Reviewerly/exHarmony huggingface dataset:
A Computer-Assisted Writing Tool for an Extended Variety of Leichte Sprache (Easy-to-Read German)

Leichte Sprache (LS; easy-to-read German) defines a variety of German characterized by simplified syntactic constructions and a small vocabulary. It provides barrier-free information for a wide spectrum of people with cognitive impairments, learning difficulties, and/or a low level of literacy in the German language. The levels of difficulty of a range of syntactic constructions were systematically evaluated with LS readers as part of the recent LeiSA project ( Bock, 2019 ). That study identified a number of constructions that were evaluated as being easy to comprehend but which fell beyond the definition of LS. We therefore want to broaden the scope of LS to include further constructions that LS readers can easily manage and that they might find useful for putting their thoughts into words. For constructions not considered in the LeiSA study, we performed a comparative treebank study of constructions attested to in a collection of 245 LS documents from a variety of sources. Employing the treebanks TüBa-D/S (also called VERBMOBIL) and TüBa-D/Z, we compared the frequency of such constructions in those texts with their incidence in spoken and written German sources produced without the explicit goal of facilitating comprehensibility. The resulting extension is called Extended Leichte Sprache (ELS). To date, text in LS has generally been produced by authors proficient in standard German. In order to enable text production by LS readers themselves, we developed a computational linguistic system, dubbed ExtendedEasyTalk . This system supports LS readers in formulating grammatically correct and semantically coherent texts covering constructions in ELS. This paper outlines the principal components: (1) a natural-language paraphrase generator that supports fast and correct text production while taking readership-design aspects into account, and (2) explicit coherence specifications based on Rhetorical Structure Theory (RST) to express the communicative function of sentences. The system’s writing-workshop mode controls the options in (1) and (2). Mandatory questions generated by the system aim to teach the user when and how to consider audience-design concepts. Accordingly, users are trained in text production in a similar way to elementary school students, who also tend to omit audience-design cues. Importantly, we illustrate in this paper how to make the dialogues of these components intuitive and easy to use to avoid overtaxing the user. We also report the results of our evaluation of the software with different user groups.
Designing an Email Security Awareness Program for State-Owned Enterprises in Namibia

Emails offer easy and timely communication enabling businesses to connect to their stakeholders in real time. State-owned enterprises are no exception to this practice. Being a global enabler of communication, emails are a preferred medium for delivering malicious payload for hackers. Emails have well-known vulnerabilities such as malware, phishing, and spamming that can cripple and devastate an organization. Most attacks depend on the user performing some action such as clicking a link or sharing sensitive information making email security a human issue. Through their insecure behavior, employees can open doors for hackers into their organizational infrastructure. Change in behavior needs more than availing information on threats, risks, and unethical behaviors, and employees must be able to know or understand what is at risk and how to mitigate the email security risks. End-users/IT professionals must be motivated and willing to apply security practices/policies, and the latter requires a change of attitude and intentions. This qualitative exploratory research study presents results of an email security awareness study conducted with one state-owned enterprise in Namibia. Findings will be used to design an email security awareness program that will enhance the email security within the state-owned enterprise organizations and improve ICT awareness.
Unsupervised Path Regression Networks

We demonstrate that challenging shortest path problems can be solved via direct spline regression from a neural network, trained in an unsupervised manner (i.e. without requiring ground truth optimal paths for training). To achieve this, we derive a geometry-dependent optimal cost function whose minima guarantees collision-free solutions. Our method beats state-of-the-art supervised learning baselines for shortest path planning, with a much more scalable training pipeline, and a significant speedup in inference time.
Self-supervised Learning for Object Detection in Autonomous Driving

Recently, self-supervised pretraining methods have achieved impressive results, matching ImageNet weights on a variety of downstream tasks including object detection. Despite their success, these methods have some limitations. Most of them are optimized for image classification and compute only a global feature vector describing an entire image. On top of that, they rely on large batch sizes, a huge amount of unlabeled data and vast computing resources to work well.To address these issues, we propose SSLAD, a Self-Supervised Learning approach especially suited for object detection in the context of Autonomous Driving. SSLAD computes local image descriptors that are invariant to augmentations and scale. In experiments, we show that our method outperforms state-of-the-art self-supervised pretraining methods, on various object detection datasets from the automotive domain. By leveraging just 20,000 unlabeled images taken from a video camera on a car, SSLAD almost matches ImageNet weights trained on 1,200,000 labeled images. If as few as 20 unlabeled images are available, SSLAD generates weights that are far better than a random initialization, whereas competing self-supervised methods just do not work given so little data. Furthermore, SSLAD is very robust with respect to batch size. Even with a batch size of one, SSLAD generates weights that are clearly superior to a random initialization while greatly outperforming other self-supervised methods. This property of SSLAD even allows for single-GPU training with only a minor decrease in performance.
Knowledge transfer for causal discovery

