Reviewerly/exHarmony huggingface dataset:
Approaches of Sentiment Analysis: A Review

The social media websites are full of information related to the public’s opinions, and emotions in different aspects such as elections, products, movies, disaster, and company brand, etc. The opinion of people is very useful in commercializing a business. Sentiment analysis is used to determine the attitude of the writer on a topic as positive or negative. Twitter is a widely used micro-blogging site for expressing opinions. In this paper, the approaches used by different researchers for sentiment analysis is discussed and based on the accuracy, precision, recall, and f1 score, it is discovered that the lexicon-based approach is widely used in NLP and Naive Bayes and SVM are highly effective and accurate classifiers used in Machine Learning. This paper also presents the deep learning approach and the major challenges faced by sentiment analysis.
Dynamic Door Modeling for Monocular 3D Vehicle Detection.

The precise 3D localization of non-ego vehicles is a crucial task for the long-term goal of autonomous driving. In urban scenarios, where pedestrians frequently interact with vehicles, this task also requires a precise modeling of dynamic vehicle parts, e.g., doors. Current state-of-the-art computer vision algorithms are in fact able to estimate a vehicle pose but do not model doors by any means. To provide a solution solely based on a monocular camera, our proposed pipeline first performs a six degree-of-freedom pose estimation and then predicts the respective states of the vehicle doors. For both problems we utilize a perspective-n-point fitting method based on key points. To this end, we jointly detect the two required sets of correspondences for the vehicle body and the doors with a neural network. Since little insight is published for the application of key point based vehicle detection in the literature, we compare different implementations of the key point prediction module and investigate algorithm details, i.e., the role of a key point visibility analysis and two differing key point layouts. Results for the body estimation and the door detection with respect to these implementation details are presented on a proprietary dataset, in which we utilize an exact vehicle model to receive precise ground truth.
Human–computer interaction based on the intelligent information retrieval method for customer satisfaction in power system service

The analysis of the information retrieval system focuses on the notion of appropriate and irrelevant documents. The performance predictor, including accuracy and reminder, is used to establish how well the device satisfies consumer requirements. The effectiveness of the indexing and retrieval is calculated by contrasting a typical collection of queries and documents with the efficacy, functionality, and systemic approach. Important evaluations are used to measure functionality, performance (precession and retraction), compilation, and interface assessments. Document and query indexing, query assessment, and system assessment are key issues in information retrieval. This paper uses the human–computer interaction based on the intelligent information retrieval method (HCI-IRM). The proposed method concentrates on customer satisfaction, the main success assessment metric. It identifies the collection of related records at a given time within the collection. An information retrieval system’s main objective is to obtain the information. It is either the actual information or the documents containing the information substitutes which completely or partly correspond to the customer’s query evaluation. The extraction and recruitment of knowledge-based data from a database are usually related to the retrieval of information. The retrieval and precise technology are used to assess the efficiency of the data recovery system framework. As a result, HCI-IRM enhances the response time, and the relevance of the outcomes is key to customer satisfaction. Comparison of Yahoo and Google search engines focused on accuracy and reminder technology.
DISTANCE TECHNOLOGIES IN THE MODERN EDUCATIONAL PROCESS: A PHILOSOPHICAL VIEW

With the formation and development of information technologies, many aspects of the usual way of society are subject to changes. This article examines the current trend of introducing distance technologies into the educational process. The pros and cons of this trend are determined from the point of view of a philosophical approach. Taking into account the existing philosophical concepts of education, it is possible to analyze the significance of current trends in education. It is concluded that remote technologies have their advantages, but there are more disadvantages. But for the objectivity of the assessment, it should be noted that this form of education has become the only rational equivalent of classical education during the isolation period during a pandemic.
Guidance, navigation and control of multirotors

