Reviewerly/exHarmony huggingface dataset:
Using Neural Architectures to Model Complex Dynamical Systems

The natural, physical and social worlds abound with feedback processes that make the challenge of modeling the underlying system an extremely complex one. This paper proposes an end-to-end deep learning approach to modelling such so-called complex systems which addresses two problems: (1) scientific model discovery when we have only incomplete/partial knowledge of system dynamics; (2) integration of graph-structured data into scientific machine learning (SciML) using graph neural networks. It is well known that deep learning (DL) has had remarkable successin leveraging large amounts of unstructured data into downstream tasks such as clustering, classification, and regression. Recently, the development of graph neural networks has extended DL techniques to graph structured data of complex systems. However, DL methods still appear largely disjointed with established scientific knowledge, and the contribution to basic science is not always apparent. This disconnect has spurred the development of physics-informed deep learning, and more generally, the emerging discipline of SciML. Modelling complex systems in the physical, biological, and social sciences within the SciML framework requires further considerations. We argue the need to consider heterogeneous, graph-structured data as well as the effective scale at which we can observe system dynamics. Our proposal would open up a joint approach to the previously distinct fields of graph representation learning and SciML.
ReadOCR: A Novel Dataset and Readability Assessment of OCRed Texts

Results of digitisation projects sometimes suffer from the limitations of optical character recognition software which is mainly designed for modern texts. Prior work has examined the impact of OCR errors on information retrieval (IR) and downstream natural language processing (NLP) tasks. However, questions remain open regarding the actual readability of the OCRed text to the end users, especially, considering that traditional OCR quality metrics consider only syntactic or surface features and are quite limited. This paper proposes a novel dataset and conducts a pilot study to investigate these questions.
Design of Symbol Visual Communication and Communication Platform Based on Web Services

Symbol visual communication design and communication platform is a new modern digital media technology. Based on computer and network, it integrates text, graphics and other elements to form an information flow with certain interactive and visual characteristics. Therefore, a symbol visual communication platform based on web is designed. Firstly, this paper introduces the symbol visual communication design, expounds the symbol visual communication elements, designs the symbol visual communication platform framework according to the application of web service framework, and tests the performance of the framework. Finally, the test results show that the operation time and delay time of the symbol visual communication framework based on Web services are maintained at about 3 s, the CPU occupancy is about 3%, and the memory usage is about 3500K. This shows that the web service framework can meet the needs of symbol visual communication design.
Solutions to the Endless Addition of Transaction Volume in Blockchain

In blockchain system, the problem of endless addition of transaction volume results in larger space occupation, heavier network transmission burden and the like.But the way of abandoning historical data is hindered by the characteristic of blockchain-tamper-proof.To solve this problem, this dissertation takes bitcoin system as an example, and gives a definition of expired transaction.By abandoning expired transactions and packing the rest transactions in several blocks into a new substitute block to replace old blocks, help to overcome the difficulty in clearing historical data.However, this solution fails to clear ineffective intermediate transactions.Thus, a follow-up solution is proposed, abandon the transactions whose outputs have been run out and retain the transactions where not all the outputs have been spent.Additionally, include records of the spending details of each transaction output, which allows us to clear ineffective intermediate transactions.In the end, an experiment is conducted to confirm the effectiveness of the two solutions as to clearing transactions.
Enhancing Documentation of Hupa with Automatic Speech Recognition

This study investigates applications of automatic speech recognition (ASR) techniques to Hupa, a critically endangered Native American language from the Dene (Athabaskan) language family.Using around 9h12m of spoken data produced by one elder who is a firstlanguage Hupa speaker, we experimented with different evaluation schemes and training settings.On average a fully connected deep neural network reached a word error rate of 35.26%.Our overall results illustrate the utility of ASR for making Hupa language documentation more accessible and usable.In addition, we found that when training acoustic models, using recordings with transcripts that were not carefully verified did not necessarily have a negative effect on model performance.This shows promise for speech corpora of indigenous languages that commonly include transcriptions produced by secondlanguage speakers or linguists who have advanced knowledge in the language of interest.
An Adaptive Approach for Preserving Privacy in Context Aware Applications for Smartphones in Cloud Computing Platform

