Reviewerly/exHarmony huggingface dataset:
Metameric Inpainting for Image Warping

Image-warping, a per-pixel deformation of one image into another, is an essential component in immersive visual experiences such as virtual reality or augmented reality. The primary issue with image warping is disocclusions, where occluded (and hence unknown) parts of the input image would be required to compose the output image. We introduce a new image warping method, Metameric image inpainting - an approach for hole-filling in real-time with foundations in human visual perception. Our method estimates image feature statistics of disoccluded regions from their neighbours. These statistics are inpainted and used to synthesise visuals in real-time that are less noticeable to study participants, particularly in peripheral vision. Our method offers speed improvements over the standard structured image inpainting methods while improving realism over colour-based inpainting such as push-pull. Hence, our work paves the way towards future applications such as depth image-based rendering, 6-DoF 360 rendering, and remote render-streaming.
Controlled Query Evaluation in OWL 2 QL: A “Longest Honeymoon” Approach

Controlled Query Evaluation (CQE) has been recently studied in the context of Semantic Web ontologies. The goal of CQE is concealing some query answers so as to prevent external users from inferring confidential information. In general, there exist multiple, mutually incomparable ways of concealing answers, and previous CQE approaches choose in advance which answers are visible and which are not. In this paper, instead, we study a dynamic CQE method, namely, we propose to alter the answer to the current query based on the evaluation of previous ones. We aim at a system that, besides being able to protect confidential data, is maximally cooperative, which intuitively means that it answers affirmatively to as many queries as possible; it achieves this goal by delaying answer modifications as much as possible. We also show that the behavior we get cannot be intensionally simulated through a static approach, independent of query history. Interestingly, for OWL 2 QL ontologies and policy expressed through denials, query evaluation under our semantics is first-order rewritable, and thus in AC $$^0$$ in data complexity. This paves the way for the development of practical algorithms, which we also preliminarily discuss in the paper.
E<sup>2</sup>CM: Early Exit via Class Means for Efficient Supervised and Unsupervised Learning

State-of-the-art neural networks with early exit mechanisms often need considerable amount of training and fine tuning to achieve good performance with low computational cost. We propose a novel early exit technique, Early Exit Class Means (E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CM), based on class means of samples. Unlike most existing schemes, ECM does not require gradient-based training of internal classifiers and it does not modify the base network by any means. This makes it particularly useful for neural network training in low-power devices, as in wireless edge networks. We evaluate the performance and overheads of E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CM over various base neural networks such as MobileNetV3, EfficientNet, ResNet, and datasets such as CIFAR-100, ImageNet, and KMNIST. Our results show that, given a fixed training time budget, E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CM achieves higher accuracy as compared to existing early exit mechanisms. Moreover, if there are no limitations on the training time budget, E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CM can be combined with an existing early exit scheme to boost the latter's performance, achieving a better trade-off between computational cost and network accuracy. We also show that E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CM can be used to decrease the computational cost in unsupervised learning tasks.
AHP-Based Systematic Approach to Analyzing and Evaluating Critical Success Factors and Practices for Component-Based Outsourcing Software Development

Component-based software development (CBSD) is a difficult method for creating complicated products or systems. In CBSD, multiple components are used to construct software or a product. A complex system or program can be created with CBSD quickly and with money while maintaining excellent quality and security. On the other hand, this research will persuade outsourced vendor companies to embrace CBSD approaches for component software development. We conducted a systemic literature review (SLR) to investigate the success factors that have a favorable impact on software outsourcing vendors’ organizations, and we selected 91 relevant research publications by creating a search string based on the study questions. This useful information was compiled using Google Scholar, IEEE Explore, MDPI, WILLEY Digital Library, and Elsevier. Furthermore, we completed all of the procedures in SLR for the full literature review, including the formulation of the SLR protocol, initial and final data collection, retrieval, assessment processes, and data synthesis. Among the ten (10) critical success factors we identified are a well-trained and skilled team, proper component selection, use of design standards, well-defined architecture, well-defined analysis and testing, well-defined integration, quality assurance, good organization of documentation, and well-organized security, and proper certification. Furthermore, the proposed SLR includes 46 best practices for these critical success factors, which could assist vendor organizations in enhancing critical success factors for CBOSD. According to our findings, the discovered success factors are similar and distinct across different periods, continents, databases, and approaches. The recommended SLR will also assist software vendor organizations in implementing the CBSD idea. We used the analytical hierarchy process (AHP) method to prioritize and analyze the success factors of component-based outsourcing software development and the result of different equations of the AHP approach to construct the pairwise comparison matrix. The largest eigenvalue was 3.096 and the CR value was 0.082, which is less than 0.1, and thus sufficient and acceptable.
Compressing and interpreting word embeddings with latent space regularization and interactive semantics probing

