Reviewerly/exHarmony huggingface dataset:
Predicting Human Psychological Factors from Movie Ratings and Genres Using Genetic Algorithms

Personality computing is emerging research in artificial intelligence and psychology. The genetic algorithms are efficient in optimization problems. The selection of features must be based on the problem in hand and can be achieved effectively using feature selection and genetic algorithms. This research focuses on feature selection using genetic algorithms to select the best subset of features and to psychological factors of human rather than his predictive behaviors in movies preferences. In this work, first the prediction of user's movie genres preferences from movie ratings. The feature selection genetic algorithm on movie preferences results high fitness features of movie genres. Then the genre preferences are mapped to big five factor model using Pearson correlations. The result concludes that the mapped preferences-traits are near optimum.
LonelyScape: Increasing Attractiveness of Escape Room Game Using Augmented Reality Technology

Escape Rooms have emerged as one of the most popular real-world games, captivating individuals worldwide in search of leisure activities. This research paper outlines the development of LonelyScape, an innovative gaming experience that combines physical props with virtual elements within an engaging 3D environment. LonelyScape challenges players' problem-solving skills while providing an entertaining and immersive adventure. By utilizing augmented reality (AR) technology, the gameplay becomes interactive, ensuring a high level of engagement. The creative development process involved the use of Xcode, ARKit, RealityKit, and SwiftUI, employing the Rapid Application Development (RAD) methodology to achieve an intuitive gameplay design. In this study, we delve deeper into the subject by presenting existing studies on similar AR-based escape room games, along with a detailed overview of LonelyScape's gameplay design structure. Through beta testing involving nine participants, LonelyScape's entertainment value was affirmed, as it successfully kept players engaged throughout their journey. The evaluation of the testing, utilizing the UEQ scales, indicated that the measure of attractiveness received the highest value with a mean of 1.78, surpassing the other measures. This categorization as ‘good’ when compared to the benchmark demonstrates the positive reception of LonelyScape.
Multi-Object Tracking Based on Prediction of Invisible Object Trajectories

In previous 3D Multi-Object Tracking (3DMOT) methods terminated tracking when the tracked object was not associated with new detected object with in few frames (usually less than 5 frames). However, this can lead to premature termination of a tracker if an object simply became dimmer, such as being temporarily occluded by other objects or leaving the field of view. This became a major cause of identity switches in tracking. Additionally, premature termination of trajectory tracking is the main cause of identity switching in modern 3D MOT systems. To address this problem, we propose a method to preserve vanished object trajectories using trajectory prediction: when the life cycle management module performs trajectory management, unlike previous 3DMOT methods, when a tracked object is not matched by association within a few frames, instead of identifying the tracked object as vanishing and terminating its trajectory, our method predicts the vanishing tracked object state by the 3D Kalman Filter, which in turn comes to preserving its trajectory. With this approach, the experimental results on the nuScenes dataset show that the 3D multi-object tracking algorithm proposed in this paper performs excellently, with an Average Multi-Object Tracking Accuracy (AMOTA) of 73.7% at the frame rate of 2hz, and the Identity Switches (IDS) are also significantly reduced compared to the current excellent 3D multi-object tracking algorithms (e.g., about 50% less than SimpleTrack).
Construction and Research of Multi-Target Tracking Method in Low Light Scene Based on PP-YOLOE+m and PP-Tracking

Multi-target tracking in low-light scenes is an important research direction under of computer vision. Effective multi-target tracking methods in low-light have scenes are of great significance for applications such as video surveillance, unmanned driving, and intelligent security. Under this environment, due to insufficient light and noise interference, it faces problems such as unstable low-light image enhancement effect, target association accuracy and robustness, algorithm real-time performance and computational efficiency. Under this paper, the PP-YOLOE+m model is used for multi-target tracking in low-light scenes. By training and optimizing the PP-YOLOE+m model, the target detection ability of the PP-YOLOE+m model in low-light scenes is improved. At the same time, combined with the PP-Tracking algorithm, the accurate tracking and ID maintenance of the target can be realized. Using the ExDark dataset to evaluate the effect of the multi-target tracking method in this paper, the accuracy of multi-target tracking has been improved by 3.6%, which verifies the effectiveness and robustness of the method in this paper. Experimental results show that, the method proposed can more accurately track multiple targets, and provides guidance for vision applications in low-light scenes in low-light environments.
Restoration of Dunhuang Murals on Large-scale pretraining

