Reviewerly/exHarmony huggingface dataset:
A Practical Guide for Analyzing Large-Scale Assessment Data Using Mplus: A Case Demonstration Using the Program for International Assessment of Adult Competencies Data

In order to promote the use of increasingly available large-scale assessment data in education and expand the scope of analytic capabilities among applied researchers, this study provides step-by-step guidance, and practical examples of syntax and data analysis using Mplus. Concise overview and key unique aspects of large-scale assessment data from the 2012/2014 Program for International Assessment of Adult Competencies (PIAAC) are described. Using commonly-used statistical software including SAS and R, a simple macro program and syntax are developed to streamline the data preparation process. Then, two examples of structural equation models are demonstrated using Mplus. The suggested data preparation and analytic approaches can be immediately applicable to existing large-scale assessment data.
Privacy by Design: Survey on Capacitive Proximity Sensing as System of Choice for Driver Vehicle Interfaces

Physiological properties are recorded everywhere with cameras. They are also used to identify people in public spaces. Vehicle manufacturers also use camera systems in cars. For example, those cameras are used in cars to identify people, measure attention and recognize gestures. Especially the recording of facial images can cause privacy concerns following the GDPR. It is therefore questionable whether the recording of facial car user images corresponds to the paradigm privacy-by-design required by the GDPR.
Deinterleaving Pulse Trains via Interleaved Markov Process Estimation

A pulse train deinterleaving framework based on interleaved Markov process (IMP) estimation is presented. The approach models interleaved pulse trains as a collection of Markov processes interleaved by a Markov switch. Deinterleaving is performed through learning an IMP representation of the interleaved pulse trains. We apply a recent unsupervised information-theoretic technique that infers the underlying Markov processes by minimizing a penalized maximum likelihood (PML) entropy cost function. The technique is able to associate groups of disparate clusters in the pulse parameter space that are produced by the same parameter-agile emitter. This enables the correct determination of the number of emitters in scenarios where traditional multi-parameter clustering techniques produce many more clusters than there are actual emitters. Pulse timing information is incorporated to the extent that it is reflected in pulse ordering; in contrast to traditional time-of-arrival analysis techniques, the approach does not require precise timing resolution and can accommodate highly complex pulse repetition interval (PRI) patterns. An experimental demonstration and performance characterization on synthetic pulse train datasets is provided.
Corpus processing service: A Knowledge Graph platform to perform deep data exploration on corpora

Abstract Knowledge Graphs have been fast emerging as the de facto standard to model and explore knowledge in weakly structured data. Large corpora of documents constitute a source of weakly structured data of particular interest for both the academic and business world. Key examples include scientific publications, technical reports, manuals, patents, regulations, etc. Such corpora embed many facts that are elementary to critical decision making or enabling new discoveries. In this paper, we present a scalable cloud platform to create and serve Knowledge Graphs, which we named corpus processing service (CPS). Its purpose is to process large document corpora, extract the content and embedded facts, and ultimately represent these in a consistent knowledge graph that can be intuitively queried. To accomplish this, we use state‐of‐the‐art natural language understanding models to extract entities and relationships from documents converted with our previously presented corpus conversion service platform. This pipeline is complemented with a newly developed graph engine which ensures extremely performant graph queries and provides powerful graph analytics capabilities. Both components are tightly integrated and can be easily consumed through REST APIs. Additionally, we provide user interfaces to control the data ingestion flow and formulate queries using a visual programming approach. The CPS platform is designed as a modular microservice system operating on Kubernetes clusters. Finally, we validate the quality of queries on our end‐to‐end knowledge pipeline in a real‐world application in the oil and gas industry.
DeepRouting: A Deep Neural Network Approach for Ticket Routing in Expert Network

Ticket routing is a part of software support process, where multiple expert groups are involved in processing incident tickets. The goal of routing is to find an expert group which can resolve a ticket at the initial assignment, or when it needs to be transferred to another group. Matching a ticket to its potential resolver effectively provides significant business value for both service providers and their customers. Previous works used hand-crafted features to train predictive models to automate or assist in routing. One of the findings shows that, the similarity between a ticket and an expert group is prominent in identifying the resolver among other groups. Meanwhile, numerous studies demonstrate the effectiveness of deep neural networks in text similarity modeling problems. In this paper, we propose a multi-view deep neural network solution to jointly learn a relevance score for a ticket-group pair, using both text and routing path information. The text relevance is modeled by a classic deep semantic matching model, while the routing graph representation is embedded using a convolutional graph network. Experimental results show that the proposed approach outperforms baseline models in resolver ranking and assistive routing tasks. Comparative experiments also show that text has higher importance than routing path information.
A model for building probabilistic knowledge-based systems using divergence distances

