Reviewerly/exHarmony huggingface dataset:
Multi-agent target search strategy optimization: Hierarchical reinforcement learning with multi-criteria negative feedback

Recently, using unmanned platforms to perform target search tasks has received extensive attention and research. The complexity of the search scenario and the unpredictability of the target movement pose significant challenges for unmanned platforms to perform the search task. Developing efficient search strategies is crucial for their success. In this study, we model the problem as a Partially Observable Markov Decision Process (POMDP) and propose a Hierarchical Deep Q Network with Multi-criteria Negative Feedback method named MNF-HDQN to solve the problem efficiently. The MNF-HDQN incorporates point and area evaluations instead of the original sparsity calculation to enhance the cooperation competence of unmanned platforms in searching for a target in various search tasks. Additionally, the integration of convex polygon theory into reward shaping and the design of a new two-stage search strategy encouragement function further improve the performance of the proposed method. We conduct detailed experiments to verify the superiority of the MNF-HDQN. And experimental results show that our method provides a high successful search rate in a shorter timestep compared with state-of-the-art baselines. This advantage is more evident when the search scenario is more complex or the target movement is more unpredictable.
Robust Clustering with Normal Mixture Models: A Pseudo <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si166.svg"><mml:mi>β</mml:mi></mml:math>-Likelihood Approach

As in other estimation scenarios, likelihood based estimation in the normal mixture set-up is highly non-robust against model misspecification and presence of outliers (apart from being an ill-posed optimization problem). A robust alternative to the ordinary likelihood approach for this estimation problem is proposed which performs simultaneous estimation and data clustering and leads to subsequent anomaly detection. To invoke robustness, the methodology based on the minimization of the density power divergence (or alternatively, the maximization of the β-likelihood) is utilized under suitable constraints. An iteratively reweighted least squares approach has been followed in order to compute the proposed estimators for the component means (or equivalently cluster centers) and component dispersion matrices which leads to simultaneous data clustering. Some exploratory techniques are also suggested for anomaly detection, a problem of great importance in the domain of statistics and machine learning. The proposed method is validated with simulation studies under different set-ups; it performs competitively or better compared to the popular existing methods like K-medoids, TCLUST, trimmed K-means and MCLUST, especially when the mixture components (i.e., the clusters) share regions with significant overlap or outlying clusters exist with small but non-negligible weights (particularly in higher dimensions). Two real datasets are also used to illustrate the performance of the newly proposed method in comparison with others along with an application in image processing. The proposed method detects the clusters with lower misclassification rates and successfully points out the outlying (anomalous) observations from these datasets.
LexiFusedNet: A Unified Approach for Imbalanced Short-Text Classification Using Lexicon-Based Feature Extraction, Transfer Learning and One Class Classifiers

The incorporation of lexicon-based feature extraction and the utilization of a one-class classification loss within a transfer learning-based deep neural network offer significant advantages. In lexicon-based feature extraction, weights are assigned to relevant words in short texts, and fine-tuning with these weighted features enables the capture of critical information. Augmenting contextualized word embeddings with lexicon-derived weights highlights the significance of specific words, thereby enriching text comprehension. One-class classification loss methods such as OCSVM or SVDD identify anomalous instances based on word relevance. A comprehensive evaluation on four benchmark datasets has confirmed an improvement in short-text classification performance, effectively addressing issues related to data imbalances, contextual limitations, and noise.
Blockchain as a Collaborative Technology - Case Studies in the Real Estate Sector in Vietnam

This paper delves into the transformative potential of blockchain technology in the real estate sector, with a particular emphasis on Vietnam, a developing country with unique challenges. Traditional real estate processes in such contexts often suffer from issues such as lack of transparency, high intermediary fees, time-consuming document validation, and potential for fraud. These limitations create a pressing need for a more efficient, transparent, and secure system. In response to this, we explore the application of collaborative technologies, specifically blockchain and smart contracts, to redefine and streamline the real estate sector. We propose a novel model that leverages these technologies to address the identified issues. The model is designed to enhance transparency, reduce transaction times, and minimize the potential for fraud, thereby instilling greater trust in the real estate transaction process. This paper provides a comprehensive model for the real estate sector based on blockchain and smart contracts, addressing the specific challenges of the real estate sector in developing countries (i.e., Vietnam). We implement this model as a proof-of-concept on the Hyperledger Fabric platform, demonstrating its practical feasibility and benefits. Lastly, we evaluate the performance and efficiency of our model using the Hyperledger Caliper platform, providing valuable insights into its strengths and areas for potential improvement. The findings of this paper could significantly contribute to the evolution of the real estate sector in Vietnam and other similar markets, paving the social computing - in a case study of an efficient and secure real estate transaction process.
Feedback delay of sports intelligent learning system based on model predictive control and artificial intelligence

