Reviewerly/exHarmony huggingface dataset:
Crowdsourced Estimation of Collective Just Noticeable Difference for Compressed Video with Flicker Test and QUEST+

&lt;p&gt;The concept of video-wise just noticeable difference (JND) was recently proposed to determine the lowest bitrate at which a source video can be compressed without perceptible quality loss with a given probability.&lt;/p&gt; &lt;p&gt;This bitrate is usually obtained from an estimate of the satisfied used ratio (SUR) at each bitrate, respectively encoding quality parameter. The SUR is the probability that the distortion corresponding to this bitrate is not noticeable. Commonly, the SUR is computed experimentally by estimating the subjective JND threshold of each subject using binary search, fitting a distribution model to the collected data, and creating the complementary cumulative distribution function of the distribution. The subjective tests consist of paired comparisons between the source video and compressed versions. However, we show that this approach typically over- or underestimates the SUR.&lt;/p&gt; &lt;p&gt;To address this shortcoming, we directly estimate the SUR function by considering the entire population as a collective observer. Our method randomly chooses the subject for each paired comparison and uses a state-of-the-art Bayesian adaptive psychometric method (QUEST+) to select the compressed video in the paired comparison.&lt;/p&gt; &lt;p&gt;Our simulations show that this collective method yields more accurate SUR results with fewer comparisons.&lt;/p&gt; &lt;p&gt;We also provide a subjective experiment to assess the JND and SUR for compressed video. In the paired comparisons, we apply a flicker test that compares a video that interleaves the source video and its compressed version with the source video. Analysis of the subjective data revealed that the flicker test provides on average higher sensitivity and precision in the assessment of the JND threshold than the usual test that compares compressed versions with the source video. &lt;/p&gt; &lt;p&gt;Using crowdsourcing and the proposed approach, we build a JND dataset for 45 source video sequences that are encoded with both advanced video coding (AVC) and versatile video coding (VVC) at all available quantization parameters. Our dataset is available at http://database.mmsp-kn.de/flickervidset-database.html.&lt;/p&gt;
Analysis using a Model-Based Approach of Path Planning for Multiple Uavs in the Context of Surveying Building Damage Following a Disaster

In the wake of disasters, efficient and timely assessment of building damage is of paramount importance for effective disaster response and recovery efforts. Unmanned Aerial Vehicles (UAVs), commonly known as drones, have emerged as indispensable tools for postdisaster surveying due to their ability to rapidly cover large areas and provide high-resolution imagery. The effective utilization of multiple UAVs in such scenarios necessitates optimal path planning to maximize coverage, minimize mission time, and ensure safe navigation. This paper presents a comprehensive model-based analysis of multi-UAV path planning strategies tailored for surveying building damage in postdisaster environments. The study commences by emphasizing the critical role of UAVs in disaster management, especially in the context of assessing structural damage. Acknowledging the challenges posed by complex urban landscapes and the need for swift response, the research focuses on the development and evaluation of path planning strategies that leverage advanced modeling techniques. The proposed model integrates spatial data, building blueprints, and environmental information to generate a realistic representation of the disaster-affected area, facilitating informed decision- making for path planning. A range of path planning approaches is examined within the model-based framework. These include grid-based methods, which discretize the area into cells and deploy UAVs to cover designated regions, and graph-based approaches such as Rapidly-exploring Random Trees (RRT), which generate paths through probabilistic exploration. The paper also delves into machine learning-enhanced strategies, leveraging reinforcement learning algorithms to adapt UAV paths based on real-time observations and evolving disaster dynamics. Furthermore, the study underscores the scalability of the model-based approach, demonstrating its applicability to scenarios involving a varying number of UAVs. Through comprehensive experimentation, the paper provides insights into the optimal deployment of UAV teams, shedding light on the balance between increased coverage and potential communication overhead.
Enhancing Earth Observation Data Processing through Optimized Multi-Modular Service

The significance of earth observation data spans diverse fields and domains, driving the need for their efficient management.Nevertheless, the exponential increase in data volume brings with it new challenges that make processing and storing data more complicated.In response to these challenges, this article proposes an optimized multi-modular service for earth observation data management.The suggested approach focuses on choosing the optimal configurations for the storage and processing layers to improve the performance and cost-effectiveness of managing data.By employing the recommended optimized strategies, earth observation data can be managed more effectively, resulting in fast data processing and reduced costs.
Pemanfaatan Remitensi Buruh Migran Perempuan untuk Meningkatkan Kesejahteraan Keluarga yang Ditinggalkan

