Reviewerly/exHarmony huggingface dataset:
Proposal of Avatar Face Generation Techniques for Immersive Metaverse Environments"Environments

The core element driving the advancement of metaverse technology is presumed to be avatar technology. Therefore, we intend to craft a research paper focused on facial expression manipulation of avatars in real-time through the integration of emotion information based on natural language processing. The central idea involves utilizing emotion information derived from natural language processing to determine the avatars
Research on the Application of Convolutional Neural Network Model in Night Surveillance Video Image Enhancement

With the popularization of professional digital imaging equipment, digital image processing is popularly used in many fields such as industrial production, video surveillance, intelligent transportation, remote sensing and monitoring, and plays a significant role. In low illumination environments such as cloudy days, nights, indoor and object occlusion, imaging devices often capture images with low brightness and contrast, severe loss of detail information, and a large amount of noise. Enhancing low illumination images can enhance their clarity, highlight the texture details of the scene, greatly enhance the quality of the image, and provide data quality assurance for completing tasks such as target recognition and tracking, image segmentation, etc. This paper proposes a low light image enhancement algorithm based on CNN model to address the problem of low brightness and unclear monitoring video images in nighttime scenes due to lighting conditions. This algorithm can effectively improve the quality of low light images and exhibit superiority on multiple public datasets. The algorithm proposed in this article not only effectively improves the brightness of the image, but also enhances the detail clarity of the image to a certain degree, and can avoid color distortion and halo phenomena to a certain degree.
Kesesuaian Lahan Sawah Pasang Surut dan Faktor Pembatas Utama Tanaman Padi di Kecamatan Sinaboi, Kabupaten Rokan Hilir

The paddy production is decreasing due to land characteristics that are not suitable for its crop requirements. This study aims to identify the inhibiting factors of growth and production and evaluate land suitability based on the factors that inhibit growth and production of tidal paddy fields. The research method used is a soil survey. Soil sampling points were determined based on the overlay results of the current paddy field map and the map of land and soil units. The suitability assessment of paddy fields is carried out using a matching system in actual and potential conditions. The actual land suitability of all SLH is not current suitable (N1) with a very high Fe inhibiting factor. Limiting factors of Fe content can be overcome by making one-way drainage channels, adding organic matter, liming, fertilizing and or using tolerant varieties. The potential land suitability of all SLH is marginally suitable (S3).
EFEKTIVITAS PEMBELAJARAN PAK DENGAN METODE SNOWBALL THROWING UNTUK MENINGKATKAN HASIL BELAJAR SISWA KELAS V SD KANISIUS KURMOSARI SEMARANG

The teaching and learning process is an ongoing interaction between teachers and students to acquire knowledge in accordance with learning objectives. Measuring the success of the teaching and learning process can be seen from the learning outcomes achieved by students. The purpose of this study was to determine whether the snowball throwing method was more effective in influencing student responsibility than the conventional method, the effect of the responsibility of students taught by the snowball throwing method on student achievement, and the effectiveness of using the snowball throwing method in improving student achievement. This type of research is quantitative with a True Experimental research design using a Pretest-Posttest Control Group Design. Data was collected by means of tests, observations, and documentation. After the data was collected, data analysis was carried out so that the average experimental class student responsibility was 3,25 higher than the control class average responsibility of 1,9 so that the snowball throwing method in the experimental class was more effective in influencing student responsibility. Student responsibility is very influential on student achievement of 83,0%. The use of the snowball throwing method was more effective in improving the learning achievement of fifth-graders at SD Kanisius Kurmosari Semarang in Catholic Religious Education subjects by 76,8497 or 76,8%. So, the use of the snowball throwing method is more effective in improving student learning outcomes.
Discussion of the Use of Concordance Programs in the EFL Classroom

Classroom concordance programs involve the analysis of concordances of language features. The use of concordancers can provide learners analytic skills to manipulate comprehensive databases for the sake of coping with real-language problems. Due to its practicability, the approach has been developed for language teaching in the ELT classroom. The purpose of this paper is to discuss the use of concordance programs in the EFL classroom. A great number of studies regarding the use of corpora and the concordancer as a resource in language teaching and learning demonstrate that concordancing as a teaching tool can be effectively used in classroom to meet some specific teaching purposes, and concordance analysis can help students discover and differentiate meanings of words and phrases, identify useful phrases and unfamiliar collocations, explore word order, and distinguish words with similar meanings. Consequently, in the EFL context, a series of concordance exercises can be carried out to assist students to investigate language data and derive patterns of language use. Furthermore, an active pedagogical approach should be oriented to the acquisition of valuable knowledge, to the solution to sophisticated problems and to the development of learning abilities. Based on this, teachers should select the right pedagogical approach to increase students’ motivation, promote their initiative, and guide them into the procedure of knowledge exploration.
GS<sup>2</sup>P: A Generative Pre-trained Learning to Rank Model with Over-parameterization for Web-Scale Search

