Reviewerly/exHarmony huggingface dataset:
Skeleton-based action recognition with hierarchical spatial reasoning and temporal stack learning network

Skeleton-based action recognition aims to recognize human actions by exploring the inherent characteristics from the given skeleton sequences and has attracted far more attention due to its great important potentials in practical applications. Previous methods have illustrated that learning discriminative spatial and temporal features from the skeleton sequences is a crucial factor to recognize human actions. Nevertheless, how to model spatio-temporal evolutions is still a challenging problem. In this work, we propose a novel model with hierarchical spatial reasoning and temporal stack learning network (HSR-TSL) to explore the discriminative spatial and temporal features for human action recognition, which consists of a hierarchical spatial reasoning network (HSRN) and a temporal stack learning network (TSLN). Specifically, the HSRN employs a hierarchical residual graph neural network to capture two-level spatial features: intra spatial information of each part and body-level structural information between each part. The TSLN models the detailed temporal dynamics of skeleton sequences by a composition of multiple skip-clip LSTMs. During training, we develop a clip-based incremental loss to effectively optimize the model. We perform extensive experiments on five challenging benchmarks to verify the effectiveness of each component of our model. The comparison results illustrate that our approach significantly boosts the performances for skeleton-based action recognition.
Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

Underwater images suffer from color cast and low visibility caused by the medium scattering and absorption, which will reduce the use of valuable information from the image. In this paper, we propose a novel method which includes four stages of pixel intensity center regionalization, global equalization of histogram, local equalization of histogram and multi-scale fusion. Additionally, this method uses a pixel intensity center regionalization strategy to perform centralization of the image histogram on the overall image. Global equalization of histogram is employed to correct color of the image according to the characteristics of each channel. Local equalization of dual-interval histogram based on average of peak and mean values is used to improve contrast of the image according to the characteristics of each channel. Dual-image multi-scale fusion to integrate the contrast, saliency and exposure weight maps of the color corrected and contrast enhanced images. Experiments on variety types of degraded underwater images show that the proposed method produces better output results in both qualitative and quantitative analysis, thus, the proposed method outperforms other state-of-the-art methods.
Voice Assistants and Smart Speakers in Everyday Life and in Education

In recent years, Artificial Intelligence (AI) has shown significant progress and its potential is growing.An application area of AI is Natural Language Processing (NLP).Voice assistants incorporate AI by using cloud computing and can communicate with the users in natural language.Voice assistants are easy to use and thus there are millions of devices that incorporates them in households nowadays.Most common devices with voice assistants are smart speakers and they have just started to be used in schools and universities.The purpose of this paper is to study how voice assistants and smart speakers are used in everyday life and whether there is potential in order for them to be used for educational purposes.
Optical-Relayed Entanglement Distribution Using Drones as Mobile Nodes

Entanglement distribution has been accomplished using a flying drone, and this mobile platform can be generalized for multiple mobile nodes with optical relay among them. Here we develop the first optical relay to reshape the wave front of photons for their low diffraction loss in free-space transmission. Using two drones, where one distributes the entangled photons and the other serves as relay node, we achieve entanglement distribution with Clauser-Horne-Shimony-Holt S parameter of 2.59±0.11 at 1 km distance. Key components for entangled source, tracking, and relay are developed with high performance and are lightweight, constructing a scalable airborne system for multinode connectio and toward mobile quantum networks.Received 6 April 2020Accepted 1 December 2020DOI:https://doi.org/10.1103/PhysRevLett.126.020503© 2021 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOptical quantum information processingQuantum communicationQuantum entanglementQuantum information architectures & platformsQuantum InformationNetworks
Commercial applications of quantum computing

Abstract Despite the scientific and engineering challenges facing the development of quantum computers, considerable progress is being made toward applying the technology to commercial applications. In this article, we discuss the solutions that some companies are already building using quantum hardware. Framing these as examples of combinatorics problems, we illustrate their application in four industry verticals: cybersecurity, materials and pharmaceuticals, banking and finance, and advanced manufacturing. While quantum computers are not yet available at the scale needed to solve all of these combinatorics problems, we identify three types of near-term opportunities resulting from advances in quantum computing: quantum-safe encryption, material and drug discovery, and quantum-inspired algorithms.
A Study of Fall Detection in Assisted Living: Identifying and Improving the Optimal Machine Learning Method

