Reviewerly/exHarmony huggingface dataset:
Learning, Education, and HCI

In this SIG, we propose a gathering of researchers and practitioners thinking about HCI in learning and educational contexts to foster an ongoing Learning and Education community at CHI. With the recent increase in CHI submissions relating to learning (40% more submissions than previous CHI), this SIG is an opportunity to foster an inclusive dialogue on designing and studying phenomena, tools, and processes related to learning and education. This SIG will bring together researchers, educators, and practitioners with three goals in mind: (1) discussing more inclusive cross-disciplinary perspectives on learning; (2) defining future directions and standards for learning and education contributions in CHI; and (3) building community across research/practice boundaries.
Multi-Wavelength Digital-Phase-Shifting Moiré Based on Moiré Wavelength

Multi-wavelength digital-phase-shifting moiré was demonstrated using multiple moiré wavelengths determined by system calibration over the full working depth. The method uses the extended noisy phase map as a reference to unwrap the phase map with a shorter wavelength, and thus achieve a less noisy and more accurate continuous phase map. The moiré wavelength calibration determines a moiré-wavelength to height relationship that permits pixelwise refinement of the moiré wavelength and height during 3D reconstruction. Only a single pattern has to be projected and, thus, a single image captured to compute each phase map with a different wavelength to perform digital-phase-shifting moiré temporal phase unwrapping. Only two captured images are required for two-wavelength phase unwrapping and three captured images for three-wavelength phase unwrapping. The method has been demonstrated in the 3D surface-shape measurement of an object with surface discontinuities and spatially isolated objects.
Systems Modeling Using Deep Elman Neural Network

In this paper, the modeling of complex systems using deep Elman neural network architecture is improved. The emphasis is to retrieve better deep Elman structure that emulates perfectly such dynamic systems. To achieve this goal, sigmoid activation functions in the hidden and output layers nodes are chosen and data files on considered systems for modeling and validation steps are given. Simulation results prove the ability and the efficiency of a deep Elman neural network with two hidden layers in this task.
Developing the Informational and Digital Environment of a University: Problem Analysis and Assessment

The research objective is to reveal and analyze the problems in developing the informational environment of a university. The authors applied theoretical and empirical methods, such as observation, polling, composing a Likert scale, calculation of Spearman ranking correlation coefficients, tabular and diagram techniques of data visualization, etc., which enabled to achieve the set goal. The score method based on Likert scale enabled to reveal the problem aspects of forming the informational and digital environment of a university. Also, these techniques enabled to quantitatively assess each problem, assess their significance and reveal the degree of each factor’s relation to each problem of using the information and communication technologies at a university. University professors were polled about using innovative information technologies in order to both enhance the education efficiency and apply new pedagogical concepts and techniques. The poll revealed the problem aspects impeding the development of the informational environment. The research results are presented as a series of diagrams, reliably characterizing the issues which the university professors face when implementing the educational process. Besides, a number of indicators were calculated to determine the correlation coefficients, which enabled to quantitatively assess the degree of correlation between the two series of the compared quantitative indicators. The research revealed the factors impeding the development of the informational environment of a university. Awareness of these factors by the university administration and teaching staff would allow overcoming them, with a view of increasing the quality of educational services. The authors are making a substantiated conclusion that, despite certain difficulties, the informational technologies integrated into the educational process comply with the modern requirements of the society and enhance the competencies of both the students and professors participating in the educational process.
Using the Google Web 1T 5-Gram Corpus for OCR Error Correction

In this paper we use the idea of context-based orthographic error corrections by taking the TREC-5 Confusion Track Data Set’s degrade5 and attempting to correct errors generated during Optical Character Recognition. We do this by identifying all errors using OCRSpell, then generate the 3-gram and searching for the first and last word in the Google Web 1T corpus of trigrams. We then select the candidates with the highest frequencies and a small Levenshtein edit distance. We report on our accuracy and precision and discuss on special situations and how to improve performance. All source code is publicly available for our readers to further our work or critique it.
The CDIO ++ Approach to University Faculty Advance Training for Research and Teaching Activities

