Reviewerly/exHarmony huggingface dataset:
Bounding Box Based Weakly Supervised Deep Convolutional Neural Network for Medical Image Segmentation Using an Uncertainty Guided and Spatially Constrained Loss

In this paper, we propose a weakly supervised deep convolutional neural network for medical image segmentation using an uncertainty guided and spatially constrained loss, which only requires bounding box annotations for model training. We utilise predictive uncertainty estimation during training to guide the model learning from the image region with high predictive confidence. Additionally, a conditional random field (CRF) based local spatial constraint is incorporated to the loss function, which regularises the predicted labels of a local region. This CRF loss term is independent to the training labels (bounding box annotation), which prevents the model over-fitted to the bounding box annotation. We evaluated our method on a public dermoscopic dataset containing different types of skin lesions. Our method achieved superior performance in comparison with the state-of-the-art learning based (DeepCut) and non-learning based (GrabCut) methods in terms of dice coefficient. The code is available on Github (https://github.com/golnarkmahani/Weakly-Supervised-Segmentation).
ProGesAR: Mobile AR Prototyping for Proxemic and Gestural Interactions with Real-world IoT Enhanced Spaces

Real-world IoT enhanced spaces involve diverse proximity- and gesture-based interactions between users and IoT devices/objects. Prototyping such interactions benefits various applications like the conceptual design of ubicomp space. AR (Augmented Reality) prototyping provides a flexible way to achieve early-stage designs by overlaying digital contents on real objects or environments. However, existing AR prototyping approaches have focused on prototyping AR experiences or context-aware interactions from the first-person view instead of full-body proxemic and gestural (pro-ges for short) interactions of real users in the real world. In this work, we conducted interviews to figure out the challenges of prototyping pro-ges interactions in real-world IoT enhanced spaces. Based on the findings, we present ProGesAR, a mobile AR tool for prototyping pro-ges interactions of a subject in a real environment from a third-person view, and examining the prototyped interactions from both the first- and third- person views. Our interface supports the effects of virtual assets dynamically triggered by a single subject, with the triggering events based on four features: location, orientation, gesture, and distance. We conduct a preliminary study by inviting participants to prototype in a freeform manner using ProGesAR. The early-stage findings show that with ProGesAR, users can easily and quickly prototype their design ideas about pro-ges interactions.
Self-Recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks

Nowadays, users upload numerous photos to social network platforms to share their daily lives. These photos contain numerous personal information, which can be easily captured by intelligent algorithms. To improve privacy security, we aim to form a protection mechanism by exploiting adversarial examples, which can mislead and disrupt intelligent algorithms. However, the existing adversarial attack lacks the study on recoverability and reversibility, which makes them unable to serve as an effective protection mechanism. To address this issue, we propose a recoverable generative adversarial network to generate self-recoverable adversarial examples. By modeling the adversarial attack and recovery as a united task, our method can minimize the error of the recovered examples while maximizing the attack ability, resulting in better recoverability of adversarial examples. To further boost the recoverability of these examples, we exploit a dimension reducer to optimize the distribution of adversarial perturbation. The experimental results prove that the adversarial examples generated by the proposed method present superior recoverability, attack ability, and robustness on different datasets and network architectures, which ensure its effectiveness as a protection mechanism in social networks.
Pandemic Displays: Considering Hygiene on Public Touchscreens in the Post-Pandemic Era

The COVID-19 pandemic created unprecedented questions for touch-based public displays regarding hygiene, risks, and general awareness. We study how people perceive and consider hygiene on shared touchscreens, and how touchscreens could be improved through hygiene-related functions. First, we report the results from an online survey (n = 286). Second, we present a hygiene concept for touchscreens that visualizes prior touches and provides information about the cleaning of the display and number of prior users. Third, we report the feedback for our hygiene concept from 77 participants. We find that there is demand for improved awareness of public displays' hygiene status, especially among those with stronger concerns about COVID-19. A particularly desired detail is when the display has been cleaned. For visualizing prior touches, fingerprints worked best. We present further considerations for designing for hygiene on public displays.
Evaluation of text-to-gesture generation model using convolutional neural network

