Reviewerly/exHarmony huggingface dataset:
WebMea: A Google Chrome Extension for Web Security and Privacy Measurement Studies

Web measurement is an integral part of web security and privacy analysis. A large-scale web measurement usually relies on a crawler. Browser-based crawlers are often preferred and they are typically implemented through one of two approaches: the DevTools approach or the browser extension approach. It is often not clear to researchers which approach should be taken to implement a crawler since the analysis of these two approaches is limited. In this paper, we analyze and compare the web measurement capability of the Google Chrome DevTools approach and the Google Chrome extension approach to help researchers make decisions. We find that both approaches have the capability to satisfy primary web measurement requirements, although their APIs and the complexity of using their APIs are different. We point out such differences to help researchers more easily choose or take the appropriate approach. Taking the former approach, Puppeteer is a crawler that can be readily used by researchers. However, it is difficult for researchers to find a good baseline Google Chrome extension to start with their extension-based crawler construction. Therefore, we further build WebMea as a baseline Google Chrome extension that can measure multiple types of web data. Researchers can use or customize WebMea according to their web measurement requirements. WebMea is applicable to different web measurement studies, and it can help make web measurement studies more reproducible and replicable.
VaultBox: Enhancing the Security and Effectiveness of Security Analytics

Security tools like Firewalls, IDS, IPS, SIEM, EDR, and NDR effectively detect and block threats. However, these tools depend on the system, application, and event logs. Logs are the key ingredient for various purposes, including troubleshooting performance issues, satisfying compliance mandates, and monitoring and improving security. In addition, logs from multiple machines are collected and fed to the Security Information and Event Management (SIEM) system for further security analysis. Therefore, a SIEM system’s efficiency and effectiveness depend heavily on the quality and quantity of logs provided. Unfortunately, logs are often targeted brutally and tampered with after a successful intrusion to cover the attack’s traces. Thus it becomes critical to protect the confidentiality, integrity, availability, and authenticity of logs at rest or transit. This paper proposes a novel scheme to prevent logs from tampering, detect any tampering, and recuperate logs if lost or corrupt. Our scheme is forward-secure, replicated, randomized, and rate-less, aiming to help securely store and transmit logs to SIEM.
Two-Stage Anomaly Detection in LEO Satellite Network

We introduce a novel two-stage method for detecting anomaly signal in Low Earth Orbit (LEO) satellite network in response to increasing clutter signals and interference. First, a convolutional neural network (CNN) classifier is trained on real sensor signals and synthesized wireless modulated signals. Then, an anomaly detector is used to detect the classified signal. To address the limited computing resources on the satellite, we utilize transfer learning to reduce the scale of the classifier and anomaly detector. Our proposed method consists of a multi-class CNN model that reliably detects the modulation methods used in a specific satellite environment by I/Q signals and a recurrent neural network model that identifies anomalies when events significantly deviate from expected or predicted values. Experimental results show the effectiveness of our proposed method.
Performance or Explainability? A Law of Armed Conflict Perspective

Machine learning techniques lie at the centre of many recent advancements in artificial intelligence (AI), including in weapon systems. While powerful, these techniques utilise opaque models whose internal workings are generally quite difficult to explain, which necessitated the development of explainable AI (XAI). In the military domain, both performance and explainability are important and legally required by international humanitarian law (IHL). In practice, however, these two desiderata are in conflict, as improving explainability may involve paying an opportunity cost in performance and vice versa. It is unclear how IHL requires States to address this dilemma. In this article, we attempt to operationalise normative IHL requirements in terms of P (performance) and X (explainability) to derive qualitative guidelines for decision-makers on this issue. We first explain the explainability-performance trade-off, what causes it, and what its consequences are. Then, we explore relevant IHL principles that include P and X as requirements, and develop four tenets derived from these principles. We demonstrate how IHL prescribes minimum values for both P and X, but that once these values are achieved, P should be prioritised over X. We conclude by formulating a general guideline and provide an example of how this would impact model choice.
Affordances

