Reviewerly/exHarmony huggingface dataset:
Localization study for the implementation of a solar power plant in Brazil using Dish-Stirling systems

This work presents a localization study for the implementation of a solar power plant in Brazil using systems with Stirling engines and parabolic dish concentrators, also known as Dish-Stirling systems. The demand for electricity in the world is constantly growing, and this will likely double over the next 40 years, creating an electrical energy deficit scenario that can be filled with renewable and clean sources. The electric power generation system presented in this paper is formed by the parabolic dish concentrator and a receiver, where the Stirling engine and the power generator are located. The Dish-Stirling system presents the greatest efficiency in the conversion of heat to electrical energy when compared to other thermal concentration technologies. The localization study employed several criteria to select the most suitable place for the implementation of the power plant. The weights of these criteria were defined with the application of the Analytic Hierarchy Process. After determining the weights of each criterion, the localization study was carried out using a Geographic Information Systems (GIS) tool in three cities of different regions of Brazil. Fuzzy logic was used to implement the thresholds for each subcriterion in the GIS system. Research in Brazil about thermal conversion with Dish-Stirling systems is still incipient. Therefore, this work intends to demonstrate the potential of solar energy in Brazil and pinpoint available locations for future projects. The municipality of Bom Jesus da Lapa in the State of Bahia stood out in comparison with the other cities evaluated in this paper.
PoseAsQuery

PoseAsQuery is an interactive browsing system used to repeatedly replay any specific segment of a video. To acquire and improve the body control skills that are essential for physical performance, it is necessary to continuously observe an individual's movement. However, previous research has several issues on the ability to repeatedly observe a user's movement without interruption during personal training. For these scenarios, the proposed system simultaneously provides a pose-based browsing method and its interface. The user can pin any frame from which a replay starts by using the person's body movement without another operation, such as pressing a button, that is not essential for improving the skill. The experimental results suggest that users can use posture to decide where to start replaying when they know that the corresponding pose enables them to replay the scene. We concluded that the proposed method enables users to repeatedly observe scenes more efficiently and in a less stressful way than pressing a button to replay from a fixed scene.
Survey on Applying GAN for Anomaly Detection

In the current days, most prominent research in machine learning was focused on the generative models. Generative Adversarial Networks (GANs) is one of the generative models used to model the complex high dimensional distribution of real-world data. GANs have two structures, generator to create new data instances resembling our training data, and discriminator to distinguish real data from the data created by the generator. As predicting abnormal data is one of the most important problems across a range of domains. Our Literature survey was conducted on applications of GAN in the field of anomaly detection and a simple experiment is conducted illustrating the usage of GAN in anomaly detection using MNIST dataset.
Spectra-GANs: A New Automated Denoising Method for Low-S/N Stellar Spectra

Numerous spectra can be obtained from sky surveys such as the Sloan Digital Sky Survey and the Large Sky Area Multi-Object Fibre Spectroscopic Telescope. However, a considerable fraction of such spectra, which are also valuable for astronomical research, are of low quality, possessing characteristics such as low signal-to-noise ratio (low-S/N). Principal component analysis is widely used to process these low-S/N spectra, but it is not efficient enough to describe the non-linear properties within the spectra. Wavelets are often used to denoise the low-S/N spectra. However, as is well known, the most optimal wavelet basis for each type of spectra needs to be determined; therefore, wavelet analysis is very difficult to use in practice. Restricted Boltzmann machine is a non-linear algorithm that performs poorly when applied to low-S/N spectra. Denoising Convolutional Neural Networks (DnCNN) is a promising denoiser, however, its performance is unsatisfactory due to the lack of suitable noise model. To better exploit the spectra with low-S/N, we propose a new method that can be used to obtain better denoised spectra when compared to those obtained using other methods. A new method called the Spectra Generative Adversarial Nets (Spectra-GANs) is introduced. Spectra-GANs is simply a feedforward neural network that learns the difference between the input vector and the target by minimizing the loss function. It can be used in spectral denoising. The performance of Spectra-GANs is better than those of other methods with regard to denoising the spectra, especially with regard to extremely low-S/N spectral processing. Thus, Spectra-GANs proposed herein is a suitable alternative to previously used methods in spectral denoising.
Keyword Search With Two-Side Verification in encrypted data using blockchain

