Reviewerly/exHarmony huggingface dataset:
Looking at the Branches and Roots

When looking at the future of Fuzzy Logic (FL), it is immediate to think at applications where FL could be used to compute with perceptions, possibly expressed in natural language, thus enabling Explainable Artificial Intelligence (XAI). Scholars in FL have been working on Interpretability, an important part of XAI, for decades. Yet, the research community in FL seems isolated from other Artificial Intelligence (AI) communities. There is a gap between FL and AI that due to the relative youth of FL when compared with the foundational theories underlying AI. If we want FL growing its branches in XAI as well as in other fields, we need to develop, both in Research and Education, more robust roots supporting all the theories, the methodologies and the technologies that are going to be developed now and in future.
Evolution of entanglement in electron–electron collisions

The collision between two electrons is treated in a time-dependent three-dimensional approach involving a two-electron wave packet, which is composed of stationary solutions of the two-electron Schroedinger equation with Coulomb interaction. As a quantitative measure of the entanglement between the two electrons we employ a renormalized linear entropy, which is readily obtainable from the one-electron reduced density matrix in real space. Starting with a non-entangled initial state consisting of two separate one-electron packets, the scattering process is visualized on a femtosecond time scale by a sequence of snap shots of the one-electron density. The corresponding evolution of the linear entropy is shown for several energy values up to 200 eV (per electron in the center-of-mass system). The maximal entanglement, which is characterized by the asymptotic value of the linear entropy, is found to decrease strongly with increasing energy. The entropy for anti-parallel spins turns out to be almost the same as for parallel spins, which means that spin entanglement is vastly dominated by spatial entanglement.
A simple crime hotspot forecasting algorithm

Crime hotspot forecasting is an important part of crime prevention and reducing the delay between a 911 call and the physical intervention.Current developments in the field focus on enriching the historical data and sophisticated point process analysis methods with a fixed grid.In the paper we present a simple spatio-temporal point process allowing one to perform exhaustive (literal) grid searches.We then show that this approach can compete with more complex methods, as evidenced by the results on data collected by the Portland Bureau of Police.Finally, we discuss the advantages and potential implications of the new method.
Reasoning about clinical guidelines based on algebraic data types and constraint logic programming

Previously, the authors presented an overall framework aimed at improving the representation, quality and application of clinical guidelines in daily clinical practice. Regarding the quality improvement of guidelines, we developed a proposal to verify specific requirements in guidelines, using the SPIN model checker as verification tool. Additionally, we established a pattern-based approach for defining commonly occurring types of requirements in guidelines, in order to help non experts in their formal specification. In particular, among such patterns, we identified several which could not be verified by using such a proposal, thus leaving their verification as future work. In this paper, we provide a revised and extended version of that work by providing an overall proposal which mainly addresses previous shortcomings, while providing additional verification functionalities. More specifically, we have defined a complementary proposal to the previous one regarding the verification of guidelines. This proposal uses Formula, a model finding and design space exploration tool that is based on Algebraic Data Types (ADT) and Constraint Logic Programming (CLP). The main contributions of this paper are twofold: (1) providing a more complete set of patterns for defining commonly occurring types of requirements in guidelines, and (2) supporting the verification of a wider range of patterns by combining the use of our previous proposal, based on the SPIN model checker, with our Formula-based method. More specifically, our Formula-based proposal provides us with a solution to the verification of those patterns we were not able to verify previously. Additionally, our proposal has been implemented as an Eclipse plug-in developed based on Model Driven Development (MDD) techniques, which enables us to automatically generate the Formula specification of a guideline, making the process faster and less error-prone than a manual translation. This Formula specification, together with the requirements to be checked in the guideline, are finally taken as input of the Formula tool to check whether the guideline verifies the requirements. We show the feasibility of our overall approach by verifying properties in different clinical guidelines with encouraging results.
An Efficient CBIR System for High Resolution Remote Sensing Images

High resolution satellite images (HRSI), contain a great range of objects and spatial patterns appearing with a large variation of scale, rotation and illumination. In this paper, we propose a Content Based Image Retrieval (CBIR) system for HRSI by employing SURF features and boost them through the addition of color, texture and structural information around key points; and by learning a category-specific dictionary for each image class. An extensive experimental evaluation on the well- known UC Merced dataset has been performed and compared with other feature extraction methods including Convolutional Neural Networks. It is demonstrated that our method is quite competitive in terms of performance.
한국어 교재의 ‘-이/히/리/기-’ 피동 단원 분석과 교수 방안 제시

