Reviewerly/exHarmony huggingface dataset:
Shorter Lattice-Based Group Signatures via “Almost Free” Encryption and Other Optimizations

We present an improved lattice-based group signature scheme whose parameter sizes and running times are independent of the group size. The signature length in our scheme is around 200KB, which is approximately a 3X reduction over the previously most compact such scheme, based on any quantum-safe assumption, of del Pino et al. (CCS 2018). The improvement comes via several optimizations of some basic cryptographic components that make up group signature schemes, and we think that they will find other applications in privacy-based lattice cryptography.
Anthropomorphizing Technology: A Conceptual Review of Anthropomorphism Research and How it Relates to Children’s Engagements with Digital Voice Assistants

'Anthropomorphism' is a popular term in the literature on human-technology engagements, in general, and child-technology engagements, in particular. But what does it really mean to 'anthropomorphize' something in today's world? This conceptual review article, addressed to researchers interested in anthropomorphism and adjacent areas, reviews contemporary anthropomorphism research, and it offers a critical perspective on how anthropomorphism research relates to today's children who grow up amid increasingly intelligent and omnipresent technologies, particularly digital voice assistants (e.g., Alexa, Google Assistant, Siri). First, the article reviews a comprehensive body of quantitative as well as qualitative anthropomorphism research and considers it within three different research perspectives: descriptive, normative and explanatory. Following a brief excursus on philosophical pragmatism, the article then discusses each research perspective from a pragmatistic viewpoint, with a special emphasis on child-technology and child-voice-assistant engagements, and it also challenges some popular notions in the literature. These notions include descriptive 'as if' parallels (e.g., child behaves 'as if' Alexa was a friend), or normative assumptions that human-human engagements are generally superior to human-technology engagements. Instead, the article reviews different examples from the literature suggesting the nature of anthropomorphism may change as humans' experiential understandings of humanness change, and this may particularly apply to today's children as their social cognition develops in interaction with technological entities which are increasingly characterized by unprecedented combinations of human and non-human qualities.
Coupling of cryptocurrency trading with the sustainable environmental goals: Is it on the cards?

Abstract Following the systematic review and bibliometric analysis of current literature, this paper attempts to investigate whether the wealth generated through cryptocurrency trading can assist in attaining the United Nations' (UN) sustainable development goal (SDG) 7, affordable and clean energy and UN SDG 13 related to climate action. The critical analysis of literature indicates a growing interest in cryptocurrency, the UN's SDGs and the negative effect that crypto mining has on the use of enormous energy. However, there is a clear gap in the literature that focuses on the possibility of using the wealth generated through cryptocurrency trading in financing environmentally friendly projects and attaining the UN's SDG 7 and SDG 13. The findings and the future research direction of this study aim to firstly expand the academic literature related to SDG 7 and SDG 13 and secondly to examine the relationship between cryptocurrency and sustainability even during an uncertain period. This study provides evidence pertaining to the theoretical models that can be applied within discussion of the complex relationship between cryptocurrency, clean energy and climate action. Our findings will provide policymakers with information regarding actions that need to be taken in order to convert cryptocurrency generated wealth and consequently attaining sustainable socio‐economic goals in the future.
The Use of Smart Speakers in Care Home Residents: Implementation Study

The use of smart speakers to improve well-being had been trialed in social care by others; however, we were not aware of their implementation in most care homes across a region in the Southwest of the United Kingdom. For the widespread adoption of new technology, it must be locally demonstrable and become normalized.The aim of this study was to install smart speakers in care homes in a rural and coastal region and to explore if and how the devices were being used, the barriers to their implementation, and their potential benefits.Email, workshops, drop-in sessions, phone, and cold calling was used to contact all 230 care homes, offering a free smart speaker and some advisory support. Care homes accepting the devices were asked to complete a feedback diary. Nonresponse rate for diary completion was high and was thus supplemented with a telephone survey.Over the course of 7 months, we installed 156 devices in 92 care homes for older people, 50 devices for people with physical or mental health needs, and 8 for others. The devices were used mainly for music but also for poetry, recipes, light controls, jokes, and video calls. Care home managers reported the benefits for the residents, including enhanced engagement with home activities, enjoyment, calming effects, and the acquisition of new skills. Implementation problems included internet connectivity, staff capacity, and skills.Affordable consumer devices such as smart speakers should be installed in all care homes to benefit residents. Voice-activated technologies are easy to use and promote interaction. This study indicates that implementation in care homes was possible and that smart speakers had multifaceted benefits for residents and staff. Most care homes in this region now use smart speakers for their residents, thereby normalizing this practice.
Towards differential access control and privacy-preserving for secure media data sharing in the cloud

