Reviewerly/exHarmony huggingface dataset:
An Overview of Invisible Solar Generation Estimating Approaches

In recent decades, residential and commercial solar photovoltaic (PV) systems have been increased. However, most of these PV systems are not monitored by power system operators. Thus, they are called invisible solar power or behind-the-meter (BTM) solar sites. The presence of these types of PV sites could pose many new challenges to the analysis of hosting capacity, net load forecasting for feeders, or volt-ampere-reactive (VAR) control. To handle these system operations, numerous approaches have been developed to estimate invisible or behind-the-meter solar generation. This paper presents a complete literature review about the estimation techniques for invisible solar generation, providing an important reference to power system operators. These up-to-date estimation techniques are classified into two categories, i.e., data-driven methods and model-based methods. This paper will summarize the required data for each method and study their strengths and drawbacks for PV estimation.
Design and development of digital e-library

Digital libraries are platforms that allow users to access structured information in a knowledge-based repository of data and access. In the end, the availability of good content and easy accessibility and use will drive the production and growth of digital libraries. Significant advancements in IT and the rapid development of online data have generated an intense interest in tools to help users find desired data. This paper describes how, particularly in college libraries, a digital library can be built and enforced in an academic library. The specifications for hardware, software, processing and recovery technologies are addressed. An elegant user interface, combined with better browsing, insertion and reporting capabilities, functionality that anyone can access from anywhere. The library’s automatic update facility helps to get a clear idea of which books are accessed by the members, allows users to create a ’hard copy’ report. The Digital library system stores information such as name, address, ID number, date of birth of library members. The information of books such as book name, book number, topic to which it refers, author, edition, edition, year of publication, total number of publications, total number of books in the library can also be stored and accessed digitally.
Data Loading and Migration Methods in the Cloud Environment

Currently, most of the databases are migrated to the cloud environment ensuring reliability, stability, and scalability. Companies do not need to have their own database managers and administrators. With the increase of the data amount, it is necessary to take loading and import methods into the account, to optimize the whole process and minimize time and resource demands. In comparison with the local server storage located physically in the company, there should be an emphasis on the communication channel reliability and process supervision. This paper summarizes the methods available to load data into the Oracle Cloud environment supplemented by the own technique definition. It is based on structure management and index extraction outside the main transaction by pointing to the Notofication_module. Thanks to that, loading and migration operations can be optimized reducing the costs.
Multilingual Sentiment Mining System to Prognosticate Governance

In the age of the internet, social media are connecting us all at the tip of our fingers. People are linkedthrough different social media. The social network, Twitter, allows people to tweet their thoughts on any particular event or a specific political body which provides us with a diverse range of political insights. This paper serves the purpose of text processing of a multilingual dataset including Urdu, English, and Roman Urdu. Explore machine learning solutions for sentiment analysis and train models, collect the data on government from Twitter, apply sentiment analysis, and provide a python library that classifies text sentiment. Training data contained tweets in three languages: English: 200k, Urdu: 200k and Roman Urdu: 11k. Five different classification models are applied to determine sentiments, and eventually, the use of ensemble technique to move forward with the acquired results is explored. The Logistic Regression model performed best with an accuracy of 75%, followed by the Linear Support Vector classifier and Stochastic Gradient Descent model, both having 74% accuracy. Lastly, Multinomial Naïve Bayes and Complement Naïve Bayes models both achieved 73% accuracy.
Opportunities and Challenges for Flagman Recognition in Autonomous Vehicles

Autonomous vehicles promise significant advances in transportation safety, efficiency and comfort. However, achieving the goal of full autonomy is impeded by the need to address several operational challenges encountered in practice. Gesture recognition of flagmen on roads is one such set of challenges. An autonomous vehicle needs to make safe decisions and facilitate forward progress in the presence of road construction workers and flagmen. However, human gestures under diverse environmental conditions are very varied and represent significant complexity. In this work, we present (i) a taxonomy of challenges for organizing traffic gestures, (ii) a sizeable flagman gesture dataset, and (iii) extensive experiments on practical algorithms for gesture recognition. We categorize traffic gestures according to their semantics, flagman appearances and the environmental context. We then collect a dataset covering a range of common flagman gestures with and without props such as signs and flags. Finally, we develop a recognition algorithm using different feature representations of the human pose and perform extensive ablation experiments on each component.
Energy Efficiency in Cloud Computing: Exploring the Intellectual Structure of the Research Field and Its Research Fronts with Direct Citation Analysis

