Reviewerly/exHarmony huggingface dataset:
Game-Set-MATCH

We use biometrics like fingerprints and facial images to identify ourselves to our mobile devices and log on to applications everyday. Such authentication is internal-facing: we provide measurement on the same device where the template is stored. If our personal devices could participate in external-facing authentication too, where biometric measurement is captured by a nearby external sensor, then we could also enjoy a frictionless authentication experience in a variety of physical spaces like grocery stores, convention centers, ATMs, etc. The open setting of a physical space brings forth important privacy concerns though. We design a suite of secure protocols for external-facing authentication based on the cosine similarity metric which provide privacy for both user templates stored on their devices and the biometric measurement captured by external sensors in this open setting. The protocols provide different levels of security, ranging from passive security with some leakage to active security with no leakage at all. With the help of new packing techniques and zero-knowledge proofs for Paillier encryption -- and careful protocol design, our protocols achieve very practical performance numbers. For templates of length 256 with elements of size 16 bits each, our fastest protocol takes merely 0.024 seconds to compute a match, but even the slowest one takes no more than 0.12 seconds. The communication overhead of our protocols is very small too. The passive and actively secure protocols (with some leakage) need to exchange just 16.5KB and 27.8KB of data, respectively. The first message is designed to be reusable and, if sent in advance, would cut the overhead down to just 0.5KB and 0.8KB, respectively.
Magnetic resonance imaging and transrectal ultrasound prostate image segmentation based on improved level set for robotic prostate biopsy navigation

Abstract Aim Transrectal ultrasound (TRUS) guided prostate biopsy is a typical early prostate examination. However, the ultrasound imaging suffers from blurred contour, intensity inhomogeneity and small surrounding soft tissue differentiation. To take advantage of clear magnetic resonance imaging (MRI) into robotic prostate biopsy navigation, the prostate regions in the MRI and TRUS images need to be segmented separately. This paper proposes an improved level set segmentation model based on prior shape, which aims to provide a better solution to the prostate segmentation problems in TRUS and MRI. Methods In our segmentation model, the Gaussian probability model is used to establish the statistical learning of the prior shape, and the cosine function is used to represent the energy term fitting of the traditional prior shape and the local intensity information. Results The experiment results show that the model can adapt to different forms of prostate in MRI and TRUS more accurately, and the prostate biopsy accuracy in our biopsy system can reach 2.24 ± 1.44 mm. Conclusion This segmentation model has high accuracy, meets the clinical needs in robotic prostate biopsy navigation.
Influence of Moodle and MS Teams on teaching-learning-studying (TLS) processes

In two decades, the Moodle established itself as favorable LMS. It is used on all levels of education and many lecturers have gained skills to use it effectively. Moodle becomes mature and many lecturers have found it to be their first choice Learning Management System (LMS). Educational systems around the world have always problems with money and the Moodle is free; it has good multi-language support; and it implements many functionalities covering most lecturers' needs. But in the recent years some new alternatives emerge. They do not want to copy the Moodle and this makes them interesting enough to try them in practice. In recent time the Microsoft Teams incorporating the Microsoft OneNote becomes increasingly popular and gained users in many fields of work. Both systems were tried in different courses. It is clear that the Moodle is the winner in traditional educational tasks (assessments, peer to peer assessment, quizzes, categorization, extendibility). The MS Teams excels in different types of communications (chat, videoconferencing, lecturing, screen shareing), and is superior in general content creation. There is no clear winner in the assignment, mathematics (symbol), and file distribution.
Investigating Immersive Virtual Reality as an Educational Tool for Quantum Computing

Quantum computing (QC) is an intrinsically complex yet exciting discipline with increasing practical relevance. A deep understanding of QC requires the integration of knowledge across numerous technical fields, such as physics, computing and mathematics. This work aims to investigate how immersive Virtual Reality (VR) compares to a desktop environment (‘web-applet’) as an educational tool to help teach individuals QC fundamentals. We developed two interactive learning tutorials, one utilising the ‘Bloch sphere’ visualisation to represent a single-qubit system, and the other exploring multi-qubit systems through the lens of ‘quantum entanglement’. We evaluate the effectiveness of each medium to teach QC fundamentals in a user study with 24 participants. We find that the Bloch sphere visualisation was well-suited to VR over a desktop environment. Our results also indicate that mathematics literacy is an important factor in facilitating greater learning with this effect being notably more pronounced when using VR. However, VR did not significantly improve learning in a multi-qubit context. Our work provides valuable insights which contribute to the emerging field of Quantum HCI (QHCI) and VR for education.
Information Leakage by Model Weights on Federated Learning

