Reviewerly/exHarmony huggingface dataset:
Text-mining in Terms of Methodology and Development

Text-mining is gaining popularity and has attained the status of cross-field technology, which brings together soft and hard sciences. This happens by virtue of its applicability in various fields, for instance, Linguistics, for language units' identification and categorization, History, for ancient texts reconstruction, Sociology, for sentiment analysis, and others. In this paper, we make a deep analysis of the methodological background of text-mining. We prove that text-mining has accumulated several methodologies, which make it flexible for various purposes and fuzzy enough to bring into proximity of human-like text comprehension and knowledge extraction. We also make a retrospective journey into the history of text-mining, providing the description of technical means that underpin its appearance and the methods which draw it nearer natural cognitive processes. The abovesaid makes the study unique, for no papers focused on the methodologic and historical background of this rapidly developing technology are available.
Detection Analysis of DeepFake Technology by Reverse Engineering Approach (DREA) of Feature Matching

In this article, we will discuss the exploitation analysis and the benefits of Scale-Invariant Feature Transform extraction to detect the existence of a DeepFake in a video. DeepFakes are becoming a trend especially due to campaign elections in 2020, and hence there is a bigger determination to be able to detect and stop this fake media. We focus on the standard deviation of the points of interest from SIFT, to detect the possibility of whether visual media has been compromised with misinformation. We use the key point matching technique to analyze the video frame by frame. Scale-invariant Feature Transform, often called SIFT is an algorithm within OpenCV that is used to cluster unique key points in sub-images, and more so the facial features in face detection. These unique detection sites are referred to as descriptors. This research focuses on the binary classification problem using a convolution neural network for detection of DeepFake Video against the original video. We feed the SIFT features as input for this detection on the original image in this experimental study and discuss the performance and benefits.
Enabling Performance Prediction in Information Retrieval Evaluation

How to model the performance of a retrieval system before its deploying has puzzled the Information Retrieval (IR)researchers for a long time. Currently, the evaluation of IR systems relies on empirical experiments. Empirical evaluation means that we need experimental collections: building them is expensive both in term of time and money. Exploiting already available collections to predict the performance of a system on new collections, would dramatically reduce such cost. With the research line described in this work,we plan to study the development of predictive models for the performance of the IR systems. In particular, the proposed research line will investigate Generalized Linear Mixed Models and Causal Inference. Furthermore, we highlight the importance of modelling the performance as distributions rather than point estimations.
Absolute testing method of shift-rotation based on the influence function

The absolute testing of an optical surface with the shift-rotation method is an effective way to obtain an optical surface with high accuracy. The traditional shift-rotation method based on Zernike polynomials has a large number of computations and poor fitting accuracy for high frequency. Additionally, the number of calculations of the pixel-level spatial frequency method in solving the test and reference error based on each pixel is too large, which leads to poor practicability in reality. An optimized absolute testing method of shift-rotation based on the influence function is presented in this paper. By introducing the concept of the influence function in adaptive optics instead of a Zernike polynomial, the calculation accuracy of the mid-and high-frequency surface is improved, and higher precision of the absolute surface can be obtained. Relevant theoretical simulation and experimental verification are carried out. The experimental results compared with the Zernike and pixel-level methods show that the reference and test surface can be well reconstructed by using the proposed shift-rotation method based on the influence function.
Cooperative and Distributed Multiobjective Optimization for Heterogeneous Belief Rule Base

So far, there has been only oneoptimization objective in belief rule base (BRB) learning-related studies, either modeling accuracy via parameter learning, modeling complexity via structure learning, or a unified objective representing them both. This article proposes a new cooperative and distributed multiobjective approach for heterogeneous BRB (CDMO-BRB) to simultaneously optimized its structure and parameters. The new CDMO-BRB approach is implemented in a parent&#x2013;child framework. In the parent process, cooperative optimization is equipped with a multipopulation strategy and another redundant-gene strategy to simultaneously optimize the structure and parameters of BRBs in heterogeneous structures. In the child process, distributed learning is designed to improve optimization efficiency and avoid early convergence and overfitting. A benchmark case on pipeline leakage detection is studied. Case study results show that the structure and parameters of BRB can be optimized simultaneously by pushing forward the Pareto front. In comparison with previous studies, CDMO-BRB has produced superior performance. Moreover, distributed learning is also separately validated.
A Blockchain based Decentralized Video Streaming Platform with Content Protection System