The aim of the study is to explore the intellectual structure of the field and fronts in research on energy efficiency in the context of cloud computing and thus to contribute to science mapping of the research field. The research process was driven by the following study questions: (1) what are the most influential publications in the research field? and (2) what are the research fronts in the research field? The method of direct citation analysis was employed in the research process. Data for analysis were obtained from the Scopus database and analyzed with the use of VOSviewer science mapping software. In response to the first question, we identified the most influential publications in the research field and analyzed their types (i.e., whether they are original research papers or rather the “context” papers e.g., survey or review papers, framework papers, challenges papers, and study papers). Moreover, a comparison analysis between the types of papers among the most cited “classical” publications and “emerging stars” was conducted. In response to the second research question, we identified five research fronts concentrated around the issues of: virtual machine management (“VM”); task-focus, concerning data replication, task consolidation, and task scheduling (“task”); energy efficiency (“energy”); modelling and optimization (“model”); and energy efficiency in the networking context (“network”).
Meeting the challenges of research bibliography in MALL

It is critical for researchers in Mobile-Assisted Language Learning (MALL) to have a thorough grasp of the published literature. This is no less true in the field of Computer-Assisted Language Lear...
Model of Multiagent Cooperation for Behavioral Testing

Modern distributed information systems (DIS), based on the complex application of many technologies, increasingly acquire the properties of multi-agent systems (MAS). Autonomy, mobility, cooperativity, intelligence, basic for MAS, become necessary properties of DIS. At the same time, the rapidly growing complexity, distribution and criticality of the use of DIS cause no less growth in the requirements for the reliability of their functioning, provided at the systemic, behavioral level by a complex of many approaches, both for project verification and for implementation testing. Most functional verification and testing problems have NP-complexity, which requires the integration of different verification tools and the use of various types of decomposition to achieve the desired result. The proposed model of MAS cooperation for testing is based on the representation of the behavior of DIS and MAS in their composition by extended Petri nets, automata behavioral experiments of check and recognition, functional hierarchical decomposition. The features of the model are the spatio-temporal decomposition of the set of events and actions of the goal and behavior of the MAS in the resource space of the DIS deployment environment, the spatio-temporal definition of the goals and behavior of all agents on the set of constrictions of events and actions of the MAS, the corresponding definition for agents of their autonomy, mobility, cooperativity and intelligence, the definition of cooperativity as a structure of agent coordination, the definition of the MAS model, reduced for events and actions outside the cooperativeness and coordination and commutation in its composition, the construction of a behavioral check model for the reduced MAS cooperation model. The proposed model is designed to reduce the computational complexity of behavioral check and recognition of cooperative interaction of the MAS and to determine the general algebraic conditions for constructing the corresponding methods of such check and recognition.
Semantic correction system using neural networks

This research work proposes a semantic correction and analysis system using neural network. Unrestricted faulty text messages which are often used for text modification and analysis. E.g. email address or worldwide internet address. These text messages are scanned and so distributed to one of many knowledgeable agents in line with explicit task criteria. Potential eventualities inside this framework embrace the educational of the routing of publication titles or news titles. In this paper we have a tendency to describe in depth experiments for linguistics text routing supported classified library titles and newswire titles. This task is difficult since incoming messages might contain constructions that haven’t been anticipated. Therefore, the contributions of this analysis in learning and generalizing neural architectures focus upon unrestricted messages. Neural networks were developed and examined for this subject since they support hardiness and learning in uproarious unrestricted real-world texts. We have a tendency to describe and compare completely different sets of experiments. The remainder of experiments analyzes a perennial neural network for the task of library title classification. The comparison of the examined models demonstrates that the techniques from data retrieval integrated into perennial plausibleness networks performed well even underneath noise and for various corpora.
Geometry-Aware Hierarchical Bayesian Learning on Manifolds

