Reviewerly/exHarmony huggingface dataset:
Sign Language Converter Using Hand Gestures

A correspondence hole has consistently been there between Sign Languages and common dialect. The aim is to build a system that can help the paralysed people or any person to convey their message via Hand Gestures. The present framework catches the motions utilizing a webcam and perceives the signal being appeared, mapping it to the comparing English Characters. In this system, the patient will pose a hand gesture which displays a message mapped with it which is delivered in the form of audio and text. The gesture will convey a message rather than an alphabet. The patient can choose the preferred language which he/she is familiar with. The benefit of the system is that it will also ring an alert if there is an emergency.
skeleton-based Action recognition with a triple-stream graph convolutional network

Skeleton data has been widely used in action recognition tasks to achieve effective recognition because it can stably adapt to dynamic environments and complex backgrounds. In those existing methods, two kinds of information, joint and bone segments, have been proven to be very useful. However, the inexplicit motion information, which is between bones, is always be ignored. Therefore, how to combine these three kinds of information, how to make full use of the joint information of bones and the relationship between bone segments and bone movement information, and how to represent bone movement information are still problems that need to be solved urgently. This paper designs a triple-stream undirected graph neural network, named 3s-AGCN, to extract motion information features of joints, bone segments, and bones for action recognition. Our final model has been tested on the large data set NTU-RGBD and has reached the latest level of the current model.
Markerless Augmented Reality in Construction Engineering Utilizing Extreme Programming

Abstract This study aims to build Android-based Markerless Augmented Reality as a learning media in the Construction field, specifically Bridge material for vocational students. This study applies the Extreme programming method, which consists of the Planning, Design, Coding, and Testing stages. The developers and potential users performed the tests. The Functional Usability test results using Black-Box testing reveal that all features in the application can operate successfully. In terms of compatibility, the application is declared very suitable for use. The application has proven successful in running on several kinds of Android smartphones with different versions and screen resolutions. Meanwhile, in terms of Usability, the application acquires an eligibility percentage of 84.2%. Users find it easy to operate the application since they can immediately run 3D objects wherever they are, without being limited by the presence of markers. They consider being freer to run AR applications. However, the Markerless application that has been built can only be operated on Android smartphones version 7.0 and above.
User Behavior Fingerprinting With Multi-Item-Sets and Its Application in IPTV Viewer Identification

User activities in cyberspace leave unique traces for user identification (UI). Individual users can be identified by their frequent activity items through statistical feature matching. However, such approaches face the data sparsity problem. In this paper, we propose to address this problem by multi-item-set fingerprinting that identifies users not only based on their frequent individual activity items, but also their frequent consecutive item sequences with different lengths. We also propose a new similarity metric between fingerprint vectors that combines the advantages of Jaccard distance and relative entropy distance. Furthermore, we develop a fusion decision scheme by consolidating matching candidates generated by different similarity metrics. It improves the precision at the price of extra rejection. Our proposed approaches can be used in both one-by-one matching and bipartite graph group matching. Through extensive experiments on three real user datasets, in particular a large-scale Internet Protocol Television (IPTV) viewer dataset, we demonstrate that the proposed approaches outperform the state-of-the-art methods. The average matching precision reaches 93.8% for a dataset of 1,000 users and 100% for a dataset of 100 users. This work is of significance for information forensics and raises a new challenge for human privacy protection in cyberspace.
Study of Full-body Virtual Embodiment Using noninvasive Brain Stimulation and Imaging

The sense of embodiment in virtual reality is a strong case of body ownership illusion, effectively allowing humans to experience the ownership of a modified, or a completely different body. Virtual embodiment has captured the attention of researchers in various fields, with applications far beyond computer science. Despite the promising applications, little is known about the neural mechanisms behind full-body virtual embodiment. This study investigates the influence of anodal transcranial direct current stimulation of the brain area linked to processing of the bodily self (right temporoparietal junction) to the subjective strength of virtual embodiment and its main constituents, using within-subject experimental design with sham-controlled stimulation. Virtual embodiment was studied using questionnaires, accompanied by brain signals gathered using EEG. Our results suggest that stimulation did not affect the sense of ownership toward the virtual avatar. Borderline strengthening of the perceived sense of agency toward the avatar’s actions was found in the sessions with stimulation.
An adaptive approach of Feature Selection applied to Semi-Supervised Fuzzy Clustering

