Reviewerly/exHarmony huggingface dataset:
Meta-interactivity and Playful Approaches for Musical Composition

The concept of gamified interactive models and its novel extensions, such as playification, has been widely approached in order to engage users in many fields. In fields such as HCI and AI, however, these approaches were not yet employed for supporting users to create different forms of artworks, like a musical corpus. While allowing novel forms of interactivity with partially-autonomous systems, these techniques could also foster the emergence of artworks not limited to experts. Hence, in this research we introduce the concept of meta-interactivity for compositional interfaces, which extends an individual’s capabilities by the translation of an effort into a proficiency. We study how this approach can be effective through the development of novel systems that enables novices to compose coherent musical pieces through the use of imagetic elements in a virtual environment.
Image Quality Assessment–driven Reinforcement Learning for Mixed Distorted Image Restoration

Due to the diversity of the degradation process that is difficult to model, the recovery of mixed distorted images is still a challenging problem. The deep learning model trained under certain degradation declines significantly in other degradation situations. In this article, we explore ways to use a combination of tools to deal with the mixed distortion. First, we illustrate the limitations of a single deep network in dealing with multiple distortion types and then introduce a hierarchical toolkit with distinguished powerful tools. Second, we investigate how an efficient representation of images combined with a reinforcement learning (RL) paradigm helps to deal with tool noise in continuous restoration. The proposed method can accurately capture the distortion preferences for selecting the optimal recovery tools by RL agent. Finally, to fully utilize random tools for unknown distortion combinations, we adopt the exploration scheme with various quality evaluation methods to achieve more quality improvements. Experimental results demonstrate that the peak signal-to-noise ratio of the proposed method is 3.30 dB higher than other state-of-the-art RL-based methods on the CSIQ single distortion dataset and 0.95 dB higher on the DIV2K mixed distortion dataset.
Prioritizing UI Items for Bidirectional Design System Development: A Suggested Set of Criteria and its Implementation

Design systems provide detailed guidelines on UI items’ appearance and behavior and serve as a primary reference for interface development. For bidirectional languages (e.g., Arabic and Hebrew), it is essential to highlight an additional parameter – the direction of the UI item. However, investigating the correct direction of all items could involve costly research. Hence, it is important to set priorities for such research. While prioritizing usability issues has long been attended by the HCI community, the specific problem of this research is new and has never been addressed. To deal with this challenge, we conducted five focus groups with HCI professionals and identified a set of prioritization criteria. We then used those criteria to prioritize the UI items for future empirical evaluation of their preferred directionality. In addition to these findings, we provide some insights from our experience of using focus groups for the prioritization task.
How HCI Adopts Service Design

Service design has gained tractions in Human-Computer Interaction (HCI) as an approach to deal with changes of technology design scopes. In the meantime, there are confusions around definitions of service design and its relevance to HCI. Despite the co-existence of interests and confusions, little research has been done for a comprehensive overview of how HCI interprets and adopts service design. This research performed a systematic literature review on extant HCI publications that claim to use service design. The review findings from the 179 publications revealed varying dimensions of service design taken up in HCI, relations between service design scopes and emerging technologies, as well as unclarity to service design in HCI and HCI's current tendency to use service design for the interaction level rather than the system level. We discuss future design and research opportunities for HCI by integrating the system level dimensions of service design.
Research on Bi-LSTM machine reading comprehension algorithm based on attention mechanism

Abstract Machine reading comprehension is a very important research field in Natural Language Processing (NLP), but it is not sufficient for military research, and traditional machine reading comprehension algorithms do not fully explore every word in different contexts. In response to the above problems, this paper proposes a machine reading comprehension model that incorporates a multi-attention mechanism. It is brave to solve machine reading comprehension tasks in the military field. The model in this paper performs word embedding, part-of-speech embedding, and knowledge base embedding for each word in the article. Among them, knowledge embedding uses military knowledge bases to make up for the lack of information on military entities and relationships in military articles, and independently carries out part-of-speech embedding to solve the problem of part of speech judgment of polysemous words in specific contexts. The experimental results show that the machine reading comprehension model with multi-attention mechanism proposed in this paper can achieve better results on machine reading comprehension tasks, and the accuracy rate is improved by 1.6% compared with ROUGE-L.
Supporting Real-time Peer-Mentoring of Rural Volunteers

