Reviewerly/exHarmony huggingface dataset:
Gated Value Network for Multilabel Classification

We introduce a gated value network (GVN) for general multilabel classification (MLC) tasks. GVN was motivated by deep value network (DVN) that directly exploits the “compatibility” metric as the learning pursuit for MLC. Meanwhile, it further improves traditional DVN on twofold. First, GVN relaxes the complex variable optimization steps in DVN inference by incorporating a feedforward predictor for straightforward multilabel prediction. Second, GVN also introduces the gating mechanism to block confounding factors from the input data that allows more precise compatibility evaluations for data and their potential multilabels. The whole GVN framework is trained in an end-to-end manner with policy gradient approaches. We show the effectiveness and generalization of GVN on diverse learning tasks, including document classification, audio tagging, and image attribute prediction.
Reproduction Package for Evaluating the Vitruvius Approach

Vitruvius for consistency in view-based system development proposes a process and mechanisms for developing complex software and software-intensive systems by means of views that are kept consistent by explicit specifications of their dependencies.This repository provides the projects of the Vitruv framework (https://vitruv.tools), which implements that approach. It contains a prepared execution environment for the framework as well as case study projects. In addition, it provides a script to run the environment and automatically resolve all dependencies based on Docker.The repository was created to be used as a reproduction package for the evaluation of the article Enabling consistency in view-based system development - Vitruvius approach by Klare et al., published in The Journal of Systems & Software.
Video Analysis Method of Basketball Training Based on Deep Learning

In order to solve the problem of poor data clustering effect of the original basketball shooting track capture and antisense capture method, a basketball shooting track automatic capture method based on background difference method is designed. Then the sensor network is designed to capture the trajectory. By using this method to improve the accuracy of basketball shooting trajectory automatic capture, ensure the integrity of the data. In the process of design, it paid attention to the installation position of the sensor and the processing process after data acquisition. Inertial sensors are used to ensure the real-time and effectiveness of data acquisition. The trapper is used to complete the data processing and trajectory capture. The background difference method is used to detect and process the target trajectory. Realize the automatic capture of shooting trajectory. This method can greatly improve the accuracy of track capture. After its popularization and application, it can effectively improve the track capture effect in basketball
Design and Implementation of Edge Extraction Algorithm for Digital Image

With the development of mathematics and artificial intelligence, various types of edge algorithms are emerging, and theories such as neural networks are applied to image edge detection. However, due to the contradiction between detection accuracy, edge positioning accuracy and anti-noise, there are still some inconsistencies in edge detection. How to achieve accurate positioning and extraction of image edges has become an important issue that people must face. In this paper, the principle method of image edge extraction is deeply studied and explored. The main work is reflected in the following aspects:First, the meaning and prospect of image edge extraction are analyzed and discussed. Then introduce the basic theory of graphics extraction, enumerate and explain the current various extraction algorithms, focus on the canny algorithm and the robot algorithm, and obtain their extracted images through experimental simulation, from image extraction effect, peak signal-to-noise ratio and time overhead. Several parties have made comparisons. Secondly, the MATLAB experimental simulation platform is introduced, and the user interface design and development is carried out on this platform, and the code is given for some functions. Finally, this paper gives a comprehensive summary of the work of the thesis, and looks forward to the further research on image edge extraction.
Construction of Ontology-based Fault Knowledge Base of Aerospace TT&C Equipment

This paper studies the construction of ontology-based fault knowledge base of aerospace TT&C equipment. Firstly, knowledge base and fault knowledge of aerospace TT&C equipment are researched. Then we construct an ontology-based knowledge base model, and the process of construction is given in detail. After that, the rule-based semantic reasoning is tested and analyzed. The research in this paper can offer some help and reference for the construction of knowledge base in other domain.
Construction of Spatiotemporal Big Data Using Environmental Impact Assessment Information

In this study, the information from environmental impact statements was converted into spatial data because environmental data from development sites are collected during the environmental impact assessment (EIA) process. Spatiotemporal big data were built from environmental spatial data for each environmental medium for 2,235 development sites during 2007-2018, available from public data portals. Comparing air-quality monitoring stations, 33,863 measurement points were constructed, which is approximately 75 times more measurement points than that 452 in Air Korea’s real-time measurement network. Here, spatiotemporal big data from 2,677,260 EIAs were constructed. In the future, such data might be used not only for EIAs but also for various spatial plans.
Cascaded U-net for Kidney and Tumor Segmentation from CT volumes