This chapter considers participatory sound artsound artparticipatory sound artworks from the perspective of affordanceaffordance theory. Its central contention is that the material (sensoryexperiencesensory experience, technologicaltechnology, spatiotemporal) and social (participatory, relationalrelationality) aspects of the artwork necessarily influence and inform each other. It uses the concept of affordance as an analytical device to investigate how participatory processes in sound art are neither limited to the artistic intent, nor completely indeterminate, being instead delimited and directed by the artwork’s materialities—which they in turn reshape. The chapter first discusses three principal affordances characteristic of participatory sound artworks in general—for creativityaffordancefor creativity, experimentationaffordancefor exploration and experimentation and connectivityaffordancefor connectivity. It then offers a classification of sound art’s materialities and medialitiesmediality into three environmentenvironment types—localenvironmentlocal (not technologically extended in any way), networkedenvironmentnetworked, and augmentedenvironmentaugmented—exploring their specific affordances. Finally, the chapter expands its scope beyond the institutionally recognised forms of sound art by considering sonic practices of digital online culture that share a sound art aestheticsaesthetics—both the participants’ documentation of their experienceexperience with sound art on social media and the “digitally native” forms of sonic participationparticipation, such as sonic memessonic memes.
Educational Development in the Postdigital Era

The recent COVID-19 pandemic has highlighted a pressing need to better theorize the role of digital technologies in education, given the widespread “mainstreaming” of software platforms and online teaching. Digital technologies are now, more than ever, structural elements of the educational landscape, rather than merely peripheral devices that augment existing practices. Drawing on the concept of the “postdigital,” recent educational research has argued for just such a shift in perspective, away from viewing technologies simply as “tools” for enhancement, and toward greater recognition of the ways digital systems and infrastructures are already profoundly interrelated with the everyday activities of teaching and learning. In particular, such perspectives emphasize the role of the digital in the economic, political, and material dimensions of education. This chapter will utilize the concept of the postdigital in order to examine the ways in which education in the Asia-Pacific region appears to be increasingly embroiled in the wider sociotechnical systems and political economies of digital technology, as well as growing environmental concerns related to technology. The chapter will focus on artificial intelligence (AI) as a prominent example of current digital technology being utilized for educational development, as well as a key illustration of the “postdigital” ways technologies are interrelated with economic, political, and material dimensions of education. The specific national context for this examination will be China, which has produced one of the most ambitious strategies for the technological development of education in the Asia-Pacific region.
Ghostlee: A Location-Based Social Augmented Reality Application

Ghostlee is a location-based pervasive social game in which players use mobile devices with location-based capabilities to create, share and reply to comments posted by others. Feedback from initial user testing involving 18 players over 2 weeks reveals patterns of play, preferred settings for play, and the limitations of location-based play. Social AR sessions are frequently short, conducted in between, around, and sometimes in conjunction with the many activities of people’s daily lives. In this study we reflect on the AR design patterns and their alignment with Ghostlee’s socio-cultural design considerations. Future directions for location-based social games are described.
Multi-sensory Interactive Experience Design of Museum Musical Instrument Collections

This paper explores museum musical instrument exhibits’ current status and development trends. It highlights the importance of multi-sensory interaction for the museum experience, especially for musical instrument exhibits. Based on existing literatures and case studies, the strengths and weaknesses of current musical instrument exhibit interaction forms are analyzed, and the possibility of applying intelligent replicas to museum musical instrument interaction is proposed. This project produced an interactive prototype in which a physical model of chimes is embedded in a digital component that serves as an interface to control the delivery of virtual instrument sounds and animation effects on screen. The aim is to restore the functionality of the chimes as musical instruments by representing their visual, acoustic, and in some cases, tactile characteristics to visitors through physical interaction.
YouNDigital: A Multidisciplinary Co-creation Strategy to Define Audiences, Users, and Contexts of Use of a Digital Newsroom

Aiming to engage young people in critical social matters, the project YouNDigital- Youth, News and Digital Citizenship (PTDC/COM-OUT/0243/2021) is developing a digital newsroom to understand young people’s interests and engagement with news. This paper discusses the methodological process of designing a Web application to host a digital newsroom that can also be attractive and user-friendly. This process was guided by a bottom-up approach with the user at its centre. It began with co-creation sessions involving a multidisciplinary team and interviews with young people from different life contexts. These inputs were valuable for the design of the Web app. Next, we will conduct user usability tests and heuristic validation with experts. The challenge was gathering all the knowledge and information and meeting project expectations. The co-creation sessions and the interviews were central to producing meaning and enlightenment about the Web app, its users and its contexts. The co-creation sessions enabled the creation of three different Personas and context scenarios, and the interviews with young people validated the outputs of the design thinking process and offered additional information to meet young people’s expectations and needs.
Evaluating the Effectiveness of Graph and Timeline-Based Visualizations for Search Engine Results: A Comparative Study

