Reviewerly/exHarmony huggingface dataset:
Tweet Data Analysis on COVID-19 Outbreak

COVID-19 outbreak as a pandemic in the month of March 2020 announced by World Health Organization and this pandemic virus was originated from Dragon country China, it is a big pandemic virus has been attacking through out of the world and killing lakhs of people. While this pandemic has persevered to have impact on the lives of many thousands of people, various countries have depended on complete lockdown. During this pandemic circumstance, i.e., in lockdown, individuals around the globe have taken long range interpersonal communication destinations or applications to convey their emotions and discover a way to remain quiet themselves as down. In this work, fleeting supposition investigation coronavirus tweets after some time April 16 to April 13 are appeared. The extremity score of each tweet with VADER calculation in NLP is predominantly engaged in this work. The scores are isolated dependent on compound extremity esteem at last tweets are distinguished into three significant assessment classes like positive, negative and impartial and by demonstrating feeling score plot, fleeting assumptions after some time the information perception is additionally accomplished in this work. This work also focuses data visualization by showing sentiment score plot.
Composable data visualizations

Let’s say we want to create the two charts in Figure 1. The chart on the left is a bar chart that shows two different values for each bar. The chart on the right consists of two line charts that share the x axis with parts of the timeline highlighted using two different colors.
Learning to Transfer Under Unknown Noisy Environments: An Universal Weakly-Supervised Domain Adaptation Method

Weakly-supervised domain adaptation has been introduced to address the source domain with label noise or/and feature noise. However, the existing weakly-supervised domain adaptation methods only work under ideal assumptions, which assume either the annotated data of the target domain can be accessed or the noise rate of all the classes is identical and already known. This limits their practical application. To tackle this, we propose a universal weakly-supervised domain adaptation method called PDCAS which relaxes the ideal assumptions and makes it more general. Specially, PD- CAS includes two stages: progressive distillation and domain alignment. In progressive distillation, we iteratively distill out potentially corrected samples whose annotated labels are consistent with the prediction of model. By exploiting intrinsic similarity to extract initial corrected samples, this process does not need any supervision. In domain alignment, besides taking the global feature distributions into consideration, we also adopt Class-Aligned Sampling which balances the samples for both source and target domains to alleviate the shift of label distributions. Extensive experiments on Office-31 and Office-Home datasets demonstrate the effectiveness and robustness of our method compared to state-of-the-art methods.
A PROCESS OF DIALOGUE WITH SITUATION IN VR AS A DESIGN SPACE

In recent years, the need to utilize vacant houses and buildings has been called for, and there are many examples of space regeneration through renovation and DIY. However, it is difficult to fully utilize the ideas for utilization of space found in the design process, and in order to expand the possibilities, it will be important for users and clients, who are the key decision makers in the construction process, to be deeply involved in the design process. On the other hand, VR(virtual reality) technology has been attracting attention in recent years, making it possible to think about design while experiencing the ever-changing design objects. It has potential to enable non-experts to become deeply involved in the design process by experiencing rich virtual images of spaces.
Interactive Personas: Towards the Dynamic Assessment of Student Motivation within ITS

An intelligent system can provide sufficient collaborative opportunities and support yet fail to be pedagogically effective if the students are unwilling to participate. One of the common ways to assess motivation is using self-report questionnaires, which often do not take the context and the dynamic aspect of motivation into account. To address this, we propose personas, a user-centered design approach. We describe two design iterations where we: identify motivational factors related to students' collaborative behaviors; and develop a set of representative personas. These personas could be embedded in an interface and be used as an alternative method to assess motivation within ITS.
Path Planning in a dynamic indoor environment for mobile robots using Q-Learning Technique

Autonomous Navigation for mobile robots has many applications for indoor and outdoor environments; however, it is still a challenge since no error-free solution for its implementation exists yet. This study attempts to present a path planning approach using Q-learning, a Reinforcement Learning technique, to be deployed and tested in a simulated warehouse-like environment. The approach used was able to generate a collision-free path for the robot to navigate through.
Explanation in Multi-Stakeholder Recommendation for Enterprise Decision Support Systems

Business agility requires support from recommendation systems, but explaining recommendations may yield information disclosure. We analyze how to provide explanations in the scenario of Multi-Stakeholder Recommendation where the sensible information of one stakeholder should not be disclosed in the explanation to another stakeholder. Among the several types of explanations analyzed, counterfactual explanations come off best as they allow the system to preserve each stakeholder’s privacy and sensitive information in terms of preferences.
A Frame-Based Analysis of Verbal Particles in Hungarian

