Reviewerly/exHarmony huggingface dataset:
Learning Complexity-Aware Cascades for Pedestrian Detection

The problem of pedestrian detection is considered. The design of complexity-aware cascaded pedestrian detectors, combining features of very different complexities, is investigated. A new cascade design procedure is introduced, by formulating cascade learning as the Lagrangian optimization of a risk that accounts for both accuracy and complexity. A boosting algorithm, denoted as complexity aware cascade training (CompACT), is then derived to solve this optimization. CompACT cascades are shown to seek an optimal trade-off between accuracy and complexity by pushing features of higher complexity to the later cascade stages, where only a few difficult candidate patches remain to be classified. This enables the use of features of vastly different complexities in a single detector. In result, the feature pool can be expanded to features previously impractical for cascade design, such as the responses of a deep convolutional neural network (CNN). This is demonstrated through the design of pedestrian detectors with a pool of features whose complexities span orders of magnitude. The resulting cascade generalizes the combination of a CNN with an object proposal mechanism: rather than a pre-processing stage, CompACT cascades seamlessly integrate CNNs in their stages. This enables accurate detection at fairly fast speeds.
Higher education student pathways to ebook usage and engagement, and understanding: Highways and cul de sacs

Ebooks have enthusiastically been adopted by academic libraries, viewed as a golden bullet by library professionals, resulting in efficient resource use, space saving, student satisfaction and accommodating millennial generation study habits. A small-scale online survey undertaken at Northumbria and Durham Universities investigated students’ ebook use, examining aspects of learning ebooks support, searching strategies, devices used for ebook access, and reading and use strategies. Ninety-two responses were analysed using a mixed methods approach. Despite many advantages of ebooks including portability, availability, functionality and searching, results, demonstrated sentiment regarding ebooks was not wholly positive. There were frustrations regarding the complexity of ebook provision, publisher’s restrictions and the lack of compatibility with reading devices. A key finding related to ebook interrogation which involved greater targeted searching of content and a ‘bite-size’ approach to reading. Caution must be observed to ensure that library collections facilitate a complexity of learning styles, and provide opportunities for students to better digest content.
Audio watermarking algorithm against synchronization attacks using global characteristics and adaptive frame division

Achieving resistance to synchronization attacks on audio watermarking is a challenging task. Available synchronization methods based on local features utilize sample alignments. The local features vanish easily after operation, hindering resynchronization. This study proposes a new audio watermarking method that divides frames adaptively; it is based on the global characteristics of the frame and achieves indirect synchronization by employing a corresponding frame sequence number. The experimental results reveal that the proposed scheme exhibits effective robustness against conventional signal processing manipulation and synchronization attack processes such as jittering, time scale modification, and pitch shift modification.
Rational closure for all description logics

Rational closure is one of the most extensively studied nonmonotonic extensions of description logics. Nonetheless, so far it has been investigated only for description logics that satisfy the disjoint model union property, or limited fragments that support nominals. In this paper we show that for sufficiently expressive description logics, the traditional correspondence between rational closure and ranked interpretations does not hold. Therefore, in order to extend rational closure to a wider class of description logics it is necessary to change the definition of rational closure, or alternatively abandon its standard semantics. Here we pursue the former approach, and introduce stable rational closure, based on stable rankings. The resulting nonmonotonic logic is a natural extension of the standard rational closure: First, its refined exceptionality criterion yields a closure that satisfies the KLM postulates. Second, when a knowledge base enjoys the disjoint model union property, then stable rational closure equals the old notion. In the other cases, stable rankings may raise the exceptionality level of some concepts. Stable rational closure has a model-theoretic semantics based on upward-closed models, that relax the canonical models adopted in the past, in order to deal with logics that do not satisfy the disjoint union model property. Unfortunately, stable rankings do not always exist, and are not necessarily unique. However, they can be effectively enumerated for all defeasible knowledge bases in SROIQ, using any algorithm for reasoning with ranked models.
Checking Security Properties of Cloud Service REST APIs

Most modern cloud and web services are programmatically accessed through REST APIs. This paper discusses how an attacker might compromise a service by exploiting vulnerabilities in its REST API. We introduce four security rules that capture desirable properties of REST APIs and services. We then show how a stateful REST API fuzzer can be extended with active property checkers that automatically test and detect violations of these rules. We discuss how to implement such checkers in a modular and efficient way. Using these checkers, we found new bugs in several deployed production Azure and Office365 cloud services, and we discuss their security implications. All these bugs have been fixed.
An efficient KP design framework of attribute‐based searchable encryption for user level revocation in cloud