At present, in terms of model predictive control theory and model applications, two-layer segmented linear grid models are dominant. The two-layer piecewise linear lattice model is more effective, does not require too many parameters and is easy to calculate. Since the multi-layer model predictive control is based on a two-layer model, it is very important to study the mechanism and optimization methods of the two-layer model. Artificial intelligence comes from computer science. It tries to understand the nature of intelligence and create new intelligent machines that can react like human intelligence. Research in this field involves robotics, speech recognition, image recognition, natural language processing, etc. Since the emergence of artificial intelligence, theory and technology have gradually matured, and its application fields have also increased. The technological products created by artificial intelligence will embody the development of human wisdom in the future. Sports intelligence learning was first proposed by Lins. Linsley found that under laboratory conditions, by observing and recording the learner's behavior frequency and reaction speed, and adjusting the learning activities based on this, a better learning effect can be obtained. The combination of sports intelligence learning and behaviorist learning theory forms a systematic and accurate teaching method. Video system feedback delayed learning is a part of multimedia education technology. In recent years, multimedia technology has been widely used in various educational fields. By reviewing the application research of multimedia teaching technology in education, we have determined the areas related to the delay of the video system feedback, and guided the application research of the video feedback system in education.
Finding the Age and Education Level of Bulgarian-Speaking Internet Users Using Keystroke Dynamics

The rapid development of information and communication technologies and the widespread use of the Internet has made it imperative to implement advanced user authentication methods based on the analysis of behavioural biometric data. In contrast to traditional authentication techniques, such as the simple use of passwords, these new methods face the challenge of authenticating users at more complex levels, even after the initial verification. This is particularly important as it helps to address risks such as the possibility of forgery and the disclosure of personal information to unauthorised individuals. In this study, the use of keystroke dynamics has been chosen as a biometric, which is the way a user uses the keyboard. Specifically, a number of Bulgarian-speaking users have been recorded during their daily keyboard use, and then a system has been implemented which, with the help of machine learning models, recognises certain acquired or intrinsic characteristics in order to reveal part of their identity. The results show that users can be categorised using keystroke dynamics, in terms of the age group they belong to and in terms of their educational level, with high accuracy rates, which is a strong indication for the creation of applications to enhance user security and facilitate their use of Internet services.
A classification method for high‐dimensional imbalanced multi‐classification data

Abstract High‐dimensional imbalanced multi‐classification problems (HDIMCPs) occur frequently in engineering applications such as medical detection, item classification, and email classification. However, there is a paucity of research in the academic community on this topic. This paper proposes an evolutionary algorithm‐based classification method for HDIMCPs, named HIMALO (high‐dimensional imbalanced multi‐classification method based on ant lion optimizer). HIMALO proposes a new individual initialization strategy that replaces the random initialization of the ant lion optimizer with Fuch chaos. Then, it encodes individuals using concatenated sample features and base classifier weights, optimizes these features and weights concurrently during the iteration process. Additionally, a multi‐classification strategy, union one versus many, that combines one versus all and one‐against‐higher‐order is proposed. Numerous experiments are conducted to prove the superior classification performance and stability of HIMALO when compared with other algorithms.
Optimal Coherent Point Selection for 3D Quality Inspection from Silhouette-Based Reconstructions

3D Geometric quality inspection involves assessing and comparing a reconstructed object to a predefined reference model or design that defines its expected volume. Achieving precise 3D object geometry reconstruction from multiple views can be challenging. In this research, we propose a camera-coherent point selection method to measure differences with the reference. The result is a point cloud extracted from the reconstruction that represents the best-case scenario, ensuring that any deviations from the reference are represented as seen from the cameras. This algorithm has been tested in both simulated and real conditions, reducing reconstruction errors by up to one fifth compared to traditional 3D reconstruction methodologies. Furthermore, this strategy assures that any existing difference with its reference really exists and it is a best-case scenario. It offers a fast and robust pipeline for comprehensive 3D geometric quality assurance, contributing significantly to advancements in the field of 3D object inspection.
One pie, many recipes: The role of artificial intelligence chatbots in influencing Malaysian solo traveler purchase intentions

