Reviewerly/exHarmony huggingface dataset:
Knowledge Elicitation Using Deep Metric Learning and Psychometric Testing

Knowledge present in a domain is well expressed as relationships between corresponding concepts. For example, in zoology, animal species form complex hierarchies; in genomics, the different (parts of) molecules are organized in groups and subgroups based on their functions; plants, molecules, and astronomical objects all form complex taxonomies. Nevertheless, when applying supervised machine learning (ML) in such domains, we commonly reduce the complex and rich knowledge to a fixed set of labels, and induce a model shows good generalization performance with respect to these labels. The main reason for such a reductionist approach is the difficulty in eliciting the domain knowledge from the experts. Developing a label structure with sufficient fidelity and providing comprehensive multi-label annotation can be exceedingly labor-intensive in many real-world applications. In this paper, we provide a method for efficient hierarchical knowledge elicitation (HKE) from experts working with high-dimensional data such as images or videos. Our method is based on psychometric testing and active deep metric learning. The developed models embed the high-dimensional data in a metric space where distances are semantically meaningful, and the data can be organized in a hierarchical structure. We provide empirical evidence with a series of experiments on a synthetically generated dataset of simple shapes, and Cifar 10 and Fashion-MNIST benchmarks that our method is indeed successful in uncovering hierarchical structures.
The role of erosion in the morphometry of composite volcanoes

&amp;lt;p&amp;gt;Volcanoes are extremely dynamic landforms. They grow by the accumulation of eruptive products and intrusions and degrade by a range of erosion processes such as superficial runoff, chemical and physical weathering, fluvial and glacial incision, and mass movements. In this study, we aim at documenting and quantifying the morphology of natural composite volcanoes using a range of morphometric indices, to better understand the factors that control erosion rates and patterns.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In addition to standard morphometric indices, including edifice ellipticity and irregularity, computed by the MORVOLC algorithm, a fractal dimension tool is developed to quantitatively report the shape complexity of stratovolcanoes. A convex hull approach is used to derive minimal erosion volumes and estimate erosion rates, considering available geochronological constraints. Morphometric parameters are derived from digital elevation models (DEMs) for a few exemplary stratovolcanoes of contrasted ages from the same volcanic region. To analyse the potential bias induced by the selected DEMs and the identification of the volcanic edifice outline, we also conduct a sensitivity analysis. The morphometric parameters are similarly extracted using the freely and globally available ALOS 30m (AW3D30), SRTM 30m (SRTMGL1), and ASTER 30m (GDEM 003), and compared to values obtained with the TanDEM-X 12m. The subjective user-drawn edifice outlines are compared to outlines generated by available algorithms, i.e. NETVOLC and MBOA, and their impact on the accuracy of morphometric indexes is quantified. &amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Our results highlight that erosion increases edifice irregularity and fractal dimension. Preliminary trends between volcano fractal dimension, eroded volume, and age suggest that fractal analysis has the potential to be used as a relative age determination tool. The proposed morphometric characterisation paves the way for a comparison between natural volcanoes and controlled lab experiments reproducing the degradation of pristine volcanic cones by surface runoff to be developed later in our project. &amp;amp;#160;&amp;lt;/p&amp;gt;
Expression Modalities: How Speaking Versus Writing Shapes What Consumers Say, and Its Impact

Word of mouth is both frequent and important. But might the way consumers communicate (i.e., speaking versus writing) shape the language they use? And, as a result, the impact of what they share? While a great deal of research has begun to examine the behavioral drivers of word of mouth, there has been less attention to how communication modality might shape what consumers share. Five studies demonstrate that compared to writing, speaking increases the emotionality of communication. Speaking often involves less time to deliberate about what to say, so consumers use more emotional language. This difference in language produced, in turn, can lead to greater persuasion. This work sheds light on drivers of word of mouth, effects of communication modality, and role of language in communication.
Education as a Strategy for Active Aging Learning Center in Taiwan

