Reviewerly/exHarmony huggingface dataset:
EvoLogic: Intelligent Tutoring System to Teach Logic

This article presents the cognitive model of the EvoLogic Intelligent Tutoring System, developed to assist in the teaching-learning process of Natural Deduction in Propositional Logic. EvoLogic consists of 3 agents, among which, the Pedagogical agent (treated here as the student model) and the Specialist agent (based on a Genetic Algorithm) compose the cognitive model. This cognitive model allows an efficient model tracing mechanism to be developed, which will follow each student's step during the theorem proof. The purpose of the article, in addition to presenting the EvoLogic, is to analyze the efficiency of the ITS in a known exercise that has already been studied in the literature (applied to 57 students). The results show that the EvoLogic obtained all the solutions presented by the students, allowing it to follow the student's steps, providing real-time feedback, based on the steps that the students are taking (in real time), known as model tracing.
Towards a gaze-contingent reading assistance for children with difficulties in reading

Reading is a key skill in learning, working and participating in society on all levels. However, in 2018, 20% of German school students possessed only insufficient levels of reading proficiency. Frustration associated with these difficulties results in avoidance of reading, such that struggling readers will often not overcome them on their own. We present a first approach towards an assistance system that recognizes reading difficulties by analyzing the user's gaze behavior and offers dynamic adaptation of the text presentation. In a formative study, including 34 fifth-grade students, letter- and syllabication-based assistance significantly and substantially increased children's motivation to read. Based on these findings, gaze-contingent assistance presents a promising approach in improving struggling readers’ reading experience and motivation to read.
Android App based Bluetooth controlled Low-cost Cloth Folding Machine

This paper describes an electromechanical device that can fold various types of clothes like shirts, pants, towels, suits, etc. The most noticeably awful piece of clothing is folding all the garments when they come out after washing and dryer. People simply dump their clothing into the storeroom without arranging them. This conduct regularly leaves a wreck and gives inconvenience when peoples are discovering garments. To dispose of this complication we are presenting an easy and economical cloth folding machine. In this system actuators control the motion of folding. The angle of rotation controlled through the program of microcontroller commanded from an app. It is predicted that by utilizing this machine cloth folding time can be reduced to a great extent compared to the traditional method.
Automated Recording and Semantics-Aware Replaying of High-Speed Eye Tracking and Interaction Data to Support Cognitive Studies of Software Engineering Tasks

The paper introduces a fundamental technological problem with collecting high-speed eye tracking data while studying software engineering tasks in an integrated development environment. The use of eye trackers is quickly becoming an important means to study software developers and how they comprehend source code and locate bugs. High quality eye trackers can record upwards of 120 to 300 gaze points per second. However, it is not possible to map each of these points to a line and column position in a source code file (in the presence of scrolling and file switching) in real time at data rates over 60 gaze points per second without data loss. Unfortunately, higher data rates are more desirable as they allow for finer granularity and more accurate study analyses. To alleviate this technological problem, a novel method for eye tracking data collection is presented. Instead of performing gaze analysis in real time, all telemetry (keystrokes, mouse movements, and eye tracker output) data during a study is recorded as it happens. Sessions are then replayed at a much slower speed allowing for ample time to map gaze point positions to the appropriate file, line, and column to perform additional analysis. A description of the method and corresponding tool, Déjà Vu, is presented. An evaluation of the method and tool is conducted using three different eye trackers running at four different speeds (60Hz, 120Hz, 150Hz, and 300 Hz). This timing evaluation is performed in Visual Studio and Eclipse IDEs. Results show that Déjà Vu can playback 100% of the data recordings, correctly mapping the gaze to corresponding elements, making it a well-founded and suitable post processing step for future eye tracking studies in software engineering.
Biogeochemical response of Pinus brutia and Olea europaea to lithological variations and Cu mineralisation in Cyprus

Regional geochemical mapping programs are predominantly based on regolith materials as the sampling media. The use of plant organs has been more limited, despite potential advantages demonstrated in more localised studies that have examined biogeochemical responses to various types of mineralisation or contamination, or sought to identify species with phytoremediation potential. This study investigated the effects of variations in lithology and mining wastes on the multielement chemistry of Pinus brutia (pine) needles and O. europaea (olive) leaves, obtained from low-density traverses across Cyprus and detailed sampling around two Cyprus-style VHMS Cu-deposits. A total of 476 samples were analysed by a combination of inductively-coupled plasma mass spectrometry (ICP-MS) and portable X-ray fluorescence spectrometry (pXRF). At the regional scale there are substantial differences in the composition of the pine needles, especially for Ni, Ba, Sr, Mn and Zn, between the major lithological groups that range from the ultramafic and mafic units of the Troodos Ophiolite to the mineralised pillow basalts and various marine carbonate units. Both pine needles and olive leaves display intense Re anomalies in areas with mine wastes and consistently low concentrations in background areas. The response for Cu and other macro- or micronutrient elements to contamination is less pronounced and may reflect barriers to uptake by the plants. The relative magnitude of variations between the different lithologies in median plant metal concentrations is equivalent to that displayed in soils of the Geochemical Atlas of Cyprus (e.g. REE, Zn and Sr), but for some elements the patterns differ substantially. There is close correlation between the ICP-MS and pXRF data for elements where most values are above pXRF detection limits. The study further demonstrates the potential of plants in regional geochemical mapping for a range of applications, in combination with or as an alternative to conventional regolith media.
Optimization of Single-Axis Discrete Solar Tracking

