Reviewerly/exHarmony huggingface dataset:
Object Detection Based on an Improved YOLOv7 Model for Unmanned Aerial-Vehicle Patrol Tasks in Controlled Areas

When working with objects on a smaller scale, higher detection accuracy and faster detection speed are desirable features. Researchers aim to endow drones with these attributes in order to improve performance when patrolling in controlled areas for object detection. In this paper, we propose an improved YOLOv7 model. By incorporating the variability attention module into the backbone network of the original model, the association between distant pixels is increased, resulting in more effective feature extraction and, thus, improved model detection accuracy. By improving the original network model with deformable convolution modules and depthwise separable convolution modules, the model enhances the semantic information extraction of small objects and reduces the number of model parameters to a certain extent. Pretraining and fine-tuning techniques are used for training, and the model is retrained on the VisDrone2019 dataset. Using the VisDrone2019 dataset, the improved model achieves an mAP50 of 52.3% on the validation set. Through the visual comparative analysis of the detection results in our validation set, we find that the model shows a significant improvement in detecting small objects compared with previous iterations.
ChatGPT and computational-based research: benefits, drawbacks, and machine learning applications

Abstract Generative artificial intelligence (GenAI) systems are disrupting how research is conducted across a wide range of disciplines. Many journals have decided not to allow these tools to be co-authors for the purposes of publication, but rather they must be acknowledged by authors as having been utilised in the writing process. Furthermore, due to the hallucinations that these models sometimes produce, authors are to review what is generated and recognise that they hold it to be true and accurate. To date, there has been varying research conducted on the accuracy of GenAI systems and their production of written text. However, new functions that allow GenAI systems to produce coding for constructing tools in computer programming languages highlights a new area that warrants investigation. Therefore, this article puts forth an account of using ChatGPT 3.5 to construct coding to be utilised for a Latent Dirichlet Allocation Topic Model (LDA-TM) for use in a Systematic Literature Review. This is hoped to address three elements of using ChatGPT 3.5 for coding: code review, error resolution, and scripting new code. The code will be aimed at designating an appropriate Hyper-parameter for the Random State for use in the LDA-TM. Within this context, this article will discuss the advantages and drawbacks of utilising this new tool and what it means for researchers who wish to augment their work with computer programming-based applications. To the authors knowledge, this is the first time this has been discussed within the context of the research being conducted.
Mining Player Behavior Patterns from Domain-Based Spatial Abstraction in Games

Identifying explainable player strategies and decision patterns that give insights into player behavior is one of the most difficult tasks for game analytics, yet yields great informative potential for various purposes. Industrial stakeholders can capture player experience and infer issues or feedback on design, content, and game balancing - while players themselves might want to leverage this technique to contrast their style of play to other players, fostering self-regulated learning. On top of that, in the case of educational games, the identification of learning strategies (as well as the discovery of popular erroneous strategies) could even elevate their potential to successfully communicate educational concepts. To advance this field, we investigate how the spatial map of a game contributes to identifying player strategies and emphasize the importance of the appropriate level of abstraction to capture strategical decisions. Using visualizations and expert domain knowledge about spatial abstraction, we illustrate how the partitioning into affordance zones can reveal patterns and strategies in gameplay. To showcase the generalizability of our methodology, we investigate two case studies for the distinct genres of educational games (Parallel) and MMORPGs (Guild Wars 2). The two case studies unveil insightful strategies between different player sets of interest – only possible by the apt level of spatial abstraction.
Explainable AI in Manufacturing: an Analysis of Transparency and Interpretability Methods for the XMANAI Platform

The use of artificial intelligence (AI) in manufacturing has become increasingly common, but the lack of transparency and interpretability of AI models can limit their adoption in critical applications, due to lack of human understanding. Explainable AI (XAI) has emerged as a solution to this problem by providing insights into the decision-making process of AI models. This paper analyses different approaches for AI transparency and interpretability, starting from explainability by-design to post-hoc explainability, pointing out trade-offs, advantages and disadvantages of each one, as well as providing an overview of different applications in manufacturing processes. The paper concludes presenting XMANAI as an innovative platform for manufacturing users to develop insightful XAI pipelines that can assist in their everyday operations and decision-making. The comprehensive overview of methods here presented serves as the ground basis for the platform draft catalogue of XAI models.
Energy Efficiency Analysis of East-West Oriented Photovoltaic Systems for Buildings: A Technical-Economic-Environmental Approach

