Reviewerly/exHarmony huggingface dataset:
Argumentation and meaning

Abstract This special issue aims to explore the semantic and pragmatic dimensions of meaning in terms of their significance and relevance in the study of argumentation. Accordingly, the contributors to the project, who have all presented their work during the 2nd Argumentation and Language conference, which took place in Lugano in February 2018, 1 have been specifically instructed to produce papers which explicitly tackle the importance of the study of meaning for that of argumentative practices. All papers therefore cover at least one aspect of this complex relationship between argumentation and meaning, which contributes to delivering a state-of-the-art panorama on the issue. Drawing from computational linguistics, semantics, pragmatics and discourse analysis, the contributions to this special issue will illuminate how the study of meaning in its different forms may provide valuable insights for the study of people’s argumentative practices in different contexts, ranging from the political to the private sphere. This introductory discussion tackles specific aspects of the intricate relationship between pragmatic inference and argumentative inference – that is, between meaning and argumentation –, provides a brief survey of existing interfaces between the study of meaning and that of argumentation, and concludes with a presentation of the contributions to this special issue.
Compositionality Meets Belief Revision: a Bayesian Model of Modification

Abstract The principle of compositionality claims that the content of a complex concept is determined by its constituent concepts and the way in which they are composed. However, for prototype concepts this principle is often too rigid. Blurring the division between conceptual composition and belief update has therefore been suggested (Hampton and Jönsson 2012). Inspired by this idea, we develop a normative account of how belief revision and meaning composition should interact in modifications such as “red apple” or “pet hamster”. We do this by combining the well-known selective modification model (Smith et al. Cognitive science 12(4):485–527 1988) with the rules of Bayesian belief update. Moreover, we relate this model to systems of defeasible reasoning as discussed in the field of artificial intelligence.
Training of Artificial Neural Network Using New Initialization Approach of Particle Swarm Optimization for Data Classification

Artificial neural network (ANN) has a wide variety of practice for the solution of problems in the area of data classification. Back propagation algorithm is a famous neural network (NN) traditional training approach. Hence, this classical training technique has many drawbacks like stuck in the local minima and maximum number of iterations required. Particle Swam Optimization (PSO) has been widely applied for the solutions of data classification problems. Population initialization is a vital factor in PSO algorithm, which considerably influences the diversity and convergence during the PSO's process. In this paper, the training of the ANN has been implemented with new initialization technique by using low discrepancies sequence, Torus termed as TO-PSO. In this paper, a detailed comparative performance analysis for the training of neural network is observed on nine benchmark data sets taken from UCI repository. The Results demonstrate that training of ANN with proposed initialization technique offer efficient and best substitute to traditional training approaches of the NN, which gives the solution of problems related to the data classification. Furthermore, the performance of TO-PSO has been compared with back propagation algorithm (BPA), standard PSO-NN and two other initialization approaches Sobol based PSO (SO-PSONN) and Halton based PSO (H-PSONN) for the training of ANN. The experimental results show that the proposed approach outperforms than BPA, traditional PSONN, SO-PSONN and H-PSONN in terms of converging speed and better accuracy Moreover, the outcomes of our work present a foresight that how the proposed initialization technique can be used as an efficient alternative to standard training approaches for the data classification problems.
Character Level Neural Architectures for Boosting Named Entity Recognition In Code Mixed Tweets

Named Entity Recognition (NER) is a highly researched topic in the field of Natural Language Processing and is an important step towards information extraction. The rampantly growing popularity of Online Social Networks (OSNs) has led to an exponential increase in the amount of raw text comments online that provides a significantly fertile ground to analyze data and procure certain insights. However, most of the state-of-the-art models existing at present which have achieved near human performance, are primarily targeted at or cater to the mining and analysis of resource rich monolingual text data. Code-mixing or multilingualism in OSNs results in significantly unstructured text which poses a serious problem for social media analytics by hindering algorithms from learning to generalize on the unstructured data. With the motive of tackling this problem and boosting the performance of NER on code mixed text, this paper proposes the usage of word embeddings and specifically character level recurrent neural networks to take into account the context in which words are used. This approach facilitates effective segmentation and classification of text into entities like person, location or organization for text data composed of Hindi-English Code mixed tweets. Our extensive experimentation indicates an improvement over the minimal existing baseline state-of-art approaches for extracting named entities.
Ambient Intelligence for Smart Home using The Internet of Things

