Reviewerly/exHarmony huggingface dataset:
Aspect Based Opinion Mining Leveraging Weighted BiGRU and CNN Module in Parallel

People have created a massive number of opinion content on the Web to support their opinions about goods and services that have a high commercial value in their everyday lives. For polarization, these remark sentences sometimes contain multiple comment components with varying sentiments, rendering the actual purpose of the sentence irrelevant. The goal of aspect-oriented sentiment analysis is just to identify the target’s perception polarities in scenario. Deep learning definitely is maturing, and it’s becoming more common to use deep learning approaches to identify emotions. The use of the convolutional neural network & the bidirectional gated recurrent device in a sentiment classification model is presented. LSTM is equivalent to BiGRU which is a time cyclic neural network with reduced processing complexity. We propose that CNN and bi-GRU be used in parallel, with weights applied to elements of the CNN and bi-GRU output. We use CNN and bi-GRU in parallel because CNN may produce more significant feature space from the original dataset than the bi-GRU components used as the CNN input. For the final sentiment categorization, the Sigmoid classifier is utilised.
The Experience of Sound: An Interview with Thomas DeLio

Abstract Thomas DeLio is a composer and theorist of international renown in both fields and especially noted for his work in computer music. In this conversation he discusses his musical thinking with over 40 years in the field. His compositions have been performed worldwide and are recorded on numerous labels. Neuma recordings has recently released five volumes of his recorded compositions in an ongoing series of his collected works. The interview itself, conducted over a series of phone conversations in the summer of 2020, begins with a look at his early student years at the New England Conservatory of Music in the late 1960s and early 1970s and navigates through to his current work and thinking today. Numerous aspects of his compositional approach and aesthetics are discussed, including his early influences, his illuminating thoughts on time and silence in his music, his applications of various technologies, and spatial projection, particularly at it relates to his work with sound installations.
Ant Colony Algorithm for Container-based Microservice Scheduling in Hybrid Cloud

Abstract The microservice model divides the application into a group of loosely coupled and collaborative fine-grained services, which results in the workflow scheduling problem, especially in the complex environment of resource diversity such as hybrid cloud. To solve this problem, this paper establishes a workflow scheduling model of microservice based on multiple container instances and proposes an ant colony optimization (ACO) algorithm to optimize finish time and resource cost. The algorithm proposed optimizes update strategies of multi-objective heuristic information to improve the selection probability of the dominant path. The comparative experiments show that the proposed optimization algorithm achieves better results in scheduling objectives in the hybrid cloud.
AVA - A Cloud-based Banking Virtual Assistant

The world is embracing a digital revolution and entering a millennial generation of banking. However, the literal and conservative bounds of the society who choose to stick to a traditional banking approach, prefer the in-person experience for banking. The staff at any bank undergo tremendous pressure doing trivial tasks to offer basic services like balance inquiry and fixed deposits, loan inquiries, etc. Given the limited time of banking hours and the highly dense population of India, it has been a burden, especially during the COVID-19 pandemic. This motivates us to introduce Ambar Virtual Assistant (AVA), which will take care of the trivial tasks aforementioned. The ability of AVA to converse in the national language (Hindi) along with English, will ensure better customer satisfaction to bridge the gap between diversity, education, and innovation.
Perceptually Dithered HDR for 8-Bit Interfaces

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In this study, we explore the performance of applying dither when quantizing high-dynamic range (HDR) achromatic images to lower bit depths and found that by applying “perceptual dither,” or dither in the International Telecommunication Union – Radiocommunication (ITU-R) BT.2100 IC<sub>T</sub>C<sub>P</sub> color representation, the bit depth necessary for visually consistent reproduction is reduced by 2 bits. We conclude that using perceptual dithering, the precision of a 10-bit source signal with only 8 bits, or the precision of a 12-bit source signal with only 10 bits, can be achieved</i> .
Mapless Humanoid Navigation Using Learned Latent Dynamics

In this paper, we propose a novel Deep Reinforcement Learning approach to address the mapless navigation problem, in which the locomotion actions of a humanoid robot are taken online based on the knowledge encoded in learned models. Planning happens by generating open-loop trajectories in a learned latent space that captures the dynamics of the environment. Our planner considers visual (RGB images) and non-visual observations (e.g., attitude estimations). This confers the agent upon awareness not only of the scenario, but also of its own state. In addition, we incorporate a termination likelihood predictor model as an auxiliary loss function of the control policy, which enables the agent to anticipate terminal states of success and failure. In this manner, the sample efficiency of the approach for episodic tasks is increased. Our model is evaluated on the NimbRo-OP2X humanoid robot that navigates in scenes avoiding collisions efficiently in simulation and with the real hardware.
Current Status and Development Trend of Crowd Counting

