Reviewerly/exHarmony huggingface dataset:
Multi-layer Representation Learning and Its Application to Electronic Health Records

Electronic Health Records (EHRs) are digital records associated with hospitalization, diagnosis, medications and so on. Secondary use of EHRs can promote the clinical informatics applications and the development of healthcare undertaking. EHRs have the unique characteristic where the patient visits are temporally ordered but the diagnosis codes within a visit are randomly ordered. The hierarchical structure requires a multi-layer network to explore the different relational information of EHRs. In this paper, we propose a Multi-Layer Representation Learning method (MLRL), which is capable of learning effective patient representation by hierarchically exploring the valuable information in both diagnosis codes and patient visits. Firstly, MLRL utilizes the multi-head attention mechanism to explore the potential connections in diagnosis codes, and a linear transformation is implemented to further map the code vectors to non-negative real-valued representations. The initial visit vectors are then obtained by summarizing all the code representations. Secondly, the proposed method combines Bidirectional Long Short-Term Memory with self-attention mechanism to learn the weighted visit vectors which are aggregated to form the patient representation. Finally, to evaluate the performance of MLRL, we apply it to patient’s mortality prediction on real EHRs and the experimental results demonstrate that MLRL has a significant improvement in prediction performance. MLRL achieves around 0.915 in Area Under Curve which is superior to the results obtained by baseline methods. Furthermore, compared with raw data and other data representations, the learned representation with MLRL shows its outstanding results and availability on multiple different classifiers.
Automated contact tracing: a game of big numbers in the time of COVID-19

One of the more widely advocated solutions for slowing down the spread of COVID-19 has been automated contact tracing. Since proximity data can be collected by personal mobile devices, the natural proposal has been to use this for automated contact tracing providing a major gain over a manual implementation. In this work, we study the characteristics of voluntary and automated contact tracing and its effectiveness for mapping the spread of a pandemic due to the spread of SARS-CoV-2. We highlight the infrastructure and social structures required for automated contact tracing to work. We display the vulnerabilities of the strategy to inadequate sampling of the population, which results in the inability to sufficiently determine significant contact with infected individuals. Of crucial importance will be the participation of a significant fraction of the population for which we derive a minimum threshold. We conclude that relying largely on automated contact tracing without population-wide participation to contain the spread of the SARS-CoV-2 pandemic can be counterproductive and allow the pandemic to spread unchecked. The simultaneous implementation of various mitigation methods along with automated contact tracing is necessary for reaching an optimal solution to contain the pandemic.
AMVP: Adaptive CNN-based Multitask Video Processing on Mobile Stream Processing Platforms

The popularity of video cameras has spawned a new type of application called multitask video processing, which uses multiple CNNs to obtain different information of interests from a raw video stream. Unfortunately, the huge resource requirements of CNNs make the concurrent execution of multiple CNNs on a single resource-constrained mobile device challenging. Existing solutions solve this challenge by offloading CNN models to the cloud or edge server, compressing CNN models to fit the mobile device, or sharing some common parts of multiple CNN models. Most of these solutions, however, use the above offloading, compression or sharing strategies in a separate manner, which fail to adapt to the complex edge computing scenario well. In this paper, to solve the above limitation, we propose AMVP, an adaptive execution framework for CNN-based multitask video processing, which elegantly integrates the strategies of CNN layer sharing, feature compression, and model offloading. First, AMVP reduces the total computation workload of multiple CNN inference by sharing some common frozen CNN layers. Second, AMVP supports distributed CNN inference by splitting big CNNs into smaller components running on different devices. Third, AMVP leverages a quantization-based feature compression mechanism to reduce the feature transmission traffic size between two separate CNN components. We conduct extensive experiments on AMVP and the experimental results show that our AMVP framework can adapt to different performance goals and execution environments. Compared to two baseline approaches that only share or offload CNN layers, AMVP achieves up to 61% lower latency and 10% higher throughput with comparative accuracy.
Security Issues of Smart Contracts in Ethereum Platforms

