Reviewerly/exHarmony huggingface dataset:
Knowledge and Reality in the Historical Epistemology

The article gives a generalized view of the historical epistemology and highlights its main problems: the nature of historical reality, historical knowledge and historical agent. The historical epistemology represents a special philosophical discourse, the purpose of which is constructing historical knowledge for cultural assimilation of the new historical reality at the intersection of science and society. A distinction is proposed between the position of a historian of science and a historical epistemologist in terms of the essence of historical event and historical fact. The historical epistemology reveals its boundaries and a position within modern epistemological approaches. On the one hand, it is the substantialist interpretation of the historical event, which loses its a priori status only by socio-epistemological explanation. On the other hand, a figure of the historical agent (hero and author) keeping the status of a theoretical fiction in historical epistemology, acquires the adequate meaning in the existential philosophy of science.
TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning.

As data science becomes increasingly mainstream, there will be an ever-growing demand for data science tools that are more accessible, flexible, and scalable. In response to this demand, automated machine learning (AutoML) researchers have begun building systems that automate the process of designing and optimizing machine learning pipelines. In this chapter we present TPOT v0.3, an open source genetic programming-based AutoML system that optimizes a series of feature preprocessors and machine learning models with the goal of maximizing classification accuracy on a supervised classification task. We benchmark TPOT on a series of 150 supervised classification tasks and find that it significantly outperforms a basic machine learning analysis in 21 of them, while experiencing minimal degradation in accuracy on 4 of the benchmarks—all without any domain knowledge nor human input. As such, genetic programming-based AutoML systems show considerable promise in the AutoML domain.
Continual Learning Using Task Conditional Neural Networks.

Conventional deep learning models have limited capacity in learning multiple tasks sequentially. The issue of forgetting the previously learned tasks in continual learning is known as catastrophic forgetting or interference. When the input data or the goal of learning change, a continual model will learn and adapt to the new status. However, the model will not remember or recognise any revisits to the previous states. This causes performance reduction and re-training curves in dealing with periodic or irregularly reoccurring changes in the data or goals. The changes in goals or data are referred to as new tasks in a continual learning model. Most of the continual learning methods have a task-known setup in which the task identities are known in advance to the learning model. We propose Multi-view Task Conditional Neural Networks (Mv-TCNN) that does not require to known the reoccurring tasks in advance. We evaluate our model on standard datasets using MNIST, CIFAR10, CIFAR100, and also a real-world dataset that we have collected in a remote healthcare monitoring study (i.e. TIHM dataset). The proposed model outperforms the state-of-the-art solutions in continual learning and adapting to new tasks that are not defined in advance.
Pipeline image haze removal system using dark channel prior on cloud processing platform

Pipeline fault detection is very important application of pipeline robots for the security of underground drainage pipeline facilities. The detection performance of existing systems is closely related to the image definition in the complex pipeline environment in terms of darkness, water fog, haze, etc. In this paper, the techniques of dark channel prior and cloud processing are combined into the framework of pipeline image haze removal system. In the system, including the user management module, system sitting module, cloud-based image management module and image processing module, we transmit the image data with the secure cloud data control mechanism, and remove the haze in each image using dark channel prior. The experimental results show that the system has good effects on haze removal of pipe images, especially for the larger reflection area. The system can be applied to engineering practice.
Deep Reinforcement Learning with Graph-based State Representations.

Deep RL approaches build much of their success on the ability of the deep neural network to generate useful internal representations. Nevertheless, they suffer from a high sample-complexity and starting with a good input representation can have a significant impact on the performance. In this paper, we exploit the fact that the underlying Markov decision process (MDP) represents a graph, which enables us to incorporate the topological information for effective state representation learning. Motivated by the recent success of node representations for several graph analytical tasks we specifically investigate the capability of node representation learning methods to effectively encode the topology of the underlying MDP in Deep RL. To this end we perform a comparative analysis of several models chosen from 4 different classes of representation learning algorithms for policy learning in grid-world navigation tasks, which are representative of a large class of RL problems. We find that all embedding methods outperform the commonly used matrix representation of grid-world environments in all of the studied cases. Moreoever, graph convolution based methods are outperformed by simpler random walk based methods and graph linear autoencoders.
DECISION SUPPORT SYSTEM OF ACHIEVED STUDENTS USING WEIGHTED PRODUCT METHOD

