Reviewerly/exHarmony huggingface dataset:
Human Action Recognition from 3D Landmark Points of the Performer

Recognizing human actions is an active research area, where pose of the performer is an important cue for recognition. However, applying the 3D landmark points of the performer in recognizing action, is relatively less explored area of research due to the challenge involved in the process of extracting 3D landmark points from single view of the performers. With the recent advancements in the area of 3D landmark point detection, exploiting the landmark points in recognizing human action, is a good idea. We propose a technique for Human Action Recognition by learning the 3D landmark points of human pose, obtained from single image. We apply an autoencoder architecture followed by a regression layer to estimate the pose parameters like shape, gesture and camera position, which are later mapped to the 3D landmark points by Skinned Multi Person Linear Model (SMPL model). The proposed method is a novel attempt to apply a CNN based 3D pose reconstruction model (autoencoder) for recognizing action. Further, instead of using the autoencoder as a classifier to classify to 3D poses, we replace the decoder part by a regressor to obtain the landmark points, which are then fed into a classifier. The 3D landmark points of the human performer(s) at each frame, are fed into a neural network classifier as features for recognizing action.
Predicting Question Responses to Improve the Performance of Retrieval-Based Chatbot

Chatbot models are built to mimic a conversation between humans and fulfill different tasks. Retrieval-based chatbot models are designed to select the most appropriate response from a pool of candidates given a past conversation and current input. During the conversation, chatbots are expected to (1) provide direct assistant when the user request is clear or (2) ask clarification questions to gather more information to better understand the user’s need. Despite its importance, few studies have looked at when to ask questions and how to retrieve relevant questions accordingly. As a result, existing retrieval-based chatbot models perform poorly when the correct response is a question. To overcome this limitation, we propose an adaptive response retrieval model. Specifically, we first predict whether the best response should be a question, and then apply different models to retrieve the responses accordingly. A novel question response retrieval model is proposed to better capture the matching patterns between question responses with the conversations. Experiments on two public data sets show the proposed adaptive model can significantly and consistently improve the retrieval performance in particular for the question responses.
Chatbots for Action-Oriented Language Learning Using Elbot to Enhance Conflict-Solving Skills in Learners of German as a Foreign Language

The aim of this study is to investigate (i) how learners perceive the non-educational chatbot Elbot as a language learning tool in a formal context (university German course), and (ii) to what extent a set of task-oriented interactions with Elbot influences the learners’ behaviour (e.g. lexical choices) during a separate activity. For these purposes, this study outlines a lesson/experiment focused on the enhancement of conflict-solving skills in German as a foreign language. A mixed-methods approach was applied to evaluate Elbot’s contribution to the lesson as well as its impact on the learners’ choice of basic strategies to solve a conflict. The study concludes with the discussion of the advantages of developing an edu-bot based on an existing non-educational chatbot. In this regard, the research also provides some concrete proposals based on the results of the data analysis.
Implementing Cloud Computing Technology on Restaurant’s Expenses Monitoring System

Abstract Cloud computing offers the advantage to increase business productivity because it combines computer technology and internet-based development that is currently needed in various fields, including the restaurant industry. The application of cloud computing in the restaurant industry that has been carried out includes building payment transaction systems, restaurant reservation systems, and customer satisfaction systems. Although there have been many studies on cloud computing applications in the restaurant industry, there is still minimal research that discusses the consolidation of many restaurant branches’ expenses. Business activities in the restaurant industry require payments that need to be monitored appropriately to avoid waste, providing information on the cost-efficiency of costs quickly and accurately, allowing easy observation of expenditure data movement. The method used is by collecting expenditure data from all restaurant branches then processing it by comparing the income data obtained to produce output in the form of the level of efficiency achieved by each store. For three months, the sample data were obtained from the Cekeran Midun restaurant, which has seven branches in Bandung and Cimahi, Indonesia. The user acceptance test results show that all functions work well and have an overall acceptance rate of 83.33%, with most comments on the improvement being on the convenience of use and the system’s layout. Further research combines the calculation of costs in a restaurant. It allows the development of a platform that provides cloud computing-based online services that can be used collectively by various restaurants (software as a service).
APPLICATION OF BIG DATA TECHNOLOGIES IN THE DEVELOPMENT OF EDUCATIONAL SYSTEMS

