Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition Using CTAL Model

In recent years, computer vision-related applications and research works are rapidly increasing daily. There are so many kinds of computer vision tasks like object detection, robotics, gait analysis, medical diagnosis, crime detection, and many more. Along with these Human Activity Recognition or HAR is also one kind. The challenges of HAR are as follows: First, it is computationally expensive and a time-consuming process, and second, it is a video data means it has spatial and temporal features, so it is hard to achieve good performance. To overcome these issues, we have proposed “a hybrid CNN model(Convolutional Triplet Attention LSTM (CTAL) model)” for activity recognition on the UCF50 dataset. In this paper, we will discuss about the used data set, proposed model, experiments, and results.
African higher education institutions catching up: The potential of 4IR in closing the digital deficits

The new digital cultures brought by the Fourth Industrial Revolution (4IR) are forcing universities to transform their practices as they prepare students for a future that will magnify the use of digital tools. Throughout the world, institutions of higher learning are gearing to maximise the benefits that could be gained from the 4IR. The concept, engaged university, ensures that the uniiversities will not leave the communities around them behind as they prepare for the future, for universities will play a huge role in contributing to the progress in societies. This conceptual article examines the opportunities of 4IR in African institutions of higher learning. The 4IR has potential to solve many of the ills in Africa, for the African societies still suffer ill health, poverty and illiteracy. The promise of the 4IR brings hope of minimising these ills as Africa moves towards advancement. Furthermore, there has been much balkanisation in African education hence some believe that the 4IR can create links between institutions within states and the entire African continent. Not only will new technologies transform research, scholarship and teaching, but these would also have impact on bringing forth new epistemologies although there is still a battle for more resources. The major challenge for 4IR in Africa will be to create links between new technological cultures and African cultures; universities are well-placed to face such challenges.
THE ROLE OF ACCOUNTING FORENSICS IN CRIMINAL PROCEEDINGS

The lack of quality forensic analysis of financial reports and records, as a result of the lack of forensic accounting experts and implementation framework, is a real problem in combating financial crime. Forensic accounting can serve as a potential tool to combat financial crime and to identify and track cases of financial crime. In criminal proceedings, accounting forensics can have different roles, starting from financial forensics, expert, witness and professional adviser. The aim of this paper is to point out the significant role of accounting forensics in criminal proceedings.
Interactive Interpretation Viewer for Typed First-order Logic

This poster describes the Interactive Interpretation Viewer - IIV, for finite interpretations in typed first-order logic written in the (new) TPTP format for interpretations.
Development of Intelligent Old-Age Medical and Health System Based on Data Fusion

Starting from the construction of an intelligent medical and health system for elderly care, it is proposed to use improved Particle Swarm Optimization Algorithm (PSOA) data fusion algorithm, network video surveillance technology, and hardware equipment such as Radio-Frequency Identification (RFID) smart bracelets and intelligent mobile robots to establish a distributed elderly care The intelligent service system collects vital signs, human movements, human posture characteristics, and position change information of the elderly, and uses improved PSOA data fusion algorithm, the data fusion processing of different types of human body information is completed, which not only greatly improves the utilization rate of medical resources for the elderly, but also ensures the data fusion accuracy of human health detection for the elderly. The improved PSOA neural network data fusion results can provide support for the intelligent care of the elderly care institutions.
College Enquiry Chatbot using Conversational AI

Abstract: Chatbots are computer programs that use text or voice-based interfaces to replicate human conversation. They are often used to automate mundane processes, provide customer support, or aid in the retrieval of information. Chatbots are built with a number of strategies that enable them to interpret and respond to user inputs in a more human-like manner. They can be employed in a variety of industries, including e-commerce, healthcare, and banking. We have analysed and compared numerous chatbot strategies in this report to establish the optimal way for our own chatbot project. We reviewed twenty-six papers on chatbot development and assessed the advantages and disadvantages of various strategies. Natural language processing techniques, such as tokenization and named entity recognition, have been shown in our research to be critical for interpreting user inputs. We also discovered that dialogue management methods, such as rule-based and machine learning-based approaches, have an important influence in influencing discussion flow. Furthermore, we discovered that natural language generation techniques, such as template-based and neural network-based methods, are critical in generating effective chatbot responses. We also investigated various services on the market in order to create a functional chatbot for our college. We also emphasized the various applications of chatbots as well as the current hurdles in the industry. Based on these findings, we chose a technique for our own chatbot project that employs advanced natural language processing and machine learning techniques to create more human-like conversations and improve overall user experience.
Errata