Summary Attribute‐based searchable encryption (ABSE) is the combination of attribute‐based encryption (ABE) and searchable encryption with the inherent benefits of fine‐grained access control and expressive searching capabilities in multiuser setting. In this paper, we have used the key‐policy (KP) design framework of ABE and named the scheme KP‐ABSE. The proposed KP‐ABSE scheme efficiently supports user revocation where the computationally intensive tasks are delegated to the cloud server. Furthermore, the proposed scheme generates constant‐size user secret keys and trapdoors and has constant number of pairing operations, which in other schemes typically varies with the number of attributes associated with them. Thus, the proposed scheme reduces computational and storage costs and supports fast searching. Finally, the proposed scheme can be proven secure under a decision linear assumption in a selective security model.
Security and Privacy of Electronic Health Records: Decentralized and Hierarchical Data Sharing using Smart Contracts

Over the last fifty years, medical treatment has seen remarkable advancement, however, the data management and storage systems of medical records has lagged in comparison. In addition, these systems are often inharmonious across platforms and do not put the privacy desires of patients first. While HIPAA and other laws are put in place to protect patient medical record security and privacy, these antiquated systems inherently hinder patient security and privacy. In this paper, we propose a novel data sharing and management scheme that empowers patients over their records by leveraging the security and privacy benefits of blockchain and smart contracts. In comparison to current methods for healthcare records management, our proposed scheme empower patients over their records and minimizes the dependencies on record-generating institutions. It also allows the patients to selectively share their records and disclose certain parts with specific data users based on the privacy preferences desired. In our security and privacy analysis, we show that patients can protect against potential threats to securely and privately share their records. Moreover, in our performance discussions, we show that smart contract design and development is key.
TalkTraces

Group Support Systems provide ways to review and edit shared content during meetings, but typically require participants to explicitly generate the content. Recent advances in speech-to-text conversion and language processing now make it possible to automatically record and review spoken information. We present the iterative design and evaluation of TalkTraces, a real-time visualization that helps teams identify themes in their discussions and obtain a sense of agenda items covered. We use topic modeling to identify themes within the discussions and word embeddings to compute the discussion "relatedness" to items in the meeting agenda. We evaluate TalkTraces iteratively: we first conduct a comparative between-groups study between two teams using TalkTraces and two teams using traditional notes, over four sessions. We translate the findings into changes in the interface, further evaluated by one team over four sessions. Based on our findings, we discuss design implications for real-time displays of discussion content.
Dynamic Hand Gesture Classification Based on Multistatic Radar Micro-Doppler Signatures Using Convolutional Neural Network

We propose a novel convolutional neural network (CNN) for dynamic hand gesture classification based on multistatic radar micro-Doppler signatures.The timefrequency spectrograms of micro-Doppler signatures at all the receiver antennas are adopted as the input to CNN, where data fusion of different receivers is carried out at an adjustable position.The optimal fusion position that achieves the highest classification accuracy is determined by a series of experiments.Experimental results on measured data show that 1) the accuracy of classification using multistatic radar is significantly higher than monostatic radar, and that 2) fusion at the middle of CNN achieves the best classification accuracy.
Participatory Design of VR Scenarios for Exposure Therapy

Virtual reality (VR) applications for exposure therapy predominantly use computer-generated imagery to create controlled environments in which users can be exposed to their fears. Creating 3D animations, however, is demanding and time-consuming. This paper presents a participatory approach for prototyping VR scenarios that are enabled by 360° video and grounded in lived experiences. We organized a participatory workshop with adolescents to prototype such scenarios, consisting of iterative phases of ideation, storyboarding, live-action plays recorded by a 360° camera, and group evaluation. Through an analysis of the participants' interactions, we outline how they worked to design prototypes that depict situations relevant to those with a fear of public speaking. Our analysis also explores how participants used their experiences and reflections as resources for design. Six clinical psychologists evaluated the prototypes from the workshop and concluded they were viable therapeutic tools, emphasizing the immersive, realistic experience they presented. We argue that our approach makes the design of VR scenarios more accessible.
Automated Cloud Infrastructure, Continuous Integration and Continuous Delivery using Docker with Robust Container Security

Recent industry trends have determined that cloud computing has proven to be a very demanding and lucrative field. Regardless of the size of an organization, they are transitioning to the cloud platform for multiple reasons. Despite cloud providers effort to make the transition smooth, we firmly believe that we have not reached to that point yet, but with the proper set of tools and leveraging best practices, we can certainly make this transition efficient and robust. There are various essential layers that all are working closely together to make cloud automation and platform orchestration successful. This paper provides a brief cloud technology overview and guideline on how cloud computing technology is transforming industrial automation for the future, enhancing productivity and cost optimization. This paper also examines a strategy for effective deployment and maintenance so that automated resources can be managed effectively, provisioned rapidly and released with little to no admin efforts.
Can Privacy Be Satisfying?