This article presents an ambience intelligence application for smart home systems for efficient use of electricity, enhance comfort zone, independence of living and security. The system integrates smart home occupant's identification, sensors-actuators deployment, a gateway Hub, machine learning, and cloud computing components to realize the objectives of smart living. A Mobile phone MAC-address based occupant identification and machine learning algorithms are proposed to address the multiple smart home occupant problems and an instant user control versus rule-based control conflicts. Results show that machine learning algorithm could learn the ambience preferences of multiple occupants when trained on a large enough dataset. The proposed system can implement ambient intelligence applications in smart home.
Color edge preserving image colorization with a coupled natural vectorial total variation

In this paper, a novel colorization model based on the natural vectorial total variation TVJ is proposed. TVJ supports a common edge direction for all channels, it leads to a better preservation of color edges. We give the existence of the minimizer of the proposed model and use the primal–dual algorithm to numerically solve this model. Experiment results both on structure images and texture images show that the proposed model can preserve the contours well and reduce color bleeding effects at edges compared with the known methods.
The Study of the Integration State in the Education System in the Conditions of Open Educational Space

The relevance of the study of the integration process in the education system and the need to prepare future teachers for its implementation is due to the increasing integration processes in the society of digital technologies, multitasking professional activity, expanding opportunities for human potential growth as the main resource of positive social change. The study was conducted on the basis of “Trans-Baikal State University” in April–June 2019. A total of 229 full-time and part-time students were interviewed. The article analyzes the results of a sociological survey future teachers, aimed at identifying the nature and state of the integration process and the willingness of students to integrate in their teaching activities. The results of the study revealed a contradiction between the need for integration in education in general and the insufficient level of implementation of joint activities of teachers to organize integrated classes at school. The authors offer strategic guidelines in the training of future teachers, taking into account the specifics of the integration processes in the open educational space: professional cooperation between students and teachers of different profiles in order to solve professional problems based on humanistic values and integrate various fields of knowledge; the inclusion of internal and external educational integration into various disciplines and special courses, in all types of practices, as well as extracurricular work; organization of integrated educational events and reflexive analysis of their implementation; development of modern didactic tools on the problem of integration, taking into account the profile of students.
TaxAA: a Reliable Tax Auditor Assistant for Exploring Suspicious Transactions

We present TaxAA, a reliable tax auditor assistant that helps tax auditors explore suspicious transactions and get reliable evidence. We construct a Tax Audit Network(TAN) and use extended algorithms based on the semi-supervised Graph Convolutional Network(GCN) to build detection models for calculating taxpayers' suspicion score, we choose Hierarchical Graph Convolutional Network(H-GCN) as our basic model according to the experimental results. Then the visual analytic system allows tax auditors to customize suspicious indicators to observe the suspicious relationships among taxpayers by the "wheel" chart. Meanwhile, it can provide detailed transactions and individual information for reference. We have evaluated the assistant based on the tax data of a province, our detection model can achieve high accuracy and the visual analytic system can provide useful guidance for tax auditors.
High-dimensional angular two-photon interference and angular qudit states

Using angular position–orbital angular momentum entangled photons, we propose an experiment to generate maximally entangled states in D -dimensional quantum systems, the so called qudits, by exploiting correlations of parametric down-converted photons. Angular diffraction masks containing N angular slits in the arms of each twin photon define a qudit space of dimension N 2 , spanned by the alternative pathways of the photons. Numerical results for N angular slits with N = 2, 4, 5, 10 are reported. We discuss relevant experimental parameters for an experimental implementation of the proposed scheme using Spatial Light Modulators (SLMs), and twin-photons produced by Spontaneous Parametric Down Conversion (SPDC). The entanglement of the qudit state can be quantified in terms of the Concurrence, which can be expressed in terms of the visibility of the interference fringes, or by using Entanglement Witnesses. These results provide an additional means for preparing entangled quantum states in high-dimensions, a fundamental resource for quantum simulation and quantum information protocols.
A method for solving scriptio continua in Javanese manuscript transliteration

Many Javanese manuscripts in Indonesia are stored in museums and libraries. Most of these manuscripts were written using local scripts that are rarely used in everyday life, and hence a software application that can help and improve the reading of these manuscripts is valuable. An essential step in automatic manuscript image transliteration is post-processing, which involves editing and concatenating syllables into words. The main problem of post-processing is that there exists no symbol for space between words in a sentence, which is called the scriptio-continua problem. This paper proposes methods based on the backtracking algorithm to solve the scriptio continua in the post-processing step of Javanese manuscript image transliteration. The proposed methods use a depth-first search in seeking relevant candidate words to determine whether to merge a new syllable or not. The results of the proposed methods to concatenate 17,687 syllables from the Hamong Tani book using a dictionary containing 49,801 words are found to be satisfactory in terms of computation and accuracy. The accuracy of the implemented greedy and brute-force methods is both 81.64%. However, the greedy-based method is more efficient and has a better performance than the brute-force method.
Guaranteed Collision Avoidance in Multivehicle Cooperative Missions Using Speed Adjustment

