Reviewerly/exHarmony huggingface dataset:
Design and Implementation Of Smart Gloves For The Specially Privileged

Human beings can see, listen and most importantly interact with their external environment. This innate ability makes them superior over the other species. Unfortunately, there are some people who are differently abled and do not possess the ability to interact with others. These members communicate with the help of sign language, but this language is understood only within their community. Thus, a huge communication barrier exists between this community and the open world. As a matter of a fact this communication barrier is not only restricted to those with disability but also to the old aged members in the society. The impact of age and diseases are so heavy on them that they are not able to carry out their simple household chores like having food, medicines or a glass of water. Thus, we propose a system called “Smart Gloves” which will be beneficial to them and address their problems. The glove will act as a gesture to text converter. The sign language can easily be converted to understandable form. The glove will also act as an obstacle detector to help the blind navigate the roads. It also comes with built in wireless connectivity so that the old aged can message their guardians in case of an emergency.
Texture Operator based Digital Image Splicing Detection using ELTP Technique

Social networking sites play a significant role in news viral. A lot of information is fake. Image editing tools play an essential role in the easy availability of these tools and software. So image authentication is the need of the time. In this paper, we proposed a texture-based image splicing detection, ELTP (Enhanced local ternary pattern) used for feature extraction, which gives us a better response than LBP. The local ternary pattern used ternary value (-1, 0, 1) code for assessment, advanced to LBP binary value code. ELTP is more sensitive compare to noise. In our proposed technique, first, we convert the RGB image into YCbCr, and the chrominance component used for feature extraction. In feature extraction, first, we implement a standard deviation filter to highlight the abrupt change in the image and ELTP to extract the features. SVM is used as a classifier gives better to some of the states of the art method.
Single Image Defogging using Depth Estimation and Scene-Specific Dark Channel Prior

Quality of outdoor images are often degraded under bad weather conditions by the extensive presence of suspended particles in the atmosphere such as fog and haze. Although, many single image defogging approaches have been proposed in the past, their restoration performance is considerably low since they fail to consider the image-specific cues. To feed that information, we propose a simple and robust defogging framework that can estimate the rough depth map of a foggy image based on the density of the fog in local regions. Obtained depth map information is used to compute the scene-specific dark channel and transmission. In addition, a histogram normalization based post-processing technique is used to enhance the restoration. Experimental evaluation performed on a benchmark dataset demonstrates the proposed defogging framework outperforms state-of-the-art approaches.
An Automated Interview Grading System in Talent Recruitment using SVM

Interview is one of the most important stages during talent recruitment to find the best candidates who fit with the corporate culture. However, an interview usually involves third-party psychology experts and professionals to conduct both the interview process and analysis, and thus can be fairly costly for the company. To this end, companies seek alternative methods to reduce manual tasks and human effort in talent recruitment by introducing automation using machine learning technology. In this paper, we investigate machine learning methods to grade the corporate culture fitness level of job applicants by analyzing the interview verbatim. To classify the interview verbatim, we compare SVM, which has been proven to be a very effective text classifier in general, with naive Bayes and KNN. In this study, SVM often demonstrates higher performance on any dataset and in many different schemes compared to naive Bayes and KNN. SVM achieves the average accuracy at 86%, better than naive Bayes at 81% and KNN at 79%.
QoE Influence Factors (IFs) classification Survey focusing on User Behavior/Engagement metrics

Over the last few years, the evolution of multimedia streaming services has resulted in growth and significant explorations of Quality of Experience (QoE) since the concept of QoE is gaining an enormous research effort, but it is still complex to estimate. This estimation can be influenced by a wide range of quality affecting factors. In this work, we provide standard definitions, we review and survey existing models to classify QoE Influence Factors (IFs). Moreover, we present a classification of the QoE IFs into four groups: system factors, context factors, human factors, and social-behavioral factors. Finally, regarding the QoE IFs, we highlight some relevant studies. We believe that our overview and findings can provide an understanding of the related quality affecting factors and QoE assessment approaches over video communications.
A Hybrid Approach for Signal Modulation Recognition Using Deep Learning Methods

Abstract In this study, a novel signal modulation recognition framework has been proposed for automatically classifying eleven different modulation types with various SNR values. The framework employs both the raw complex-valued I/Q signal and its time-frequency description to represent the radio signal. And, a hybrid deep neural network is presented to recognize different modulation types from the representation data by leveraging the appealing properties of a convolutional neural network (CNN) and a long short-term memory (LSTM) network. Extensive validation of our scheme is performed on a large public dataset by comparing it with three existing 。 methods from literature, and our scheme yields quite promising results in terms of recognition accuracy.
Robotic Visual Servoing Based on Convolutional Neural Network

