Reviewerly/exHarmony huggingface dataset:
Exploring the Utility of Dutch Question Answering Datasets for Human Resource Contact Centres

We explore the use case of question answering (QA) by a contact centre for 130,000 Dutch government employees in the domain of questions about human resources (HR). HR questions can be answered using personnel files or general documentation, with the latter being the focus of the current research. We created a Dutch HR QA dataset with over 300 questions in the format of the Squad 2.0 dataset, which distinguishes between answerable and unanswerable questions. We applied various BERT-based models, either directly or after finetuning on the new dataset. The F1-scores reached 0.47 for unanswerable questions and 1.0 for answerable questions depending on the topic; however, large variations in scores were observed. We conclude more data are needed to further improve the performance of this task.
Design and research of robot hand in life test of flush tank panel

At present, the flushing water tank on the market has different shapes and styles, and the pressing panel is also different. There is no specification limit on the shape and size, and the shape is different, which puts forward higher requirements for the versatility of equipment fixtures. Now the general testing equipment on the market for different samples are customized according to the sample fixture method to solve this problem, resulting in a wide variety of equipment fixture, replacement and inconvenient operation resulting in low efficiency of the test. The experimental device described in this paper uses an adjustable universal fixture to solve the problem of sample installation. Only one fixture can solve the problem of sample installation. The flexibility of the robot hand is used to solve the problems caused by different pressing modes of different samples, so that the experimental device can be matched with different flushing water tanks and pressing panels on the market, and there is no need to solve the adaptability problem by customizing the replacement fixture. The cost of the equipment is reduced, the installation operation is convenient, the sample installation time is saved and the efficiency is improved, the equipment is truly universal and convenient, and the test method is more concise and effective, and the test efficiency is improved.
Obstacle avoidance of mobile robots using modified artificial potential field algorithm based on vortex and PID adjustment

In recent years, autonomous mobile robot has become a key research direction in academia due to their good application prospects. The ability to autonomously avoid obstacles and reach a preset target point is the basic requirement of an intelligent mobile robot. As a common obstacle avoidance algorithm for mobile robots, artificial potential field algorithm (APF) has defects such as local minima problem and GNRON problem. Aiming at the shortcomings of the traditional APF algorithm, this paper proposes an improved artificial potential field algorithm based on point vortex and PID adjustment. The implementation of the improved algorithm is based on the two following main points. Firstly, an irrotational point vortex flow field is added to the original repulsion field of the obstacle to form a composite potential field. Properties of point vortex are used to provide additional virtual deflection force to the robot to avoid getting stuck. Secondly, aiming at GNRON problem, the strength of vortex and the range of action of the vortex repulsive composite potential field are adjusted in real time by means of PID adjustment. Finally, in order to verify the feasibility of the improved algorithm proposed in this paper, a comparison with the traditional APF algorithm was performed in seven environments with different obstacle layouts. The results show that the improved algorithm can be effectively applied to a variety of obstacle environments. Moreover, the local minimal value problem and GNRON problem of the traditional APF algorithm are successfully solved. The mobile robot can flexibly and efficiently avoid obstacles and reach the end point.
GCENet: Global contextual exploration network for RGB-D salient object detection

Representing contextual features at multiple scales is important for RGB-D SOD. Recently, due to advances in backbone convolutional neural networks (CNNs) revealing stronger multi-scale representation ability, many methods achieved comprising performance. However, most of them represent multi-scale features in a layer-wise manner, which ignores the fine-grained global contextual cues in a single layer. In this paper, we propose a novel global contextual exploration network (GCENet) to explore the performance gain of multi-scale contextual features in a fine-grained manner. Concretely, a cross-modal contextual feature module (CCFM) is proposed to represent the multi-scale contextual features at a single fine-grained level, which can enlarge the range of receptive fields for each network layer. Furthermore, we design a multi-scale feature decoder (MFD) that integrates fused features from CCFM in a top-down way. Extensive experiments on five benchmark datasets demonstrate that the proposed GCENet outperforms the other state-of-the-art (SOTA) RGB-D SOD methods.
Multiple approaches to predicting flake mass