This paper presents a speed-adjustment method for avoiding obstacles during multivehicle missions. This method enables multiple autonomous agents to cooperatively avoid collision against popup moving obstacles while at the same time guaranteeing intervehicle safety. The collision-avoidance algorithm assumes knowledge of a nominal trajectory for the obstacle and takes into account bounded deviations in the speed profile. Observing such deviations, the algorithm varies the pace of the mission accordingly, guaranteeing collision avoidance. A rigorous mathematical analysis based on Lyapunov stability theory is performed to provide performance bounds and safety guarantees of the algorithm. Finally, simulation and experimental results are presented to validate the efficacy of the method in realistic multivehicle mission scenarios.
Experiencing the Sheffield Team Software Project: A project-based learning approach to teaching Agile

Graduates of computer science and software engineering degrees are often expected by employers to possess various technical skills as well as competencies in project management, testing, teamwork, and other soft skills. Extant literature has identified that these competencies are often not addressed by traditional teaching approaches such as lectures and labs. In this paper, we present a project-based learning approach to teaching agile software development where students work in multicultural teams to develop software for clients. This approach to teaching software development addresses some of the competencies required by employers, and the feedback from students, clients, and tutors are discussed and analysed critically.
Collision Avoidance for Multiple Micro Aerial Vehicles using Fast Centralized Nonlinear Model Predictive Control

This article proposes a novel control architecture using a centralized nonlinear model predictive control (CNMPC) scheme for controlling multiple micro aerial vehicles (MAVs). The control architecture uses an augmented state system to control multiple agents and performs both obstacle and collision avoidance. The optimization algorithm used is OpEn, based on the proximal averaged Newton type method for optimal control (PANOC) which provides fast convergence for non-convex optimization problems. The objective is to perform position reference tracking for each individual agent, while nonlinear constraints guarantee collision avoidance and smooth control signals. To produce a trajectory that satisfies all constraints a penalty method is applied to the nonlinear constraints. The efficacy of this proposed novel control scheme is successfully demonstrated through simulation results and comparisons, in terms of computation time and constraint violations, which are provided with respect to the number of agents.
Probabilistic Linear Discriminant Analysis Based on <i>L</i> <sub>1</sub>-Norm and Its Bayesian Variational Inference

Probabilistic linear discriminant analysis (PLDA) is a very effective feature extraction approach and has obtained extensive and successful applications in supervised learning tasks. It employs the squared L2 -norm to measure the model errors, which assumes a Gaussian noise distribution implicitly. However, the noise in real-life applications may not follow a Gaussian distribution. Particularly, the squared L2 -norm could extremely exaggerate data outliers. To address this issue, this article proposes a robust PLDA model under the assumption of a Laplacian noise distribution, called L1-PLDA. The learning process employs the approach by expressing the Laplacian density function as a superposition of an infinite number of Gaussian distributions via introducing a new latent variable and then adopts the variational expectation-maximization (EM) algorithm to learn parameters. The most significant advantage of the new model is that the introduced latent variable can be used to detect data outliers. The experiments on several public databases show the superiority of the proposed L1-PLDA model in terms of classification and outlier detection.
The Method of Modelling the Mechanism of Random Access Memory of System for Natural Language Processing

It is considered the method of modelling the mechanism of memory, which functionally provides a solution to a number of the tasks of the NLP system. It is proposed to apply the formal concepts, the ensemble of images and the focus of attention for constructing a new method. It is defined the principles of modelling the mechanism of image exchange in the ensemble.
End-to-end Spatiotemporal Attention Model for Autonomous Driving

In recent years, end-to-end autonomous driving has become an emerging research direction in the field of autonomous driving. This method attempts to map the road images collected by the vehicle camera to the decision control of the vehicle. We propose a spatiotemporal neural network model with a visual attention mechanism to predict vehicle decision control in an end-to-end manner. The model is composed of CNN and LSTM and can extract temporal and spatial features from road image sequences. The visual attention mechanism in the model helps the model to focus on important areas in the image. We evaluated the model in the open racing car simulator TORCS, and the experiments showed that our model is better at predicting driving decisions than the simple CNN model. In addition, the visual attention mechanism in the model is conducive to improving the performance of the end-to-end autonomous driving model.
System to Fight Counterfeit Drugs