Pervasive photo sharing in online social media platforms can cause unintended privacy violations when elements of an image reveal sensitive information. Prior studies have identified image obfuscation methods (e.g., blurring) to enhance privacy, but many of these methods adversely affect viewers' satisfaction with the photo, which may cause people to avoid using them. In this paper, we study the novel hypothesis that it may be possible to restore viewers' satisfaction by 'boosting' or enhancing the aesthetics of an obscured image, thereby compensating for the negative effects of a privacy transform. Using a between-subjects online experiment, we studied the effects of three artistic transformations on images that had objects obscured using three popular obfuscation methods validated by prior research. Our findings suggest that using artistic transformations can mitigate some negative effects of obfuscation methods, but more exploration is needed to retain viewer satisfaction.
Using TopGear in Overdrive: A More Efficient ZKPoK for SPDZ

The HighGear protocol (Eurocrypt 2018) is the fastest currently known approach to preprocessing for the SPDZ Multi-Party Computation scheme. Its backbone is formed by an Ideal Lattice-based Somewhat Homomorphic Encryption Scheme and accompanying Zero-Knowledge proofs. Unfortunately, due to certain characteristics of HighGear such current implementations limit the security parameters in a number of places. This is mainly due to memory and bandwidth consumption constraints. In this work we present a new approach to the ZKPoKs for the SPDZ Multi-Party Computation scheme. We rigorously formalize the original approach of HighGear and show how to improve upon it using a different proof strategy. This allows us to increase the security of the underlying protocols, whilst simultaneously also increasing the performance in terms of memory and bandwidth consumption as well as overall throughput of the SPDZ offline phase.
A novel edge-oriented framework for saliency detection enhancement

Mixed visual scenes and cluttered background commonly exist in natural images, which forms a challenge for saliency detection. In dealing with complex images, there are two kinds of deficiencies in the existing saliency detection methods: ambiguous object boundaries and fragmented salient regions. To address these two limitations, we propose a novel edge-oriented framework to improve the performance of existing salient detection methods. Our framework is based on two interesting insights: 1) human eyes are sensitive to the edges between foreground and background even there is hardly any difference in terms of saliency, 2) Guided by semantic integrity, human eyes tend to view a visual scene as several objects, rather than pixels or superpixels. The proposed framework consists of the following three parts. First, an edge probability map is extracted from an input image. Second, the edge-based over-segmentation is obtained by sharpening the edge probability map, which is ultilized to generate edge-regions using an edge-strength based hierarchical merge model. Finally, based on the prior saliency map generated by existing methods, the framework assigns each edge-region with a saliency value. Based on four publically available datasets, the experiments demonstrate that the proposed framework can significantly improve the detection results of existing saliency detection models, which is also superior to other state-of-the-art methods.
Care and Design

While there have been considerable developments in designing for dementia within HCI, there is still a lack of empirical understanding of the experience of people with advanced dementia and the ways in which design can support and enrich their lives. In this paper, we present our findings from a long-term ethnographic study, which aimed to gain an understanding of their lived experience and inform design practices for and with people with advanced dementia in residential care. We present our findings using the social theory of recognition as an analytic lens to account for recognition in practice and its challenges in care and research. We discuss how we, as the HCI community, can pragmatically engage with people with advanced dementia and propose a set of considerations for those who wish to design for and with the values of recognition theory to promote collaboration, agency and social identity in advanced dementia care.
BP$k$ NN: $k$ -Nearest Neighbor Classifier With Pairwise Distance Metrics and Belief Function Theory

The k-nearest neighbor (kNN) rule is one of the most popular classification algorithms in pattern recognition field because it is very simple to understand but works quite well in practice.However, the performance of the kNN rule depends critically on its being given a good distance metric over the input space, especially in small data set situations.In this paper, a new kNN-based classifier, called BPkNN, is developed based on pairwise distance metrics and belief function theory.The idea of the proposal is that instead of learning a global distance metric, we first decompose it into learning a group of pairwise distance metrics.Then, based on each learned pairwise distance metric, a pairwise kNN (PkNN) sub-classifier can be adaptively designed to separate two classes.Finally, a polychotomous classification problem is solved by combining the outputs of these PkNN sub-classifiers in belief function framework.The BPkNN classifier improves the classification performance thanks to the new distance metrics which provide more flexibility to design the feature weights and the belief function-based combination method which can better address the uncertainty involved in the outputs of the sub-classifiers.Experimental results based on synthetic and real data sets show that the proposed BPkNN can achieve better classification accuracy in comparison with some state-of-the-art methods.
Examining the "Global" Language of Emojis

