Reviewerly/exHarmony huggingface dataset:
Pacific identity discourses on Twitter: constructing cyberspaces of belonging

This article investigates the performance of Pacific identities on Twitter during a high-profile cultural and sporting event, Tonga and Australia’s first-ever rugby league test match in late 2018. More than 9000 tweets were analysed using quantitative and qualitative methods to map different publics orienting to the event on Twitter, including a Pacific diasporic public that emerged through locative practices of identity and cultural performance. This study finds that Pacific users’ tweets were differentiated by textual and visual cues, including the use of emojis as a paralanguage of Pacificness and a racialised visual discourse of ‘Brownness’, in ways that suggest an emerging Pacific counter-public. The findings discussed here further demonstrate the ways in which social media affordances enable the construction of race and ethnicity online, and the ways in which marginalised groups are using social media to create alternative public spheres. This study also demonstrates the importance of looking more closely at the different discursive practices within Twitter public to both foreground marginalised groups’ practice and de-Westernise social media studies.
Video abnormal behaviour detection based on pseudo‐3D encoder and multi‐cascade memory mechanism

Frame prediction methods based on Auto-Encoder (AE) composed of convolutional neural networks (CNN) are very popular in detecting abnormal behaviour. The methods predict normal behaviour accurately and abnormal behaviour incorrectly, which is considered a criterion for abnormality discrimination. However, the emergence of problems such as too strong AE representation leading to detection failure, the insufficient ability of the network to extract spatio-temporal information, a large number of model parameters and slow running speed leads to the need for the method to be further improved. In this work, the authors propose a network framework for abnormal behaviour detection in video based on a pseudo-3D encoder and a multi-cascade memory mechanism (MMP3D). First of all, the encoder consisting of pseudo-3D convolution is used to extract spatio-temporal information from the video. Then, the multi-cascade memory mechanism (MM) and the multi-headed prototype attention mechanism are used to store and aggregate features of normal behaviour, which solves to some extent the problem of detection failure caused by strong AE representation power. Finally, the decoder designed by the 2D deconvolution layers is used to recover the prediction information. The efficiency and superiority of our method is validated on the Ped2 dataset, Avenue dataset, and ShanghaiTech dataset.
A Lattice-Based Redactable Signature Scheme using Cryptographic Accumulators for Trees

Abstract Redactable signatures allow the signature holder to remove admissible data blocks in the signed data while generating valid signatures about different redacted data without communicating with the primary signer. Now, this sort of signature has attracted widespread attention due to its many application scenarios such as electronic medical records, smart grids and XML files. However, there are rarely redactable signature schemes that can resist quantum attacks so far. In the wake of quantum calculation era, it is essential to blossom more quantum-resistant redactable signatures for different data structures. Moreover, it is popular to use accumulators to design redactable signature schemes. Unfortunately, the existing accumulators do not support tree data structures. Therefore, this paper first gives the definition of accumulator schemes for trees, and designs a lattice-based accumulator scheme for trees. Our accumulator scheme features shorter accumulator values and a faster witness generation algorithm than existing lattice trapdoor accumulators. Second, this paper resorts to approximate trapdoors and the preimage sampleable technique, and presents a lattice-based redactable signature scheme for trees using our accumulator scheme. Meanwhile, this scheme fulfills unforgeability, transparency and privacy under adaptive chosen-message attacks. Furthermore, the experiment results show that the redactable signature scheme meets actual revision requirements well.
Battery storage system plug-and-play information flow implementation technology

In order to solve the problems that may exist in the large-scale application of energy storage, the “plug-and-play” technology is realized through battery storage power plants. The concept of plug-and-play originates from the network, which refers to the ability of the computer system to configure expansion boards and other devices automatically. This paper researches plug-and-play key technologies for battery storage power stations, aiming to overcome the grid-connected bottlenecks after large-scale application of energy storage systems, improve the effectiveness and convenience of the grid-connected operation of energy storage systems, and effectively improve the practicality of energy storage systems level, supporting the company’s energy Internet development strategy. Based on the technical framework of plug-and-play realization of battery storage power stations, this paper will conduct technical research on the realization of information flow. First, based on the IED modeling method and the actual functional requirements of BESS, we select logical nodes with corresponding functions to form logical devices and establish the BESS information model. Then, we express the self-describing characteristics through the SCL configuration file and analyze the configuration process of the configuration description file CID. Finally, the plug-and-play information flow implementation architecture is constructed with the heterogeneous information model mapping and data conversion module as the core part. Our design can realize the real plug-and-play operation of the battery storage power station connected to the regional power grid, thereby promoting the friendly interaction between the regional power grid and the battery storage power station and improving the adaptability of the battery storage power station in different application scenarios.
Lessons Learned from Reevaluating Big Deals with Unsub