This paper makes four scientific contributions to the field of fall detection in the elderly to contribute to their assisted living in the future of Internet of Things (IoT)-based pervasive living environments, such as smart homes. First, it presents and discusses a comprehensive comparative study, where 19 different machine learning methods were used to develop fall detection systems, to deduce the optimal machine learning method for the development of such systems. This study was conducted on two different datasets, and the results show that out of all the machine learning methods, the k-NN classifier is best suited for the development of fall detection systems in terms of performance accuracy. Second, it presents a framework that overcomes the limitations of binary classifier-based fall detection systems by being able to detect falls and fall-like motions. Third, to increase the trust and reliance on fall detection systems, it introduces a novel methodology based on the usage of k-folds cross-validation and the AdaBoost algorithm that improves the performance accuracy of the k-NN classifier-based fall detection system to the extent that it outperforms all similar works in this field. This approach achieved performance accuracies of 99.87% and 99.66%, respectively, when evaluated on the two datasets. Finally, the proposed approach is also highly accurate in detecting the activity of standing up from a lying position to infer whether a fall was followed by a long lie, which can cause minor to major health-related concerns. The above contributions address multiple research challenges in the field of fall detection, that we identified after conducting a comprehensive review of related works, which is also presented in this paper.
A Systematic Literature Review of Blockchain-Enabled Supply Chain Traceability Implementations

In recent years, traceability systems have been developed as practical tools for improving supply chain (SC) transparency and visibility, especially in health and safety-sensitive sectors like food and pharmaceuticals. Blockchain-related SC traceability research has received significant attention during the last several years, and arguably blockchain is currently the most promising technology for providing traceability-related services in SC networks. This paper provides a systematic literature review of the various technical implementation aspects of blockchain-enabled SC traceability systems. We apply different drivers for classifying the selected literature, such as (a) the various domains of the available blockchain-enabled SC traceability systems and relevant methodologies applied; (b) the implementation maturity of these traceability systems along with technical implementation details; and (c) the sustainability perspective (economic, environmental, social) prevalent to these implementations. We provide key takeaways regarding the open issues and challenges of current blockchain traceability implementations and fruitful future research areas. Despite the significant volume and plethora of blockchain-enabled SC traceability systems, academia has so far focused on unstructured experimentation of blockchain-associated SC traceability solutions, and there is a clear need for developing and testing real-life traceability solutions, especially taking into account feasibility and cost-related SC aspects.
The Joy of Forgetting: Faster Anytime Search via Restarting

Anytime search algorithms solve optimisation problems by quickly finding a (usually suboptimal) first solution and then finding improved solutions when given additional time. To deliver an initial solution quickly, they are typically greedy with respect to the heuristic cost-to-go estimate h. In this paper, we show that this low-h bias can cause poor performance if the greedy search makes early mistakes. Building on this observation, we present a new anytime approach that restarts the search from the initial state every time a new solution is found. We demonstrate the utility of our method via experiments in PDDL planning as well as other domains, and show that it is particularly useful for problems where the heuristic has systematic errors.
Fast Large-Scale Spectral Clustering via Explicit Feature Mapping

We propose an efficient spectral clustering method for large-scale data. The main idea in our method consists of employing random Fourier features to explicitly represent data in kernel space. The complexity of spectral clustering thus is shown lower than existing Nyström approximations on large-scale data. With m training points from a total of n data points, Nyström method requires O(nmd+m3+nm2) operations, where d is the input dimension. In contrast, our proposed method requires O(nDd+D3+n'D2) , where n' is the number of data points needed until convergence and D is the kernel mapped dimension. In large-scale datasets where n' << n hold true, our explicitly mapping method can significantly speed up eigenvector approximation and benefit prediction speed in spectral clustering. For instance, on MNIST (60 000 data points), the proposed method is similar in clustering accuracy to Nyström methods while its speed is twice as fast as Nyström.
Privacy-preserving data integrity verification by using lightweight streaming authenticated data structures for healthcare cyber–physical system

