Reviewerly/exHarmony huggingface dataset:
Disagreements in a feminist digital safe space

Abstract Research on impoliteness has underlined the need for multidisciplinary approaches to impoliteness ( Sifianou 2019a ). In this article, I adopt the concept of “safe space”, as discussed in works on feminist digital media ( Clark-Parsons 2018 ; Rentschler 2014 ), to explore how impoliteness relates to identity construction and the negotiation of power dynamics in the context of a Greek feminist website, constructed as a safe space. Both the concept of “safe space” and discursive and relational approaches to impoliteness ( Angouri and Locher 2012 ; Graham 2007 ; Locher and Watts 2005 , 2008 ) relate to norms of appropriate behaviour in particular contexts and relational issues, such as power and identity. The data for this article comprise comments posted in discussions taking place in the Greek website of A, mpa , in which participants became involved in disagreements. Culpeper’s (2010) framework for conventionalised impoliteness formulae is the starting point of the analysis, which takes a discourse analytical approach to investigate the intersection of identity and power in the site. Results show that criticisms and challenging questions are linked to the identification of participants with particular feminist positions, and to the negotiation of the power distribution between the administrator and themselves. Furthermore, the paradoxical nature of safe spaces plays an important role in these processes, as the use of impoliteness points to the negotiation of the norms for interaction among participants and reveals the struggle between individual expression and maintenance of safety.
Temporal Collaborative Filtering with Graph Convolutional Neural Networks

Temporal collaborative filtering (TCF) methods aim at modelling non-static aspects behind recommender systems, such as the dynamics in users' preferences and social trends around items. State-of-the-art TCF methods employ recurrent neural networks (RNNs) to model such aspects. These methods deploy matrix-factorization-based (MF -based) approaches to learn the user and item representations. Recently, graph-neural-network-based (GNN-based) approaches have shown improved performance in providing accurate recommendations over traditional MF -based approaches in non-temporal CF settings. Motivated by this, we propose a novel TCF method that leverages GNNs to learn user and item representations, and RNNs to model their temporal dynamics. A challenge with this method lies in the increased data sparsity, which negatively impacts obtaining meaningful quality representations with GNNs. To overcome this challenge, we train a GNN model at each time step using a set of observed interactions accumulated time-wise. Comprehensive experiments on real-world data show the improved performance obtained by our method over several state-of-the-art temporal and non-temporal CF models.
Explainable Software vulnerability detection based on Attention-based Bidirectional Recurrent Neural Networks

Software vulnerability detection in source code is a fundamental problem in cyber-security. Aiming at discovering the vulnerability automatically, this paper proposes an open source software vulnerability detection method based on attention-based bidirectional recurrent neural networks. Based on the high-level and generalizable function representations that obtained from the abstract syntax tree(AST), an attention-based bidirectional recurrent neural networks is devised to capture the sequential and important code elements in vulnerability detection from the large number of features that the deep learning model has learned. Experimental results confirm that the huge potential of the proposed new vulnerability detection method which is not only more effective than Convolutional Neural Networks(CNN) but also better than traditional Bidirectional Recurrent Neural Networks(BRNN) in reducing the false negative rate at the price of increasing the false positive rate.
Estimating daily diffuse solar radiation using neuro-fuzzy, fuzzy C-means and subtractive clustering

This work presents two optimization techniques, fuzzy c-means (FCM) and subtractive clustering, to improve the efficiency and generalization ability of back propagation neuro-fuzzy model for estimating daily diffuse solar radiation Hd, four parameters including extraterrestrial irradiation H0, the global radiation Hg, the average, air temperature (Tavg) and the relative humidity (Rh). The predictions from the neuro-fuzzy optimized by fuzzy c-means (FCM) and subtractive clustering were analyzed and evaluate with 02 statistical parameters. The results show that the proposed neurofuzzy optimized by FCM model has potential in accurately predicting the daily diffuse solar radiation.
Visual effects creation to improve user performance in fully immersive virtual environment of automobile engine assembly

Abstract Virtual reality (VR) technologies and computer-generated technologies are very useful for many industries, such as medical, education, gaming, mining, engineering, and other sectors. In engineering sector specifically for assembly department, the major problem they are facing was that traditional components of automotive engine are complex and very difficult to assemble using current method which use paper manual. The goal of this project is to create an interactive VR application such that users can quickly integrate and perform the work easily. In this work, the authors designed and developed an immersive VR application with several stages, such as 3D model creation, visual effects creation and gaming scene building. The application was built using Unreal and HTC vive headset. As this is an on-going project work, further functionality will be added to render the application immensely for users and assist the sense of pedagogy in terms of the digital method of learning.
Impact of Map-Reduce framework on Hadoop and Spark MR Application Performance

