Reviewerly/exHarmony huggingface dataset:
BRCEA: Bootstrapping Relation-Aware Cross-Lingual Entity Alignment

Entity alignment aims to align entities referring to the same identity in the real world across different Knowledge Graphs (KGs), which is a fundamental task of KG construction and KG fusion. Recent works focus on embedding-based approaches. With the pre-aligned entity pairs, these approaches mainly embed entities based on relation triples to capture structural information and then try to refine the entity embeddings by self-characteristics contained in attribute triples. However, insufficient training data, diverse expressions of attributes, and different importance between self-characteristics and structural information in different KGs are three obstacles to entity embedding. In order to tackle these problems, we propose a novel Bootstrapping Relation-aware model for Cross-lingual Entity Alignment using both relation triples and attribute triples of KGs (BRCEA). Firstly, given the base prior alignments, it separately embeds entities from two aspects, namely self-characteristics and structural information. Then, bootstrapping component discovers two sets of new alignments. Finally, the two sets will be used to construct new training data for the next iteration to overcome the sparsity of training data. We performed our model on several real-world datasets, and the results show that our model outperforms the state-of-art models for cross-lingual entity alignment.
A Location Recall Strategy for Improving Efficiency of User-Generated Short Text Geolocalization

Currently, users’ daily lives are closely related to social sites. Geolocalizing user-generated short texts (UGSTs) can provide services for the wider fields of business and society. The existing works focus on improving the accuracy of geolocalization algorithms and reducing the prediction error. However, little work has been done on improving the computing efficiency of geolocalization algorithms. When dealing with large-scale data, the existing algorithm efficiency is greatly reduced. To solve this problem, we summarize three modes of user access to point of interests (PoIs), further propose a variety of location recall methods, and then introduce these geolocation recall methods into the existing algorithm. Geolocation recall generates a candidate location set for each user according to the user’s historical behavior records to reduce the search space of the geolocalization algorithm and improve its efficiency. We carried out experiments on the ground-truth datasets, compared the performance of various location recall methods, and verified the excellent location recall performance of three geolocalization algorithms. The experimental results show that location recall shortens the calculation time of the three algorithms by approximately 70% and further improves the accuracy of these algorithms.
Application of Neural Networks in Sentiment Analysis of Social Media Text Data

The paper describes the results of experiments comparing the use of different neural network architectures in sentiment analysis of text messages from social networks. Experiments were carried out using two text vectorization algorithms “word2vec” and “BERT”. The following types of neural networks were also used: LSTM, Bidirectional LSTM, CNN, MLP. The experiments were carried out on text data extracted from the VKontakte social network. As a result, an indicator of accuracy in determining the emotional color of posts was achieved at 87%.
Predictive Modeling for the Classification of Child Behavior from Children Stories

Emotions finding from stories is a wide range area of research with lot of different applications. Through this research work, we are trying to predict the effect of emotion from stories with the help of ensemble classifiers. Stories are an essential part of childhood. It is an effective way for children to understand the environment and things happening around the world. The story telling will help them to develop good manners and intellectual power. It will definitely impact the behavior of children like through the situations described in the story. The basic emotions in kids square measure joy, fear, anger, disgust, surprise, disappointment and neutral. The degree of those emotions depends upon the essential character of kid. Classification is an important data mining technique which is used here to classify the sentences in the stories based on the emotion reflected in the child. Here, we are examining the efficiency of classification algorithms for creating the prediction models from children stories. For that, we are using both single and ensemble classifiers, which help us to make good comparison for story-based emotion experiment because it shows 80% accuracy and takes only less time to build model using both classifiers.
Deep Learning Applied to Scientific Discovery: A Hot Interface with Philosophy of Science

We review publications in automated scientific discovery using deep learning, with the aim of shedding light on problems with strong connections to philosophy of science, of physics in particular. We show that core issues of philosophy of science, related, notably, to the nature of scientific theories; the nature of unification; and of causation loom large in scientific deep learning. Therefore, advances in deep learning could, and ideally should, have impact on philosophy of science, and vice versa. We suggest lines of further research, and highlight the role ‘theory-based’ AI could have in future developments of the field.
How to Improve E-commerce Search Engines? Evaluating Transformer-Based Named Entity Recognition on German Product Datasets

