Reviewerly/exHarmony huggingface dataset:
Distillation of Best Practices from Refactoring FLASH for Exascale

Abstract FLASH is a multiphysics software package that was created in 1998 by combining three preexisting packages and has undergone three major revisions. Software design and engineering practices were integrated early in the development and maintenance processes of FLASH, and these processes have evolved strongly at each of the revisions. As high-performance computing enters the age of exascale, challenges along the orthogonal axes of node-level hardware and solver heterogeneity force developers of complex multiphysics software to consider a software architecture overhaul. Because of the nature and scope of necessary changes, an effort to refactor and grow the architecture of the FLASH code has been launched as a separate software project. For this project to succeed, its development team must evaluate, improve, and modernize software processes and policies to meet the unique challenges posed by the exascale era. We describe here our experiences, lessons we have learned, and the methods that we have developed as part of this ongoing project. Within the context of the challenges posed by exascale, we review the FLASH design approach as well as some of the main software engineering processes and tools that have been implemented or updated throughout the lifetime of FLASH. Modernization applied to these processes and tools is also detailed. Reviewing and reevaluating the FLASH experience of establishing and updating software design and engineering practices have been helpful in understanding the needs of the project as it transitions to exascale and in planning the transition. We find that our historical design methodology is still important and relevant. We also believe that using a mixture of plan-based and agile methods is still the best for our project and is in accord with the guidance found in the literature. We present a section on inferences and lessons learned related to software design and engineering practices.
Rationality and maximal consistent sets for a fragment of ASPIC + without undercut

Structured argumentation formalisms, such as ASPIC + , offer a formal model of defeasible reasoning. Usually such formalisms are highly parametrized and modular in order to provide a unifying framework in which different forms of reasoning can be expressed. This generality comes at the price that, in their most general form, formalisms such as ASPIC + do not satisfy important rationality postulates, such as non-interference. Similarly, links to other forms of knowledge representation, such as reasoning with maximal consistent sets of rules, are insufficiently studied for ASPIC + although such links have been established for other, less complex forms of structured argumentation where defeasible rules are absent. Clearly, for a formal model of defeasible reasoning it is important to understand for which range of parameters the formalism (a) displays a behavior that adheres to common standards of consistency, logical closure and logical relevance and (b) can be adequately described in terms of other well-known forms of knowledge representation. In this paper we answer this question positively for a fragment of ASPIC + without the attack form undercut by showing that it satisfies all standard rationality postulates of structured argumentation under stable and preferred semantics and is adequate for reasoning with maximal consistent sets of defeasible rules. The study is general in that we do not impose any other requirements on the strict rules than to be contrapositable and propositional and in that we also consider priorities among defeasible rules, as long as they are ordered by a total preorder and lifted by weakest link. In this way we generalize previous similar results for other structured argumentation frameworks and so shed further light on the close relations between assumption-based argumentation and ASPIC + .
HIGnet: Hierarchical and Interactive Gate Networks for Item Recommendation

Existing research exploits the semantic information from reviews to complement user-item interactions for item recommendation. However, as these approaches either defer the user-item interactions until the prediction layer or simply concatenate all the reviews of a user/item into a single review, they fail to capture the complex correlations between each user-item pair or introduce noises. Thus, we propose a novel Hierarchical and Interactive Gate Network (HIGnet) model for rating prediction. Modeling local word informativeness and global review semantics in a hierarchical manner enable us to exploit textual features of users/items and capture complex semantic user-item correlations at different levels of granularities. Experiments on five challenging real-world datasets demonstrate the state-of-the-art performance of the proposed HIGnet model. To facilitate community research, the implementation of the proposed model is made publicly available (https://github.com/uqjwen/higan).
RT-BDI: A Real-Time BDI Model

Currently, distributed cyber-physical systems (CPS) rely upon embedded real-time systems, which can guarantee compliance with time constraints. CPS are increasingly required to act and interact with one another in dynamic environments. In the last decades, the Belief-Desire-Intention (BDI) architecture has proven to be ideal for developing agents with flexible behavior. However, current BDI models can only reason about time and not in time. This lack prevents BDI agents from being adopted in designing CPS, and particularly in safety-critical applications. This paper proposes a revision of the BDI model by integrating real-time mechanisms into the reasoning cycle of the agent. By doing so, the BDI agent can make decisions and execute plans ensuring compliance with strict timing constraints also in dynamic environments, where unpredictable events may occur.
Exploring the Relative Effects of Body Position and Spatial Cognition on Presence When Playing Virtual Reality Games

