Reviewerly/exHarmony huggingface dataset:
Automatic Isolated Arabic Speech Recognition and Its Transformation into Signs

In this technological era, providing a decent social integration of the mute communities or for the people with special needs still stands as a challenge. Currently, Sign Language (SL) is the main tool of communication between literate mute communities. About 18 million of the mute community is from the Arabic world [1]. In this framework, this work focuses on the design and development of an effective approach for automatic isolated Arabic speech based message recognition. The objective is to achieve an effective solution with a high level of precision. It is realizable by smartly using the hybrid features extraction and the robust classification techniques. The incoming speech segment is enhanced by the application of appropriate pre-conditioning. The Mel-Frequency Cepstral Coefficients (MFCCs) and the Perceptive Linear Prediction Coding Coefficients (PLPCC) are extracted from the enhanced speech segment. Later specifically designed voting based robust classifier issued to compare these extracted features with the reference templates. The comparison outcomes are the basis of classification decisions. The classification decision is transformed into systematic visual signs. The system functionality is tested with the help of a prototype realization. An average subject dependent Arabic isolated speech recognition accuracy of 92.6% is achieved.
Who you gonna call? seeking information and expertise for decision making in local government

ABSTRACT This paper analyzes the findings of a research project into the information seeking practices of local government decision‐makers in terms of collaborative information seeking (CIS) and expertise seeking. It finds that the participants do collaborate in information seeking and that there is a clear preference for personal information sources. The generally accepted definition of CIS as groups collectively seeking information in the performance of common projects or tasks is found to have limited applicability in many real‐life situations. Conversely, viewing expertise seeking as an individual activity in which another person is selected as an information source based on their high level of knowledge or skill does not reflect the collaborative nature of the modern organization. It is argued that neither approach adequately accounts for the full range of behaviors observed in practice, and that research into organizational information behavior would benefit from a conceptual amalgamation of CIS and expertise seeking.
SoC as IoT sensor network hub

Abstract Nowadays SoC’s miniaturization provide smaller yet more powerful devices that are perfect to be used as local hubs for small to medium sensor networks. Although sensors can now be easily connected directly to the cloud, a hub can simplify the process of bringing sensor to the IoT cloud. One of the most popular SoC board, Raspberry PI, is perfect for the hub role due to its small form factor, price, processing power and connectivity. Our proposed system consists in a SoC based low cost raspberry pi hub that connects two Bluetooth sensortag CC2650 modules to a mongoDB cloud database.
Robust Weighted Measurement Fusion Kalman Predictors with Uncertain Noise Variances

For the multisensor system with uncertain noise variances, using the minimax robust estimation principle, the local and weighted measurement fusion robust time-varying Kalman predictors are presented based on the worst-case conservative system with the conservative upper bound of noise variances. The actual prediction error variances are guaranteed to have a minimal upper bound for all admissible uncertainties of noise variances. A Lyapunov approach is proposed for the robustness analysis and their robust accuracy relations are proved. It is proved that the robust accuracy of weighted measurement robust fuser is higher than that of each local robust Kalman predictor. Specially, the corresponding steady-state robust local and weighted measurement fusion Kalman predictors are also proposed. A Monte-Carlo simulation example shows the effectiveness of the robustness and accuracy relations.
A Deep Hybrid Collaborative Filtering Based on Multi-dimension Analysis

In order to solve the problem that the existing neural collaborative filtering methods are not comprehensive to mine the latent information of embedded vectors, a deep hybrid collaborative filtering based on multi-dimension analysis is proposed. The idea is to use different feature fusion methods for the embedded vectors of users and items to obtain multiple dimensional fusion features, so that the information explored by different methods can complement each other, and the model can better discover the interaction between users and items. Experimental results show that, compared with the single-method of dimension analysis, the multi-dimension analysis can effectively improve the model’s ability to mine the interaction between users and items, and improve the performance of the recommender system.
An Investigation of Electrode Design for Physical Touch Extensions on a Capacitive Touch Surface

A simple way to prototype touch interaction is to extend electrodes from a capacitive touch screen to off-screen areas. With that we aim to develop a toolkit that transforms a user-designed layout into a layout of screen-extension electrodes that realizes touch for rapid prototyping. Nevertheless, this kind of extension cannot detect touch if the physical properties of the electrodes become large. In this work, we decompose the physical design properties of the extension electrode into two factors, the target area, and line bridge, and investigate the limitation of each separately. While revealing some factors, such as area, is extremely limited in term of designing freely, we look into the causes by measuring the capacitive charge on-screen and on-extensions.
An accelerated asynchronous advantage actor-critic algorithm applied in papermaking