Word embedding, a high-dimensional (HD) numerical representation of words generated by machine learning models, has been used for different natural language processing tasks, for example, translation between two languages. Recently, there has been an increasing trend of transforming the HD embeddings into a latent space (e.g. via autoencoders) for further tasks, exploiting various merits the latent representations could bring. To preserve the embeddings’ quality, these works often map the embeddings into an even higher-dimensional latent space, making the already complicated embeddings even less interpretable and consuming more storage space. In this work, we borrow the idea of [Formula: see text] VAE to regularize the HD latent space. Our regularization implicitly condenses information from the HD latent space into a much lower-dimensional space, thus compressing the embeddings. We also show that each dimension of our regularized latent space is more semantically salient, and validate our assertion by interactively probing the encoding-level of user-proposed semantics in the dimensions. To the end, we design a visual analytics system to monitor the regularization process, explore the HD latent space, and interpret latent dimensions’ semantics. We validate the effectiveness of our embedding regularization and interpretation approach through both quantitative and qualitative evaluations.
Education in the New Normal: A Need for Alternative Strategies in Supporting Teaching and Learning in South African Schools in the Post-COVID-19 Era

The outbreak of the COVID-19 pandemic has negatively impacted the educational system all over the world in many ways, including teaching and learning. Schools had to abandon face-to-face teaching and learning and adopt online teaching approaches. This means that teaching through mobile digital tools and blended approaches is to be enhanced post-COVID-19 era. This paper critically reviewed the literature on the strategies used to support teaching and learning in South African schools during the post-COVID-19 era amid the challenges experienced by the education system in South Africa. The literature review approach was adopted to gather literature on strategies to support teaching and learning in South African schools. Having considered teaching and learning in South African schools which is primarily based on face-to-face traditional pedagogical strategies before the outbreak of COVID-19 and the challenges experienced during the pandemic. The paper argues the need for educational planners to rethink how teaching and learning could be enhanced in the new normal. The digital learning, blended teaching, and eLearning/online learning approaches response of South Africa during the outbreak of the pandemic should be sustained. It emerged from the literature that although teachers and learners have embraced technology, there is no clear policy on how ICT should integrate with teaching, learning and assessment. Therefore, this study recommends that the Department of Basic Education develop an ICT integration policy providing a clear strategy that will accommodate both rural and urban schools. Furthermore, it is recommended that increased funding on education resources and the provision of continuous teacher professional development program be considered. Keywords: New normal, Post-COVID-19, Blended teaching and learning, Online learning
LIP: Local Importance-Based Pooling

Spatial downsampling layers are favored in convolutional neural networks (CNNs) to downscale feature maps for larger receptive fields and less memory consumption. However, for visual recognition tasks, these layers might lose discriminative details due to improper pooling strategies. In this paper, we present a unified framework (LAN) over the common downsampling layers (e.g., average pooling, max pooling, and strided convolution) from a view of local aggregation based on importance. In this LAN framework, we analyze the issues of these widely-used pooling layers and figure out the criteria of designing an effective downsampling layer. Based on this analysis, we propose a simple, general, and effective pooling operation based on local importance modeling, termed as Local Importance-based Pooling (LIP). LIP is able to enhance discriminative features during the downsampling procedure by learning adaptive importance weights based on inputs. To further modulate different pooling windows for more effective pooling, we present the improved version of LIP, termed LIP++, by introducing an explicit margin term and efficient logit modules. Our LIP++ can yield consistent accuracy improvement over the original LIP yet with a smaller computational cost. Extensive experiments show that our presented LIP method consistently yields notable gains with different CNN architectures on the image classification task. In the challenging MS COCO dataset, detectors with our LIP-ResNets as backbones obtain a consistent performance improvement over the vanilla ResNets on both bounding box detection and instance segmentation. Finally, we also verify the effectiveness of LIP on the tasks of pose estimation and semantic segmentation, demonstrating its generalization to the dense prediction task.
Overwatch: learning patterns in code edit sequences

