Reviewerly/exHarmony huggingface dataset:
A ternary bitwise calculator based genetic algorithm for improving error correcting output codes

This paper proposes a novel genetic algorithm (GA) for the error correction output coding (ECOC) framework. Different from other GA algorithms, a new individual structure is designed by setting a gene as the combination of three types of operators: (1) the column selector; (2) the ternary bitwise calculator; (3) the feature selector. In our GA algorithm, two column selectors first extract two columns to from a codematrix pool, then a Ternary bitwise Calculator (TC) transfers them to a new column through a ternary number calculation process. The feature selector selects a feature subset for training the associated dichotomizer. By doing so, an individual contains a set of genes to form an ECOC ensemble. The TCs deployed in our algorithm include both the traditional TCs and some newly proposed TCs, which aid to generate diverse codematrices. When the evolutionary process terminates, the best individual in the last generation is regarded as the final solution. The performance of our algorithm is verified on both the UCI and microarray data sets. Experiment results demonstrate that our GA based ECOC achieves promising performance comparing to other ECOC algorithms. Furthermore, results also confirm that various TCs contribute to the generation of discriminative individuals.
Changing Roles and Contexts: Symbolic Interactionism in the Sharing of Food and Eating Practices between Remote, Intergenerational Family Members

Cooking and eating together is a prominent social experience amongst families. Older adults and their adult children who live apart often communicate about these experiences to stay aware of each other's health and wellbeing. In this paper, we examine current practices surrounding the communication of eating habits and meal preparation between older adults and their adult children living apart. We interviewed 18 older parents and nine adult children to understand their experiences. While most participants found the sharing of eating experiences to be rewarding and enlightening of family health behaviors, family roles and contexts could create tensions around this type of conversation. Applying the lens of symbolic interactionism theory, we examine how changing roles and contexts influence the conversation of eating and meal preparation and how participants manage tensions. We discuss future design opportunities to support family collaboration around food and eating, accounting for the transition of roles and contexts.
Augmented Reality in Education: Current Status and Advancement of the Field

Despite the substantial body of work and positive reported outcomes of AR (Augmented Reality) usage for education, researchers claim that compared to other digital technologies such as multimedia and web services for teaching and learning, research in AR is still at an early stage, with the majority of studies being short-term, one-time experiments with high variability in the effect size. AR-based applications targeting the educational sector are becoming ever more available with even low-cost smartphones supporting visualization of AR content. Regardless of the fact that the number of downloads of these applications exceeds millions, and assuming they are used in educational settings, we still lack longitudinal reports on how AR affects learning outcomes (compared to other technologies). This chapter looks at the current status of AR in education from different points of view: widely available technologies, types of AR learning experiences for education, capabilities for authoring AR, suitable audiences and topics, and the status of research of AR in education. The chapter summarizes each of these points of view and looks at the possibilities to advance the field.
Does recent research evidence support the hyperpersonal model of online impression management?

The hyperpersonal model of communication was conceived in the 1990s and has driven much of the research into online impression management. Based on four principal tenets (increased control, asynchronicity of communication, increased physical distance and reallocation of cognitive resources) it has largely received empirical support, especially by research involving text-only communication. This review briefly summarises this research before identifying four areas in which it is not supported by findings: the wider context of online communication, the expanding nature of online platforms to include pictures and video, use of language in online environments, and online self-disclosure. We suggest that the model is modified and updated, or its limitations defined, with respect to this evidence.
Neural Message Passing for Multi-label Classification

Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample. Modeling the combinatorial label interactions in MLC has been a long-haul challenge. We propose Label Message Passing (LaMP) Neural Networks to efficiently model the joint prediction of multiple labels. LaMP treats labels as nodes on a label-interaction graph and computes the hidden representation of each label node conditioned on the input using attention-based neural message passing. Attention enables LaMP to assign different importances to neighbor nodes per label, learning how labels interact (implicitly). The proposed models are simple, accurate, interpretable, structure-agnostic, and applicable for predicting dense labels since LaMP is incredibly parallelizable. We validate the benefits of LaMP on seven real-world MLC datasets, covering a broad spectrum of input/output types and outperforming the state-of-the-art results. Notably, LaMP enables intuitive interpretation of how classifying each label depends on the elements of a sample and at the same time rely on its interaction with other labels (We provide our code and datasets at https://github.com/QData/LaMP .).
Learning Dynamic Textures for Neural Rendering of Human Actors

