Reviewerly/exHarmony huggingface dataset:
Classification and Regression Combined Model on Accessing Humor Score with Explanatory Feature

Humor recognition and generation are still challenges for machine learning. There is a competition named Assessing Humor in Edited News Headlines. In the competition dataset, original headlines, edit words, and humor grades are provided. We utilized this dataset and tried to tackle the task, predicting the humor score of headlines after using the edit word to switch a word inside the original one. We divided the task into two major sections, the classification part and the regression part. Support vector machine (SVM) models are used for the classification task, and Bidirectional Encoder Representations from Transformers (BERT) models are used for the regression task. We proposed a new combined model by using classification labels to restrain the regression results. Through the task, we found that the models with a defining characteristic have a better performance. A further study on the explanatory models was conducted. Unfortunately, our classification model does not have an outstanding performance. Therefore, using classification labels to restrain the regression results does not get an excellent final model performance.
Establishment of Performance Evaluation Model of Highway Environmental Protection Based on Deep Learning

As the foundation of the social economic system, public transportation roads play an important role in the development of the national economy. At the same time, the opposition between public transportation roads and the natural environment, ecological environment, and natural resources is becoming more and more obvious. This paper is aimed at studying the performance evaluation model of highway environmental protection based on deep learning. For this reason, related methods such as environmental protection, performance evaluation, and deep learning algorithms are proposed in the article, and corresponding experiments are also carried out in the article. The experimental results show that the highway environmental protection performance evaluation model based on deep learning can quickly help environmental supervisors to inspect the work of relevant highway environmental protection agencies and also accurately mention the environmental protection performance detection to 80%.
Exploring Technology Usage of Southeast Asian Elderly Women for Better Mental Health during COVID-19 Pandemic

The elderly population worldwide got immensely affected by the increased isolation and risk for complications due to the COVID-19 pandemic. Notably, elderly women get more affected by social isolation and distress irrespective of health factors. We aim to understand how urban elderly women in Southeast Asia – typically highly dependent on the other family members due to cultural practices – took care of their mental health with uncertainty and distress using technology during the social distance period. Through 19 semi-structured interviews with participants from six Southeast Asian countries and analyzing the data using thematic analysis, we surfaced that our participants started learning different technology with great enthusiasm and used them for their mental well-being during the pandemic period. This paper portrays how our participants enhanced interpersonal bonding, cultivated self-care and creative outlets, and facilitated positivism around their social circle using different technology platforms to mitigate their stress and uncertainty during the pandemic. Our participants’ technology usage for better mental well-being during the COVID-19 period provides HCI researchers with valuable design guidelines. Here, we contribute by expanding the HCI community’s understanding of technology design within the intersection of the elderly population and mental health for the Southeast Asian cultural context.
Image Retrieval Technology of Smart Archives from the Perspective of National Reading

In order to improve the image retrieval effect of smart archives, reduce retrieval time, and enhance retrieval performance, this paper proposes an image retrieval technology for smart archives from the perspective of national reading. According to the concept of nationwide reading and considering the internal and external influencing factors, mobile visual retrieval service for users of smart archives is provided, and the mobile visual retrieval model for users of smart archives is constructed by using a standardized structure and mixed structure. During the operation of the model, the image feature similarity of the wisdom archive is calculated, and the image is processed by the corresponding module, so as to realize the image retrieval technology research of the wisdom archive from the national reading perspective. The experimental results show that the designed technology has better retrieval effect, shorter time, and better performance.
Graft-RRT: An efficient method to find and converge global paths through intermediate points

In this paper, an improved algorithm for rapidly exploring random trees (RRT) is proposed. This method is called Graft-RRT. the main advantages of this method are the low cost of the explored global path and the fast exploration speed. Based on the idea of bi-directional random exploration tree (B-RRT), the algorithm generates a third random tree, Graft-Tree, by finite random sampling of the feasible region between the starting point and the ending point, and then extends the starting point random tree and the ending point random tree. When the global path is obtained, the global path cost is reduced by compressing the effective cost on the path several times. The simulation results show that the global path cost is significantly lower than B-RRT and RRT*, and the time consumed is lower than RRT and RRT* in the same complex and simple environment.
Prototyping and Design of Transparent and Flexible Electrochromic Displays

