Reviewerly/exHarmony huggingface dataset:
Incremental Learning Based on Data Translation and Knowledge Distillation

Recently, deep convolutional neural networks (DCNNs) have achieved remarkable results in image classification tasks.Despite convolutional networks' great successes, their training process relies on a large amount of data prepared in advance, which is often challenging in real-world applications, such as streaming data and concept drift.For this reason, incremental learning (continual learning) has attracted increasing attention from scholars.However, incremental learning is associated with the challenge of catastrophic forgetting: the performance on previous tasks drastically degrades after learning a new task.In this paper, we propose a new strategy to alleviate catastrophic forgetting when neural networks are trained in continual domains.Specifically, two components are applied: data translation based on transfer learning and knowledge distillation.The former translates a portion of new data to reconstruct the partial data distribution of the old domain.The latter uses an old model as a teacher to guide a new model.The experimental results on three datasets have shown that our work can effectively alleviate catastrophic forgetting by a combination of the two methods aforementioned.
Sistem Penentuan Lokasi Menara Base Transceiver Station dengan Algoritma AHP-TOPSIS

Cellular telecommunication technology has now become an inseparable part of the increasingly mobile pattern of human life, this of course has an impact on the number of uses of cellular telecommunication technology which is increasing every day. This increase has encouraged cellular telecommunications technology vendors to continuously improve the quantity and quality of their telecommunications networks by establishing telecommunications equipment such as Base Transceiver Station (BTS) towers at strategic points. This study aims to build a system for determining the location of BTS towers that can provide visualization of information on the distribution of existing BTS towers and their coverage area radius. Analysis and location determination using a combination of AHP and TOPSIS methods. The AHP method is used to determine the objectivity of the weight and importance of the BTS tower location criteria. Furthermore, the results of the comparison of criteria will be used in the TOPSIS method to assess the rating of each candidate location. The results of the study obtained that the criteria for the location of BTS towers were population density with a weight of 0.42, the distance of the existing tower was 0.2, location access was 0.31, and the installation cost was 0.08. Meanwhile, the candidate locations were measured using linguistic variables with a weight of 0.67 for very good criteria, 0.23 for good, and 0.10 for good enough. System testing was carried out on determining the location of the BTS tower in Sinjai Borong District by analyzing three candidate locations and the results obtained that the Samaenre location was the best candidate with a rating value of 0.74.
Adaptation Strategies for Human-Machine Interactions in Dialect Specific Environment

Offering human machine interfaces capable of handling cultural variation in speech is an exciting topic in cognitive info-communication.From a machine learning point-of-view, this paper examines Automatic Speech Recognition (ASR) with respect to transcribing interactions occurring in a language environment where a particular dialect of a pluricentric language is spoken.Conservative retraining, transfer learning, multi-task training, matrix factorization, i-vector based techniques as well as adversarial and teacherstudent training are all considered suitable approaches for the adaptation of deep neural acoustic models in ASR.Facing a problem of adaptation for accented speech, which method should be preferred?Comparing these techniques is often complicated, as different experiments are carried out on diverse datasets and within various frameworks.It is also worthwhile analysing possible combination of such techniques within complex systems.The objective of this work is to systematically compare and analyse a number of domain adaptation techniques for ASR using the same framework, in order to allow for a fair comparison on adapting US English acoustic models for particular accents.Our results indicate that, when properly hyperparametrized and carefully regularized, the easiest approaches, requiring less complexity and reduced computational power, can perform equally well as the more complex ones.
AutoSpill: Credential Leakage from Mobile Password Managers

Password managers (PMs) are becoming increasingly popular on mobile devices, especially on small-screen devices, mainly due to the convenience of automatically filling credentials into login forms. Modern mobile OSes advocate for system-wide autofill frameworks to support autofilling on browsers as well as other apps. Mobile OSes also empower apps to directly render web content within WebView controls without redirecting users to the main browser. \par We present a novel technique, called AutoSpill, to leak users' saved credentials during an autofill operation on a webpage loaded into an app's WebView. AutoSpill conveniently dodges the secure autofill process. The majority of popular Android PMs considered in our experiments were found vulnerable to AutoSpill; even when the app hosting the WebView is not actively participating in the leak. Android intermediates in the autofill process because of its app sandboxing. Hence, the responsibility for any credential leakage is often stranded between PMs and the Android system. We investigate the root causes of AutoSpill and propose countermeasures to fundamentally fix AutoSpill for both the parties. We responsibly disclosed our findings to the affected PMs and Android security team.
Tool/Dataset Paper: Realistic ABAC Data Generation using Conditional Tabular GAN

