Reviewerly/exHarmony huggingface dataset:
Rational Interpretation of Numerical Quantity in Argumentative Contexts

Numerical descriptions furnish us with an apparently precise and objective way of summarising complex datasets. In practice, the issue is less clear-cut, partly because the use of numerical expressions in natural language invites inferences that go beyond their mathematical meaning, and consequently quantitative descriptions can be true but misleading. This raises important practical questions for the hearer: how should they interpret a quantitative description that is being used to further a particular argumentative agenda, and to what extent should they treat it as a good argument for a particular conclusion? In this paper, we discuss this issue with reference to notions of argumentative strength, and consider the strategy that a rational hearer should adopt in interpreting quantitative information that is being used argumentatively by the speaker. We exemplify this with reference to United Kingdom universities’ reporting of their REF 2014 evaluations. We argue that this reporting is typical of argumentative discourse involving quantitative information in two important respects. Firstly, a hearer must take into account the speaker’s agenda in order not to be misled by the information provided; but secondly, the speaker’s choice of utterance is typically suboptimal in its argumentative strength, and this creates a considerable challenge for accurate interpretation.
Machine learning algorithms for smart data analysis in the Internet of things: an overview

Machine learning (ML) techniques will benefit immensely from the avalanche of data readily available from various (IoT) applications considered as the major contributor of new data for future intelligent network. Based on this new concept, network systems will further magnify their capacity to exploit variety of experimental data across a plethora of network devices, study the data information, obtain knowledge and make informed decisions based on the dataset at their disposal. Smart IoT data analysis are performed utilizing supervised learning, unsupervised learning and reinforced learning. This study is limited to supervised and unsupervised ML techniques. In other to achieve the set objectives, reviews and discussions of substantial issues related to supervised or unsupervised machine learning techniques were executed, highlighting the advantages and limitations of each algorithm as well presenting the recent research trends and recommendations for future study.
Special C&amp;G and G&amp;VC session at EuroVis

The authors of a selection of visualization-related articles from Computers & Graphics and Graphics and Visual Computing will present their work in a special at this years EuroVis conference. The journals plan to turn this session into a recurring event to give the authors of such pure journal articles a better visibility of their work.
Explainable Artificial Intelligence (XAI) towards Model Personality in NLP task

In recent years, the development of Deep Learning in the field of Natural Language Processing, especially in sentiment analysis, has achieved significant progress and success. It is because of the availability of large amounts of text data and the ability of deep learning techniques to produce sophisticated predictive results from various data features. However, the sophisticated predictions that are not accompanied by sufficient information on what is happening in the model will be a major setback. Therefore, the significant development of the Deep Learning model must be accompanied by the development of the XAI method, which helps provide information about what drives the model to get predictable results. Simple Bidirectional LSTM and complex Bi-GRU-LSTM-CNN model for Sentiment Analysis were proposed in the present research. Both models were analyzed further using three different XAI methods (LIME, SHAP, and Anchor) in which they were used and compared to two proposed models, proving that XAI is not limited to giving information about what happens in the model but can also help us to understand and distinguish models’ personality and behaviour.
Difference Value Network for Image Super-Resolution

Recently, improved performance has been achieved in image super-resolution (SR) by using deep convolutional neural networks (CNNs). However, most existing networks neglect the feature correlations of adjacent layers, causing features at different levels to not be fully utilized. In this paper, a novel difference value network (DVN) is proposed to address this problem. The proposed network makes full use of different levels of features by using the difference values (D-values) of adjacent layers. Specifically, a difference value block (DVB) is designed to extract the difference values of adjacent layers. The extracted difference value can highlight which regions should be paid more attention to, so as to guide image SR. Further, a difference value group (DVG) is designed to integrate the difference values extracted by the difference value block into its output. In this way, the DVG can provide additional structure prior for image SR. Finally, to make our network more stable, a multipath supervised reconstruction block is proposed to supervise the reconstruction process. The experimental results on five benchmark datasets show that the proposed network can achieve better reconstruction results than the compared SR methods.
Reinforcement and Imitation Learning Applied to Autonomous Aerial Robot Control