The knowledge-based systems (KBSs) in general and solving the knowledge merging problem in particular have seen a great surge of research activity in recent years. However, there still exist two main shortcomings that need to be addressed in the probabilistic framework. Firstly, the current methods only deal with the problems in which original probabilistic knowledge bases (PKBs) are required to be consistent and formed in the same structure. It is a very strong requirement and difficult to apply in practice. Secondly, only a few measures of distance between probability distributions have been studied to apply in existing models. To overcome these disadvantages, in this paper, we introduce a novel framework for merging PKBs. To this end, a theoretical model is introduced and several experiments are implemented. In theoretical model, some theorems are pointed out and proved to provide mathematical background to construct the merging model. Moreover, a deep survey on how to employ divergence distance functions (DDFs) between probability distributions to carry out the merging process are performed. In experimental aspect, a consistency recovery algorithm and some merging algorithms based on DDFs are proposed. Through the results of conducted experiments, issues about the time cost of merging process, the number of iterations, and CPU Time Elapsed parameter to solve the class of optimization problems in the merging process are analyzed, compared, and evaluated.
Non-classical properties of coherent States in magnetized anisotropic 2D dirac materials

Novel properties of 2D Dirac materials make them attractive candidates to study the quantum effects. In anisotropic 2D materials, using an annihilation operator, coherent states of the electrons can be defined. In this paper we have considered non-classical properties of such states including entanglement, statistical properties, squeezing and polarization properties. Introducing bases, we find that these states are entangled in some ranges of coherence parameters. Also, with the use of Mandel parameter, we realize that the states may have sub-Poissonian statistics. In addition, coherent states may be squeezed and polarized in some values of parameters. As well as, the entanglement will vanish and the polarization degree goes to its maximum for the large values of parameters.
Bangla Text Generation Using Bidirectional Optimized Gated Recurrent Unit Network

Natural language processing is a vital branch of Artificial Intelligence, a bridge to communicate between computer and human in their languages and is used to generate automatic text, summarize articles, machine translation, etc. In this paper, we proposed a model of Bangla sentence generation that uses the Optimized Gated Recurrent Unit network based Recurrent Neural network model. We used OGRU based model instead of the regular Gated Recurrent Unit network because OGRU generates better results than GRU. For this purpose, we have used three Bangla corpus datasets, which consist of more than 49 million words. Here we used Bangla Natural Language Toolkit to preprocess the datasets. We trained our model with those three different datasets and achieved an accuracy of 97% on average and it can generate a paragraph of text based on the context. With this accuracy and ability to generate text, it is proved that our model is much efficient in Generating Bengali Sentences.
A new data augmentation method based on local image warping for medical image segmentation

Purpose The segmentation accuracy of medical images was improved by increasing the number of training samples using a local image warping technique. The performance of the proposed method was evaluated in the segmentation of breast masses, prostate and brain tumors, and lung nodules. Methods We propose a simple data augmentation method which is called stochastic evolution (SE). Specifically, the idea of SE stems from our thinking about the deterioration of the diseased tissue and the healing process. In order to simulate this natural process, we implement it according to the local distortion algorithm in image warping. In other words, the irregular deterioration and healing processes of the diseased tissue is simulated according to the direction of the local distortion, thereby producing a natural sample that is indistinguishable by humans. Results The proposed method is evaluated on four segmentation tasks of breast masses, prostate, brain tumors, and lung nodules. Comparing the experimental results of four segmentation methods based on the UNet segmentation architecture without adding any expanded data during training, the accuracy and the Hausdorff distance obtained in our approach remain almost the same as other methods. However, the dice similarity coefficient (DSC) and sensitivity (SEN) have both improved to some extent. Among them, DSC is increased by 5.2%, 2.8%, 1.0%, and 3.2%, respectively; SEN is increased by 6.9%, 4.3%, 1.2%, and 4.5%, respectively. Conclusions Experimental results show that the proposed SE data augmentation method could improve the segmentation accuracy of breast masses, prostate, brain tumors, and lung nodules. The method also shows the robustness with different image datasets and imaging modalities.
Using Syntactic Co-occurrences to Trace Phraseological Complexity Development in Learner Writing: Verb + Object Structures in LONGDALE

This study investigates phraseological complexity development in the writing of French learners of English as a Foreign Language from the Longitudinal Database of Learner English (LONGDALE). More particularly, it examines the effects of proficiency and time spent learning English on the phraseological sophistication of verb + object relations, which is measured with mutual information at three data collection points. Results of a mixed-effects model in which we control for potential effects of topic/prompt show that learner proficiency, as assessed by a standardized test, is a better predictor of phraseological complexity in each learner writing sample than the actual data collection point at which the essay was written, thus suggesting that the time dimension does not add anything to, or modify what we already know about, EFL learners’ use of statistical co-occurrences as represented in this longitudinal corpus.
Qualitative HD Image and Video Recovery via High-Order Tensor Augmentation and Completion