Predicting original flake mass is a major goal of lithic analysis. Predicting original flake mass allows for researchers to make estimations of remaining mass, lost mass, and other features. All these measures relate to the organization of lithic technology by past societies. The present work tests three different models to predict log of flake mass: multiple linear regression, random forest regression, and artificial neural networks (ANN). Estimations of flake mass were performed using the remaining features of flakes from an experimental assemblage. This assemblage was obtained by the expansion of a previous dataset through the inclusion of bigger flakes, allowing the analysis to account for the effects of sample size and value distribution. Correlation results show a large/strong relation between predictions and real outcome (r2 = 0.78 in the best case). Comparison of the models affords insights into variable importance for predicting flake mass. Results show that (for the present dataset) multiple linear regression still stands as the best method for predicting log of flake weight. Additionally, transformation of predicted values from the multiple linear regression and true values to the linear scale reinforces the linear correlation above the 0.8 threshold.
An efficient saliency prediction model for Unmanned Aerial Vehicle video

Visual saliency prediction plays an important role in Unmanned Aerial Vehicle (UAV) video analysis tasks. In this paper, an efficient saliency prediction model for UAV video is proposed based on spatial–temporal features, prior information and the relationship of frames. It can achieve high efficiency by designing a simplified network model. Since UAV videos usually cover a wide range of scenes containing various background disturbances, a cascading architecture module is proposed for feature extraction from coarse to fine, in which a saliency related feature sub-network is utilized to obtain useful clues from each frame, then a new convolution block is designed to capture spatial–temporal features. This structure can achieve advanced performance and high speed based on a 2D CNN framework. Moreover, a multi-stream prior module is proposed to model the bias phenomenon in viewing behavior for UAV video scenes. It can automatically learn prior information based on the video context, and can also combine other priors. Finally, based on the spatial–temporal features and learned priors, a temporal weighted average module is proposed to model the inter-frame relationship and generate the final saliency map, which can make the generated saliency maps look smoother in the temporal dimension. The proposed method is compared with 17 state-of-the-art models on two public UAV video saliency prediction datasets. The experimental results demonstrate that our model outperforms other competitors. Source code is available at: https://github.com/zhangkao/IIP_UAVSal_Saliency .
Dismissals for Social Media Hate Speech in South Africa: Animalistic Dehumanisation and the Circulation of Racist Words and Images

Social media is changing the way humans create and exchange information. Not all social media communications are, however, civil: the 'dark side' of social media cultivates various 'anti-social' exchanges including hate speech. Parallel accelerating social media use has been an increase in decision-makers having to consider the legalities of dismissing employees for social media misconduct. This paper through an analysis of first instance South African employee dismissal decisions, identifies an economy of hate within South African workplaces. In 30% of social media misconduct decisions (120/400), employees were dismissed for circulating racialised hate speech. This hate speech took three forms. First was the use of animality discourse and animal metaphors to dehumanise colleagues and employers. Second, employees used words that had specific racist connotations within South Africa. Third, there was the direct deployment of signs or symbols connected with South Africa's racialised past.
Sift Descriptor for Social Media User Accounts Matching

The task of user accounts matching in different social media and determining those belonging to the same user is relevant in various contexts related to the analysis of social media. The solution of this problem is of both theoretical importance and allows you to expand the understanding of the behavior of users in social media, as well as practical and can be applied to collect data about a single user. This work is devoted to solving the problem of automation of matching the accounts of social media users by analyzing the graphic content posted in the accounts. Previously, a method was proposed to solve this problem, based on the use of a number of content features, the current study develops the proposed approach by searching for duplicate images using SIRF. As a result, a new model for classifying pairs of accounts into the classes “belongs to one user” and “does not belong to one user” was proposed, which achieved higher accuracy and f-score compared to previous results.
Mathematical Models and Algorithms of an Intelligent Platform for the Implementation of an Individual Learning Trajectory

Mathematical models and algorithms underlying the intellectual educational platform (IEP) of a new type, aimed at independent assimilation of the given disciplines by the student, are considered. Training is carried out in the process of interaction with the electronic learning system, which adapts to the level of knowledge of the student, his psychotype, system of preferences and individual characteristics of the nervous system. The concept is based on the combined use of an electronic textbook on educational disciplines, created on the basis of the programmed learning approach, and gamification implemented in the web development of the IEP shell. A methodology for evaluating learning outcomes at a university as a level of a student's competence is built based on the results of certification by means of standard a set of evaluation tools. The methodology is based on a fuzzy-multiple aggregation of the results obtained when a student completes a set of assessment tasks recommended by experts and selected by a teacher to check the level of formation of the selected competencies.
Development of a Model of User Interface Logic Representation

