Reviewerly/exHarmony huggingface dataset:
Dense Semantics-Assisted Networks for Video Action Recognition

Most existing action recognition approaches directly leverage the video-level features to recognize human actions from videos. Although these methods have made remarkable progress, the accuracy is still unsatisfied. When the test video involves complex backgrounds and activities, existing methods usually suffer from a significant drop in accuracy. Human action is inherently a high-level concept. Merely applying a video classification model without a detailed semantic understanding of the video content, e.g., objects, scene context, object motions, object interactions, is inadequate to tackle the challenges for action recognition. Fine-level semantic understanding of videos generates elementary semantic concepts from the raw video data, such as the semantics of objects and background regions. It can be employed to bridge the gap between the raw video data and the high-level concept of human actions. In this work, we leverage dense semantic segmentation masks, which encode rich semantic details, provide extra information for the network training, and improve the performance of action recognition. We propose a novel deep architecture which is named as Dense Semantics-Assisted Convolutional Neural Networks (DSA-CNNs) to effectively utilize dense semantic information of video by a bottom-up attention way in the spatial stream, while by the way of branch fusion in the temporal stream. To verify the effectiveness of our approach, we conduct extensive experiments on publicly available datasets – UCF101, HMDB51, and Kinetics. The experimental results demonstrate that our approach substantially improves existing methods and achieves very competitive performance. It also shows that our approach is superior to other related methods that utilize extra information for action recognition.
Leveraging Semantic Technologies for Digital Interoperability in the European Railway Domain

The European Union Agency for Railways is an European authority, tasked with the provision of a legal and technical framework to support harmonized and safe cross-border railway operations throughout the EU. So far, the agency relied on traditional application-centric approaches to support the data exchange among multiple actors interacting within the railway domain. This lead however, to isolated digital environments that consequently added barriers to digital interoperability while increasing the cost of maintenance and innovation. In this work, we show how Semantic Web technologies are leveraged to create a semantic layer for data integration across the base registries maintained by the agency. We validate the usefulness of this approach by supporting route compatibility checks, a highly demanded use case in this domain, which was not available over the agency's registries before. Our contributions include (i) an official ontology for the railway infrastructure and authorized vehicle types, including 28 reference datasets; (ii) a reusable Knowledge Graph describing the European railway infrastructure; (iii) a cost-efficient system architecture that enables high-flexibility for use case development; and (iv) an open source and RDF native Web application to support route compatibility checks. This work demonstrates how data-centric system design, powered by Semantic Web technologies and Linked Data principles, provides a framework to achieve data interoperability and unlock new and innovative use cases and applications. Based on the results obtained during this work, ERA officially decided to make Semantic Web and Linked Data-based approaches, the default setting for any future development of the data, registers and specifications under the agency's remit for data exchange mandated by the EU legal framework. The next steps, which are already underway, include further developing and bringing these solutions to a production-ready state.
Framework for determining the suitability of blockchain: Criteria and issues to consider

Abstract Various distributed ledger technologies (DLTs), such as blockchain, have evolved significantly in recent years. These technologies provide a robust and effective solution for providing confidentiality, integrity, nonrepudiation, authentication, and transparency. While blockchain has many advantages, it has various limitations as well, such as complexity, low throughput, privacy, and so on. We discuss the issues that must be considered when deciding whether to use these technologies in a given case or not. We describe the operation of blockchain, application areas where blockchain is suitable, and those where it is not. We also discuss the applicability of other emerging DLTs, apart from blockchain technology, such as Hashgraph, Zcash, Nano coin, and IOTA.
Deep learning-based optimization for motion planning of dual-arm assembly robots

With the rapid technological and economic development, a growing number of companies are employing robots for their production and service operations. Motion planning is a fundamental topic in robotics that has received wide attention due to its importance in the development of industry 4.0 and intelligent manufacturing systems. This study sought to develop a deep learning-based optimization algorithm for planning collision-free trajectories of dual-arm assembly robots in complex operational environments. Given the high dimensionality of the robotic motion patterns, a Bi-directional Rapidly-exploring Random Tree integrated with the Long Short-term Memory (LSTM-BiRRT) method is proposed to enhance the effectiveness and efficiency of the planning process. Numerical experiments demonstrated that the LSTM-BiRRT algorithm outperforms the state-of-the-art approaches developed for motion planning of dual-arm robots in both two- and three-dimensional environments. The developed algorithm reduces the path length of the robotic operations at a significantly shorter computational time. The LSTM-BiRRT algorithm can serve as a strong benchmark for future developments as well as applications in the process autonomy across intelligent supply chains.
Elastic Parameter Server: Accelerating ML Training With Scalable Resource Scheduling