Artificial intelligence (AI) chatbots are pervasive in the travel industry and have significantly alleviated solo travelers' concerns in trip planning and booking. However, many existing AI chatbots have yet to meet the expectations of solo travelers, especially when they demand more personalized information to assist in travel decision-making. Based on complexity theory, this research examines the factors that stimulate solo travelers' purchase intentions when using AI chatbots, particularly covering the three main aspects of marketing efforts, communication quality, and affective characteristics. Drawing from a sample of 281 solo travelers, partial least squares-structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) are used to examine the proposed relationships. The PLS-SEM results illustrate that interaction, entertainment, trendiness, communication competence, and satisfaction have significant direct effects on purchase intentions. The fsQCA results further revealed four solutions exhibiting high purchase intentions among solo travelers. Different core, peripheral, and necessary causal conditions in each configuration path were identified. The findings enrich the AI chatbot literature by examining the underlying reasons why solo travelers react differently to this emerging technology and produce practical recommendations for designing AI chatbot systems.
New bounds for covering codes of radius 3 and codimension $ 3t+1 $

The smallest possible length of a $ q $-ary linear code of covering radius $ R $ and codimension (redundancy) $ r $ is called the length function and is denoted by $ \ell_q(r, R) $. In this work, for $ q $ an arbitrary prime power, we obtain the following new constructive upper bounds on $ \ell_q(3t+1,3) $:$ \begin{equation*} \begin{split} &amp;\bullet\; \ell_q(r,3)\lessapprox \sqrt[3]{k}\cdot q^{(r-3)/3}\cdot\sqrt[3]{\ln q}, \; r = 3t+1, \; t\ge1, \; q\ge\lceil \mathcal{W}(k)\rceil, \\ &amp;\phantom{\bullet\; }18 &lt;k\le20.339, \; \mathcal{W}(k){\text{ is a decreasing function of }}k ;\\ &amp;\bullet\; \ell_q(r,3)\lessapprox \sqrt[3]{18}\cdot q^{(r-3)/3}\cdot\sqrt[3]{\ln q}, \; r = 3t+1, \; t\ge1, \; q{\text{ large enough}}. \end{split} \end{equation*} $For $ t = 1 $, we use a one-to-one correspondence between codes of covering radius 3 and codimension 4, and 2-saturating sets in the projective space $ {\mathrm{PG}}(3, q) $. A new construction providing sets of small size is proposed. The codes, obtained by geometrical methods, are taken as the starting ones in the lift-constructions (so-called '$ q^m $-concatenating constructions') to obtain infinite families of codes with radius 3 and growing codimension $ r = 3t + 1 $, $ t\ge1 $. The new bounds are essentially better than the known ones.
FS-DINO: Improved Detection Method for Full-Scale Objects Based on DINO From High-Resolution Remote Sensing Imagery

Object detection from high-resolution remote sensing (RS) imagery plays an important role in a wide range of applications, including traffic management, urban planning, and automatic surveying and mapping. Meanwhile, the end-to-end model for object detection is becoming a hot topic in computer vision area. However, owing to the complex background in the RS image, the results of object detection based on the end-to-end model are not satisfactory. Particularly, the issue lies in the small and large objects, which are easily omitted. We introduce advanced end-to-end object detection with transformers with improved deNoising anchOr box (DINO) and propose a new framework improvement for RS image object detection, called improved detection method for full-scale objects based on DINO. First, we propose a feature-rich hybrid stacking module. To retain more multiscale features during channel mapping, we add two additional mapping superposition channels to conventional channel mapping. Second, we propose a low-level attention reuse module and believe that the information in the low-level attention model is incomplete and needs to be reused. Therefore, we add the output of low-level attention to the input of next-level attention through a simple activation module. We conducted experimental verification on the AIcrowd dataset and the DOTA-v1.0 dataset. On the AIcrowd and DOTA-v1.0 datasets, compared with the mainstream algorithms, our algorithm has improved the accuracy of full-scale objects, especially for large objects. In addition, our model surpasses several state-of-the-art methods, including recent anchor-free methods, in terms of accuracy on the DOTA-v1.0 dataset.
BPpy: Behavioral programming in Python

This paper presents BPpy, a novel framework for behavioral programming (BP) in Python. Designed with a flexible architecture, BPpy is crafted for easy integration with various Python platforms and systems, enabling the utilization of scenario-based programming for creating enhanced models for artificial intelligence (AI)-driven systems and vice versa. We illustrate how BPpy implements the principles of BP and extends them towards integrating with existing AI-based tools. The framework’s core principles and design are presented, followed by a comparison of its main features against those of current state-of-the-art BP-based frameworks.
Target Indicators for Assessing the Results of the Implementation of State Programmes for Regional Cultural Development

