Reviewerly/exHarmony huggingface dataset:
The ontology-based approach to data storage systems technical diagnostics

Monitoring and diagnosing the state of data storage systems, as well as assessing reliability and troubleshooting, require a formalized health model. A comparative analysis of existing knowledge representation methods has shown that an ontological approach is well suited for this task. This paper introduces a machine-represented data storage reliability ontology with an expert health model as baseline data. Classes of the ontology include the key terms of the reliability domain. Stated requirements for data interpretation tools allow further processing of the ontology-based knowledge base. Described ontology-based diagnostic systems have shown their applicability in the case of data storage systems in the construction industry.
Factors hindering Indonesian lecturers from publishing articles in reputable international journals

&lt;p class="WW-Default"&gt;The purpose of this study is to investigate the possible factors inhibiting the university lecturers or professors in Indonesia from publishing their research results in reputable international journals in English. The research used a mixed (quantitative and qualitative) method, employing a national survey and in-depth interviews with selected lecturers and researchers in five different universities in Indonesia (i.e., Bengkulu University, Padang State University, Makasar State University, Unika Atma Jaya University and Mataram University). The results show that three main factors have hindered the lecturers from submitting their articles to reputable international journals: lack of self-confidence on the quality of their own research and article, finding it very difficult and very time consuming to prepare an article in English and no adequate reward for those who can successfully publish in reputable international journals. However, the majority of them have a positive attitude toward international journal publication for their own benefits or for the sake of their institution.&lt;strong&gt;&lt;em&gt;&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;
The efficacy of Euler diagrams and linear diagrams for visualizing set cardinality using proportions and numbers

This paper presents the first empirical investigation that compares Euler and linear diagrams when they are used to represent set cardinality. A common approach is to use area-proportional Euler diagrams but linear diagrams can exploit length-proportional straight-lines for the same purpose. Another common approach is to use numerical annotations. We first conducted two empirical studies, one on Euler diagrams and the other on linear diagrams. These suggest that area-proportional Euler diagrams with numerical annotations and length-proportional linear diagrams without numerical annotations support significantly better task performance. We then conducted a third study to investigate which of these two notations should be used in practice. This suggests that area-proportional Euler diagrams with numerical annotations most effectively supports task performance and so should be used to visualize set cardinalities. However, these studies focused on data that can be visualized reasonably accurately using circles and the results should be taken as valid within that context. Future work needs to determine whether the results generalize both to when circles cannot be used and for other ways of encoding cardinality information.
Confluence Competition 2019

We report on the 2019 edition of the Confluence Competition, a competition of software tools that aim to prove or disprove confluence and related (undecidable) properties of rewrite systems automatically.
Mining aspects of customer’s review on the social network

This study represents an efficient method for extracting product aspects from customer reviews and give solutions for inferring aspect ratings and aspect weights. Aspect ratings often reflect the user’s satisfaction on aspects of a product and aspect weights reflect the degree of importance of the aspects posed by the user. These tasks therefore play a very important role for manufacturers to better understand their customers’ opinion on their products and services. The study addresses the problem of aspect extraction by using aspect words based on conditional probability combined with the bootstrap technique. To infer the user’s rating for aspects, a supervised approach called the Naïve Bayes classification method is proposed to learn the aspect ratings in which sentiment words are considered as features. The weight of an aspect is estimated by leveraging the frequencies of aspect words within each review and the aspect consistency across all reviews. Experimental results show that the proposed method obtains very good performance on real world datasets in comparison with other state-of-the-art methods.
Utility of a Shuffled Differential Evolution algorithm in designing of a Pi-Sigma Neural Network based predictor model

Since the last few decades, Artificial Neural Networks have been the center of attraction of a large number of researchers for solving diversified problem domains. Due to its distinguishing features such as generalization ability, robustness and strong ability to tackle nonlinear problems, it appears to be more popular in financial time series modeling and prediction. In this paper, a Pi-Sigma Neural Network is designed for foretelling the future currency exchange rates in different prediction horizon. The unrevealed parameters of the network are interpreted by a hybrid learning algorithm termed as Shuffled Differential Evolution (SDE). The main motivation of this study is to integrate the partitioning and random shuffling scheme of Shuffled Frog Leaping algorithm with evolutionary steps of a Differential Evolution technique to obtain an optimal solution with an accelerated convergence rate. The efficiency of the proposed predictor model is actualized by predicting the exchange rate price of a US dollar against Swiss France (CHF) and Japanese Yen (JPY) accumulated within the same period of time.
Predicting Second Language Writing Proficiency in Learner Texts Using Computational Tools