This course is a hands-on introduction to the fabrication of flexible, transparent free-form displays based on electrochromism for an audience with a variety of backgrounds, including artists and designers with no prior knowledge of physical prototyping. Besides prototyping using screen printing or ink-jet printing of electrochromic ink and an easy assembly process, participants will learn essentials for designing and controlling electrochromic displays.
Multi-view Attention Mechanism Learning for POI Recommendation

Abstract POI(point of interest) recommendation is a very necessary research field in both academic and commerce, however, predicting users’ potential points of interest is always faced with the problems of data sparsity and context semantics. Some studies have shown that graph embedding technology alleviates the problem of data sparsity to a certain extent. However, neither graph embedding techniques nor unsupervised learning models can adaptively learn the different effects of multiple relations between users and POIs, respectively. In view of this, we leverage the contextual information of users and POIs to build the multi-view affinity graphs(e.g. User-User, POI-POI and User-POI), and learn the latent representations of users and POIs based on the Graph Embedding technology and Attention mechanism, namely the GEA model. In particular, we first construct multi-view affinity graphs by using user’s social relationship, geographical distance and check-in behaviour, and embed them into a low dimensional shared space to learn the latent representation of users and POIs. Afterwards, in order to take advantage of the different effects of multiple relationships in the final recommendation task, we exploit the attention mechanism to obtain the fused latent representation and make recommendation according users’ potential preferences. Finally, we design a multi-task objective function for joint optimization to obtain more accurate recommendation results. Extensive experiments on Gowalla have verified the effectiveness of our model.
An Effective Approach for Sonar Image Recognition with Improved Efficientdet and Ensemble Learning

Abstract Precisely recognition of underwater targets plays a critical role in the field of underwater unmanned exploration. According to the characteristics of sonar image, in order to improve the accuracy of underwater automatic target recognition, this paper proposes an underwater target sonar image recognition method combined with improved Efficientdet and ensemble learning. According to the regional dominance principle and threshold segmentation method, the sonar image is preprocessed, and preliminary effective features extracted from preprocessed data by Efficientnet backbone, so as to balance the efficiency and power consumption of the algorithm. The feature fusion adopts the improved weighted bi-directional feature pyramid ( BiFPN ) structure to aggregate the global information and strengthen the feature representation of the shallow feature map, which optimizes the problem that the small feature extraction is not ideal due to the low resolution of the sonar image and less detail information. By building a two-level classifier to complete the learning task in an ensemble way, a better classification effect is obtained than a single classifier. This method is applied to the underwater intelligent perception group of the 10th National Marine Aircraft Design and Production Competition in 2021, and won the first prize in the country. The experimental results show that the designed ensemble network model is more accurate than the conventional convolution neural network in identifying underwater targets on the measured sonar image data set while ensuring a certain prediction speed.
Effect of relativistic motion on superconducting quantum bits under decoherence

In the present manuscript, we consider a quantum system of two super-conducting (SC) qubits that move at relativistic speeds and each qubit coupled to a resonator mode (RM). Here, the relativistic motion of the SC-qubits results from the modulation of the time-varying coupling between the SC-qubits and the resonator for which the system resembles the rotating approximation and anti-Jaynes-Cummings (JC) dynamics. We study the time variation of some interesting current quantumness measures with respect to the time-varying coupling (TVC) considering von Neumann entropy, negativity and Mandel parameter. We illustrate the dynamical behavior of the SC-qubits–RMs entanglement, SC-qubit–SC-qubit entanglement and photon statistics of the RMs at relativistic motion in the absence and presence of dissipative environment.
CAPIRS: COVID-19-Based Application Programming Interface Recommendation System for the Developers

Context: From the past few years, Application Programming Interface (API) is widely used for mobile- and web-based application developments. Software developers can integrate third-party services into their projects to achieve their development goals efficiently using APIs; however, with the rapid increase in the number of APIs, the manual selection of Mashup-oriented API is becoming more difficult for the developer. Objective: In the COVID-19 pandemic, everyone wants an update about the latest Standard Operating Procedures (SOPs) and the latest information on COVID-19. Additionally, a software developer wants to develop an application that provides the SOPs and latest information of COVID-19; a developer can add these functionalities into an application using COVID-19-based APIs. Moreover, the current work aims at proposing a COVID-19-based API recommendation system for the developers. Method: In this study, we propose a COVID-19-based API recommendation system for developers. The recommendation system takes a developer query as input and recommends top-3 APIs and supported features, which help the developer during software development. Furthermore, the proposed COVID-19-based API recommendation system ensures the maximum participation of the developers by validating the recommended APIs and recommendation system from the expert developers using research questionnaires. Results: Additionally, the proposed COVID-19-based API recommendation system’s output is validated by expert developers and evaluated on 120 expert developers’ queries. In addition, experiment results show that single value decomposition achieves better prediction. Conclusion: We conclude that it is significantly important to recommend APIs along with supported features to the developer for project development, and future work is needed to take more developer’s queries also to build Integrated Development Environment for the developers.
Designing Interactive Experiences in the Interplay between Ambient Intelligence and Mixed Reality