Federated learning aggregates data from multiple sources while protecting privacy, which makes it possible to train efficient models in real scenes. However, although federated learning uses encrypted security aggregation, its decentralised nature makes it vulnerable to malicious attackers. A deliberate attacker can subtly control one or more participants and upload malicious model parameter updates, but the aggregation server cannot detect it due to encrypted privacy protection. Based on these problems, we find a practical and novel security risk in the design of federal learning. We propose an attack for conspired malicious participants to adjust the training data strategically so that the weight of a certain dimension in the aggregation model will rise or fall with a pattern. The trend of weights or parameters in the aggregation model forms meaningful signals, which is the risk of information leakage. The leakage is exposed to other participants in this federation but only available for participants who reach an agreement with the malicious participant, i.e., the receiver must be able to understand patterns of changes in weights. The attack effect is evaluated and verified on open-source code and data sets.
Design Sensibilities - Designing for Cultural Sensitivity

When designing interactive systems and applications, we need to take into account different user groups, contexts, and cultural sensitivities. The challenges in culturally sensitive design can arise e.g. from historical, ideological, or ethical factors, and need to be considered when conducting HCI research, e.g. with cultural heritage, under-represented user groups, topics of cultural rituals, or in cross-cultural interfaces. The digitalizing world crosses old borders by bringing technology connectivity for new domains, and provides the means to distribute information that has previously been harder to access, and which may contain strong cultural meanings. With emerging technology use, new etiquette and social practices are formed to reflect the new sub-cultures. This workshop addresses the cultural sensitivities when designing interactive systems. The workshop invites researchers and practitioners to present and discuss about related case studies, applications, research methods, and experiences.
Sensors-based Human Activity Recognition with Convolutional Neural Network and Attention Mechanism

Recently, Human Recognition Activity (HAR) has received more and more attention. At present, Recurrent neural networks (RNN), long short-term memory in particular, are main approaches in HAR. However, RNN suffers from the fact that it cannot process sequences in parallel and longer sequences cannot be remembered well. Therefore, this paper applies the attention mechanism to explore the relevant time context, and proposes a new model, named DeepConvAttn. DeepConvAttn is based on a well-known deep learning model, DeepConvLSTM. These two models are compared in experiments, and the results show DeepConvAttn is better than DeepConvLSTM on two popular HAR benchmark datasets.
The Future Supermarket

Ethnographic Experiential Futures (EXF) is an approach to foresight that aims to increase the accessibility, variety and depth of images of the future for design. In this paper, we present a EXF-based case study imagining the future supermarket. Rooted in already-existing images of future supermarkets, we propose that the future supermarket experience is autonomous, efficient, informed, local and personal. Based on the idea of four "generic futures" we illustrate each of these categories with a set of scenarios in a variety of experiential forms. These were exhibited first in an open exhibition and second in a closed exhibition for experts. To close, we offer reflections on the use of Ethnographic Experiential Futures in the form of a set of questions to inspire future research.
Deep Image Translation for Enhancing Simulated Ultrasound Images

Ultrasound simulation based on ray tracing enables the synthesis of highly realistic images. It can provide an interactive environment for training sonographers as an educational tool. However, due to high computational demand, there is a trade-off between image quality and interactivity, potentially leading to sub-optimal results at interactive rates. In this work we introduce a deep learning approach based on adversarial training that mitigates this trade-off by improving the quality of simulated images with constant computation time. An image-to-image translation framework is utilized to translate low quality images into high quality versions. To incorporate anatomical information potentially lost in low quality images, we additionally provide segmentation maps to image translation. Furthermore, we propose to leverage information from acoustic attenuation maps to better preserve acoustic shadows and directional artifacts, an invaluable feature for ultrasound image interpretation. The proposed method yields an improvement of 7.2% in Fréchet Inception Distance and 8.9% in patch-based Kullback-Leibler divergence.
On modeling and identification of empirical partially intelligible white noise processes

Abstract The paper discusses the identification of the empirical, partially intelligible white noise processes generated by deterministic numerical algorithms. The introduced fuzzy‐random complementary approach can identify the inner hidden correlational patterns of the empirical white noise process if the process has a real hidden structure of this kind. We have shown how the characteristics of autocorrelated white noise processes change as the order of autocorrelation increases. Based on this approach, the original empirical white noise process transformed by the autocorrelation operator can be considered to be random data series (randomlikeness), and at the same time, it has function‐like characteristics (functionlikeness), as well. We approach the analysis of the mentioned complementarity by modeling the autocorrelation functions of the empirical white noise processes using tensor product (TP) model transformation.
Periodicity-Oriented Data Analytics on Time-Series Data for Intelligence System

