Reviewerly/exHarmony huggingface dataset:
ACTUARIAL APPLICATIONS OF WORD EMBEDDING MODELS

Abstract In insurance analytics, textual descriptions of claims are often discarded, because traditional empirical analyses require numeric descriptor variables. This paper demonstrates how textual data can be easily used in insurance analytics. Using the concept of word similarities, we illustrate how to extract variables from text and incorporate them into claims analyses using standard generalized linear model or generalized additive regression model. This procedure is applied to the Wisconsin Local Government Property Insurance Fund (LGPIF) data, in order to demonstrate how insurance claims management and risk mitigation procedures can be improved. We illustrate two applications. First, we show how the claims classification problem can be solved using textual information. Second, we analyze the relationship between risk metrics and the probability of large losses. We obtain good results for both applications, where short textual descriptions of insurance claims are used for the extraction of features.
Social presence and place illusion are affected by photorealism in embodied VR

Photorealism of virtual characters and environments is becoming more achievable in Virtual Reality (VR). With this development comes the need for further investigation into the role it plays on people’s responses to characters. Whether or not these improvements make any difference to the perception and response towards the virtual character was the central question of the present study. In order to evaluate this, we designed a within-subjects experiment, where participants were embodied in a high-fidelity virtual body in VR and were observing an animated character, rendered in photorealistic and simplified style. The character displayed a simple interactive behaviour with the participant (eye-gaze) and was designed to express an emotional reaction to induce an empathetic response in participants. Our goal was to evaluate if photorealism alone is enough to increase self-reported and behavioural signs (interpersonal distance or proximity) of social presence, place illusion, and empathetic concern for the character in virtual reality. This was found to be the case for self-reported social presence and place illusion, while empathetic concern depended on the order of condition. behavioural measure proximity was not affected by render style.
Current limitations in cyberbullying detection: On evaluation criteria, reproducibility, and data scarcity

The detection of online cyberbullying has seen an increase in societal importance, popularity in research, and available open data. Nevertheless, while computational power and affordability of resources continue to increase, the access restrictions on high-quality data limit the applicability of state-of-the-art techniques. Consequently, much of the recent research uses small, heterogeneous datasets, without a thorough evaluation of applicability. In this paper, we further illustrate these issues, as we (i) evaluate many publicly available resources for this task and demonstrate difficulties with data collection. These predominantly yield small datasets that fail to capture the required complex social dynamics and impede direct comparison of progress. We (ii) conduct an extensive set of experiments that indicate a general lack of cross-domain generalization of classifiers trained on these sources, and openly provide this framework to replicate and extend our evaluation criteria. Finally, we (iii) present an effective crowdsourcing method: simulating real-life bullying scenarios in a lab setting generates plausible data that can be effectively used to enrich real data. This largely circumvents the restrictions on data that can be collected, and increases classifier performance. We believe these contributions can aid in improving the empirical practices of future research in the field.
ORGANIZING EDUCATIONAL ACTIVITIES BASED ON INTERACTIVE METHODS ON MATHEMATICS SUBJECT

This article deals with some suggestions on how effectively organize math classes. Some interactive methods used in the learning process have been analyzed in the Functional Analysis lesson on the topic of “The spectrum and resolvent of linear operators”.
Analysing concatenation approaches to document-level NMT in two different domains

In this paper, we investigate how different aspects of discourse context affect the performance of recent neural MT systems.We describe two popular datasets covering news and movie subtitles and we provide a thorough analysis of the distribution of various document-level features in their domains.Furthermore, we train a set of context-aware MT models on both datasets and propose a comparative evaluation scheme that contrasts coherent context with artificially scrambled documents and absent context, arguing that the impact of discourse-aware MT models will become visible in this way.Our results show that the models are indeed affected by the manipulation of the test data, providing a different view on document-level translation quality than absolute sentence-level scores.
Pose-Assisted Multi-Camera Collaboration for Active Object Tracking

Active Object Tracking (AOT) is crucial to many vision-based applications, e.g., mobile robot, intelligent surveillance. However, there are a number of challenges when deploying active tracking in complex scenarios, e.g., target is frequently occluded by obstacles. In this paper, we extend the single-camera AOT to a multi-camera setting, where cameras tracking a target in a collaborative fashion. To achieve effective collaboration among cameras, we propose a novel Pose-Assisted Multi-Camera Collaboration System, which enables a camera to cooperate with the others by sharing camera poses for active object tracking. In the system, each camera is equipped with two controllers and a switcher: The vision-based controller tracks targets based on observed images. The pose-based controller moves the camera in accordance to the poses of the other cameras. At each step, the switcher decides which action to take from the two controllers according to the visibility of the target. The experimental results demonstrate that our system outperforms all the baselines and is capable of generalizing to unseen environments. The code and demo videos are available on our website https://sites.google.com/view/pose-assisted-collaboration.
nn-dependability-kit: Engineering Neural Networks for Safety-Critical Autonomous Driving Systems