The value of big deals is increasingly unclear. This article briefly discusses factors others have considered in evaluating big deals and covers the four factors that should be considered moving forward: open access, interlibrary loan, post-termination access, and a-la-carte costs. Unsub, a tool for reevaluating big deals created by the nonprofit OurResearch, is introduced. Lessons learned are shared from two years of helping libraries reevaluate big deals to provide insight into the complexities and tradeoffs involved in evaluating big deals across many libraries.
Accelerating BPC-PaCo through Visually Lossless Techniques

Fast image codecs are a current need in applications that deal with large amounts of images. Graphics Processing Units (GPUs) are suitable processors to speed up most kinds of algorithms, especially when they allow fine-grain parallelism. Bitplane Coding with Parallel Coefficient processing (BPC-PaCo) is a recently proposed algorithm for the core stage of wavelet-based image codecs tailored for the highly parallel architectures of GPUs. This algorithm provides complexity scalability to allow faster execution at the expense of coding efficiency. Its main drawback is that the speedup and loss in image quality is controlled only roughly, resulting in visible distortion at low and medium rates. This paper addresses this issue by integrating techniques of visually lossless coding into BPC-PaCo. The resulting method minimizes the visual distortion introduced in the compressed file, obtaining higher-quality images to a human observer. Experimental results also indicate 12% speedups with respect to BPC-PaCo.
Open Knowledge, Open Archive. An Approach to Open Culture

The proposed contribution aims to describe an approach developed as part of doctoral research, the purpose of which is to identify methodologies and tools useful for the archiving, preservation, enhancement, and enjoyment of culture, through the design of digital platforms equipped with different investigation tools. The analysis of the scientific literature and the state of the art on the subject shows that there is a growing need for tools useful for the preservation and protection of memory and culture. To do this, digital design and visual communication have the role of conforming architectures and systems to enhance the archived heritage, with particular attention to different navigational models and to the principles of user interface. These aspects were investigated during the research course, through the study of methods of filing and presenting information, focusing also on the use of diagrammatic representation methods, which are increasingly being employed in digital collections, and participation at various levels and by various types of users, to consolidate the relationship between culture, digital memories, and community.
Designing a Speculative Design Toolkit for Community Biodiversity Activism

This paper forms part of a doctoral research project based on the proposition that a diverse Speculative Design practice can support community biodiversity activism, from which a Strategic Speculative Design Framework rooted in queer and more-than-human perspectives and theories was developed. This Framework forms the basis of the ongoing development of a Design Toolkit, which aims to be a practical application of the framework’s principles that can be applied in community biodiversity activism settings. The toolkit draws on speculative gaming elements to make the framework concepts accessible for a wide audience, and has been tested, both in terms of the overall concepts and in play-testing specific game elements, as part of the design process. The conclusions drawn from this iterative design and testing phase, which is still underway, are that the speculative strategies in the framework can indeed be a useful tool to engage communities in considering possible futures for the environment, but that this depends on the toolkit being designed in order that they can be understandable to a wide audience and being well structured that there is a logical flow between activities that will scaffold the participants to think speculatively and craft transformative speculations about the future of the ecosystem.
Constructing adversarial examples to investigate the plausibility of explanations in deep audio and image classifiers

