Reviewerly/exHarmony huggingface dataset:
MÜZELERDE ARTIRILMIŞ GERÇEKLİK UYGULAMALARI: SAKIP SABANCI MÜZESİ ÖRNEĞİ

Museums, as indispensable tourist attractions within the scope of cultural tourism, should therefore interact and communicate with visitors using up-to-date technology whilst further developing technology in order to sustain their existence.Augmented Reality (AR) is a contemporary technology that has the potential to be used in museology.This research aims to determine the evaluations of visitors to Sakıp Sabancı Museum of the use of current AR applications.For this research, 19 semistructured interviews were conducted using a convenience sampling method.The content of the interviews were analyzed using a descriptive analysis method.The results of the research reveal that most of the interviewees after the Atlı Köşk visits, in which Sakıp Sabancı Museum AR applications were used, they considered AR applications to be useful and believed they should be adopted by other museums.
Gradations of Error Severity in Automatic Image Descriptions

Earlier research has shown that evaluation metrics based on textual similarity (e.g., BLEU, CIDEr, Meteor) do not correlate well with human evaluation scores for automatically generated text. We carried out an experiment with Chinese speakers, where we systematically manipulated image descriptions to contain different kinds of errors. Because our manipulated descriptions form minimal pairs with the reference descriptions, we are able to assess the impact of different kinds of errors on the perceived quality of the descriptions. Our results show that different kinds of errors elicit significantly different evaluation scores, even though all erroneous descriptions differ in only one character from the reference descriptions. Evaluation metrics based solely on textual similarity are unable to capture these differences, which (at least partially) explains their poor correlation with human judgments. Our work provides the foundations for future work, where we aim to understand why different errors are seen as more or less severe.
Intensification of the educational process of higher educational institutions by tools of interactive technologies

Intensification of the educational process of a higher educational institution by tools of interactive technologies (IT) is studied in terms of efficiency of distance learning platforms. A good deal more IT tools are integrated into the educational process than platforms, the main function of which is the exchange of materials, task supervision, registration of activity and attendance. Within the scope of on the survey and data analysis of the Moodle platform for the period from September to January 2019 (230 students and 8 educators of Ivan Franko National University of Lviv from the following faculties have been involved, namely: economic, financial and business management, international relations of the 2nd year of study). The scale of intensification of the educational process due to the integration of different types of IT has been estimated. The analysis of users’ activities of the distance learning platform has been carried out. An improvement of the communication level, self-monitoring, assessment, reducing time for preparation of materials, as well as increasing the efficiency of tasks’ performance has been revealed. Increasing the level of attendance and increasing the level of control and responsibility are the most common manifestations of the intensification of the educational process. The reduction in the time for obtaining and searching for materials and saving in the time for preparing materials by educators has been recorded. As a result, there was a significant reduction of time for the course’s organization. The educators have given higher assessment of the intensification of the educational process in terms of using IT tools compared to students’ assessment. However, students’ assessments of the processing of materials and tasks’ performance are higher compared to the assessments of educators; it characterizes a certain level of subjectivity of students in assessing their own educational activities. The results obtained can be used to theorize the experience of using IT in the organization of the educational process of higher educational institutions, as well as for further experimental research in this area in order to reveal effective models of “teacher / student” interaction and search for effective ways to intensify the educational process of higher educational institutions in the paradigm of introducing distance education in the realities of the Covid-19 pandemic.
Bitcoin and Its Energy Usage: Existing Approaches, Important Opinions, Current Trends, and Future Challenges

Recent years have shown a great interest of public in buying and selling of crypto/digital currency.With hundreds of digital currencies in financial market, bitcoin remains the most widely used, adapted, and accepted currency around the world.However, the critics of bitcoin still consider it a threat to modern day power usage.This paper discusses the important pitfalls, pros, and cons related to bitcoin's energy consumption.The paper begins by highlighting the flexibilities cryptocurrency can bring to online money transfers compared to traditional 'fiat' architecture.Then, the focus of the paper entirely remains on listing various facts related to bitcoin's energy utilization including a brief description of several emerging approaches for energy optimization.This paper is concluded by revealing key current challenges associated to bitcoin's energy usage..
Artificial Intelligence, Robotics and Autonomous Systems in SCM