A review of the literature on university faculty advanced training for research and teaching activities is presented. Based on the analysis of the experience of Russian and foreign universities, the principles for developing a modern faculty advanced training system at the university are set out responding to the challenges associated with the ongoing and expected changes in the content and technology of student training, primarily in the field of STEM. It is proposed to use the CDIO ++ approach for advanced training of faculty of various categories (professor, associate professor, assistant) to research and teaching activities in the conditions of division of labor when creating and implementing educational products at various stages of their life cycle. The distribution of academic staff potential is shown and an assessment of the resources of a program for faculty advanced training is given on the basis of the CDIO ++ approach for a hypothetical university with a given structure and established priorities for faculty members of various categories.
Machine Learning Techniques for IoT Intrusions Detection in Aerospace Cyber-Physical Systems

Aeronautical systems are no longer traditional masterpieces of autonomous mechanical engineering. Today, they are characterized by many intelligent technologies that include sensors, wireless standards and data analysis tools. Known as Aerospace Cyber-physical Systems (CPS), these CPSes are undergoing a massive transformation to increase the safety, efficiency and reliability of their operations. The physical system has created the Internet of Things IoT by integrating sensors, controllers and actuators. Nevertheless, the cyberspace of these aerospace CPSes offers many opportunities for malicious actors who threaten the security and privacy of vehicles/aircraft and their applications. Unprotected or poorly protected systems can easily be exploited for malicious purposes. Indeed, aerospace CPSes are always under threat from an increasing number of cyber-attacks through sensory or wireless channels, hardware, software or actuators. Recently, due to the significant advances and impressive results of machine learning techniques in the fields of image recognition, natural language processing and speech recognition for various long-standing artificial intelligence tasks, there has been a great interest in applying them to intrusion detection in the field of cybersecurity. In this chapter, we present different machine learning techniques for IoT intrusion detection in aerospace cyber-physical systems. The application of machine learning for cybersecurity in IoT requires the availability of substantial data on IoT attacks, but the lack of data on IoT attacks is a significant problem. In our study, the Cooja IoT simulator was used to generate high fidelity attack data in IoT 6LoWPAN networks. The efficient network architecture for all machine models is chosen based on comparing the performance of various network topologies and network scenarios. The experimental results show that Machine learning models for intrusion detection give better results by more than 99% in terms of accuracy, efficiency and detection rate. Also, it requires a low energy consumption overhead and memory, which proves that the proposed models can be used in constrained environments such as IoT sensors.
Semantic web and knowledge graphs as an educational technology of personnel training for nuclear power engineering

The technologies of knowledge representation and inference in an artificial intelligence system focused on the domain of nuclear physics and nuclear power engineering are considered. The possibilities of description logics and graph databases of nuclear knowledge for the generation of cognitive hypotheses, using in addition to deduction and other ways of reasoning, such as inductive inference and reasoning based on analogies, are discussed. The use of adequate description logic and measures of semantic similarity is substantiated. Interactive visual navigation and reasoning on the knowledge graphs are performed by means of special retrieval widgets and the smart RDF browser. Operations with semantic repositories are implemented on cloud platforms using SPARQL queries and RESTful services. The proposed software solutions are based on cloud computing using DBaaS and PaaS service models to ensure scalability of data warehouses and network services. Example of use of the offered technologies and software has been given.
Automation of Bone Tissue Histology

In abstract methods of automation of histology of bone structure are considered. Possible inputs are snapshots of a microscope or computed tomography slices. An algorithm is proposed that differentiates object according to their color (or grayscale) and recover morphology topology. Algorithm to separate morphological objects by their dimensions and color parameters was built. Measured parameters are bone surface, bone area, porosity, cortical thickness, canal number, canal area and etc. Additionally was measured the anisotropy properties of the bone tissue: distribution of porosity direction and degree of porosity elongation. A bone example was scanned by computed tomography. All data were measured by the proposed method and the results presented. As an example algorithm of work on computed tomography data is shown in this work.
Ilities Semantic Basis: Research Progress and Future Directions

This paper summarizes progress on the investigation of an ilities semantic basis. One of the fundamental challenges for developing a clearer understanding of the semantics of “ilities” (sometimes referred to as system qualities) is continuing ambiguity in these terms. Prior and ongoing research on use of a prescriptive semantic basis has resulted in a framework for formulating ility “definitions.” Progress has been made on evolving the semantic basis construct, a translation layer, and ilities metrics. Synergies with other relevant research are explored. Potential application of the semantic basis approach is discussed, including near-term application in education and longer-term application in technical document comprehension of non-technical requirements. Several future directions for ilities research within the systems community are proposed.
Transfantome