The development of cloud computing techniques has promoted the continuous sharing of a tremendous amount of media data. However, the involved parties in such processes usually share different or even conflicting benefits, causing non-orthogonal security concerns. The data owners worry about their private data being abused through illegal access or unauthorized redistribution by the requesters, while the latter ones are vulnerable to being profiled (e.g., interests, age) through their data access histories. Since most existing approaches address these problems individually, the security requirements of different roles cannot be properly satisfied. In this paper, we attempt to provide a joint effort that flexibly addresses the above security concerns. We first design a new cryptographic primitive (i.e., TFPRE-OT) by exploiting type-based proxy re-encryption for fine-grained sharing and oblivious transfer for hiding the access histories of data requesters. Using TFPRE-OT as the building block, a novel data sharing scheme is proposed to achieve differential access control and protect privacy disclosure in general cloud computing settings. We provide a thorough security analysis, showing that the proposed method can fulfill all desired security requirements. Finally, we validate the effectiveness of our design with both case studies and extensive experiments.
Taking Art School Online in Response to COVID 19: From Rapid Response to Realising Potential

Abstract In response to COVID 19 higher education worldwide underwent a rapid shift, moving learning and teaching to the online environment. This shift in pedagogy and practice fundamentally challenged a number of disciplines and disciplinary norms, none more so than studio‐based art and design institutions. As a result, academic staff and those in professional service roles were required to rapidly engage with digital technologies with which they were unfamiliar, adjusting their pedagogic approach and innovating within the online environment. This article presents a small‐scale study investigating the impact of this rapid change on a small specialist studio‐based higher education institution which prioritises physical making and in person teaching within its educational provision. The study focused on how the shift influenced the perceptions and practices of teaching and professional support staff in their use of learning technologies and what newly adopted approaches to technology enhanced art and design education may likely continue post COVID 19.
Addressing and Modeling the Challenges Faced in the Implementation of Blockchain Technology in the Food and Agriculture Supply Chain

Blockchain technology has gained a lot of attention in the supply chain of many organizations. This technology is being used in sectors like food, healthcare, manufacturing, retail, automobile, etc. The aim of this research is to identify the problems faced in the food and agriculture supply chain in implementing blockchain technology in their organizations. To determine the barriers to blockchain technology in the food and agriculture supply chain, the authors have used technological, organizational, and environmental frameworks. All the barriers are divided into three constructs having some variables. Data is collected through questionnaires using survey methods. Empirical methods used are exploratory factor analysis and structural equation modelling. This study also provides empirical evidence and developed three structural equation models. This study will help the service providers to address the problems that are being faced by the firms in the implementation of blockchain technology in their firms.
Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling

Open-vocabulary instance segmentation aims at segmenting novel classes without mask annotations. It is an important step toward reducing laborious human supervision. Most existing works first pretrain a model on captioned images covering many novel classes and then finetune it on limited base classes with mask annotations. However, the high-level textual information learned from caption pretraining alone cannot effectively encode the details required for pixelwise segmentation. To address this, we propose a cross-modal pseudo-labeling framework, which generates training pseudo masks by aligning word semantics in captions with visual features of object masks in images. Thus, our framework is capable of labeling novel classes in captions via their word semantics to self-train a student model. To account for noises in pseudo masks, we design a robust student model that selectively distills mask knowledge by estimating the mask noise levels, hence mitigating the adverse impact of noisy pseudo masks. By extensive experiments, we show the effectiveness of our framework, where we significantly improve mAP score by 4.5% on MS-COCO and 5.1 % on the large-scale Open Images & Conceptual Captions datasets compared to the state-of-the-art. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code is available at https://github.com/hbdat/cvpr22_cross_modal_pseudo_labeling.
Deep-learning-based solution for data deficient satellite image segmentation

Satellite images have become more easily accessible due to government programmes and the availability of commercial earth observation satellites. The increased volume of data and the recent advancement in deep learning-based techniques have made possible the development of fast and accurate methods for satellite image segmentation, which aims at automatically giving each pixel in the satellite image a label of the underlying object/ground type. However, despite the abundant supply of raw satellite images, the labelling of these images is a manual task and is therefore time and labour intensive. When a large labelled dataset is absent, resorting to a smaller data source significantly hampers the performance of the machine learning model. Therefore, an approach which mitigates the effects of using small labelled datasets should be investigated. This paper provides a solution to the problem of insufficient data samples for satellite image segmentation. To this end, two methods are developed and compared with the benchmark U-Net model: the use of transfer learning on an Xception architecture, and the use of steerable filters. The first model transfers knowledge from a pre-trained Xception model and predicts with a multi-resolution feature fusion module, specifically designed to recover fine details. The latter further improves the capability of our neural network model in handling data with high rotational variation. Both models are experimentally shown to significantly enhance the segmentation performance despite a small-sized training dataset.
Is Bitcoin a safe haven? Application of FinTech to safeguard Australian stock markets