In order to simplify the problems of image feature choosing and extraction, and the nonlinear mapping estimation in traditional visual servoing, we present a visual servoing method based on convolutional neural network (CNN) to realize the precise, robust, and real-time 6DOF control of robotic manipulation. Herein we propose an approach to design and generate a dataset from a single image, by simulating the motion of eye-in-hand robotic system. This dataset is utilized to train our visual servoing CNN which computes the relative pose between robotic system and external environment. Then the output of the network is employed in the visual control schemes. This method converges robustly with experimental result of a position error less than one millimeter and a rotation error less than half a degree in average in simulation.
Crossed-Time Delay Neural Network for Speaker Recognition

Time Delay Neural Network (TDNN) is a well-performing structure for deep neural network-based speaker recognition systems. In this paper we introduce a novel structure, named Crossed-Time Delay Neural Network (CTDNN) to enhance the performance of current TDNN for speaker recognition. Inspired by the multi-filters setting of convolution layers from convolution neural networks, we set multiple time delay units with different context size at the bottom layer and construct a multilayer parallel network. The proposed CTDNN gives significant improvements over original TDNN on both speaker verification and identification tasks. It outperforms in VoxCeleb1 dataset in verification experiment with a 2.6% absolute Equal Error Rate improvement. In few shots condition, CTDNN reaches 90.4% identification accuracy, which doubles the identification accuracy of original TDNN. We also compare the proposed CTDNN with another new variant of TDNN, Factorized-TDNN, which shows that our model has a 36% absolute identification accuracy improvement under few shots condition. Moreover, the proposed CTDNN can handle training with a larger batch more efficiently and hence, utilize calculation resources economically.
Realtime flicker removal for fast video streaming and detection of moving objects

Abstract High-speed cameras are used in computer vision systems to track balls, shuttlecocks, or players in many different sports. Collected information is used for statistics, as coaches’ and players’ aids to improve technique and tactics or as referees’ aids to verify their decisions or to enrich television broadcasts. Sports arenas in which games are played are often equipped with lights generating flickering effects in captured movies. A fast and yet effective enough algorithm is necessary to remove flickering so movement detection and object tracking algorithms could be used. In this paper, we propose a fast flicker removal algorithm working as an online filter on frame streams at speeds exceeding 200 frames per second. Most of the solutions found in literature concentrate on effectiveness and accuracy and not on the speed of operation. In contrast, our original solution is designed with speed in mind with sufficient accuracy to be used before calculating differential frames to detect movement in streams. Our algorithm is adaptive and works when lighting conditions are changing (new light sources) and performs well with various light sources that are causing flickering. The results of the experiments carried out show the high effectiveness of the method implemented on CPU and GPU, allowing effective tracking of objects of interest in preliminary applications of a commercially offered instant review system for badminton.
Privacy Games

The problem of analyzing the effect of privacy concerns on the behavior of selfish utility-maximizing agents has received much attention lately. Privacy concerns are often modeled by altering the utility functions of agents to consider also their privacy loss [4, 14, 20, 28]. Such privacy-aware agents prefer to take a randomized strategy even in very simple games in which non-privacy-aware agents play pure strategies. In some cases, the behavior of privacy-aware agents follows the framework of Randomized Response, a well-known mechanism that preserves differential privacy. Our work is aimed at better understanding the behavior of agents in settings where their privacy concerns are explicitly given. We consider a toy setting where agent A , in an attempt to discover the secret type of agent B , offers B a gift that one type of B agent likes and the other type dislikes. As opposed to previous works, B ’s incentive to keep her type a secret isn’t the result of “hardwiring” B ’s utility function to consider privacy, but rather takes the form of a payment between B and A . We investigate three different types of payment functions and analyze B ’s behavior in each of the resulting games. As we show, under some payments, B ’s behavior is very different than the behavior of agents with hardwired privacy concerns and might even be deterministic. Under a different payment, we show that B ’s BNE strategy does fall into the framework of Randomized Response.
A Stochastic Part of Speech Tagger for the Sinhala Language based on Social Media Data mining