While learning to rank (LTR) is widely employed in web searches to prioritize pertinent webpages from the retrieved contents based on input queries, traditional LTR models stumble over two principal stumbling blocks leading to subpar performance: 1) the lack of well-annotated query-webpage pairs with ranking scores to cover search queries of various popularity, debilitating their coverage of search queries across the popularity spectrum, and 2) ill-trained models that are incapable of inducing generalized representations for LTR, culminating in overfitting. To tackle the above challenges, we proposed a Generative Semi-Supervised Pre-trained (GS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> P) Learning to Rank model. Specifically, GS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> P first generates pseudo-labels for the unlabeled samples using tree-based LTR models after a series of co-training procedures, then learns the representations of query-webpage pairs with self-attentive transformers via both discriminative (LTR) and generative (denoising autoencoding for reconstruction) losses. Finally, GS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> P boosts the performance of LTR through incorporating Random Fourier Features to over-parameterize the models into “interpolating regime”, so as to enjoy the further descent of generalization errors with learned representations. We conduct extensive offline experiments on a publicly available dataset and a real-world dataset collected from a large-scale search engine. The results show that GS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> P can achieve the best performance on both datasets, compared to baselines. We also deploy GS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> P at a large-scale web search engine with realistic traffic, where we can still observe significant improvement in real-world applications. GS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> P performs consistently in both online and offline experiments.
Enhancing the Security of the BB84 Quantum Key Distribution Protocol against Detector-Blinding Attacks via the Use of an Active Quantum Entropy Source in the Receiving Station

True randomness is necessary for the security of any cryptographic protocol, including quantum key distribution (QKD). In QKD transceivers, randomness is supplied by one or more local, private entropy sources of quantum origin which can be either passive (e.g., a beam splitter) or active (e.g., an electronic quantum random number generator). In order to better understand the role of randomness in QKD, I revisit the well-known “detector blinding” attack on the BB84 QKD protocol, which utilizes strong light to achieve undetectable and complete recovery of the secret key. I present two findings. First, I show that the detector-blinding attack was in fact an attack on the receiver’s local entropy source. Second, based on this insight, I propose a modified receiver station and a statistical criterion which together enable the robust detection of any bright-light attack and thus restore security.
A Detailed Survey on Sports Video Summarization

In response to the surging demand for efficient sports content consumption, sports video summarization has emerged as a critical technology for saving time and delivering key moments to sports enthusiasts. This motivates the study to enhance sports video summarization techniques, tackling the problem of effectively providing users with the most recent sports information. This study presents various feature extraction and video segmentation methods for segmenting video, accurately recognizing features and actions in sports videos. This article sets the path for significant improvements by leveraging advanced techniques, such as CNNs and deep learning models in identifying essential elements, even within occluded regions. This advancement addresses the core challenge of providing timely concise sports content to a demanding audience.
Resource-Constrained Multisource Instance-Based Transfer Learning

In today's machine learning (ML), the need for vast amounts of training data has become a significant challenge. Transfer learning (TL) offers a promising solution by leveraging knowledge across different domains/tasks, effectively addressing data scarcity. However, TL encounters computational and communication challenges in resource-constrained scenarios, and negative transfer (NT) can arise from specific data distributions. This article presents a novel focus on maximizing the accuracy of instance-based TL in multisource resource-constrained environments while mitigating NT, a key concern in TL. Previous studies have overlooked the impact of resource consumption in addressing the NT problem. To address these challenges, we introduce an optimization model named multisource resource-constrained optimized TL (MSOPTL), which employs a convex combination of empirical sources and target errors while considering feasibility and resource constraints. Moreover, we enhance one of the generalization error upper bounds in domain adaptation setting by demonstrating the potential to substitute the H ∆ H divergence with the Kullback-Leibler (KL) divergence. We utilize this enhanced error upper bound as one of the feasibility constraints of MSOPTL. Our suggested model can be applied as a versatile framework for various ML methods. Our approach is extensively validated in a neural network (NN)-based classification problem, demonstrating the efficiency of MSOPTL in achieving the desired trade-offs between TL's benefits and associated costs. This advancement holds tremendous potential for enhancing edge artificial intelligence (AI) applications in resource-constrained environments.
Measuring data access and re-use in the European Legal Framework for Data, from the General Data Protection Regulation (GDPR) law to the Proposed Data Act: the case of vehicle data

