Reviewerly/exHarmony huggingface dataset:
Smart healthcare framework for ambient assisted living using IoMT and big data analytics techniques

In the era of pervasive computing, human living has become smarter by the latest advancements in IoMT (Internet of Medical Things), wearable sensors and telecommunication technologies in order to deliver smart healthcare services. IoMT has the potential to revolutionize the healthcare industry. IoMT interconnects wearable sensors, patients, healthcare providers and caregivers via software and ICT (Information and Communication Technology). AAL (Ambient Assisted Living) enables integration of new technologies to be part of our daily life activities. In this paper, we have provided a novel smart healthcare framework for AAL to monitor the physical activities of elderly people using IoMT and intelligent machine learning algorithms for faster analysis, decision making and better treatment recommendations. Data is collected from multiple wearable sensors placed on subject’s left ankle, right arm, and chest, is transmitted through IoMT devices to the integrated cloud and data analytics layer. To process huge amounts of data in parallel, Hadoop MapReduce techniques are used. Multinomial Naïve Bayes classifier, which fits into the MapReduce paradigm, is utilized to recognize the motion experienced by different body parts and provides higher scalability and better performance with parallel processing when compared to serial processor. Our proposed framework predicts 12 physical activities with an overall accuracy of 97.1%. This can be considered as an optimal solution for recognizing physical activities to remotely monitor health conditions of elderly people.
A comprehensive study for robot navigation techniques

An intelligent autonomous robot is required in various applications such as space, transportation, industry, and defense. Mobile robots can also perform several tasks like material handling, disaster relief, patrolling, and rescue operation. Therefore, an autonomous robot is required that can travel freely in a static or a dynamic environment. Smooth and safe navigation of mobile robot through cluttered environment from start position to goal position with following safe path and producing optimal path length is the main aim of mobile robot navigation. Regarding this matter, several techniques have been explored by researchers for robot navigation path planning. An effort has been made in this article to study several navigation techniques, which are well suited for the static and dynamic environment and can be implemented for real-time navigation of mobile robot.
Relational Collaborative Filtering

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity - i.e., the item similarity evidenced by user interactions like ratings and purchases. Nevertheless, there exist multiple relations between items in real-world scenarios, e.g., two movies share the same director, two products complement with each other, etc. Distinct from the collaborative similarity that implies co-interact patterns from the user's perspective, these relations reveal fine-grained knowledge on items from different perspectives of meta-data, functionality, etc. However, how to incorporate multiple item relations is less explored in recommendation research.
PReMVOS: Proposal-Generation, Refinement and Merging for Video Object Segmentation

We address semi-supervised video object segmentation, the task of automatically generating accurate and consistent pixel masks for objects in a video sequence, given the first-frame ground truth annotations. Towards this goal, we present the PReMVOS algorithm (Proposal-generation, Refinement and Merging for Video Object Segmentation). Our method separates this problem into two steps, first generating a set of accurate object segmentation mask proposals for each video frame and then selecting and merging these proposals into accurate and temporally consistent pixel-wise object tracks over a video sequence in a way which is designed to specifically tackle the difficult challenges involved with segmenting multiple objects across a video sequence. Our approach surpasses all previous state-of-the-art results on the DAVIS 2017 video object segmentation benchmark with a $$\mathcal {J}$$ & $$\mathcal {F}$$ mean score of 71.6 on the test-dev dataset, and achieves first place in both the DAVIS 2018 Video Object Segmentation Challenge and the YouTube-VOS 1st Large-scale Video Object Segmentation Challenge.
Cyber threat intelligence sharing: Survey and research directions

Cyber Threat Intelligence (CTI) sharing has become a novel weapon in the arsenal of cyber defenders to proactively mitigate increasing cyber attacks. Automating the process of CTI sharing, and even the basic consumption, has raised new challenges for researchers and practitioners. This extensive literature survey explores the current state-of-the-art and approaches different problem areas of interest pertaining to the larger field of sharing cyber threat intelligence. The motivation for this research stems from the recent emergence of sharing cyber threat intelligence and the involved challenges of automating its processes. This work comprises a considerable amount of articles from academic and gray literature, and focuses on technical and non-technical challenges. Moreover, the findings reveal which topics were widely discussed, and hence considered relevant by the authors and cyber threat intelligence sharing communities.
TreeUNet: Adaptive Tree convolutional neural networks for subdecimeter aerial image segmentation

