Reviewerly/exHarmony huggingface dataset:
Evaluation of CNN Models with Transfer Learning for Recognition of Sign Language Alphabets with Complex Background

Sign language acts as a mediator for deaf and speech-impaired community to visually communicate with one another and engage in their environment. As a state-of-the-art recognition technique in computer vision, deep learning models have demonstrated success for several tasks. In this paper, we present a Convolutional Neural Network (CNN) approach with transfer learning to recognize Arabic and American Sign Languages alphabets with complex background. The descent underlying concept of transfer learning is to adopt a model pretrained on a massive annotated dataset and fine tune the later layers on the target dataset. We applied different techniques to improve the accuracy of the proposed approach such as data augmentation, batch-normalization, and early stopping. The proposed model is evaluated on three datasets and experiments reveal improved results with high recognition rates.
Compact Scrutiny of Current Video Tracking System and its Associated Standard Approaches

With an increasing demands of video tracking systems with object detection over wide ranges of computer vision applications, it is necessary to understand the strengths and weaknesses of the present situation of approaches.However, there are various publications on different techniques in the visual tracking system associated with video surveillance application.It has been seen that there are prime classes of approaches that are only three, viz.point-based tracking, kernelbased tracking, and silhouette-based tracking.Therefore, this paper contributes to studying the literature published in the last decade to highlight the techniques obtained and brief the tracking performance yields.The paper also highlights the present research trend towards these three core approaches and significantly highlights the open-end research issues in these regards.The prime aim of this paper is to study all the prominent approaches of video tracking system which has been evolved till date in various literatures.The idea is to understand the strength and weakness associated with the standard approach so that new approaches could be effectively act as a guideline for constructing a new upcoming model.The prominent challenge in reviewing the existing approaches are that all the approaches are targeted towards achieving accuracy, whereas there are various other connected problems with internal process which has not been considered for e.g.feature extraction, processing time, dimensional problems, non-inclusion of contextual factor, which has been an outcome of the proposed review findings.The paper concluded by highlighting this as research gap acting as contribution of this review work and further states that there are some good possibilities of new work evolution if these issues are considered prior to developing any video tracking system.Overall, this paper offers an unbiased picture of the current state of video tracking approaches to be considered for developing any upcoming model.
Towards harnessing based learning algorithms for tweets sentiment analysis

In the digital era of information, the number of web users and the volume of data being generated and exchanged over the internet has drastically increased. Sentiment analysis and opinion mining are open research fields having various everyday life applications, such as Twitter. Twitter is flooded with opinions, emotions, views and discussions among different communities across the world. Therefore, the tweet is related to various domains, such as stock market, product opinion and political elections. In this study, a classification problem is considered having a given set of tweets with various features and diverse opinions. The goal is to obtain expressions of opinion representing a target feature and to categorize these tweets as positive, neutral, or negative sentiment using different machine learning techniques. The dataset sentiment140 (STS-Test) is preprocessed and very commonly used for research purposes. The studied machine learning classifiers are random Forest, libSVM, IBk (kNN), decision table, and AdaBoostMl. Several evaluation measures are used for evaluating the performance of studied classifier on the selected dataset. These measures include accuracy, precision, recall, and F-measure. The confusion matrix is also obtained as a result of each classifier. The obtained results revealed that the RF classifier obtained the best accuracy results whereas the AdaBoostMl classifier obtained the worst accuracy results when tested for STS-Test dataset.
Behavioral Analysis to Detect Social Spammer in Online Social Networks (OSNs)

The faster and regular usage of Web 2.0 technologies like Online Social Networks (OSNs) addicted to millions of users worldwide. This popularity made target for spammers and fake users to spread phishing attack, viruses, false news, pornography and unwanted advertisements like URLs, images and videos etc. The present paper proposes a behavioral analysis-based framework for classifying spam contents in real time by aggregating machine learning techniques and genetic algorithm. The main procedure of the work is, firstly based on social networks spam policy, novel profile based and content-based features are proposed to facilitate spam detection. Secondly, accumulate a dataset from various social networks like Facebook, Twitter, and Instagram including spam and non-spam profiles. For suitable feature selections, we have used a genetic algorithm and various classifiers for decision making. In order to attest the effectiveness of our proposed framework, we have compared with existing techniques.
Verification, Reproduction and Replication of NLP Experiments: a Case Study on Parsing Universal Dependencies