With the widespread use of mobile phones and smartphone applications, protecting one's privacy has become a major concern.Because active defensive strategies and temporal connections between situations relevant to users are not taken into account, present privacy preservation systems for cell phones are often ineffective.This work defines secrecy maintenance issues similar to optimizing tasks, thereby verifying their accuracy and optimization capabilities through a hypothetical study.Many optimal issues arise while preserving one's privacy and these optimal issues are to be addressed as linear programming issues.By addressing linear programming issues, an effective context-aware privacy-preserving algorithm (CAPP) was created that uses an active defence strategy to determine how to release a user's current context to enhance the quality of service (QoS) regarding context-aware applications while maintaining secrecy.CAPP outperforms other standard methodologies in lengthy simulations of actual data.Additionally, the minimax learning algorithm (MLA) optimizes the policy users and improves the satisfaction threshold of the contextaware applications.Moreover, a cloud-based approach is introduced in the work to protect the user's privacy from third parties.The obtained performance measures are compared with existing approaches in terms of privacy policy breaches, context sensitivity, satisfaction threshold, adversary power, and convergence speed for online and offline attacks.
The Comparison of Most Popular Mobile Applications for Learning Foreign Languages

Mobile-assisted language learning (MALL) stimulates students' motivation and improves their foreign language skills.The primary aim of this paper is to compare the most frequently used mobile applications for learning foreign languages, primarily English, by students in Serbia.Additionally, we wanted to reflect on students' views and experiences.Qualitative research, in the form of a focus group, was conducted in order to determine the clearest possible picture of students' views concerning the usefulness of different language apps, primarily Duolingo, HelloTalk and WordUp app.The respondents are Singidunum University students who voluntarily agreed to participate in this research.The results showed that innovative ways of perfecting foreign languages, such as mobile applications, are captivating for students.In addition, they highly value visual and engaging materials.They believe Duolingo is the best app for improving vocabulary and grammar, whereas HelloTalk is an ideal app for developing speaking skills.WordUp is recommended as a dictionary and as a revolutionary app for learning new English words.Students agreed that the combination of all three mobile applications would be optimal, depending on their language level and learning goals.
Brain Shape Correspondence Analysis Using Variational Mixtures for Gaussian Process Latent Variable Models

Analyzing brain structures in the medical imaging field poses challenging problems due to neurological diseases’ heterogeneity. Besides, measuring brain changes quantitatively in neurodevelopmental is crucial to evaluate clinical outcomes correctly. From a computer-vision perspective, establishing correspondences between shapes often requires computing similarity measures that, in most cases, are unavailable. This paper proposes an unsupervised probabilistic framework for shape correspondence analysis on brain structures by using variational unsupervised learning. The probabilistic framework comprehensively captures the form of brain shapes from surface descriptors. Then, we learned clustered latent space representations of surface descriptors by using mixtures distributions for Gaussian process latent variable models to avoid computing similarity measures, which classify the resulting latent vectors to establish group-wise correspondences. The experimental results show how the proposed model captures non-linearities in non-rigid 3D shapes even when they present occlusion or partialities. These results demonstrated that the proposed model is suitable for shape correspondence analysis.
An Efficient Deep Feature Splitting for Hash-Based Retrieval of Remote Sensing Images

The remote sensing image retrieval (RSIR) models face two problems in practical scenarios- like i) repetitive or bursty features dominated in the final image representation, resulting in less distinguishable representations, and ii) more time complexity involved in the exhaustive search in large-scale remote sensing image (RSI) archives. The complex pattern of RSI with large within-class and small between-class variance in pixel values of region-of-interest and time complexity incorporated with large-scale retrieval tasks motivated to propose an efficient deep feature splitting (DFS) strategy for hash-based RSI retrieval (DFSHash-RSIR) model. The DFS is designed to incorporate a split module, which splits the fully-connected (FC) layer feature into equal-sized blocks. The center-cluster loss (CCL) function utilized strengthens subjective information of the classes and minimizes the distance between the image descriptors of similar classes to a great extent. The DFSHash strategy employed reduces the feature dimensionality with improved overall retrieval performance. Also, the classification errors in the feature descriptors and the DFS-based binary codes are minimized to learn the highly discriminative binary codes effectively. The proposed DFSHash-RSIR model provides efficient search and improved retrieval performance. Experiments on benchmark RSI archives reveal that the DFSHash-RSIR model achieved state-of-the-art results with better mAP values.
Chapter 2 Smittestopp for Android and iOS

Abstract Contact tracing is currently a manual and laborious task that requires individuals to recall their interactions with people many days in the past. As a remedy, phones can be used to play a significant role in the response to the COVID-19 pandemic, by easing the burden of healthcare staff. Through novel and sophisticated technology, apps can be used to track infected people, issue quarantine guidelines, and provide the latest news to the public. Along with general public measures, apps can contribute significantly to keeping infection levels low. Generally, digital contract tracing can identify and warn people who may be at risk of being infected because they were in close physical proximity of someone who later tested positive for COVID-19.
Intraday Return Predictability in the Cryptocurrency Markets: Momentum, Reversal, or Both