Transfantome is a novel robot interaction in which the user seamlessly changes or simultaneously uses different "Bodies" of different sizes, structures, and positions. By combining various multiple bodies, we expand our range of power, dexterity, and the place we act, and it brings us unexplored experience or highly efficient work.
Transdimensional transformation based Markov chain Monte Carlo

Variable dimensional problems, where not only the parameters, but also the number of parameters are random variables, pose serious challenge to Bayesians.Although in principle the Reversible Jump Markov Chain Monte Carlo (RJMCMC) methodology is a response to such challenges, the dimension-hopping strategies need not be always convenient for practical implementation, particularly because efficient "move-types" having reasonable acceptance rates are often difficult to devise.In this article, we propose and develop a novel and general dimensionhopping MCMC methodology that can update all the parameters as well as the number of parameters simultaneously using simple deterministic transformations of some low-dimensional (often one-dimensional) random variable.This methodology, which has been inspired by Transformation based MCMC (TMCMC) of (Stat.Mehodol.(2014) 16 100-116), facilitates great speed in terms of computation time and provides reasonable acceptance rates and mixing properties.Quite importantly, our approach provides a natural way to automate the move-types in variable dimensional problems.We refer to this methodology as Transdimensional Transformation based Markov Chain Monte Carlo (TTMCMC).Comparisons with RJMCMC in gamma and normal mixture examples demonstrate far superior performance of TTMCMC in terms of mixing, acceptance rate, computational speed and automation.Furthermore, we demonstrate good performance of TTMCMC in multivariate normal mixtures, even for dimension as large as 20.To our knowledge, there exists no application of RJMCMC for such high-dimensional mixtures.As by-products of our effort on the development of TTMCMC, we propose a novel methodology to summarize the posterior distributions of the mixture densities, providing a way to obtain the mode of the posterior distribution of the densities and the associated highest posterior density credible regions.Based on our method, we also propose a criterion to assess convergence of variable-dimensional algorithms.These methods of summarization and convergence assessment are applicable to general problems, not just to mixtures. IntroductionMarkov chain Monte Carlo (MCMC) is known to have revolutionized Bayesian computation.In modern times, it is often required to analyze high-dimensional,
SSwWS: Structural Model of Information Architecture

The Web Technologies allow a representation of a domain of knowledge. This facilitates the conversion of an explicit and tacit knowledge to the possibility of adding knowledge to the Web for automatic processing by the computer. For this reason, it has been designed to be an architecture known as SSwWS (Search Semantic with Web Services) or Search Semantic Web Services, to show how to extend the functionality of the Web search and semantic raised by Berners-Lee, on the meta-references, defined in a Web ontology, so that a user on the Internet can find the answers to their questions through Web services in a simple and fast.
Taming the Noisy Gradient: Train Deep Neural Networks with Small Batch Sizes

Deep learning architectures are usually proposed with millions of parameters, resulting in a memory issue when training deep neural networks with stochastic gradient descent type methods using large batch sizes. However, training with small batch sizes tends to produce low quality solution due to the large variance of stochastic gradients. In this paper, we tackle this problem by proposing a new framework for training deep neural network with small batches/noisy gradient. During optimization, our method iteratively applies a proximal type regularizer to make loss function strongly convex. Such regularizer stablizes the gradient, leading to better training performance. We prove that our algorithm achieves comparable convergence rate as vanilla SGD even with small batch size. Our framework is simple to implement and can be potentially combined with many existing optimization algorithms. Empirical results show that our method outperforms SGD and Adam when batch size is small. Our implementation is available at https://github.com/huiqu18/TRAlgorithm.
Feature Modeling vs. Decision Modeling

Modeling variability, i.e., defining the commonalities and variability of reusable artifacts, is a central task of software product line engineering. Numerous variability modeling approaches have been proposed in the last three decades. Most of these approaches are based on feature modeling (FM) or decision modeling (DM), two classes of variability approaches that go back to initial proposals made in the early 1990ies, i.e., FODA for FM and Synthesis for DM. This extended abstract summarizes the history of FM and DM as well as the results of a systematic comparison between FM and DM published earlier. We also outline perspectives, especially regarding potential synergies and key common elements that should be part of a standard variability modeling language.
Brand disclosure and source partiality affect native advertising recognition and media credibility

This study explores how textual characteristics of native advertising affected audiences’ advertising recognition and perceived message credibility and media channel credibility. Findings show that repeated mentioning of brand names increased audiences’ advertising recognition but did not affect perceived message credibility or media credibility. Using sponsor-affiliated sources increased audiences’ advertising recognition but decreased perceived message credibility and media credibility. The study recommends frequent and early sponsorship disclosure and cautions against using sponsor-affiliated sources in native advertising.
PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space

