Reviewerly/exHarmony huggingface dataset:
SenseJoy, a pluggable solution for assessing user behavior during powered wheelchair driving tasks

Abstract Background The complex task of Electric Powered Wheelchairs (EPW) prescription relies mainly on personal experience and subjective observations despite standardized processes and protocols. The most informative measurements come from joystick monitoring, but recording direct joystick outputs require to disassemble the joystick. We propose a new solution called “SenseJoy” that is easy to plug on a joystick and is suitable to characterize the driver behavior by estimating the joystick command. Methods SenseJoy is a pluggable system embedded on EPW built with a 3D accelerometer and a 2D gyrometer placed within the joystick and another 3D accelerometer located at the basis of the joystick. Data is sampled at 39 Hz and processed offline. First, SenseJoy sensitivity is assessed on wheelchair driving tasks performed by a group of 8 drivers (31 ± 8 years old, including one driver with left hemiplegia, one with cerebral palsy) in a lab environment. Direct joystick measurements are compared with SenseJoy estimations in different driving exercises. A second group of 5 drivers is recorded in the ecological context of a rehabilitation center (41 ± 10 years old, with two tetraplegic drivers, one tetraplegic driver with cognitive disorder, one driver post-stroke, one driver with right hemiplegia). The measurements from all groups of drivers are evaluated with an unsupervised statistical analysis, to estimate driving profile clusters. Results The SenseJoy is able to measure the EPW joystick inclination angles with a resolution of 1.31% and 1.23% in backward/forward and left/right directions respectively. A statistical validation ensures that the classical joystick-based indicators are equivalent when acquired with the SenseJoy or with a direct joystick output connection. Using an unsupervised methodology, based on a similarity matrix between subjects, it is possible to characterize the driver profile from real data. Conclusion SenseJoy is a pluggable system for assessing the joystick controls during EPW driving tasks. This system can be plugged on any EPW equipped with a joystick control interface. We demonstrate that it correctly estimates the performance indicators and it is able to characterize driving profile. The system is suitable and efficient to assist therapists in their recommendation, by providing objective measures with a fast installation process.
Adaptive soft subspace clustering combining within-cluster and between-cluster information

For the uncertain problem that between-cluster distance influences clustering in the soft subspace clustering (SSC) process, a novel clustering technique called adaptive soft subspace clustering (ASSC) is proposed by employing both within-cluster and between-cluster information. First, a new object ive function is constructed by minimizing the within-cluster compactness and maximizing the between-cluster distance based on the framework of SSC algorithm. Based on this objective function, a new way of computing clusters’ feature weights, centers and membership is then derived by using Lagrange multiplier method. The uniqueness of ASSC is that the objective function does not increase any control parameters, which can avoid the sensitivity of clustering results to the initial points of the control parameters. The properties of this algorithm are investigated and the performance is evaluated experimentally using UCI datasets. The contrastive experiment results demonstrate that the accuracy and the stability of the proposed algorithm outperform the four existing clustering algorithms, i.e., ESSC, EWKM, FWKM and CIM_QPSO_SSC.
Fault Tree Analysis based Virtual Machine Migration for Fault-Tolerant Cloud Data Center

Though cloud data center is highly adapted for flexible, scalable and highly available computing and storage resources, it is vulnerable to failures. Predicting the occurrence of server failure and taking appropriate preventive measures are essential in the cloud data center. In order to develop fault-tolerant cloud data center, a Fault Tree Analysis (FTA) based failure aware virtual machine (VM) migration technique is proposed. A fault tree is constructed for server failure event. The server failure is estimated by analyzing the fault tree and the virtual machines are migrated proactively to an alternate server before the failure. Simulations have been carried out and performance of the proposed technique is analyzed in terms of throughput. The results show that the proposed technique outperforms the other state of art techniques.
Extremely Low Light Video Enhancement along with No-Reference Video Quality Measurements

Low light video enhancement is a challenge many researchers have been facing.Low illumination is the cause for low light videos.Noise present in the videos adds to the low quality of the low light video.So to improve the quality of videos the noise present in the videos has to be suppressed before enhancement is attempted.This paper addresses Kalman filter to suppress the noise from the extremely low light video.Though entire noise could not be suppressed by this technique a Non local means filter helps to remove the remaining noise after amplification.The Gray World technique is used for amplification.After removing the noise by using NLM filter image enhancement of extremely low light video is carried out.
Application of systems approach at early stages of designinng unmanned towed underwater systems for shallow water areas

