Reviewerly/exHarmony huggingface dataset:
Comparing Two Webcam-Based Eye Gaze Trackers for Users with Severe Speech and Motor Impairment

This paper aims to develop and evaluate two different webcam-based gaze-controlled interfaces for users with severe speech and motor impairment (SSMI). We configured two webcam-based gaze trackers using open-source software (Python and JavaScript) and developed cursor control algorithm using the gaze tracker. We designed a quiz application to evaluate the webcam-based gaze trackers for both users with SSMI and their able-bodied counterparts. We also collected data using a commercial infrared-based eye gaze tracker. We noted that users with SSMI and able- bodied users could use the webcam-based gaze-controlled interface. It was found that for users with SSMI, speed of interaction was significantly faster for a low-cost infrared-based commercial gaze. Results from this study can be used to develop as well as select low-cost eye gaze trackers for users with SSMI. This might be the first study to evaluate webcam-based gaze trackers in a gaze-controlled interface for users with different range of abilities
Reinforcement Learning-based Control Allocation for the Innovative Control Effectors Aircraft

Established Control Allocation (CA) methods rely on knowledge of the control effectiveness for distributing control effector utilization for control of (overactuated) systems. The Innovative Control Effectors (ICE) aircraft model is highly overactuated with its 13 control effectors, CA is a preferred method to distribute control effector utilization. In this paper it is envisioned to use Reinforcement Learning (RL) for distributing control effector utilization, which requires no knowledge of the control effectiveness. RL allows to pursue more abstract and timescale separated objectives. The ICE aircraft’s altitude, considering only longitudinal motion, is controlled by distributing the control effector utilization using RL, from an initial offset, while pursuing secondary objectives such as decreasing effector utilization and thrust.
Exploring Aerospace Design in Virtual Reality with Dimension Reduction

One of the today’s most propitious immersive technologies is virtual reality (VR). This term is colloquially associated with head-sets that transport users to a bespoke, built-for-purpose immersive 3D virtual environment. It has given rise to the field of immersive visual analytics—a new field of research that aims to use immersive technologies for enhancing and empowering data analytics. In this paper we present a VR aerospace design environment with the objective of aiding the component aerodynamic design process by interactively visualizing performance and geometry. This virtual environment uses ideas from parameter-space dimension reduction to enhance the exploration and exploitation of the design space. We decompose the design of such an environment into function structures, present an implementation of the system, and verify the interface in terms of usability and expressiveness.
A diachronic perspective on formality in students’ writing: empirical findings from the UK

One of the missions of language education internationally is to enable students to use language in a context-appropriate manner. Against this backdrop, students’ ability to linguistically encode formality emerges as an important issue. Using extracts of writing produced by 16-year-old students in 2004 and 2014, this study sought to investigate whether the level of formality in students’ writing changed over time. The 858 extracts of writing sampled were composed as part of a high-stakes examination in English Language. The linguistic analysis carried out focused on a number of features serving either as discriminators between spoken and written discourse (e.g. lexical diversity, lexical sophistication), or as markers of informal electronic communication (e.g. abbreviations, omitted stops, non-capitalised sentences). A statistical comparison of the prevalence of these features in the 2004 and 2014 corpora indicated that students’ writing underwent informalisation. This trend was found to be stronger among lower attaining students.
A Computational, Cognitive, and Narratological Approach to Narrative Generation

This chapter surveys and discusses interdisciplinary approaches to primarily artificial intelligence (AI)-based computational narrative or story generation systems by way of introducing cognitive science, and narratology and related literary theories. The first part of this chapter provides a general description (from the perspective of the research framework of the author) and the second part presents processes, theories, designs, and implementations of narrative generation by the author. In particular, the first part includes an overview of narratology and the relevant literary theories, computational and cognitive theories and techniques related to narratology and narrative generation, and narrative generation systems. The second part presents, in relative detail, components that constitute a systematic study for narrative generation by the author and an integrated narrative generation system of all of the previous attempts.
The Inflected Construction in the dialects of Sicily

