Reviewerly/exHarmony huggingface dataset:
Towards reference-aware FrameNet representations : Bridging generic and specific event knowledge

FrameNet (Baker et al., 2003) is a resource that encodes conceptual and linguistic knowledge in the form of frames: information packages defining word senses and semantic roles associated with a particular type of event, situation or concept. FrameNet is a rich resource for describing how events and situations can be conceptualized in language in different ways, but is limited by its focus on lexical semantics and lack of a notion of reference: a frame-semantic analysis of the event descriptions in (1) would tell us that both describe the same event type (i.e., a commercial transaction, conceptualized from two different perspectives), but not whether they in fact describe the same event token in the real world. (1) a. Yesterday, John sold Mary a book. b. A woman bought a novel in the shop. To address this limitation, we are currently developing a new FrameNet-based resource, comprising a lexical database, annotated corpus and a semantic parser, that is ‘referentially enriched’ in two ways: frame annotations are linked, on one hand, to referential information from an ontology of real-world event tokens, and on the other hand to truth-conditional meaning representations.
THE APPLICATION OF GAMES IN DEVELOPING STUDENTS’ SPEAKING SKILL IN EFL CLASSROOM

This research was aimed at developing students speaking skill in EFL classroom through the application of games. The research was designed in the form of Classroom Action Research. It was conducted in two cycles and six meetings. Each cycle consisted of plan, implementation, observation and Reflection. The researcher employed observation checklist, field notes, questionnaire, and test to collect the data. The main criterion of success of this research was that the students’ scores on speaking test should achieve the score of 70; and it must be achieved by 75% of the total number of students. The second criterion was that the teacher’s classroom performance should meet “Success” category. The results of the first speaking test showed that from 24 students in class TBI-1 FTIK IAIN Palu, there were 15 students got the scores of 70 or higher. It was equal to 62.5 %. In cycle 2, the total number of students who got the scores of 70 or higher was 21. It was equal to 87.5 %. The result of this research indicated that the application of games was effective to develop the students’ speaking skills.Keywords:
JomAttendance: My face is the attendance

The attendance system in education currently use in the world are commonly sign on paper is not effective in recording attendance because people can alter and change the data easily. With modern technology, the attendance system should use a facial recognition system when recording attendance. From the mixed-mode research, we concluded that university students have agreed that traditional attendance is a hassle just to verify their attendance in the classroom because of the huge class size, therefore this system plans to solve problems such as reliability, prevent truant and improve the time taken when taking attendance. The system is using a ‘facial recognition attendance algorithm’ in this system. The research of this paper demonstrates the benefits of having a facial detection attendance system in a university setting. The facial detection approach towards a new standard for attendance for universities because it is faster and effective to record student attendance.
Intrinsic Argument Strength in Structured Argumentation: A Principled Approach

Abstract argumentation provides us with methods such as gradual and Dung semantics with which to evaluate arguments after potential attacks by other arguments. Some of these methods can take intrinsic strengths of arguments as input, with which to modulate the effects of attacks between arguments. Coming from abstract argumentation, these methods look only at the relations between arguments and not at the structure of the arguments themselves. In structured argumentation the way an argument is constructed, by chaining inference rules starting from premises, is taken into consideration. In this paper we study methods for assigning an argument its intrinsic strength, based on the strengths of the premises and inference rules used to form said argument. We first define a set of principles, which are properties that strength assigning methods might satisfy. We then propose two such methods and analyse which principles they satisfy. Finally, we present a generalised system for creating novel strength assigning methods and speak to the properties of this system regarding the proposed principles.
Organizational Model for Pedagogical Practices of Informatics and IT

The article presents the current standards for pedagogical practices for the subjects Informatics and Informational Technologies for students in Sofia University “St. Kliment Ohridski” and the evaluation criteria for them. Some difficulties with online education due to the COVID-19 pandemics are noted. The author presents an organizational model for pedagogical practices of students in Informatics and Informational Technologies in which the trainees swap between primary and high school in the middle of their semester. The research is in pilot stage and presents results only from the author observations and non-formal interviews with students.
DP-YOLOv5: Computer Vision-Based Risk Behavior Detection in Power Grids