Constitution of the Republic of Indonesia No. 12 of 2012 that part of the national education system has a strategic role in educating the life of the nation. Students are people who are trying to develop themselves through the process of education on the path, level and type of education. The Weighted Product method is chosen because the method is solved by using multiplication to connect the value of the attribute, where the value must be raised with the attribute weight value in question. Criteria that have been determined include: average value, discipline, attendance, extracurricular, and Non-Academic. The results of this study there are 6 alternatives, obtained the value of Agus = 0.1618 as the student with the lowest value and the value of Intan = 0.1748 as the best student
Feature Learning Using Random Forest and Binary Logistic Regression for ATDS

The “Automatic Text Document Summarization” (ATDS) is multifaceted research field of machine learning, text analytics, data mining, linguistic, and natural language processing. This can be used in field of news summarization, e-governance, information retrieval, search engine optimization, etc. This work is based on extension of work done by authors [1, 2]. Yadav and Sharan [1] proposed a linear model for ATS, and this work finding an optimal feature vector to decide the importance of different features. This is done by random forest and logistic regression. The structure of proposed work is followed by, Sect. 1 contains about introduction to summarization, Sect. 2 have related work, Sect. 3 about logistic regression, random forest, dataset, in Sect. 4 proposed model is presented, Sect. 5 about experiments and results, followed by Sect. 6 about conclusion. To evaluate summary the “Recall Oriented Understudy for Gisting Evaluation” (ROUGE) measure is considered, that is introduced by Lin [3].
Formal vs. Case-Study-Based Approaches for the Identification of Cultural Influences in Requirements Engineering

Requirements Engineering (RE) activities require intensive communication to capture clients' requirements. As culture influences the way in which people communicate and interact, individuals' culture might influence the implementation of RE activities. In this paper, we discuss the differences between formal approach (using the framework we propose) and case-study-based approaches for the identification of cultural influences in RE. We compared the results of the application of our framework with the results of case studies conducted by other researchers from several cultures. The results showed that the framework provided a much higher degree of precision than expected: the framework identified almost all of the cultural influences mentioned in the identified cases studies. This provides evidence of the accuracy and validity of our framework, which can provide similar conclusions based on information about the cultures involved rather than in-depth analysis.
Perspectives for software development using the naturalistic language SN

The evolution of programming techniques prompted researchers to develop more expressive languages from the point of view of natural languages. However, although current languages allow structuring the code so that it is easier to understand the relationship that abstractions have, the level of expressivity is low. Another problem that currently arises is the shortage of documentation as a result of the lack of discipline of the programmers or the short time to make the delivery of the software product, which hinders the maintenance and evolution tasks. As part of the solution to these problems, naturalistic programming proposes to take elements of natural languages to create programming languages that reduce the gap between the domains of the problem and the solution. Therefore, naturalistic languages offer greater expressiveness and self-documented code. This article proposes the revision of a programming language that integrates grammatical elements of the English language, whose objective is to develop source code, through the composition of phrases, to achieve a level of expressiveness that allows reducing the gap between the dominion of the problem and the solution.
A Hybrid Evolutionary Algorithm for Offline UAV Path Planning

This paper investigates the offline path planning problem of unmanned aerial vehicles (UAVs) for surveillance mission in complex urban environments. A new idea by coupling the differential evolution (DE) with A* algorithm is suggested to address the problem in large urban areas with narrow street and infrastructure of built environment. The proposed method consists of two phase: the first phase adopts DE to divide the straight line between source and destination into several smaller regions, while the second one utilizes A* for each region to find a collision-free and shortest path in parallel. In order to assess the efficiency of the suggested algorithm, a real-world scenario is examined. Evaluations exhibited promising results with proper accuracy and minimum computational time.
Integrating Multi-Index Materials Classification and Inventory Control in Discrete Manufacturing Industry: Using a Hybrid ABC-Chaos Algorithm