The population of individuals who are over 65 years-old in Taiwan has grown from less than 10% in 2008 to 14% in 2018. Since 2008, Taiwan has officially been an aging nation as defined by the World Health Organization. With the current rate of growth, we expect the country to become a super-aged society with 20% of the population being older than 65 years of age in 2025. The challenge Taiwan currently faces in bracing for a rapidly aging society is more difficult than ever. In response to a rapidly aging population, universities have been cooperating with the government since 2008 in promoting a community-based lifelong learning-oriented "active aging" education policy, of which we collectively call "Senior Learning" (“Le-Ling Learning” in Mandarin, meaning “Active Aging Learning”). 12 years into implementing the program, 369 senior learning centers have been established in Taiwan by 2020. These established centers with their extensions can be found throughout Taiwan, including 360 towns and cities as well as 3,175 villages. These centers, supported by local resources (e.g. schools, and non-governmental organizations), provide senior learning courses and activities. There are thousands of community volunteers participating every year, as well as over 200,000 hours of Active Aging Learning course duration prepared by the government; moreover, there are currently 200 instructors trained for teaching these programs. Active Aging Learning program has truly become a “zeitgeist” in Taiwan in response to the elderly society. In caring for the elderly, meaningful educational learning has become the best strategy as reflected by the public.&#x0D; The purpose of this article is to report on the developmental background, promotion model, effectiveness of, and the prospects of Active Aging Learning in Taiwan. The contents of this article include: (1) the conceptual basis of Active Aging Learning, (2) the practice mode and strategy of Active Aging Learning, (3) the implementation effect of Active Aging Learning, and (4) the future progress of Active Aging Learning. The research data in this article originate from policy documents, both qualitative and quantitative data for the implementation of the Active Aging Learning program, as well as my 12 years of practical observations and experience as the overall project principal investigator. Through reading this article, the readers can quickly understand the implementation process, effectiveness, and problems of senior learning in Taiwan, as well as the key findings of our 12 years of experience. We found that the best strategy for an aging society is not to emphasize "care" in the context of traditional pathology, but to learn in order to "prevent". Educational strategies and talent cultivation are the broad directions of our active efforts.
Parallelization of Frequent Itemset Mining Methods with FP-tree: An Experiment with PrePost+ Algorithm

Parallel processing has turn to be a common programming practice because of its efficiency and thus becomes an interesting field for researchers. With the introduction of multi- core processors as well as general purpose graphics processing units, parallel programming has become affordable. This leads to the parallelization of many of the complex data processing algorithms including algorithms in data mining. In this paper, a study on parallel PrePost+ is presented. PrePost+ is an efficient frequent itemset mining algorithm. The algorithm has been modified as a parallel algorithm and the obtained result is compared with the result of sequential PrePost+ algorithm
RETRACTED ARTICLE: Innovation performance of listed companies based on machine computing and data envelopment analysis

The introduction and creation of new technological means can change and promote the process and results of economic development, thus promoting the steady progress of economic development. In these areas of economic development, some small-scale scientific and technological innovation enterprises occupy a large proportion, and the economic development in other areas cannot do without the promotion of these enterprises, which is a typical model in China Is the gem listing. The listing of gem not only symbolizes that China has made great progress in the field of scientific and technological innovation, but also directly confirms that innovation is the source of economic development. Innovation can not only promote the growth of economic development, but also promote the reform and optimization of economic development system. This paper takes the gem as an example, based on the computer calculation and data envelopment analysis, from the data analysis of the project, summarizes the key factors affecting the innovation and development of an enterprise, and studies the specific path to effectively promote innovation.
Keyword Search over the COVID-19 Data

Keyword search is typically associated with information retrieval systems. However, recently, keyword search has been expanded to relational databases and RDF datasets, as an attractive alternative to traditional database access. With this motivation, this paper first introduces a platform for data and knowledge retrieval, called DANKE, concentrating on the keyword search component. It then describes an application that uses DANKE to implement keyword search over two COVID-19 data scenarios.
A comparison of the lexical, syntactic, and discourse features between the listening and the reading texts in the College Scholastic Ability Test

The aim of this study is to compare the lexical, syntactic, and discourse features between the listening and the reading texts in the College Scholastic Ability Test (CSAT). Dialogues, monologues and reading texts from the 1994 to 2020 CSATs, were analyzed using Coh-Metrix. A total of 30 separate ANOVAs, with the text as the independent variable and the numerical index obtained via Coh-Metrix as the dependent variable of each ANOVA, were conducted. The results reveal that the texts show differences in terms of lexical, syntactic, and discourse features. The texts mostly match the descriptions of the relevant literature on spoken and written languages. However, there are some discrepancies with respect to lexical diversity and syntactic density in the listening texts. These findings indicate possible differences between actual spoken data and the listening texts in the CSAT. This study suggests that test developers should pay attention to the linguistic features of spoken texts when writing test items for the CSAT. Implications for ways to enhance the authenticity of the CSAT are also discussed.
A Robust Method for Dehazing of Single Image with Sky Region Detection and Segmentation

In recent times, there has been a tremendous progress in image dehazing for computer vision applications, while the sky region processed by these algorithms tends to degrade by noise and color distortion. In this paper, an improved dark channel prior algorithm is proposed which detects the sky region first and divides the image into sky region and non-sky region and then estimates the transmission of two parts separately, followed by combining with refining step. The proposed algorithm also accurately corrects the transmission of the sky region to avoid noise and color distortion. Experimental results show a greater quality improvement in the output images than the existing strategies.
Risks Associated with Threats Related to Disruptive Technologies in the Current Financial Systems Context

