Reviewerly/exHarmony huggingface dataset:
The Efficiency of Distance Learning in Ukrainian Higher Education Institutions During the Martial Law Period

The full-scale invasion of Ukraine by the Russian Federation has become a severe challenge for the entire Ukrainian society, including the higher education system. The decision to switch to distance learning was made due to the increased threat to the safety of students, teachers and staff of higher education institutions. The article examines the effectiveness of the introduction of distance learning in Ukrainian higher education institutions during martial law period; analyses the level of student satisfaction with the organization of the distance learning system during martial law period; analyses the difficulties and the indicators of success of students' learning outcomes in various specialities before and during martial law period. The authors created a questionnaire for students using Google Forms to solve these problems. The obtained results were processed with the help of content analysis, calculation of the share of elections, and ranking. In order to make a comparative characterization of students' learning outcomes before and during martial law period, the authors analyzed the overall and qualitative performance of students by speciality. It has been determined that the most significant difficulties that arise in Ukrainian distance learning in the context of war are: interruptions in the work of the Internet and communication, psychological stress of students and teachers, lack of effective interaction, lack of practical training, systematic physical and moral fatigue, and reduced motivation to learn. It was found that the indicators of qualitative performance decreased, while the indicators of overall performance remained almost unchanged. The authors conclude that although distance learning requires some compromises and adaptation, its implementation in Ukraine has shown that educational institutions can function effectively in a crisis and provide students with quality education. The results of the study can be useful for educational institutions and students to improve the quality of education in times of crisis.
Evaluation of a Telemergency Service for Older People Living at Home: A Study Protocol

Ageing is a critical challenge for public health and healthcare systems worldwide, as it is associated with an increased risk of falls, disability, cognitive impairment, and comorbidities. Among strategies to reduce negative fall outcomes, fall detection devices, such as Personal Emergency Response Systems (PERS), seem to be beneficial tools. PERS enable the detection and reporting of potentially dangerous situations in and outside home. However, more studies are needed to understand who can best benefit from these devices, as most did not associate alert data with user health information. For this purpose, a retrospective cohort study is being conducted to describe two years of alerts activation and the characteristics of users, distinguishing between those who privately pay for the service and those who receive a fund from public local authorities.
Sentiment Analysis of Amazon Product Reviews using VADER and RoBERTa Models

Natural language processing's rapidly expanding field of sentiment analysis seeks to extract subjective information from text data. This article offers a thorough overview of the most advanced lexicon-based, machine learning-based, and hybrid sentiment analysis methodologies. The study discusses the challenges and limitations of sentiment analysis, such as the lack of labelled data, the ambiguity of language, and ethical considerations. The study also presents a critical analysis of the current applications of sentiment analysis in various domains, including marketing, customer service, social media, healthcare, and politics. Furthermore, the research work highlights future directions and opportunities for research in the field, such as the development of more accurate sentiment analysis techniques, the integration of multimodal data, and the exploration of ethical and privacy implications. This study offers a thorough grasp of sentiment analysis, including its current applications, difficulties, and potential future research areas.
News Alert App

The News Alert Android App is a revolutionary mobile application designed to keep users informed with real- time access to breaking news from across the globe. With its user-friendly interface, the app ensures that users can easily navigate through various news categories and receive instant notifications for critical updates and emergencies. Offering personalized news preferences, users can customize their experience by selecting specific topics, regions, or industries of interest. Beyond text-based news, the app provides multimedia content, including images and videos, to enhance the storytelling experience. Social media integration allows users to share news updates with their networks, encouraging meaningful discussions and raising awareness about important issues. With AI-driven algorithms, the app delivers smart notifications to avoid overwhelming users with redundant or irrelevant information. Whether online or offline, users can access up-to-date news, bookmark articles, and stay connected to the world's events. The News Alert Android App is a powerful tool for those seeking timely and reliable information, fostering an informed and engaged community in today's fast-paced information age. The news alert app includes modules like home, science technology, sports, health, entertainment for users. Keyword: Real-Time News Delivery, User Experience, User- Centric Design, Push Notification.
A Novel Japanese Character Recognition Algorithm for Automatic Smart Courseware Generation Tasks