Label information in the corresponding semi-supervised fuzzy clustering cannot be used efficiently due to feature redundancy. To address the problem, we propose an adaptive approach of feature selection applied to semi-supervised fuzzy clustering. There are three phases in our approach: 1) feature-score by fisher-score; 2) Min Mean Square Error of Feature Select criterion; 3) the number of features is selected by Min Mean Square Error of Feature Select criterion. We apply our approach to three semi-supervised fuzzy clustering methods. Experiments show that the adaptive approach of feature selection applied to semi-supervised fuzzy clustering can improve the clustering performance.
Feature-aided GM-PHD Algorithm for Sea-surface Target Tracking

Target tracking in sea clutter is always a challenging problem due to the weak target returns, especially in terrible sea conditions. Since the radar returns are frequently submerged by strong clutter, conventional tracking algorithms, which only use the dynamic characteristics of the target like position, speed, etc., failed to achieve satisfying tracking performance. Hence, additional features could be introduced in order to distinguish target and clutter effectively. Specifically, this paper introduces three features in the time domain, frequency domain, and time-frequency domain respectively, and combines them into the conventional Gaussian Mixture Probability Hypothesis Density (GM-PHD) multi-target tracking algorithm. Moreover, we utilize Multiple Hypothesis Tracking (MHT) to manage the track for further improving the tracking results. The experimental results on the CSIR public set verity the effectiveness of the proposed method.
A Novel Fraud Detection Scheme for Credit Card Usage Employing Random Forest Algorithm Combined with Feedback Mechanism

As electronic commerce has gained wide spread popularity payments made for the transactions performed by users through credit card also gained an equal amount of reputation. Whenever shopping through web is made the chance for the occurrence of fraudulent activities are escalating. In this paper we have proposed a three phase scheme to detect the fraudulent activities. A profile for the card users based on their behavior is created by employing machine learning technique. In the second phase extraction of group of precise communicative pattern for the card users depending upon the accumulated transactions and the users earlier transactions. A collection of classifiers are then trained based on all behavioral pattern. The trained collection of classifiers are then used to detect the online fraudulent activities occurred and if an emerging transaction is found to be fraudulent, a feedback is taken which resolves the quandary caused by the drift in the notion. Experiments performed indicated that the proposed scheme works better than other schemes.
An Experimental Evaluation of Content-based Recommendation Systems: Can Linked Data and BERT Help?

Content-Based Recommendation Systems suggest items (e.g., articles, products, objects, services, or places) that are relevant to the user based on the features describing the items. In many content-based recommendation systems we can find, along with discrete attributes, textual features (e.g., text summaries or comments) obtained from web pages, news articles, etc. Traditionally, to enable its exploitation, the textual information of items is represented by using basic information retrieval models (such as the vector space model), which do not take into account natural language challenges involving the semantics of the words (synonymy, polysemy and hiperonymy, etc.) or language understanding. Other solutions try to exploit those semantics. In this paper, we present an experimental evaluation where we compare several recommendation approaches, including a content-based recommender based on vector space models, a deep learning and content-based recommendation approach, and a semantic-aware content-based recommendation model. This last approach exploits textual features of items obtained from the Linked Open Data (LOD) and BERT (Bidirectional Encoder Representations from Transformers) for language modelling. Deep Learning transformers are achieving good results in different NLP (Natural Language Processing) problems, but using them to build content-based recommendation systems has not been explored in depth so far. Our experimental results, focused on the domain of movie recommendations, show that a approach based on the use of BERT can provide good results if enough training data are available.
Merging Naive Bayes and Causal Rules for Text Sentiment Analysis

Abstract Trad itional machine learning sentiment analysis models are d ifficult to achieve good classification results from small sample data. This paper proposes to merging naive Bayes and causal rule(MNBACR) for small sample data sentiment analysis scenarios. This model is based on the causal analysis theory, and introduce the causal inference algorithm into the field of text sentiment analysis. The causal inference algorithm extracts the causal ru les of Chinese texts, and the causal rules can be used as the features of the naive Bayes algorithm to predict the sentiment polarity of small sample texts. In experiments, the model in this paper is evaluated on financial news datasets which have a small number for sample, and the results show that the proposed method achieves the best performance compared to the existing state-of-the-art models on the small sample data onto sentiment analysis
Short-term Photovoltaic Power Prediction Based on Moderate-resolution Imaging Spectroradiometer Clear Sky Data

