Reviewerly/exHarmony huggingface dataset:
A Machine Learning Approach for an HPC Use Case: the Jobs Queuing Time Prediction

High-Performance Computing (HPC) domain provided the necessary tools to support the scientific and industrial advancements we all have seen during the last decades. HPC is a broad domain targeting to provide both software and hardware solutions as well as envisioning methodologies that allow achieving goals of interest, such as system performance and energy efficiency. In this context, supercomputers have been the vehicle for developing and testing the most advanced technologies since their first appearance. Unlike cloud computing resources that are provided to the end-users in an on-demand fashion in the form of virtualized resources (i.e., virtual machines and containers), supercomputers' resources are generally served through State-of-the-Art batch schedulers (e.g., SLURM, PBS, LSF, HTCondor). As such, the users submit their computational jobs to the system, which manages their execution with the support of queues. In this regard, predicting the behaviour of the jobs in the batch scheduler queues becomes worth it. Indeed, there are many cases where a deeper knowledge of the time experienced by a job in a queue (e.g., the submission of check-pointed jobs or the submission of jobs with execution dependencies) allows exploring more effective workflow orchestration policies. In this work, we focused on applying machine learning (ML) techniques to learn from the historical data collected from the queuing system of real supercomputers, aiming at predicting the time spent on a queue by a given job. Specifically, we applied both unsupervised learning (UL) and supervised learning (SL) techniques to define the most effective features for the prediction task and the actual prediction of the queue waiting time. For this purpose, two approaches have been explored: on one side, the prediction of ranges on jobs' queuing times (classification approach) and, on the other side, the prediction of the waiting time at the minutes level (regression approach). Experimental results highlight the strong relationship between the SL models' performances and the way the dataset is split. At the end of the prediction step, we present the uncertainty quantification approach, i.e., a tool to associate the predictions with reliability metrics, based on variance estimation.
Community Governance Based on Sentiment Analysis: Towards Sustainable Management and Development

The promotion of community governance by digital means is an important research topic in developing smart cities. Currently, community governance is mostly based on reactive response, which lacks timely and proactive technical means for emergency monitoring. The easiest way for residents to contact their properties is to call the property call center, and the call centers of many properties store many speech data. However, text sentiment classification in community scenes still faces challenges such as small corpus size, one-sided sentiment feature extraction, and insufficient sentiment classification accuracy. To address such problems, we propose a novel community speech text sentiment classification algorithm combining two-channel features and attention mechanisms to obtain effective emotional information and provide decision support for the emergency management of public emergencies. Firstly, text vectorization based on word position information is proposed, and a SKEP-based community speech–text enhancement model is constructed to obtain the corresponding corpus. Secondly, a dual-channel emotional text feature extraction method that integrates spatial and temporal sequences is proposed to extract diverse emotional features effectively. Finally, an improved cross-entropy loss function suitable for community speech text is proposed for model training, which can achieve sentiment analysis and obtain all aspects of community conditions. The proposed method is conducive to improving community residents’ sense of happiness, satisfaction, and fulfillment, enhancing the effectiveness and resilience of urban community governance.
Significance of Cross-Correlated QoS Configurations for Validating the Subjective and Objective QoE of Cloud Gaming Applications

In this paper, utilising real-internet traffic data, we modified a popular network emulator to better imitate real network traffic and studied its subjective and objective implications on QoE for cloud-gaming apps. Subjective QoE evaluation was then used to compare cross-correlated QoS metric with the default non-correlated emulator setup. Human test subjects showed different correlated versus non-correlated QoS parameters affects regarding cloud gaming QoE. Game-QoE is influenced more by network degradation than video QoE. To validate our subjective QoE study, we analysed the experiment’s video objectively. We tested how well Full-Reference VQA measures subjective QoE. The correlation between FR QoE and subjective MOS was greater in non-correlated QoS than in correlated QoS conditions. We also found that correlated scenarios had more stuttering events compared to non-correlated scenarios, resulting in lower game QoE.
A Systematic Review of Technology Reviews in Language Teaching and Learning Journals

