Reviewerly/exHarmony huggingface dataset:
Online multi-object tracking with unsupervised re-identification learning and occlusion estimation

Occlusion between different objects is a typical challenge in Multi-Object Tracking (MOT), which often leads to inferior tracking results due to the missing detected objects. The common practice in multi-object tracking is re-identifying the missed objects after their reappearance. Though tracking performance can be boosted by the re-identification, the annotation of identity is required to train the model. In addition, such practice of re-identification still can not track those highly occluded objects when they are missed by the detector. In this paper, we focus on online multi-object tracking and design two novel modules, the unsupervised re-identification learning module and the occlusion estimation module, to handle these problems. Specifically, the proposed unsupervised re-identification learning module does not require any (pseudo) identity information nor suffer from the scalability issue. The proposed occlusion estimation module tries to predict the locations where occlusions happen, which are used to estimate the positions of missed objects by the detector. Our study shows that, when applied to state-of-the-art MOT methods, the proposed unsupervised re-identification learning is comparable to supervised re-identification learning, and the tracking performance is further improved by the proposed occlusion estimation module.
What Is Metaverse?—A Definition Based on Qualitative Meta-Synthesis

The term Metaverse has received much attention in various industries, in society, and, increasingly, in scientific communities. This creates demand for a comprehensive and broadly accepted definition of the subject, which is well grounded in research. At the same time, the Metaverse is rather a vision under evolution than an examinable phenomenon. Therefore, this study applied an adapted version of the meta-synthesis method to analyze the existing literature and distill a proposal for a Metaverse definition. The adapted method takes the nature of the subject into account by weighing younger publications with many citations over older, less influential documents. Initially, 47 publications were fed into the process, of which 24 were left after the primary analysis. As a result of the analysis, the following definition is proposed: “The Metaverse is an interconnected web of ubiquitous virtual worlds partly overlapping with and enhancing the physical world. These virtual worlds enable users represented by avatars to connect and interact with each other, to experience and consume user-generated content in an immersive, scalable, synchronous and persistent environment. An economic system provides incentives for contributing to the Metaverse.” The meta-synthesis method ensures a systematic and reproducible approach while at the same time preserving the original voice and notion of the analyzed literature in order to create new knowledge based on the existing literature; thus the proposed Metaverse definition might serve as a helpful foundation for future research.
A Novelty Detector and Extreme Verification Latency Model for Nonstationary Environments

Safe and reliable operation of systems relies on the use of online condition monitoring and diagnostic systems that aim to take immediate actions upon the occurrence of a fault. Model-based solutions are often not practical in nonstationary environments. Thus, the evolving data stream requires the data-driven model to be adaptive. In this paper, we propose a framework for the fault detection and classification that is accomplished on the data stream with both the gradual and abrupt drifts. The framework is only provided with prior information about the possible faults at the initial step; however, despite this, the framework can still detect the novel faults without receiving any update. Furthermore, an efficient fault classification algorithm is presented to maximize the efficiency of the proposed framework. Finally, the proposed framework is applied for diagnosing bearing defects in the induction motors to demonstrate its feasibility for industrial applications.
Adversarial Action Prediction Networks

Different from after-the-fact action recognition, action prediction task requires action labels to be predicted from partially observed videos containing incomplete action executions. It is challenging because these partial videos have insufficient discriminative information, and their temporal structure is damaged. We study this problem in this paper, and propose an efficient and powerful deep network for learning representative and discriminative features for action prediction. Our approach exploits abundant sequential context information in full videos to enrich the feature representations of partial videos. This information is encoded in latent representations using a variational autoencoder (VAE), which are encouraged to be progress-invariant. Decoding such latent representations using another VAE, we can reconstruct missing information in the features extracted from partial videos. An adversarial learning scheme is adopted to differentiate the reconstructed features from the features directly extracted from full videos in order to well align their distributions. A multi-class classifier is also used to encourage the features to be discriminative. Our network jointly learns features and classifiers, and generates the features particularly optimized for action prediction. Extensive experimental results on UCF101, Sports-1M and BIT datasets demonstrate that our approach remarkably outperforms state-of-the-art methods, and shows significant speedup over these methods. Results also show that actions differ in their prediction characteristics; some actions can be correctly predicted even though only the beginning 10% portion of videos is observed.
Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data

