Reviewerly/exHarmony huggingface dataset:
A New Split for Evaluating True Zero-Shot Action Recognition

Zero-shot action recognition is the task of classifying action categories that are not available in the training set. In this setting, the standard evaluation protocol is to use existing action recognition datasets (e.g. UCF101) and randomly split the classes into seen and unseen. However, most recent work builds on representations pre-trained on the Kinetics dataset, where classes largely overlap with classes in the zero-shot evaluation datasets. As a result, classes which are supposed to be unseen, are present during supervised pre-training, invalidating the condition of the zero-shot setting. A similar concern was previously noted several years ago for image based zero-shot recognition, but has not been considered by the zero-shot action recognition community. In this paper, we propose a new split for true zero-shot action recognition with no overlap between unseen test classes and training or pre-training classes. We benchmark several recent approaches on the proposed True Zero-Shot (TruZe) Split for UCF101 and HMDB51, with zero-shot and generalized zero-shot evaluation. In our extensive analysis we find that our TruZe splits are significantly harder than comparable random splits as nothing is leaking from pre-training, i.e. unseen performance is consistently lower, up to 9.4% for zero-shot action recognition. In an additional evaluation we also find that similar issues exist in the splits used in few-shot action recognition, here we see differences of up to 14.1%. We publish our splits and hope that our benchmark analysis will change how the field is evaluating zero- and few-shot action recognition moving forward.
Development of a New Consistent Color Detection Algorithm Based on Omnidirectional Images Using Reference Color Patches

Observed color in images is easily affected by lighting conditions such as the sunlight location, weather, or time of day. Especially for image recognition processing on an outdoor navigated autonomous mobile robot, we should consider the effect of lighting conditions to achieve consistent robust color detection regardless of lighting condition change. In this paper, we employ new reference color patches to perform robust color detection to identify surrounding images by using the omnidirectional camera. As a demonstration of color detecting capability for the proposed approach, we apply specified human finding tasks defined by Tsukuba Challenge 2019 rules. Using the proposed method, we can demonstrate that the mobile robot was stably detected color, determining specified humans regardless of surrounding light change due to mobile robots.
A Comparative Analysis of Credit Card Fraud Detection Using Machine Learning and Deep Learning Techniques

E-commerce is growing rapidly around the world and this causes a significant increase in credit card transactions, both normal and fraud transactions. Financial institutions throughout the world lose billions because of credit card fraud. Fraudsters have no fixed styles; they always change their behavior and try to learn new technologies that allow them to commit frauds through online transactions. Moreover, they assume that the regular behavior of consumers and fraud patterns change fast. Fraud detection systems have become necessary for banks and financial institutions to minimize their losses. However, not much research has been conducted on credit card fraud detection methodologies, due to the unavailability of credit card transaction dataset for researchers. Also, most of the researchers apply either machine learning or deep learning techniques without comparing the results of applying both techniques in the same dataset. This research aims to apply different machine learning (Logistic Regression, K-Nearest Neighbor, Random Forest) and deep learning (Deep Neural Network, Convolutional Neural Network) techniques in a real-life credit card dataset to choose the most efficient algorithm for detecting fraud transactions. After applying different experiments with different parameters using all the algorithms mentioned before, the Random Forest Algorithm gives slightly better accuracy than Deep Neural Networks.
Stablecoins and Business Ethics

As blockchain technology develops and advances, stablecoins, a new class of cryptocurrency, is attracting interest around the world. However, few studies have examined stablecoins from the perspective of business ethics. In this study, we examine the ethical issues of blockchain technology and stablecoins, as well as the potential impact of stablecoin applications from a business ethics perspective. We review the characteristics and classifications of blockchain technology and stablecoins and investigate their relationship with business ethics and risk. We analyze stablecoins using a multi-layered and multi-dimensional framework based on anticipatory technology ethics (ATE) framework focusing on the technology, artefacts, and application level of stablecoins. We also discuss the moral good, bad, and ambivalence of stablecoins, considering these questions in the context of individuals, corporations, and systems. We identify the risks associated with stablecoins and suggest possible ways to mitigate them, as well as proposing future research directions related to stablecoin applications.
Gabor and Maximum Response Filters with Random Forest Classifier for Face Recognition in the Wild

