Reviewerly/exHarmony huggingface dataset:
Fuzzy based energy efficient workload management system for flash crowd

Many organizations are moving towards the cloud to meet sudden spikes due to a flash crowd. The exponential growth in requests and the offloading of the computations have increased data centers which in turn increases energy consumption and carbon footprint. This paper aims to propose an energy-efficient workload management scheme for the green cloud. The novelty of the work entails i) Developing a three-staged scheduling scheme that maps Virtual Machines with the active Physical Machines resulting in the consolidation of workload. ii) Deploying Fuzzy Decision Maker to analyze under-utilized and over-utilized servers based on the cores and the type of workload. The efficiency of the proposed system is tested through a trace-based simulation. The Power Usage Effectiveness benchmarked with standard algorithms claims that this work averagely conserves 1.9% energy by triggering 60% lesser migrations and reduces the execution time of cloud instances averagely by 6 clock cycles with a minimum Service Level Agreement Violations of 0.1%
A Study of Light Field Streaming for An Interactive Refocusing Application

Light fields are able to capture light rays from a scene arriving at different angles, which allows post-capture rendering applications such as interactive viewpoint selection or refocusing. However, this additional angular information comes at the price of a significant increase of the data volume compared to traditional 2D images. While light field compression is still an ongoing research effort, showing impressive compression gain with the latest coding standard, light fields are in practice often stored on remote servers to avoid consuming unnecessary storage of the user devices. A typical cost-effective solution for light field visualisation is then to render the requested image on the server and transmit the result to the user. Another trivial solution would be to directly send the light field to the user and perform the rendering process directly on the client side to avoid transmission delay. While the latter solution seems instinctively less optimal and is usually discarded in previous work because of an expected unacceptable startup delay, we propose a quantitative study to compare both solutions in terms of rate-distortion (RD) performance. A counterintuitive finding of this paper is that accepting a reasonable startup delay (a few seconds) can provide a significant improvement of the RD performances.
Context-Aware Correlation Filter Learning Toward Peak Strength for Visual Tracking

Recently, the correlation filter (CF) has been catching significant attention in visual tracking for its high efficiency in most state-of-the-art algorithms. However, the tracker easily fails when facing the distractions caused by background clutter, occlusion, and other challenging situations. These distractions commonly exist in the visual object tracking of real applications. Keep tracking under these circumstances is the bottleneck in the field. To improve tracking performance under complex interference, a combination of least absolute shrinkage and selection operator (LASSO) regression and contextual information is introduced to the CF framework through the learning stage in this article to ignore these distractions. Moreover, an elastic net regression is proposed to regroup the features, and an adaptive scale method is implemented to deal with the scale changes during tracking. Theoretical analysis and exhaustive experimental analysis show that the proposed peak strength context-aware (PSCA) CF significantly improves the kernelized CF (KCF) and achieves better performance than other state-of-the-art trackers.
Efficient and Accurate Face Shape Reconstruction by Fusion of Multiple Landmark Databases

We propose an efficient and accurate regression-based 3D face shape reconstruction method. We use an encoder based on MobileNet to estimate parameters including face pose and coefficients of a parametric face model from a single face image. The encoder is trained only by 2D landmarks. Faces can be reconstructed by these parameters. Three contributions of our method are: 1) we propose a databases fusion method to train our network which can easily utilize multiple 2D landmark databases which have different landmark numbers and positions; 2) with the fusion method, we propose a simple MobileNet based network which is efficient, accurate and robust for face reconstruction even without complex training strategies; 3) we add an additional deformation field for shape correction to further improve our network's performance. Experiments demonstrate our method can bring about great performance improvement on most test databases and also compare favorably to some state-of-the-art methods in performance and speed.
Creating a Knowledge Base to Research the History of UK Museums through Rapid Application Development

Several studies have highlighted the absence of an integrated comprehensive dataset covering all of the UK’s museums, hence impeding research into the emergence, evolution, and wider impact of the UK’s museums sector. “Mapping Museums” is an interdisciplinary project aiming to develop a comprehensive database of UK museums in existence since 1960, and to use this to undertake an evidence-based analysis of the development of the UK’s museum sector during 1960--2020 and the links to wider cultural, social, and political concerns. A major part of the project has been the iterative, participatory design of a new RDF/S Knowledge Base to store data and metadata relating to the UK’s museums, and a Web Application for the project’s humanities scholars to browse, search, and visualise the data to investigate their research questions. This article presents the challenges we faced in developing the Knowledge Base and Web Application, our methodology and methods, the design and implementation of the system, and the design, outcomes, and implications of a user trial undertaken with a group of experts from the UK’s museums sector.
Securing visual search queries in ubiquitous scenarios empowered by smart personal devices

