Reviewerly/exHarmony huggingface dataset:
Enhancing Online Knowledge Graph Population with Semantic Knowledge

Knowledge Graphs (KG) are becoming essential to organize, represent and store the world’s knowledge, but they still rely heavily on humanly-curated structured data. Information Extraction (IE) tasks, like disambiguating entities and relations from unstructured text, are key to automate KG population. However, Natural Language Processing (NLP) methods alone can not guarantee the validity of the facts extracted and may introduce erroneous information into the KG. This work presents an end-to-end system that combines Semantic Knowledge and Validation techniques with NLP methods, to provide KG population of novel facts from clustered news events. The contributions of this paper are two-fold: First, we present a novel method for including entity-type knowledge into a Relation Extraction model, improving F1-Score over the baseline with TACRED and TypeRE datasets. Second, we increase the precision by adding data validation on top of the Relation Extraction method. These two contributions are combined in an industrial pipeline for automatic KG population over aggregated news, demonstrating increased data validity when performing online learning from unstructured web data. Finally, the TypeRE and AggregatedNewsRE datasets build to benchmark these results are also published to foster future research in this field.
Best Fits and Dark Horses: Can Design Teams Tell the Difference?

Abstract Design teams are often asked to produce solutions of a certain type in response to design challenges. Depending on the circumstances, they may be tasked with generating a solution that clearly follows the given specifications and constraints of a problem (i.e., a Best Fit solution), or they may be encouraged to provide a higher risk solution that challenges those constraints, but offers other potential rewards (i.e., a Dark Horse solution). In the current research, we investigate: what happens when design teams are asked to generate solutions of both types at the same time? How does this request for dual and conflicting modes of thinking impact a team’s design solutions? In addition, as concept generation proceeds, are design teams able to discern which solution fits best in each category? Rarely, in design research, do we prompt design teams for “normal” designs or ask them to think about both types of solutions (boundary preserving and boundary challenging) at the same time. This leaves us with the additional question: can design teams tell the difference between Best Fit solutions and Dark Horse solutions? In this paper, we present the results of an exploratory study with 17 design teams from five different organizations. Each team was asked to generate both a Best Fit solution and a Dark Horse solution in response to the same design prompt. We analyzed these solutions using rubrics based on familiar design metrics (feasibility, usefulness, and novelty) to investigate their characteristics. Our assumption was that teams’ Dark Horse solutions would be more novel, less feasible, but equally useful when compared with their Best Fit solutions. Our analysis revealed statistically significant results showing that teams generally produced Best Fit solutions that were more useful (met client needs) than Dark Horse solutions, and Dark Horse solutions that were more novel than Best Fit solutions. When looking at each team individually, however, we found that Dark Horse concepts were not always more novel than Best Fit concepts for every team, despite the general trend in that direction. Some teams created equally novel Best Fit and Dark Horse solutions, and a few teams generated Best Fit solutions that were more novel than their Dark Horse solutions. In terms of feasibility, Best Fit and Dark Horse solutions did not show significant differences. These findings have implications for both design educators and design practitioners as they frame design prompts and tasks for their teams of interest.
Resource optimization and task scheduling in cloud computing

Cloud computing is a need of current and modern technology. Task scheduling and resource optimization are important aspects of cloud computing. Resource scheduling in cloud is a tough task and the planning of right resources to cloud workloads counts on the QoS needs of cloud applications, while in parallel with this task, gain is one of the most important aspect From which the cloud service suppliers point of view it is mainly determined by the structure of a cloud service platform under given market demand, However, facing the numerous cloud suppliers as well as their heterogeneous pricing strategies, customers may well be confused with which clouds are appropriate for storing their facts and what hosting strategy is cheaper. In particular, recently numerous studies have focused on determining the bond between server side system data and performance undergo for reducing resource wastage. Like all businesses, the benefit of a specialist system in distributed computing is identified with two sections, which are the cost and the income. The resource optimization and scheduling consist of allocating a single type of resource among the waiting jobs.
Predicting financial statement fraud using fuzzy neural networks

