Reviewerly/exHarmony huggingface dataset:
Connecting Discourse and Domain Models in Discourse Analysis through Ontological Proxies

Argumentation-oriented discourse analysis usually focuses on what is being said and how, following the text under analysis quite literally, and paying little attention to the things in the world to which the text refers. However, to perform argumentation-oriented discourse analysis, one must assume certain conceptualisations by the speaker in order to interpret and reconstruct propositions and argumentation structures. These conceptualisations are rarely captured as a product of the analysis process. In this paper, we argue that considering the ontology to which a discourse refers as well as the text itself provides a richer and more useful representation of the discourse and its argumentation structures, facilitates intertextual analysis, and improves understandability of the analysis products. To this end, we propose the notion of ontological proxies, i.e., conceptual artefacts that connect elements in the argumentation structure to the associated ontology elements.
Sub-aperture phase error stitching for full aperture airborne SAL data processing method based on azimuth deramp

Synthetic Aperture Ladar (SAL) is a combination of synthetic aperture and lidar. At present, sub-aperture stitching with PGA algorithm is used to realize SAL imaging. However, this algorithm has a low rate of data utilization, resulting in a worse image SNR and resolution. For solving this problem, a method of sub-aperture phase error stitching for full aperture airborne SAL data processing based on azimuth deramp is proposed in this paper. The data is divided into a sequence of sub-apertures and the standard Standard Minimum Entropy Autofocus (MEA) is used to estimate the phase error of each sub-aperture. Then all the phase errors of the sub-apertures are stitched to obtain the full-aperture phase error. After the compensation of the full-aperture phase error, the full-aperture image is obtained by deramp. Compared with the SAL image obtained by sub-aperture algorithm, the method we proposed can offer better resolution and SNR. Finally, the imaging results of airborne SAL real data verify the effectiveness of the proposed algorithm.
Quantum Ciphertext Dimension Reduction Scheme for Homomorphic Encrypted Data

At present, in the face of the huge and complex data in cloud computing, the parallel computing ability of quantum computing is particularly important. Quantum principal component analysis algorithm is used as a method of quantum state tomography. We perform feature extraction on the eigenvalue matrix of the density matrix after feature decomposition to achieve dimensionality reduction, proposed quantum principal component extraction algorithm (QPCE). Compared with the classic algorithm, this algorithm achieves an exponential speedup under certain conditions. The specific realization of the quantum circuit is given. And considering the limited computing power of the client, we propose a quantum homomorphic ciphertext dimension reduction scheme (QHEDR), the client can encrypt the quantum data and upload it to the cloud for computing. And through the quantum homomorphic encryption scheme to ensure security. After the calculation is completed, the client updates the key locally and decrypts the ciphertext result. We have implemented a quantum ciphertext dimensionality reduction scheme implemented in the quantum cloud, which does not require interaction and ensures safety. In addition, we have carried out experimental verification on the QPCE algorithm on IBM's real computing platform. Experimental results show that the algorithm can perform ciphertext dimension reduction safely and effectively.
Impact of Personal Factors on Software Quality

Software development is a people-oriented process. Applications designed for the solution of human problems are developed by people. While examining the quality of the software product, ignoring the individual characteristics of individuals will lead to incomplete research. This paper is a road map for the study we are conducting. We are aiming to do a comprehensive examination based on the factors examined in human factor researches in the literature. In our research, the quality metrics of the software projects to be carried out by students will be analyzed. Personal characteristics of the participants will be determined, and inferences about how much they affect the application they developed will be made.
Advancing Autonomy in Aviation: A Holistic Approach

Safely implementing increased automation and autonomy in aviation systems offers the promise of improved operational safety, increased air mobility, and economic benefits for millions of people across the globe. However, many technical approaches to autonomy do not follow a holistic approach, taking into account the challenges associated with terminology, certification, fundamental design principles, and current human-centric regulations. Solving challenges in one or some of these areas will not be enough to move aviation into a high level of automation towards future autonomy. For example, even if the technology is available, the regulator must have evidence to know it is safe, and the flying public expects the regulator to certify the aircraft before entrusting their safety to it. Thus, as aviation moves into the age of autonomy, it is essential to have consensus standards that understand the need for a holistic approach to support the safe and effective use of autonomous aircraft. To meet these challenges, in 2017 ASTM International established Task Group AC377 for “Autonomy Design and Operations in Aviation.” The AC377 Task Group has the responsibility for harmonizing terminology, certification requirements constructs, and fundamental autonomy concepts among ASTM aviation committees that produce consensus standards for aviation - F37 for Light Sport Aircraft, F38 for Unmanned Aircraft Systems, F39 for Aircraft Systems, and F44 for General Aviation - with the goal of promoting consistency and transferability in standards relating to aviation autonomy. ASTM AC377 uses a holistic approach to develop guidance materials in the form of technical reports. This paper will discuss the progress to date in addressing these challenges and recommend next steps to close remaining gaps.
WITHDRAWN: Fast RNS elliptic curve point multiplication on FPGAs