The subject of the study is the analysis of the risks associated with threats generated by disruptive technologies in the context of current financial information systems of the entities. The phenomenon of cybercrime, facilitated by the development of Artificial Intelligence, Deep Learning and the disruptive frequency of security incidents represents the foundation of this paper. The aim of the article is to integrate, compare and investigate the impact of disruptive technologies, current security risks and incidents, and to design measures in order to manage risk. The results of this paper highlight Advanced Persistent Threats (APTs), malware, ransomware, sabotage of external actors, third-party threats in the top 5 most common security incidents. The paper acknowledges the complexity of digitization and transposes a practical model of risk management. The paper contributes to informing stakeholders about the forced penetration of hackers into victims' devices, under the pretext of COVID-19.
Chinese Named Entity Recognition: Applications and Challenges

Chinese Named Entity Recognition (NER) is an important task in Chinese natural language processing, which has been widely used in automatic question answering, reading comprehension, knowledge graph, machine translation and other fields. With the development of natural language processing techniques and the enhancement of text mining, the acquisition of semantic knowledge in text area becomes very important, and named entity recognition is the foundation of information application technology such as relationship extraction, which has important significance. In this chapter, we first provide a comprehensive review of traditional approaches. Then, we present the differences of between Chinese NER and NER, and describe the details of its application. We also introduce the challenge of Chinese entity recognition. Last, we compare recent works, give a vision about future work and propose the application in the Multi-dimensional Data Association and inTelligent Analysis (MDATA) model.
Simultaneous and precision measurement of yaw and pitch using digital speckle pattern interferometry

A method of simultaneous and precision measurement of yaw and pitch based on digital speckle pattern interferometry is proposed. The relationship between the yaw/pitch and phase distribution is analyzed in detail, resulting in the establishment of the yaw and pitch simultaneous measurement model. A plane fitting method is proposed to separate the two angle motions from a single phase map. The proposed method enjoys the advantages of being free of cooperative target, high-resolution measurement, and compact optical setup. Experimental results show the mean absolute error of the measurement is <1  μrad.
Enhance capability of separable ball criterion for the bipartite quantum states

Abstract The largest separable ball is a meaningful geometric sufficient condition for separability of bipartite quantum states which tells that all the states in the unit ball in Frobenius norm centered at the identity matrix are separable. In this paper, we propose an algorithm that can improve the capability of this criterion to detect the separability outside the unit ball by solving an optimization problem based on the invertible local operators, that is, to optimize <?CDATA ${{\Vert}\left(A\otimes B\right)\rho {\left(A\otimes B\right)}^{{\dagger}}-\mathbb{1}{\Vert}}_{F}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mfenced close="‖" open="‖"> <mml:mrow> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mi>A</mml:mi> <mml:mo>⊗</mml:mo> <mml:mi>B</mml:mi> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mi>ρ</mml:mi> <mml:msup> <mml:mrow> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mi>A</mml:mi> <mml:mo>⊗</mml:mo> <mml:mi>B</mml:mi> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:mrow> <mml:mrow> <mml:mo>†</mml:mo> </mml:mrow> </mml:msup> <mml:mo>−</mml:mo> <mml:mn mathvariant="double-struck">1</mml:mn> </mml:mrow> </mml:mfenced> </mml:mrow> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> </mml:msub> </mml:math> with respect to the invertible square matrices A and B . The numerical examples have shown that our algorithm is more powerful than the original separable ball criterion.
Quantum supremacy in end-to-end intelligent IT. Pt. 2: State -of -art of quantum SW/HW computational gate-model toolkit

Several paradigms of quantum computing are considered. Quantum computer simulators are de-scribed. Models of learning quantum systems from experiments are considered. Quantum speed-up limitation in two-level systems (qubit) is discussed. The approaches to the formation of a quantum variational intrinsic solver are considered.
Online education empowerment with artificial intelligence tools