The quality of e-commerce search engines often suffers from data that online retailers poorly maintain. This situation can be observed on consumer-to-consumer marketplaces as well as on business-to-consumer platforms. One way to improve search quality is to perform linguistic enhancement of the product data. In this case, Named Entity Recognition is primarily used to identify important content and give it a higher weighting in the search. Our approach detects e-commerce entity types, such as products, brands, and various product attributes. Because of the low availability of existing resources and linguistic complexity identifying these entity types is challenging. Therefore, we acquire data from two online e-commerce marketplaces to build six German datasets based on product titles and descriptions. For these datasets, we evaluate the NER performance of the state-of-the-art models BERT, RoBERTa, and XLM-RoBERTa. The best performance archived the XLM-RoBERTa model with an F1 score of 0.8611 averaged over all datasets.
Word Boundary Detection Using Convolutional Neural Network (CNN) and Decision Tree Method

Speech recognition is a vast area for research. In speech recognition word boundary detection is an important part. Understanding and fixing the problems of efficiently detecting where the words are present in a signal is still challenging. In this paper, we have discussed our work on word boundary detection using two different approaches: (1) Convolutional Neural Network and (2) Decision Tree Method. CNN is efficiently used in areas like face recognition, object detection, image classification, etc. whereas a decision tree is broadly used in decision analysis. This study can further help with speech recognition.
Challenging the Spatial Consciousness of a Psychoanalytically inspired Agent through a Use Case

The science of Artificial Intelligence (AI) has made considerable progress in recent decades. However, considerable weaknesses are still evident where complex processes need to be executed. As an alternative approach, we study a functional top-down model inspired by the findings of psychoanalysis. This model has recently been extended to include functions of location awareness and navigation. The purpose of this paper is to verify the functionality added to the psychoanalytically inspired agents by means of a simulation. To this end, we start from a psychoanalytically formulated use case. The narrative is examined for its main challenges and translated and subsequently implemented in a technical simulation environment. Results of the simulations are discussed and interpreted with respect to the original psychoanalytically formulated use case.
Permian Flexifenestella from Perm Region (Bryozoa)

The paper pioneers the paleontological descriptions of Flexifenestella (Bryozoa) sampled from boreholes in Perm Region. A new description algorithm for the curved branches which shape defines qualitative features with quantitative parameters is developed and applied. The new species of Flexifenestella venusta is described.
Phobia Treatment with Face-Your-Fear VR App

Technology plays an important role in people's daily life, for they depend on it to perform many tasks.Among the prominent technologies are smartphones and Virtual Reality (VR).Developing mobile apps and integrating VR technology allow users to experience an artificial environment as if it is real such as the simulation of phobias.This research paper proposes a mobile application named Face-Your-Fear (FyF) integrating VR to help any user to live a cybernetic experience, overcome the illness, know about phobia symptoms, and get extra information on two types of phobia: aquaphobia and achluophobia.Its architecture, features, development, interfaces and limitations are presented, including the main results of the qualitative survey to users.
Attentive Sequence-to-Sequence Modeling of Stroke Gestures Articulation Performance

Production time of stroke gestures is a fundamental measure of user performance with graphical user interfaces. However, production time represents an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">overall</i> quantification of the user's gesture articulation process and therefore provides an incomplete picture of such process. Moreover, previous approaches assumed stroke gestures as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">synchronous</i> point sequences, when most gesture-driven applications have to deal with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">asynchronous</i> point sequences. Furthermore, deep generative models of human handwriting ignore the temporal information, thereby missing a key component of the user's gesture articulation process. To solve these issues, we introduce <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ditto</small> , a sequence-to-sequence deep learning model that estimates the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">velocity profile</i> of any stroke gesture using spatial information only, providing thus a fine-grained estimation of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">moment-by-moment</i> behavior of the user's articulation performance. We show that this unique capability makes <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ditto</small> remarkably accurate while handling gestures of any type: unistrokes, multistrokes, and multitouch gestures. Our model, code, and associated web application are available as open source software.
Human Capital Vulnerability and Cybersecurity Risk Management: An Integrated Approach