As the volume of information on the internet continues to expand, search engines play a crucial role in helping users navigate this vast data landscape. While traditional list-based search results enable users to find information efficiently, they often require manual exploration to discover meaningful connections between results. The focus of this research lies in addressing this limitation by proposing and evaluating a prototype that leverages graph-based and timeline-based visualization techniques to highlight temporal and semantic relationships within search results. These visualization methods can potentially enhance user experience and comprehension by minimizing the manual effort required to explore such relationships. Despite the promising potential of combining these two techniques, limited research has been conducted to measure their effectiveness compared to traditional search engine result displays. This research intends to bridge this gap by outlining a comparative study using a prototype that encapsulates the idea of presenting search engine results in these visualizations. The main focus of this work is to present and describe a prototype and shape the background necessary to understand how these visualization techniques can improve search engine design, promote efficient knowledge discovery, and ultimately enhance the user experience.
Serendipitous Book Explorer Using Personalized Associative Dictionaries

This paper proposes a system called AssocSearcher that utilizes associative dictionaries as assistance for exploratory search for books. Users of the system can extend their horizons to explore books by getting subtopic or related words from the other people’s associative dictionaries. The associative dictionaries are created as substitutes for individual thoughts. We generated associative dictionaries of individual persons using Word2vec and GloVe from sentences read or written by the individuals. This allows each associative dictionary to respond to related words that are characteristics of each person with a particular topic or keyword. Experimental results obtained by searching books using multiple associative dictionaries showed that participants are more aware of the possibility of exploration. We also found a dictionary familiar to a particular participant improves comprehension.
Enhanced Quantified Local Implicit Neural Representation for Image Compression

Recently, implicit neural representation (INR) has been applied to image compression. However, the rate-distortion performance of most existing INR-based image compression methods is still obviously inferior to the state-of-the-art image compression methods. In this paper, we propose an Enhanced Quantified Local Implicit Neural Representation (EQLINR) for image compression by enhancing the utilization of local relationships of INR and narrow the quantization gap between training and encoding to further improve the performance of INR-based image compression. Our framework consists of latent representation and the corresponding implicit neural network consisting of MLP and CNN, which can transform the latent representation into the image space. To enhance local relationships utilization, we design a local enhancement module (LEM) consisted of CNN to capture the neighborhood relationships of the reconstructed image from MLP. Furthermore, to mitigate the performance loss caused by quantization of latent representation, we employ an enhanced quantization scheme (EQS) in our training process. We use uniform noise for network initialization and then use Stochastic Gumbel Annealing (SGA) with dynamic temperature regulation as a proxy function for quantization during training. Extensive experimental results demonstrate that our approach significantly the compression performance of INR-based image compression, and even better than BPG
Exploring the Topics of Audio Words for Detecting Alzheimer's Disease From Spontaneous Speech

Many studies have utilized speech data to automatically detect Alzheimer's Disease (AD). However, most of them simply take the speech data from a subject participating in a task (e.g., picture description) as a whole audio sequence, and lack considerations on separate utterances that may be related to different topics in the picture and are of different importance for discriminating AD patients from healthy controls. To this end, this paper proposes an AD detection method with topic modeling for utterances composed of audio words. First, an audio word discovery algorithm using Byte Pair Encoding (BPE) is designed to tokenize speech data without relying on text transcriptions. Then, a topic model is built that assigns each utterance a topic label in an unsupervised way. Finally, an Audio-Word HuBERT (AW-HuBERT) model integrating utterance-level topic labels is constructed for AD detection. This model is pretrained by training the existing HuBERT model with audio word sequences. The final decision for a recording is made by the weighted sum of utterance-level classification possibilities, and topic-dependent Area-Under-Curve (AUC) values are used as the weights. Experimental results on the DementiaBank dataset show that the proposed method achieves a better AD detection accuracy than state-of-the-art methods.
Affine Non-local Means Image Denoising

In subsequent activities like image identification and medical diagnosis, digital pictures are frequently damaged by noise during capture and transmission. When damaged photos must be utilized, several denoising techniques have been suggested to increase the accuracy of these jobs. However, the majority of these techniques either call for making assumptions on the statistical characteristics of the corrupting noise or are specifically created solely for a certain kind of noise. The performance of traditional image denoising algorithms that use lone noisy images and general image databases will soon be reached. In this article, we suggest denoising images with specific external image databases. Denoising is formulated as an optimum filter design issue, and we use the focused databases to (1) find the fundamental properties of the ideal filter using group sparsity, and (2) find spectral coefficients of the optimal filter using localized priors. Research exhibits better denoising outcomes than current methods for a range of circumstances, adding photos and text, multitier pictures, as well as face images. In contrast to employing a general database, we present an adaptive picture denoising approach in this study. In this context, a focused database is one that only contains photos pertinent to the noisy image. Targeted external databases might be acquired in several real-world settings, including text pictures, human faces, and photos taken by multi-view camera systems, as will be demonstrated in subsequent sections of this work.
Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion-Based Transformer Network for Remote Sensing Image Super-Resolution