The experience of organizing the educational process during the quarantine caused by the COVID-19 pandemic is considered. Using of interactive technologies that allow organizing instant audio communication with a remote audience, as well as intelligent tools based on artificial intelligence that can help educational institutions to work more efficiently. Examples of sufficient use of artificial intelligence in distance learning are given. Particular attention is paid to the development of intelligent chatbots intended for use in communications with students of online courses of educational web portals. The use of technologies of ontology formation based on automatic extraction of concepts from external sources is offered, what can lead to greater acceleration of construction of the intellectual component of chatbots. Artificial intelligence tools can become an essential part of distance learning during this global COVID-19 pandemic. While educational institutions are closed to quarantine and many of them transitioned to distance learning lecturers and schoolteachers, as well as students and schoolchildren faced with the necessity to study in this new reality. The impact of these changes depends on people's ability to learn and on the role that the education system will play in meeting the demand for quality and affordable training. The experience of organizing the educational process at the University of Education Management of the National Academy of Pedagogical Sciences of Ukraine in the quarantine caused by the COVID-19 pandemic showed that higher and postgraduate institutions were mostly ready to move to distance learning. However, most distance learning systems, on whatever platform they are organized, need to be supplemented: the ability to broadcast video (at least ‒ one-way streaming), providing fast transmission of various types of information, receiving instant feedback when voting, polls and more. The structure of each section of the training course for the online learning system should fully cover the training material and meet all the objectives of the course. Appropriate language should be used, and wording, syntax, and presentation of tasks should be considered. One of the areas of application of artificial intelligence technologies in online learning is the use of chatbots which are characterrized by the following properties. It is advisable to use computer ontologies to ensure the intellectualization of chatbots. In this case, the metadata must be understandable to both humans and software and meet the requirements of modern standards in the field of information technology. The extraction of concepts from external data sources was carried out to build the ontology.
Environment-Triggered Microlearning

In ubiquitous (micro)learning, any place is a potential learning environment, be it a couch at home or a noisy place in public. For an optimal experience, learning should, therefore, be adjusted to the learner’s environment and needs in a specific environment, e.g., with respect to available attentional resources. At the same time, changing environments provide a unique opportunity as a source of immediately relevant learning material, e.g., context-based vocabulary in language learning. In my thesis, I explore environment-triggered learning from both perspectives. I investigate how a learner’s physical location or mental state can trigger learning activities at a suitable moment and keep learners engaged. On the content side, I investigate how session content can be matched to the learner’s surroundings. In these two areas, I conceptualize and evaluate tools that utilize context to improve the environment-based learning experience. This paper is presented at MobileHCI 2020 Doctoral Consortium.
Cybersecurity Education in the Age of Artificial Intelligence

The 2019 Federal Cybersecurity Research and Development Strategic Plan highlighted the mutual needs and benefits of artificial intelligence (AI) and cybersecurity. AI techniques are expected to enhance cybersecurity by assisting human system managers with automated monitoring, analysis, and responses to cybersecurity attacks. Conversely, it is essential to guard AI technologies from unintended uses and hostile exploitation by leveraging cybersecurity practices. Research results at the intersection of AI and cybersecurity can help us to be better equipped with tools and techniques to tackle the growing cybersecurity challenges, while also presenting an opportunity to devise fundamentally new ways to motivate and educate students about cybersecurity in the age of AI. Likewise, a June 2019 technical workshop on 'Artificial Intelligence and Cybersecurity: Opportunities and Challenges' noted how the interplay between AI, machine learning, and cybersecurity will continue to introduce new opportunities and challenges in the security of AI as well as AI for cybersecurity. Basic research at the intersection of AI, cybersecurity, and education has the potential to expand existing AI opportunities and resources in cybersecurity education and workforce development. Education efforts are needed to foster workforce knowledge and skills about applying AI expertise to cybersecurity as well as building robust and trustworthy AI. This BOF session will bring together researchers who are interested in these collaborative explorations.
Survey on Deep Learning-Based Kuzushiji Recognition

Owing to the overwhelming accuracy of the deep learning method demonstrated at the 2012 image classification competition, deep learning has been successfully applied to a variety of other tasks. The high-precision detection and recognition of Kuzushiji, a Japanese cursive script used for transcribing historical documents, has been made possible through the use of deep learning. In recent years, competitions on Kuzushiji recognition have been held, and many researchers have proposed various recognition methods. This study examines recent research trends, current problems, and future prospects in Kuzushiji recognition using deep learning.
Snapshot 3D imaging with a gigapixel-scale multi-aperture microscope

We present a gigapixel-scale multi-aperture microscope capable of measuring a sample’s 3D height profile over multi-centimeter-scale fields of view with a series of single synchronized camera snapshots. Exploiting the overlap redundancy in our multi-aperture camera array microscope, we developed a novel, end-to-end photogrammetric reconstruction algorithm that simultaneously calibrates the cameras’ 3D positions and poses, stitches the acquired images, and generates a coregistered, pixel-wise 3D height map of the sample. Our work opens the door to video-rate 3D monitoring of dynamic scenes at micrometer-scale resolutions and centimeter-scale fields of view.