The level of safety in power grid construction has been improved by Computer Vision (CV) recently with deep Convolutional Neural Networks (CNN). However, due to environmental complexity and risk behaviors diversity, the current detection algorithms still have false and missing detection problems. This paper quantitatively analyses these practical problems and proposes a Double Precise YOLOv5 (DP-YOLOv5) method. Compared to other state-of-art detectors, DP-YOLOv5 highlights three points: integrating multi objects for each classification to avoid false detection in complex environments, adding standard operation samples for guidance to reduce the missing detection caused by risk behaviors diversity, and using Depthwise Separable convolutional networks to reduce model parameters. The proposed DP-YOLOv5 method is evaluated on a dataset with 2.5k images generated in real power grid operation environments provided by State Grid Jiangsu Electric Power Co., Ltd. Compared with the state-of-art YOLOv5s detector. Experimental results show that the precision of DP-YOLOv5 is 7.1% higher, while the model size is 20% less.
ZipLine: An Optimized Algorithm for the Elastic Bulk Synchronous Parallel Model

The bulk synchronous parallel (BSP) is a celebrated synchronization model for distributed training of deep learning models. A shortcoming of the BSP is that it requires workers to wait for the straggler at every iteration. Therefore, employing BSP increases the waiting time of the faster workers of a cluster and results in an overall prolonged training time. To ameliorate this shortcoming of BSP, we proposed ElasticBSP [1], a model that aims to relax its strict synchronization requirement with an elastic synchronization by allowing delayed synchronization to minimize the waiting time. ELASTICBSP is realized by the algorithm named ZipLine. In this work, we show the theoretical proof of ZipLine and further propose algorithmic and implementation optimizations of ZipLine, namely ZipLineOpt and Ziplineoptbs, which reduce the time complexity of ZipLine to linearithmic time. The experiments show that ZipLineOpt and ZipLineOptBs enable the scalability of ElasticBSP. Further experimental evaluation on large deep neural networks on large ImageNet dataset demonstrate that our proposed Elas-ticbspmodel, materialized by the proposed optimized ZipLine variants, converges faster and to a higher accuracy than the predominant BSP.
A Study on the Recognition and utterance of Accents according to the Meaning of sentence-closing ending of Korean Learners ―For the sentence-closing ending of ‘-고’, ‘-다고’, ‘-네’, ‘-다더라’, ‘-다니’, ‘-(으)ㄹ걸’―

이 연구는 한국어 학습자들이 의미에 따라 분화되는 종결어미의 억양을 어떻게 판별하고 산출하는지를 밝히는 논문이다. 연구에 참여한 학습자들은 한국어의 억양이 의사소통에 영향을 미친다는 것을 인지하고 있었다. 그러나 한국어 억양을 익힌 방법이 그동안 들었던 수업과는 별 관련성이 없는 것으로 응답하였고 억양 교육의 만족도가 매우 낮았다. 판별 실험에서 오류를 많이 보인 종결어미는 ‘감탄(놀람)’의 ‘&#8211;다니’였으며 산출 실험의 경우는 ‘빈정거림’의 ‘-고’였다. 또한 과거의 억양 학습 유무는 억양 판별과는 크게 상관이 없었지만 산출 능력과 유의미한 관계가 있었다. 또 한국어 학습 기간이 길수록 억양 판별을 좀 더 잘했으나 산출 능력과는 관계가 없었다.This study is a paper that reveals how Korean learners discriminate and utter sentence-closing ending accents that differentiate according to their meanings. In the discrimination experiment, the sentence-closing ending, who showed the most errors by foreign learners, was -다니 of Amazed, and in the case of utterance experiment, -고 of Criticism.“ The presence or absence of intonation learning in the past had little to do with intonation recognition. However, it has been shown to have a significant relationship with the ability to produce. In addition, the longer the Korean language learning period, the better the intonation was determined, but it was not related to the utter ability. Therefore, hyperpartition elements, including intonation, should be taught consciously in terms of speech.
Research Guides: Library 101: Digital Learning and Academic Innovations