Parameter server (PS) based on worker-server communication is designed for distributed machine learning (ML) training in clusters. In feedback-driven exploration of ML model training, users exploit early feedback from each job to decide whether to kill the job or keep it running so as to find the optimal model configuration. However, PS does not support adjusting the number of workers and servers of a job at runtime. It becomes the bottleneck of scalable distributed ML training because the cluster resources cannot be dynamically allocated or deallocated to jobs, resulting in significant early feedback latency and resource under-utilization. This article rethinks the principle of PS architecture. We present Elastic Parameter Server (EPS), a lightweight and user-transparent PS that accelerates feedback-driven exploration for distributed ML training. EPS allows to remove a subset of workers and servers from running jobs and allocate the released resources to an incoming job at runtime so as to reduce its early feedback latency. It can also use the released resources from a killed job to add workers and servers to running jobs to improve resource utilization and the training speed. We develop a heuristic scheduler that leverages EPS and offers scalable resource scheduling for multiple ML jobs. We implement EPS in Tencent Angel and the scheduler in Apache Yarn, and conduct evaluations with various ML models. Experimental results show that EPS achieves up to 1.5x improvement on the ML training speed compared to PS.
Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study

Data science offers an unparalleled opportunity to identify new insights into many aspects of human life with recent advances in health care. Using data science in digital health raises significant challenges regarding data privacy, transparency, and trustworthiness. Recent regulations enforce the need for a clear legal basis for collecting, processing, and sharing data, for example, the European Union's General Data Protection Regulation (2016) and the United Kingdom's Data Protection Act (2018). For health care providers, legal use of the electronic health record (EHR) is permitted only in clinical care cases. Any other use of the data requires thoughtful considerations of the legal context and direct patient consent. Identifiable personal and sensitive information must be sufficiently anonymized. Raw data are commonly anonymized to be used for research purposes, with risk assessment for reidentification and utility. Although health care organizations have internal policies defined for information governance, there is a significant lack of practical tools and intuitive guidance about the use of data for research and modeling. Off-the-shelf data anonymization tools are developed frequently, but privacy-related functionalities are often incomparable with regard to use in different problem domains. In addition, tools to support measuring the risk of the anonymized data with regard to reidentification against the usefulness of the data exist, but there are question marks over their efficacy.In this systematic literature mapping study, we aim to alleviate the aforementioned issues by reviewing the landscape of data anonymization for digital health care.We used Google Scholar, Web of Science, Elsevier Scopus, and PubMed to retrieve academic studies published in English up to June 2020. Noteworthy gray literature was also used to initialize the search. We focused on review questions covering 5 bottom-up aspects: basic anonymization operations, privacy models, reidentification risk and usability metrics, off-the-shelf anonymization tools, and the lawful basis for EHR data anonymization.We identified 239 eligible studies, of which 60 were chosen for general background information; 16 were selected for 7 basic anonymization operations; 104 covered 72 conventional and machine learning-based privacy models; four and 19 papers included seven and 15 metrics, respectively, for measuring the reidentification risk and degree of usability; and 36 explored 20 data anonymization software tools. In addition, we also evaluated the practical feasibility of performing anonymization on EHR data with reference to their usability in medical decision-making. Furthermore, we summarized the lawful basis for delivering guidance on practical EHR data anonymization.This systematic literature mapping study indicates that anonymization of EHR data is theoretically achievable; yet, it requires more research efforts in practical implementations to balance privacy preservation and usability to ensure more reliable health care applications.
The age of abundant scholarly information and its synthesis– A time when ‘just google it’ is no longer enough

Academic research has changed in recent years. It has entered the age of abundant scholarly information. New scientometric data shows impressive increases in both the quantity and quality of information researchers produce. Since 2007 about the same number of publications have become accessible on databases as more than the hundred years prior. At the same time, evidence synthesis has become key in making this wealth of information understandable and useful. Researchers need to be increasingly proficient in identifying relevant information - to be able to build on an increasingly comprehensive research base and to adhere to rising standards in evidence synthesis. Both these requirements make a 'true partnership between librarians and researchers' in demand like never before.
Prosodic Differences in Human- and Alexa-Directed Speech, but Similar Local Intelligibility Adjustments

