Reviewerly/exHarmony huggingface dataset:
Technologies for fall risk assessment and conceptual design in personal health record system

Falls among older people are a major economic and public health problem. Due to the demographic change and aging of populations, there is an urgent need for accurate screening tools to identify those at risk to target effective falls prevention strategies. Clinical fall risk assessments are costly and time-consuming and thus cannot be performed frequently. Technologies provide means for assessing fall risk during daily living, making self-evaluations and fast methods for fall risk assessment for professional use.&#x0D; This study collects and evaluates existing technological solutions for fall risk assessment including various different sensor technologies. The study also presents one easy to use solution for assessing fall risk and suggests a concept-design for integrating sensor-based solutions into the Finnish national Kanta Personal Health Record.&#x0D; The optimal solution for technological fall risk assessment is still unclear. A wide implementation still requires extensive validation studies, adoption to health care processes and novel IoT -solutions for collecting large amounts of sensor data. Thorough methods should be utilised in designing the privacy and security aspects of fall risk assessment solutions, as well as different user profiles, to allow suitable interfaces and visualisations to users. It should always be clear what kind of data are collected from users and how the data are utilised. The consent of the users should also always be collected.
Visual sensor image enhancement based on non-sub-sampled shearlet transform and phase stretch transform

Acquiring clear images is a requisite in visual sensor networks. Image enhancement is an effective way to improve image quality. In this paper, non-sub-sampled shearlet transform (NSST) multi-scale analysis is combined with phase stretch transform (PST) to nonlinearly enhance the images captured by visual sensors. The components of different scales after NSST multi-scale decomposition are processed by nonlinear models with different thresholds. The thresholds of the enhanced model are determined by the local standard deviation of PST feature map. The noise is well suppressed, and the detail features are enhanced obviously. Experiments show that the proposed algorithm can improve image distortion, clear details, and enhance image contrast effectively.
Arbitrary phase-locking in Mach–Zehnder interferometer

Based on the demand for the stability of the Mach–Zehnder optical interferometer, we proposed and designed a system that can achieve fast and precise locking at any phase point in one period. The locking system is simple and is easy to operate. The core elements are an electro-optic modulator (EOM), which can generate different phase shift on the vertically polarized light and horizontally polarized light, and a PID feedback. The experimental results show that for the MZ interferometer, the system can achieve fast, accurate and long-term locking at any phase point. The accuracy of the phase lock can be controlled within 1.05°or 0.0029λ with the intensity of the interference beams of 0.335 mW. This locking technology can be applied in the field of precision measurement in the future.
Influence of Pre-service and In-service Teachers’ Gender and Experience on the Acceptance of AR Technology

Augmented reality has received more and more attention by researchers in the field of education. Because of its rich visual presentations and various user interaction, AR leaning environment has great potential for learning activities. This study used mature UTAUT questionnaires to investigate pre-service teachers and in-service teachers’ acceptance of AR technology. The effective participants included 70 pre-service and 50 in-service teachers. Through data analysis of effective participants from the perspectives of gender and experience, we found that 1) Pre-service teachers are more sensitive to social influences than in-service teachers are. For all the participants, 2) Male teachers are more sensitive to social influences than females. 3) Effort expectancy has a negative impact on AR behavioral intention of high-experience teachers. Based on the research results, specific discussions and suggestions are proposed for different teacher groups to improve the technical acceptance of AR in teaching.
A LeNet Based Convolution Neural Network for Image Steganalysis on Multiclass Classification

It is increasingly difficult to identify data hiding techniques used in the stego-images in this era of great advancement in technology. The purpose of this paper is to identify the types of data hiding techniques of stego-images. This paper proposed the use deep learning and convolutional neural networks (CNN) to train and generate a binary classification model for prediction. This paper proposed three types of detection algorithms for stego-images. The algorithms are based on the LeNet technique of convolutional neural network (CNN) in deep learning. The first algorithm detects the DCT stego-images. The second detects the histogram stego-images. The third and final algorithm detects the LSB stego-images. Experimental results from LeNet deep learning show significantly high detection rate. The DCT stego-images showed a significant high detection accuracy of 99%, while the LSB and histograms stego-images showed 70% accuracy rate.
A New Cipher Based on Feistel Structure and Chaotic Maps

