Reviewerly/exHarmony huggingface dataset:
Tort Liability for Damages of Artificial Intelligence Robots in The Jordanian Civil Law

Abstract This research aims to shed light on the general provisions resulting from the harmful act (tort liability) and their applicability and actions to the harmful act for the damage resulting from artificial intelligence embodied in smart robots, as this research sought to clarify the concept of artificial intelligence embodied in robots and its legal nature, as the goal The research aims to determine the subjectivity of the damage caused by robots, which is reflected in its determination of the extent to which compensation is permissible when establishing responsibility for the harmful act caused by robots. In addition, this research touched on the cornerstone of the causal relationship between the harmful act and the damage resulting from the use of robots, in terms of determining the content and nature of this causal relationship between the harmful act and the damage. Keywords: Artificial intelligence, Robot, Tort, Legal nature of the robot.
Cloud Computing Opportunities and Challenges in Electronic Document Management

Background of the study: Technological developments have encouraged the creation of cloud computing technology that allows access to information from everywhere via the internet. The use of cloud computing in managing electronic documents can manage document storage without having storage specialists, reducing the need for physical storage space, and access flexibility. But behind the benefits offered, some risks must be anticipated by the organization. Purpose: This study aims to determine the opportunities and challenges of using cloud computing in electronic document management. Method: This study uses the Systematic Literature Review method on the literature available in the Google Scholar database from 2017 to 2022. Findings: Based on the results of an analysis conducted on 20 selected articles, it was found that the opportunity of cloud computing in electronic document management is to increase efficiency in managing electronic documents, especially in terms of centralized document storage, increasing productivity, and response time of business processes, and cost efficiency. Meanwhile, the challenges are related to data security, system stability, authentication, and disaster preparedness, both from nature and cybercrime. Conclusion: The research recommends that organizations using cloud computing to manage their electronic document must provide compatible and good security systems.
A Hierarchical Model for Quality Evaluation of Mixed Source Software Based on ISO/IEC 25010

With the emergence of mixed source software, the existing quality models are not able to better assess the community quality and autonomy controllability of mixed source software. To fill this gap, we propose a hierarchical model in this paper for quality assessment of mixed source software. In our model, the new attributes are proposed to meet the quality requirements of mixed source software based on the ISO/IEC 25010 standards, and a set of metrics are designed for the new attributes. The model evaluates the quality of mixed source software through quality attributes that have been quantified by the metrics. Applying our quality model to some mixed source software and comparing the model results with the actual situation, we verify whether our proposed two quality attributes, community intensity and autonomy controllability, can effectively assess the quality of mixed source software. The results of the experiments show that our model is indeed effective in assessing the quality of mixed source software. An important feature of our model is that the model has good flexibility, and the set of quality attributes and metrics can be adjusted freely, which provides a flexible and feasible way for various software quality assessment requirements.
Lightweight Multi-Attention Fusion Network for Image Super-Resolution

Single image super-resolution reconstruction (SISR) is one of the important techniques in computer vision and image processing. Most of the existing SISR methods adopt equal processing for different spatial domains and channel domains, resulting in a large amount of computational resources wasted on unimportant features. In order to address these problems, a novel lightweight multi-attention fusion network (LMAFN) is proposed, in which the multiple attention fusion block allocates computational resources more efficiently by capturing the weight information implied by the channel domain and the spatial domain separately, thus effectively reducing the number of parameters. The synthetic channel attention block in the multiple attention fusion block makes full use of inter-channel correlation by introducing both global standard deviation pooling and maximum pooling. Global features are fused through residual linking to alleviate the problem of high frequency information loss. Experimental results on several benchmark datasets show that the proposed method effectively reduces the number of parameters and computational effort without excessive loss of reconstruction performance, and achieves better performance than the compared models.
Chaos and multifold complexity for an inverted harmonic oscillator

A bstract We examine the multifold complexity and Loschmidt echo for an inverted harmonic oscillator. We give analytic expressions for any number of precursors, implementing multiple backward and forward time evolutions of the quantum state, at the leading order in the perturbation. We prove that complexity is dominated by the longest permutation of the given time combination in an alternating “zig-zag” order, the exact same result obtained with holography. We conjecture that the general structure for multifold complexity should hold true universally for generic quantum systems, in the limit of a large number of precursors.
An underwater image enhancement method based on color correction and local unsharp masking