Attribute-based Access Control (ABAC) is increasingly being used in a wide variety of applications that include cloud services, IoT, smart homes, healthcare and several others. Conducting systematic and reproducible experiments with benchmark realistic datasets, however, still remains a challenge. To address this shortcoming, in this paper we introduce a method called ConGRASS (Conditional Tabular GAN for Realistic ABAC Simulation Studies) for generating large ABAC datasets. Starting with a given real world dataset of (potentially) limited size, we first train a conditional tabular generative adversarial network for learning its distribution. The trained model is used to generate realistic datasets of arbitrarily large sizes having distribution similar to the original dataset. ConGRASS has been implemented as a free to use web-based tool in which a user can choose the name of a listed real dataset along with the desired dataset size. A CSV file containing ABAC data is generated as output. Extensive evaluation shows the ability of the model to faithfully learn the statistical properties of the selected real data. When such a dataset is used in an actual problem, significant improvement in performance is achieved, proving the utility of ConGRASS.
Role Models: Role-based Debloating for Web Applications

The process of debloating, i.e., removing unnecessary code and features in software, has become an attractive proposition to managing the ever-expanding attack surface of ever-growing modern applications. Researchers have shown that debloating produces significant security improvements in a variety of application domains including operating systems, libraries, compiled software, and, more recently, web applications. Even though the client/server nature of web applications allows the same backend to serve thousands of users with diverse needs, web applications have been approached monolithically by existing debloating approaches. That is, a feature can be debloated only if none of the users of a web application requires it. Similarly, everyone gets access to the same "global" features, whether they need them or not. Recognizing that different users need access to different features, in this paper we propose role-based debloating for web applications. In this approach, we focus on clustering users with similar usage behavior together and providing them with a custom debloated application that is tailored to their needs. Through a user study with 60 experienced web developers and administrators, we first establish that different users indeed use web applications differently. This data is then used by DBLTR, an automated pipeline for providing tailored debloating based on a user's true requirements. Next to debloating web applications, DBLTR includes a transparent content-delivery mechanism that routes authenticated users to their debloated copies. We demonstrate that for different web applications, DBLTR can be 30-80% more effective than the state-of-the-art in debloating in removing critical vulnerabilities.
SCAtt-man: Side-Channel-Based Remote Attestation for Embedded Devices that Users Understand

From the perspective of end-users, IoT devices behave like a black box: As long as they work as intended, users will not detect any compromise. Users have minimal control over the software. Hence, it is very likely that the user misses that illegal recordings and transmissions occur if a security camera or a smart speaker is hacked. In this paper, we present SCAtt-man, the first remote attestation scheme that is specifically designed with the user in mind. SCAtt-man deploys software-based attestation to check the integrity of remote devices, allowing users to verify the integrity of IoT devices with their smartphones. The key novelty of SCAtt-man resides in the utilization of user-observable side-channels such as light or sound in the attestation protocol. Our proof-of-concept implementation targets a smart speaker and an attestation protocol that is based on a data-over-sound protocol. Our evaluation demonstrates the effectiveness of \toolname against a variety of attacks and its usability based on a user study with 20 participants.
Upgrading Conventional Space Maintainers Using GrADS Appliance: A Case Report

rehabilitation, including multiple extractions, was planned chair-side under nitrous oxide and oxygen inhalational sedation.Owing to the parental demands of esthetics in the anterior region, apart from the need for space preservation, it was decided to customize a unique design of SM for the maxilla.
Statistical Learning

One of the main goals of statistical learning is to characterize how the excess risk depends on the sample size n, on the complexity of the hypothesis class, and on the underlying complexity of the prediction problem itself. A related problem is to control the generalization error, which is a measure of how accurately an algorithm is able to predict outcome values for previously unseen data. Establishing probability error bounds for these problems can be converted into a problem of uniform convergence. We first introduce some commonly used technical tools for uniform convergence. Along the way, we highlight the recent development of learning theory for deep neural networks (DNNs) and explain the theoretical benefit to improve the generalization error in practice. Furthermore, we present the generalization of DNNs for robust adversarial learning with ℓ∞ attacks. For general machine learning tasks, we show that adversarial Rademacher complexity is always larger than natural counterpart, but the effect of adversarial perturbations can be limited under the weight normalization framework.
Comparing Zealous and Restrained AI Recommendations in a Real-World Human-AI Collaboration Task

