Reviewerly/exHarmony huggingface dataset:
Embr: A Creative Framework for Hand Embroidered Liquid Crystal Textile Displays

Conductive thread is a common material in e-textile toolkits that allows practitioners to create connections between electronic components sewn on fabric. When powered, conductive threads are used as resistive heaters to activate thermochromic dyes or pigments on textiles to create interactive, aesthetic, and ambient textile displays. In this work, we introduce Embr, a creative framework for supporting hand-embroidered liquid crystal textile displays (LCTDs). This framework includes a characterization of conductive embroidery stitches, an expanded repertoire of thermal formgiving techniques, and a thread modeling tool used to simulate mechanical, thermal, and electrical behaviors of LCTDs. Through exemplar artifacts, we annotate a morphological design space of LCTDs and discuss the tensions and opportunities of satisfying the wider range of electrical, craft, cultural, aesthetic, and functional concerns inherent to e-textile practices.
SonarID: Using Sonar to Identify Fingers on a Smartwatch

The diminutive size of wrist wearables has prompted the design of many novel input techniques to increase expressivity. Finger identification, or assigning different functionality to different fingers, has been frequently proposed. However, while the value of the technique seems clear, its implementation remains challenging, often relying on external devices (e.g., worn magnets) or explicit instructions. Addressing these limitations, this paper explores a novel approach to natural and unencumbered finger identification on an unmodified smartwatch: sonar. To do this, we adapt an existing finger tracking smartphone sonar implementation—rather than extract finger motion, we process raw sonar fingerprints representing the complete sonar scene recorded during a touch. We capture data from 16 participants operating a smartwatch and use their sonar fingerprints to train a deep learning recognizer that identifies taps by the thumb, index, and middle fingers with an accuracy of up to 93.7%, sufficient to support meaningful application development.
Reversible data hiding for JPEG images based on improved mapping and frequency ordering

Due to the good balance between file size and visual quality, JPEG images are widely used in our daily lives. Many reversible data hiding (RDH) schemes have been developed for JPEG images, most of which are based on the modification of one-dimensional (1D) histograms of DCT coefficients. Recently, two-dimensional (2D) histograms of DCT coefficients are used to improve embedding efficiency. However, the existing coefficient pairing, coefficient pair mapping and embedding order can be further optimized. In this paper, a new RDH scheme for JPEG images based on 2D histogram modification is proposed. First, according to the statistical distribution of the AC coefficients, two non-zero coefficients with the same frequency in adjacent blocks, instead of two neighboring coefficients within the same block, are paired. More coefficient pairs with higher embedding efficiency can be obtained. Then, an optimized coefficient pair mapping is presented, more specifically, the coefficient pairs that occur more frequently are given priority to use the transfer mode with higher embedding efficiency. Lastly, a new method is proposed to accurately estimate the embedding efficiency of different frequencies. The data is embedded into the coefficient pairs at the frequency position where the embedding efficiency is higher. Extensive experiments on several commonly-used images and two popular image databases show that our proposed scheme can achieve better visual quality and smaller file size increments compared to the state-of-the-art RDH schemes for JPEG images.
Enhancing Core Public Service Vocabulary to Enable Public Service Personalization

The provision of public services (PS) is at the heart of public authority operations as it directly affects citizens’ lives and the prosperity of society. Part of PS provision is publishing PS descriptions in an online catalogue to inform citizens and promote transparency. The European Commission has developed Core Public Service Vocabulary Application Profile (CPSV-AP), as a standard European PS data model to facilitate PS catalogue creation and semantic interoperability. However, CPSV-AP is not sufficient to model complex PS with different versions based on rules and citizens’ circumstances (e.g., getting a passport for a child or for an emergency). As a result, citizens cannot obtain personalized information on PS. The aim of this paper is to enhance CPSV-AP in order to support the modeling of complex PS. We illustrate the use of the proposed model in a real-life case study. Specifically, we use the proposed model to develop a knowledge graph and a chatbot that provides personalized information to citizens of the city of Bjelovar (Croatia) regarding the life-event “having a baby”. We believe our research is of interest to researchers on PS data models and public authorities interested in providing personalized PS information to their citizens using chatbots.
Classification of Indian media titles using deep learning techniques

Automatic speech recognition is being used everywhere these days. An essential part of this is language identification. Our goal here is to identify the language of the media title, such as song names and movie titles, to help in speech recognition. The focus here is to classify solely using the title of the media without any additional data in their transliterated form to classify them into their original native language using natural language processing, machine learning, and deep learning techniques. Transliterated titles of the song and movie names are being used. This work explores and implements various natural language processing and machine learning methods such as N-grams, SVMs, LSTMs, and MuRIL to classify the text titles according to their language. The results of various implementations are compared and contrasted as an approach of its own to classify the data.
Attack or Block? Repertoires of Digital Censorship in Autocracies