This paper presents a new framework for severely distorted image and video recovery via tensor augmentation and completion. By considering the task of representing a matrix by a high-order-$n$ tensor as that of encoding the matrix's 2-D indices $(i,j)$ by $n$-digit words $i_1i_2\dots i_n$, we develop a new high order tensor augmentation to cast a third order tensor of color images or video sequences containing missing pixels into a higher order tensor, which is similar to the ket augmentation of quantum physics, is capable of capturing all correlations and entanglements between entries of the original third order tensor. Accordingly, the resuling high-order tensor is completed by our previously developed parallel matrix factorization via tensor train decomposition. Simulations are provided to show the clear advantages of our approach in enhancing important metrics of visual quality such as relative square error and structural similarity index in image and video processing that help to achieve high recovery rates even for high-definition images and videos with 95% missing pixels.
Asymptotic behavior of free energy when optimal probability distribution is not unique

Bayesian inference is a widely used statistical method. The free energy and the generalization loss, which are used to estimate the accuracy of Bayesian inference, are known to be small in singular models that do not have a unique optimal parameter. However, their characteristics have not yet been clarified when there are multiple optimal probability distributions. In this paper, we theoretically derive the asymptotic behaviors of the generalization loss and the free energy in the case that the optimal probability distributions are not unique and show that they contain asymptotically different terms from those of the conventional asymptotic analysis.
Extracting Information from Weighted Contact Networks via Genetic Algorithms

Epidemic contact tracing examines the movement of infection through a population based upon links in a contact network, and weighted networks represent the potential of transfer of the contagion. Graph compression reduces the size of a network by merging groups of nodes into supernodes. This study considers the use of genetic algorithms to select the nodes to be merged, grouping together highly connected sections of the graphs. Examined is a dataset that is extracted from contacts that occurred during several days of the "Infectious: Stay Away" event. The incorporation of weights, to indicate the strength of interactions between individuals, is an important contribution of this work. The demonstrated outcomes are that by including weighted information on the edges, there is more effective detection of highly interacting subgroups when compared to the unweighted version of graphs. These methods not only compress the networks with a low rate of distortion, but also the identification of supernodes in the networks allows for better targeting of interventions by public health upon individuals in such groups. This is crucial because when one member becomes infected, all members of the group are exposed to the contagion.
Heterogeneous Graphical Granger Causality by Minimum Message Length

The heterogeneous graphical Granger model (HGGM) for causal inference among processes with distributions from an exponential family is efficient in scenarios when the number of time observations is much greater than the number of time series, normally by several orders of magnitude. However, in the case of “short” time series, the inference in HGGM often suffers from overestimation. To remedy this, we use the minimum message length principle (MML) to determinate the causal connections in the HGGM. The minimum message length as a Bayesian information-theoretic method for statistical model selection applies Occam’s razor in the following way: even when models are equal in their measure of fit-accuracy to the observed data, the one generating the most concise explanation of data is more likely to be correct. Based on the dispersion coefficient of the target time series and on the initial maximum likelihood estimates of the regression coefficients, we propose a minimum message length criterion to select the subset of causally connected time series with each target time series and derive its form for various exponential distributions. We propose two algorithms—the genetic-type algorithm (HMMLGA) and exHMML to find the subset. We demonstrated the superiority of both algorithms in synthetic experiments with respect to the comparison methods Lingam, HGGM and statistical framework Granger causality (SFGC). In the real data experiments, we used the methods to discriminate between pregnancy and labor phase using electrohysterogram data of Islandic mothers from Physionet databasis. We further analysed the Austrian climatological time measurements and their temporal interactions in rain and sunny days scenarios. In both experiments, the results of HMMLGA had the most realistic interpretation with respect to the comparison methods. We provide our code in Matlab. To our best knowledge, this is the first work using the MML principle for causal inference in HGGM.
Calibration Venus: An Interactive Camera Calibration Method Based on Search Algorithm and Pose Decomposition