Sicilian dialects display an instance of verbal Pseudo-Coordination (V1[TAM.Agr] a V2[TAM.Agr]), here referred to as the Inflected Construction or IC (cf. Cardinaletti and Giusti 2001, 2003), that can occur in very different configurations. Aim of this paper is to discuss the following parameters of microvariation, by providing new data from recent fieldwork: (i) the restriction of the IC to some V2s; (ii) the possibility for the IC to occur across moods and tenses and to display complete paradigms for person features; (iii) the reduction of go as V1, and of other possible V1s, to a prefix-like invariable form. In the paper, three major types of IC are identified: Type 1 IC is only possible in some persons of the indicative present and in the 2sg of the imperative; Type 2 IC features the extension of the paradigm to other simple tenses of the indicative; Type 3 IC displays a full-fledged paradigm in the indicative (3 simple tenses), subjunctive and imperative.
Index Matrices as a Decision-Making Tool for Job Appointment

The paper explores the process of decision making, related to the appointment of the human factor in an incomplete information environment. We propose for the first time a new approach to optimization of the process of appointment and reappointment, based on partial knowledge about the values of evaluation criteria of the human resources over time, using the apparatuses of index matrices and of intuitionistic fuzzy sets. In the paper, the 3-dimensional optimal appointment problem is formulated and an algorithm for its optimal solution is proposed, where the evaluations of candidates against criteria formulated by several experts in a certain time (or location), are intuitionistic fuzzy pairs. The proposed algorithm for the solution takes into account the ratings of the experts and the weighting coefficients of the assessment criterion according to its priority for the respective position.
Model-based System Health Management and Contingency Planning for Autonomous UAS

Safe autonomous operations of an Unmanned Aerial System (UAS) requires that the UAS can react to unforeseen circumstances, for example, after a failure has occurred. In this paper we describe a model-based run-time architecture for autonomous on-board diagnosis, system health management, and contingency management. This architecture is being instantiated on top of NASA's Core Flight System (cFS/cFE) as amajor component of the on-board AutonomousOperating System (AOS). We will describe our diagnosis and monitoring components, which continuously provide system health status. Automated reasoning with constraint satisfaction form the core of our decision-making component, which assesses the current situation, aids in failure disambiguation, and constructs a contingency plan to mitigate the failure(s) and allow for a safe end of the mission. We will illustrate our contingency management system with two case studies, one for a fixed-wing aircraft in simulation, and one for an autonomous DJI S1000+ octo-copter.
DeepXplore

Over the past few years, Deep Learning (DL) has made tremendous progress, achieving or surpassing human-level performance for a diverse set of tasks, including image classification, speech recognition, and playing games like Go. These advances have led to widespread adoption and deployment of DL in security- and safety-critical systems, such as selfdriving cars, malware detection, and aircraft collision avoidance systems.
A measurement framework for software product maturity assessment

Abstract The need to ensure the quality of software is growing in importance on a daily basis due to the growing role of software in critical products and application areas, such as defense, aerospace, aviation, and medicine. To meet this need, many organizations use the Capability Maturity Model Integration process model to assess and improve software development processes. This paper proposes a framework for measuring software product maturity as an indicator of product quality. The proposed framework consists of two parts: a reference model and an assessment method. The reference model provides a platform for gathering product quality indicators as evidence of product capability, which reflects the product's maturity. The quality indicators are then used to assess the product maturity level. The assessment method utilizes standard steps for assessing product maturity that are reflected in the degree of the product's conformance with the relevant quality attributes defined and agreed upon by the product's stakeholders. The proposed framework enables measuring the quality of the product from the developers' and the users' perspective. The proposed maturity model and the assessment method can help software organizations and software clients ensure that software products meet the appropriate quality levels.
Agile Development of Physical Products—A Case Study of Medical Device Product Development

Agile development gets more and more relevant in non-IT development processes. Main reasons are the flexibility and the shorter reaction times. The different conditions of physical products compared to software products are current challenges for the use of agile product development methods. This project focuses especially on a medical product development by using scrum to develop agile. After each sprint, the scrum process was adapted to the respective circumstances. Additionally, the team participated a survey, which focuses on collaboration and team behaviour aspects. The survey is based on the team climate inventory. Results of the research were amongst others a 3D-printed microtiter plate, best practices for scrum in medical device development and the survey. Based on the implementation of agile methods in this project and the survey, a recommendation model of an agile medical device development is derived.
Energy Efficiency in Cloud Computing