Contrast-enhanced Computed Tomography (CT) imaging is most useful tool in diagnosing and locating the kidney lesions. An automated kidney and tumor segmentation are very helpful because it can provide the precise information about the location and size of lesions which can be used in quantitative analysis of the tumor. Semantic segmentation of kidney is very challenging as it requires large dataset for training and its morphological heterogeneity makes it a difficult problem. The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) has publicly released a 210 cross sectional CT images with kidney tumors along with corresponding semantic segmentation masks. In this work we proposed a novel two stage 2D segmentation method to automatically segment kidney and tumor using the combination of Unet++ and squeeze and excite approach. The proposed network is trained in keras framework. Our method achieves a dice score of 0.98 and 0.965 with kidney and tumor respectively on training data and the results demonstrates the accuracy of our proposed method. Proposed method was able to segment kidney and tumor from abdominal CT images which can provide the exact location and size of the tumor. This information can also be used to analyze treatment response.
IMPROVING STUDENTS WRITING SKILL BY USING FREE WRITING TECHNIQUE

The aimed of this research is to find out does the use of free writing technique improve students’ writing skill or not. The research conducted at SMPN 1 Kembang Tanjong with experimental research design. The population was all of the eighth grade students with the total number of students were 106. The sample was class VIII 1 as experimental class and VIII 2 as control class. The instrument used in pre-test and post-test was a set of written test. Rubric scoring system by Heaton used to analyze the students writing score and independent T-test used to analyze the data. The result of data analysis showed that in pre-test the students at experimental class got mean score 53,5 while at control class got 52,10. In post-test, the students at experimental class who taught writing by using free writing technique got a mean score 78,08 and at control class students who was not got the treatment as in experimental class only got a mean score 57,70. The result of independent T-test, at pretest t-count ? t-table (0,154 ? 1,685) and P-value ? level of significance (0,879 ? 0,05) it means there were no significantly differences between experimental class and control class at pre-test and at posttest t-count ? t-table (3.845 ? 1,685) and P-value ? level of significance (0,00 ? 0,05) it means teaching writing by using free writing technique improve significantly the students writing skill. So, the hypothesis was accepted. &#x0D; &#x0D; Key Words: Writing, Free Writing, Descriptive Text
Hybrid Adaptive Wavelet-Based Optical Flow Algorithm for Background Oriented Schlieren (BOS) Experiments

Quantitative analysis of the flow field is an effective method to study hydrodynamics. As a flow field measurement technology, the Background Oriented Schlieren (BOS) is widely used. However, it is difficult to measure the complex transparent flow field (flow field with large refractive index gradient) using the BOS experiment. In order to overcome this difficulty and improve the accuracy of the BOS experiment, this paper presents a hybrid adaptive wavelet-based optical flow algorithm for the BOS. The current algorithm is a combination of the traditional optical flow algorithm and the wavelet-based optical flow algorithm. By adding the initial value constraints, the adaptive scale constraints, and the adaptive regularization constraints, the algorithm can effectively overcome the above-mentioned difficulty and also improve its accuracy. To further illustrate the feasibility of the proposed method, this paper uses the simulation data, the data of the DNS datasets, and the data of the BOS experiment to verify the performance of the algorithm. The experiment of comparing the proposed algorithm with the existing ones is carried out on the DNS datasets and the data of the BOS experiment. Finally, the proposed method is verified by a practical BOS experiment. The results show that the proposed algorithm can effectively improve the measurement accuracy of displacements.
Efficient Multilevel Architecture for Depth Estimation from a Single Image

Monocular depth estimation is an important task in scene understanding with applications to pose, segmentation and autonomous navigation. Deep Learning methods relying on multilevel features are currently used for extracting local information that is used to infer depth from a single RGB image. We present an efficient architecture that utilizes the features from multiple levels with fewer connections compared to previous networks. Our model achieves comparable scores for monocular depth estimation with better efficiency on the memory requirements and computational burden.
STANDAR KELENGKAPAN FITUR E-LEARNING SUPPLY CHAIN MANAGEMENT PADA PRODUK BACKLOG MENGGUNAKAN METODOLOGI SCRUM