Our physical environments integrate more and more sensing, processing, and communications technology toward the realization of the vision of Ambient Intelligence (AmI). At the same time, virtual worlds are increasingly more linked to physical spaces by means of applications delivering the vision of Augmented and Mixed Reality (AR/MR). In this context, several opportunities emerge at the intersection of these two visions of computing. The goal of this course is to achieve the realization that AmI and MR are two sides of the same coin in order to deliver a fresh perspective to HCI researchers and practitioners interested in designing interactive experiences beyond the desktop and mobile computing paradigms by employing the concepts, principles, and methods of AmI and MR.
Helping Voice Shoppers Make Purchase Decisions

Online shoppers have a lot of information at their disposal when making a purchase decision. They can look at images of the product, read reviews, make comparisons with other products, do research online, read expert reviews, and more. Voice shopping (purchasing items via a Voice assistant such as Amazon Alexa or Google Assistant) is different. Voice introduces novel challenges as the communication channel is limited in terms of the amount of information people can and are willing to absorb. Because of this, the system should choose the single most effective nugget of information to help the customer, and present the information succinctly. In this paper we report on a within-subject user study (N = 24), in which we employed three template-based methods that use information from customer reviews, product attributes and search relevance signals to generate helpful supporting information. Our results suggest that: (1) supporting information from customer reviews significantly improves participants perception of system effectiveness (helping them make good decisions); (2) supporting information based on search relevance signals improves user perception of system transparency (providing insight into how the system works). We discuss the implications of our findings for providing supporting information for customers shopping by Voice.
Hourly solar irradiance prediction based on enhanced incremental extreme learning machine

As more and more photovoltaic power generation systems are connected to the grid, the intermittency and fluctuation of their output power have placed a great burden on the grid. Solar irradiance forecasts are critical for planning and managing photovoltaic system power generation. In order to improve the prediction accuracy of solar irradiance, this paper introduces an hourly solar irradiance prediction method. The enhanced incremental extreme learning machine algorithm is used to establish a solar irradiance prediction model. Only the historical irradiance data is used as the Input features, and output the hourly average solar irradiance at the time to be predicted. The experimental results of this paper show that, compared with the original incremental extreme learning machine, the solar irradiance prediction model established by the enhanced incremental extreme learning machine algorithm can obtain smaller prediction errors. At the same time, the enhanced incremental extreme learning machine can obtain more stable and accurate prediction results with fewer nodes.
Access to Music for People with Dementia

How can smart technology support people with mild-moderate dementia to benefit from the positive effects of listening to music in daily life? The quality of life of people with dementia decreases rapidly when they experience difficulties in using everyday products and lose initiative. With a focus on the interaction with music, we study how smart technology can enable human-product-interaction while adapting to loss of initiative. As a result, knowledge on interaction design will be developed to help designers create better products for people with dementia.
Respecting and Facilitating Children's Contributions to Research, Design and Evaluation in HCI

Child Computer Interaction is concerned with the research, design, and evaluation of interactive technologies for children. Whilst many aspects of general HCI can be applied into this field, there are important adaptions to be made when conducting work for and with children throughout all stages of the design cycle. This course overviews the main tools and techniques in use by the CCI community against a backdrop of ensuring that children who work with our community feel valued and see the value in the work they contribute to. presented alongside examples and experiences from academia and industry. The course draws on the authors’ experience within academia and industry hands on and provides useful checklists and tips to ensure children (and researchers and developers) get the most out of participation in HCI activities.
The Sound of Hallucinations: Toward a more convincing emulation of internalized voices

