Reviewerly/exHarmony huggingface dataset:
Immersive Services over 5G and Beyond Mobile Systems

5G and beyond mobile systems target a plethora of emerging industrial and entertainment verticals that incur extra overhead to the network. These verticals are characterized by vigorous, continuous, and conflicting requirements that make the desired system's mission strenuous and more challenging. These verticals, such as autonomous driving, will accommodate immersive services, including virtual reality/augmented reality (VR/AR) and Holography services. Immersive services, in particular, have strict requirements for latency, throughput, and positioning. This article discusses VR-based remote services' potential, which occupies an important place among immersive services such as remote surgery, remote space control, and remote driving of Unmanned Aerial Vehicles (UAVs) or cars. Such services require an ultra-low Glass-to-Glass latency to avoid any failure and accidents when remotely controlling devices. We evaluate an immersive remote control service from the end-to-end communication perspectives using different camera devices that stream real-time 360° videos to a VR Head Mounted Device (HMD). The obtained results demonstrate the challenges of such service and the need for more advanced and optimized techniques, devices, and protocols to achieve less than 20 ms of Glass-to-Glass latency.
YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs

Performance of object detection models has been growing rapidly on two major fronts, model accuracy and efficiency. However, in order to map deep neural network (DNN) based object detection models to edge devices, one typically needs to compress such models significantly, thus compromising the model accuracy. In this paper, we propose a novel edge GPU friendly module for multi-scale feature interaction by exploiting missing combinatorial connections between various feature scales in existing state-of-the-art methods. Additionally, we propose a novel transfer learning backbone adoption inspired by the changing translational information flow across various tasks, designed to complement our feature interaction module and together improve both accuracy as well as execution speed on various edge GPU devices available in the market. For instance, YOLO-ReT with MobileNetV2×0.75 backbone runs real-time on Jetson Nano, and achieves 68.75 mAP on Pascal VOC and 34.91 mAP on COCO, beating its peers by 3.05 mAP and 0.91 mAP respectively, while executing faster by 3.05 FPS. Furthermore, introducing our multi-scale feature interaction module in YOLOv4-tiny and YOLOv4-tiny (3l) improves their performance to 41.5 and 48.1 mAP respectively on COCO, outperforming the original versions by 1.3 and 0.9 mAP.
OrderSketch: An Unbiased and Fast Sketch for Frequency Estimation of Data Streams

Estimating the frequency of each distinct item in data streams is a fundamental problem in data mining. The speed of existing algorithms is not fast enough, and at the same time, some algorithms improve accuracy through complex configuration, which is a heavy burden for users. To address this issue, we propose a new sketch, OrderSketch, which has a simple structure and operation that is effortless to understand and use. The OrderSketch is significantly faster than existing algorithms while maintaining high accuracy. We theoretically prove that OrderSketch can provide unbiased estimation and then give an error bound of our algorithm. To verify the effectiveness and efficiency of OrderSketch, we compare it with five other widely used and excellent performance algorithms. Experimental results show that OrderSketch has 3 times higher insertion speed compared with the state-of-the-art work. We have released our source codes at Github [1].
Fully Hyperbolic Graph Convolution Network for Recommendation

Recently, Graph Convolution Network (GCN) based methods have achieved outstanding performance for recommendation. These methods embed users and items in Euclidean space, and perform graph convolution on user-item interaction graphs. However, real-world datasets usually exhibit tree-like hierarchical structures, which make Euclidean space less effective in capturing user-item relationship. In contrast, hyperbolic space, as a continuous analogue of a tree-graph, provides a promising alternative. In this paper, we propose a fully hyperbolic GCN model for recommendation, where all operations are performed in hyperbolic space. Utilizing the advantage of hyperbolic space, our method is able to embed users/items with less distortion and capture user-item interaction relationship more accurately. Extensive experiments on public benchmark datasets show that our method outperforms both Euclidean and hyperbolic counterparts and requires far lower embedding dimensionality to achieve comparable performance.
SW_Qsim

Classical simulation of quantum computation plays a critical role in numerical studies of quantum algorithms and the validation of quantum devices. Here, we introduce SW_Qsim, a tensor-network-based quantum simulator, which is designed with a two-level parallel structure for efficient implementation on the many-core New Sunway Supercomputer. We propose a minimize-memory contraction path algorithm for rectangular quantum grids to reduce the memory overhead, and provide the memory-limited simulation capacity of SW26010pro. Moreover, tensor operations are carefully optimized on the SW processor to achieve high performance. We design a fault tolerance mechanism to improve the extreme-scale parallel stability. We benchmark SW_Qsim's simulation of RQCs up to 400-qubits, achieving near-linear strong and weak scaling with up to 28.75 million cores, far beyond the previous state of the art. Our work sheds light on the development of efficient quantum algorithms for use in the physical, chemical, and engineering science fields.
Privacy-aware detection framework to mitigate new-age phishing attacks