Existing approaches to user interface design of technical systems are more focused on organizing algorithms for user-system interaction (scenarios for system specific tasks) and selecting the interface structure according to these algorithms. At the same time, the logic of user interaction with the system is considered at the design stage, and is not shown to user as part of the finished system. The interaction logic remains an internal part of system and ceases to correspond to the professional activity of the user, since it undergoes transformation at the system implementation stages. The results of the transformation are expressed in the classic problems of the user interface: learning time and human errors increase, overall user subjective satisfaction and productivity decrease. To solve these problems, a new model of the user's communication logic with the object management system is needed, consisting of solving problems logic and the logic of data collecting and processing. Such model should be reflected in the system interface. Then the interface will be a unity of solving problems logic and data collecting and processing logic. And it is focused on user general professional activity. The paper considers a model for representing the unity of two logic types. The paper analyzes the existing approaches to user interface design of technical systems in terms of reflecting the user ‘s professional activity logic, the concept of the logic model of user- system communication, and the methodology of its obtaining is presented.
Complex Modeling of Inductive and Deductive Reasoning by the Example of a Planimetric Problem Solver

The paper is devoted to the problem of computer simulation of inductive and deductive reasonings based on the example of automatic solutions to planimetric problems. The study has been carried out using an experimental system that includes a natural language interface. The deductive component of the system is based on the geometry axiomatics; the inductive component is based on the Polya concept. We have described the interaction of components in solving problems and given some solution examples, while noting the important role of cognitive patterns for formalizing the problem description in a natural language, supporting the solver, and generating a drawing. We have emphasized the applied value of the research results for the education sector.
Analysis of Micro-images of Skin Neoplasms Using Convolutional Neural Networks in an Intelligent Medical Information System for the Early Diagnosis of Melanoma

AI-based clinical decision support is a particularly promising area of research in oncology. Successful treatment is possible only if melanoma is detected in the early stages of its biological development and is accompanied by a good long-term prognosis without reducing the quality of life of patients. The article proposes an approach to detecting nests in nevus tissues using a convolutional neural network. The neural network used in the study is based on the weights of cells trained to search for nuclei. After that, these weights were corrected taking into account the search for the necessary objects, namely nests of nevus cells. The article discusses examples of images that serve as a training sample, the configuration and parameters of the neural network. The main results of the experiments obtained during the study are also presented.
Information Granulating and Its Use in Descriptive and Predictive Analysis of Medical Data

The article discusses the task of descriptive and predictive analysis based on information granulation, focused on the processing of medical data, the specificity of which is the heterogeneity of values and different frequency of admission. Systematically outlines approaches to information granulation of multidimensional data were given and a meaningful statement of the problem of information granulation of medical data using introduced expert-defined and data-extracted granules was provided. The approach of the solution to the problem of information granulating in the proposed setting was proposed for medical observation data and applied for a set of numerical cardiovascular indicators of patients. The textual descriptions as a set of propositions were generated that characterize the state and changes in the patient's cardiovascular health. Based on obtained results, the doctor can predict diseases more precise and make patient-oriented recommendations more reasonably.
Probabilistic Graphical Models with Continuous Variables for the Decision Making About Risky Episodic Behavior in the Framework of Gamma Poisson Model with Application to Public Posting Data

Significant amount of accidents are related to a human factor in Socio-Cyber-Technical-Physical Systems that are used in cybersecurity or healthcare. Assessment of this factor is a part of a risk management process and it relies on an estimate of some characteristic of the behavior in interest. The data for this estimation procedure may be obtained via self–reports and interviews, that often give incomplete and imprecise information. In this paper the use of the continuous probabilistic graphical models is proposed to capture the uncertainty that accompanies the heterogeneous and incomplete behavior data and their continuity. The vine mediated Bayesian Belief network is proposed for the problem, that relies on lesser number of parameters. The data on public posting in the online social media is used to compare this approach with hybrid Bayesian Belief network that is approximated with mixtures of truncated exponentials.
Cross-Layer Security of Embedded and Cyber-Physical Systems

Cyber-physical systems (CPS) are engineered systems that are built from, and depend upon, the seamless integration of computation and physical components [NSF]. Embedded systems comprising of hardware and software systems are the major enabling technology for these cyber-physical systems. Today, CPSs can be found in security-sensitive areas such as aerospace, automotive, energy, healthcare, manufacturing transportation, entertainment, and consumer appliances. Compared to the traditional information processing systems, due to the tight interactions between cyber and physical components in CPSs and closed-loop control from sensing to actuation, new vulnerabilities are emerging from the boundaries between various layers and domains. In this keynote talk, Prof. Al Faruque will discuss how new vulnerabilities are emerging at the intersection of various components and subsystems and their various hardware, software, and physical layers. Several recent examples from various cyber-physical systems will be presented in this talk. To understand these new vulnerabilities, a very different set of methodologies and tools are needed. Defenses against these vulnerabilities demand also new hardware/software co-design approaches. The talk will highlight recent developments in this regard. The major goal of this talk will be to highlight various research challenges and the need for novel scientific solutions from the larger research community.
Context-Based Clustering to Mitigate Phishing Attacks