Emojis are becoming an increasingly popular mode of communication between individuals worldwide, with researchers claiming them to be a type of "ubiquitous language'' that can span different languages due to its pictorial nature. Our study uses a combination of methods to examine how emojis are adopted and perceived by individuals from diverse cultural backgrounds and 45 countries. Our survey and interview findings point to the existence of a cultural gap between user perceptions and the current emoji standard. Using participatory design, we sought to address this gap by designing 40 emojis and conducted another survey to evaluate their acceptability compared to existing Japanese emojis. We also draw on participant observation from a Unicode Consortium meeting on emoji addition. Our analysis leads us to discuss how emojis might be made more inclusive, diverse, and representative of the populations that use them.
Bayesian State Estimation for Markovian Jump Systems: Employing Recursive Steps and Pseudocodes

In this article, we review several existing Bayesian state-estimation algorithms for Markovian jump systems (MJSs) and illustrate their distinctive properties. Specifically, we analyze the jump Markov linear systems (JMLSs), for which autonomous multiple-model (AMM) algorithms, first-order generalized pseudo-Bayesian (GPB1) algorithms, second-order generalized pseudo-Bayesian (GPB2) algorithms, interacting multiple-model (IMM) algorithms, and reweighed IMM (RIMM) algorithms are introduced. For nonlinear MJSs, we consider the sampling importance resampling (SIR) particle filter (PF) as well as the IMM PF to demonstrate how to handle nonlinearity in the framework of MJSs. For convenience, the realization codes of GPB1 and IMM algorithms and SIR PFs are provided.
Engaging Gentrification as a Social Justice Issue in HCI

Gentrification-the spatial expression of economic inequality-is fundamentally a matter of social justice. Yet, even as work outside of HCI has begun to discuss how computing can enable or challenge gentrification, HCI's growing social justice agenda has not engaged with this issue. This omission creates an opportunity for HCI to develop a research and design agenda at the intersection of computing, social justice, and gentrification. We begin this work by outlining existing scholarship describing how the consumption side dynamics of gentrification are mediated by contemporary socio-technical systems. Subsequently, we build on the social justice framework introduced by Dombrowski, Harmon, and Fox to discuss how HCI may resist or counter such forces. We offer six modes of research that HCI scholars can pursue to engage gentrification.
Augmented Go &amp; See: An approach for improved bottleneck identification in production lines

Bottlenecks in production lines are often shifting and thus hard to identify. They lead to decreased output, longer throughput times and higher work in progress. Go & See is a well-established Lean practice where managers go to the shop floor to see the problems first hand. Mixed reality is a promising technology to improve transparency in complex production environments. Until recently, mixed reality applications have been very demanding in terms of computing power requiring high performance hardware. This paper presents an approach for real-time KPI visualization using mixed reality for bottleneck identification in production lines relying on the bring-your-own device principle. The developed application uses image recognition to identify work stations and visualizes cycle times and work in progress in augmented reality. With this additional information, it is possible to discern different root causes for bottlenecks, for example systematically higher or varying cycle times due to breakdowns. This solution can be classified according to the acatech industry 4.0 maturity model as a level 3 - transparency - application. It could be shown that the identification of bottlenecks and underlying reasons has been improved compared to standard Go & See.
A Practice-Led Account of the Conceptual Evolution of UX Knowledge

The contours of user experience (UX) design practice have been shaped by a diverse array of practitioners and disciplines, resulting in a diffuse and decentralized body of UX-specific disciplinary knowledge. The rapidly shifting space that UX knowledge occupies, in conjunction with a long-existing research-practice gap, presents unique challenges and opportunities to UX educators and aspiring UX designers. In this paper, we analyzed a corpus of question and answer communication on UX Stack Exchange using a practice-led approach, identifying and documenting practitioners' conceptions of UX knowledge over a nine year period. Specifically, we used natural language processing techniques and qualitative content analysis to identify a disciplinary vocabulary invoked by UX designers in this online community, as well as conceptual trajectories spanning over nine years which could shed light on the evolution of UX practice. We further describe the implications of our findings for HCI research and UX education.