Reviewerly/exHarmony huggingface dataset:
Research and Analysis for Real-Time Streaming Big Data Based on Controllable Clustering and Edge Computing Algorithm

Aiming at the low efficiency, poor performance and weak stability of traditional clustering algorithms and the poor response to the processing of massive data in real time, a real-time streaming controllable clustering edge computing algorithm (SCCEC) is proposed. First, the data tuples that arrive in real time are pre-processed by coarse clustering, the number of clusters, and the position of the center point are determined, and a set formed by macro clusters having differences is formed. Secondly, the macro cluster set obtained by the coarse clustering is sampled, and then K-means parallel clustering is performed with the largest and smallest distances, thereby realizing fine clustering of data. Finally, the completely clustering algorithm and the edge-computing algorithm are combined to realize the clustering analysis under the edge-computing framework. The experimental results show that the proposed algorithm has the advantages of high efficiency, good quality, and strong stability. It can quickly obtain the global optimal solution, and deal with massive data with high real-time performance. It can be used for real-time streaming data aggregation under big data background.
A Morpho-Syntactically Informed LSTM-CRF Model for Named Entity Recognition

We propose a morphologically informed model for named entity recognition, which is based on LSTM-CRF architecture and combines word embeddings, Bi-LSTM character embeddings, part-ofspeech (POS) tags, and morphological information.While previous work has focused on learning from raw word input, using word and character embeddings only, we show that for morphologically rich languages, such as Bulgarian, access to POS information contributes more to the performance gains than the detailed morphological information.Thus, we show that named entity recognition needs only coarse-grained POS tags, but at the same time it can benefit from simultaneously using some POS information of different granularity.Our evaluation results over a standard dataset show sizeable improvements over the state-of-the-art for Bulgarian NER.
Designing and modeling of a multi-agent adaptive learning system (MAALS) using incremental hybrid case-based reasoning (IHCBR)

Several researches in the field of adaptive learning systems has developed systems and techniques to guide the learner and reduce cognitive overload, making learning adaptation essential to better understand preferences, the constraints and learning habits of the learner. Thus, it is particularly advisable to propose online learning systems that are able to collect and detect information describing the learning process in an automatic and deductive way, and to rely on this information to follow the learner in real time and offer him training according to his dynamic learning pace. This article proposes a multi-agent adaptive learning system to make a real decision based on a current learning situation. This decision will be made by performing a hypride cycle of the Case-Based Reasonning approach in order to follow the learner and provide him with an individualized learning path according to Felder Silverman learning style model and his learning traces to predict his future learning status. To ensure this decision, we assign at each stage of the Incremental Hybrid Case-Based Reasoning at least one active agent performing a particular task and a broker agent that collaborates between the different agents in the system.
On the performance of quick artificial bee colony algorithm for dynamicdeployment of wireless sensor networks

In recent years, the use of wireless sensor networks (WSNs) has increased and there have been significant improvements in this field. Especially with smarter, cheaper, and smaller sensor nodes, various kinds of information can be detected and collected in different environments and under different conditions. WSNs have thus been used in many applications such as military, surveillance, target tracking, home, medical, and environmental applications. As the popularity of WSNs increases, problems related to these networks are being realized. The dynamic deployment problem is one of the main challenges that have a direct effect on the performance of WSNs. In this study, a novel optimization technique named the quick artificial bee colony (qABC) algorithm was applied to the dynamic deployment problem of WSNs. qABC is a new version of the artificial bee colony algorithm (ABC) and it redefines the onlooker bee phase of ABC in a more detailed way. In order to see the performance of qABC on this problem, WSNs that include only mobile sensors or both stationary and mobile sensors were considered with binary and probabilistic detection models. Some experimental studies were conducted for tuning the colony size ($CS$) and neighborhood radius ($r$) parameters of the qABC algorithm, and the performance of the proposed method was compared with the standard ABC algorithm and some other recently introduced approaches including a parallel ABC, a cooperative parallel ABC, a version of ABC powered by a transition control mechanism (tlABC), and a parallel version of tlABC. Additionally, some CPU time analyses were provided for qABC and ABC considering different dimensions of the problem. Simulation results show that the qABC algorithm is an effective method that can be used for the dynamic deployment problem of WSNs, and it generally improves the convergence performance of the standard ABC on this problem when $r \geq 1$.
Virtual Reality Application for Stress Therapy: Issues and Challenges

Given that stresshas affected many people and is becoming an important issue to address, the development of an application that can help people to relax and therefore reduce stress is becoming important. One of the solutions is the use of virtual reality (VR) in stress therapy. In this paper, we evaluate a virtual reality application developed for stress therapy, and highlighted the issues associated with such application. Based on the issues, we proposed a component framework which can be used to develop a VR application for stress therapy which provides holistic experience to the users.
The MeSH-Gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for Semantic Similarity.