Subject-specific causal discovery methods could help to make better decisions in domains where there are variations on causal relations across subjects, such as neuroscience and genetics. However, discovering subject-specific causal models from limited data sets could be challenging. Although there are several causal discovery methods, most of them are focused on finding the common causal relations of a population from data sets with enough samples. The issue of discovering subject-specific causal relations from limited data sets has not been sufficiently explored. In this paper, we propose a knowledge transfer method for discovering subject-specific causal models. Our method discovers causal probabilistic graphical models, up to Markov equivalence classes, from discrete and continuous data sets. We hypothesized that transferring weighted instances of additional sources according to their discrepancy with target instances helps to compensate for the lack of target data and improves the performance of a scored-based causal discovery method. Experimental results on synthetic data sets and benchmark causal Bayesian networks show that our proposal, under few target samples, outperforms significantly in adjacency and arrowhead recovery the baseline and alternative methods.
A framework to predict early news popularity using deep temporal propagation patterns

The increasing competition among the news industries puts editors under the pressure of posting news articles that should gain more user attention. News popularity is predicted using different content and metadata features. Some approaches use retweet paths formed on social media when a tweet is retweeted. However, before a piece of news spreads by retweeting, there are several initial tweets made by multiple different users that spread the same news. Retweeting behavior serves as the secondary features in this case while the initial tweets serve as the primary features. In this work, the popularity of a news item published on a certain website is predicted by exploiting the initial tweeting behavior of the news item on Twitter. The temporal characteristics of a news item are exploited as the news propagates via tweets. Additionally, other content and metadata features have also been used to predict news popularity. Data is extracted from different websites of cybersecurity news and Twitter. A deep neural network is proposed to predict early news popularity. The proposed model yields the macro averaged F-score of 92% which shows the effectiveness of temporal propagation patterns in predicting news popularity. The proposed model is compared with the baseline models and state-of-the-art techniques, and it is shown that the proposed model outperforms all the existing techniques.
Korean Dialect Identification Based on Intonation Modeling

Korean dialect identification (K-DID) is a challenging task due to its relatively unexplored field of study, mutual comprehensibility between the dialects, and lack of sufficient Korean dialect datasets available in the past. With large-scaled dialect datasets now available, this paper proposes intonational modeling of the Korean dialects by feeding frame-wise acoustic features on sequential modeling of a neural network. Compared to previous prosodic labeling with syllable-based pitch marking, our approach of intonation modeling is realized with the combination of a set of spectral features, including fundamental frequency, trained on a bidirectional LSTM network with attention mechanism. We believe the attention mechanism enables the detection of dialect-rich segments hidden among the dominant non-dialect segments within the same utterance. We test the networks on different combinations of speaker ages and speech styles. The best performance of the K-DID is achieved with 68.51 % in utterance-level accuracy, which surpasses our previous work.
Entropy-Based Locally Adaptive Thresholding for Image Segmentation

We presented a local thresholding method that is adaptively computed to segment images. The construction of the locally adaptive threshold mimics how human visual system interprets images, that is, identifying homogeneous regions in images in the presence of noise, clutters, and low contrast. Using a sliding window to raster scan an image, the method uses local statistics to measure homogeneity of pixel distribution in the window. The locally adaptive threshold is determined by evaluating a pixel against the local mean and an offset term, which is built by the local standard deviation and entropy. In implementation, except for the size of the sliding window, the method involves no parameters and, thus, requires no user input. For this reason, the method has a consistent and robust performance across users. Test results on different types of images demonstrated the robust performance of the method in separating fine details about an image and suppressing false positives. In practice, the method is extendable to parallel processing as the calculations are locally determined.
Manipuri-English Cross-lingual Word Embeddings using a Temporally Aligned Comparable Corpus

