural Network and Capsule Network in Image Recognition- A Case Study of Microbiopsy Images of Breast Tumor

Cancer is now the leading causes of death. During treatment, if an abnormal mass is detected by routine mammography after histological examination, a biopsy will be performed for further diagnosis. However, the time required to review and evaluate the biopsy slide is very long. An effective algorithm that can identify cancer tissues and reduce the rate of misdiagnosis is urgently needed. The current research methods mainly include: Capsule Network, and Deep Convolutional Neural Networks (CNNs) used in image classification, etc. This paper mainly uses Capsule Network, CNN, CNN trained on Histogram of Oriented Gradient (HOG) images, and Grey Level Co-occurrence Matrix (GLCM) to classify benign and malignant images of the tumor micro-biopsy images. The first step is to download the breast tumor micro-biopsy images from Breakhis. The second step is to preprocess the images, mainly image enhancement, augmentation, and HOG feature extraction. In the third step, the processed image data is used to train CNN and Capsule Network. The last step is to visualize the accuracy of model training and use confusion matrix to calculate the accuracy of image classification, recall rate, and so on. The results show that the accuracy of CNN with the help of transfer learning can reach 98.32%, the accuracy of CNN trained with HOG image is only 82.99%, the accuracy of CNN trained with GLCM is 92.79%, and the accuracy of capsule network can reach 92.39%.
FEMINIST THEMATIC DISCOURSE ANALYSIS IN CS

How conversation takes place is a well-researched area in the field of Linguistics, particularly when it comes to how Feminist Methodologies are applied to this. What remains to be seen, however, is the application of these techniques in the field
Research on the Design of Project-based Teaching &amp; Learning Mode Assisted by Educational Robot

In view of the problems existing in the current implementation of project-based learning, such as teachers pay too little attention to students' basic theoretical knowledge, students' low participation in learning and the limitations of using information technology, this paper considers the application of autonomous mobile educational robots to assist project-based learning under the background of artificial intelligence (AI). First, according to the six characteristics of robot-assisted instruction, we discuss the functional modules of autonomous mobile educational robots that assist project-based learning should have, also, guided by the constructivism theory, combined with the general project-based teaching mode and the functional characteristics of autonomous mobile educational robots assisting project-based teaching, we reconstruct the elements of teaching system, and a project-based teaching mode assisted by autonomous mobile educational robots is designed. Then, a learning platform corresponding to the teaching mode of this paper is developed by using enterprise WeChat to further assist the teaching & learning process. Finally, in order to verify the feasibility and effectiveness of the teaching model, the experimental course of” Principles of Computer Composition” for sophomore is selected to carry out corresponding teaching practice, the results demonstrate that the teaching mode has positive effects on improving students' motivation, participation, interest in the material, and collaborative learning abilities.
User-Centered Virtual and Augmented Reality Environments to Support the Classroom Needs of Elementary School Children

Virtual reality (VR) and augmented reality (AR) are emerging technologies that in recent years have been the focus of attention in various fields of application. Particularly, in education, they have shown to have a potential in the teaching and learning process allowing students and teachers to have interactive content in which they can explore and discover practically and playfully the topics that are carried out in the classroom. This work focuses on identifying existing educational models in elementary education in order to propose a model that considers VR and AR as part of the activities carried out in the classroom and that allows teachers and students to expand their imagination and creativity and above all to enrich their knowledge by immersing them in activities according to their curricula.
What Philosophy Does the Leader Have Related to Crisis Management?: The Linguistic Characteristics of Presidents' Speech in Korea

Modern society is a risk society characterized by 'uncertainty'. The leaders in a risk society play an important role in maintaining order and safety. The leader's message in a risk situation has a positive effect on actions and motivation to protect citizens and contributes to the minimization of damage. In this study, under recognition of the importance of the leader's role in disaster safety, leaders' messages were investigated. For the purpose, a language-network analysis was performed on the speeches related to disaster and safety of recent Korean four presidents from the 16th to the 19th. The results are as follows: First, Korea's crisis management revealed in the President's speech reflects the situation at that time. Second, the speech contains the Presidents' political philosophy and values. Third, many of the words found in the speeches of the four Presidents show the governments' struggles to induce a higher level of safety behaviors and attitudes of citizens while giving highest priority to the safety of citizens.
Analysis of Preparation Postures in Timing of Titling the torso and Opening the Elbow during Carrying Heavy Object for Improving Humanoid Robot Movements

In recent years, labor shortages and the aging of the workforce have become problems. One solution to this problem is the introduction of humanoid robots to perform tasks on behalf of humans. Since humanoid robots have the same structure as humans, they can have a variety of motions by extracting features from human motions and estimating posture. In this study, we focus on the redundant parts of a humanoid robot and human that are not directly related to reaching the target positions of the hands and aim to analyze the stabilization of humanoid robot motions and the preparation posture for movements that require force. In the experiments, we focused on the putting and pulling movements of the shelf, which requires maintaining its balance and exerting force and confirmed the differences in the movements by changing the weight of the objects. We analyzed the movements by focusing on the flexion and extension of the torso and the opening of the elbows while holding the object close to the torso. From the results of the experiments, when the object’s weight was larger, the timing of grasping was earlier, the opening angle of the elbow was larger, and the torso was closer to the target object.
Collision-free Robot Path Planning on Arbitrary Optimization Criteria for Multi-DoF Robot Arms Using cGANs

In robot motion, collision avoidance is essential to ensure safety, and generation of multiple paths according to the objective is beneficial. Conventional methods have been pointed out to have difficulty in achieving both collision avoidance and optimization, and to have large computational costs. It is also challenging to compensate for safety using deep learning methods. In this paper, we propose a method that can generate an optimal trajectory with obstacles in a short time and guarantee collision avoidance. The optimal trajectory is calculated by updating the latent variables with the collision avoidance posture of the robot obtained as a latent representation of the conditionally generated adversarial network (cGANs). As a result, the optimal trajectory considering collision avoidance could be generated in less time than the conventional method. In addition, We also experimented with collision avoidance using a real robot, UR5e.
A Cyber4Dev Security Culture Model

Researchers have conducted studies to investigate factors influencing information security and cybersecurity culture, and to develop models and frameworks. However, very little research has been done on cybersecurity culture for developing countries, Cyber4Dev. The purpose of this paper is to develop an Ubuntu Cyber4Dev Security Culture Model. The current study deployed the PRISMA and scoping literature review methods to review existing cybersecurity culture models and to identify significant factors influencing cybersecurity culture to address unique challenges in developing countries like Africa. An Ubuntu Cyber4Dev Security Culture Model is proposed integrating African languages and the Ubuntu philosophy with 13 factors derived from literature that influence cybersecurity culture. The proposed conceptual model can be used as a point of reference to conduct further research on Cyber4Dev security culture in Africa and other developing countries. This study is one of the first studies on Cyber4Dev security culture and can inform policymakers on how to foster a cybersecurity culture with the aim of influencing the behaviour of end users to improve the protection of information.