Reviewerly/exHarmony huggingface dataset:
Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video

Predicting throughput is essential to reduce latency in time-critical services like video streaming, which constitutes a significant portion of mobile network traffic. The video player continuously monitors network throughput during playback and adjusts the video quality according to the network conditions. This means that the quality of the video depends on the player’s ability to predict network throughput accurately, which can be challenging in the unpredictable environment of mobile networks. To improve the prediction accuracy, we grouped the throughput trace into clusters taking into account the similarity of their mean and variance. Once we distinguished the similar trace fragments, we built a separate LSTM predictive model for each cluster. For the experiment, we used traffic captured from 5G networks generated by individual user equipment (UE) in fixed and mobile scenarios. Our results show that the prior grouping of the network traces improved the prediction compared to the global model operating on the whole trace.
A neural machine translation method based on split graph convolutional self-attention encoding

With the continuous advancement of deep learning technologies, neural machine translation (NMT) has emerged as a powerful tool for enhancing communication efficiency among the members of cross-language collaborative teams. Among the various available approaches, leveraging syntactic dependency relations to achieve enhanced translation performance has become a pivotal research direction. However, current studies often lack in-depth considerations of non-Euclidean spaces when exploring interword correlations and fail to effectively address the model complexity arising from dependency relation encoding. To address these issues, we propose a novel approach based on split graph convolutional self-attention encoding (SGSE), aiming to more comprehensively utilize syntactic dependency relationships while reducing model complexity. Specifically, we initially extract syntactic dependency relations from the source language and construct a syntax dependency graph in a non-Euclidean space. Subsequently, we devise split self-attention networks and syntactic semantic self-attention networks, integrating them into a unified model. Through experiments conducted on multiple standard datasets as well as datasets encompassing scenarios related to team collaboration and enterprise management, the proposed method significantly enhances the translation performance of the utilized model while effectively mitigating model complexity. This approach has the potential to effectively enhance communication among cross-language team members, thereby ameliorating collaborative efficiency.
Social media and human development in Zimbabwe: opportunities and challenges

This article employs the concepts of human development and social media as an open space/public sphere to frame the nexus between media and development. It seeks to answer questions, particularly on how human development can be facilitated and enhanced by social media through increasing citizen participation in the governance process and enhancing media expression in a media-constrained and politically restrictive environment. Tracing social media’s coverage of some key political and social events in Zimbabwe, this article provides insights into how social media has contributed to the governance process by offering the public with significant opportunities to ‘interact’ with the authorities and other developmental stakeholders, and bettering media expression. The paper concludes that social media has enhanced how and when people articulate issues, thereby proactively contributing to the governance process and media expression. Considering the ways the digital era has transformed the governance process, it needs to be earnestly harnessed. Albeit its weaknesses, social media presents the citizens with visible and enhanced opportunities to participate in issues critical to the socio-economic and political development of the citizens.
A novel automatic annotation method for whole slide pathological images combined clustering and edge detection technique

Abstract Pixel‐level labeling of regions of interest in an image is a key step in building a labeled training dataset for supervised deep learning networks of images. However, traditional manual labeling of cancerous regions in digital pathological images by doctors is time‐consuming and inefficient. To address this issue, this paper proposes an automatic labeling method for whole slide images, which combines clustering and edge detection techniques. The proposed method utilizes the multi‐level feature fusion model and the Long‐Short Term Memory network to discriminate the cancerous nature of the whole slide images, thereby improving the classification accuracy of the whole slide images. Subsequently, the automatic labeling of cancerous regions is achieved by integrating a density‐based clustering algorithm and an edge point extraction algorithm, both based on the discriminated results of the cancerous properties of whole slide images. The experimental results demonstrate the effectiveness of the proposed method, which offers an efficient and accurate solution to the challenging task of cancerous region labeling in digital pathological images.
FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning

Abstract Federated learning (FL) is a promising approach for learning a model from data distributed on massive clients without exposing data privacy. It works effectively in the ideal federation where clients share homogeneous data distribution and learning behavior. However, FL may fail to function appropriately when the federation is not ideal, amid an unhealthy state called Negative Federated Learning (NFL), in which most clients gain no benefit from participating in FL. Many studies have tried to address NFL. However, their solutions either (1) predetermine to prevent NFL in the entire learning life-cycle or (2) tackle NFL in the aftermath of numerous learning rounds. Thus, they either (1) indiscriminately incur extra costs even if FL can perform well without such costs or (2) waste numerous learning rounds. Additionally, none of the previous work takes into account the clients who may be unwilling/unable to follow the proposed NFL solutions when using those solutions to upgrade an FL system in use. This paper introduces FL-GUARD, a holistic framework that can be employed on any FL system for tackling NFL in a run-time paradigm. That is, to dynamically detect NFL at the early stage (tens of rounds) of learning and then to activate recovery measures when necessary. Specifically, we devise a cost-effective NFL detection mechanism, which relies on an estimation of performance gain on clients. Only when NFL is detected, we activate the NFL recovery process, in which each client learns in parallel an adapted model when training the global model. Extensive experiment results confirm the effectiveness of FL-GUARD in detecting NFL and recovering from NFL to a healthy learning state. We also show that FL-GUARD is compatible with previous NFL solutions and robust against clients unwilling/unable to take any recovery measures.
Enhancing blockchain scalability with snake optimization algorithm: a novel approach

Scalability remains a critical challenge for blockchain technology, limiting its potential for widespread adoption in high-demand transactional systems. This paper proposes an innovative solution to this challenge by applying the Snake Optimization Algorithm (SOA) to a blockchain framework, aimed at enhancing transaction throughput and reducing latency. A thorough literature review contextualizes our work within the current state of blockchain scalability efforts. We introduce a methodology that integrates SOA into the transaction validation process of a blockchain network. The effectiveness of this approach is empirically evaluated by comparing transaction processing times before and after the implementation of SOA. The results show a substantial reduction in latency, with the optimized system achieving lower average transaction times across various transaction volumes. Notably, the latency for processing batches of 10 and 100 transactions decreased from 30.29 ms to 155.66 ms–0.42 ms and 0.37 ms, respectively, post optimization. These findings indicate that SOA is exceptionally efficient in batch transaction scenarios, presenting an inverse scalability behavior that defies typical system performance degradation with increased load. Our research contributes a significant advancement in blockchain scalability, with implications for the development of more efficient and adaptable blockchain systems suitable for high throughput enterprise applications.
Automated hearing impairment diagnosis using machine‐learning: An open‐source software development undergraduate research project

Abstract Approximately 700 million people will have disabling hearing loss by 2050. Underdeveloped and developing countries, which encompass a considerable proportion of people with disabling hearing impairment, have a sparse number of audiologists and otolaryngologists. The lack of specialists leaves most hearing impairments undiagnosed for a long time, resulting in negative societal and economic impacts. In this article, we propose an automated hearing impairment diagnosis software—based on machine learning—to support audiologists and otolaryngologists in accurately and efficiently diagnosing and classifying hearing loss. We present the design, implementation, and performance analysis of an open‐source automated hearing impairment diagnosis software, which consists of two modules: a hearing test data‐generation module and a machine‐learning model. The data‐generation module produces a diverse and exhaustive data set for training and evaluating the machine‐learning model. By employing multiclass and ultilabel classification techniques to learn from the hearing test data, the model can swiftly predict the type, degree, and configuration of hearing loss with high reliability. Our proposed machine‐learning model demonstrates promising results with a prediction time of 634 ms, a log‐loss reduction rate of 0.9848 and accuracy, precision, recall, and f1‐score of 1.0000—showing the model's applicability to assist audiologists and otolaryngologists in rapidly and accurately classifying the type, degree, and configuration of hearing loss. In addition to the technical contributions, this article also highlights the importance of involving undergraduate students in open‐source software development projects which have a direct impact on improving the quality of human life.
Recent advances in earthquake seismology using machine learning

Abstract Given the recent developments in machine-learning technology, its application has rapidly progressed in various fields of earthquake seismology, achieving great success. Here, we review the recent advances, focusing on catalog development, seismicity analysis, ground-motion prediction, and crustal deformation analysis. First, we explore studies on the development of earthquake catalogs, including their elemental processes such as event detection/classification, arrival time picking, similar waveform searching, focal mechanism analysis, and paleoseismic record analysis. We then introduce studies related to earthquake risk evaluation and seismicity analysis. Additionally, we review studies on ground-motion prediction, which are categorized into four groups depending on whether the output is ground-motion intensity or ground-motion time series and the input is features (individual measurable properties) or time series. We discuss the effect of imbalanced ground-motion data on machine-learning models and the approaches taken to address the problem. Finally, we summarize the analysis of geodetic data related to crustal deformation, focusing on clustering analysis and detection of geodetic signals caused by seismic/aseismic phenomena. Graphical Abstract
Successive data injection in conditional quantum GAN applied to time series anomaly detection

