Reviewerly/exHarmony huggingface dataset:
Memory-Efficient Algorithm for Scalar Multiplications on Twisted Edwards Curves for Isogeny-Based Cryptosystems

Scalar multiplications are considered an essential aspect of implementations of isogeny-based cryptography. The efficiency of scalar multiplication depends on the equation of the underlying elliptic curves and the addition chain employed. Bos and Friedberger recently stated that, for larger scalar multiplication, addition-subtraction chains will become more useful for twisted Edwards curves because of the differential restriction on Montgomery curves in the setting of isogeny-based cryptosystem. Motivated by these comments, we attempt to increase the efficiency of scalar multiplication in twisted Edwards curves in terms of the memory of algorithms. In this paper, we present a double-base addition-subtraction chain algorithm with memory efficiency for scalar multiplication. The memory usage of this part is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>O</mi> <mfenced open="(" close=")" separators="|"> <mrow> <mrow> <mrow> <msup> <mrow> <mfenced open="(" close=")" separators="|"> <mrow> <mi mathvariant="normal">log</mi> <mi>n</mi> </mrow> </mfenced> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> <mo>/</mo> <mrow> <mi mathvariant="normal">log</mi> <mi mathvariant="normal">log</mi> <mi>n</mi> </mrow> </mrow> </mrow> </mfenced> </math> , which is better than the result of the tree-based approach, which is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>O</mi> <mfenced open="(" close=")" separators="|"> <mrow> <msup> <mrow> <mfenced open="(" close=")" separators="|"> <mrow> <mi mathvariant="normal">log</mi> <mi>n</mi> </mrow> </mfenced> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </mfenced> </math> .
GAN’SDA Wrap: Geographic And Network Structured DAta on surfaces that Wrap around

There are many methods for projecting spherical maps onto the plane. Interactive versions of these projections allow the user to centre the region of interest. However, the effects of such interaction have not previously been evaluated. In a study with 120 participants we find interaction provides significantly more accurate area, direction and distance estimation in such projections. The surface of 3D sphere and torus topologies provides a continuous surface for uninterrupted network layout. But how best to project spherical network layouts to 2D screens has not been studied, nor have such spherical network projections been compared to torus projections. Using the most successful interactive sphere projections from our first study, we compare spherical, standard and toroidal layouts of networks for cluster and path following tasks with 96 participants, finding benefits for both spherical and toroidal layouts over standard network layouts in terms of accuracy for cluster understanding tasks.
Person Re‐identification Based on Feature Erasure and Diverse Feature Learning

Robust and comprehensive modelling of targets is the key to successful person re-identification. However, some useful information may be ignored, since CNNs tend to learn from the most distinctive feature region of the human body. In the present study, a multi-branch lightweight network structure that can enhance the ability of diverse feature retrieval is introduced. The proposed network consists of three branches. In the feature erasure branch, a drop block model is added to remove the horizontal region with the highest activation degree from feature vectors so as to allow the network to learn relatively low discrimination features. The global branch is used as an essential supplement to the feature erasure branch. A unified horizontal segmentation strategy is adopted in the local branch to avoid the influence of feature dislocation. Finally, diverse feature learning is achieved through the branch network structure. The proposed method can achieve state-of-the-art results on Market-1501, CUHK03 and DukeMTMC-Reid data sets, thereby demonstrating the effectiveness of the method.
Triple-discriminator GAN for semi-supervised generalized zero-shot learning

Generalized zero-shot learning (GZSL) is a newly rising topic that has been attracting an increasing amount of attention. However, there is currently a paradox in existing semi-supervised GZSL methods, in that most of them are transductive. This requires prior knowledge of unseen classes, which violates the premise that unseen class samples cannot be obtained in GZSL. In this paper, we consider a more practical setting, the semi-supervised inductive problem. This setting only requires the data of seen classes in the training stage, and each class only needs to label a few samples. After that, we migrate some transductive methods to this the new setting and reevaluate their accuracy. However, most of these methods perform poorly in new setting. We analyze their pros and cons deduce a more reasonable model, and finally, use a trainable network to instantiate it. The model has a dual structure of mutual learning, which can make full use of both labeled data and unlabeled data. Extensive experiments demonstrate the state-of-the-art performance of our model in transductive and semi-supervised inductive settings.
Unsupervised Anomaly Detection in 3D Brain MRI Using Deep Learning with Impured Training Data