This chapter investigates the industrial applications of intelligent robots and autonomous systems in Industry 4.0 perspective. Artificial Intelligence (AI) and autonomous technology have yielded the most remarkable applications that impact our lives since the beginning of the 2000s. Some of the most prominent applications include, but are not limited to, self-driving cars, drones, weapon systems, unmanned spacecraft exploring space, underwater robots exploring the deep ocean, software agents used in financial decision making, and deep learning methods used in medical diagnosis. Robots are also powerful devices that take part in AI research efforts. AI and artificial neural networks (ANN) are interconnected domains in computer science. AI can be successfully executed through ANN. Machine learning (ML) deals with the building process of computers that can improve themselves through experience. The field of ML is the foundation of AI and data science, and it is situated at the intersection of computer science and statistics.
The Effect of Knowledge Management Integration on E Learning System toward Lecturers Performance at University Institutions

This study was designed to 1) measure the effect of knowledge integration and e-learning on lecturers’ performance and 2) examine the effect of work procedures and information technology on the performance of lecturers at State University of Malang, Indonesia. Anchored by a survey study and document analysis, the present study revealed that knowledge management indirectly affects lecturers’ performance. Our findings also uncovered that there is a significant influence between personal knowledge on work procedures, and the most dominant factor affecting their performance is information technology. Based on the findings, this study can be a catalyst for improving the quality of higher education through the application of knowledge management on e-learning. Furthermore, the results are worthwhile to be used as lecturers’ performance enhancement in university levels.
Weighting Passages Enhances Accuracy

We observe that in curated documents the distribution of the occurrences of salient terms, e.g., terms with a high Inverse Document Frequency, is not uniform, and such terms are primarily concentrated towards the beginning and the end of the document. Exploiting this observation, we propose a novel version of the classical BM25 weighting model, called BM25 Passage (BM25P), which scores query results by computing a linear combination of term statistics in the different portions of the document. We study a multiplicity of partitioning schemes of document content into passages and compute the collection-dependent weights associated with them on the basis of the distribution of occurrences of salient terms in documents. Moreover, we tune BM25P hyperparameters and investigate their impact on ad hoc document retrieval through fully reproducible experiments conducted using four publicly available datasets. Our findings demonstrate that our BM25P weighting model markedly and consistently outperforms BM25 in terms of effectiveness by up to 17.44% in NDCG@5 and 85% in NDCG@1, and up to 21% in MRR.
Studying the Impact of Filling Information Gaps on the Output Quality of Neural Data-to-Text

It is unfair to expect neural data-to-text to produce high quality output when there are gaps between system input data and information contained in the training text. Thomson et al. (2020) identify and narrow information gaps in Rotowire, a popular data-to-text dataset. In this paper, we describe a study which finds that a state-of-the-art neural data-to-text system produces higher quality output, according to the information extraction (IE) based metrics, when additional input data is carefully selected from this newly available source. It remains to be shown, however, whether IE metrics used in this study correlate well with humans in judging text quality.
Transfer Learning and Ensemble Learning

In this chapter, we start from transfer learning and introduce the relationship between different learners; we use ensemble learning to combine them together and hope to get a strong learner from a weak learner by changing the training dataset or adjusting parameters of networks. Our ultimate goal is to implement a robust and stable classifier.
Designing a Framework for Training Teachers on Mobile Learning in Sub-Sahara Africa

Mobile technology has the potential to enhance the capabilities of students and educators in effective teaching and learning process.Understanding the factors that hinder integration of mobile technology in instructional delivery is key to the effective uptake of mobile technology for instructional purposes.The benefits of using mobile technology in the classroom can only be fully harnessed when teachers and other educational stakeholders are guided by framework that captures factors that enhance and hinder it use.It is against this background that this study seeks to design a framework for training teachers in the uptake of mobile learning in classrooms in Sub-Sahara Africa.The interactions among the factors considered suggest that Personal and Societal beliefs, Ownership of devices, Cost of devices and Type of devices contribute significantly to the teachers' technology self-efficacy, mobile phone self-efficacy and readiness to use mobile learning for instruction.Training on pedagogy could also enhance uptake, likewise, technical, and instructional support need to be considered in content development, delivery and evaluation.Feedback constitutes an important variable throughout the process leading up to uptake.Therefore, these factors need to be properly considered in the uptake of mobile learning in classrooms in sub-Sahara Africa.This would engender seamless integration of mobile learning in instructional delivery at different levels of education.
Understanding quotation extraction and attribution: towards automatic extraction of public figure’s statements for journalism in Indonesia