The untrustable nature of the cloud server and the information protection of clients is important to scramble the information before redistributing the cloud. Main aim is to acknowledge the protected keyword search over encrypted data against noxious clients and malcioust cloud specialist organizations, we discover a traded off technique into the square chain into SSE the distributed storage utilized in accessible symmetric encryption plans (SSE) is given in a private way, which can't be viewed as a genuine cloud. Also, the cloud server verifies the data working on that open chain. We present a framework that influences blockchain innovation to furnish protected dispersed information stockpiling with keyword search administration. The System enables the customer to transfer there in scrambled structure disseminates the information substance to cloud hubs and guarantee information accessibility utilizing cryptographic methods we present a framework that influences blockchain innovation to furnish a safe appropriated information stockpiling with catchphrase search administration. Trustworthy keyword search verified the server-side verifiability which hide the authorized cloud servers from being encircled by vindictive information proprietors in the information storage stage. Moreover, the blockchain findings and hash applications are applied to the values to cost of keyword searching without presenting any intruder checking the client-side harmful. Our proposed security keyword checking and execution will prove this keyword search trustable and more secure and efficient for cloud computing applications.
Unsupervised acquisition of idiomatic units of symbolic natural language: An n-gram frequency-based approach for the chunking of news articles and tweets

Symbolic sequential data are produced in huge quantities in numerous contexts, such as text and speech data, biometrics, genomics, financial market indexes, music sheets, and online social media posts. In this paper, an unsupervised approach for the chunking of idiomatic units of sequential text data is presented. Text chunking refers to the task of splitting a string of textual information into non-overlapping groups of related units. This is a fundamental problem in numerous fields where understanding the relation between raw units of symbolic sequential data is relevant. Existing methods are based primarily on supervised and semi-supervised learning approaches; however, in this study, a novel unsupervised approach is proposed based on the existing concept of n-grams, which requires no labeled text as an input. The proposed methodology is applied to two natural language corpora: a Wall Street Journal corpus and a Twitter corpus. In both cases, the corpus length was increased gradually to measure the accuracy with a different number of unitary elements as inputs. Both corpora reveal improvements in accuracy proportional with increases in the number of tokens. For the Twitter corpus, the increase in accuracy follows a linear trend. The results show that the proposed methodology can achieve a higher accuracy with incremental usage. A future study will aim at designing an iterative system for the proposed methodology.
Evaluating Change in the Conversation of a Person With Mild Aphasia After Attentive Reading With Constrained Summarization-Written Treatment

Purpose The purpose of this project was to evaluate the effect of a discourse-level treatment, Attentive Reading with Constrained Summarization–Written (ARCS-W), on conversational discourse. ARCS-W aims to improve spoken and written output by addressing the cognitive–linguistic requirements of discourse production through constrained summarization of novel material. Method This is an experimentally controlled case study with a single participant, Bill. Three conversation samples were collected at pretreatment, and a single conversation was collected 1 month after treatment. The participant completed 24 ARCS-W treatment sessions, and each session included reading and then summarizing a novel current event article following specific constraints (use lexically precise words, stay on topic, use complete sentences) in speaking and writing. Conversation outcomes evaluated the success of each utterance (1–4 scale), grammaticality, and the proportion of utterances with relevant content (relevant utterances). Additionally, behavioral manifestations of word-finding difficulty were evaluated in conversation. Results Bill improved communicative success at the utterance level based on the minimal detectable change. He also demonstrated reductions in behavioral manifestations of lexical retrieval difficulty based on decreases in the percentage of false starts (e.g., t*, t*), mazes (e.g., uh, s*, um), and abandoned utterances. Bill did not increase the proportion of relevant utterances or grammatical utterances in conversation. Conclusions This case study provides preliminary evidence of the potential impact of ARCS-W treatment in conversation. Additionally, the measures implemented to evaluate conversation represent a promising adaptation of a novel methodology to capture change in conversation. Supplemental Material https://doi.org/10.23641/asha.12375053
A Relational Conceptual Model in GIS for the Management of Photovoltaic Systems

The aim of this manuscript is to define an operational pipeline of work, from data acquisition to the report creation, for the smart management of PV plants. To achieve such an ambitious result, we exploit the implementation of a conceptual model, deployed through a relational database to retrieve any kind of information related to the PV plant. The motivation that drove this research is due to the increasing construction of PV plants. In fact, following European and international investments that heavily stimulated the use of clean energy, the need to maintain PV plants in their maximum efficiency for their whole lifecycle emerged, to bring about benefits from both the ecological and the economic points of view. While the research community focuses on finding new and automatic ways to detect faults automatically, few efforts have been made considering the so-called Operation and Maintenance (O&amp;M). A relational conceptual model may facilitate the management of heterogeneous sources of information, which are common in complex PV plants. The purpose of the present study is to provide companies and insiders with a GIS-based tool to maintain the energy efficiency of a PV plant. Indeed, it is a common practice used by companies dealing with O&amp;M of PV plants to create technical reports about the health status of the plants. This operation, made manually, is very time consuming and error prone. To overcome this latter drawback, this work attempts to encourage the use of GIS in the PV plants O&amp;M, which proves to be efficient to deal with fault management and to assure a good level of energy production. The developed conceptual model, tested on two real case studies, proved to be complete, cost-effective and efficient to be replicated in other existing plants.
Road Traffic Outlier Detection Technique based on Linear Regression