• Proposed new three-phase framework to detect and mitigate new-age phishing attacks by Cyber-criminals, adopting sophisticated tactics. Implemented Python-based lightweight secure DNS infrastructure framework on user systems and IoT devices . • Proposed unique phishing taxonomy that classifies phishing attacks based on new-age major methods being adopted by the cyber attackers. End users and imperfectly configured Internet of Things devices are the weakest links and present low-hanging targets to attack. In workplaces, online work and business are performed over insecure internet. Phishing presents privacy and safety threats to the personal credentials and identity of unsuspecting victims. Unaware untrained users end up as the main targets for the simplest methods of stealing credentials, identities, and personal details. Clever attackers do not just depend on tools and technologies; they have increased the use of targeted Phishing as the new-age attack vector. With increasing users and devices, attackers resort to building one-to-one relationships with unsuspecting victims. The authors present a new privacy-aware framework to detect and mitigate Phishing attacks against new-age tactics to achieve maximum impact against unsuspecting victims and the Internet of things. The authors proposed unique Phishing taxonomy. Results show the proposed framework is capable of identifying multiple Phishing attacks.
Nominal Group Grammar: System and Structure

In this introductory paper for this series of special issues, we review the development of nominal group description in SFL – focusing on its syntagmatic and paradigmatic modeling, and stepping through a number of the principles that underpin SFL description more broadly. We then explore a number of issues that have arisen in relation to nominal groups as more languages have been described in SFL, such as how to deal with structural markers that have typically been left aside in explicit SFL modeling, and how to reconcile logical and experiential perspectives on the nominal group. As part of this, the paper proposes analyses aimed at extending SFL again – in particular suggesting a clearer division of labor between the word rank and the group rank, and proposing a type of structure called 'subjacency' for non-iterative dependency structures. Our aim is to further promote SFL as an evolving model of language and to encourage researchers to further develop SFL descriptions of languages around the world.
An Entropy-Guided Reinforced Partial Convolutional Network for Zero-Shot Learning

Zero-Shot Learning (ZSL) aims to transfer learned knowledge from observed classes to unseen classes via semantic correlations. A promising strategy is to learn a global-local representation that incorporates global information with extra localities (i.e., small parts/regions of inputs). However, existing methods discover localities based on explicit features without digging into the inherent properties and relationships among regions. In this work, we propose a novel Entropy-guided Reinforced Partial Convolutional Network (ERPCNet), which extracts and aggregates localities progressively based on semantic relevance and visual correlations without human-annotated regions. ERPCNet uses reinforced partial convolution and entropy guidance; it not only discovers global-cooperative localities dynamically but also converges faster for policy gradient optimization. We conduct extensive experiments to demonstrate ERPCNet’s performance through comparisons with state-of-the-art methods under ZSL and Generalized Zero-Shot Learning (GZSL) settings on four benchmark datasets. We also show that ERPCNet is time efficient and explainable through visualization analysis.
A Cognitive Diagnostic Module Based on the Repair Theory for a Personalized User Experience in E-Learning Software

This paper presents a novel cognitive diagnostic module which is incorporated in e-learning software for the tutoring of the markup language HTML. The system is responsible for detecting the learners’ cognitive bugs and delivering personalized guidance. The novelty of this approach is that it is based on the Repair theory that incorporates additional features, such as student negligence and test completion times, in its diagnostic mechanism; also, it employs a recommender module that suggests students optimal learning paths based on their misconceptions using descriptive test feedback and adaptability of learning content. Considering the Repair theory, the diagnostic mechanism uses a library of error correction rules to explain the cause of errors observed by the student during the assessment. This library covers common errors, creating a hypothesis space in that way. Therefore, the test items are expanded, so that they belong to the hypothesis space. Both the system and the cognitive diagnostic tool were evaluated with promising results, showing that they offer a personalized experience to learners.
Comparing automated vs. manual data collection for COVID-specific medications from electronic health records