When designing an AI-assisted decision-making system, there is often a tradeoff between precision and recall in the AI's recommendations. We argue that careful exploitation of this tradeoff can harness the complementary strengths in the human-AI collaboration to significantly improve team performance. We investigate a real-world video anonymization task for which recall is paramount and more costly to improve. We analyze the performance of 78 professional annotators working with a) no AI assistance, b) a high-precision "restrained" AI, and c) a high-recall "zealous" AI in over 3,466 person-hours of annotation work. In comparison, the zealous AI helps human teammates achieve significantly shorter task completion time and higher recall. In a follow-up study, we remove AI assistance for everyone and find negative training effects on annotators trained with the restrained AI. These findings and our analysis point to important implications for the design of AI assistance in recall-demanding scenarios.
Graphical Perception of Saliency-based Model Explanations

In recent years, considerable work has been devoted to explaining predictive, deep learning-based models, and in turn how to evaluate explanations. An important class of evaluation methods are ones that are human-centered, which typically require the communication of explanations through visualizations. And while visualization plays a critical role in perceiving and understanding model explanations, how visualization design impacts human perception of explanations remains poorly understood. In this work, we study the graphical perception of model explanations, specifically, saliency-based explanations for visual recognition models. We propose an experimental design to investigate how human perception is influenced by visualization design, wherein we study the task of alignment assessment, or whether a saliency map aligns with an object in an image. Our findings show that factors related to visualization design decisions, the type of alignment, and qualities of the saliency map all play important roles in how humans perceive saliency-based visual explanations.
Reality Rifts: Wonder-ful Interfaces by Disrupting Perceptual Causality

Reality Rifts are interfaces between the physical and the virtual reality, where incoherent observations of physical behavior lead users to imagine comprehensive and plausible end-to-end dynamics. Reality Rifts emerge in interactive physical systems that lack one or more components that are central to their operation, yet where the physical end-to-end interaction persists with plausible outcomes. Even in the presence of a Reality Rift, users can still interact with a system—much like they would with the unaltered and complete counterpart—leading them to implicitly infer the existence and imagine the behavior of the lacking components from observable phenomena and outcomes. Therefore, dynamic systems with Reality Rifts trigger doubt, curiosity, and rumination—a sense of wonder that users experience when observing a Reality Rift due to their innate curiosity.
“It can bring you in the right direction”: Episode-Driven Data Narratives to Help Patients Navigate Multidimensional Diabetes Data to Make Care Decisions

Engaging with multiple streams of personal health data to inform self-care of chronic health conditions remains a challenge. Existing informatics tools provide limited support for patients to make data actionable. To design better tools, we conducted two studies with Type 1 diabetes patients and their clinicians. In the first study, we observed data review sessions between patients and clinicians to articulate the tasks involved in assessing different types of data from diabetes devices to make care decisions. Drawing upon these tasks, we designed novel data interfaces called episode-driven data narratives and performed a task-driven evaluation. We found that as compared to the commercially available diabetes data reports, episode-driven data narratives improved engagement and decision-making with data. We discuss implications for designing data interfaces to support interaction with multidimensional health data to inform self-care.
MyCareBudget: Co-creating a Healthcare Digital Commons with and for Disabled Citizens and their Unpaid Carers

Supporting disabled populations and their unpaid carers through designing sustainable healthcare interventions and infrastructures, is an important, yet challenging, area in HCI research. We report on a collaboration with 23 disabled citizens, unpaid carers, and a care organisation, wishing to co-develop digital responses to challenges they face in the management of self-directed care budgets. We describe how leveraging participatory methods, including asynchronous and remote engagements, enabled the co-creation of a sustainable digital common-pool resource, used by over 5,000 people worldwide. This study contributes novel configurations of methods and tools for co-design with 'seldom heard' populations. Demonstrating how these enabled the collective articulation of what constitutes trust, governance, and responsibility, in the design of a digital commons, "MyCareBudget", offering peer-produced care documents for use by disabled citizens and their unpaid carers. We discuss implications for HCI interested in co-designing sustainable socio-technical interventions with underserved and marginalised populations, in healthcare settings.
Polagons: Designing and Fabricating Polarized Light Mosaics with User-Defined Color-Changing Behaviors

Polarized light mosaics (PLMs) are color-changing structures that alter their appearance based on the orientation of incident polarized light. While a few artists have developed techniques for crafting PLMs by hand, the underlying material properties are difficult to reason about; there exist no tools to bridge the high-level design objectives with the low-level physics knowledge needed to create PLMs. In this paper, we introduce the first system for creating Polagons: machine-made PLMs crafted from cellophane with user-defined color changing behaviors. Our system includes an interface for designing and visualizing Polagons as well as a fabrication process based on laser cutting and welding that requires minimal assembly by the user. We define the design space for Polagons and demonstrate how formalizing the process for creating PLMs can enable new applications in fields such as education, data visualization, and fashion.
Deep Learning Super-Resolution Network Facilitating Fiducial Tangibles on Capacitive Touchscreens