As a challenging task, crowd counting has attracted the attention of researchers due to its wide application in the fields of smart video surveillance, smart city construction, and public safety. But at the same time, the impact of many factors, including occlusion, scale changes, and perspective distortion, on task accuracy is still a problem that needs to be solved urgently. On the basis of combing and summarizing the relevant literature, the mainstream population counting methods are reviewed to lay the foundation for more in-depth research in the future. Firstly, it analyzes the research background, current situation and development trend of crowd counting method as a whole. Secondly, the traditional counting method is summarized with the three angles of detection, regression and density estimation as the starting point. Then, it focuses on the crowd counting method based on CNN. Once again, a brief introduction to commonly used counting data sets is given, and Ground Truth generation methods and mainstream evaluation indicators are explained. Finally, based on a series of analyses, the main characteristics and development prospects of population counting are summarized.
Evolving Fuzzy Modeling with Variable Step-Size applied to Gas Prices Forecasting in Brazil

Fuel represents one of the main transport costs and, consequently, of a logistical operation. So, a computational tool that allows reliable forecasts on the fuel prices becomes a competitive differential for the logistics operator, especially in a country of continental dimensions such as Brazil. The present study developed a participatory evolving fuzzy model, with a variable step-size, denominated VS-ePL-KRLS, applied to the forecast of weekly prices of diesel oil S500 and S10, at the national level, for biweekly and monthly horizons. The proposed model presented better accuracy in relation to the existing ones of this class in the literature, without loss of computational performance for all four analyzed scenarios.
Exponential Auto Regressive Model for Nonlinear Modeling of Noisy and Clean Speech

This paper proposes a method for non-linear modeling of speech signals using Exponential Auto-Regressive (ExpAR) model. This paper shows the non-suitability of linear Auto-Regressive (AR) model during noisy speech using prediction error from the model. The error signal and Akaike’s Information Criterion (AIC) measures are used to compare the performance of ExpAR model with respect to AR model for speech. The proposed method gives more than 2X improvement in terms of error with respect to the linear auto-regressive model in low SNR situations. Analysis of AR and ExpAR is done for clean speech as well; the proposed ExpAR model attains approximately 2X factor better performance with respect to the linear AR model. The AIC measure obtained also suggests the better suitability of ExpAR with respect to AR for modeling speech signals.
Planning approach trajectories to enable late aborts for fixed-wing UAV recovery on ships

For a fixed-wing UAV performing an autonomous recovery in a ship-mounted arrest system, several events can make it preferrable to abort the landing, including issues with communication systems, navigation sensors, or wind gusts exceeding required limits, in order to retry when conditions improve. If the recovery involves impacting an arrest system, aborts cannot necessarily be performed arbitrarily late without a risk of colliding with other parts of the ship. Automatic aborts should therefore only be possible when a safe abort is feasible. We propose a concept where optimization is used to generate an approach trajectory which enables aborts as late as safely possible. The concept is demonstrated using a simple three dimensional UAV model, for several different scenarios. This is then compared to less complex approach plans. The implemented example demonstrates that optimization can be useful for planning approach trajectories and allow later aborts than the simple methods.
Image classification based on self-attention convolutional neural network

Image classification technology is the most basic and important technical branch of computer vision. How to effectively extract effective information from images has become more and more urgent. First, we use the self-attention module to use the correlation between the features to weight and sum the features to get the image category. The self-attention mechanism is simpler to calculate, which greatly reduces the complexity of the model. Secondly, we have also made an optimization strategy for the complex CNN (Convolutional Neural Network) model. This article uses the global average pooling method to replace the fully connected method, which reduces the complexity of the model and generates fewer features. Finally, we verified the feasibility and effectiveness of our model on two data sets.
Design and implementation of augmented reality system for paper media based on ARtoolKit