Online censorship has become a common feature in autocracies. Previous work has investigated different online censorship tactics such as website blocking or cyberattacks independently. In reality, however, autocratic governments rely on a repertoire of censorship techniques to control online communication, which they are likely to use depending on the respective political situation on the ground. In this article, we study the interplay of different online censorship techniques empirically. Focusing on new Internet measurement techniques and large existing datasets, we study the relationship between website blocking and cyberattacks (Denial-of-Service). Our results provide evidence that autocrats select tactics from their censorship repertoire depending on the current level of contention. During quiet times, we find some evidence that governments rely on different censorship tactics in parallel. In weeks with protest, however, website blocking is negatively associated with Denial-of-Service attacks against opposition websites. This shows that when the stakes are high, autocrats become more selective in their use of censorship.
Cultural Influences on Chinese Citizens’ Adoption of Digital Contact Tracing: A Human Infrastructure Perspective

Digital contact tracing is an ICT approach for controlling public health crises. It identifies users' risk of infection based on their healthcare and travel information. In the COVID-19 pandemic, many countries implemented digital contact tracing to contain the coronavirus outbreak. However, the adoption rates vary significantly across different countries. In this study, we investigate Chinese people's adoption of digital contact tracing. We aim at finding the influence of Chinese culture on people's attitudes and behaviors toward the technology. We interviewed 26 Chinese participants and used thematic analysis to interpret the data. Our findings showed that Chinese culture shaped citizens' interactions with the digital contact tracing at multiple levels; driven by the culture, Chinese citizens accepted digital contact tracing and contributed to making digital contact tracing a socio-technical infrastructure of people's daily lives. We also discuss such cultural influences with the growing literature of human infrastructure and crisis informatics.
Reducing the Cognitive Load of Playing a Digital Tabletop Game with a Multimodal Interface

Multimodal Interfaces (MMIs) combining speech and spatial input have the potential to elicit minimal cognitive load. Low cognitive load increases effectiveness as well as user satisfaction and is regarded as an important aspect of intuitive use. While this potential has been extensively theorized in the research community, experiments that provide supporting observations based on functional interfaces are still scarce. In particular, there is a lack of studies comparing the commonly used Unimodal Interfaces (UMIs) with theoretically superior synergistic MMI alternatives. Yet, these studies are an essential prerequisite for generalizing results, developing practice-oriented guidelines, and ultimately exploiting the potential of MMIs in a broader range of applications. This work contributes a novel observation towards the resolution of this shortcoming in the context of the following combination of applied interaction techniques, tasks, application domain, and technology: We present a comprehensive evaluation of a synergistic speech & touch MMI and a touch-only menu-based UMI (interaction techniques) for selection and system control tasks in a digital tabletop game (application domain) on an interactive surface (technology). Cognitive load, user experience, and intuitive use are evaluated, with the former being assessed by means of the dual-task paradigm. Our experiment shows that the implemented MMI causes significantly less cognitive load and is perceived significantly more usable and intuitive than the UMI. Based on our results, we derive recommendations for the interface design of digital tabletop games on interactive surfaces. Further, we argue that our results and design recommendations are suitable to be generalized to other application domains on interactive surfaces for selection and system control tasks.
Severity classification of software code smells using machine learning techniques: A comparative study

Abstract Code smell is a software characteristic that indicates bad symptoms in code design which causes problems related to software quality. The severity of code smells must be measured because it will help the developers when determining the priority of refactoring efforts. Recently, several studies focused on the prediction of design patterns errors using different detection tools. Nowadays, there is a lack of empirical studies regarding how to measure severity of code smells and which learning model is best to detect the severity of code smells. To overcome such gap, this paper focuses on measuring the severity classification of code smells depending on several machine learning models such as regression models, multinominal models, and ordinal classification models. The Local Interpretable Model Agnostic Explanations (LIME) algorithm was further used to explain the machine learning model's predictions and interpretability. On the other side, we extract the prediction rules generated by the Projective Adaptive Resonance Theory (PART) algorithm in order to study the effectiveness of using software metrics to predict code smells. The results of the experiments have shown that the accuracy of severity classification model is enhanced than baseline and ranking correlation between the predicted and actual model reaches 0.92–0.97 by using Spearman's correlation measure.
Can Anybody Help Me?: Using Community Help Desk Call Records to Examine the Impact of Digital Divides During a Global Pandemic