Technology reviews are a specific article genre published in several language teaching and learning journals. Their emergence is likely due to the proliferation of technology that facilitates and supports language acquisition. These reviews aim to increase teachers’ and learners’ awareness of the forms of technology available and succinctly describe their benefits and limitations for language teaching and learning. As education enters a new digital age, accelerated by the COVID-19 pandemic, it is essential to determine which forms of technology support teachers’ professional learning and how they can be utilized. This article presents a systematic review of technology reviews published between 2017 and 2022 in five Q1 SCOPUS-indexed language teaching and learning journals: RELC Journal, TESOL Journal, CALICO Journal, Language Learning and Technology and Teaching English as a Second Language Electronic Journal (TESL-EJ). These five journals were the only Q1 language teaching and learning journals we identified that publish technology reviews. All 73 technology reviews published in the 5-year period in the 5 journals were reviewed. Using professional digital competence as a framework, we explore what the articles reveal about how technology is used, as well as the knowledge gaps that remain. We examine the types and features of the technology described in the reviews, the language skills and educational levels of the learners, and the pedagogical implications of technology use. We found that the technology reviews mainly introduced the technological features and functions of the reviewed technologies with less emphasis placed on their pedagogical utilities and social aspects of their use for language teaching. The article concludes with recommendations for authors and journal editors regarding the content that should be included in a technology review to ensure that it is helpful to the profession. We also offer advice to practitioners on how to benefit from technology reviews.
A Feature-Trajectory-Smoothed High-Speed Model for Video Anomaly Detection

High-speed detection of abnormal frames in surveillance videos is essential for security. This paper proposes a new video anomaly-detection model, namely, feature trajectory-smoothed long short-term memory (FTS-LSTM). This model trains an LSTM autoencoder network to generate future frames on normal video streams, and uses the FTS detector and generation error (GE) detector to detect anomalies on testing video streams. FTS loss is a new indicator in the anomaly-detection area. In the training stage, the model applies a feature trajectory smoothness (FTS) loss to constrain the LSTM layer. This loss enables the LSTM layer to learn the temporal regularity of video streams more precisely. In the detection stage, the model utilizes the FTS loss and the GE loss as two detectors to detect anomalies. By cascading the FTS detector and the GE detector to detect anomalies, the model achieves a high speed and competitive anomaly-detection performance on multiple datasets.
Artificial Intelligence Implications in Engineering and Production

Humans have undergone technological innovation in their daily personal lives as a result of the invention of the computer, but, afterward, the web, as well as we, carry on living in a technologically advanced world. Artificial intelligence (AI) is a term that we always keep hearing among computer scientists because we are all aware that it is a fast-expanding discipline in a multitude of areas. Numerous problems, including dynamic, unpredictable behaviors, and intricacy, are present in the architecture of research and production tools and procedures. Several research and engineering experts&rsquo; working methods have recently changed as a result of the development of larger datasets, fast processing speed, cloud technology, and artificial intelligence procedures. For researchers and producers, such techniques provide exhilarating, cutting-edge solutions to difficult problems. In contrast, artificial intelligence (AI) is a broad field. The availability of a wide range of concepts, strategies, and techniques makes it difficult to select the best AI approach for the correct technical or production industry and settings. Our examination of the works on numerous Automatic industrial claims and production through using suggested classification provided valuable understanding. Additionally, we identified plans for potential AI application investigation in the fields of engineering besides production.
WLiT: Windows and Linear Transformer for Video Action Recognition

The emergence of Transformer has led to the rapid development of video understanding, but it also brings the problem of high computational complexity. Previously, there were methods to divide the feature maps into windows along the spatiotemporal dimensions and then calculate the attention. There are also methods to perform down-sampling during attention computation to reduce the spatiotemporal resolution of features. Although the complexity is effectively reduced, there is still room for further optimization. Thus, we present the Windows and Linear Transformer (WLiT) for efficient video action recognition, by combining Spatial-Windows attention with Linear attention. We first divide the feature maps into multiple windows along the spatial dimensions and calculate the attention separately inside the windows. Therefore, our model further reduces the computational complexity compared with previous methods. However, the perceptual field of Spatial-Windows attention is small, and global spatiotemporal information cannot be obtained. To address this problem, we then calculate Linear attention along the channel dimension so that the model can capture complete spatiotemporal information. Our method achieves better recognition accuracy with less computational complexity through this mechanism. We conduct extensive experiments on four public datasets, namely Something-Something V2 (SSV2), Kinetics400 (K400), UCF101, and HMDB51. On the SSV2 dataset, our method reduces the computational complexity by 28% and improves the recognition accuracy by 1.6% compared to the State-Of-The-Art (SOTA) method. On the K400 and two other datasets, our method achieves SOTA-level accuracy while reducing the complexity by about 49%.
Automatic Extraction of Medication Mentions from Tweets—Overview of the BioCreative VII Shared Task 3 Competition

