Reviewerly/exHarmony huggingface dataset:
Detection of Criminogenic Content in Natural Language Texts

This paper considers the application of the vocabulary method in the task of classifying texts in natural language for the class "criminogenic". The definition of criminogenicity is also given in the text of this paper. A 274word criminogenic dictionary is manually created, which is then automatically increased by methods of machine learning to 2286 semantically close to the original words. The model of classification of criminogenic texts is constructed. Experiments were made and the quality of classification was measured on two samples of news texts with the size of five thousand and one thousand texts.
IT Security In Radar Sensor Systems - A Methodical Approach

IT security is getting more and more in the focus of the development of radar systems, especially due to cyber threats, highly connected systems or governmental regulations. IT security takes place as an integrated activity along the entire life cycle of a product development - a new company challenge!
Bayesian semiparametric multivariate density deconvolution via stochastic rotation of replicates

In multivariate density deconvolution, the distribution of a random vector needs to be estimated from replicates contaminated with measurement errors. A novel approach to multivariate deconvolution is proposed by stochastically rotating and stretching or contracting the replicates toward the corresponding true latent values. The method further accommodates conditionally heteroscedastic measurement errors commonly observed in many real data applications. The estimation and inference schemes are developed within a Bayesian framework implemented via an efficient Markov chain Monte Carlo algorithm, appropriately accommodating uncertainty in all aspects of the analysis. The method's efficacy is demonstrated empirically through simulation experiments and practically in estimating the long-term joint average intakes of different dietary components from their measurement error-contaminated 24-hour dietary recalls.
Keiki: Towards Realistic Danmaku Generation via Sequential GANs

The time-series GAN and periodic spatial GAN show different yet competitive performance in terms of the evaluation metrics adopted, their deviation from human-designed danmakus, and the diversity of generated danmakus. The preliminary experimental studies presented here showcase that potential of time-series GANs for sequential content generation in games.
Proof Search and Certificates for Evidential Transactions

Abstract Attestation logics have been used for specifying systems with policies involving different principals. Cyberlogic is an attestation logic used for the specification of Evidential Transactions (ETs). In such transactions, evidence has to be provided supporting its validity with respect to given policies. For example, visa applicants may be required to demonstrate that they have sufficient funds to visit a foreign country. Such evidence can be expressed as a Cyberlogic proof, possibly combined with non-logical data ( e.g. , a digitally signed document). A key issue is how to construct and communicate such evidence/proofs. It turns out that attestation modalities are challenging to use established proof-theoretic methods such as focusing. Our first contribution is the refinement of Cyberlogic proof theory with knowledge operators which can be used to represent knowledge bases local to one or more principals. Our second contribution is the identification of an executable fragment of Cyberlogic, called Cyberlogic programs, enabling the specification of ETs. Our third contribution is a sound and complete proof system for Cyberlogic programs enabling proof search similar to search in logic programming. Our final contribution is a proof certificate format for Cyberlogic programs inspired by Foundational Proof Certificates as a means to communicate evidence and check its validity.
What Factors Contribute to the Perceived Usability of Virtual Reality Technology of Conservative Industrial Consumers

Our study investigated what factors contribute to the perceived usability of Virtual Reality technology by small firms operating in a conservative industry. For this purpose, we interviewed the developers and users of a newly developed Virtual Reality-equipped heavy machinery. We conducted eleven semi-structured interviews and used thematic analysis to identify the main themes that influenced the perceived usability of the technology by the consumers. This was significant as it contributed to their decisions about purchasing and continuing to use the technology. Consequently, it impacted the developers of the technology in their competitive capability in a conservative and homogenous industrial arena. The dimension and depth of perceived usability by this specific groups are discussed, and the practical implications are provided.
Design and Implementation of Data Adapter in SWIM

In the background of the globalized interaction needs of civil aviation information services, the definition of System Wide Information Management (SWIM) was proposed by International Civil Aviation Organization (ICAO). However, the issue of interaction between traditional business systems and heterogeneous systems still exists on the SWIM platform. The data adapter is an important functional component to solve this problem, which can not only solve the problems of high coupling among systems, poor compatibility, low sensitivity, non-standard syntax and poor semantics, but also realize the high sharing of information between systems. Based on the SWIM platform, this article redefines SWIM. First, this article analyzes the concept and architecture of SWIM as well as the role and logical architecture of adapters. Then, according to the requirements of the adapter, a design method of the adapter structure is proposed, and the process design methods of the two main functional modules, data transformation and service encapsulation, are proposed respectively, including data standard and service standard. Extensible Markup Language (XML) technology is a platform independent language, 90% of systems support XML, it is self-descriptive and extensible, suitable for storage and transport on the network, therefore it is the preferred language for software development and is important for the design of SWIM data adapter. Finally, the data adapter is designed and implemented.
How Much Immersive is Virtual Reality? Evidence from VR-Based Managerial Competency Assessment Tool Testing