An energy analysis of a single-axis discrete solar PV tracking system is presented. The motion is performed from East to West with an axis of rotation parallel to the surface. Several orientations per day are studied, and optimal configurations are found for discrete tracking systems. The angular motion range is subdivided in discrete angles, and the irradiance is calculated for each angle by assuming a clear sky model. Moreover, according to the number of discrete positions, all the combinations are checked. The obtained results are based entirely on simulations, the energy performance of a solar array is maximized according to the discrete movements. With respect to fixed tilted panels, energy gains between 22.9% and 31.9% are obtained, which correspond to 71% and 97% respectively to the gain obtained with a continuos tracking.
Finding Idioms in Source Code Using Subtree Counting Techniques

This paper is dedicated to extracting idioms from source code written in Python language. Programming language idiom is the fragment of code which often occur in different programs. In this research and idiom is represented as the part of program abstract syntax tree (AST). For idiom extracting the subtree computing techniques are used. Idiom extracting process is similar to numeric function optimization: starting with root node, on each step we add one node to the subtree and compute subtree efficiency metric. When metric stops to grow, we consider subtree obtained the idiom. As subtree efficiency metric different functions can be used. These functions can have subtree length or subtree frequency as an arguments.
Artificial Intelligence – a new topic in Computer Science curriculum at primary and secondary schools: challenges, opportunities, tools and approaches

Within the context of the Industrial Revolution 4.0, the issue of Artificial Intelligence is becoming an important challenge throughout society. School plays an important role in preparing the young generation for emerging labor market changes. This paper presents opportunities for Computer Science curriculum innovations at primary and secondary schools that are currently being addressed in Slovakia. We present main ideas how to implement new available tools and approaches to teaching the topic of Artificial Intelligence. Based on the new educational materials we have developed, we have gained valuable experience from teacher training, pupil workshops as well as the pilot phase of testing directly in schools confirming that this topic can also be taught in an attractive and age-appropriate way.
Extended Wigner function for the harmonic oscillator in the phase space

New time dependent extended Wigner functions for the quantum harmonic oscillator have been obtained in this work. The Moyal equation for the harmonic oscillator has been associated with the wave equation of a 2D membrane in the phase plane. The values of the extended Wigner function are equal to the deviation values of the points on the surface of the membrane from the equilibrium state. The positive and negative values of the extended Wigner function correspond to the direction of the deviation from the equilibrium state. As an example, a time dependent extended Wigner function corresponding to the standing wave of quasi-probability density arising in the phase plane is considered.
Alternative Characterizations of Probabilistic Trace Equivalences on Coherent Resolutions of Nondeterminism

For nondeterministic and probabilistic processes, the validity of some desirable properties of probabilistic trace semantics depends both on the class of schedulers used to resolve nondeterminism and on the capability of suitably limiting the power of the considered schedulers. Inclusion of probabilistic bisimilarity, compositionality with respect to typical process operators, and backward compatibility with trace semantics over fully nondeterministic or fully probabilistic processes, can all be achieved by restricting to coherent resolutions of nondeterminism. Here we provide alternative characterizations of probabilistic trace post-equivalence and pre-equivalence in the case of coherent resolutions. The characterization of the former is based on fully coherent trace distributions, whereas the characterization of the latter relies on coherent weighted trace sets.
Environmental-Perception Modeling and Reference Architecture for Cyber Physical Systems

In cyber-physical systems, physical and software components are deeply intertwined, blurring the boundaries between the cyber and physical worlds.Perceiving environmental information is a prerequisite for a cyber-physical system to be reliable and adaptive to the environment.However, the intrinsically open and dynamic nature of the environment brings challenges to the systematic realization of environmentalperception.This article regards environmental-perception as a first-class entity with self-maintenance and abstracts it into three levels: data capture, context awareness, and situation identification.Then, a five-tier reference architecture is proposed, which not only explicitly defines the perception ability at different abstraction levels, but also provides for the storage and management of environmental information, thus facilitating information reuse and the adaptation ability of cyber-physical systems.We evaluate the reference architecture with a case study on a widely used smart room environment, demonstrating its broad applicability and the ability to reuse environmental information.We also performed a survey on the proposed reference architecture to reveal its industrial motivations and benefits.
Bias Busters: Robustifying DL-Based Lithographic Hotspot Detectors Against Backdooring Attacks