This paper aims to examine the presentation aspects of ‘-i/hi/li/gi-’ passive unit appearing in Korean textbooks, define problems, and create barriers to complement them. As a result of examining the textbooks of 15 universities, three problems were found in the presentation of the passive unit. First, the learning objective is to memorize the passive verbs themselves in which each affix is combined, rather than create proper passive sentences appropriately using passive verbs. Second, the list of passive verbs in textbooks contains mixed passive verbs that do not fulfill the original features of passive verbs. Further, only passive verbs without sample sentences are presented. Third, there are many difficulties in teaching and learning situations by presenting various passive sentences without identifying various passive sentences with differences in their features. To solve these problems, it was emphasized that the core of the passive unit is to learn passive verbs and further create passive sentences by taking the educational contents of the passive unit to a sentence level rather than a word level. Also, the paper asserted that considering the features and differences in passive sentences, the passive sentences be divided and presented by type and teach students the rules and constraints for creating passive sentences in order. (Soongsil University)
Underwater Computer Vision of the ZEABUS AUV

this paper presents the implementation of underwater computer vision of the ZEABUS Autonomous Underwater Vehicle (AUV) at Kasetsart University. The purpose of the implementation is to augment previously used algorithm for the 2018 International RoboSub Competition and Singapore AUV 2018 Challenge. The result shows that new algorithm has higher precision level and can achieve better object detection.
DeepStego: Protecting Intellectual Property of Deep Neural Networks by Steganography

Deep Neural Networks (DNNs) has shown great success in various challengingtasks. Training these networks is computationally expensive and requires vastamounts of training data. Therefore, it is necessary to design a technology toprotect the intellectual property (IP) of the model and externally verify theownership of the model in a black-box way. Previous studies either fail to meetthe black-box requirement or have not dealt with several forms of security andlegal problems. In this paper, we firstly propose a novel steganographic schemefor watermarking Deep Neural Networks in the process of training. This schemeis the first feasible scheme to protect DNNs which perfectly solves theproblems of safety and legality. We demonstrate experimentally that such awatermark has no obvious influence on the main task of model design and cansuccessfully verify the ownership of the model. Furthermore, we show a ratherrobustness by simulating our scheme in a real situation.
Modeling simple structures and geometry for better stochastic optimization algorithms

We develop model-based methods for stochastic optimization problems, introducing the approximate-proximal point, or aProx, family, which includes stochastic subgradient, proximal point, and bundle methods. For appropriately accurate models, the methods enjoy stronger convergence and robustness guarantees than classical approaches and typically add little to no computational overhead over stochastic subgradient methods. For example, we show thatmethods using the improved models converge with probability 1; these methods are also adaptive to a natural class of what we term easy optimization problems, achieving linear convergence under appropriate strong growth conditions on the objective. Our experimental investigation shows the advantages of more accurate modeling over standard subgradient methods across many smooth and non-smooth, convex and non-convex optimization problems.
Online English Vocabularies Autonomous Learning Model Based on EM Algorithm

Under the background of the rapid spread of the Internet, a variety of English vocabularies learning models have emerged in mobile Internet terminals. On the one hand, its development inherits the traditional English learning theory. On the other hand, it also reveals its own uniqueness. The acquisition of human information is time-sensitive and the generalization of information is full of the horizons of language learners. The problem is how to effectively reduce negative impacts and improve learning efficiency. The EM algorithm can effectively filter English vocabularies and generate a mean value from a mixture of k different normal distributions, which makes English vocabulary online learning more efficient.
Using Argumentative Semantic Feature for Summarization

The last decade has witnessed digitization of many government organization's data. Text summarization of the political discourse, particularly the parliamentary proceedings is relatively a lesser explored area of research. In this paper, we investigate the role of semantics especially theory of argumentation in debate summarization and use it to design a semi automatic pipeline for generating these summaries. The proposed approach considers the topic-relevance, argumentative nature, sentiment and context features. We test our approach on the dataset of debates mined from Lok Sabha, the elected house of representatives in India. Our proposed methodology and pipeline show significant improvement over the high performing popular systems for ROUGE-1, ROUGE-2 and ROUGE-L metrics.
SBIR Based Screening for Lung Cancer