In robotics, the ultimate goal of reinforcement learning is to endow robots with the ability to learn, improve, adapt, and reproduce tasks with dynamically changing constraints based on exploration and autonomous learning. Reinforcement Learning (RL) aims at addressing this problem by enabling a robot to learn behaviors through trial-and-error. With RL, a Neural Network can be trained as a function approximator to directly map states to actuator commands making any predefined control structure not-needed for training. However, the knowledge required to converge these methods is usually built from scratch. Learning may take a long time, not to mention that RL algorithms need a stated reward function. Sometimes, it is not trivial to define one. Often it is easier for a teacher, human or intelligent agent, do demonstrate the desired behavior or how to accomplish a given task. Humans and other animals have a natural ability to learn skills from observation, often from merely seeing these skills’ effects: without direct knowledge of the underlying actions. The same principle exists in Imitation Learning, a practical approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator. In this scenario, this work’s primary objective is to design an agent that can successfully imitate a prior acquired control policy using Imitation Learning. The chosen algorithm is GAIL since we consider that it is the proper algorithm to tackle this problem by utilizing expert (state, action) trajectories. As reference expert trajectories, we implement state-of-the-art on and off-policy methods PPO and SAC. Results show that the learned policies for all three methods can solve the task of low-level control of a quadrotor and that all can account for generalization on the original tasks.
Upsampling of 16-Channel LiDAR Depth Data using Weighted Median Filter

Three-dimensional (3D) LiDAR is widely used because it can accurately measure 360° 3D depth. One of the most widely used sensors to this end is VLP-16, a 16-channel LiDAR manufactured by Velodyne. This 16-channel LiDAR provides dense 3D depth data in the horizontal direction. However, in the vertical direction, it provides only sparse depth data owing to the LiDAR architecture. We propose an upsampling method to obtain a dense 3D depth map from a 16-channel LiDAR by applying an edge-aware filter to the LiDAR data. A camera- LiDAR sensor system composed of a 16-channel LiDAR and six vision cameras is used for this purpose. First, the LiDAR depth data are projected onto the camera image coordinates to generate a 2.5D depth map. Next, bilinear interpolation and a weighted median filter are applied to upsample the original depth map. Compared with the original LiDAR data, we can generate an extremely dense depth map by using the proposed method. The results of quantitative analysis show that the proposed method can dramatically increase the number of 3D depth points and reduce the depth reconstruction error.
WS-RCNN: Learning to Score Proposals for Weakly Supervised Instance Segmentation

Weakly supervised instance segmentation (WSIS) provides a promising way to address instance segmentation in the absence of sufficient labeled data for training. Previous attempts on WSIS usually follow a proposal-based paradigm, critical to which is the proposal scoring strategy. These works mostly rely on certain heuristic strategies for proposal scoring, which largely hampers the sustainable advances concerning WSIS. Towards this end, this paper introduces a novel framework for weakly supervised instance segmentation, called Weakly Supervised R-CNN (WS-RCNN). The basic idea is to deploy a deep network to learn to score proposals, under the special setting of weak supervision. To tackle the key issue of acquiring proposal-level pseudo labels for model training, we propose a so-called Attention-Guided Pseudo Labeling (AGPL) strategy, which leverages the local maximal (peaks) in image-level attention maps and the spatial relationship among peaks and proposals to infer pseudo labels. We also suggest a novel training loss, called Entropic OpenSet Loss, to handle background proposals more effectively so as to further improve the robustness. Comprehensive experiments on two standard benchmarking datasets demonstrate that the proposed WS-RCNN can outperform the state-of-the-art by a large margin, with an improvement of 11.6% on PASCAL VOC 2012 and 10.7% on MS COCO 2014 in terms of mAP50, which indicates that learning-based proposal scoring and the proposed WS-RCNN framework might be a promising way towards WSIS.
Problems of education in the post-industrial era

Considering the problems of education in the post-industrial era, the authors present the key concepts and make an attempt to describe the post-industrial reality as a volatile and complex system. Тhe authors discuss new approaches in technical and corporate education. A model of the development of «post-industrial education» is proposed, which helps to understand the main trends and challenges that await the education system in the future. The problems of «post-visual reality» and distance education are discussed, technical and organizational methods of its implementation are considered. In accordance with the concept of «life-long learning», conclusions are drawn about the changing roles of students and teachers.
The Impact of Control Point Dispersion on Measurement Accuracy in a New Type of Light-Pen Coordinate Measuring System

Light-pen coordinate measuring machines are widely used due to their portability; however, the measurement accuracy is affected by camera calibration, light-pen probe calibration, control points, etc.To study the influence of control point dispersion on measurement accuracy, a light-pen coordinate measuring system is constructed in this paper.In this system, a new type of control point distribution is designed.Based on this distribution, five light-pens with different control point dispersions are created and an algorithm is proposed to match control points with their images.The experimental repeatability results for the five light-pen probes indicate satisfactory repeatability, which ensures the effectiveness of the measurement results.The experimental measurement results for five light-pens demonstrate that the measurement error decreases with the decline of the control point dispersion at first, but begins to increase when the control point dispersion reaches a certain value.INDEX TERMS Light-pen coordinate measuring system, light-pen
Development of a Drawing Application for Communication Support during Endoscopic Surgery and Its Evaluation Using Steering Law

