Reviewerly/exHarmony huggingface dataset:
Multi-object tracking with robust object regression and association

Tracking-by-regression is a new paradigm for online Multi-Object Tracking (MOT). It unifies detection and tracking into a single network by associating targets through regression, significantly reducing the complexity of data association. However, owing to noisy features from nearby occlusions and distractors, the regression is vulnerable and unaware of the inter-object occlusions and intra-class distractors. Thus the regressed bounding boxes can be wrongly suppressed or easily drift. Meanwhile, the commonly used bounding box-based post-processing is unable to remedy false negatives and false assignments caused by regression. To address these challenges, we present to leverage regression tubes as input for the regression-based tracker, which provides spatial–temporal information to enhance the tracking performance. Specially, we propose a novel tube re-localization strategy that obtains robust regressions and recovers missed targets. A tube-based NMS (T-NMS) strategy to manage the regressions at the tube level is also proposed, including a tube IoU (T-IoU) scheme for measuring positional relation and tube re-scoring (T-RS) to evaluate the quality of candidate tubes. Finally, a tube re-assignment strategy is further employed for robust cost measurement and to revise false assignments using motion cues. We evaluate our method on benchmarks, including MOT16, MOT17, and MOT20. The results show that our method can significantly improve the baseline, mitigate the challenges of the regression-based tracker, and achieve very competitive tracking performance.
WITHDRAWN: Auction, Learning and Bargaining based Control Scheme for Edge assisted Metaverse System

This article has been withdrawn at the request of the author. The Publisher apologizes for any inconvenience this may cause The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies/article-withdrawal.
AnimalTrack: A Benchmark for Multi-Animal Tracking in the Wild

Multi-animal tracking (MAT), a multi-object tracking (MOT) problem, is crucial for animal motion and behavior analysis and has many crucial applications such as biology, ecology and animal conservation. Despite its importance, MAT is largely under-explored compared to other MOT problems such as multi-human tracking due to the scarcity of dedicated benchmarks. To address this problem, we introduce AnimalTrack, a dedicated benchmark for multi-animal tracking in the wild. Specifically, AnimalTrack consists of 58 sequences from a diverse selection of 10 common animal categories. On average, each sequence comprises of 33 target objects for tracking. In order to ensure high quality, every frame in AnimalTrack is manually labeled with careful inspection and refinement. To our best knowledge, AnimalTrack is the first benchmark dedicated to multi-animal tracking. In addition, to understand how existing MOT algorithms perform on AnimalTrack and provide baselines for future comparison, we extensively evaluate 14 state-of-the-art representative trackers. The evaluation results demonstrate that, not surprisingly, most of these trackers become degenerated due to the differences between pedestrians and animals in various aspects (e.g., pose, motion, and appearance), and more efforts are desired to improve multi-animal tracking. We hope that AnimalTrack together with evaluation and analysis will foster further progress on multi-animal tracking. The dataset and evaluation as well as our analysis will be made available upon the acceptance.
Deep Learning for Victims Detection from Virtual and Real Search and Rescue Environments

Robotic interventions in post-disaster environments to carry out search and rescue explorations allow optimizing time for identifying victims and safeguarding the rescuer’s integrity. The rising in the neural networks field and their application in image detection algorithms have made it possible to facilitate the early detection of victims in these first phases of exploration. This article analyses the effectiveness of applying neural network models obtained from training with different datasets of both: images captured in real environments and synthetic images from recreated virtual post-disaster environments for detecting victims. For this development, tests have been carried out in environments at the ETSII-Universidad Politécnica de Madrid, generating models from images obtained with the ARTU-R robot (A1 Rescue Task UPM Robot) and have been validated with real search and rescue exercises at the University of Malaga. The main results show that the models obtained from virtual environments apply to real ones.
From Augmented Reality to Deep Learning-Based Cognitive Assistance: An Overview for Industrial Wire Harnesses Assemblies

The wire harness assembly process is a typical manufacturing work. Such activity is becoming more complex as transport systems evolve to more digitalized and driven by electric motors. The complexity of the tasks carried out in the harnessing operation is in most cases related to a high degree of manual work. This paper presents an overview of the different industrial work case scenarios and shows the advantages of traditional Augmented Reality technology as an effective tool to reduce workers’ cognitive overload. Furthermore, the paper presents an overview of the state of the art in approaches dedicated to understanding the harnessing task through deep learning-based segmentation methods. Moreover, the document presents a summary of publicly available datasets and techniques for training such semantic segmentation. To conclude, the fusion of Augmented Reality tools and semantic segmentation systems is postulated, to reduce both errors and cognitive overload of harnessing workers.
Familial concentration of crime in a digital era: Criminal behavior among family members of cyber offenders