Abstract Classical GAN architectures have shown interesting results for solving anomaly detection problems in general and for time series anomalies in particular, such as those arising in communication networks. In recent years, several quantum GAN (QGAN) architectures have been proposed in the literature. When detecting anomalies in time series using QGANs, huge challenges arise due to the limited number of qubits compared to the size of the data. To address these challenges, a new high‐dimensional encoding approach, named Successive Data Injection (SuDaI) is proposed. In this approach, SuDaI explores a larger portion of the quantum state, compared to the conventional angle encoding method used predominantly in the literature. This is achieved through repeated data injections into the quantum state. SuDaI encoding allows the authors to adapt the QGAN for anomaly detection with network data of a much higher dimensionality than with the existing known QGANs implementations. In addition, SuDaI encoding applies to other types of high‐dimensional time series and can be used in contexts beyond anomaly detection and QGANs, opening up therefore multiple fields of application.
Physics-Guided Data Augmentation Combined with Unsupervised Learning Improves Stability and Accuracy of Bit Wear Deep Learning Model

Abstract Data is one of the most important limiting factors of deep machine learning (ML) model in drilling applications. Though a big size of historical data can be available, high-quality cleaned and labeled data is usually limited. In this case study, we show that with limited labeled data, physics-based data augmentation combined with unsupervised learning significantly improves both stability and accuracy in bit wear ML model. It provides a pathway to overcome labeled data shortage and field data quality limitations. Labeled bit wear data is usually limited because only the final bit dull state can be labeled using dull photos for the entire drilling distance. To overcome this difficulty, an encoder-decoder unsupervised ML framework based on Bi-directional LSTM architecture is first applied to the data of entire drilling distance to extract and maximize data learnings. Then, a physics-based wear estimator is implemented in the learned latent space to augment labeled wear dataset, guided by true wear labels and offset-well data. Weighting factor in loss function is applied to optimize augmented and true datasets in final supervised learning step. The proposed real-time bit wear model is built on a combination of physics-based data augmentation and unsupervised data learning method. The model is applied on multiple field bit runs. Results show that by implementing the unsupervised data learnings only, the prediction accuracy is improved by 30% compared to the baseline ML model. By combining physics-based augmentation, the accuracy is further improved by 10%. More importantly, adding physics-based data augmentation significantly reduces prediction variance and unphysical wear outputs, therefore improving prediction stability by more than 30%. It should be noted that the prediction stability of AI/ML model is crucial in real-time application and decision making. The results show physics-based data augmentation not only increases the size of label dataset and prevents model overfitting, but also applies physics-based guidance to the ML model effectively. It is learned weighting factor plays a crucial role in balancing loss contributions from true wear labels and physics-based labels. While low weighting factor of physics-based labels diminishes the augmented data, high weighting factor disrespects the true wear labels leading to a high prediction bias. Overall, the bit wear model study shows physic-based data augmentation combined with unsupervised data learning can effectively improve model accuracy, stability, and overcome labeled data shortage difficulty. The proposed paper shows a case study for the bit wear ML model using a combination of physics-based data augmentation and unsupervised data learning. While labeled data is one of the major challenges of ML models in many drilling applications, this study provides a pathway to improve both accuracy and stability of deep ML models with limited labeled data.
Drill Bit Forensics Using Videos Captured on Mobile Phones