Presents corrections to the paper, A New Cellular Vehicle-to-Everything Application: Daytime Visibility Detection and Prewarning on Expressways
Mixer: DNN Watermarking using Image Mixup

It is crucial to protect the intellectual property rights of DNN models prior to their deployment. The DNN should perform two main tasks: its primary task and watermarking task. This paper proposes a lightweight, reliable, and secure DNN watermarking that attempts to establish strong ties between these two tasks. The samples triggering the watermarking task are generated using image Mixup either from training or testing samples. This means that there is an infinity of triggers not limited to the samples used to embed the watermark in the model at training. The extensive experiments on image classification models for different datasets as well as exposing them to a variety of attacks, show that the proposed watermarking provides protection with an adequate level of security and robustness.
GeoGebra as a Tool for Improving Mathematics Instruction and Learning by Ghanaian Teachers

This study is mainly about using GeoGebra as a tool for improving mathematics instruction and learning by Ghanaian teachers. The quantitative method was employed for the study and the descriptive statistics of the study were the mean, standard deviation, percentages, and frequencies were used. Two-way analysis of variance (ANOVA) was used in answering the Hypothesis test. The sample size for the study comprised 10 junior high schools and 13 primary schools. A total of 74 samples made of mathematics teachers were used of which 40 were junior high teachers and 34 were primary school teachers. A questionnaire with 10 questions/items with 4 options or 4 Likert scales for each was used in measuring the use of GeoGebra as a tool to improve mathematics instructions or learning by Ghanaian teachers (primary teachers and junior high school teachers). The study indicated that teachers believe that GeoGebra could effectively improve mathematics instruction and learning in the classroom. It also showed that teachers are ready to learn and adopt the use of technology (ICT) in their lesson delivery.
Special Issue on Unsupervised Anomaly Detection

Anomaly detection (also known as outlier detection) is the task of finding instances in a dataset which deviate markedly from the norm [...]
Hot-Fit Model to Measure the Effectiveness and Efficiency of Information System in Public Sector

The research aimed to determine the effectiveness and efficiency of the information systems’ implementation. The research used the modified Human Organization Technology-Fit (HOT-Fit) as the model to measure information systems’ effectiveness and efficiency. Each indicator of the HOT-Fit model is mapped into two categories, namely effectiveness and efficiency. Data collection was conducted through interviews with the users of population administration information system (SIAK), and observation of activities at the public office. The data were analyzed qualitatively by assessing the achievement of each indicator of effectiveness and efficiency found in the interview and observation. It is found that effectiveness indicators are level of use, attitude, perceived usefulness, communication, availability, security, completeness, accuracy, timeliness, empathy, and transparency. Meanwhile, indicators to measure efficiency are users, training, staff support, ease of use, ease of learning, response time, access to technical support, and quick responsiveness. The research also expands the scope of the HOT-Fit model in evaluating information systems. Practically, the research can be a reference for evaluating the effectiveness and efficiency of information system implementation in the public sector. It also can be used to improve the achievement of indicators that have not achieved maximum results to improve services to the community.
Embedding dialog reading into AR picture books

Parent–child reading critically affects children’s learning and development, and dialog reading (DR) is a highly effective strategy for parent–child reading. This study employed the prompt, expand, evaluate, and repeat (PEER) DR strategy to develop a picture book incorporating augmented reality (AR) technology. AR information is extracted from the book using a mobile device to guide children through PEER-based reading. To determine the effectiveness of the AR picture book, a total of 56 preschool children participated in an experiment. The children were divided into an experimental group and a control group. The participants in the control group were accompanied by their parents, and they engaged in conventional parent–child reading; however, the participants in the experimental group engaged alone in AR-assisted reading. The results indicated that the experimental group achieved learning effectiveness significantly higher than that of the control group. A cycle of behavioral changes,“scanning with a mobile device → reading augmented information → answering the questions on the mobile device → scanning with a mobile device", was observed. This cycle indicates that child–mobile device–picture book interaction increased the children’s learning effectiveness.
A Probabilistic Model of Human Activity Recognition with Loose Clothing