The development of E-Learning features is a concern among developers.Making E-Learning features that meet the standards and in accordance with the needs of students and lecturers is one of the requirements in its application.The effectiveness of learning that is expected to be effective using E-Learning is still difficult to achieve due to various factors, one of which is lack of features in the system.Supply Chain Management (SCM) is a supply chain learning that has several series of activities, so it is important to ensure all chapters in SCM are mastered by students on time.We will compare several features of the 3 Learning Management System (LMS) applications.Among them are SEVIMA EdLink, Coursera, and Blackboard CourseSites.The results of this study will produce a standard set of features specific to SCM E-Learning to support learning effectiveness.The standard setting for features completeness will use the SCRUM framework in the product backlog.The results of the analysis are Prototype using Website-based MOODLE Tools.
INFORMATION SYSTEMS DESIGN FOR EARLY CHILDHOOD EDUCATION SERVICES

Parents are working harder due to the increase needs of necessities of life. This causes mothers to work so that they meet these necessities. As a result, mothers leave their children aged 2 (two) to 4 (four) years in the play group. However, the children development is often out of hand because it is not properly recorded. Recording children development manually has several weaknesses, one of them is damaged children development recording book. Based on this problem, this research aims to develop information systems design for early childhood education services to monitor children development. The method used was SDLC. The result of the research is information system as children development monitor in early childhood education services and tool for teachers in managing the information regarding children growth and development effectively and efficiently.
Multistage adaptive spectral subtraction of seismic signals

&amp;lt;p&amp;gt;The detection of microearthquakes is an important task in various seismological applications as volcano seismology, induced seismicity, and mining safety. In this work we have developed a novel technique in order to improve the quality and efficiency of STA/LTA based detection of microearthquakes. This technique consists of different stages of filtering employing an adaptive spectral subtraction method, which allows greatly improving the signal/noise ratio.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The implemented technique consists in a preliminary band-pass filtering of the signal followed by different stages of an adaptive spectral subtraction. The spectral subtraction technique is a non-linear filtering which allows taking into account the actual noise spectrum shape. It allows achieving a good filtering even in cases where the signal and noise spectrum overlaps. In order to take into account of the temporal variation in the background noise spectrum, we designed an adaptive technique. We first divide the incoming signals into short temporal windows. Each window is classified as &amp;amp;#8220;noise only&amp;amp;#8221; or &amp;amp;#8220;meaningful signal&amp;amp;#8221; (which can be either a microearthquake or any other relevant transient signal) using different features as the signal energy and the zero-crossing rate. Windows classified as &amp;amp;#8220;noise only&amp;amp;#8221; are continuously accumulated in a dynamic buffer which allows the average noise spectrum to be estimated and updated in an adaptive manner. This technique can be applied on subsequent stages to further improve the signal/noise ratio. This technique has been implemented in Python for the automatic detection of the microearthquakes on both off-line and near-real time data.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In order to check the efficiency of the results, we compared the results of an STA/LTA based automatic detection on the initial band-pass filtered signal and on the spectral subtracted signals after different stages of filtering. A notable improvement of the quality of the detection process is observed when repeated spectral subtraction stages are applied.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We applied this procedure to seismic data recorded by Red S&amp;amp;#237;smica Canaria, managed by Instituto Volcanol&amp;amp;#243;gico de Canarias (INVOLCAN), on Tenerife (Canary Islands), comparing results from the proposed detection algorithm with standard approaches as well as with manual detections. We present an extensive statistical analysis of the results, determining the percentage of correct detections, novel detections, false positives and false negatives after each stage of filtering. First results have shown that this technique is also able to detect automatically microearthquakes which went undetected after a manual analysis.&amp;lt;/p&amp;gt;
Robust stabilization of differential‐algebraic systems

Summary For an uncertain differential‐algebraic system subject to perturbations acting additively on coprime factors of its transfer function matrix, we construct a robust stabilizing controller and give a formula for the maximum stability margin in terms of realizations and solutions to appropriate Riccati equations.
Computing Ex Ante Coordinated Team-Maxmin Equilibria in Zero-Sum Multiplayer Extensive-Form Games

Computational game theory has many applications in the modern world in both adversarial situations and the optimization of social good. While there exist many algorithms for computing solutions in two-player interactions, finding optimal strategies in multiplayer interactions efficiently remains an open challenge. This paper focuses on computing the multiplayer Team-Maxmin Equilibrium with Coordination device (TMECor) in zero-sum extensive-form games. TMECor models scenarios when a team of players coordinates ex ante against an adversary. Such situations can be found in card games (e.g., in Bridge and Poker), when a team works together to beat a target player but communication is prohibited; and also in real world, e.g., in forest-protection operations, when coordinated groups have limited contact during interdicting illegal loggers. The existing algorithms struggle to find a TMECor efficiently because of their high computational costs. To compute a TMECor in larger games, we make the following key contributions: (1) we propose a hybrid-form strategy representation for the team, which preserves the set of equilibria; (2) we introduce a column-generation algorithm with a guaranteed finite-time convergence in the infinite strategy space based on a novel best-response oracle; (3) we develop an associated-representation technique for the exact representation of the multilinear terms in the best-response oracle; and (4) we experimentally show that our algorithm is several orders of magnitude faster than prior state-of-the-art algorithms in large games.
TECNOLOGÍAS DE INFORMACIÓN Y COMUNICACIÓN (TIC) COMO RECURSO ALTERNATIVO EN EDUCACIÓN SECUNDARIA A DISTANCIA

