Reviewerly/exHarmony huggingface dataset:
Digital Copyright Management Mechanism Based on Dynamic Encryption for Multiplatform Browsers

In recent years, the internet and smart devices have developed rapidly. Many people no longer rely on newspapers, magazines, or television to receive news. They can see the latest news using computers or mobile phones. According to a study by the Taiwan Internet Information Center, nearly 90% of Taiwanese people have used the internet. Many online streaming services have emerged, and people can easily watch movies and TV programs through computers or mobile phones. Hence, some websites use digital copyright management mechanisms to protect videos from being directly downloaded. However, 30% of websites use AES-128 encryption to protect their content. If the key access mechanism is not well protected, the encryption methodology may be useless. Therefore, this paper proposes a cross-platform digital copyright management mechanism for adaptive streaming. With this mechanism, users do not need to download additional applications, as the mechanism implements Web-Assembly language through the browser.
Optimizing Cloud Energy Consumption Using Static Task Scheduling Algorithms: A Comparative Study

Cloud data centers, comprising a diverse set of heterogeneous resources working collaboratively to achieve high-performance computing, face the challenge of resource dynamism, where performance fluctuates over time. This dynamism poses complexities in task scheduling, warranting further research on the resilience of existing static task scheduling algorithms when deployed in dynamic cloud environments. This study adapts three well-known task scheduling algorithms to the cloud computing context and conducts a comprehensive comparison to assess their resilience to dynamic conditions. The evaluation, employing simulation techniques, analyzes total energy consumption and total response time as key metrics. The results offer detailed insights into the effectiveness of the adapted algorithms, providing valuable guidance for optimizing task scheduling in dynamic cloud data centers.
New User Engagement Prediction Using Machine Learning Algorithms

This research paper presents our participation in the User Engagement Prediction Challenge, a competition hosted on the Zindi platform. The challenge's objective is to develop a predictive model that accurately predicts user activity in the subsequent month. Throughout the competition, we encountered the challenge of data imbalance, which we addressed by employing various techniques, including oversampling, undersampling, and ensemble learning. Among these approaches, ensemble learning proved to be the most effective in mitigating the data imbalance issue and improving prediction performance. In our study, we implemented different machine learning algorithms, including stack, hard voting, and CatBoost. We achieved a noteworthy F-score of 49.56% through extensive experimentation using the CatBoost algorithm. This achievement allowed us to secure the third position in the competition.
Automatic Disease Diagnosis System Using Deep Q-Network Reinforcement Learning

Disease diagnosis is a pivotal and indispensable step in the overall treatment process of diseases. The rapid advancement of machine learning and deep learning methodologies has revolutionized automatic disease diagnosis and improved healthcare accessibility. This paper introduces an automatic diagnostic system leveraging both Explicit and Implicit Symptoms utilized by the Deep-Q Network Reinforcement Policy. The proposed approach revolves around a dialogue-based system, employing a task-oriented framework that is designed for disease diagnosis. The cornerstone of the used methodology lies in the utilization of reinforcement learning, specifically employing the Deep Q-Network (DQN) policy. The utilized DQN network architecture comprises four linear layers, with the Rectified Linear Units (ReLUs) activation function. We conducted a comparative analysis between two deep learning techniques: the DQN and the Policy Gradients (PG). The experimental data was obtained from the extensive Medical Diagnosis Dialogue repository that encompasses 2,374 dialogues, 12 distinct disease categories, and 118 types of symptoms.
Utilizing the Blockchain Technology for Higher Education in the Era of Pandemics: A Systematic Review

The availability of important characteristics such as decentralization, permanence, anonymity, and audacity has driven interest in blockchain technology more recently than ever before. This technology has been employed in a variety of applications, including education. Therefore, this research is a systematic review of research that investigates the applications of blockchain technology in higher education institutions in addition to those that present the role of this technology in combating the Corona pandemic, as it focuses on the following main topics: (1) blockchain-based applications and systems that can be used in higher education institutions; and (2) the current state of blockchain challenges in this area that need to be addressed in the future. A detailed analysis of the results of each topic as well as an extensive discussion were conducted based on the results. This review also provides insight into other areas that could benefit from blockchain technology during and after the pandemic.
Multiple Sentinel-2 Images Super-Resolution with Google Earth Pro Images