Abstract Still images captured using mobile phone cameras have been shown to be very useful for bit forensics purposes. However, since still images can only capture views at certain viewing angles, they often provide insufficient information on each cutter's diamond table and substrate. Videos solve the problem of lack of view angles. This paper demonstrates how a video captured on a phone can provide additional bit information benefiting forensics, and provides recommendations for capturing videos to maximize information content. Bit forensics as well as the future IADC dull grading system require meticulous data collection for various drill bit regions, including critical information such as blade numbers and cutter locations. To automate this process, this paper introduces a multi-stage approach using bit videos. Initially, a detection model is employed to accurately locate the drill bit within the video. Next, computer vision algorithms are utilized to segment the different blades. Spatial geometry algorithms are then applied to reconstruct the camera trajectory, which aids in estimating the blade numbers. Finally, each cutter within the segmented blades is further segmented into different regions. This study explores the utilization of videos for the automated location segmentation of drill bits, a crucial aspect of the revised IADC dull grading system currently being proposed. The videos capture the entire drill bit from multiple angles, encompassing top-down views and a full 360-degree rotation. The IADC dull grading system necessitates the precise recording of position information including blade numbers, pocket numbers, and bit zones. By employing videos instead of still images, this study demonstrates that spatial geometric information of the drill bit can be obtained more completely and efficiently. Given a video that conforms to established shooting standards, the proposed automatic position calculation algorithm efficiently completes the segmentation of different parts of the drill bit and labels them in accordance with the relevant standards. Notably, video capturing offers several advantages over still photography; it obviates the need for complex training for operators, only necessitating adherence to basic camera trajectory guidelines, and substantially reduces the time needed to collect such data. Importantly, the location segmentation algorithm employed in this study is capable of running in real-time, thereby streamlining and accelerating the IADC dull grading and bit forensics processes. This paper introduces a novel video-based algorithm for drill bit segmentation. The algorithm automatically segments and labels various components of the drill bit as per established criteria, generating comprehensive data vital for damage analysis. By employing this algorithm, videos of PDC drill bits can be processed with remarkable speed and accuracy. This represents a substantial advancement in data collection methods, with implications for improving the quality of bit damage assessment.
The Implementation of Safety-II: Learning from Normal Work in Practice

Abstract The oil and gas industry has for decades learned about safety from the analysis of failure, adverse events, incidents, and accidents. This approach is called "safety-I". Safety-I has helped the industry reach a plateau of general high levels safety, measured as the absence of incidents, compared to other industries, but there is still room for further and continuous improvements. Researchers in human factors and safety have suggested that safety critical industries, in the future, should not only learn from failure, but also from the normal everyday successful work. This approach is called "safety-II" (Hollnagel et al. 2015), and, in a report from The International Association of Oil &amp; Gas Producers (2022), "learning from normal work". The paper describes the practical implementation and roll-out of safety-II and learning from normal work on board operating drilling rigs. The learning from normal work project covers 3 phases: Phase 1: The onboard studies of positive practice, i.e., ethnographic observations of normal daily work and conversations with crew members, e.g., on the drill floor.Phase 2: The development and prototype testing of learning material based on the onboard observations and training of onboard facilitators in the learning team format.Phase 3: The final validation of the learning from normal work concept in a ramp-up case study on a rig starting a new drilling campaign. More than 160 examples of positive practice from 5 different rigs (2 drillships, 2 jackups and 1 semisubmersibles) were collected in the first phase of the learning from normal work project. The examples illustrate how crews add safety to the process by, e.g., the way they communicate and interact, the way they use technology or the way they plan and discuss their work. The positive examples were filtered and used to generate learning material in the form of learning cards for interactive discussions of safety in learning teams and positive examples to be used in safety meetings and presentations for inspiration as an alternative to failure and incident cases. One important conclusion from the learning from normal work project is that the crews contribute to the safety of the process through their behavior and small actions in the daily routine work. Thereby, they put many small "safety nets" in place in the system making it resilient and strong. Looking at positive practice and positive events is new when discussing safety. It changes the narrative and the focus of the discussion from "what to avoid" to "what to do more" and "what to develop and support". This philosophy is based on positive reinforcement as a motivating and driving factor.
Human-Centered Interaction in Virtual Worlds: A New Era of Generative Artificial Intelligence and Metaverse

The metaverse has emerged as an exciting new paradigm for human-computer interaction (HCI) and virtual collaboration. This paper presents a comprehensive review of the metaverse to address the gap in the existing literature where there is a lack of a survey that reviews the nature of the metaverse and its building blocks from a human-centric perspective. We first synthesize a definition of the metaverse from existing literature and delineate key affordances. We then introduce a detailed framework encompassing the metaverse’s nature, infrastructure technologies, and input/output technologies that facilitate multi-sensory HCI, alongside applications across diverse domains. The components within this framework are explained in depth, offering insights into the metaverse’s nature and the readiness level of current technologies. Based on this comprehensive analysis, we outline major open challenges and propose promising directions demanding further exploration and investigation. By clarifying the vision for the metaverse and characterizing the building blocks required to realize it, this review provides essential insights and serves as an invaluable resource for metaverse developers and researchers working to advance this transformative new medium.