Smart personal devices are assuming a fundamental role in the ubiquitous communication and computing arena. They provide new sophisticated cameras and new visual search interfaces and facilities that can drastically improve their presence and role in complex IoT-based critical infrastructures, such as healthcare monitoring and emergency systems, or remote access control facilities and smart authentication services. This new scenario calls for strong secure and resilient visual query mechanisms for these devices. In this work we propose an innovative secure visual search system, which is well-suited for ubiquitous computing scenarios empowered by modern smart personal devices. More precisely, we show how to insert, at the visual data acquisition time, a watermark inside the already compressed descriptor characterizing an MPEG-CDVS data stream used in visual queries, to make it possible to decode the watermark on the server side in order to improve the robustness against image-based identity spoofing. Such a security enforcement solution may be practical in several real-life applications involving visual queries performed from personal trusted devices, and it is particularly suitable in all those application domains that require performing visual queries with a high degree of security. It has been extensively tested and achieved satisfactory results: the presence of such a watermark does not affect the image matching performance and functionality.
Multi-View Metric Learning for Multi-Label Image Classification

Multi-label image classification is a very challenging task, where data are often associated with multiple labels and represented with multiple views. In this paper, we propose a novel multi-view distance metric learning approach to dealing with the multi-label image classification problem. In particular, we attempt to concatenate multiple types of features after learning one optimal distance metric for each view, so as to obtain a better joint representation across multi-view spaces. To preserve the intrinsic geometric structure of the data in the low-dimensional feature space, we introduce a manifold regularization with the adjacency graph being constructed based on all labels. Moreover, we learn another distance metric to capture the dependency of labels, which can further improve the classification performance. Experimental results on publicly available image datasets demonstrate that our method achieves superior performance, compared with the state-of-the-arts.
Joints Relation Inference Network for Skeleton-Based Action Recognition

Recently, the graph convolutional networks based methods have achieved remarkable performance in skeleton-based action recognition. However, current methods do not make full use of the topology of the skeleton graph, because they set and fixed the relation of skeleton joints or adjacency matrix for all input samples manually. In order to solve the problem, we design an end-to-end architecture consisting of joints relation inference network (JRIN) and skeleton graph convolutional network (SGCN). JRIN can aggregate spatial-temporal feature of every two joints globally, then infer the optimal relation between every two joints automatically. These relations of joints is quantified as the optimal adjacency matrices. Then SGCN will take the optimal matrices to do the final action recognition. During training, special initialization and alternate training strategies are proposed to optimize the model. Our method achieves very competitive performance on widely used NTU-RGB+D and Kinetics datasets.
Authorization Transparency for Accountable Access to IoT Services

Highly distributed smart environments, such as Smart Cities, require scalable architectures to support a large number of stakeholders that share Internet of Things (IoT) resources and services. We focus on authorization solutions that regulate access of users to smart objects and consider scenarios where a large number of smart objects owners want to share the resources of their devices in a secure way. A popular solution is to delegate third parties, such as public Cloud services, to mediate authorization procedures among users and smart objects. This approach has the disadvantage of assuming third parties as trusted proxies that guarantee correctness of all authorization procedures. In this paper, we propose a system that allows to audit authorizations managed by third parties, to detect and expose their misbehaviors to users, smart objects owners and, possibly, to the public. The proposed system is inspired by the transparency projects used to monitor Web Certification Authorities, but improves over existing proposals through a twofold contribution. First, it is specifically designed for IoT devices, provided with little resources and distributed in constrained environments. Second, it complies to current standard authorization protocols and available open-source software, making it ready to be deployed.
Learning Workflow Embeddings to Improve the Performance of Similarity-Based Retrieval for Process-Oriented Case-Based Reasoning