Periodic pattern mining models analyze patterns which occur periodically in a time-series database, such as sensor readings of smartphones and/or Internet of Things devices. The extracted patterns can be utilized for risk prediction, system management, and decision-making. In this article, we propose an efficient periodicity-oriented data analytics approach. It ignores intermediate events deliberately by adopting the concept of flexible periodic patterns, so it can be applied to more diverse real-life scenarios and systems. Moreover, the proposed approach adopts a novel symbol-centered data structure instead of existing data structures for state-of-the-art approaches of periodic pattern mining. Performance evaluations on real-life datasets, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Diabetes, Oil Prices</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bike Sharing</i> , and requirements show that our approach has better runtime, memory usage, number of visited patterns, and sensitivity than efficient periodic pattern mining (EPPM) and flexible periodic pattern mining (FPPM), which are the state-of-the-art approaches in the same field. The experimental results show that the proposed algorithm will require less runtime and smaller memory than the existing algorithms on most data and requirements in real life.
On the Success Probability of Solving Unique SVP via BKZ

As lattice-based key encapsulation, digital signature, and fully homomorphic encryption schemes near standardisation, ever more focus is being directed to the precise estimation of the security of these schemes. The primal attack reduces key recovery against such schemes to instances of the unique Shortest Vector Problem (uSVP). Dachman-Soled et al. (Crypto 2020) recently proposed a new approach for fine-grained estimation of the cost of the primal attack when using Progressive BKZ for lattice reduction. In this paper we review and extend their technique to BKZ 2.0 and provide extensive experimental evidence of its accuracy. Using this technique we also explain results from previous primal attack experiments by Albrecht et al. (Asiacrypt 2017) where attacks succeeded with smaller than expected block sizes. Finally, we use our simulators to reestimate the cost of attacking the three lattice KEM finalists of the NIST Post Quantum Standardisation Process.
Assessing Mock Classes: An Empirical Study

During testing activities, developers frequently rely on dependencies (e.g., web services, etc) that make the test harder to be implemented. In this scenario, they can use mock objects to emulate the dependencies' behavior, which contributes to make the test fast and isolated. In practice, the emulated dependency can be dynamically created with the support of mocking frameworks or manually hand-coded in mock classes. While the former is well-explored by the research literature, the latter has not yet been studied. Assessing mock classes would provide the basis to better understand how those mocks are created and consumed by developers and to detect novel practices and challenges. In this paper, we provide the first empirical study to assess mock classes. We analyze 12 popular software projects, detect 604 mock classes, and assess their content, design, and usage. We find that mock classes: often emulate domain objects, external dependencies, and web services; are typically part of a hierarchy; are mostly public, but 1/3 are private; and are largely consumed by client projects, particularly to support web testing. Finally, based on our results, we provide implications and insights to researchers and practitioners working with mock classes.
Multiclass Classification for Bangla News Tags with Parallel CNN Using Word Level Data Augmentation

Text mining is the procedure of exploring large unorganized text data. Due to the availability of numerous amounts of text data through online blogs, newspapers and other media, text classification and categorization is the hot topic nowadays. Many researches have been done on this topic on English and other western languages. However, very few notable researches have been on Bangla language. Unavailability of a notable dataset in Bangla language is another burden to develop a highperformance text classification tool. In this paper, we have presented a Bangla news tags classification approach. The classification has been done entirely based on news titles only with parallel Convolutional Neural Network (CNN) which is a category of deep neural networks utilizing word-level data augmentation approach. Due to the unavailability of a proper and updated dataset on Bangla news titles and tags, we have developed our own dataset which consists of 88,968 news titles and tags by scrapping online newspapers. According to the classification result, our approach shows an accuracy of 93.47% which is the highest amongst the similar works.
CLAPS: Client-Location-Aware Path Selection in Tor