Remote sensing image super-resolution (RSISR) plays a vital role in enhancing spatial detials and improving the quality of satellite imagery. Recently, Transformer-based models have shown competitive performance in RSISR. To mitigate the quadratic computational complexity resulting from global self-attention, various methods constrain attention to a local window, enhancing its efficiency. Consequently, the receptive fields in a single attention layer are inadequate, leading to insufficient context modeling. Furthermore, while most transform-based approaches reuse shallow features through skip connections, relying solely on these connections treats shallow and deep features equally, impeding the model’s ability to characterize them. To address these issues, we propose a novel transformer architecture called Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network (SPIFFNet) for RSISR. Our proposed model effectively enhances context cognition and understanding of the entire image, facilitating efficient integration of features cross-stages. The model incorporates Cross-Spatial Pixel Integration Attention (CSPIA) to introduce contextual information into a local window, while Cross-Stage Feature Fusion Attention (CSFFA) adaptively fuses features from the previous stage to improve feature expression in line with the requirements of the current stage. We conducted comprehensive experiments on multiple benchmark datasets, demonstrating the superior performance of our proposed SPIFFNet in terms of both quantitative metrics and visual quality when compared to state-of-the-art methods. Our code is available at https://github.com/Dr-Lyt/SPIFFNet.
LEI: Livestock Event Information Schema for Enabling Data Sharing

Data-driven advances have resulted in significant improvements in dairy production. However, the meat industry has lagged behind in adopting data-driven approaches, underscoring the crucial need for data standardisation to facilitate seamless data transmission to maximise productivity, save costs, and increase market access. To address this gap, we propose a novel data schema, Livestock Event Information (LEI) schema, designed to accurately and uniformly record livestock events. LEI complies with the International Committee for Animal Recording (ICAR) and Integrity System Company (ISC) schemas to deliver this data standardisation and enable data sharing between producers and consumers. To validate the superiority of LEI, we conducted a structural metrics analysis and a comprehensive case study. The analysis demonstrated that LEI outperforms the ICAR and ISC schemas in terms of design, while the case study confirmed its superior ability to capture livestock event information. Our findings lay the foundation for the implementation of the LEI schema, unlocking the potential for data-driven advances in livestock management. Moreover, LEI's versatility opens avenues for future expansion into other agricultural domains, encompassing poultry, fisheries, and crops. The adoption of LEI promises substantial benefits, including improved data accuracy, reduced costs, and increased productivity, heralding a new era of sustainability in the meat industry.
Practice of Working People Re-Skilling through University DX Promotion Co-Creation Activities and Its Effects

Kagawa University and Ricoh Company, Ltd. are working together to promote university DX through industry-academia collaboration. Various activities related to the promotion of DX at Kagawa University are carried out there. The activities also have a function of “Re-skilling” engineers at Ricoh Company, Ltd. This paper describes an overview of the university’ s DX co-creation activities. The In-house development of business systems is described from among the activities, and the effect of “Re-skilling” of the engineers of Ricoh Company, Ltd. who participate in the activities is discussed.
Transfer Learning-based Human Motion Capture and Classification System using Multiple Low-cost IMU and Flex Pressure Sensors

In this paper, we present a transfer learning-based human motion capture and classification system using multiple IMU and flex sensors without camera. The proposed system consists of 6 low-cost IMUs and 12 flex pressure sensors. For the human motion classification, the signals of multiple IMUs were reconstructed into images using wavelet transform. The transformed image is designed to be used as an input to the convolution neural network. The network was designed using Inception V3-based transfer learning. For motion capture without using a camera, the presented system was constructed through a combination of IMUs (MPU6050) and flex pressure sensors (thin film pressure sensors) rather than multiple IMUs. The proposed method showed 86.67% accuracy and 0.869 points of f1-score. The proposed system is expected to be applicable to a healthcare system that can provide exercise coaching without cameras, and without space restrictions.