Reviewerly/exHarmony huggingface dataset:
A Study of Applying Simulacre Ideologies to an Interactive Online Ink and Wash Painting Education Program

현대의 4차 산업혁명에 따른 디지털 시대로의 변화는 팬데믹 상황을 야기한 코로나19로 인하여 예상치 않게 다양한 분야로가속화 되어졌다. 이에 미술교육의 실기 영역에서도 비대면 원격강의와 상호소통적 시스템의 개발에 대한 요구가 증가하였다. 본 연구에서는 이론적 사유로써 가상개념인 시뮬라크르에 대한 고찰을 통해 이러한 비대면 원격강의에 대한 인문학적의미와 이해를 돕고자 한다. 그리고 실제적인 동양화 미술실기에 대한 원격강의 예시 수업안을 제시하여 수업 사례의 결과를분석하였다. 서양의 형이상학 안에서 이데아적 원본개념에 대한 복제인 현상계의 복제물로 제시되었던 시뮬라크르는 원본의최하위개념에 지나지 않았다. 하지만 현대철학의 맥락 속에서 시뮬라크르의 개념은 발터벤야민의 가상개념인 복제의 중요성으로 강조되었고, 이후 쟝 보드리야르에 의해 실제보다 더 실제 같은 시뮬라시옹의 과정으로 소개되었다. 또한 들뢰즈에의하여 원본과 가상의 경계가 해체되고, 생성과 변화를 계속하는 사건으로서의 시뮬라크르가 제시된다. 이러한 시뮬라크르의개념을 기반으로 본 연구는 가상공간의 주제를 통한 실기수업으로 진행되었다. 연구대상은 16명의 미술대학 동양화과 학생들이었으며, 모두 코로나19로 인해 학교 실기실을 사용하지 못한 채 온라인 수업으로 대체되는 과정을 경험하였다. 이러한전형적인 실기실 수업이 쌍방향 원격수업으로 이루어질 경우에 대한 구체적인 원격수업 진행과정을 제시하고, 학생들이 제출한 발표 자료들과 실기 결과물들을 중심으로 미술교육적 실행효과와 발전가능성을 확인하였다.The new digital era caused by the fourth industrial revolution of today is being accelerated due to the COVID-19 pandemic. During this period, there is a higher demand for online education, calling art education to transition into online methods. This paper aims to demonstrate how the understanding of humanities aids the understanding of simulacre. Through an in-depth understanding of the idea of simulacre I proposed a practical lecture where students can visually express their interactions with the idea of simulacre provided by the philosophy of Walter Benjamin, Jean Baudillard, and Gilles Deleuze. This class plans to express the virtual reality space as the main concept and design to instruct 17 oriental painting major students through an interactive online education program. Furthermore, This study analyzes students’ artwork to see how effectively they have developed their simulacre ideas. This research demonstrates that recognition and expression of simulacre was enabled by an interactive online art education program.
Game-Based Coding Challenges to Foster Programming Practice.

The practice is the crux of learning to program. Automated assessment plays a key role in enabling timely feedback without access to teachers but alone is insufficient to engage students and maximize the outcome of their practice. Graphical feedback and game-thinking promote positive effects on students' motivation as shown by some serious programming games, but those games are complex to create and adapt. This paper presents Asura, an environment for assessment of game-based coding challenges, built on a specialized framework, in which students are invited to develop a software agent (SA) to play it. During the coding phase, students can take advantage of the graphical feedback to complete the proposed task. Some challenges also encourage students to think of a SA that plays in a setting with interaction among SAs. In such a case, the environment supports the creation and visualization of tournaments among submitted agents. Furthermore, the validation of this environment from the learners' perspective is also described.
TRANSFORMING INTERACTIONS: MOUSE-BASED TO VOICE-BASED INTERFACES

Recent technological advancements in natural language processing and voice-based technology have led to transformations in the way humans, and machines interact. From the conventional mouse-based interactions, the demand has switched over to more advanced voice-based interactions. The naturalness of speech as a communication medium coupled with technological advancements has influenced this demand. This paper presents an application of computers and technology to develop a voice-based interface in the travel and tourism domain. The primary aim of this research work is to develop a human-computer interface for effective and efficient communication. NLP techniques ASR, POS, TTS has been combined with query processing techniques to retrieve the information. The user's perspective to the interface has been recorded, and response to the usability of the voice-based interface is observed. While attaining these goals, the focus was towards achieving high accuracy of speech processing engine along with efficient modeling of the natural language to generate SQL query. The end to end system performs with an accuracy of 94%. The purpose of this system development is to move a step ahead to empower the citizens of India digitally. The integration of the TTS engine extends the use of the system for visually impaired people.
Introduction to the Special Section on Contextual Object Analysis in Complex Scenes