Lung cancer is the most dangerous type of cancer in the world. Early detection can save the life and increase the survivability of patients. In this project we obtain a solution for lung cancer symptom detection by applying Shape based image retrieval (SBIR). Our algorithm is broadly divided into three parts, at first part we accept the data set of cancer symptoms which is a generalized way for creating the patterns for Lung Cancer Framework, and in the second part we find the relevant data from the patterns using segmentation approach. We can choose the frequent symptoms only by using the threshold value. Based on the threshold value we decide whether it’s a cancer cell or non-cancer cell. We initialize the cancer cell value to support the pattern of cancer symptoms. It is updated in each trial. By updating the cancer cell value in each step we can check the symptom precision which either increases the accuracy or decreases it. Finally, result analysis can be proved by the appropriately using artificial neural network algorithm.
Comparison between Supervised and Unsupervised Feature Selection Methods

The reduction of the feature set by selecting relevant features for the classification process is an important step within the image processing chain, but sometimes too little attention is paid to it. Such a reduction has many advantages. It can remove irrelevant and redundant data, improve recognition performance, reduce storage capacity requirements, computational time of calculations and also the complexity of the model. Within this paper supervised and unsupervised feature selection methods are compared with respect to the achievable recognition accuracy. Supervised Methods include information of the given classes in the selection, whereas unsupervised ones can be used for tasks without known class labels. Feature clustering is an unsupervised method. For this type of feature reduction, mainly hierarchical methods, but also k-means are used. Instead of this two clustering methods, the Expectation Maximization (EM) algorithm was used in this paper. The aim is to investigate whether this type of clustering algorithm can provide a proper feature vector using feature clustering. There is no feature reduction technique that provides equally best results for all datasets and classifiers. However, for all datasets, it was possible to reduce the feature set to a specific number of useful features without losses and often even with improvements in recognition performance.
TOWARDS A SEMANTIC RESEARCH INFORMATION SYSTEM BASED ON RESEARCHERS' CV DOCUMENTS

Curricula vitae are widely used as the main mechanisms for evaluation of researchers.They also reflect a fraction of the data about the research activity of an organization and geographic area.The nature of the academic world causes researchers to have to continuously keep the information updated.There are tools to help manage this information, usually offered by an organization such as a university or the national government.However, the reports generated by these tools have a local scope when in most of the cases they are used outside the organization.We explore Semantic Web as a solution to this problem because of its benefits on interoperability, inference capabilities when merging data from different sources, and expedience when responding to semantic queries made by evaluators.We design a software solution using the existing tools, able to get data from a CV document, insert it into a semantic system, and then build a document CV from the existing amount of semantic data of a researcher in that system.
A novel breakpoint detection method based on dynamic programming for linear laser scanner

Linear laser scanner has been widely used in industrial applications, such as 3D reconstruction. Breakpoint detection of the laser stripe centerline is a fundamental step. In practical applications, the centerline is always not perfect due to various noises. A novel breakpoint detection method named Fixed Dynamic Programming (FDP) is proposed. Firstly, the centerline fitting error is illustrated in detail, which is to be used as the criterion in the FDP. Secondly, principle of the FDP method is described, which modified the traditional DP method by utilizing the foreknown breakpoints information of the centerline. Finally, experiment is implemented to detect breakpoints of the noisy laser stripe centerline by using a linear laser scanning system. From the experiment result verifies that the prosed FDP method can avoid the influence of noise and improve the computation efficiency.
An Overview About the Security Architecture of the Mobile Operating System iOS

The use of mobile operating systems is rapidly increasing as the number of smartphone users is increasing day by day. iOS is one of the most popular and widely used mobile operating systems in the world. This document describes an overview about the security features of iOS in its low-level system framework and in its user-level applications framework. The document discusses processes like how security is implemented in the core components of the system, how data protection and encryption is achieved, and how applications are secured by sandboxing.
Mixture of Multilayer Perceptron Regressions

This paper investigates mixture of multilayer perceptron (MLP) regressions. Although mixture of MLP regressions (MoMR) can be a strong fitting model for noisy data, the research on it has been rare. We employ soft mixture approach and use the Expectation-Maximization (EM) algorithm as a basic learning method. Our learning method goes in a double-looped manner; the outer loop is controlled by the EM and the inner loop by MLP learning method. Given data, we will have many models; thus, we need a criterion to select the best. Bayesian Information Criterion (BIC) is used here because it works nicely for MLP model selection. Our experiments showed that the proposed MoMR method found the expected MoMR model as the best for artificial data and selected the MoMR model having smaller error than any linear models for real noisy data.
What Is It Like to Be a Tree? The Transformative Potential of Virtual Reality