Contents that are uploaded on the centralized video platform are mostly controlled by the platform owner instead of the content creator. On the other hand, decentralized blockchain-based video platforms are trying to decrease ad pressure and removes intermediaries but some content creators upload garbage content to earn free cryptocurrency, generated by the blockchain algorithm which demoralizes other creative content makers. Also, copyright infringement, illegal distribution of protected content is a big issue in mainstream video platforms. Therefore, copyright protection, illegal access control and legitimate video file distribution are needed with proper pay off for the creator's creative content. The paper proposes a composition of robust copyright protection algorithm, a secured encryption method and access control system designed with modified version of IPFS and Ethereum Smart Contract to develop an effective Blockchain-based decentralized video streaming platform.
Differential neural network identifier with composite learning laws for uncertain nonlinear systems

This manuscript describes the design and numerical implementation of a novel composite differential neural network aimed to estimate nonlinear uncertain systems. A differential neural network (DNN) with a composite feedback matrix approximates the structure of non-linear uncertain systems. The feedback matrix is assumed to belong to a convex set as well as the free parameters of the DNN (weights) at any instant of time. Therefore, ɩ-different DNN works in parallel. A composite Lyapunov function finds the convex hull approximation of the set of DNN working together to improve the approximation capabilities of classical neural networks. The main result of this study shows the practical stability of the estimation error. Numerical simulations demonstrate the approximation capabilities of the composite DNN implemented in a Van Der Pol oscillator where the presence of high-frequency components makes difficult a classical DNN approximation.
Teleclinical Support System via MR-HMD Displaying Doctor's Instructions and Patient Information

A possession telepresence system that enables nurses to “possess” a doctor in a remote place is proposed. The possession telepresence system is a system in which a nurse can substitute a part of the medical practice of the doctor by using mixed-reality head-mounted displays, while receiving instructions from a remote doctor. In this study, nurses collaborated with remote doctors to perform clinical examinations. This system enables nurses not only to perform examinations themselves, but also to perform further examinations when the knowledge of the doctor is needed, while possessing the remote doctor and asking for instructions. As a result of the experiment using the possession telepresence system, the nurse was able to perform examinations under the instructions from the remote doctor.
On uniform concentration bounds for Bi-clustering by using the Vapnik–Chervonenkis theory

Bi-clustering refers to the task of partitioning the rows and columns of a data matrix simultaneously. Although empirically useful, the theoretical aspects of bi-clustering techniques have not been studied in-depth. We present a framework for investigating the statistical guarantees behind the sparse bi-clustering algorithm by using the Vapnik–Chervonenkis (VC) theory.
Accented Visualization User Interfaces in Augmented Reality

The features of using modern augmented reality devices (tablets, glasses, and helmets) in practice require the development of new principles for building user interfaces. Due to the three-dimensionality of the observed scene and the need to combine real objects with virtual ones in one scene, specific difficulties arise in ensuring effective human-computer interaction in industrial applications. To solve this problem, it is proposed to adapt the user interface in accordance with the concept of accented visualization and implement adaptive generation of virtual scene elements as necessary, as a result of analysis of the user’s focus and current context. The chapter discusses the basic patterns and requirements for augmented reality interfaces for decision-making support systems that will improve the usability and effectiveness of augmented reality devices application. In order to personalize the interface and adapt it to the current operations and user interests, it is proposed to implement machine learning algorithms.
DRTaint: A Dynamic Taint Analysis Framework Supporting Correlation Analysis Between Data Regions

Abstract Taint analysis is a common technology for software analysis, which is widely used in the region of information security. Aiming at the problem that the existing binary program dynamic taint analysis framework does not support “replay” analysis and the analysis efficiency is low, a taint analysis framework called DRTaint is proposed. DRTaint is a universal taint analysis framework that supports correlation analysis between data regions. Experiments show that DRTaint shows good performance advantages when performing taint analysis many times on one program. On the basis of DRTaint, this paper studies the malicious code behavior extraction method based on taint analysis, and the research finds that DRTaint can provide better support for malicious code behavior extraction.
Efficient Privacy Preserving Distributed K-Means for Non-IID Data

Privacy is becoming a crucial requirement in many machine learning systems. In this paper we introduce an efficient and secure distributed K-Means algorithm, that is robust to non-IID data. The base idea of our proposal consists in each client computing the K-Means algorithm locally, with a variable number of clusters. The server will use the resultant centroids to apply the K-Means algorithm again, discovering the global centroids. To maintain the client's privacy, homomorphic encryption and secure aggregation is used in the process of learning the global centroids. This algorithm is efficient and reduces transmission costs, since only the local centroids are used to find the global centroids. In our experimental evaluation, we demonstrate that our strategy achieves a similar performance to the centralized version even in cases where the data follows an extreme non-IID form.
WITHDRAWN: Video event classification using KNN classifier with hybrid features