The detection of lesions in magnetic resonance imaging (MRI)-scans of human brains remains challenging, time-consuming and error-prone. Recently, unsupervised anomaly detection (UAD) methods have shown promising results for this task. These methods rely on training data sets that solely contain healthy samples. Compared to supervised approaches, this significantly reduces the need for an extensive amount of labeled training data. However, data labelling remains error-prone. We study how unhealthy samples within the training data affect anomaly detection performance for brain MRI-scans. For our evaluations, we consider autoencoders (AE) as a well-established baseline method for UAD. We systematically evaluate the effect of impured training data by injecting different quantities of unhealthy samples to our training set of healthy samples. We evaluate a method to identify falsely labeled samples directly during training based on the reconstruction error of the AE. Our results show that training with impured data decreases the UAD performance notably even with few falsely labeled samples. By performing outlier removal directly during training based on the reconstruction-loss, we demonstrate that falsely labeled data can be detected and that this mitigates the effect of falsely labeled data.
NUDF: Neural Unsigned Distance Fields for High Resolution 3D Medical Image Segmentation

Medical image segmentation is often considered as the task of labelling each pixel or voxel as being inside or outside a given anatomy. Processing the images at their original size and resolution often result in insuperable memory requirements, but downsampling the images leads to a loss of important details. Instead of aiming to represent a smooth and continuous surface in a binary voxel-grid, we propose to learn a Neural Unsigned Distance Field (NUDF) directly from the image. The small memory requirements of NUDF allow for high resolution processing, while the continuous nature of the distance field allows us to create high resolution 3D mesh models of shapes of any topology (i.e. open surfaces). We evaluate our method on the task of left atrial appendage (LAA) segmentation from Computed Tomography (CT) images. The LAA is a complex and highly variable shape, being thus difficult to represent with traditional segmentation methods using discrete labelmaps. With our proposed method, we are able to predict 3D mesh models that capture the details of the LAA and achieve accuracy in the order of the voxel spacing in the CT images.
Hybrid Atlas Building with Deep Registration Priors

Registration-based atlas building often poses computational challenges in high-dimensional image spaces. In this paper, we introduce a novel hybrid atlas building algorithm that fast estimates atlas from large-scale image datasets with much reduced computational cost. In contrast to previous approaches that iteratively perform registration tasks between an estimated atlas and individual images, we propose to use learned priors of registration from pre-trained neural networks. This newly developed hybrid framework features several advantages of (i) providing an efficient way of atlas building without losing the quality of results, and (ii) offering flexibility in utilizing a wide variety of deep learning based registration methods. We demonstrate the effectiveness of this proposed model on 3D brain magnetic resonance imaging (MRI) scans.
Self-Ensemble Distillation Using Mean Teachers with Long &amp; Short Memory

Ensemble of deep learning models is widely used to increase performance, however, doing so requires training and deploying several models. This can be mitigated by distilling the knowledge of several models into a single network. Yet, the cost of training numerous models remains. We propose a new consistency regularisation based methodology that eliminates the requirement of training several teacher networks, thus lowering training costs. We efficiently generate several teacher networks by taking exponential moving averages of student network parameters with varying decay rates that provide long and short memory from training routine. Random augmentation is applied individually to each teacher input, and a consistency loss is obtained between teacher & student output to improve model generalisation. We test our proposed method of self-ensembling distillation on two segmentation datasets - The MICCAI 2019 Challenge dataset & the Kaggle Prostate cANcer graDe Assessment (PANDA) Challenge dataset, and show significant gain in performance over baseline well as ensemble knowledge distillation.
Software Development to Detecting the use of Mask using Convolutional Neural Networks

This research aims to develop software that can detect faces that use masks, do not use masks, and use masks in incorrect positions with the TensorFlow library applying the Convolutional Neural Network (CNN) method. The software development method used is the Incremental Method. Incremental one focuses on building a CNN model using the evaluation of the confusion matrix and incremental two focuses on developing a software display using black box testing. The result in incremental one is a CNN model with a confusion matrix evaluation resulting in a model that has 98.83% accuracy, 98.84% precision, 98.78% recall, and 98.81% fl-score. The second incremental result is the display of the software that has been black box tested and is ready to be used for detection. The final result of this research is software that can detect human face objects using masks, not using masks, and using masks in the incorrect position
ANNs and Unifying Explanations: Reply to Erasmus, Brunet, and Fisher