Cloud computing is one of the recent emerging technologies that provides services to consumers in a pay as you go model.Cloud computing offers ITC based services over the internet and the use of virtualization allows it to provide computing resources.Data Centers are the core of cloud computing, which consists of: networked servers, cables, power sources, etc. which host the running applications and store Business information.High performance has always been the most critical concern in cloud data centers, which comes at the cost of energy consumption.The vital challenge is balancing between system performance and power consumption by reducing energy consumption without prejudicial impact on the performance and quality of services delivered.There are many techniques and algorithms proposed to achieve efficient energy utilization in cloud computing, these techniques include: VM Migration, Consolidation and Resources orchestration in cloud computing.This paper provides a survey of approaches and techniques for energy efficiency in cloud computing.
Using knowledge anchors to facilitate user exploration of data graphs

This paper investigates how to facilitate users' exploration through data graphs. The prime focus is on knowledge utility, i.e. increasing a user's domain knowledge while exploring a data graph, which is crucial in the vast number of user-facing semantic web applications where the users are not exp erts in the domain. We introduce a highly unique exploration support mechanism underpinned by the subsumption theory for meaningful learning. A core algorithmic component for operationalising the subsumption theory for meaningful learning is the automatic identification of knowledge anchors in a data graph (KADG). We present several metrics for identifying KADG which are evaluated against familiar concepts in human cognitive structures. The second key component is a subsumption algorithm that utilises KADG for generating exploration paths for knowledge expansion. The implementation of the algorithm is applied in the context of a Semantic data browser in a music domain. The resultant exploration paths are evaluated in a task-driven experimental user study compared to free data graph exploration. The findings show that exploration paths, based on subsumption and using knowledge anchors, lead to significantly higher increase in the users' conceptual knowledge and better usability than free exploration of data graphs. The work opens a new avenue in semantic data exploration which investigates the link between learning and knowledge exploration. We provide the first framework that adopts educational theories to inform data graph exploration for knowledge expansion which extends the value of exploration and enables broader applications of data graphs in systems where the end users are not experts in the specific domain.
3D Face Recognition Based on Volumetric Representation of Range Image

In this paper, a 3D face recognition system has been developed based on the volumetric representation of 3D range image. The main approach to build this system is to calculate volume on some distinct region of 3D range face data. The system has mainly three steps. In the very first step, seven significant facial landmarks are identified on the face. Secondly, six distinct triangular regions A to F are created on the face using any three individual landmarks where nose tip is common to all regions. Further 3D volumes of all the respective triangular regions have been calculated based on plane fitting on the input range images. Finally, KNN and SVM classifiers are considered for classification. Initially, the classification and recognition are carried out on the different volumetric region, and a further combination of all the regions is considered. The proposed approach is tested on three useful challenging databases, namely Frav3D, Bosphorous, and GavabDB.
Evaluating the quality of word representation models for unstructured clinical Text based ICU mortality prediction

In modern hospitals, the role of Clinical Decision Support Systems (CDSS) in assisting care providers is well-established. Most conventional CDSS systems are built on the availability of patient data in the form of structured Electronic Health Records. However, a significant percentage of patient data is still stored in the form of unstructured clinical text notes, especially in developing countries. These contain abundant patient-specific information, which has so far remained largely under-utilized in powering CDSS applications. In this paper, we attempt to build one such CDSS system for patient mortality prediction, using unstructured clinical records. Effectiveness of such prediction models largely depends on optimally capturing latent concept features, thus, word representation quality is of utmost importance. We experiment with three popular word embedding models - Word2Vec, FastText and GloVe for generating word embeddings of unstructured nursing notes of patients from a standard, open dataset, MIMIC-III. These word representations are used as features to train machine learning classifiers to build ICU mortality prediction models, a critical CDSS in ICUs of hospitals. Experimental validation showed that a model built on Word2Vec Skipgram based Random Forest classifier was the most optimal word embedding based mortality prediction model, that outperformed traditional severity scores like SAPS-II, SOFA, APS-III and OASIS, by a significant margin of 43-52%.
AWX: An Integrated Approach to Hierarchical-Multilabel Classification

The recent outbreak of works on artificial neural networks (ANNs) has reshaped the machine learning scenario. Despite the vast literature, there is still a lack of methods able to tackle the hierarchical multilabel classification (HMC) task exploiting entirely ANNs. Here we propose AWX, a novel approach that aims to fill this gap. AWX is a versatile component that can be used as output layer of any ANN, whenever a fixed structured output is required, as in the case of HMC. AWX exploits the prior knowledge on the output domain embedding the hierarchical structure directly in the network topology. The information flows from the leaf terms to the inner ones allowing a jointly optimization of the predictions. Different options to combine the signals received from the leaves are proposed and discussed. Moreover, we propose a generalization of the true path rule to the continuous domain and we demonstrate that AWX’s predictions are guaranteed to be consistent with respect to it. Finally, the proposed method is evaluated on 10 benchmark datasets and shows a significant increase in the performance over plain ANN, HMC-LMLP, and the state-of-the-art method CLUS-HMC. Code related to this paper is available at: https://github.com/lucamasera/AWX .
Radar maneuvering target tracking algorithm based on human cognition mechanism