Data extraction from electronic health record (EHR) systems occurs through manual abstraction, automated extraction, or a combination of both. While each method has its strengths and weaknesses, both are necessary for retrospective observational research as well as sudden clinical events, like the COVID-19 pandemic. Assessing the strengths, weaknesses, and potentials of these methods is important to continue to understand optimal approaches to extracting clinical data. We set out to assess automated and manual techniques for collecting medication use data in patients with COVID-19 to inform future observational studies that extract data from the electronic health record (EHR).For 4,123 COVID-positive patients hospitalized and/or seen in the emergency department at an academic medical center between 03/03/2020 and 05/15/2020, we compared medication use data of 25 medications or drug classes collected through manual abstraction and automated extraction from the EHR. Quantitatively, we assessed concordance using Cohen's kappa to measure interrater reliability, and qualitatively, we audited observed discrepancies to determine causes of inconsistencies.For the 16 inpatient medications, 11 (69%) demonstrated moderate or better agreement; 7 of those demonstrated strong or almost perfect agreement. For 9 outpatient medications, 3 (33%) demonstrated moderate agreement, but none achieved strong or almost perfect agreement. We audited 12% of all discrepancies (716/5,790) and, in those audited, observed three principal categories of error: human error in manual abstraction (26%), errors in the extract-transform-load (ETL) or mapping of the automated extraction (41%), and abstraction-query mismatch (33%).Our findings suggest many inpatient medications can be collected reliably through automated extraction, especially when abstraction instructions are designed with data architecture in mind. We discuss quality issues, concerns, and improvements for institutions to consider when crafting an approach. During crises, institutions must decide how to allocate limited resources. We show that automated extraction of medications is feasible and make recommendations on how to improve future iterations.
Brick: Asynchronous Incentive-Compatible Payment Channels

Off-chain protocols (channels) are a promising solution to the scalability and privacy challenges of blockchain payments. Current proposals, however, require synchrony assumptions to preserve the safety of a channel, leaking to an adversary the exact amount of time needed to control the network for a successful attack. In this paper, we introduce Brick, the first payment channel that remains secure under network asynchrony and concurrently provides correct incentives. The core idea is to incorporate the conflict resolution process within the channel by introducing a rational committee of external parties, called wardens. Hence, if a party wants to close a channel unilaterally, it can only get the committee's approval for the last valid state. Additionally, Brick provides sub-second latency because it does not employ heavy-weight consensus. Instead, Brick uses consistent broadcast to announce updates and close the channel, a light-weight abstraction that is powerful enough to preserve safety and liveness to any rational parties. We formally define and prove for Brick the properties a payment channel construction should fulfill. We also design incentives for Brick such that honest and rational behavior aligns. Finally, we provide a reference implementation of the smart contracts in Solidity.
A Novel Attention-Based Convolution Neural Network for Human Activity Recognition

Human Activity Recognition (HAR) has been widely used for various applications, such as smart homes, healthcare, security and human-robot interaction. In this paper, a novel deep learning model is proposed based on different Convolution Neural Networks (CNN) and the attention mechanism for addressing the HAR problem by using sensory data from accelerometers and gyroscopes and providing high recognition accuracy. The architecture of the proposed model (named LGSTNet) is designed to be able to analyse and extract Local and Global Spatial-Temporal features from sensory data for HAR. The idea of LGSTNet is to segment an activity window into several sub-windows and join the Attention mechanism into an 2D CNN (named Attention-based 2D-CNN module) to precisely learn local spatial-temporal features from those sub-windows. Meanwhile, an 3D CNN (named 3D-CNN module) is designed to learn global spatial-temporal features from the whole window. Both comparison and ablation experiments are conducted on two real-world datasets, named <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WISDM</monospace> and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UCI-HAR</monospace> . By comparing LGSTNet to eight other well-known existing models and its variants on both datasets, LGSTNet achieves the best performance (over 0.8064 and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.9569\,\,{F}_{w}-\textit {Meansure}$ </tex-math></inline-formula> on <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WISDM</monospace> and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UCI-HAR</monospace> , respectively) and is also more robust.
Accuracy Evaluation of Touch Tasks in Commodity Virtual and Augmented Reality Head-Mounted Displays

An increasing number of consumer-oriented head-mounted displays (HMD) for augmented and virtual reality (AR/VR) are capable of finger and hand tracking. We report on the accuracy of off-the-shelf VR and AR HMDs when used for touch-based tasks such as pointing or drawing. Specifically, we report on the finger tracking accuracy of the VR head-mounted displays Oculus Quest, Vive Pro and the Leap Motion controller, when attached to a VR HMD, as well as the finger tracking accuracy of the AR head-mounted displays Microsoft HoloLens 2 and Magic Leap. We present the results of two experiments in which we compare the accuracy for absolute and relative pointing tasks using both human participants and a robot. The results suggest that HTC Vive has a lower spatial accuracy than the Oculus Quest and Leap Motion and that the Microsoft HoloLens 2 provides higher spatial accuracy than Magic Leap One. These findings can serve as decision support for researchers and practitioners in choosing which systems to use in the future.
Revisting Quantization Error in Face Alignment

