Reviewerly/exHarmony huggingface dataset:
Integrated security of cyber-physical vehicle networked systems in the age of 5G

In recent years, a series of serious catastrophic traffic accidents, such as the Chongqing bus crash and Wuxi Road bridge collapse, revealed some serious issues in the mobile vehicle safety and emergency response mechanisms.The advent of 5G communication has undoubtedly created some great opportunities for solving these issues.In order to fulfill the requirements of serious traffic accident prevention and forensic analysis, this paper proposes an event-based mobile vehicle cyber-physical security governance framework based on 5G communication technology.The proposed framework aims to resolve the issues of mobile vehicle security, including the availability of network resources in high-speed motion and the complexity of security objectives within cyberphysical systems.Relying on precise perception of insecure events at the physical, communication, and society layers, this paper constructs an integrated intelligent safety response strategy for physical equipment information security, state vehicle security, environmental vehicle security, and network security by intelligent perception, edge-cloud computing, and other technologies.The proposed framework achieves the goals of real-time event prediction before the event, immediate alarm during the event, and replay for evidence forensics after the event.
14 Ensemble Kalman filter for neural network-based one-shot inversion

We study the use of novel techniques arising in machine learning for inverse problems. Our approach replaces the complex forward model by a neural network, which is trained simultaneously in a one-shot sense when estimating the unknown parameters from data, i. e., the neural network is trained only for the unknown parameter. By establishing a link to the Bayesian approach to inverse problems we develop an algorithmic framework that ensures the feasibility of the parameter estimate with respect to the forward model. We propose an efficient, derivative-free optimization method based on variants of the ensemble Kalman inversion. Numerical experiments show that the ensemble Kalman filter for neural network-based one-shot inversion is a promising direction combining optimization and machine learning techniques for inverse problems.
Multilayer network based comparative document analysis (MUNCoDA)

The proposed multilayer network-based comparative document analysis (MUNCoDA) method supports the identification of the common points of a set of documents, which deal with the same subject area. As documents are transformed into networks of informative word-pairs, the collection of documents form a multilayer network that allows the comparative evaluation of the texts. The multilayer network can be visualized and analyzed to highlight how the texts are structured. The topics of the documents can be clustered based on the developed similarity measures. By exploring the network centralities, topic importance values can be assigned. The method is fully automated by KNIME preprocessing tools and MATLAB/Octave code.•Networks can be formed based on informative word pairs of a multiple documents•The analysis of the proposed multilayer networks provides information for multi-document summarization•Words and documents can be clustered based on node similarity and edge overlap measures
Vissen naar variatie

Belgian Dutch (BD) and Netherlandic Dutch (ND) are known to exhibit phonetic and lexical differences, but national variation in the syntax of Dutch has often been claimed to be quasi non-existent. This view is rooted in the fact that both laypersons and researchers are oblivious to national divergences in the grammar of Dutch (unless they are categorical and/or heavily mediatized), but also in the undisputed belief that BD and ND are different surface manifestations of ‘the same grammatical motor’. As a result, only a few syntactic phenomena have hitherto been shown to be sensitive to national constraints. In this paper we illustrate a computational bottom-up approach (pioneered in Bannard &amp; Callison-Burch 2005) to cast the net as widely as possible. Building on statistical machine translation and a parallel corpus of Dutch translations of English subtitles, we identify plausible mappings between English n-grams and their Dutch translations. We do this in order to obtain paraphrases, i.e., stretches of interchangeable Dutch text that carry approximately the same meaning. In a first case study, we found corroborating evidence among the discovered paraphrases for many syntactic variables that have previously been attested in Dutch, including complementizer variation, existential er-variation, word order phenomena, and inflection variation. Crucially, we also discovered a number of alternations we had not anticipated as interesting variables. In order to detect national constraints on the newly found variables, we carried out a second experiment with a smaller corpus of Belgian and Netherlandic subtitles: the two variables we investigated in this light – deictic strength variation and subordination variation – did indeed manifest national sensitivity.
Modeling relation paths for knowledge base completion via joint adversarial training