The COVID-19 global pandemic has ignited lightning-fast adoption of digital tools in our communities, organizations, and systems of governance. It also inspired an unprecedented level of providing access to digital devices to communities and individuals lacking prior access. The situation and circumstances provide a unique opportunity to understand digital divides through a new lens. In this work, we contribute a contemporaneous understanding of digital divides beyond access by qualitatively analyzing over 300 calls made to a volunteer-based community IT help desk. We highlight the intertwined network of challenges leading to ecosystem digital divides and contribute new insights into how the complex socio-technical systems of practice, and the tools to support them, must adapt to bridge digital divides more effectively.
Dually Noted: Layout-Aware Annotations with Smartphone Augmented Reality

Sharing annotations encourages feedback, discussion, and knowledge passing among readers and can be beneficial for personal and public use. Prior augmented reality (AR) systems have expanded these benefits to both digital and printed documents. However, despite smartphone AR now being widely available, there is a lack of research about how to use AR effectively for interactive document annotation. We propose Dually Noted, a smartphone-based AR annotation system that recognizes the layout of structural elements in a printed document for real-time authoring and viewing of annotations. We conducted experience prototyping with eight users to elicit potential benefits and challenges within smartphone AR, and this informed the resulting Dually Noted system and annotation interactions with the document elements. AR annotation is often unwieldy, but during a 12-user empirical study our novel structural understanding component allows Dually Noted to improve precise highlighting and annotation interaction accuracy by 13%, increase interaction speed by 42%, and significantly lower cognitive load over a baseline method without document layout understanding. Qualitatively, participants commented that Dually Noted was a swift and portable annotation experience. Overall, our research provides new methods and insights for how to improve AR annotations for physical documents.
The design, construction and evaluation of annotated Arabic cyberbullying corpus

Cyberbullying (CB) is classified as one of the severe misconducts on social media. Many CB detection systems have been developed for many natural languages to face this phenomenon. However, Arabic is one of the under-resourced languages suffering from the lack of quality datasets in many computational research areas. This paper discusses the design, construction, and evaluation of a multi-dialect, annotated Arabic Cyberbullying Corpus (ArCybC), a valuable resource for Arabic CB detection and motivation for future research directions in Arabic Natural Language Processing (NLP). The study describes the phases of ArCybC compilation. By way of illustration, it explores the corpus to discover strategies used in rendering Arabic CB tweets pulled from four Twitter groups, including gaming, sports, news, and celebrities. Based on thorough analysis, we discovered that these groups were the most susceptible to harassment and cyberbullying. The collected tweets were filtered based on a compiled harassment lexicon, which contains a list of multi-dialectical profane words in Arabic compiled from four categories: sexual, racial, physical appearance, and intelligence. To annotate ArCybC, we asked five annotators to classify 4,505 tweets into two classes manually: Offensive/non-Offensive and CB/non-CB. We conducted a rigorous comparison of different machine learning approaches applied on ArCybC to detect Arabic CB using two language models: bag-of-words (BoW) and word embedding. The experiments showed that Support Vector Machine (SVM) with word embedding achieved an accuracy rate of 86.3% and an F1-score rate of 85%. The main challenges encountered during the ArCybC construction were the scarcity of freely available Arabic CB texts and the deficiency of annotating the texts.
Tracking of Gymnast’s Limb Movement Trajectory Based on MEMS Inertial Sensor

In order to track the limb movement trajectory of gymnasts, a method based on MEMS inertial sensor is proposed. The system mainly collects the acceleration and angular velocity data of 11 positions during gymnastics by constructing sensor network. Based on the two kinds of preprocessed data, the parameters such as sample mean, standard deviation, information entropy, and mean square error are calculated as classification features, the support vector machine (SVM) classification model is established, and the movements of six kinds of gymnastics are effectively recognized. The experimental results show that when the human body is doing gymnastics, the measured three-axis acceleration values are between -0.5 g~2.2 g, -1 g~2.8 g, and -1.8 g~1 g, respectively, and the static error range accounts for only 1.6%~2% of the actual measured data range. Therefore, it is considered that such static error has little effect on the accuracy of data feature extraction and action recognition, which can be ignored. It is proved that MEMS inertial sensor can effectively track the movement trajectory of gymnasts' limbs.
Designing and Assessing a Virtual Reality Simulation to Build Resilience to Street Harassment