Human activity recognition has become an attractive research area with the development of on-body wearable sensing technology. Textiles-based sensors have recently been used for activity recognition. With the latest electronic textile technology, sensors can be incorporated into garments so that users can enjoy long-term human motion recording worn comfortably. However, recent empirical findings suggest, surprisingly, that clothing-attached sensors can actually achieve higher activity recognition accuracy than rigid-attached sensors, particularly when predicting from short time windows. This work presents a probabilistic model that explains improved responsiveness and accuracy with fabric sensing from the increased statistical distance between movements recorded. The accuracy of the comfortable fabric-attached sensor can be increased by 67% more than rigid-attached sensors when the window size is 0.5s. Simulated and real human motion capture experiments with several participants confirm the model’s predictions, demonstrating that this counterintuitive effect is accurately captured.
THE ROLE AND IMPORTANCE OF FACEBOOK AMONG STUDENTS IN TIMIȘOARA

With more than 1 billion users on the planet and more than 7.8 million users in Romania, the social network Facebook enjoys great popularity, mainly among people under the age of 35. This paper present s the results of a study conducted among students in Timișoara regarding the place, importance and role Facebook has in everyday life.
The Use of Personal Inarisk Media in Improving the Concept of Disaster Information Literacy in social Studies Learning

The purpose of this study is to determine the improvement of the concept of disaster information literacy using InaRisk Personal learning media in social studies learning. This research approach uses quantitative with quasi-experimental method with nonquivqlent control group design. The population of this research is the seventh grade students of SMP Negeri 1 Tarogong Kaler. The sample was taken by purposive sampling technique with class VII A as the experimental class and class VII B as the control class. Based on the results of statistical analysis, the Asymp.sig.2-tailed value is 0.000 and a is 0.05, which means 0.000 &lt;0.05, then Ho is rejected. From these data it can be concluded that the value of Asymp.sig. of 0.000 &lt; 0.05, then there is a significant increase or learning that uses InaRisk Personal learning media is effective to be applied. Overall, the ability of the concept of disaster information literacy affects the preparedness of students in dealing with disasters in the surrounding environment. the ability of the concept of disaster information literacy in the experimental class increased significantly in terms of seeking information, identifying information, evaluating information, and using information.
From Predictive Algorithms to Automatic Generation of Anomalies

We ask how machine learning can change a crucial step of the scientific process in economics: the advancement of theories through the discovery of "anomalies." Canonical examples of anomalies include the Allais Paradox and the Kahneman-Tversky choice experiments, which are concrete examples of menus of lotteries that highlighted flaws in expected utility theory and spurred the development of new theories for decision-making under uncertainty. We develop an econometric framework for anomaly generation and develop two algorithmic procedures to generate anomalies (if they exist) when provided a formal theory and data that the theory seeks to explain. Our algorithmic procedures are general since anomalies play an important role across a wide variety of fields in economics. As an illustration, we apply our procedures to generate anomalies for expected utility theory using simulated lottery choice data by an individual who behaves according to cumulative prospect theory. We produce novel anomalies for the independence axiom based on the probability weighting function that to our knowledge have not been noticed before.While this illustration is specific, it is our view that automatic anomaly generation can accelerate the development of new theories.
Identifying spammers and fake users identification in online social networking sites

Online social networking sites are new platforms for spreading spammers and fake news for attackers.Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs).In this paper, we perform a review of techniques used for detecting spammers on Twitter.Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users.The presented techniques are also compared based on various features, such as user features, content features, graph features, structure features, and time features.
Multi-scale Attention Map Guided Image Rain Removal Network

To solve the problem of low running speed of image rain removal models, a multi scale attention map guided image rain removal network is proposed.Firstly, an original resolution main network with residual structure is constructed for image rain removal.Secondly, in order to enhance the rain removal ability of the main network, especially the recognition ability of multi-scale features, a multi-scale attention map generation network is designed as an auxiliary to the main network.The generated multi-scale attention maps are used to enhance the features of rain stripes in the image, guiding the main network to identify and remove them in turn.In addition, this paper also designed the dual attention guided convolution block, which adds the ability to pay attention to spatial features on the basis of traditional channel attention mechanisms to achieve joint attention to spatial and channel features.Experimental results show that the proposed method maintains the comparability and stability of the restored image quality while significantly reducing the amount of network parameters and effectively improving the speed.
On quantum traversability of wormholes

In this paper we study the possibility of non-relativistic quantum particles to traverse the generalized Ellis-Bronnikov wormholes by considering quantum effects, such as tunneling. We have used the generalized Ellis-Bronnikov wormhole metric and found that for $n=2$ we have a single barrier shaped effective potential centered at the throat of the wormhole for any value of orbital angular momentum. For $n\neq2$ we have a symmetric double barrier shaped potential when the orbital angular momentum is zero and a single barrier for nonzero angular orbital momentum. Analytical solutions for the Schr\"{o}dinger equation in the generalized Ellis-Bronnikov spacetime could be found only for $n=2$. Such solutions were given in terms of the confluent Heun functions. Finally, by using a delta-barrier approximation we could find the transmission and reflection coefficients for a non-relativistic particle to traverse the generalized Ellis-Bronnikov wormhole.
Deep Reinforcement Learning Guided Decision Tree Learning For Program Synthesis