Chaotic systems have been proved to be useful and effective for cryptography. Through this work, a new Feistel cipher depend upon chaos systems and Feistel network structure with dynamic secret key size according to the message size have been proposed. Compared with the classical traditional ciphers like Feistel-based structure ciphers, Data Encryption Standards (DES), is the common example of Feistel-based ciphers, the process of confusion and diffusion, will contains the dynamical permutation choice boxes, dynamical substitution choice boxes, which will be generated once and hence, considered static,&#x0D; While using chaotic maps, in the suggested system, called Chaotic-based Proposed Feistel Cipher System (CPFCS), we made the confusion and diffusion in dynamical behavior based on Standard and Lorenz maps. The first is used for substitution, and the second one for permutation operations .A proposed cryptographic system uses the same work (the same way) for both enciphering and deciphering. The proposed cipher operates on more than 500 bytes (4000-bit) readable text blocks by six round computing. Within the basic operator of the cipher, i.e., in the function of the round F, a dynamical lookup table 2D standard map system is used to enhance the complexity and diffusion of the unreadable text. Also, a 3D Logistic map used for key sequence generator and chaos based dynamical Initial Permutation (dynamical IP) are used to increase the diffusion and confusion. Three different image sizes and three different text length were implemented in CPFCS. The results of the proposed system and security tests improve the applicability of PFCS in the data protection and security.
Understanding Effects of Collaborations in Developing Mobile Computing Systems: Popularity, Efficiency, and Quality

The development of mobile computing systems is an inherently collaborative activity, and the nature of the collaboration likely affects systems success. Collaboration can lead to faster development of systems and increased creativity, but it may also adversely affect the quality of systems. As one of the most popular and active Web services for developing open source software, Github has been a place of origin of many novel mobile computing systems with the Internet of Things, such as OpenEdge and CloudSim. Meanwhile, Github provides a place for people to collaborate with each other on developing novel mobile service computing systems. In this paper, we explore the effects of collaboration in developing systems hosted by Github and use a model of collaboration to detect bugs of systems aiming to enhance their quality. We conduct an at-scale study of more than five million projects on GitHub to reveal collaboration's effects on the popularity of software systems and speed of development. We find that having more collaborators results in higher popularity but slower development. Projects that use Objective-C are at an even more significant risk for lower development speeds than other projects. We define collaboration networks and co-commit networks, which capture the projects' collaboration patterns and use structural features of these networks to identify files within commits with bugs. By examining 18 large popular projects, we find that involving these features statistically significantly improves bug detection by 6.7% on average.
Accurate Method for Picking Up the First Arrival Time of Microseismic Signals Based on Entropy Theory

The entropy method describes the change in the signal state from a macroscopic angle and weakens the interference generated by random noise. From the angle of chaos, which describes the first arrival of microseismic signals, it predicts the trend of noise entropy based on the grey theory. The results indicate that the entropy method is sensitive to amplitude, frequency, and seismic phase changes. Compared with traditional methods, the entropy method has better noise immunity. In terms of the influence of the pickup parameter on the resulting error, the entropy method demonstrated the best result and produces the smallest error.
WEBOMETRICS ANALYSIS OF IRANIAN UNIVERSITIES ABOUT MEDICAL SCIENCES’ WEBSITES BETWEEN SEPTEMBER 2016 AND MARCH 2017

This study aimed to analyze the websites' ranks on Iranian Medical Sciences Universities in the period of 11 September 2016 to 1March 2017.It was performed on Webometrics method which contains 41 Websites of Iranian Medical Sciences Universities.For instance, Moz, Google, Google Scholar and so on.University of Shiraz's website topped the table.Tehran, Shahid Beheshti and Bandar Abbas Universities' websites followed Shiraz in order.Meanwhile, Kerman
Quantitative Statistics and Identification of Tool‐Marks

Abstract This study was designed to establish a feature identification method of tool‐mark 2D data. A uniform local binary pattern histogram operator was developed to extract the tool‐mark features, and the random forest algorithm was adopted to identify these. The presented method was used to conduct five groups of experiments with a 2D dataset of known matched and nonmatched tool‐marks made by bolt clippers, cutting pliers, and screwdrivers. The experimental results show that the proposed method achieved a high rate of identification of the tool‐mark samples generated under identical conditions. The proposed method effectively overcomes the disadvantage of unstable illumination of 2D tool‐mark image data and avoids the difficulty in mark inspection caused by manually preset parameters in the existing methods, thus reducing the uncertainty of inspected results.
Sensitivity-Oriented Layer-Wise Acceleration and Compression for Convolutional Neural Network