Phishing is by far the most common and disruptive type of cyber-attack faced by most organizations. Phishing messages may share common attributes such as the same or similar subject lines, the same sending infrastructure, similar URLs with certain parts slightly varied, and so on. Attackers use such strategies to evade sophisticated email filters, increasing the difficulty for computing support teams to identify and block all incoming emails during a phishing attack. Limited work has been done on grouping human-reported phishing emails, based on the underlying scam, to help the computing support teams better defend organizations from phishing attacks. In this paper, we explore the feasibility of using unsupervised clustering techniques to group emails into scams that could ideally be addressed together. We use a combination of contextual and semantic features extracted from emails and perform a comparative study on three clustering algorithms with varying feature sets. We use a range of internal and external validation methods to evaluate the clustering results on real-world email datasets. Our results show that unsupervised clustering is a promising approach for scam identification and grouping, and analyzing reported phishing emails is an effective way of mitigating phishing attacks and utilizing the human perspective.
Dual Instance-Consistent Network for Cross-Domain Object Detection

Cross-domain object detection aims to transfer knowledge from a labeled dataset to an unlabeled dataset. Most existing methods apply a unified embedding model to generate the tightly coupled source and target descriptions for domain alignment, leading to the destroyed feature distribution of the target domain because the embedding model is mainly controlled by the source domain. To reduce the representation bias of the target domain, we apply two independent networks to extract two types of discriminative descriptions with mutual consistency, i.e., a novel Dual Instance-Consistent Network (DICN) is proposed for cross-domain object detection. Especially, Dual Instance-Consistent Module containing the instance mutual consistency between Primary Network and Auxiliary Network is applied to align two domains, where Primary and Auxiliary Networks are used to obtain the source-specific and target-specific information, respectively. The instance mutual consistency consists of two terms: feature consistency and detection consistency, which is applied to align the instance feature and the output of detection head, respectively. With the instance mutual consistency, optimizing the Primary (Auxiliary) Network only with source (target) images by fixing the Auxiliary (Primary) Network can generate the source(target)-specific description. Extensive experiments on several benchmarks demonstrate the effectiveness of the proposed DICN, e.g., obtaining mAP of 44.10% for Cityscapes → Foggy Cityscapes, AP on car of 76.50% for Cityscapes → KITTI, MR -2 of 8.87%, 12.66%, 22.27%, and 42.06% for COCOPersons → Caltech, CityPersons → Caltech, COCOPersons → CityPersons, and Caltech → CityPersons, respectively.
Content Analysis of Social Media Data

This chapter focuses on content analysis and introduces the collection of data from Twitter by either select keywords or languages. It then develops computerized content analysis techniques for use on tweets, covering the particular challenges of adapting these techniques for usage on the text from social media (for instance, dealing with the often very short passages of text, the especially dense usage of colloquialisms, and the frequent mixing of different languages within a particular source of social media text data). It also covers the download of other forms of content (such as video and images) and the handling of meta-objects such as mentions and hashtags.
The influence of the living sports center program on the body build up and blood cortisol density of participants

This study has been undertaken regarding the influence of the living sports center program on the body build up and blood cortisol density of participants. For data processing, Subjects for the study were residents in B city P center members with a total of 12 persons (6 persons for exercise group and 7 persons for control group). SPSS 21.0 statistics was used to calculate the mean (M) and standard deviation (SD), and the analysis is made for each group and period, verification of difference for group and period. The noticeable level (α) on all statistics figures is set for .05. The frequency and period of exercise was implemented 4 times a week with 50 minutes (10 minutes of preparatory exercise, 40 minutes of main exercise and 10 minutes of wrap-up exercise) per session for a total of 8 weeks and the exercise intensity was 40~50% HRR in 1~4 weeks and 51~60% HRR in 5~8 weeks. Under this study outcome, the body fat rate showed noticeable difference in period and group, and the difference was not shown in period and group. The bodily fat volume showed noticeable difference in period, and group, period and group did not show noticeable difference. The cortisol showed noticeable difference in period, period and group and noticeable difference was now shown in group.