Reviewerly/exHarmony huggingface dataset:
A Cloud-Guided Feature Extraction Approach for Image Retrieval in Mobile Edge Computing

Mobile Edge Computing (MEC) can facilitate various important image retrieval applications for mobile users by offloading partial computation tasks from resource-limited mobile devices to edge servers. However, existing related works suffer from two major limitations. (i) High network bandwidth cost: they need to extract numerous features from the image and upload these feature data to the cloud server. (ii) Lowretrieval accuracy: they separate the feature extraction processes from the image data set in the cloud server, thus unable to provide effective features for accurate image retrieval. In this paper, we propose a cloud-guided feature extraction approach for mobile image retrieval. In the proposed approach, the cloud server first leverages the relationships among labeled images in the data set to learn a projection matrix P. Then, it uses the matrix P to extract discriminative features from the image data set and form a low-dimensional feature data set. Following that, the cloud server sends the matrix P to the edge server and uses it to multiply the image χ. The result PTχ, i.e., image features, is uploaded to the cloud server to find the label of the image with the most similar multiplying result. The label is regarded as the retrieval result and returned to the mobile user. In the cloud-guided feature extraction approach, the matrix P can extract a small number of effective image features, which not only reduces network traffic but also improves retrieval accuracy. We have implemented a prototype system to validate the proposed approach and evaluate its performance by conducting extensive experiments using a real MEC environment and data set. The experimental results show that the proposed approach reduces the network traffic by nearly 93 percent and improves the retrieval accuracy by nearly 6.9 percent compared with the state-of-the-art image retrieval approaches in MEC.
Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning

Proteomics data encode molecular features of diagnostic value and accurately reflect key underlying biological mechanisms in cancers. Histopathology imaging is a well-established clinical approach to cancer diagnosis. The predictive relationship between large-scale proteomics and H&E-stained histopathology images remains largely uncharacterized. Here we investigate such associations through the application of machine learning, including deep neural networks, to proteomics and histology imaging datasets generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) from clear cell renal cell carcinoma patients. We report robust correlations between a set of diagnostic proteins and predictions generated by an imaging-based classification model. Proteins significantly correlated with the histology-based predictions are significantly implicated in immune responses, extracellular matrix reorganization, and metabolism. Moreover, we showed that the genes encoding these proteins also reliably recapitulate the biological associations with imaging-derived predictions based on strong gene-protein expression correlations. Our findings offer novel insights into the integrative modeling of histology and omics data through machine learning, as well as the methodological basis for new research opportunities in this and other cancer types.
Blockchain-Based Intelligent Network Management for 5G and Beyond

The concept of spectrum and infrastructure sharing appeared to solve the problem by allowing operators to cooperate between each other. However, the current architecture of mobile network, which is mostly based on the centralized management entities is not suitable for the spectrum and infrastructure sharing due to the complexity of billing procedure. Moreover, the operator oriented mobile network is slowing the overall technological development, because operators must balance between capital expenditures and profit margins. Such situation negatively affects users, because they are either experience lower quality of service or forced to pay higher price. In this paper, we propose a new intelligent network architecture, which leverage the blockchain technology to handle relationship between operators and users based on smart contracts. The new unlicensed spectrum sharing algorithm between operators using the virtual cryptocurrency has been developed based on game theory. Simulation results show that proposed algorithm achieves Nash equilibrium between operators within few seconds.
On the Capacity of Leaky Private Information Retrieval

Private information retrieval (PIR) allows users to retrieve data from databases without revealing the identity of that data. An extensive body of works has investigated efficient schemes to achieve computational and information-theoretic privacy. The latter guarantees that no information is revealed to the databases, irrespective of their computational power. Although information-theoretic PIR (IT-PIR) provides a strong privacy guarantee, it can be too taxing for certain applications. In this paper, we initiate the study of leaky private information retrieval (L-PIR), where a bounded amount of privacy leakage is allowed and measured through a parameter ε. The classical IT-PIR formulation is obtained by setting ε = 0, and for ε > 0, we explore the opportunities offered for reducing the download cost. We derive new upper and lower bounds on the download cost of L-PIR for any arbitrary ε, any number of messages K, and for N = 2 databases.
Interpretable Visual Question Answering by Reasoning on Dependency Trees