There is a long-standing claim that cybersecurity and digital information system protection is primarily a technological issue falling into the information technology domain. However, empirical evidence demonstrates that human and behavioural factors are usually the main vulnerability causing cybersecurity accidents. This chapter examines the role of the human capital of being both a vulnerability and strength in cybersecurity risk management. It provides recommendations to align firms’ corporate governance and internal control systems to human-related cybersecurity risk.
A System for Fusing Color and Near-Infrared Images in Radiance Domain

We designed and demonstrated a system that fused color and near-infrared (NIR) images in the radiance domain. The system is designed to enhance image quality captured in outdoor environments, especially in hazy weather conditions. Previous dehazing methods based on RGB-NIR fusion exist but have rarely addressed the issue of color fidelity and potential see-through effect of fusing with NIR image. The proposed system can dehaze and enhance image details while maintaining the color fidelity and protect privacy. By working in the radiance domain, the system could handle large brightness differences among the color and NIR images and achieve High Dynamic Range (HDR). We proposed two methods to correct the fusion color: linear scalings when raw images were used and color swapping with base-detail image decomposition in the presence of nonlinearity in the ISP pipeline. The system also had two clothing see-through prevention mechanisms to avoid ethical issue arising from the see-through effect of NIR image.
Neuro-Positionality in User-Centered Design

This study examines the principles of universal design and neurodiversity theory as they interact during the onboarding processes of Student Disability Services departments. Finding fundamental conflicts between these two schools of thought, particularly in terms of self-identification and disability negotiation, I advance a novel framework of neuro-positionality in user-centered design.
Relationship between personal values and adaptation of students: transfer of the method of "learning by action"

The article reveals the relationship between personal values and adaptation of students in the transfer of the method of "learning by doing". It is shown that the value of creativity in the stable component was associated with maladaptation, and in the dynamic on the contrary, which indicates that respondents are able to show creativity when needed, "here and now", ie in critical situations. Contradiction is observed in the professional component with the importance of the value of spiritual satisfaction - material well-being. That is, if the team or the environment of the respondents are dominated by spiritual values (morality, morality, belief in the good), the process of adaptation will be faster and easier, and if the material (money, benefits), the adaptation is difficult. This may indicate altruism and selflessness. Values have been shown to play an important role in the adaptation process. Regarding the constituent values, both dynamic and stable components are important. Of particular importance is the experience of transferring the method of "Learning by Action" (Service Learning) in higher education institutions. This method belongs to the methods of education in the interests of sustainable development and involves the integration into the educational process of higher education institutions of social activity of students. The effectiveness of the method is that students of higher education institutions who have experienced volunteer work in social projects in the educational process are more prepared in practical terms for the labor market and are able to see the meaning of activities in various social institutions. The main goal is to develop students' social responsibility and other key competencies through the use of the "Learning by Action" method. Key words: students, values, adaptation, learning, action, activity.
Teaching Machines to Read and Comprehend Tibetan Text

Teaching machine to comprehend a passage and answer corresponding questions, the machine reading comprehension (MRC) has attracted much attention in current years.However, most models are designed to finish English or Chinese MRC task, Considering lack of MRC dataset, the low-resource languages MRC tasks, such as Tibetan, it is hard to get high performance.To solve this problem, this paper constructs a span-style Tibetan MRC dataset named TibetanQA and proposes a hierarchical attention network model for Tibetan MRC task which includes word-level attention and re-read attention.And the experiments prove the effectiveness of our model.
Role of evaluation in curricular projection of instruiry

The efficiency of the educational process is dependent on rigorous pedagogical design, the choice and use of the most appropriate teaching-learning strategies and the application of a correct evaluation system. In this article we presented the epistem of the evaluation, demonstrating that this represents an essential moment in the teaching process, being an important factor in the proper functioning of the educational process. Evaluation is necessary at the beginning of the teaching activity, during it, but also at the end, as it allows to identify the difficulties encountered by the students in learning and to determine the level of preparation of the pupils in the discipline taught, compared to the expectations of the teacher and the requirements of the curriculum.
AHP and BP neural network-based evaluation model of higher vocational PBL english learning