Conversational gestures have a crucial role in realizing natural interactions with virtual agents and robots. Data-driven approaches, such as deep learning and machine learning, are promising in constructing the gesture generation model, which automatically provides the gesture motion for speech or spoken texts. This study experimentally analyzes a deep learning-based gesture generation model from spoken text using a convolutional neural network. The proposed model takes a sequence of spoken words as the input and outputs a sequence of 2D joint coordinates representing the conversational gesture motion. We prepare a dataset consisting of gesture motions and spoken texts by adding text information to an existing dataset and train the models using specific speaker's data. The quality of the generated gestures is compared with those from an existing speech-to-gesture generation model through a user perceptual study. The subjective evaluation shows that the model performance is comparable or superior to those by the existing speech-to-gesture generation model. In addition, we investigate the importance of data cleansing and loss function selection in the text-to-gesture generation model. We further examine the model transferability between speakers. The experimental results demonstrate successful model transferability of the proposed model. Finally, we show that the text-to-gesture generation model can produce good quality gestures even when using a transformer architecture.
ComputableViz: Mathematical Operators as a Formalism for Visualisation Processing and Analysis

Data visualizations are created and shared on the web at an unprecedented speed, raising new needs and questions for processing and analyzing visualizations after they have been generated and digitized. However, existing formalisms focus on operating on a single visualization instead of multiple visualizations, making it challenging to perform analysis tasks such as sorting and clustering visualizations. Through a systematic analysis of previous work, we abstract visualization-related tasks into mathematical operators such as union and propose a design space of visualization operations. We realize the design by developing ComputableViz, a library that supports operations on multiple visualization specifications. To demonstrate its usefulness and extensibility, we present multiple usage scenarios concerning processing and analyzing visualization, such as generating visualization embeddings and automatically making visualizations accessible. We conclude by discussing research opportunities and challenges for managing and exploiting the massive visualizations on the web.
Designing Tools and Interfaces for Ecological Restoration: An Investigation into the Opportunities and Constraints for Technological Interventions

As interest within the HCI community expands beyond urban settings, novel tools and devices are being developed to support more sustainable interactions with natural environments and inform conservation action. Yet little is known about the users of these devices, and how their requirements and priorities might affect the usability or operationalization of the devices in the real world. Using the 'e-seed', a biomimetic self-drilling interface as a 'research probe', we conducted a qualitative user study with 14 subject matter experts in areas like forestry and agriculture to understand the value and limits for devices and systems in ecological restoration and monitoring. We highlight unique challenges in existing ecological practices, opportunities for technological interventions, and the policies and economic constraints affecting the feasibility of such interventions. We present a set of critical design considerations for building and deploying novel devices in natural and semi-natural ecosystems and discuss implications for future research.
WearSkill

We introduce WearSkill, a web application that implements personalized and interchangeable input for wearable computing. We outline functional and quality requirements for WearSkill and present the engineering details of its implementation using web technology. We emphasize the interchangeability of input modalities with various wearables, e.g., touch input on a smart ring vs. mid-air gestures of the hand wearing a smartwatch vs. voice input detected by the microphone from a pair of smartglasses, towards personalized input for users with various motor abilities. Our findings, from a study involving twenty-one people with motor impairments, show that WearSkill can provide accurate recommendations for personalized input modalities that match 85.3% with users' own preferences.
An improved invasive weed optimization enabled Shepard convolutional neural network for classification of breast cancer

Abstract One of the most predominant types of cancer affecting women is breast cancer. In computer‐assisted analysis of pathology, the most crucial task is classifying breast cancer automatically from the histopathological images. Most of the patients experienced everlasting effects and loss of life because of the absence of timely detection and thereby delay in treatment. Thus, the timely detection as well as precise identification of breast cancer using the histopathological image is highly required. An improved invasive weed water wave optimization (IIWWWO) algorithm is devised here, which is employed in training the Shepard convolutional neural networks (ShCNN) classifier. The developed optimization algorithm combines the improved invasive weed optimization algorithm and water wave optimization algorithm. Initially, the Bayesian filtering process is applied in the input image to remove the noises present in the input image. Moreover, the Hessian‐based Laplacian of Gaussian texture feature model is utilized in segmenting blood cells for an effective classification process. The shape features, namely shape area, diameter, and perimeter, are effectively extracted for better performance. Besides, ShCNN classifier is the most important one for classifying breast cancers. The proposed IIWWWO optimization technique is utilized in tuning the employed classifier termed as ShCNN classifier. Classification of breast cancer is accomplished using two levels, such as malignant and benign. The developed IIWWWO‐based ShCNN model is compared with various metrics, like accuracy, true negative rate (TNR) and true positive rate (TPR). The developed technique used to classify breast cancer obtained better accuracy of 95.83%, maximal TPR of 94.72%, and high TNR of 93.15%.
Hybrid Guidance Optimization for Multipulse Glideslope Approach with Bearing-Only Navigation