Recently, heatmap regression models have become the mainstream in locating facial landmarks. To keep computation affordable and reduce memory usage, the whole procedure involves downsampling from the raw image to the output heatmap. However, how much impact will the quantization error introduced by downsampling bringƒ The problem is hardly systematically investigated among previous works. This work fills the blank and we are the first to quantitatively analyze the negative gain. The statistical results show the NME generated by quantization error is even larger than 1/3 of the SOTA item, which is a serious obstacle for making a new breakthrough in face alignment. To compensate for the impact of quantization effect, we propose a novel method, called Heatmap In Heatmap (HIH), which leverages two categories of heatmaps as label representation to encode the coordinate. And in HIH, the range of one heatmap represents a pixel of the other category of heatmap. Also, we even combine the face alignment with solutions of other fields to make a comparison. Extensive experiments on various benchmarks show the feasibility of HIH and superior performance than other solutions. Moreover, the mean error reaches to 4.18 on WFLW, which exceeds SOTA a lot.
Multi-heterogeneous neighborhood-aware for Knowledge Graphs alignment

Entity alignment is an important task for the Knowledge Graph (KG) completion, which aims to identify the same entities in different KGs. Most of previous works only utilize the relation structures of KGs, but ignore the heterogeneity of relations and attributes of KGs. However, these information can provide more feature information and improve the accuracy of entity alignment. In this paper, we propose a novel Multi-Heterogeneous Neighborhood-Aware model (MHNA) for KGs alignment. MHNA aggregates multi-heterogeneous information of aligned entities, including the entity name, relations, attributes and attribute values. An important contribution is to design a variant attention mechanism, which adds the feature information of relations and attributes to the calculation of attention coefficients. Extensive experiments on three well-known benchmark datasets show that MHNA significantly outperforms 12 state-of-the-art approaches, demonstrating that our approach has good scalability and superiority in both cross-language and monolingual KGs. An ablation study further supports the effectiveness of our variant attention mechanism.
A Comprehensive Review of One-stage Networks for Object Detection

Object detection has always been a hot topic in image processing, which is important in a variety of applications. With the advent of the era of big data and the continuous improvement of hardware computing power, deep learning gets more attention in object detection. One popular branch is regression-based (One-stage) model, which uses a single neural network to directly predict bounding boxes and class probabilities from the entire image by one evaluation. One-stage networks can effectively increase the detection speed. This article mainly describes object detection methods based on regression object detectors (One-stage methods), such as You Only Look Once (YOLO) series and Single Shot Multibox Detector (SSD) series. Then, their applications are briefly introduced. The development trend and future development direction of this type of object detection are discussed in the end.
Human Activity Recognition Using 1D Convolutional Neural Network

Humans are more oriented toward innovative research objectives to recognize objects and understand the environment, evaluating time series and forecasting outcomes patterns due to rapid artificial intelligence (AI) advances. Human activity recognition (HAR) is a domain that targets on recognizing, interpreting, and evaluating human movement behavior. HAR has significantly benefitted from deep learning. Despite its immense potential, deep learning models face significant challenges, such as the need for a large dataset for training in the real world. On the other hand, the current study needs to be improved to distinguish static and dynamic behavior with more remarkable achievements. Our main goal is to use one-dimensional convolutional neural network (1D CNN) to create a system that can identify movements such as sitting, standing, walking, sleeping, reading, and tilting, and also we are trying to reduce the time optimization for training the neural network. Human–computer interaction (HCI) mechanization is becoming more in demand for recording behaviors using sensors like gyroscope and accelerometer, and HAR can be done with sensors, images, smartphones, or clips. This paper introduces a technique that employs a 1D CNN to expecting human behavior that is based totally on the dataset given, and we have got finished 90.73% accuracy.
Multisample-Based Contrastive Loss for Top-K Recommendation

Top-k recommendation is a fundamental task in recommendation systems that is generally learned by comparing positive and negative pairs. The contrastive loss (CL) is the key in contrastive learning that has recently received more attention, and we find that it is well suited for top-k recommendations. However, CL is problematic because it treats the importance of the positive and negative samples the same. On the one hand, CL faces the imbalance problem of one positive sample and many negative samples. On the other hand, there are so few positive items in sparser datasets that their importance should be emphasized. Moreover, the other important issue is that the sparse positive items are still not sufficiently utilized in recommendations. Consequently, we propose a new data augmentation method by using multiple positive items (or samples) simultaneously with the CL loss function. Therefore, we propose a multisample-based contrastive loss (MSCL) function that solves the two problems by balancing the importance of positive and negative samples and data augmentation. Based on the graph convolution network (GCN) method, experimental results demonstrate the state-of-the-art performance of MSCL. The proposed MSCL is simple and can be applied in many methods. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/haotangxjtu/MSCL</uri> .