Eliciting semantic similarity between concepts remains a challenging task. Recent approaches founded on embedding vectors have gained in popularity as they have risen to efficiently capture semantic relationships. The underlying idea is that two words that have close meaning gather similar contexts. In this study, we propose a new neural network model, named MeSH-gram, which relies on a straightforward approach that extends the skip-gram neural network model by considering MeSH (Medical Subject Headings) descriptors instead of words. Trained on publicly available PubMed/MEDLINE corpus, MeSH-gram is evaluated on reference standards manually annotated for semantic similarity. MeSH-gram is first compared to skip-gram with vectors of size 300 and at several windows' contexts. A deeper comparison is performed with twenty existing models. All the obtained results with Spearman's rank correlations between human scores and computed similarities show that MeSH-gram (i) outperforms the skip-gram model and (ii) is comparable to the best methods that need more computation and external resources.
Research on Situational Perception of Power Grid Business Based on User Portrait

The user portrait is to analyze the user data, label the user information, construct a virtual representative of the real user, and facilitate the business personnel to quickly and accurately understand the user's information, so as to take targeted measures to achieve the desired goal. With the standardization of the business travel business of the grid, the collection and analysis of user data for the commercial business of the grid is becoming more and more important. The comprehensive strengthening of the concentration, uniform and lean management of travel expenses has become a hot research topic. This paper selects the case based on the analysis of user's portraits to predict the customers of the grid business travel business, and describes the modeling process and method from the aspects of design, data analysis and preprocessing, feature engineering and algorithm model construction of the image label of the business travel business customer.
RAS: A Data‐Driven Rigidity‐Aware Skinning Model For 3D Facial Animation

Abstract We present a novel data‐driven skinning model—rigidity‐aware skinning (RAS) model, for simulating both active and passive 3D facial animation of different identities in real time. Our model builds upon a linear blend skinning (LBS) scheme, where the bone set and skinning weights are shared for diverse identities and learned from the data via a sparse and localized skinning decomposition algorithm. Our model characterizes the animated face into the active expression and the passive deformation: The former is represented by an LBS‐based multi‐linear model learned from the FaceWareHouse data set, and the latter is represented by a spatially varying as‐rigid‐as‐possible deformation applied to the LBS‐based multi‐linear model, whose rigidity parameters are learned from the data by a novel rigidity estimation algorithm. Our RAS model is not only generic and expressive for faithfully modelling medium‐scale facial deformation, but also compact and lightweight for generating vivid facial animation in real time. We validate the efficiency and effectiveness of our RAS model for real‐time 3D facial animation and expression editing.
Annotation Guideline No. 1: Cover Sheet for Narrative Boundaries Annotation Guide

Narratives and stories are found all over the world, in every culture, and they are used by every person every day. For computers to communicate with people in a natural and respectful manner, they need to understand stories. Unfortunately, computational understanding of stories is currently in its early stages: computers cannot yet identify even basic characteristics of a narrative, such as where it begins and ends. To train and test a computer’s ability to identify the beginnings and endings of narratives (what we call here narrative boundaries) we are collecting human judgments.
ICT and its purpose in the pedagogical practice

Information and communications technology (ICT) is currently inserted in the daily activities of a human being and has become a necessary resource, vital for their development up to the point of not conceiving activities without using these. In this sense, the education field is not the exception and has been partially including ICT as support for the teaching practice; however, there is an increasing demand from the students to implement more resources and activities supported by these technologies. A significant distance exists (the known digital divide) between the possibilities offered by different web applications that can support the pedagogical practices and the actual reality of the teaching practice, where the students realize the flaws and the necessity of strengthening the technology skills of the teachers to efficiently develop and orientate such use. In this regard, it is necessary to critically reflect on the possibilities that are offered by different web applications and their impact, their purpose in the pedagogical practice in a way that can serve as a pretext to motivate the teachers in their use and application in the development of their classes, achieving real formative processes. By doing this, students can be offered real, significant learning experiences from the technological, social, and pedagogical view, consolidating in a first instance the abilities of the teachers in the ICT area, as well as the comprehensive learning of their students, being updated on the advances that ICT offers globally, and implementing these permanently in their job as teachers, thus innovating their classes and motivating students towards learning and toward consolidating the use of ICT in the different disciplines, with the human and social component that must characterize us, accomplishing from this standpoint and reflection a pedagogical practice of quality.
Cooperative Model in Language Reasoning: Indonesian Language Teaching Materials in Teacher Training and Education Faculty of Muhammadiyah Sumatera Utara University