This guide will take you through the ins and outs of your library! Learn about services, policies, and all of the opportunities that await you!
A New Plugin to Include FuzzyPred in KNIME

Knowledge discovery from databases is a very attractive and challenging task. The elicitation of fuzzy predicates (called FuzzyPred) in conjunctive and disjunctive normal form provides a convenient and effective general way to identify and to represent certain dependencies among items in fuzzy transactions. Konstanz Information Miner (KNIME) is a strong and comprehensive free platform for drag-and-drop analytics, machine learning, statistics, and data processing. It already offers a large variety of nodes, which enables easy execution of data pipelines. This paper presents a new plug-in that integrates FuzzyPred into KNIME. It allows reducing the amount of knowledge and experience required by users to use the method. A case of study is given to illustrate the use of the proposal.
A Multi-Simplex Imperialist Competitive Paradigm for Solving Nonlinear Physical Systems

This paper proposes a novel gradient free multi-simplex topology fabric aided imperialist competitive algorithm (ICA) for solving nonlinear systems of algebraic equations by transforming them to equivalent global optimization problems. The high dependence of traditional gradient based solvers of such systems on initial guesses and the Jacobeans resulting in false convergence is the main motivation behind the present work. The present work provides a mechanism for enhancing exploitation powers of imperialist search phase of the algorithm and hence improves the convergence speed. The variants emerging from the proposed approach are applied to diverse nonlinear systems arising in different scientific areas and the results so obtained are analyzed in details. Based on the analysis of empirical results on complex benchmark models, it is observed that the proposed enhancement of ICA has not only boosted the problem solving power of the imperialist system when applied multidimensional nonlinear physical systems but also emerged as an efficient gradient/initial guess free alternative solver with high accuracy for nonlinear systems.
Geographical Labeling of Web Objects Through Maximum Marginal Classification

Web search engines have become extremely popular in providing requested information to the user. The result set effectiveness of Web search engines has been continuously improving over the years. However, the documents of the result set may also contain irrelevant information having no importance to the user. So, the user has to spend some effort in searching for relevant information in these result set documents. To overcome this searching overhead, Web object search engines have been proposed. Such systems are built by extracting object information from various Web documents and integrating them into object repository. The user is provided with the facility to submit object search queries and the required object information is retrieved. Unlike, Web search engines, providing results to geography-specific queries is still in nascent stage for Web object search engines. Recently, Gaussian Mixture Model based technique for geographical labeling of Web objects was proposed in the literature. However, there is significant scope to improve the labeling accuracy results obtained in this technique. In this chapter, maximum marginal classifier-based technique for Web object geographical labeling is proposed. The advantages of this proposed technique are empirically exhibited on a real-world data set. This proposed technique, outperforms the contemporary technique by at least 40% in labeling accuracy, and is twice better in execution efficiency.
A Proposed of Multimedia Compression System Using Three - Dimensional Transformation

Video compression has become especially important nowadays with the increase of data transmitted over transmission channels, the reducing the size of the videos must be done without affecting the quality of the video. This process is done by cutting the video thread into frames of specific lengths and converting them into a three-dimensional matrix. The proposed compression scheme uses the traditional red-green-blue color space representation and applies a three-dimensional discrete Fourier transform (3D-DFT) or three-dimensional discrete wavelet transform (3D-DWT) to the signal matrix after converted the video stream to three-dimensional matrices. The resulting coefficients from the transformation are encoded using the EZW encoder algorithm. Three main criteria by which the performance of the proposed video compression system will be tested; Compression ratio (CR), peak signal-to-noise ratio (PSNR) and processing time (PT). Experiments showed high compression efficiency for videos using the proposed technique with the required bit rate, the best bit rate for traditional video compression. 3D discrete wavelet conversion has a high frame rate with natural spatial resolution and scalability through visual and spatial resolution Beside the quality and other advantages when compared to current conventional systems in complexity, low power, high throughput, low latency and minimum the storage requirements. All proposed systems implement using MATLAB R2020b.
Periodic Activation Functions Induce Stationarity

