Reviewerly/exHarmony huggingface dataset:
Enabling Remote Patient Monitoring Through the Use of Smart Thermostat Data in Canada: Exploratory Study

Background Advances in technology have made the development of remote patient monitoring possible in recent years. However, there is still room for innovation in the types of technologies that are developed, used, and implemented. The smart thermostat solutions provided in this study can expand beyond typically defined features and be used for improved holistic health monitoring purposes. Objective The aim of this study is to validate the hypothesis that remote motion sensors could be used to quantify and track an individual’s movements around the house. On the basis of our results, the next step would be to determine if using remote motion sensors could be a novel data collection method compared with the national census-level surveys administered by governmental bodies. The results will be used to inform a more extensive implementation study of similar smart home technologies to gather data for machine learning algorithms and to build upon pattern recognition and comprehensive health monitoring. Methods We conducted a pilot study with a sample size of 8 to validate the use of remote motion sensors to quantify movement in the house. A large database containing data from smart home thermostats was analyzed to compare the following indicators; sleep, physical activity, and sedentary behavior. These indicators were developed by the Public Health Agency of Canada and are collected through traditional survey methods. Results The results showed a significant Spearman rank correlation coefficient of 0.8 (P&lt;.001), which indicates a positive linear association between the total number of sensors activated and the total number of indoor steps traveled by study participants. In addition, the indicators of sleep, physical activity, and sedentary behavior were all found to be highly comparable with those attained by the Public Health Agency of Canada. Conclusions The findings demonstrate that remote motion sensors data from a smart thermostat solution are a viable option when compared with traditional survey data collection methods for health data collection and are also a form of zero-effort technology that can be used to monitor the activity levels and nature of activity of occupants within the home.
Performance analysis of nonlinear activated zeroing neural networks for time-varying matrix pseudoinversion with application

By exploiting two simplified nonlinear activation functions, two zeroing neural network (ZNN) models are designed and studied to efficiently tackle the time-varying matrix pseudoinversion problem. Compared with ZNN activated by previously presented activation functions, these two simplified finite-time ZNN (SFTZNN) models (called SFTZNN1 and SFTZNN2) not only achieve faster finite-time convergence, but also possess better robustness. In addition, the SFTZNN1 and SFTZNN2 models have simpler structure compared with the widely used sign-bi-power activated ZNN model. Theoretical analysis is presented to obtain the maximum convergence time for the SFTZNN models in ideal conditions. Besides, when external perturbations are injected into the proposed SFTZNN models, upper bounds of the steady-state residual error are theoretically calculated. Comparative simulations and one engineering application case validate the feasibility and superiority of the two new SFTZNN models when solving time-varying matrix pseudoinversion.
Stargazing in the Dark: Secure Skyline Queries with SGX

Skylining for multi-criteria decision making is widely applicable and often involves sensitive data that should be encrypted, especially when the database and query engine are outsourced to an untrusted cloud platform. The state-of-the-art designs (ICDE’17) of skylining over encrypted data, while relying on two non-colluding servers, are still slow – taking around three hours to get the skyline for 9000 2-D points.This paper proposes a very efficient solution with a trusted processor such as SGX. A challenge is to support dynamic queries while keeping the memory footprint small and simultaneously preventing unintended leakage with only lightweight cryptographic primitives. Our proposed approach iteratively loads data to the memory-limited SGX on-demand and builds a binary-tree-like index for logarithmic query time. For millions of points, we gain \({6000} - {28000}\times \) improvement in query time (ICDE’17).
Big data analysis and empirical research on the financing and investment decision of companies after COVID-19 epidemic situation based on deep learning

The novel corona virus pneumonia has brought pressure on economic development. Large and medium-sized companies will also play a key role in the recovery of growth after the outbreak. Therefore, it is particularly important to pay attention to the impact of the epidemic on large and medium-sized companies and on the investment and financing of companies. Firstly, the structure of the network model of data analysis is designed in this paper, including the design of the network level, the selection of the number of neurons in each level, the determination of the initial weight and other related parameters. According to the design of network structure, the evaluation model of investment and financing of listed companies is established. Python is used to preprocess the data and train the sample data. By comparing the data processed by two training methods, the optimal network classification model is selected. The experimental results show that the proposed method can improve the effectiveness of investment and financing decisions of listed companies.
Pareto Optimal Multirobot Motion Planning