The aim of this study is to determine student’s language reasoning of Mathematics Education and Accounting Education in Teaching Training and Education Faculty of Muhammadiyah Sumatera Utara University. The research method used is an experimental method, where Mathematics Education as a control class or without using cooperative models and Accounting Education as a class experiment or use a cooperative learning models. The sample of this study is 65 students consisting of Mathematics Education students as many as 40 students, Accounting Education students as many as 25 students. Research results on Mathematics Education students through tests without using cooperative learning models obtained Mean= 73.50. Research results on Accounting Education through tests after using cooperative learning models obtained Mean= 76.20. Hypothesis tests conducted by statistical test or t test, it is obtained t count = 1.78 and t table = 1.68. Thus, t count is greater than t table, it can be concluded that cooperative models applied to Accounting Education in learning can improve student’s language reasoning.
"The Magic of Science:" Beyond Action, a Case Study on Learning Through Socioenaction

Recent advances in Human-Computer Interaction (HCI) can significantly affect technology-enhanced educational contexts. Our evolving relationship technology is a challenging topic of investigation, but alternative theories to cognition and socially aware empirical studies can shed light on the subject. In this paper, we explore "The Magic of Science" workshop, conducted in an educational museum context. With a background on learning through action and enactivism, our objective is to observe how people can individually and socially experience pervasive digital technology in educational contexts. Our study included 15 participant children and adolescents, who explored an exhibit of three interactive artworks and then built an interactive artifact from scratch during the workshop. We observed how these interactions took place and collected feedback on the experience of the workshop. Our results indicate that new ways of interacting with pervasive technologies allow us to expand the concept of learning through action, towards learning through socioenaction.
Research on Intelligent Learning APP in Junior English Vocabulary Teaching

Vocabulary, as one of the most difficult problems for students and teachers, is also one of the most important elements in traditional teaching. This thesis is based on the problems existing in traditional vocabulary teaching and under the guidance of the mobile learning theory, the constructivism theory and the humanism theory. This thesis researches the application of intelligent Learning APP in Junior English vocabulary teaching .The research shows that the application of intelligent Learning APP, as one of the products of modern science and technology, is applied in Junior English vocabulary teaching. It effectively combines after-class teaching with in-class teaching, pays attention to students' individuality, improves teachers' teaching quality and reduces teaching pressure. The application of intelligent Learning APP has made up for the shortcomings of traditional teaching, stimulated students' interest in learning and made the teaching process more interesting.
A Novel Key Influencing Factors Selection Approach of P2P Lending Investment Risk

Recent frequent “thunderstorm incidents” of P2P lending industry have caused the panic of industry investors. To predict the investment risk of P2P lending, we should scientifically and rationally analyze the key influencing factors of P2P lending investment risk. Existing key influencing factors selection methods mainly involve traditional statistical approaches and artificial intelligence methods. The traditional statistical approaches cannot deal with the high-dimensional nonlinear problems, and it cannot find the exact key influencing factors of the P2P lending investment risk. The artificial intelligence methods cannot recognize and learn the application background, and the selected attributes without active thinking and personal perception may not be the key influencing factors of P2P lending investment risk. To address the above issues, a novel key influencing factors selection approach of P2P lending investment risk is proposed by combining the proposed fireworks coevolution binary glowworm swarm optimization (FCBGSO), multifractal dimension (MFD), probit regression, and artificial prior knowledge. First, multifractal dimension combined with the proposed FCBGSO is used to select the preliminary influencing factors of the investment risk; second, the nonsignificant relevant attributes in the preliminary influencing factors are removed using the probit regression, and we add the influencing factors extracted from the original dataset of P2P lending using the artificial prior knowledge into the retaining influencing factors after removing one by one. A small and reasonable number of influencing factor subsets are achieved. Finally, we evaluate each influencing factors subset using extreme learning machine (ELM), and the subset with the best classification accuracy is efficiently achieved, i.e., it is the key influencing factors of P2P lending investment risk. Experimental results on the real P2P lending dataset from the Renrendai platform demonstrate that the proposed approach performs better than other state-of-the-art methods and that it has validity and effectiveness. It provides a new research idea for the key influencing factors selection of P2P lending investment risk.
Leukocyte Segmentation via End-to-End Learning of Deep Convolutional Neural Networks