Human resource management (HRM) is one of the rapidly evolving areas of VR application. Recently, there has been an increasing focus on the development of staff selection and competency assessment tools. The purpose of this paper is to disclose the key points of immersive VR applications based on real work situations for the assessment of managerial competencies. In doing this, first, 29 and later, after improving the VR applications, 50 experiments were performed testing 11 different VR applications based on managerial issues. Immersion in VR was assessed using the Slater-Usoh-Steed questionnaire (SUS), supplemented by questions allowing for assessing the immersiveness of simulated managerial situations and subjective feelings of stress while in VR. In line with the research findings, insights are provided on how to enhance the immersive VR applications based on real managerial situations for the assessment of competencies.
Cyber-physical System based Cooperative Maneuver Planning for Multiple Tractor-trailer Vehicles in a Cluttered Environment

Tractor-trailer vehicles are commonly deployed in a logistics scenario. Cooperative maneuver planning focuses on the situation that multiple tractor-trailer vehicles work in the same warehouse. The existing sample-and-search-based or numerical-optimization-based planners suitable for a single rigid-body vehicle cannot deal with multiple multi-body vehicles because of the complex vehicle kinematics and the intractably scaled inter-vehicle collision-avoidance constraints. This paper formulates the cooperative maneuver planning scheme as a centralized optimal control problem (OCP) and introduces the Adaptive-Scaling Constrained Optimization (ASCO) method to deal with the intractable OCP in a tractable way. Simulation results show the efficacy of the proposed method.
A Method of Evaluating MOOC Education Ability of English Teachers Based on Fuzzy Analytic Hierarchy Process

To evaluate the teaching quality of college teachers scientifically and reasonably, a fuzzy comprehensive evaluation method based on AHP is proposed. Considering the integration of online course evaluation standards and the basic requirements of college English teaching, this paper establishes an index framework of MOOC teaching quality evaluation index. We acquire the evaluation data through questionnaire and expert scoring method, and use AHP method to calculate the weight of quality evaluation index. Then, the evaluation data is substituted into the fuzzy comprehensive evaluation model, and the membership function is used to establish the single factor evaluation matrix to determine the maximum component of the evaluation grade as the final evaluation grade. Through the example analysis, it shows the application effect of AHP and fuzzy comprehensive evaluation method in the system, and proves that our scheme can obtain more objective and accurate results in the evaluation process.
Accelerating Deep Convolutional Neural on GPGPU

This paper is focused on the improvement of the efficiency of the sparse convolutional neural networks (CNNs) layers on graphic processing units (GPU). The Nvidia deep neural network (cuDnn) library provides the most effective implementation of deep learning (DL) algorithms for GPUs. GPUs are one of the most efficient and commonly used accelerators for deep learning computations. The modern CNN models need megabytes of coefficients and needed millions MAC operations to perform convolution. One of the most common techniques for compressing CNN models is weight pruning. There are two main types of pruning: structural (based on removing whole weight channels) and non-structural (removing individual weights). The first enables much easier acceleration, but with this type it is difficult to achieve a sparsity level and accuracy as high as that obtained with the second type. Non-structural pruning with retraining can generate a matrix-weight up to $${\sim }90\%$$ or more of sparsity in some deep CNN models. This work shows when is worth using a direct sparse operation to speed-up the calculation of the convolution layers. The VGG-16, CNN-non-static and $$1 \times 1$$ layers from ResNet models were used as a benchmarks. In addition, we present the impact of using reduced precision on time efficiency.
Survey on Unsupervised Techniques for Person Re-Identification

Unsupervised person re-identification (ReID) might be difficult if lacking labeling information. The feature extraction scheme generally divides existing methods into handcrafted feature-based methods, unsupervised domain adaptation (UDA) based methods, and pseudo-labels estimation-based methods. Feature representations are extracted or learnt directly from unlabeled datasets to address the scalability issue by hand-crafted feature-based methods. The purpose of unsupervised domain adaptation is to relieve the domain bias as the learnt features are transferred to an unlabeled target from a labeled source. For pseudo-labels estimation-based methods, they take supervised pseudo-labels to learn feature representations and labels are estimated together for unlabeled datasets. In this paper, the state-of-the-art unsupervised techniques are reviewed to solve the task of person re-identification, a brief review of each method along with their evaluations on a set of widely used datasets in included. In addition, we give a detail comparison among these methods according to corresponding category.
Guideline Proposal for Quality Evaluation of for Research in Education Research in Software Engineering

