Reviewerly/exHarmony huggingface dataset:
Verb Meaning Representation Based on Structured Semantic Components

Meaning representation is a key task in computational linguistics and natural language processing. Many current meaning representation models fail to represent the accurate meanings of some sentences because of the flexibility of natural language meanings. This paper proposes a meaning representation scheme based on semantic components. Through a case study of Chinese strike verbs, za(smash) and tou(hurl), the paper shows the method of annotating the semantic components for the event participants of each instance of a verb in sentences. The actual meaning of each verb is summarized according to the annotation of semantic features. The change of verb meaning or the generation of new meaning is also explained more reasonably according to the semantic feature annotation proposed in this paper.
Highly Differentiated Target Detection Method Based on Yolov3 Model Under Extremely Low Light Conditions

Because of the dark atmosphere created by YOLO v3's poor detection accuracy, the photographs obtained in reality usually considerably improve the difficulty of target detection tasks in extremely low light settings. Under extremely low light conditions, the YOLOv3 model is used to suggest a high-discrimination target detection approach. Target detection is an essential task in the field of computer vision, in which the image is the source of input, but the image obtained in reality may make the target detection task in extremely low light conditions because of the dark environment and poor-quality imaging equipment, which increases the difficulty of target detection and affects the accuracy of detection. Therefore, based on the above reasons, this paper proposes a high-discrimination target detection method based on the YOLOv3 model under extremely low light conditions. The low light enhancement algorithm based on dehazing algorithm is improved for restraining the part of the transmission rate less than 0.5 in the pixel, to enhance the contrast of the processed images. The improved low light enhancement algorithm is 5.32% higher based on the evaluation index of target detection than the original images. Coiflet wavelet transform is applied to achieve high-discrimination feature extraction of images and decompose the low-frequency features of image information at different resolutions to obtain high-frequency features in the horizontal, vertical, and diagonal directions. The eventually constructed YOLOv3 network has reduced the negative impact of the detected target on the detection accuracy under the illumination change. The results reveal that the target detection method established in this paper has been enhanced by 40.6% on low light photos when compared to the traditional YOLOv3 model, and it still has considerable advantages in comparison to other benchmark approaches in target detection models.
Simultaneously Monitoring System Design for Infants’ Temperature: A Human-Centred Design (HCD) in Fashion Practice

Finding a convenient way to simultaneously monitor an infant's temperature is a major concern in the domain of childcare [26]. This study proposes a novel system for simultaneously monitoring the skin temperature of infants using a thermochromic textile in a modified garment design. Thermochromic materials use pigment activation to provide an easy to comprehend visual indication of an infant's temperature change. They can detect signs of infants' skin temperature change, making it possible to monitor a fever by simply watching garments and accessories change color. This paper presents a human-centered methodological approach towards thermochromic textile design for use with infants. It details the approaches taken to explore problems and find solutions, including in-depth semi-structured interviews, laboratory experiments and experimental prototyping. This interdisciplinary project seeks to achieve low-cost and convenient real-time monitoring of infants' skin surface temperature via a smart textile design. The feedback collected after the textile experiments and design prototyping demonstrated positive potential when applying thermochromic textiles to manage infant health.
Tracing People’s Experiences with Registrated Spatio-Temporal Photographs in AR Spaces

We propose a new style smartphone application that presents past photographs in AR (Augmented Reality) at the location in the real world where they were taken. The application consists of a process that provides precise location information of the photograph and a process that changes the present of the photograph depending on the user's movements. The goal of our research is to support the sharing of place-related stories with photographs. First, spatial awareness using a smartphone camera enables a high accurate location information of centimeter-order for user to obtain more real experience. AR is effective to assists in registering the photograph on a third-dimensional space. Second, the AR image is animated depending on the smartphone camera's position and direction so that the user can easily and naturally be guided to the location where the photograph was taken. The new framework for easily registering photographs to the AR space using a smartphone realize a new innovative form of real world visual representation of photographs by adding physical actions. Additionally, the networked service will be introduced to the framework so that users could share rich experiences, for example, recording personal or historical stories and providing interesting content for tourist spots.
Elicitation of Requirements for a NLP-Model Store for Abusive Language Detection