Open data plays a fundamental role in the 21st century by stimulating economic growth and by enabling more transparent and inclusive societies. However, it is always difficult to create new high-quality datasets with the required privacy guarantees for many use cases. In this paper, we developed a differential privacy framework for privacy preserving data publishing using Generative Adversarial Networks. It can be easily adapted to different use cases, from the generation of time-series, to continuous, and discrete data. We demonstrate the efficiency of our approach on real datasets from the French public administration and classic benchmark datasets. Our results maintain both the original distribution of the features and the correlations among them, at the same time providing a good level of privacy.
A Memetic Chaotic Gravitational Search Algorithm for unconstrained global optimization problems

Metaheuristic optimization algorithms address two main tasks in the process of problem solving: i) exploration (also called diversification) and ii) exploitation (also called intensification). Guaranteeing a trade-off between these operations is critical to good performance. However, although many methods have been proposed by which metaheuristics can achieve a balance between the exploration and exploitation stages, they are still worse than exact algorithms at exploitation tasks, where gradient-based mechanisms outperform metaheuristics when a local minimum is approximated. In this paper, a quasi-Newton method is introduced into a Chaotic Gravitational Search Algorithm as an exploitation method, with the purpose of improving the exploitation capabilities of this recent and promising population-based metaheuristic. The proposed approach, referred to as a Memetic Chaotic Gravitational Search Algorithm, is used to solve forty-five benchmark problems, both synthetic and real-world, to validate the method. The numerical results show that the adding of quasi-Newton search directions to the original (Chaotic) Gravitational Search Algorithm substantially improves its performance. Also, a comparison with the state-of-the-art algorithms: Particle Swarm Optimization, Genetic Algorithm, Rcr-JADE, COBIDE and RLMPSO, shows that the proposed approach is promising for certain real-world problems.
Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation

We present a simple yet powerful data augmentation method for boosting Neural Machine Translation (NMT) performance by leveraging information retrieved from a Translation Memory (TM).We propose and test two methods for augmenting NMT training data with fuzzy TM matches.Tests on the DGT-TM data set for two language pairs show consistent and substantial improvements over a range of baseline systems.The results suggest that this method is promising for any translation environment in which a sizeable TM is available and a certain amount of repetition across translations is to be expected, especially considering its ease of implementation.
Human–Robot–Environment Interaction Interface for Smart Walker Assisted Gait: AGoRA Walker

The constant growth of the population with mobility impairments has led to the development of several gait assistance devices. Among these, smart walkers have emerged to provide physical and cognitive interactions during rehabilitation and assistance therapies, by means of robotic and electronic technologies. In this sense, this paper presents the development and implementation of a human–robot–environment interface on a robotic platform that emulates a smart walker, the AGoRA Walker. The interface includes modules such as a navigation system, a human detection system, a safety rules system, a user interaction system, a social interaction system and a set of autonomous and shared control strategies. The interface was validated through several tests on healthy volunteers with no gait impairments. The platform performance and usability was assessed, finding natural and intuitive interaction over the implemented control strategies.
Explanations can be manipulated and geometry is to blame

Explanation methods aim to make neural networks more trustworthy and interpretable. In this paper, we demonstrate a property of explanation methods which is disconcerting for both of these purposes. Namely, we show that explanations can be manipulated arbitrarily by applying visually hardly perceptible perturbations to the input that keep the network's output approximately constant. We establish theoretically that this phenomenon can be related to certain geometrical properties of neural networks. This allows us to derive an upper bound on the susceptibility of explanations to manipulations. Based on this result, we propose effective mechanisms to enhance the robustness of explanations.
Write-a-video

We present Write-A-Video , a tool for the creation of video montage using mostly text-editing. Given an input themed text and a related video repository either from online websites or personal albums, the tool allows novice users to generate a video montage much more easily than current video editing tools. The resulting video illustrates the given narrative, provides diverse visual content, and follows cinematographic guidelines. The process involves three simple steps: (1) the user provides input, mostly in the form of editing the text, (2) the tool automatically searches for semantically matching candidate shots from the video repository, and (3) an optimization method assembles the video montage. Visual-semantic matching between segmented text and shots is performed by cascaded keyword matching and visual-semantic embedding, that have better accuracy than alternative solutions. The video assembly is formulated as a hybrid optimization problem over a graph of shots, considering temporal constraints, cinematography metrics such as camera movement and tone, and user-specified cinematography idioms. Using our system, users without video editing experience are able to generate appealing videos.
Why ADAM Beats SGD for Attention Models

