Reviewerly/exHarmony huggingface dataset:
On logical and extensional characterizations of attributed feature models

Software-intensive systems can have thousands of interdependent configuration options across different subsystems. Feature models (FMs) allow designers to organize the configuration space by describing configuration options using interdependent features: a feature is a name representing some functionality and each software variant is identified by a set of features. Attributed feature models (AFMs) extend FMs to describe the, possibly constrained, choice of a value from domains such as integers or strings: each attribute is associated to one feature, and when the feature is selected then the attribute brings some additional information relative to the selected features. Different representations of FMs and AFMs have been proposed in the literature. In this paper we focus on the logical representation (which works well in practice) and the extensional representation (which has been recently shown well suited for theoretical investigations). We provide an algebraic and a logical characterization of operations and relations on FMs and AFMs, and we formalize the connection between the two characterizations as monomorphisms from lattices of logical FMs and AFMs to lattices of extensional FMs and AFMs, respectively. This formalization sheds new light on the correspondence between the algebraic and logical characterizations of operations and relations for FMs and AFMs. It aims to foster the development of a formal framework for supporting practical exploitation of future theoretical developments on FMs, AFMs and multi software product lines.
Enedis IoT smart grid solutions for more efficiency

Electricity demand aggregation, development of Electric Vehicle and renewable energy and active users like prosumers are already key drivers in the development of the distribution grid, which will rely on efficient digital smart grid solutions operated by innovative DSOs, such as IoT equipment and infrastructure based on advanced telecommunication technologies. In France, in order to face these challenges and answer in a cost effective way to public authorities and private stakeholders&apos; expectations, Enedis has established a smart grid roadmap and developed smart grid solutions with two main objectives: Modernising network management processes and infrastructure, with predictive maintenance solutions such as big data, analytics, monitoring of primary substations, and the improvement of its quality of service using new sensors, more data and smart automation. Providing assistance to electric power system stakeholders and regional territories in the French energy transition, with innovative grid connection solutions and processes/automations for facilitating the Renewable Energy Sources integration. This paper presents the impact of several innovative solutions, based on different technologies, especially the IoT, on Enedis Smart Grid Strategy. Thanks to smart metering (more than 30 million of Linky smart meters have been already deployed in mid-January 2021), other connected objects and all digital solutions Enedis is now able to know what happens on its network better and in real-time.
Adjustment of In-Depth Interview and Focus Group Discussion Guidelines for Disaster Victims Evacuation Model Development with Corona Virus Disease 2019 Pandemic

In developing the disaster victim evacuation (DVE) model, we started with exploratory study is needed to identify the related variables.The exploratory studies are in the form of qualitative and quantitative research.In this qualitative study conducted agent-based in the nursing perspective.To conduct this research, guidelines are needed that are in accordance with the actual situation, such as the global COVID-19 pandemic.Although many articles have been published regarding in-depth interview (II) and Focus Group Discussion (FGD) guidelines, all of them have to be readjusted according to the situation.This adjustment is important to do minimize problems that arise in the data collection process, such as the emergence of new clusters during pandemic of the Covid 19.This study aims to provide a detailed standard in the implementation of the data collection process using the II & FGD method for the DVE Model development and in accordance with pandemic conditions.This literature review study begins with article search and identification of scientific articles.Before the scientific articles are synthesized, a variable identification process is carried out through 2 stages, namely identification to sort articles according to variables and article selection for research main reference needs and references for discussion..The results of this study are in the form of qualitative research guidelines presented in the form of standard data collection methods using II & FGD.In this article, we recommend aligning the II & FGD guidelines with the development of information technology in exploring Agent-based information.
Continuous Sign Language Recognition Based on 3D Hand Skeleton Data

<p indent="0mm">In sign language recognition, it is necessary to eliminate the visual problems caused by interference factors such as background and light. Therefore, an end-to-end continuous sign language recognition model is designed using low-redundant skeleton data. Firstly, the shape and trajectory features are extracted from intra frame and inter frame respectively, which can reduce the discreteness of the original samples. Secondly, a series of parallel two-stream residual networks are constructed to fuse shape and trajectory features, further generate the spatial-temporal feature sequence. Finally, the attention-based encoder-decoder network is used to realize the mapping of the fused feature sequence to the translated text. In addition, a new skeleton-based sign language dataset using Leap Motion is collected named LMSLR. Experimental results on the LMSLR dataset and the public CSL dataset show that the proposed model has higher accuracy and lower computational complexity than most models based on video processing.
Cascading Style Sheets

