Reviewerly/exHarmony huggingface dataset:
Why Users Comply with Wearables: The Role of Contextual Self-Efficacy in Behavioral Change

Wearables provide great opportunities for improving personal health, but research challenges their capacity to evoke behavioral change effectively. Realizing the full potential of wearables requires a better understanding of users’ behavior change processes. Based on self-efficacy theory, we investigate how wearables influence users’ perceptions of their self-efficacy and subsequent health behavior. Using narrative interviews with twenty-five long-term wearable users, we show that wearables can have both positive and negative effects on users’ perceptions of their self-efficacy and that these perceptions are subject to internal and external contexts, which can positively or negatively affect users’ compliance. We also find that the internal context may have a compounding or neutralizing effect on self-efficacy, despite an adverse external context. Our study shows the contextual and transient nature of self-efficacy, thus contributing to self-efficacy theory and research on wearables and offering practical design implications.
UAV Autonomous Tracking and Landing Based on Deep Reinforcement Learning Strategy

Unmanned aerial vehicle (UAV) autonomous tracking and landing is playing an increasingly important role in military and civil applications. In particular, machine learning has been successfully introduced to robotics-related tasks. A novel UAV autonomous tracking and landing approach based on a deep reinforcement learning strategy is presented in this paper, with the aim of dealing with the UAV motion control problem in an unpredictable and harsh environment. Instead of building a prior model and inferring the landing actions based on heuristic rules, a model-free method based on a partially observable Markov decision process (POMDP) is proposed. In the POMDP model, the UAV automatically learns the landing maneuver by an end-to-end neural network, which combines the Deep Deterministic Policy Gradients (DDPG) algorithm and heuristic rules. A Modular Open Robots Simulation Engine (MORSE)-based reinforcement learning framework is designed and validated with a continuous UAV tracking and landing task on a randomly moving platform in high sensor noise and intermittent measurements. The simulation results show that when the moving platform is moving in different trajectories, the average landing success rate of the proposed algorithm is about 10% higher than that of the Proportional-Integral-Derivative (PID) method. As an indirect result, a state-of-the-art deep reinforcement learning-based UAV control method is validated, where the UAV can learn the optimal strategy of a continuously autonomous landing and perform properly in a simulation environment.
A Novel Reputation-aware Client Selection Scheme for Federated Learning within Mobile Environments

This paper studies the problem of training federated deep learning models over a mobile environment. Stemming from the federated learning (FL) concept, deep learning models on mobile devices can be trained for various use cases including but not limited to image sorting and prediction of upcoming words. Mobile devices have access to rich data sets through embedded sensors and as well as installed software, and these feature rich data can facilitate solid training models, including personal images and other behaviometric features. However, utilizing the data through conventional approaches can potentially lead to privacy leakages. In this paper, we propose an alternate strategy that builds on the Federated Learning (FL) concept, to keep the training data on distributed mobile devices, and train a shared model by aggregating updated local models. The contribution of this study is an optimal user selection method for the federated learning environment based on reputation scores. Through extensive validation experiments considering two different model architectures and three datasets, our experiments show that the proposed approach is stable over data that is not independent nor identically distributed (i.e., non-IID) and under imbalanced distribution. Experimental results show that the proposed reputation-aware FL scheme can achieve improvements in the test accuracy up to 9.30% under different data sets.
PRIPEL: Privacy-Preserving Event Log Publishing Including Contextual Information

Event logs capture the execution of business processes in terms of executed activities and their execution context. Since logs contain potentially sensitive information about the individuals involved in the process, they should be pre-processed before being published to preserve the individuals’ privacy. However, existing techniques for such pre-processing are limited to a process’ control-flow and neglect contextual information, such as attribute values and durations. This thus precludes any form of process analysis that involves contextual factors. To bridge this gap, we introduce PRIPEL, a framework for privacy-aware event log publishing. Compared to existing work, PRIPEL takes a fundamentally different angle and ensures privacy on the level of individual cases instead of the complete log. This way, contextual information as well as the long tail process behaviour are preserved, which enables the application of a rich set of process analysis techniques. We demonstrate the feasibility of our framework in a case study with a real-world event log.
How has forking changed in the last 20 years?