Fine-grained semantic segmentation results are typically difficult to obtain for subdecimeter aerial imagery segmentation as a result of complex remote sensing content and optical conditions. Recently, convolutional neural networks (CNNs) have shown outstanding performance on this task. Although many deep neural network structures and techniques have been applied to improve accuracy, few have attended to improving the differentiation of easily confused classes. In this paper, we propose TreeUNet, a tool that uses an adaptive network to increase the classification rate at the pixel level. Specifically, based on a deep semantic model infrastructure, a Tree-CNN block in which each node represents a ResNeXt unit is constructed adaptively in accordance with the confusion matrix and the proposed TreeCutting algorithm. By transmitting feature maps through concatenating connections, the Tree-CNN block fuses multiscale features and learns best weights for the model. In experiments on the ISPRS two-dimensional Vaihingen and Potsdam semantic labelling datasets, the results obtained by TreeUNet are competitive among published state-of-the-art methods. Detailed comparison and analysis show that the improvement brought by the adaptive Tree-CNN block is significant.
Practical Full Resolution Learned Lossless Image Compression

We propose the first practical learned lossless image compression system, L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and JPEG 2000. At the core of our method is a fully parallelizable hierarchical probabilistic model for adaptive entropy coding which is optimized end-to-end for the compression task. In contrast to recent autoregressive discrete probabilistic models such as PixelCNN, our method i) models the image distribution jointly with learned auxiliary representations instead of exclusively modeling the image distribution in RGB space, and ii) only requires three forward-passes to predict all pixel probabilities instead of one for each pixel. As a result, L3C obtains over two orders of magnitude speedups when sampling compared to the fastest PixelCNN variant (Multiscale-PixelCNN). Furthermore, we find that learning the auxiliary representation is crucial and outperforms predefined auxiliary representations such as an RGB pyramid significantly.
Unsupervised Domain Adaptation Using Feature-Whitening and Consensus Loss

A classifier trained on a dataset seldom works on other datasets obtained under different conditions due to domain shift. This problem is commonly addressed by domain adaptation methods. In this work we introduce a novel deep learning framework which unifies different paradigms in unsupervised domain adaptation. Specifically, we propose domain alignment layers which implement feature whitening for the purpose of matching source and target feature distributions. Additionally, we leverage the unlabeled target data by proposing the Min-Entropy Consensus loss, which regularizes training while avoiding the adoption of many user-defined hyper-parameters. We report results on publicly available datasets, considering both digit classification and object recognition tasks. We show that, in most of our experiments, our approach improves upon previous methods, setting new state-of-the-art performances.
Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing

Workflow scheduling is a largely studied research topic in cloud computing, which targets to utilize cloud resources for workflow tasks by considering the objectives specified in QoS. In this paper, we model dynamic workflow scheduling problem as a dynamic multi-objective optimization problem (DMOP) where the source of dynamism is based on both resource failures and the number of objectives which may change over time. Software faults and/or hardware faults may cause the first type of dynamism. On the other hand, confronting real-life scenarios in cloud computing may change number of objectives at runtime during the execution of a workflow. In this study, we propose a prediction-based dynamic multi-objective evolutionary algorithm, called NN-DNSGA-II algorithm, by incorporating artificial neural network with the NSGA-II algorithm. Additionally, five leading non-prediction based dynamic algorithms from the literature are adapted for the dynamic workflow scheduling problem. Scheduling solutions are found by the consideration of six objectives: minimization of makespan, cost, energy and degree of imbalance; and maximization of reliability and utilization. The empirical study based on real-world applications from Pegasus workflow management system reveals that our NN-DNSGA-II algorithm significantly outperforms the other alternatives in most cases with respect to metrics used for DMOPs with unknown true Pareto-optimal front, including the number of non-dominated solutions, Schott’s spacing and Hypervolume indicator.
Adaptive Density Map Generation for Crowd Counting

The following topics are dealt with: learning (artificial intelligence); feature extraction; neural nets; image representation; object detection; convolutional neural nets; image segmentation; image classification; computer vision; video signal processing.
A Comparative Analysis of Distributed Ledger Technology Platforms