Dunhuang murals are a precious cultural heritage and their restoration is of vital importance. Traditional image restoration methods and methods based on generative adversarial networks (GANs) have limitations in the mural restoration task. In this paper, we propose a diffusion model-based method for restoring Dunhuang murals using the RePaint model for image restoration. We first collected and produced 5135 Dunhuang mural image data that had undergone data augmentation methods such as cropping and panning, and then pre-trained the RePaint model on a large public dataset and fine-tunedthe model on the Dunhuang mural data. The experimental results show that the fine-tuned RePaint model has significantly improved the evaluation metrics such as PSNR and SSIM in the Dunhuang mural restoration task compared to other recent excellent image restoration models. This suggests that the RePaint model has superior performance in mural restoration tasks, especially in the areas of texture generation and structure retention. This study provides a novel and effective method for the field of Dunhuang mural restoration, which is expected to provide more support for heritage conservation.
Harnessing Technological Transformation: An Exploration of the Impact of Mobile-Assisted Language Learning on English Vocabulary Acquisition

"This paper delves into the profound impact and the multifaceted influence of MobileAssisted Language Learning (MALL) on the acquisition of English vocabulary. MALL, a by-product of digital innovation, has revolutionized the traditional paradigms of language learning, offering flexibility, convenience, and personalization. The integration of mobile technology in language education has facilitated an environment conducive to continuous and engaging vocabulary learning, significantly enhancing learners' interaction with the target language. However, alongside its benefits, the implementation of MALL introduces unique challenges, such as issues of digital equity and the potential for distraction. This paper seeks to provide a comprehensive examination of the transformative potential of MALL in bolstering the process of English vocabulary learning, with a balanced evaluation of its myriad advantages, disadvantages, and the effects of globalization on its implementation"
Influence of Errors on the Evaluation of Text Classification Systems

Accuracy metrics and explanation of outputs can provide users with useful information about the performance of machine learning-based systems. However, the availability of this information can result in users’ overlooking potential problems in the system. This paper investigates whether making errors obvious to the user can influence trust towards a system that has high accuracy but has flaws. In order to test this hypothesis, a series of experiments with different settings were conducted. Participants were shown examples of the predictions of text classification systems, the explanation of those predictions and the overall accuracy of the systems. The participants were then asked to evaluate the systems based on those pieces of information and to indicate the reason for their evaluation decision. The results show that participants who were shown examples where there was a pattern of errors in the explanation were less willing to recommend or choose a system even if the system’s accuracy metric was higher. In addition, fewer participants reported that the accuracy metric was the reason for their choice, and more participants mentioned the prediction explanation.
Neural Network Approaches for Data Estimation in Unique Word OFDM Systems

Data estimation is conducted with model-based estimation methods since the beginning of digital communications. However, motivated by the growing success of machine learning, current research focuses on replacing model-based data estimation methods by data-driven approaches, mainly neural networks (NNs). In this work, we particularly investigate the incorporation of existing model knowledge into data-driven approaches, which is expected to lead to complexity reduction and / or performance enhancement. We describe three different options, namely “model-inspired” pre-processing, choosing an NN architecture motivated by the properties of the underlying communication system, and inferring the layer structure of an NN with the help of model knowledge. Most of the current publications on NN-based data estimation deal with general multiple-input multiple-output (MIMO) communication systems. In this work, we investigate NN-based data estimation for so-called unique word orthogonal frequency division multiplexing (UW-OFDM) systems. We highlight differences between UW-OFDM systems and general MIMO systems one has to be aware of when using NNs for data estimation, and we introduce measures for a successful utilization of NN-based data estimators in UW-OFDM systems. Further, we investigate the use of NNs for data estimation when channel coded data transmission is conducted, and we present adaptions to be made, such that NN-based data estimators provide satisfying performance for this case. We compare the presented NNs concerning achieved bit error ratio performance and computational complexity, we show the peculiar distributions of their data estimates, and we also point out their downsides compared to model-based equalizers.
Empirical Performance of CART, C5.0 and Random Forest Classification Algorithms for Decision Trees

This study compares the performance of &lt;i&gt;CART&lt;/i&gt;, &lt;i&gt;C5.0&lt;/i&gt; and Random Forest (&lt;i&gt;RF&lt;/i&gt;) algorithms. 25 continuous predictors and 25 factors were simulated using a population size of 10,000. Based on this data, sample data were generated by varying the number of predictors, the proportion of categorical versus continuous predictors and the sample size. The performance of the tree algorithms increases with sample size and the number of variables, but for &lt;i&gt;RF&lt;/i&gt;, it is highly greater than the one of &lt;i&gt;CART&lt;/i&gt; and &lt;i&gt;C5.0&lt;/i&gt;. Irrespective of the algorithms, the performance decreases when there are more categorical variables than continuous variables.
A New Image Watermarking Scheme Using Genetic Algorithm and Residual Numbers with Discrete Wavelet Transform