Abstract Given the rise of deep learning and its inherent black-box nature, the desire to interpret these systems and explain their behaviour became increasingly more prominent. The main idea of so-called explainers is to identify which features of particular samples have the most influence on a classifier’s prediction, and present them as explanations. Evaluating explainers, however, is difficult, due to reasons such as a lack of ground truth. In this work, we construct adversarial examples to check the plausibility of explanations, perturbing input deliberately to change a classifier’s prediction. This allows us to investigate whether explainers are able to detect these perturbed regions as the parts of an input that strongly influence a particular classification. Our results from the audio and image domain suggest that the investigated explainers often fail to identify the input regions most relevant for a prediction; hence, it remains questionable whether explanations are useful or potentially misleading.
A Defense of Friction—Or Why Ubiquitous Computing Should not Recede to the Background

The idea that friction is problematic and artefacts which draw attention to themselves are inefficient has significantly influenced how we think about ubiquitous computing. This paper shows that friction’s ambivalent nature as something that enables, and limits activity makes it an inescapable topic for design and an effective hermeneutical tool for approaching the relationship between complexity and privacy. We argue that by striving to make smart devices “self-effacing” and “seamless”, designers are bringing more underlying complexities and dangers for users. We contend the main challenge of designers is not avoiding or disguising frictions but helping users understand them and manage them. Finally, we argue that when balancing the unavoidable trade-offs between privacy and ease of use that seem to be intrinsic to ubiquitous computing, designers cannot continue to rely on legislation but adopt an ethical stance as a critical aspect of their practice.
Ascertaining the Influence of Style on the Credibility and Appeal of a Digital Health Avatar

As part of a project to develop a mobile app, with the capability to aid users in accessing their full lifelong healthcare history, a digital patient avatar was proposed, to be used as a visual display of information and a navigation tool within the app. With this article we aim to build upon previous research to identify how the appearance of this sort of avatar might influence the perceived credibility and trust placed on the information displayed in the app, specifically within the healthcare context. To this end, a survey was executed in which participants would be presented with a series of avatars with different visual styles, and their attitudes towards each of the avatars were recorded. The results fell mostly in line with preconceived hypotheses, that realistic avatars hold greater credibility but higher discomfort while stylized avatars generate increased affinity. However, some additional observations were made as to the discomfort caused by the improper combination of stylized and realistic elements within the same avatar. These results indicated that, for further development of the avatar’s visual style, a more comprehensive study would be needed to fine-tune the most adequate point between realistic and stylized.
Mapping Persona Interaction with Scenarios

The following article describes a Persona Method through Visual Clues and Socio-Economic Scenarios Interference (PMVC+) in a learning setting performed in the course of Design of Interfaces I, School of Arts, University of Evora, Portugal, in the academic year of 2021/22. The study follows a descriptive case methodology. Supported by previous work, the proposed PMVC+ concerns the project’s early stage and reveals how a mosaic assemblage of visual clues concerning Persona construction and socio-economic scenarios allows an empowered perception of consumers and how those scenarios can influence consumption behaviors. In project guidance, a discovery map was developed to provide an itinerary on cohesive information acquisition. The proposed PMVC+ allowed students to implicitly reason the influence of external variables in their constructions of reality and value external socio-economic scenarios when projecting a persona, enabling a strong focus on consumers and scenario fluidity. Although in part working in fictionalized settings, the PMVC+ triggers the reasoning on the complexity and influence of socio-economic scenarios and delivers more immersive mapping solutions in working with Personas allowing students to retrieve reasoned product opportunities.
Designing Physical and Virtual Walkshop Methods for Speculative Internet of Things Research

This paper describes a novel method combining speculative design with walking workshops to reveal the ethical and social challenges of connected technology in public spaces for policymaking. In the digitally hyper-connected society, digital technologies such as sensors, the Internet of Things (IoT), and Artificial intelligence (AI) enable people, and ordinary objects, to be connected in almost every place. This project tested different methods such as a walking method called walkshop and digital recreation of the physical environment to allow policymakers to explore a future connected place. We introduce a comparative analysis to show the opportunities and challenges of both physical and digital methods. The findings show the value of physical and virtual walkshops to engage communities and place managers on building a connected place in a participatory manner.
An improved adaptive FastSLAM algorithm with time‐varying noise estimator

