Reviewerly/exHarmony huggingface dataset:
A Survey on Chatbot: Futuristic Conversational Agent for User Interaction

A chatbot aspires to make the interaction between mankind and machine through acoustic and textual techniques. There are enormous types of speech-based assistants. Few of them are Siri (developed by Apple), Google Assistant, Microsoft's Cortana, and Amazon's Alexa. NLTK for Python, which is a Natural Language Processing (NLP) technique, is pragmatic to analyze speech as well as smart replies or responses that appear by designing an engine to deliver responses as a human does. For a chatbot to accurately excel a dialogue of a human, it should anatomize the input provided by a user appropriately and articulate a relevant and appropriate response. This paper describes a survey on the strategies used to design chatbot and comparison that is prepared by examining different design techniques to the main ones adopted. The survey is done on the researches carried out in representing the significant prolongation in chatbot over the last few decades.
Launching a Micro–Scout UAV from a Mobile Robotic Manipulator Arm

This paper addresses the problem of autonomously deploying an unmanned aerial vehicle in non-trivial settings, by leveraging a manipulator arm mounted on a ground robot, acting as a versatile mobile launch platform. As real-world deployment scenarios for micro aerial vehicles such as search-and-rescue operations often entail exploration and navigation of challenging environments including uneven terrain, cluttered spaces, or even constrained openings and passageways, an often arising problem is that of ensuring a safe take-off location, or safely fitting through narrow openings while in flight. By facilitating launching from the manipulator end-effector, a 6-DoF controllable take-off pose within the arm workspace can be achieved, which allows to properly position and orient the aerial vehicle to initialize the autonomous flight portion of a mission. To accomplish this, we propose a sampling-based planner that respects a) the kinematic constraints of the ground robot / manipulator / aerial robot combination, b) the geometry of the environment as autonomously mapped by the ground robots perception systems, and c) accounts for the aerial robot expected dynamic motion during takeoff. The goal of the proposed planner is to ensure autonomous collision-free initialization of an aerial robotic exploration mission, even within a cluttered constrained environment. At the same time, the ground robot with the mounted manipulator can be used to appropriately position the take-off workspace into areas of interest, effectively acting as a carrier launch platform. We experimentally demonstrate this novel robotic capability through a sequence of experiments that encompass a micro aerial vehicle platform carried and launched from a 6-DoF manipulator arm mounted on a four-wheel robot base.
Emotion-Aware Event Summarization in Microblogs

Microblogs have become the preferred means of communication for people to share information and feelings, especially for fast evolving events. Understanding the emotional reactions of people allows decision makers to formulate policies that are likely to be more well-received by the public and hence better accepted especially during policy implementation. However, uncovering the topics and emotions related to an event over time is a challenge due to the short and noisy nature of microblogs. This work proposes a weakly supervised learning approach to learn coherent topics and the corresponding emotional reactions as an event unfolds. We summarize the event by giving the representative microblogs and the emotion distributions associated with the topics over time. Experiments on multiple real-world event datasets demonstrate the effectiveness of the proposed approach over existing solutions.
HSV model based data hiding in video for watermark applications

This manuscript proposes a new data hiding approach that is used in watermark applications in video by transforming the RGB model to HSV model. This method initially estimates the number of frames needed to embed the data (watermark). Then two sets of RGB (Red, Green, Blue) coefficients (R1, G1, B1), (R2, G2, B2) are converted to HSV (Hue, saturation, values) Coefficients (H1, S1, V1) and (H2, S2, V2). The ‘Value’ Coefficients V1 and V2 are used to embed the watermark, since there exists a strong correlation between the adjacent ‘Value’ Coefficients. The same process is repeated on adjacent HSV coefficients till the watermark is fully embedded. After embedding the data HSV coefficients are again converted back to RGB coefficients. During the extraction phase, the data is extracted by transforming the RGB coefficient to HSV coefficients. One bit of information can be extracted from two adjacent HSV coefficients. Experimental outcomes show that the proposed watermarking approach is efficiently against attacks, viz noise, filtering, etc. Also, the proposed method performs better than traditional watermarking methods with the help of embedding rate (bpp), Structural similarity index measurement (SSIM), Visual quality (PSNR), Normalized cross-correlation (NC).
Grid-Based Concise Hash for Solar Images