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.
Vehicle-Related Distance Estimation Using Customized YOLOv7

With the popularity of autonomous driving, the development of ADAS (Advanced Driver Assistance Systems), especially collision avoidance systems, has become an important branch in the field of deep learning. In the face of complex traffic environments, collision avoidance systems need to detect vehicles quickly and accurately in traffic distance to the vehicle in front. Against this background, in this paper, we aim at investigating how to build a fast and robust model for vehicle distance estimation. The theoretical insights are synthesized in the context of odometry and customized YOLOv7 based on what a conceptual framework is proposed. In this paper, KITTI is employed as the dataset for model training and testing. Being one of the pioneer works on distance estimation based on KITTI, the unique value of this research work lies in the first time using YOLOv7 with attention model as a distance estimation model and getting 4.253 on RMSE.
A Multifactor Comparative Assessment of Augmented Reality Frameworks in Diverse Computing Settings

Research and development on different augmented reality (AR) frameworks have come a long way when it comes to image tracking, object tracking, plane tracking and light estimation. However, there might be trade-offs and varying results obtained from different AR frameworks, depending on the use cases, and this is critical for consideration during immersive application development. Besides the current literature effort, this research proposes a multifactor comparative analysis of two core AR frameworks, which aims to analyze and evaluate ARKit and ARCore in diverse computing settings. This research developed a structural application which evaluated three major test parameters across ten devices spanning ARKit and ARCore. The first parameter relates to evaluating AR measurements using four different distance criteria. The second parameter evaluated resource utilization, relating to the central processing unit (CPU) and random access memory (RAM), while the last parameter evaluated plane detection based on light estimation. Findings conclude that ARKit is the preferable AR framework for AR measurement accuracy and reliability within the tested distance criteria. ARCore is the most optimized AR framework in terms of RAM utilization. Regarding plane detection based on light estimation, ARCore is the preferable choice under low lighting conditions, however, ARKit is the most suitable AR framework under adequate ambient lighting conditions. The findings of this research could guide future prototyping and immersive mobile application development within the context of the parameters used.
Attention U-Net Based on Bi-ConvLSTM and Its Optimization for Smart Healthcare

As an important part of cyber–physical–social intelligence, artificial intelligence (AI)-driven smart healthcare is committed to promoting the application of human–machine hybrid augmented intelligence in the medical field, including AI-assisted medical image analysis and lesion recognition. Among them, deep learning models represented by fully convolutional networks (FCNs) have achieved excellent performance in medical image segmentation. However, limited by the complex structure of segmentation networks and the inherently redundant characteristics of convolutional operation, the scale of these models is extremely large. To further promote the application of machine intelligence in the field of medical image analysis, we propose an attention U-Net based on Bi-ConvLSTM (AUBC-Net) for accurate segmentation of medical images in this article. Different from classical U-Net, the proposed model deals with the potential association between decoding features and encoding features by bidirectional convolution LSTM. Furthermore, for the inherent redundancy characteristics of FCNs, we propose a lightweight feature generation strategy and optimize the calculation process of Bi-ConvLSTM based on tensor multilinear algebra, which can greatly reduce the number of network parameters. In addition, we have conducted the image segmentation experiments on two benchmark medical datasets, and the experimental results demonstrate that the proposed model can not only achieve better performance than existing methods, but also effectively compress network parameters while ensuring performance, which greatly facilitates AI-driven smart medical applications.
Extended Object Tracking Using Random Matrix With Extension-Dependent Measurement Numbers