While in social media users most commonly interact with each other without a guiding entity, the discussion space of newspaper platforms is moderated by community managers, who invest considerable amounts of time and effort in keeping comment sections clean. To reduce this effort and allow community managers again to more freely interact with their users, automated comment moderation systems (ACMS) be be utilized. However, most newspapers do not have the expertise to create, update, and maintain machine learning (ML)-models. Thus, they are forced to rely on proprietary off-the-shelf solutions. However, if they want to keep their sovereignty over their data and systems, they would need access to models which could be integrated within their current moderation or content management systems. One option could be a platform for newspapers and data scientists where the data scientists could sell their pre-trained models and where newspapers could hire data scientists to create tailor-made models for them. In order to identify the requirements, community managers have for such systems, we conducted a series of semi-structured interviews with community managers of newspapers of varying size (from local to national). Furthermore, the information was enriched by the participation in multiple workshops on content moderation. We were able to elicit five major technical requirements necessary to create the described design artifact.
Confidence Value Calculation for Cultural-Based Misunderstanding Warning in Multilingual Chat

Previously, the Cultural Difference Detection (CDD) concept had been introduced to predict possible misunderstandings when machine translation (MT) users communicate across cultures. CDD compared images that linked to a concept in two languages with the assumption that the low similarity of images can indicate cultural misunderstanding. Its result has been used for warning MT users of words that could possibly cause misunderstanding. However, how it should be applied in the warning system has not yet been studied. One of the complications with the warning system is the accuracy of CDD. This paper proposes a method to calculate confidence value and to use this value to predict the accuracy of CDD. This confidence value could be used to decide whether to warn or not to warn the user when a suspect word is typed in the text box. This confidence value is calculated from the similarity among the images used during the CDD calculation. To examine the proposed confidence value, we conducted an experiment by comparing the accuracy of CDD and the confidence value. The experiment confirms that our proposed confidence concept reflects the CDD accuracy as there is a strong correlation between the confidence value and the CDD accuracy.
Immersive Sims: A New Paradigm or a New Game Genre?

In this paper, first we discuss how sets of common principles constitute paradigms in HCI and what creates a paradigm shift; second, we analyse characteristics of immersive sims and examine if they qualify as a new paradigm. The first HCI paradigm, an amalgam of engineering and human factors, accepts interaction as a form of man-machine coupling in ways inspired by industrial engineering and ergonomics. The second HCI paradigm, in contrast, hinges on a metaphor of mind and computer as symmetric, coupled information processors. The third HCI paradigm focuses on embodied interaction, meaning and meaning construction, situated knowledge, an explicit focus on values and the centrality of context. In this paper, we build a rigorous speculative set of concepts to test our hypothesis that immersive sims present a paradigm shift. We align this conceptual clarity with a structured and systematic analysis of the high and low-level characteristics of immersive sims based on Samoylenko’s description of the five pillars of immersive sim design. We propose more refined low-level characteristics within the three high-level characteristics of immersion, simulation, and interaction. This methodical analysis of principles and practices results in a set of reliable tools supported by examples drawn from the full history of immersive sims. Using these principles, we develop an immersive sim and conduct a usability test to verify the low-level characteristics listed above in a UX case study. Finally, we conclude by stating that the immersive sim design principles constitute a paradigm shift in HCI, rather than a new genre.
User-Centered Information Architecture of Vehicle AR-HUD Interface

Augmented reality head-up display (AR-HUD) presents a more natural way to process images with breakthroughs in optics. The technology also become the primary development trend of automobile human-machine interface (HMI). However, too much virtual information might interfere with drivers’ attention to their surroundings and affect their driving judgments. To effectively manage the complexity of vehicle information systems while further improving driving safety, this study aims to establish the information architecture of two driver groups (beginner drivers and skilled drivers) in different road environments. We developed a priority classification model and recruited 60 university students to participate in a card sorting experiment based on the criteria of beginner and skilled drivers. The cluster analysis findings were used to determine the AR-HUD information's grouping structure and hierarchical connection for the two driving user groups. Additionally, we investigated the impact of common road types and driving experience on the diversity of information architecture. The findings of this study constructed a user-centered information architecture and provided a theoretical foundation and role for the automobile industry in managing AR-HUD information and interface design.
The Experimental JavaScript-Based Blockchain Platform for the Trustless Communication of Two Peers

This study letter presents an approach to the implementation of a truly peer-to-peer blockchain-driven web service. The proposed service can be used for electronic signatures, proof-of-history ledgers, and decentralized communication between two parties that do not trust each other.
An Idea of the Web Development Effort Estimation driven by Web3 Technologies