Fraud is a common phenomenon in business, and according to Section 24 of the Iranian Auditing Standards, it is the fraudulent act of one or more managers, employees, or third parties to derive unfair advantage and any intentional or unlawful conduct. Financial statements are a means of transmitting confidential management information about the financial position of a company to shareholders and other stakeholders. In this paper, by reviewing the literature, 6 indicators of current ratio, debt ratio, inventory turnover ratio, sales growth index, total asset turnover ratio, and capital return ratio as input and detection of financial fraud as output are considered for the fuzzy neural network. The database was compiled for 10 companies in the period from 2010 to 2018 after clearing and normalizing qualitatively between 1 to 5 discrete numbers with very low or very high meanings, respectively. The fuzzy neural network model with 161 nodes, 448 linear parameters, 36 nonlinear parameters, and 64 fuzzy laws with two methods of accuracy approximation of mean squared error and root mean squared error has been set to zero and 0.0000001 respectively. This neural network can be used for prediction.
A comparative and correlation analysis of experimental work for the development of organisational and managerial competences in university teachers

This article reports on the development of a framework for university teachers’ organizational and managerial competence. The work is based on the content analysis of scientific research in new generation educational program management, ways for expanding the functions of university teachers and transforming their activities with due regard to the modern reality of higher education institutions. This competence is an integrative teacher's personality trait that reflects four structural components: value and motivation-based component, cognitive component, design�based component and knowledge-based component. Expert surveys were conducted in 2017- 2019 to provide empirical support for the structural components. The sample included over 280 respondents at Samara University. Means and methods for university teacher organizational and managerial competence formation were assessed within the scope of an advanced training program (ATP) named "Educational Program Management", implemented by the Professional Competence Development Centre of Samara University. The content of the ATP’s sections was developed, and teaching methods contributing to the development of indicators of the relevant competence components were identified. Summative and formative assessments of the level of competence development used self-evaluation by a sample of 140 teachers, organizing the educational process at 35 departments of the University. A correlation analysis of summative and formative assessment data was carried out to establish ties between the components of organizational and managerial competence.
An Examination of Influence on Weight Perception by Visual Change of the Own Arm

The purpose of this research is to make a change in weight perception by utilizing the potential impression that a person has acquired from visual information. Recently, the illusion caused by changes in visual information, which is represented by the rubber hand illusion and the Proteus effect, has been reported. In this research, we make one’s self-awareness change by the appearance of the own arm through AR technology. We conduct an experiment to verify that the change of the self-awareness influences the weight perception of grasping an actual object.
DIDACTIC INNOVATION, DISABILITY, SPORT AND TRAINING EVALUATION DURING COVID-19

Among the sectors most affected by the ongoing health and social pandemic there is certainly that of education, which suddenly found itself forced to overturn its habits and its methodological settings in teaching provision. Technology has become central, especially in university contexts characterized by courses with a high number of students, and distance learning has established itself as an essential rule to guarantee the continuation of the training processes. The purpose of this paper is to investigate whether the transformation that took place was also the cause of the start of a didactic innovation process based on the adoption of technological supports and new methodological settings with specific reference also to the sphere of disability and the environment. sports education.
Bayesian Hierarchical Models with Applications Using RPeter D.Congdon (2019). Second Edition, CRC Press, pages 580, ISBN: 9781498785754

Biometrical JournalVolume 63, Issue 1 p. 213-214 BOOK REVIEW Bayesian Hierarchical Models with Applications Using R , Peter D. Congdon (2019). Second Edition, CRC Press, pages 580, ISBN: 9781498785754 Christian Stock, Christian Stock stock@imbi.uni-heidelberg.de Institute of Medical Biometry and Informatics (IMBI), University of Heidelberg, Heidelberg, GermanySearch for more papers by this author Christian Stock, Christian Stock stock@imbi.uni-heidelberg.de Institute of Medical Biometry and Informatics (IMBI), University of Heidelberg, Heidelberg, GermanySearch for more papers by this author First published: 29 October 2020 https://doi.org/10.1002/bimj.202000297Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume63, Issue1January 2021Pages 213-214 RelatedInformation
Evolution of Cyber-Security Research in an Industrial Setting

The software that increasingly undergirds our daily lives, national infrastructure, and defense systems continues to grow in size and complexity, incorporating far-reaching networks of open-source components and third-party dependencies. The task of analyzing and protecting these bloated and sprawling software systems is qualitatively more difficult than it was just a decade ago. We are also faced with a growing need to protect against cyber threats that continue to increase in frequency and sophistication.
Genre Annotation of Web Corpora: Scheme and Issues

Unlike traditional corpora made from printed media in the past decades, sources of web corpora are not categorised and described well, thus making it difficult to control the content of the corpus. This paper presents an attempt to classify genres in a large English web corpus through supervised learning. A set of genres suitable for web corpora users is defined based on a research of related work. A genre annotation scheme with active learning rounds is introduced. A collection of web pages representing various genres that was created for this task and a scheme of consequent human annotation of the data set is described. Measuring the inter-annotator agreement revealed that either the problem may not be well defined, or that our expectations concerning the precision and recall of the classifier cannot be met. Eventually, the project was postponed at that point. Possible solutions of the issue are discussed at the end of the paper.
THE INFUSION SPEECH SKELETON APPLICATION (APP) AS A TEACHING MEDIUM FOR STUDENTS OF OFFICE MANAGEMENT &amp; TECHNOLOGY (OMT): A CONCEPTUAL PAPER