Convolutional neural networks (CNNs) have achieved excellent performance improvement in image processing and other machine learning tasks. However, tremendous computation and memory consumption for most classical CNN models pose a great challenge to the deployment in portable and power-limited devices. In this paper, by analyzing the sensitivity of the rank in each layer of the network accuracy, we propose a sensitivity-oriented layer-wise low-rank approximation algorithm. With specific compression and acceleration requirement, the convolutional layer with higher sensitivity keeps more kernels than that with lower sensitivity. In addition, we also demonstrated that global optimization can obtain a better classification performance than layer-wise fine-tuning. The experimental results show that the proposed method can achieve 20% acceleration ratio gaining compared with the traditional rank-reducing methods. When deployed on the VGGNet-16 model, the proposed method can achieve 2.7x compression/acceleration ratio on convolutional layers and 10.9x compression/acceleration ratio on fully connected (FC) layers with 0.05% top-1 accuracy loss and 0.01% top-5 accuracy loss.
Water Sink Model for Robot Motion Planning

There are various motion planning techniques for robots or agents, such as bug algorithm, visibility graph, Voronoi diagram, cell decomposition, potential field, and other probabilistic algorithms. Each technique has its own advantages and drawbacks, depending on the number and shape of obstacles and performance criteria. Especially, a potential field has vector values for movement guidance to the goal, and the method can be used to make an instantaneous and smooth robot movement path without an additional controller. However, there may be some positions with zero force value, called local minima, where the robot or agent stops and cannot move any further. There are some solutions for local minima, such as random walk or backtracking, but these are not yet good enough to solve the local minima problem. In this paper, we propose a novel movement guidance method that is based on the water sink model to overcome the previous local minima problem of potential field methods. The concept of the water sink model is to mimic the water flow, where there is a sink or bathtub with a plughole and floating piece on the water. The plughole represents the goal position and the floating piece represents robot. In this model, when the plug is removed, water starts to drain out via the plughole and the robot can always reach the goal by the water flow. The water sink model simulator is implemented and a comparison of experimental results is done between the water sink model and potential field.
Analysis of an Optimal Fault Attack on the LED-64 Lightweight Cryptosystem

This paper presents an optimal method for recovering the secret keys of the light encryption device (LED) by combining the impossible differential fault attack with the algebraic differential fault attack. The proposed optimal method effectively improves the performance of fault attacks. A fault attack model, named the redundant random nibble fault model (RRNFM), is proposed to simulate a multiple fault injection in a real-world environment. Using the optimal method and the RRNFM, the 64-bit secret key of LED can be recovered by injecting no more than six faults in 1300 experiments. We establish an equation of inverse proportionality between the number of faults injected and the remaining amount of the secret key. Using the equation, the number of fault injection can be accurately predicted, providing an effective way of evaluating fault attacks.
Allocating Planning Effort When Actions Expire

Making plans that depend on external events can be tricky. For example, an agent considering a partial plan that involves taking a bus must recognize that this partial plan is only viable if completed and selected for execution in time for the agent to arrive at the bus stop. This setting raises the thorny problem of allocating the agent’s planning effort across multiple open search nodes, each of which has an expiration time and an expected completion effort in addition to the usual estimated plan cost. This paper formalizes this metareasoning problem, studies its theoretical properties, and presents several algorithms for solving it. Our theoretical results include a surprising connection to job scheduling, as well as to deliberation scheduling in time-dependent planning. Our empirical results indicate that our algorithms are effective in practice. This work advances our understanding of how heuristic search planners might address realistic problem settings.
Distributed Elections Using Site-Push Majority Winner Monitoring