This research is based on achieving modular solutions for photovoltaic solar systems ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PVSS</i> ) in buildings. Several studies tend to inadequately address the analysis of photovoltaic ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PV</i> ) module orientation, often relying on the conventional south-facing orientation. The objective is to analyze and compare the influence of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PV</i> module orientation. Therefore, two proposed PVSS were analyzed, incorporating module orientation and inclination variations. A hotel electrical consumption study was conducted in 2018-2019. The incidence of shadows over the roofed surfaces of the building was analyzed from a three-dimensional model using Sketchup software. The annual and monthly generation, as well as the performance of the installation, were estimated using PVsyst software for each projected arrangement. An economic and environmental study was carried out for these proposals to remark on their feasibility and influence on environmental protection. As a result, the east-west oriented <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PV</i> system is the most efficient and feasible with a peak power of 113.24 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">kW<sub>p</sub></i> and a Performance Ratio ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PR</i> ) of 0.80. The east-west oriented proposal allows avoiding emissions of 301 421 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TCO</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> into the atmosphere. These promising results were due to both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PV</i> modules physical orientation and their lower daily working temperature. Also, an increase in electrical production per module and an optimization of the surface are achieved. These results highlight the significance of this research as they encourage the adoption of these systems, leading to improved investment recovery periods and enhanced environmental conservation efforts.
Low-Light Image Enhancement with Wavelet-Based Diffusion Models

Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. To address these issues, we propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL. Specifically, we present a wavelet-based conditional diffusion model (WCDM) that leverages the generative power of diffusion models to produce results with satisfactory perceptual fidelity. Additionally, it also takes advantage of the strengths of wavelet transformation to greatly accelerate inference and reduce computational resource usage without sacrificing information. To avoid chaotic content and diversity, we perform both forward diffusion and denoising in the training phase of WCDM, enabling the model to achieve stable denoising and reduce randomness during inference. Moreover, we further design a high-frequency restoration module (HFRM) that utilizes the vertical and horizontal details of the image to complement the diagonal information for better fine-grained restoration. Extensive experiments on publicly available real-world benchmarks demonstrate that our method outperforms the existing state-of-the-art methods both quantitatively and visually, and it achieves remarkable improvements in efficiency compared to previous diffusion-based methods. In addition, we empirically show that the application for low-light face detection also reveals the latent practical values of our method. Code is available at https://github.com/JianghaiSCU/Diffusion-Low-Light.
Blockchain-based connectivity within digital platforms and ecosystems in international business

Digital globalization enabled by disruptive technologies has opened a myriad of ways to create value, warranting a cross-disciplinary research agenda on digital connectivity in international business (IB). Given the scarcity of understanding about the role of specific technologies enabling value creation through digital connectivity, we investigate how blockchain-based digital connectivity shapes value creation in international digital platforms and ecosystems (DPEs). Building on a comparative empirical analysis of international DPEs enabled by different types of blockchain technology, we develop a typology of blockchain-based connectivity in IB. The typology demonstrates how different blockchain technology types enable different kinds of connectivity, resulting in diverse approaches to value creation within newly emerging forms of DPEs. These findings contextualize the concept of digital connectivity, enabling more precise explanations of blockchain's adoption in DPEs, and expand the conceptualization of DPEs, with three new types emerging from the utilization of blockchain-based connectivity. Moreover, this interdisciplinary explanation reveals why certain features theoretically attributed to blockchain (e.g., decentralization, trust) do not always realize or support value creation in practice in IB based on the DPE business model.
Determination of consciousness, self-consciousness and subjectness within the framework of the information concept