In Recent years, with the vast development of deep learning techniques, a great deal of effort has been devoted in the computer vision and multimedia community toward the problems of visual object analysis, such as object representation, recognition, detection, identification, etc. Especially at the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2014, the computers have successfully outperformed humans with a lower error rate at image recognition for the first time. However, most of existing algorithms focus more on analyzing objects in a relatively simple and restricted situation, which may perform poorly in natural environments.
Unified Architectural Support for Secure and Robust Deep Learning

Recent advances in Deep Learning (DL) have enabled a paradigm shift to include machine intelligence in a wide range of autonomous tasks. As a result, a largely unexplored surface has opened up for attacks jeopardizing the integrity of DL models and hindering the success of autonomous systems. To enable ubiquitous deployment of DL approaches across various intelligent applications, we propose to develop architectural support for hardware implementation of secure and robust DL. Towards this goal, we leverage hardware/software co-design to develop a DL execution engine that supports algorithms specifically designed to defend against various attacks. The proposed framework is enhanced with two real-time defense mechanisms, securing both DL training and execution stages. In particular, we enable model-level Trojan detection to mitigate backdoor attacks and malicious behaviors induced on the DL model during training. We further realize real-time adversarial attack detection to avert malicious behavior during execution. The proposed execution engine is equipped with hardware-level IP protection and usage control mechanism to attest the legitimacy of the DL model mapped to the device. Our design is modular and can be tuned to task-specific demands, e.g., power, throughput, and memory bandwidth, by means of a customized hardware compiler. We further provide an accompanying API to reduce the nonrecurring engineering cost and ensure automated adaptation to various domains and applications.
Automatic Story Generation: State of the Art and Recent Trends

Throughout history, human beings have used tales as a way of communicating ideas and transmitting knowledge. One of the great advances in this area was obtained thanks to writing, a fact that substantially facilitated the transmission of these stories. Being such an ancient activity in human history, it is not surprising that it has been the object of study of artificial intelligence, from a very early time in the development of the latter, leading, among other things, to the emergence of the automatic generation of stories. The automatic generation of stories has been a challenge that has been sought to be solved using different approaches. This review brings together some of these guidelines for carrying out this endeavour.
Control of programmable photonic integrated meshes for free-space optics applications

We report on the automated control of self-configuring programmable photonics meshes employed for the manipulation of free-space optical beams. Applications include optical beam coupling and identification, phase front correction and transmission through scattering media.
SCOBO: Sparsity-Aware Comparison Oracle Based Optimization.

We study derivative-free optimization for convex functions where we further assume that function evaluations are unavailable. Instead, one only has access to a comparison oracle, which, given two points $x$ and $y$, and returns a single bit of information indicating which point has larger function value, $f(x)$ or $f(y)$, with some probability of being incorrect. This probability may be constant or it may depend on $|f(x)-f(y)|$. Previous algorithms for this problem have been hampered by a query complexity which is polynomially dependent on the problem dimension, $d$. We propose a novel algorithm that breaks this dependence: it has query complexity only logarithmically dependent on $d$ if the function in addition has low dimensional structure that can be exploited. Numerical experiments on synthetic data and the MuJoCo dataset show that our algorithm outperforms state-of-the-art methods for comparison based optimization, and is even competitive with other derivative-free algorithms that require explicit function evaluations.
Enhanced Fourier-transform method for high-density fringe analysis by iterative spectrum narrowing

We propose a model-based fringe analysis technique that enables the Fourier transform method to analyze dense fringe patterns with large phase variations. The conventional Fourier-transform method has a limited dynamic range of measurable phase because the Fourier spectra broadened by large phase variations cannot be separated by the spatial carrier frequency. Our model-based iterative technique effectively narrows the broad spectrum and reduces phase errors. Results of simulations and experiments are presented that demonstrate the validity of the proposed spectrum-narrowing technique for high-density fringe patterns.
CNN-Based Cascade with Skipping Connections for Semantic Segmentation