Part of Speech (POS) taggers plays a critical role in NLP applications for analyzing the behavior and the construction of a language. POS tagging is an essential step in NLP for further analysis of a language. Even though there are various POS taggers available for the English language, still there is no official POS tagger for the Sinhala language. To overcome this problem, this paper presents a stochastic based POS tagger for the Sinhala language based on social media data mining. This tagger uses a Hidden Markov model (HMM) with bi-gram probabilities for training and tagging data. The tagger is capable of handling lexical items with multiple POS tags while predicting the POS tags of unknown words. The Viterbi algorithm is used to decide the best POS tag for each word based on the results of HMM. An annotated corpus was developed using social media data during the research for HMM parameter estimation. The tagger has shown 63% overall accuracy and nearly 90% accuracy for known words. The proposed approach has demonstrated higher accuracy level compared to SVM and hybrid approaches.
Q-Learning Dynamic Path Planning for an HCV Avoiding Unknown Threatened Area

For a hypersonic cruise vehicle (HCV) avoiding unknown threats from ground or space, dynamic path planning is an essential issue to fly safely in near space. Q-learning is the most typical one of reinforcement learning methods that has been effectively used for robotic path planning. However, few works is reported for an HCV with hypersonic flying characteristics considered. In this paper, a Q-learning based dynamic path planning method is presented. In the method, the vehicle is assumed as a mass point with time-invariant altitude and velocity for simplicity and the flight zone of the vehicle is modeled as an average grid map, where an ε-greedy strategy is designed to establish the Q-learning algorithm. A simulation case has illustrated the effectiveness of the given method and shows that the method is suitable for the cruise phase path planning of an HCV.
Research and application of digital watermark tracing technology in data security protection of power grid

The purpose of this paper is to study the security control measures when data is distributed and shared, and necessary privacy data protection measures should be taken. On the one hand, when data leakage occurs after data is distributed, it can accurately locate which responsibility link occurs according to the added data watermark information, and then trace the responsibility and strengthen the weak link control. On the other hand, through the joint query technology in the data encryption sharing scenario, this paper studies how to build a trusted security system to ensure the complete trust between the data participants and provide effective support for big data analysis.
VIPNet: A Fast and Accurate Single-View Volumetric Reconstruction by Learning Sparse Implicit Point Guidance

With the advent of deep neural networks, learning-based single-view reconstruction has gained popularity. However, in 3D, there is no absolutely dominant representation that is both computationally efficient and accurate yet allows for reconstructing high-resolution geometry of arbitrary topology. After all, the accurate implicit methods are time-consuming due to dense sampling and inference, while volumetric approaches are fast but limited to heavy memory usage and low accuracy. In this paper, we propose VIPNet, an end-to-end hybrid representation learning for fast and accurate single-view reconstruction under sparse implicit point guidance. Given an image, it first generates a volumetric result. Meanwhile, a corresponding implicit shape representation is learned. To balance the efficiency and accuracy, we adopt PointGenNet to learn some representative points for guiding the voxel refinement with the corresponding sparse implicit inference. A strategy of patch-based synthesis with global-local features under implicit guidance is also applied for reducing memory consumption required to generate high-resolution output. Extensive experiments demonstrate the effectiveness of our method both qualitatively and quantitatively, which indicates that our proposed hybrid learning outperforms separate representation learning. Specifically, our network not only runs 60 times faster than implicit methods but also contributes to accuracy gains. We hope it will inspire a re-thinking of hybrid representation learning.
Communication practices to maintain the student contingent in the distance education system

Modern educational programs of distance education are gaining popularity and demand among applicants with different educational needs. Such programs are aimed at overcoming educational inequality and equalization of educational opportunities, solving problems of inclusive education, incorporation in the system of continuous education. The study objective is to identify the role of educational communications and their effect on preserving the student contingent in distance education. The paper focuses on identifying the reasons to discontinue by students their studies in vocational educational programs conducted using distance learning technologies; determining the communication aspects of the problem to maintain a contingent of students in the professional educational programs of Bachelor and Master. The authors determine the motives for choosing the program, involvement in educational communications, satisfaction with the educational process and its components, as well as difficulties in mastering the program based on the student opinions in «the group at risk of expulsion» and studied in such educational programs, but for various reasons do not complete the studies. The research has solved the following tasks: considering educational communication practices in the system of distance learning; identifying the reasons of difficulties and expulsions of students in obtaining higher professional education by the programs of distance education; forming proposals on the organization of educational communications aimed more successful mastering of the educational program and preservation of the student contingent in the distance learning system. The study results should be used to organize communication and methodological support of professional educational programs of distance education.
MSCANet: Adaptive Multi-scale Context Aggregation Network for Congested Crowd Counting