This article studies a class of multirobot coordination problems where a team of robots aim to reach their goal regions with minimum time and avoid collisions with obstacles and other robots. A novel numerical algorithm is proposed to identify the Pareto optimal solutions where no robot can unilaterally reduce its traveling time without extending others'. The consistent approximation of the algorithm in the epigraphical profile sense is guaranteed using set-valued numerical analysis. Experiments on an indoor multirobot platform and computer simulations show the anytime property of the proposed algorithm, i.e., it is able to quickly return a feasible control policy that safely steers the robots to their goal regions and it keeps improving policy optimality if more time is given.
SIDU: Similarity Difference And Uniqueness Method for Explainable AI

A new brand of technical artificial intelligence ( Explainable AI ) research has focused on trying to open up the 'black box' and provide some explainability.This paper presents a novel visual explanation method for deep learning networks in the form of a saliency map that can effectively localize entire object regions.In contrast to the current state-of-the art methods, the proposed method shows quite promising visual explanations that can gain greater trust of human expert.Both quantitative and qualitative evaluations are carried out on both general and clinical data sets to confirm the effectiveness of the proposed method.
Context-aware in-process crowdworker recommendation

Identifying and optimizing open participation is essential to the success of open software development. Existing studies highlighted the importance of worker recommendation for crowdtesting tasks in order to detect more bugs with fewer workers. However, these studies mainly focus on one-time recommendations with respect to the initial context at the beginning of a new task. This paper argues the need for in-process crowdtesting worker recommendation. We motivate this study through a pilot study, revealing the prevalence of long-sized non-yielding windows, i.e., no new bugs are revealed in consecutive test reports during the process of a crowdtesting task. This indicates the potential opportunity for accelerating crowdtesting by recommending appropriate workers in a dynamic manner, so that the non-yielding windows could be shortened.
Preparing Future UX Professionals

The field of user experience (UX) is growing and rapidly evolving. As instructors who teach in and help design UX programs for students in higher education, we are interested in developing compelling and relevant educational experiences for our students who aim to work as UX professionals. To gain a better understanding of the current expectations for working in the UX industry, we conducted 64 interviews with 71 senior UX industry professionals to learn about their current practices and their expectations of what skills new employees need to be successful working in UX. In this paper, we present a subset of findings from the study that focus on desired skills (both human and technical) and dispositions of UX employees. We discuss the implications of this research for teachers and students and a call for future work.
Practical and White-Box Anomaly Detection through Unsupervised and Active Learning

To ensure quality of service and user experience, large Internet companies often monitor various Key Performance Indicators (KPIs) of their systems so that they can detect anomalies and identify failure in real time. However, due to a large number of various KPIs and the lack of high-quality labels, existing KPI anomaly detection approaches either perform well only on certain types of KPIs or consume excessive resources. Therefore, to realize generic and practical KPI anomaly detection in the real world, we propose a KPI anomaly detection framework named iRRCF-Active, which contains an unsupervised and white-box anomaly detector based on Robust Random Cut Forest (RRCF), and an active learning component. Specifically, we novelly propose an improved RRCF (iRRCF) algorithm to overcome the drawbacks of applying original RRCF in KPI anomaly detection. Besides, we also incorporate the idea of active learning to make our model benefit from high-quality labels given by experienced operators. We conduct extensive experiments on a large-scale public dataset and a private dataset collected from a large commercial bank. The experimental resulta demonstrate that iRRCF-Active performs better than existing traditional statistical methods, unsupervised learning methods and supervised learning methods. Besides, each component in iRRCF-Active has also been demonstrated to be effective and indispensable.
Integer Programming Techniques for Minor-Embedding in Quantum Annealers

A major limitation of current generations of quantum annealers is the sparse connectivity of manufactured qubits in the hardware graph. This technological limitation has generated considerable interest, motivating efforts to design efficient and adroit minor-embedding procedures that bypass sparsity constraints. In this paper, starting from a previous equational formulation by Dridi et al. (arXiv:1810.01440), we propose integer programming (IP) techniques for solving the minor-embedding problem. The first approach involves a direct translation from the previous equational formulation to IP, while the second decomposes the problem into an assignment master problem and fiber condition checking subproblems. The proposed methods are able to detect instance infeasibility and provide bounds on solution quality, capabilities not offered by currently employed heuristic methods. We demonstrate the efficacy of our methods with an extensive computational assessment involving three families of random graphs of varying sizes and densities. The direct translation as a monolithic IP model can be solved with existing commercial solvers yielding valid minor-embeddings but it is outperformed, overall, by the decomposition approach. Our results demonstrate the promise of our methods for the studied benchmarks, highlighting the advantages of using IP technology for minor-embedding problems.
Lic-Sec: An enhanced AppArmor Docker security profile generator