The main purpose of this research was to analyze the meaning of Information and Communication Technologies (ICT), as an alternative resource in Secondary Distance Education in the Educational Institution Ciudad Latina of the Municipality of Soacha, Department of Cundinamarca-Colombia, it was based on the theoretical approaches of Dellepiane (2010), Molina (2012), Cacurri (2013), among others. The research was qualitative, using the post-positivist paradigm, with a phenomenological method; the population was two (02) managers, two (02) secondary education teachers and two (02) students. The instrument was through the participant observation technique, through the in-depth interview, open modality. The results affirm that there is an attitude towards technological culture, but managers must be able to promote meetings and mechanisms so that teachers can include new technologies in their pedagogical practices, requiring greater participation of teachers in technological educational activity. . Concluding in a general way, based on the results that the members of the educational institutions require to handle the knowledge about ICTs, so that they can include them as learning tools, indicating that they have to be developed in educational management; as well as promoting adequate spaces with updated technological equipment that contributes to the commitment and shared responsibility for achieving the permanence of technological tools in student learning.
Authors’ Reply to “Comments on “A new approach for the verification of infinite-step and K-step opacity using two-way observers” [Automatica, 2017(80)162-171]”

First, through a scalar counterexample, it is pointed out that Lemma 4.1 of Mazo et al. (2010), which serves as a stepping stone to the central result (Theorem 4.2) of that paper, is incorrect as stated, and so is that theorem. Then, a corrected result is proposed and proved. Finally, the statement of Theorem 4.2 in Mazo et al. (2010) is corrected accordingly.
The impact of texting language on Nigerian students: a case study of final year linguistics students

This article reports on research which examined whether the use of mobile phone text messaging is responsible for the reported presence of abbreviations in students’ written work at the University of Benin. I argue that the frequent use of short messaging service (SMS) abbreviations may not be attributed only to the reported increase in the use of abbreviations in the written work of students. Other factors, such as the purpose of the writing and the students’ state of mind, might also be determinants of whether students use abbreviations or not. The research was based on the analysis of a questionnaire distributed to final-year linguistics students of the University of Benin in 2015, during their regular classes at the main campus of the University of Benin. In total, 62 final-year students from the Department of Linguistics and African Studies at the University of Benin participated in the in-class survey. The professor of the students obtained ethical clearance and provided 72 notebooks, 126 written assignments and 85 examination scripts of the same students to the researcher for analysis and validation of their responses to the questionnaire. The analysis indicated that SMS abbreviations were carried over into students’ written classwork. However, one cannot categorically state that SMSs are the reason why students use abbreviations in their written work as widely reported because the evidence from this study does not support such a claim.
ForecastQA: A Question Answering Challenge for Event Forecasting

Event forecasting is a challenging, yet consequential task, as humans seek to constantly plan for the future. Existing automated forecasting approaches rely mostly on structured data, such as time-series or event-based knowledge graphs, to help predict future events. In this work, we formulate the forecasting problem as a restricted-domain, multiple-choice, question-answering (QA) task that simulates the forecasting scenario. To showcase the usefulness of this task formulation, we introduce a dataset ForecastQA, a question-answering dataset consisting of 10,392 event forecasting questions, which have been collected and verified via crowdsourcing efforts. We also present our experiments on ForecastQA using BERT-based models and find that our best model achieves 61.0\% accuracy on the dataset, which is still far behind human performance by about 18%. We hope ForecastQA will support future research efforts in bridging this gap.\footnote{\url{this https URL}}
JHUBC’s Submission to LT4HALA EvaLatin 2020

We describe the JHUBC submission to the EvaLatin Shared task on lemmatization and part-of-speech tagging for Latin. We modify a hard-attentional character-based encoder-decoder to produce lemmas and POS tags with separate decoders, and to incorporate contextual tagging cues. While our results show that the dual decoder approach fails to encode data as successfully as the single encoder, our simple context incorporation method does lead to modest improvements.