Research on face recognition has been evolving for decades. There are numerous approaches developed with highly desirable outcomes in constrained environments. In contrast, approaches to face recognition in an unconstrained environment where varied facial posing, occlusion, aging, and image quality still pose vast challenges. Thus, face recognition in the unconstrained environment still an unresolved problem. Many current techniques are not performed well when experimented in unconstrained databases. Additionally, most of the real-world application needs a good face recognition performance in the unconstrained environment. This paper presents a comprehensive process aimed to enhance the performance of face recognition in an unconstrained environment. This paper presents a face recognition system in an unconstrained environment. The fusion between Gabor filters and Maximum Response (MR) filters with Random Forest classifier is implemented in the proposed system. Gabor filters are a hybrid of Gabor magnitude filters and Oriented Gabor Phase Congruency (OGPC) filters. Gabor magnitude filters produce the magnitude response while the OGPC filters produce the phase response of Gabor filters. The MR filters contain the edge- and bar-anisotropic filter responses and isotropic filter responses. In the face features selection process, Monte Carlo Uninformative Variable Elimination Partial Least Squares Regression (MC-UVE-PLSR) is used to select the optimal face features in order to minimize the computational costs without compromising the accuracy of face recognition. Random Forests is used in the classification of the generated feature vectors. The algorithm performance is evaluated using two unconstrained facial image databases: Labelled Faces in the Wild (LFW) and Unconstrained Facial Images (UFI). The proposed technique used produces encouraging results in these evaluated databases in which it recorded face recognition rates that are comparable with other state-of-the-art algorithms.
Overview of the Nigerian Cyber Crime Act 2015

In an increasingly digitalized, globalized and technological skyrocketing World, the extent of computer related crimes both locally and globally is reaching mammoth proportions.There is a revolution going on in criminal activity. The revolution lies in the way that networked computers permits crimes to be committed remotely. A criminal no longer needs to be at the actual scene of the crime to prey on his victim as the world is now a global community interlinked by computer devices.
LibGuides: LibAnswers Training Guide for UL Employees: Chat Demo

This is a guide for how to use LibAnswers at the CMU University Libraries. The guide includes materials for learning about the system, and our policies for use. This is a page to practice and demo the Chat service before going live.
Analysis on the Construction and Innovation of Classroom Teaching Quality Evaluation System for College Teachers

Teaching quality evaluation is a part of teaching quality monitoring in colleges and universities. It is an effective way to test students’ learning effect and teachers’ teaching effect. Based on this, this paper analyzes the problems existing in the classroom teaching quality evaluation system of college teachers, and puts forward the optimization and innovation methods for reference.
From Internet and Extended Reality to Metaverse: Technology Survey, Ecosystem, and Future Directions

Since the popularisation of the Internet in the 1990s, the cyberspace has kept evolving. We have created various computer-mediated virtual environments including social networks, video conferencing, virtual 3D worlds (e.g., VR Chat), augmented reality applications (e.g., Pokemon Go), and Non-Fungible Token Games (e.g., Upland). Such virtual environments, albeit non-perpetual and unconnected, have bought us various degrees of digital transformation. The term `metaverse' has been coined to further facilitate the digital transformation in every aspect of our physical lives. At the core of the metaverse stands the vision of an immersive Internet as a gigantic, unified, persistent, and shared realm. While the metaverse may seem futuristic, catalysed by emerging technologies such as Extended Reality, 5G, and Artificial Intelligence, the digital `big bang' of our cyberspace is not far away. This survey paper presents the first effort to offer a comprehensive framework that examines the latest metaverse development under the dimensions of state-of-the-art technologies and metaverse ecosystems, and illustrates the possibility of the digital `big bang'. First, technologies are the enablers that drive the transition from the current Internet to the metaverse. We thus examine eight enabling technologies rigorously - Extended Reality, User Interactivity (Human-Computer Interaction), Artificial Intelligence, Blockchain, Computer Vision, Edge and Cloud computing, and Future Mobile Networks. In terms of applications, the metaverse ecosystem allows human users to live and play within a self-sustaining, persistent, and shared realm. Therefore, we discuss six user-centric factors -- Avatar, Content Creation, Virtual Economy, Social Acceptability, Security and Privacy, and Trust and Accountability. Finally, we propose a concrete research agenda for the development of the metaverse.
Combining Predictions Under Uncertainty: The Case of Random Decision Trees

A common approach to aggregate classification estimates in an ensemble of decision trees is to either use voting or to average the probabilities for each class. The latter takes uncertainty into account, but not the reliability of the uncertainty estimates (so to say, the “uncertainty about the uncertainty”). More generally, much remains unknown about how to best combine probabilistic estimates from multiple sources. In this paper, we investigate a number of alternative prediction methods. Our methods are inspired by the theories of probability, belief functions and reliable classification, as well as a principle that we call evidence accumulation. Our experiments on a variety of data sets are based on random decision trees which guarantees a high diversity in the predictions to be combined. Somewhat unexpectedly, we found that taking the average over the probabilities is actually hard to beat. However, evidence accumulation showed consistently better results on all but very small leafs.
THE INFORMATION SYSTEM FOR DECISION MAKING WHEN EVALUATING THE MASTER’S THESIS