Abstract The verbal particle in Hungarian raises a number of intriguing issues for any theory of the syntax-semantics interface. In this article, we aim at a formal account of the semantic contribution of various verbal particles in Hungarian and we show how the semantic representation of the clause can be compositionally derived. We will concentrate on the four frequent particles meg- , le- , el- and fel- . Our approach makes use of a formalized version of Role and Reference Grammar and the framework of decompositional frame semantics. In particular, we give a formal representation of the boundary-setting function of the verbal particle in terms of decompositional frames which builds on a scalar change analysis. We furthermore analyze the interaction of the particle with resultative adjectives and provide a formal model of how their syntactic representations drive their frame-semantic composition.
Spatial-frequency domain representation of interferogram formation in coherence scanning interferometry

As the requirements concerning lateral resolution, robustness and reliability continuously increase, appropri- ate modeling of coherence scanning interferometry (CSI) as an important method prerequisite of virtual CSI instruments gains in importance. We recently published the so-called double foil model that is based on the three-dimensional (3D) spatial frequency representation of interference signals. The model is consistent with Kirchhoff’s diffraction theory applied to surface reflection and scattering. Scattered light contributions belong- ing to certain plane wave components of incident light are superimposed incoherently. For an instrument of given numerical aperture the maximum lateral resolution provided by the diffraction limit and the capability of measuring steep surface slopes are closely related to the evaluation wavelength, i.e. the wavelength, at which the interference phase is analyzed. In this contribution we extend the model in order to describe the complete measuring process including the depth scan. Our approach introduces 3D representations of both, the surface under investigation as well as the reference mirror as thin foils in cartesian coordinates. Interference is shown to occur after Fourier transformation with respect to the axial coordinate z in the hybrid xyqz coordinate system, where the surface under investigation is treated as a phase object. Consequently, an axial shift of the measurement object or the reference mirror results in different phase shifts of the monochromatic interference patterns depending on the angle of incidence and the scattering angle. Our study combines theoretical considerations and simulations with exemplary experimental results. Conclusions are drawn with respect to signal filtering and analysis aiming at high topography fidelity of CSI systems.
Conceptual Aspects of Strategic Human Resources Management in the Context of Digitalization

In the context of business digitalization and fierce competition, the search for modern and progressive ways to manage human resources is of particular importance. With the development of technology, the digital economy, telecommunications, the most profitable are investments in the development of human resources. The main goal of this article is to develop the conceptual aspects of strategic human resources management in the context of business digitalization. The tasks of the work include the refinement of the conceptual apparatus in the field of human resources management of an organization, the identification of key factors affecting the development of human resources in the context of digitalization, as well as the refinement of conceptual elements of strategic human resources management in an organization. In the research process, general scientific methods of cognition were used: analysis, synthesis, comparison, grouping, dialectic and integrated approaches that allow the formation of reasonable conclusions. The result of the study is the development of an algorithm for strategic human resource management taking into account the requirements of the digitalization of business. The results can be used in the activities of commercial organizations.
An Adversarial Variational Autoencoder Approach Toward Transfer Learning for mTBI Identification

Subject variability in mild traumatic brain injury (mTBI) data has often been an obstacle in accurate injury-related feature extraction and biomarker identification for successful mTBI diagnosis. To address this issue, we propose an adversarial variational autoencoder model as a novel regularization approach to extract subject-invariant representations for transfer learning in mTBI identification. The proposed method consists of a variational autoencoder with an attached adversarial network. The autoencoder attempts to learn the latent space mappings from neural activity, while the adversary network is used in a discriminative setting to detach the subject individuality from the representations. The trained encoder is then transferred to extract the representations from new subject's data. Several classifiers are utilized to classify the extracted representations into two categories of normal and mTBI. To evaluate the performance of the proposed method, recorded cortical activity of GCaMP6s transgenic calcium reporter mice before and after inducing an injury is used. Experimental results on cross-subject transfer learning exhibit the efficiency of the proposed framework by achieving 89.7% classification accuracy, suggesting the feasibility of the proposed method in learning invariant representations in mTBI data.
Embedded Stochastic Syntactic Processes: A Class of Stochastic Grammars Equivalent by Embedding to a Markov Process