In process-oriented case-based reasoning, similarity-based retrieval of workflow cases from large case bases is still a difficult issue due to the computationally expensive similarity assessment. The two-phase MAC/FAC ("Many are called, but few are chosen") retrieval has been proven useful to reduce the retrieval time but comes at the cost of an additional modeling effort for implementing the MAC phase. In this paper, we present a new approach to implement the MAC phase for POCBR retrieval, which makes use of the StarSpace embedding algorithm to automatically learn a vector representation for workflows, which can be used to significantly speed-up the MAC retrieval phase. In an experimental evaluation in the domain of cooking workflows, we show that the presented approach outperforms two existing MAC/FAC approaches on the same data.
Services as Enterprise Smart Contracts in the Digital Factory

The implementation of remote services in the factory of the future is gaining more and more attention. A distributed process can be considered, where an Original Equipment Manufacturer supplies services based on data collected from the machines of its clients. Examples of services are those implementing anomaly detection and predictive maintenance techniques. Data on which remote services are based, as well as their execution logic, must be transparently shared among participants, to enforce trust among OEM and its clients. The goal of this paper is to propose an approach where blockchain technology and Smart Contracts are used to ensure the required level of trust when implementing the aforementioned services. Events occurring on monitored machines are stored as transactions in a blockchain-based system, to ensure non repudiation of data on which remote services are based. Moreover, trust-demanding tasks in the execution logic of services are considered to design Smart Contracts, that guarantee the required level of trustworthiness among participants. The example featuring Remote Monitoring Services for anomaly detection is used to demonstrate the feasibility of the approach.
Sentiment Analysis on Indian Government Schemes Using Twitter data

People use social media for entertainment, fetching information, news, business, communication and many more. Few of such social media applications are Facebook, Twitter, WhatsApp, Snapchat and so on. Twitter is one among the micro blogging websites. We are using Twitter mainly because it has gained a lot of media attention. The text written is referred to as tweets, where a common man can tweet or can write their hearts out. We would be fetching the direct responses from the public and hence the data is more real-time. First step is to fetch the tweets on a particular scheme using python language code followed by the cleaning process then comes the creation of bag of words. Later these bags of words are given as an input to the algorithms. Finally, after training the algorithms, we will be getting the sentiment of the public on that scheme.
IPTV video quality assessment model based on neural network

In recent years, with the continuous development of network science and technology and the continuous promotion of “three networks convergence”, IPTV (Interactive Network Television) has shown a rapid development trend. IPTV is different from the traditional one-way broadcasting mode of television, it can achieve interaction with the audience, and provide more personalized and diversified videos. With the rapid development of new media, massive video resources and a large number of video-related information are sweeping in. The evaluation of video can no longer be limited to the ratings of traditional platforms. Video playback on new media platforms, network impact, video content and other related data are also important indicators affecting video evaluation. Video quality is closely related to video content characteristics. Different videos have different sensitivity to the same packet loss rate. The IPTV video quality evaluation model based on content features considers the video content characteristics. Firstly, the QP, bit rate and motion vector (MV) information of the video is obtained by analyzing the video stream. Then, the time complexity of the video is calculated by using the obtained MV, and the spatial complexity of the video is calculated by quantization parameters and bit rate. Videos are classified by clustering analysis. On this basis, the BP neural network is used to establish the model and evaluate the video quality, so as to better reflect the visual perception of the human eye. The model has low computational complexity and is suitable for IPTV video quality assessment with certain computing power in network nodes.
Estimation of Multiple Atmospheric Pollutants Through Image Analysis

Multiple atmospheric pollutants, such as PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> , PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> , and NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , degrades air quality in many parts of the world. Fine-grained air pollution data can help combat the problem, but conventional monitoring stations are too expensive to support high spatial resolution; image-based estimates have the potential to improve spatial coverage. We estimate pollutant concentrations from images using the position-and color-dependent properties of scattering and absorption. We are the first to use images to estimate pollutant concentrations in systems with multiple pollutants. We achieve this by considering the differences in scattering and absorption spectra between different pollutants. Our system improves the accuracy of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> , PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> , and NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> estimation by 22% for single-scene images in Beijing and Shanghai compared to the best existing image-based techniques.
LPOD: A Local Path Based Optimized Scheduling Algorithm for Deadline-Constrained Big Data Workflows in the Cloud