Typical configuration of an unmanned towed underwater system (UTUS) was described and advantages of using this type of marine equipment for shallow depth works were outlined.A list of basic operating modes of the UTUS which are key modes for design calculations of the system was determined. Basic requirements to the design and construction of a competitive UTUS for shallow water areas have been formed. The main requirements include the need for high-performance underwater technologies and the use of state-of-the-art computer research and design software. The need to maximize use of materials, parts and units that are commercially available as components for the UTUS was also indicated.Expediency and possibility of applying the systems approach methodology at early stages of the UTUS design were shown. Equations of existence were formulated in a matrix form for the UTUS components which make it possible to check engineering solutions for compliance with the requirements of the Requirements Specification already at the stages of technical proposal and sketch design. Structural, power, information and operational characteristics of the UTUS components were used as the compliance criteria.Sequence of the design works was completed in a form of a generalized design algorithm that implements the systems approach using modern computer technologies and equations of the UTUS existence. The developed algorithm allows the designer to check requirements of the Requirements Specification already at the early stages of the UTUS design and forms a scientifically substantiated methodological basis for creation of competitive underwater equipment
Embedding Cybersecurity Into Design Education: Increasing Designers’ Awareness of Cybersecurity Throughout the Design Process

Abstract As more digital devices with sensing capabilities are introduced into users’ daily lives, the risks of threats to data and privacy and security have increased. While cybersecurity has been acknowledged as an important concern in developing products with digital services, currently available design methodologies and practices offer limited effective guidance to designers to explicitly address cybersecurity issues. In this paper, we present a case study from a product design course at the University of California, Berkeley, where the course’s teaching team implemented an intervention in the form of cybersecurity-focused educational materials into the design process. The baseline and post-intervention survey results indicate that the cybersecurity intervention throughout the course had positively influenced the students’ awareness of cybersecurity (p &amp;lt; 0.001, SD = 0.79, 26% increase in score, Cohen’s d = 0.81). The intervention provoked the designers to consider and include aspects of cybersecurity in developing their design solutions throughout most of the design process. However, their increased awareness aside, the extent of the student teams considering cybersecurity had tapered off over the 6-week design course with little noticeable influence in the final design.
Image Denoising via Fast and Fuzzy Non-local Means Algorithm

Non-local means (NLM) algorithm is an effective and successful denoising method, but it is computationally heavy. To deal with this obstacle, we propose a novel NLM algorithm with fuzzy metric (FM-NLM) for image denoising in this paper. A new feature metric of visual features with fuzzy metric is utilized to measure the similarity between image pixels in the presence of Gaussian noise. Similarity measures of luminance and structure information are calculated using a fuzzy metric. A smooth kernel is constructed with the proposed fuzzy metric instead of the Gaussian weighted L2 norm kernel. The fuzzy metric and smooth kernel computationally simplify the NLM algorithm and avoid the filter parameters. Meanwhile, the proposed FMNLM using visual structure preferably preserves the original undistorted image structures. The performance of the improved method is visually and quantitatively comparable with or better than that of the current state-ofthe- art NLM-based denoising algorithms.
Grammar-based Tree Swarm Optimization

Particle Swarm Optimization (PSO) has been successfully applied to find good solutions through a guided search. This optimization technique usually works with vectors as individuals of the population conforming the search space. Nevertheless, there exist problems such that the search space cannot be transformed into a vector search space. In this paper we propose a novel technique based on the intuition behind PSO but overcoming its limitations concerning search spaces. Specifically, we present a PSO framework where the individuals conforming the search space are tree-like structures. In particular, our framework naturally includes classical PSO but also search spaces where elements are structures that can be represented as trees (in addition to usual trees, linear structures such as lists, queues and stacks).
Android Radio Streaming Apps for Songgolangit FM Ponorogo

Abstract Songgolangit FM Radio is one of the information media in Ponorogo society. The broadcast signal has limited covered areas because of the geographical condition of Ponorogo. Then, the other media is needed to solve a problem. This research aims to expand the broadcast area using Android radio streaming apps. This research used the waterfall steps. The test using black box shows that the apps running well and have no error. The feedback from Songgolangit FM peoples expected that these apps hope could expand new user with especially to Android users. These apps can be download by all Android phone through the Google Play Store (https://play.google.com/store/apps/details?id=com.amoled.radiosonggolangit&amp;hl=en). On the next research can measure the effectivity of radio streaming Android apps. Besides, future research also can develop its apps in other operating systems such as iOS, Tizen, Hongmeng and Blackberry mobile apps.
PEMILIHAN KEPLING TELADAN MENERAPKAN METODE RANK ORDER CENTROID (ROC) DAN METODE ADDITIVE RATIO ASSESSMENT (ARAS) DI KECAMATAN MEDAN AREA