Purpose Extracting information from unstructured data becomes a challenging task for computational linguistics. Public figure’s statement attributed by journalists in a story is one type of information that can be processed into structured data. Therefore, having the knowledge base about this data will be very beneficial for further use, such as for opinion mining, claim detection and fact-checking. This study aims to understand statement extraction tasks and the models that have already been applied to formulate a framework for further study. Design/methodology/approach This paper presents a literature review from selected previous research that specifically addresses the topics of quotation extraction and quotation attribution. Research works that discuss corpus development related to quotation extraction and quotation attribution are also considered. The findings of the review will be used as a basis for proposing a framework to direct further research. Findings There are three findings in this study. Firstly, the extraction process still consists of two main tasks, namely, the extraction of quotations and the attribution of quotations. Secondly, most extraction algorithms rely on a rule-based algorithm or traditional machine learning. And last, the availability of corpus, which is limited in quantity and depth. Based on these findings, a statement extraction framework for Indonesian language corpus and model development is proposed. Originality/value The paper serves as a guideline to formulate a framework for statement extraction based on the findings from the literature study. The proposed framework includes a corpus development in the Indonesian language and a model for public figure statement extraction. Furthermore, this study could be used as a reference to produce a similar framework for other languages.
Wavefront measurement of membrane diffractive lens using line structured light deflectometry

In this paper, a wavefront testing method of membrane diffractive lens based on line structured light deflectormetry is proposed. The test membrane diffractive lens was placed between a screen and a camera, and a series of artificially coded line structured light that scanned on the screen were shot by the camera through the diffraction of the test lens. After the difference calculation of coordinates where light on the test lens and the screen, wavefront of the test diffraction order could be obtained by the integral of slopes. To improve the measuring accuracy, systematic errors were removed by N-step averaging method. In the experiment, a membrane diffractive lens with a diameter of 80mm, F/4.5 was measured using this method, which proved the feasibility after self-consistent results were obtained.
Impact of initial states on the quantum speed limits metric in linear molecules

Quantum speed limit (QSL) in a quantum system originates from the essential principle of the quantum mechanics. It gives a maximum speed of evolution or a minimum evolution time of the quantum system, which has potential applications in the fields of quantum information, quantum communication and quantum control and so on. In the last decades, the QSL bounds have been explored from the isolated quantum systems to the open quantum systems, several different geometric measures have been adopted to investigate the distinguishability between the initial and the evolved state. The QSL bounds in many systems have been discussed, indicating that the tightness of the QSL metric is related to the researched dynamical system. However, the QSL problem for the molecular system has rarely been reported. The study of the quantum speed limits in the different molecular systems is helpful for realizing the quantum information technology based on the molecules. In this paper, the generalized quantum speed limit metric for linear molecular dynamics is studied by the algebraic approach. The quantum Fisher information metric and the Wigner-Yanase information metric are both used to study the QSLs in the dynamical evolution of the two linear molecules. Here the dynamical evolutions begin with the two kinds of vibrational states, Fock initial state and coherent initial state. The results show that the quantum Fisher information metric is more appropriate than the Wigner-Yanase information metric for HCN and DCN molecules. The relative differences between the generalized geometric QSL and the two geodesic QSL metrics become bigger gradually with the increase of the initial vibrational quantum number. However, the relative difference for the DCN molecule is smaller than for the HCN molecule. The relative difference between the strong coherent states is smaller, which indicates that the generalized geometric quantum speed limit is suitable to describing high-coherent case. In conclusion, the different QSL metrics reveal the discrepancy in the evolution of the molecular system, and the relative difference is related to the initial state of the molecules and molecular parameters. More molecular systems need to be investigated in order to obtain the criteria between the QSL metrics and the molecular parameters.
Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training

In this paper we consider the problem of optimizing neural Referring Expression Generation (REG) models with sequence level objectives. Recently reinforcement learning (RL) techniques have been adopted to train deep end-to-end systems to directly optimize sequence-level objectives. However, there are two issues associated with RL training: (1) effectively applying RL is challenging, and (2) the generated sentences lack in diversity and naturalness due to deficiencies in the generated word distribution, smaller vocabulary size, and repetitiveness of frequent words and phrases. To alleviate these issues, we propose a novel strategy for training REG models, using minimum risk training (MRT) with maximum likelihood estimation (MLE) and we show that our approach outperforms RL w.r.t naturalness and diversity of the output. Specifically, our approach achieves an increase in CIDEr scores between 23%-57% in two datasets. We further demonstrate the robustness of the proposed method through a detailed comparison with different REG models.
Cybersecurity Resiliency of Marine Renewable Energy Systems‐Part 1: Identifying Cybersecurity Vulnerabilities and Determining Risk