<ns3:p>This article delves into the difficulties and opportunities associated with the acquisition, sharing, and re-purposing of vehicle data, particularly information derived from black boxes used by insurance companies and event data recorders installed by manufacturers. While this data is usually utilized by insurers and car makers, it may benefit consumers, rival firms, and public institutions profiting from access to data for objectives such as data portability between insurance companies, traffic and transportation management, and the development of intelligent mobility solutions. Among other regulations, the authors examine the proposed Data Act as the European chosen champion to address the legal and technical hurdles surrounding the reuse of privately held corporate data, including privacy and intellectual property, competition, and data interoperability issues. The text also offers an overview of the sorts of data obtained through vehicle recording systems and their potential benefits for various stakeholders.</ns3:p><ns3:p> This paper presents a methodology for comparing and evaluating, in an ordinal fashion, the degree of access conferred by various regulations and put it to practical use to assess how much data is currently left out from access by the existing legislation, how much of such data is covered by the Data Act, and ultimately, how much still remains inaccessible for reuse.</ns3:p>
Robot Untuk Mendeteksi Objek dengan Citra Digital dan Notifikasi Short Message Service (SMS)

The use of the camera can only be used as a monitor which is usually used not equipped with other devices that allow it to hit an object, be it a human or an object and by using a camera is a hardware device that functions to capture images and convert them into digital images that can be read and processed. by a computer using a robot controlled by a smartphone in approaching objects, the camera sensor is useful for detecting colored ball objects. The detected colors consist of Red, Green, Blue, all colors are processed in the microcontroller which functions as the brain of the robot that is made and later the robot will send information in the form of SMS the results of color objects that have been detected. Research results On the camera sensor, the larger the megapixel camera used, the clearer the resulting image will be in the detection process. The time for sending SMS to the destination number is based on the signal quality of the GSM provider used and in developing information, real-time object information can be added. for object detection will be displayed directly by the smartphone connected to the robot.&#x0D;
Virtual Dis-Engagement: Exploring Digital Inequality in African Higher Education Institutions

The spread of the Coronavirus 2019 (COVID-19) is by far one of the most serious global threats to academics, teaching and learning in African Higher Education Institutions (HEI) in decades. Unsurprisingly, the surge in digitalisation and internet use has played a critical role in salvaging the academic environment. Universities have and are still increasingly shifting courses online at home. Whilst the internet is an important resource in efforts to stay informed and proceed with daily lives during the COVID-19 pandemic, these online approaches to reducing risk are not available to everyone in the same way. It is evident that poorer students who live in less affluent areas pay more for less reliable internet service provision. Although smartphones dominate most socioeconomic groups, they are still a poor alternative for broadband internet access for tasks such as online classes. The digital divide leaves the vulnerable student population who lack access to a reliable broadband internet connection, at a significant disadvantage when it comes to accessing and engaging with forms of knowledge transfer and acquisition. These educational setbacks can have significant impacts on academic success, research and career opportunities. Rural communities have long been confronted with unique education challenges and chief among them is the digital divide. While millions of African students are grappling with the same challenges, HEIs have scrambled to provide students with resources, but many who live in low-income and rural communities continue to have difficulties. The digital divide is a problem that predates the pandemic, and is a problem that will persist long after it passes. Considering that some form of hybrid learning will likely continue for years to come, the broadband coverage gap is an issue that must be addressed.
Mining for Better: An Energy-Recycling Consensus Algorithm to Enhance Stability with Deep Learning

As the most popular consensus algorithm for blockchain, the Proof-of-Work (PoW) is suffering from the inability of handling computing power fluctuations. Meanwhile, PoW consumes a significant amount of energy without producing actual value. To address these issues, this paper proposes a deep learning-based consensus framework called Proof-of-Improvement (PoI), which recycles the energy from mining blocks to improve the blockchain itself. In PoI, a new reward mechanism is used to encourage miners to include the high-accuracy model in their blocks. Then, based on PoI, a difficulty adjustment algorithm is designed. Experiments are done on real-world data and the result shows the proposed algorithm’s proficiency in preserving block time stability with fluctuating hash rates. To the best of the authors’ knowledge, PoI is the first to handle both energy recycling and difficulty adjustment concurrently.
From Whistle to Echo: Data Leaks in Web-Based Whistleblowing Channels