Purpose of the study: This study aimed to utilize the usage of App in Public Speaking course to ease and engage students of Office Management &amp; Technology (OMT) in their learning experience by providing the simplified version of speech draft development.&#x0D; Methodology: The development of the App divided into two divisions, (a) the App content and, (b) the App design and concept. The content of the App was from the main textbook, simplified to serve the needs of OMT students, whereas the Apps’ design and concept were based on interactive design and waterfall models. Analysis, Design, Development, Implementation, and Evaluation (ADDIE) and Focus Group were also included.&#x0D; Main Findings: The Infusion Speech Skeleton App is projected to be a vital platform for students to improve their understanding of the course. The App is also expected to improve students’ performance particularly those who have trouble comprehending speech draft development.&#x0D; Applications of this study: Because the App is mainly developed with OMT students in the loop, most of the settings will be in OMT students’ favors. However, the Apps also can be beneficial to those taking literature, and other communications studies for basic reference.&#x0D; Novelty/Originality of this study: The App usage in learning was introduced in many courses, however, to our best knowledge this is the first attempt of App development and implementation in the course of public speaking, focusing on the need for non-communication students, OMT students.
Human Abnormality Detection Based on Bengali Text

In the field of natural language processing and human-computer interaction, human attitudes and sentiments have attracted the researchers. However, in the field of human-computer interaction, human abnormality detection has not been investigated extensively and most works depend on image-based information. In natural language processing, effective meaning can potentially convey by all words. Each word may bring out difficult encounters because of their semantic connection with ideas or categories. In this paper, an efficient and effective human abnormality detection model is introduced, that only uses Bengali text. This proposed model can recognize whether the person is in a normal or abnormal state by analyzing their typed Bengali text. To the best of our knowledge, this is the first attempt in developing a text based human abnormality detection system. We have created our Bengali dataset (contains 2000 sentences) that is generated by voluntary conversations. We have performed the comparative analysis by using Naive Bayes and Support Vector Machine as classifiers. Two different feature extraction techniques count vector, and TF - IDF is used to experiment on our constructed dataset. We have achieved a maximum 89&#x0025; accuracy and 92&#x0025; Fl-score with our constructed dataset in our experiment.
Machine Learning-Based Evolutionary Neural Network Approach Applied in Breast Cancer Tumor Classification

Recently, the particle swarm optimization (PSO) has successfully applied to many optimization problems such as artificial neural network (ANN). In this paper, we propose an Adaptive Particle Swarm Optimization (APSO) algorithm to optimize parameters of neural network model for the breast cancer classification task. The aim of the model was to classify two types of tumor: benign and malignant. The dataset used in this study was the popular Wisconsin Diagnosis Breast Cancer (WDBC) which was partitioned into 70% for training phase and 30% for testing phase. The model had been trained by three difference optimization algorithms as follow: Back-propagation (BP), classical PSO and APSO. The results show that the proposed APSO method significantly improved the performance of model in test accuracy, convergence speed and local minimum avoidance in comparison with BP and classical PSO.
D-GHNAS for Joint Intent Classification and Slot Filling

Intent classification and slot filling are two classical problems for spoken language understanding and dialog systems. The existing works, either accomplishing intent classification or slot filling separately or using a joint model, are all human-designed models with trial and error. In order to explore the variety of network architecture and to find whether there exist possible network architectures with better results, we proposed the D-GHNAS (Deep deterministic policy gradient based Graph Hypernetwork Neural Architecture Search) to accomplish intent classification and slot filling via a NAS (Neural Architecture Search) method. NAS based techniques can automatically search for network architectures without experts’ trial and error. Different from early NAS methods with hundreds of GPU days to find an ideal neural architecture that takes too much computation resource, in this work, hypernetwork is used to decrease the computation cost. Experimental results demonstrate that our model improves intent classification and slot filling results on public benchmark datasets ATIS and SNIPS compared with other joint models for these tasks.
Influence of classical field driving on survival probability in quantum Zeno and anti-Zeno effect