Identification and analysis of leukocytes (white blood cells, WBC) in blood smear images play a vital role in the diagnosis of many diseases, including infections, leukemia, and acquired immune deficiency syndrome (AIDS). However, it remains difficult to accurately segment and identify leukocytes under variable imaging conditions, such as variable light conditions and staining degrees, the presence of dyeing impurities, and large variations in cell appearances, e.g., size, color, and shape of cells. In this paper, we propose an end-to-end leukocyte segmentation algorithm that uses pixel-level prior information for supervised training of a deep convolutional neural network. Specifically, a context-aware feature encoder is first introduced to extract multi-scale leukocyte features. Then, a feature refinement module based on the residual network is designed to extract more discriminative features. Finally, a finer segmentation mask of leukocytes is reconstructed by a feature decoded based on the feature maps. Quantitative and qualitative comparisons of real-world datasets show that the proposed method achieves state-of-the-art leukocyte segmentation performance in terms of both accuracy and robustness.
Cyber-Physical Museum Exhibits Based on Additive Technologies, Tangible Interfaces and Scientific Visualization

This paper is devoted to the new methods of organizing interactive museum exhibits by means of additive technologies, programmable microelectronics and multimedia.Traditionally museum exhibition is distanced from the visitor appearing just as a visual image.But new trend is to let the visitor inside the exhibition, providing different forms of interactions.Modern museums constantly search for the new ways of presenting cultural and natural heritage taking into account that exhibitions should be at the same time scientifically accurate, attractive, memorable and, ideally, accessible for the wide audience including disabled people.In this regard, tangible user interfaces based on the Internet of Things and combined with the scientific visualization build up very promising set of technologies that brings to live cyberphysical museum exhibits.These exhibits are an alloy of physical museum items with virtual multimedia content, providing the visitor a unique experience of interactivity.The key feature of cyber-physical exhibits is their tangibility, whereby they become accessible for visually impaired people and deliver much more information for the regular visitors.There are a lot of successful attempts to build cyber-physical exhibits within the museum space.However, there is a lack of methodological basis for this, as well as the lack of highlevel tools to seamlessly integrate related technologies into the existing museum infrastructure.In this paper we propose using ontology-driven adaptive multiplatform scientific visualization system SciVi as a software basis for cyber-physical museum exhibits.This system was previously successfully used in solving problems related to steering hardware and software solvers in different application domains, monitoring lightweight robotics systems and supporting custom hardware human-machine interfaces.So, it contains necessary mechanisms to adapt to the third-party digital infrastructure (including the museum one) and build all the required middleware and visualizers within it.We tested our approach by developing two cyber physical exhibits: tangible bonobo skull in the State Darwin Museum (Moscow) and tangible titanophone skulls in the Museum of Permian Antiquities (Perm).Bonobo exhibit appears to be a custom joystick in a form of corresponding skull to steer the Sketchfab-rendered 3D model of bonobo monkey head.Titanophone exhibit allows visitors to discover the age variability of titanophone synapsid in an interactive way.
Assessing the Mental States of Fallback-Ready Drivers in Automated Driving by Electrooculography

During automated driving at SAE level 2 or level 3, the driver's role changes from an active driver to a fallback-ready driver, whose mental state must still be prepared for a takeover request (TOR) when prompted. This preliminary study investigates the sensitivity of eye-movement measures in assessing the mental state of fallback-ready drivers in automated driving. The mental state of a fallback-ready driver is considered to be defined by two major factors: the state of workload imposed by non-driving related task, and the state of drowsiness. A visual workload (using a tablet computer), a non-visual workload (performing a cognitive task), as well as a state of drowsiness (after a prolonged interval of automated driving) were induced, and their effects on driving performance were studied in a high-fidelity driving simulator. The number and size of saccadic eye-movements, as well as the durations of eyeblinks, were measured by using electrooculography. The results indicate that the percentage of small saccades was sensitive to both visually and non-visually loaded states and can therefore be used to distinguish the two types of workload, and eyeblink duration was sensitive to the state of drowsiness. These results should be helpful in understanding the fallback-ready driver's mental state in automated driving, and can provide a new perspective on the selection of input features in developing camera-based driver state monitoring systems.
Complete weight enumerators of a class of linear codes over finite fields

<p style='text-indent:20px;'>We investigate a class of linear codes by choosing a proper defining set and determine their complete weight enumerators and weight enumerators. These codes have at most three weights and some of them are almost optimal so that they are suitable for applications in secret sharing schemes. This is a supplement of the results raised by Wang <i>et al.</i> (2017) and Kong <i>et al.</i> (2019).