Collaborative reasoning for understanding image-question pairs is a very critical but underexplored topic in interpretable visual question answering systems. Although very recent studies have attempted to use explicit compositional processes to assemble multiple subtasks embedded in questions, their models heavily rely on annotations or handcrafted rules to obtain valid reasoning processes, which leads to either heavy workloads or poor performance on compositional reasoning. In this paper, to better align image and language domains in diverse and unrestricted cases, we propose a novel neural network model that performs global reasoning on a dependency tree parsed from the question; thus, our model is called a parse-tree-guided reasoning network (PTGRN). This network consists of three collaborative modules: i) an attention module that exploits the local visual evidence of each word parsed from the question, ii) a gated residual composition module that composes the previously mined evidence, and iii) a parse-tree-guided propagation module that passes the mined evidence along the parse tree. Thus, PTGRN is capable of building an interpretable visual question answering (VQA) system that gradually derives image cues following question-driven parse-tree reasoning. Experiments on relational datasets demonstrate the superiority of PTGRN over current state-of-the-art VQA methods, and the visualization results highlight the explainable capability of our reasoning system.
Salient Segmentation based Object Detection and Recognition using Hybrid Genetic Transform

Object detection and recognition is an effective and fundamental technique used to track the objects accurately in complex scenes. Over the last decade, object analysis has caught the attention of researchers to explore and cover the aspects of object detection and recognition related problems in the technologies such as robotics, surveillance, agriculture, medical and marketing. In this paper, we present a unique method for accurate object recognition. Firstly, the clustering of similar colors and regions is achieved by applying K-mean clustering algorithm. Secondly, segmentation is performed by merging the previously achieved clusters, which are similar and connected. Thirdly, Generalized Hough transform is used for the detection of salient objects. Finally, Genetic algorithm is applied as recognizer engine to recognize the salient objects under different environmental settings. The accuracy of our experimental work has been evaluated on the benchmark dataset MSRC.
A Fast Feature Extraction Algorithm for Image and Video Processing

Medical images and videos are utilized to discover, diagnose and treat diseases. Managing, storing, and retrieving stored images effectively are considered important topics. The rapid growth of multimedia data, including medical images and videos, has caused a swift rise in data transmission volume and repository size. Multimedia data contains useful information; however, it consumes an enormous storage space. Therefore, high processing time for that sheer volume of data will be required. Image and video applications demand for reduction in computational cost (processing time) when extracting features. This paper introduces a novel method to compute transform coefficients (features) from images or video frames. These features are used to represent the local visual content of images and video frames. We compared the proposed method with the traditional approach of feature extraction using a standard image technique. Furthermore, the proposed method is employed for shot boundary detection (SBD) applications to detect transitions in video frames. The standard TRECVID 2005, 2006, and 2007 video datasets are used to evaluate the performance of the SBD applications. The achieved results show that the proposed algorithm significantly reduces the computational cost in comparison to the traditional method.
Indonesian Abstractive Text Summarization Using Bidirectional Gated Recurrent Unit

Abstractive text summarization is more challenging than the extractive one since it is performed by paraphrasing the entire contents of the text, which has a higher difficulty. But, it produces a more natural summary and higher inter-sentence cohesion. Recurrent Neural Network (RNN) has experienced success in summarizing abstractive texts for English and Chinese texts. The Bidirectional Gated Recurrent Unit (BiGRU) RNN architecture is used so that the resulted summaries are influenced by the surrounding words. In this research, such a method is applied for Bahasa Indonesia to improve the text summarizations those are commonly developed using some extractive methods with low inter-sentence cohesion. An evaluation on a dataset of Indonesian journal documents shows that the proposed model is capable of summarizing the overall contents of testing documents into some summaries with high similarities to the provided abstracts. The proposed model resulting success in understanding source text for generating summarization.
Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