The current study tests whether individuals ( n = 53) produce distinct speech adaptations during pre-scripted spoken interactions with a voice-AI assistant (Amazon’s Alexa) relative to those with a human interlocutor. Interactions crossed intelligibility pressures (staged word misrecognitions) and emotionality (hyper-expressive interjections) as conversation-internal factors that might influence participants’ intelligibility adjustments in Alexa- and human-directed speech (DS). Overall, we find speech style differences: Alexa-DS has a decreased speech rate, higher mean f0, and greater f0 variation than human-DS. In speech produced toward both interlocutors, adjustments in response to misrecognition were similar: participants produced more distinct vowel backing (enhancing the contrast between the target word and misrecognition) in target words and louder, slower, higher mean f0, and higher f0 variation at the sentence-level. No differences were observed in human- and Alexa-DS following displays of emotional expressiveness by the interlocutors. Expressiveness, furthermore, did not mediate intelligibility adjustments in response to a misrecognition. Taken together, these findings support proposals that speakers presume voice-AI has a “communicative barrier” (relative to human interlocutors), but that speakers adapt to conversational-internal factors of intelligibility similarly in human- and Alexa-DS. This work contributes to our understanding of human-computer interaction, as well as theories of speech style adaptation.
Transductive Few-Shot Classification on the Oblique Manifold

Few-shot learning (FSL) attempts to learn with limited data. In this work, we perform the feature extraction in the Euclidean space and the geodesic distance metric on the Oblique Manifold (OM). Specially, for better feature extraction, we propose a non-parametric Region Self-attention with Spatial Pyramid Pooling (RSSPP), which realizes a trade-off between the generalization and the discriminative ability of the single image feature. Then, we embed the feature to OM as a point. Furthermore, we design an Oblique Distance-based Classifier (ODC) that achieves classification in the tangent spaces which better approximate OM locally by learnable tangency points. Finally, we introduce a new method for parameters initialization and a novel loss function in the transductive settings. Extensive experiments demonstrate the effectiveness of our algorithm and it outperforms state-of-the-art methods on the popular benchmarks: mini-ImageNet, tiered-ImageNet, and Caltech-UCSD Birds-200-2011 (CUB).
Physical Reservoir Computing in Robotics

In recent years, there has been an increasing interest in using the concept of physical reservoir computing in robotics. The idea is to employ the robot's body and its dynamics as a computational resource. On one hand, this has been driven by the introduction of mathematical frameworks showing how complex mechanical structures can be used to build reservoirs. On the other hand, with the recent advances in smart materials, novel additive manufacturing techniques, and the corresponding rise of soft robotics, a new and much richer set of tools for designing and building robots is now available. Despite the increased interest, however, there is still a wide range of unanswered research questions and a rich area of under-explored applications. We will discuss the current state of the art, the implications of using robot bodies as reservoirs, and the great potential and future directions of physical reservoir computing in robotics.
Users’ perception toward Bitcoin Green with big data analytics

Purpose Nowadays, Bitcoin is facing many environmental problems arising from the proof of work based on blockchain. For this reason, Bitcoin Green (BITG) has been created and would solve these issues. The purpose of this paper is to visualize the users’ perception toward BITG through Twitter text analysis. Design/methodology/approach The big data used in this study includes two sources. The first data were extracted from the “Google Trends” engine during the period between 20 September 2015 and 15 September 2020. The second data were extracted from the Twitter application. This research explores the perceived ease of use, the perceived usefulness, the social influence, the perceived control and the user attitudes toward BITG. Therefore, lexicon-based sentiment analysis techniques combined with different dictionaries are built to visualize the drivers of investor attitudes toward the BITG using Twitter text messages. Besides, this study has checked the validity of two main assumptions using the normality (Jarque-Bera) and Kruskal-Wallis rank sum tests capable to conclude whether users mostly perceive BITG as a sustainable technology. Findings This empirical work affords insights into users’ intentions by exploring the drivers of BITG perception. The results show that users positively perceive the use of BITG as a sustainable blockchain. Besides, its usefulness is more appreciated from its ethical and technological characteristics, and its perceived application is mainly based on investment and coin offering use. Similarly, users are mostly showing positive emotions toward BITG. Research limitations/implications Tweets related to “BITG” are not as voluminous as the other cryptocurrencies like Bitcoin and Ethereum, which make it difficult to extract all the characteristics and use cases. Originality/value To the best of the authors’ knowledge, this work is the first one that uses the theory of planned behavior and the theory of acceptance model to explore cognitive factors in understanding investor intentions in adopting BITG.
Multi-Attention Convolutional Neural Network for Video Deblurring

