Reviewerly/exHarmony huggingface dataset:
Feeling good: humanitarian virtual reality film, emotional style and global citizenship

Virtual Reality (VR) film has been described as an empathy machine. Filmmakers and producers have claimed that VR film’s immersive qualities can amplify empathy for victims of humanitarian crises and move the viewer to support humanitarian aid organizations. This paper questions these transformative assumptions about VR film. We call attention to how humanitarian VR films are techniques that promote emotional styles like empathy through the script of suffering and hope. Through analysis of humanitarian VR films, the use of character, narrative, and formal VR film devices, we show how empathy is created. Thereby, we focus specifically on the simulation of particular locales, intimate encounters with the suffering Other, and gratification of viewer needs. The paper concludes that humanitarian VR films simulate an engagement with global problems when, in fact, they are catering to the emotional needs of people engaging with those problems. The global citizen as a feeling self becomes caught in interpersonal affective textures, which obscure geopolitical causes of humanitarian crises. Hence, the paper questions empathy as a universal way to better the world and diverges from the celebration of humanitarian VR film as a universal empathy machine.
Automatic Text Simplification Tools for Deaf and Hard of Hearing Adults: Benefits of Lexical Simplification and Providing Users with Autonomy

Automatic Text Simplification (ATS), which replaces text with simpler equivalents, is rapidly improving. While some research has examined ATS reading-assistance tools, little has examined preferences of adults who are deaf or hard-of-hearing (DHH), and none empirically evaluated lexical simplification technology (replacement of individual words) with these users. Prior research has revealed that U.S. DHH adults have lower reading literacy on average than their hearing peers, with unique characteristics to their literacy profile. We investigate whether DHH adults perceive a benefit from lexical simplification applied automatically or when users are provided with greater autonomy, with on-demand control and visibility as to which words are replaced. Formative interviews guided the design of an experimental study, in which DHH participants read English texts in their original form and with lexical simplification applied automatically or on-demand. Participants indicated that they perceived a benefit form lexical simplification, and they preferred a system with on-demand simplification.
"It Should Be a Game for Fun, Not Exercise": Tensions in Designing Health-Related Features for Pokémon GO

Leveraging existing popular games such as Pokémon GO to promote health can engage people in healthy activities without sacrificing gaming appeal. However, little is known about what potential tensions arise from incorporating new health-related features to already existing and popular games and how to resolve those tensions from players' perspectives. In this paper, we identify design tensions surrounding the appeals of Pokémon GO, perspectives on different health needs, and mobile health technologies. By conducting surveys and design workshops with 20 avid Pokémon GO players, we demonstrate four design tensions: (1) diverse goals and rewards vs. data accuracy, (2) strong bonds between players and characters vs. gaming obsession, (3) collaborative play vs. social anxiety, and (4) connection of in-real-life experiences with the game vs. different individual contexts. We provide design implications to resolve these tensions in Pokémon GO and discuss how to extend our findings to the broader context of health promotion in location-based games.
Unmet Needs and Opportunities for Mobile Translation AI

Translation apps and devices are often presented in the context of providing assistance while traveling abroad. However, the spectrum of needs for cross-language communication is much wider. To investigate these needs, we conducted three studies with populations spanning socioeconomic status and geographic regions: (1) United States-based travelers, (2) migrant workers in India, and (3) immigrant populations in the United States. We compare frequent travelers' perception and actual translation needs with those of the two migrant communities. The latter two, with low language proficiency, have the greatest translation needs to navigate their daily lives. However, current mobile translation apps do not meet these needs. Our findings provide new insights on the usage practices and limitations of mobile translation tools. Finally, we propose design implications to help apps better serve these unmet needs.
Augmented Reality to Enable Users in Learning Case Grammar from Their Real-World Interactions

Augmented Reality (AR) provides a unique opportunity to situate learning content in one's environment. In this work, we investigated how AR could be developed to provide an interactive context-based language learning experience. Specifically, we developed a novel handheld-AR app for learning case grammar by dynamically creating quizzes, based on real-life objects in the learner's surroundings. We compared this to the experience of learning with a non-contextual app that presented the same quizzes with static photographic images. Participants found AR suitable for use in their everyday lives and enjoyed the interactive experience of exploring grammatical relationships in their surroundings. Nonetheless, Bayesian tests provide substantial evidence that the interactive and context-embedded AR app did not improve case grammar skills, vocabulary retention, and usability over the experience with equivalent static images. Based on this, we propose how language learning apps could be designed to combine the benefits of contextual AR and traditional approaches.
Digital Fabrication Tools at Work