The efficient recognition and smart analysis of the character is essential for the digital information processing. To deal with the traditional algorithms of the low efficiency or noise-sensitive features, this paper propoes novel Japanese character recognition algorithm for the automatic smart courseware generation tasks. The proposed algorithm has 3 aspects of considerations: 1) The traditional ELM is optimized considering the unsupervised features, to then obtain the fast deep learning network training speed; 2) The proposed OCR method is studied, the pre-processing steps are combined to make the framework complete; 3) The automatic smart courseware generation scheme is integrated as the application. In the experimentation section, the proposed model is compared with the SVM and PCA, the results on accuracy has proven that proposed model is efficient.
Secure Color Image Encryption: An Innovative Algorithm based on 8D Hyperchaotic System and DNA Encoding Strategies

In recent years, the increasing demand for secure image transmission and storage has necessitated the development of robust and efficient image encryption algorithms. This study presents a novel color image encryption algorithm based on an 8-dimensional (8D) hyperchaotic system and DNA encoding techniques. The proposed algorithm exploits the inherent complexity and unpredictability of the 8D hyperchaotic system to enhance security, while leveraging the unique properties of DNA sequences for encoding and data manipulation. The proposed encryption process consists of three main stages: (1) generation of chaotic sequences from the 8D hyperchaotic system, (2) DNA encoding of the color image using a set of predefined DNA encoding rules, and (3) an encryption process that combines chaotic sequences with DNA-encoded image data through a series of substitution and permutation operations. The decryption process reverses these stages to recover the original image. Comprehensive security analysis, including key space analysis, sensitivity analysis, and correlation analysis, demonstrates that the proposed algorithm provides a high level of security against various attacks. Additionally, performance evaluation based on image quality metrics, such as Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), confirms the efficiency and effectiveness of the proposed algorithm for color image encryption applications.
Packaging Virtual Image Auxiliary Generation Algorithm based on Large Language Model (LLM)

When applying the Large Language Model (LLM) to image processing, it is crucial to control the training size and the accuracy of the algorithm. This research study proposes a novel LLM-based algorithm for the generation of auxiliary virtual images. The proposed approach is based on a two-step strategy, namely the optimized LLM and the joint pix2pix model, which integrates the neural structure into the traditional processing pipelines. For the designed LLM, this study uses the Transformer's global interactive ability that combines with the local characteristics of CNN to enrich the feature diversity, then the input feature maps are divided into multiple groups and further, then fuse with the updated regulation to achieve the initial generation task. For the joint pix2pix mode, the original image is generated by the generator to generate a new image, the new image and the original image are fused together as fake data and sent to the discriminator for training. The experimental results on the small and large datasets show that the proposed approach outperforms.
A Weighted Regular Heterogeneous-Center Triplet Loss for Visible-Infrared Person Re-identification

Cross-modal person re-identification (Re-ID) compensates for the supervision of suspicious persons under dark conditions and has important significance in real life. Existing related studies based on triplet loss adopt either too strong or too weak constraints to aggregate features, so in order to further improve accurate person Re-ID, this paper proposes a weighted regular heterogeneous-center loss, which adopts a weighted approach to adaptively keep the centers of person features of different identities away from each other while pulling the centers of same identities of different modalities closer. Besides, this paper also proposes a network for adaptive extraction of local features, which adaptively extracts local features through an attention mechanism, and then employs a combination of global features and local features to effectively enhance the robustness of person features and improve the detection accuracy of person Re-ID. Experiments on two publicly available cross-modal person re-identification datasets SYSU-MM01 and RegDB in this paper demonstrate the effectiveness of the proposed method, which achieves comparable or leading detection results compared with current mainstream algorithms.
Revisiting Learned Image Compression With Statistical Measurement of Latent Representations

Recent learned image compression models surpass manually designed methods in rate-distortion performance by introducing nonlinear transforms and end-to-end optimization. However, there still lack quantitative measurements that efficiently evaluate the latent representations inferred by learned image compression models. To address this problem, we develop novel measurements on robustness and importance of the latent representations. We first propose an admissible range that can be efficiently estimated via gradient ascent and descent for establishing the empirical distribution of latent representations. Consequently, the in-distribution region within the admissible range is derived to measure the robustness and channel importance of latent representations of natural images. Visualization demonstrates the statistics of latent representations are significantly distinguishing in the properties of robustness and linearity within and outside the in-distribution region. To our best knowledge, this paper proposes the first statistically meaningful measurements for learned image compression and successfully applies the measurements in corruption alleviation during successive image compression and post-training pruning in a training-free fashion. Compared with existing methods, the shrunk in-distribution constraint derived from the in-distribution region achieves superior robustness and rate-distortion performance in successive compression. The channel importance allows post-training pruning to achieve comparable rate-distortion performance with a reduction of up to 60% entropy coding time.
Online Federated Learning via Non-Stationary Detection and Adaptation Amidst Concept Drift