Road traffic anomaly detection is a very important aspect of intelligent traffic management system. Traffic anomaly may arise due to several reasons like unusual traffic incidents and malfunctioning of sensors deployed over the road network to capture traffic information. Unusual traffic incident includes road accident, road blockage due to construction, any major events and so on. For smooth mobility of the citizens, it is very important to identify these kinds of scenarios with minimum delay so that traffic management authority can take proper measures. This paper proposes a technique based on statistical model which identifies the temporal outliers in the road traffic. Z-score and linear regression model are two statistical models have been used in combination for detection of temporal outliers. The proposed technique can be used to detect unusual traffic incident or sensors failure.
Sentiment Analysis from Students’ Feedback : A Romanian High School Case Study

The education system is a source that generates significant amounts of data, daily, in various formats and, often, hiding valuable information. Finding a good way to unravel those hidden gems, represents one of the most challenging problems of natural language processing, namely sentiment analysis. This involves, applying NLP and text analysis techniques to identify and classify subjective opinions in different materials such as documents or sentences. In our work, we used as raw data, the opinions of students from eleven high schools in Suceava, related to various aspects of the educational process. They were collected through a Google Docs form, and analyzed through the Orange environment (an open source tool for machine learning and data visualization). In this paper, we make a comparative study of the obtained results using the Ekman and Plutchik models. Each model extracts from the analyzed texts, a different emotion, based on which the students’ sentiments towards the educational process will be analyzed.
Conversational implicatures in English dialogue: Annotated dataset

Human dialogue often contains utterances having meanings entirely different from the sentences used and are clearly understood by the interlocutors. But in human-computer interactions, the machine fails to understand the implicated meaning unless it is trained with a dataset containing the implicated meaning of an utterance along with the utterance and the context in which it is uttered. In linguistic terms, conversational implicatures are the meanings of the speaker’s utterance that are not part of what is explicitly said. In this paper, we introduce a dataset of dialogue snippets with three constituents, which are the context, the utterance, and the implicated meanings. These implicated meanings are the conversational implicatures. The utterances are collected by transcribing from listening comprehension sections of English tests like TOEFL (Test of English as a Foreign Language) as well as scraping dialogues from movie scripts available on IMSDb (Internet Movie Script Database). The utterances are manually annotated with implicatures.
Augmented Reality (AR) based application to introduce animals for children

Abstract The development of technology today is very helpful in the multimedia-based learning process. Children will be more interested in learning if the application can be installed on a Smartphone, because children enjoy playing Smartphones. In teaching children to recognize animals, teachers at school use pictures. With the presence of multimedia based on Augmented Reality (AR), it is helpful for displaying images of animals in 3-dimensional form so it is more interesting. Applications created can help children to classify, explain about animals through information about sounds, habitat, breeding about animals in the form of 3D objects. In making Augmented Reality Animal Recognition, the researcher adopted the SDLC (System Development Life Cycle) method. At the trial stage, the Alpha version of the application was carried out to determine the quality, shortcomings and smoothness of the application that had been made. Application test results could run well. Therefore, through this application, children are more interested in getting to know animals so that it provides a learning experience and is easily accessible on smart phones.
Inattentive, imprudent and inapt: discovering inadequacies of ICT during life-changing events through the lens of non-users

Life-changing events (or LCEs) can alter a person's status quo and threaten well-being. Previous research investigated distinct LCEs, where participants already used technology routinely. This paper reports the results of two field studies through which we compared supports people refer to when experiencing different LCEs. Together with users of technology, our sampling included participants who specifically did not refer to online services and tools to seek help during their LCE. We found that popular services people refer to are inattentive to the needs of people experiencing an LCE as they do not allow forms of progressive engagement and disclosure within the service. We also found that popular services are imprudent as their design might expose users experiencing an LCE to more sources of stress. Finally, we found that these services are inapt to support these users as they do not provide direct forms of interactions with experts.
Machine learning algorithms to empower Indian women entrepreneur in E-commerce clothing