Can engineering neural networks be approached in a disciplined way similar to how engineers build software for civil aircraft? We present nn-dependability-kit, an open-source toolbox to support safety engineering of neural networks for autonomous driving systems. The rationale behind nn-dependability-kit is to consider a structured approach (via Goal Structuring Notation) to argue the quality of neural networks. In particular, the tool realizes recent scientific results including (a) novel dependability metrics for indicating sufficient elimination of uncertainties in the product life cycle, (b) formal reasoning engine for ensuring that the generalization does not lead to undesired behaviors, and (c) runtime monitoring for reasoning whether a decision of a neural network in operation is supported by prior similarities in the training data. A proprietary version of nn-dependability-kit has been used to improve the quality of a level-3 autonomous driving component developed by Audi for highway maneuvers.
Multiple Query Content-Based Image Retrieval Using Relevance Feature Weight Learning

We propose a novel multiple query retrieval approach, named weight-learner, which relies on visual feature discrimination to estimate the distances between the query images and images in the database. For each query image, this discrimination consists of learning, in an unsupervised manner, the optimal relevance weight for each visual feature/descriptor. These feature relevance weights are designed to reduce the semantic gap between the extracted visual features and the user’s high-level semantics. We mathematically formulate the proposed solution through the minimization of some objective functions. This optimization aims to produce optimal feature relevance weights with respect to the user query. The proposed approach is assessed using an image collection from the Corel database.
Validation of an Indicator Model (INCODIES) for Assessing Student Digital Competence in Basic Education

Digital competence is a basic skill for citizens and should be systematically assessed, taking into account characteristics such as knowledge, skills and attitudes. However, measuring and identifying the digital competence of students is still a major challenge. To this end, the indicator model (INCODIES) has been developed and validated following the structure of the DIGCOMP model, which can be used to create assessment tests that measure student digital competence when they have completed their compulsory education. This article presents the results of a process of content validation carried out by 77 experts, in both the school and the university context. The analysis of the experts’ assessments and the level of agreement among those rating them were carried out following an original method which consists of determining a quantitative index for the validity of content called the Content Validity Ratio (CVR). The results obtained allowed us to confirm that the competences included in the reference framework DIGCOMP are correctly represented in the items formulated for the INCODIES model, which obtained high content validity. The model can serve as a structure and basis for the designing of specific evaluation tests on this topic.
Tablets and apps for promoting robotics, mathematics, STEM education and literacy in early childhood education

New interactive technologies in terms of smart mobile devices and accompanied applications (apps) attract an increasing attention in the field of preschool and early-primary education. This has risen a great amount of academic literature, and numerous implementation initiatives. Despite this widespread interest, successful integration of interactive technologies in preschool and early-primary education still faces unresolved issues and challenges. This paper refers mostly to smart mobile devices and their accompanied mobile applications (apps) at the device/platform level. Robotics, Mathematics, STEM and Literacy are discussed below, since these are the fields found to provide most opportunities in early childhood, especially promising to cultivate interests early in computing. The ultimate objective is to present a greater comprehension of the influence of new technologies on young children's learning procedure and its potential for early childhood education. The study ends up with a general analysis of the research findings and a short proposal for the extension of the under-study subject as well.
An incremental explanation of inference in Bayesian networks for increasing model trustworthiness and supporting clinical decision making

Various AI models are increasingly being considered as part of clinical decision-support tools. However, the trustworthiness of such models is rarely considered. Clinicians are more likely to use a model if they can understand and trust its predictions. Key to this is if its underlying reasoning can be explained. A Bayesian network (BN) model has the advantage that it is not a black-box and its reasoning can be explained. In this paper, we propose an incremental explanation of inference that can be applied to ‘hybrid’ BNs, i.e. those that contain both discrete and continuous nodes. The key questions that we answer are: (1) which important evidence supports or contradicts the prediction, and (2) through which intermediate variables does the information flow. The explanation is illustrated using a real clinical case study. A small evaluation study is also conducted.
Three-way decisions based blocking reduction models in hierarchical classification