In a recent article, Erasmus, Brunet, and Fisher (2021) argue that Artificial Neural Networks (ANNs) are explainable. They survey four influential accounts of explanation: the Deductive-Nomological model, the Inductive-Statistical model, the Causal-Mechanical model, and the New-Mechanist model. They argue that, on each of these accounts, the features that make something an explanation is invariant with regard to the complexity of the explanans and the explanandum. Therefore, they conclude, the complexity of ANNs (and other Machine Learning models) does not make them less explainable. In this reply, it is argued that Erasmus et al. left out one influential account of explanation from their discussion: the unificationist model. It is argued that, on the unificationist model, the features that make something an explanation is sensitive to complexity. Therefore, on the unificationist model, ANNs (and other Machine Learning models) are not explainable. It is emphasized that Erasmus et al.’s general strategy is correct. The literature on explainable Artificial Intelligence can benefit by drawing from philosophical accounts of explanation. However, philosophical accounts of explanation do not settle the problem of whether ANNs are explainable because they do not unanimously declare that explanation is invariant with regard to complexity.
graphiti: Sketch-based Graph Analytics for Images and Videos

Graph analytics is currently performed using a combination of code, symbolic algebra, and network visualizations. The analyst has to work with symbolic and abstract forms of data to construct and analyze graphs. We locate unique design opportunities at the intersection of computer vision and graph analytics, by utilizing visual variables extracted from images/videos and some direct manipulation and pen interaction techniques. We also summarize commonly used graph operations and graphical representations (graphs, simplicial complexes, hypergraphs), and map them to a few brushes and direct manipulation actions. The mapping enables us to visually construct and analyze a wide range of graphs on top of images, videos, and sketches. The design framework is implemented as a sketch-based notebook interface to demonstrate the design possibilities. User studies with scientists from various fields reveal innovative use cases for such an embodied interaction paradigm for graph analytics.
Metadata Integration Framework for Data Integration of Socio-Cultural Anthropology Digital Repositories: A Case Study of Princess Maha Chakri Sirindhorn Anthropology Centre

Data integration is one of the most challenging tasks for digital collections whose data are stored across various repositories. Data integration across digital repositories has several challenges. First, data heterogeneity in terms of data schema and data values usually occurs across diverse data sources. Second, heterogeneity in data representation and semantic issues are among the problems. The same data may appear in different repositories with varied data representations, i.e., metadata schema. Recent research has focused on matching several related metadata schemas. In this paper, a metadata integration framework is proposed to support digital repositories in socio-cultural anthropology at the Princess Maha Chakri Sirindhorn Anthropology Centre (SAC), Thailand. The proposed framework is defined based on the Metadata Lifecycle Model (MLM). It utilizes non-procedural schema mappings to express data relationships in diverse schemas. A case study of metadata integration over the SAC digital repositories was conducted to validate the framework. The SAC common metadata schema was designed to support data mapping across 13 digital repositories. The SAC “One Search” system was developed to exemplify the system implementation of the framework. Evaluation results showed that the proposed metadata integration framework can support domain experts in socio-cultural anthropology in unified searching across the repositories.
Designing Tangibles to Support Emotion Logging for Older Adults: Development and Usability Study

The global population is aging, leading to shifts in health care needs. In addition to developing technology to support physical health, there is an increasing recognition of the need to consider how technology can support emotional health. This raises the question of how to design devices that older adults can interact with to log their emotions.We designed and developed 2 novel tangible devices, inspired by existing paper-based scales of emotions. The findings from a field trial of these devices with older adults are reported.Using interviews, field deployment, and fixed logging tasks, we assessed the developed devices.Our results demonstrate that the tangible devices provided data comparable with standardized psychological scales of emotion. The participants developed their own patterns of use around the devices, and their experience of using the devices uncovered a variety of design considerations. We discuss the difficulty of customizing devices for specific user needs while logging data comparable to psychological scales of emotion. We also highlight the value of reflecting on sparse emotional data.Our work demonstrates the potential for tangible emotional logging devices. It also supports further research on whether such devices can support the emotional health of older adults by encouraging reflection of their emotional state.
What is Your Current Mindset?

Is recommendation the new search? Recommender systems have shortened the search for information in everyday activities such as following the news, media, and shopping. In this paper, we address the challenges of capturing the situational needs of the user and linking them to the available datasets with the concept of Mindsets. Mindsets are categories such as “I’m hungry” and “Surprise me” designed to lead the users to explicitly state their intent, control the recommended content, save time, get inspired, and gain shortcuts for a satisficing exploration of POI recommendations. In our methodology, we first compiled Mindsets with a card sorting workshop and a formative evaluation. Using the insights gathered from potential end users, we then quantified Mindsets by linking them to POI utility measures using approximated lexicographic multi-objective optimisation. Finally, we ran a summative evaluation of Mindsets and derived guidelines for designing novel categories for recommender systems.
Datasets of trace elements in shallow marine sediments along the Egyptian shore of the Mediterranean and Red Seas