Continuous full-disk observations of the solar chromosphere and corona are provided nowadays by the Solar Dynamics Observatory. Such data are crucial for analysing the Sun-Earth system and life on our planet. Part of the data is an enormous number of high-resolution images. We create a compact grid-based solar image hash to classify or retrieve similar solar images. To compute the hash, we design intermediate hand-crafted features. Then, we use a convolutional autoencoder to encode the descriptors to the form of a concise hash.
Assessing the Effect of Data Augmentation on Occluded Frontal Faces Using DWT-PCA/SVD Recognition Algorithm

The drift towards face-based recognition systems can be attributed to recent advances in supportive technology and emerging areas of application including voting systems, access control, human-computer interactions, entertainments, and crime control. Despite the obvious advantages of such systems being less intrusive and requiring minimal cooperation of subjects, the performances of their underlying recognition algorithms are challenged by the quality of face images, usually acquired from uncontrolled environments with poor illuminations, varying head poses, ageing, facial expressions, and occlusions. Although several researchers have leveraged on the property of bilateral symmetry to reconstruct half-occluded face images, their approach becomes deficient in the presence of random occlusions. In this paper, we harnessed the benefits of the multiple imputation by the chained equation technique and image denoising using Discrete Wavelet Transforms (DWTs) to reconstruct degraded face images with random missing pixels. Numerical evaluation of the study algorithm gave a perfect (100%) average recognition rate each for recognition of occluded and augmented face images. The study also revealed that the average recognition rate for the augmented face images (75.5811) was significantly lower than the average recognition rate (430.7153) of the occluded face images. MICE augmentation is recommended as a suitable data enhancement mechanism for imputing missing data/pixel of occluded face images.
A decomposition-based algorithm for learning the structure of multivariate regression chain graphs

We extend the decomposition approach for learning Bayesian networks (BNs) proposed by Xie et al. (2006) [54] to learning multivariate regression chain graphs (MVR CGs), which include BNs as a special case. The same advantages of this decomposition approach hold in the more general setting: reduced complexity and increased power of computational independence tests. Moreover, latent (hidden) variables can be represented in MVR CGs by using bidirected edges, and our algorithm correctly recovers any independence structure that is faithful to an MVR CG, thus greatly extending the range of applications of decomposition-based model selection techniques. Simulations under a variety of settings demonstrate the competitive performance of our method in comparison with the PC-like algorithm (Sonntag and Peña, 2012) [43]. In fact, the decomposition-based algorithm usually outperforms the PC-like algorithm except in running time. The performance of both algorithms is much better when the underlying graph is sparse.
Transfer Learning for Autonomous Vehicles Obstacle Avoidance with Virtual Simulation Platform

Over the last few years, autonomous vehicles have expanded considerably. Autonomous driving systems are getting more complex and must be tested successfully prior to implementation. We are exploring a model for high quality prediction of obstacle avoidance for autonomous vehicles based on images created by a virtual simulation platform and then using a VGG 16 deep learning technique, including transfer learning. This paper proposes a transfer of learning strategy using the VGG16 architecture, while the output of the proposed architecture is further compared to the existing NVIDIA architecture. Experimental results indicate that the VGG16 with the transfer learning architecture has surpassed other tested methods.
Formalizing Calculus without Limit Theory in Coq

Formal verification of mathematical theory has received widespread concern and grown rapidly. The formalization of the fundamental theory will contribute to the development of large projects. In this paper, we present the formalization in Coq of calculus without limit theory. The theory aims to found a new form of calculus more easily but rigorously. This theory as an innovation differs from traditional calculus but is equivalent and more comprehensible. First, the definition of the difference-quotient control function is given intuitively from the physical facts. Further, conditions are added to it to get the derivative, and define the integral by the axiomatization. Then some important conclusions in calculus such as the Newton–Leibniz formula and the Taylor formula can be formally verified. This shows that this theory can be independent of limit theory, and any proof does not involve real number completeness. This work can help learners to study calculus and lay the foundation for many applications.
Controlling as a Tool to Reduce the Risks of Smart University in the Digital Economy

Modern conditions are characterized by the uncertainty and volatility of the economic environment. This situation is associated with increased risks, accompanied by uncertainty and uncertainty in achieving the expected results. In this regard, both prompt and preventive measures are needed to reduce the likelihood of the occurrence of risks or localize the negative consequences of their occurrence. The activities of a smart university are subject to the influence of the external environment, which causes certain threats and risks. In addition, one cannot but take into account the influence of internal risks on the results of its functioning. To solve such problems in a smart-organization, it is necessary to strengthen the controlling system, which involves targeted actions to reduce risks. The article analyzes the existing risks of a smart university, clarifies the principles and stages of building controlling in a smart university in a digital economy. The result of the study is the proposed controlling algorithm to reduce risks in a smart university in the context of digital transformation. The obtained results were tested with risk reduction in the Smart-division of the university using the example of the department.
Automatic Recognition of ISL Dynamic Signs with Facial Cues

