Reviewerly/exHarmony huggingface dataset:
Asian CHI Symposium: HCI Research from Asia and on Asian Contexts and Cultures

The Asian CHI symposium is an annual event organized by researchers and practitioners in Asia. The symposium aims to bring together both early-career and senior HCI academia and UX practitioners from industries in Asia and bring about cross-exchange of information and transfer of knowledge in a multidisciplinary environment and multi-socio-economic aspects of HCI research and also foster social ties and collaboration in the field of HCI. Beyond showcasing the latest Asian-inspired HCI work and those focusing on incorporating Asian sociocultural factors in their design, implementation, evaluation, and improvement, the Asian CHI Symposium 2023 is a sandbox for academically rigorous discourse platform for both HCI academic and UX practitioners to present their latest research findings and solutions that reflect the expansion of HCI theory and applications towards culturally inclusive design for diverse audiences in Asia. In addition to circulating ideas and envisioning future research in HCI, this symposium aims to foster social networks among academics (researchers and students) and practitioners and grow a research community from Asia.
Frames and descriptions

Abstract This chapter looks at frames associated with common nouns. Frames are construed as situations (modelled as records in TTR). A common noun like dog is associated with a type of situation which is common for dogs, for example, where the dog has a name, an age, and various other attributes we commonly attribute to dogs and this plays an important role in interpreting utterances such as the dog is nine. Some nouns, such as temperature, represent frame level predicates. Nouns which normally predicate of individuals can be coerced to predicates of frames. An example is ship in four thousand ships passed through the lock which can either mean that four thousand distinct ships passed through the lock or that there were four thousand ship-passing-through-the-lock events some of which may have involved the same ship. Such examples are analysed by associating an appropriate frame type with the noun ship.
Deep Learning Based Marathi Sentence Recognition using Devnagari Character Identification

There are multiple algorithms available to recognize Marathi Devnagari characters. Most of these methods are limited because of the large variety of character variations due to Kana, Matra, Ukar, Velanti, and Anusvar, which are specific to the Marathi grammar called Barakhadi. There is a need to have a dictionary-based word formulation to achieve full Marathi sentence recognition. In the proposed work, a Marathi sentence is recognized using a combination of full 454 variation detection of Devnagari characters and nearest dictionary word mapping using the k-nearest neighbour (KNN) model to achieve full sentence recognition. This is the first time full 454 (Vyanjan variation as per Barakhadi) character recognition instead of the traditional 58 characters (Vyanjans) has been attempted which leads to sentence recognition. The proposed method could achieve a sentence recognition accuracy of 86.84%, a 454 character classification accuracy was 89.52%, and the execution speed of the proposed system was 1.464 secs per word. For the training of the character recognition network, a separate dataset was created for all Vyanjan variations as per Barakhadi. This novel contribution of the proposed system will surely inspire researchers to explore Devnagari sentence recognition.
Workflow aware analytical model to predict performance and cost of serverless execution

Summary Serverless computing has emerged as a powerful deployment model based on the Function‐as‐a‐Service (FaaS) paradigm, where applications are orchestrated through a set of independent functions. The function orchestration within an application can be represented through a serverless workflow, which defines the overall execution plan of the application. To ensure the quality of service for serverless computing platforms, it is essential to develop performance and cost models that can predict the service quality that can be obtained from deploying and executing applications in the cloud platform. While several analytical models have been developed for various cloud deployment frameworks in recent years, there has been a lack of performance and cost analysis models for serverless computing platforms. The existing performance and cost monitoring tools available in serverless frameworks face several challenges, such as complexity, lack of transparency, and incomplete monitoring data. In this paper, we fill the gap by proposing an efficient workflow‐based analytical model that can estimate the end‐to‐end response time and cost of the serverless execution plan. The proposed model can handle complex structures like loop, cycles, self‐loop, and parallel substructures that exist in serverless workflows. Additionally, we propose a heuristic optimization algorithm to identify the optimal resource configuration to achieve the optimal response time under a given budget constraint. We evaluated the effectiveness of the proposed model by considering seven serverless applications in both AWS Lambda and Microsoft Azure platforms. We compared the accuracy of the proposed model with the real values of response time and cost obtained in AWS Lambda and Microsoft Azure serverless platforms. The proposed performance and cost model in the AWS Lambda platform has been observed to have an average accuracy of 99.2% and 98.7% respectively. In the Microsoft Azure platform, the average accuracy of the performance and cost model has been observed to be 98.6% and 98.2% respectively.
Exploiting Local Shape and Material Similarity for Effective SV-BRDF Reconstruction from Sparse Multi-Light Image Collections