Hierarchical classification (HC) is effective when categories are organized hierarchically. However, the blocking problem makes the effect of hierarchical classification greatly reduced. Blocking means that samples are easily getting misclassified in high-level classifiers so that the samples are blocked at the high-level of the hierarchy. This issue is caused by the inconsistency between the artificially defined hierarchy and the actual hierarchy of the raw data. Another issue is that it is flippant to strictly process data following the hierarchy. Therefore, special treatment is required for some uncertain data. To address the first issue, we learn category relationships and modify the hierarchy. To address the second issue, we introduce three-way decisions (3WD) to targetedly deal with the ambiguous data. We extend original studies and propose two HC models based on 3WD, collectively referred to as TriHC, for carefully modifying the hierarchy to alleviate the blocking problem. The proposed TriHC model learns new category hierarchies by the following three steps: (1) mining category relations; (2) modifying category hierarchies according to the latent category relations; and (3) using 3WD to divide observed objects into three regions: positive region, boundary region, and negative region, and making decisions based on different strategies. Specifically, based on different category relation mining methods, there are two versions of TriHC, cross-level blocking priori knowledge based TriHC (CLPK-TriHC) and expert classifier based TriHC (EC-TriHC). The CLPK-TriHC model defines a cross-level blocking distribution matrix to mine the category relations between the higher and lower levels. To better exploit category hierarchical relations, the EC-TriHC model builds expert classifiers using topic model to learn latent category topics. Experimental results validate that the proposed methods can simultaneously reduce the blocking and improve the classification accuracy.
CorNet: Generic 3D Corners for 6D Pose Estimation of New Objects without Retraining

We present a novel approach to the detection and 3D pose estimation of objects in color images. Its main contribution is that it does not require any training phases nor data for new objects, while state-of-the-art methods typically require hours of training time and hundreds of training registered images. Instead, our method relies only on the objects' geometries. Our method focuses on objects with prominent corners, which covers a large number of industrial objects. We first learn to detect object corners of various shapes in images and also to predict their 3D poses, by using training images of a small set of objects. To detect a new object in a given image, we first identify its corners from its CAD model; we also detect the corners visible in the image and predict their 3D poses. We then introduce a RANSAC-like algorithm that robustly and efficiently detects and estimates the object's 3D pose by matching its corners on the CAD model with their detected counterparts in the image. Because we also estimate the 3D poses of the corners in the image, detecting only 1 or 2 corners is sufficient to estimate the pose of the object, which makes the approach robust to occlusions. We finally rely on a final check that exploits the full 3D geometry of the objects, in case multiple objects have the same corner spatial arrangement. The advantages of our approach make it particularly attractive for industrial contexts, and we demonstrate our approach on the challenging T-LESS dataset.
Bringing Cyber to School: Integrating Cybersecurity Into Secondary School Education

Based on three one-day workshops with teachers, we identify drivers and barriers for introducing cybersecurity into secondary school education.We found that students, although more knowledgeable in cybersecurity than their teachers, lacked understanding of career pathways and online safety.Teachers, however, lacked adequate knowledge and resources.
Efficient heuristics for learning Bayesian network from labeled and unlabeled data

Bayesian network classifiers (BNCs) are powerful tools to mine statistical knowledge from data and infer under conditions of uncertainty. However, most of the traditional BNCs focus on mining the dependency relationships existed in labeled data while neglecting the information hidden in unlabeled d ata, which may result in the biased decision boundaries. To address this issue, we introduce a new order-based greedy search heuristic based on mutual information for building efficient structures in tree-augmented naive Bayes (TAN), which is a highly accurate learner while maintaining simplicity and efficiency. Target learning is used to dynamically describe the dependency relationships in each unlabeled test instance. Extensive experimental results on UCI (University of California at Irvine) machine learning repository demonstrate that our proposed algorithm is a competitive alternative to state-of-the-art classifiers like weighted averaged TAN and k-dependence Bayesian classifier, as well as Random forest.
Spatial pattern-shifting method for complete two-wavelength fringe projection profilometry