Sign language recognition systems help the hearing and vocally impaired people to communicate with the verbally speaking community. This paper proposes an Indian sign language (ISL) recognition system capable of recognizing isolated double-handed dynamic gestures involving facial expressions. The recognition system uses skin color segmentation for segmenting face and hand regions from the video frames. Histogram of oriented gradients (HOG) features are computed over the segmented frames, and Multi-class neural network is used for classification. The proposed system can recognize ten single and double-handed dynamic gestures with an accuracy of 81%. The most important thing regarding this work is that it does not use any sophisticated sensors or even does not use any computationally intensive methods like hand tracking and still achieve a remarkable accuracy.
Social Network Opinion Mining and Sentiment Analysis: Classification Approaches, Trends, Applications and Issues

Sentiment Analysis (SA) or Opinion Mining (OM) probes the content stated about a topic of interest and classifies them as +ve, −ve or neutral hold on the person's opinion, emotion, judgment articulated in it. Nowadays people’s opinions and reviews on blogs, forums, review websites, E-commerce sites are most vital information in the process decision-making for both organization and users. SA is an information revelation and knowledge discovery technique inherited from data mining. The manual investigation of such immense number of reviews is actually unthinkable so computerized frameworks are expected to do analysis and accordingly classify of information to take related decisions. In general, SA attempts to make some decisions on the base of an author standpoint that may be his or her judgment, mood or evaluation. In this study, we have described the essential concepts, various methods, trends, applications and key issues of OMSA in social networks. We tried to analyze SA literatures from various perspectives and presented those techniques and methods in a systematic manner. With brief descriptions of the algorithms, different SA taxonomical techniques classified along with their advantages and limitations. The critical opportunities that lie ahead for SA are defined and addressed on the basis of those sources. We attempted to present the recent applications and challenging issues in Sentiment Identification and presents evaluation metrics for SA.
Heterogeneous Integration of Big Data Using Semantic Web Technologies

Semantic web offers information for both individuals and computers to preserve large data scale semantically and provide a meaningful content of unstructured data. It offers new benefits for big-data research and applications. Big Data and Semantic Web are the epitome of computer sciences latest trend study subjects. Big data is a new tendency relates to a huge set of datasets including structured, semi-structured and unstructured data collected from different sources. Their integration faces many issues, as it is difficult to process this information using traditional databases and software methods. Recent works on the incorporation of both these technologies have provided a scalable approach in Data Analytics. This article attempts to give a comparative study of methods in integrating Big Data with Semantic Web, describing how Semantic Web makes Big Data smarter, revisits the difficulties and possibilities of Big Data and Semantic Web, and lastly summarizes the future direction of this inclusion.
Medical Knowledge Graph in Chinese Using Deep Semantic Mobile Computation Based on IoT and WoT

In this paper, a novel medical knowledge graph in Chinese approach applied in smart healthcare based on IoT and WoT is presented, using deep neural networks combined with self-attention to generate medical knowledge graph to make it more convenient for performing disease diagnosis and providing treatment advisement. Although great success has been made in the medical knowledge graph in recent studies, the issue of comprehensive medical knowledge graph in Chinese appropriate for telemedicine or mobile devices have been ignored. In our study, it is a working theory which is based on semantic mobile computing and deep learning. When several experiments have been carried out, it is demonstrated that it has better performance in generating various types of medical knowledge graph in Chinese, which is similar to that of the state-of-the-art. Also, it works well in the accuracy and comprehensive, which is much higher and highly consisted with the predictions of the theoretical model. It proves to be inspiring and encouraging that our work involving studies of medical knowledge graph in Chinese, which can stimulate the smart healthcare development.
Anomaly Detection Method for Substation Equipment Based on Feature Matching and Multi-Semantic Classification