Neural network models are known to reinforce hidden data biases, making them unreliable and difficult to interpret. We seek to build models that `know what they do not know' by introducing inductive biases in the function space. We show that periodic activation functions in Bayesian neural networks establish a connection between the prior on the network weights and translation-invariant, stationary Gaussian process priors. Furthermore, we show that this link goes beyond sinusoidal (Fourier) activations by also covering triangular wave and periodic ReLU activation functions. In a series of experiments, we show that periodic activation functions obtain comparable performance for in-domain data and capture sensitivity to perturbed inputs in deep neural networks for out-of-domain detection.
DeInStance: Creating and Evaluating a German Corpus for Fine-Grained Inferred Stance Detection

We introduce deInStance, a corpus of 1000 politicians’ answers in German (de) containing sentences labeled with explicitly expressed and inferred stances - pro and con relations - by 3 annotators. They achieved an acceptable inter-rater agreement given the inherent subjective nature of the task. A first baseline, a fine-tuned BERT-based token classifier, achieved F1-scores of around 70% . Our focus is on the difficult subclass of sentences comprising only non-polar words, but still with an (implicit) pro or con perspective of the writer.
A Plea to Tool Vendors: Do Not Mislead How Technical Debt Is Managed

All systems have technical debt, and it has to be continuously managed. Thanks to the past decade of research, software engineering teams have now built an awareness and understanding of technical debt as a concept,1 have an improved appreciation of code analysis to drive down unintentional implementation mistakes that may lead to technical debt,2 and even started using some practices to improve its management.3 In fact, arguably, the managing technical debt research agenda, which was initially summarized at the 2010 National Science Foundation Future of Software Engineering Workshop,4 has been one of the few bodies of work in software engineering where we have seen upfront and engaged collaboration among academia, industry, and tool vendors.
Research on Air Quality Analysis Based on Cluster Methods

Abstract Research on air quality analysis is a hot field. Here we describe an analysis process based on cluster methods for the data of ambient air quality. In this paper, we use the process to cluster on the air quality data which from the National Urban Air Quality Report in December 2020 on the official website of the Ministry of Ecology and Environment of the People’s Republic of China. We find that cities in different clusters with different main pollutants and pollution levels. Ambient air quality analysis aims to provide guidance for reducing the impact of air pollution on health.
METHODOLOGY FOR ASSESSING THE EFFECTIVENESS OF METHODS FOR EMBEDDING DIGITAL WATERMARKS

In recent years, we have seen a significant increase in traffic moving across various networks and channels. The development of technology and global network leads to an increase in the amount of multimedia traffic. To authenticate and avoid abuse, data should be protected with watermarks. This paper discusses various robust and invisible watermarking methods in the spatial domain and the transform domain. The basic concepts of digital watermarks, important characteristics and areas of application of watermarks are considered in detail. The paper also presents the most important criteria for assessing the digital watermark effectiveness. Based on the analysis of the current state of the digital watermarking methods, robustness, imperceptibility, security and payload have been determined as the main factors in most scientific works. Moreover, researchers use different methods to improve / balance these factors to create an effective watermarking system. Our research identified the main factors and new techniques used in modern research. And the assessment of watermark method effectiveness was proposed.
Event-Triggered Distributed Maximum Correntropy Filtering in Non-Gaussian Environments