Video deblurring, which aims at restoring the sharp video from blurry video, is drawing increasing attention in the field of computer vision. In this paper, a method called Multi-Attention Convolutional Neural Network (MACNN) consisting of the temporal-spatial attention module, the frame channel attention module, and the feature extraction-reconstruction module is proposed. First, we use the temporal-spatial attention module and the frame channel attention module to capture features with temporal and spatial information existing across neighboring frames. Then, these captured features are fused and reconstructed to restore the sharp frame. Last but not least, we train MACNN together with a content loss and a perceptual loss in an end-to-end manner to recover realistic video details. Both quantitative and qualitative evaluation results on standard benchmarks demonstrate the proposed MACNN is superior to the state-of-the-art methods in terms of accuracy, efficiency, and visual effect.
A study on transformer-based Object Detection

This paper focuses on transformers based end-to-end object detection methods. End to end object detection is a new paradigm that has got attention in recent times. It does not require complex hand-engineered components such as non-max suppression to detect objects inside an image. Various methods are proposed to date to enhance fully end-to-end object detectors, most of them are based on the attention mechanism. In this work, we analyze some algorithms which involve transformers for the purpose of object detection. We discuss end-to-end models in which we have focused on Adaptive clustering-based transformers which solve attention encoder redundancy, Deformable Detection Transformers in which the attention module attends a limited collection of key sampling points, Unsupervisedly pre-trained Detection Transformers which are pre-trained on random query patches from the given image to improve accuracy and finally the Transformer-based Set Prediction using FCOS. These enhanced models not only improve the mean average precision of the model but also improves the total convergence time.
Confidential Serverless Made Efficient with Plug-In Enclaves

Serverless computing has become a fact of life on modern clouds. A serverless function may process sensitive data from clients. Protecting such a function against untrusted clouds using hardware enclave is attractive for user privacy. In this work, we run existing serverless applications in SGX enclave, and observe that the performance degradation can be as high as 5.6× to even 422.6×. Our investigation identifies these slowdowns are related to architectural features, mainly from page-wise enclave initialization. Leveraging insights from our overhead analysis, we revisit SGX hardware design and make minimal modification to its enclave model. We extend SGX with a new primitive—region-wise plugin enclaves that can be mapped into existing enclaves to reuse attested common states amongst functions. By remapping plugin enclaves, an enclave allows in-situ processing to avoid expensive data movement in a function chain. Experiments show that our design reduces the enclave function latency by 94.74-99.57%, and boosts the autoscaling throughput by 19-179×.
AGO-Net: Association-Guided 3D Point Cloud Object Detection Network

The human brain can effortlessly recognize and localize objects, whereas current 3D object detection methods based on LiDAR point clouds still report inferior performance for detecting occluded and distant objects: The point cloud appearance varies greatly due to occlusion, and has inherent variance in point densities along the distance to sensors. Therefore, designing feature representations robust to such point clouds is critical. Inspired by human associative recognition, we propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We bridge the gap between the perceptual domain, where features are derived from real scenes with sub-optimal representations, and the conceptual domain, where features are extracted from augmented scenes that consist of non-occlusion objects with rich detailed information. A feasible method is investigated to construct conceptual scenes without external datasets. We further introduce an attention-based re-weighting module that adaptively strengthens the feature adaptation of more informative regions. The network's feature enhancement ability is exploited without introducing extra cost during inference, which is plug-and-play in various 3D detection frameworks. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed. Experiments on nuScenes and Waymo datasets also validate the versatility of our method.
ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation

Self-supervised learning of depth has been a highly studied topic of research as it alleviates the requirement of having ground truth annotations for predicting depth. Depth is learnt as an intermediate solution to the task of view synthesis, utilising warped photometric consistency. Although it gives good results when trained using stereo data, the predicted depth is still sensitive to noise, illumination changes and specular reflections. Also, occlusion can be tackled better by learning depth from a single camera. We propose ADAA, utilising depth augmentation as depth supervision for learning accurate and robust depth. We propose a relational self-attention module that learns rich contextual features and further enhances depth results. We also optimize the auto-masking strategy across all losses by enforcing L1 regularisation over mask. Our novel progressive training strategy first learns depth at a lower resolution and then progresses to the original resolution with slight training. We utilise a ResNet18 encoder, learning features for prediction of both depth and pose. We evaluate our predicted depth on the standard KITTI driving dataset and achieve state-of-the-art results for monocular depth estimation whilst having significantly lower number of trainable parameters in our deep learning framework. We also evaluate our model on Make3D dataset showing better generalization than other methods.