Convolutional Neural Networks (CNN) are becoming a common presence in many applications and services, due to their superior recognition accuracy.They are increasingly being used on mobile devices, many times just by porting large models designed for server space, although several model compression techniques have been considered.One model compression technique intended to reduce computations is channel pruning.Mobile and embedded systems now have GPUs which are ideal for the parallel computations of neural networks and for their lower energy cost per operation.Specialized libraries perform these neural network computations through highly optimized routines.As we find in our experiments, these libraries are optimized for the most common network shapes, making uninstructed channel pruning inefficient.We evaluate higher level libraries, which analyze the input characteristics of a convolutional layer, based on which they produce optimized OpenCL (Arm Compute Library and TVM) and CUDA (cuDNN) code.However, in reality, these characteristics and subsequent choices intended for optimization can have the opposite effect.We show that a reduction in the number of convolutional channels, pruning 12% of the initial size, is in some cases detrimental to performance, leading to 2× slowdown.On the other hand, we also find examples where performance-aware pruning achieves the intended results, with performance speedups of 3× with cuDNN and above 10× with Arm Compute Library and TVM.Our findings expose the need for hardware-instructed neural network pruning.
Volcano-Seismic Transfer Learning and Uncertainty Quantification With Bayesian Neural Networks

Over the past few years, deep learning (DL) has emerged as an important tool in the fields of volcano and earthquake seismology. However, these methods have been applied without performing thorough analyses of the associated uncertainties. Here, we propose a solution to enhance volcano-seismic monitoring systems, through probabilistic Bayesian DL; we implement and demonstrate a workflow for waveform classification, rapid quantification of the associated uncertainty, and link these uncertainties to changes in volcanic unrest. Specifically, we introduce Bayesian neural networks (BNNs) to perform event identification, classification, and their estimated uncertainty on data gathered at two active volcanoes, Mount St. Helens, Washington, USA, and Bezymianny, Kamchatka, Russia. We demonstrate how BNNs achieve excellent performance (92.08%) in discriminating both the type of event and its origin when the two data sets are merged together, and no additional training information is provided. Finally, we demonstrate that the data representations learned by the BNNs are transferable across different eruptive periods. We also find that the estimated uncertainty is related to changes in the state of unrest at the volcanoes and propose that it could be used to gauge whether the learned models may be exported to other eruptive scenarios.
MeCap: Whole-Body Digitization for Low-Cost VR/AR Headsets

Low-cost, smartphone-powered VR/AR headsets are becoming more popular. These basic devices - little more than plastic or cardboard shells - lack advanced features, such as controllers for the hands, limiting their interactive capability. Moreover, even high-end consumer headsets lack the ability to track the body and face. For this reason, interactive experiences like social VR are underdeveloped. We introduce MeCap, which enables commodity VR headsets to be augmented with powerful motion capture ("MoCap") and user-sensing capabilities at very low cost (under $5). Using only a pair of hemi-spherical mirrors and the existing rear-facing camera of a smartphone, MeCap provides real-time estimates of a wearer's 3D body pose, hand pose, facial expression, physical appearance and surrounding environment - capabilities which are either absent in contemporary VR/AR systems or which require specialized hardware and controllers. We evaluate the accuracy of each of our tracking features, the results of which show imminent feasibility.
R you ready? Using the R programme for statistical analysis and graphics

Abstract For conducting research, nurses typically use commercial statistical packages. R software is a free, powerful, and flexible alternative, but is less familiar and used less frequently in nursing research. In this paper, we use data from a previous study to demonstrate a few typical steps in exploratory data analysis using R. A step‐by‐step description of some basic analyses in R is provided here, including examples of specific functions to read and manipulate the data, calculate scores from individual questionnaire items, and prepare a correlation plot and summary table.
Recurrent Neural Networks for Early Detection of Heart Failure From Longitudinal Electronic Health Record Data

We determined the impact of data volume and diversity and training conditions on recurrent neural network methods compared with traditional machine learning methods.Using longitudinal electronic health record data, we assessed the relative performance of machine learning models trained to detect a future diagnosis of heart failure in primary care patients. Model performance was assessed in relation to data parameters defined by the combination of different data domains (data diversity), the number of patient records in the training data set (data quantity), the number of encounters per patient (data density), the prediction window length, and the observation window length (ie, the time period before the prediction window that is the source of features for prediction). Data on 4370 incident heart failure cases and 30 132 group-matched controls were used. Recurrent neural network model performance was superior under a variety of conditions that included (1) when data were less diverse (eg, a single data domain like medication or vital signs) given the same training size; (2) as data quantity increased; (3) as density increased; (4) as the observation window length increased; and (5) as the prediction window length decreased. When all data domains were used, the performance of recurrent neural network models increased in relation to the quantity of data used (ie, up to 100% of the data). When data are sparse (ie, fewer features or low dimension), model performance is lower, but a much smaller training set size is required to achieve optimal performance compared with conditions where data are more diverse and includes more features.Recurrent neural networks are effective for predicting a future diagnosis of heart failure given sufficient training set size. Model performance appears to continue to improve in direct relation to training set size.
Learning Cooperative Personalized Policies from Gaze Data