Convolutional neural networks (CNNs) have produced significant improvements in semantic segmentation. They can collect relevant contextual information directly from the RGB images via stacks of convolutional and subsampling layers. In this paper, semantic segmentation is solved using a novel cascade of CNNs. The proposed method employs skipping connections for combining the input images with intermediary results, thus enabling successive corrections of the label maps. The cascade can easily integrate additional corrective algorithms, as exemplified for a graph-cut algorithm with confidence-dependent weight cues. The design allows a separate training of each component network, with reduced computational resources. The performance of the proposed approach is investigated for RGB images acquired by a wearable assistive device, in the framework of an application assisting the navigation of visually impaired persons. The experimental results indicate that the component CNNs can gradually improve the accuracy of the semantic segmentation.
Optimizing DDR5 address signal integrity using stochastic learning algorithms

With DDR5, the DRAMs will have the ability to support On Die Termination (ODT). The address topology is expected to continue to be a fly-by topology, with each DRAM loading the address bus driven by the controller. Each DRAM is expected to allow multiple ODT settings. The number of potential settings grows exponentially with the number of DRAMs in the system. Finding the optimal setting within the valid option space becomes a challenge with large number of DRAMs. At the same time, finding this optimal setting also becomes critical at the data rates of DDR5.
Single Satellite Imagery Superresolution Based on Hybrid Nonlocal Similarity Constrained Convolution Sparse Coding

The traditional superresolution methods based on image patches often ignore the consistency between the overlapped patches, causing block effects in produced images. The convolutional sparse coding based superresolution method uses the translation invariance of the convolution filter to directly encode the entire image, maintaining consistency and good performance. In this article, we propose a novel approach to single-image superresolution reconstruction based on hybrid nonlocal similarity constrained convolution sparse coding. We first decompose the input image into a smooth part and a texture part. The Bayesian nonparametric model can use more prior information of the original image, so we replace the previous bicubic interpolation with this method to better reconstruct the residual high-frequency information in the smooth part. When reconstructing the texture part, this article proposes a nonlocal similarity constrained convolutional sparse coding method, which transforms the reconstruction of the texture part to minimize the convolution sparse coding noise of the feature maps and classifies the image patches in the search space by using the correlation coefficients as the structural information, avoiding unnecessary weight calculation. Several methods were tested on satellite images extensively. Both visual inspection and quantitative analysis results demonstrate that our method outperforms other state-of-the-art methods and increases noise immunity effectively.
The Use of Informational Technologies in the Outdoor Educational Activities in Times of Special Educational Situations

In the modern educational systems, informational technologies had become part of the daily teaching learning routines. Outdoor education, as part of educational curricula, in most of the educational systems, uses informational technologies in order to make the instructive-educative process at all age groups more enjoyable and more efficient. During outdoor learning activities within the classroom experiences, there are lots of elements from the digital area that can be use&amp; Being part of new educational types, outdoor education among with ITC, has become an imperative need to be practiced in the modern educational systems especially in times of special educational times. They are complementary, especially in times of pandemic when regular education types within the classrooms cannot be achieved. The present article shows practical ways in which informational technologies can be used within outdoor learning experiences and a brief research about the way primary school teachers within 3 schools from Arad County use informational technologies during outdoor educational classes. The main method used was the survey, based on the questionnaire instrument. There were 75 participants and the schools chosen belong to 3 different environments, such as city, suburb area and country side. The preliminary results show that teachers within the rural area use outdoor settings the least and also the modern informational technologies during their instructive routines.
Digital morphology analyzers in hematology: a French survey

Digital morphology hematology analyzers are becoming more prevalent in laboratories Aims: investigate practices and assess the benefits and limits of digital automated microscopy in hematology. Methods questionnaire sent by e-mail in 2018 to French public and private laboratories. Results out of 118 responses (56 private, 62 public), 117 participants had a CellaVision® microscope, 1 had a West Medica®. Practices were sometimes different, especially in the choice of smears to be digitized or for quality controls (16.1% had internal quality controls, 48.3% external quality controls); 62.1% never used the red blood cell (RBC) characterization tool; the number of cells counted varied from 100 to 400. The study reported a high rate of agreement for these benefits: traceability (95.7%), staff training (94.1%), eye strain (91.4%), risk of error (87.2%), time saving (83.6%). Among the disadvantages, apart from the inadequate search for platelets clumps (93.2%), the agreement rates were often lower: adaptation to digital images (61.2%), difficult assessment of atypical morphologies (49.6%) or RBC morphology (49.6%). Conclusion despite well-established benefits, standardization of practices and technical improvement are still needed.
Classification of Natural Language Semantic Relations under Deep Learning