The notion of forking has changed with the rise of distributed version control systems and social coding environments, like GitHub. Traditionally forking refers to splitting off an independent development branch (which we call hard forks); research on hard forks, conducted mostly in pre-GitHub days showed that hard forks were often seen critical as they may fragment a community Today, in social coding environments, open-source developers are encouraged to fork a project in order to contribute to the community (which we call social forks), which may have also influenced perceptions and practices around hard forks. To revisit hard forks, we identify, study, and classify 15,306 hard forks on GitHub and interview 18 owners of hard forks or forked repositories. We find that, among others, hard forks often evolve out of social forks rather than being planned deliberately and that perception about hard forks have indeed changed dramatically, seeing them often as a positive noncompetitive alternative to the original project.
Efficient Bimanual Manipulation Using Learned Task Schemas

We address the problem of effectively composing skills to solve sparse-reward tasks in the real world. Given a set of parameterized skills (such as exerting a force or doing a top grasp at a location), our goal is to learn policies that invoke these skills to efficiently solve such tasks. Our insight is that for many tasks, the learning process can be decomposed into learning a state-independent task schema (a sequence of skills to execute) and a policy to choose the parameterizations of the skills in a state-dependent manner. For such tasks, we show that explicitly modeling the schema's state-independence can yield significant improvements in sample efficiency for model-free reinforcement learning algorithms. Furthermore, these schemas can be transferred to solve related tasks, by simply re-learning the parameterizations with which the skills are invoked. We find that doing so enables learning to solve sparse-reward tasks on real-world robotic systems very efficiently. We validate our approach experimentally over a suite of robotic bimanual manipulation tasks, both in simulation and on real hardware. See videos at http://tinyurl.com/chitnis-schema.
Breast ultrasound lesion classification based on image decomposition and transfer learning

Purpose In medical image analysis, deep learning has great application potential. Discovering a method for extracting valuable information from medical images and integrating that information closely with medical treatment has recently become a major topic of interest. Because obtaining large volumes of breast lesion ultrasound image data is difficult, transfer learning is usually employed to obtain benign and malignant classification of breast lesions. However, because of blurred unclear regions of interest in breast lesion ultrasound images and severe speckle noise interference, convolutional neural networks have proven ineffective in extracting features, thus providing unreliable classification results. Methods This study employs image decomposition to obtain fuzzy enhanced and bilateral filtered images to enrich input information of breast lesions. Fuzzy enhanced, bilateral filtered, and original ultrasound images comprise multifeature data, which are presented as inputs to a pre‐trained model to realize knowledge fusion. Therefore, effective features of breast lesions are extracted and then used to train fully connected layers with ground truths provided by a doctor to accomplish the classification. Results A pre‐trained VGG16 model was used to extract features from multifeature data, and these features were fused to train the fully connected layers to realize classification. The performance score reported is as follows: accuracy of 93%, sensitivity of 95%, specificity of 88%, F1 score of 0.93, and AUC of 0.97. Conclusions Compared with using a single original ultrasound image for feature extraction, multifeature data based on image decomposition enables the pre‐trained model to extract more relevant features, thereby providing better classification results than those from traditional transfer learning techniques.
Fair and Efficient Gossip in Hyperledger Fabric

Permissioned blockchains are supported by identified but individually untrustworthy nodes, collectively maintaining a replicated ledger whose content is trusted. The Hyperledger Fabric permissioned blockchain system targets high-throughput transaction processing. Fabric uses a set of nodes tasked with the ordering of transactions using consensus. Additional peers endorse and validate transactions, and maintain a copy of the ledger. The ability to quickly disseminate new transaction blocks from ordering nodes to all peers is critical for both performance and consistency. Broadcast is handled by a gossip protocol, using randomized exchanges of blocks between peers.We show that the current implementation of gossip in Fabric leads to heavy tail distributions of block propagation latencies, impacting performance, consistency, and fairness. We contribute a novel design for gossip in Fabric that simultaneously optimizes propagation time, tail latency and bandwidth consumption. Using a 100-node cluster, we show that our enhanced gossip allows the dissemination of blocks to all peers more than 10 times faster than with the original implementation, while decreasing the overall network bandwidth consumption by more than 40%. With a high throughput and concurrent application, this results in 17% to 36% fewer invalidated transactions for different block sizes.
Interactive Human Centered Artificial Intelligence