The purpose of this study is to construct a hands-free endoscopic surgical communication support system that can draw lines in space corresponding to head movements using AR technology and evaluate the applicability of the drawing motion by the head movement to the steering law, one of the HCI models, for the potential use during endoscopic surgery. In the experiment, the participants manipulated the cursor by using head movements through the pathway and movement time (MT); the number of errors and subjective evaluation of the difficulty of the task was obtained. The results showed that the head-movement-based line drawing manipulation was significantly affected by the tracking direction and by the task difficulty, shown as the Index of Difficulty (ID). There was high linearity between ID and MT, with a coefficient of determination R2 of 0.9991. The Index of Performance was higher in the horizontal and vertical directions compared to diagonal directions. Although the weight and biocompatibility of the AR glasses must be overcome to make the current prototype a viable tool for supporting communication in the operating room environment, the prototype has the potential to promote the development of a computer-supported collaborative work environment for endoscopic surgery purposes.
Exploiting augmented intelligence in the modeling of safety-critical autonomous systems

Abstract Machine learning (ML) is used increasingly in safety-critical systems to provide more complex autonomy to make the system to do decisions by itself in uncertain environments. Using ML to learn system features is fundamentally different from manually implementing them in conventional components written in source code. In this paper, we make a first step towards exploring the architecture modeling of safety-critical autonomous systems which are composed of conventional components and ML components, based on natural language requirements. Firstly, augmented intelligence for restricted natural language requirement modeling is proposed. In that, several AI technologies such as natural language processing and clustering are used to recommend candidate terms to the glossary, as well as machine learning is used to predict the category of requirements. The glossary including data dictionary and domain glossary and the category of requirements will be used in the restricted natural language requirement specification method RNLReq, which is equipped with a set of restriction rules and templates to structure and restrict the way how users document requirements. Secondly, automatic generation of SysML architecture models from the RNLReq requirement specifications is presented. Thirdly, the prototype tool is implemented based on Papyrus. Finally, it presents the evaluation of the proposed approach using an industrial autonomous guidance, navigation and control case study.
Decentralised Artificial Intelligence Enabled Blockchain Network Model

Blockchain and Artificial intelligence are novel technologies which take prominent place across all industries. AI refers to intelligent tasks that are carried out by machines which in-turn are done by humans in olden days. Blockchain a collection of decentralised networks which revolutionizes and upgrades the business operations. Blockchain creates a decentralised environment that shares data which is in encrypted form between ledgers in a confidential way without the involvement of any third party. Blockchain and Artificial Intelligence has been shaping their paths with a slight overlap of their own. Combined decentralised AI networks enables the businesses to take decisions without the need of centralised control activity. Block chain consists of nodes and it follows distributed ledger technology; Proof of Work (PoW) consensus algorithm used in blockchain makes use of lot of computing power and energy for the miners to get reward. To overcome this difficulty, this paper proposes AI enabled Miner Node Selection Algorithm in Block chain Networks based on PoW consensus algorithm. The paper presents a miner node selection with PoW in blockchain which reduce data storage in blockchain by data pruning technique. By executing this algorithm in blockchain network, unbiased blockchain implementation will be easier. The integration of AI with the implementation of blockchain enhances the efficiency of blockchain network by reducing the computational power, energy and time spent in selection of the node which is evident in our experimental results.
The use of virtual reality in education

Recent technological developments in the field of teaching have brought significant changes in the daily practice of teaching. Due to the great development of computer resources, we must adapt the old and traditional teaching methods to the new technological conditions existing in our social environment. One of the latest developments in the field of education are the virtual reality techniques, which will change the course of our educational systems in few years. This transformation is currently present in the technologically advanced countries.
Leveraging Linguistic Coordination in Reranking N-Best Candidates For End-to-End Response Selection Using BERT