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Vyasa Sai, Anand Paul and Ramachandran Varatharajan, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal’s standard workflow.
Multi-scale Attention Consistency for Multi-label Image Classification

Human has well demonstrated its cognitive consistency over image transformations such as flipping and scaling. In order to learn from human’s visual perception consistency, researchers find out that convolutional neural network’s capacity of discernment can be further elevated via forcing the network to concentrate on certain area in the picture in accordance with the human natural visual perception. Attention heatmap, as a supplementary tool to reveal the essential region that the network chooses to focus on, has been developed and widely adopted by CNNs. Based on this regime of visual consistency, we propose a novel end-to-end trainable CNN architecture with multi-scale attention consistency. Specifically, our model takes an original picture and its flipped counterpart as inputs, and then send them into a single standard Resnet with additional attention-enhanced modules to generate a semantically strong attention heatmap. We also compute the distance between multi-scale attention heatmaps of these two pictures and take it as an additional loss to help the network achieve better performance. Our network shows superiority on the multi-label classification task and attains compelling results on the WIDER Attribute Dataset.
Reinforcement Learning with Converging Goal Space and Binary Reward Function

Usage of a sparse and binary reward function remains one of the most challenging problems in reinforcement learning. In particular, when the environments wherein robotic agents learn are sufficiently vast, it is much more difficult to learn tasks because the probability of reaching the goal is minimal. A Hindsight Experience Replay algorithm was proposed to overcome these difficulties; however, problems persist that affect the learning speed and delay learning when a learning agent cannot receive proper rewards at the beginning of the learning process. In this paper, we present a simple method called Converging Goal Space and Binary Reward Function, which helps agents learn tasks easily and efficiently in large environments while providing a binary reward. At an early stage in training, a larger goal space margin facilitates the reward function for a more rapid policy learning. As the number of successes increases, the goal space is gradually reduced to the size used to the size used in the test. We apply this reward function to two different task experiments: sliding and throwing, which must be explored at a wider range than the reach of the robotic arms, and then compare the learning efficiency to that of experiments that only employ a sparse and binary reward function. We show that the proposed reward function performs better in large environments using physics simulation, and we demonstrate that the function is applicable to real world robotic arms.
An implementation of a multivariate discretization for supervised learning using Forestdisc

Discretization is a key pre-processing step in Machine Learning that transforms continuous attributes into discrete ones, through different methods available in the literature. In this regard, this work provides the ForestDisc framework that discretizes data based on a supervised, multivariate and hybrid approach. It uses, at first, a splitting process relying on a tree learning ensemble to generate a large set of cut points. It then uses a merging process based on moment matching optimization, to transform this set into a reduced and representative one. ForestDisc is a non-parametric discretizer in the sense that it does not require the user to introduce any initial setting parameters. We implemented ForestDisc algorithm in the "ForestDisc" R package.
A survey of tag clouds as tools for information retrieval and content representation

Tag clouds are tools that have been widely used on the Internet since their conception. The main applications of these textual visualizations are information retrieval, content representation and browsing of the original text from which the tags are generated. Despite the extensive use of tag clouds, their enormous popularity and the amount of research related to different aspects of them, few studies have summarized their most important features when they work as tools for information retrieval and content representation. In this paper we present a summary of the main characteristics of tag clouds found in the literature, such as their different functions, designs and negative aspects. We also present a summary of the most popular metrics used to capture the structural properties of a tag cloud generated from the query results, as well as other measures for evaluating the goodness of the tag cloud when it works as a tool for content representation. The different methods for tagging and the semantic association processes in tag clouds are also considered. Finally we give a list of alternative for visual interfaces, which makes this study a useful first help for researchers who want to study the content representation and information retrieval interfaces in greater depth.
Handwritten Text Recognition Using Reconstructed Pen Trace with Medial Representation