Big Data can become an indispensable tool for transforming the education system, modernizing approaches, narrowing existing gaps and introducing expertise to improve the efficiency of the educational system. Today, the task of presenting technologies for working with big data is considered relevant, which is aimed at the development of educational systems through the identification of formed patterns in the education system. The author of the article defines the methodological aspects of the implementation of big data technology in education, as well as methods of working with them. The main approach in this case is modeling the development of the educational system with the inclusion of regulation measures based on the results of the analysis of the identified formed patterns.
Predictive Analytics Service for Security of Blockchain and Peer-to-Peer Payment Solutions

The blockchain and Peer-To-Peer Payment solutions become adopted by financial institutions. While these changes bring significant service benefits they also increase the risks and vulnerabilities of the financial services. In this paper, we investigate, develop, and evaluate machine learning (ML) algorithms for predicting attacks on blockchain nodes and a Peer to Peer payment system. We have evaluated a set of machine learning algorithms that include classification ML algorithms from the scikit-learn library. We demonstrate that the proposed solution is able to predict cyber-physical attacks close to 100% accuracy. We have implemented a service prototype as a proof of concept. The prediction is done based on the collected data of the blockchain and peer-to-peer payment nodes. For the evaluation of the algorithms, a set of highly reputable classification metrics has been selected and applied.
IoT-Blockchain Integration: The Way Ahead

IoT allows communication to take place globally between human to human, human to things, things to things. As data are shared among different participants in a distributed network, there are lots of security issues that are to be handled properly to secure the data. Blockchain technology which is an emerging technology can play a vital role to solve different security issues in IoT devices, the data they retrieve, produce, and process. Apart from security issues, the blockchain has different perspectives. Smart contracts can be created using the blockchain technology to make data immutable in a distributed peer-to-peer networking environment apart from cryptocurrencies like “BitCoin.” This paper focuses on the study of the Internet of Things (IoT), different perspectives of blockchain technology, how integration of IoT with blockchain can achieve the trustworthiness of data in a distributed network, different distributed IoT applications are discussed which are implemented through blockchain methodology.
Incremental Evolution of Stylized Images

This paper examines and showcases a framework to generate artworks using evolutionary algorithms. Based on the idea of an abstract artistic process stylized images are generated from different input images without human supervision. After explaining the underlying concept, the solution space of different styles is explored and its properties are discussed. Given this insights into the framework, current shortcomings are evaluated and improvements are discussed.
A Study of Data Processing for Object Recognition in Scene Image using FRCNN: A Smart Grid Technology

This paper proposes a new learning about the significance of part elements of a scene image with the effort of data processing. A top down tree structure with every node representing an annotation or bounding box having labeled visual features of an object existing in the image is studied in the paper. The images and its object annotations are from a trained dataset and are parsed to obtain the proposed representation. The images from the datasets and their parsed tree representations will be trained using a network called LSTM (Long Short Term Memory) network. The object detection may not be agnostic to the entire content of the image due to being influenced by the image composition and the discovered parts. The attempt has been made to show the object detection as a representation of the objects and their locations, parts of these objects, and the accuracy of the object detection method has been noted to have an efficient record with the implementation of the baseline Fast Region Based Convolutional Neural Network (FRCNN) method. The tested Google open images datasets are used and found to have increased object detection record in notably respect to the use of high cost sensors in digital devices.
Two-stream Adaptive Convolutional Neural Network for Crowd Counting

Due to the high crowd density in some scenes and the large differences in crowd feature scales, it is difficult to count the number of people. The structure of the multi-branch CNN can deal with the features of different scales, improve the accuracy of crowd counting, but also increase the parameters and become more complicated. In this paper, a 2S-ACNN with a simple structure and balanced performance is proposed for crowd counting in a single frame image. The network structure uses different sizes of receptive field to deal with features of different scales, and adds a multi-scale pooling structure to carry out feature fusion, which improve the representation ability of the model. The experimental results show that this method can maintain fewer parameters and achieve lower loss and higher accuracy.
Security Enhancement in Image Watermarking Using Combined Medium Sub-Band Wavelet Approach for Copyright Protection