In order to predict photovoltaic power effectively, a method based on improved Bird model and FPGA fast neural network is proposed in article. This method forecast the thickness of Aerosol Optical (Aerosol Optical the Depth, AOD) through FPGA (Field Programmable Gate Array) constructed neural network first, then ameliorate BIRD model. We used AOD and meteorological data to predict different period of time's solar radiation intensity through BIRD model in clear sky, corrected cloud model with fuzzy variable, built irradiance forecast model and then calculated the photovoltaic panels outputs with reference of specifications. The result shows that the proposed method based on MODIS has high accuracy and it is a practical significance to a certain degree.
Research on Digital Oil Painting Based on Digital Image Processing Technology

With the development of the times and the progress of society, the achievements of human civilization have been accumulated. Under the background of the rapid development of computer network and information technology, the traditional way of information transmission based on words can no longer meet the needs of people in the new era. Therefore, in this era of widespread data and image processing technology, image as a way of information dissemination has been more and more well known. In order to meet the challenges of the new era to the research of digital oil painting, this paper puts forward the method of applying digital image processing technology to the research of digital oil painting. Through the digital image processing technology to analyze the large amount of information contained in the digital oil painting, combined with the new requirements of the new era for digital oil painting research, a set of digital oil painting research and development most suitable for the new era is formulated The new plan of the exhibition. Through long-term research and analysis, it is found that digital image processing technology has a profound impact on the research of digital oil painting. The research method proposed in this paper successfully provides a new idea for the research and development of digital oil painting.
Appearance-Based Gaze Tracking Through Supervised Machine Learning

Applications that use human gaze have become increasingly more popular in the domain of human-computer interfaces, and advances in eye gaze tracking technology over the past few decades have led to the development of promising gaze estimation techniques. In this paper, a low-cost, in-house video camera-based gaze tracking system was developed, trained and evaluated. Seminal gaze detection methods constrained the application space to indoor conditions, and in most cases techniques required intrusive hardware. More modern gaze detection techniques try to eliminate the use of any additional hardware to reduce monetary cost as well as undue burden to the user, all the while maintaining accuracy of detection. In this work, image acquisition was achieved using a low-cost USB web camera mounted at a fixed position on the viewing screen or laptop. In order to determine the point of gaze, the Viola Jones face detection algorithm is used to extract facial features from the image frame. The gaze is then calculated using image processing techniques to extract gaze features, namely related to the image position of the pupil. Thousands of images are classified and labeled to form an in-house database. A multi-class Support Vector Machine (SVM) was trained and tested on this data set to distinguish point of gaze from input face image. Cross validation was used to train the model. Confusion matrices, accuracy, precision, and recall are used to evaluate the performance of the classification model. Evaluation of the proposed appearance-based technique using two different kernel functions is also assessed in detail.
Mixed Reality Supermarket: A Modern Approach into Day - to - Day Grocery Shopping

In the modern world where there are massive trends in development and implementation of new technologies, combination of Virtual Reality and Augmented Reality is one which has key potential in an everyday developing world. The main concept behind Virtual Reality is simply immersing the user in a virtual environment at the comfort of their own place. This is done by creating a computer-generated 3D environment with hand gestured navigation system combined with concepts of voice recognition, image processing and machine learning that explores intense human interactions. As we are in the 21st century, where technological transformations are most certainly creating blurry lines between fiction and reality, more and more people have the need to fulfill their daily requirements easily without wasting their valuable time. Buying day to day needs from a supermarket is one of the main activities that each one of us struggle to go through during the day. Targeting the above simple daily activities, we are making an effort to apply VR Technology to this area through this research and thus trying to provide a rather new technological experience for purchasing items from a supermarket. This can be beneficial to the consumers to minimize their valuable time wasted, and also, they will be able to get the real experience of shopping while getting exposure to marketing.
Performance Comparison of SRCNN, VDSR, and SRDenseNet Deep Learning Models in Embedded Autonomous Driving Platforms