Abstract The underwater images collected by optical cameras have different problems due to the camera equipment, underwater environment and light source, which lead to the problems of chromatic aberration and low contrast of the images collected by ordinary people for subsequent use. The reasons are that the reflection of light by impurities in the water and the absorption efficiency of different wavelengths of light under water, and under different depths of field conditions often leads to color Bias and contrast are low. To solve this problem, we adopt a method based on color correction and local unsharp masking for the fusion of underwater images. Firstly, color is improved by correcting for color shift using the red channel and gray world methods, combined with CLAHE. secondly, for the low contrast problem, the adaptive mask coefficient and the local unsharp mask are used to separate the background and foreground, which enhances the recognition of underwater target and background, and effectively improves the contrast of images. Target. Finally, the weighted fusion of Gaussian pyramid is completed by using the method of combining four weights. The experimental results show that the algorithm proposed in this paper has a strong generalization ability, and can improve the quality of images in different underwater scenes which is according to different objective evaluation indicators, the algorithm in this paper also achieves the best results.
Optimizing the Training of Models for Automated Post-Correction of Arbitrary OCR-ed Historical Texts

Systems for post-correction of OCR-results for historical texts are based on statistical correction models obtained by supervised learning. For training, suitable collections of ground truth materials are needed. In this paper we investigate the dependency of the power of automated OCR post-correction on the form of ground truth data and other training settings used for the computation of a post-correction model. The post-correction system A-PoCoTo considered here is based on a profiler service that computes a statistical profile for an OCR-ed input text. We also look in detail at the influence of the profiler resources and other settings selected for training and evaluation. As a practical result of several fine-tuning steps, a general post-correction model is achieved where experiments for a large and heterogeneous collection of OCR-ed historical texts show a consistent improvement of base OCR accuracy. The results presented are meant to provide insights for libraries that want to apply OCR post-correction to a larger spectrum of distinct OCR-ed historical printings and ask for "representative" results.
Internet of Things: A systematic Literature Review

Internet of things (IoT) is an emerging technology that is being used widely.The literature has no agreement regarding the factors that affect the adoption of IoT.The purpose of this study is to review the literature systematically using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).Following this method, 69 articles were included in this review.A frequency analysis was conducted.The findings showed that number of articles reduced during COVID19.Higher education has the highest numbers of articles.Emerging economies are active in research about IoT.Technology acceptance model (TAM) is still the dominant adoption theory with majority of the reviewed articles are using quantitative method and large sample size to meet the assumption of using structural equation modeling.The most important predictors are the perceived usefulness, perceived ease of use, social influence, privacy, security, and trust.Other factors also included the variables of UTAUT.Decision makers are recommended to focus on usefulness and simplifies the process of using IoT as well as to create awareness about the application of IoT.Future studies are recommended to narrow the scope to one industry and to conduct more studies using mixed method or qualitative approach.More studies in developing countries are needed to explain the adoption of IoT.Povzetek: V tem
Blockchain for Secure Healthcare: Opportunities, Challenges and Solutions

With the tremendous advancement in information and communication technologies (ICT), gradually human lives have been relying on them by means of healthcare, communication, and many other aspects.However, ICT has a major issue in terms of security which is one of the hottest areas of research at present.Among the numerous modern technologies that help improving the security and privacy, Blockchain has grabbed its place and remarkably enhanced many industries.Furthermore, Blockchain is executed within or on top of the cloud computing paradigm that allows the users to obtain the essential data with more security.With that, the inherent security and privacy related issues in the cloud computing paradigm are radically mitigated.Currently, among several industrial systems, healthcare systems are maintained through cloudbased data centers to facilitate diverse stakeholders like patients, practices, labs, pharmacies, and other healthcare entities, in terms of data processing and its ubiquity.However, that is not adequate in terms security and privacy thus vulnerable to several breaches.As a potential solution, Blockchain-based Cloud Computing offers security and availability for these records which is what Healthcare systems are mainly lacking.In this paper, we will state how Blockchain is affecting the Healthcare field, and what enhancements could happen if medical systems use Blockchain.Moreover, opportunities for Blockchain for secure healthcare systems, potential challenges and their solutions are discussed.
Research on the Carbon Peak and Carbon Neutralization Model in Colleges and Universities: Taking China University of Geosciences (Beijing) as an Example