Much research has investigated improving the security and performance of Tor by having Tor clients choose paths through the network in a way that depends on the client's location. However, this approach has been demonstrated to lead to serious deanonymization attacks. Moreover, we show how in some scenarios it can lead to significant performance degradation. For example, we demonstrate that using the recently-proposed Counter-RAPTOR system when guard bandwidth isn't abundant could increase median download times by 28.7%. We propose the CLAPS system for performing client-location-aware path selection, which fixes the known security and performance issues of existing designs. We experimentally compare the security and performance of CLAPS to Counter-RAPTOR and DeNASA. CLAPS puts a strict bound on the leakage of information about the client's location, where the other systems could completely reveal it after just a few connections. It also guarantees a limit on the advantage that an adversary can obtain by strategic relay placement, which we demonstrate to be overwhelming against the other systems. Finally, due to a powerful formalization of path selection as an optimization problem, CLAPS is approaching or even exceeding the original goals of algorithms to which it is applied, while solving their known deficiencies.
Deep Generative Model Using Unregularized Score for Anomaly Detection With Heterogeneous Complexity

Accurate and automated detection of anomalous samples in an image dataset can be accomplished with a probabilistic model. Such images have heterogeneous complexity, however, and a probabilistic model tends to overlook simply shaped objects with small anomalies. The reason is that a probabilistic model assigns undesirable lower likelihoods to complexly shaped objects, which are nevertheless consistent with the current set standards. This difficulty is critical, especially for a defect detection task, where the anomaly can be a small scratch or grime. To overcome this difficulty, we propose an unregularized score for deep generative models (DGMs). We found that the regularization terms of the DGMs considerably influence the anomaly score depending on the complexity of the samples. By removing these terms, we obtain an unregularized score, which we evaluated on toy datasets, two in-house manufacturing datasets, and on open manufacturing and medical datasets. The empirical results demonstrate that the unregularized score is robust to the apparent complexity of given samples and detects anomalies selectively.
A novel approach with dual-sampling convolutional neural network for ultrasound image classification of breast tumors

Breast cancer is one of the leading causes of female cancer deaths. Early diagnosis with prophylactic may improve the patients' prognosis. So far ultrasound (US) imaging has been a popular method in breast cancer diagnosis. However, its accuracy is bounded to traditional handcrafted feature methods and expertise. A novel method, named dual-sampling convolutional neural networks (DSCNNs), was proposed in this paper for the differential diagnosis of breast tumors based on US images. Combining traditional convolutional and residual networks, DSCNN prevented gradient disappearance and degradation. The prediction accuracy was increased by the parallel dual-sampling structure, which can effectively extract potential features from US images. Compared with other advanced deep learning methods and traditional handcrafted feature methods, DSCNN reached the best performance with an accuracy of 91.67% and an area under curve of 0.939. The robustness of the proposed method was also verified by using a public dataset. Moreover, DSCNN was compared with evaluation from three radiologists utilizing US-BI-RADS lexicon categories for overall breast tumors assessment. The result demonstrated that the prediction sensitivity, specificity and accuracy of the DSCNN were higher than those of the radiologist with 10 year experience, suggesting that the DSCNN has the potential to help doctors make judgements in clinic.
Signal Processing on Signed Graphs: Fundamentals and Potentials

A wide range of data science problems can be modeled in terms of a graph (or network), e.g., social, sensor, communication, infrastructure, and biological networks. The nodes in a graph/network represent the entities of interest, and the edges reflect relations between these entities, such as geographic proximity (e.g., wireless networks), social relations (e.g., Facebook), biological mechanisms (e.g., brain networks), similarity (e.g., texts by identical authors), and statistical dependencies (e.g., gene expression data). Graph signal processing (GSP) advocates the use of graphs as combined data and computation models and has become a groundbreaking and powerful paradigm for solving diverse learning and inference tasks in the area of data science. GSP uses tools from linear algebra, (spectral) graph theory, computational harmonic analysis, and optimization theory to extend conventional signal processing concepts to data located on irregular domains that are characterized by graphs (networks). For entry-level expositions of GSP fundamentals and the relevant state of the art, we refer to [1]-[4] and the May 2018 Proceedings of the IEEE.
Weakly Supervised Short Text Categorization Using World Knowledge

Short text categorization is an important task in many NLP applications, such as sentiment analysis, news feed categorization, etc. Due to the sparsity and shortness of the text, many traditional classification models perform poorly if they are directly applied to short text. Moreover, supervised approaches require large amounts of manually labeled data, which is a costly, labor intensive, and time-consuming task. This paper proposes a weakly supervised short text categorization approach, which does not require any manually labeled data. The proposed model consists of two main modules: (1) a data labeling module, which leverages an external Knowledge Base (KB) to compute probabilistic labels for a given unlabeled training data set, and (2) a classification model based on a Wide & Deep learning approach. The effectiveness of the proposed method is validated via evaluation on multiple datasets. The experimental results show that the proposed approach outperforms unsupervised state-of-the-art classification approaches and achieves comparable performance to supervised approaches.