Finding the right reviewers to assess the quality of conference submissions is a time consuming process for conference organizers.Given the importance of this step, various automated reviewer-paper matching solutions have been proposed to alleviate the burden.Prior approaches, including bag-ofwords models and probabilistic topic models have been inadequate to deal with the vocabulary mismatch and partial topic overlap between a paper submission and the reviewer's expertise.Our approach, the common topic model, jointly models the topics common to the submission and the reviewer's profile while relying on abstract topic vectors.Experiments and insightful evaluations on two datasets demonstrate that the proposed method achieves consistent improvements compared to available state-of-the-art implementations of paper-reviewer matching.* We leave it to future work to experiment with other useful measures of semantic similarity.
Population inversion and entanglement in single and double glassy Jaynes-Cummings models

We find that a suppression of the collapse and revival of population inversion occurs in response to insertion of Gaussian quenched disorder in atom-cavity interaction strength in the Jaynes-Cummings model. The character of suppression can be significantly different in the presence of non-Gaussian disorder, which we uncover by studying the cases when the disorder is uniform, discrete, and Cauchy-Lorentz. Interestingly, the quenched averaged atom-photon entanglement keeps displaying nontrivial oscillations even after the population inversion has been suppressed. Subsequently, we show that disorder in atom-cavity interactions helps to avoid sudden death of atom-atom entanglement in the double Jaynes-Cummings model. We identify the minimal disorder strengths required to eliminate the possibility of sudden death. We also investigate the response of entanglement sudden death in the disordered double Jaynes-Cummings model in the presence of atom-atom coupling.
Map-type Modelling and Analysis of Children Stunting Case Data in Indonesia with Interactive Multimedia Method

Stunting is a major nutritional problem in Indonesia for years. Although the government has done some actions to solve this, the severity of this problem still persist, which means we have to improve the effectiveness of solving this problem. In this journal, we propose an application to integrate the data about stunting cases in Indonesia with its causes details, in hope to increase the effectiveness of government interventions in solving this problem. The application we propose implements interactive multimedia method to increase simplicity, user engagement, intuitiveness, and availability in processing data on stunting case.
Model Update Strategies about Object Tracking: A State of the Art Review

Object tracking has always been an interesting and essential research topic in the domain of computer vision, of which the model update mechanism is an essential work, therefore the robustness of it has become a crucial factor influencing the quality of tracking of a sequence. This review analyses on recent tracking model update strategies, where target model update occasion is first discussed, then we give a detailed discussion on update strategies of the target model based on the mainstream tracking frameworks, and the background update frameworks are discussed afterwards. The experimental performances of the trackers in recent researches acting on specific sequences are listed in this review, where the superiority and some failure cases on each of them are discussed, and conclusions based on those performances are then drawn. It is a crucial point that design of a proper background model as well as its update strategy ought to be put into consideration. A cascade update of the template corresponding to each deep network layer based on the contributions of them to the target recognition can also help with more accurate target location, where target saliency information can be utilized as a tool for state estimation.
Differential Privacy via a Truncated and Normalized Laplace Mechanism

When querying databases containing sensitive information, the privacy of individuals stored in the database has to be guaranteed. Such guarantees are provided by differentially private mechanisms which add controlled noise to the query responses. However, most such mechanisms do not take into consideration the valid range of the query being posed. Thus, noisy responses that fall outside of this range may potentially be produced. To rectify this and therefore improve the utility of the mechanism, the commonly-used Laplace distribution can be truncated to the valid range of the query and then normalized. However, such a data-dependent operation of normalization leaks additional information about the true query response, thereby violating the differential privacy guarantee. Here, we propose a new method which preserves the differential privacy guarantee through a careful determination of an appropriate scaling parameter for the Laplace distribution. We adapt the privacy guarantee in the context of the Laplace distribution to account for data-dependent normalization factors and study this guarantee for different classes of range constraint configurations. We provide derivations of the optimal scaling parameter (i.e., the minimal value that preserves differential privacy) for each class or provide an approximation thereof. As a result of this work, one can use the Laplace distribution to answer queries in a range-adherent and differentially private manner. To demonstrate the benefits of our proposed method of normalization, we present an experimental comparison against other range-adherent mechanisms. We show that our proposed approach is able to provide improved utility over the alternative mechanisms.