Purpose This study aims to address the timely question of whether Bitcoin exhibited a safe haven property against the major Australian stock indices during the first and second waves of the COVID-19 pandemic in Australia and whether such property is similar or different in one year time from the first wave of the COVID-19. Design/methodology/approach The authors used the bivariate Dynamic Conditional Correlation, Generalized Autoregressive Conditional Heteroskedasticity model, on the five-day returns of Bitcoin and Australian stock indices for the sample period between 23 April, 2011 and 19 April, 2021. Findings The results show that Bitcoin offered weak safe haven and hedging benefits when combined in a portfolio with S&amp;P/ASX 200 Financials index, S&amp;P/ASX 200 Banks index or S&amp;P/ASX 300 Banks index. In regard to the S&amp;P/ASX All Ordinaries Gold index, the authors found Bitcoin a risky candidate with inconsistent safe haven and hedging benefits. Against S&amp;P/ASX 50 index, S&amp;P/ASX 200 index and S&amp;P/ASX 300 index, Bitcoin was nothing more than a diversifier. The outset of the second COVID-19 wave, which was comparatively more severe than the first, is also reflected in the results with considerably higher correlations. Originality/value There is a lack of in-depth empirical evidence on the safe haven capabilities of Bitcoins for various Australian stock indices during the first and second waves of the COVID-19 pandemic. The study bridges this void in research.
MP2ML

We present an extended abstract of MP2ML, a machine learning framework which integrates Intel nGraph-HE, a homomorphic encryption (HE) framework, and the secure two-party computation framework ABY, to enable data scientists to perform private inference of deep learning (DL) models trained using popular frameworks such as TensorFlow at the push of a button. We benchmark MP2ML on the CryptoNets network with ReLU activations, on which it achieves a throughput of 33.3 images/s and an accuracy of 98.6%. This throughput matches the previous state-of-the-art frameworks.
View-Wise Versus Cluster-Wise Weight: Which Is Better for Multi-View Clustering?

Weighted multi-view clustering (MVC) aims to combine the complementary information of multi-view data (such as image data with different types of features) in a weighted manner to obtain a consistent clustering result. However, when the cluster-wise weights across views are vastly different, most existing weighted MVC methods may fail to fully utilize the complementary information, because they are based on view-wise weight learning and can not learn the fine-grained cluster-wise weights. Additionally, extra parameters are needed for most of them to control the weight distribution sparsity or smoothness, which are hard to tune without prior knowledge. To address these issues, in this paper we propose a novel and effective Cluster-weighted mUlti-view infoRmation bottlEneck (CURE) clustering algorithm, which can automatically learn the cluster-wise weights to discover the discriminative clusters across multiple views and thus can enhance the clustering performance by properly exploiting the cluster-level complementary information. To learn the cluster-wise weights, we design a new weight learning scheme by exploring the relation between the mutual information of the joint distribution of a specific cluster (containing a group of data samples) and the weight of this cluster. Finally, a novel draw-and-merge method is presented to solve the optimization problem. Experimental results on various multi-view datasets show the superiority and effectiveness of our cluster-wise weighted CURE over several state-of-the-art methods.
Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

Solar energy is one of the most strategic energy sources for the world’s economic development. This has caused the number of solar photovoltaic plants to increase around the world; consequently, they are installed in places where their access and manual inspection are arduous and risky tasks. Recently, the inspection of photovoltaic plants has been conducted with the use of unmanned aerial vehicles (UAV). Although the inspection with UAVs can be completed with a drone operator, where the UAV flight path is purely manual or utilizes a previously generated flight path through a ground control station (GCS). However, the path generated in the GCS has many restrictions that the operator must supply. Due to these restrictions, we present a novel way to develop a flight path automatically with coverage path planning (CPP) methods. Using a DL server to segment the region of interest (RoI) within each of the predefined PV plant images, three CPP methods were also considered and their performances were assessed with metrics. The UAV energy consumption performance in each of the CPP methods was assessed using two different UAVs and standard metrics. Six experiments were performed by varying the CPP width, and the consumption metrics were recorded in each experiment. According to the results, the most effective and efficient methods are the exact cellular decomposition boustrophedon and grid-based wavefront coverage, depending on the CPP width and the area of the PV plant. Finally, a relationship was established between the size of the photovoltaic plant area and the best UAV to perform the inspection with the appropriate CPP width. This could be an important result for low-cost inspection with UAVs, without high-resolution cameras on the UAV board, and in small plants.
Groundwater Quality Monitoring Using In-Situ Measurements and Hybrid Machine Learning with Empirical Bayesian Kriging Interpolation Method