The aim of the article was to conduct research on the evaluation potential of target indicators used to analyze the effectiveness of the implementation of state programmes for cultural development of the regions of Southern Russia, its optimality and sufficiency. New tasks in the field of management activity in the sphere of culture, initiated by a number of factors, are analyzed. The most significant factors are identified: informatization and introduction of innovative methods in the management of cultural institutions, the change of generations of visitors to cultural institutions, the change in preferences of the target audience, oriented towards more modern forms of cultural product consumption. The study was based on the analysis of strategic planning documents presented on the official websites of the ministries of culture of the regions of Southern Russia: Krasnodar and Stavropol Krai, Rostov and Astrakhan Territories, the Republics of Karachay-Cherkessia, Kabardino-Balkaria, North Ossetia — Alania, and Dagestan. The authors analyze the composition of target indicators used in the state programmes of cultural development of the regions, describe their evaluation potential, and classify them. It is concluded that the target indicators of different groups are connected in a complex system of interdependence and complement each other. At the same time, there is a certain bias towards quantitative methods of assessment, which only indirectly and approximately indicate the quality of work of cultural institutions. We propose innovative target indicators focused on the qualitative assessment of the effectiveness of the implementation of state programmes for the cultural development of regions. These target indicators make it possible to measure the media and network activity of cultural institutions, their presence in virtual space, positive practices of cultural institutions, the development and operation of historical and cultural, literary, ethno-national, cultural and recreational brands in the region.
Law Enforcement Perspectives on Police Vehicle Cybersecurity

Cyberattacks on law enforcement officers’ vehicles can compromise and disrupt emergency response, yet there is little knowledge of how law enforcement officers understand and respond to cybersecurity concerns within their work vehicles. This study gathered data on law enforcement officers’ perceptions of vehicle cybersecurity. A survey study was conducted with 80 law enforcement officers, focusing on gathering information to quantify the relationship between their experience as an officer, their cybersecurity experience, and their perception of vehicle cybersecurity. Overall, there was a lack of understanding and knowledge regarding vehicle cybersecurity. Results also revealed a small relationship between how many hours an officer spends in the vehicle and vehicle cybersecurity and an even stronger relationship between the officer’s cybercrime experience and their perceptions of vehicle cybersecurity. With appropriate interventions (e.g., training), there is an opportunity to impact law enforcement behavior to positively promote vehicle cybersecurity resiliency.
SmartORC: smart orchestration of resources in the compute continuum

The promise of the compute continuum is to present applications with a flexible and transparent view of the resources in the Internet of Things–Edge–Cloud ecosystem. However, such a promise requires tackling complex challenges to maximize the benefits of both the cloud and the edge. Challenges include managing a highly distributed platform, matching services and resources, harnessing resource heterogeneity, and adapting the deployment of services to the changes in resources and applications. In this study, we present SmartORC, a comprehensive set of components designed to provide a complete framework for managing resources and applications in the Compute Continuum. Along with the description of all the SmartORC subcomponents, we have also provided the results of an evaluation aimed at showcasing the framework's capability.
A Methodology for Generating Virtual Reality Immersion Metrics based on System Variables

Technological advances in recent years have promoted the development of virtual reality systems that have awide variety of hardware and software characteristics, providing varying degrees of immersion. Immersionis an objective property of the virtual reality system that depends on both its hardware and softwarecharacteristics. Virtual reality systems are currently attempting to improve immersion as much as possible.However, there is no metric to measure the level of immersion of a virtual reality system based onits characteristics. To date, the influence of these hardware and software variables on immersion hasonly been considered individually or in small groups. The way these system variables simultaneously affectimmersion has not been analyzed either. In this paper, we propose immersion metrics for virtualreality systems based on their hardware and software variables, as well as the development process that ledto their formulation. From the conducted experiment and the obtained data, we followed a methodology togenerate immersion models based on the variables of the system. The immersion metrics presented in thiswork offer a useful tool in the area of virtual reality and immersive technologies, not only to measurethe immersion of any virtual reality system but also to analyze the relationship and importance of thevariables of these systems.
Border Collie Shuffled Shepherd optimization-based image reconstruction using visual cryptography

The visual secret sharing (VSS) technique is an encryption model to conceal secret messages into two or more meaningless images, called shares. Information loss is a huge drawback in existing systems. An efficient model has been developed to reconstruct the original image using the Border Shuffled Shepherd Optimization (BSSO) based on the visual cryptography technique. Visual cryptography is performed on the transmitter and receiver sides. At the transmitter side, the input image is partitioned into numerous shares and the B-noisy matrix is optimally generated using BSSO, which is an incorporation of Border Collie Optimization (BCO) and Shuffled Shepherd Optimization (SSO). At the receiver end, all the shares are integrated to reconstruct the original image. The BSSO has maximum Peak-to-Signal Noise Ratio (PSNR) of 56.591, distortion ratio of 0.908, noise level of 0.834, Mean squared error (MSE) of 0.038, and Structural Similarity Index (SSIM) of 0.981, for Lena image.