A vast and growing body of research has shown that crime tends to run in families. However, previous studies focused only on traditional crimes and research on familial risk factors for cyber offending is very scarce. To address this gap in the literature, the present study examines the criminal behavior of the family members of a sample of cyber offenders prosecuted in the Netherlands. The sample consists of 979 cyber offenders prosecuted for computer trespassing between 2001 and 2018, and two matched groups of 979 traditional offenders and 979 non-offenders. Judicial information and kinship data from Dutch Statistics were used to measure criminal behavior among family members. Both traditional offenders and cyber offenders were found to be more likely to have criminal fathers, mothers, and siblings than non-offenders. Additional analyses, however, showed different patterns between cyber offenders who were only prosecuted for cyber offenses and those who also committed traditional crimes. While the former group of cyber offenders were similar to non-offenders in terms of family offending, the latter group of cyber offenders were more similar to traditional offenders. Overall, these results suggest that the traditional mechanisms of intergenerational transmission of crime can only partially explain cybercrime involvement.
SCGG: A deep structure-conditioned graph generative model

Deep learning-based graph generation approaches have remarkable capacities for graph data modeling, allowing them to solve a wide range of real-world problems. Making these methods able to consider different conditions during the generation procedure even increases their effectiveness by empowering them to generate new graph samples that meet the desired criteria. This paper presents a conditional deep graph generation method called SCGG that considers a particular type of structural conditions. Specifically, our proposed SCGG model takes an initial subgraph and autoregressively generates new nodes and their corresponding edges on top of the given conditioning substructure. The architecture of SCGG consists of a graph representation learning network and an autoregressive generative model, which is trained end-to-end. More precisely, the graph representation learning network is designed to compute continuous representations for each node in a graph, which are not only affected by the features of adjacent nodes, but also by the ones of farther nodes. This network is primarily responsible for providing the generation procedure with the structural condition, while the autoregressive generative model mainly maintains the generation history. Using this model, we can address graph completion, a rampant and inherently difficult problem of recovering missing nodes and their associated edges of partially observed graphs. The computational complexity of the SCGG method is shown to be linear in the number of graph nodes. Experimental results on both synthetic and real-world datasets demonstrate the superiority of our method compared with state-of-the-art baselines.
Query Optimizer as a Service

Query optimization is a critical technology that is common across all modern data processing systems. However, it is traditionally implemented in silos and is deeply embedded in different systems. Furthermore, over the years, query optimizers have become less understood and rarely touched pieces of code that are brittle to changes and very expensive to maintain, thus slowing down the pace of innovation. In this paper, we argue that it is time to think of query optimizer as a service in modern cloud architectures. Such a design can help build a common set of well-maintained optimizations that are externalized from the query engines and that could be learned and improved using the large workloads present in modern clouds. We present, Oasis, a reference architecture for query optimizer as a service and describe our success in deploying the early version of it in Cosmos. Finally, we discuss the risks and responsibilities involved with Oasis to ensure it is a win-win for everyone.
Leveraging multimodal sensory information in cybersickness prediction

Cybersickness is one of the problems that undermines user experience in virtual reality. While many studies are trying to find ways to alleviate cybersickness, only a few have considered cybersickness through multimodal perspectives. In this paper, we propose a multimodal, attention-based cybersickness prediction model. Our model was trained based on a total of 24,300 seconds of data from 27 participants and yielded the F1-score of 0.82. Our study results highlight the potential to model cybersickness from multimodal sensory information with a high level of performance and suggest that the model should be extended using additional, diverse samples.
Precueing Sequential Rotation Tasks in Augmented Reality

Augmented reality has been used to improve sequential-task performance by cueing information about a current task step and precueing information about future steps. Existing work has shown the benefits of precueing movement (translation) information. However, rotation is also a major component in many real-life tasks, such as turning knobs to adjust parameters on a console. We developed an AR testbed to investigate whether and how much precued rotation information can improve user performance. We consider two unimanual tasks: one requires a user to make sequential rotations of a single object, and the other requires the user to move their hand between multiple objects to rotate them in sequence.
State-of-the-Art Evidence Retriever for Precision Medicine: Algorithm Development and Validation