Digital transformation involves utilizing information technologies and digital tools to restructure the business processes and increase their efficiency. During the COVID-19 lockdown, this topic becomes even more relevant. This paper covers the digitalization of such processes as preparing the master’s research work and presenting the results. We modeled a system for supporting decision-making when evaluating master's thesis based on the results of expert assessment and automated text verification for plagiarism following the regulatory documents of a higher education institution and general recommendations for observing academic integrity. This paper suggests system implementation via LMS Moodle, determines the resources for loading the master's thesis and accompanying documents, conducting peer review, organizing consulting undergraduates, and communication per the distribution of the roles. To verify texts for plagiarism, the Unicheck module can be integrated with LMS Moodle. The availability of an educational environment in the universities along with the creation of a single user base will allow for implementing the proposed information system with minimal costs.
A CODE MIXING USED BY LECTURER AND STUDENT IN TEACHING AND LEARNING PROCESS AT YOUTUBE VIDEOS

This research is aimed at finding Kinds and Influencing Factors of Code Mixing in several YouTube Videos that contain the Teaching and Learning Process. The data were collected from several videos on a streaming website called “YouTube” which contain the Teaching and Learning Process, the data were examined using qualitative method. The findings reveal that there are three out of six kinds of Code Mixing that are found in several YouTube videos and also three out of eight Influencing Factors of Code Mixing. The kinds of Code Mixing that are found are as follows: The Insertion of Word, The Insertion of Phrase, and The Insertion of Hybrid. It also found some Influencing Factors on the Using of Code Mixing, which is “Because of Real Lexical Need”, “Repetition Used for Clarification”, To Soften or Strengthen Request or Command”. The result of this research shows that there are 37 data of Code Mixing that are found in several YouTube videos.
Sentiment Analysis of Student Emotion During Online Learning Using Recurrent Neural Networks (RNN)

There are many limitations in online learning process where communication effect student productivity, such as interpretation in the delivery of information can be different if it is in text form . The unstable internet network in some parts of Indonesia is also an obstacle in the learning process. Emotional factors are very influential on student motivation in learning, in online learning emotions can be read from textual dialogue in providing responses. We propose trainable model capable of identifying  the tendency of emotions / responses felt by students. With using natural language processing we can extract information and insights contained in conversations from WhatsApp, then organize them into their respective categories. The selection of the RNN algorithm can increase the accuracy by 75% in analyzing student emotions in online learning.
Development of digital literacy of students with disabilities

The article is devoted to the formation of digital literacy in teaching children with disabilities on the basis of two components: digital user component, digital correction-intellectual component, which are developed by the authors. Discussed the importance of the state program Digital Kazakhstan and one of these areas of human capital. Described by the authors created a course on computer science aimed at improving the digital literacy of students with disabilities. The main research methods are the theoretical analysis of educational and methodological, scientific literature and Internet content on digital literacy, and components of digital literacy, generalization of practical experience in the formation and development of digital literacy of students with disabilities. The content of digital literacy of students with disabilities was investigated, the thematic content of the educational sections of the course on the formation and development of digital literacy was substantiated, corresponding to the main components of digital literacy: digital user component, digital correction-intellectual component.
Adaptive State Estimation of UAV Information Physical System Under Network Attack

with the idea of information physics system put forward, governments, enterprises and scientific research in-stitutions have joined the research and construction of information physics system. The construction and development of information physics system is bound to be restricted by the security and privacy of information physical system, which provides theoretical reference for clearing the current security threats of information physical system and providing theoretical reference for the security and privacy protection of information physical system. UAV is an intelligent infor-mation physics system which relies on ground communication and flight control system to realize autonomous flight. The concept of IT system was first proposed by NASA in 1992. As a new intelligent system, UAV has emerged and is widely used. It is a complex system of computing, network and physical entity integration and deep cooperation. Thus, the real-time perception, dynamic control and information service of large physical system and information system can be achieved.
The design and development of the i-Jury management system / Mohamad Rahimi Mohamad Rosman ... [et al.]