Cyberpsychology, Behavior, and Social NetworkingVol. 22, No. 3 CyberSightingsWhat Is It Like to Be a Tree? The Transformative Potential of Virtual RealityCompiled by: Andrea GaggioliCompiled by: Andrea GaggioliAddress correspondence to: Dr. Andrea Gaggioli, Department of Psychology, Catholic University of Milan, Largo Gemelli 1-20123 Milan, Italy E-mail Address: andrea.gaggioli@unicatt.itDepartment of Psychology, Catholic University of Milan, Milan, Italy.Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy.Search for more papers by this authorPublished Online:11 Mar 2019https://doi.org/10.1089/cyber.2019.29144.csiAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"What Is It Like to Be a Tree? The Transformative Potential of Virtual Reality." Cyberpsychology, Behavior, and Social Networking, 22(3), p. 232FiguresReferencesRelatedDetails Volume 22Issue 3Mar 2019 InformationCopyright 2019, Mary Ann Liebert, Inc., publishersTo cite this article:Compiled by: Andrea Gaggioli.What Is It Like to Be a Tree? The Transformative Potential of Virtual Reality.Cyberpsychology, Behavior, and Social Networking.Mar 2019.232-232.http://doi.org/10.1089/cyber.2019.29144.csiPublished in Volume: 22 Issue 3: March 11, 2019PDF download
Foresight Technologies In The Development Of Communicative Reflection

The article is devoted to the problem of searching for innovative educational technologies aimed at developing key communicative competencies. Communicative reflection, being one of the key features of the "21st century skills", demands innovative communicative technologies for its formation and development, among which the authors of the article include foresight technologies. In the article their use is substantiated by the essential characteristics and innovative content of the foresight, as well as the operational structure and composition of methods that contribute to the development of students communicative reflection. The article emphasizes the communicative and reflective potential of foresight technologies, discusses the positive and negative effects of the foresight method. Particular attention is paid to the value of open communication strategies for building variable images of the future. Foresight is considered as a technology that expands the variability of thinking, communicative and reflexive repertoires. The ethical potential of foresight technologies results from the use of open communication strategies built on the basis of dialogue and facilitation. Based on the review of scientific literature and their own research and teaching experience, the authors confirm the feasibility of using foresight technologies in both real and virtual communication environment. The use of foresight technologies in the educational process will allow to expand the variability of thinking, communication and reflexive repertoires, to make the transition from a closed to an open variable future strategy.
pSPARQL: A Querying Language for Probabilistic RDF Data

More and more linked data (taken as knowledge) can be automatically generated from nonstructured data such as text and image via learning, which are often uncertain in practice. On the other hand, most of the existing approaches to processing linked data are mainly designed for certain data. It becomes more and more important to process uncertain linked data in theoretical aspect. In this paper, we present a querying language framework for probabilistic RDF data (an important uncertain linked data), where each triple has a probability, called pSRARQL, built on SPARQL, recommended by W3C as a querying language for RDF databases. pSPARQL can support the full SPARQL and satisfies some important properties such as well-definedness, uniqueness, and some equivalences. Finally, we illustrate that pSPARQL is feasible in expressing practical queries in a real world.
Deception Tree Model for Cyber Operation

Modern cyber operations are evolving from direct attacks and defense to complex cyber operations that involve deception. As deceptions is included in cyber-attacks and defenses, elements should be identified to respond to cyber operations. If appropriate countermeasures can be taken for identified elements, they can gain a strategic advantage in cyberspace. Related cyber research includes developing response tools for attackers from a defensive standpoint and developing attack techniques that exploit human cognitive vulnerabilities. Other research has classified tools according to their purposes and has studied procedures for effectively carrying out deception. However, existing studies neither consider specific objectives nor classify in complex cyber operations. Classifying in cyber operations requires dividing cyberspace into physical, logical, and persona layers, the targets of cyber operations should be identified from machines to humans, and procedures should be identified from TTPs to objectives. In response, this paper proposes a deception tree that can be categorized from a cyber-deceitful TTP perspective. The tree model can distinguish targets from humans and machines in terms of attack and defense and systematically establish the effects, tactics, techniques, and procedures of selected targets. Three cases were applied and analyzed to verify the performance of the tree model. The first case is the cyber incident that occurred at KHNP in 2014 in which a deceitful attack was conducted on humans, the second case is using Honeynet technology to deceive the attacker, and third case is using Anti-Ransomware technology to deceive malware.