The issue of counterfeit drugs is prevalent worldwide. Developing countries are particularly affected by this problem. The financial loss incurred due to counterfeit drugs is in billions. The main reason for this distribution of counterfeit drugs is the current fragmented supply chain system in pharmaceutical industry. Drugs change hands from several entities of the supply chain like manufacturer, stockist, retailer, etc. It becomes difficult to keep track of the drug in supply chain at any given point of time. In this paper, we review all the technologies that can be used and all the literature available and work done on this issue. In our proposed solution, we discuss how using a distributed ledger we can add nodes to the blockchain network and monitor all the transactions on the blockchain, thus introducing traceability, security, and transparency.
Comparative analysis of the denoising effect of unstructured vs. convolutional autoencoders

Adding noise to data affects the prediction of a discriminator. Some like a deep neural network extract features directly from inputs, where the quality of the features may be affected by the amount of the noise. A deep neural model specifically meant for feature extraction is the autoencoder, while it has also been extended to perform denoising. In this paper, we investigate the denoising effect of an encoder on different nature as well as different amounts of additive noise. The experiments are evaluated on a linearized autoencoder as well as a convolutional autoencoder, which is especially meant for image data. Denoising Autoencoders (DAE) and Convolutional Denoising Autoencoders (CDAE) are evaluated by introducing with Gaussian, Salt and Pepper, and Poisson types of noise with a factor of 0.5. The results shows 0.12, 0.09, 0.47 Mean Squared Error (MSE) for DAE and 0.13, 0.10 and 0.9 MSE in case of CDAE with the same amount of noise factor added, alluding to the insight that a lack of focus on structure in the model may help it focus more on the denoising task.
A Survey Of Different Workflow Scheduling Algorithms In Cloud Computing

Cloud computing is a suitable platform to execute large applications with huge amount of resources to provide a variety of services to the users. Workflow scheduling plays a major role in cloud computing for the optimal resource utilization. It mainly focuses on the effective application of resources and to reduce the resource allocation time. Hence, workflow scheduling is the essential part in Cloud Computing. There are various scheduling algorithms considering parameters such as Throughput, Energy Consumption, Makespan, Resource Utilization, Cost, Computational Time, Bandwidth and Resource Availability. A number of scheduling algorithms are present in cloud computing but the examination of those algorithms are less likely to find. In order to get a clear idea on various scheduling algorithms, the proposed paper gives a survey on various scheduling algorithms. The proposed survey tries to shed light on the comparison of scheduling algorithms present in cloud computing.
A Real-Time Super Multiview Rendering Pipeline for Wide Viewing-Angle and High-Resolution 3D Displays Based on a Hybrid Rendering Technique

Super multiview (SMV) 3D displays with a directional diffuser can provide dense viewpoints, wide viewing angles and high image quality. However, traditional SMV 3D rendering pipelines have many disadvantages, such as high time costs, low image quality, incorrect lighting effects and limited viewing angles. To overcome the abovementioned drawbacks, we propose a new SMV 3D rendering pipeline based on a hybrid rendering technique (HRT) that effectively combines and improves the Turing multiview rendering technique, the multiple view plus depth (MVD) technique and the deferred shading technique, introducing additional normal information, diffuseness information and shininess information into the SMV 3D rendering pipeline. The SMV HRT pipeline can generate SMV 3D images with accurate lighting in real time when the viewpoint number of an SMV 3D display is greater than 50, the viewing angle is greater than 100 degrees, the resolution of a single viewpoint image is more than 512*512 and the resolution of the LCD panel is 7680*4320; in particular, complex scenes can be generated in real time. The proposed HRT pipeline is the only viable real-time rendering pipeline with accurate lighting effects for super multiview 3D displays with wide viewing angle and high resolution that runs on a common PC with standard graphics hardware. An 8k SMV 3D display with an 80° viewing angle and 100 viewpoints is applied to demonstrate the validity of the HRT method in experiments. When the number of reference views is four, the experimental results show that the rendering frame rate is more than 35 fps on average for complex virtual scenes, and the peak signal-to-noise ratio (PSNR) value of the HRT is greater than 36 dB, illustrating its high image quality and accurate lighting. Compared with state-of-the-art MVD methods, HRT processes more lighting information and is more suitable for virtual scene rendering. Therefore, the HRT method has advantages regarding the image quality of the reconstructed 3D image and can easily achieve real-time rendering for SMV 3D displays with wide viewing angles.
Cross-Fertilisation Between Human-Computer Interaction and Artificial Intelligence

Human-Computer Interaction (HCI) and Artificial Intelligence (AI) are two disciplines that followed parallel trajectories for about four decades. They also both complement each other and overlap in various problem-rich domains. This chapter is far from being exhaustive, but provides a representative story of how HCI and AI cross-fertilise each other since their inception. It reviews the following domains: intelligent user interfaces and more specifically conversational animated affective agents; capitalisation, formulation and use of ergonomic knowledge for the design and evaluation of interactive systems; synergy between visualisation and data mining.