Telephone-driven community forums have been a widely proposed solution to address the unreliable internet connectivity and large geographic scope that characterizes many international NGO contexts. Primarily, these applications support asynchronous activities, such as information portals or forums to access rural journalism, but opportunities for real-time experience sharing remain largely under-explored. In this paper, we explore the potential of such forums to support remote mentoring of NGO volunteers, a practice that requires synchronous, dialogical formats for experience sharing and peer discussion. We engaged 28 participants from a rural Indian NGO in the design of peer-mentoring sessions that leverage synchronous audio discussions, using the structure and format of traditional talk-show radio as a starting point. The participants favored an entertaining approach to mentoring and discussed the logistics required to achieve this within their resource constraints. We conclude with design implications for designing media-driven community engagement platforms and the ethical challenges around protecting marginalized community interests.
On tuning a mean-field model for semi-supervised classification

Semi-supervised learning (SSL) has become an interesting research area due to its capacity for learning in scenarios where both labeled and unlabeled data are available. In this work, we focus on the task of transduction - when the objective is to label all data presented to the learner - with a mean-field approximation to the Potts model. Aiming at this particular task we study how classification results depend on $\beta$ and find that the optimal phase depends highly on the amount of labeled data available. In the same study, we also observe that more stable classifications regarding small fluctuations in $\beta$ are related to configurations of high probability and propose a tuning approach based on such observation. This method relies on a novel parameter $\gamma$ and we then evaluate two different values of the said quantity in comparison with classical methods in the field. This evaluation is conducted by changing the amount of labeled data available and the number of nearest neighbors in the similarity graph. Empirical results show that the tuning method is effective and allows NMF to outperform other approaches in datasets with fewer classes. In addition, one of the chosen values for $\gamma$ also leads to results that are more resilient to changes in the number of neighbors, which might be of interest to practitioners in the field of SSL.
Intelligent Music Interfaces: When Interactive Assistance and Augmentation Meet Musical Instruments

The interactive augmentation of musical instruments to foster self-expressiveness and learning has a rich history. Over the past decades, the incorporation of interactive technologies into musical instruments emerged into a new research field requiring strong collaboration between different disciplines. The workshop ”Intelligent Music Interfaces” consequently covers a wide range of musical research subjects and directions, including (a) current challenges in musical learning, (b) prototyping for improvements, (c) new means of musical expression, and (d) evaluation of the solutions.
Active Sets for Explicitly Constrained Evolutionary Optimization

Active-set approaches are commonly used in algorithms for constrained numerical optimization. We propose that active-set techniques can beneficially be employed for evolutionary black-box optimization with explicit constraints and present an active-set evolution strategy. We experimentally evaluate its performance relative to those of several algorithms for constrained optimization and find that the active-set evolution strategy compares favourably for the problem set under consideration.
Momentum feature comparison network based on generative adversarial network for single image super-resolution

Most super-resolution methods are trained on datasets where high-resolution images and corresponding low-resolution images are obtained by the fixed degradation method. However, these external-based methods would fail to recover the detailed information of the test images if it could not be found in datasets. In this paper, we propose the momentum feature comparison network to generate the super-resolution image with rich texture information. Without the participation of high-resolution images in the training process, our method belongs to the completely unsupervised super-resolution method. A siamese structure with momentum update in generator is proposed to expand the content information of the low-resolution image and maintains the continuous consistency of intermediate feature maps. Furthermore, the results of two branches are fused through the feature fusion module to retain the global distribution of features and enhance local high-frequency details. The experimental results show that our method achieves great results compared with the state-of-the-art methods. • A feature comparison network model based on momentum update is proposed to solve the single image super-resolution problem. • Using generative adversarial network algorithms to improve the quality of reconstructed details for image super-resolution. • In the process of network training, only the input low-resolution images are used, which is a completely unsupervised image super-resolution algorithm.
Temporal Relation Extraction with Joint Semantic and Syntactic Attention

Determining the temporal relationship between events has always been a challenging natural language understanding task. Previous research mainly relies on neural networks to learn effective features or artificial language features to extract temporal relationships, which usually fails when the context between two events is complex or extensive. In this paper, we propose our JSSA (Joint Semantic and Syntactic Attention) model, a method that combines both coarse-grained information from semantic level and fine-grained information from syntactic level. We utilize neighbor triples of events on syntactic dependency trees and events triple to construct syntactic attention served as clue information and prior guidance for analyzing the context information. The experiment results on TB-Dense and MATRES datasets have proved the effectiveness of our ideas.
Generating Full-Field Digital Mammogram From Digitized Screen-Film Mammogram for Breast Cancer Screening With High-Resolution Generative Adversarial Network