Street harassment is a widespread problem that can constrain people's freedom to enjoy public spaces safely, along with many other negative psychological impacts. However, very little research has looked at how immersive technology can help in addressing it. We conducted three studies to investigate the design decisions, ethical issues and efficacy of an immersive simulation of street harassment: an online design study (n=20), an interview study with experts working in the area (n=9), and a comparative lab study investigating design, ethics and efficacy (n=44). Our results deepen understanding of the design decisions that contribute to a realistic psychological experience, such as the effects of screen-based video vs passive VR vs interactive VR. They also highlight important ethical issues such as traumatisation and potential for victim blaming, and how they can be approached in an ethical manner. Finally, they provide insights into efficacy in terms of perceived usefulness, competence and empathy.
Defect Prediction Technology in Software Engineering Based on Convolutional Neural Network

Software defect prediction has become a significant study path in the field of software engineering in order to increase software reliability. Program defect predictions are being used to assist developers in identifying potential problems and optimizing testing resources to enhance program dependability. As a consequence of this strategy, the number of software defects may be predicted, and software testing resources are focused on the software modules with the most problems, allowing the defects to be addressed as soon as feasible. The author proposes a research method of defect prediction technology in software engineering based on convolutional neural network. Most of the existing defect prediction methods are based on the number of lines of code, module dependencies, stack reference depth, and other artificially extracted software features for defect prediction. Such methods do not take into account the underlying semantic features in software source code, which may lead to unsatisfactory prediction results. The author uses a convolutional neural network to mine the semantic features implicit in the source code and use it in the task of software defect prediction. Empirical studies were conducted on 5 software projects on the PROMISE dataset and using the six evaluation indicators of Recall, F1, MCC, pf, gm, and AUC to verify and analyze the experimental results showing that the AUC values of the items varied from 0.65 to 0.86. Obviously, software defect prediction experimental results obtained using convolutional neural networks are still ideal. Defect prediction model in software engineering based on convolutional neural network has high prediction accuracy.
Classroom Dandelions: Visualising Participant Position, Trajectory and Body Orientation Augments Teachers’ Sensemaking

Despite the digital revolution, physical space remains the site for teaching and learning embodied knowledge and skills. Both teachers and students must develop spatial competencies to effectively use classroom spaces, enabling fluid verbal and non-verbal interaction. While video permits rich activity capture, it provides no support for quickly seeing activity patterns that can assist learning. In contrast, position tracking systems permit the automated modelling of spatial behaviour, opening new possibilities for feedback. This paper introduces the design rationale for "Dandelion Diagrams" that integrate participant location, trajectory and body orientation over a variable period. Applied in two authentic teaching contexts (a science laboratory, and a nursing simulation) we show how heatmaps showing only teacher/student location led to misinterpretations that were resolved by overlaying Dandelion Diagrams. Teachers also identified a variety of ways they could aid professional development. We conclude Dandelion Diagrams assisted sensemaking, but discuss the ethical risks of over-interpretation.
Uncertainty-Aware Deep Ensemble Model For Targeted Ultrasound-Guided Prostate Biopsy

Reliable prostate cancer detection using ultrasound imaging can significantly improve patient outcomes and survival. One outstanding challenge for developing machine learning models that can accurately detect cancer is labeling the ultrasound data based on histopathology reports, where only a coarse label is provided for an entire biopsy sample. As a result, training a model for pixel level analysis of ultrasound images with high rate of label noise leads to model overfitting the noisy data and poor generalization performance. To address this challenge, we leverage the deep ensemble technique for uncertainty estimation to detect out-of-distribution inputs and data with noisy labels. We use Masksembles idea to overcome the computational cost associated with the ensemble techniques. We further improve the performance of the model by using co-teaching framework with a regularized loss. We demonstrate that the proposed method helps with the overconfident predictions of learning models, and noisy nature of the labels data.
TapGazer: Text Entry with Finger Tapping and Gaze-directed Word Selection

While using VR, efficient text entry is a challenge: users cannot easily locate standard physical keyboards, and keys are often out of reach, e.g. when standing. We present TapGazer, a text entry system where users type by tapping their fingers in place. Users can tap anywhere as long as the identity of each tapping finger can be detected with sensors. Ambiguity between different possible input words is resolved by selecting target words with gaze. If gaze tracking is unavailable, ambiguity is resolved by selecting target words with additional taps. We evaluated TapGazer for seated and standing VR: seated novice users using touchpads as tap surfaces reached 44.81 words per minute (WPM), 79.17% of their QWERTY typing speed. Standing novice users tapped on their thighs with touch-sensitive gloves, reaching 45.26 WPM (71.91%). We analyze TapGazer with a theoretical performance model and discuss its potential for text input in future AR scenarios.