Despite using cross-lingual knowledge to learn word embeddings for various NLP tasks, there is no comprehensive analysis of the multiple methodologies on the Manipuri-English language pair in the literature. Manipuri is a low-resource language spoken in the northeastern states of India. This study provides an extensive evaluation of two popular unsupervised approaches of inducing cross-lingual word embeddings, namely MUSE and Vecmap, on the language pair bilingual dictionary induction task. We found that the Vecmap consistently outperforms the MUSE. We also propose a novel model to enhance the embeddings by exploiting a temporally aligned comparable corpus. From various experimental results, it is evident that the proposed model outperforms its counterparts.
Time Series and Stochastic Processes

This chapter addresses some statistical modeling approaches for time series data and discusses their potential for psychometric applications. We adopt a broad conceptualization of time series, including under this rubric any type of data that involves serial statistical dependence. Such dependence may be represented in continuous time, discrete time, or in a purely sequential manner. This chapter begins by discussing the relationships among these three representations and offers some general advice on when each might prove useful. We then provide an overview of three modeling frameworks that exemplify the different representations of statistical dependence: Markov decision processes, state-space modeling, and temporal point processes. For each modeling framework, we discuss its specification, its psychometric interpretation, and provide a brief numeric example including R code.
Joint learning of morphology and syntax with cross-level contextual information flow

Abstract We propose an integrated deep learning model for morphological segmentation, morpheme tagging, part-of-speech (POS) tagging, and syntactic parsing onto dependencies, using cross-level contextual information flow for every word, from segments to dependencies, with an attention mechanism at horizontal flow. Our model extends the work of Nguyen and Verspoor ((2018). Proceedings of the CoNLL Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies . The Association for Computational Linguistics, pp. 81–91.) on joint POS tagging and dependency parsing to also include morphological segmentation and morphological tagging. We report our results on several languages. Primary focus is agglutination in morphology, in particular Turkish morphology, for which we demonstrate improved performance compared to models trained for individual tasks. Being one of the earlier efforts in joint modeling of syntax and morphology along with dependencies, we discuss prospective guidelines for future comparison.
Modeling of professional self-determination of future young scholars in the process of professional training in the universities of the science city

The work deals with the problem of professional self-determination of students in the process of professional training in the universities of the science city. We consider the types of self-realization, present a model of professional self-determination of future young scholars, as well as methodological approaches to building a model and its components. The organizational and pedagogical conditions for the professional self-determination of students on the basis of the cluster approach are presented: the orientation of the educational process of universities in science cities to interact with enterprises, research institutes of the science city, the business community; active organizational and educational position of universities in science cities to promote employment and job placement of students at enterprises in science cities; a combination of various forms, methods of supporting the professional self-determination of students; accompaniment of the educational process with a digital ecosystem for practice-oriented interaction of students with employers and other interested social partners. The implementation of the model in practice will allow students to engage in professionally oriented interaction, including through project activities and interaction through Internet technologies with the world professionally oriented scientific community.
Cross-Domain Explicit–Implicit-Mixed Collaborative Filtering Neural Network

Collaborative filtering (CF) is a classical model for recommender systems. Though neural network-based CF models have been shown to perform well in some cases, they still suffer from the sparsity and cold-start issues. To address these issues, we propose a novel neural network-based CF model, termed the cross-domain explicit–implicit-mixed CF neural network (CEICFNet). The proposed model utilizes deep neural networks to learn latent factors not only from the explicit ratings and the implicit interactions but also in a cross-domain manner. In particular, domain-shared multilayer perception (MLP) networks are designed to learn the user rating latent factors and the user interaction latent factors from the explicit ratings and the implicit interactions, respectively, which act as bridges for transferring knowledge across domains. On the other hand, domain-specific MLP networks are designed to learn the item rating latent factors and the item interaction latent factors from the explicit ratings and the implicit interactions, respectively. Then, in each domain, based on the user rating latent factors and the item rating latent factors, the rating predictive representation for each user–item pair can be learned by an MLP. Similarly, the interaction predictive representation for each user–item pair can be learned. For integrating the explicit ratings and the implicit interactions, in each domain, a fully connected layer is used to automatically assign different weights to the rating predictive representations and the interaction predictive representations, based on which the final interaction probability can be generated. Extensive experiments are conducted on five datasets and the results have confirmed the effectiveness of our model.
Cross-Modality Generation of Amyloid PET from FDG PET for Alzheimer’s Disease Diagnosis