Knowledge Base Completion (KBC), which aims at determining the missing relations between entity pairs, has received increasing attention in recent years. Most existing KBC methods focus on either embedding the Knowledge Base (KB) into a specific semantic space or leveraging the joint probability of Random Walks (RWs) on multi-hop paths. Only a few unified models take both semantic and path-related features into consideration with adequacy. In this paper, we propose a novel method to explore the intrinsic relationship between the single relation (i.e. 1-hop path) and multi-hop paths between paired entities. We use Hierarchical Attention Networks (HANs) to select important relations in multi-hop paths and encode them into low-dimensional vectors. By treating relations and multi-hop paths as two different input sources, we use a feature extractor, which is shared by two downstream components (i.e. relation classifier and source discriminator), to capture shared/similar information between them. By joint adversarial training, we encourage our model to extract features from the multi-hop paths which are representative for relation completion. We apply the trained model (except for the source discriminator) to several large-scale KBs for relation completion. Experimental results show that our method outperforms existing path information-based approaches. Since each sub-module of our model can be well interpreted, our model can be applied to a large number of relation learning tasks.
An Initial Investigation on Optimizing Tandem Speaker Verification and Countermeasure Systems Using Reinforcement Learning

The spoofing countermeasure (CM) systems in automatic speaker verification (ASV) are not typically used in isolation of each other. These systems can be combined, for example, into a cascaded system where CM produces first a decision whether the input is synthetic or bona fide speech. In case the CM decides it is a bona fide sample, then the ASV system will consider it for speaker verification. End users of the system are not interested in the performance of the individual sub-modules, but instead are interested in the performance of the combined system. Such combination can be evaluated with tandem detection cost function (t-DCF) measure, yet the individual components are trained separately from each other using their own performance metrics. In this work we study training the ASV and CM components together for a better t-DCF measure by using reinforcement learning. We demonstrate that such training procedure indeed is able to improve the performance of the combined system, and does so with more reliable results than with the standard supervised learning techniques we compare against.
A sufficient entanglement criterion based on quantum fisher information and variance

We derive criterion in the form of inequality based on quantum Fisher information and quantum variance to detect multipartite entanglement. It can be regarded as complementary of the well-established PPT criterion in the sense that it can also detect bound entangled states. The inequality is motivated by Y.Akbari-Kourbolagh $et\ al.$[Phys. Rev A. 99, 012304 (2019)] which introduced a multipartite entanglement criterion based on quantum Fisher information. Our criterion is experimentally measurable for detecting any $N$-qudit pure state mixed with white noisy. We take several examples to illustrate that our criterion has good performance for detecting certain entangled states.
Scaling up and rolling out through the Web

The purpose of this paper is to investigate online public participation and engagement in science through crowdsourcing platforms. In order to fulfil this purpose, this paper will use the crowdsourcing platform Zooniverse as a case study, as it constitutes the most prominent and established citizen science platform today. The point of departure for the analysis is that Zooniverse can be seen as a “platformization” of citizen science and scientific citizenship. The paper suggests that the mobilisation of individuals who participate and engage in science on the Zooniverse platform takes place through an epistemic culture that emphasises both authenticity and prospects of novel discoveries. Yet, in the process of turning “raw” data into useable data, Zooniverse has implemented a framework that structures the crowd, something that limits the sort of participation that is offered on the platform. This limitation means that the platform as a whole hardly be seen as fostering a more radical democratic inclusion, for example in the form of a co-production of scientific knowledge, that dissolves the institutional borders between scientists and non-professional volunteers.
A method for qualitative evaluation of service-dominant business models

The widespread deployment of digital technologies has driven many contemporary organisations to adopt a service perspective as a means to extend their business practices or to cater to customer needs. To reduce service complexity, organisations engage in collaborative networks to exchange and integrate resources from many concurrent actors to co-create value. As a consequence, in contrast to firm-centric business models, the resulting service-dominant business model becomes networked in nature, describing the logic of how many concurrent actors co-create value and exchange resources. As business models take a pivotal position with respect to organisational performance and business-IT alignment, this places significant emphasis on the valid design and subsequent evaluation of these models, taking into account these service-dominant characteristics. However, we see that limited work is available to support business model evaluation from a service-dominant perspective, to address these concerns, design decisions and the resulting quality of the model. In response, this paper proposes a method for the qualitative evaluation of service-dominant business models. Following a design science research methodology, we have iteratively designed the method supported by theory on service-dominant logic, business model design and business model evaluation. We illustrate the application of the method by means of a case study.
Object Extraction Using Probabilistic Maps of Color, Depth, and Near-Infrared Information