This study explores whether linguistic features can predict second language writing proficiency in the Michigan English Language Assessment Battery (MELAB) writing tasks.Advanced computational tools were used to automatically assess linguistic features related to lexical sophistication, syntactic complexity, cohesion, and text structure of writing samples graded by expert raters.The findings of this study show that an analysis of linguistic features can be used to significantly predict human judgments of the essays for the MELAB writing tasks.Furthermore, the findings indicate the relative contribution of a range of linguistic features in MELAB essays to overall second language (L2) writing proficiency scores.For instance, linguistic features associated with text length and lexical sophistication were found to be more predictive of writing quality in MELAB than those associated with cohesion and syntactic complexity.This study has important implications for defining writing proficiency at different levels of achievement in L2 academic writing as well as improving the current MELAB rating scale and rater training practices.Directions for future research are also discussed.
Discernible image mosaic with edge-aware adaptive tiles

We present a novel method to produce discernible image mosaics, with relatively large image tiles replaced by images drawn from a database, to resemble a target image. Compared to existing works on image mosaics, the novelty of our method is two-fold. Firstly, believing that the presence of visual edges in the final image mosaic strongly supports image perception, we develop an edge-aware photo retrieval scheme which emphasizes the preservation of visual edges in the target image. Secondly, unlike most previous works which apply a pre-determined partition to an input image, our image mosaics are composed of adaptive tiles, whose sizes are determined based on the available images in the database and the objective of maximizing resemblance to the target image. We show discernible image mosaics obtained by our method, using image collections of only moderate size. To evaluate our method, we conducted a user study to validate that the image mosaics generated present both globally and locally appropriate visual impressions to the human observers. Visual comparisons with existing techniques demonstrate the superiority of our method in terms of mosaic quality and perceptibility.
Enhancing Network Embedding with Implicit Clustering

Network embedding aims at learning the low dimensional representation of nodes. These representations can be widely used for network mining tasks, such as link prediction, anomaly detection, and classification. Recently, a great deal of meaningful research work has been carried out on this emerging network analysis paradigm. The real-world network contains different size clusters because of the edges with different relationship types. These clusters also reflect some features of nodes, which can contribute to the optimization of the feature representation of nodes. However, existing network embedding methods do not distinguish these relationship types. In this paper, we propose an unsupervised network representation learning model that can encode edge relationship information. Firstly, an objective function is defined, which can learn the edge vectors by implicit clustering. Then, a biased random walk is designed to generate a series of node sequences, which are put into Skip-Gram to learn the low dimensional node representations. Extensive experiments are conducted on several network datasets. Compared with the state-of-art baselines, the proposed method is able to achieve favorable and stable results in multi-label classification and link prediction tasks.
Lattice-Based Proof of a Shuffle

In this paper we present the first fully post-quantum proof of a shuffle for RLWE encryption schemes. Shuffles are commonly used to construct mixing networks (mix-nets), a key element to ensure anonymity in many applications such as electronic voting systems. They should preserve anonymity even against an attack using quantum computers in order to guarantee long-term privacy. The proof presented in this paper is built over RLWE commitments which are perfectly binding and computationally hiding under the RLWE assumption, thus achieving security in a post-quantum scenario. Furthermore we provide a new definition for a secure mixing node (mix-node) and prove that our construction satisfies this definition.
Information Fusion for Industrial Mobile Platform Safety via Track-Before-Detect Labeled Multi-Bernoulli Filter

This paper presents a novel Track-Before-Detect (TBD) Labeled Multi-Bernoulli (LMB) filter tailored for industrial mobile platform safety applications. At the core of the developed solution is two techniques for fusion of color and edge information in visual tracking. We derive an application specific separable likelihood function that captures the geometric shape of the human targets wearing safety vests. We use a novel geometric shape likelihood along with a color likelihood to devise two Bayesian updates steps which fuse shape and color related information. One approach is sequential and the other is based on weighted Kullback–Leibler average (KLA). Experimental results show that the KLA based fusion variant of the proposed algorithm outperforms both the sequential update based variant and a state-of-art method in terms of the performance metrics commonly used in computer vision literature.
Swarm Algorithms for NLP - The Case of Limited Training Data