Context: The lack of rigor in research in Computer Science Education, including Software Engineering, has already been discussed and examined in previous researches of the literature in both the theoretical and methodological aspects, which may result in questionable evidence. Aim: Improvement of planning conditions for research in Education in Software Engineering through a Guideline. Methodology: To achieve this objective, a Systematic Literature Review will be carried out to ascertain the gaps found in the literature in Education in Software Engineering, in addition to Interviews with researchers in Education in Software Engineering to report the problems found in research. From this, a Guideline will be modeled using the method Grounded Theory. For model validation, a Survey and Focus groups with researchers in education in Software Engineering. And finally, apply it in a Case Study with researchers in Education in Software Engineering to present the practical contributions of the proposed Guideline.
Self-supervised pre-training on industrial time-series

Machine Learning (ML) and Deep Learning (DL) techniques have achieved remarkable results in many domains resulting in excellent performances in fields including Natural Language Processing (NLP), Computer Vision (CV) and Speech Recognition (SR). In light of these achievements, the application of ML algorithms to the industry world is appealing and very interesting results have already been produced by the scientific community. However, industrial data are often significantly different from those commonly employed in the disciplines where DL has had a larger impact. Industrial datasets are often limited in size and are constituted by large amounts of unlabelled data. In contrast, in CV and NLP, large deep neural networks can benefit from large labelled datasets, that can thus be used to train DL models in a fully-supervised fashion. In this work, we adopt a self-supervised approach to process heterogeneous dataset characterized by a relatively large number of unlabelled data and few labelled ones. We apply the proposed approach to two industrial time-series data and show that leveraging unlabelled data results in improved performance on classification downstream tasks.
Malaria Cell Detection Model

Bajpai, Prasannajeet Chourawar, SanchitaMalaria is the most ubiquitous endemic and a deadly disease which is caused by parasites which are transmitted to people through bites of an infected female Anopheles mosquito. This disease is present in several continents, which is a matter of concern for several bodies and associations. All the governments around the world are trying to reduce this morbidity by taking various preventive measures. Model will help us to detect five species of Plasmodium (Malaria Parasites) that have long been recognized to infect humans. Now with our model which is a pre-trained CNN model with 13,775 images of infected cells and 13,775 images of uninfected cells with valid efficiency more than 93% and then all it is needed is just upload image or images that we want to test which then will be fed to our mode and then the model will give the output with concerning images in the same sequence in which it was uploaded. By using this model, we are primarily promoting reusability, accessibility, cost-efficient, eco-friendly as we are not using any kits to create more plastic waste.
A. V. Khvastunova’s Graphical Works: Art Nouveau as Means to Aesthetize Everyday Reality in the XXI Century

The paper examines the modern painter A. V. Khvastunova’s creative activity whose works are based on everyday reality aesthetization. The article clarifies ways to solve the problem that proved to be a sticking point for the Miriskusniks (members of the artistic movement “Mir Iskusstva (World of Art)”). Scientific originality of the study lies in a description of the artistic method which involves positive perception of the aesthetized reality represented by the Art Nouveau artistic means. As a result, it is shown that rich figurativeness of A. V. Khvastunova’s works allows describing everyday reality by means of a refined artistic language.
VBSRL: A Semantic Frame-Based Approach for Data Extraction from Unstructured Business Documents

The definition of alternative processing techniques as applied to business documents is inevitably at odds with long-standing issues derived by the unstructured nature of most business-related information. In particular, more and more refined methods for automated data extraction have been investigated over the years. The last frontier in this sense is Semantic Role Labeling (SRL), which extracts relevant information purely based on the overall meaning of sentences. This is carried out by mapping specific situations described in the text into more general scenarios (semantic frames). FrameNet originated as a semantic frame repository by applying SRL techniques to large textual corpora, but its adaptation to languages other than English has been proven a difficult task. In this paper, we introduce a new implementation of SRL called Verb-Based SRL (VBSRL) for information extraction. VBSRL relies on a different conceptual theory used in the context of natural language understanding, which is language-independent and dramatically elevates the importance of verbs to abstract from real-life situations.
Earthquake Early Warning (EEW) System: System Architecture, Data Modelling, and User Interface Design