This article addresses the problem of suitably defining statistical models of languages derived from context-free grammars (CFGs), where the observed strings may be corrupted by noise or other mechanisms. This article uses the concept of a stochastic syntactic process (SSP), which we have introduced in previous work. An SSP is a stochastic process taking values in the set of all parse trees of a CFG. Inference problems such as estimating a parse tree for “noisy” processes are of obvious significance, particularly in the motivating example of metalevel target tracking. This article demonstrates that by careful application of the theory of probability, an SSP can be embedded into a Markov random field (MRF), thus opening up the possibility of the application of advanced machine learning algorithms based on graphical models to inference problems involving sophisticated target behavior at the “meta” level. This article provides a simple example of how a simple CFG can be embedded in an MRF. Extensions to context-sensitive grammars are discussed.
Image Summarization Using Unsupervised Learning

Recent trends show that a large amount of image data generated rapidly. Image Collection Summarization used to provide an automated collection of condensed representations for the collection of images. Most existing works on image summarization follow the supervised method. But the absence of appropriate ground truth data makes it impossible to solve the problem with traditional supervised methods. This paper proposes two unsupervised methods for extractive image summarization. The first method named Image Summarization using Clustering (ISUC) uses OneClassSVM for clustering followed by outlier detection to extract representative images. The second method named Image Summarization using Auto encoder (ISUA) uses auto-encoder to generate a representative score to each image in the collection and then the summary is created with images having diverse scores. Extensive experiments carried out on Div400 dataset shows the effectiveness of proposed methods over supervised methods and ISUA outperforms ISUC.
Encrypted Network Traffic Classification: A data driven approach

With the growing number of the network based applications, the quality and usability of various applications require distinguished requirements in light of network performance. The Internet is a vast network of independently-managed networks. The traffic is heterogeneous and consists of flows from multiple applications. For the sake of Internet governance, identifying who is who has become an impressing demand. The purpose of Internet governance is to properly distinguish the demands across the limited recourse. Traffic classification is the very beginning that helps identify who and how much the application requires. Hence, the classification and identification of encrypted traffic has drawn much research attention. Compare to the traditional methods such as port-based methods and payload-based methods, this study establishes a two-stage traffic classification framework based on convolutional neural network. Experimental results suggest that the developed method has a good degree of traffic classification subject to fewer bytes in the packet.
DNAP: Detection of News Article Plagiarism

With the rapid development of we media, enormous news articles are produced online, where plagiarism becomes easier. Hence, article plagiarism detection has become more and more important. Besides, existing state-of-the-art article plagiarism detector suffers from several limitations in fine grained plagiarism detection. In this paper, we propose a tool DNAP that can detect plagiarism at word-level granularity. In our approach, token based fingerprinting is designed for matching and asymmetric similarity coefficient is used for measuring similarity. We thoroughly evaluate DNAP by both mutation framework and real-world datasets. Compared with existing state-of-the-art tool, all experiment results demonstrate that DNAP is the best performing article plagiarism detection tool, shown as better recall and F1-score with high precision.
Blockchain Law: A New Beginning

A blockchain is a time-stamped series of immutable record of data that is managed by a cluster of computers not owned by any single entity. Each of these blocks of data is secured and bound to each other using cryptographic principles. Bitcoin is the most popular example that is intrinsically tied to blockchain technology. It is also the most controversial one since it helps to enable a multibillion-dollar global market of anonymous transactions without any governmental control. Hence it has to deal with a number of regulatory issues involving national governments and financial institutions.Since blockchain technology only creates an infrastructure or medium enabling a variety of applications, it is yet to stir up much regulatory controversy in and of itself. This is as opposed to, say, crypto-assets, which may disrupt traditional value-transfer systems and have governments around the world scrambling to understand their legal consequences.Legal frameworks are based on national borders, it is highly relevant from a legal perspective especially in the countries in which any legal act has been performed; it is relevant to understand which law applies and which government has the authority to enforce the law. However, the actual relevance of the law remains limited.
Attention-Based Explanation in a Deep Learning Model For Classifying Radiology Reports

Although deep learning techniques have obtained remarkable results in clinical text analysis, the delicacy of this application domain requires also that these models can be easily understood by the hospital staff. The attention mechanism, which assigns numerical weights representing the contribution of each word to the predictive task, can be exploited for identifying the textual evidence the prediction is based on. In this paper, we investigate the explainability of an attention-based classification model for radiology reports collected from an Italian hospital. The identified explanations are compared with a set of manual annotations made by the domain experts in order to analyze the usefulness of the attention mechanism in our context.
Pipeline for Development of 3 Dimensional motion animation using the 2 Dimensional video