Hadoop and Spark are popular Map Reduce frameworks, and are under active maintenance. Various benchmark suites and micro-benchmarks have been developed in order to measure and understand the performance and behaviour of the framework. The performance and behaviour of the micro-benchmarks (such as data motifs) are derived from core computations of Big Data applications can also be better understood by such an analysis. Data motifs have been previously studied and each data motif has been shown to behave differently due to changes in two external factors: its input size or input pattern. However, what is not considered by these studies or by any other Hadoop/Spark micro-benchmark distribution, is how the underlying framework can impact the behaviour of the benchmark, in terms of system resource usage and in terms of micro-architectural behaviour. The framework is the third external factor which impacts the behaviour of a big data application. In this work, we analyse the various Hadoop and Spark micro-benchmarks with different input sizes, and differing Hadoop/Spark versions and demonstrate through our results that the behaviour of the motif must also include the underlying framework as a third influencing factor.
Path optimization for multiple humanoid robot using TLBO based ANFIS controller in obscure environment

Humanoid robot NAO is an intelligent reprogrammable agent designed to execute desired work using wireless control system by sensing the working environment at the same time. This article concentrates on the analysis of hybrid intelligent navigation approaches for single and multiple humanoid NAOs. It describes the optimization of a collision-free path in a static and dynamic terrain. The hybridization introduced is designed in two steps. ANFIS (Adaptive Network-based Fuzzy Inference System) controller produces a transitional driving angle (TDA) using a robot location with respect to obstacle distance in all directions. It is fed to Teacher Learner based Optimization (TLBO) approach that produces optimum driving angle (ODA) for the humanoid robot to guide along with its predefined target. It optimizes the path selection by taking Euclidean distance and ODA as the key factor. The hybridized controller is examined in an environment that consists of single and multiple NAOs. Case of inter-collision may occur in the path planning of multiple humanoid NAOs. It is eliminated with the integration of the dining philosopher controller in the base algorithm to prioritized one robot towards the assigned target. Simulation results demonstrate the proposed controller's efficacy and explain that it gives optimized path length and travel time. In addition, a comparative study with previously applied effective algorithms is done.
Adjoint Reactive GUI Programming

Abstract Most interaction with a computer is via graphical user interfaces. These are traditionally implemented imperatively, using shared mutable state and callbacks. This is efficient, but is also difficult to reason about and error prone. Functional Reactive Programming (FRP) provides an elegant alternative which allows GUIs to be designed in a declarative fashion. However, most FRP languages are synchronous and continually check for new data. This means that an FRP-style GUI will “wake up” on each program cycle. This is problematic for applications like text editors and browsers, where often nothing happens for extended periods of time, and we want the implementation to sleep until new data arrives. In this paper, we present an asynchronous FRP language for designing GUIs called $$\lambda _{\mathsf {Widget}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>λ</mml:mi> <mml:mi>Widget</mml:mi> </mml:msub> </mml:math> . Our language provides a novel semantics for widgets, the building block of GUIs, which offers both a natural Curry–Howard logical interpretation and an efficient implementation strategy.
Remote Healthcare Monitoring System for Aging Population Based on IoT and Big Data Analysis

As the population ages, this will put tremendous pressure on pensions and the medical system. Health insurance for the elderly poses a major challenge to the allocation of limited medical resources. The Internet of Things (IoT) is widely regarded as a potential solution to relieve pressure on healthcare systems. With the rapid development of wireless communication technologies such as backscatter communication, Wi-Fi and 5G, in vivo and in vitro medical devices have been greatly developed. In this article, we propose a real-time big data healthcare analysis system based on the combination of shallow neural networks and deep neural networks. The system consists of two parts: the detection of fog layer abnormality based on shallow neural network and the diagnosis of cloud disease based on deep neural network. The system can not only make full use of the real-time nature of the fog layer but also give full play to its powerful computing power based on reducing the computing pressure of the cloud layer.
A Strategy Towards Cost-Effective Content Delivery in the Higher Learning Institutions of Tanzania