Cameras are widely used in many scenes such as robot positioning and unmanned driving, in which the camera calibration is a major task in this field. The interactive camera calibration method based on a plane board is becoming popular due to its stability and handleability. However, most methods choose suggestions subjectively from a fixed pose dataset, which is error-prone and limited for different camera models. In addition, these methods do not provide clear guidelines on how to place the board in the specified pose. This paper proposes a new interactive calibration method, named ‘Calibration Venus’, including two main parts: pose search and pose decomposition. First, a pose search algorithm based on simulated annealing (SA) algorithm is proposed to select the optimal pose in the entire pose space. Second, an intuitive and easy-to-use user guidance method is designed to decompose the optimal pose into four sub-poses: translation, each rotation along X-, Y-, Z-axes. Thereby the users could follow the guide step by step to accurately complete the placement of the calibration board. Experimental results evaluated on simulated and real datasets show that the proposed method can reduce the difficulty of calibration, and improve the accuracy of calibration, as well as provide better guidance.
VFDP: Visual Analysis of Flight Delay and Propagation on a Geographical Map

The propagation of flight delays is challenging to analyze because delay events depend on multiple variables. This phenomenon has become even worse with the increasing number of aircraft in China, and research into delay propagation has shown limited progress. In this paper, we design a visual analysis system for flight delay propagation. Unlike conventional flight delay research, this work focuses on the flight delay propagation trends in one region and representing the relationship of delays occurring in multiple airports. First, we construct a Bayesian network to analyze the delay parameters and select delay factors for visualization. Second, the system employs a series of visualization methods to present the propagation of flight delays, including density and flow visualizations. Third, the system combines multiple available visual representations for analyzing flight delays from different aspects. We demonstrate our methods with real data in multiple types of cases, and we evaluate our visual design through user studies. The results help identify several benefits of our system and confirm its usefulness for delay propagation analysis.
A Blockchain handling Data in a Waste Recycling Scenario and Fostering Participation

When having to improve waste collection and recycling, the citizen involvement is paramount. However, the lack of transparency about everyday activities and progresses towards long term goals could result in a loss of interest for the community and more difficulties in achieving desired results. In our proposed approach, data related to waste collection would be automatically passed from sensors to a blockchain smart contract. Such data include the amount of waste collected for each category from a citizen. By using a blockchain storing data related to collected waste, maximum data integrity and transparency would be ensured. Furthermore, citizens are provided with a smartphone app to read data from the blockchain, register other data concerning their activities, and check their fee balance. The overall solution, by ensuring that each citizen can monitor their progress and the global results achieved by the community, would address and foster participation.
Evaluation of multiple surface irradiance-based clear sky detection methods at Xianghe—A heavy polluted site on the North China Plain

Surface irradiance measurements with high temporal resolution can be used to detect clear skies, which is a critical step for further study, such as aerosol and cloud radiative effects. Twenty-one clear-sky detection (CSD) methods are assessed based on five years of 1-min surface irradiance data at Xianghe—a heavily polluted station on the North China Plain. Total-sky imager (TSI) discrimination results corrected by manual checks are used as the benchmark for the evaluation. The performance heavily relies on the criteria adopted by the CSD methods. Those with higher cloudy-sky detection accuracy rates produce lower clear-sky accuracy rates, and vice versa. A general tendency in common among all CSD methods is the detection accuracy deteriorates when aerosol loading increases. Nearly all criteria adopted in CSD methods are too strict to detect clear skies under polluted conditions, which is more severe if clear-sky irradiance is not properly estimated. The mean true positive rate (CSD method correctly detects clear sky) decreases from 45% for aerosol optical depth (AOD) ≤ 0.2% to 6% for AOD > 0.5. The results clearly indicate that CSD methods in a highly polluted region still need further improvements. 摘要 根据位于华北平原的重污染站点——香河5年的分钟级别地表太阳辐射和人工订正的全天空成像仪数据, 对21种晴空识别 (CSD) 方法进行了评估:晴空识别准确率较高的方法云天识别准确率较低, 反之亦然;由于CSD 方法采用的参数阈值不适用于污染情况, 当气溶胶含量增加时, 识别准确率呈下降趋势.研究结果显示, 利用太阳辐射数据识别晴空的方法在高污染地区使用时需进行改进.
Information Security Awareness of Students on Academic Information System Using Kruger Approach

With the rapid development of technology, security awareness of information plays an important role. It encompasses the user's consciousness to commit the rules, realize the potentiality, comprehend the responsibilities, and conduct accordingly. The adverse effect of technology is boosting crime in cyberspace. Therefore, it needs protection for a system to prevent this kind of crime on the internet. One of the most vulnerable and essential systems in higher education that needs protection is the Academic Information System (AIS), which facilitates students and lecturers in academics. As one of the universities that use AIS, Universitas Islam Negeri Syarif Hidayatullah (UIN Syahid) requires action to increase information security awareness in the academic information system to understand information security. It is necessary to measure AIS users' security awareness level. We use the Kruger approach adapted from psychology theory that developed into three equivalent dimensions. These three dimensions are related to confidentiality, integrity, and availability. The result shows most AIS users in UIN Syahid have a medium level with 75% of security awareness, with the constraint of this study only conducted to students.