Autonomous driving is one of the most popular research areas. The camera is the eyes of autonomous driving. The computer vision technology using such a camera is a core element for the popularization of autonomous driving. Autonomous driving is not only improving the quality of the images but also processing real-time images. In the last few years, the performance of vision tasks have been largely improved via the deep-learning-based approaches. Despite of this advantages, deep-learning-based approaches have a limitation in that resulting in expensive computation. In this context, we analyze these approaches in the point of the processing time on the embedded autonomous driving platform, NVIDIA TX2. We used the three representative deep-learning-based super-resolution methods, SRCNN, VDSR, and SRDenseNet for evaluation. In the evaluation, the comparison of processing speed over different resolutions was conducted.
Seniors’ ability to decode differently aged facial emotional expressions

The present investigation aims at assessing elders' ability to decode facial emotional expressions conveyed by differently aged people in order to confirm (or disconfirm) the appropriateness of the “own age bias” theory, as well as investigate effects of different ages and different emotional categories. The study, involves 44 healthy elders (23 females), aged 65+ (mean age=75.09; SD=±7.9) which were requested to label 76 pictures depicting elders, middle-aged and young women and men displaying the six facial emotional expressions of disgust, anger, fear, sadness, happiness and neutrality. Results show a complex pattern of influences that calls for more deep investigations on the features to be accounted by providing socially and emotionally believable interfaces of effective and efficient algorithms to detect and decode their users' emotional facial expressions.
<i>α</i>-MON: Traffic Anonymizer for Passive Monitoring

Packet measurements at scale are essential for several applications, such as cyber-security, accounting and troubleshooting. They, however, threaten users’ privacy by exposing sensitive information. Anonymization has been the answer to this challenge, i.e., replacing sensitive information with obfuscated copies. Anonymization of packet traces, however, comes with some challenges and drawbacks. First, it reduces the value of data. Second, it requires to consider diverse protocols because information may leak from many non-encrypted fields. Third, it must be performed at high speeds directly at the monitor, to prevent private data from leaking, calling for real-time solutions. We present <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -MON, a flexible tool for privacy-preserving packet monitoring. It replicates input packet streams to different consumers while anonymizing protocol fields according to flexible policies that cover all protocol layers. Beside classic anonymization mechanisms such as IP address obfuscation, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -MON supports <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${z}$ </tex-math></inline-formula> -anonymization, a novel solution to obfuscate rare values that can be uniquely traced back to limited sets of users. Differently from classic anonymization approaches, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z-anonymity</i> works on a streaming fashion, with zero delay, operating at high-speed links on a packet-by-packet basis. We quantify the impact of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z-anonymity</i> on traffic measurements, finding that it introduces minimal error when it comes to finding heavy-hitter services. We evaluate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -MON performance using packet traces collected from an ISP network and show that it achieves a sustainable rate of 40 Gbit/s on a Commercial Off-the Shelf server. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -MON is available to the community as an open-source project.
Mobile Augmented Reality (MAR) in Distance Learning: Present and Future

Covid-19 pandemic affects our life suddenly and dramatically. The most affected area was our teaching methods. Students and teachers move to distance learning without good experience and background especially for non-IT teachers.In this paper, we will discuss the opportunity to enhance distance learning and how to make it more effective, easier, and more enjoyable by activating Augmented Reality (AR) in the education field.Recently, AR has been used in various contexts to enhance our experience in mobile and wearable devices. This paper discusses the use of AR in the field of education where it has been observed that learning results have been improved. This type of application required specialized teams of software to create and maintain it and we will explain the benefits and limitations of using AR in distance learning. Using AR in education will provide powerful paradigms for the next generation advanced learning system.
5:3 compressor based neural network with LM Algorithm in Multiplier as Application

Abstract In conventional approach for VLSI, implementing large numbers of operations in parallel is possible with NN. The fundamental task of a neural network hardware is not dependent on the implementation technology and are quite constructive in simulating the digital circuits. These NN representations can be incorporated in various applications where in the behaviour of these circuits are essential to get a solution for discrete problems. Therefore, supervised learning algorithms are used to train the computer software. One such algorithm is the L-M method which is one of the powerful and widely used approaches. This paper accounts to the designing and training of a neural network model as computing technique in 5:3 compressor with Lavenberg-Marquardt Algorithm. The performance parameters are significantly remarkable for real time implementation, are presented in the paper.