Transmission of data over the internet has become a critical issue as a result of the advancement in technology, since it is possible for pirates to steal the intellectual property of content owners. This paper presents a new digital watermarking scheme that combines some operators of the Genetic Algorithm (GA) and the Residue Number (RN) System (RNS) to perform encryption on an image, which is embedded into a cover image for the purposes of watermarking. Thus, an image watermarking scheme uses an encrypted image. The secret image is embedded in decomposed frames of the cover image achieved by applying a three-level Discrete Wavelet Transform (DWT). This is to ensure that the secret information is not exposed even when there is a successful attack on the cover information. Content creators can prove ownership of the multimedia content by unveiling the secret information in a court of law. The proposed scheme was tested with sample data using MATLAB2022 and the results of the simulation show a great deal of imperceptibility and robustness as compared to similar existing schemes.
Pengembangan Lembar Kerja Siswa Berbasis Keanekaragaman Dan Kepadatan Populasi Tridacna Di Perairan Kepulauan Widi

One of the rich fauna in the Widi Island, including coral reefs, seagrass beds, shellfish and fish. To preserve these fauna, especially shellfish, you can develop student worksheets that are used for the learning process on invertebrate material. The aims of the research were to develop LKS (Student Worksheets) based on the diversity and population density of Tridacna in the waters of the Widi Islands and to analyze the validity of student worksheets based on the diversity and population density of Tridacna in the waters of the Widi Islands. The development of LKS based on diversity and population density of Tridacna in the waters of the Widi Islands uses the ADDIE development model which consists of five stages. However, in this study the authors only reached the development stage, due to time and cost constraints. As for the development stage that the author did, namely the Analysis stage (Analyze), Design (Design) and Development stage (Develop). The validity level of LKS based on diversity and population density of Tridacna in the waters of the Widi Islands obtained an average score for the material expert validator, namely 3.87 with valid criteria. For media expert validators, an average score of 4.05 is obtained with valid criteria. The total average number of the two validators is 3.96 with valid criteria, so it can be concluded that student worksheets are feasible to use.Keywords: Development of LKS, Tridacna, Widi Islands
An improved Sparrow search path planning algorithm for UAV

In order to address the problems of easily sink into local optima and insufficient convergence when solving the SSA, this paper proposes an improved sparrow search path planning algorithm for UAV. First, the periodic adaptive convergence factor is introduced, improves the rate of convergence of the algorithm, and solves the problem that the algorithm is easy to fall into local optimization. Secondly, a dynamic adjustment strategy for the number of discoverers was introduced to address transitioning algorithms from global exploration to local exploration.Then, an adaptive spiral search strategy was introduced to enable the improved follower position update formula to fully utilize the information of the optimal individual and conduct spiral searches around the optimal individual. Finally, compared and simulated with the original algorithm and PSO algorithm in different environments, The improved algorithm has a shorter path and better search ability.
Implementing rover speed control in Paparazzi UAV

This paper presents the design, implementation and testing of a speed controller for an autonomous rover with an unicycle drive. The aim is to get the rover to follow the speed setpoint as accurately as possible based on low cost GPS measurements. The use of IMU and GPS measurements was investigated and a moving average filter was designed to use the GPS signal as feedback for the speed controller. This filter and speed control were implemented on a flight controller board on a small autonomous rover using the the Paparazzi UAV development framework. Outdoor tests were performed.
Closed-Loop ACAS Xu Neural Network Verification

Benchmark Proposal: Neural Network Control Systems (NNCS) play critical roles in autonomy. However, verifying their correctness is a substantial challenge. In this paper, we consider the neural network compression of ACAS Xu, a popular benchmark usually considered for open-loop neural network verification. ACAS Xu is an air-to-air collision avoidance system for unmanned aircraft issuing horizontal turn advisories to avoid collision with an intruder aircraft. We propose specific properties and different system assumptions to use this system as a closed-loop NNCS benchmark. We present experimental results for our properties based on randomly generated test cases and provide simulation code.
Interpersonal Communication Channels and Alcohol Consumption among Students in Government Tertiary Medical Colleges in Kenya

The study sought to evaluate how interpersonal communication channels influenced the consumption of alcohol among college students. Specifically, the study focused on public tertiary colleges in Kenya. The study was motivated by the increased cases of dropout, ill-health, poor performance, and other related effects of alcohol among college students. This is despite the continued efforts to curb alcohol abuse among students by agencies such as the United Nations Office for Drug and Crime, the National Authority for the Campaign Against Drug Abuse, and the school management, among other agencies. The study was anchored on social cognitive theory. Through a descriptive cross-sectional survey design, both qualitative and quantitative data were obtained using a questionnaire and interview schedule. Both quantitative and qualitative data were analyzed. The findings revealed that alcohol consumption among college students was significantly influenced by interpersonal communication channels (t = 14.381, p&lt;0.05). The study recommended the need for stakeholders, including the government and other agencies, to spearhead campaigns to sensitize the students on the ills of alcohol consumption and uphold measures that reduce the encouragement of alcohol consumption through interpersonal communication channels.
Reducing Perceived Transactional Distance in Distance Education: The Impact of the Chatbot