In recent years, the application of neural networks in the field of satellite image super-resolution has become increasingly widespread. This paper aims to explore how to utilize images from two completely different data sources (Sentinel-2 and Google Earth Pro) as training data to surpass the spatial resolution limitation of 10 meters provided by the Sentinel-2 satellite and obtain higher resolution image. The Sentinel-2 satellite offers open data with four bands with spatial resolution of 10 meters and a resampling cycle every 5 days. This provides abundant data that can be used as Low-Resolution (LR) images for research. Adopting a multi-image super-resolution processing approach allows for the full utilization of geographical information contained within different images of the same location captured at shorter time intervals, while also eliminating concerns regarding information loss due to weather-affected low-quality images. Google Earth Pro provides images with a resolution of up to 0.15 meters, allowing the High-Resolution (HR) image resolution to be adjusted according to the needs. In our experiments, we proposed Sentinel-2 Google Earth Pro Network (SGNET). We used HR images with a resolution of 2 meters, and through SGNET implementation, a five-fold increase in spatial resolution for Sentinel-2 images was achieved, yielding very satisfactory results.
Assessing a Private 5G SA and a Public 5G NSA Architecture for Networked Music Performances

Networked Music Performance (NMP) systems enable displaced musicians to play together. Recent advances in 5G technologies open novel possibilities for running NMPs over cellular wireless networks. However, tests on 5G support for NMPs are still limited to date, and typically restricted to specific use cases or architectures. In this paper we consider two 5G architectures, a private 5G standalone (SA) network with edge computing infrastructure, and a pre-commercial public 5G non-standalone (NSA) network. We analyze their capability to support NMPs in terms of four network metrics, namely end-to-end latency, packet error ratio, missed packets, and maximum number of consecutive missed packets. For our measurements we involved a jitter buffer of 10.66 ms. The results for the private 5G SA architecture show that the network was stable and capable of guaranteeing the latency and reliability requirements needed to ensure a realistic music interplay. Latency was constantly below 23 ms, whereas packet losses occurred with a probability of less than 0.01 on average. Conversely, the public 5G NSA architecture was insufficient to support NMPs, as the performance of the network in terms of latency and reliability were well above the perceptual thresholds that musicians can tolerate. These results suggest that public cellular 5G SA architectures with edge computing support are required for realistic real-time musical interactions.
Live Coding in the Metaverse

Networked, immersive, shared virtual environments are on the rise under the concept of the metaverse. Behind all the hype, however, it is an excellent opportunity for networked and virtual music performances using live coding. Especially in recent years, the use of metaverse environments for live coding performances has seen an upsurge due to the constraints of the global pandemic. While the performances are mostly integrated as 2D video streams into environments such as Mozilla Hubs, it seems reasonable to integrate browser-based live coding languages into these environments. This paper describes developing and integrating two live coding languages based on the Bytebeat concept into metaverse systems. They enable real-time live coding within a virtual environment, even collaboratively, with immersive capabilities through spatial audio rendering and the use of VR and AR devices. In addition, different art experiences through the visual composition of the environments can be created. We will show the development and benefits of the live coding languages implemented and the artistic possibilities these virtual environments offer.
Regression-Based Approach for Proactive Predictive Modeling of Efficient Cloud Cost Estimation

Businesses increasingly lean on cloud-based solutions in the contemporary digital landscape, drawn by their scalability and adaptability. Navigating the financial intricacies of cloud subscription models, particularly when intertwined with software-defined systems, remains a formidable challenge. This challenge is accentuated by dynamic pricing structures, making accurate cost forecasting a critical but complex endeavor. This research unveils an innovative methodology designed to meticulously forecast the financial costs of cloud subscription tasks based on resource allocation characteristics. Our approach centers around a robust model carefully created through particular preparation and modeling stages that incorporate a pricing model for various virtual machines and utilize different advanced regression algorithms to navigate the complex world of cloud subscription services. The results of our study, which analyzed real cloud workloads, indicate that equipping businesses with a powerful predictive tool can lead to improved financial planning and strategic decision-making in their cloud operations. This study aims to guide businesses in the ever-changing field of cloud technology, focusing on promoting financial efficiency and improving decision-making strategies in their cloud initiatives.
Study on the Use of Defect Metrics in the Software Development Process. Flaws and Vulnerabilities

The quality compliance has become an aspect of big importance in the development of software engineering, here the term ‘defect metrics’ comes in the prevention of errors, failures and defects in the software. As a result of applying this type of metrics we can obtain quality and more reliable systems for today's world. Therefore, establishing criteria for defect metrics is a relevant step within the components that make up the software product. The main objective of this research is to show an overview of defect metrics in software products. We aim to show their benefits and limitations and the causes of vulnerabilities in applications in order to obtain better built applications. This study was carried out using different sources of information obtained from academic databases, which were: ACM Digital Library, IEEE Xplore, Springer Link and Science Direct. The study recovered a total of 20 research papers that met the necessary guidelines to belong to the systematic literature review; these primary studies were previously subjected to multiple quality tests. The use of software metrics contributes significantly to the quality of a software product, however, limiting factors that interfere with the full use of defect metrics are also discussed. Finding the option with the lowest risk of limiting the use of these metrics and greater gain is relevant to the creation of a software product. In addition to supporting the testing process early on, they also prevent software vulnerabilities due to their nature and incorporation in the components of a system.
Development of an Autonomous Mobile Robot Mechatronic System at Scale for Industrial Logistics in Warehouses