Nowadays the healthcare system is undergoing a paradigm shift to integrate cloud computing and Internet-of-Things with physical medical equipments into a distributed network ensuring real-time and near real-time data transfer from the physical world to the cyber space for computation, storage and analysis. Namely, the healthcare system can be seen as a cyber–physical system (CPS) for patient-centric healthcare applications and services, called Health-CPS, based on cloud computing, Wireless Body Area Networks (WBANs) and big data analytics technologies. Health-CPS has played an important role in healthcare for improving treatment quality and patients’ assistance speed. However, despite these advantages, the development of Health-CPS will be restricted by serious security threats, especially the security threats to the healthcare-related data. Because Health-CPS forward Electronic health records (EHRs), biomedical signals of patients and public health to the cloud which may operate in distributed and hostile environments, novel security mechanisms are required to prevent malicious interactions to the storage infrastructure. Therefore, the cloud providers must take strong security measures to protect the integrity and privacy of the healthcare related data. But most of the researchers do not simultaneously pay attention to both integrity and privacy for Health-CPS. Therefore, we proposed a privacy-preserving data integrity verification model by using lightweight streaming authenticated data structures for Health-CPS. We have given the design idea, architecture, formal definition, security definition, communication protocols of our model in detail. The key construction processes of our model which include initialization, data appending, scale expansion, data query and verification, are also given in this paper. Finally, the security and performance analysis show that our scheme is not only secure but also efficient.
Chaff from the Wheat: Characterizing and Determining Valid Bug Reports

Developers use bug reports to triage and fix bugs. When triaging a bug report, developers must decide whether the bug report is valid (i.e., a real bug). A large amount of bug reports are submitted every day, with many of them end up being invalid reports. Manually determining valid bug report is a difficult and tedious task. Thus, an approach that can automatically analyze the validity of a bug report and determine whether a report is valid can help developers prioritize their triaging tasks and avoid wasting time and effort on invalid bug reports. In this study, motivated by the above needs, we propose an approach which can determine whether a newly submitted bug report is valid. Our approach first extracts 33 features from bug reports. The extracted features are grouped along 5 dimensions, i.e., reporter experience, collaboration network, completeness, readability and text. Based on these features, we use a random forest classifier to identify valid bug reports. To evaluate the effectiveness of our approach, we experiment on large-scale datasets containing a total of 560,697 bug reports from five open source projects (i.e., Eclipse, Netbeans, Mozilla, Firefox and Thunderbird). On average, across the five datasets, our approach achieves an F1-score for valid bug reports and F1-score for invalid ones of 0.74 and 0.67, respectively. Moreover, our approach achieves an average AUC of 0.81. In terms of AUC and F1-scores for valid and invalid bug reports, our approach statistically significantly outperforms two baselines using features that are proposed by Zanetti et al. [104] . We also study the most important features that distinguish valid bug reports from invalid ones. We find that the textual features of a bug report and reporter's experience are the most important factors to distinguish valid bug reports from invalid ones.
Reconstructing Perceived Images From Human Brain Activities With Bayesian Deep Multiview Learning

Neural decoding, which aims to predict external visual stimuli information from evoked brain activities, plays an important role in understanding human visual system. Many existing methods are based on linear models, and most of them only focus on either the brain activity pattern classification or visual stimuli identification. Accurate reconstruction of the perceived images from the measured human brain activities still remains challenging. In this paper, we propose a novel deep generative multiview model for the accurate visual image reconstruction from the human brain activities measured by functional magnetic resonance imaging (fMRI). Specifically, we model the statistical relationships between the two views (i.e., the visual stimuli and the evoked fMRI) by using two view-specific generators with a shared latent space. On the one hand, we adopt a deep neural network architecture for visual image generation, which mimics the stages of human visual processing. On the other hand, we design a sparse Bayesian linear model for fMRI activity generation, which can effectively capture voxel correlations, suppress data noise, and avoid overfitting. Furthermore, we devise an efficient mean-field variational inference method to train the proposed model. The proposed method can accurately reconstruct visual images via Bayesian inference. In particular, we exploit a posterior regularization technique in the Bayesian inference to regularize the model posterior. The quantitative and qualitative evaluations conducted on multiple fMRI data sets demonstrate the proposed method can reconstruct visual images more accurately than the state of the art.
Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection

Digital pathology enables remote access or consults and powerful image analysis algorithms. However, the slide digitization process can create artifacts such as out-of-focus (OOF). OOF is often only detected on careful review, potentially causing rescanning, and workflow delays. Although scan time operator screening for whole-slide OOF is feasible, manual screening for OOF affecting only parts of a slide is impractical.We developed a convolutional neural network (ConvFocus) to exhaustively localize and quantify the severity of OOF regions on digitized slides. ConvFocus was developed using our refined semi-synthetic OOF data generation process and evaluated using seven slides spanning three different tissue and three different stain types, each of which were digitized using two different whole-slide scanner models ConvFocus's predictions were compared with pathologist-annotated focus quality grades across 514 distinct regions representing 37,700 35 μm × 35 μm image patches, and 21 digitized "z-stack" WSIs that contain known OOF patterns.When compared to pathologist-graded focus quality, ConvFocus achieved Spearman rank coefficients of 0.81 and 0.94 on two scanners and reproduced the expected OOF patterns from z-stack scanning. We also evaluated the impact of OOF on the accuracy of a state-of-the-art metastatic breast cancer detector and saw a consistent decrease in performance with increasing OOF.Comprehensive whole-slide OOF categorization could enable rescans before pathologist review, potentially reducing the impact of digitization focus issues on the clinical workflow. We show that the algorithm trained on our semi-synthetic OOF data generalizes well to real OOF regions across tissue types, stains, and scanners. Finally, quantitative OOF maps can flag regions that might otherwise be misclassified by image analysis algorithms, preventing OOF-induced errors.
Characteristics of Co-Allocated Online Services and Batch Jobs in Internet Data Centers: A Case Study From Alibaba Cloud

In order to reduce power and energy costs, giant cloud providers now mix online and batch jobs on the same cluster.Although the co-allocation of such jobs improves machine utilization, it challenges the data center scheduler and workload assignment in terms of quality of service, fault tolerance, and failure recovery, especially for latency critical online services.In this paper, we explore various characteristics of co-allocated online services and batch jobs from a production cluster containing 1.3k servers in Alibaba Cloud.From the trace data, we find the following: 1) For batch jobs with multiple tasks and instances, 50.8% failed tasks wait and halted after a very long time interval when their first and the only one instance fails.This wastes much time and resources as the remaining instances are running for an impossible successful termination.2) For online services jobs, they are clustered in 25 categories according to their requested CPU, memory, and disk resources.Such clustering can help the co-allocation of online services jobs with batch jobs.3) Servers are clustered into seven groups by CPU utilization, memory utilization, and their correlations.Machines with a strong correlation between CPU and memory utilization provides an opportunity for job co-allocation and resource utilization estimation.4) The MTBF (mean time between failures) of instances are in the interval [400, 800] seconds while the average completion time of the 99th percentile is 1003 seconds.We also compare the cumulative distribution functions of jobs and servers and explain the differences and opportunities for workload assignment between them.Our findings and insights presented in this paper can help the community and data center operators better understand the workload characteristics, improve resource utilization, and failure recovery design.
Quantum Image Encryption Scheme Using Arnold Transform and S-box Scrambling

The paper proposes a lossless quantum image encryption scheme based on substitution tables (S-box) scrambling, mutation operation and general Arnold transform with keys. First, the key generator builds upon the foundation of SHA-256 hash with plain-image and a random sequence. Its output value is used to yield initial conditions and parameters of the proposed image encryption scheme. Second, the permutation and gray-level encryption architecture is built by discrete Arnold map and quantum chaotic map. Before the permutation of Arnold transform, the pixel value is modified by quantum chaos sequence. In order to get high scrambling and randomness, S-box and mutation operation are exploited in gray-level encryption stage. The combination of linear transformation and nonlinear transformation ensures the complexity of the proposed scheme and avoids harmful periodicity. The simulation shows the cipher-image has a fairly uniform histogram, low correlation coefficients closed to 0, high information entropy closed to 8. The proposed cryptosystem provides 2256 key space and performs fast computational efficiency (speed = 11.920875 Mbit/s). Theoretical analyses and experimental results prove that the proposed scheme has strong resistance to various existing attacks and high level of security.