Synthesizing realistic videos of humans using neural networks has been a popular alternative to the conventional graphics-based rendering pipeline due to its high efficiency. Existing works typically formulate this as an image-to-image translation problem in 2D screen space, which leads to artifacts such as over-smoothing, missing body parts, and temporal instability of fine-scale detail, such as pose-dependent wrinkles in the clothing. In this article, we propose a novel human video synthesis method that approaches these limiting factors by explicitly disentangling the learning of time-coherent fine-scale details from the embedding of the human in 2D screen space. More specifically, our method relies on the combination of two convolutional neural networks (CNNs). Given the pose information, the first CNN predicts a dynamic texture map that contains time-coherent high-frequency details, and the second CNN conditions the generation of the final video on the temporally coherent output of the first CNN. We demonstrate several applications of our approach, such as human reenactment and novel view synthesis from monocular video, where we show significant improvement over the state of the art both qualitatively and quantitatively.
Exploring Eyes-free Bezel-initiated Swipe on Round Smartwatches

Bezel-based gestures expand the interaction space of touch-screen devices (e.g., smartphones and smartwatches). Existing works have mainly focused on bezel-initiated swipe (BIS) on square screens. To investigate the usability of BIS on round smartwatches, we design six different circular bezel layouts, by dividing the bezel into 6, 8, 12, 16, 24, and 32 segments. We evaluate the user performance of BIS on these layouts in an eyes-free situation. The results show that the performance of BIS is highly orientation dependent, and varies significantly among users. Using the Support-Vector-Machine (SVM) model significantly increases the accuracy on 6-, 8-, 12-, and 16-segment layouts. We then compare the performance of personal and general SVM models, and find that personal models significantly improve the accuracy for 8-, 12-, 16-, and 24-segment layouts. Lastly, we discuss the potential smartwatch applications enabled by the BIS.
Detecting Gender Stereotypes: Lexicon vs. Supervised Learning Methods

Biases in language influence how we interact with each other and society at large. Language affirming gender stereotypes is often observed in various contexts today, from recommendation letters and Wikipedia entries to fiction novels and movie dialogue. Yet to date, there is little agreement on the methodology to quantify gender stereotypes in natural language (specifically the English language). Common methodology (including those adopted by companies tasked with detecting gender bias) rely on a lexicon approach largely based on the original BSRI study from 1974.
Responsive Drone Autopilot System for Uncertain Natural Language Commands

The purpose of this investigation is to realize how possible is it to control a drone with human's English natural language commands, even the command contains some quantitative uncertain words. Basically, the system interprets the commands with uncertain words, into a machine understandable format. According to the literature review, human-robot association does not go with uncertain words and it is a considerable gap. This proposed system has a client side mobile application to input voice. To build a machine understandable command, it refers to an ontology based knowledge store. The knowledge store gains information and expands its knowledge by crawling websites. Once the user commands, the drone to make a movement, the system will analyze the instruction to check if there is uncertainty and if it points to a surrounding object that is visible to the drone's camera. For extracting the uncertainty it uses NLTK along with a specific grammar rule and that methodology is succeeded according to the results. After collecting that information the system can navigate through the user's instruction. This research helps non-skilled drone pilots to have a smooth flying experience, and also other researchers to discover about natural language processing with robotics.
Party brand hate in political market: antecedents and consequences

Brand hate is defined as a severe dislike for a product or service by the consumer. Although brand hate has been recognized as a critical element, in the political market, there is a lack of understanding of the impact of brand hate on voters. This study is an attempt to examine the antecedents and consequences of brand hate in the political market. The study reveals that unmet expectations, symbolic incongruity, and ideological incompatibility of electorates have a significant positive impact on brand hate intensity. The result shows that political product involvement significantly mediates the relationship between unmet expectations, symbolic incongruity, and ideological incompatibility and brand hate intensity. The study further reveals that as an outcome of political brand hate, electorates put emphasis on either brand avoidance and/or brand extremism. The outcome of this study may help political parties gain knowledge about the impact of political brand hate.
LYRICS: A General Interface Layer to Integrate Logic Inference and Deep Learning