Artificial Intelligence (AI) has become the buzzword of the last decade. Advances so far have been largely technical with a focus on machine learning (ML). Only recently have we begun seeing a shift towards focusing on the human aspects of artificial intelligence, centered on the narrow view of making AI interactive and explainable. In this paper I suggest a definition for "Interactive Human Centered Artificial Intelligence and outline the required properties. Staying in control is essential for humans to feel safe and have self-determination. Hence, we need to find ways for humans to understand AI based systems and means to allow human control and oversight. In our work, we argue that levels of abstractions and granularity of control are a general solution to this. Furthermore, it is essential that we make explicit why we want AI and what are the goals of AI research and development. We need to state the properties that we expect of future intelligent systems and who will benefit from a system or service. For me, AI and ML are very much comparable to raw materials (like stone, iron, or bronze). Historical periods are named after these materials as they fundamentally changed what humans can build and what tools humans can engineer. Hence, I argue that in the AI age we need to shift the focus from the material (e.g. the AI algorithms, as there will be plenty of material) towards the tools and infrastructures that are enabled which are beneficial to humans. It is apparent that AI will allow the automation of mental routine tasks and that it will extend our ability to perceive the world and foresee events. For me, the central question is how to create these tools for amplifying the human mind without compromising human values.
Satellite quantum repeaters for a quantum Internet

Quantum EngineeringVolume 2, Issue 4 e55 ORIGINAL PAPER Satellite quantum repeaters for a quantum Internet Mario Mastriani, Corresponding Author mmastria@fiu.edu School of Computing and Information Sciences, Florida International University, Miami, Florida, USA Correspondence Mario Mastriani, School of Computing and Information Sciences, Florida International University, 11200 S.W. 8th Street, Miami, Florida 33199, USA. Email: mmastria@fiu.eduSearch for more papers by this authorSundaraja Sitharama Iyengar, School of Computing and Information Sciences, Florida International University, Miami, Florida, USASearch for more papers by this author Mario Mastriani, Corresponding Author mmastria@fiu.edu School of Computing and Information Sciences, Florida International University, Miami, Florida, USA Correspondence Mario Mastriani, School of Computing and Information Sciences, Florida International University, 11200 S.W. 8th Street, Miami, Florida 33199, USA. Email: mmastria@fiu.eduSearch for more papers by this authorSundaraja Sitharama Iyengar, School of Computing and Information Sciences, Florida International University, Miami, Florida, USASearch for more papers by this author First published: 30 September 2020 https://doi.org/10.1002/que2.55Citations: 8Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Summary This work presents a satellite alternative to quantum repeaters based on the terrestrial laid of optical fiber, where the latter have the following disadvantages: a propagation speed (v) equal to 2/3 of the speed of light (c), losses and an attenuation in the material that requires the installation of a repeater every 50 km, while satellite repeaters can cover greater distances at a speed v = c, with less attenuation and losses than in the case of optical fiber except for relative environmental aspects to the ground-sky link, that is, clouds that can disrupt the distribution of entangled photons. Two configurations are presented, the first one of a satellite and the second one of two satellites in the event that both points on the ground cannot access the same satellite. Finally, a series of implementations for evaluating the performance and robustness of both configurations are implemented on a 5 qubits IBM Q processor. Open Research DATA AVAILABILITY STATEMENT The experimental data that support the findings of this study are available in ResearchGate with the identifier https://doi.org/10.13140/RG.2.2.28199.29600. Citing Literature Volume2, Issue4December 2020e55 RelatedInformation
Gradients as a Measure of Uncertainty in Neural Networks

Despite tremendous success of modern neural networks, they are known to be overconfident even when the model encounters inputs with unfamiliar conditions. Detecting such inputs is vital to preventing models from making naive predictions that may jeopardize real-world applications of neural networks. In this paper, we address the challenging problem of devising a simple yet effective measure of uncertainty in deep neural networks. Specifically, we propose to utilize backpropagated gradients to quantify the uncertainty of trained models. Gradients depict the required amount of change for a model to properly represent given inputs, thus providing a valuable insight into how familiar and certain the model is regarding the inputs. We demonstrate the effectiveness of gradients as a measure of model uncertainty in applications of detecting unfamiliar inputs, including out-of-distribution and corrupted samples. We show that our gradient-based method outperforms state-of-the-art methods by up to 4.8% of AUROC score in out-of-distribution detection and 35.7% in corrupted input detection.
Exploring differences and commonalities between feature flags and configuration options

Feature flags for continuous deployment and configuration options for customizing software share many similarities, both conceptually and technically. However, neither academic nor practitioner publications seem to clearly compare these two concepts. We argue that a distinction is valuable, as applications, goals, and challenges differ fundamentally between feature flags and configuration options. In this work, we explore the differences and commonalities of both concepts to help understand practices and challenges, and to help transfer existing solutions (e.g., for testing). To better understand feature flags and how they relate to configuration options, we performed nine semi-structured interviews with feature-flag experts. We discovered several distinguishing characteristics but also opportunities for knowledge and technology transfer across both communities. Overall, we think that both communities can learn from each other.
Residual attention-based multi-scale script identification in scene text images