Event detection from videos has become a demanding and challenging subject with the creation of huge video data. Detection of complex interesting events automatically in user generated videos is a difficult job because of its features that differ from video broadcasting. We present an effective technique for detecting actions or events in UCF 101 video large scale data. Histogram equalization is initially used on UCF101 database for contrast enhancement in the pre processing stage. In the later stage gradient local ternary pattern, histogram of oriented gradient and tamuras features are extracted from the enhanced images to accomplish feature extraction. Finally k nearest neighbor (KNN) classifier is used to classify the events. In this work we compared Random Forest classifier with KNN classifier in terms of accuracy, specificity, sensitivity, false discovery rate, false omission rate and error rate. KNN gave 90.25% accuracy where as Random forest classifier yielded 77.18% accuracy. The result exhibits that KNN classifier gives better performance than RF classifier.
Constructing Explainable Opinion Graphs from Reviews

The Web is a major resource of both factual and subjective information. While there are significant efforts to organize factual information into knowledge bases, there is much less work on organizing opinions, which are abundant in subjective data, into a structured format.
Development of English-to-Bengali Neural Machine Translation Systems

The development strategies of English-to-Bengali neural machine translation (NMT) systems are reported in this paper. Several attention mechanisms on the development of English-to-Bengali NMT system are reported transparently. Further, the translation system has been experimented with some preprocessing steps such as tokenization, truecasing to obtain a better translation. The automatic evaluation techniques are used to measure the performance of the translated systems followed by human judgment.
Maneuver Classification for Road Vehicles with Constrained Filtering Techniques

Environment perception and situation awareness are keystones for autonomous road vehicles. The problem of maneuver classification for road vehicles in the context of multi-model state estimation under model uncertainty is addressed in this paper. The conventional approach is to define different motion models that match the desired type of movements. In this work we used a single motion model as a starting point and applied constraints to construct such filters that are fine tuned for the predefined maneuvers. The estimation is carried out in the interacting multiple model framework, where the elemental filters are constrained Kalman filters. To capture the characteristics of the considered maneuvers linear equality and non-equality state constraints were used. The performance of the proposed method is demonstrated in a simulation environment participating an observer and a maneuvering vehicle.
A study on the change in service quality in the era of the 4th industrial revolution compared to the existing service quality evaluation

This thesis is to review the existing service quality and methods used for evaluation, and to determine whether it is suitable for the rapidly changing 4th industrial revolution era. In the era of the 4th industrial revolution, service targets and processes are expected to change completely, so the existing service quality evaluation method is not expected to be appropriate anymore. Through this investigation, it is necessary to study the direction and method of reviewing service quality suitable for the 4th industrial revolution era.
Multi-stream pose convolutional neural networks for human interaction recognition in images

Recognizing human interactions in still images is quite a challenging task since compared to videos, there is only a glimpse of interaction in a single image. This work investigates the role of human poses in recognizing human–human interactions in still images. To this end, a multi-stream convolutional neural network architecture is proposed, which fuses different levels of human pose information to recognize human interactions better. In this context, several pose-based representations are explored. Experimental evaluations in an extended benchmark dataset show that the proposed multi-stream pose Convolutional Neural Network is successful in discriminating a wide range of human–human interactions and human poses when used in conjunction with the overall context provides discriminative cues about human–human interactions.
Graph-Structured Context Understanding for Knowledge-grounded Response Generation

In this work, we establish a context graph from both conversation utterances and external knowledge, and develop a novel graph-based encoder to better understand the conversation context. Specifically, the encoder fuses the information in the context graph stage-by-stage and provides global context-graph-aware representations of each node in the graph to facilitate knowledge-grounded response generation. On a large-scale conversation corpus, we validate the effectiveness of the proposed approach and demonstrate the benefit of knowledge in conversation understanding.
A Deployment Management of High-Availability Microservices for Edge Computing

In recent years, due to the emergence of Edge Computing architecture, the microservice computing originally located in the cloud has been extended to the edge to reduce network latency. At the same time, the data transmission bandwidth is dispersed on the edge, reducing the usage of network bandwidth, and thus shortening the response time of the service. In addition, the way which the cloud manages the edge is called cloud-edge collaboration. This is the unified management of microservice operations at the edge by the cloud. And, cloud-edge collaboration can also reduce maintenance costs. KubeEdge is an open source cloud-edge collaboration architecture by using the cloud to manage multiple edge microservices. However, if there are multiple microservices that need to be continuously deployed and updated, a more complete solution is still needed in microservice management. Hence, this study proposes an edge computing architecture that apply additional component to KubeEdge to deploy and manage containerized microservices automatically.
TSMiner: from TSBuilder to Ecosystem