The head of the environment is an extension of the head of the village head in assisting or providing services to the community both in the administration of administration in the village and to other problems. It is natural for a kepling to be appreciated for their performance during their special tenure in the kecamatan field area. Previously, the selection of a dipling in a sub-district was very inefficient and seemed unfair for this exemplary selection to use a system to produce an accurate value, and no intentional element. To overcome the process of selecting an exemplary kepling that experiences these obstacles by using an application called a Decision Support System. Decision Support System (SPK) is a system that can solve a problem, and this system is also assisted with several methods, namely the Rank Order Centroid (ROC) method that can assign weight values to each of the criteria based on their priority level. And to do the ranking or determine an exemplary set using the Additive Ratio Assessment (ARAS) method, this method provides decision making that takes decisions based on ranking or the highest value.Keywords: Head of Medan Area Subdistrict, SPK, Centroid Rank Order, Additive Ratio Assessment (ARAS).
StoMADS: Stochastic blackbox optimization using probabilistic estimates

This work introduces StoMADS, a stochastic variant of the mesh adaptive direct-search (MADS) algorithm originally developed for deterministic blackbox optimization. StoMADS considers the unconstrained optimization of an objective function f whose values can be computed only through a blackbox corrupted by some random noise following an unknown distribution. The proposed method is based on an algorithmic framework similar to that of MADS and uses random estimates of function values obtained from stochastic observations since the exact deterministic computable version of f is not available. Such estimates are required to be accurate with a sufficiently large but fixed probability and satisfy a variance condition. The ability of the proposed algorithm to generate an asymptotically dense set of search directions is then exploited to show convergence to a Clarke stationary point of f with probability one, using martingale theory.
Accurate phase-shift estimation for fringe-pattern profilometry

Fringe-pattern profilometry (FPP) has been widely used for phase reconstruction. It involves the use of phase shifting for phase retrieval. Phase-shift errors can affect the accuracy of phase reconstruction, and limited studies have been dedicated to studying phase-shift errors due to experimental, human, or environmental factors. We propose a simple and yet accurate phase-shift estimation method. Our study shows that the method is able to accurately estimate the actual phase shifts used in the FPP technique. The proposed method can find its applications in FPP and other phase shifting-based three-dimensional imaging techniques.
A Real-Time Gesture Recognition System with FPGA Accelerated ZynqNet Classification

This paper presents a real-time hand gesture recognition system by accelerating a convolutional neural network (CNN) using FPGA platform. More specifically, ZynqNet is adopted and modified to fulfill the classification task of recognizing the Swedish manual alphabet, which is used by sign language users for spelling purposes, also known as fingerspelling. Data augmentation and transfer learning techniques have been used during the training phase to improve the classification accuracy up to 80.1%, even with an 8-bit ZynqNet model. Extensive analysis of memory requirements and data processing patterns has been performed to enable optimization techniques, including memory partitioning and register arrays. The resulting FPGA implementation on a Xilinx UltraScale device avoids the use of off-chip memories, which together with block-wise processing scheduling, achieves an image rate of 23.5 frames per second (FPS) at 200 MHz clock frequency.
Security Analysis of IoT Systems Using Attack Trees

Attack trees are graphical representations of the different scenarios that can lead to a security failure. In combination with model checking, attack trees are useful to quantitatively analyse the security of a system. Such analysis can help in the design phase of a system to decide how and where to modify the system in order to meet some security specifications. In this paper we propose a security-based framework for modeling IoT systems where attack trees are defined alongside the model. A malicious entity uses the attack tree to exploit the vulnerabilities of the system. Successful attacks can be rare events in the system’s execution, in which case they are hard to detect with usual model checking techniques. Hence, we use importance splitting as a statistical model checking technique for rare events. This technique requires a decomposition of an attack into sub parts, similarly to an attack tree. We argue that therefore, importance splitting is well suited, and benefits, from our modeling framework. We implemented our approach in a tool-set and verified its effectiveness by running a set of experiments over a real-word example.
Structural Analysis of Contract Renewals

In the present paper we sketch an automated procedure to compare different versions of a contract. The contract texts used for this purpose are structurally differently composed PDF files that are converted into structured XML files by identifying and classifying text boxes. A classifier trained on manually annotated contracts achieves an accuracy of 87% on this task. We align contract versions and classify aligned text fragments into different similarity classes that enhance the manual comparison of changes in document versions. The main challenges are to deal with OCR errors and different layout of identical or similar texts. We demonstrate the procedure using some freely available contracts from the City of Hamburg written in German. The methods, however, are language agnostic and can be applied to other contracts as well.
Requirements Culture: A Case Study on Product Development and Requirement Perspectives