In the process of knowing and understanding the world, the traditional media of books and paper have always played a very important role. However, with the emergence of new media and the skilled use of digital media technology, the traditional medium of book and paper media has gradually faded away in the eyes of mankind. Stereoscopic paper media is a three-dimensional paper media that can display three-dimensional virtual scenes on the display device based on the traditional paper books by using the camera of the display device to identify the logo card or the real scene, and then collect the virtual information and the real scene. In this paper, under Windows 10 operating system, the program is written by VC++ and ARtoolKit software development kit development tool, and based on augmented reality technology, virtual 3D scenes are built through 3D model modeling to achieve matching and integration with real scenes of paper books and realize 3D interaction. Based on this, a 3D interactive augmented reality system with a high degree of realism is realized. The main research contents include: customizing the paper media markers, reducing the system's misjudgment rate of the markers by combining the single-response matrix and calculating the image matching value with an optimization algorithm, realizing and tracking the recognition of the markers in real time; and optimizing the image segmentation method of the ARtoolKit software development kit, using the marker card to rotate 45&deg; relative to the camera's position to simulate the situation when the scene has changed significantly. We use the minimum error method to calculate the gaps in real time to realize augmented reality; build a 3D model with animation effects based on the ARtoolKit software development tool and Unity development platform; accurately align the recognized markers with the virtual 3D model; add audio to the 3D model to match the content and add interactive functions to realize the control of 3D animation. Stereoscopic paper media breaks the traditional paper book book reading method, no longer just read the content of paper books, you can see and content-related virtual information on the display device of the mobile terminal, such as three-dimensional scenes, videos and so on. Stereoscopic paper media improves the reader's interest in reading and gives the reader a vivid and realistic reading experience.
Estimating the Size of Hidden Groups

NSU studies have been applied in different countries. For example, this technique has been used in the United States to estimate the number of women who had been raped in the last year.
Learned What-If Cost Models for Autonomous Clustering

Clustering database tables, i.e., storing similar values in physical proximity, can enhance the performance of database systems. Optimizing clustering configurations manually is cumbersome because the solution space is large and the performance impact depends on the system's workload. Autonomous clustering approaches can support database administrators in this task. However, determining the optimal clustering by physically applying all possible clusterings is prohibitively expensive. Our learned cost model simulates the effects of a hypothetical clustering and estimates the resulting workload latency based on previously trained cost models. The model accurately estimates the latencies of TPC-H workloads (scale factor 1) for different clusterings with a relative error of at most 5%, thereby enabling the determination of optimal clustering configurations.
Natural Language Text to RDF Schema Conversion and OWL Mapping for an e-Recruitment Domain

The recruitment process has been one of the most hazardous processes to date within the organization. Sometimes, it even takes months to complete the hiring process. The manual recruitment process is also not cost effective in terms of time and money. Therefore, our motive is to develop an e-recruitment system using domain ontology which will not only reduce cost-per-hire and time-per-hire of recruitment but also reduces semantic heterogeneity. An ontology-based e-recruitment system to decide the most suitable candidate for the introduced job profile from the resume itself required linked data and extended vocabularies. Our approach is to develop domain ontologies for both candidates and recruiters, and then match the ontologies using semantic techniques. To develop ontologies of both candidates and recruiters, firstly, the Natural Language Text will be converted into RDF Schema and further mapped into Web Ontology Language (OWL). In this paper, we are presenting an Ontology Conversion Module that generates RDF triples from the Natural Language Text and maps it to OWL after applying some property restrictions as mentioned in W3C.
SUPPORTING HUMAN-CENTERED DESIGN IN PSYCHOLOGICALLY DISTANT PROBLEM DOMAINS: THE DESIGN FOR CYBERSECURITY CARDS

Abstract Increasingly digital products and services make cybersecurity a crucial issue for designers. However, human-centered designers struggle to consider it in their work, partially a consequence of the high psychological distance between designers and cybersecurity. In this work, we build on the Design for Cybersecurity (DfC) Cards, an intervention to help designers consider cybersecurity, and examine a project-based design course to understand how and why specific DfC cards were used. Three findings result. First, designers found the intervention useful across all design phases and activities. Second, the cards helped design teams refocus their attention on the problem domain and project outcome. Third, we identify a need for support in framing and converging during user research, opportunity identification, and prototyping. We argue that the psychological distance between designers and the problem space of cybersecurity partially explains these findings, and ultimately exacerbates existing challenges in the design process. These findings suggest that design interventions must consider the psychological distance between designer and problem space, and have application in design practice across many complex problem domains.
Data Mining Techniques for Producing Clustering in Big Data with MapReduce Function

Big data is a large collection of dataset from heterogeneous sources of data which may be terabytes or petabytes of data. The big data is useful for existing business growth and also supports to create the new business. Handling this much of data is very difficult in database management system. The problems of big data are storing, processing, analyzing, extracting, and privacy. This survey paper, mainly focused on challenges of big data, how to extract the required data from large volume of data, and also various clustering algorithm. For the extraction of data, mapreduce function is used which is mainly used in Google search engine.
Efficient Improved Estimation Method for Non-Gaussian Regression from Discrete Data