The paper reviews a project on the automation of term system construction. TSBuilder (Term System Builder) was developed in 2014 for supervised machine learning, namely 1-3 word terms identification in natural language texts and their categorization. TSGraph (Term System Graph) was developed in 2019 for visualization of terminologies' overlappings. TSMiner is the integration of existing projects into an ecosystem allowing to move smoothly from search to storage and visualization of terminologies. We added the use of synonymic matching for single- and multiple-word terms at the morphemic level, which is beyond the scope of a traditional linguistic analysis. This allows us to replenish the database with new words close in meaning to the existing ones but having dropped out of our radar for not being cognit to the ones included into the database. Algorithms for translating natural language words into a vector form were used to estimate the degree of synonymy.
Emoji Score and Polarity Evaluation Using CLDR Short Name and Expression Sentiment

The detection and analysis of sentiment have received great importance by professionals from ecommerce, medical services, physiologists, and social critics. The computerization has resulted in tremendous increase of online users. The users have adapted posting their day to day life events, expressions, judgement, memories, etc. on social media platforms like Facebook, WhatsApp, and Twitter as a part of life. Online postings consist of text and huge number of emojis as well. To evaluate the correct sentiment of such online postings, an accurate approach is required which evaluates the score and polarity of emoji used in an online sentiment. In the current paper, we have proposed an approach to evaluate score and polarity of 4158 emojis. Emoji evaluation is a task of determining the score and polarity of emoji as positive, negative, and neutral based on the sentiment of sentence and the short name description of emoji. The accuracy of the proposed approach is evaluated through machine learning approaches like Linear Regression, SVM and Random Forest. Our approach outperforms in SVM with an accuracy of 85%.
Bodily ownership and agency sensations in a natural state

Our bodily sensation is a fundamental cue for our self-consciousness. Whereas experimental studies have uncovered characteristics of bodily sensation, these studies investigated bodily sensations through manipulating bodily sensations to be apart from one's own body and to be assigned to external, body-like objects. In order to capture our bodily sensation as it is, this questionnaire survey study explored the characteristics of bodily sensation using a large population-based sample (N = 580, comprising 20s to 70s age groups) without experimental manipulations. We focused on the sensations of ownership, the feeling of having a body part as one's own, and agency, the feeling of controlling a body part by oneself, in multiple body parts (the eyes, ears, hands, legs, nose, and mouth). The ownership and agency sensations were positively related to each other in each body part. Interestingly, the agency sensation of the hands and legs had a positive relationship with the ownership sensations of the other body parts. We also found the 60s age group had a unique internal configuration, assessed by the similarity of rating scores, of the body parts for each bodily sensation. Our findings revealed the existence of unique characteristics for bodily sensations in a natural state.
QoS Optimization of DNN Serving Systems Based on Per-Request Latency Characteristics

Deep Neural Networks (DNNs) have been extensively applied in a variety of tasks, including image classification, object detection, etc. However, DNNs are computationally expensive, making on-device inference impractical due to limited hardware capabilities and high energy consumption. This paper first incorporates downside risk into characteristics of per-request processing latency for DNN serving systems. Then, considering applications' diverse preferences of latency and accuracy, we introduce a scheme for assigning applications to different DNN models in an edge site, in order to maximize QoS of all applications while reducing the risk of having large processing latency and to meet requirements of minimum accuracy at the same time. Empirical results show that our approach improves system performance and takes an acceptable amount of time for computation.
Closed-Form Results for Prior Constraints in Sum-Product Networks

Incorporating constraints is a major concern in probabilistic machine learning. A wide variety of problems require predictions to be integrated with reasoning about constraints, from modeling routes on maps to approving loan predictions. In the former, we may require the prediction model to respect the presence of physical paths between the nodes on the map, and in the latter, we may require that the prediction model respect fairness constraints that ensure that outcomes are not subject to bias. Broadly speaking, constraints may be probabilistic, logical or causal, but the overarching challenge is to determine if and how a model can be learnt that handles a declared constraint. To the best of our knowledge, treating this in a general way is largely an open problem. In this paper, we investigate how the learning of sum-product networks, a newly introduced and increasingly popular class of tractable probabilistic models, is possible with declared constraints. We obtain correctness results about the training of these models, by establishing a relationship between probabilistic constraints and the model's parameters.