Smart contracts belong to the most rapidly growing aspect of the world of cryptocurrencies. This phenomenon attracts great attention from researchers but also the business community, and the development brings daily a lot of novel applications. Smart contracts allow running contract code transparently for all parties, without the need for a centralized authority. One of the smart contract applications is the issuance of digital assets, so-called tokens, serving as fundraising fuel for Initial Coin Offerings. ICOs bring a new easy, and bureaucracyless way for startups to raise considerable funds from crowds with incredible speed. However, the technology hides a dark side in the form of speculative scams, hardly distinguishable from genuine fundraising activities. Evaluation audit of ICOs associated with the underlying security of smart contracts is a complex issue requiring many efforts. This paper focuses on one of the most popular blockchain frameworks, Ethereum, a prominent ICO and smart contract platform, and its dominant programming language, Solidity.
Low-Latency Live Streaming Over HTTP in Bandwidth-Limited Networks

HTTP adaptive streaming (HAS) can achieve low latency for live streaming with the Common Media Application Format. However, the legacy mechanisms inaccurately measure the available bandwidth because the download time is affected by not only the network but also idle times between chunks at the live encoder side. These wrong measurements may condemn users to a low quality-of-experience (QoE). This letter presents a HAS client with a novel bandwidth measurement heuristic. Our experiments show that it provides a better QoE with fewer video freezes and higher video quality while achieving a live latency down to one second in bandwidth-limited networks.
Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.
Smart and Autonomous Door Unlocking System Using Face Detection and Recognition

Recently, face recognition's popularity has grown beyond imagination and has given a new dimension to the existing security system. From being theoretically proposing the idea on paper to making it a reality, it has gone through a lot of phases. This paper implements ease of access security system to the disabled people. The security is provided in terms of sending a text message to the registered mobile number and buzzing the alarm. The system is trained with images of only known faces of people for whom the door will be unlocked automatically. When the unknown person tries to enter the access to the door is denied. With leading-edge innovations, smart door unlocking system has become more push on it should be well designed to as it is related to home security and impart easy access to the user. This autonomous door unlocking system concerns to keyless door unlock system with the help of face recognition and detection.
A Study on Nine Years of Bitcoin Transactions: Understanding Real-world Behaviors of Bitcoin Miners and Users

Bitcoin is the world's first blockchain-based, peer-to-peer cryptocurrency system. Being tremendously successful, the Bitcoin system is designed to support reliable, secure, and trusted transactions between untrusted peers. Since its release in 2009, the Bitcoin system has rapidly grown to an unprecedentedly large scale. However, the real-world behaviors of miners and users in the system and the efficacy of the original Bitcoin system design in the field deployment still remain unclear, hindering us from understanding its internals and developing the next-generation cryptocurrency system. In this paper, we study the behaviors of Bitcoin miners and users and their interactions based on quantitative analysis of more than nine years of Bitcoin transaction history, from its first release on January 3rd, 2009 to April 30th, 2018. We have analyzed over 300 million transaction records to study the transactions' processing, confirmation, and implementation. We have obtained several critical findings regarding how the miners and users exploit the high degree of freedom provided by the Bitcoin system to achieve their own interests. For example, we find that miners often attempt to maximize their profits even by sacrificing system performance; users could try to speed up the transaction processing by mistakenly trading off security for reduced latency. Such unexpected behaviors, to some degree, deviate from the original design purposes of the Bitcoin system and could bring undesirable consequences. Besides revealing several unexpected behaviors of the Bitcoin miners and users in the real world, we have also discussed the associated system implications as well as optimization opportunities in the future.
Remittances and value added across economic sub-sectors in Sub-Saharan Africa

This research assesses the relevance of enhancing remittances on value added across economic sectors in sub-Saharan Africa for the period 1980–2014 using the Generalised Method of Moments. First, no significant net effects on added value to the agricultural sector are apparent. Second, enhancing remittances engenders a positive net effect on added value to the manufacturing sector. Third, there are negative net effects on added value to the service sector. Given that the unfavourable net incidence of remittances to the service sector is associated with a positive marginal or conditional effect, the analysis is extended by computing thresholds at which remittances induce net positive effects on added value to the service sector. The extended analysis shows that a remittance threshold of 48.5% of GDP is the critical mass needed for further enhancement of remittances to engender positive net effects on value added to the service sector.
Improving Unimodal Object Recognition with Multimodal Contrastive Learning