As in any field of inquiry that depends on experiments, the verifiability of experimental studies is important in computational linguistics. Despite increased attention to verification of empirical results, the practices in the field are unclear. Furthermore, we argue, certain traditions and practices that are seemingly useful for verification may in fact be counterproductive. We demonstrate this through a set of multi-lingual experiments on parsing Universal Dependencies treebanks. In particular, we show that emphasis on exact replication leads to practices (some of which are now well established) that hide the variation in experimental results, effectively hindering verifiability with a false sense of certainty. The purpose of the present paper is to highlight the magnitude of the issues resulting from these common practices with the hope of instigating further discussion. Once we, as a community, are convinced about the importance of the problems, the solutions are rather obvious, although not necessarily easy to implement.
Consensus-driven illuminant estimation with GANs

We present a method for illuminant estimation that exploits a generative adversarial network architecture to generate a spatially-varying illuminant map. This map is then transformed by consensus into a global illuminant estimation, in the form of a single RGB triplet. To this end, different consensus strategies are designed and compared in this paper. The best solution won second place in the 2nd International Illumination Estimation Challenge, specifically for the indoor track.
A Semi-Supervised BERT Approach for Arabic Named Entity Recognition

Named entity recognition (NER) plays a significant role in many applications such as information extraction, information retrieval, question answering, and even machine translation. Most of the work on NER using deep learning was done for non-Arabic languages like English and French, and only few studies focused on Arabic. This paper proposes a semi-supervised learning approach to train a BERT-based NER model using labeled and semi-labeled datasets. We compared our approach against various baselines, and state-of-the-art Arabic NER tools on three datasets: AQMAR, NEWS, and TWEETS. We report a significant improvement in F-measure for the AQMAR and the NEWS datasets, which are written in Modern Standard Arabic (MSA), and competitive results for the TWEETS dataset, which contains tweets that are mostly in the Egyptian dialect and contain many mistakes or misspellings.
Fingerspelling recognition using synthetic images and deep transfer learning

Although gesture recognition has been intensely studied for decades, it is still a challenging research topic due to difficulties posed by background complexity, occlusion, viewpoint, lighting changes, the deformable and articulated nature of hands, etc. Numerous studies have shown that extending the training dataset with real images about synthetic images improves the recognition accuracy. However, little work is devoted to demonstrate what improvements in recognition can be achieved thanks to transferring the style onto synthetically generated images from the real gestures. In this paper, we propose a novel method for Japanese fingerspelling recognition using both real and synthetic images generated on the basis of a 3D hand model. We propose to employ a neural style transfer to include information from real images onto synthetically generated dataset. We demonstrate experimentally that neural style transfer and discriminative layer training applied to training deep neural models allow obtaining considerable gains in the recognition accuracy.
Algorithm Research of Face Recognition System Based on Haar

Face detection is to detect the face from the original image, which is the basis of face recognition technology. Face is a kind of natural structural object with quite complex details, which will be challenges to this kind of recognition. Face has the variability of pattern because of the different appearance, expression and skin color, and in general, there may be appendages such as glasses, beard and so on. Face image as a three-dimensional object has shadows caused by light also unavoidable. In this paper, the most mainstream Harr feature classifier algorithm is used, innovatively propose non-human rejection algorithm to reduce the impact of complex environment on detection results and improve detection accuracy.
Synthetic Expressions are Better Than Real for Learning to Detect Facial Actions

Critical obstacles in training classifiers to detect facial actions are the limited sizes of annotated video databases and the relatively low frequencies of occurrence of many actions. To address these problems, we propose an approach that makes use of facial expression generation. Our approach reconstructs the 3D shape of the face from each video frame, aligns the 3D mesh to a canonical view, and then trains a GAN-based network to synthesize novel images with facial action units of interest. To evaluate this approach, a deep neural network was trained on two separate datasets: One network was trained on video of synthesized facial expressions generated from FERA17; the other network was trained on unaltered video from the same database. Both networks used the same train and validation partitions and were tested on the test partition of actual video from FERA17. The network trained on synthesized facial expressions outperformed the one trained on actual facial expressions and surpassed current state-of-the-art approaches.
RETRACTED: Application of preschool education major in applied universities based on multimedia technology research on process management innovation in teaching process