Deep learning (DL) offers potential improvements throughout the CAD tool-flow, one promising application being lithographic hotspot detection. However, DL techniques have been shown to be especially vulnerable to inference and training time adversarial attacks. Recent work has demonstrated that a small fraction of malicious physical designers can stealthily “backdoor” a DL-based hotspot detector during its training phase such that it accurately classifies regular layout clips but predicts hotspots containing a specially crafted trigger shape as nonhotspots. We propose a novel training data augmentation strategy as a powerful defense against such backdooring attacks. The defense works by eliminating the intentional biases introduced in the training data but does not require knowledge of which training samples are poisoned or the nature of the backdoor trigger. Our results show that the defense can drastically reduce the attack success rate from 84% to ~0%.
Erasing Appearance Preservation in Optimization-Based Smoothing

Optimization-based Image smoothing is routinely formulated as the game between a smoothing energy and an appearance preservation energy. Achieving adequate smoothing is a fundamental goal of these Image smoothing algorithms. We show that partially “erasing” the appearance preservation facilitate adequate Image smoothing. In this paper, we call this manipulation as Erasing Appearance Preservation (EAP). We conduct an user study, allowing users to indicate the “erasing” positions by drawing scribbles interactively, to verify the correctness and effectiveness of EAP. We observe the characteristics of human-indicated “erasing” positions, and then formulate a simple and effective 0-1 knapsack to automatically synthesize the “erasing” positions. We test our synthesized erasing positions in a majority of Image smoothing methods. Experimental results and large-scale perceptual human judgments show that the EAP solution tends to encourage the pattern separation or elimination capabilities of Image smoothing algorithms. We further study the performance of the EAP solution in many image decomposition problems to decompose textures, shadows, and the challenging specular reflections. We also present examinations of diversiform image manipulation applications like texture removal, retexturing, intrinsic decomposition, layer extraction, recoloring, material manipulation, etc. Due to the widespread applicability of Image smoothing, the EAP is also likely to be used in more image editing applications.
Face Detection Using an Implementation Running in a Web Browser

A face recognition system using machine learning has been developed. Node.js, Face-recognition.js, Dlib technology stacks were chosen to implement the system. The choice of means of realization of the client and server parts is carried out, the architecture of the application is defined. A desktop application has been created. An interface based on the HTML framework of the Twitter Bootstrap framework was developed and a comprehensive system testing was performed.
A Component-Based Evolution Model for Service-Based Software Architectures

Microservices architecture has emerged as an architectural style which focuses on the design and development of software systems as a set of small independent services. Although MSA is inspired by the Service-Oriented Architecture style (both are service-based architectures), it presents important differences. Likewise, software architecture must evolve as new architectural styles and software frameworks arise, and the evolution of the software architecture is considered as a central feature of any software system. In this way, this paper proposes an approach to evolve SOA to MSA architectures through the use of Sarch language. To accomplish this, the language was extended to allow the modeling of the two architectural styles from the component-and-connector view, the inclusion of a decomposition view for functional properties, and the use of a model-to-model transformation algorithm.
Investigating Effects and User Preferences of Extra- and Intradiegetic Virtual Reality Questionnaires

Virtual realities (VR) are becoming an integral part of product development across many industries, for example to assess aesthetics and usability of new features in the automotive industry. The recording of the evaluation is typically conducted by filling out questionnaires after the study participants left the virtual environment. In this paper, we investigate how questionnaires can be best embedded within the virtual environment and compare how VR-questionnaires differ from classical post-test evaluations regarding preference, presence, and questionnaire completion time.
A multi-scale context encoder for high quality restoration of facial images

In recent years, with the growth of data, storage and computing power many challenging problems can be solved. One of the most challenging and interesting tasks is to recover data in pixel level of image without using any additional information. Context encoder, a combination of auto-encoder and GAN, uses an unsupervised visual feature learning algorithm to predict missing pixel data in images. This paper presents a novel method to extract features in context encoder to restore high quality facial image. Instead of computing features from a single scale input image, the proposed method utilizes features from multiscale input images to create features in the encoder step. The features are then fed into the decoder to create image with the needed information after feature aggregation in different ways. The restored images after using decoder and the original ones are then provided to a discriminator to make the network creating a looked-real image. Experimental results on some public data sets such as CELEBA-HQ and LFW show that the proposed method with features in multi-scale achieves a promising result compared with the original one.
Assessing Deep Learning Models for Human-Robot Collaboration Collision Detection in Industrial Environments