Abstract The present article describes a novel phrasing model which can be used for segmenting sentences of unconstrained text into syntactically-defined phrases. This model is based on the notion of attraction and repulsion forces between adjacent words. Each of these forces is weighed appropriately by system parameters, the values of which are optimised via particle swarm optimisation. This approach is designed to be language-independent and is tested here for different languages. The phrasing model’s performance is assessed per se, by calculating the segmentation accuracy against a golden segmentation. Operational testing also involves integrating the model to a phrase-based Machine Translation (MT) system and measuring the translation quality when the phrasing model is used to segment input text into phrases. Experiments show that the performance of this approach is comparable to other leading segmentation methods and that it exceeds that of baseline systems.
Advanced Digital Competence of the Teacher

This chapter is devoted to the issues of changing the structure of the competence of teachers in the transformation of the school of the twenty-first century—the school of digital civilization. With this in mind, this chapter takes a fresh look at the advanced competencies of teachers of the near future in the context of these real school changes, the structure of which can be represented as the “triangle of digital competencies”: life, social, and professional. As shown by analytical studies and experimental experience of teacher training, the results of which are presented in this chapter, this triangle of competences will allow children to be included in the digital school environment and form their further activity in the smart education system in the profession throughout their lives. It is also important to note that new competencies have become the basis of a new profession in the school—a digital curator, which is also discussed in this chapter.
A Generic Research Data Infrastructure for Long Tail Research Data Management

The advent of data intensive science has fueled the generation of digital scientific data. Undoubtedly, digital research data plays a pivotal role in transparency and re-producibility of scientific results as well as in steering the innovation in a research process. However, the main challenges for science policy and infrastructure projects are to develop practices and solutions for research data management which in compliance with good scientific standards make the research data discoverable, citeble and accessible for society potential reuse. GeRDI – the Generic Research Data (RD) Infrastructure – is such a research data management initiative which targets long tail content that stems from research communities belonging to different domain and research practices. It provides a generic and open software which connects research data infrastructures of communities to enable the investigation of multidisciplinary research questions.
Examining the sustainability and development challenge in agricultural-forest frontiers of the Amazon Basin through the eyes of locals

Abstract. The Amazon basin is the world's largest rainforest and the most biologically diverse place on Earth. Despite the critical importance of this region, Amazon forests continue inexorably to be degraded and deforested for various reasons, mainly a consequence of agricultural expansion. The development of novel policy strategies that provide balanced solutions, associating economic growth with environmental protection, is still challenging, largely because the perspective of those most affected – local stakeholders – is often ignored. Participatory fuzzy cognitive mapping (FCM) was implemented to examine stakeholder perceptions towards the sustainable development of two agricultural-forest frontier areas in the Bolivian and Brazilian Amazon. A series of development scenarios were explored and applied to stakeholder-derived FCM, with climate change also analysed. Stakeholders in both regions perceived landscapes of socio-economic impoverishment and environmental degradation driven by governmental and institutional deficiencies. Under such abject conditions, governance and well-integrated social and technological strategies offered socio-economic development, environmental conservation, and resilience to climatic changes. The results suggest there are benefits of a new type of thinking for development strategies in the Amazon basin and that continued application of traditional development policies reduces the resilience of the Amazon to climate change, whilst limiting socio-economic development and environmental conservation.
Robust Detection of Cyberbullying in Social Media

The potentially detrimental effects of cyberbullying have led to the development of numerous automated, data–driven approaches, with an emphasis on classification accuracy. Cyberbullying, as a form of abusive online behavior, although not well–defined, is a repetitive process, i.e., a sequence of aggressive messages sent from a bully to a victim over a period of time with the intent to harm the victim. Existing work has focused on aggression (i.e., using profanity to classify toxic comments independently) as an indicator of cyberbullying, disregarding the repetitive nature of this harassing process. However, raising a cyberbullying alert immediately after an aggressive comment is detected can lead to a high number of false positives. At the same time, three key practical challenges remain unaddressed: (i) detection timeliness, which is necessary to support victims as early as possible, (ii) scalability to the staggering rates at which content is generated in online social networks, (iii) reliance on high quality annotations from human experts for training of highly accurate supervised classifiers.
Investigation of non-markovian dynamics of two dipole-dipole interacting Qubits based on numerical solution of the non-linear stochastic schrödinger equation

In this paper, we investigate non-markovian dynamics of a system of two interacting qubits. With the help of stochastic calculus we derive the non-Markovian non-linear stochastic Schrödinger equation. This equation is solved by the direct computer simulation. The simulation is used to obtain some dynamic properties of the system.
A distributed ledger based platform for community-driven flexibility provision