Robots perceive their environment using various sensor modalities, e.g., vision, depth, sound or touch. Each modality provides complementary information for perception. However, while it can be assumed that all modalities are available for training, when deploying the robot in real-world scenarios the sensor setup often varies. In order to gain flexibility with respect to the deployed sensor setup we propose a new multimodal approach within the framework of contrastive learning. In particular, we consider the case of learning from RGB-D images while testing with one modality available, i.e., exclusively RGB or depth. We leverage contrastive learning to capture high-level information between different modalities in a compact feature embedding. We extensively evaluate our multimodal contrastive learning method on the Falling Things dataset and learn representations that outperform prior methods for RGB-D object recognition on the NYU-D dataset. Our code and details on the used datasets are available at: https://github.com/meyerjo/MultiModalContrastiveLearning.
Hierarchical Attention for Ship Detection in SAR Images

Considering the difficulty of ship detection in Synthetic Aperture Radar (SAR) images lacking color and texture details, we propose a method for SAR ship detection based on hierarchical attention mechanism. Compared with the optical images, the detection methods based on deep-learning for SAR images are aiming at designing a network that is sensitive to high-level features. The proposed method, containing Global Attention Module (GAM) and Local Attention Module (LAM), presents a hierarchical attention strategy respectively from the image level and the target level. GAM in both spatial and channel domain is constructed to highlight target characteristics. Anchor generation is guided by the LAM for locating more accurate candidate regions. Hierarchical attention improves the performance of extracting significant features of SAR images, which makes the algorithm more efficient in detecting small and multiple ship targets. Experiments results show that our method has achieved 0.7 to 4.1 points higher Average Precision (AP) than several state-of-the-art detection methods on SAR datasets of GF-3 and Sentinel-1.
FedPacket: A Federated Learning Approach to Mobile Packet Classification

In order to improve mobile data transparency, various approaches have been proposed to inspect network traffic generated by mobile devices and detect exposure of personally identifiable information (PII), ad requests, etc. State-of-the-art approaches use features extracted from HTTP packets and train classifiers in a centralized way: users collect and label network packets on their mobile devices, then upload data to a central server; the server uses the data contributed by all users to train a packet classifier. However, training datasets from network traffic collected on user devices may contain sensitive information that users may not want to upload. In this article, we propose a federated learning approach to mobile packet classification, which enables devices to collaboratively train a global model, without uploading the training data collected on devices. We apply our framework to two packet classification tasks (i.e., to predict PII exposure or ad requests in individual packets) and we demonstrate its effectiveness in terms of classification performance, communication and computation cost, using three real-world datasets. Methodological challenges we address in the process include model and feature selection, as well as tuning the federated learning parameters specifically for our packet classification tasks. We also discuss privacy limitations and mitigation approaches.
Factor Graph based 3D Multi-Object Tracking in Point Clouds

Accurate and reliable tracking of multiple moving objects in 3D space is an essential component of urban scene understanding. This is a challenging task because it requires the assignment of detections in the current frame to the predicted objects from the previous one. Existing filter-based approaches tend to struggle if this initial assignment is not correct, which can happen easily.We propose a novel optimization-based approach that does not rely on explicit and fixed assignments. Instead, we represent the result of an off-the-shelf 3D object detector as Gaussian mixture model, which is incorporated in a factor graph framework. This gives us the flexibility to assign all detections to all objects simultaneously. As a result, the assignment problem is solved implicitly and jointly with the 3D spatial multi-object state estimation using non-linear least squares optimization.Despite its simplicity, the proposed algorithm achieves robust and reliable tracking results and can be applied for offline as well as online tracking. We demonstrate its performance on the real world KITTI tracking dataset and achieve better results than many state-of-the-art algorithms. Especially the consistency of the estimated tracks is superior offline as well as online.
Multi-element geochemical data mining: Implications for block boundaries and deposit distributions in South China