Two-wavelength fringe projection profilometry (FPP) unwraps a phase with the unambiguous phase range (UPR) of the least common multiple (LCM) of the two wavelengths. It is accurate, convenient, and robust, and thus plays an important role in shape measurement. However, when two non-coprime wavelengths are used, only a small UPR can be generated, and the unwrapping performance is compromised. In this Letter, a spatial pattern-shifting method (SPSM) is proposed to generate the maximum UPR (i.e., the product of the two wavelengths) from two non-coprime wavelengths. For the first time, to the best of our knowledge, the SPSM breaks the constraint of wavelength selection and enables a complete (i.e., either coprime or non-coprime) two-wavelength FPP. The SPSM, on the other hand, only requires spatially shift of the low-frequency pattern with the designed amounts and accordingly adjusting the fringe order determination, which is extremely convenient in implementation. Both numerical and experimental analyses verify its flexibility and correctness.
“Hey assistant, how can I become a donor?” The case of a conversational agent designed to engage people in blood donation

People have insufficient knowledge and many misconceptions about the blood donation process, which hampers donors recruitment. Therefore, novel strategies and resources are needed to provide information and improve these circumstances. We aimed at an interactive conversational agent to explain about blood donation. We used the Dialogflow framework to develop a conversational agent and deployed it publicly. Afterward, we conducted an assessment of user experience (UX) with 50 participants who interacted with the agent. We analyzed participants’ opinions, the different UX scales, and their association with participants’ demographic variables. The conversational agent is available on the Google Assistant platform in Brazil. It is capable of responding to utterances related to 30 common questions and concerns about donating blood. The user can interact and explore freely and in any order by typing, speaking and selecting interface elements. The agent responds by speaking and displaying visual information, some multimedia content, and suggestions for continuing the dialogue. It enables a conversational sequence in which knowledge is imparted to the user in stages as the dialogue evolves. The overall UX assessed was very satisfactory, and people with specific demographic characteristics were more likely to have better UX. All participants had positive opinions and attitudes towards the conversational agent. A conversational agent is a creative and captivating strategy of imparting knowledge and engage people regarding blood donation. The findings reaffirm the potential of using this technology for information outreach, especially for socially relevant purposes.
The case for blockchain in IoT identity management

Purpose The aim of this paper is to evaluate the use of blockchain for identity management (IdM) in the context of the Internet of things (IoT) while focusing on privacy-preserving approaches and its applications to healthcare scenarios. Design/methodology/approach The paper describes the most relevant IdM systems focusing on privacy preserving with or without blockchain and evaluates them against ten selected features grouped into three categories: privacy, usability and IoT. Then, it is important to analyze whether blockchain should be used in all scenarios, according to the importance of each feature for different use cases. Findings Based on analysis of existing systems, Sovrin is the IdM system that covers more features and is based on blockchain. For each of the evaluated use cases, Sovrin and UniquID were the chosen systems. Research limitations/implications This paper opens new lines of research for IdM systems in IoT, including challenges related to device identity definition, privacy preserving and new security mechanisms. Originality/value This paper contributes to the ongoing research in IdM systems for IoT. The adequacy of blockchain is not only analyzed considering the technology; instead the authors analyze its application to real environments considering the required features for each use case.
Cursive Character Recognition in Natural Scene Images Using a Multilevel Convolutional Neural Network Fusion

The accuracy of current natural scene text recognition algorithms is limited by the poor performance of character recognition methods for these images.The complex backgrounds, variations in the writing, text size, orientations, low resolution and multi-language text make recognition of text in natural images a complex and challenging task.Conventional machine learning and deep learning-based methods have been developed that have achieved satisfactory results, but character recognition for cursive text such as Arabic and Urdu scripts in natural images is still an open research problem.The characters in the cursive text are connected and are difficult to segment for recognition.Variations in the shape of a character due to its different positions within a word make the recognition task more challenging than non-cursive text.Optical character recognition (OCR) techniques proposed for Arabic and Urdu scanned documents perform very poorly when applied to character recognition in natural images.In this paper, we propose a multi-scale feature aggregation (MSFA) and a multi-level feature fusion (MLFF) network architecture to recognize isolated Urdu characters in natural images.The network first aggregates multi-scale features of the convolutional layers by up-sampling and addition operations and then combines them with the high-level features.Finally, the outputs of the MSFA and MLFF networks are fused together to create more robust and powerful features.A comprehensive dataset of segmented Urdu characters is developed for the evaluation of the proposed network models.Synthetic text on the patches of images with real natural scene backgrounds is generated to increase the samples of infrequently used characters.The proposed model is evaluated on the Chars74K and ICDAR03 datasets.To validate the proposed model on the new Urdu character image dataset, we compare its performance with the histogram of oriented gradients (HoG) method.The experimental results show that the aggregation of multi-scale and multilevel features and their fusion is more effective, and outperforms other methods on the Urdu character image and Chars74K datasets.