The basis weight and moisture system with multivariate, strong coupling, large time lag and large inertia has always been mcky in papermaking. In this paper, a framework which applies the reinforcement learning methods is introduced to solve. Inspired by the previous work, we improve the Markov Decision Process (MDP) in our model, moreover we propose a new form of eligibility traces which can realize data multiplexing to speed up learning, aiming at remitting the costly expense of industrial process online training, where we use it in asynchronous advantage actor-critic (A3C) for illustration. The effectiveness of accelerated A3C in aforementioned system and the superiority of fast learning have been proved in the simulation.
Apply Lagrange Interpolation Based Access Control Mechanism in Personal Health Record Medical System

This study applied a Lagrange Interpolation based access control mechanism in the cloud Personal Health Record (PHR) system. We proposed an access control mechanism to make sure that health care information sharing is secure through the public key cryptosystem and Lagrange interpolation. Additionally, the access authority and the privacy setting have to be confined severely when we consider personal health records and private information. The PHR ought to prevent unauthorized users on the top of providing users with the access authority. As a result, we analyzed the mechanism’s security of the network attackers. On the basis of our analysis consequent, the access control and key management mechanism that we proposed can protect the health care information which can be shared among medical institutions efficiently and effectively.
Restoration of Horizontal Motion Blurred Images Based on Wiener Filtering

This paper proposes to use Wiener filtering for restoring the horizontal motion blurred images and designs a method for estimating the required parameters. In this paper, the spectrum data of motion blurred images are processed, and an effective method for measuring the motion blurred length is designed. After the wavelet decomposition of the blurred image, suitable processing of low frequency coefficients and high frequency coefficients may get the noise to signal power ratio which is needed by Wiener filtering. MATLAB simulation results show that the motion blur length obtained by this method is more accurate, the restoration method proposed by this paper is suitable for horizontal motion blurred images, the effect is better and the ringing artifact is not obvious.
A Redundant Recurrent Cerebellar Model Articulation Control System for Industrial Applications

Because of increasing reliability requirements of the applications in industrial areas, the control systems must guarantee the stability and robustness during operation and continuously maintain control and supervisory process. In order to meet the above requirements, this study proposes a redundant recurrent cerebellar model articulation control system (RRCMACS) for controlling industrial applications, in particular the water level and the pressure in a tank. Therein, the stability and the robustness of the applications are guaranteed by the recurrent cerebellar model articulation controller (RCMAC) and the redundant solution maintains continuously control and supervisory process. To adapt industrial applications, a Programmable Logic Controller (PLC) was used to build the control system and industrial networks were used to transfer data between the control system and field devices.
Best Practices for Increasing Data Return: Case Study From Indian Ocean Observation Network

Abstract Sustained real-time ocean observation systems using moored data buoys are vital for understanding ocean dynamics and variability, which are essential for improving oceanographic services including weather prediction, ocean state forecast, cyclone tracking, tsunami monitoring, and climate change studies. This paper describes the significant rapid restoration techniques implemented to increase the availability of the Indian Ocean observation networks over the past two decades. The efforts have helped in achieving availability of 97.9%, 82.3%, and 98.7% for the meteorological sensors, subsea surface oceanographic sensors, and tsunami buoy network, respectively.
Efficient Inter-Centroid Distance Estimation for the Data Clustering Using RCKM Algorithm

In this paper an efficient random centroid k-means (RCKM) method is used to provide better centroid distance selection in the clustering. For the experimentation and comparative analysis Pima Indians diabetes database has been used. In this approach the distance algorithm considered here are Euclidean, Pearson Coefficient, Chebyshev and Canberra. First we have randomized the centroid value in each iteration and process the value for clustering in the minimum centroid distance weight and maximum centroid distance weight for finding the clustering in several iterations. Based on the above distance algorithms centroid distances have been calculated to find out the minimum and maximum centroid distance for the clustering process. Here we also calculate the time taken to process the whole dataset. The Experimental result shows that the RCKM approach is better than previous work in both minimum and maximum spans for finding centroid distance in all the cases.
TEXT TASKS – PREREQUISITE FOR DEVELOPMENT OF CREATIVE THINKING IN STUDENTS FROM GRADES 1-4