The purpose of this study was to determine the effectiveness of the use of augmented reality learning media on geography subjects in class X SMAN 36 Jakarta. This research uses an experimental method with Nonrandomized Control Group Pretest Posttest Design. The population of this study were students of class X SMAN 36 Jakarta with research samples selected by randomized sampling technique, namely X E1 as the experimental class and X E2 as the control class. Data collection techniques used were tests on two classes and distributing questionnaires filled out by experimental class students. Some of the data analysis techniques used are instrument tests, data tests as analysis requirements and data analysis tests. Based on the test results, the instrument was declared valid and reliable and the data was declared normal and homogeneous. The results showed that augmented reality learning media was declared effective to improve students' learning outcomes in geography class X SMAN 36 Jakarta with a significance value of 0.000 &lt;0.05 and an N-Gain value of 71.069%, which included the effective category. So educators can utilize the media to be used as a praga tool that facilitates the learning process because it is proven to be able to provide an increase in student learning outcomes. For future research, it is hoped that this augmented reality media can be used in other learning materials.
Creating Custom Immersive 360-Degree Videos for Use in Clinical and Nonclinical Settings: Tutorial (Preprint)

<sec> <title>UNSTRUCTURED</title> The use of virtual reality (VR) stimulation in clinical settings has increased in recent years. In particular, there has been increasing interest in the use of VR stimulation for a variety of purposes, including medical training, pain therapy, and relaxation. Unfortunately, there is still a limited amount of real-world 360-degree content that is both available and suitable for these applications. Therefore, this tutorial paper describes a pipeline for the creation of custom VR content. It covers the planning and designing of content; the selection of appropriate equipment; the creation and processing of footage; and the deployment, visualization, and evaluation of the VR experience. This paper aims to provide a set of guidelines, based on first-hand experience, that readers can use to help create their own 360-degree videos. By discussing and elaborating upon the challenges associated with making 360-degree content, this tutorial can help researchers and health care professionals anticipate and avoid common pitfalls during their own content creation process. </sec>
Tracing metals from source to sink in Irish-type Zn-Pb(-Ba-Ag) deposits

The Irish Midlands Zn-Pb(-Ba-Ag) Orefield represents one of the best studied palaeo-hydrothermal systems worldwide. Despite this, the detailed origins of ore and trace metals and the controls of their variability in deposits across the Orefield, have not been considered in detail. In this contribution, I have used diverse data sources in an attempt to track the metals from their source and assess how they were fractionated at various stages along the reconstructed flow path. This includes: (1) circulation of fluids to depths of up to ~7 km in the Lower Palaeozoic wedge of arc-derived detritus that underlies the Orefield and through a primary ‘reaction zone’; (2) fluid transit within the wedge and then up through the redox boundary represented by the Old Red Sandstone; and (3) fluid ingress into the receptive Lower Carboniferous host rocks in the ore deposits themselves, initially through focused feeder pipes and then spreading out upwards and especially laterally into the surrounding carbonate units. The results show that the Lower Palaeozoic package was a particularly fertile source from which metals were easily extracted and that this exerted a primary control on metal tenor in the overlying deposits. However, additional fractionation steps, including loss to and extraction of metals from, the Old Red Sandstone, and sequential deposition of metals during cooling, neutralisation and fluid mixing were also important. The presence beneath the Irish Midlands Basin of a weakly metamorphosed rock package formed from arc-derived detritus is considered to be a critical component of the mineral system.
P2FLF: Privacy-Preserving Federated Learning Framework Based on Mobile Fog Computing

Mobile IoT devices provide a lot of data every day, which provides a strong base for machine learning to succeed. However, the stringent privacy demands associated with mobile IoT data pose significant challenges for its implementation in machine learning tasks. In order to address this challenge, we propose privacy-preserving federated learning framework (P2FLF) in a mobile fog computing environment. By employing federated learning, it is possible to bring together numerous dispersed user sets and collectively train models without the need to upload datasets. Federated learning, an approach to distributed machine learning, has garnered significant attention for its ability to enable collaborative model training without the need to share sensitive data. By utilizing fog nodes deployed at the edge of the network, P2FLF ensures that sensitive mobile IoT data remains local and is not transmitted to the central server. The framework integrates privacy-preserving methods, such as differential privacy and encryption, to safeguard the data throughout the learning process. We evaluate the performance and efficacy of P2FLF through experimental simulations and compare it with existing approaches. The results demonstrate that P2FLF strikes a balance between model accuracy and privacy protection while enabling efficient federated learning in mobile IoT environments.
Deep-Aware Network for Removing Single Haze