Crowd counting has achieved significant progress with deep convolutional neural networks. However, most of the existing methods don’t fully utilize spatial context information, and it is difficult for them to count the congested crowd accurately. To this end, we propose a novel Adaptive Multi-scale Context Aggregation Network (MSCANet), in which a Multi-scale Context Aggregation module (MSCA) is designed to adaptively extract and aggregate the contextual information from different scales of the crowd. More specifically, for each input, we first extract multi-scale context features via atrous convolution layers. Then, the multi-scale context features are progressively aggregated via a channel attention to enrich the crowd representations in different scales. Finally, a $$1\times 1$$ convolution layer is applied to regress the crowd density. We perform extensive experiments on three public datasets: ShanghaiTech Part_A, UCF_CC_50 and UCF-QNRF, and the experimental results demonstrate the superiority of our method compared to current the state-of-the-art methods.
Fast Prototyping of a Deep Neural Network on an FPGA

This paper describes a prototyping methodology for implementing deep neural network (DNN) models in hardware. From a DNN model developed in C or C++ programming language, we develop a hardware architecture using a SoC virtual platform and verify the functionality using FPGA board. It demonstrates the viability of using FPGAs for accelerating specific applications written in a high-level language. With the use of High-level Synthesis tools provided by Xilinx [3], it is shown to be possible to implement an FPGA design that would run the inference calculations required by the MobileNetV2 [1] Deep Neural Network. With minimal alterations to the C++ code developed for a software implementation of the MobileNetV2 where HDL code could be directly synthesized from the original C++ code, dramatically reducing the complexity of the project. Consequently, when the design was implemented on an FPGA, upwards of 5 times increase in speed was able to be realized when compared to similar processors (ARM7).
Automatic classification and identification algorithms for single-environment risk and their application in social changes

This article focuses on the risk assessment about social environmental safety-. Based on multi-rule, multi-standard, multi-level, multi-factor, and multi-dimension classification principles, this paper proposes a layer-by-layer classification and automatic identification algorithm for single-environment risks. The layer-by-layer classification, k-classification, single-environment risk, and risk information are defined. This paper first proposes an algorithm for the layer-by-layer classification of risk, and -an automatic identification algorithm of layer-by-layer classificationfor single-environment risk. By designing a rule for thelayer-by-layer classification of risk, this paper innovatively studies the automatic identification, evaluation, measurement, and classification of risk in a single environment. The algorithms are applied to a real case where a woman in Dalian, China was suddenly beaten by a stranger in the middle of the night on 22 June 2019. Through four iterations of gradual classification and automatic identification, the environment is finally labeled as "top risk," and a corresponding numerical risk value is calculated. This research is practical and can be used to provide guidance to populations on how to identify and avoid certain risks.
Mobile Learning: Modelling the Influencing Factors on Mobile Devices

With the popularity of mobile devices, learning through mobile device proliferated in the education sector. Now a day's mobile devices and mobile technologies are advancing rapidly. Therefore, mobile learning surpassing all other educational servicing methods. On the other hand, human life style averts stagnated education and demanding synchronous mode education such as learning while working. In this study we set out to look-over influencing factors on mobile devices for applicable and sustainable mobile learning environment. In this study we propose an impact model consists six influencing factors i.e. screen size, supportive software, screen zooming, video playback control, touch screen keyboard, and language predictive tools. 120 university teachers and learners with equal portion in each category participated on the model evaluation through pre and post survey questionnaires. Initially they faced pre survey questionnaire with their prior mobile learning experience and finally took the post survey questionnaire after successfully working on the Moodle mobile app modified by first author. The results revealed that the most significant impact factor in the proposed model was `screen zooming'. Hence, when designing applicable and sustainable mobile learning environment it should be considered that, the screen zooming as the most significant mobile device feature for learner and teacher to adopt in mobile learning.
A Deep Learning Model for Investigation on Human Body Movements and Action

Recognizing body movements are vital for fields like virtual reality, human computer interaction, and safety monitoring. The research seeks to overcome the limitations of existing body movement recognition systems, which include individual human body recognition rather than multiple recognition and also fails when the body is partially visible. Since most of the real-world applications, like accident monitoring requires recognition of multiple people; at the same time, the existing methodology has reached an impasse. Classification algorithms are predominantly used to recognize various body movements and actions. Even Kinect, which is well known for recognizing human actions and movements, uses a random forest algorithm to track the movements. With recent technological advancements, researchers are keen to increase the accuracy of tracking the movements. New research methodologies seek to overcome the above limitations by using deep neural networks and many other machine learning algorithms. With the inspiration of the success of convolution neural networks (CNNs), the implementation explores usage of the mentioned deep learning algorithm in estimating human poses and recognizes corresponding actions. The idea is to build a scalable model that can be used for many applications and get accurate results as much as possible.