Bayesian learning with Gaussian processes demonstrates encouraging regression and classification performances in solving computer vision tasks. However, Bayesian methods on 3D manifold-valued vision data, such as meshes and point clouds, are seldom studied. One of the primary challenges is how to effectively and efficiently aggregate geometric features from the irregular inputs. In this paper, we propose a hierarchical Bayesian learning model to address this challenge. We initially introduce a kernel with the properties of geometry-awareness and intra-kernel convolution. This enables geometrically reasonable inferences on manifolds without using any specific hand-crafted feature descriptors. Then, we use a Gaussian process regression to organize the inputs and finally implement a hierarchical Bayesian network for the feature aggregation. Furthermore, we incorporate the feature learning of neural networks with the feature aggregation of Bayesian models to investigate the feasibility of jointly learning on manifolds. Experimental results not only show that our method outperforms existing Bayesian methods on manifolds but also demonstrate the prospect of coupling neural networks with Bayesian networks.
Virtual Dream Reality Check: A Case of Interactive Digital Theatre from the Royal Shakespeare Company

The proliferation of digital theatre signals a new era of theatrical experiences. The Royal Shakespeare Company's (RSC) Dream is a recent example of how theatre companies are integrating cutting-edge technology to revolutionise their performances. Emerging from the Audience of the Future (AF) program, Dream combined gaming and theatre technology to create a virtual world for audiences to inhabit and explore digitally. The production incorporated motion-capture technology and Virtual Reality (VR) headsets, allowing seven actors to perform live from a purpose-built studio while audiences simultaneously accessed their performance from a compatible device via a bespoke website (https://dream.online). The resulting experience combined live and virtual performance elements to connect global audiences.This venture into technologically enhanced theatre, however, raises questions about the potential implications of audience engagement with digital productions. In this paper, we question how RSC's Dream combined both live and virtual theatre experiences to offer a more interactive viewing experience. We argue that while Dream signalled an exciting step in the development of digital theatre, the interactive features revealed some discrepancies between RSC's goals and the degree of involvement delivered. We thus contend that for theatre organisations to incorporate digital technologies, they must tend to the nuances of technological interventions and weave them seamlessly with theatrical elements to retain the fidelity of the theatre experience.
The challenges of machine translation of academic publications

Clear translation remains a major challenge to better communication and understanding of the international academic literature, despite advances in Machine Translation (MT). Automatic translation systems which captured the detail and the sense of any manuscript in any language for a reader from any other linguistic background would find global applications.In this article, we discuss the current opportunities and constraints to the wider use of machine translation and computer-assisted human translation (CAT). At the present stage of technology development, these instruments offer a number of advantages to specialists working with scientific texts. These include the facility to skim and scan large amounts of information in foreign languages, and to act as digital dictionaries, thesauri and encyclopedias. Word-to-word and phrase-to-phrase translation between many languages and scripts is now well advanced.The availability of modern machine translation has therefore changed the work of specialist scientific translators, placing greater emphasis on more advanced text and sense editing skills. However, machine translation is still challenged by the nuances of language and culture from one society to another, particularly in the freestyle literature of the arts and humanities. Scientific papers are generally much more structured, but the quality of machine translation still largely depends on the quality of the source text. This varies considerably between different scientific disciplines and from one author to another.The most advanced translation systems are making steady progress. It is timely to revisit traditional training programmes in the field of written translation to focus on the development of higher-level research competencies, such as terminology search, and so to make best use of evolving machine translation technologies.More widely, we consider that there is a challenge across the higher education systems in all countries to develop a simple, clear and consistent “international” writing style to assist fast, reliable and low-cost machine translation and hence to advance mutual understanding across the global scientific literature.
An Integrated Web-based Approach for Security Enhancement by Identification and Prevention of Scam Websites

Scam websites or illegitimate internet portals are widely used to mislead users into fraud or malicious attacks, which may involve compromise of vital information. Scammers misuse the secrecy and anonymity of the internet of facade their true identity and purposes behind numerous disguises. These can include false security alerts, information betrayal, and other misleading presentations to give the impression of legality and lawfulness. The proposed research is a web-based application - Scam Website Analyser- which enables checking whether a website is a scammed one.. The main aim of the research is to improve security and prevent scams of public websites. It ensures maintaining the details of scam websites in a database and also requests the websites of other databases using external APIs. The basic idea behind the research is the concept of user -orienteers where the user is able to get information about scam websites and prevent themselves from using those sites in future.
Intelligent Car Security System Based on Fingerprint Identification and Internet of Things