The recent development of ubiquitous technologies such as mobile computing facilities in developing countries promises a cost-effective approach for learning in higher learning institutions (HLIs). For instance, in Tanzania, new technologies in HLIs are being blended through eLearning and m-learning. However, HLIs are facing challenges in the delivery of eLearning contents due to the distributed nature of learning environments, the advancement of a large amount of data, shortage in ICT resources, and bandwidth. This study presents the state-of-the-art challenges and opportunities on eLearning in Tanzania. The survey results show that above 75% of students own multiple mobile devices that can support access to M-learning. Then, a cost-effective strategy for content delivery supported by mobile edge computing and cloud computing is proposed. Moreover, it is recommended that HLIs in developing countries should enact ICT policy that integrates issues associated with how to effectively utilize the emerging ubiquitous technologies applicable in their learning environments.
Visualization of similarity queries in an immersive virtual reality environment

The virtual reality provides new possibilities for data interaction in visualization systems, assisting users in the analysis of data through improved intuition and perception. In content-based image retrieval systems the visualization of queries results allows adding the human visual perception in the analysis, improving the knowledge discovery process. Visualization allows users to better understand the distribution of data related to the queries and to incorporate their prior knowledge in the process of data retrieval and analysis. This work proposes the interactive visualization of similarity queries on complex data in a low cost immersive virtual reality environment. Thus, we proposed rendering options and techniques for interaction to avail advantages of the immersive environment and enable the cheap and practical visualization of similarity queries. We present visualization examples, through the virtual reality prototype produced, to demonstrate the advantages of its use in data analysis.
Weakly supervised object-aware convolutional neural networks for semantic feature matching

We address the task of establishing visual correspondences between two images depicting main objects of the same semantic category. This task encounters various challenges such as background clutter, intra-class variation, and viewpoint variations. Existing works are dominated by end-to-end training methods that rely on redundant calculation or large amounts of manual annotations, and cannot generalize to unseen object categories. In this paper, we propose to construct a weakly supervised object-aware convolutional neural network architecture for semantic feature matching, while being trainable end-to-end without the requirement for manual annotations. The main component of this architecture is a similarity filter module containing a trainable neural nearest neighbors network. Since training data for semantic feature matching is rather limited, we introduce a simple and effective foreground selection strategy to produce the foreground masks. Using these masks as a form of weak supervision signal for correspondence task and tackle the background clutter. Extensive experiments illustrate that the proposed approach outperforms the state-of-the-art methods for semantic feature matching on multiple public standard benchmark datasets.
ExNav: An Interactive Big Data Exploration Framework for Big Unstructured Data

Driven by the increasing gap between the exponential growth of data and the limited human ability to comprehend them, recently, a novel interactive data exploration approach called Explore-by-Examples has generated a lot of attention for its capabilities to bridge this gap and to help the user obtain high-value content from the data that are often hidden using the traditional search methods. However, despite their effectiveness in extracting valuable information, existing Explore-by-Examples systems focus solely on structured data, which represents a small portion of the data available today. In this work, we present a novel data exploration framework, namely ExNav (Exploration Navigator), which enables the user to effortlessly explore the world of unstructured data for insights that are often unreachable from traditional search and exploration methods. In particular, we exploit the space of advanced machine learning, data embedding, and active learning algorithms to design effective exploration and space pruning approaches tailored for unstructured datasets. Our experimental evaluation using multiple real-world unstructured datasets (i.e., text, image, and graph) show that ExNav can reduce users' effort by up to 9x while still achieving the same accuracy as the state-of-the-art alternative. Moreover, ExNav is also able to identify relevant data items that are often undetectable by current techniques, even when a large number of samples are explored.
Forensic Analysis of E-mail for Authorship Attribution: Research Perspective

E-mail has emerged as an important means of communication. Lots of criminal activities have been reported using e-mail as a primary source for the scam. Extracting the information from the e-mail text has become more important for forensic investigation and evidence collection. Identifying the actual author of an e-mail has become necessary and is also a significant area of research in digital forensics as each author has a unique style of writing. Yet, it is one of the most demanding and tedious tasks due to the characteristics of the e-mail dataset. Recently, lots of cybercrimes, hacking, and many forms of organized crimes are penetrating the network which breaches the IT security. With various sources of evidential data that is required for analysis, one technique used for analysis will be more beneficial than the other depending on the time and constraints considered. There exist several machine learning algorithms that ease the work of efficient analysis of author identification from the evidence gathered. This paper emphasis such varied algorithms and justifies the efficient use of the support vector machine algorithm for forensic analysis of data and also provides the related works and techniques for author attribution.
Integrating 3D and 2D Views of Medical Image Data in Virtual Reality for Efficient Navigation