Along with the rapid development of cloud computing technology, containerization technology has drawn much attention from both industry and academia. In this paper, we perform a comparative measurement analysis of Docker-sec, which is a Linux Security Module proposed in 2018, and a new AppArmor profile generator called Lic-Sec, which combines Docker-sec with a modified version of LiCShield, which is also a Linux Security Module proposed in 2015. Docker-sec and LiCShield can be used to enhance Docker container security based on mandatory access control and allows protection of the container without manual configurations. Lic-Sec brings together their strengths and provides stronger protection. We evaluate the effectiveness and performance of Docker-sec and Lic-Sec by testing them with real-world attacks. We generate an exploit database with 40 exploits effective on Docker containers selected from the latest 400 exploits on Exploit-DB. We launch these exploits on containers spawned with Docker-sec and Lic-Sec separately. Our evaluations show that for demanding images, Lic-Sec gives protection for all privilege escalation attacks for which Docker-sec and LiCShield failed to give protection.
Synthesized View Quality Assessment Using Feature Matching and Superpixel Difference

Depth Image-Based Rendering (DIBR) is the key technique in many multi-view 3D applications to synthesize virtual views using texture and depth information. However, DIBR induces distortions that disturb the visual quality of experience; therefore, image quality assessment (IQA) methods are essential to evaluate the quality of the synthesized views. The characteristics of the DIBR-related distortions are different from those of the traditional video coding distortions and conventional objective IQA methods often fail to provide accurate quality predictions for synthesized views. In this letter, we proposed a new Full Reference (FR) objective metric for evaluation of DIBR synthesized views. We used a feature matching method at feature (key) points of reference and synthesized images to quantify the local differences. Moreover, global quality loss is computed in shift-compensated views by measuring the gradient difference in image superpixels. Performance evaluation on the three public data sets shows the effectiveness of the proposed model. A software release of the proposed method is available at https://gitlab.com/etro/ssdi.
Exploring Algorithmic Fairness in Deep Speaker Verification

To allow individuals to complete voice-based tasks (e.g., send messages or make payments), modern automated systems are required to match the speaker's voice to a unique digital identity representation for verification. Despite the increasing accuracy achieved so far, it still remains under-explored how the decisions made by such systems may be influenced by the inherent characteristics of the individual under consideration. In this paper, we investigate how state-of-the-art speaker verification models are susceptible to unfairness towards legally-protected classes of individuals, characterized by a common sensitive attribute (i.e., gender, age, language). To this end, we first arranged a voice dataset, with the aim of including and identifying various demographic classes. Then, we conducted a performance analysis at different levels, from equal error rates to verification score distributions. Experiments show that individuals belonging to certain demographic groups systematically experience higher error rates, highlighting the need of fairer speaker recognition models and, by extension, of proper evaluation frameworks.
Skinny: A Lightweight U-Net For Skin Detection And Segmentation

The use of deep-learned features has recently allowed for improving the performance of skin detection and segmentation. In particular, fully-convolutional U-Nets, proposed for segmenting medical images, occurred to be extremely effective here. However, the spatial context, which is rather narrow for U-Nets, may be more important for skin segmentation than for segmenting other image structures. We propose Skinny-a lightweight U-Net-based architecture that extends the range of multi-scale analysis. The results of our experiments indicate that Skinny outperforms the state-of-the-art skin segmentation techniques, rendering the F-score of 92.3% and 94.9% for the ECU and HGR datasets, respectively.
Enhanced Privacy and Data Protection using Natural Language Processing and Artificial Intelligence

Artificial Intelligence systems have enabled significant benefits for users and society, but whilst the data for their feeding are always increasing, a side to privacy and security leaks is offered. The severe vulnerabilities to the right to privacy obliged governments to enact specific regulations to ensure privacy preservation in any kind of transaction involving sensitive information. In the case of digital and/or physical documents comprising sensitive information, the right to privacy can be preserved by data obfuscation procedures. The capability of recognizing sensitive information for obfuscation is typically entrusted to the experience of human experts, who are over-whelmed by the ever increasing amount of documents to process. Artificial intelligence could proficiently mitigate the effort of the human officers and speed up processes. Anyway, until enough knowledge won't be available in a machine readable format, automatic and effectively working systems can't be developed. In this work we propose a methodology for transferring and leveraging general knowledge across specific-domain tasks. We built, from scratch, specific-domain knowledge data sets, for training artificial intelligence models supporting human experts in privacy preserving tasks. We exploited a mixture of natural language processing techniques applied to unlabeled domain-specific documents corpora for automatically obtain labeled documents, where sensitive information are recognized and tagged. We performed preliminary tests just over 10.000 documents from the healthcare and justice domains. Human experts supported us during the validation. Results we obtained, estimated in terms of precision, recall and F1-score metrics across these two domains, were promising and encouraged us to further investigations.
Multi-Task Learning for Single Image Depth Estimation and Segmentation Based on Unsupervised Network