In order to accurately classify the semantic relations of natural language, the classification method of natural language semantic relations based on gated convolutional neural network is stated. The classification model of gated convolutional neural network is designed. The model is improved by combining dependent syntactic analysis and other external features. The analysis is carried out by calculating the macro average F1 value of the benchmark data set. The results show that the gated convolutional neural network model proposed in this study has the best classification results for evaluation using SemEval data set under different dimensional word vectors. In conclusion, the model proposed in this study can make full use of the depth of network model to improve the classification effect of semantic relations on small sample data sets. It is better to use models to classify semantic relations when incorporating multiple external features. This study provides an important reference for the application of deep learning in the classification of semantic relations.
MASDES-DWMV: Model for Dynamic Ensemble Selection Based on Multiagent System and Dynamic Weighted Majority Voting

One of the main challenges of machine learning algorithms is to maximize the result and generalization. Thus, the committee machines, i.e., the combination of more than one learning machine (an approach also called in the literature by ensemble), together with agent theory, become a promising alternative in this challenge. In this sense, this research proposes a dynamic selection model of machine committees for classification problems based on the multi-agent system and the weighted majority voting dynamic. This model has an agent-based architecture, with its roles and behaviors modeled and described throughout the life cycle of the multiagent system. The steps of generalization, selection, combination, and decision are performed through the behavior and interaction of agents with the environment, in the exercise of their respective roles. In order to validate the model, experiments were performed on 20 datasets from four repositories and compared with seven state-of-the-art dynamic committee selection models. At the end, the results of these experiments are presented, compared and analyzed, in which the proposed model obtained considerable gains in relation to the other models.
Utilising Perspectives to Improve Completeness in Industrial Requirements Specifications

In projects with requirements-related troubles the reason is often plainly that too little effort has been spent on requirements engineering (RE). Our varied industrial experience repeatedly showed, however, that even in projects with plenty of RE-budget and -efforts spent numerous problems are often found during acceptance testing that can still be traced back to incomplete or even missing requirements. How is that possible? After observing this repeatedly throughout more than eight years of industrial experience we performed a careful, in-depth qualitative analysis on the underlying root causes that lead us to the hypothesis we present in this paper: requirements engineers often tacitly lay a strong focus on one particular perspective in textbased requirements documentation, while the others typically remain un- or underspecified. Thus, incompleteness arises. In this paper we summarise our observations and present a solution idea that more explicitly considers the different perspectives during the RE process. This can significantly increase requirements completeness, lead to an improved overall balancing of RE efforts and, hence, also lead to higher chances for project success.
Towards Automatic Capturing of Traceability Links by Combining Eye Tracking and Interaction Data

Despite its numerous scientifically cited benefits, traceability is still rarely established in industrial settings. Years of research in the area have brought several different approaches to create traceability links including Information Retrieval (IR) and Machine Learning approaches. However, their accuracy and overall traceability support is not sufficient yet to be properly applied in practice. In my research, I want to investigate the usage of eye tracking and interaction data in the field of traceability. By tracking how software engineers interact with documents, where they focus on and recording gaze links between those documents, an algorithm is designed to obtain trace links between artifacts from these data. Eye tracking and interaction data have the advantage that they can be recorded in an automatic, non-intrusive way without requiring manual effort. They give detailed insight about where people focus on when working on tasks. However, software support of eye tracking is still limited, especially in the context of dynamic content such as switching between, scrolling or editing documents. Therefore, one essential step of my research is to provide software support for recording eye tracking data in dynamic document environments. By combining eye tracking data with additionally recorded metadata such as interactions, this eye tracking framework shall enable the automatic capturing of gaze links and gaze durations during software engineering tasks. The approach of using eye tracking in the context of traceability will be evaluated in several usage scenarios such as requirements coverage assessment.
Towards a General Solution for Layout of Visual Goal Models with Actors

Goal models help stakeholders make trade-off decisions in the early stages of project development. While these approaches have significant analysis capabilities, they have yet to see broad industrial adoption, with the construction of scalable, realistic goal models acting as a significant barrier. Over the last decade, researchers have used force-directed algorithms, specifically GraphViz, to layout goal models and have called for improved layout algorithms to better accommodate the unique challenges presented by actor-based models. We extend a force-directed algorithm to include goal model heuristics, and independently arrived at a domain specific version of a generic layout algorithm for undirected compound graphs. As initial validation of the effectiveness and scalability of our algorithm, we implement our approach in BloomingLeaf, a goal model analysis tool. Initial results are promising; yet, further collaboration and validation across the various goal modeling approaches (e.g., GRL, iStar, Tropos) is required before we can recommend our approach to be adopted in tooling. This paper presents early results and lays a foundation for discussion within our GORE community.