An ideal Mixed Reality (MR) system would only present virtual information (e.g., a label) when it is useful to the person. However, deciding when a label is useful is challenging: it depends on a variety of factors, including the current task, previous knowledge, context, etc. In this paper, we propose a Reinforcement Learning (RL) method to learn when to show or hide an object's label given eye movement data. We demonstrate the capabilities of this approach by showing that an intelligent agent can learn cooperative policies that better support users in a visual search task than manually designed heuristics. Furthermore, we show the applicability of our approach to more realistic environments and use cases (e.g., grocery shopping). By posing MR object labeling as a model-free RL problem, we can learn policies implicitly by observing users' behavior without requiring a visual search model or data annotation.
PAUSE THE BLOCKCHAIN LEGAL REVOLUTION

Abstract When bitcoin was released by the mysterious Satoshi Nakamoto in 2008, few could have predicted that it would attract as much attention as it has today. It has spawned a veritable host of other cryptocurrencies, including ether on the upstart Ethereum network, which boasts smart contract functionality. The underlying blockchain technology has also attracted attention, with some within the blockchain community suggesting that it can solve such diverse problems as secured digital voting to tracking food provenance. In the legal context, blockchains have been envisaged as capable of revolutionising registries for assets ranging from land to intellectual property, modernising clearing and settlement, and even fundamentally transforming the contracting process. This article critically evaluates the popular claims surrounding the potential of blockchain technologies to disrupt the legal system by separating hype from fact.
Efficient Object Detection Framework and Hardware Architecture for Remote Sensing Images

Object detection in remote sensing images on a satellite or aircraft has important economic and military significance and is full of challenges. This task requires not only accurate and efficient algorithms, but also highperformance and low power hardware architecture. However, existing deep learning based object detection algorithms require further optimization in small objects detection, reduced computational complexity and parameter size. Meanwhile, the generalpurpose processor cannot achieve better power efficiency, and the previous design of deep learning processor has still potential for mining parallelism. To address these issues, we propose an efficient contextbased feature fusion single shot multibox detector (CBFFSSD) framework, using lightweight MobileNet as the backbone network to reduce parameters and computational complexity, adding feature fusion units and detecting feature maps to enhance the recognition of small objects and improve detection accuracy. Based on the analysis and optimization of the calculation of each layer in the algorithm, we propose efficient hardware architecture of deep learning processor with multiple neural processing units (NPUs) composed of 2D processing elements (PEs), which can simultaneously calculate multiple output feature maps. The parallel architecture, hierarchical onchip storage organization, and the local register are used to achieve parallel processing, sharing and reuse of data, and make the calculation of processor more efficient. Extensive experiments and comprehensive evaluations on the public NWPU VHR10 dataset and comparisons with some stateoftheart approaches demonstrate the effectiveness and superiority of the proposed framework. Moreover, for evaluating the performance of proposed hardware architecture, we implement it on Xilinx XC7Z100 field programmable gate array (FPGA) and test on the proposed CBFFSSD and VGG16 models. Experimental results show that our processor are more power efficient than general purpose central processing units (CPUs) and graphics processing units (GPUs), and have better performance density than other stateoftheart FPGAbased designs.
Learning Where to Look While Tracking Instruments in Robot-Assisted Surgery

Directing of the task-specific attention while tracking instrument in surgery holds great potential in robot-assisted intervention. For this purpose, we propose an end-to-end trainable multitask learning (MTL) model for real-time surgical instrument segmentation and attention prediction. Our model is designed with a weight-shared encoder and two task-oriented decoders and optimized for the joint tasks. We introduce batch-Wasserstein (bW) loss and construct a soft attention module to refine the distinctive visual region for efficient saliency learning. For multitask optimization, it is always challenging to obtain convergence of both tasks in the same epoch. We deal with this problem by adopting 'poly' loss weight and two phases of training. We further propose a novel way to generate task-aware saliency map and scanpath of the instruments on MICCAI robotic instrument segmentation dataset. Compared to the state of the art segmentation and saliency models, our model outperforms most of the evaluation metrics.