As PBL (Project-based learning) mode has been widely and intensively integrated into many curriculums of higher vocational education, a lot of research has been done in order to assess the impact of project-based learning on higher vocational students’ English learning process. Yet, most measures are relatively subjective and lack in accuracy. This article aims to propose the method of combining AHP and BP neutral Network to construct the evaluation model of project-based English learning. AHP is used to comprehensively consider the selection of evaluation indicators from various factors affecting learning effectiveness based on PBL English learning and to set the weight of each evaluation index for PBL learning according to a questionnaire and experts’ scores. Then applying the calculation data of the AHP method as the target value of the BP neural network training and thus a BP neutral network-based evaluation model of PBL English learning is built. The simulation experiment denotes improvement on the objectivity, accuracy and efficiency of the evaluation on learning effectiveness of PBL English learning.
Improving Multimodal Data Labeling with Deep Active Learning for Post Classification in Social Networks

Automatic user post classification is an important task in the field of social network analysis. Being effectively solved, post classification could be used for thematic user feed composition or inappropriate content identification. Commonly addressed by applying various Machine Learning approaches, the task often involves manual processes related to ground truth sourcing, which is known to be a hardly-scalable and increasingly expensive procedure. At the same time, Active Learning for automatic user post classification is a promising way to bridge such a gap, as it does not require massive ground truth availability aligning our research with the real world settings. In this work, we put our focus on leveraging textual and visual data modalities for the application of user post classification and investigate how batch size and batch normalization disabling techniques could affect active deep neural network learning process. We solve the problem of automatic user post classification by employing our novel multimodal neural network architecture with multi-head tunable loss function components. We show that the proposed approach, coupled with Active Learning, allows for the achievement of a significant classification performance boost in terms of crowd assessing resources as compared to the passive learning approaches.
AITIA: Embedded AI Techniques for Industrial Applications

Motivated by an increasing interest from startups in embedded Artificial Intelligence (AI) and by their limited expertise, the AITIA Project targets the development of embedded AI techniques for industrial applications. This extended abstract presents the motivation and the solutions being developed towards four use cases: smart sensors, network intrusion detection, driver-assistance systems, and Industry 4.0.
Improving Deep Object Detection Algorithms for Game Scenes

The advancement and popularity of computer games make game scene analysis one of the most interesting research topics in the computer vision society. Among the various computer vision techniques, we employ object detection algorithms for the analysis, since they can both recognize and localize objects in a scene. However, applying the existing object detection algorithms for analyzing game scenes does not guarantee a desired performance, since the algorithms are trained using datasets collected from the real world. In order to achieve a desired performance for analyzing game scenes, we built a dataset by collecting game scenes and retrained the object detection algorithms pre-trained with the datasets from the real world. We selected five object detection algorithms, namely YOLOv3, Faster R-CNN, SSD, FPN and EfficientDet, and eight games from various game genres including first-person shooting, role-playing, sports, and driving. PascalVOC and MS COCO were employed for the pre-training of the object detection algorithms. We proved the improvement in the performance that comes from our strategy in two aspects: recognition and localization. The improvement in recognition performance was measured using mean average precision (mAP) and the improvement in localization using intersection over union (IoU).
Large Receptive Field Boundary Matching Networks for Generating Better Proposals

Temporal Action Detection is a research hotspot in Video Understanding. It is necessary not only to give the specific moments of the beginning and end of each action instance in the video, but also to give the category of the action instance. At present, most of the methods in temporal action detection are divided into two steps: The video is divided into a series of video clips to determine whether each clip is an action instance, and the fragments that are not an action instance are deleted; Segments that may be action instances are then classified to get the final result. At present, the difficulties in action detection research mainly include the following two points: 1) The boundary is not clear. Different from action recognition, action detection requires precise positioning, but an action in life is often not very definite. It is also the reason why the mean average precision (mAP) of action detection is low at present. 2) The time span is large. In life, an action often spans a very long scale. Short movements such as waving can last for a few seconds, while long movements such as rock climbing or cycling can last for tens of minutes, which makes it extremely difficult for us to extract proposals. In view of the above difficulties, this paper proposes a large receptive field boundary matching network(LRFBMN) model which takes advantage of the relationship between proposals to improve the accuracy of proposal generation. The model is mainly divided into two parts: 1) Clipping feature map is processed by large kernel convolution, and then proposal feature is generated by ROI-pooling; 2) The proposals are arranged in a certain order to form a fixed graph, and the information exchange between graph nodes is realized by using convolution with dilation. Through experiments, this model is 2.35% higher than baseline and 1.06% higher than state-of-the-art in THUMOS14 data set.