Deep neural networks have significantly enhanced the performance of various computer vision tasks, including single image depth estimation and image segmentation. However, most existing approaches handle them in supervised manners and require a large number of ground truth labels that consume extensive human efforts and are not always available in real scenarios. In this paper, we propose a novel framework to estimate disparity maps and segment images simultaneously by jointly training an encoder-decoder-based interactive convolutional neural network (CNN) for single image depth estimation and a multiple class CNN for image segmentation. Learning the neural network for one task can be beneficial from simultaneously learning from another one under a multi-task learning framework. We show that our proposed model can learn per-pixel depth regression and segmentation from just a single image input. Extensive experiments on available public datasets, including KITTI, Cityscapes urban, and PASCAL-VOC demonstrate the effectiveness of our model compared with other state-of-the-art methods for both tasks.
Toward Hardware-Efficient Optical Neural Networks: Beyond FFT Architecture via Joint Learnability

As a promising neuromorphic framework, the optical neural network (ONN) demonstrates ultrahigh inference speed with low energy consumption. However, the previous ONN architectures have high area overhead which limits their practicality. In this article, we propose an area-efficient ONN architecture based on structured neural networks, leveraging optical fast Fourier transform for efficient computation. A two-phase software training flow with structured pruning is proposed to further reduce the optical component utilization. Experimental results demonstrate that the proposed architecture can achieve 2.2- 3.7× area cost improvement compared with the previous singular value decomposition-based architecture with comparable inference accuracy. A novel optical microdisk-based convolutional neural network architecture with joint learnability is proposed as an extension to move beyond Fourier transform and multilayer perception, enabling hardware-aware ONN design space exploration with lower area cost, higher power efficiency, and better noise-robustness.
Proof-of-Work cryptocurrency mining: a statistical approach to fairness

In Proof-of-Work Blockchain-based systems, the ledger is kept consistent through some participants solving cryptopuzzles, usually referred to as block mining.Conventional wisdom asserts that the mining protocol is incentive-compatible.However, whether some strategic mining behaviors occur in practice or not, has been the subject of extensive debate.In this paper, we target this question by detecting anomalies in the statistics of consecutive blocks among several popular cryptocurrency systems.Firstly, we measure the inequality of mining revenue distribution in each system.Secondly, we propose a statistical method to identify the selfish mining (SM) behavior, a mining attack strategy posited by Eyal and Sirer in 2014.Our method is based on abnormal (statistically significant) high probability of continuously mining blocks.Finally, we extend our method to detect the mining cartels, in which miners secretly get together and share information about newly mined blocks.Our analysis will contribute to the research of fairness in cryptocurrency mining by providing evidence that the aforementioned strategic mining behaviors do take place in practice.
EvoQ: Mixed Precision Quantization of DNNs via Sensitivity Guided Evolutionary Search

Network quantization can effectively reduce computation and memory costs without modifying network structures, facilitating the deployment of deep neural networks (DNNs) on edge devices. However, most of the existing methods usually need time-consuming training or fine-tuning and access to the original training dataset that may be unavailable due to privacy or security concerns. In this paper, we introduce a novel method named EvoQ that employs evolutionary search to achieve mixed precision quantization with limited data, which can optimize the resource allocation without adding computation consumption. Considering the shortage of samples and expensive search costs, we use 50 samples to measure the output difference between the quantization model and the pre-trained model for the evaluation of quantization policy, which can save the time obviously while maintaining high accuracy. To improve the search efficiency, we analyze the quantization sensitivity of each layer and utilize the results to optimize the mutation operation. At last, we calibrate the outputs and intermediate features of the quantization model using the selected 50 samples to improve the performance further. We implement extensive experiments on a diverse set of models, including ResNet18/50/101, SqueezeNet, ShuffleNetV2, and MobileNetV2 on ImageNet, as well as SSD-VGG and SSD-ResNet50 on PASCAL VOC. Our method can improve the performance apparently and outperforms the existing post-training quantization methods, demonstrating the effectiveness of EvoQ.