Digital fabrication tools have transformed how people work in micro- and small-scale manufacturing settings. While increasing efficiency and precision, these tools raise concerns around user agency and control. This paper describes an exploratory study investigating the felt work experience and desired futures of professionals who use fabrication tools. We conducted co-design workshops with 23 professionals who use 3D printers, laser cutters, and CNC routers. We probed about current practices; machine awareness and autonomy; and user agency. Our findings reveal that current tools are not very professional. They are unreliable and untrustworthy. Participants desired smarter tools that can actively prevent errors and perform self-calibration and self-maintenance. They had few concerns that more intelligence would impact agency. They desired tools that could negotiate trade-offs between time, cost, and quality; and that can operate as super-human shop assistants. We discuss the implications of these findings as opportunities for research that can improve professionals' work experience.
Anomaly Detection by Using Streaming K-Means and Batch K-Means

This paper introduces K-Means algorithm as new technique for detecting anomaly. Data analysis has been applied to industry field widely and plays important role in it. However, conventional data analysis method cannot process large-scale data in considerable time and waste lots of computing resources. Conversely, Batch processing and Stream processing are equipped with property of processing data in short time interval, especially stream processing, can process data in real-time. This paper also compares Batch K- Means processing with Streaming K-Means processing according to distance, cost value and cluster distribution factors. Moreover, this paper also discusses how to reach optimized K value of Batch K-means model and Streaming K- means model, analyzes attributes of Batch K-Means processing and Streaming K-Means processing and finds limitations of these two processing models. Finally, the paper proposes limitations of research experiment and future improvement of clustering technique.
A Probabilistic Grammar of Graphics

Visualizations depicting probabilities and uncertainty are used everywhere from medical risk communication to machine learning, yet these probabilistic visualizations are difficult to specify, prone to error, and their designs are cumbersome to explore. We propose a Probabilistic Grammar of Graphics (PGoG), an extension to Wilkinson's original framework. Inspired by the success of probabilistic programming languages, PGoG makes probability expressions, such as P(A|B), a first-class citizen in the language. PGoG abstractions also reflect the distinction between probability and frequency framing, a concept from the uncertainty communication literature. It is expressive, encompassing product plots, density plots, icon arrays, and dotplots, among other visualizations. Its coherent syntax ensures correctness (that the proportions of visual elements and their spatial placement reflect the underlying probability distribution) and reduces edit distance between probabilistic visualization specifications, potentially supporting more design exploration. We provide a proof-of-concept implementation of PGoG in R.
Nursing students’ experiences in virtual simulation practice

Purpose: The purpose of this study was to explore the meaning of nursing students’ experiences in virtual simulation practice.Methods: The participants were six nursing students who have experience in a virtual simulation. Data were collected from August to September 2019 through a focus group interview. Giorgi’s phenomenological method was used for analysis.Results: The study results revealed 3 constituents and 6 sub-constituents that are essential for nursing student’s experiences in a virtual simulation. The three constituents were: “Chaos in the virtual reality”, “The process of adjusting to chaos”, and “Becoming an independent nurse in a safe virtual reality”.Conclusion: Based on the results of this study, the following suggestions are made. Results suggest the development of virtual simulations in Korean, the use of simulations as a group activity first, and the use of simulation between lecture and practice or in the regular curriculum. The results of the study can be used as primary data for providing virtual simulation in nursing education.
Designing Voice Interfaces: Back to the (Curriculum) Basics

Voice user interfaces (VUIs) are rapidly increasing in popularity in the consumer space. This leads to a concurrent explosion of available applications for such devices, with many industries rushing to offer voice interactions for their products. This pressure is then transferred to interface designers; however, a large majority of designers have been only trained to handle the usability challenges specific to Graphical User Interfaces (GUIs). Since VUIs differ significantly in design and usability from GUIs, we investigate in this paper the extent to which current educational resources prepare designers to handle the specific challenges of VUI design. For this, we conducted a preliminary scoping scan and syllabi meta review of HCI curricula at more than twenty top international HCI departments, revealing that the current offering of VUI design training within HCI education is rather limited. Based on this, we advocate for the updating of HCI curricula to incorporate VUI design, and for the development of VUI-specific pedagogical artifacts to be included in new curricula.
EmoG: Supporting the Sketching of Emotional Expressions for Storyboarding