This book on Cascading Style Sheets (CSS) takes a very different approach from most. It isn’t trying to teach you how to design web pages and, aside from a cursory overview, isn’t focused on teaching you how to use CSS. This chapter introduces the focus of this book, which is how (and why) to treat CSS as a programming language.
Lessons Learned From a Centers for Disease Control and Prevention Virtual Partner Services Technical Assistance Pilot Project to Respond to a Local Syphilis Outbreak

A virtual partner services technical assistance (TA) project was piloted with the Minnesota Department of Health to address an ongoing syphilis outbreak. The TA reduced the health department's disease intervention specialist workload, achieved partner services outcomes comparable with in-person methods, and identified lessons learned to replicate with other jurisdictions.
The Aesthetics of Hybrid Space

Instigated from the fundamental characteristics of virtual reality (VR)–immersion and interaction–this project presents hybrid reality environments that explore the properties of ‘spatial continuity’. The “Digital Hybrid” project consists of various virtual spaces with unique aesthetics, fused together by superimposing real living spaces onto digitized fragments of remote homes to produce augmented living spaces. The topic of digital continuity is investigated on multiple levels, enabling new modes of interaction and communication by virtually merging remote spaces, producing new forms of aesthetics, transporting spaces from real to virtual and vice versa, and exploring the transition from one space into another.
Development and Evaluation of Bluetooth Low-Energy Device for Electronic Encounter Metrics

The coronavirus disease 2019 (COVID-19) pandemic led to the need for tracking of physical contacts and potential exposure to disease. Traditional contact tracing can be augmented by electronic tools called “electronic contact tracing” or “exposure notification.”. Some methods were built to work with smartphones; however, smartphones are not prevalent in some high-contact areas (e.g., schools and nursing homes). We present the design and initial testing of low-cost, highly privacy preserving wearable exposure notification devices. Several devices were constructed based on existing hardware and operated independently of a smartphone. The method (devices and analyses) was not able to reliably use the received signal strength indicator (RSSI) as a proxy for distance between pairs of devices; the accuracy of RSSI as a proxy for distance decreased dramatically outside of the idealized conditions. However, even an imperfect device could be useful for research on how people use and move through spaces. With some improvement, these devices could be used to understand disease spread and human or animal interaction in indoor environments.
On the Importance of Regularisation and Auxiliary Information in OOD Detection

Neural networks are often utilised in critical domain applications, even though they exhibit overconfident predictions for ambiguous inputs. This deficiency demonstrates a fundamental flaw: that neural networks often overfit on spurious correlations. We address this limitation by presenting two novel objectives that improve out-of-distribution (OOD) detection. We empirically demonstrate that our methods outperform the baseline while still maintaining a competitive performance against the rest. Additionally, we empirically demonstrate the robustness of our approach against common corruptions and the importance of regularisation and auxiliary information in OOD detection.
Single Image Super-Resolution Reconstruction Algorithm with Generative Adversarial Network Based on Multi-Size Convolution and Laplacian Filtering

<p indent="0mm">At present, the image super-resolution reconstruction algorithm using deep learning still faces problems such as insufficient texture perception and insufficient realism of the reconstructed image. In order to improve the quality of the reconstructed image, a single image super-resolution reconstruction algorithm with generative adversarial network based on multi-size convolution and Laplacian filtering is proposed. First, multi-size convolution feature extraction, separable Laplacian filtering and composite residual dense block are used to build a generation network which can extract more comprehensive image information. Secondly, multi-dimensional soft labels are utilized to construct the adversarial network, which can train the generative adversarial network easily and reconstruct image texture richly. Finally, the <italic>L</italic><sub>1</sub> loss function and the VGG low-level features are taken to obtain the overall features in the pre-training stage and the VGG high-level features, Charbonnier loss, and generative loss are used to make the reconstruction result more meticulously during training. Div2k and Flickr2K are chosen for model training, and Set5 and other data sets are used for testing. The experiment results show that the network size of this algorithm is 40% less than USRNet and other related algorithms, and the perceptual index is 0.76% lower than USRNet on average. The reconstruction result has more details and is more authentic.
Paraphrasing Compound Nominalizations