While stochastic gradient descent (SGD) is still the de facto algorithm indeep learning, adaptive methods like Adam have been observed to outperform SGDacross important tasks, such as attention models. The settings under which SGDperforms poorly in comparison to Adam are not well understood yet. In thispaper, we provide empirical and theoretical evidence that a heavy-taileddistribution of the noise in stochastic gradients is a root cause of SGD's poorperformance. Based on this observation, we study clipped variants of SGD thatcircumvent this issue; we then analyze their convergence under heavy-tailednoise. Furthermore, we develop a new adaptive coordinate-wise clippingalgorithm (ACClip) tailored to such settings. Subsequently, we show howadaptive methods like Adam can be viewed through the lens of clipping, whichhelps us explain Adam's strong performance under heavy-tail noise settings.Finally, we show that the proposed ACClip outperforms Adam for both BERTpretraining and finetuning tasks.
Blockchain and Internet of Things in Higher Education

Building the innovative blockchain-based architecture across the Internet of Things (IoT) platform for the education system could be an enticing mechanism to boost communication efficiency among all participants within the 5G network.Wireless networking would have been the main research area allowing people to communicate without using the wires.It was established at the start of the Internet by retrieving the web pages to connect from one computer to another.Moreover, high-speed, intelligent, powerful networks with numerous contemporary technologies, such as low power consumption, and so on, appear to be available in today's world to connect among each other.The cloud features on physical things under IoT is allowed to store and process IoT and Blockchain data in any situation.One of the complex tasks throughout the area of mobile communications would be to design a new virtualization framework based on blockchain across the Internet of Things architecture.The goal of this research is to connect a new study for an educational system that contains Blockchain to the internet of things or keeping things cryptographically secure on the Internet.This research combines with its improved blockchain and IoT to create an efficient interaction system among students, teachers, employers, developers, facilitators, recruiters, and accreditors on the Internet.This specified framework is detailed research's great estimation.
Probability Transform Based on the Ordered Weighted Averaging and Entropy Difference

Dempster-Shafer evidence theory can handle imprecise and unknown information, which has attracted many people. In most cases, the mass function can be translated into the probability, which is useful to expand the applications of the D-S evidence theory. However, how to reasonably transfer the mass function to the probability distribution is still an open issue. Hence, the paper proposed a new probability transform method based on the ordered weighted averaging and entropy difference. The new method calculates weights by ordered weighted averaging, and adds entropy difference as one of the measurement indicators. Then achieved the transformation of the minimum entropy difference by adjusting the parameter r of the weight function. Finally, some numerical examples are given to prove that new method is more reasonable and effective.
Temporal Common Sense Acquisition with Minimal Supervision

Temporal common sense (e.g., duration and frequency of events) is crucial for understanding natural language. However, its acquisition is challenging, partly because such information is often not expressed explicitly in text, and human annotation on such concepts is costly. This work proposes a novel sequence modeling approach that exploits explicit and implicit mentions of temporal common sense, extracted from a large corpus, to build TacoLM, a temporal common sense language model. Our method is shown to give quality predictions of various dimensions of temporal common sense (on UDST and a newly collected dataset from RealNews). It also produces representations of events for relevant tasks such as duration comparison, parent-child relations, event coreference and temporal QA (on TimeBank, HiEVE and MCTACO) that are better than using the standard BERT. Thus, it will be an important component of temporal NLP.
NeurASP: Embracing Neural Networks into Answer Set Programming

We present NeurASP, a simple extension of answer set programs by embracing neural networks. By treating the neural network output as the probability distribution over atomic facts in answer set programs, NeurASP provides a simple and effective way to integrate sub-symbolic and symbolic computation. We demonstrate how NeurASP can make use of a pre-trained neural network in symbolic computation and how it can improve the neural network's perception result by applying symbolic reasoning in answer set programming. Also, NeurASP can make use of ASP rules to train a neural network better so that a neural network not only learns from implicit correlations from the data but also from the explicit complex semantic constraints expressed by the rules.
A QR Code–Based Contact Tracing Framework for Sustainable Containment of COVID-19: Evaluation of an Approach to Assist the Return to Normal Activity