The “3060” goal demonstrates China’s responsibility in actively responding to global climate, enhances China’s voice and influence in climate governance, and sets an example of energy conservation and emission reduction for developing countries. As the main institutions that educate people for the Party and the country, colleges and universities have the responsibility to explore the realization path of carbon peak and carbon neutralization against the new problems encountered in the practice of energy conservation and emission reduction in colleges and universities. In response to the national strategy and the action plan of the Ministry of Education, taking the carbon practice of China University of Geosciences (Beijing) as the starting point, this paper conducts benchmarking research on the path of carbon peak and carbon neutralization in universities through the ecological factor method, campus carbon footprint, and questionnaire analysis.
GradFreeBits: Gradient-Free Bit Allocation for Mixed-Precision Neural Networks

Quantized neural networks (QNNs) are among the main approaches for deploying deep neural networks on low-resource edge devices. Training QNNs using different levels of precision throughout the network (mixed-precision quantization) typically achieves superior trade-offs between performance and computational load. However, optimizing the different precision levels of QNNs can be complicated, as the values of the bit allocations are discrete and difficult to differentiate for. Moreover, adequately accounting for the dependencies between the bit allocation of different layers is not straightforward. To meet these challenges, in this work, we propose GradFreeBits: a novel joint optimization scheme for training mixed-precision QNNs, which alternates between gradient-based optimization for the weights and gradient-free optimization for the bit allocation. Our method achieves a better or on par performance with the current state-of-the-art low-precision classification networks on CIFAR10/100 and ImageNet, semantic segmentation networks on Cityscapes, and several graph neural networks benchmarks. Furthermore, our approach can be extended to a variety of other applications involving neural networks used in conjunction with parameters that are difficult to optimize for.
State of the Art and Future Prospects of Virtual and Augmented Reality in Veterinary Medicine: A Systematic Review

Virtual reality and augmented reality are new but rapidly expanding topics in medicine. In virtual reality, users are immersed in a three-dimensional environment, whereas in augmented reality, computer-generated images are superimposed on the real world. Despite advances in human medicine, the number of published articles in veterinary medicine is low. These cutting-edge technologies can be used in combination with existing methods in veterinary medicine to achieve diagnostic/therapeutic and educational goals. The purpose of our review was to evaluate studies for their use of virtual reality and augmented reality in veterinary medicine, as well as human medicine with animal trials, to report results and the state of the art. We collected all of the articles we included in our review by screening the Scopus, PubMed, and Web of Science databases. Of the 24 included studies, 11 and 13 articles belonged to virtual reality and augmented reality, respectively. Based on these articles, we determined that using these technologies has a positive impact on the scientific output of students and residents, can reduce training costs, and can be used in training/educational programs. Furthermore, using these tools can promote ethical standards. We reported the absence of standard operation protocols and equipment costs as study limitations.
A Location–Time-Aware Factorization Machine Based on Fuzzy Set Theory for Game Perception

Game user perception is of great significance for game developers and network operators for improving service quality and operational efficiency. At present, the most common approach is to use the linear model that considers only the impact of network factors evaluation on user perception. The interpretation process is complex and useful, but invisible feature interaction data are not taken into account. As a result, user perception evaluation can only be interpreted by experienced experts, which is both time-consuming and laborious. In this paper, aiming at the shortcomings of existing algorithms, a location–time-aware factorization machine model (LTFM) is proposed by exploiting the location projection and time projection of users and services and fuzzy set theory. Our proposed LTFM can be decomposed into two parts: first, an original game quality of experience (QoE) dataset is extended. LTFM uses location and time information to map to latent vectors, which increases the number of records in each game data, involving no additional information. Then, LTFM utilizes fuzzy set theory to strengthen the positive feature interactions and reduce the negative feature interactions. The factorization machine is used to mine a number of potential features in the user’s invoking service behavior. The multiplayer online battle arena (MOBA) game perception dataset is obtained with reference to the ITU-T standard to verify the advanced nature of the proposed model. Experimental results show that LTFM outperforms existing algorithms in terms of prediction accuracy and model interpretability. Not only can accurate user experience quality categories be produced, but also the impact of individual characteristics and their feature interactions can be explained, which helps operators to make better optimization decisions.
Gallery Walk technique in enhancing reading comprehension and oral English language proficiency of junior high school students