This paper reports evidence of intraday return predictability, consisting of both intraday momentum and reversal, in the cryptocurrency market. Using high-frequency price data on Bitcoin from March 3, 2013, to May 31, 2020, it shows that the patterns of intraday return predictability change in the presence of large intraday price jumps, FOMC announcement release, liquidity levels, and the outbreak of the COVID-19. Intraday return predictability is also found in other actively traded cryptocurrencies such as Ethereum, Litecoin, and Ripple. Further analysis shows that the timing strategy based on the intraday predictors produces higher economic value than the benchmark strategy such as the always-long or the buy-and-hold. Evidence of intraday momentum can be explained in light of the theory of late-informed investors, whereas evidence of intraday reversal, which is unique to the cryptocurrency market, can be related to investorsâ€™ overreaction to non-fundamental information and overconfidence bias.
News Article Retrieval in Context for Event-centric Narrative Creation

Writers such as journalists often use automatic tools to find relevant content to include in their narratives.In this paper, we focus on supporting writers in the news domain to develop eventcentric narratives.Given an incomplete narrative that specifies a main event and a context, we aim to retrieve news articles that discuss relevant events that would enable the continuation of the narrative.We formally define this task and propose a retrieval dataset construction procedure that relies on existing news articles to simulate incomplete narratives and relevant articles.Experiments on two datasets derived from this procedure show that state-of-the-art lexical and semantic rankers are not sufficient for this task.We show that combining those with a ranker that ranks articles by reverse chronological order outperforms those rankers alone.We also perform analysis of the results that sheds light on the characteristics of this task.
An Error Analysis on Students’ Writing of Descriptive Text at SMK Negeri 1 Pedan, Klaten

This study aims to determine the types of errors and causes of errors made by 10th grade students of SMK Negeri 1 Pedan in writing descriptive texts.This type of research is descriptive qualitative using the error analysis method.The subjects of this study were 30 students of SMK Negeri 1 Pedan who were then categorized and taken into 12 students and 2 teachers.This study used document review and interview techniques in data collection.The data of this study were taken from the works of students who sent their descriptive texts a person, famous figure or place through emails.12 students and 2 teachers had problems with interlingual transfer, intralingual transfer and context of learning, the results of this study indicate that there were several errors that occured namely 1) spelling error 2) punctuation error 3) grammar error 4) usage error.From the results of this study, the types of errors that often appeared and made by the students were spelling error.The reasons why students made these errrors as follow: 1) students were not thorough 2) not understanding of English vocabulary 3) lack of understanding the material.Even though they experience some difficulties, students are still trying to do the assignment given and have enthusiasm when describing the character as seen from the results.
Learning target class eigen subspace (LTC-ES) via eigen knowledge grid

In one-class classification (OCC) tasks, only the target class (class-of-interest (CoI)) samples are well defined during training, whereas the other class samples are totally absent.In OCC algorithms, the high dimensional data adds computational overhead apart from its intrinsic property of curse of dimensionality.For target class learning, conventional dimensionality reduction (DR) techniques are not suitable due to negligence of the unique statistical properties of CoI samples.In this context, the present research proposes a novel target class guided DR technique to extract the eigen knowledge grid that contains the most promising eigenvectors of variance-covariance matrix of CoI samples.In this process the lower and higher eigenvalued eigenvectors are rejected via statistical analysis because the high variance may split the target class itself, whereas the lower variance do not contribute significant information.Furthermore, the identified eigen knowledge grid is utilized to transform high dimensional samples to the lower dimensional eigen subspace.The proposed approach is named as learning target class eigen subspace (LTS-ES) that ensures strong separation of the target class from other classes.To show the effectiveness of transformed lower dimensional eigen subspace, oneclass support vector machine (OCSVM) has been experimented on wide variety of benchmark datasets in presence of: original feature space, transformed features obtained via eigenvectors of approximately 80%-90% cumulative variance, transformed features obtained via knowledge grid and transformed features obtained via eigenvectors of approximately 50% cumulative variance.Finally, a new performance measure parameter called stability factor is introduced to validate the robustness of the proposed approach.
Analysis Of the Impact of Online Learning on Cognitive Ability of Children Aged 5-6 Years In Nafiri Kasih Kindergarten Sibuntuon