Developing deep learning algorithms for breast cancer screening is limited due to the lack of labeled full-field digital mammograms (FFDMs). Since FFDM is a new technique that rose in recent decades and replaced digitized screen-film mammograms (DFM) as the main technique for breast cancer screening, most mammogram datasets were still stored in the form of DFM. A solution for developing deep learning algorithms based on FFDM while leveraging existing labeled DFM datasets is a generative algorithm that generates FFDM from DFM. Generating high-resolution FFDM from DFM remains a challenge due to the limitations of network capacity and lacking GPU memory.In this study, we developed a deep-learning-based generative algorithm, HRGAN, to generate synthesized FFDM (SFFDM) from DFM. More importantly, our algorithm can keep the image resolution and details while using high-resolution DFM as input. Our model used FFDM and DFM for training. First, a sliding window was used to crop DFMs and FFDMs into 256 × 256 pixels patches. Second, the patches were divided into three categories (breast, background, and boundary) by breast masks. Patches from the DFM and FFDM datasets were paired as inputs for training our model where these paired patches should be sampled from the same category of the two different image sets. U-Net liked generators and modified discriminators with two-channels output, one channel for distinguishing real and SFFDMs and the other for representing a probability map for breast mask, were used in our algorithm. Last, a study was designed to evaluate the usefulness of HRGAN. A mass segmentation task and a calcification detection task were included in the study.Two public mammography datasets, the CBIS-DDSM dataset and the INbreast dataset, were included in our experiment. The CBIS-DDSM dataset includes 753 calcification cases and 891 mass cases with verified pathology information, resulting in a total of 3568 DFMs. The INbreast dataset contains a total of 410 FFDMs with annotations of masses, calcifications, asymmetries, and distortions. There were 1784 DFMs and 205 FFDM randomly selected as Dataset A. The remaining DFMs from the CBIS-DDSM dataset were selected as Dataset B. The remaining FFDMs from the INbreast dataset were selected as Dataset C. All DFMs and FFDMs were normalized to 100μm × 100μm in our experiments. A study with a mass segmentation task and a calcification detection task was performed to evaluate the usefulness of HRGAN.The proposed HRGAN can generate high-resolution SFFDMs from DFMs. Extensive experiments showed the SFFDMs were able to help improve the performance of deep-learning-based algorithms for breast cancer screening on DFM when the size of the training dataset is small.
QuAD: Deep-Learning Assisted Qualitative Data Analysis with Affinity Diagrams

Affinity diagramming is an effective and efficient method for forming nuanced interpretations of wide-ranging, unstructured qualitative data; however, this method does not scale well to large data sets. We propose a novel affinity diagramming system, called Qualitative Affinity Diagrammer (QuAD) that leverages computer-generated suggestions using deep learning to address the scalability of the diagramming process. QuAD features automatic grouping suggestions to jump-start the affinity diagramming process and provides grouping suggestions throughout the diagramming process to reduce sifting through notes. In this paper, we present a prototype of QuAD that uses Bidirectional Encoder Representations from Transformers (BERT) and Girvan-Newman to generate grouping suggestions. This work is the first step towards creating a powerful tool for assisting in the analysis of large qualitative data sets in a variety of contexts, including human-computer interaction.
Designing Augmented Reality Based Interventions to Encourage Physical Activity During Virtual Classes

This paper presents an exploratory study of designing augmented reality (AR) based interventions to encourage physical activity among students attending virtual classes. We conducted a focus group study with four HCI students to understand the current behaviour of students during breaks between classes. Based on these insights we designed two AR interventions: AR Exergame, an interactive game that requires the user to move their hands to grab virtual apples; AR micro-movements, a method that requires the user to switch to a different physical space when starting a new virtual class. These AR interventions were aimed at encouraging students to perform micro-activities in between virtual classes, i.e. small non-strenuous activities. The effectiveness of these methods to reduce video conferencing fatigue, a.k.a Zoom fatigue, and also to compare it with the students current methods was tested through a user study with six participants.
Leveraging Transformative Justice in Organizing Collective Action Towards Community Safety