This paper proposes a modified glideslope guidance method that optimizes a hybrid multiobjective of bearing-only navigation error and fuel consumption. The traditional glideslope guidance fixes uniform maneuver intervals and the initial approach velocity as a predetermined value, making this approach inflexible. In this paper, the maneuver intervals and the initial approach velocity were used as optimization variables, and a hybrid cost function was designed. The tradeoff between the two objectives was analyzed with a bearing-only navigation simulation conducted to reveal the navigation performance following different resulting trajectories. The result showed that the optimal scheduled times of maneuvers remained relatively stable under different tradeoff weights, while a strong correlation between the optimal initial approach velocity and the tradeoff weight was revealed. Therefore, when the optimization has to be solved several times online with different tradeoff weights, the initial approach velocity can be the only optimization variable, leaving the scheduled times of maneuvers fixed in the optimal values achieved offline. These findings provide a potential reference for far-approach trajectory design of bearing-only navigation.
“It Basically Started Using Me:” An Observational Study of Password Manager Usage

There is limited information regarding how users employ password managers in the wild and why they use them in that manner. To address this knowledge gap, we conduct observational interviews with 32 password manager users. Using grounded theory, we identify four theories describing the processes and rationale behind participants' usage of password managers. We find that many users simultaneously use both a browser-based and a third-party manager, using each as a backup for the other, with this new paradigm having intriguing usability and security implications. Users also eschew generated passwords because these passwords are challenging to enter and remember when the manager is unavailable, necessitating new generators that create easy-to-enter and remember passwords. Additionally, the credential audits provided by most managers overwhelm users, limiting their utility and indicating a need for more proactive and streamlined notification systems. We also discuss mobile usage, adoption and promotion, and other related topics.
VibEmoji: Exploring User-authoring Multi-modal Emoticons in Social Communication

Emoticons are indispensable in online communications. With users’ growing needs for more customized and expressive emoticons, recent messaging applications begin to support (limited) multi-modal emoticons:, enhancing emoticons with animations or vibrotactile feedback. However, little empirical knowledge has been accumulated concerning how people create, share and experience multi-modal emoticons in everyday communication, and how to better support them through design. To tackle this, we developed VibEmoji, a user-authoring multi-modal emoticon interface for mobile messaging. Extending existing designs, VibEmoji grants users greater flexibility to combine various emoticons, vibrations, and animations on-the-fly, and offers non-aggressive recommendations based on these components’ emotional relevance. Using VibEmoji as a probe, we conducted a four-week field study with 20 participants, to gain new understandings from in-the-wild usage and experience, and extract implications for design. We thereby contribute to both a novel system and various insights for supporting users’ creation and communication of multi-modal emoticons.
Affinder: Expressing Concepts of Situations that Afford Activities using Context-Detectors

Context-aware applications have the potential to act opportunistically to facilitate human experiences and activities, from reminding us of places to perform personal activities, to identifying coincidental moments to engage in digitally-mediated shared experiences. However, despite the availability of context-detectors and programming frameworks for defining how such applications should trigger, designers lack support for expressing their human concepts of a situation and the experiences and activities they afford (e.g., situations to toss a frisbee) when context-features are made available at the level of locations (e.g., parks). This paper introduces Affinder, a block-based programming environment that supports constructing concept expressions that effectively translate their conceptions of a situation into a machine representation using available context features. During pilot testing, we discovered three bridging challenges that arise when expressing situations that cannot be encoded directly by a single context-feature. To overcome these bridging challenges, Affinder provides designers (1) an unlimited vocabulary search for discovering features they may have forgotten; (2) prompts for reflecting and expanding their concepts of a situation and ideas for foraging for context-features; and (3) simulation and repair tools for identifying and resolving issues with the precision of concept expressions on real use-cases. In a comparison study, we found that Affinder’s core functions helped designers stretch their concepts of how to express a situation, find relevant context-features matching their concepts, and recognize when the concept expression operated differently than intended on real-world cases. These results show that Affinder and tools that support bridging can improve a designer’s ability to express their concepts of a human situation into detectable machine representations—thus pushing the boundaries of how computing systems support our activities in the world.
Study on the Evolutionary Game of Information Security Supervision in Smart Cities under Different Reward and Punishment Mechanisms