This research was executes with the purpose of understanding the effect of online learning at the cognitive competencies of student elderly five until six years.Information were collected using interview data series technique and interview.The validity of the data is checked through triangulation of methods and sources.The consequences confirmed the cognitive skills of youngsters at Nafiri Kasihis Kindergarten for the duration of the net studying pandemic in phrases of citing, including and subtracting simple, bringing up, grouping, sorting objects based on shape, shade and length.The fulfillment of children's cognitive development at this school has decreased because teachers continually try to supply obligations to students that are able to stimulate children's cognitive competencies.
Solutions and Advanced Hints

Abstract This chapter contains high-level solutions to the end-of-chapter Test Yourself questions together with some advanced hints on how to get your R-scripts to run faster, smoother and with greater efficiency.
Sovereign Digital Currency – A New Reality

One of the most relevant and widely discussed trends in the financial sector is the development of a new payment technology – central bank digital currency (CBDC). CBDC is central bank-issued digital money denominated in the national unit of account, and it represents a liability of the central bank. It differs from electronic reserves (which cannot currently be accessed by individuals), physical cash, cryptocurrencies and other private digital tokens. Central banks around the world have been researching the concept and design of CBDC for several years and their work continues apace amid the COVID&amp;#8209;19 pandemic. This general trend encompasses large differences across jurisdictions and types of economies. Each country should make its own choice of the CBDC design, taking into account its particular circumstances. Over the last years, the share of central banks actively engaging in some form of CBDC work has increased significantly. Central banks are moving from conceptual research of CBDC to experimentation and pilot projects. CBDC projects differ starkly across countries, both in their motivations and their economic and technical design. Rising number of central banks are considering retail CBDC rather than wholesale, and “hybrid” or “intermediated” operational architectures where the CBDC is a direct claim on the central bank, but the private sector manages customer-facing activity. The paper reviews the recent advances in CBCD projects, and the experience of the countries in more advanced stages of CBCD engagement, namely the particularly advanced CBDC Chinese project of the e-yuan of the People’s Bank of China and Swedish e-krona project of Sveriges Riksbank.
Sarcasm of News Headlines on National Electronic Mass Media and Its Relevance to Learning Indonesian

This research aims to describe sarcasm on the national electronic mass media through the principles of language politeness, the form of sarcasm, compare the national online news that contains the most sarcasm, and explain the relevance of sarcasm language to Indonesian language learning in the Eight Grade of Junior High School.This research used a qualitative descriptive method as the research method.The object of this research was the sarcasm language contained in the news headlines in the national electronic newspapers.The data in this research consisted of several titles with sarcasm on national electronic mass media.The data were collected by using the listening method with advanced technique namely writing technique.Then, the researcher analyzed the data using extra lingual matching method.The results showed that there were six violations of language politeness principles in the news headlines on national electronic mass media, namely the approbation maxim; the tact maxim; the agreement maxim; the sympathy maxim; the modesty maxim; and the generosity maxim.The most common violation of the language politeness principle was the approbation maxim.As for the sarcasm language in news headlines in the national electronic mass media found two forms of sarcasm, namely dispraise and satire.Dispraise was the most common form of sarcasm.Based on the comparison of the four types of national online news that contained a lot of sarcasm, in the first place was CNN Indonesia.com;Sindonews.com;Tempo.co; and Tribunnews.com.
Prediction Modeling

Predictive modeling can be defined as modeling the historical data using statistical and machine learning techniques to predict future observations. Prediction modeling tasks can be grouped into three categories: supervised learning, unsupervised learning and reinforcement learning.
How to Specify Software Users Want and Need? Execution Time, Team Configuration and Contingency Factors for Optimizing Little Design Up-Front (Lduf)

Context: A trend in software development is combining Agile Software Development (ASD) and User Centered Design (UCD), to deliver products with expected quality, time and budget, also meeting customers’ needs. However, literature review and field research suggest that the customization of the Little Design Up-Front (LDUF), one of the most common forms of integration, is still largely unexplored. Objective: This research reveals the most relevant aspects of LDUF considering time of execution, team configuration and contingency factors. Results were obtained from a systematic literature review and from the confrontation of theory and practice based on the assessment of how contingency factors influences LDUF. Method: We systematically reviewed the literature on LDUF from a final sample of 95 relevant papers for the area. Then an in-depth single case study was conducted to evaluate how factors affect work done in practice. Results: We summarize LDUF considering different perspectives: i. time for execution, ii. team configuration, iii. contingency factors, and discuss how the process should be planned, adapted, and executed considering different contexts. Conclusion: Our research contributes to the literature by analyzing LDUF considering time and team configuration and summarizing the impact of contingency factors by areas of responsibility.