The article is devoted to the study of the nature of consciousness within the framework of the information concept. The paper proposes a definition of consciousness as an informational environment in which an extended model of reality is realized. The process of realization of this extended model is defined as thinking. The result of thinking is information objects that form a system in the form of information environment. Information objects are reflections of the real world properties, not directly, but by means of translation through a special object – a carrier of consciousness. In the case of human consciousness, such a carrier is a human being (represented in the form of his nervous system). As a result, consciousness can be qualified as a simulacrum of reality, i.e., a model of a model: an information model of the carrier of consciousness, which in turn is a means of physical modeling of the real "big" world. Possible mechanisms mediating thinking are considered. For this purpose, two new concepts are introduced: neural circuit and neurophysical pattern. An approach to the study of self-consciousness based on the localization of the consciousness carrier in the multidimensional space of states of initial real objects, as well as their reflections in the form of information objects is proposed. This localization is ensured by the presence of feedbacks. Summarizing the results of the study, the article states the following connection between consciousness, self-consciousness and subjectness: under certain conditions (when consciousness is localized in the state space of the carrier of consciousness), consciousness acquires the property of self-consciousness, a special case of which (when the initiator of changes determining localization is the carrier of consciousness) is self-consciousness endowed with subjectness.
LiDAR Point Clouds Semantic Segmentation in Autonomous Driving Based on Asymmetrical Convolution

LiDAR has become a vital sensor for autonomous driving scene understanding. To meet the accuracy and speed of LiDAR point clouds semantic segmentation, an efficient model ACPNet is proposed in this paper. In the feature extraction stage, the backbone is constructed with asymmetric convolutions, so the skeleton of the square convolution kernel is enhanced, which leads to greater robustness to target rotation. Moreover, a contextual feature enhancement module is designed to extract richer contextual features. During training, global scaling and global translation are performed to enrich the diversity of datasets. Compared with the baseline network PolarNet, the mIoU of ACPNet on the SemanticKITTI, SemanticPOSS and nuScenes datasets are improved by 5.1%, 1.6% and 2.9%, respectively. Meanwhile, the speed of ACPNet is 14 FPS, which basically meets the real-time requirements in autonomous driving scenarios. The experimental results show that ACPNet significantly improves the performance of LiDAR point cloud semantic segmentation.
Extending the variational quantum eigensolver to finite temperatures

Abstract We present a variational quantum thermalizer (VQT), called quantum-VQT (qVQT), which extends the variational quantum eigensolver to finite temperatures. The qVQT makes use of an intermediate measurement between two variational circuits to encode a density matrix on a quantum device. A classical optimization provides the thermal state and, simultaneously, all associated excited states of a quantum mechanical system. We demonstrate the capabilities of the qVQT for two different spin systems. First, we analyze the performance of qVQT as a function of the circuit depth and the temperature for a one-dimensional Heisenberg chain. Second, we use the excited states to map the complete, temperature dependent phase diagram of a two-dimensional J 1 –J 2 Heisenberg model. Numerical experiments on both quantum simulators and real quantum hardware demonstrate the efficiency of our approach, which can be readily applied to study various quantum many-body systems at finite temperatures on currently available noisy intermediate-scale quantum devices.
METAseen: Analyzing network traffic and privacy policies in Web 3.0 based Metaverse

Metaverse is a new emerging concept building up a virtual environment for the user using Virtual Reality (VR) and blockchain technology but introduces privacy risks. Now, a series of challenges arise in Metaverse security, including massive data traffic breaches, large-scale user tracking, analysis activities, unreliable Artificial Intelligence (AI) analysis results, and social engineering security for people. In this work, we concentrate on Decentraland and Sandbox, two well-known Metaverse applications in Web 3.0. Our experiments analyze, for the first time, the personal privacy data exposed by Metaverse applications and services from a combined perspective of network traffic and privacy policy. We develop a lightweight traffic processing approach suitable for the Web 3.0 environment, which doesn't rely on complex decryption or reverse engineering techniques. We propose a smart contract interaction traffic analysis method capable of retrieving user interactions with Metaverse applications and blockchain smart contracts. This method provides a new approach to de-anonymizing users' identities through Metaverse applications. Our system, METAseen, analyzes and compares network traffic with the privacy policies of Metaverse applications to identify controversial data collection practices. The consistency check experiment reveals that the data types exposed by Metaverse applications include Personal Identifiable Information (PII), device information, and Metaverse-related data. By comparing the data flows observed in the network traffic with assertions made in the privacy regulations of the Metaverse service provider, we discovered that far more than 49% of the Metaverse data flows needed to be disclosed appropriately.
Orchestrating the Execution of Serverless Functions in Hybrid Clouds