In order to efficiently reduce the high inventory cost of discrete manufacturing firms, which is caused by the large production scale, complex processes, and varieties of materials, this article presents a two-stage decision framework integrating multi-index material classification and inventory control policy. In the first stage, a multi-index material classification method based on analytic hierarchy process-information entropy is implemented to categorize materials in production process into three classes: class A (i.e., strategic materials), class B (i.e., bottleneck materials), and class C (i.e., general materials). In the following stage, based on the results of material classification, an order quantity forecasting model for strategic materials based on ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> ) is constructed with the objective to minimize the total inventory cost over a planning horizon. Then, a hybrid Artificial Bee Colony-Chaos (ABC-Chaos) algorithm is employed to solve the proposed model. To be specific, the chaotic local search strategy is introduced to help local extreme point escape from bondage in the random search phase of scout bee. Finally, a real-world case study from a typical foundry enterprise is illustrated to demonstrate the applicability and feasibility of the proposed decision framework. The experimental results show that the proposed hybrid ABC-chaos algorithm performs better than the other algorithms.
Explainable Detection of Fake News and Cyberbullying on Social Media

While social media had ubiquitously penetrate into into people’s daily life, where allows interactions between people, user-generated text data not only enables novel applications, but also provides user digital footprints for us to analyze a variety of human behaviors. In this talk, we will share two of our recent studies on combating anti-social behaviors: detecting fake news and identifying cyberbullying behaviors on social media. We will reveal three important insights. First, it is possible to predict anti-social behaviors without social network information. Second, graph neural networks (GNN) is effective in improving the performance of such two tasks. Third, our models can provide model explainability to understand the language use of anti-social behaviors. In the end of this talk, we will point out future directions on fighting with fake news and cyberbullying in social media.
Mining GitHub classroom commit behavior in elective and introductory computer science courses

Git and GitHub are the industry standard tools used for version control today. Consequently, integrating Git and GitHub into CS curriculums is of increasing importance for educators. With GitHub Cl...
Emerging Opportunities for Blockchain Use by Small and Medium Enterprises (SMEs) in Developing Economies

The blockchain digital technology holds immense possibilities for the growth and development of developing economies such as Ghana. Since the emergence of blockchain technology, many developing economies have as yet to tap into its limitless possibilities with Ghana being no exception. In this chapter, the authors explore the possibilities for the blockchain for SMEs in developing economies like Ghana with a view to identifying the properties of a blockchain ecosystem that will facilitate their development, adoption, and use by SMEs.
Stratified Sampling and Cluster Sampling

Complex survey designs involve at least one of the three features: (i) stratification; (ii) clustering; and (iii) unequal probability selection of units. In this chapter we provide some basic results on stratified sampling and cluster sampling. In Sect. 3.5 we provide a brief discussion on stratified two-stage cluster sampling, which reveals the notational complexities for complex surveys. General unequal probability sampling methods will be discussed in the next chapter.
Information Ontology as Anti-Metaphysics

Followed by the general review of various kinds of information ontology as metaphysical thoughts, two main approaches can be illustrated. The basic feature of this kind of metaphysics is its theoretical explanation for the constructivism of the whole world. However, metaphysics is undermining its study of existence itself by taking existence as substantial entities, attempting to construct the whole world, or separating existence and thoughts. With the insight from anti-metaphysical approaches, this paper discusses three aspects of the information ontology.
Making it Simple: Expanding Access and Lowering Barriers to Novel Interaction Devices for Virtual and Augmented Reality

The open source community has been an integral part in the rapid growth of the maker and DIY communities, mainly by facilitating easy exchange of information and ideas, thereby lowering the jump from ideation to a testable prototype. In this work, we add a new class of open-source tools and resources which, when used in conjunction with existing virtual reality or augmented reality systems, provide for an improved virtual experience. We focus on making our processes accessible and reproducible while keeping the costs of production of the devices as low as possible. Additionally, we describe three successful use-case projects that live in the interface between hardware and software, and we open-source the designs and software for easy reproduction by interested researchers. We hope that this paper will give AR/VR researchers the confidence in taking the next step of making virtual reality as realistic as possible and will hopefully snowball into a new class of devices/ecosystem.
Development Of Creativity As The Basis Of Innovative Activity Of A University Teacher

There is an active search for policies and methods to improve the quality of education in the vocational studies system, with all that it represents. Pedagogical work is a special type of highly skilled mental work that has a creative character and is characterized by a high degree of tension. The work of a teacher is a conscious and appropriate activity for the training, education and development of students and of society as a whole. The objective of the research is to discuss the development of creativity based on the innovative activity of teachers. It is concluded that the teacher has a main role as an agent in the formation of professional knowledge among students. The real problems of psychology and pedagogy in the modern education system are the definition of the professional requirements for the personality of a modern teacher and the psycho-diagnosis of the level of his professional qualities. However, these questions take on a political meaning because they concern the human development of a society and must therefore also be politically debated.
Global Examples of COVID-19 Surveillance Technologies : Flash Report