Object extraction is one of the important and chal-lenging tasks in the computer vision and/or robotics ? elds.This task is to extract the object from the scene using anypossible cues. The scenario discussed in this study was the objectextraction which considering the Space of Interest (SOI), i.e.,the three dimensional area where the object probably existed.To complete such task, the object extraction method based onthe probabilistic maps of multiple cues was proposed. Thanksto the Kinect V2 sensor, multiple cues such as color, depth, andnear-infrared information can be acquired simultaneously. TheSOI was modeled by a simple probabilistic model by consideringthe geometry of the possible objects and the reachability of thesystem acquired from depth information. To model the color andnear-infrared information, a Gaussian mixture models (GMM)was used. All of the models were combined to generate theprobabilistic maps that were used to extract the object fromthe scene. To validate the proposed object extraction, severalexperiments were conducted to investigate the best combinationof the cues used in this study.
Edge-First Resource Management for Video-Based Applications: A Face Detection Use Case

The edge computing paradigm introduces a hierarchy of multiple processing elements between the edge devices, the gateways, and the cloud endpoints, in order to address the Internet-of-Things (IoT) challenges in a scalable way. In order to support the computational demands of latency-sensitive video applications and efficiently utilize the available network resources, we present an edge-based resource management methodology for serving video processing applications in an IoT environment. In this letter, we propose a scalable solution for providing low-latency video analytics at the edge level, while maximizing the quality of service under device and network constraints. As use case, we evaluate the proposed methodology on a face detection video processing application. The experimental results show that the proposed methodology satisfies specific time thresholds, comparing to cloud-only, local-only, and other video-optimized offloading techniques, while taking into consideration the video resolution.
Upper and lower densities of Gabor Gaussian systems

We study the upper and the lower densities of complete and minimal Gabor Gaussians systems. In contrast to the classical lattice case when they are both equal to 1, we prove that the lower density may reach 0 while the upper density may vary at least from 1π to e. In the case when the upper density exceeds 1, we establish a sharp inequality relating the upper and the lower densities.
Uso de plataforma virtual para retroalimentar la formación del profesorado

Given the current importance of the use of Information and Communication Technologies (ICT) in training processes, it is interesting to know how these tools are integrated into teaching and learning processes, specifically in the evaluation and feedback of practices in teacher training. This study deals with the types of written feedback provided by tutors and mentors to teachers in training through the use of the virtual platform Sistema de Evaluacion De Practicas Docentes (SEPRAD) and to know the level of satisfaction that future teachers have when using this technological support. For that reason, a retrospective qualitative design with an interpretative approach was applied. The participants were 34 university tutors, 96 schools mentors and 315 teachers in training from different Pedagogy careers. Among the most relevant results, it stands out that the tutors provide mostly authoritative feedback, with a marked orientation for correction. Regarding the use of the virtual platform, teachers in training value it positively, but demand possibilities of a higher interactivity between peers, tutor and mentor. The implications of these findings for teacher in training are discussed, as well as the potential of the virtual platform to bring on the feedback.
Predicting the Survival of AIDS Patients Using Two Frameworks of Statistical Joint Modeling and Comparing Their Predictive Accuracy

Background: The present study aimed to estimate the survival of HIV-positive patients and compare the accuracy of two commonly used models, Shared Random-Effect Model (SREM) and Joint Latent Class Model (JLCM) for the analysis of time to death among these patients. Methods: Data on a retrospective survey among HIV-positive patients diagnosed during 1989-2014 who referred to the Behavioral Diseases Consultation Center of Mashhad University of Medical Sciences was used in this study. Participants consisted of HIV-positive high-risk volunteers, referrals of new HIV cases from prisons, blood transfusion organization and hospitals. Subjects were followed from diagnosis until death or the end of study. SREM and JLCM were used to predict the survival of HIV/AIDS patients. In both models age, sex and addiction were included as covariates. To compare the accuracy of these alternative models, dynamic predictions were calculated at specific time points. The receiver operating characteristic (ROC) curve was used to select the more accurate model. Results: Overall, 213 patients were eligible that met entry conditions for the present analysis. Based on BIC criteria, three heterogeneous sub-populations of patients were identified by JLCM and individuals were categorized in these classes (“High Risk”, “Moderate Risk” and “Low Risk”) according to their health status. JLCM had a better predictive accuracy than SREM. The average area under ROC curve for JLCM and SREM was 0.75 and 0.64 respectively. In both models CD4 count decreased with time. Based on the result of JLCM, men had higher hazard rate than women and the CD4 counts levels of patients decreased with increasing age. Conclusion: Predicting risk of death (or survival) is vital for patients care in most medical research. In a heterogeneous population, such as HIV-positive patients fitting JLCM can significantly improve the accuracy of the risk prediction. Therefore, this model is preferred for these populations.
Deep Learning from Incomplete Data: Detecting Imminent Risk of Hospital-acquired Pneumonia in ICU Patients.