A nominalization uses a deverbal noun to describe an event associated with its underlying verb.Commonly found in academic and formal texts, nominalizations can be difficult to interpret because of ambiguous semantic relations between the deverbal noun and its arguments.Our goal is to interpret nominalizations by generating clausal paraphrases.We address compound nominalizations with both nominal and adjectival modifiers, as well as prepositional phrases.In evaluations on a number of unsupervised methods, we obtained the strongest performance by using a pre-trained contextualized language model to re-rank paraphrase candidates identified by a textual entailment model.
Automatic development of requirement linking matrix based on semantic similarity for robust software development

With growing complexity of modern software, it is important that the relevant textual requirements are correctly linked into a ‘requirement liking matrix’ during early system development stages. The resulting requirement linking matrix highlights direct and indirect interactions between different requirements, thus facilitating improved design, development, and testing of complex software systems, e.g. automotive software, electrical/electronic architectures. The sheer volume of textual requirements collected in real-life coupled with data noises makes the task of automatic requirement linking a non-trivial exercise. In this paper, we propose a novel semantic similarity model for automatically linking different requirements to organize them into a requirement linking matrix. The model computes the similarity in terms of term-to-term, tuple-to-tuple, and text-to-text scores. The scores are ranked to determine whether the links are having “High”, “Low”, or “No” relationship with each other in a requirement linking matrix. The model is deployed as a prototype tool and its performance is validated by using the real-life data. We also compare our approach with the alternative approaches proposed in literature. The system achieved the average F1 score of 0.93 in correctly linking the heterogeneous requirements.
Asymmetric Hybrids: Dialogues for Computational Concept Combination

When people combine concepts these are often characterised as “hybrid”, “impossible”, or “humorous”. However, when simply considering them in terms of extensional logic, the novel concepts understood as a conjunctive concept will often lack meaning having an empty extension (consider “a tooth that is a chair”, “a pet flower”, etc.). Still, people use different strategies to produce new non-empty concepts: additive or integrative combination of features, alignment of features, instantiation, etc. All these strategies involve the ability to deal with conflicting attributes and the creation of new (combinations of) properties. We here consider in particular the case where a Head concept has superior ‘asymmetric’ control over steering the resulting concept combination (or hybridisation) with a Modifier concept. Specifically, we propose a dialogical approach to concept combination and discuss an implementation based on axiom weakening, which models the cognitive and logical mechanics of this asymmetric form of hybridisation.
Teachers' Perceptions of Principals' Supportive Behavior during COVID-19

After the sudden shutdown of schools during the COVID-19 pandemic, schools were faced with providing online instruction for students.Some schools conducted teacher professional development and provided electronic devices to the students, while other districts lacked that capability.According to one study, schools that offered online learning during the pandemic provided a stable environment that reduced stress and uncertainty.Although there is a dearth of research about how to manage such a crisis, some parallels can be drawn from available information and applied to the current pandemic.The purpose of the present study was to determine what professional development and support were provided and how that impacted teachers' instructional delivery during the shutdown.In the present mixed methods study, public school teachers completed a Likert survey with an option for an open-ended comment after each question regarding teachers' perceptions of professional support from administration and perceptions of their own professional practice.Mean responses are displayed as descriptive statistics and sorted by geographic regions.One implication from the findings is that teachers need professional development for online instruction and time to adapt to it.
THE MEMORIZE-SPEAK UP-HABITUATION METHOD AS AN EFFORT TO IMPROVE THE MASTERY OF FOREIGN LANGUAGE SKILLS