Medical imaging techniques, e.g., CT or MRI, produce large sets of 2D and 3D data for diagnosis and operative planning. Virtual Reality (VR) could provide a more intuitive and efficient way of interacting with such data. While 3D representations are well suited to show 3D spatial relationships, 2D image slices are important to show details, i.e., both 3D and 2D are required. While researchers have investigated separated approaches for presenting 3D and 2D data in VR, we present a solution that closely integrates 3D and 2D and provides an intuitive and efficient interaction technique for navigating the information by syncing the position of a 3D cursor inside the 3D representation to matching 2D images on different axes. We conducted an empirical evaluation comparing our approach to navigating the data in 2D and found that the VR method was significantly faster, although, at the cost of significantly increased task load.
Handling redundant processing in OBDA query execution over relational sources

Redundant processing is a key problem in the translation of initial queries posed over an ontology into SQL queries, through mappings, as it is performed by ontology-based data access systems. Examples of such processing are duplicate answers obtained during query evaluation, which must finally be discarded, or common expressions evaluated multiple times from different parts of the same complex query. Many optimizations that aim to minimize this problem have been proposed and implemented, mostly based on semantic query optimization techniques, by exploiting ontological axioms and constraints defined in the database schema. However, data operations that introduce redundant processing are still generated in many practical settings, and this is a factor that impacts query execution. In this work we propose a cost-based method for query translation, which starts from an initial result and uses information about redundant processing in order to come up with an equivalent, more efficient translation. The method operates in a number of steps, by relying on certain heuristics indicating that we obtain a more efficient query in each step. Through experimental evaluation using the Ontop system for ontology-based data access, we exhibit the benefits of our method.
A summarization glyph for sets of unreadable visual items in treemaps

The use of glyphs in data visualization aims to create a visual object that represents a data set through a set of graphic representations. Even though glyphs can help visualization techniques increase visual data representation, this approach sometimes can suffer from large amounts of data, making the glyph susceptible to perception errors of its visual variables. The grids and geomaps layouts are the most common layouts used with glyphs. However, when a more complex arrangement is considered, such as a treemap layout, the problem is even more challenging, as the small and variable areas directly influence glyph readability. Thus, this article proposes a summarization glyph to replace a set of treemap items whose areas are tiny, and that consequently would not be suitable for a glyph of their own. The proposed glyph enables data analysts to have some useful insights about the data in the overview exploration. The summarization glyph is a stacked bar chart representing the data dimensions of the tiny items. A decision tree trained on user perception tests selects the glyphs with low readability, deciding if they are kept, or included in the summarization glyph.
An Adaptive Algorithm for Lexical Ambiguity in Word Sense Disambiguation

The lexical ambiguity in word sense disambiguation is a crucial problem in the fields of machine translation and information retrieval. The lexical ambiguity causes because of polysemy words in a natural language understanding. The polysemy words may confuse the machine while processing user inputs. In machine translation, the polysemy words are processed with the help of available context information. The objective of this research work is to present a supervised deep neural network model dedicated to the task of maximizing the accuracy. The input layer of the neural network will consist of nodes having binary values depending on the presence or absence of frequently occurring context words related to the ambiguous words. The output layer will consist of nodes equal to the number of senses the ambiguous word has. Training and testing of the model will be done using lexical resources such as SemCor or OMSTI. The accuracy will be calculated based on All-Word tasks from SemEval international workshops. The purpose of this research is to improve better communication between man and machine.
Digitalizing Commodity Trading Value Chain: An Assessment for Turkey

In the value chain of the agricultural commodity sector, innovative solutions have been introduced in order to reduce deficiencies, to improve efficiency and to secure food supply as well as to advance cooperative advantages from farm to fork. Likewise, traditional commodity trading venues have also been challenged. In this study, we analyse digital technologies that can be utilized to transform commodity-trading venues in the agricultural sector in Turkey. A framework for agricultural commodity trading chain digitalization is proposed and its implications for research and practices are discussed. Based on the literature review, blockchain technology is proposed for digitalization of contract farming, commodity financing via token based smart contracts, commodity-backed token and smart contract trading and commodity hedging.