List based scheduling algorithms have been proven an optimistic strategy with a shorter response time to generate feasible solutions for the workflow scheduling problem. Data-intensive and computation-intensive workflow applications have different characteristics in terms of the ratio between data transfer time and task execution time. Workflow scheduling algorithms in a cloud-based environment should adequately consider the characteristics of the underlying cloud platform such as the on-demand resource provisioning strategy, the practically unlimited compute capacities, the booting times of virtual machines, the homogeneous network and the pay-as-you-go price model to produce an optimal scheduling solution within the deadline constraint of a given workflow. In this paper, a path based scheduling algorithm, named LPOD, is proposed to find the best workflow schedule solution with minimum monetary cost in a cloud computing environment. A series of case studies have been carefully conducted using synthetic workflows based on DATAVIEW, which is a popular open-source big data workflow management system. The experimental results show that the proposed algorithm is efficient and can generate better workflow schedules than the state-of-the-art algorithms such as IC-PCP and SGX-E2C2D.
Bright: an augmented reality assistive platform for visual impairment

283 million people suffer from moderate to severe vision impairment or blindness; many today rely on lens-based optical aids of limited use. Electronic pass-through zoom headsets are extremely expensive and provide narrow feature sets with compromised comfort and ease-of-use, while mobile app-based aids provide useful functionality but in an inconvenient form factor. The present project explores the development of an aid for the visually impaired, designed as a software solution running on off-the-shelf optical see-through augmented reality head-mounted display devices such as the Microsoft HoloLens, to deliver a broad feature set while reducing cost and optimizing comfort and usability.
The LMU Munich Unsupervised Machine Translation System for WMT19

We describe LMU Munich's machine translation system for German→Czech translation which was used to participate in the WMT19 shared task on unsupervised news translation.We train our model using monolingual data only from both languages.The final model is an unsupervised neural model using established techniques for unsupervised translation such as denoising autoencoding and online back-translation.We bootstrap the model with masked language model pretraining and enhance it with back-translations from an unsupervised phrase-based system which is itself bootstrapped using unsupervised bilingual word embeddings.
Biometric Fusion System Using Face and Voice Recognition

This paper presents a biometric fusion system for human recognition which utilizes voice and face biometric features. The aim of the system is to verify or identify the users by their face and/or voice characteristics. The system allows reliable human recognition based on physiological biometric feature—face, and physiological–behavioral biometric feature—voice. The outcome yields better identification results using voice biometrics and better verification results using face biometrics. The approach used here for face recognition consists of an image processing being histogram equalization and application of Gabor filter, and extracting a feature vector from the image. Such a feature extraction method has been successfully applied in the iris recognition [3]. The speech recognition process uses a recording divided into frames out of which only the frames containing speech are considered. The feature vector is extracted from Mel frequency cepstral coefficient. The classification process is performed by a basic dynamic time warping algorithm. In this paper, evaluation of the above approaches for face and voice recognition is performed.
Efficient Instance and Semantic Segmentation for Automated Driving

Environment perception for automated vehicles is achieved by fusing the outputs of different sensors such as cameras, LIDARs and RADARs. Images provide a semantic understanding of the environment at object level using instance segmentation, but also at background level using semantic segmentation. We propose a fully convolutional residual network based on Mask R-CNN to achieve both semantic and instance level recognition. We aim at developing an efficient network that could run in real-time for automated driving applications without compromising accuracy. Moreover, we compare and experiment with two different backbone architectures, a classification type of network and a faster segmentation type of network based on dilated convolutions. Experiments demonstrate top results on the publicly available Cityscapes dataset.
Spatial Constraint Multiple Granularity Attention Network For Clothesretrieval

Clothes retrieval is very popular on e-commerce websites, and it is still a very challenging problem. These difficulties come from clothing deformation, variations in angle, noisy background and so on. According to our observation, although the overall shape of clothing varies greatly, there is slightly deformation in some local parts (such as collar, pocket, etc.) which has a significant impact on the final retrieval performance. In this paper, we propose an end-to-end attention-based deep network. It consists of two main branches, one of which is used to extract the key parts in clothing from different granularities. The other is designed to simulate the spatial contextual between different parts to obtain more discriminative feature representation by means of Long Short-Term Memory (LSTM). Experimental results demonstrate that our method is superior to previous methods in terms of performance and effectiveness.