The problem of distributed election majority-winner monitoring for checkpoint-based protocols has been of interest for some time now, and the approach that most of the major algorithms take on this is to employ a center-based pull from all voting sites in conjunction with a count tracker, to continually monitor the information about the incoming voter stream. This paper presents an alternative solution to this problem, using site-based voter information push to the center, with reduced communication complexity, using a unique sampling technique. This technique is utilized to come up with a winner or an approximate winner with very high probability for Approval-based and Scoring-based rules. The site-push algorithm introduced here has been validated with correctness results, and has also been analyzed for different scenarios to see the improvement in the number of messages initiated, in comparison to the center-based pull algorithm considered for a checkpoint-based protocol. The effect of this algorithm is to reduce the communication complexity by a factor of (1 + log k/log n/k) in comparison to the center-pull algorithm (where k is the number of voting sites, and n the number of voters), which leads to a large reduction in communication complexity in democratic election scenarios. Also, to analyze other scenarios, simulation results are presented to show how the algorithm fares under randomized voter assignment, with variations in the value of total voters, total sites and the approximation parameter ϵ.
An Efficient Streaming Accelerator for Low Bit-Width Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have been widely applied in various fields, such as image recognition, speech processing, as well as in many big-data analysis tasks. However, their large size and intensive computation hinder their deployment in hardware, especially on the embedded systems with stringent latency, power, and area requirements. To address this issue, low bit-width CNNs are proposed as a highly competitive candidate. In this paper, we propose an efficient, scalable accelerator for low bit-width CNNs based on a parallel streaming architecture. With a novel coarse grain task partitioning (CGTP) strategy, the proposed accelerator with heterogeneous computing units, supporting multi-pattern dataflows, can nearly double the throughput for various CNN models on average. Besides, a hardware-friendly algorithm is proposed to simplify the activation and quantification process, which can reduce the power dissipation and area overhead. Based on the optimized algorithm, an efficient reconfigurable three-stage activation-quantification-pooling (AQP) unit with the low power staged blocking strategy is developed, which can process activation, quantification, and max-pooling operations simultaneously. Moreover, an interleaving memory scheduling scheme is proposed to well support the streaming architecture. The accelerator is implemented with TSMC 40 nm technology with a core size of 0.17 mm 2 . It can achieve 7.03 TOPS/W energy efficiency and 4.14 TOPS/mm 2 area efficiency at 100.1 mW, which makes it a promising design for the embedded devices.
GARSAaaS: group activity recognition and situation analysis as a service

Human activity recognition using embedded mobile and embedded sensors is becoming increasingly important. Scaling up from individuals to groups, that is, group activity recognition, has attracted significant attention recently. This paper proposes a model and specification language for group activities called GroupSense-L, and a novel architecture called GARSAaaS (GARSA-as-a-Service) to provide services for mobile Group Activity Recognition and Situation Analysis (or GARSA) applications. We implemented and evaluated GARSAaaS which is an extension of a framework called GroupSense (Abkenar et al., 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), 2016) where sensor data, collected using smartphone sensors, smartwatch sensors and embedded sensors in things, are aggregated via a protocol for these different devices to share information, as required for GARSA. We illustrate our approach via a scenario for providing services for bush walking leaders and bush walkers in a bushwalking group activity. We demonstrate the feasibility of our model and expressiveness of our proposed model.
Recent trends in big data using hadoop

&lt;span&gt;Big data refers to huge set of data which is very common these days due to the increase of internet utilities. Data generated from social media is a very common example for the same. This paper depicts the summary on big data and ways in which it has been utilized in all aspects. Data mining is radically a mode of deriving the indispensable knowledge from extensively vast fractions of data which is quite challenging to be interpreted by conventional methods. The paper mainly focuses on the issues related to the clustering techniques in big data. For the classification purpose of the big data, the existing classification algorithms are concisely acknowledged and after that, k-nearest neighbor algorithm is discreetly chosen among them and described along with an example. &lt;/span&gt;
Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications

Existing application performance management (APM) solutions lack robust anomaly detection capabilities and root cause analysis techniques, that do not require manual efforts and domain knowledge.In this paper, we develop a density-based unsupervised machine learning model to detect anomalies within an enterprise application, based upon data from multiple APM systems.The research was conducted in collaboration with a European automotive company, using two months of live application data.We show that our model detects abnormal system behavior more reliably than a commonly used outlier detection technique and provides information for detecting root causes.
On Soft Errors in the Conjugate Gradient Method: Sensitivity and Robust Numerical Detection

The conjugate gradient (CG) method is the most widely used iterative scheme for the solution of large sparse systems of linear equations when the matrix is symmetric positive definite.Although more than sixty year old, it is still a serious candidate for extreme-scale computations on large computing platforms.On the technological side, the continuous shrinking of transistor geometry and the increasing complexity of these devices affect dramatically their sensitivity to natural radiation, and thus diminish their reliability.One of the most common effects produced by natural radiation is the single event upset which consists in a bit-flip in a memory cell producing unexpected results at application level.Consequently, the future extreme scale computing facilities will be more prone to errors of any kind, including bit-flips, during their calculations.These numerical and technological observations are the main motivations for this work, where we first investigate through extensive numerical experiments the sensitivity of CG to bit-flips in its main computationally intensive kernels, namely the matrix-vector product and the preconditioner application.We further propose numerical criteria to detect the occurrence of such soft errors and assess their robustness through extensive numerical experiments.