Abstract A desire to better understand requirements and their uses within product development inspired this case study conducted at a large power tool company. Researchers were interested in determining if a requirements culture existed within the company investigated, how this culture varied across departments, and how individuals at the company viewed requirements in engineering design. Ten semi-structured interviews were conducted by five teams of interviewers over the course of two months to answer these questions. Employees in various positions across the company were interviewed in the study including Project Engineers, a CAD Designer, an Industrial Designer, Project Managers, and Vice Presidents. Through a detailed analysis of the interview transcripts and documentation, it was determined that the company did have a definite requirement culture and that the transfer of requirements across departments must remain consistent for the individual groups to be effective. Furthermore, it was found that employee engagement with requirements was more significant with those who were involved in the early stages of product development.
Harassment Detection Using Machine Learning and Fuzzy Logic Techniques

Social networks, instant messaging applications, smartphones and the Internet are the main technological tools used by adolescents for communication.While they can benefit from those tools, they can also be used as a weapon for harassment.Cyberbullying is the name used for a current global social problem derived from harassment that uses offensive messages, which is severely affecting the youngest.Different types of software to identify and filter offensive contents have been developed in the last years.However, most of them are time consuming, not scalable and focused on very specific environments.To address this problem, we propose a mobile application for smartphones that provides a potential offensive content detection in order to determine whether a cyberbullying attack exists or not.In particular, we have developed an application that combines data pre-processing, fuzzy logic and machine learning to predict cyberbullying content.The main idea is to install a mobile application on the smartphone of a possible victim, so that it runs in the background.The system analyzes all received messages and notifications using data processing and decision-making algorithms.Finally, a fuzzy logic technique helps the system to reach a conclusion under a certain degree of imprecision.
Un nuovo testimone della "Lettera sul Romanticismo"

The paper describes and publishes a new witness of Lettera sul Romanticismo, contained in the same manuscript, preserved at Museo Manzoni in Villa «Il Caleotto» in Lecco, which includes the Sposi Promessi Summary, published in «Annali Manzoniani», n. 1 (III series, 2018, pp. 122-154). The interest of the document – which can be traced back to the circle of mutual friends between Manzoni, Achille Mauri and Monsignor Tosi – lies in the peculiarities of the title (which links it to a copy in the CNSM Finazzi Collection) and in an unpublished addition, made ‘by heart’, to fill a gap in the copy, due to a saut du meme au meme of the antigraph, by Manzoni himself.
Comparative Study for Outlier Detection In Air Quality Data Set

Outlier detection considered as non-trivial task because it can be misclassified as noise, also sometimes it can be misclassified as normal data or otherwise.Difficulties level of detecting outliers arose in time series data, it happens because different context has different amount of outlier data set.One of type data which considered as time series is air quality data set, because level of air quality can be different over time.In order to overcome the difficulties of detects outlier in time series data set, this research compare three different techniques of outlier detection are : statistical, density ratio, and forecasting technique, using the same data set about air quality data set in Madrid in three year period.The result of this research concludes that density ratio has outperformed the other two techniques that obtain the highest balanced accuracy and the lowest percentage of FAD.The lowest percentage of FAD means that those technique have the ability to not misclassified outlier data set as normal data or otherwise.
CAD System for Breast US Images with Speckle Noise Reduction and Bio-Inspired Segmentation

Ultrasound (US) images are highly susceptible to speckle-like noise which makes imperative to use specific techniques for image smoothing. However, this process can lead to undesirable side effects such as the degradation of the real contour of the region of interest (ROI). In such context, this paper presents a new methodology for computer aided diagnosis (CAD) systems whose heart is the combination of a method for speckle noise reduction, with histogram equalization and a technique for image segmentation that uses the bio-inspired firefly algorithm and Bayesian model. The segmentation approach and the equalization are applied in two distinct stages: globally and locally. The global application produces an initial coarse estimate of the ROI, and the local application defines this region more precisely. In the classification step we carried out experiments which show that the combination of features computed both within and below the lesion strongly influences the final accuracy. We show that the gray-scale distribution and statistical moments within the lesion together with gray-scale distribution and contrast of the region below the lesion is the combination that produces the better classification results. Experiments in a database of 250 US images of breast anomalies (100 benign and 150 malignant) show that the proposed methodology reaches performance of 95%.
Quantum Neural Networks Learning Algorithm Based on a Global Search

This paper reports the performance of a novel training algorithm for quantum neural networks (QNN) using a variation of the quantum search algorithm. The proposed algorithm trains a QNN exploring all possible weights with a sublinear cost. The training cost is theoretically O(√(N/t)), as a function of the quantity N of possible weights and t is the number of possible solutions. Initial experimental results demonstrate that the algorithm always converges to existing solutions, in addition to having the mean and maximum values, almost in total, lower than the expected maximum amount, as theoretically expected. The training algorithm is applied to classification problems.