Distributed Ledger Technology (DLT) has emerged as one of the most disruptive technologies in the last decade. It promises to change the way people do their business, track their products, and manage their personal data. Though the concept of DLT was first implemented in 2009 as Bitcoin, it has gained significant attention only in the past few years. During this time, different DLT enthusiasts and commercial companies have proposed and developed several DLT platforms. These platforms are usually categorized as public vs private, general purpose vs application specific and so on. As a growing number of people are interested to build DLT applications, it is important to understand their underlying architecture and capabilities in order to determine which DLT platform should be leveraged for a specific DLT application. In addition, the platforms need to be evaluated and critically analyzed to assess their applicability, resiliency and sustainability in the long run. In this paper, we have surveyed several leading DLT platforms and evaluated their capabilities based on a number of quantitative and qualitative criteria. The comparative analysis presented in this paper will help the DLT developers and architects to choose the best platform as per their requirement(s).
A new application of fractional Atangana–Baleanu derivatives: Designing ABC-fractional masks in image processing

Based on a new definition for derivative and integral of fractional-order, several fractional masks have been presented for the use of image denoising. In each method, the process involves constructing a square and then applying it to all the corresponding blocks in the noisy image. We have measured the denoising performance of our proposed masks by employing some known indexes. They are the peak signal-to-noise ratio (PSNR), ENTROPY, and SSIM. The obtained experimental results show that our proposed masks are computationally efficient, and their performances are compatible with other standard and fractional smoothing filters.
Learning to Discover Novel Visual Categories via Deep Transfer Clustering

We consider the problem of discovering novel object categories in an image collection. While these images are unlabelled, we also assume prior knowledge of related but different image classes. We use such prior knowledge to reduce the ambiguity of clustering, and improve the quality of the newly discovered classes. Our contributions are twofold. The first contribution is to extend Deep Embedded Clustering to a transfer learning setting; we also improve the algorithm by introducing a representation bottleneck, temporal ensembling, and consistency. The second contribution is a method to estimate the number of classes in the unlabelled data. This also transfers knowledge from the known classes, using them as probes to diagnose different choices for the number of classes in the unlabelled subset. We thoroughly evaluate our method, substantially outperforming state-of-the-art techniques in a large number of benchmarks, including ImageNet, OmniGlot, CIFAR-100, CIFAR-10, and SVHN.
QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

We provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a high level description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging applications. We provide a survey of the state-of-the-art of QoE management techniques categorized into three different groups: a) QoE-aware/driven strategies using SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over emerging architectures such as multi-access edge computing, cloud/fog computing, and information-centric networking; and c) extended QoE management approaches in new domains such as immersive augmented and virtual reality, mulsemedia and video gaming applications. Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative service management in softwarized networks. Finally, we provide a discussion on future research directions with a focus on emerging research areas in QoE management, such as QoE-oriented business models, QoE-based big data strategies, and scalability issues in QoE optimization.
Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification

Aspect-level sentiment classification aims to distinguish the sentiment polarities over one or more aspect terms in a sentence. Existing approaches mostly model different aspects in one sentence independently, which ignore the sentiment dependencies between different aspects. However, such dependency information between different aspects can bring additional valuable information for aspect-level sentiment classification. In this paper, we propose a novel aspect-level sentiment classification model based on graph convolutional networks (GCN) which can effectively capture the sentiment dependencies between multi-aspects in one sentence. Our model firstly introduces bidirectional attention mechanism with position encoding to model aspect-specific representations between each aspect and its context words, then employs GCN over the attention mechanism to capture the sentiment dependencies between different aspects in one sentence. The proposed approach is evaluated on the SemEval 2014 datasets. Experiments show that our model outperforms the state-of-the-art methods. We also conduct experiments to evaluate the effectiveness of GCN module, which indicates that the dependencies between different aspects are highly helpful in aspect-level sentiment classification1 .
A new color image encryption scheme based on 2DNLCML system and genetic operations

Based on the spatiotemporal chaos of the two Dimensional Nonlinear Coupled Map Lattices (2DNLCML) and genetic operations, a new optical color image encryption scheme is proposed. Because of the spatial nonlinear coupling which replaces traditional adjacent coupling, the 2DNLCML system contains good features such as ergodic pseudo-random sequence, less periodic windows in bifurcations and larger range of parameters in chaotic dynamics, which is more suitable for image encryption. The scheme also performs genetic operations including mutation and crossover for binary coding, thus reducing the computational complexity. Simulations have been carried out and the results demonstrate that the proposed algorithm has properties of large key space, high sensitivity to key, strong resisting attack. So, it is more secure and effective algorithm for encryption of digital images.
Image Dehazing by an Artificial Image Fusion Method Based on Adaptive Structure Decomposition