Radar Maneuvering Targets Tracking (RMTT) in clutter is a quite challenging issue due to the errors in the models and the varying dynamics of the processes. Modern radar tracking system calls for the adaptive signal and data processing algorithm urgently to adapt the uncertainty of the environment. The mechanism of human cognition can help persons cope with the similar difficulties in visual tracking. Inspired by human cognition mechanism, a comprehensive method for RMTT is proposed. In the method, the model transition probability in Interacting Multiple Model (IMM) and the validation gate can be adjusted dynamically with target maneuver; the waveform in radar transmitter can vary with the perception of the environment. Experimental results in cluttered scenes show that the proposed algorithm is more accurate for perceiving the environment and targets, and the waveform selection algorithm is better than that with fixed waveform.
Weakly sensitive analysis for JavaScript object‐manipulating programs

Summary While JavaScript programs have become pervasive in web applications, they remain hard to reason about. In this context, most static analyses for JavaScript programs require precise call graph information, since the presence of large numbers of spurious callees significantly deteriorates precision. One of the most challenging JavaScript features that complicate the inference of precise static call graph information is read/write accesses to object fields, the names of which are computed at runtime. JavaScript framework libraries often exploit this facility to build objects from other objects, as a way to simulate sophisticated high‐level programming constructions. Such code patterns are difficult to analyze precisely, due to weak updates and limitations of unrolling techniques. In this paper, we observe that precise field origination relations can be inferred by locally reasoning about object copies, both regarding to the object and to the program structure, and we propose an abstraction that allows to separately reason about field read/write access patterns working on different fields and to carefully handle the sets of JavaScript object fields. We formalize and implement an analysis based on this technique. We evaluate the performance and precision of the analysis on the computation of call graph information for examples from jQuery tutorials.
Eureka: Edge-Based Discovery of Training Data for Machine Learning

The generation of high-quality training data has become the key bottleneck in the use of deep learning across many domains. In this paper, we describe Eureka, an interactive system that leverages edge computing and early discard to greatly improve the productivity of experts in the construction of a labeled dataset. Our experimental results show that Eureka reduces the labeling effort needed to construct a training set by two orders of magnitude relative to a brute-force approach.
Feature-oriented contract composition

A software product line comprises a set of products that share a common code base, but vary in specific characteristics called features. Ideally, features of a product line are developed in isolation and composed subsequently. Product lines are increasingly used for safety–critical software, for which quality assurance becomes indispensable. While the verification of product lines gained considerable interest in research over the last decade, the subject of how to specify product lines is only covered rudimentarily. A challenge to overcome is composition; similar to inheritance in object-oriented programming, features of a product line may refine other features along with their specifications. To investigate how refinement and composition of specifications can be established, we derive a notion of feature-oriented contracts comprising preconditions, postconditions, and framing conditions of a method. We discuss six mechanisms to perform contract composition between original and refining contracts. Moreover, we identify and discuss desired properties for contract composition and evaluate which properties are established by which mechanism. Our three main insights are that (a) contract refinement is seldom but crucial, (b) the Liskov principle does not apply to features, and (c) it is sufficient to accommodate techniques from object-orientation in the contract-composition mechanisms for handling frame refinements.
A Unifying Computational Framework for Teaching and Active Learning

Traditionally, learning has been modeled as passively obtaining information or actively exploring the environment. Recent research has introduced models of learning from teachers that involve reasoning about why they have selected particular evidence. We introduce a computational framework that takes a critical step toward unifying active learning and teaching by recognizing that meta-reasoning underlying reasoning about others can be applied to reasoning about oneself. The resulting Self-Teaching model captures much of the behavior of information-gain-based active learning with elements of hypothesis-testing-based active learning and can thus be considered as a formalization of active learning within the broader teaching framework. We present simulation experiments that characterize the behavior of the model within three simple and well-investigated learning problems. We conclude by discussing such theory-of-mind-based learning in the context of core cognition and cognitive development.