In this research letter we propose a new approach to web development effort estimation using tokenization-alike technique. According to the proposed approach, a website could be estimated using the Constructive Cost Model (COCOMO) and the obtained assumption can be represented using a Web3 wallet to reflect the monetary value.
Effects of Immediate Feedback in Operating Information Device by Finger Tap Gesture

In this paper, we proposed a robust finger tap gesture recognition method and examined the feedback function when operating information devices using finger tap gestures. A finger tap gesture recognition method using hall sensors and gyro sensors achieved a high recognition rate without causing false detection. However, when operating information devices with finger tap gestures, there are the following two problems. The first is that it is recognized as a gesture different from the intended gesture, and the second is that the user's subjective gesture success or failure differs from the actual gesture success or failure. If the above phenomenon occurs, the user cannot operate the information device smoothly. In this paper, we aimed to make it possible for the user to quickly grasp the state of the information device and take appropriate actions by presenting the user with immediate feedback according to the type of gesture immediately after the successful gesture. As for the types of immediate feedback, tactile feedback and sound feedback were prepared and compared those feedback condition. As a result, it was found that by giving immediate feedback, the user can properly grasp the state of the information device and quickly per-form appropriate operations. Furthermore, when comparing tactile feedback and sound feedback, the operational performance tended to be higher for tactile feedback, and the subjective evaluation tended to be higher for sound feedback.
Hagnifinder: Predicting Magnification of Digital Histological Images Using Deep Learning

Background and Objective: Training a robust cancer diagnostic or prognostic artificial intelligent model using histology images requires a large number of representative cases with labels or annotations, which are difficult to obtain. The histology snapshots available in published papers or case reports could be used to enrich the training dataset. However, the magnifications of these invaluable snapshots are generally unknown, which limits their usage. Therefore, a robust magnification predictor is required for utilizing those diverse snapshot repositories consisting of different diseases. This paper presents a magnification prediction model named Hagnifinder for H&E-stained histological images.Methods: Hagnifinder is a regression model based on a convolutional neural network (CNN) that contains three modules: Feature Extraction Module, Regression Module, and Adaptive Scaling Module (ASM). In the training phase, the Feature Extraction Module first extracts the image features, followed by feature values redistribution performed by the ASM. Finally, the Regression Module estimated the mapping between the extracted features and the magnifications. We constructed a new dataset for training a robust model, named Hagni40, consisting of 94,643 H&E-stained histology image patches at 40 different magnifications of 13 types of cancers based on The Cancer Genome Atlas. To verify the performance of the Hagnifinder, we measured the accuracy of the predictions by setting the maximum allowable difference values (0.5, 1, 5) between the predicted magnification and the actual magnification. We compared Hagnifinder with state-of-the-art methods on a public dataset BreakHis and the Hagni40.Results: The Hagnifinder provides consistent prediction accuracy, with a mean accuracy of 98.9%, across 40 different magnifications and 13 different cancer types when Resnet50 was used as the feature extractor. Compared with the state-of-the-art methods focusing on 4 to 5 levels of magnification classification, the Hagnifinder achieved the best and most comparable performance in BreakHis and Hagni40 datasets. Conclusions: The experimental results suggest that Hagnifinder could be a valuable tool for predicting the associated magnification of any given histology image.
Delivering Simulation Scenarios to Law Enforcement

To be useful in training, simulation needs to be accessible to the target population. Law enforcement simulators are often located in training centers that are a considerable distance from the agency or municipality employing sworn officers. Scheduling training on a simulator is difficult because of travel time, operating costs, and officer availability. Therefore, role play is often used as a form of simulation that can be done without special equipment. This has drawbacks as it is more difficult for officers to suspend disbelief and expect their fellow officers to behave like the general public or suspected criminals. There are many ways of making the experience of simulation more accessible to the law enforcement community. However, few studies have been done to examine the benefits of increasing access to simulation for the purpose of training. The approach taken in this paper looks at the feasibility of tailoring a simulated scenario for a specific location and then delivering it to officers in that location via Zoom. This type of tailoring and delivery is more likely to fit in the training budget of smaller departments and builds a set of training materials that may be more effective for their personnel. This presentation shows how one small police department was able to make use of a simulator in a remote location to gain practice in social interaction skills.
Quantized Neural Network Synthesis for Direct Logic Circuit Implementation