To explore the macroscopic characteristics of geological block boundaries, Mesozoic magmatic rocks, and various ore deposit distributions in South China, we processed large-scale stream sediment geochemical data covering the entire South China region. Two unsupervised data mining algorithms, cluster analysis and correlation analysis, were used to identify the geochemical distribution patterns. The combined geochemical patterns of nine major elements show two gradient lines that divide the South China region into three blocks. These gradient lines reveal the eastern and western boundaries of the intracontinental orogenic belts between the Yangtze and Cathaysia blocks. We suggest that the Nanning-Pingxiang segment is located on the southern end of the Yangtze-Cathaysia block boundary, and the Jishou-Kaili segment is a part of the boundary between the Yangtze block and the eastern orogenic belt. In the Mesozoic, the distribution of magmatic rocks migrated from the inland to the southeast coast. Meanwhile, the mantle-derived compatible elements were gradually depleted, whereas the crust-derived incompatible elements were gradually enriched. The migration and enrichment of geochemical elements provide material sources for the formation of various ore deposits. High-temperature metallogenic deposits related to Mesozoic magmatic activity are widely developed in eastern South China, while low-temperature metallogenic, sedimentary, and metamorphic deposits are more developed in inland South China. The regional geochemical patterns indicate the distributions of deposits to varying degrees.
Sentiment Analysis of Service Quality of Online Healthcare Platform Using Fast Large-Margin

Mobile technology is a tool by which healthcare users are assisted. Health information technology has the ability to enhance individual health outcomes and increase healthcare quality, allowing better independent health management. The implementation of information technology in healthcare, particularly the development of healthcare services based on mobile technology (m-health), has already changed healthcare delivery by making it more available and affordable across developing world. Alodokter is Indonesia's number one digital health firm, that has significantly changed the axis of Indonesian health services in providing easily understood, reliable, and available medical information to everyone. This research use Alodokter as our analysis to determine its service quality from customer review from Google Play Store that will be measured by using sentiment analysis. We use Fast Large-Margin as classification methodology and sentiment analysis to classify these following service dimensions: system quality, interaction quality, and information quality. The result of this research is system quality get the most review from customer and interaction quality get the most positive sentiment from customer review.
Generative RGB-D Face Completion for Head-Mounted Display Removal

Head-mounted displays (HMDs) are an essential display device for the observation of virtual reality (VR) environments.However, HMDs obstruct external capturing methods from recording the user's upper face.This severely impacts social VR applications, such as teleconferencing, which commonly rely on external RGB-D sensors to capture a volumetric representation of the user.In this paper, we introduce an HMD removal framework based on generative adversarial networks (GANs), capable of jointly filling in missing color and depth data in RGB-D face images.Our framework includes an RGBbased identity loss function for identity preservation and several components aimed at surface reproduction.Our results demonstrate that our framework is able to remove HMDs from synthetic RGB-D face images while preserving the subject's identity.
A Collaborative Control Method of Dual-Arm Robots Based on Deep Reinforcement Learning

Collaborative control of a dual-arm robot refers to collision avoidance and working together to accomplish a task. To prevent the collision of two arms, the control strategy of a robot arm needs to avoid competition and to cooperate with the other one during motion planning. In this paper, a dual-arm deep deterministic policy gradient (DADDPG) algorithm is proposed based on deep reinforcement learning of multi-agent cooperation. Firstly, the construction method of a replay buffer in a hindsight experience replay algorithm is introduced. The modeling and training method of the multi-agent deep deterministic policy gradient algorithm is explained. Secondly, a control strategy is assigned to each robotic arm. The arms share their observations and actions. The dual-arm robot is trained based on a mechanism of “rewarding cooperation and punishing competition”. Finally, the effectiveness of the algorithm is verified in the Reach, Push, and Pick up simulation environment built in this study. The experiment results show that the robot trained by the DADDPG algorithm can achieve cooperative tasks. The algorithm can make the robots explore the action space autonomously and reduce the level of competition with each other. The collaborative robots have better adaptability to coordination tasks.
Research on Zero-trust Security Protection Technology of Power IoT based on Blockchain

Abstract With the access of massive terminals in the power Internet of things (IoT), the efficiency of information sharing is improved, but the grid boundary is increasingly blurred. The traditional centralized security management mode is facing a severe security situation, which is difficult to meet the high security requirements of the system operation. There are three main risks: 1) the execution authority of control information is not strictly controlled at the equipment level; 2) Data and authority are not separated, which makes the security management of data layer have hidden dangers; 3) Power supply chain is faced with the problems of data island, data trust and performance guarantee. Based on the characteristics of blockchain technology (BcT), such as non-tampering, collective maintenance and transaction non repudiation, this paper constructs a data protection architecture covering device layer, data layer and business layer, realizes high-level security protection of data flow in all links of power network, and improves the security management and control ability of the system. Through the scheme proposed in this paper, the implementation of control information is strictly managed, the use rights management of data is more standardized, and the credibility and interaction rate of business data are greatly improved.