Under the paradigm of precision medicine (PM), patients with the same disease can receive different personalized therapies according to their clinical and genetic features. These therapies are determined by the totality of all available clinical evidence, including results from case reports, clinical trials, and systematic reviews. However, it is increasingly difficult for physicians to find such evidence from scientific publications, whose size is growing at an unprecedented pace.In this work, we propose the PM-Search system to facilitate the retrieval of clinical literature that contains critical evidence for or against giving specific therapies to certain cancer patients.The PM-Search system combines a baseline retriever that selects document candidates at a large scale and an evidence reranker that finely reorders the candidates based on their evidence quality. The baseline retriever uses query expansion and keyword matching with the ElasticSearch retrieval engine, and the evidence reranker fits pretrained language models to expert annotations that are derived from an active learning strategy.The PM-Search system achieved the best performance in the retrieval of high-quality clinical evidence at the Text Retrieval Conference PM Track 2020, outperforming the second-ranking systems by large margins (0.4780 vs 0.4238 for standard normalized discounted cumulative gain at rank 30 and 0.4519 vs 0.4193 for exponential normalized discounted cumulative gain at rank 30).We present PM-Search, a state-of-the-art search engine to assist the practicing of evidence-based PM. PM-Search uses a novel Bidirectional Encoder Representations from Transformers for Biomedical Text Mining-based active learning strategy that models evidence quality and improves the model performance. Our analyses show that evidence quality is a distinct aspect from general relevance, and specific modeling of evidence quality beyond general relevance is required for a PM search engine.
Effect of learning parameters on the performance of the U-Net architecture for cell nuclei segmentation from microscopic cell images

Nuclei segmentation of cells is the preliminary and essential step of pathological image analysis. However, robust and accurate cell nuclei segmentation is challenging due to the enormous variability of staining, cell sizes, morphologies, cell adhesion or overlapping of the nucleus. The automation process to find the cell's nuclei is a giant leap in this direction and has an important step toward bioimage analysis using software tools. This article extensively analyzes deep U-Net architecture and has been applied to the Data Science Bowl dataset to segment the cell nuclei. The dataset undergoes various preprocessing tasks such as resizing, intensity normalization and data augmentation prior to segmentation. The complete dataset then undergoes the rigorous training and validation process to find the optimized hyperparameters and then the optimized model selection. The mean (m) ± standard deviation (SD) of Intersection over Union (IoU) and F1-score (Dice score) have been calculated along with accuracy during the training and validation process, respectively. The optimized U-Net model results in a training IoU of 0.94 ± 0.16 (m ± SD), an F1-score of 0.94 ± 0.17 (m ± SD), a training accuracy of 95.54 and validation accuracy of 95.45. With this model, we applied a completely independent test cohort of the dataset and obtained the mean IOU of 0.93, F1-score of 0.9311, and mean accuracy of 94.12, respectively to measure the segmentation performance.
On the Discoverability of npm Vulnerabilities in Node.js Projects

The reliance on vulnerable dependencies is a major threat to software systems. Dependency vulnerabilities are common and remain undisclosed for years. However, once the vulnerability is discovered and publicly known to the community, the risk of exploitation reaches its peak, and developers have to work fast to remediate the problem. While there has been a lot of research to characterize vulnerabilities in software ecosystems, none have explored the problem taking the discoverability into account. Therefore, we perform a large-scale empirical study examining 6,546 Node.js applications. We define three discoverability levels based on vulnerabilities lifecycle (undisclosed, reported, and public). We find that although the majority of the affected applications (99.42%) depend on undisclosed vulnerable packages, 206 (4.63%) applications were exposed to dependencies with public vulnerabilities. The major culprit for the applications being affected by public vulnerabilities is the lack of dependency updates; in 90.8% of the cases, a fix is available but not patched by application maintainers. Moreover, we find that applications remain affected by public vulnerabilities for a long time (103 days). Finally, we devise DepReveal, a tool that supports our discoverability analysis approach, to help developers better understand vulnerabilities in their application dependencies and plan their project maintenance.
VLH team at ALQAC 2022: Retrieving legal document and extracting answer with BERT-based model

Legal AI is a growing subfield of AI that deals with the application of AI techniques to legal problems. In legal AI, there are many different tasks that can be performed, such as legal retrieval, legal question answering, and legal document generation. In this paper, we describe our approach to the legal retrieval and legal question answering tasks of the Automated Legal Question Answering Competition (ALQAC) 2022. For legal retrieval, we use a combination of lexical matching with deep learning models. For legal question answering, we treat the inputs in multiple aspects and come up with different strategies to answer the questions. Our system achieves impressive results in both tasks.
Enhanced Task-based Knowledge for Lexicon-based Approach in Vietnamese Hate Speech Detection

The explosion of free-text content on social media has brought the exponential propagation of hate speech. The definition of hate speech is well-defined in the community guidelines of many popular platforms such as Facebook, Tiktok, and Twitter, where any communication judges towards the minor, protected groups are considered hateful content. This paper first points out the sophisticated word-play of malicious users in a Vietnamese Hate Speech (VHS) Dataset. The Center Loss in the training process to disambiguate the task-based sentence embedding is proposed for improving generalizations of the model. Moreover, a task-based lexical attention pooling is also proposed to highlight lexicon-level information and then combined into sentence embedding. The experimental results show that the proposed method improves the F1 score in the ViHSD dataset, while the training time and inference speed are insignificantly changed.
Pulse coupled neural network optimized with chaotic grey wolf algorithm for breast cancer classification using mammogram images