This thesis presents contributions to the Guidance, Navigation and Control (GNC) systems for multirotor vehicles by applying and developing diverse control techniques and machine learning theory with innovative results. The aim of the thesis is to obtain a GNC system able to make the vehicle follow predefined paths while avoiding obstacles in the vehicle's route. The system must be adaptable to different paths, situations and missions, reducing the tuning effort and parametrisation of the proposed approaches. The multirotor platform, formed by the Asctec Hummingbird quadrotor vehicle, is studied and described in detail. A complete mathematical model is obtained and a freely available and open simulation platform is built. Furthermore, an autopilot controller is designed and implemented in the real platform. The control part is focused on the path following problem. That is, following a predefined path in space without any time constraint. Diverse control-oriented and geometrical algorithms are studied, implemented and compared. Then, the geometrical algorithms are improved by obtaining adaptive approaches that do not need any parameter tuning. The adaptive geometrical approaches are developed by means of Neural Networks. To end up, a deep reinforcement learning approach is developed to solve the path following problem. This approach implements the Deep Deterministic Policy Gradient algorithm. The resulting approach is trained in a realistic multirotor simulator and tested in real experiments with success. The proposed approach is able to accurately follow a path while adapting the vehicle's velocity depending on the path's shape. In the navigation part, an obstacle detection system based on the use of a LIDAR sensor is implemented. A model of the sensor is derived and included in the simulator. Moreover, an approach for treating the sensor data to eliminate the possible ground detections is developed. The guidance part is focused on the reactive path planning problem. That is, a path planning algorithm that is able to re-plan the trajectory online if an unexpected event, such as detecting an obstacle in the vehicle's route, occurs. A deep reinforcement learning approach for the reactive obstacle avoidance problem is developed. This approach implements the Deep Deterministic Policy Gradient algorithm. The developed deep reinforcement learning agent is trained and tested in the realistic simulation platform. This agent is combined with the path following agent and the rest of the elements developed in the thesis obtaining a GNC system that is able to follow different types of paths while avoiding obstacle in the vehicle's route.
The Towards Silence Project: Multiple String Quartets Organize to Perform a New Musical Form in Spatially Variable and Distributed Conditions

This case describes an example of a collective making process in the field of performing arts. In 2009, multiple string quartets (many considered world class) organized to perform a new musical composition by Sir John Tavener. The composition challenged four quartets at a time to perform as an integrated ensemble while sitting apart, in various configurations, and at spatial distances up to 70 feet. The process unfolded in three phases: pre-rehearsals of the first group of quartets in the United Kingdom (UK), a series of rehearsals leading to one premiere performance by the second group of quartets in New York, and a series of rehearsals integrated with additional performances in four distinct venues in the UK. Mid-way through the process, the musicians chose to integrate a simple coordinating technology into their process, to address the difficulties produced by distance. This telling of the case story describes what the musicians did to achieve these unprecedented performances, given the unusual circumstances, emphasizing how they made decisions and evaluated their work along the way. The case is based on comprehensive fieldwork, including observation, interviews, spatial measurement and diagramming, questionnaires, and analysis of videotape of the rehearsal process.
Technologies and implementation of an integration with geolocation and maps in mobile application development

Abstract This article describes native and cross-platform technologies used to implement the functionality of geolocation and geographic maps in the development of mobile applications for iOS and Android platforms. The implementation for iOS platform using Swift programming language, for Android platform using Kotlin programming language and cross-platform implementation using Flutter framework are considered in detail.
Rock Units Erosion Susceptibility Detection and Classification Using Nonlinear Correlation Analysis and Landsat ETM+ Data

the lithological maps is inevitable in the preparation of rock unit’s erosion susceptibility maps. In this study, rock unit outcrops in the Soh Basin (50 km Northern Isfahan) were extracted using nonlinear correlation analysis of satellite data. Moreover, rock unit’s erosion susceptibility such as marl, shale, and quaternary deposits and resistant rock units such as sandstone and limestone were extracted based on soil erosion intensity factors. The lithology of the basin was studied usingthe virtual variables method. Initially, rock units, as a virtual independent variable, and the PC1 (the first principal component) of ETM+ multispectral bands were by amultiple linear regression model. Afterward, rock units were in logistic regression analysis as virtual dependent variables. The results revealed that logistic regression analysis is a suitable model for rock unit’s extraction.
INNOVATIVE TRENDS IN EDUCATION: THE SYMBIOSIS OF CULTURE, LAW AND MANAGEMENT

In this article, the author considers methodological innovations that are extremely relevant in the era of the digital educational space. Special attention is paid to the development of effective educational methods aimed at the intellectual, cultural, creative and personal development of students using the maximum potential of their capabilities.
Robust Background Template for Saliency Detection