Federated Learning (FL) is an emerging domain in the broader context of artificial intelligence research. Methodologies pertaining to FL assume distributed model training, consisting of a collection of clients and a server, with the main goal of achieving optimal global model with restrictions on data sharing due to privacy concerns. It is worth highlighting that the diverse existing literature in FL mostly assume stationary data generation processes; such an assumption is unrealistic in real-world conditions where concept drift occurs due to, for instance, seasonal or period observations, faults in sensor measurements. In this paper, we introduce a multiscale algorithmic framework which combines theoretical guarantees of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAvg</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedOMD</i> algorithms in near stationary settings with a non-stationary detection and adaptation technique to ameliorate FL generalization performance in the presence of concept drifts. We present a multi-scale algorithmic framework leading to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Tilde{\mathcal{O}} (\min \{ \sqrt{LT}, \Delta^{({1}/{3})}T^{({2}/{3})} + \sqrt{T} \})$</tex-math> </inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic regret</i> for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$</tex-math> </inline-formula> rounds with an underlying general convex loss function, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L$</tex-math> </inline-formula> is the number of times non-stationary drifts occurred and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta$</tex-math> </inline-formula> is the cumulative magnitude of drift experienced within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$</tex-math> </inline-formula> rounds.
A Filtering-Based General Approach to Learning Rational Constraints of Epistemic Graphs

Epistemic graphs are a generalization of the epistemic approach to probabilistic argumentation. Hunter proposed a 2-way generalization framework to learn epistemic constraints from crowd-sourcing data. However, the learnt epistemic constraints only reflect users’ beliefs from data, without considering the rationality encoded in epistemic graphs. Meanwhile, the current framework can only generate epistemic constraints that reflect whether an agent believes an argument, but not the degree to which it believes in it. The major challenge to achieving this effect is that the time performance will become unacceptable when the number of restricted values increase. To address these problems, we propose a filtering-based approach using a multiple-way generalization step to generate a set of rational rules which are consistent with their epistemic graphs from a dataset. This approach is able to learn a wider variety of rational rules that reflect information in both the domain model and the users model, and therefore more suitable to be applied to some situations, e.g. automated persuasion system, where the statistical information about the beliefs of a group of users is exploited to predict the behaviours of a specific user. Moreover, to improve computational efficiency, we introduce a new function to exclude meaningless rules. The empirical results show that our approach significantly outperforms the existing framework when expanding the variety of rules.
SVCNet: Scribble-Based Video Colorization Network With Temporal Aggregation

In this paper, we propose a scribble-based video colorization network with temporal aggregation called SVCNet. It can colorize monochrome videos based on different user-given color scribbles. It addresses three common issues in the scribble-based video colorization area: colorization vividness, temporal consistency, and color bleeding. To improve the colorization quality and strengthen the temporal consistency, we adopt two sequential sub-networks in SVCNet for precise colorization and temporal smoothing, respectively. The first stage includes a pyramid feature encoder to incorporate color scribbles with a grayscale frame, and a semantic feature encoder to extract semantics. The second stage finetunes the output from the first stage by aggregating the information of neighboring colorized frames (as short-range connections) and the first colorized frame (as a long-range connection). To alleviate the color bleeding artifacts, we learn video colorization and segmentation simultaneously. Furthermore, we set the majority of operations on a fixed small image resolution and use a Super-resolution Module at the tail of SVCNet to recover original sizes. It allows the SVCNet to fit different image resolutions at the inference. Finally, we evaluate the proposed SVCNet on DAVIS and Videvo benchmarks. The experimental results demonstrate that SVCNet produces both higher-quality and more temporally consistent videos than other well-known video colorization approaches. The codes and models can be found at https://github.com/zhaoyuzhi/SVCNet.
Describing and Animating Quantum Protocols

We introduce a quantum protocol simulator, the Qtpi simulator, which allows description of quantum protocols with multiple agents. It has protection against cloning and sharing of qbits within a simulation. Its symbolic calculator is adequate for the protocols we have examined, and can be pushed to simulate some non-protocol algorithms.
Audience Irrelevance in Strategic Argumentation Games