Previous research has suggested that cognitive functions such as execution, attention, memory, and perception differ when body position changes. However, the influence of body position on these cognitive functions is still not fully understood. In particular, little is known about how physical self-positioning may affect the cognitive process of perceptual responses in a virtual environment (VE). In this regard, we compared three body positions (standing, sitting, and half-sitting) in two types of virtual reality (VR) game with different degrees of freedom in navigation (finite and infinite) to explore the association between body position and the sense of presence in VEs. A two-way ANOVA yielded a main effect for body position, with the sense of presence significantly higher for a standing position than for a half-sitting position. However, there was no main effect for game type, nor was there an interaction between body position and game type. In addition, a one-way ANOVA revealed that, for the game with infinite navigation, participants in a standing position felt stronger presence than those who were sitting or half-sitting, whereas no significant difference was found between the sitting and half-sitting positions. In contrast, there was no statistical difference between the three body positions when playing the game with finite navigation. Taken together, it can be speculated that the cognitive effects of body position on presence are correlated with the degree of freedom in navigation in a VE.
An Access Control Mechanism Based on Risk Prediction for the IoV

The information sharing among vehicles provides intelligent transport applications in the Internet of Vehicles (IoV), such as self-driving and traffic awareness. However, due to the openness of the wireless communication (e.g., DSRC), the integrity, confidentiality and availability of information resources are easy to be hacked by illegal access, which threatens the security of the related IoV applications. In this paper, we propose a novel Risk Prediction-Based Access Control model, named RPBAC, which assigns the access rights to a node by predicting the risk level. Considering the impact of limited training datasets on prediction accuracy, we first introduce the Generative Adversarial Network (GAN) in our risk prediction module. The GAN increases the items of training sets to train the Neural Network, which is used to predict the risk level of vehicles. In addition, focusing on the problem of pattern collapse and gradient disappearance in the traditional GAN, we develop a combined GAN based on Wasserstein distance, named WCGAN, to improve the convergence time of the training model. The simulation results show that the WCGAN has a faster convergence speed than the traditional GAN, and the datasets generated by WCGAN have a higher similarity with real datasets. Moreover, the Neural Network (NN) trained with the datasets generated by WCGAN and real datasets (NN-WCGAN) performs a faster speed of training, a higher prediction accuracy and a lower false negative rate than the Neural Network trained with the datasets generated by GAN and real datasets (NN-GAN), and the Neural Network trained with the real datasets (NN). Additionally, the RPBAC model can improve the accuracy of access control to a great extent.
Analysis on Various Feature Extraction Methods for Medical Image Classification

Soft Computing is an emerging technique of machine intelligence that includes methods like Neural Networks, Fuzzy Logic, Support Vector Machines and Genetic Algorithms. The aim of soft computing is to mimic the reasoning and decision making of human. Neural networks are prominently being used in applications that involve processing of voluminous data and hence it can be highly applicable to the areas like image processing, stock market prediction and weather forecasting. Medical image processing involves four phases like Preprocessing, Segmentation, Feature Extraction and Classification. The aim of this work is to find the feature extraction method that is best for classifying the medical images. Local Binary Patterns (LBP), Gray-Level-Run-Length-Matrix (GLRM), Completed Local Binary Patterns (CLBP), Gray-Level Co-occurrence Matrix (GLCM) and Local Tetra Patterns (LTrP) are the most prominent feature extraction methods for medical images and are considered in this study. Two well-known classifiers Multi-Layer Perceptron using Backpropagation Network (MLPBPN) and Support Vector Machine (SVM) are used to analyse the efficiency of above specified five feature extraction techniques. Five different medical image Datasets are considered for experimentation. The experimental results illustrate that GLCM method is the best method compared with the other four feature extraction methods for medical image classification.
A technique for supporting decision process of global software project monitoring and rescheduling based on risk analysis

We present a novel, non-deterministic technique for global software project monitoring and re-scheduling that can be used by IT project managers to react on dynamically changing project conditions as the project knowledge grows and available resources are changed. The technique is adaptive, allows controlling overall risk level in each control step and makes advantage of the full PM Iron Triangle. We also shortly discuss the proposed technique in application to projects that are spread globally in different countries. The technique is verified on sample project data and its quality is assessed. What is more, we present the benefits and drawbacks of the proposed technique and consider suggestions for future work.
Multi-task Learning for Aspect and Polarity Recognition on Vietnamese Datasets