Abstract:This study aimed to analyze and examine the MSH (Memorize-Speak Up-Habituation) method on foreign language mastery skills, especially English. This research used a descriptive qualitative approach with case study research at the LIPs (Language Intensive Program of Boarding Junior High School of Nurul Jadid) Paiton, Probolinggo. The instruments used were observation, in-depth interviews, and documentation to support the completeness of the data. The data analysis technique was carried out through the stages of data collection, data reduction, data presentation, and conclusion of research results. The findings indicated that the MSH method was applied with the stages of Memorizing new vocabulary and pronunciation, Speak Up, or integrating the use of new vocabulary in speaking activities, and habituation of foreign language activities. This study shows the implication that with the MSH method, LIPs members can master 4 English skills (listening, writing, reading, and speaking) correctly even though it requires high discipline.Abstrak:Penelitian ini bertujuan untuk menganalisis serta mengkaji penggunaan metode MSH (Memorize-Speak Up-Habituation) terhadap keterampilan penguasaan berbahasa asing, khususnya bahasa Inggris. Penelitian ini menggunakan pendekatan kualitatif deskriptif dengan jenis penelitian studi kasus di lembaga LIPs (Language Intensive Program of SMP Nurul Jadid) Paiton, Probolinggo. Instrumen dalam penelitian ini mengunakan observasi dan wawancara mendalam serta dokumentasi untuk mendukung kelengkapan data. Teknik analisis data pada penelitian ini dilakukan dengan tahap pengumpulan data, reduksi data, penyajian data, dan penyimpulan hasil penelitian. Hasil penelitian menunjukkan bahwa metode MSH diterapkan dengan tahapan Memorize (mengingat) kosakata baru serta pelafalannya; Speak Up (berbicara) atau mengintegrasikan penggunaan kosakata baru dalam kegiatan speaking (berbicara); dan Habituation (pembiasaan) kegiatan berbahasa asing. Penelitian ini menunjukkan implikasi bahwa dengan metode MSH, anggota LIPs dapat menguasai 4 kompetensi bahasa Inggris (listening, writing, reading, speaking) dengan tepat walau membutuhkan kedisiplinan tinggi.
Smart Voting System Using Facial Recognition

Abstract: In this paper a new authentication technique is discussed i-e; facial recognition verification for online voting system. It aims to develop a computerized voting system to make the election process more secure and user friendly. The electorate want to visit distinct locations like polling cubicles and stand in an extended queue to cast their vote, because of such reasons most of the people skip their chance of voting. The voter who isn't eligible also can forged its vote via way of means of faux way which can also additionally cause many problems. That's why in this project we have proposed a system or way for voting which is very effective or useful in voting. This system can also save money of the government which is spent in the election process. Overall this project is being developed to help staff of election commission of India and also reduce the human efforts. Keywords: Online Voting, Biometric Authentication, Security System.
Cell Tracking based on Multi-frame Detection and Feature Fusion

Cell tracking is a challenging task in computer vision because of dramatic changes of cell morphology, unregular movement pattern, and complex physiological phenomena such as mitosis and apoptosis. In recent years, cell image processing benefits a lot from the rapid development of deep learning: cell detection, segmentation, classification, especially tracking. In this paper, we propose a multiple cell tracking framework based-on multi-feature fusion. First, we propose an improved cell detection algorithm, which can detect cell mitosis and cell centroid with higher efficiency and accuracy. Second, we design a tracking framework based on the fusion of deep appearance feature and deep motion feature. Experimental results show that our proposed tracking method outperforms most traditional method and some state-of-the-art methods.
“Bundle of Sticks” and the Value of Interdependence: Building a Tools and Services Collective

In this article, we raise questions about how bundling and independence show up in the scholarly publishing industry today, both for large conglomerates and for smaller commercial and nonprofit players. We then contemplate what interdependence might look like and how it might help to transform academic publishing. We end with findings from the Next Generation Library Publishing (NGLP) project (2019-2022) and its Collaborative Frameworks Working Group regarding a set of initial steps that we believe publishers, tools, and service providers might take together towards developing a collective publishing framework for open source, values-aligned tools and services.
Blended learning and digital education

In this chapter I explore the possibilities offered by the integration between a Foucauldian biopolitical perspective and theories of embodiment for understanding the making of the learner in the context of digital education and blended learning. As known, biopolitical studies provide a critical body-oriented analytic of power and social epistemology, where the body is conceptualized as an object of knowledge, the target of power and an ethical matter of concern. However, they lack analytical tools to recognize the diverse and complex ways in which epistemic, ordering and ethical aspects of digital education experiences are made sense of, mediated, struggled over and sometimes ignored. I argue that theories of embodiment can fill this gap, offering the tools to produce synthetic accounts of the bodily, the cognitive and the social in the investigation of the digital and blended learner subjectification. The chapter identifies in the changing relationships between social relations, forms of embodiment and selfhood in digitally mediated educational environments a key area of inquiry for those researchers interested in examining the making of the learner through the experience of digital education and blended learning.