Social media has become the hub for sharing and communicating the information among people. Due to increase in popularity of social media, it led the people to share the information through the internet. Thus, it helps to understand and predict user behavior in social media platforms, where it helps the entrepreneurs to understand improve the quality of the products or services they offered. It also helps them to understand the requirements of customers in better way. In this research paper, SVM and REPTree classifiers are used to classify the customer reviews on women clothing e-commerce, whether to recommend the product or not. The classifier performances are evaluated using accuracy, precision and recall parameters. REPTree outperforms SVM and achieves highest classification accuracy of 91.43% with precision and recall values as 93.75% and 96.04% respectively.
Cooking Activity Recognition in Egocentric Videos with a Hand Mask Image Branch in the Multi-stream CNN

Fine-grained activity recognition, especially cooking activity one, with egocentric videos is a hot topic and a challenging task in computer vision. To tackle this problem, many researchers have tried to leverage the information of cooking tools such as knife, peeler, etc., or that of equipment in the background. Although these are useful to improve the recognition performance on general cooking activity categories, the information does not provide sufficient evidences to recognize fine-grained cooking activities such as slicing, mincing, etc., because these belong same the general category and we often utilize the same type of tools. In addition, since the types of tools and equipment differs for each kitchen, a recognition model can over-fit to some specific environments in training data due to the over-confidence on such information.Therefore, a method having a high discriminating power of object classification and robustness for the environment difference is required. For the first step to realize such a method, in this research, we focus on the characteristics of egocentric video, i.e., capturing hands of camera wearer without occlusions. Hand shape is useful to recognize the objects manipulated by camera wearer and sequential hand positions are also effective to analyze hand movement. By using these advantages, in this paper, we proposed a new multi-stream CNN, which has a mask image branch to leverage the hand shape and position information, in addition to the RGB and optical flow branches. From the empirical experiments for fine-grained cooking activity recognition in three types of kitchens, our proposed method outperformed the conventional methods and we confirmed that our proposed method has higher robustness for environmental difference compared with conventional methods.
Scene Understanding for Safety Analysis in Human-Robot Collaborative Operations

Human-robot collaboration (HRC) depends on the detection of objects in the environment to perform critical tasks in a safe manner. This is usually done through object detection on robot's raw sensors data. However, this approach can not capture the semantic and contextual information from the environment. From that, scene understanding overcomes this limitation by combining different perception tasks to gather semantic knowledge from a given scene. Moreover, robots may take full benefit of semantic knowledge during safety analysis, as it will provide a richer information of the environment, commonly through scene graphs. We present our work on the comparison of two AI-based scene understanding methods for risk management in HRC. The first implementation is a contextual semantics based scene graph generator method that is integrated with the state-of-the-art Mask R-CNN object detection. Whereas the second implementation adopts an end-to-end multi-level scene description neural network (MSDN) to predict the scene graphs and region captions. The object detection methods employed in both solutions were evaluated by measuring the mean average precision. The overall scene understanding performance was based on the average inference time and top (50 and 100) recall on predicted scene graph relationships. From the experiments, MSDN consistently outperformed contextual semantics based method in top (50 and 100) recall by 20-25% for scene graph generation task. Although, contextual semantics based implementation has lower scene graph generation inference time, which is 2.4 times faster than the end-to-end neural network based implementation. All the experiments were performed in a simulated warehouse scenario where autonomous mobile robots and humans have close interaction.
Flexible development of location-based mobile augmented reality applications with AREA

Abstract Mobile applications have garnered a lot of attention in the last years. The computational capabilities of mobile devices are the mainstay to develop completely new application types. The provision of augmented reality experiences on mobile devices paves one alley in this field. For example, in the automotive domain, augmented reality applications are used to experience, inter alia, the interior of a car by moving a mobile device around. The device’s camera then detects interior parts and shows additional information to the customer within the camera view. Another application type that is increasingly utilized is related to the combination of serious games with mobile augmented reality functions. Although the latter combination is promising for many scenarios, technically, it is a complex endeavor. In the AREA (Augmented Reality Engine Application) project, a kernel was implemented that enables location-based mobile augmented reality applications. Importantly, this kernel provides a flexible architecture that fosters the development of individual location-based mobile augmented reality applications. The work at hand shows the flexibility of AREA based on a developed serious game. Furthermore, the algorithm framework and major features of it are presented. As the conclusion of this paper, it is shown that mobile augmented reality applications require high development efforts. Therefore, flexible frameworks like AREA are crucial to develop respective applications in a reasonable time.