In recent years, serverless computing, especially Function as a Service (FaaS), is rapidly growing in popularity as a cloud programming model. The serverless computing model provides an intuitive interface for developing cloud-based applications, where the development and deployment of scalable microservices has become easier and cost-effective. An increasing number of batch-processing applications are deployed as pipelines that comprise a sequence of functions that must meet their deadline targets to be practical. In this paper, we present our Hybrid Cloud Scheduler (HCS) for orchestrating the execution of serverless batch-processing pipelines deployed over heterogeneous infrastructures. Our framework enables developers to (i) automatically schedule and execute batch-processing applications in heterogeneous environments such as the private edge and public cloud serverless infrastructures, (ii) benefit from cost reduction through the utilization of their own resources in a private cluster, and (iii) significantly improves the probability of meeting the deadline requirements of their applications. Our experimental evaluation demonstrates the efficiency and benefits of our approach.
Optimizing Forest Fire Prevention: Intelligent Scheduling Algorithms for Drone-Based Surveillance System

Given the importance of forests and their role in maintaining the ecological balance, which directly affects the planet, the climate, and the life on this planet, this research presents the problem of forest fire monitoring using drones. The forest monitoring process is performed continuously to track any changes in the monitored region within the forest. During fires, drones' capture data is used to increase the follow-up speed and enhance the control process of these fires to prevent their spread. The time factor in such problems determines the success rate of the fire extinguishing process, as appropriate data at the right time may be the decisive factor in controlling fires, preventing their spread, extinguishing them, and limiting their losses. Therefore, this research presented the problem of monitoring task scheduling for drones in the forest monitoring system. This problem is solved by developing several algorithms with the aim of minimizing the total completion time required to carry out all the drones' assigned tasks. System performance is measured by using 990 instances of three different classes. The performed experimental results indicated the effectiveness of the proposed algorithms and their ability to act efficiently to achieve the desired goal. The algorithm RID achieved the best performance with a percentage rate of up to 90.3% with a time of 0.088 seconds.
Socio-Economic Factors and Engagement Work in Teachers of Educational Institutions, State and Private

Objective: The objective of this research was to determine the relationship between sociodemographic factors and engagement in teachers of public and private institutions in the city of Juliaca, 2021. Methodology: The approach was quantitative with a cross-sectional correlational design, the sample consisted of 104 teachers from public and private educational institutions. A sociodemographic form was used for data collection, as well as the UWES-17. Results and conclusions: Descriptive and inferential statistics were used for data processing through the SPSS statistical package. It is concluded that the sociodemographic factors are not related to engagement, except for the age factor, in which a chi-square value of 15.966 was obtained with a p-value of 0.043 which is less than the proposed significance (p&lt;0.05), therefore, it is affirmed that there is a relationship between the factor and engagement in teachers of public and private institutions in the city of Juliaca, 2021. Implications of the research: In the other factors such as gender, marital status, labor condition, labor regime, length of service, academic training, average remuneration the p value obtained was greater than 0.05 so it is affirmed that there is no relationship between these and engagement in teachers. Originality/value: This article contributes to analyze the socioeconomic factors and work engagement in teachers from different schools in Puno, and it also shows the different relationships with the dimensions of the study.
Internet Scam (Phishing): Issues of Counteraction and Prevention

Internet technologies are so common today that they literally extend over almost all areas of life. However, due to the ongoing growth of Internet technologies, security threats to systems and networks are also rising. One of these serious threats is «phishing», in which attackers try to illicitly obtain user credentials. Phishing is a fraudulent practice, the purpose of which is to create a fake website, illicitly asking for passwords, bank account numbers, etc. to induce users to reveal confidential information. This type of scam is increasingly gaining in popularity, since fraudsters incur a minimum amount of expenses, and the tools used by them for illegal purposes are relatively accessible and easy to use. The purpose of this study is to examine common features of phishing attacks, as well as to analyze means and methods of countering these wrongdoings. The article looks into the existing ways of countering phishing attacks and analyzes measures aimed at identifying and suppressing such crimes. The analysis of judicial and investigative practice has shown that in the current state the government needs to provide law enforcement agencies with cutting-edge technical equipment, software, and highly qualified staff in order to successfully overcome new challenges in cyberspace in the context of the digital economy.