Storyboarding is an important ideation technique that uses sequential art to depict important scenarios of user experience. Existing data-driven support for storyboarding focuses on constructing user stories, but fail to address its benefit as a graphic narrative device. Instead, we propose to develop a data-driven design support tool that increases the expressiveness of user stories by facilitating sketching storyboards. To explore this, we focus on supporting the sketching of emotional expressions of characters in storyboards. In this paper, we present EmoG, an interactive system that generates sketches of characters with emotional expressions based on input strokes from the user. We evaluated EmoG with 21 participants in a controlled user study. The results showed that our tool has significantly better performance in usefulness, ease of use, and quality of results than the baseline system.
PPO-DFK: A Privacy-Preserving Optimization of Distributed Fractional Knapsack With Application in Secure Footballer Configurations

In the optimized footballer configurations, the team coach selects the players to participate in the game based on the training status of all players. As the number of excellent players increases, the costs and budget owned by the club boss need to be considered. Obviously, the players' costs are considered as private and sensitive and the training status are also sensitive. Therefore, the private information might be revealed in the process of data sharing and processing in this distributed manner. Considering the privacy-revealing issues in the above-mentioned scenario, this article proposes a privacy-preserving optimization for distributed fractional knapsack (PPO-DFK) problem, in which it achieves the secure footballer configurations, i.e., it is able to win the game but the sum of cost does not exceed the budget, without revealing either the expenditure/money owned by the club boss or the players' training status owned by the team coach. In the proposed PPO-DFK scheme, it employs a novel transformation approach (TA), a secure comparison protocol and a secure sorting protocol as the building blocks to ensure the privacy protection of distributed optimization procedure, then it uses the greedy algorithm to find an efficient solution. The security of proposed PPO-DFK scheme is strictly analyzed and its effectiveness is demonstrated by the experimental results on concrete examples.
Combination of fuzzy and cognitive theories for adaptive e-assessment

A crucial factor for successful educational results in computer-based educational systems and e-learning systems is the learner’s assessment. Assessment is more effective when it is tailored to each individual student’s learning needs and abilities. Therefore, a significant research challenge is to create tests that include exercises/questions/activities etc., which conform to each learner’s knowledge level and learning needs and abilities. This goal constitutes the need for creating adaptive tests. However, the area of adaptive e-assessment has not yet been explored sufficiently and thus there is scope for a lot of improvement. To this end, in this paper we present a novel solution for adaptive e-assessment. The novelty and significance lie in the blending of fuzzy logic and cognitive theories for further enhancing the personalization and adaptivity in e-assessment. Particularly, fuzzy sets are used to describe the knowledge level of students in a more realistic way. Furthermore, the cognitive theory of Revised Bloom Taxonomy is used to express the learning objectives that are required to be assessed through the created test. In addition, a fuzzy rule-based reasoner, which decides about the number and the difficulty level of the test items that have to be included into the created personalized test for each level of the Revised Bloom Taxonomy, is used. The fuzzy rules are applied to the fuzzy sets that describe the learners’ knowledge level. For the formation of the fuzzy sets and rules, the opinion of several tutors, holding experience in the educational process and instruction, was taken into consideration. The created adaptive test comprises distinct test items based on the individual learning needs of each student. The presented method has been used in two tutoring systems and has been fully evaluated. The evaluation results show great accuracy in the selection of test items for each individual student.
Leveraging blockchain’s potential – The paradox of centrally legitimate, decentralized solutions to institutional challenges in Kenya

Blockchain technologies and business models offer a multitude of decentralized and distributed services, but our understanding of the conditions necessary to reach the emerging technology’s full potential is incomplete. The benefits are perhaps most pronounced in developing economies where high institutional misalignment, or even dysfunction, leaves much room for improvement, but we know even less about these contexts due to a dearth of primary research. In this article, we survey thirteen key entrepreneurs and managers in Kenya to identify and observe the current challenges associated with unlocking the potential of blockchain-enabled services. We identify three conditions that need to be met: sufficient technical capacity, appropriate regulative interventions, and most importantly, the adoption of decentralization logics. To truly leverage blockchain technology, market and institutional actors need to collaborate to overcome the paradox of centrally legitimate, decentralized solutions.
On the use of Jargon and Word Embeddings to Explore Subculture within the Reddit’s Manosphere

Understanding the identities, needs, realities and development of subcultures has been a long term target of sociology and cultural studies. Socio-cultural linguistics, in particular, examines the use of language and, in particular, the existence and use of neologisms, slang and jargon. These terms capture concepts and expressions that are not in common use and represent the new realities, norms and values of subcommunities. Identifying and understanding such terms, however, is a very complex task, particularly considering the vast amount of content that is currently available online for many such groups. In this paper, we propose a combination of computational and socio-linguistic methods to automatically extract new terminology from large amounts of data, using word-embeddings to semantically contextualise their meaning. As a use case, we explore subculture on the platform Reddit. More specifically, we investigate groups considered part of the manosphere, a loose online community where men's perspectives, gripes, frustrations and desires are explicitly expressed and where women are typically targets of hostility. Characterisations of this group as a subculture are then provided, based on an in-depth analysis of the identified jargon.
BlankIt library debloating: getting what you want instead of cutting what you don’t