This article is dedicated to the reduction of the offline handwritten character recognition task (using a handwriting image as input) to the online recognition task (using a pen trace as input). A method of the pen trace reconstruction from handwritten text image using its medial representation (a skeleton) is proposed. Experimental comparison of symbols recognition quality using the image, the true pen trace and the reconstructed pen trace is carried out.
Investigating Learning in Deep Neural Networks Using Layer-Wise Weight Change

Understanding the learning dynamics of deep neural networks is of significant interest to the research community as it can provide insights into the black box nature of neural nets. In this work, we conduct a study which analyzes layer-wise learning trends by measuring the relative change in the weights of a Deep Neural Net during training. Through our controlled yet exhaustive set of experiments we were able to identify key trends which could lead to better understanding of how neural networks learn and make way for better training regimes. In our work we explore the learning trends in ubiquitous convolutional neural networks and datasets. Our work provides a simple yet novel approach to interpreting neural networks and is different from previous investigative studies.
How Users Reciprocate to Alexa

With the launch of Siri, a conversational assistant presented by Apple in 2011, voice-enabled personal assistants have since been accessible to the masses (Hoy 2018). As speech is the main channel for communication between humans (Flanagan 1972; Schafer 1995) and is considered to be an innate human behavior (Pinker 1994), interacting with a voice interface is intuitive (Cohen, Giangola and Balogh 2004). Studies conducted under the Computers Are Social Actors (CASA) paradigm indicate that speech-output and interactivity are two main factors to elicit social reactions in users (Nass et al. 1993). Users adopt human principles like reciprocity and team affiliation when interacting with computers (Nass and Moon 2000). As voice assistants are able to send social cues we assumed that subjects will show social reactions towards an Amazon Echo. Focussing on the social norm of reciprocity, we measured if people provide more help to the assistant after being told that they are interdependent of each other when compared to being independent. A laboratory experiment with 120 participants was conducted. Participants played an interactive game using an Amazon Echo. Team affiliation was manipulated by telling one group their game performance would be rated individually while telling the interdependent group that they are being assessed on their joint performance with the assistant. To operationalize reciprocity, we opted for a behavioral measurement to assess participants’ willingness to help the assistant: Participants were asked to name potential cities in which the game could take place. Participants were able to name any number of cities, assuming a relationship between the number of cities named and the level of cooperativeness towards the assistant. Results show a significant main effect of interdependence on the evaluation of Alexa as well as the number of cities named meaning participants did show reciprocal behavior towards the voice assistant.
DCT: An Scalable Multi-Objective Module Clustering Tool

Maintaining complex software systems is a time-consuming and challenging task. Practitioners must have a general understanding of the system's decomposition and how the system's developers have implemented the software features (probably cutting across different modules). Re-engineering practices are imperative to tackle these challenges. Previous research has shown the benefits of using software module clustering (SMC) to aid developers during re-engineering tasks (e.g., revealing the architecture of the systems, identifying how the concerns are spread among the modules of the systems, recommending refactorings, and so on). Nonetheless, although the literature on software module clustering has substantially evolved in the last 20 years, there are just a few tools publicly available. Still, these available tools do not scale to large scenarios, in particular, when optimizing multi-objectives. In this paper we present the Draco Clustering Tool (DCT), a new software module clustering tool. DCT design decisions make multi-objective software clusterization feasible, even for software systems comprising up to 1,000 modules. We report an empirical study that compares DCT with other available multi-objective tool (HD-NSGA-II), and both DCT and HD-NSGA-II with mono-objective tools (BUNCH and HD-LNS). We evidence that DCT solves the scalability issue when clustering medium size projects in a multi-objective mode. In a more extreme case, DCT was able to cluster Druid (an analytics data store) 221 times faster than HD-NSGA-II.
Pseudo-gradient algorithms for forecasting and filtering cylindrical and circular images

Currently, most of the image processing works deal with images defined on rectangular two-dimensional grids or grids of higher dimension. In some practical situations, images are set on a cylinder. For example, images of a section of a pipeline, a blood vessel, parts during turning, etc. The peculiarity of the domain for specifying such images requires its consideration in their models and processing algorithms. In the present work, autoregressive models of cylindrical images are considered, expressions of the correlation function depending on the autoregression parameters are given. To represent heterogeneous images with random heterogeneities, «doubly stochastic» models in which one or more images control the parameters of the resulting image are used. Note that the spiral scan of a cylindrical image can be considered as a quasiperiodic process due to the correlation of image lines, for example, voiced speech sounds and musical tones. The scans of two or more correlated cylindrical images can serve as a model of a system of correlated quasiperiodic processes. Pseudo-gradient algorithms for filtering and predicting images and processes and for identifying their models are proposed. The conducted statistical modeling showed that these algorithms are close in efficiency to the optimal algorithms constructed with known values of the parameters of the processed data.
Effects of the slit width on the subsidiary maxima in multiple-slit diffraction: Analytical solutions