Abstract Technology innovation, market demand, and the potential impacts of a changing climate are driving the marine renewable energy (MRE) industry to develop market-ready systems to provide low-carbon electricity for emerging, off-grid markets. The advanced operational and information technology devices used in MRE systems create a pathway for a cyber threat actor to gain unauthorized access to data or disrupt operation. To improve the resiliency of MRE systems as a predictable, affordable, and reliable source of energy from oceans and rivers, guidance was developed for an end users' organization that describes a framework for identifying and managing cybersecurity risk. The development of the cybersecurity guidance is based on standards described in the Risk Management Framework and Cybersecurity Framework developed by the National Institute of Standards and Technology (NIST). This paper is the first of a two-part series that describes an approach to determine the cybersecurity risk for MRE systems based on assessing potential cyber threats, identifying vulnerabilities (people, processes, and technology, including physical and operational environment), and evaluating the consequences a cyberattack would have on operation of the MRE system and impact on end users' mission and business objectives. MRE developers and stakeholders can use this approach to assess their current cybersecurity risk posture to incorporate appropriate cybersecurity controls to reduce the consequences and impacts from a cyberattack on MRE systems. This approach can be refined further as MRE systems are deployed and operational configurations are available.
Packaging Information as a Polycode Advertising Text: Impact Tools

The article is devoted to the analysis of polycode advertising texts placed on food packaging. The relevance of the study is determined by the fact that at present, when the decision to purchase is often made directly at the point of sale, the role of information on the packaging has increased many times over. This fact makes it necessary to analyze modern technologies of influence and manipulation that are relevant for this type of discourse. The author considers the text on the packaging as a communicative and structural holistic message, consisting of semiotically heterogeneous elements, characterized by high pragmatic and expressive saturation, aimed at encouraging consumers to make a purchase of a particular product. A typology of functions performed by the information on the packaging is proposed. The main polycode tools that ensure the effective implementation of these functions are considered. As the most frequent polycode techniques used in the text on food packaging, the author identifies the following: an emphasis is made on a unique component in the composition, categorization of the target audience according to certain criteria, the use of means of expression, including provocative metaphorization, appeal to authoritative opinion, the use of stylistic visualization, technologies of narrative and storytelling, etc.
Chinese Grammatical Correction Using BERT-based Pre-trained Model

In recent years, pre-trained models have been extensively studied, and several downstream tasks have benefited from their utilization. In this study, we verify the effectiveness of two methods that incorporate a pre-trained model into an encoder-decoder model on Chinese grammatical error correction tasks. We also analyze the error type and conclude that sentence-level errors are yet to be addressed.
Web-Based Flood Hazard Monitoring

Flood is a natural disaster. It occurs in several cities in Indonesia. Floods caused by rivers that overflowed and then flooded residential areas. It comes mostly unexpectedly without early warning. It causes many losses, especially the loss of material, and health threats to surrounding communities. The advance of network technology can reduce the adverse effects of flooding by providing warning alarms and water level monitoring system in real-time that can be accessed via the web. Based on the problem, a monitoring system was designed to monitor water levels via a web that work in real time for 24 hours, and store water level data into the database. The use of this website requires an internet connection, so that internet services must be available.
Towards Generating Query to Perform Query Focused Abstractive Summarization using Pre-trained Model

Query Focused Abstractive Summarization (QFAS) represents an abstractive summary from the source document based on a given query. To measure the performance of abstractive summarization tasks, different datasets have been broadly used. However, for QFAS tasks, only a limited number of datasets have been used, which are comparatively small and provide single sentence summaries. This paper presents a query generation approach, where we considered most similar words between documents and summaries for generating queries. By implementing our query generation approach, we prepared two relatively large datasets, namely CNN/DailyMail and Newsroom which contain multiple sentence summaries and can be used for future QFAS tasks. We also implemented a pre-processing approach to perform QFAS tasks using a pretrained language model, BERTSUM. In our pre-processing approach, we sorted the sentences of the documents from the most query-related sentences to the less query-related sentences. Then, we fine-tuned the BERTSUM model for generating the abstractive summaries. We also experimented on one of the largely used datasets, Debatepedia, to compare our QFAS approach with other models. The experimental results show that our approach outperforms the state-of-the-art models on three ROUGE scores.