In this paper, we present a multi-task deep neural network model based on deep learning approach to address two tasks in document-level aspect-based sentiment analysis in Vietnamese datasets: Aspect Detection and Sentiment Polarity Classification. Besides, we conduct a series of experiments with different pre-trained embeddings on two VLSP benchmark datasets for the restaurant and hotel domains. Experimental results show that our model outperforms than stable baseline approaches on both tasks for two domains and achieves the new state-of-the-art F1-scores with 64.78% for the restaurant domain and 70.90% for the hotel domain.
Case-study based development of professional communicative competence of agricultural and environmental engineering students

Thisarticledemonstratestheeffectivenessofemployingthecase study method to teach English as a foreign language to the agricultural and environmental engineering students. It should be useful to those who are trying to contribute to the development of the technology relevant to the contemporary educational challenges based on the unity of applied and theoretical aspects of teaching English within a perspective of developing students’ environmental responsibility. This research includes three-year study conducted both in the institution of higher professional education and in one of the largest concentrating enterprise sharing strong environmental ethics in Russia from 2017 to 2019. The research methodology includes: analysis of literature on the main purposes of the engineering training as well as environmental concerns; the analysis of the waysofteachingcasestudy;anassessmentofpurposes,needs andfunctions for which English is required. The findings of this research are as follows: firstly, engineering student’s professional communicative competence is effectively developed within a case study analysis based on real cases from current companies. Secondly, students are more satisfied with their English course and more willing to put their English to practical use when foreign language skills and their professional engineering issues are brought together.
Reconstructing Arguments from Noisy Text

Abstract Social media are of paramount importance to public discourse. RANT aims to contribute methods and formalisms for extracting, representing, and processing arguments from noisy text found in social media discussions, using a large corpus of pre-referendum Brexit tweets as a running case study. We identify recurring linguistic argumentation patterns in a corpus-linguistic analysis and formulate corresponding corpus queries to extract arguments automatically. Given the huge amount of social media data available, our approach aims at high precision at the possible price of low recall. Argumentation patterns are directly associated with logical patterns in a dedicated formalism and accordingly, individual arguments are directly parsed as logical formulae. The logical formalism for argument representation features a broad range of modalities capturing real-life modes of expression. We cast this formalism as a family of instance logics in the generic framework of coalgebraic logic and complement it by a flexible framework to represent relationships between arguments; including standard relations like attack and support but also relations extracted from metadata. Some relations are inferred from the logical content of individual arguments. We are in the process of developing suitable generalizations of various extension semantics for argumentation frameworks combined with corresponding algorithmic methods to allow for the automated retrieval of large-scale argumentative positions.
Improved rider optimization for optimal container resource allocation in cloud with security assurance

Purpose The containerization application is one among the technologies that enable microservices architectures, which is observed to be the model for operating system (OS) virtualization. Containers are the virtual instances of the OS that are structured as the isolation for the OS atmosphere and its file system, which are executed on the single kernel and a single host. Hence, every microservice application is evolved in a container without launching the total virtual machine. The system overhead is minimized in this way as the environment is maintained in a secured manner. The exploitation of a microservice is as easy to start the execution of a new container. As a result, microservices could scale up by simply generating new containers until the required scalability level is attained. This paper aims to optimize the container allocation. Design/methodology/approach This paper introduces a new customized rider optimization algorithm (C-ROA) for optimizing the container allocation. The proposed model also considers the impact of system performance along with its security. Moreover, a new rescaled objective function is defined in this work that considers threshold distance, balanced cluster use, system failure, total network distance and security as well. At last, the performance of proposed work is compared over other state-of-the-art models with respect to convergence and cost analysis. Findings For experiment 1, the implemented model at 50th iteration has achieved minimal value, which is 29.24%, 24.48% and 21.11% better from velocity updated grey wolf optimisation (VU-GWO), whale random update assisted LA (WR-LA) and rider optimization algorithm (ROA), respectively. Similarly, on considering Experiment 2, the proposed model at 100th iteration attained superior performance than conventional models such as VU-GWO, WR-LA and ROA by 3.21%, 7.18% and 10.19%, respectively. The developed model for Experiment 3 at 100th iteration is 2.23%, 5.76% and 6.56% superior to VU-GWO, WR-LA and ROA. Originality/value This paper presents the latest fictional optimization algorithm named ROA for optimizing the container allocation. To the best of the authors’ knowledge, this is the first study that uses the C-ROA for optimization.
IoT Architecture Based on ABAC Smart Contract