Whistleblowing refers to reporting misconduct to responsible authorities. With accelerating digitalization and the European Union’s new whistleblower directive, large numbers of whistleblowing channels and company web pages that act as gateways to these services have been deployed. At the same time, on modern websites rife with third-party services such as web analytics, this development introduces privacy challenges. In the current study, we analyze websites of 15 Finnish companies and the whistleblowing services they employ in order to assess whether they inadvertently reveal identifying personal data to the employee’s company and third parties. Results indicate there is reason for serious concern about the privacy of whistleblowers who report wrongdoings online.
Federated Boolean Neural Networks Learning

In this paper, we propose a new centralized Federated Learning (FL) for training Deep Neural Networks (DNNs) in resource-constrained environments. Despite its popularity, federated learning faces the increasingly difficult task of scaling communication over large wireless networks with limited bandwidth. Moreover, this distributed training paradigm requires clients to perform intensive computations for multiple iterations, which may exceed the capacity of a typical edge device with limited processing power, storage capacity, and energy budget. Therefore, practical deployment of FL requires a balance between energy efficiency due to resource constraints and latency due to bandwidth constraints. In this work, we overcome both constraints by integrating low-precision arithmetic on clients and exchanging only highly compressed vectors during training. Experimental results show that the proposed algorithms FedBool and MajBool perform better than current methods on standard image classification tasks.
Handwritten digit recognition by spin waves in a Skyrmion reservoir

Abstract By performing numerical simulations for the handwritten digit recognition task, we demonstrate that a magnetic skyrmion lattice confined in a thin-plate magnet possesses high capability of reservoir computing. We obtain a high recognition rate of more than 88%, higher by about 10% than a baseline taken as the echo state network model. We find that this excellent performance arises from enhanced nonlinearity in the transformation which maps the input data onto an information space with higher dimensions, carried by interferences of spin waves in the skyrmion lattice. Because the skyrmions require only application of static magnetic field instead of nanofabrication for their creation in contrast to other spintronics reservoirs, our result consolidates the high potential of skyrmions for application to reservoir computing devices.
Strategic Management Practices and Performance of Non-Profit Organizations; A Case of Care International Rwanda

The general objective of the study is to examine the influence of strategic management practices and performance of Non-Profit Organizations with a case study of Care International Rwanda. The study objectives were: To investigate the effect of strategic formulation on the performance of Care International Rwanda, to establish the influence of strategic implementation on the performance Care International Rwanda, to assess the influence of strategic evaluation and control on the performance of Care International Rwanda. The study used descriptive research design while population and sample size were 83 meanwhile census sampling technique was applied. In data collection, questionnaires will be used to collect primary data and presentation was used through tables. Data was analyzed using SPSS software to generate the needed statistics. The study performed pilot study to determine validity and reliability of data collection instrument. The findings showed that strategic formulation is correlated with performance of non-profit organization at the level of 81.6%; Further, strategic implementation correlated with performance of non-profit organization at strong positive correlation of .842. The results affirmed that there is positive correlation between strategic evaluation, control and performance of non-profit organization at 0.795. The study concluded that strategic formulation, strategic implementation, strategic evaluation and control contribute significantly to the performance of Care International Rwanda. In recommendation, research recommended that management needs to put more effort in implementation the formulated strategies to be sure that objectivities, mission and vision will be achieved as the they planned. Further, Effective utilization of resources in suitable manner contributes to the performance of non-profit organization. Keywords: strategic management, non-profit organizations, strategic evaluation, strategy formulation.
Optimal qubit circuits for quantum-enhanced telescopes

We propose two optimal phase-estimation schemes that can be used for quantum-enhanced long-baseline interferometry. By using distributed entanglement, it is possible to eliminate the loss of stellar photons during transmission over the baselines. The first protocol is a sequence of gates using nonlinear optical elements, optimized over all possible measurement schemes to saturate the Cram\'er-Rao bound. The second approach builds on an existing protocol, which encodes the time of arrival of the stellar photon into a quantum memory. Our modified version reduces both the number of ancilla qubits and the number of gate operations by a factor of two.