Watermarking in multimedia content has secured attractive approaches in research society since nowadays the transmission of digital content in wireless medium has been enormous. Along with the distribution of digital content, it is important to claim ownership. A non-blind detection technique is proposed in this paper. First, an original image undergoes the wavelet decomposition such as LL1, LH1, HL1, and HH2. Afterward, the medium level sub-band coefficients of the image are subject to the comfortable shares for merging. Next, one of the shares from the LH1 band is merging with another one of the shares in HL1 band. In this stage, the copyright mark is fetched into the merged sub-band coefficients. Finally, an inverse wavelet transform is applied to receive the watermarked image. To prove the authentication, the original image and the watermarked image undergo the same operation and acquire copyright information. Experimental results achieve that the proposed approach can withstand various image processing attacks.
Internet Censorship in India

Time keeps on slipping and accordingly making the things to move or change from their present form keeping in mind the end goal of to take a new frame. This is true in case of censorship too, the extent of which was previously limited to print media. With the reach of internet at every nook and corner of the society, internet has moved toward becoming something which has turned out to be effective in drawing in an ever-increasing number of people thus resulting in increased tenfold of internet users from 1999 to 2013 according to a report of internet stats. At one end it is benefiting the society but at the other it is turning out into a center point of illegal matter which is harmful for the wellbeing of the society. In order to cooperate with the menace of internet use, there is a need to censor the internet. This research paper mainly focuses upon the concept of internet censorship in Indian context.
Using Triangle Exchange Mechanism to Accelerate the Pre-training Convergence Speed of BERT

As a current-leading language representation model, BERT is entirely constructed by self-attention. The special weighted sum method of the self-attention mechanism can capture the longer-term dependency, meanwhile unable the modeling of timing sequence information structurally. BERT added position embedding to alleviate this problem, but we noticed that this would also lead to inaccurate context modeling in the early stage of training. We propose a novel approach, Triangle Exchange (TE), optimizing the model internal structure to make context modeling more accurate. The method enables the attention values of each token to its above and below tokens to be calculated from different heads by special disposing of self-attention matrices, thus expanding the variable range of the difference be-tween contextual attention values for a given token. Experiments show that TE can be flexibly applied to the self-attention mechanism, significantly improves the convergence speed of the masked LM model, and obtains higher accuracy on the pre-training procedures compared with BERT.
Construction of College English Ecological Teaching Mode under Computer Network Environment

This paper first analyzes the concept of computer network environment in China, and then expounds the shortcomings in the ecological teaching mode of college English under the computer network environment; finally, the paper analyzes the strategies of constructing the ecological teaching mode of college English under the computer network environment, including the detailed discussion and planning of diversified teaching methods, communication among subjects in the teaching process and the connection of classroom contents. Based on the current situation of ecological college English teaching in China, this paper makes full use of computer network environment to promote its application and development in ecological college English teaching mode.
Background Linking of News Articles

Nowadays, it is very rare to find a single news article that solely contains all the information about a certain subject or event. Very recently, a number of methods were proposed to find background articles that can be linked to a query article to help readers understand its context, whenever they are reading it. These methods, however, are still far from reaching an optimal performance. In my thesis, I propose techniques that aim to improve the background linking process for online news articles. For example, I propose to exploit different techniques to construct representative search queries from the query article, that be can effectively employed to retrieve the required background links in an ad-hoc setting. Moreover, I aim to study how to train neural models that can learn the background relevance between pairs of articles. Through the proposed techniques, I aim to experiment with the possible criteria that may distinguish useful background articles from non-relevant ones, such as their semantic and lexical similarities, and the granularity of the topics discussed in each. Defining these criteria will enable understanding the notion of background relevance, and accordingly allow for effective background links retrieval.
Mathematical Methods of Randomized Machine Learning

In this paper, a review of mathematical methods of randomized machine learning is presented.
Cold Start in Function as a Service: A Systematic Study, Analysis and Evaluation

Function as a Service is one of the most popular and used offerings of cloud paradigm and hence is continuously evolving with both changes in the offering and the implementation. To the service user, it still seems to be a black box completely abstracting the hardware implementation underneath. However, it still suffers from the common pitfalls of virtualization, and the evaluation of these pitfalls becomes inevitable. Particularly, reusing the container for the next call, having minimal idle time to avoid the cost, and meeting the demand or auto-scaling are few of the key stakeholders in controlling the instantiation of new containers. Hence, cold starts within the function call. This study aims to outline the key factors that are mainly responsible for the cold starts and their impact on the duration of the cold start. We conducted a series of AWS Lambda executions with different configurations and dependencies to understand the implications of different cold start factors.
Multi-Feature Learning with Canonical Correlation Analysis Constraint for Text-Independent Speaker Verification