This paper investigates the distributed filtering issue for general stochastic nonlinear systems with non-Gaussian noises. A novel adaptive event-triggered scheme is designed to reduce the communication consumption. A new event-triggered distributed maximum correntropy filter (E-DMCC) is proposed by means of the maximum correntropy criterion, and the corresponding explicit expressions of desired filter gains and the upper bound of filtering error covariance are given in light of fixed-point iterative update rules and some typical matrix inequalities. Finally, a ballistic object tracking system is utilized to show the merit of the constructed distributed filtering in non-Gaussian noise environments.
An Adaptive Anisotropic Method for Speckle Suppression

The speckle noise affects the interpretation of the synthetic aperture radar (SAR) images. It is important to preserve the information of high frequency in the filtering process. A novel local adaptive anisotropic method is proposed. The method is applied to the partial differential equation (PDE) to calculate the diffusion coefficient for de-speckling of SAR image. By experimenting with real SAR images, the new method has better filtering effect and better real-time performance compared than other filtering methods.
D2.3 Set of FAIR data repositories features

This report presents the results of the first year of Task 2.3 from the FAIRsFAIR project. It gives guidelines to enable features for repositories which allow them not only to host FAIR digital objects, but also to be FAIR themselves. The recommendations were collected in the workshop “Building the data landscape of the future: FAIR Semantics and FAIR Repositories” (22 October 2019, Espoo Finland) that was hosted by this task together with the FAIRsFAIR task 2.2. It derived input from more than 70 participants from 6 communities: the European Life Sciences Infrastructure for Biological Information (ELIXIR), the European Incoherent Scatter Scientific Association (EISCAT), the Social Sciences and Humanities (SSH), the Integrated Carbon Observation System (ICOS), the European network of Long-Term Ecosystem Research sites (eLTER), and the Data Publisher for Earth & Environmental Science ( Pangea). The background of participants lied in infrastructures, research and libraries.
Defend Against Poisoning Attacks in Federated Learning

The rapid development of artificial intelligence not only brings convenience to people’s lives, but also leads to many privacy leaks. In order to resolve the contradiction between data availability and privacy protection, Google proposed the framework of federated learning. In federated learning, local clients upload model update values to the server, and the server aggregates all update values to obtain a new global model. However malicious attackers can upload malicious update values to perform poisoning attacks, making the global model unavailable or introducing a backdoor. In this paper, we deploy an AutoEncoder on the server side to calculate the reconstruction error of model update values. According to the size of the reconstruction error, we remove malicious update values and retain benign update values. Finally, the server aggregates all benign update values to obtain a new global model. Experimental results show that our scheme can effectively defend against poisoning attacks.
Airport Small Target Algorithm Based on Convolution Kernel Attention Mechanism

Aiming at the complex background of the airport target and small targets, a YOLOv3 airport small target detection algorithm based on convolution kernel attention mechanism is proposed. Firstly, the convolution kernel attention mechanism is embedded in the three feature fusion layers of YOLOv3 to improve the score of the airport small target. Secondly, design a Gaussion IOU(Intersection over Union) algorithm to obtain the JS divergence by constructing a three-dimensional Gaussian function, and replace the original IOU with Gaussion IOU to improve the recall rate of target detection. Finally, an improved non-maximum suppression algorithm is proposed, which uses Gaussian function as the attenuation coefficient to screen anchor frames. Using the designed algorithm, the mAP@0.5 for the three targets of airplanes, cars, and people is 0.951, which is 0.1 higher than the original YOLOv3. The results show that the designed algorithm can accurately detect small targets at the airport.
Didactic Model for Generating a System of Problems Based on the Technological Approach

The article describes a model of didactic technology for generating a system of problems. A combination of the stages of knowledge acquisition, staging of systems design, described in the literature are observed when specifying the technology, which are partially supplemented. The structure and the components, of a specific technology for creating a system of problems for the formation of skills in students to solve irrational equations with one radical, are described. A specific system of problems is proposed.