考虑一个受经典场驱动的二能级系统与零温玻色子库相互作用, 研究经典场驱动对量子Zeno效应和量子反Zeno效应中量子系统存活概率的影响. 结果表明, 经典场驱动可以降低量子系统的有效衰减率, 即提高量子系统的存活概率. 此外, 环境的欧姆性对于提高量子系统的存活概率也起着重要作用, 设置适当的环境欧姆参数可降低量子系统的有效衰减率. 再者, 随着二能级系统与经典场之间失谐量的增加, 量子系统的存活概率降低, 而通过增加经典场驱动的强度或选择合适的环境欧姆参数, 可以抑制失谐带来的负面影响.
Utility Function Derived Off-Road Vehicle Path Planning

Abstract Optimal navigation of ground vehicles in an off-road setting is a challenging task. One must accurately model the properties of the terrain and reconcile it with vehicle capabilities, while simultaneously addressing mission requirements. An important part of navigation is path planning, the selection of the route a vehicle takes between the start and end points. It is often seen that, given the starting and end points for a vehicle, the optimal path that the vehicle should take varies considerably with the mission requirements. While most commonly used algorithms use a local cost function, mission requirements are typically defined over the entire run of the vehicle. Utility theoretic methods provide a normative tool to model tradeoffs over attributes (mission requirements) that the operator cares about. It is critical therefore, that preferences embedded in the utility function influence the local cost functions used. In this paper, we provide a framework for a feedback-based method to update the parameters of the local cost-function. We do so by using a geodesic-based method for path planning given the terrain inputs, followed by a physics-based simulation of a vehicle to evaluate the attributes. These attributes are then combined into a multiattribute utility function. An optimization-based approach is used to find the parameters of the cost function that maximizes this multiattribute utility. We present our approach on a vehicle navigation example over a terrain acquired from United States Geological Survey data.
Measuring Unique Changes

Size metrics are commonly cited features in studies that analyze influencing factors on pull request lifetime. These metrics are also important for integrators, as based on them, they may prefer to prioritize pull requests easy to assess. However, code changes that form pull requests may not be unique, and repetitive changes may represent less complexity than expected by considering size metrics like lines of code or source files. The goal of this study is to analyze the influence of unique changes over pull requests relative to its size and lifetime. We collected data from 83,000+ pull requests of 26 projects hosted on GitHub. Also, we proposed a metric called unique changes rate to measure the proportion of unique changes over the total changes made by a pull request. We conducted experiments with Random Forest regression models and association rules to examine the influence of unique changes rate. Results show that unique changes have more influence over the lifetime of large pull requests, is determined mainly by the number of source files, and low levels of unique changes rate affect more the relationship between pull request size and lifetime than high levels. We conclude that unique changes can figure as an interesting feature in the context of the pull request lifetime. Results indicate that unique changes may increase or decrease the influence of pull request size on its lifetime. Our work has implications for researchers and core team members in software projects since unique changes represent helpful information.
Adaptive Convolution Kernel for Text Classification via Multi-channel Representations

Although existing text classification algorithms with LSTM-CNN-like structures have achieved great success, these models still have deficiencies in text feature representation and extraction. Most of the text representation methods based on LSTM-like models often adopt a single-channel form, and the size of convolution kernel is usually fixed in further feature extraction by CNN. Hence, in this study, we propose an Adaptive Convolutional Kernel via Multi-Channel Representation (ACK-MCR) model to solve the above two problems. The multi-channel text representation is formed by two different Bi-LSTM networks, extracting time-series features from forward and backward directions to retain more semantic information. Furthermore, after CNNs, a multi-scale feature attention is used to adaptively select multi-scale feature for classification. Extensive experiments show that our model obtains competitive performance against state-of-the-art baselines on six benchmark datasets.
Self-guided Stereo Correspondence Estimation Algorithm

This paper introduces an innovative algorithm, “Self-guided Stereo Correspondence” (SGSC), that is directed by photometric properties of the candidate pixels. As the photometric properties of reference image (left image) pixel and its neighbor’s pixel are similar in most cases, so the upcoming corresponding pixel exists in the surrounding of the previous matching pixel. Searching performance is greatly improved by utilizing this photometric property of the candidate pixels. The searching performance is further improved by applying the pioneering threshold technique. These two key techniques sufficiently reduced the computational cost with no degradation of accuracy. The achievements of the proposed method are testified on Middlebury standard Stereo Datasets of 2003 and 2006 and the Middlebury latest Optical Flow Dataset. Finally, the proposed method is compared with present state-of-the-art methods and our SGSC outdoes the latest methods in terms of speed, visualization of hidden ground truth, 3D reconstruction and accuracy.