The need to generate convincing simulation of voices often arises in the context of avatar therapy, a treatment approach for disorders such as schizophrenia. This treatment involves patients interacting with simulations of the entity they imagine to be responsible for the voices they hear, for which there is often no external reference available. However, in such scenarios, there is little knowledge of how to design and reproduce these voices in a convincing manner. Existing voice manipulation interfaces are often complex to use, and highly limited in their ability to modify vocal characteristics beyond small adjustments. To address these challenges, we designed a framework that allows users to explore and select from a large set of voices, and thereafter manipulate the voice(s) to converge towards an effective match for one they have in mind. We demonstrated both the usability and superior performance of this system compared to existing voice manipulation interfaces.
A Hybrid Algorithm for Face Recognition System Based Smart Attendance System

Face recognition aims to detect, track, identify, or verify person from video or images using recognition system. Its application varies from security, authentication, social media, and so on. With the advancement of technologies, still it impedes in accuracy. The challenges involved in face recognition are variations in facial expression involve diverging light, facial changes interfered with facial hair, pose, and so on. This research work presents a new hybrid approach, which improves the accuracy in identification of a person. The accuracy of the proposed work clearly depicts that it can be best suited in real time environments for tracking persons. It can be used for applications like authentication, security, and so on. The proposed work is validated by tracking the student presence. It captures the image of the student and detect the face by pre-processing, extracting the features and recognizing the person. For recognition it uses Local Binary Pattern Histogram (LBPH) algorithm and Haar Cascade classifier. LBP Algorithm uses Histogram, which improves the accuracy of the face recognition. It is integrated with Haar classifier, which uses machine learning algorithm. Adaboost Learning algorithm, which selects small features from large set, which improves the efficiency of recognition.
The Future is Red: Visualizing Wildfire Predictions using Contactless Interaction

This paper presents our approach for visualizing, sonifying, and predicting wildfire in the near future and contactless interaction. We provide an interaction tool to depict the causes and results of the wildfire and promote awareness of the environmental issues by giving forecasting results of the wildfire to the audience. Multimodal interaction allows the audience to dynamically experience the changes of the wildfire over time in two representative locations. (California, United States, and South Korea) The interactive multimodal data visualization and sonification depict the past, present, and future of the wildfire. This data-driven design was installed in an art gallery and presented to audience members. Contactless user interaction with Leap Motion cameras was used during the pandemic period for hygienic interaction. In this paper, we describe the design process and how this interface was developed based on environmental issues, and informal user responses from the art gallery setup are discussed.
Research on the Evaluation Model of Dance Movement Recognition and Automatic Generation Based on Long Short-Term Memory

With the development of random image processing technology and in-depth learning, it is possible to recognize human movements, but it is difficult to recognize and evaluate dance movements automatically in artistic expression and emotional classification. Aiming at the problems of low efficiency, low accuracy, and unsatisfactory evaluation in dance motion recognition, this paper proposes a long short-term memory (LSTM) model based on deep learning to recognize dance motion and automatically generate corresponding features. This paper first introduces the related deep learning model recognition methods and describes the related research background. Secondly, the method of identifying dance movements is identified concretely, and the process of identifying concretely is given. Finally, through the comparison of different dance movements through experiments, it shows that there are obvious advantages in the accuracy of action recognition, error rate, similarity, and model evaluation method.
AR Music Visualizers: Application Space and Design Guidelines

Music visualization has recently become a popular application of augmented-reality (AR) technology. However, despite their various designs and functionalities, their potential benefits and the effectiveness of their designs are not well explored. By interviewing experts and generating and evaluating design ideas, we offer two contributions: (1) the application space of AR music visualizers, defined by the categories of their potential benefits and the constraints from their contexts of usage; and (2) a four-step design guidelines to design a visualizer that achieves the intended benefit(s), while appropriately handling the contextual constraints. We believe that this paper can provide a concrete basis for designing effective AR music visualizers in the future.
From-The-Wild: Towards Co-Designing For and From Nature

Here we present in-progress methodological research exploring how to co-design technology for nature-related experiences. To support increasingly situated and participatory practices in this space, we propose a turn towards co-designing from-the-wild, i.e. ideating during raw engagements that are radically situated in nature. Our approach extends existing in-the-wild practices by (1) enacting co-design in natural (rather than human-made) environments, and (2) avoiding techniques that privilege the designer's agenda over other stakeholders' and compromise the situated nature of ideation. Our contribution includes: (1) the proposal of co-designing from-the-wild as a response to the limitations of existing in-the-wild methods when designing for and from nature; and (2) early reflections from our hands-on engagement with said approach, which begin to surface exciting opportunities and constraints emerging in this methodological space. By sharing our work-in-progress with the HCI community, we hope to spark a conversation stimulating future co-design methods research in increasingly wilder directions.