The main idea of this project is to propose an efficient set of techniques to perform the process of motion capture. Motion capture is basically a process of capturing the motion of a person and using it to transfer it to a 3D model created to the user's desire. The process basically obtains this motion by making the actor wear a motion capture suit which contains markings on their body joints and the changes in the position of these markers are used to obtain the motion. This project has established a set of techniques to make this process easier and cost-efficient. Fabricating a mobile robot that can follow the actor and record the video by keeping him in the required position in the video frame. Then the recorded video is inputted to several deep learning models which in turn can give us the 3D pose of the actor. This motion can be transferred to the required 3D model. Thus, avoiding the use of the mocap suit and multiple cameras to get the motion from the actor.
Multi-Knowledge Fusion Network for Generalized Zero-Shot Learning

Suffering from the semantic insufficiency and domain-shift problems, most of existing state-of-the-art methods fail to achieve satisfactory results for Zero-Shot Learning (ZSL). In order to alleviate these problems, we propose a novel generative ZSL method to learn more generalized features from multi-knowledge in semantic-to-visual embedding. In our approach, the proposed Multi-Knowledge Fusion Net-work (MKFNet) alleviates the semantic insufficiency problem by fusing the domain information of different knowledge, which enables more relevant semantic features to be trained for semantic-to-visual feature embedding. The pro-posed knowledge regularization L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KR</inf> greatly improves the intersection between the synthesized visual features generated by MKFNet and the unseen visual features, which can alleviate the domain-shift problem. Empirically, we show that our approach consistently outperforms these state-of-the-art methods on a large number of available benchmarks on the generalized ZSL (GZSL).
A Representation Learning Perspective on the Importance of  Train-Validation Splitting in Meta-Learning

An effective approach in meta-learning is to utilize multiple "train tasks" to learn a good initialization for model parameters that can help solve unseen "test tasks" with very few samples by fine-tuning from this initialization. Although successful in practice, theoretical understanding of such methods is limited. This work studies an important aspect of these methods: splitting the data from each task into train (support) and validation (query) sets during meta-training. Inspired by recent work (Raghu et al., 2020), we view such meta-learning methods through the lens of representation learning and argue that the train-validation split encourages the learned representation to be low-rank without compromising on expressivity, as opposed to the non-splitting variant that encourages high-rank representations. Since sample efficiency benefits from low-rankness, the splitting strategy will require very few samples to solve unseen test tasks. We present theoretical results that formalize this idea for linear representation learning on a subspace meta-learning instance, and experimentally verify this practical benefit of splitting in simulations and on standard meta-learning benchmarks.
Video Streaming Platform Using Distributed Environment In Cloud Platform

Nowadays, there are so many Video content delivery platforms. Each platform has its own design and technology for delivering the content. There are so many Video streaming algorithms, from those we approach an Adaptive Bitrate Streaming algorithm. As so many Organizations are using the Content caching mechanism for delivering the content. We thought that we take a step forward to that which is multi-Caching for producing the best experience. There are many advantages to multi-tier caching Technology. Content delivery and Quality of Experience is increased, it is going to be a better experience for the end-users. Here, a distributed environment plays a key role in it. There are few challenges to implement for Video streaming platform which are jitter, bandwidth, and data loss. We accept those challenges by using the cloud platforms. This video streaming platform is useful to distinct types of users. This is the way of enjoyable experience to the user over the internet. We are done the research on the special video streaming platform. Here, we used an algorithm which is Adaptive Bitrate Algorithm. It is hard to write the code so, we choose the S3Bubble, for storing and delivering the content by using Amazon web services. To access the user for the content, we create a user interface by using the UI-UX, it makes easy to access what he wants. We develop a design by using the WordPress. For deploying the content over the internet by using AWS server.
High-Dimensional Data Clustering with Fuzzy C-Means: Problem, Reason, and Solution

Fuzzy C-Means (FCM) clustering algorithm is a popular unsupervised learning approach that has been extensively utilized in various domains. However, in this study, we point out a major problem faced by FCM when it is applied to the high-dimensional data, i.e., quite often the obtained prototypes (cluster centers) could not be distinguished with each other. Many studies have claimed that the concentration of the distance (CoD) could be a major reason for this phenomenon. This paper has therefore revisited this factor, and highlight that the CoD could not only lead to decreased performance, but sometimes also positively contribute to enhanced performance of the clustering algorithm. Instead, this paper point out the significance of features that are noisy and correlated, which could have a negative effect on FCM performance. Hence, to tackle the mentioned problem, we resort to a neural network model, i.e., the autoencoder, to reduce the dimensionality of the feature space while extracting features that are most informative. We conduct several experiments to show the validity of the proposed strategy for the FCM algorithm.