Modern software systems make extensive use of libraries derived from C and C++. Because of the lack of memory safety in these languages, however, the libraries may suffer from vulnerabilities, which can expose the applications to potential attacks. For example, a very large number of return-oriented programming gadgets exist in glibc that allow stitching together semantically valid but malicious Turing-complete and -incomplete programs. While CVEs get discovered and often patched and remedied, such gadgets serve as building blocks of future undiscovered attacks, opening an ever-growing set of possibilities for generating malicious programs. Thus, significant reduction in the quantity and expressiveness (utility) of such gadgets for libraries is an important problem.
Personalized QoE Improvement for Networking Video Service

Personalized networking video service, as an inevitable trend recently, has become the core part for users' quality of experience (QoE) improvement. Unfortunately, existing schemes of QoE improvement are far from personalized since most of them only focus on network-level or user-level optimization. How to realize personalized QoE improvement for networking video service has been widely considered as a fundamental technical challenge. To get over this dilemma, this work proposes a personalized QoE improvement scheme by fully taking advantage of the time-varying influences on users' QoE, including user-awareness, device-awareness and contextawareness. The highlights of this work lie in that, the proposed scheme realizes the personalization comprehensively considering all these three dimensions, meanwhile, it is so robust that can be applied to the application scenario where the observable users' data is not sufficient. Specifically, we firstly design a comprehensive data collection strategy and accurately classify these collected data. Then, an efficient deep learning (DL)-based model for personalized characteristics extraction is proposed to precisely characterize personalization with temporal, spatial and periodic correlations. Subsequently, to resolve the data sparsity issue, a federated learning (FL)-based architecture with privacy-protection is designed by securely exchanging encrypted parameters with other users. Importantly, we design an optimization scheme based on comprehensive MOS formula for personalized QoE improvement. Experimental results demonstrate that the proposed scheme has a significantly better performance on the personalized QoE improvement.
Type error feedback via analytic program repair

We introduce Analytic Program Repair, a data-driven strategy for providing feedback for type-errors via repairs for the erroneous program. Our strategy is based on insight that similar errors have similar repairs. Thus, we show how to use a training dataset of pairs of ill-typed programs and their fixed versions to: (1) learn a collection of candidate repair templates by abstracting and partitioning the edits made in the training set into a representative set of templates; (2) predict the appropriate template from a given error, by training multi-class classifiers on the repair templates used in the training set; (3) synthesize a concrete repair from the template by enumerating and ranking correct (e.g. well-typed) terms matching the predicted template. We have implemented our approach in Rite: a type error reporting tool for OCaml programs. We present an evaluation of the accuracy and efficiency of Rite on a corpus of 4,500 ill-typed Ocaml programs drawn from two instances of an introductory programming course, and a user-study of the quality of the generated error messages that shows the locations and final repair quality to be better than the state-of-the-art tool in a statistically-significant manner.
Optimizing homomorphic evaluation circuits by program synthesis and term rewriting

We present a new and general method for optimizing homomorphic evaluation circuits. Although fully homomorphic encryption (FHE) holds the promise of enabling safe and secure third party computation, building FHE applications has been challenging due to their high computational costs. Domain-specific optimizations require a great deal of expertise on the underlying FHE schemes, and FHE compilers that aims to lower the hurdle, generate outcomes that are typically sub-optimal as they rely on manually-developed optimization rules. In this paper, based on the prior work of FHE compilers, we propose a method for automatically learning and using optimization rules for FHE circuits. Our method focuses on reducing the maximum multiplicative depth, the decisive performance bottleneck, of FHE circuits by combining program synthesis and term rewriting. It first uses program synthesis to learn equivalences of small circuits as rewrite rules from a set of training circuits. Then, we perform term rewriting on the input circuit to obtain a new circuit that has lower multiplicative depth. Our rewriting method maximally generalizes the learned rules based on the equational matching and its soundness and termination properties are formally proven. Experimental results show that our method generates circuits that can be homomorphically evaluated 1.18x – 3.71x faster (with the geometric mean of 2.05x) than the state-of-the-art method. Our method is also orthogonal to existing domain-specific optimizations.