Positron Emission Tomography (PET) has been widely used in the early diagnosis and treatment monitoring of Alzheimer’s Disease (AD). As two radiotracers of neurodegeneration, [18F]Fluorodeoxyglucose ([18F]FDG) and [18F]Florbetapir ([18F]AV45) PET have been used to measure cerebral glucose metabolism and $\beta$-amyloid $(A\beta)$ deposition, respectively. The combination of different modality PET images, such as FDG PET and AV45 PET, can provide complementary information for clinical diagnosis and evaluation. However, compared to the actual and available FDG PET data, AV45 PET data is always deficient due to the institution-specific tracers. In this paper, we propose a lightweight Generative Adversarial Network (GAN)-based model, which is termed “dual perceptual loss based generative adversarial network (DPGAN) for fast 2. 5D-based cross-modality generation of AV45 PET from FDG PET. This model provides a potential supplementary solution to those clinical situations that only the FDG PET image is acquired, but the AV45 PET is missing. Our experimental results showed that the DPGAN outperformed recent CycleGAN and pGAN, given its stronger ability in capturing the $A \beta$ deposition patterns on the whole-brain scale. All qualitative and quantitative metrics demonstrated the strong similarity between the generated AV45 PET images using DPGAN and the original AV45 PET images.
Privacy-Preserving Big Data Exchange: Models, Issues, Future Research Directions

Big data exchange is an emerging problem in the context of big data management and analytics. In big data exchange, multiple entities exchange big datasets beyond the common data integration or data sharing paradigms, mostly in the context of data federation architectures. How to make big data exchange while ensuring privacy preservation constraintsƒ The latter is a critical research challenge that is gaining momentum on the research community, especially due to the wide family of application scenarios where it plays a critical role (e.g., social networks, bio-informatics tools, smart cities systems and applications, and so forth). Inspired by these considerations, in this paper we provide an overview of models and issues in the context of privacy-preserving big data exchange research, along with a selection of future research directions that will play a critical role in next-generation research.
Evaluation of Vector Transformations for Russian Static and Contextualized Embeddings

The authors of Word2Vec claimed that their technology could solve the word analogy problem using the vector transformation in the introduced vector space. By default, the same is true for both static and contextualized models. However, the practice demonstrates that sometimes such an approach fails. In this paper, we investigate several static and contextualized models trained for the Russian language and find out the reasons of such inconsistency. We found out that words of different categories demonstrated different behavior in the semantic space. Contextualized models tend to find phonological and lexical analogies, while static models are better in finding relations among geographical proper names. In most cases, the average accuracy for contextualized models is better than for static ones. Our experiments have demonstrated that in some cases the length of the vectors could differ more than twice, while for some categories most of the vectors could be perpendicular to the vector connecting average beginning and ending points.
Analysis of the application status and prospect of smart bracelets in the elderly

With the rapid development of the aging population, artificial intelligence and big data in China, changes have occurred to the health needs, health care systems and services for the elderly, which shift from nursing equipment to medical facilities. In this paper, 472 elderly people in Wuhan, China, are taken as the research objects. The questionnaire survey is selected as a way to understand the use status and demand of the elderly for smart health bracelets. Moreover, how to combine the development of clinical nursing in the new era on the existing basis, so as to realize the informatization, intelligence and convenience of the smart health bracelet for the elderly, is discussed. It is hoped to provide a theoretical reference for the development of such smart health bracelets.
MTRFN: Multiscale Temporal Receptive Field Network for Compressed Video Action Recognition at Edge Servers

With the wide deployment of Internet of Things monitoring terminals, a tremendous number of videos are accumulated continuously. Big data processing and analysis-based action recognition has an increasingly important role in making cities simpler, better, and smarter. The traditional cloud server-centered analysis mode has to spend extra time transmitting vast video data terminals to remote cloud servers, which is always violated in real implementation. Edge servers with limited caching and computation capacities near the monitoring terminals enable implementation. However, due to the dependency on training data and the high complexity of extracting information and network architecture, existing image domain-based methods cannot be implemented at edge servers. Moreover, recognizing actions with different durations is still challenging. Due to these issues, we extend the traditional image domain to the compressed domain to efficiently extract the information of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I$ </tex-math></inline-formula> frames and physical knowledge motion vectors (MVs), which can reflect the multiscale temporal feature just by partial decoding. To recognize the actions with different durations, a multiscale temporal receptive field network (MTRFN), including short-term and long-term branches, is proposed to simultaneously capture the action’s instant change based on the extracted MVs, the long temporal feature between adjacent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I$ </tex-math></inline-formula> frames, and the interaction between them. The results show that our algorithm can achieve a better balance between accuracy and computational complexity.