Haze can seriously affect the visible and visual quality of outdoor images. As a challenge in practice, image dehazing techniques are always used to remove haze from the captured images. Existing image dehazing algorithms focus on enhancing both global image contrast and saturation, but ignore the local enhancement. So the dehazed images do not often have good performance in the visual quality of local details. This paper proposes a new single-image dehazing solution based on the adaptive structure decomposition integrated multi-exposure image fusion (PADMEF). A set of underexposed image sequences are extracted from a single blurred image first by a series of gamma correction and the spatial linear adjustment of saturation. Then different exposure-level images are fused into a haze-free image by applying a multi-exposure image fusion (MEF) scheme based adaptive structure decomposition to each image patch. The proposed image dehazing scheme can effectively eliminate the visual degradation caused by haze without the physical model inversion of haze formation. Both apriori estimation of scene depth and the expensive refinement process of depth mapping can be avoided. The entropy of image texture named as texture energy is used to measure the image energy and obtain the information size contained in an image. Meanwhile, a texture energy based method is presented to adaptively select the corresponding patch size for the decomposition of image structure. In addition, this paper verifies that the dehazed images obtained by the patch based MEF algorithm always meet the requirements of intensity decrease. The comparative experiment results are evaluated in both qualitative and quantitative aspects, which confirm the effectiveness of the proposed solution in haze removal.
Streaming Automatic Speech Recognition with the Transformer Model

Encoder-decoder based sequence-to-sequence models have demonstrated state-of-the-art results in end-to-end automatic speech recognition (ASR). Recently, the transformer architecture, which uses self-attention to model temporal context information, has been shown to achieve significantly lower word error rates (WERs) compared to recurrent neural network (RNN) based system architectures. Despite its success, the practical usage is limited to offline ASR tasks, since encoder-decoder architectures typically require an entire speech utterance as input. In this work, we propose a transformer based end-to-end ASR system for streaming ASR, where an output must be generated shortly after each spoken word. To achieve this, we apply time-restricted self-attention for the encoder and triggered attention for the encoder-decoder attention mechanism. Our proposed streaming transformer architecture achieves 2.8% and 7.3% WER for the "clean" and "other" test data of LibriSpeech, which to our knowledge is the best published streaming end-to-end ASR result for this task.
Relation Matters: Relational Context-Aware Fully Convolutional Network for Semantic Segmentation of High-Resolution Aerial Images

Most current semantic segmentation approaches fall back on deep Convolutional Neural Networks (CNNs).However, their use of convolution operations with local receptive fields causes failures in modeling contextual spatial relations.Prior works have sought to address this issue by using graphical models or spatial propagation modules in networks.But such models often fail to capture long-range spatial relationships between entities, which leads to spatially fragmented predictions.Moreover, recent works have demonstrated that channel-wise information also acts a pivotal part in CNNs.In this work, we introduce two simple yet effective network units, the spatial relation module and the channel relation module, to learn and reason about global relationships between any two spatial positions or feature maps, and then produce relation-augmented feature representations.The spatial and channel relation modules are general and extensible, and can be used in a plug-and-play fashion with the existing Fully Convolutional Network (FCN) framework.We evaluate relation module-equipped networks on semantic segmentation tasks using two aerial image datasets, namely ISPRS Vaihingen and Potsdam datasets, which fundamentally depend on long-range spatial relational reasoning.The networks achieve very competitive results, a mean F1 score of 88.54% on the Vaihingen dataset and a mean F1 score of 88.01% on the Potsdam dataset, bringing significant improvements over baselines.
Swift

We report on experiences with Swift congestion control in Google datacenters. Swift targets an end-to-end delay by using AIMD control, with pacing under extreme congestion. With accurate RTT measurement and care in reasoning about delay targets, we find this design is a foundation for excellent performance when network distances are well-known. Importantly, its simplicity helps us to meet operational challenges. Delay is easy to decompose into fabric and host components to separate concerns, and effortless to deploy and maintain as a congestion signal while the datacenter evolves. In large-scale testbed experiments, Swift delivers a tail latency of <50μs for short RPCs, with near-zero packet drops, while sustaining ~100Gbps throughput per server. This is a tail of <3x the minimal latency at a load close to 100%. In production use in many different clusters, Swift achieves consistently low tail completion times for short RPCs, while providing high throughput for long RPCs. It has loss rates that are at least 10x lower than a DCTCP protocol, and handles O(10k) incasts that sharply degrade with DCTCP.