This study investigates the impact of utilizing a chatbot named 'YakınDost' on the perceptions of transactional distance among distance education students. Transactional distance, which involves communication and interaction between students and instructional personnel, is a critical aspect of distance education. To address this, chatbots are increasingly being employed as tools to facilitate engagement. Developed using IBM Watson Assistant, 'YakınDost' serves as a conversational agent to address common inquiries related to courses, assignments, instructors, exams, and technical matters. The primary objective of this research is to assess how the integration of the 'YakınDost' chatbot influences students' perceptions of transactional distance. To achieve this, the 'Transactional Distance Perception Scale' was administered at the outset and conclusion of the fall semester to first-year students at a state university. Additionally, during the mid-semester, students were introduced to the YakınDost chatbot and were invited to participate in the 'Chatbot Usability Scale.' Out of 2000 students, the data analysis focused on 583 students who completed both pre-test and post-test surveys, among which 398 students interacted with the chatbot. The findings highlight a significant reduction in students' perceptions of transactional distance due to the implementation of the YakınDost chatbot.
The Impact of Green Entrepreneurship on the Sustainable Development of African Nations

Abstract This study adopted a quantitative technique to examine the impact of green entrepreneurship on the sustainable development of African nations. It reviewed thematically proof from 45 previous works that examined the effect of green product or service offering on social progress index.in Africa. It also found that adopting a single theory is insufficient to explain the rationale of the relationships between green entrepreneurship and sustainable development. While most studies adopted secondary data, future studies may focus on using primary data and other methods instead of regression or multiple regression analysis. From the findings, product or service offering positively correlates the green innovation and green product. In conclusion, the priory expectations were found to be positive outcomes because of the correlations among the moderating variable, explanatory variable and response variables. Hence, the regression model revealed that the alternate hypotheses were accepted, unlike the null hypotheses. Based on the findings and conclusion, this study contributes to the knowledge that green entrepreneurship is perceived to foster sustainable development in countries through the SMEs activities. It therefore could be recommended that green entrepreneurs should develop and design an analytical framework to synchronized green product or service offering with social progress index. Entrepreneurs should always facilitate continuous improvement on devising green innovation towards the sustainability, survival and success. Keywords Green entrepreneurship. Green Product or Service offering. Investment in environmental technologies. Environmental Reporting and Disclosure. Sustainable development JEL Classification: M1, M10, M13, M16
Data Augmentation Techniques in Automatic Translation of Vietnamese Sign Language for the Deaf

Automatic translation of Vietnamese sign language is a new and meaningful problem. Automatic translation of Vietnamese sign language is a meaningful solution as it promises to eliminate communication obstacles and improve the lives of deaf people. With the support of automatic translation technologies, many effective translation methods exist. However, the important problem in the current problem is that there is not a large enough amount of data available to evaluate and develop translation models. With Wordnet, automatic data augmentation is possible. By applying the hyponym and hypernym in Wordnet with the criteria of this study, we enrich the data based on the original data built. The measure of data similarity between the sentences generated from the original sentence is evaluated accordingly based on the combination of the cosine measure and meets the data requirements of the Vietnamese sign language automatic translation problem. Experiments show that BLEU scores on some translation models achieve high results after data augmentation.
A Learning Mechanism to Improve Skills Acquisition for Autistic Children in Mauritania

The learning of autistic children is an issue for care and treatment centers for children with autism spectrum disorder, however many studies seek to find learning mechanisms and models in order to facilitate their social integration. The aim of this study is to propose a learning mechanism based on the analysis and annotation of a corpus video filmed during the learning and training sessions of autistic children in Mauritania. We have analyzed these videos with the objective of identifying a classical and software annotation and then producing a coding schema while importing it into ANVIL software. In this respect, we are interested in the XML files generated by ANVIL presenting details of the appearance of attributes in the annotated videos. The match between the two annotations illustrates that the data in the files converge in showing that the children who had an improvement in skill acquisition in the treatment sessions due to the fact that the specialist educators in the videos emphasized attributes that promote collaboration more than others to get the children's attention. Indeed, we wish to obtain a learning mechanism that is as complete as possible for the schooling of autistic children in Mauritania and that can later be implemented as a pedagogical conversational agent.