In this paper, we propose an effective saliency detection method based on dense and sparse representation in-terms of an optimized background template. Firstly, the input image is divided into compact and uniform super-pixels. Then, the optimized background template is produced by introducing boundary conductivity measurement to improve the dense and sparse representation of the image's super-pixels in terms of the optimized background, where the reconstruction error represents a saliency measure. Based on the optimized template, two saliency maps are generated by dense and sparse representation. Finally, the Bayesian framework used to integrate the two saliency maps to obtain the final one. Experimental results show that the proposed method performs favorably against eight state-of-the-art methods. In addition, the proposed method is shown to be more effective in highlighting the challenging salient objects that touch the image boundary.
Secure Big Data Management based on Secret Sharing and Verifiable Computing

Of late, more and more sensitive data have been connected with cyberspace, which has led to an increase in the importance of secure cloud computing. This paper shows an overall scheme of securely managing big data in a cloud supported by two technological methods, secret sharing and verifiable computing. While each of them was implemented and analyzed individually in previous works, this paper shows a comprehensive framework combining them to realize both security and efficiency in cloud data management. Specifically, secret sharing enables hiding the outsourced encrypted data from any unauthorized entity, and verifiable computing enable resource-constraint users to access the database in which the correctness is undoubtedly confirmed. This paper also compares the database scheme based on blockchain and the proposed method in terms of performance. The analysis shows that the proposed scheme entirely based on the conventional Internet computes the operations lighter than the blockchain scheme.
Learning multi-layer latent variable model with short run MCMC inference dynamics

This paper studies the fundamental problem of learning deep generative models that consist of multiple layers of latent variables organized in top-down architectures. Such models have high expressivity and allow for learning hierarchical representations. Learning such a generative model requires inferring the latent variables for each training example based on the posterior distribution of these latent variables. The inference typically requires Markov chain Monte Caro (MCMC) that can be time consuming. In this paper, we propose to use noise initialized non-persistent short run MCMC, such as nite step Langevin dynamics initialized from the prior distribution of the latent variables, as an approximate inference engine, where the step size of the Langevin dynamics is variationally optimized by minimizing the Kullback-Leibler divergence between the distribution produced by the short run MCMC and the posterior distribution. Our experiments show that the proposed method outperforms variational auto-encoder (VAE) in terms of reconstruction error and synthesis quality. The advantage of the proposed method is that it is simple and automatic without the need to design an inference model.
Problematika Penggunaan Fitur E-Learning 2 dan 3 di Jurusan Sejarah Fakultas Ilmu Sosial Universitas Negeri Padang pada Masa Pandemi Covid-19

The purpose of this study was to describe the obstacles felt by lecturers and students of the History Department, Faculty of Social Science, Universitas Negeri Padang in using the features contained in e-learning. The research is motivated by initial findings which show that there are various obstacles experienced by lecturers and students in utilizing and accessing the features available in Universitas Negeri Padang e-learning. This study uses a mixed-method. The results of the study indicate that the obstacles experienced by lecturers in utilizing e-learning features; detailed technical knowledge in managing certain features have not been mastered by lecturers. These features are the BigBlueButton, Chat, Choice, Glossary, Lesson, Quiz, Wiki, Book, Folder, Label, Page, and URL features (still limited in use; under 10%). The features that are commonly used by lecturers are the assignment, attendance, forum, and file features (utilization in the 10%-20% range). The obstacles in accessing e-learning that is felt by students, which are often errors, especially in the morning, do not know in detail the functions of each feature.
Development of Island Model Genetic Algorithms with Different Genetic Operators

The genetic algorithms (GA) are evolutionary algorithms that imitate the evolution process of many living things. GA operates multipoint search, therefore the parallelization of the genetic operators are effective to shorten computational time. The island model GA is one of extended model of GA such that introducing parallel computation. This paper develops new algorithm of island model GA by introducing the concept of TCPSO (Two-swarm Cooperative Particle Swarm Optimization). In the proposed method, the individuals are updated by using different procedures which are determined depending on the categorized groups. Additionally, this paper conducts numerical experiments and the result indicates the effectiveness of the proposed algorithm.
Few-shot Deep Representation Learning based on Information Bottleneck Principle

In a standard anomaly detection problem, a detection model is trained in an unsupervised setting, under an assumption that the samples were generated from a single source of normal data. In practice, however, normal data often consist of multiple classes. In such settings, learning to differentiate between normal instances and anomalies among discrepancies between normal classes without large-scale labeled data presents a significant challenge. In this work, we attempt to overcome this challenge by preparing few examples from each normal class, which is not excessively costly. The above setting can also be described as a few-shot learning for multiple, normal classes, with the goal of learning a useful representation for anomaly detection. In order to utilize the limited labeled examples in training, we integrate the inter-class distances among the labeled examples in the deep feature space into the MAP loss. We derive their relations from an information-theoretic principle. Our empirical study shows that the proposed model improves the segmentation of normal classes in the deep feature space which contributes to identifying the anomaly class examples.
Exploration and Practice of the Undergraduate Training Mode in Electronic and Information Engineering with Marine Characteristics