The study used quasi-experimental research with pre-test and post-test design approach to analyse the effectiveness of using the Gallery Walk technique in enhancing reading comprehension and oral English language proficiency among junior high school students. Cluster sampling technique was used in determining the respondents who experienced the Gallery Walk teaching technique and those who experienced the conventional teaching technique. Pre-test result showed no differences in the level of reading comprehension and oral English language proficiency prior to the experiment. After the implementation of the study, post-test was given, and results revealed differences in the students’ performance. Findings revealed that the students who were taught using the Gallery Walk technique showed improvement in reading comprehension and oral English language proficiency as compared to those who underwent the conventional teaching technique. In addition, students had positive feedback about the implementation of Gallery Walk. Finally, the study recommended the use of the Gallery Walk technique in teaching English 8 for enhancing reading comprehension and oral English language proficiency.
ERGCN: Enhanced Relational Graph Convolution Network, an Optimization for Entity Prediction Tasks on Temporal Knowledge Graphs

Reasoning on temporal knowledge graphs, which aims to infer new facts from existing knowledge, has attracted extensive attention and in-depth research recently. One of the important tasks of reasoning on temporal knowledge graphs is entity prediction, which focuses on predicting the missing objects in facts at current time step when relevant histories are known. The problem is that, for entity prediction task on temporal knowledge graphs, most previous studies pay attention to aggregating various semantic information from entities but ignore the impact of semantic information from relation types. We believe that relation types is a good supplement for our task and making full use of semantic information of facts can promote the results. Therefore, a framework of Enhanced Relational Graph Convolution Network (ERGCN) is put forward in this paper. Rather than only considering representations of entities, the context semantic information of both relations and entities is considered and merged together in this framework. Experimental results show that the proposed approach outperforms the state-of-the-art methods.
An Adaptive Refinement Scheme for Depth Estimation Networks

Deep learning has proved to be a breakthrough in depth generation. However, the generalization ability of deep networks is still limited, and they cannot maintain a satisfactory performance on some inputs. By addressing a similar problem in the segmentation field, a feature backpropagating refinement scheme (f-BRS) has been proposed to refine predictions in the inference time. f-BRS adapts an intermediate activation function to each input by using user clicks as sparse labels. Given the similarity between user clicks and sparse depth maps, this paper aims to extend the application of f-BRS to depth prediction. Our experiments show that f-BRS, fused with a depth estimation baseline, is trapped in local optima, and fails to improve the network predictions. To resolve that, we propose a double-stage adaptive refinement scheme (DARS). In the first stage, a Delaunay-based correction module significantly improves the depth generated by a baseline network. In the second stage, a particle swarm optimizer (PSO) delineates the estimation through fine-tuning f-BRS parameters—that is, scales and biases. DARS is evaluated on an outdoor benchmark, KITTI, and an indoor benchmark, NYUv2, while for both, the network is pre-trained on KITTI. The proposed scheme was effective on both datasets.
A Semi-Analytical Model for Separating Diffuse and Direct Solar Radiation Components

The knowledge of the solar irradiation components is required by most solar applications. When only the global horizontal irradiance is measured, this one is typically broken down into its fundamental components, beam and diffuse, by applying an empirical separation model. This study proposes a semi-analytical model for diffuse fraction, defined as the ratio of diffuse to global solar irradiance. Starting from basic knowledge, a general equation for diffuse fraction is derived. Clearness index, relative sunshine, and the clear-sky atmospheric transmittance are highlighted as robust predictors. Thus, the model equation implicitly provides hints for developing accurate empirical separation models. The proposed equation is quasi-universal, allowing for temporal (from 1-min to 1-day) and spatial (site specificity) customization. As a proof of theory, the separation quality is discussed in detail on the basis of radiometric data retrieved from Baseline Surface Radiation Network (BSRN), station Magurele, Romania. For temperate continental climate, overall results show for the diffuse fraction estimation a maximum possible accuracy around 7%, measured in terms of normalized root mean square error. One of the many options of implementing the semi-analytical model is illustrated in a case study.
Eco-Sensory Technologies and the Surrealist Impulse

Critically responding to the contemporary capture and redistribution of experience through digital technologies, we focus on a series of creative ethnographic experiments with sensory technologies conducted by members of the Manifold Lab in collaboration with young people in Manchester, UK. We draw on theories of the haptic eye and a/signification from Deleuze and Guattari, to problematise conventional descriptions of sensation, in search of the intensive, the imperceptible, and the eco-sensory. Our focus in this chapter is on sensor technologies as potential agents of surrealist ethnographic experimentation. We engage with digital devices as ecological agents, tracking intensive flows, layering affect, invaginating power, ramifying insight, and entangling concepts through playful inversion, pursuing a kind of surreal participation that avoids de-scription and conventional causal exegesis. We argue that critical and creative engagements with digital technology helps to counter simplistic models of ethnographic representation.