Over the last few years, we have seen many approaches using tangibles to address the limited expressiveness of touchscreens. Mainstream tangible detection uses fiducial markers embedded in the tangibles. However, the coarse sensor size of capacitive touchscreens makes tangibles bulky, limiting their usefulness. We propose a novel deep-learning super-resolution network to facilitate fiducial tangibles on capacitive touchscreens better. In detail, our network super-resolves the markers enabling off-the-shelf detection algorithms to track tangibles reliably. Our network generalizes to unseen marker sets, such as AprilTag, ArUco, and ARToolKit. Therefore, we are not limited to a fixed number of distinguishable objects and do not require data collection and network training for new fiducial markers. With extensive evaluation, including real-world users and five showcases, we demonstrate the applicability of our open-source approach on commodity mobile devices and further highlight the potential of tangibles on capacitive touchscreens.
Perceptual Pat: A Virtual Human Visual System for Iterative Visualization Design

Designing a visualization is often a process of iterative refinement where the designer improves a chart over time by adding features, improving encodings, and fixing mistakes. However, effective design requires external critique and evaluation. Unfortunately, such critique is not always available on short notice and evaluation can be costly. To address this need, we present Perceptual Pat, an extensible suite of AI and computer vision techniques that forms a virtual human visual system for supporting iterative visualization design. The system analyzes snapshots of a visualization using an extensible set of filters—including gaze maps, text recognition, color analysis, etc—and generates a report summarizing the findings. The web-based Pat Design Lab provides a version tracking system that enables the designer to track improvements over time. We validate Perceptual Pat using a longitudinal qualitative study involving 4 professional visualization designers that used the tool over a few days to design a new visualization.
Perspective and Geometry Approaches to Mouse Cursor Control in Spatial Augmented Reality

Spatial augmented reality (SAR) can extend desktop computing out of the monitor and into our surroundings, but extending the standard style of mouse input is challenging due to real-world geometry irregularity, gaps, and occlusion. We identify two general approaches for controlling a mouse cursor in SAR: perspective-based approaches based on raycasting, such as Nacenta et. al's Perspective Cursor, and geometry-based approaches that closely associate cursor movement with surface topology. For the latter, we introduce Everywhere Cursor, a geometry-based approach for indirect mouse cursor control for complex 3D surface geometry in SAR. A controlled experiment compares approaches. Results show the geometry-based Everywhere Cursor improves accuracy and precision by 29% to 60% on average in a tracing task, but when traversing long distances, the perspective-based Perspective Cursor and Raycasting techniques are 22% to 49% faster, albeit with 4% to 10% higher error rates.
Promoting Inclusiveness and Fairness through NFTs: The Case of Student-Athletes and NILs

Recent regulatory changes have enabled NCAA student-athletes to profit from their name, image, and likeness (NIL), departing from previous policies requiring those athletes to maintain their amateur status. However, despite the changes, it is unlikely that all the approximately 500,000 NCAA student-athletes will profit from NIL contracts. Within this context, we study how to design a fair and inclusive solution that may help all student-athletes secure NIL financial resources. Following a design science approach, we define design requirements after interviewing student-athletes. Subsequently, we derive three design principles: inclusiveness, fairness, and transparency. Thereafter, we suggest a blockchain-based artifact that satisfies all design principles. Our idea lies in designing collectibles as non-fungible tokens (NFTs) that pay different royalties whenever a transaction (purchase or exchange) happens in different markets (primary or secondary). Finally, we evaluate our solution by discussing its features with current student-athletes.
Independence for Whom? A Critical Discourse Analysis of Onboarding a Home Health Monitoring System for Older Adult Care

Home health monitoring systems (HHMS) are presented as a cost-effective solution that will assist with collaborative care of older adults. However, instead of care recipients feeling like collaborators, such systems often disempower them. In this paper, we examine the dissemination, onboarding, and initial use of an HHMS to see how the discourse used by developers and participants affects users' collaborative care efforts. We found that the textual information provided often contrasted with how our participants managed their care. Instead of providing participants with 'independence,' 'safety,' and 'peace of mind,' care recipients were placed in a more dependent, less proactive role, and care providers were pressured to take on more responsibilities. We position HHMS, as they are currently marketed and onboarded, as normalizing pseudo-institutionalization. As an alternative we advocate that the discourse and design of such systems should reflect and re-enforce the varied roles care recipients take in managing their care.