The health status of substation equipment is an important guarantee for the safe and stable operation of substation, and promptly detection of anomaly conditions in substation equipment can avoid serious safety hazards. To address this issue, this paper proposes an anomaly detection method for substation equipment based on image registration and deep learning. First, the anomaly categories of substation equipment are detected based on multi-semantic feature network and fine-grained classification. Then, a sparse cross-domain feature matching algorithm is introduced to register the image of substation equipment, and edge detection and image denoising is used to detect the anomaly areas in the image. Finally, the image registration and multi-semantic recognition are merged for the integrated anomaly detection. The experimental results illustrate that the proposed method can rapidly and accurately detect the anomaly of substation equipment, and significantly improve the automation level of substation equipment and the safety of substation operation.
Achieving Pairing-Free Aggregate Signatures using Pre-Communication between Signers

Most aggregate signature schemes are relying on pairings, but high computational and storage costs of pairings limit the feasibility of those schemes in practice. Zhao proposed the first pairing-free aggregate signature scheme (AsiaCCS 2019). However, the security of Zhao's scheme is based on the hardness of a newly introduced non-standard computational problem. The recent impossibility results of Drijvers et al. (IEEE S&P 2019) on two-round pairing-free multi-signature schemes whose security based on the standard discrete logarithm (DL) problem have strengthened the view that constructing a pairing-free aggregate signature scheme which is proven secure based on standard problems such as DL problem is indeed a challenging open problem. In this paper, we offer a novel solution to this open problem. We introduce a new paradigm of aggregate signatures, i.e., aggregate signatures with an additional pre-communication stage. In the pre-communication stage, each signer interacts with the aggregator to agree on a specific random value before deciding messages to be signed. We also discover that the impossibility results of Drijvers et al. take effect if the adversary can decide the whole randomness part of any individual signature. Based on the new paradigm and our discovery of the applicability of the impossibility result, we propose a pairing-free aggregate signature scheme such that any individual signature includes a random nonce which can be freely generated by the signer. We prove the security of our scheme based on the hardness of the standard DL problem. As a trade-off, in contrast to the plain public-key model, which Zhao's scheme uses, we employ a more restricted key setup model, i.e., the knowledge of secret-key model.
Enhanced Automatic Modulation Classification using Deep Convolutional Latent Space Pooling

In our work, we investigate alternative forms of automatic modulation classification with deep learning and statistical methods. With a growing number of devices communicating through wireless transmission mediums, automatic modulation classification plays a critical role in reading an observed signal. We compare our proposed method with the current state of the art and show that traditional convolutional neural networks can outperform residual neural networks for the task of modulation classification. Using an approach inspired by research from the speaker verification community, we show that the modulation method used to transmit a signal can be classified into one of 24 candidate modulation types with greater than 98% accuracy for signals with high signal to noise ratios.
Weak texture information map guided image super-resolution with deep residual networks

Limited by the poor quality of the camera, transmission bandwidth, excessive compression and other factors, low-resolution images widely exist in our lives. Single image super-resolution method is a kind of image processing task which can obtain high-resolution image from corresponding source low-resolution image. With the development of deep learning technology, a series of deep learning methods have brought crucial improvement for SISR problem. However, we observe that no matter how deep the network structures for SISR are designed, they usually have poor performances on restoration of tiny or irregular details, we call them weak texture information. The main reason for this phenomenon is that weak texture information is not obvious relative to the salient features, so as weak texture information feature is less extracted in the process of neural network feature extraction. To address this problem, we propose a SISR method which owns unique weak texture information prediction module and call it as weak texture information map guided image super-resolution with deep residual networks. In our network structure, two auxiliary sub-networks work together to capture grab details information and predict weak texture information. Then, predicted weak texture information will be fused into main sub-network. Therefore, our network can obtain more irregular details which usually miss in deep learning based methods. Finally, both qualitative experiments and visual effects demonstrate the effectiveness of our proposed algorithm. Specifically, our method can restore more irregular image details.
Cyber Terrorism-Related Multimedia Detection Using Deep Learning—A Survey

In this era of technological know-how crime has changed from bodily assault to virtual assault and due to increase of Internet applications, i.e., social media has made conversation simpler as nicely as given an open supply for terrorist to layout their undertaking with the assist of social media structures it is easy to terrify and create chaos in society which leads in excessive cyber terrorism ratio from last few years. Due to this extend in cyber terrorism ratio we would like to discover such exercise the usage of multimedia dataset which could become aware of such undertaking the usage of live API of social media in combination with deep learning to obtain accurate solution to this growing trouble of cyber terrorism due to the fact keyboards used through terrorists are extra dangerous than a bomb.