For extended object tracking (EOT), a fluctuating number of measurements are generated by a sensor at a time instant. In practice, the measurement number depends on the object extension, sensor resolution, and sensor-to-object geometry. Given the sensor resolution, the number, thus, contains information on the object state and extension. This article proposes a random-matrix approach to EOT utilizing this information to improve the performance of state and extension estimation. First, a Gamma-alike distribution of the measurement number is proposed to model the dependence of the number on sensor resolution and the object state and extension. This model also fits the random-matrix framework. Second, a Bayesian approach to jointly estimating the state and extension based on an extension-dependent number of measurements is derived. Facilitated by the form of the distribution, the derived approach has an analytical form and it can naturally reduce to an EOT approach without directly using measurement numbers. The proposed number model can also be incorporated with different random-matrix approaches. The effectiveness of the proposed approach is demonstrated by evaluation results using one simulation and two real-data experimental scenarios compared with existing random-matrix algorithms.
Video Quality Assessment Considering the Features of the Human Visual System

Nowadays, numerous video compression quality assessment metrics are available. Some of these metrics are “objective” and only tangentially represent how a human observer rates video quality. On the other hand, models of the human visual system have been shown to be effective at describing spatial coding. In this work we propose a new quality metric which extends the peak signal to noise ratio metric with features of the human visual system measured using modern LCD screens. We also analyse the current visibility models of the early visual system and compare the commonly used quality metrics with metrics containing data modelling human perception. We examine the Pearson’s linear correlation coefficient of the various video compression quality metrics with human subjective scores on videos from the publicly available Netflix data set. Of the metrics tested, our new proposed metric is found to have the most stable high performance in predicting subjective video compression quality.
S2CGNet: A Robust Aircraft Detector Based on the Sword-Shaped Component Geometry

Aircraft detection is a challenging task for remote sensing images. The anchor-based methods are of high complexity, and the keypoint-based detectors suffer the grouping difficulty. Some line-based models relying on local features are hindered by adhesion and disintegrity problems. Moreover, those detection representations rarely take into account the sword-shaped component geometric semantics (e.g., fuselage and the wing) of the aircraft itself, leading to being less robust and unfavorable for downstream tasks, such as ones needing the detailed size and shape of aircraft. Accordingly, we model the sword-shaped component geometry and propose S2CGNet, a more robust appearance-based aircraft detector. The sword attenuation mask (SAM) module is devised to encode a “sword-shaped mask” for each aircraft while exploring more robustness via the geometric surface embedding. The SAM can provide clearer borders to separate different aircraft more precisely. Besides, to address the instance disintegrity problem and further boost the quality of SAM, we propose an instance aware graph (IAG) module to jointly optimize the parameters of the fuselage/wing detection heads. Experimental results show that the performance of S2CGNet can reach the state-of-the-art (SOTA) level. Specifically, it achieves 98.5% in terms of AP50 on the combined dataset of Aircraft-KP and NWPU VHR-10, boosting 3.8% than the baseline. Besides, S2CGNet boosts the quality of detection results greatly, e.g., it yields a significant improvement of 21.3% on AP75 compared to the baseline. Furthermore, the generalization comparisons on the FAIR1M dataset strongly demonstrate the robustness of our model surpasses other oriented detectors by a large margin.
Anomaly Search Over Discrete Composite Hypotheses in Hierarchical Statistical Models

Detection of anomalies among a large number of processes is a fundamental task that has been studied in multiple research areas, with diverse applications spanning from spectrum access to cyber-security. Anomalous events are characterized by deviations in data distributions, and thus can be inferred from noisy observations based on statistical methods. In some scenarios, one can often obtain noisy observations aggregated from a chosen subset of processes. Such hierarchical search can further minimize the sample complexity while retaining accuracy. An anomaly search strategy should thus be designed based on multiple requirements, such as maximizing the detection accuracy; efficiency, be efficient in terms of sample complexity; and be able to cope with statistical models that are known only up to some missing parameters (i.e., composite hypotheses). In this paper, we consider anomaly detection with observations taken from a chosen subset of processes that conforms to a predetermined tree structure with partially known statistical model. We propose Hierarchical Dynamic Search (HDS), a sequential search strategy that uses two variations of the Generalized Log Likelihood Ratio (GLLR) statistic, and can be used for detection of multiple anomalies. HDS is shown to be order-optimal in terms of the size of the search space, and asymptotically optimal in terms of detection accuracy. An explicit upper bound on the error probability is established for the finite sample regime. In addition to extensive experiments on synthetic datasets, experiments have been conducted on the DARPA intrusion detection dataset, showing that HDS is superior to existing methods.