Integrated Development Environments (IDEs) provide tool support to automate many source code editing tasks. Traditionally, IDEs use only the spatial context, i.e., the location where the developer is editing, to generate candidate edit recommendations. However, spatial context alone is often not sufficient to confidently predict the developer’s next edit, and thus IDEs generate many suggestions at a location. Therefore, IDEs generally do not actively offer suggestions and instead, the developer is usually required to click on a specific icon or menu and then select from a large list of potential suggestions. As a consequence, developers often miss the opportunity to use the tool support because they are not aware it exists or forget to use it. To better understand common patterns in developer behavior and produce better edit recommendations, we can additionally use the temporal context, i.e., the edits that a developer was recently performing. To enable edit recommendations based on temporal context, we present Overwatch, a novel technique for learning edit sequence patterns from traces of developers’ edits performed in an IDE. Our experiments show that Overwatch has 78% precision and that Overwatch not only completed edits when developers missed the opportunity to use the IDE tool support but also predicted new edits that have no tool support in the IDE.
Exploring Dimensionality Reduction Techniques in Multilingual Transformers

Abstract In scientific literature and industry, semantic and context-aware Natural Language Processing-based solutions have been gaining importance in recent years. The possibilities and performance shown by these models when dealing with complex Human Language Understanding tasks are unquestionable, from conversational agents to the fight against disinformation in social networks. In addition, considerable attention is also being paid to developing multilingual models to tackle the language bottleneck. An increase in size has accompanied the growing need to provide more complex models implementing all these features without being conservative in the number of dimensions required. This paper aims to provide a comprehensive account of the impact of a wide variety of dimensional reduction techniques on the performance of different state-of-the-art multilingual siamese transformers, including unsupervised dimensional reduction techniques such as linear and nonlinear feature extraction, feature selection, and manifold techniques. In order to evaluate the effects of these techniques, we considered the multilingual extended version of Semantic Textual Similarity Benchmark (mSTSb) and two different baseline approaches, one using the embeddings from the pre-trained version of five models and another using their fine-tuned STS version. The results evidence that it is possible to achieve an average reduction of $$91.58\% \pm 2.59\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>91.58</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>2.59</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> in the number of dimensions of embeddings from pre-trained models requiring a fitting time $$96.68\% \pm 0.68\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>96.68</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>0.68</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> faster than the fine-tuning process. Besides, we achieve $$54.65\% \pm 32.20\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>54.65</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>32.20</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> dimensionality reduction in embeddings from fine-tuned models. The results of this study will significantly contribute to the understanding of how different tuning approaches affect performance on semantic-aware tasks and how dimensional reduction techniques deal with the high-dimensional embeddings computed for the STS task and their potential for other highly demanding NLP tasks.
Data Protection Law and Multi-Party Computation

Pushes for increased power of Law Enforcement (LE) for data retention and centralized storage result in legal challenges with data protection law and courts-and possible violations of the right to privacy. This is motivated by a desire for better cooperation and exchange between LE Agencies (LEAs), which is difficult due to data protection regulations, was identified as a main factor of major public security failures, and is a frequent criticism of LE. Secure Multi-Party Computation (MPC) is often seen as a technological means to solve privacy conflicts where actors want to exchange and analyze data that needs to be protected due to data protection laws. In this interdisciplinary work, we investigate the problem of private information exchange between LEAs from both a legal and technical angle. We give a legal analysis of secret-sharing based MPC techniques in general and, as a particular application scenario, consider the case of matching LE databases for lawful information exchange between LEAs. We propose a system for lawful information exchange between LEAs using MPC and private set intersection and show its feasibility by giving a legal analysis for data protection and a technical analysis for workload complexity. Towards practicality, we present insights from qualitative feedback gathered within exchanges with a major European LEA.
Type-directed synthesis of visualizations from natural language queries

We propose a new technique based on program synthesis for automatically generating visualizations from natural language queries. Our method parses the natural language query into a refinement type specification using the intents-and-slots paradigm and leverages type-directed synthesis to generate a set of visualization programs that are most likely to meet the user's intent. Our refinement type system captures useful hints present in the natural language query and allows the synthesis algorithm to reject visualizations that violate well-established design guidelines for the input data set. We have implemented our ideas in a tool called Graphy and evaluated it on NLVCorpus, which consists of 3 popular datasets and over 700 real-world natural language queries. Our experiments show that Graphy significantly outperforms state-of-the-art natural language based visualization tools, including transformer and rule-based ones.
First-class names for effect handlers