We present a practical solution to create a relightable model from small Multi-light Image Collections (MLICs) acquired using standard acquisition pipelines. The approach targets the difficult but very common situation in which the optical behavior of a flat, but visually and geometrically rich object, such as a painting or a bas relief, is measured using a fixed camera taking a limited number of images with a different local illumination. By exploiting information from neighboring pixels through a carefully-crafted weighting and regularization scheme, we are able to efficiently infer subtle and visually pleasing per-pixel analytical Bidirectional Reflectance Distribution Functions (BRDFs) representations from few per-pixel samples. The method has a low memory footprint and is easily parallelizable. We qualitatively and quantitatively evaluated it on both synthetic and real data in the scope of image-based relighting applications.
Child-Centred AI Design: Definition, Operation, and Considerations

AI systems and related algorithms are starting to play a variety of roles in the digital ecosystems of children - being embedded in the connected toys, smart home IoT technologies, apps, and services they interact with on a daily basis. Going forward, AI systems will, in all likelihood, become even more pervasive in children’s applications simply due to their sheer usefulness in creating compelling, adaptive, and personal user experiences. Yet, understanding the ways that AI-driven systems used by children operate, and how AI could be designed to better anticipate and respond to children’s diverse requirements is still a new and emerging area of investigation. Our goals of this workshop are to (1) extend the current critically constructive dialogue around the meaning of child-centred AI design and (2) explore ways to operationalise such child-centred AI design in practice, and finally (3) further expand and foster a community for those who are interested in designing and developing child-centred AI systems.
Using Tello EDU quadrocopters to study the theme "Linear algorithms" of a school informatics course

The article discusses the possibilities of using Tello EDU quadrocopters when studying the section "Algorithms and programming" of a school informatics course. Quadrocopters can be used in lessons on the following themes: "Algorithms and executors. A quadrocopter is an executor of algorithms", "Linear algorithms. Programming an autonomous flight of a quadrocopter in the DroneBlocks environment", "Branching algorithms. Programming an autonomous flight of a quadrocopter in the DroneBlocks environment", "Cyclic algorithms. Programming an autonomous flight of a quadrocopter in the DroneBlocks environment". The lesson on the theme "Linear algorithms. Programming an autonomous flight of a quadrocopter in the DroneBlocks environment" is described. The tasks are suggested for each stage of the lesson. At the stage of updating knowledge, students are offered an interactive poster for organizing self-control. The lesson uses such methodological techniques as "Epigraph", "Skipping words", for the reflection of educational activities the technique "Ladder of success" is used.
“You said da da...”: A Short Echoing Tweak for Journaling with VA

AI and deep learning have enabled development of voice agents (VAs) and conversational agents (CAs) for more varied uses, including audio journaling which requires longer, more nuanced conversations. Current technology is capable of utilizing additional data, but VAs still remain limited to single-turn, task-oriented interactions. In this study, we investigate whether current turn-taking system is suitable for interactions with long speech in the form of audio journaling, and suggest ways that would make VAs more appropriate to handle longer sentences and turns by the user. To this end, we designed three types of responses, Empathetic, Reading-back, and Mixed, which all echo the user's speech in different ways. From a week-long journaling experiment with 23 subjects, we collected long speech data (average number of syllables: Empathetic=425, Reading-back=438, Mixed=304). The most effective response in this case is a Reading-back in terms of quality of data and journaling experience.
The Internet of Bananas

In this paper we recount the "Internet of Bananas" (IoB), a design-led exploration of citizen sensing as well as an exercise in "useful futility". The project mixed critical design and satire to explore social justice attitudes towards data collection and electronic literacy. In the IoB a number of participants from around the world collaboratively created and set up a small IoT network by programming and interconnecting a set of sensor kits with which they monitored the colour, surface temperature and humidity of a network of bananas for the duration of one week. The main goal of the creation of such network was to be a platform for reflection, discussion and exchange on themes related to data production and collection. The IoB was active for a week, the "Banana Jam", after which a debriefing provided an occasion to gather precious feedback from the participants and to assess, with their help, the entire process. The feedback, articulated around five main themes, has let to the creation of six final design considerations related to the combination of natural elements and digital technologies, the values of weirdness and play for critical design and its dissemination, and the importance of inclusivity and openness in regard of data collection and visualization.
Comparison of ANN Global Horizontal Irradiation predictions with Satellite Global Horizontal Irradiation using Statistical evaluation