Abstract In this study, an adaptive FastSLAM (AFastSLAM) algorithm, which is obtained by estimating the time‐varying noise statistics and improving FastSLAM algorithm, is proposed. This improvement was accomplished by using maximum likelihood estimation and expectation maximization criterion and a one‐step smoothing algorithm in importance sampling. In addition, innovation covariance estimation (ICE) method was used to prevent loss of positive definiteness of the process and measurement noise covariance matrices. The proposed method was compared with FastSLAM by calculating the root mean square error (RMSE) using different particle numbers at varying initial process and measurement noise values. Simulation studies have shown that AFastSLAM provides much more accurate, consistent, and successful estimates than FastSLAM for both robot and landmark positions.
Multi-scale gradient attention guidance and adaptive style fusion for image inpainting

Image inpainting aims to fill in the missing regions of damaged images with plausible content. Existing inpainting methods tend to produce ambiguous artifacts and implausible structures. To address the above issues, our method aims to fully utilize the information of known regions to provide style and structural guidance for missing regions. Specifically, the Adaptive Style Fusion (ASF) module reduces artifacts by transferring visual style features from known regions to missing regions. The Gradient Attention Guidance (GAG) module generates accurate structures by aggregating semantic information along gradient boundary regions. In addition, the Multi-scale Attentional Feature Extraction (MAFE) module extracts global contextual information and enhances the representation of image features. The sufficient experimental results on the three datasets demonstrate that our proposed method has superior performance in terms of visual plausibility and structural consistency compared to state-of-the-art inpainting methods.
How Should We Regulate Cryptocurrencies via Consensus?: A Strategic Framework for Optimal Legal Transaction Throughput

Permissionless blockchain consensus protocols have been leveraged for defining decentralized economies for the (commercial or private) trade of virtual and physical assets, using cryptocurrencies. In most instances, the assets being traded are regulated , which mandates that the legal right to their trade and their trade value are determined by the governmental regulator of the jurisdiction in which the trade occurs. Unfortunately, existing blockchains do not formalize proposal of legal cryptocurrency transactions, as part of the execution of their respective consensus protocols, resulting in illegal activities in the associated crypto-economies. In this contribution, unlike existing non-consensus solutions, which are prone to be more compute-time and audit-time intensive, we present a novel regulatory framework for blockchain protocols, for ensuring legal transaction confirmation as part of the blockchain consensus. As per our regulatory framework, we derive, through a stochastic game analysis, block proposal strategies under which legal transaction throughput supersedes throughput of traditional transactions, which are, in the worst case, an indifferentiable mix of legal and illegal transactions. Finally, we show that when a majority of the consensus protocol participants are licensed by the regulator to propose legal transactions, there exists a fair consensus execution policy to maximize the legal transaction throughput in the blockchain network.
Noise Modulation-Based Reversible Data Hiding with McEliece Encryption

McEliece cryptosystem is expected to be the next generation of the cryptographic algorithm due to its ability to resist quantum computing attacks. Few research studies have combined it with reversible data hiding in the encrypted domain (RDH-ED). In this article, we analysed and proved that there is a redundancy in the McEliece encryption process that is suitable for embedding. Then, a noise modulation-based scheme is proposed, called NM-RDHED, which is suitable for any signal and not only for images. The content owner scrambles the original image and then encrypts it with the receiver’s public key. The data hider generates a load noise by modulating additional data. After that, the load noise is added to the encrypted image, which achieves the data embedding. The reconstructed image is without any distortion after the direct decryption of the marked image, and the extracted data are no errors. The experimental results demonstrate our scheme has a higher embedding rate and more security, which is superior to existing schemes.
An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research.The safety criteria for medical imaging are highly stringent, and models are required for an explanation.However, existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs.Thus, the interpretability of CNNs has come into the spotlight.Since medical imaging data are limited, many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public ImageNet datasets by the transfer learning method.Unfortunately, this generates many unreliable parameters and makes it difficult to generate plausible explanations from these models.In this study, we trained from scratch rather than relying on transfer learning, creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac MRI.Our enhanced GPU training system implemented interpretable global average pooling for graphics using deep learning.The deep learning tasks were simplified.Simplification included data management, neural network architecture, and training.Our system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment model.Our results demonstrated that the proposed method was feasible and efficient: the Dice coefficient reached 94.48%, and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning architectures.This model is lightweight and more convenient to deploy on mobile devices than transfer learning models.