Handling an event requires a repetitive and complex task, from initial preparation until its conclusion. One of the important elements in an innovation event is the jurying process. The jurying process usually involves tasks with higher priority and must be completed in a limited duration, usually within 4 to 5 days. The inexistence of inexpensive jurying information systems leads to the development of the i-Jury management system (i-Jury). The i-jury management system was developed based on PHP programming language, MySQL database, and jQuery. The development of the information system follows the System Development Lifecycle methodology. The findings show that the development of the information system speeds up the process of managing jury, project management, as well as speeding up the disbursement of results, comparing to previous traditional evaluation methods
Human Activity Recognition using Switching Structure Model

To communicate with people interactive systems often need to understand human activities in advance. However, recognizing activities in advance is a very challenging task, because people perform their activities in different ways, also, some activities are simple while others are complex and comprised of several smaller atomic sub-activities. In this paper, we use skeletons captured from low-cost depth RGB-D sensors as high-level descriptions of the human body. We propose a method capable of recognizing simple and complex human activities by formulating it as a structured prediction task using probabilistic graphical models (PGM). We test our method on three popular datasets: CAD-60, UT-Kinect, and Florence 3D. These datasets cover both simple and complex activities. Also, our method is sensitive to clustering methods that are used to determine the middle states, we evaluate test different clustering, methods.
Lawyer’s emotional intelligence and professional ethics development: aspects of the widespread introduction of legal clinic

Legal clinic is an innovative form of legal education. There is the need to critically reflect on functioning of a legal clinic in order to determine the possibility of clinical education to be a factor in the development of students’ emotional intelligence and professional ethics. The article covers possible positive and negative aspects of the widespread introduction of legal clinic as a means of individual students’ qualities development into law education. The study is based on the method of systematic analysis, allowing comprehensively cover legal clinics functions and critically evaluate the impact of legal clinics on emotional intelligence and professional ethics of future lawyers’ development. The article shows that legal clinic, being an innovative form of legal education aimed at developing practical skills of future lawyers, performs both educational and social functions. Legal clinic promotes the development of emotional intelligence (allowing students to better understand and control their emotion, as well as to adequately perceive the client's psychological state, choosing appropriate tactics of behavior) and professional ethics (understanding the importance of the principle of the rule of law, the priority of human rights, awareness of the role of a lawyer in society and the importance of pro bono activities).
Test sequence selection method for video criticality evaluation

Criticality is a measure of the difficulty of video compression. Image sequences with high criticality are required for the evaluation of video compression algorithms. The selected test sequences are usually determined by experts from large amounts of material,which is labor-intensive and subjective. In order to solve this problem, a test sequence selection algorithm for video criticality evaluation is proposed in this paper. Based on basic principles of video coding, four types of metrics including texture map variance, AC energy, motion vector difference and motion angle entropy are selected in this paper. The least squares method is applied to fit the parameters of these four types of metrics to their corresponding criticality values for multiple resolution image sequences, and the fitting results are verified by experiments to effectively select the test sequences.
Application of Standardized Nursing Procedures Combined with Specialized Management Education in Extracorporeal Membrane Oxygenation in ICU

To analyze the application effect of standardized nursing procedures combined with specialized management education in extracorporeal membrane oxygenation (ECMO) in ICU. 18 patients treated with ECMO in our department from July 1, 2020 to December 30, 2020 were chosen as observation group, and 18 patients receiving ECMO in our department (January 2017-June 2018) were selected as control group. The control group received routine procedures in the implementation of ECMO nursing while the observation group received standardized ECMO nursing procedures combined with specialized management education to analyze the effect of different nursing management methods on ECMO patients by comparing the indexes before and after nursing management. There were no obvious differences in gender ratio, average age, average weight, average BMI, marital status, education and residence between the two groups (P &gt; 0.05). Personnel arrival time, start time of rescue, preparation time of items, pipeline prefilling time and catheter completion time in observation group were obviously better compared with control group (P &lt; 0.001). The total clinical efficacy rate in observation group was obviously higher compared with control group (P &lt; 0.05). There were no significant differences in the Pa02, SP02 and PaC02 levels between the two groups before nursing management (P &gt; 0.05), and the levels in observation group were obviously higher compared with control group after nursing management (P &lt; 0.001). The clinical nursing satisfaction in observation group was obviously higher compared with control group (P &lt; 0.05). The sequential organ failure assessment (SOFA) score in observation group after nursing management was obviously lower compared with control group (P &lt; 0.001). The incidence of complications in observation group was obviously lower compared with control group (P &lt; 0.05). The implementation of standardized nursing procedures combined with specialized management education for ECMO patients can effectively shorten rescue time, the preparation time before catheterization and catheter completion time, improve the therapeutic effect, and improve blood gas indicators, safe and effective. At the same time, it can facilitate the doctor-nurse cooperation, worthy of promotion and application.