Being relevant is an essential evaluation criterion for the rationality of persuasion in argumentation theory. However, it is often observed that people, especially politicians, intend to use irrelevant arguments. Persuasive argumentation, such as those by politicians, can be thought of as equivocal actions to acquire the polls of voters with diverse values. The rationale for such persuasive activities is worth explaining more. In this paper, we establish a model of strategic argumentation by combining abstract argumentation theory and game theory. In our argumentation game model with multiple audiences and diverse values, we show the rationale for people’s strategies of audience-irrelevant arguments.
The Explainable Model to Multi-Objective Reinforcement Learning Toward an Autonomous Smart System

The mission of this chapter is to add an explainable model to multi-goal reinforcement learning toward an autonomous smart system to design both complex behaviors and complex decision making friendly for a human user. At the front of the introduction section, and a relation between reinforcement learning including an explainable model and a smart system is described. To realize the explainable model, this chapter formalizes the exploration of various behaviors toward sub-goal states efficiently and in a systematic way in order to collect complex behaviors from a start state towards the main goal state. However, it incurs significant learning costs in previous learning methods, such as behavior cloning. Therefore, this chapter proposes a novel multi-goal reinforcement learning method based on the iterative loop-action selection strategy. As a result, the complex behavior sequence is learned with a given sub-goal sequence as a sequence of macro actions. This chapter reports the preliminary work carried out under the OpenAIGym learning environment with the CartPoleSwingUp task.
Multi-agent Path Planning in Apple Orchard with Tight Tree Rows Based on Improved A-Star Algorithm for Frost Protection

<b><sc>Abstract.</sc></b> Frost is one of the severe weather events causing economic losses in agriculture. The traditional heating method is to use gas heaters at fixed locations during frost events in orchards, but fixed heaters have limited heating capacity. To solve this problem, unmanned ground vehicles (UGVs) can be applied to carry gas heaters to different low-temperature locations in orchards for multiple heating cycles. In this study, a path-planning algorithm with multiple UGV-based heaters (agents) was proposed based on the A-star algorithm to create collision free paths in a 2D grid orchard with tight row apple trees. A novel method was proposed to estimate the path cost matrix in the orchard environment and a linear optimization method was used to improve the A-star algorithm by coupling the multiple start points and goal points, i.e., the optimal task assignment. The canopy that was heated in any cycle was taken as being protected and an estimating method of protection performance was presented. The simulated results show that the improved A-star algorithm had higher search efficiency, resulting in 36.8% and 98.7% less total path cost and computational time than the traditional A-Star algorithm. More importantly, under the paths generated by the improved A-Star algorithm, 72.8% of the tree rows in the simulated orchard environment were protected by multiple agents in multiple heating cycles. Overall, this study provides a concept of preventing orchard frost damage with multiple UGV-based heaters based on the improved A-Star algorithm.
A Class of LCD codes through cyclic codes over $ZpR$

In recent time, some mixed types of alphabets have been considered for constructing error correcting codes. These constructions include $Z_{2}Z_{4}-$additive codes, $Z_{2}Z_{2}[u]-$linear codes et cetera. In this paper, we studied a class of codes over a mixed ring $Z_{p}R$ where $R=Z_{p}+vZ_{p}+v^{2}Z_{p}, v^{3}=v.$ We determined an algebraic structure of these codes under certain conditions. We have also constructed a class of LCD cyclic codes over $\bbbz_{p}R$. A necessary and sufficient condition for a cyclic code to be a complementary dual (LCD) code has been obtained.
Design and Implementation of Health Supplies Inventory Monitoring System Using First Expired First Out Method

The manual management of health supplies inventory by pharmacists at UPTD Puskesmas Toari, Southeast Sulawesi, poses a challenge in monitoring the stock of health supplies due to the manual recording, resulting in less effective monitoring of the inventory. This study aims to design a monitoring system for health supplies inventory using the First Expired First Out (FEFO) method. The FEFO method is suitable for managing the dispensing of health supplies that are closest to their expiration date by utilizing them first to avoid any expired supplies being consumed. The development of this health supplies inventory monitoring system will utilize the waterfall method, consisting of five stages: Requirement Analysis, Design, Implementation, Testing, and Maintenance. The system will be web-based, providing efficient management of health supplies inventory, including medications, disposable medical materials (BMHP), and medical equipment, while also providing timely and accurate information. The research findings indicate that the system can effectively monitor the health supplies inventory at the UPTD Puskesmas Toari Pharmacy Warehouse, utilizing the FEFO method to prioritize the dispensing of health supplies with the nearest expiration date. The system also includes a notification feature to inform the need for immediate dispensing of health supplies. Questionnaire results showed that respondents agreed with the implementation of the monitoring system at the UPTD Puskesmas Toari Pharmacy Warehouse