Algebraic effects and handlers are a promising technique for incorporating composable computational effects into functional programming languages. Effect handlers enable concisely programming with different effects, but they do not offer a convenient way to program with different instances of the same effect. As a solution to this inconvenience, previous studies have introduced _named effect handlers_, which allow the programmer to distinguish among different effect instances. However, existing formalizations of named handlers are both involved and restrictive, as they employ non-standard mechanisms to prevent the escaping of handler names. In this paper, we propose a simple and flexible design of named handlers. Specifically, we treat handler names as first-class values, and prevent their escaping while staying within the ordinary λ-calculus. Such a design is enabled by combining named handlers with _scoped effects_, a novel variation of effects that maintain a scope via rank-2 polymorphism. We formalize two combinations of named handlers and scoped effects, and implement them in the Koka programming language. We also present practical applications of named handlers, including a neural network and a unification algorithm.
SIGCHI at 40

research-article SIGCHI at 40: celebrations and aspirations Authors: Andrew L. Kun University of New Hampshire University of New HampshireView Profile , Elizabeth Churchill Google GoogleView Profile , Tamara L. Clegg University of Maryland University of MarylandView Profile , Jonathan Grudin Microsoft and University of Washington Microsoft and University of WashingtonView Profile , Kristina Höök KTH Royal Institute of Technology KTH Royal Institute of TechnologyView Profile , Daria Loi Fishtail FishtailView Profile , Yolanda Rankin Florida State University Florida State UniversityView Profile , Elizabeth Rosenzweig Brandeis University Brandeis UniversityView Profile , Kentaro Toyama University of Michigan University of MichiganView Profile , Susan Dray Dray & Associates Dray & AssociatesView Profile Authors Info & Claims InteractionsVolume 29Issue 6November - December 2022 pp 24–29https://doi.org/10.1145/3564036Published:03 November 2022Publication History 1citation140DownloadsMetricsTotal Citations1Total Downloads140Last 12 Months140Last 6 weeks29 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Indonesian Traffic Signs Recognition Using Convolutional Neural Network

A traffic violation is one of the reasons for the increasing mortality every year. Traffic Sign Recognition (TSR) is an important component within the scope of Advanced Driver Assistance System (ADAS) and autonomous vehicles, which concern the problem of Traffic Sign Classification (TSC) and Traffic Sign Detection (TSD). The detection system of traffic signs purpose is to warn the driver about the traffic condition they will pass, so it can help the driver to decrease the accident. The traffic signs in every country have different shapes and colors, so the traffic signs have wide variability. This paper shows a traffic signs classification study using a Convolutional Neural Network (CNN). The data is from the traffic signs in Indonesia which consist of 41 traffic signs. The model proposed has shown good enough performance in which the accuracy score is 93% and the average F1-score is 94% in the recognition of traffic signs in Indonesia.
Application of intervention mapping in cybersecurity education design

Education in Cybersecurity is considered one of the key challenges facing the modern digitized world. Several frameworks, e.g., developed by NIST or ENISA, have defined requirements for cybersecurity education but do not give recommendations for their development. Developing appropriate education offerings need to incorporate theory-based approaches that are evidence supported. Adopting the Intervention Mapping paradigm, we propose an educational framework incorporating validated theoretical and evidence-based approaches to cybersecurity education encompassing stakeholders' input, identified competency needs, and how to implement and evaluate learning outcomes. This paper presents a case study of how Intervention Mapping can be used to help design cybersecurity education, discuss challenges in educational and professional aspects of cybersecurity, and present an applied educational approach based on Intervention Mapping and its evaluation.
ESCHER

As distributed applications become increasingly complex, so do their scheduling requirements. This development calls for cluster schedulers that are not only general, but also evolvable. Unfortunately, most existing cluster schedulers are not evolvable: when confronted with new requirements, they need major rewrites to support these requirements. Examples include gang-scheduling support in Kubernetes [6, 39] or task-affinity in Spark [39]. Some cluster schedulers [14, 30] expose physical resources to applications to address this. While these approaches are evolvable, they push the burden of implementing scheduling mechanisms in addition to the policies entirely to the application.