We discuss a pandemic management framework using symptom-based quick response (QR) codes to contain the spread of COVID-19. In this approach, symptom-based QR health codes are issued by public health authorities. The codes do not retrieve the location data of the users; instead, two different colors are displayed to differentiate the health status of individuals. The QR codes are officially regarded as electronic certificates of individuals' health status, and can be used for contact tracing, exposure risk self-triage, self-update of health status, health care appointments, and contact-free psychiatric consultations. This approach can be effectively deployed as a uniform platform interconnecting a variety of responders (eg, individuals, institutions, and public authorities) who are affected by the pandemic, which minimizes the errors of manual operation and the costs of fragmented coordination. At the same time, this approach enhances the promptness, interoperability, credibility, and traceability of containment measures. The proposed approach not only provides a supplemental mechanism for manual control measures but also addresses the partial failures of pandemic management tools in the abovementioned facets. The QR tool has been formally deployed in Fujian, a province located in southeast China that has a population of nearly 40 million people. All individuals aged ≥3 years were officially requested to present their QR code during daily public activities, such as when using public transportation systems, working at institutions, and entering or exiting schools. The deployment of this approach has achieved sizeable containment effects and played remarkable roles in shifting the negative gross domestic product (-6.8%) to a positive value by July 2020. The number of cumulative patients with COVID-19 in this setting was confined to 363, of whom 361 had recovered (recovery rate 99.4%) as of July 12, 2020. A simulation showed that if only partial measures of the framework were followed, the number of cumulative cases of COVID-19 could potentially increase ten-fold. This approach can serve as a reliable solution to counteract the emergency of a public health crisis; as a routine tool to enhance the level of public health; to accelerate the recovery of social activities; to assist decision making for policy makers; and as a sustainable measure that enables scalability.
Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception

Microscopic image analysis plays a significant role in initial leukaemia screening and its efficient diagnostics. Since the present conventional methodologies partly rely on manual examination, which is time consuming and depends greatly on the experience of domain experts, automated leukaemia detection opens up new possibilities to minimize human intervention and provide more accurate clinical information. This paper proposes a novel approach based on conventional digital image processing techniques and machine learning algorithms to automatically identify acute lymphoblastic leukaemia from peripheral blood smear images. To overcome the greatest challenges in the segmentation phase, we implemented extensive pre-processing and introduced a three-phase filtration algorithm to achieve the best segmentation results. Moreover, sixteen robust features were extracted from the images in the way that haematological experts do, which significantly increased the capability of the classifiers to recognize leukaemic cells in microscopic images. To perform the classification, we applied two traditional machine learning classifiers, the artificial neural network and the support vector machine. Both methods reached a specificity of 95.31 %, and the sensitivity of the support vector machine and artificial neural network reached 98.25 and 100 %, respectively.
w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices

Human activity recognition (HAR) is growing in popularity due to its wide-ranging applications in patient rehabilitation and movement disorders. HAR approaches typically start with collecting sensor data for the activities under consideration and then develop algorithms using the dataset. As such, the success of algorithms for HAR depends on the availability and quality of datasets. Most of the existing work on HAR uses data from inertial sensors on wearable devices or smartphones to design HAR algorithms. However, inertial sensors exhibit high noise that makes it difficult to segment the data and classify the activities. Furthermore, existing approaches typically do not make their data available publicly, which makes it difficult or impossible to obtain comparisons of HAR approaches. To address these issues, we present wearable HAR (w-HAR) which contains labeled data of seven activities from 22 users. Our dataset’s unique aspect is the integration of data from inertial and wearable stretch sensors, thus providing two modalities of activity information. The wearable stretch sensor data allows us to create variable-length segment data and ensure that each segment contains a single activity. We also provide a HAR framework to use w-HAR to classify the activities. To this end, we first perform a design space exploration to choose a neural network architecture for activity classification. Then, we use two online learning algorithms to adapt the classifier to users whose data are not included at design time. Experiments on the w-HAR dataset show that our framework achieves 95% accuracy while the online learning algorithms improve the accuracy by as much as 40%.