At present, the information security problems of smart city show a high incidence, and it is necessary to strengthen the information security supervision of smart city. In the process of supervision, there is a game relationship between local government and smart city enterprises. This paper firstly constructs the game matrices of local government and enterprises under the static and three dynamic reward and punishment mechanisms, then conducts numerical simulation with the help of MATLAB to arrive at the optimal reward and punishment mechanism through comparison, and finally explores the influence of the change of the upper limit value of each key variable on the directionality and sensitivity of the decision-making behavior of game subjects under the optimal mechanism. The result shows that initial value is one of the decisive factors influencing the choice of management strategy by enterprise. Dynamic reward and dynamic punishment mechanism is the best reward and punishment mechanism for information security supervision in smart cities. In case the upper limit value of key parameters is increased, a larger punishment has a strong influence on the positive strategy choice of the enterprise, and a reasonable adjustment of the reward policy can likewise mobilize the probability that the enterprise actively chooses to strengthen information security management. Based on the simulation results, we propose a feasible strategy.
Google Tesseract: Optical Character Recognition (OCR) on HDD / SSD Labels Using Machine Vision

This paper is designed to have an optical character recognition system capable of interpreting captured images of hard disk drive and solid-state drive labels with high accuracy. Manual checking of the disk capacity size and part number found on the labels is time consuming, more prone to errors and utilizes more manpower. Automating the inspection through optical character recognition using image pre-processing and machine vision contributes to an easier inspection process, better management of records and faster cycle time. The images captured using a vision camera went through different stages of image pre-processing via OpenCV-Python and recognition through Google Tesseract. Different categorical variables including exposure time and location of texts in a captured image were used to determine and improve the overall recognition accuracy. By improving the lighting condition through the addition of light sources, the developed OCR system was able to achieve a character recognition accuracy of 99.375%.
Study supporting restoration of rapakivi granite in St. Petersburg

The focus of this ongoing study has been on two rapakivi granites, wiborgite and pyterlite, that have been utilized in St Petersburg since the 1760s but originate from south-eastern Finland. The aims of this study have been to describe the two types of rapakivi granites in their geological settings, to assess the needs for restoration of stones utilized in constructions and to establish databases of stones utilized in historical buildings and of the original and alternative quarries. The focus of this study has been on defining the original quarries for the old building stones or finding other similar substitute granite deposits. Chemical analysis with a handheld XRF instrument was found to be the most promising approach when combined with multivariate mathematical tools. In addition, the applicability of chemical treatment of the surface of rapakivi granite for changing the appearance of stones was investigated. The visual appearance and texture of natural stones used in restoration are the key factors, in addition to the stone type and quality. The chemical treatment of stone surfaces using acidic or metal salt treatments altered the colour tone and visual pattern. Thermal treatment was also found to be effective, but it might be harmful to the surface structure. With further optimisation, chemical treatment could be a feasible method to change the visual appearance of fresh stone in the case of substitution of weathered stone. In addition, this project has initiated cross-border activities that aim to create opportunities for establishing first contacts between the authorities, construction companies, stone producers and the manufacturer who produces the needed high-quality stone pieces.
ActiSight: Wearer Foreground Extraction Using a Practical RGB-Thermal Wearable

Wearable cameras provide an informative view of wearer activities, context, and interactions. Video obtained from wearable cameras is useful for life-logging, human activity recognition, visual confirmation, and other tasks widely utilized in mobile computing today. Extracting foreground information related to the wearer and separating irrelevant background pixels is the fundamental operation underlying these tasks. However, current wearer foreground extraction methods that depend on image data alone are slow, energy-inefficient, and even inaccurate in some cases, making many tasks-like activity recognition- challenging to implement in the absence of significant computational resources. To fill this gap, we built ActiSight, a wearable RGB-Thermal video camera that uses thermal information to make wearer segmentation practical for body-worn video. Using ActiSight, we collected a total of 59 hours of video from 6 participants, capturing a wide variety of activities in a natural setting. We show that wearer foreground extracted with ActiSight achieves a high dice similarity score while significantly lowering execution time and energy cost when compared with an RGB-only approach.