In order to improve the performance and robustness of text-independent speaker verification systems, various speaker embedding representation learning algorithms have been developed. Typically, exploring manifold kinds of features to describe speaker-related embeddings is a common approach, such as introducing more acoustic features or different resolution scale features. In this paper, a new multi-feature learning strategy with canonical correlation analysis (CCA) constraint is proposed to learn the instinct speaker embeddings, which maximizes the correlation between two features from the same utterance. Based on the multi-feature learning structure, the CCA constraint layer and the CCA loss are utilized to explore the correlation representation between the two kinds of features and alleviate the redundancy. Therefore, two multi-feature learning strategies are studied, using the pairwise acoustic features, and the pair of short-term and long-term features. Furthermore, we improve the long short-term feature learning structure by replacing the LSTM block with the Bidirectional-GRU (B-GRU) block and introducing more dense layers. The effectiveness of these improvements are shown on the VoxCeleb 1 evaluation set, the noisy Vox-Celeb 1 evaluation set and the SITW evaluation set.
An Automated System to Identify Sentiment from Micro-Blog Texts of Tweets

The recent trendsMondal, Anupam of Internet help to produce the sentiment and emotion-based opinion at the time of conversation between human beings. During the Coronavirus disease (COVID-19) pandemic,Dey, Monalisa we are spending maximum time of the day on Internet especially on social networking Web sites.Sarkar, Darothi The Web sites are Twitter, Facebook, and WhatsApp, which are taking a crucial role to communicate with each other in the form of messages. The messages are representing such as short-texts and micro-texts and carrying sentiment. The sentiment identification is challenging due to the length of the message. In the present paper, we are motivated to design a sentiment analysis system for Twitter micro-texts in the topic of Coronavirus disease (COVID-19). Hence, we have scrawled a dataset from Twitter using Twitter API and presented as our experimental dataset. Additionally, we have developed two state-of-the-art techniques, viz. unsupervised and supervised to build this system. The unsupervised technique helps to understand the characteristic of the dataset, whereas supervised technique assists in improving the accuracy of the system. The developed system may help to design various domain-specific applications such as annotation and emotion identification system for micro-texts in future.
A Generic Model for Identifying QoS Parameters Interrelations in Cloud Services Selection Ontology during Runtime

Nowadays, many business organizations are operating on the cloud environment in order to diminish their operating costs and to select the best service from many cloud providers. The increasing number of Cloud Services available on the market encourages the cloud consumer to be conscious in selecting the most apt Cloud Service Provider that satisfies functionality, as well as QoS parameters. Many disciplines of computer-based applications use standardized ontology to represent information in their fields that indicate the necessity of an ontology-based representation. The proposed generic model can help service consumers to identify QoS parameters interrelations in the cloud services selection ontology during run-time, and for service providers to enhance their business by interpreting the various relations. The ontology has been developed using the intended attributes of QoS from various service providers. A generic model has been developed and it is tested with the developed ontology.
Influence of Membership Function Type on the Accuracy of Video Tracking Algorithm

The article presents the results of intuitionistic fuzzy sets using in practice, as well as the study of the used belonging and non-belonging functions influence on the result of the video object tracking algorithm work. Fuzzy logic is used for evaluating the degree of assigned correspondences truth between keypoints of the object being followed image and the search window. The algorithm's accuracy estimates are based on synthetically generated data provided with a ground true markup that allows evaluating the degree of calculated correspondences truth. In an effort to obtain objective algorithm estimates, several different metrics are used.
Robust Image Encryption Model Using Hyperchaotic System and Deoxyribose Nucleic Acid Sequence

Recently, DNA sequences and hyperchaotic sequence are jointly used for building secure and efficient image encryption model. However, the state-of-art model are not efficient (robust) against noise and cropping attack. Since, in existing model most digits of each pixel are not altered. For enhancing security for encrypting high dimensional images, this work use both hyperchaotic and DNA sequence. Firstly, pseudorandom sequence is generated using hyperchaotic system. This is done to use hyperchaotic sequence for nearly entire process of the encryption where intensity parameters of a high dimensional image are transformed to a serial binary digit stream. Then, this stream of bits is scrambled using hyperchaotic sequence. DNA complementation and algebraic function are conducted among the DNA and the hyperchaotic sequences to attain efficient and robust image encryption performance. Experimental outcome shows proposed image encryption model attain superior performance than stat-of-art model in terms of robustness against cropping attack and noise.