Summary Breast cancer is a very hazardous disease that mainly affects women and leads to a high mortality rate. Early detection of this disease only can reduce the mortality rate. Previously, several methods were used to detect this cancer, but none of them provides sufficient accuracy. To deal this issue, a pulse coupled neural network optimized with chaotic grey wolf algorithm is proposed in this article for the classification of breast cancer using mammogram images. The breast cancer images are gathered from MAMMOSET dataset. The images are preprocessed and segmented with the help of Tsalli's entropy based multilevel 3D Otsu (TE‐3D‐Otsu) thresholding method. Then the parameters of the TE‐3D‐Otsu are optimized using the improved shuffled shepherd optimization algorithm. The features are extracted using moment invariant wavelet feature extraction technique. The images are classified using pulse coupled neural network (PCNN). Then, PCNN mass parameters are optimizes utilizing chaotic grey wolf optimization algorithm (chaotic‐GWOA) for categorizing the mammogram breast cancer imageries, such as malignant, benign, and normal. The proposed method is activated on MATLAB, its performance is analyzed with existing methods. The simulation results show that the proposed BC‐PCNN‐CGWOA attains the accuracy 57.86%, 85.94%, and 53.45% is greater than the existing methods.
A Certificateless Anonymous Cross-Domain Authentication Scheme Assisted by Blockchain for Internet of Vehicles

With the development of the Internet of Things and the increase of intelligent vehicles, the Internet of Vehicles (IoVs) have been widely used in the information communication such as road and traffic conditions. However, heavy overhead of certificate management, high computing load of identity and message authentication, and the privacy disclosure of vehicle nodes have hindered the development of intelligent transportation. In this study, we propose a certificateless cross-domain anonymous authentication scheme based on blockchain for IoVs. Specifically, the vehicle identity information is authenticated by the first roadside unit (RSU), and transactions are recorded permanently and immutably in the blockchain to reduce the repeated authentication load of other RSUs. To achieve conditional privacy, the trusted authority (TA) generates pseudonyms for each registered user. The relation between the pseudonym and the real identity is kept confidential by the TA and only can only be revealed in case of disputes. Meanwhile, the private key of the vehicle is generated anonymously on the basis of certificateless technology and the pairing-free signature verification. Correctness and security proof demonstrate that our proposed scheme is provably secure and can withstand different types of attacks. A simulation environment has been built to test the packet loss rate and delay of messages in the network. Results show that the proposed scheme is more efficient than the related schemes.
Model-predictive control for dispatch planning of concentrating solar power plants under real-time spot electricity prices

The aim at dispatch scheduling for concentrating solar power plants is to utilize thermal storage to maximize profit from electricity generation. The dispatching plan, however, must contend with significant uncertainty in both weather forecasts (particularly solar irradiation) and, in some locations, real-time electricity prices governed by potentially volatile markets. This paper proposes a Model-Predictive Control (MPC) which employs a novel similarity-based forecast for weather variables and exploits the predictions of electricity prices provided by the market operator. A Monte-Carlo simulation is developed to evaluate true performance of the proposed MPC against two benchmarks, with perfect knowledge (PK) forecasts and day-ahead optimized using prototypical weather (DAPW). A case study is performed on a 115 MW Solar Tower plant with 8-hour storage, hypothetically located in South Australia, and operates under either a fixed-price or a real-time spot price scenario. A Monte-Carlo simulation is conducted for 150 tests for January (summer) and August (winter). The results show in fixed-price scenario MPC achieves 82.4 % of optimal profit (i.e. obtained via PK) in January and 72.4 % in August whereas that in DAPW falls to 71.5 % and 56.9 % of optimal, respectively. In the spot market scenario, MPC reaches 71.3 % and 63.6 % of optimal profit in January and August, whereas that for DAPW reaches 61.4 % and 55.1 %, respectively. In conclusion, PK forecast assumption in dispatch planning over-estimates the achievable profit by ∼ 28–40 % particularly in spot market scenario. Moreover, MPC can mitigate the influence of uncertainties on the plant economic performance by 8.5 %–15.5 % compared to DAPW benchmarks.
The effects of gamification method and cognitive style on children’s early reading ability

Fun and pervasive education into students' daily lives is an important part of children's development. Learning methods and cognitive styles are one of the important aspects of the dynamics of learning. This research was a quasi-experimental study using the pretest-posttest of a nonequivalent control group design. Therefore, research based on these is expected to impact students' early reading ability. There are 20 students in the experimental and 20 students in the control classes. The age range is 5–6 years, with heterogeneous gender. The experimental group used the gamification method, while the control group used the Beyond Center and Circle Times (BCCT) method. The gamification method and students with field independence cognitive style are proven to have superiority in improving early reading ability.