Syntax-Guided Synthesis (SyGuS) is a general solving framework for program synthesis. Previous researchers have proposed a divide-and-conquer strategy to divide the grammar search space in order to search separately. This framework converts the program synthesis problem into a decision tree classification problem. It unifies the obtained partial results into a complete program by learning decision trees according to conditional expression statements. However, due to the unknowns and uncertainties of sample label categories and sample attribute features in program synthesis, as well as the limitation of traditional decision tree learning methods to immediate gains, it results in long solving times and the large size of the candidate program. To address the above problems, we propose a deep reinforcement learning-based synthesis strategy for syntax-guided programs: a) the decision tree learning process is modeled as a Markov chain decision process, addressing the learning of knowledge patterns under conceptual drift and the weighting of the importance of sample attribute features; b) a graph neural network is used to extract the feature information of the sample attributes as action embedding feature values; (c) a reward function is proposed to evaluate the classification accuracy of the sample attributes as a feedback mechanism. We have implemented our approach in a tool called RLSolver. According to the experiments, the solving times ofRLSolver are comparable to EUSolver on small-scale task datasets. RLSolver substantially reduces the solving times on large-scale task datasets, solving two more SyGus tasks than EUSolver in the same solving time limit. In addition, RLSolver reduces the number of decision tree learning rounds by 60% and the size of decision tree size by 40% after using the multi-round model training strategy and early-stop strategy.
Towards Privacy Preserving Cross Project Defect Prediction with Federated Learning

Defect prediction models can predict defects in software projects, and many researchers study defect prediction models to assist debugging efforts in software development. In recent years, there has been growing interest in Cross Project Defect Prediction (CPDP), which predicts defects in a project using a defect prediction model learned from other projects’ data when there is insufficient data to construct a defect prediction model. Since CPDP uses other projects’ data, data privacy preservation is one of the most significant issues. However, prior CPDP studies still require data sharing among projects to train models, and do not fully consider protecting project confidentiality. To address this, we propose a CPDP model FLR employing federated learning, a distributed machine learning approach that does not require data sharing. We evaluate FLR, using 25 projects, to investigate its effectiveness and feature interpretation. Our key results show that first, FLR outperforms the existing privacy-preserving methods (i.e., LACE2). Meanwhile, the performance is relatively comparable to the conventional methods (e.g., supervised and unsupervised learning). Second, the results of the interpretation analysis show that scale-related features have a common effect on the prediction performance of the FLR. In addition, further insights demonstrate that parameters of federated learning (e.g., learning rates and the number of clients) also play a role in the performance. This study is served as a first step to confirm the feasibility of the employment of federated learning in CPDP to ensure privacy preservation and lays the groundwork for future research on applying other machine learning models to federated learning.
Data-Driven Approach for Dehazing of High-Resolution Multispectral Remote Sensing Images

Haze is caused due to presence of dust, light vapors, or smoke, causing a lack of transparency in the air. This creates a significant issue for satellite images as the image regions affected by haze suffer a lack of contrast and definition, resulting in difficulty interpreting the scene. Traditionally, this issue was solved by using atmospheric correction methods, a tedious process requiring estimating several geophysical quantities at once to give reliable results. A set of algorithms to recover the clarity in hazed images, called dehazing algorithms, are becoming popular in practice for their simplicity and efficacy. This paper introduces a Convolution Neural Network based solution in which using a compound loss function to prioritize the clarity and similarity to the original has improved performance to solve the dehazing problem for high-resolution multispectral images.
Design and Research of Robot Image Perception System Based on Artificial Intelligence

Abstract In recent years, with the rapid development of science and technology, the performance and functions of traditional robots have gradually failed to meet people’s needs for production and life, and the development of robots has gradually become intelligent and networked. Among them, as the core technology of mobile robot positioning and navigation, environment perception technology has always been the focus and hot research issue in the field of robotics. This paper mainly studies the design of a robot image perception system based on artificial intelligence. In this paper, a tetrahedral robot image-sensing system based on a panoramic camera is proposed and designed, and a U-net convolutional image semantic segmentation network is designed.