The most important factor to take into account when building solar energy systems is solar irradiation. It is impossible to measure sun irradiation everywhere due to its high cost and difficulties. Additionally, in some places, the GHI was overpredicted by 25% by NASA satellite data. The main goal of this study was to develop an artificial neural network (ANN) model that can reduce the error in satellite data by predicting global horizontal irradiation (GHI) using inputs from satellite data obtained from the NASA Power Data viewer. The MAPE in the satellite was decreased by 35.8% in Peshawar, 10.2% in Islamabad, and 8.9% in Multan using the ANN models. Additionally, the results showed that all ANN models' predictions were more precise than satellite data.
Spelland: Situated Language Learning with a Mixed-Reality Spelling Game through Everyday Objects

This work explores the use of mixed-reality (MR) technology to enable situated language learning using everyday objects in the environment around the learners. The learning method is based on Presentation, Practice, and Production (PPP), which cultivates the habit of independent learning through repetition, practice, and demonstration. In our game design, the learners first interact with real-world objects via MR, and the objects' spelling and their pronunciation will appear (Presentation), the learners repeat the pronunciation (Practice) to collect the letters of this objects, and finally the learner use the collected letters to spell out the target words (Production), which then transform into interactive 3D objects. We designed the learning experience and content tools using gestural UI, voice input, and object-to-word engine. Children in the preliminary user study found the game to be immersive, helpful in learning the spelling of the everyday objects and the target words, and additionally showed increased interests in learning about other nearby objects after playing the game.
Witness-based quantification

Abstract This chapter looks at generalized quantifiers from the perspective of dialogic interaction. Traditionally generalized quantifiers are treated as sets of sets or sets of properties. Barwise and Cooper also introduced the auxiliary notion of witness set for quantifiers under the heading “Processing quantified statements”. This chapter turns things around and make the characterization of witness sets the primary notion in defining quantifiers. This makes it more straightforward to account for the anaphoric possibilities relating to quantified expressions in dialogue. We often use quantified statements in dialogue when we have inadequate information to determine their truth. This is particularly true of determiners like every and most when talking about large sets. We suggest that this phenomenon can be analyzed by estimating a probability based on the evidence presented in our cognitive resources (long-term memory or beliefs as discussed in Chapters 4 and 6).
Noninvasive material anisotropy estimation using oblique incidence reflectometry and machine learning

Anisotropy reveals interesting details of the subsurface structure of a material. We aim at noninvasive assessment of material anisotropy using as few measurements as possible. To this end, we evaluate different methods for detecting anisotropy when observing (1) several sample rotations, (2) two perpendicular planes of incidence, and (3) just one observation. We estimate anisotropy by fitting ellipses to diffuse reflectance isocontours, and we assess the robustness of this method as we reduce the number of observations. In addition, to support the validity of our ellipse fitting method, we propose a machine learning model for estimating material anisotropy.
From VizBlocks to the Data-Driven Actor: Reimagining an open-ended data physicalisation prototype with a creative business

In this case study, we document the process of engaging in an initially unplanned and informal knowledge exchange activity between academic researchers and a local performing arts company. This knowledge exchange activity quickly became a fruitful collaboration during which an academic design research prototype was reimagined as a wholly new product to expand the offering of a creative business. We document the factors that led to the success of this collaboration, reflecting on both features of the collaboration itself and how the design of the initial research prototype configured its repurposing. In terms of the latter, we consider how the original prototype's ambiguity, open-endedness, customisability and flexible assembly afforded its reimagining. Through the case study, we demonstrate that there is much to be gained from facilitating access to research prototypes for small and medium enterprises and supporting them in appropriating these toward their own goals.
CO2LLAB: Creating an Eco-Conscious Community through Habit Tracking and Augmented Reality Visualisation

CO2e emissions remain a substantial problem for the environment despite efforts from numerous initiatives over the years to curb their output. The role of younger generations is critical in this problem space. From user research, we found that young adults rarely volunteer but do engage frequently in individual actions geared towards the environment. They are also motivated when they see others' contributions. We decided to leverage this willingness for individual environmental actions and turn it into a collective effort by creating CO2LLAB, a platform that reimagines how young adults can reduce CO2e emissions with their community. It incorporates a habit-tracking app for eco-friendly actions and augmented reality technology to visualise their impact. Our prototype evaluation demonstrated that CO2LLAB not only educated them on the topic, but also motivated them to consider their impact on their community more.
Active metal deposition in a giant geothermal system

Abstract Metallogenic models are vital to mineral exploration. However, there is debate concerning the origin of hydrothermal‐type ore deposits such as vein‐type U and W deposits. The formation, migration, heating and controls on the composition of metal‐fertile fluids in active geothermal systems provide insights into the formation of ore‐forming fluids. Fluid’s metal contents are relatively high in the Yadong–Yangbajing–Naqu (YYN) geothermal belt of South Tibet, and a similar association of enrichment has been found in saline lakes of the region. This study analysed major ions and trace metal contents together with stable (O, H) isotopes of water samples from geothermal springs and wells and regional saline lakes and rivers along the Beng and YYN fault systems. Results demonstrate that metal‐fertile geothermal brines, were sourced from the Bangor saline lake. This study highlights the role of evaporation of saline lake water in the formation of metal‐fertile brines and the contrasting behaviors of U and W in hydrothermal conditions.
Conversational Recommender System based on Functional Requirement using Knowledge Graph for Building Personal Computer