Retrieval-based dialogue systems select the best response from many candidates. Although many state-of-the-art models have shown promising performance in dialogue response selection tasks, there is still quite a gap between R@1 and R@10 performance. To address this, we propose to leverage linguistic coordination (a phenomenon that individuals tend to develop similar linguistic behaviors in conversation) to rerank the N-best candidates produced by BERT, a state-of-the-art pre-trained language model. Our results show an improvement in R@1 compared to BERT baselines, demonstrating the utility of repairing machine-generated outputs by leveraging a linguistic theory.
Neural Networks for Learning Counterfactual G-Invariances from Single Environments

Despite -- or maybe because of -- their astonishing capacity to fit data, neural networks are believed to have difficulties extrapolating beyond training data distribution. This work shows that, for extrapolations based on finite transformation groups, a model's inability to extrapolate is unrelated to its capacity. Rather, the shortcoming is inherited from a learning hypothesis: Examples not explicitly observed with infinitely many training examples have underspecified outcomes in the learner's model. In order to endow neural networks with the ability to extrapolate over group transformations, we introduce a learning framework counterfactually-guided by the learning hypothesis that any group invariance to (known) transformation groups is mandatory even without evidence, unless the learner deems it inconsistent with the training data. Unlike existing invariance-driven methods for (counterfactual) extrapolations, this framework allows extrapolations from a single environment. Finally, we introduce sequence and image extrapolation tasks that validate our framework and showcase the shortcomings of traditional approaches.
Topological error correction with a Gaussian cluster state

Topological error correction provides an effective method to correct errors in quantum computation. It allows quantum computation to be implemented with a higher error threshold and high tolerating loss rates. We present a topological error correction scheme with continuous variables based on an eight-partite Gaussian cluster state. We show that topological quantum correlation between two modes can be protected against a single quadrature phase displacement error occurring on any mode and some of two errors occurring on two modes. More interestingly, some cases of errors occurring on three modes can also be recognized and corrected, which is different from the topological error correction with discrete variables. We show that the final error rate after correction can be further reduced if the modes are subjected to identical errors occurring on all modes with equal probability. The presented results provide a feasible scheme for topological error correction with continuous variables and it can be experimentally demonstrated with a Gaussian cluster state.
A Multi-View Fusion Method via Tensor Learning and Gradient Descent for Image Features

In many computer vision applications, one image can be represented by multiple heterogeneous features from different views, most of them commonly locate in high-dimensional space. These features can reflect different characteristics of one same object, they contain compatible and complementary information among each other. How to construct an uniform low-dimensional embedding features which represent useful information of multi-view features is still an important and urgent issue to be solved. Therefore, we propose a multi-view fusion method via tensor learning and gradient descent (MvF-TG) in this paper. MvF-TG reconstructs a lowdimensional mapping subspace of each object by utilizing its k nearest neighbors, which preserves the underlying neighborhood structure of the original local manifold. The new method can effectively exploit the spatial correlation information from the multi-view features by tensor learning. Furthermore, the method constructs a gradient descent optimization model to generate the better unified low dimensional embedding. The proposed method is compared with several single-view and multi-view dimensional reduction methods in these indicators of P, R, MAP and F-measure. In the retrieval experiments, the P values of the newmethod respectively are 86.80%, 52.00%, 68.56% and 78.80% on datasets of Corel1k, Corel5k, Corel10k and Holidays. In the classification experiments, the mean accuracies of it respectively are 47.94% and 87.58% on datasets of Caltech101 and Coil. These values are higher than those obtained by other comparison methods, various evaluations based on the applications of image classification and retrieval demonstrates the effectiveness of our proposed method on multi-view feature fusion dimension reduction.
Impact of Organizational Culture on Effectiveness with the Mediating Role of Innovativeness and Knowledge Management: Evidence from Pakistan

This study examines the mediating effect played by knowledge management and innovation between organizational culture and organization effectiveness. We modified W. Zheng et al. (2010) model, to propose a framework depicting how different facets of organizational culture affects its performance. We also include a mediatory role of innovativeness and knowledge management in culture and effectiveness relationships. Measures assessing organizational culture were adapted from Denison (1990) that encompassed four functional dimensions: adaptability, consistency, involvement, and mission. Knowledge management encompasses its infrastructure as well as its acquisition, Conversion, application, and protection processes. To establish the validity of this framework, we conducted a survey of 179 respondents working in the corporate sectors of Pakistan by means of questionnaires. The data was analyzed using Structural Equation Modeling and Confirmatory Factor Analysis. The results suggested that there exists a strong relationship among these variables and culture has a profound impact on the overall organization, whereas the culture of knowledge sharing and innovation can improve the effectiveness of the organization. It can be concluded that Knowledge management and innovation fully mediates the relationship between organizational culture and its effectiveness.