Earthquake Early Warning Systems (EEWSs) development is essential to provide service to stakeholders and the public. The service is the information access regarding the information on the earthquake source area and its impact on the surrounding environment. This study aims to conduct a systematic literature review of peer-published studies focusing on the development of earthquake Early Warning Systems (EEWSs). The method of systematic review is well-established in research by Kitchenham et al. (2005). It is used to analyze the literature and answer defined research questions systematically. We found 16 papers related to system architecture, data modelling and user interface design of Earthquake Early Warning Systems (EEWSs) published in 2009-2020. Research that discusses data modelling is 6%, the user interface design is 38%, and system architecture is 56%. Overall, our findings show that the system architecture, data modelling and user interface of the development of Earthquake Early Warning Systems (EEWSs) in several countries have significant similarities. It can be modelled as a framework for the development of Earthquake Early Warning Systems (EEWSs).
Multi-penalty Functions GANs via Multi-task Learning

Adversarial learning stability is one of the difficulties of generative adversarial networks (GANs), which is closely related to networks convergence and generated images quality. For improving the stability, the multi-penalty functions GANs (MPF-GANs) is proposed. In this novel GANs, penalty function method is used to transform unconstrained GANs model into constrained model to improve adversarial learning stability and generated images quality. In optimization divergence tasks, two penalty divergences (Wassertein distance and Jensen-Shannon divergence) are added in addition to the main optimization divergence (reverse Kullback-Leibler divergence). In network structure, in order to realize the multi-divergence optimization tasks, the generator and discriminator are multi-task networks. Every generator subtask corresponds to a discriminator subtask to optimize the corresponding divergence. In CELEBA and CIFAR10 data sets, the experimental results show that although the number of parameters is increased, the adversarial learning stability and generated images quality are significantly improved. The performance of the novel GANs is better than most GANs models, close to state-of-the-art models, SAGANs and SNGANs.
GPU-Accelerated Fuzzy Inference Based on Fuzzy Truth Values

The paper deals with the computational complexity of fuzzy inference in the case of multiple fuzzy inputs and/or rules. We consider an inference method based on the fuzzy truth values and its parallel implementation employing the CUDA technology. The main problem of the method consists of multiple independent reductions of discrete fuzzy sets through an associative binary operation of high computational complexity. This fact raises the problem of barrier synchronization among specific ranges of blocks. The article describes the design of a parallel inference algorithm and considers the capabilities of CUDA technology, which might be suitable for the implementation of the algorithm. The article also provides profiling results.
Supervised Bayesian specification inference from demonstrations

When observing task demonstrations, human apprentices are able to identify whether a given task is executed correctly long before they gain expertise in actually performing that task. Prior research into learning from demonstrations (LfD) has failed to capture this notion of the acceptability of a task’s execution; meanwhile, temporal logics provide a flexible language for expressing task specifications. Inspired by this, we present Bayesian specification inference, a probabilistic model for inferring task specification as a temporal logic formula. We incorporate methods from probabilistic programming to define our priors, along with a domain-independent likelihood function to enable sampling-based inference. We demonstrate the efficacy of our model for inferring specifications, with over 90% similarity observed between the inferred specification and the ground truth—both within a synthetic domain and during a real-world table setting task.
ABLTagger (PoS) - 3.0.0

A Part-of-Speech (PoS) tagger for Icelandic.In this submission, you will find pretrained models for ABLTagger v3.0.0. In this submission we provide two versions, small and large, of PoS taggers that work with the revised tagset that achieve an accuracy of ~96.7% and ~97.8% on MIM-Gold (cross-validation, excluding x and e tags), respectively.For installation, usage, and other instructions see https://github.com/cadia-lvl/POS/releases/tag/m5You should also check if a newer version is out (see README.md - versions)on CLARIN:- Model files-------------------------------------------------------------------------------------------Markari fyrir islensku.I þessum pakka er ABLTagger v3.0.0. I þessari utgafu eru tvo forþjalfuð likon, litið og stort, sem virka fyrir nýja markamengið og na 96,7% og 97,8% nakvaemni a MIM-Gull (krossprofanir, an x og e marka).Fyrir uppsetningar-, notenda- og aðrar leiðbeiningar sja https://github.com/cadia-lvl/POS/releases/tag/m5Einnig er gott að athuga þar hvort ný utgafa se komin ut (sja README.md - versions)A CLARIN:- Gogn fyrir likan
Digital Batik Museum Website Design using User Centred Design (UCD) Method

At present the use of batik is more focused as a fashion that has unique motifs with high artistic value. But the history of batik is not widely known by the public because of the difficulty of getting clear information about the meaning of a batik motif. So, in this study the authors propose to create a digital batik museum website design using the User Centered Design method that is focused on the user with persona tools as a support. the results of this design are expected to help the user in learning information about batik.