Hospital acquired pneumonia (HAP) is the second most common nosocomial infection in the ICU and costs an estimated $3.1 billion annually. The ability to predict HAP could improve patient outcomes and reduce costs. Traditional pneumonia risk prediction models rely on a small number of hand-chosen signs and symptoms and have been shown to poorly discriminate between low and high risk individuals. Consequently, we wanted to investigate whether modern data-driven techniques applied to respective pneumonia cohorts could provide more robust and discriminative prognostication of pneumonia risk. In this paper we present a deep learning system for predicting imminent pneumonia risk one or more days into the future using clinical observations documented in ICU notes for an at-risk population (n = 1, 467). We show how the system can be trained without direct supervision or feature engineering from sparse, noisy, and limited data to predict future pneumonia risk with 96% Sensitivity, 72% AUC, and 80% F1-measure, outperforming SVM approaches using the same features by 20% Accuracy (relative; 12% absolute).
Association Rule Mining on Big Data Sets

An accurate, complete, and rapid establishment of customer needs and existence of product recommendations are crucial points in terms of increasing customer satisfaction level in various different sectors such as the banking sector. Due to the significant increase in the number of transactions and customers, analyzing costs regarding time and consumption of memory becomes higher. In order to increase the performance of the product recommendation, we discuss an approach, a sample data creation process, to association rule mining. Thus instead of processing whole population, processing on a sample that represents the population is used to decrease time of analysis and consumption of memory. In this regard, sample composing methods, sample size determination techniques, the tests which measure the similarity between sample and population, and association rules (ARs) derived from the sample were examined. The mutual buying behavior of the customers was found using a well-known association rule mining algorithm. Techniques were compared according to the criteria of complete rule derivation and time consumption.
A Natural Logic System for Large Knowledge Bases

This paper describes principles and structure for a software system that implements a dialect of natural logic for knowledge bases. Natural logics are formal logics that resemble stylized natural language fragments, and whose reasoning rules reflect common-sense reasoning. Natural logics may be seen as forms of extended syllogistic logic. The paper proposes and describes realization of deductive querying functionalities using a previously specified natural logic dialect called Natura-Log. In focus here is the engineering of an inference engine employing as a key feature relational database operations. Thereby the inference steps are subjected to computation in bulk for scaling-up to large knowledge bases. Accordingly, the system eventually is to be realized as a general-purpose database application package with the database being turned logical knowledge base.
Learning parametric policies and transition probability models of markov decision processes from data

We consider the problem of estimating the policy and transition probability model of a Markov Decision Process from data (state, action, next state tuples). The transition probability and policy are assumed to be parametric functions of a sparse set of features associated with the tuples. We propose two regularized maximum likelihood estimation algorithms for learning the transition probability model and policy, respectively. An upper bound is established on the regret, which is the difference between the average reward of the estimated policy under the estimated transition probabilities and that of the original unknown policy under the true (unknown) transition probabilities. We provide a sample complexity result showing that we can achieve a low regret with a relatively small amount of training samples. We illustrate the theoretical results with a healthcare example and a robot navigation experiment.
Profiling Italian Misogynist: An Empirical Study

Hate speech may take different forms in online social environments. In this paper, we address the problem of automatic detection of misogynous language on Italian tweets by focusing both on raw text and stylometric profiles. The proposed exploratory investigation about the adoption of stylometry for enhancing the recognition capabilities of machine learning models has demonstrated that profiling users can lead to good discrimination of misogynous and not misogynous contents.
ICDAR'20: Intelligent Cross-Data Analysis and Retrieval

The First International Workshop on "Intelligence Cross-Data Analytics and Retrieval" (ICDAR'20) welcomes any theoretical and practical works on intelligence cross-data analytics and retrieval to bring the smart-sustainable society to human beings. We have witnessed the era of big data where almost any event that happens is recorded and stored either distributedly or centrally. The utmost requirement here is that data came from different sources, and various domains must be harmonically analyzed to get their insights immediately towards giving the ability to be retrieved thoroughly. These emerging requirements lead to the need for interdisciplinary and multidisciplinary contributions that address different aspects of the problem, such as data collection, storage, protection, processing, and transmission, as well as knowledge discovery, retrieval, and security and privacy. Hence, the goal of the workshop is to attract researchers and experts in the areas of multimedia information retrieval, machine learning, AI, data science, event-based processing and analysis, multimodal multimedia content analysis, lifelog data analysis, urban computing, environmental science, atmospheric science, and security and privacy to tackle the issues as mentioned earlier.