The increasing adoption of industrial robots to boost production efficiency is turning human-robot collaborative scenarios much more frequent. In this context, technical factory workers need to be safe at all times from collisions and prepare for emergencies and potential accidents. Another trend in industrial automation is the usage of machine learning techniques - specifically, deep learning algorithms - for image classification. Following these tendencies, this work evaluates the application of deep learning models to detect physical collision in human-robot interactions. Security camera images are used as the primary information source for intelligent collision detection. Unlike other proposed approaches in the literature that apply sensors like Light Detection And Ranging (LIDAR), Laser Range Finder (LRF), or torque sensors from robots, this work does not consider extra sensors, using only 2D cameras. Results show more than 99% of accuracy in the evaluated scenarios, revealing that approaches adopting deep learning algorithms could be promising for human-robot collision avoidance in industrial scenarios. The proposed models may support safety in industrial environments and reduce the impact of collision accidents.
Autoscaling High-Throughput Workloads on Container Orchestrators

High-throughput computing (HTC) workloads seek to complete as many jobs as possible over a long period of time. Such workloads require efficient execution of many parallel jobs and can occupy a large number of resources for a long time. As a result, full utilization is the normal state of an HTC facility. The widespread use of container orchestrators eases the deployment of HTC frameworks across different platforms, which also provides an opportunity to scale up HTC workloads with almost infinite resources on the public cloud. However, the autoscaling mechanisms of container orchestrators are primarily designed to support latency-sensitive microservices, and result in unexpected behavior when presented with HTC workloads. In this paper, we design a feedback autoscaler, High Throughput Autoscaler (HTA), that leverages the unique characteristics of the HTC workload to autoscales the resource pools used by HTC workloads on container orchestrators. HTA takes into account a reference input, the real-time status of the jobs' queue, as well as two feedback inputs, resource consumption of jobs, and the resource initialization time of the container orchestrator. We implement HTA using the Makeflow workload manager, Work Queue job scheduler, and the Kubernetes cluster manager. We evaluate its performance on both CPU-bound and IO-bound workloads. The evaluation results show that, by using HTA, we improve resource utilization by 5.6× with a slight increase in execution time (about 15%) for a CPU-bound workload, and shorten the workload execution time by up to 3.65× for an IO-bound workload.
Topological Computing of Arrangements with (Co)Chains

In many areas of applied geometric/numeric computational mathematics, including geo-mapping, computer vision, computer graphics, finite element analysis, medical imaging, geometric design, and solid modeling, one has to compute incidences, adjacencies, and ordering of cells, generally using disparate and often incompatible data structures and algorithms. This article introduces computational topology algorithms to discover the two-dimensional (2D)/3D space partition induced by a collection of geometric objects of dimension 1D/2D, respectively. Methods and language are those of basic geometric and algebraic topology. Only sparse vectors and matrices are used to compute both spaces and maps, i.e., the chain complex, from dimension zero to three. The prototype software is written in Julia, the novel language for scientific computing. The applications may vary from 3D graphics to 3D printing, from images to scene understanding, and from games to building information modeling.
Evaluating Worksharing Tasks on Distributed Environments

Hybrid programming is a promising approach to exploit clusters of multicore systems. Our focus is on the combination of MPI and tasking. This hybrid approach combines the low-latency and high throughput of MPI with the flexibility of tasking models and their inherent ability to handle load imbalance. However, combining tasking with standard MPI implementations can be a challenge. The Task-Aware MPI library (TAMPI) eases the development of applications combining tasking with MPI. TAMPI enables developers to overlap computation and communication phases by relying on the tasking data-flow execution model. Using this approach, the original computation that was distributed in many different MPI ranks is grouped together in fewer MPI ranks, and split into several tasks per rank. Nevertheless, programmers must be careful with task granularity. Too fine-grained tasks introduce too much overhead, while too coarse-grained tasks lead to lack of parallelism. An adequate granularity may not always exist, especially in distributed environments where the same amount of work is distributed among many more cores. Worksharing tasks are a special kind of tasks, recently proposed, that internally leverage worksharing techniques. By doing so, a single worksharing task may run in several cores concurrently. Nonetheless, the task management costs remain the same than a regular task. In this work, we study the combination of worksharing tasks and TAMPI on distributed environments using two well known mini-apps: HPCCG and LULESH. Our results show significant improvements using worksharing tasks compared to regular tasks, and to other state-of-the-art alternatives such as OpenMP worksharing.