Introduction: Creative work with text tasks helps to improve the educational process in mathematics for Grades 1-4, to create opportunities for developing education, to provoke the cognitive activity of the students and to develop their thinking. &#x0D; Object: The object of this research work is to study certain tendencies for creative work with text tasks and to develop their respective methodology options for work in the education of mathematics for Grades 1-4.&#x0D; This research work presents some theoretical concepts related to: text tasks; the stages for their solving, some of the tendencies for creative work with them; and the problem-productive strategy for education in mathematics in the primary school. &#x0D; Methods: The experimental work includes qualitative and quantitative research of the problems specified in the above paragraph. The following methods were applied: didactical experiment, observation, test, content analysis, mathematical-statistical method for data processing.&#x0D; Results: As a result of the experimental work it was found out that the percentage of the students from Grade 4 who failed to solve the text tasks with direct and indirect use of relations decreased from 31.2 % to 8.5 %. The percentage of the students who failed to describe situations from their surrounding environment using a mathematical model decreased from 45.3 % to 10.1 %. The percentage of the students who correctly composed text tasks increased from 29.5% to 71.6%. The difference in the results was proven to be statistically significant.&#x0D; Conclusion: The applied methodology system of work with text tasks helped to develop knowledge, skills and competencies from a competency cluster “Numbers” and “Modelling”, and to develop creative thinking in the students from Grades 1-4.
Stability of Nonlinear Stochastic Time Varying Systems

Stochastic systems are widely used in many fields, such as scientific research, industry, communication, population model, network control and so on. Some issues of deterministic systems are extended to stochastic systems with the research of stochastic systems. As a basic structural property of the system, stability is a core concept and an important problem in the theory of system control. In this paper, we will provide the criteria of mean square stability and mean square asymptotic stability respectively by defining the Lyapunov function of the system and the corresponding diffusion operator. We also construct the auxiliary delayed differential equation, which proves the conclusion by means of the basic theory of stochastic differential equations and the comparison principle of differential equations. The proposed criteria are more generous and can be generalized to random systems with time delay or stochastic hybrid systems. Two examples, the Hopfield neural network and a nonlinear stochastic system with time-delay, will be analyzed by the proposed criteria. It will be illustrated in the second example that the criteria proposed in this paper are suitable for the situation that time delays are time-varying and not continuously differentiable.
RSF model with SCE‐based global constraint for image segmentation

A novel region-scalable fitting (RSF) active contour model with symmetric cross entropy (SCE)-based global constraint is proposed to segment various kinds of images. The energy functional is mainly composed of two parts, namely the local and global constraints. The local constraint is the weighted region-scalable fitting term, which computes the local intensity information. The global constraint is constructed by measuring the difference between the intensity distribution of the object image and the intensity distribution of the original image using SCE. Moreover, some regularised terms are incorporated into the energy functional. Experimental results for synthetic and real images show that the proposed model achieves better segmentation performance and is insensitive to initial contour and noise.
GP-HD: Using Genetic Programming to Generate Dynamical Systems Models for Health Care

The huge wealth of data in the health domain can be exploited to create models that predict development of health states over time. Temporal learning algorithms are well suited to learn relationships between health states and make predictions about their future developments. However, these algorithms: (1) either focus on learning one generic model for all patients, providing general insights but often with limited predictive performance, or (2) learn individualized models from which it is hard to derive generic concepts. In this paper, we present a middle ground, namely parameterized dynamical systems models that are generated from data using a Genetic Programming (GP) framework. A fitness function suitable for the health domain is exploited. An evaluation of the approach in the mental health domain shows that performance of the model generated by the GP is on par with a dynamical systems model developed based on domain knowledge, significantly outperforms a generic Long Term Short Term Memory (LSTM) model and in some cases also outperforms an individualized LSTM model.
Image restoration in water turbulence using image registration and blind deconvolution

When image through water turbulence, captured images will appear severe geometrical distortion. Restoration of image with unknown geometrical distortion is a challenging problem. In this paper, we propose an iterative method to acquire a geometrically corrected high-quality image from an observed video sequence which contains serious geometrical distortion. Firstly, we use a blind deconvolution method to deblur the temporal mean image of the observed video sequence. Next, an image registration based on B-spline is employed to obtain a new video sequence with less geometrical distortion. After several iterative computations of deconvolution and registration, we carry out a robust principal component analysis to remove residual noise of latest video sequence. Finally, a single geometrically corrected image is acquired by a temporal mean operation on final video sequence. Experimental results demonstrate that our proposed method is capable to greatly remove geometrical distortion in video sequence.
Enhancing the Quality of Cellular Camera Video With Convolutional Neural Network