In spite of the amazing results obtained by deep learning in many applications, a real intelligent behavior of an agent acting in a complex environment is likely to require some kind of higher-level symbolic inference. Therefore, there is a clear need for the definition of a general and tight integration between low-level tasks, processing sensorial data that can be effectively elaborated using deep learning techniques, and the logic reasoning that allows humans to take decisions in complex environments. This paper presents LYRICS, a generic interface layer for AI, which is implemented in TersorFlow (TF). LYRICS provides an input language that allows to define arbitrary First Order Logic (FOL) background knowledge. The predicates and functions of the FOL knowledge can be bound to any TF computational graph, and the formulas are converted into a set of real-valued constraints, which participate to the overall optimization problem. This allows to learn the weights of the learners, under the constraints imposed by the prior knowledge. The framework is extremely general as it imposes no restrictions in terms of which models or knowledge can be integrated. In this paper, we show the generality of the approach showing some use cases of the presented language, including model checking, supervised learning and collective classification.
Self-Expression by Design: Co-Designing the ExpressiBall with Minimally-Verbal Children on the Autism Spectrum

Expressing one's thoughts and feelings is a fundamental human need - the basis for communication and social interaction. We ask, how do minimally-verbal children on the autism spectrum express themselves? How can we better recognise instances of self-expression? And how might technologies support and encourage self-expression? To address these questions, we undertook co-design research at an autism-specific primary school with 20 children over one school year. This paper contributes six Modalities of Self-Expression, through which children self-express and convey their design insights. Each modality of self-expression can occur across two different dimensions (socio-expressive and auto-expressive) and can be of a fundamental or an integrative nature. Further, we contribute the design trajectory of a tangible ball prototype, the ExpressiBall, which - through voice, sounds, lights, and motion sensors - explores how tangible technologies can support this range of expressive modalities. Finally, we discuss the concept of Self-Expression by Design.
BlyncSync: Enabling Multimodal Smartwatch Gestures with Synchronous Touch and Blink

Input techniques have been drawing abiding attention along with the continual miniaturization of personal computers. In this paper, we present BlyncSync, a novel multi-modal gesture set that leverages the synchronicity of touch and blink events to augment the input vocabulary of smartwatches with a rapid gesture, while at the same time, offers a solution to the false activation problem of blink-based input. BlyncSync contributes the concept of a mutual delimiter, where two modalities are used to jointly delimit the intention of each other's input. A study shows that BlyncSync is 33% faster than using a baseline input delimiter (physical smartwatch button), with only 150ms in overhead cost compared to traditional touch events. Furthermore, our data indicates that the gesture can be tuned to elicit a true positive rate of 97% and a false positive rate of 1.68%.
Feature Selection and Extraction for Graph Neural Networks

Graph Neural Networks (GNNs) have been a latest hot research topic in data science, due to the fact that they use the ubiquitous data structure graphs as the underlying elements for constructing and training neural networks. In a GNN, each node has numerous features associated with it. The entire task (for example, classification, or clustering) utilizes the features of the nodes to make decisions, at node level or graph level. In this paper, (1) we extend the feature selection algorithm presented in via Gumbel Softmax to GNNs. We conduct a series of experiments on our feature selection algorithms, using various benchmark datasets: Cora, Citeseer and Pubmed. (2) We implement a mechanism to rank the extracted features. We demonstrate the effectiveness of our algorithms, for both feature selection and ranking. For the Cora dataset, (1) we use the algorithm to select 225 features out of 1433 features. Our experimental results demonstrate their effectiveness for the same classification problem. (2) We extract features such that they are linear combinations of the original features, where the coefficients for extracted features are non-negative and sum up to one. We propose an algorithm to rank the extracted features in the sense that when using them for the same classification problem, the accuracy goes down gradually for the extracted features within the rank 1 - 50, 51 - 100, 100 - 150, and 151 - 200.
The Upcycled Home: Removing Barriers to Lightweight Modification of the Home's Everyday Objects

The Internet-of-things (IoT) embeds computing in everyday objects, but has largely focused on new devices while ignoring the home's many existing possessions. We present a field study with 10 American families to understand how these possessions could be included in the smart home through upcycling. We describe three patterns for how families collaborate around home responsibilities; we explore families' mental models of home that may be in tension with existing IoT systems; and we identify ways that families can more easily imagine a smart home that includes their existing possessions. These insights can help us design an upcycled approach to IoT that supports users in reconfiguring objects (and social roles as mediated by objects) in a way that is sensitive to what will be displaced, discarded, or made obsolete. Our findings inform the design of future lightweight systems for the upcycled home.
PrefixFPM: A Parallel Framework for General-Purpose Frequent Pattern Mining