Hardware acceleration enables neural network (NN) inferencing on edge devices and for high throughput applications. Most approaches use neural processing elements for computation while storing weights in memory blocks. To avoid costly memory access, recent efforts seek direct logic implementation with weights hardwired into the circuit. However, special training strategies are often needed, and they could not maintain accuracy. In contrast, we take a trained and quantized NN as input and synthesize it by Booth encoding and logic sharing, resulting in a hardware accelerator without degrading accuracy. Experiments demonstrate that our method outperforms existing work in area reduction and/or throughput and power efficiency.
The Influence of Interaction Design on Relation Making: A Scoping Review

Dating applications and dating sites are designed interventions that can change behaviour and influence user wellbeing. However, research from the design perspective around relation-making interventions is still scarce. This paper presents findings of a scoping review that aimed to collect current published knowledge on the influence of online communication on user behaviour, to understand its implications for relation-making. The study gathered findings from across disciplines to provide a holistic understanding of the various influences that online environment and interactions can have on user behaviour. Keyword combinations were run through five databases with a priori criteria and produced 1651 results published from the date range of 2016 to 2020. From the results, 717 abstracts were screened, and 82 papers were selected for full screening, out of which 46 were included for thematic analysis. The findings of the review show how interaction design and the online environment can influence user behaviour and thus impact how users form relationships. This scoping review is an initial study to provide an overview in a currently under-researched area. Its contribution is in presenting the needs and opportunities for future research and summarises the practical implications for interaction design that nurtures relationships.
The Elf-AR-QA System Based on IoT Cross-Media and Discussion of Emotional and Pleasure Design

Taiwan’s Hsinchu Assembly Hall is a historic building leftover from the Japanese occupation. Recently, “Memory Texture Interactive Experience Museum in Hsinchu Assembly Hall” was held after reconstruction. It integrates different technologies to allow people to learn the history and architecture of Hsinchu Assembly Hall. In this case, we developed the Interactive Elf-AR-QA system on the interactive experience museum, which is based on IoT Cross-Media System we created before. In the system, we used the knowledge of Hsinchu Assembly Hall as its data and used the smartphone as a tangible user interface. Using their own smartphones, the audience can participate with the exhibits by the “fish” and “pour” actions. The “fish” action can collect and answer question data of the exhibit art-works. The “pour” action can deliver the result into the iPad, where the audience can get the Elf number corresponding to the correct answers and then start to play the AR game based on our interactive Elf-AR-QA system in the Stone Lantern. This research used marker-based AR technology to augment cute Elf creatures on the Stone Lantern surface. In our system, we designed the “crack” pattern as the AR marker, which subtly conforms to the feature of the Stone Lantern and the demand for AR recognition. The iPad’s tangible feature also allows the audience to explore the Elf creatures among the Stone Lantern. This research integrates the cultural knowledge of Hsinchu Assembly Hall into the QA System, and according to the result audience can play with the Interactive Elf-AR system on the Stone Lantern; such application is rare in museums in Taiwan. This research also discussed emotional design and pleasure theory and produced questionnaires for verification. The result shows the Elf design’s success and proves that the system did increase the motivation of the audience, which made them want to know more about the culture of Hsinchu Assembly Hall. After the experience, the audience did gain pleasure. We hope that through this kind of interactive design, we can increase the motivation to understand Taiwan’s culture and history and get exciting and enjoyable experiences in the process. In the future, we also hope that this system and research can be applied to more cultural and creative industries.
Leveraging Conducts in the Digital Economy: A Competition and Regulatory Perspective

Without resorting to an overly simplistic statement, it remains difficult to evade the perception that the digital economy is prone to leveraging conducts directed at extending dominance between services. Navigating the internet with Google will often yield Google services at preferential placements. Similarly, Amazon might artificially favor its own retail products on Amazon Marketplace, while Meta (formerly known as Facebook) appears to be using data collected on its social networks to outcompete competitors on Facebook Marketplace. Naturally, tech companies deny bias and self-favoring, referring to misunderstandings, the evolution of the internet, or other plausible explanations. Yet, the anti-competitive effects of leveraging conducts have formed the core of recent EU antitrust investigations, such as Google Shopping and Google Android as well as vintage cases as Microsoft I. In particular, the matter of self-favoring discussed in the Google Shopping case has crystallized the concept of leveraging infringement, where a super dominant position is extended to an adjacent activity or market. Similar allegations are at the center of pending EU antitrust investigations into Facebook or Amazon. Besides whether they are liable, the relevant questions are how these companies benefit from self-favoring and leveraging and if this should raise concerns.These matters are developed in this paper.