The fast spread of coronavirus (COVID-19) around the world has put health services under an enormous strain. Turning to digital means for collating data on the spread of the virus, the associated symptoms, as well as the routes through which it may be spreading has been a common response. The situation, the associated technologies and the practices of their use vary across the globe and evolve rapidly. This report provides a global snapshot of the different types of technologies in use or in development for surveillance of COVID-19 at the beginning of April 2020. By the time this report is published, the situation will have developed further. The body of the report presents short descriptions of a selection of different apps from around the world. More detailed data tables are in Appendix 1 and include references used.
COMMUNITY COLLEGE CORNERCyber2yr2020

column COMMUNITY COLLEGE CORNERCyber2yr2020: ACM guidelines for associate-degree cybersecurity programs Share on Author: Cara Tang Portland Community College Portland Community CollegeView Profile Authors Info & Claims ACM InroadsVolume 11Issue 2June 2020 pp 8–11https://doi.org/10.1145/3388862Published:12 May 2020 2citation130DownloadsMetricsTotal Citations2Total Downloads130Last 12 Months22Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
CNN-based Criteria for Classifying Artists by Illustration Style

In works of art, the drawing style appears in pictures as an artist feature. The style can be applied to identify fake works of art, and this task comes down to identifying the artists of the pictures. Artist classification is defined as a type of image classification task; therefore, it is suitable to use a convolutional neural network (CNN) which achieves high performance in image classification to identify an artist. However, the internal function of the CNN is not clear, and it is difficult to understand how a CNN identifies the artists of pictures. There is a possibility that a CNN identifies artists by not the style of the pictures but by other criteria. In this study, we investigated criteria of the CNN model which is performed end-to-end training. We clarified that a CNN classifies artists using the style of the illustration.
A blockchain-based database management system

Abstract The software and hardware applications are clearly on the way of becoming an integral tool of business, communication and popular culture in many parts of the world. People are interacting with the environment via the Internet to perform physical activities remotely. These applications are hosted in the public or private servers under the control of the server admin. The users’ online usage data can be stored in public or private cloud platforms, used for processing and monitoring users’ online behaviour and emotional factors and shared with third parties to facilitate making their business decisions. When users allow their data to be collected via software applications and mobile devices, users need to have some level of trust and control over their data. But, software applications or mobile devices connected to the cloud server using client–server architecture does not ensure the reliability, security and integrity among their data. To get over these kinds of limitations, we propose a database management system using blockchain technology that can be used by any software applications. The blockchain database connected to the cloud server can be used to increase the trustfulness of the application. Blockchain has the capability to provide decentralization, immutability and owner-controlled digital assets among software applications. Since users can save their data in a shared transaction repository with tamper-resistant records, it enables related parties to access and control users’ data without the need for a central control system.
Sign Language Interfaces: Discussing the Field's Biggest Challenges

Sign language interfaces offer rich, timely research problems. Recent advances in computational methods have made a wider range of sign language interfaces possible. At the same time, a recent interdisciplinary review and call-to-action outlines the most pressing challenges for the field, given these recent advances. This special interest group (SIG) will meet to discuss and make progress along these challenges, providing continuity for researchers working in this space, while exchanging ideas with the broader HCI research community.
Building Language Models for Morphological Rich Low-Resource Languages using Data from Related Donor Languages: the Case of Uyghur

Huge amounts of data are needed to build reliable statistical language models. Automatic speech processing tasks in low-resource languages typically suffer from lower performances due to weak or unreliable language models. Furthermore, language modeling for agglutinative languages is very challenging, as the morphological richness results in higher Out Of Vocabulary (OOV) rate. In this work, we show our effort to build word-based as well as morpheme-based language models for Uyghur, a language that combines both challenges, i.e. it is a low-resource and agglutinative language. Fortunately, there exists a closely-related rich-resource language, namely Turkish. Here, we present our work on leveraging Turkish text data to improve Uyghur language models. To maximize the overlap between Uyghur and Turkish words, the Turkish data is pre-processed on the word surface level, which results in 7.76% OOV-rate reduction on the Uyghur development set. To investigate various levels of low-resource conditions, different subsets of Uyghur data are generated. Morpheme-based language models trained with bilingual data achieved up to 40.91% relative perplexity reduction over the language models trained only with Uyghur data.