With the development of education, education management, innovation and practice have become very important concerns. This paper proposes an innovative research on the process management of practical teaching links in pre-school education based on multimedia technology. At present, the preschool education major is deeply loved by college students. Many students who have just finished the college entrance examination want to apply for this major. However, due to the popularity of people in this profession, many colleges have exposed many problems in the teaching management. At present, for the problems existing in the practice teaching links of various universities, the current situation at this time urgently requires innovative research on process management. In this paper, if we want to do a good job in this aspect, we should establish a practical teaching system that is suitable for applied universities in preschool education, use multimedia technology to adjust practical teaching objectives from outstanding features, focusing on capacity building, and increasing investment. Intensify, improving the construction of practical teaching bases, increase the use of practical teaching bases, build a reasonable team of practical teaching faculty, strengthen the management of practical teaching links, ensure the smooth development of practical teaching, etc.
The Rise of Robo-Reviews: The Effects of Chatbot-Mediated Review Elicitation on Online Reviews

Chatbots have been introduced as a new way to receive product reviews. However, little is known about how chatbot-mediated solicitation influences review characteristics such as valence and length, or its helpfulness, which, in turn, affects companies' sales and prospective customers' decision-making. We build upon social presence and audience-tuning theories and conduct four studies comprising 1,012 respondents to narrow this literature gap. Our results show that chatbot-mediated reviews are positively biased and shorter compared to reviews collected using a conventional review form. The effect persists even when controlling for the review elicitation source (seller vs. third-party). Further, we find that the positivity bias varies depending on the level of anthropomorphism in the chatbot (human vs. machine-like). Finally, chatbot-mediated solicitation negatively influences a range of review dimensions (e.g., helpfulness) mainly due to decreased review length. Our study is among the first to introduce chatbots as a new technology to drive the elicitation of online reviews, uncovering a direct link between chatbot use and review valence, length, and helpfulness. The results of our experiment inform platforms, sellers, consumers, and regulators of the advantages but also potential drawbacks of implementing robotic automation on review platforms.
Self-Organizing IoT Device-Based Smart Diagnosing Assistance System for Activities of Daily Living

Activity of daily living (ADL) is a criterion for evaluating the performance ability of daily life by recognizing various activity events occurring in real life. However, most of the data necessary for ADL evaluation are collected only through observation and questionnaire by the patient or the patient’s caregiver. Recently, Internet of Things (IoT) device studies using various environmental sensors are being used for ADL collection and analysis. In this paper, we propose an IoT Device Platform for ADL capability measurement. Wearable devices and stationary devices recognize activity events in real environments and perform user identification through various sensors. The user’s ADL data are sent to the network hub for analysis. The proposed IoT platform devices support many sensor devices such as acceleration, flame, temperature, and humidity in order to recognize various activities in real life. In addition, in this paper, using the implemented platform, ADL measurement test was performed on hospital patients. Through this test, the accuracy and reliability of the platform are analyzed.
Demystifying the blockchain technology

The blockchain paradigm is being widely touted by many as the innovative and disruptive one capable of bringing in a few exemplary and elegant transformations in the IT space. As business operations and offerings are substantially enabled through the various crucial accomplishments and advancements in the IT field, business executives across the globe are equally keen to experiment with and embrace this new and futuristic technology to reap a slew of business benefits. Interestingly, blockchain has the inherent potential and promise to bring forth a bevy of strategically sound implications for various industry verticals. Cryptocurrency is one of the finest and foremost applications of the blockchain technology. The supply chain domain is exploring this new phenomenon for realizing some crucial advantages. The IoT discipline is another one capable of attaining a number of distinct benefits out of all the trendsetting improvisations being realized in the blockchain space. This chapter is specially crafted to tell what, why, how, and where the indispensable blockchain paradigm is being used toward real digital transformations.
Detection of cyber-attacks in systems with distributed control based on support vector regression