Abstrak— At present technological developments, especially in the field of computer vision, are showing significant performance such as the application of convolutional neural networks that have a very high degree of accuracy, for example improving video quality which recently has image restoration such as super resolution (VSR) thanks to deep learning with the aim of helping produce better visual videos. The use of video cameras for mobile devices is now increasingly highly developed. Nowadays mobile devices are experiencing a rapid increase in quality especially in cameras. However, physical limitations such as the small sensor size, compact lens and the lack of supporting hardware can prevent cellular devices from achieving good video camera quality results. For that many method approaches are applied, one of which is the CNN (Convolutional Neural Network) method. This method can improve the image of video recordings that have poor quality.&#x0D; Keywords—Convolutional neural network, computer vision, Improved video quality ;
Business Hall Abnormal Behavior Detection Based on Cascading Deep Neural Network Model

In the business halls of our country, video cameras are very common, but many videos are analyzed manually, which is time-consuming and labor-intensive, not smart enough. Many of them are used after-the-fact, unable to meet real-time requirements, and the time is relatively tight. However, our method can analyze the video, get real-time analysis results, and perform early warnings. Neuron network is a common method of artificial intelligence for identifying objects in images and videos. This method requires a large amount of sample data to obtain higher accuracy. This paper proposes a behavior detection framework based on cascading depth neural network model. Based on the advanced drfcn neural network, we trained two models and the two models are cascaded. Based on this framework, we implemented a drfcn-service system, which implements the features of drfcn, and flexibly adds plugins to support single model, two-model cascading and even multiple model cascading. Experiments show that the cascading system can significantly improve the accuracy of recognition.
An efficient reversible authentication scheme for demosaiced images with improved detectability

Abstract The existing probability based reversible authentication schemes for demosaiced images embed authentication codes into rebuilt components of image pixels. The original demosaiced image can be totally recovered if the marked image is unaltered. Although these schemes offer the goal of pixel-wise tamper detection, the generated authentication codes are irrelevant to the image pixels, causing some undetectable intentional alterations. The proposed method pre-processes the rebuilt components of demosaiced images and hashes them to generate authentication codes. With the guide of a randomly-generated reference table, authentication codes are embedded into the rebuilt components of demosaiced images. Since the distortions of image pixels are sensitive to the embedded authentication codes, the proposed method further alters the pre-processed pixels to generate a set of authentication codes. One of the authentication codes that minimizes the distortion is embedded to generate marked demosaiced images. The results show that the proposed method offers a better image quality than prior state-of-the-art works, and is capable of detecting a variety of tampering.
How much reading between the lines is there in online game playing?

Abstract This paper discusses to what extent people can convey and understand intentions and messages in Monster Strike, which has only one tool for intentional messaging – called ‘good job’ (GJ) – to send messages to other players, and it is, therefore, interesting to analyze how players exchange ideas and infer each other’s communicative intentions using a limited means of communication towards a common goal. This paper describes the significance of GJ first through an analysis of actual game playing, with supplemental information from players’ discussion spaces on line. The communications encompass (1) the original meaning (i.e., ‘good job’); (2) extended interpretations; and (3) an attention signal, with shared temporary and cumulative knowledge of the game and the players’ own dispositions.
An Adaptive Interface for Spatial Augmented Reality Workspaces

A promising feature of wearable augmented reality devices is the ability to easily access information on the go. However, designing AR interfaces that can support user movement and also adjust to different physical environments is a challenging task. We present an interaction system for AR windows that uses adaptation to automatically perform level window movement while allowing high-level user control.
A Rule-based Cooperative Exploration Method for Dual Robots in Unknown Environment

Aiming at the problem about disaster relief under the situation of unknown environment and unknown target orientation, a set of rules for mobile robot autonomous search and map navigation is formulated. Firstly, the mobile robot attempts to read the map of the environment. If it fails, the robot searches for the target according to the wall-touching algorithm in the unknown environment and utilizes the improved RBPF-SLAM algorithm to implement the map computation. When the map is successfully read, the robot moves along the optimal path planned by A* algorithm. Meanwhile, dynamic window approach is used for dynamic local path planning for the unknown environment which changes indefinitely. Finally, the simulation experiment is carried out to verify the practicability and validity of the set of rules.
Modifying FCM with grids and density peaks

In this paper, we modify the FCM algorithm with grids and density peaks. We apply the density peak clustering algorithm mixed with grid technology to determine the initial clustering positions of FCM. We also use cluster cores instead of cluster centroids, so that the modified algorithm can effectively discover arbitrary shape clusters. Experimental results show that the performance of the proposed algorithm is found to be superior to its ECTs.