In this paper we propose and analyze a community-driven platform for flexibility provision based on a distributed ledger. We introduce and analyze the platform for the use case of a self-organized decentralized virtual power plant consisting of a local community of individual prosumers with photovoltaic-storages located on a low voltage feeder. Like a virtual power plant, it aggregates small-scale assets and is able to provide ancillary services in the form of active power provision to the electrical power system. However, the decentralized virtual power plant provides a direct flexibility market access of the distributed assets without the need for a third party. Thus, balancing group managers or system operators can directly interact with the distributed assets in an aggregated way without the need for an aggregator. The solution approach uses a heuristic algorithm for the coordination combined with a distributed ledger and democratic consensus within the community. We propose the concept in detail, describe the prototypical implementation based on a consortium Ethereum blockchain and discuss results of the proof-of-concept. Our numerous test runs with up to 20 participants showed that the coordinated flexibility provision, energy sharing and according financial settlement works in practice, but would need an upgrade concerning the smart-meter hardware for an implementation in the field. We analyze the impact of the coordination interval on the community self-sufficiency and determine that one minute intervals are enough to reach 96% of the optimum. We evaluate the storage and communication effort and conclude with suggestions for future improvements and other possible applications of the decentralized platform like aggregated flexibility coordination between balancing group managers and system operators.
An Architecture for Real-Time Retinex-Based Image Enhancement and Haze Removal and Its FPGA Implementation

In recent years, much research interest has developed in image enhancement and haze removal techniques. With increasing demand for real time enhancement and haze removal, the need for efficient architecture incorporating both haze removal and enhancement is necessary. In this paper, we propose an architecture supporting both real-time Retinex-based image enhancement and haze removal, using a single module. Efficiently leveraging the similarity between Retinex-based image enhancement and haze removal algorithms, we have successfully proposed an architecture supporting both using a single module. The implementation results reveal that just 1% logic circuits overhead is required to support Retinex-based image enhancement in single mode and haze removal based on Retinex model. This reduction in computation complexity by using a single module reduces the processing and memory implications especially in mobile consumer electronics, as opposed to implementing them individually using different modules. Furthermore, we utilize image enhancement for transmission map estimation instead of soft matting, thereby avoiding further computation complexity which would affect our goal of realizing high frame-rate real time processing. Our FPGA implementation, operating at an optimum frequency of 125MHz with 5.67M total block memory bit size, supports WUXGA (1,920×1,200) 60fps as well as 1080p60 color input. Our proposed design is competitive with existing state-of-the-art designs. Our proposal is tailored to enhance consumer electronic such as on-board cameras, active surveillance intrusion detection systems, autonomous cars, mobile streaming systems and robotics with low processing and memory requirements.
Research on Forensic Model of Online Social Network

With the excessive use of online social network, criminals can get enough information about person or organization. Law enforcing agencies to get digital evidence against criminals could use the data from online social network. This paper proposes a forensic model of online social network that provides the functions of evidence acquisition and solidification, evidence document verification, and evidence analysis. This model is illustrated the feasibility with Sina microblog examples of text analysis, hotword frequency analysis, physical location analysis.
A Bounding Volume of the Cable Span for Fast Collision Avoidance Verification

The problem of verifying the absence of collision between a cable and the mobile part(s) of a device located on-board the mobile platform of a Cable-Driven Parallel Robot (CDPR) is addressed. The set of all positions taken by one cable of the CDPR for all the poses of the mobile platform in a prescribed workspace is called the cable span. A simple bounding volume approximation of the cable span is proposed in this paper. This bounding volume is a polyhedron and the characterization of the faces of this polyhedron is discussed. Using this polyhedron as a bounding volume of the cable span allows to accelerate computations related to collision avoidance checking.
Toward Comprehensive Understanding of a Sentiment Based on Human Motives

In sentiment detection, the natural language processing community has focused on determining holders, facets, and valences, but has paid little attention to the reasons for sentiment decisions. Our work considers human motives as the driver for human sentiments and addresses the problem of motive detection as the first step. Following a study in psychology, we define six basic motives that cover a wide range of topics appearing in review texts, annotate 1,600 texts in restaurant and laptop domains with the motives, and report the performance of baseline methods on this new dataset. We also show that cross-domain transfer learning boosts detection performance, which indicates that these universal motives exist across different domains.