With the improvement of people's economic level, many families have already owned compact cars. As a convenient tool for daily travel, cars are important private property, thus the safety of car is a problem that can't be ignored. And the design in this article adopts the working mode that combines single chip microcomputer with App. Employing 52 single chip microcomputer as detection center and control core of the antitheft system, new automobile has two kinds of unlocking method: password and fingerprint. Between them, fingerprint unlocking is safe and quick, which realizes Internet of things through communication module, enabling users to receive accurate and real-time security information of the car on APP.
Log pseudonymization: Privacy maintenance in practice

Mobile phones, social media, and Internet of Things (IoT) devices are examples of day-to-day technologies that collect large amounts of data, including people's location, habits, and preferences. The first regulations on digital data collection and processing privacy were created decades ago, but such an increased amount of collected digital data and the risks associated with the illegal processing and exposure of personal information led to several new regulations, including the European General Data Protection Regulation. Recent regulations require that personal data controllers implement several technical and organizational measures to protect data privacy. Much attention was given to data gathering, storage, and processing at system and database levels. But at the system administration level, log files usually store data that can lead to the identification of an individual, which means they must be processed to guarantee personal data privacy. In this work, we deal with pseudonymization. We discuss log sources, formats and data, log management architectures, and the log processing pipeline, considering pseudonymization and security requirements. We describe an architecture for log pseudonymization during the ingestion phase and present its implementation using Elasticsearch, Logstash, and Kibana, providing conclusions and helpful insights on log pseudonymization for privacy protection.
AssistML: A Concept to Recommend ML Solutions for Predictive Use Cases

The adoption of machine learning (ML) in organizations is characterized by the use of multiple ML software components. Citizen data scientists face practical requirements when building ML systems, which go beyond the known challenges of ML, e.g., data engineering or parameter optimization. They are expected to quickly identify ML system options that strike a suitable trade-off across multiple performance criteria. These options also need to be understandable for non-technical users. Addressing these practical requirements represents a problem for citizen data scientists with limited ML experience. This calls for a method to help them identify suitable ML software combinations. Related work, e.g., AutoML systems, are not responsive enough or cannot balance different performance criteria. In this paper, we introduce AssistML, a novel concept to recommend ML solutions, i.e., software systems with ML models, for predictive use cases. AssistML uses metadata of existing ML solutions to quickly identify and explain options for a new use case. We implement the approach and evaluate it with two exemplary use cases. Results show that AssistML proposes ML solutions that are in line with users' performance preferences in seconds.
ChainAccess: Blockchain based Web-Access through Biometrics

The information process of each and every individual is dependent on the Digital Identity Management (DIM) system. To decrease the requirement of remembering multiple passwords and authenticate digital Identity with improved security, we propose an optimized solution by using distributed ledger technology and smart contracts for the DIM system, i.e., Blockchain. The smart contract ensures that the user's registration process occurs only once and the required details are securely shared to the websites with the user's consent. Compared to traditional and existing digital identity management systems, the proposed Blockchain-based Web-Access through Biometrics verification ensures the user's identity in one-time registration. It provides secure and easy access to respective websites.
Designing an AR urban game (HAMAJA) for increasing engagement in city

Have you ever had this feeling that you want to design and build something to put in your city? HAMAJA is the application to design and build whatever you want and place it wherever in the city you desire. What you build is set within a digital layer of that place, granting the opportunity to be watched by anyone using HAMAJA while passing through that spot. This relation goes beyond just a visit and gives you the permission to interact with others and their work of art. In other words, HAMAJA is considered a digital layer built by the citizens for their city. To go through the project briefly, our main concern is to create an environment in which people could have a chance to engage with their city and their fellow citizens by using both game and art as we believe are two of the most powerful tools to use.
Watershed Segmentation and Extended-maxima Transformation based on Minima Imposition for Overlapped Object

Overlap is one of the problems in the segmentation process. This research proposes a mini-imposition method to optimize the watershed segmentation and extended-maxima transformation methods. Morphological operations using the bwareopen function are also added at the end of the watershed segmentation process. This method is implemented to create a counter object on a migratory bird image that has many overlapping objects. The test results of this method are proven to improve the performance of the previous method and get an average value that is very satisfying with an average value of accuracy of 0.94, recall of 0.95, and precision of 0.99.