In recent years, the combination of IoT and daily life is increasing. However, incidents caused by IoT security problems have also emerged endlessly. This paper analyzes the requirements of current IoT structure and the benefits of the combination of blockchain and IoT in this environment. Based on this, this paper uses the smart contract to implement the ABAC access control model on the blockchain and combines the IPFS file system to build a decentralized IoT architecture that can provide secure access control. The prototype of this system is tested in this paper, which verifies the feasibility of the system and confirms certain performance advantages of the architecture.
Comparative Study of Various Lightweight Cryptographic Algorithms for Data Security Between IoT and Cloud

IoT consists of a collection of constrained sensor-based devices and connected for communication. During this process, the sensors produce a huge quantity of data and transmitted in the network. Due to the uninterrupted transmission of sensitive data over the network, IoT devices are targeted for a different type of attacks. To nullify the attacks and to safeguard the sensitive information security is required. Given this, lightweight cryptographic algorithms are introduced. In this paper, various lightweight cryptographic algorithms are compared for data security between and cloud.
A Graph-Based Keyphrase Extraction Model with Three-Way Decision

Keyphrase extraction has been a popular research topic in the field of natural language processing in recent years. But how to extract keyphrases precisely and effectively is still a challenge. The mainstream methods are supervised learning methods and graph-based methods. Generally, the effects of supervised methods are better than unsupervised methods. However, there are many problems in supervised methods such as the difficulty in obtaining training data, the cost of labeling and the limitation of the classification function trained by training data. In recent years, the development of the graph-based method has made great progress and its performance of extraction is getting closer and closer to the supervised method, so the graph-based method of keyphrase extraction has got a wide concern from researchers. In this paper, we propose a new model that applies the three-way decision theory to graph-based keyphrase extraction model. In our model, we propose algorithms dividing the set of candidate phrases into the positive domain, the boundary domain and the negative domain depending on graph-based attributes, and combining candidate phrases in the positive domain and the boundary domain qualified by graph-based attributes and non- graph-based attributes to get keyphrases. Experimental results show that our model can effectively improve the extraction precision compared with baseline methods.
Improved Potential Field Method for Robot Path Planning with Path Pruning

Path planning is vital for a robot deployed in a mission in a challenging environment with obstacles around. The robot needs to ensure that the mission is accomplished without colliding with any obstacles and find an optimal path to reach the goal. Three important criteria, i.e., path length, computational complexity, and completeness, need to be taken into account when designing a path planning method. Artificial Potential Field (APF) is one of the best methods for path planning as it is fast, simple, and elegant. However, the APF has a major problem called local minima, which will cause the robot fails to reach the goal. This paper proposed an Improved Potential Field method to solve the APF limitation. Despite that, the path length produced by the Improved APF is not optimal. Therefore, a path pruning technique is proposed in order to shorten the path generated by the Improved APF. This paper also compares the performance on the path length and computational time of the Improved APF with and without path pruning. Through simulation, it is proven that the proposed technique could overcome the local minima problem and produces a relatively shorter path with fast computation time.
Imbalanced Ensemble Learning for Enhanced Pulsar Identification

Pulsars can be detected based on their emitted radio waves. Machine learning methods can be employed to support automated screening of a large number of radio signals for pulsars. This is however a challenging task since training these methods is affected by an inherent imbalance in the acquired data with signals relating to actual pulsars being in the minority. In this paper, we demonstrate that ensemble classification methods that are dedicated to imbalanced classification problems can be successfully employed for pulsar identification. Classifier ensembles combine several individual classifiers to yield more robust and improved classification, while class imbalance can be addressed through careful sampling or through cost-sensitive classification. Experimental results, based on HTRU2 data, show that the investigated ensembles outperform methods that do not consider class balance, and suggest their use for other applications in astrophysics.
Assessing User Behavior by Mouse Movements

In this working paper, we study user identification via mouse movement. Instead of treating the problem as a multi-class classification task, we cast user identification as a one-class problem and propose to learn an individual model for every user. Preliminary empirical results show that our approach works for some but not all users. We report on lessons learned.