We present analytical solutions for the subsidiary maxima in multiple-slit diffraction, considering a nonzero slit width. To find the angular position of the subsidiary and principal maxima, the intensity distribution function is differentiated with respect to the phase associated with the angular position. To solve the resulted transcendental equation analytically, the derivative and phase are expanded in the order of a∕d (a: slit width; d: slit separation), and a set of equations is established by setting each term of the derivative as zero. Solving the equations for the coefficients in the phase, we analytically obtain the phase for the subsidiary maxima as a function of the slit number and a∕d up to the twelfth order in a∕d.
Beta-Binomial stick-breaking non-parametric prior

A new class of nonparametric prior distributions, termed Beta-Binomial stick-breaking process, is proposed. By allowing the underlying length random variables to be dependent through a Beta marginals Markov chain, an appealing discrete random probability measure arises. The chain’s dependence parameter controls the ordering of the stick-breaking weights, and thus tunes the model’s label-switching ability. Also, by tuning this parameter, the resulting class contains the Dirichlet process and the Geometric process priors as particular cases, which is of interest for MCMC implementations. Some properties of the model are discussed and a density estimation algorithm is proposed and tested with simulated datasets.
Data collection of 3D spatial features of gestures from static peruvian sign language alphabet for sign language recognition

Peruvian Sign Language Recognition (PSL) is approached as a classification problem. Previous work has employed 2D features from the position of hands to tackle this problem. In this paper, we propose a method to construct a dataset consisting of 3D spatial positions of static gestures from the PSL alphabet, using the HTC Vive device and a well-known technique to extract 21 keypoints from the hand to obtain a feature vector. A dataset of 35,400 instances of gestures for PSL was constructed and a novel way to extract data was stated. To validate the appropriateness of this dataset, a comparison of four baselines classifiers in the Peruvian Sign Language Recognition (PSLR) task was stated, achieving 99.32% in the average in terms of F1 measure in the best case.
Coronavirus containment depends on human rights: freedom of expression and press are needed to fight pandemic

Purpose The purpose of this paper is to describe restrictions on freedoms of expression and press that have arisen during the coronavirus pandemic and to show the public health impact of these restrictions. Design/methodology/approach General PubMed and Google searches were used to review human rights violations both historically and during the current coronavirus pandemic. Special attention was paid to publications produced by groups dedicated to monitoring human rights abuses. Findings During the coronavirus pandemic, many governments have used the guise of controlling the virus to silence critics and stifle the press. Though these restrictions were supposedly orchestrated to fight the virus, they have done just the opposite: suppression of expression and press has hindered public health efforts and exacerbated the spread of the virus. By reducing case reporting, allowing for the spread of misinformation and blocking productive debate, violations of human rights to free expression and press have worsened the coronavirus outbreak. Originality/value This study shows the ways in which human rights are both threatened and particularly important in crises.
Traffic Intersection Re-Identification Using Monocular Camera Sensors

Perception of road structures especially the traffic intersections by visual sensors is an essential task for automated driving. However, compared with intersection detection or visual place recognition, intersection re-identification (intersection re-ID) strongly affects driving behavior decisions with given routes, yet has long been neglected by researchers. This paper strives to explore intersection re-ID by a monocular camera sensor. We propose a Hybrid Double-Level re-identification approach which exploits two branches of Deep Convolutional Neural Network to accomplish multi-task including classification of intersection and its fine attributes, and global localization in topological maps. Furthermore, we propose a mixed loss training for the network to learn the similarity of two intersection images. As no public datasets are available for the intersection re-ID task, based on the work of RobotCar, we propose a new dataset with carefully-labeled intersection attributes, which is called “RobotCar Intersection” and covers more than 30,000 images of eight intersections in different seasons and day time. Additionally, we provide another dataset, called “Campus Intersection” consisting of panoramic images of eight intersections in a university campus to verify our updating strategy of topology map. Experimental results demonstrate that our proposed approach can achieve promising results in re-ID of both coarse road intersections and its global pose, and is well suited for updating and completion of topological maps.