Marine higher education plays an important role in ocean countries in the world. Interdisciplinary integration of ocean and other disciplines has been the development trend and characteristics since the 21st century. In this paper, some explorations and practices have been carried out about the talents cultivation in Electronic Information Engineering with marine characteristics in Ocean University of China. This is a practical choice based on the national marine strategy and regional development demand for marine talents, and also benefiting the discipline intersection of information and ocean in College of Information Science and Engineering. We develop the professional training program with marine characteristics, including curriculum system and course syllabus. In order to strengthen the students’ engineering practice, we construct multi-level, multi-stage and diversified practical teaching system; offer students various innovative practice out of class; open advanced experimental platform. As a result, the specialty construction in Electronic Information Engineering based on all of these measures is feasible and effective in improving the engineering ability of students, especially the application ability of electronic information in the ocean.
Bi-directional Attention Feature Enhancement for Video Instance Segmentation.

As a recently proposed task, video instance segmentation (VIS) can classify, detect, segment and track each instance in a given video, which is very useful for driving environment perception of autonomous vehicles. In this paper, we propose a novel method called bi-directional attention feature enhancement (BAFE) to make up for the lack of feature processing in existing works of VIS. BAFE contains a top-down attention branch and a bottom-up attention branch. It uses attention to perform two-way information transmission between high-level, semantic features and low-level, local features to combine them efficiently and can be utilized in any network that uses both high-level and low-level features. We add BAFE before the mask-specialized regression branch of the latest work, spatial information preservation for VIS (SipMask- VIS), to enhance features for mask generating. Besides, we introduce modified path aggregation network (Mod-PAN) to further enhance features. Different from existing works which first train their models on image datasets and then use the pre-trained models to finish the VIS task, we obtain VIS results in an end-to-end way without any pre-training. Our method outperforms SipMask-VIS by an absolute gain of 2.5%, which strongly proves its effectiveness.
ポアソン確率に基づく地震活動表現方法の検証

The characteristics of eMAP (evaluation Method of Appearance Probability), a method for visualizing seismic rate changes (SRCs) based on Poisson probability, were discussed in comparison with Matsumura’s method and ZMAP. For SRCs made by the changes in the magnitude scale of seismic events (ΔM), from －0.5 to ＋0.5 in the Richter scale, the analytical values of Matsumura’s method and ZMAP showed almost linear changes, while those of eMAP were particularly sensitive to changes in ΔM from about －0.3 to ＋0.3 and saturated to 1 or 0 for further increases or decreases in ΔM. As for the spatial distribution of quiescence and activation in SRC given in a grid of 50 km spacing, Matsumura’s method and ZMAP did not have high resolution due to spatial fluctuation of SRCs generated naturally. In contrast to these, eMAP showed the best resolution due to its robustness to the SRC fluctuation.
Learning Highlight Separation of Real High Resolution Portrait Image

research-article Share on Learning Highlight Separation of Real High Resolution Portrait Image Authors: Ruikang Ju MRAD of Beijing Institute of Technology, Beijing Institute of Technology, China MRAD of Beijing Institute of Technology, Beijing Institute of Technology, ChinaView Profile , Dongdong Weng MRAD of Beijing Institute of Technology, Beijing Institute of Technology, China and AICFVE of Beijing Film Academy, AICFVE of Beijing Film Academy, China MRAD of Beijing Institute of Technology, Beijing Institute of Technology, China and AICFVE of Beijing Film Academy, AICFVE of Beijing Film Academy, ChinaView Profile , Bin Liang MRAD of Beijing Institute of Technology, Beijing Institute of Technology, China MRAD of Beijing Institute of Technology, Beijing Institute of Technology, ChinaView Profile Authors Info & Claims ICCCV'21: 2021 4th International Conference on Control and Computer VisionAugust 2021 Pages 18–23https://doi.org/10.1145/3484274.3484278Online:23 November 2021Publication History 0citation25DownloadsMetricsTotal Citations0Total Downloads25Last 12 Months25Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access