Some dehazing methods based on traditional statistical theory are prone to suffering from the higher complexity and resulting in some lower degeneration phenomenon such as a few halo with discontinuities and low contrast. To tackle these issues, we present an effective and efficient end-to-end method based on deep-aware channel information for single image removal haze. We compute an effective feature map by integrating the deep-aware channel attention mechanism with multi-scale residual connection to build a feature aware attention block, and then more these blocks are combined to form the final feature block for directly learning the statistical information of some haze-free images. In some pubic synthetic datasets and some real-world haze images, extensive experimental results show that the proposed model is not only superior than some state-of-the-art models including some traditional dehazing methods and methods based on convolutional neural network, but also achieves better image quality than other models such as high contrast, better details, and vivid color. Meanwhile, we argue that our model is a lightweight and low computation cost network.
Students' Perception About Powerpoint Used by the Teacher as a Teaching Media in English Language Teaching and Learning

Education is very important the frist one is for every child to have as the next generation of the nation. The development of ICT is currently having a major impact on human life in any aspect, one of which is in the aspect of education. One of the conveniences and sophistication of current ICT developments is the availability of many tools or applications that educators can use as learning media. Powerpoint is a tool that is currently widely used by teachers to support learning. This research aims to find out what are the students' perceptions about the use of powerpoint by English teachers as a teaching media. The method used in this study is a quantitative method. The sample of this study was 30 students from SMAIT IQRA Kota Bengkulu who were taken randomly. The research instrument used was a questionnaire. In analyzing the data, the study used descriptive analysis. Basd on the results, it is shows that the use of powerpoint media in english teaching and learning is fearlu good for the students with percentage responses of 57%. It can be concluded that the use of PowerPoint media by teachers in the process of teaching and learning English at SMAIT IQRA Kota Bengkulu is quite good. Teachers can innovate and be even more creative in making material for making the learning English with this media is better and maximal, especially for students.
Quantum AI and hybrid simulators for a Universal Quantum Field Computation Model

Quantum field theory (QFTh) simulators simulate physical systems using quantum circuits that process quantum information (qubits) via single field (SF) and/or quantum double field (QDF) transformation. This review presents models that classify states against pairwise particle states |ij〉, given their state transition (ST) probability P|ij〉. A quantum AI (QAI) program, weighs and compares the field’s distance between entangled states as qubits from their scalar field of radius R≥|rij|. These states distribute across 〈R〉 with expected probability 〈Pdistribute〉 and measurement outcome 〈M(Pdistribute)〉=P|ij〉. A quantum-classical hybrid model of processors via QAI, classifies and predicts states by decoding qubits into classical bits. For example, a QDF as a quantum field computation model (QFCM) in IBM-QE, performs the doubling of P|ij〉 for a strong state prediction outcome. QFCMs are compared to achieve a universal QFCM (UQFCM). This model is novel in making strong event predictions by simulating systems on any scale using QAI. Its expected measurement fidelity is 〈M(F)〉≥7/5 in classifying states to select 7 optimal QFCMs to predict 〈M〉’s on QFTh observables. This includes QFCMs’ commonality of 〈M〉 against QFCMs limitations in predicting system events. Common measurement results of QFCMs include their expected success probability 〈Psuccess〉 over STs occurring in the system. Consistent results with high F’s, are averaged over STs as 〈Pdistribute〉yielding 〈Psuccess〉≥2/3 performed by an SF or QDF of certain QFCMs. A combination of QFCMs with this fidelity level predicts error rates (uncertainties) in measurements, by which a P|ij〉=〈Psuccess〉<∼1 is weighed as a QAI output to a QFCM user. The user then decides which QFCMs perform a more efficient system simulation as a reliable solution. A UQFCM is useful in predicting system states by preserving and recovering information for intelligent decision support systems in applied, physical, legal and decision sciences, including industry 4.0 systems.
Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO

While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. Consequently, particles that are not widely known also have the opportunity to be selected as the learning exemplars. In addition, we propose a Euclidean distance-based adaptive topology to cooperate with SPA, where each particle only connects to k number of particles with the shortest Euclidean distance during each iteration. We also introduce the adaptive topology into heterogeneous populations to better solve large-scale problems. Specifically, the exploration sub-population better preserves the diversity of the population while the exploitation sub-population achieves fast convergence. Therefore, large-scale problems can be solved in a collaborative manner to elevate the overall performance. To evaluate the performance of our method, we conduct extensive experiments on various optimization problems, including three benchmark suites and two real-world optimization problems. The results demonstrate that our Euclidean distance-based adaptive topology outperforms the other widely adopted topologies and further suggest that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems.
Supervised Learning of Hierarchical Image Segmentation