Script identification is an essential step in the text extraction pipeline for multi-lingual application. This paper presents an effective approach to identify scripts in scene text images. Due to the complicated background, various text styles, character similarity of different languages, script identification has not been solved yet. Under the general classification framework of script identification, we investigate two important components: feature extraction and classification layer. In the feature extraction, we utilize a hierarchical feature fusion block to extract the multi-scale features. Furthermore, we adopt an attention mechanism to obtain the local discriminative parts of feature maps. In the classification layer, we utilize a fully convolutional classifier to generate channel-level classifications which are then processed by a global pooling layer to improve classification efficiency. We evaluated the proposed approach on benchmark datasets of RRC-MLT2017, SIW-13, CVSI-2015 and MLe2e, and the experimental results show the effectiveness of each elaborate designed component. Finally, we achieve better performances than those competitive models, where the correct rates are 89.66%, 96.11%, 98.78% and 97.20% on PRC-MLT2017, SIW-13, CVSI-2015 and MLe2e, respectively.
SparseRT

In recent years, there has been a flurry of research in deep neural network pruning and compression. Early approaches prune weights individually. However, it is difficult to take advantage of the resulting unstructured sparsity patterns on modern hardware like GPUs. As a result, pruning strategies which impose sparsity structures in the weights have become more popular. However,these structured pruning approaches typically lead to higher losses in accuracy than unstructured pruning. In this paper, we present SparseRT, a code generator that leverage unstructured sparsity toaccelerate sparse linear algebra operations in deep learning inference on GPUs. For 1x1 convolutions and fully connected layers, we demonstrate geometric mean of speedups of 3.4x over the equivalent dense computation at 90% sparsity and 5.4x at 95% sparsity when evaluated on hundreds of test cases in deep learning. For sparse 3x3 convolutions, we show speedups of over 5x on use casesin ResNet-50.
3D PostureNet: A unified framework for skeleton-based posture recognition

Image-based posture recognition is a very challenging problem as it is difficult to acquire rich 3D information from postures in 2D images. Existing methods founded on 3D skeleton cues could alleviate this issue, but they are not particularly efficient due to the application of handcrafted features and traditional classifiers. This paper presents a novel and unified framework for skeleton-based posture recognition, applying powerful 3D Convolutional Neural Network (CNN) to this issue. Technically, bounding-box-based normalization for the raw skeleton data is proposed to eliminate the coordinate differences caused by diverse recording environments and posture displacements. Moreover, Gaussian voxelization for the skeleton is employed to expressively represent the posture configuration. Thereby, an end-to-end framework based on 3D CNN, called 3D PostureNet, is developed for robust posture recognition. To verify its effectiveness, a large-scale writing posture dataset is created and released in this work, including 113,400 samples of 30 subjects with 15 postures. Extensive experiments on the public MSRA hand gesture dataset, body pose dataset and the proposed writing posture dataset demonstrate that 3D PostureNet achieves significantly superior performance on both skeleton-based human posture and hand posture recognition tasks.
Graph Attention Multi-instance Learning for Accurate Colorectal Cancer Staging

Colorectal Cancer (CRC) is one of the most common cancer diagnosed in humans. Outcomes vary significantly among patients with different tumor status. Accurate staging of colorectal cancer for personalized treatment is thus highly desired. Whole slide pathological images (WSIs) serves as the gold standard for Tumour Node Metastasis (TNM) staging. However, TNM staging for colorectal cancer relies on labor-intensive manual discriminative patch labeling, which is not suitable and scalable for large-scale WSIs TNM staging. Though various methods have been proposed to select key image patches to perform staging, they are unable to consider the structure of tissue types in biopsy samples which is a key evidence for determining tumor status. In this paper, we propose a Graph Attention Multi-instance Learning (Graph Attention MIL) with texture features, which encodes a spatial structure between patches and jointly predicts the TNM staging. We evaluated our proposed method on a large cohort of colorectal cancer dataset. The proposed framework improves the performance over the existing state-of-the-art methods indicating the future research towards graph based learning for TNM staging.
Lunar: A Toolbox for More Efficient Universal and Updatable zkSNARKs and Commit-and-Prove Extensions

We study how to construct zkSNARKs whose SRS is universal and updatable, i.e., valid for all relations within a size-bound and to which a dynamic set of participants can indefinitely add secret randomness. Our focus is: efficient universal updatable zkSNARKs with linear-size SRS and their commit-and-prove variants. We both introduce new formal frameworks and techniques, as well as systematize existing ones.