A comprehensive dataset concerning the geochemical composition of unconsolidated shallow marine sediments collected along coastal areas of Northern Nile Delta and Egyptian sector of Red Seas is presented. The sediment samples were analyzed using instrumental neutron activation analysis (INAA) in Frank Laboratory of Neutron Physics FLNP - Joint Institute for Nuclear Research JINR and inductively coupled plasma - mass spectrometer (ICP-MS) in Actlabs - Canada. Data thus collected supported the research published and published articles conducted to evaluate the geochemistry of shallow marine sediments covering mentioned areas [1], [2], [3]. The mass fractions of 43 and 39 trace elements and oxides were determined in the unconsolidated marine sediments of Northern Nile Delta and Egyptian sector of Red Sea, respectively. Final data were expressed in wt.% and mg/kg for major and trace elements, respectively. Different statistical tests such as Shapiro-Wilk, Anderson-Darling, Lilliefors and Jarque-Bera were used to check the normality of data. At the same time, distribution patterns of the rare earth elements (lanthanides) as well as Eu and Ce anomalies were investigated. Quality control of analytical measurements was carried out using certified reference materials. Different univariate and multivariate as well as graphic statistical analyses were performed. Presented data were used in identifying, by means of more pollution indices, the degree of local contamination. The present dataset could be further used in establishing geochemical background for the studied areas and tracking eventually changes posing significant threat to environment and humans.
Overall hyperbolic-singular-value-decomposition-based square-root solutions in Kalman filters with deterministically sampled mean and covariance for state estimation in continuous-discrete nonlinear stochastic systems

The paper aims at square-rooting continuous-discrete Kalman filters with deterministically sampled mean and covariance. These cover all methods devised within the quadrature, cubature and unscented Kalman filtering frameworks. The main problem addressed is a potential negativity of some weights used in calculations of the sampled expectations and covariances, which precludes from an orthogonal square-rooting procedure to be designed and applied. However, square-root solutions are often requested because of their exceptional robustness to round-off and other errors committed. These also preserve the symmetry and positivity of the covariances computed in automatic mode. Here, we apply the J-orthogonal square-rooting approach devised earlier to designing our overall square-root solutions in the realm of quadrature, cubature and unscented Kalman filtering techniques, which are adjusted easily to any state estimator in use by setting its weights and deterministically selected nodes. Our J-orthogonal square-rooting procedure is grounded on the hyperbolic singular value decomposition. It leads to two algorithms covering all Itô-Taylor-approximation-based Kalman filters with deterministically sampled mean and covariance. Performances of such square-root solutions are assessed and compared to their non-square-root counterparts in a simulated flight control scenario with ill-conditioned measurements.
Towards Conviviality in NavigatingHealth Information on Social Media

HCI is increasingly concerned with health information quality and spread of misinformation on social media. Despite many major platforms having been adopted across the world, the situated evaluation and sharing of health information is underexplored across diverse health systems and cultural and political contexts. Drawing on semi-structured interviews, we study the navigation of health information on social media in urban and rural South India, backdropped by plural knowledges around health and the specific politics and sociality of health and social media in this setting. We use Ivan Illich's concept of tools for conviviality [49] to distinguish between how people creatively use tools versus how tools manage and impose values on people—participants aimed to use health information towards care beyond institutionalized healthcare, but insidious misinformation and information-sharing practices served to commodify, spark uncertainty in, and discipline caring behavior. We use our findings to expand understandings of the use of health information on social media and how positionality shapes how people are affected by and respond to misinformation. We also draw attention to the structural aspects of health misinformation in the Indian context and how the design of social media platforms might play a role in addressing it.
Context-driven Encrypted Multimedia Traffic Classification on Mobile Devices

The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, network operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network service the traffic needs for maintaining an acceptable quality of experience. Therefore, end devices are a natural fit for performing traffic classification since end devices have more contextual information about the device usage and traffic. This paper proposes a novel approach that classifies multimedia traffic types produced and consumed on mobile devices. The technique relies on a mobile device&#x2019;s detection of its multimedia context characterized by its utilization of different media input/output components, e.g., camera, microphone, and speaker. We develop an algorithm, MediaSense, which senses the states of multiple I/O components and identifies the specific multimedia context of a mobile device in real-time. We demonstrate that MediaSense classifies encrypted multimedia traffic in real-time as accurately as deep learning approaches and with even better generalizability.