This article discusses the assessment of groundwater quality using a hybrid technique that would aid in the convenience of groundwater (GW) quality monitoring. Twenty eight (28) GW samples representing 62 barangays in Calapan City, Oriental Mindoro, Philippines were analyzed for their physicochemical characteristics and heavy metal (HM) concentrations. The 28 GW samples were collected at suburban sites identified by the coordinates produced by Global Positioning System Montana 680. The analysis of heavy metal concentrations was conducted onsite using portable handheld X-Ray Fluorescence (pXRF) Spectrometry. Hybrid machine learning—geostatistical interpolation (MLGI) method, specific to neural network particle swarm optimization with Empirical Bayesian Kriging (NN-PSO+EBK), was employed for data integration, GW quality spatial assessment and monitoring. Spatial map of metals concentration was produced using the NN-PSO-EBK. Another, spot map was created for observed metals concentration and was compared to the spatial maps. Results showed that the created maps recorded significant results based on its MSEs with values such as 1.404 × 10−4, 5.42 × 10−5, 6.26 × 10−4, 3.7 × 10−6, 4.141 × 10−4 for Ba, Cu, Fe, Mn, Zn, respectively. Also, cross-validation of the observed and predicted values resulted to R values range within 0.934–0.994 which means almost accurate. Based on these results, it can be stated that the technique is efficient for groundwater quality monitoring. Utilization of this technique could be useful in regular and efficient GW quality monitoring.
Breast Cancer Histopathological Image Classification with Adversarial Image Synthesis

Data limitation is one of the major challenges in applying deep learning to medical images. Data augmentation is a critical step to train robust and accurate deep learning models for medical images. In this research, we increase the size of a small dataset by using an Auxiliary Classifier Generative Adversarial Network (ACGAN) which generates realistic images along with their class labels.We evaluate the effectiveness of our ACGAN augmentation method by performing breast cancer histopathological image classification with deep convolutional neural network (dCNN) classifiers trained on our enhanced dataset. For our classifier, we use a transfer learning approach where the convolutional features are extracted from a pertained model and subsequently fed into several extreme gradient boosting (XGBoost) classifiers. Our experimental results on Breast Cancer Histopathological (BreakHis) dataset show that ACGAN data augmentation, along with our XGBoost classifier increases the classification accuracy by 9.35% for binary classification (benign vs. malignant) and 8.88% for four-class tumor sub-type classification compared with standard transfer learning approach.
Supporting Sign Language Narrations in the Museum

The accessibility of Cultural Heritage content for the diverse user population visiting Cultural Heritage Institutions and accessing content online has not been thoroughly discussed. Considering the penetration of new digital media in such physical and virtual spaces, lack of accessibility may result in the exclusion of a large user population. To overcome such emerging barriers, this paper proposes a cost-effective methodology for the implementation of Virtual Humans, which are capable of narrating content in a universally accessible form and acting as virtual storytellers in the context of online and on-site CH experiences. The methodology is rooted in advances in motion capture technologies and Virtual Human implementation, animation, and multi-device rendering. This methodology is employed in the context of a museum installation at the Chios Mastic Museum where VHs are presenting the industrial process of mastic processing for chewing gum production.
Presence and Flow in the Context of Virtual Reality Storytelling: What Influences Enjoyment in Virtual Environments?

Virtual reality (VR) has widely been implemented in a variety of industries, with immersive storytelling now gaining popularity in the field of journalism. In this regard, there are many important questions about which direction modern journalism should take as a whole. To test the feasibility of immersive storytelling, this study developed a cognitive experience model containing paths for presence, flow, credibility, empathy, understanding, and enjoyment. A total of 131 participants were divided into three groups and exposed to either VR, 360-degree videos, or two-dimensional (2D) videos, then asked to rate their experiences. With the exception of understanding, results showed that the VR and 360-degree videos were more highly evaluated in each path when compared with 2D videos. In this case, technical attributes did not greatly affect the study variables. However, presence played a vital role in news promotion, while flow was positively affected by presence; flow also affected other variables when combined with presence. Finally, a structural equation model was tested and the weights of the different influences on the enjoyment impact were presented. In sum, this study found that immersive storytelling improved both presence, flow, and credibility, which jointly and positively affected enjoyment. Understanding negatively affected enjoyment. Empathy had little effect on enjoyment in the model.