Frequent pattern mining (FPM) has been a focused theme in data mining research for decades, but there lacks a general programming framework that can be easily customized to mine different kinds of frequent patterns, and existing solutions to FPM over big transaction databases are IO-bound rendering CPU cores underutilized even though FPM is NP-hard. This paper presents, PrefixFPM, a general-purpose framework for FPM that is able to fully utilize the CPU cores in a multicore machine. PrefixFPM follows the idea of prefix projection to partition the workloads of PFM into independent tasks by divide and conquer. PrefixFPM exposes a unified programming interface to users who can customize it to mine their desired patterns, and the parallel execution engine is transparent to end-users and can be reused for mining all kinds of patterns. We have adapted the state-of-the-art serial algorithms for mining frequent patterns including subsequences, subtrees, and subgraphs on top of PrefixFPM, and extensive experiments demonstrate an excellent speedup ratio of PrefixFPM with the number of cores. A demo is available at https://youtu.be/PfioC0GDpsw; the code is available at https://github.com/yanlab19870714/PrefixFPM.
High Water Pants: Designing Embodied Environmental Speculation

In this paper, we present the High Water Pants: speculative wearable technology which makes climate change tangible for everyday cyclists. The pants work by mechanically shortening when a cyclist wearing the pants enters an area of Seattle, USA, which is projected to be impacted by sea-level rise in 30-80 years. This interaction 'bends time' by allowing cyclists to feel future climate change data in the present. First, we discuss the research through design process of creating the High Water Pants including foundational research, a description of the design concept and results of a preliminary study with the pants. Second, we discuss three implications of the pants for human-computer interaction (HCI): (1) they offer the concept of a 'present/future' paradigm for embodied speculation, (2) our research process demonstrates how to successfully involve more-than-human perspectives, and (3) we articulate how the High Water Pants respond to shifts in HCI's framing of sustainability.
Facilitating Democracy: Concerns from Participatory Design with Asymmetric Stakeholder Relations in Health Care

This paper addresses how facilitation can implicate what, whose and how perspectives and values become embedded in the results from participatory design activities. Inspired by Donald Schön's reflection-on-action theory, an analysis of our facilitator performances in three design activities involving health care stakeholder groups with asymmetric relations has been performed. The analysis highlights the often subtle and unforeseen ways by which facilitator actions influence who "has a say". The results emphasize how continuous introspective analyses and reflections may improve the facilitator's attentiveness to actions that may inadvertently impede the disfavored party. In the long-term, neglect may threaten the integrity of participatory design as a democratic and empowering design approach. The shift towards a practice-perspective on facilitation goes beyond the efforts of the individual practitioner. The cultivation of the reflective facilitator, a concern of relevance for the Human?Computer Interaction and Participatory Design community as a whole, is considered.
An Ontology-Based Conversation System for Knowledge Bases

Domain-specific knowledge bases (KB), carefully curated from various data sources, provide an invaluable reference for professionals. Conversation systems make these KBs easily accessible to professionals and are gaining popularity due to recent advances in natural language understanding and AI. Despite the increasing use of various conversation systems in open-domain applications, the requirements of a domain-specific conversation system are quite different and challenging. In this paper, we propose an ontology-based conversation system for domain-specific KBs. In particular, we exploit the domain knowledge inherent in the domain ontology to identify user intents, and the corresponding entities to bootstrap the conversation space. We incorporate the feedback from domain experts to further refine these patterns, and use them to generate training samples for the conversation model, lifting the heavy burden from the conversation designers. We have incorporated our innovations into a conversation agent focused on healthcare as a feature of the IBM Micromedex product.
Using Grammar Checkers in an ESL Context

Our study examines written corrective feedback generated by two online grammar checkers (GCs), Grammarly and Virtual Writing Tutor, and by the grammar checking function of Microsoft Word. We tested the technology on a wide range of grammatical error types from two sources: a set of authentic ESL compositions and a series of simple sentences we generated ourselves. The GCs were evaluated in terms of (1) coverage (number of errors flagged), (2) appropriacy of proposed replacement forms, and (3) rates of “false alarms” (forms mistakenly flagged as incorrect). Although Grammarly and Virtual Writing Tutor outperformed Microsoft Word, neither of the online GCs had high rates of overall coverage (&lt;50%). Consequently, they cannot be relied on to supply comprehensive feedback on student compositions. The finding of higher identification rates for errors from simple rather than authentic sentences reinforces this conclusion. Nonetheless, since few inaccurate replacement forms and false alarms were observed, only rarely is the feedback actively misleading. In addition, the GCs were better at handling some error types than others. Ultimately, we suggest that teachers use GCs with specially designed classroom activities that target selected error types before learners apply the technology to their own writing.