Autonomous Mobile Robots - also known as AMRs - nowadays are becoming part of multiple automated systems in the industry. That is why this project presents a different approach for the automatization regarding the transfer and relocation of materials employing AMRs. Developing a scale prototype that, through the development of a coordinates detection system within a closed space, with the help of a camera located on the ceiling of said space through QR codes, it will be able to follow a path to simulate the behavior that they would have in case they are implemented in the industry, making use of well-accessible market components. Likewise, it will be a collaborative-type robot due to the control program to develop through the camera that will be able to identify the coordinates of multiple robots utilizing a QR code as well as the coordinates of an external object located in the surroundings which will facilitate the collaboration between robots to accomplish different tasks only by using the detection and control of coordinates to execute their movements.
Proposal of an Instrument to Evaluate Teaching Performance in Virtual Mode

The quality of teaching is a key factor; educational modalities need to develop reliable mechanisms and rigorous evaluations to identify the strengths and weaknesses of teachers. The virtual modality is one that requires the construction of digital thematic content in accordance with the learning scenario itself; given its complexity and to verify that the teaching-learning process meets its purpose, it is necessary to propose evaluation mechanisms that allow a critical analysis of the teaching work with the intention of improving the teaching process. The objective of the research is to assess the design process and validity of an instrument to evaluate teaching performance in virtual modality. The research approach was mixed, descriptive, exploratory and explanatory, analyzing specialized sources on the subj ect, and characterizing the problem to identify its causes. The results show that there is a valid and reliable instrument, after applying Spearman's correlation, Cronbach's Alpha and scale processing methods, so it is concluded given the methodological rigor carried out that the instrument can be used to assess teacher performance.
TwiTrace: A New Approach of Contact Tracing Based on Multilingual Tweets

By the end of 2019, the world has known a resurgence of epidemics by the sudden outbreak of the COVID-19 virus, that claimed millions of lives, and overwhelmed public health systems. Compared to other known viruses, this one is thought to be more contagious. An individual may be infectious without showing symptoms, so until he tests positive, he might infect many people who meet him. Therefore, to keep the numbers under control, governments apply various strategies such social distancing, remote work and investigative methods like contact tracing to identify potential infected people. Many researchers proposed effective digital contact tracing solutions; nevertheless, they rely on user's cooperation to install applications or to carry sensor devices. However, social networks present a suitable alternative to gather contact-tracing data since they are public and available. In this paper, we propose TwiTrace a new contact tracing approach based on multilingual tweets and graph modelling. TwiTrace creates a dataset for Covid-19 positive cases using Twitter API, and then identifies close contacts and risk places. Next, a graph is generated and queried using Neo4j database. Our approach indicates high accuracy and shows good results in terms of size compared to suspected cases and high-risk places extracted manually.
UAV-Based Search and Rescue System Using a Probability Distribution Map

This paper introduces a comprehensive framework for enhancing search and rescue (SAR) missions using unmanned aerial vehicles (UAVs). The framework includes a probability-based mapping technique that identifies areas with the highest probability of target presence using a Probability Distribution Map (PDM). Additionally, optimized waypoint generation techniques are employed to enhance mission efficiency. Furthermore, the framework addresses UA V S’ kinematic and dynamic constraints by incorporating smoothing techniques, namely Dubin and Pythagorean Hodograph curves (PH) for safe and feasible flight trajectories. Extensive simulations show the effectiveness of the proposed SAR framework.
Breaking Blockchain Rationality with Out-of-Band Collusion

Blockchain systems often rely on rationality assumptions for their security, expecting that nodes are motivated to maximize their profits. These systems thus design their protocols to incentivize nodes to execute the honest protocol but fail to consider out-of-band collusion. Existing works analyzing rationality assumptions are limited in their scope, either by focusing on a specific protocol or relying on non-existing financial instruments. We propose a general rational attack on rationality by leveraging an external channel that incentivizes nodes to collude against the honest protocol. Our approach involves an attacker creating an out-of-band bribery smart contract to motivate nodes to double-spend their transactions in exchange for shares in the attacker’s profits. We provide a game theory model to prove that any rational node is incentivized to follow the malicious protocol. We discuss our approach to attacking the Bitcoin and Ethereum blockchains, demonstrating that irrational behavior can be rational in real-world blockchain systems when analyzing rationality in a larger ecosystem. We conclude that rational assumptions only appear to make the system more secure and offer a false sense of security under the flawed analysis.
PoSynDA: Multi-Hypothesis Pose Synthesis Domain Adaptation for Robust 3D Human Pose Estimation