We study a robust adaptive nonparametric estimation problem for periodic functions observed in discrete fixed time moments with non-Gaussian Ornstein–Uhlenbeck noises. For this problem we develop a model selection method, based on the shrinkage (improved) weighted least squares estimates. We found constructive sufficient conditions for the observations frequency under which sharp oracle inequalities for the robust risks are obtained. Moreover, on the basis of the obtained oracle inequalities we establish for the proposed model selection procedures the robust efficiency property in adaptive setting. Then, we apply the constructed model selection procedures to estimation problems in Big Data models in continuous time. Finally, we provide Monte - Carlo simulations confirming the obtained theoretical results.
An improved system for large population text independent Speaker Recognition with short utterances

This paper presents a new text independent speaker recognition system based on new cepstral Coefficients and i-vector based on probabilistic linear discriminant analysis. This system is designed to be used as a biometric authentication system based on the speaker voice. The experiments were performed on speech data taken from a large number of users and consist of 630 speakers from TIMIT database for different training conditions. The efficiency of the proposed system is observed with the increase of Speaker identification Rate. Experimental results show that the system gives higher recognition performance for different numbers of speakers.
The Effects of Using Tablet PCs on Student Self-regulated Learning and Learning Achievement

This study aimed to investigate the effects of using tablet PCs on junior high school students’ self-regulated learning and learning achievement in Chinese language courses. An experiment was conducted using a sample of 100 students from two classes of a junior high school in Northwest China. One class, as the experimental group, employed an instructional approach with tablet PCs. Other class, as the control group, used a traditional lecture-based approach without the tablet PCs. Students in the two classes studied Chinese language courses for two months. The results showed that the students with tablet PCs have significantly higher learning achievement and self-regulated learning abilities than those of students without tablet PCs.
A hybrid image retrieval system for microscopy images

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Real-time vehicle occupation status detection system by integrating cloud computation and machine learning

In the past decades, with the rapid development of computer technology, machine learning and machine vision have been applied to many field for automatic detection purpose. However, most applications are based on CNN network or DNN network, in which the computation cost is very high. In this research, we focused on the application of real-time vehicle occupation status detection. An intelligent parking management system based on machine vision and machine learning was developed. With this system, detection of vehicle occupation status can be realized only using cameras, and the computation speed is very fast. The effectiveness of this system was tested by two actual parking lots, which have already applied this system.
VAMDLE: Visitor and Asset Management Using Deep Learning and ElasticSearch

Visitor management and asset management are crucial in restricted places. This paper focuses on how artificial intelligence and image recognition can drive innovation in both visitor and asset management spaces by employing the latest advances in technology. The proposed solution, VAMDLE, is an Android application which uses deep transfer learning and Elasticsearch to facilitate the registration of visitors as well as the management of borrowed assets. TensorFlow was used to train a pre-trained model for assets image recognition and the new model was integrated into the Android application with the aid of the TFLite library. A restful web API was developed with the aid of Spring Boot to manage all the data used by the client application. The unique identifiers of the assets and of the employees were read and recognized using text recognition and regular expressions and Elasticsearch was used to automatically fill in forms. The use of these various tools and technologies resulted in an app with a simple interface, a very good classification accuracy and good average response time. The proposed system was able to register a classification accuracy of up to 97%.
Globalization and Female Economic Participation in MINT and BRICS countries

This study examines the effect of globalization on female economic participation (FEP) in MINT (Mexico, Indonesia, Nigeria & Turkey) and BRICS (Brazil, Russia, India, China & South Africa) countries between 2004 and 2018. Four measures of globalization are employed and sourced from KOF globalization index, 2018, while the female labour force participation rate is a proxy for FEP. The empirical evidence is based on Pooled Mean Group (PMG) estimators. The findings of the PMG estimator from the Panel ARDL method reveal that political and overall globalization in MINT and BRICS countries have a positive impact on FEP, whereas social globalization exerts a negative impact on FEP in the long-run. It is observed that economic globalization has no long-run effect on FEP. Contrarily, all the measures of globalization posit no short-run effect on FEP in the short-run. This supports the argument that globalization has no immediate effect on FEP. Thus, it is recommended that both MINT and BRICS countries should find a way of improving the process of globalization generally to empower women to be involved in economic activities. This study complements the extant literature by focusing on how globalization dynamics influence FEP in the MINT and BRICS countries.