Concept of Industry 4.0 and implementation of Cyber Physical Systems (CPS) and Internet of Things (IoT) in industrial plants are changing the way we manufacture. Introduction of industrial IoT leads to ubiquitous communication (usually wireless) between devices in industrial control systems, thus introducing numerous security concerns and opening up wide space for potential malicious threats and attacks. As a consequence of various cyber-attacks, fatal failures can occur on system parts or the system as a whole. Therefore, security mechanisms must be developed to provide sufficient resilience to cyber-attacks and keep the system safe and protected. In this paper we present a method for detection of attacks on sensor signals, based on e insensitive support vector regression (e-SVR). The method is implemented on publicly available data obtained from Secure Water Treatment (SWaT) testbed as well as on a real-world continuous time controlled electro-pneumatic positioning system. In both cases, the method successfully detected all considered attacks (without false positives).
RETRACTED: Research on Improving Computer Teaching Quality in Colleges and Universities in the Context of Big Data

Abstract With the continuous development of information technology, big data has entered all walks of life. As an important part of college education, computer teaching is very significant for enhancing students’ computer application ability and promoting comprehensive development. However, there are still many problems in actual computer teaching in Chinese colleges and universities, and the teaching effect is not ideal. Based on this, this article will discuss the characteristics of big data, analyze the problems in computer teaching in colleges and universities, and propose measures to improve the quality under the big data environment, hoping to have certain reference.
Knowledge Mangement System sebagai Dasar Pengembangan Sistem Informasi Kinerja Dosen

In the current digitalization era, the application of knowledge management systems can improve the performance of information systems for the better. The knowledge management system is very important to be implemented in higher education. Having different abilities in implementing the tri dharma of higher education, therefore it is necessary to share knowledge to increase the career advancement of lecturers. The development of a lecturer performance information system based on a knowledge management system is an information system used to carry out community service activities. This system can also generate information on the achievement of targets every semester. The target achieved will be used for determining rewards and punishments. The method used for software development is a waterfall. The classical method is still often used because the stages are simpler and easier to implement. Lecturer performance information systems can help programs and supervise, run and improve the performance of lecturers at Universitas IGM
Data Management Techniques in Hadoop Framework for Handling Small Files: A Survey

Hadoop is an open-source software framework, which offers cost-efficient solutions to store, manage and analyze a large amount of data; it provides distributed processing and storage of huge data across thousands of computers. Hadoop has two main components HDFS and Map Reduce. HDFS can easily handle large files but the performance of HDFS degrades when handling small size files. The usage of Name Node memory is not efficient as there are vast amount of small files to store without considering the correlation between them for data placement therefore overall performance of the Hadoop is not up to the mark. This paper provides a comparative study of various efficient data management techniques for handling small files in the Hadoop framework.
A Modular Vision Language Navigation and Manipulation Framework for Long Horizon Compositional Tasks in Indoor Environment

In this paper we propose a new framework—MoViLan (Modular Vision and Language) for execution of visually grounded natural language instructions for day to day indoor household tasks. While several data-driven, end-to-end learning frameworks have been proposed for targeted navigation tasks based on the vision and language modalities, performance on recent benchmark data sets revealed the gap in developing comprehensive techniques for long horizon, compositional tasks (involving manipulation and navigation) with diverse object categories, realistic instructions and visual scenarios with non reversible state changes. We propose a modular approach to deal with the combined navigation and object interaction problem without the need for strictly aligned vision and language training data (e.g., in the form of expert demonstrated trajectories). Such an approach is a significant departure from the traditional end-to-end techniques in this space and allows for a more tractable training process with separate vision and language data sets. Specifically, we propose a novel geometry-aware mapping technique for cluttered indoor environments, and a language understanding model generalized for household instruction following. We demonstrate a significant increase in success rates for long horizon, compositional tasks over recent works on the recently released benchmark data set -ALFRED.
Fast phase extraction of a synthetic wavelength from a dual-wavelength interferogram through Hilbert transformation

Phase imaging is an important measurement method in optical metrology, and phase extraction is one of its essential components. A method using one-frame dual-wavelength interferometry (DWI) for fast phase extraction is proposed. Using this method, the Hilbert transform (HT) is applied twice to a wavelength-multiplexed interferogram to obtain two images. The HT is then applied twice to the sum of the squares of these two images. The synthetic wavelength phase can then be obtained. Compared with other common multiple-frame DWI methods, the proposed approach requires only one multiplexed interferogram and does not have to consider parameter changes in interferograms of different frames. Furthermore, the proposed method was determined to require the least calculation time, and the simulation and experimental results confirmed its high accuracy and efficiency. It suggests that the proposed method is suitable for real-time measurements.