When a person wants to build a personal computer, this person needs to browse many kinds of computer components. Besides that, this person needs to consider the compatibility between hardware and an affordable price. This will be a problem for people who are still unfamiliar with the computer, due to their lack of understanding of how compatibility between computer components works and the time-consuming nature of market research. To deal with this problem, the recommender system will assist in finding and matching compatibility efficiently based on the functional requirements of the user. The recommender system will issue various products based on the preferences and interests of the user, but some recommendations still need to be checked for compatibility. With the help of developing a Conversational Recommender System by utilizing the Knowledge Graph, it will be easier to construct the relationship between component compatibility. We propose this research by using Knowledge Graph as alternative from ontology to build Conversational Recommender system in Building Personal Computer. This research will involve the user to prove whether the recommendations from this system meet the needs and accuracy of the recommendations requested. The main results of this study will issue a recommendation for the development of personal computers by considering compatibility using the Conversational Recommender System using the Knowledge Graph approach.
WebJump: AR-facilitated Distributed Display of Web Pages

Head-mounted displays (HMD) like AR glasses support powerful 3D displays and intuitive input modalities. However, there is a lack of collaboration between the HMD and other displays like PC monitors. In this paper, we propose WebJump, a software tool that analyzes HTML web pages and enables UI elements 'jump' from the PC monitor to the HMD. This way, contents like high-resolution images and long texts can take full advantage of the high-quality monitor display. Auxiliary contents like sidebars and menus, on the other hand, are offloaded to the HMD for easier access albeit with a lower display quality. We describe our software tool and explain in detail its implementation. We develop two applications to demonstrate WebJump's feasibility and potential.
Memento Player: Shared Multi-Perspective Playback of Volumetrically-Captured Moments in Augmented Reality

Capturing and reliving memories allow us to record, understand and share our past experiences. Currently, the most common approach to revisiting past moments is viewing photos and videos. These 2D media capture past events that reflect a recorder's first-person perspective. The development of technology for accurately capturing 3D content presents an opportunity for new types of memory reliving, allowing greater immersion without perspective limitations. In this work, we adopt 2D and 3D moment-recording techniques and build a moment-reliving experience in AR that combines both display methods. Specifically, we use AR glasses to record 2D point-of-view (POV) videos, and volumetric capture to reconstruct 3D moments in AR. We allow seamless switching between AR and POV videos to enable immersive moment reliving and viewing of high-resolution details. Users can also navigate to a specific point in time using playback controls. Control is synchronized between multiple users for shared viewing.
A prediction model based on an adaptive genetic algorithm and optimized Elman neural network for PV power

Abstract Due to the high volatility and randomness of PV power generation, fast change frequency, limited data samples and poor data conditions, the reliability, stability and economy of grid operation are affected seriously. Therefore, the accurate prediction of PV power generation can mitigate the impact of its volatility on the stability of national grid power operations. And it improves PV consumption capacity and enhances the security of microgrid and distribution grid interconnection. This paper first proposed a PV power prediction method. Secondly, through the introduction and analysis of three types of prediction methods, the first type of prediction is selected using statistical analysis and modeling methods. Thirdly, an optimized Elman neural network-based PV power prediction model is proposed to allow accurate prediction of PV power prediction. Finally, an example is given to verify the advantages of the algorithm.
Above-Screen Fingertip Tracking with a Phone in Virtual Reality

Using a phone as a VR controller is challenging because users cannot see their fingers when aiming for targets on the touchscreen. We propose using two mirrors mounted above the screen that reflect the front camera and a purpose-built deep neural network to robustly infer the 3D position of fingertips manipulating the phone. Network training is self-supervised after only a few initial labelled images and does not require any external sensor. We present a few example scenarios showing potential applications that use our phone-based fingertip tracker for precise touch input and above-screen interaction.
Type-based underspecification

Abstract This chapter gives an account of how TTR types can be used to talk of content which is underspecified. The idea is to exploit the notion of types which can have several witnesses as “underspecifications” of those witnesses. Rather than associating contents with utterances as we have done in the earlier chapters, we associate types of contents with utterances. Thus a dialogue participant when observing a speech event associates it with a type of content rather than a particular content. We show how earlier ideas about the treatment of underspecification of quantifier scope and anaphora can be accommodated in this view