We study the problem of predicting hierarchical image segmentations using supervised deep learning. While deep learning methods are now widely used as contour detectors, the lack of image datasets with hierarchical annotations has prevented researchers from explicitly training models to predict hierarchical contours. Image segmentation has been widely studied, but it is limited by only proposing a segmentation at a single scale. Hierarchical image segmentation solves this problem by proposing segmentation at multiple scales, capturing objects and structures at different levels of detail. However, this area of research appears to be less explored and therefore no hierarchical image segmentation dataset exists. In this paper, we provide a hierarchical adaptation of the Pascal-Part dataset [2], and use it to train a neural network for hierarchical image segmentation prediction. We demonstrate the efficiency of the proposed method through three benchmarks: the precision-recall and F-score benchmarks for boundary location, the level recovery fraction for assessing hierarchy quality, and the false discovery fraction. We show that our method successfully learns hierarchical boundaries in the correct order, and achieves better performance than the state-of-the-art model trained on single-scale segmentations.
The Relationship Between Innovation, Digital Transformation, Organizational Learning, Perceived Organizational Support, and Employee Performance

This research paper investigates the connections between employee overall performance, organizational getting to know, perceived organizational support, and creativity within the digital age. For a firm to prevail within the dynamic company environment of today, it's miles important to realize these traits. By analyzing how these factors engage, the research hopes to close a expertise gap. This observe uncovers the tremendous have an effect on of innovation, digital transformation, organizational getting to know, and perceived organizational help on employee overall performance through an intensive exam of the literature and quantitative evaluation of facts from 500 respondents in the UAE academic zone. The outcomes have programs for companies trying to boost worker overall performance in the context of the dynamic virtual ecosystem.
The Mediating Role of Trust in National Digital Identity Verification Platform Acceptance: A Conceptual Paper

Government and private organisation services online have been adopted rapidly during the digitalisation post-COVID-19 pandemic. The lack of digital identity exacerbates the authentication and verification of online services. This study investigates the mediating role of trust on determinants of national digital identity verification platform acceptance among young investors. The study develops an integrated theoretical framework from the Protection Motivation Theory and Uncertainty Reduction Theory. The study model incorporates three constructs, namely perceived severity, response efficacy, and transparency and accesses how trust as a mediator affects the acceptance of the NDIV platform. The online quantitative survey approach will be used as the method of this study, and data will be distributed among young investors in Malaysia. This study employs a comprehensive framework to understand the acceptance level of the NDIV platform. The study's findings will contribute to the body of knowledge regarding the NDIV platform in the Malaysian context.
Private Distribution Testing with Heterogeneous Constraints: Your Epsilon Might Not Be Mine

Private closeness testing asks to decide whether the underlying probability distributions of two sensitive datasets are identical or differ significantly in statistical distance, while guaranteeing (differential) privacy of the data. As in most (if not all) distribution testing questions studied under privacy constraints, however, previous work assumes that the two datasets are equally sensitive, i.e., must be provided the same privacy guarantees. This is often an unrealistic assumption, as different sources of data come with different privacy requirements; as a result, known closeness testing algorithms might be unnecessarily conservative, "paying" too high a privacy budget for half of the data. In this work, we initiate the study of the closeness testing problem under heterogeneous privacy constraints, where the two datasets come with distinct privacy requirements. We formalize the question and provide algorithms under the three most widely used differential privacy settings, with a particular focus on the local and shuffle models of privacy; and show that one can indeed achieve better sample efficiency when taking into account the two different "epsilon" requirements.
PreciseBugCollector: Extensible, Executable and Precise Bug-fix Collection

Bug datasets are vital for enabling deep learning techniques to address software maintenance tasks related to bugs. However, existing bug datasets suffer from precise and scale limitations: they are either small-scale but precise with manual validation or large-scale but imprecise with simple commit message processing. In this paper, we introduce PreciseBugCollector, a precise, multi-language bug collection approach that overcomes these two limitations. PreciseBugCollector is based on two novel components: a) A bug tracker to map the codebase repositories with external bug repositories to trace bug type information, and b) A bug injector to generate project-specific bugs by injecting noise into the correct codebases and then executing them against their test suites to obtain test failure messages. We implement PreciseBugCollector against three sources: 1) A bug tracker that links to the national vulnerability data set (NVD) to collect general-wise vulnerabilities, 2) A bug tracker that links to OSS-Fuzz to collect general-wise bugs, and 3) A bug injector based on 16 injection rules to generate project-wise bugs. To date, PreciseBugCollector comprises 1057818 bugs extracted from 2968 open-source projects. Of these, 12602 bugs are sourced from bug repositories (NVD and OSS-Fuzz), while the remaining 1045216 project-specific bugs are generated by the bug injector. Considering the challenge objectives, we argue that a bug injection approach is highly valuable for the industrial setting, since project-specific bugs align with domain knowledge, share the same codebase, and adhere to the coding style employed in industrial projects.