Criminologists and sociologists have documented the ways that technologies for community safety can perpetuate structural racism. Among these include policing technologies that encode Black criminality [4] and surveillance technologies which extends the reach of the carceral state [17]. Fortunately prior work in HCI has also investigated how the design of technologies for community safety can resist structural racism by employing strategies that stand as alternatives to traditional policing activities, including story-telling, community-building, and care practices [10, 11, 25, 27]. This body of work amplifies the practices of community organizations and employs their expertise to build safer community. I contribute to this body of research and ask how technologies can empower a layperson in the goal of building safer communities. This is an area of great potential— just as community policing technologies extend the reach of traditional policing, new technologies can be designed to extend the reach of community organizations aiming to dismantle oppressive social structures. Towards this goal, I leverage Transformative Justice (TJ), a community-based approach to addressing violence which asks us to not only examine the ways in which our current systems, structures, and norms perpetuate harm, but to also dismantle and replace those systems so that the conditions that enabled the harm are transformed. In this paper, I describe the current state of research, the principles of TJ, my goals for the PhD, and where I would benefit from further support.
Construction of a Digital Platform for Enterprise Financial Management Based on Visual Processing Technology

Along with the continuous socioeconomic growth, more capital injection is needed to meet the daily operating cash flow demand compared to the traditional development model, so it is necessary to study the digital capability of enterprise financial management. Based on the evolutionary logic of financial risk identification, evaluation, and control, this paper summarizes the most suitable feature processing algorithm process for enterprise financial management systems by visual processing technology; for the visual guidance system, it needs to be able to identify artifacts quickly and accurately and compare different visual recognition algorithms experimentally. Considering the accuracy and real time of the visual guidance system, the efficiency of the algorithm is improved as much as possible under the condition of ensuring the accuracy, including the collection of work area features and the filtering preprocessing of data features, edge detection, and feature extraction, and, finally, the coordinate conversion can be carried out effectively to meet the requirements of the system for speed and accuracy. The digital platform for enterprise financial management built in this paper gives risk management strategies and implementation plans in six aspects, including improving the internal environment for risk management, setting control objectives in line with the development of the company, focusing on the response and control of major risks, ensuring efficient communication of information, and strengthening supervision. In terms of profitability risk, we propose to improve asset utilization, establish additional investment risk management departments, enhance capital utilization efficiency, and create core mobile products; in terms of asset operation, we propose to increase asset risk management, expand product sales scale, standardize cash flow system, and use centralized bank accounts.
Enhancing Students’ Social and Emotional Learning in Educational Virtual Heritage through Projective Augmented Reality

The global agenda in education foresees the integration of social and emotional learning to equip students to succeed in our evolving digital society. In this paper, we focus on deepening into the research on AR/MR technologies to foster students' comprehension of socio-cultural values in historical contexts. We address this challenge by exploring the potential of the interaction paradigm called World-as-Support (WaS). We present a case study of an educational Virtual Heritage experience with primary students for a bomb shelter built during the Spanish Civil War. Our findings showed that the experience enhanced students' capabilities to reflect upon high-level issues related to value human dignity, to grasp some of the essential qualities of the value solidarity and to connect historical events with present political situations in Spain. Finally, we discuss four design recommendations for learning activities based on the WaS concerning (1) the enhancement of students' competences in collaboration, and communication; (2) critical thinking; (3) contextualization of historical contents; and (4) moral and ethical considerations for digital augmentation.
Take a line for a walk! A Hands-on Introductory Course on Sketching in HCI

Sketching is a universal activity that first appears when we play as children, but later, it is often overlooked as a useful skill in adult work – yet it can bring multiple benefits to research and practice. Specifically, our field of Human-Computer Interaction (HCI) embraces interdisciplinary practices, and amongst those, sketching has proven to be a valuable addition to the skill set of researchers, practitioners, and educators in both academia and industry. Many individuals lack the confidence to take up pen and paper after years of non-practice, but it is possible to re-learn these lost skills, improve on them, and apply them in practical ways to all areas of work and research. This course offers a journey in sketching, from scribbles and playful interpretations to hands-on and theoretical information inherent in sketching practice. Attending individuals will learn techniques and applied methods for utilizing sketching within the context of HCI, guided by experienced instructors.
On the weighted average number of subgroups of ℤm × ℤn with mn ≤ x

Let [Formula: see text] be the additive group of residue classes modulo [Formula: see text]. For any positive integers [Formula: see text] and [Formula: see text], let [Formula: see text] and [Formula: see text] denote the total number of subgroups and cyclic subgroups of the group [Formula: see text], respectively. Define [Formula: see text] In this paper, we study the asymptotic behavior of the functions [Formula: see text] and [Formula: see text].