The current 3D human pose estimators face challenges in adapting to new datasets due to the scarcity of 2D-3D pose pairs in target domain training sets. We present the Multi-Hypothesis Pose Synthesis Domain Adaptation (PoSynDA) framework to overcome this issue without extensive target domain annotation. Utilizing a diffusion-centric structure, PoSynDA simulates the 3D pose distribution in the target domain, filling the data diversity gap. By incorporating a multi-hypothesis network, it creates diverse pose hypotheses and aligns them with the target domain. Target-specific source augmentation obtains the target domain distribution data from the source domain by decoupling the scale and position parameters. The teacher-student paradigm and low-rank adaptation further refine the process. PoSynDA demonstrates competitive performance on benchmarks, such as Human3.6M, MPI-INF-3DHP, and 3DPW, even comparable with the target-trained MixSTE model. This work paves the way for the practical application of 3D human pose estimation1. The source code is available at https://github.com/hbing-l/PoSynDA.
Revisiting Hard Negative Mining in Contrastive Learning for Visual Understanding

Efficiently mining and distinguishing hard negatives is the key to Contrastive Learning (CL) in various visual understanding tasks. By properly emphasizing the penalty of hard negatives, Hard Negative Mining (HNM) can improve the CL performance. However, there is no method to quantitatively analyze the penalty strength of hard negatives, which makes training difficult to converge. In this paper, we propose a method for measuring and controlling the penalty strength. We first define a penalty strength metric to provides a quantitative analysis tool for HNM. Then, we propose a Triplet loss with Penalty Strength Control (T-PSC), which can balance the penalty strength of hard negatives and the difficulty of model optimization. In order to verify the effectiveness of the proposed T-PSC method in different modalities, we applied it to two visual understanding tasks: Image–Text Retrieval (ITR) for multi-model processing, and Temporal Action Localization (TAL) for video processing. T-PSC can be applied to existing ITR and TAL models in a plug-and-play manner without any changes. Experiments combined with existing models show that a reasonable control of the penalty strength can speed up training and improve the performance on higher-level tasks.
A pXRF‐based geochemical dataset of typical boreholes in Xuancheng Qiaomaishan copper–sulphur deposit

Abstract Drilled cores provide first‐hand data for deposit research. The element compositions at different depths of cores are an important basis for evaluating the reserves of mineral deposits. Therefore, a dataset containing a complete drill core element content has great value on practicality. In this study, a pXRF analyser was used to measure the element contents in eight cores of the Xuancheng Qiaomaishan copper–sulphur deposit ZK16 + 02, ZK16 + 03, ZK16 + 04, ZK1804, ZK18 + 02, ZK1403, ZK1602, and ZK1604, and a geochemical dataset was obtained. The dataset includes Cu, S, Fe, W, Mg, Al, Si, As, Mn, Co, Ni, Zn, Se, Ti, V, Cr, Ca, Cl, P, Sr, Y, Zr, Nb, Mo, Pb, Th, U, and other 27 major and trace elements. Based on this dataset, the vertical variation pattern of main and trace element content in drill cores can be examined, the distributions of elements in full hole or exploration line profile can be drawn, and element correlation analysis and principal component analysis can be performed.
Robust Watermarking Algorithm for Building Information Modeling Based on Element Perturbation and Invisible Characters

With the increasing ease of building information modeling data usage, digital watermarking technology has become increasingly crucial for BIM data copyright protection. In response to the problem that existing robust watermarking methods mainly focus on BIM exchange formats and cannot adapt to BIM data, a novel watermarking algorithm specifically designed for BIM data, which combines element perturbation and invisible character embedding, is proposed. The proposed algorithm first calculates the centroid of the enclosing box to locate the elements, and establishes a synchronous relationship between the element coordinates and the watermarked bits using a mapping mechanism, by which the watermarking robustness is effectively enhanced. Taking into consideration both data availability and the need for watermark invisibility, the algorithm classifies the BIM elements based on their mobility, and perturbs the movable elements while embedding invisible characters within the attributes of the immovable elements. Then, the watermark information after dislocation is embedded into the data. We use building model and structural model BIM data to carry out the experiments, and the results demonstrate that the signal-to-noise ratio and peak signal-to-noise ratio before and after watermark embedding are both greater than 100 dB. In addition, the increased information redundancy accounts for less than 0.15% of the original data., which means watermark embedding has very little impact on the original data. Additionally, the NC coefficient of watermark extraction is higher than 0.85 when facing attacks such as translation, element addition, element deletion, and geometry–property separation. These findings indicate a high level of imperceptibility and robustness offered by the algorithm. In conclusion, the robust watermarking algorithm for BIM data fulfills the practical requirements and provides a feasible solution for protecting the copyright of BIM data.