Reviewerly/exHarmony huggingface dataset:
Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentation

Deep convolutional neural networks for semantic segmentation achieve outstanding accuracy, however they also have a couple of major drawbacks: first, they do not generalize well to distributions slightly different from the one of the training data; second, they require a huge amount of labeled data for their optimization. In this paper, we introduce feature-level space-shaping regularization strategies to reduce the domain discrepancy in semantic segmentation. In particular, for this purpose we jointly enforce a clustering objective, a perpendicularity constraint and a norm alignment goal on the feature vectors corresponding to source and target samples. Additionally, we propose a novel measure able to capture the relative efficacy of an adaptation strategy compared to supervised training. We verify the effectiveness of such methods in the autonomous driving setting achieving state-of-the-art results in multiple synthetic-to-real road scenes benchmarks.
Counterexamples to New Circular Security Assumptions Underlying iO

We study several strengthening of classical circular security assumptions which were recently introduced in four new lattice-based constructions of indistinguishability obfuscation: Brakerski-Döttling-Garg-Malavolta (Eurocrypt 2020), Gay-Pass (STOC 2021), Brakerski-Döttling-Garg-Malavolta (Eprint 2020) and Wee-Wichs (Eprint 2020). We provide explicit counterexamples to the 2-circular shielded randomness leakage assumption w.r.t. the Gentry-Sahai-Waters fully homomorphic encryption scheme proposed by Gay-Pass, and the homomorphic pseudorandom LWE samples conjecture proposed by Wee-Wichs. Our work suggests a separation between classical circular security of the kind underlying un-levelled fully-homomorphic encryption from the strengthened versions underlying recent iO constructions, showing that they are not (yet) on the same footing. Our counterexamples exploit the flexibility to choose specific implementations of circuits, which is explicitly allowed in the Gay-Pass assumption and unspecified in the Wee-Wichs assumption. Their indistinguishabilty obfuscation schemes are still unbroken. Our work shows that the assumptions, at least, need refinement. In particular, generic leakage-resilient circular security assumptions are delicate, and their security is sensitive to the specific structure of the leakages involved.
A CNN sign language recognition system with single &amp; double-handed gestures

This work aims at presenting a novel Computer Vision approach in the development of a real-time, web-camera based, British Sign Language recognition system. A literature review focused on current (1) state of sign language recognition systems and (2) techniques used is conducted. This review process is used as a foundation on which a Convolutional Neural Network (CNN) based system is designed and then implemented. A bespoke British Sign Language dataset -containing 11,875 images - is then performed to train and test the CNN which is used for the classification of human hand performed gestures. Finally, the CNN architecture recognized 19 static British Sign Language gestures, incorporating both single and double-handed gestures. During testing, the system achieved an average recognition accuracy of 89%.
Multi-Scale Grid Network for Image Deblurring With High-Frequency Guidance

It has been demonstrated that the blurring process reduces the high-frequency information of the original sharp image, so the main challenge for image deblurring is to reconstruct high-frequency information from the blurry image. In this paper, we propose a novel image deblurring framework to focus on the reconstruction of high-frequency information, which consists of two main subnetworks: a high-frequency reconstruction subnetwork (HFRSN) and a multi-scale grid subnetwork (MSGSN). The HFRSN is built to reconstruct latent high-frequency information from multiple scale blurry images. The MSGSN performs deblurring processes with high-frequency guidance at different scales simultaneously. Besides, in order to better use high-frequency information to restore sharpening images, we designed a high-frequency information aggregation (HFAG) module and a high-frequency information attention (HFAT) module in MSGSN. The HFAG module is designed to fuse high-frequency features and image features at the feature extraction stage, and the HFAT module is built to enhance the feature reconstruction stage. Extensive experiments on different datasets show the effectiveness and efficiency of our method.
MasterplanGAN: Facilitating the smart rendering of urban master plans via generative adversarial networks

This study proposes a prototype for the smart rendering of urban master plans via artificial intelligence algorithms, a process which is time-consuming and relies on professionals’ experience. With the help of crowdsourced data and generative adversarial networks (GAN), a generation model was trained to provide colorful rendering of master plans similar to those produced by experienced urban designers. Approximately 5000 master plans from Pinterest were processed and CycleGAN was applied as the core algorithm to build this model, the so-called MasterplanGAN. Using the uncolored input design files in an AutoCAD format, the MasterplanGAN can provide master plan renderings within a few seconds. The validation of the generated results was achieved using quantitative and qualitative judgments. The achievements of this study contribute to the development of automatic generation of previously subjective and experience-oriented processes, which can serve as a useful tool for urban designers and planners to save time in real projects. It also contributes to push the methodological boundaries of urban design by addressing urban design requirements with new urban data and new techniques. This initial exploration indicates that a large but clear picture of computational urban design can be presented, integrating scientific thinking, design, and computer techniques.
Cryogenic Low-Noise Amplifiers: Noise Performance and Power Dissipation

Cryogenically cooled low-noise amplifiers (LNAs) have had a profound impact on experimental science. For instance, these amplifiers allow us to communicate with distant spacecraft, probe the history and composition of the universe through radio astronomy, study basic phenomena through low-temperature physics research, and read out the state of quantum systems as required for quantum computing. These devices-which can achieve noise performance within an order of magnitude of the fundamental limits imposed by quantum mechanics-find use from low frequencies through several hundreds of gigahertz. Without cryogenic LNAs, whole branches of experimental science simply could not exist.
VRdeo: Creating engaging educational material for asynchronous student-teacher exchange using virtual reality

Educational videos are traditional means of communicating scientific findings to a broader audience. Nowadays, they are also a very common medium in distant teaching. However, creating videos using the existing software tools can be very challenging for inexperienced users. Also, the student’s engagement in standard videos is often very limited as the experience and exploration of the presented phenomena is indirect and far from being interactive. To overcome this, we propose a novel tool, called VRdeo, for creating and presenting educational material that utilizes the advantages of virtual reality (VR). Each stage of the production is represented by an operating mode, where the user acts in a predefined role. VRdeo is a versatile platform that enables tutors to immerse into the virtual scene, explore 3D models of scientific data, and record a narrated story where the tutor is an active element, represented by a virtual avatar. The recording can be exported as a virtual scene. The observers (e.g., students) can then enter such a scene where they can move around and have several options for replaying and interacting with the tutor’s story. In cases when the observer cannot use a virtual reality device, VRdeo enables to generate a traditional 2D video as well. To evaluate VRdeo, we asked several experts in diverse fields for their feedback and conducted a user study with students, which gave us valuable information about its usefulness for both creating and consuming the narrated stories.
BERT-Capsule Model for Cyberbullying Detection in Code-Mixed Indian Languages

In this work, we have created a benchmark corpus for cyberbullying detection against children and women in Hindi-English code-mixed language. Both these languages are the medium of communication for a large majority of India, and mixing of languages is widespread in day-to-day communication. We have developed a model based on BERT, CNN along with GRU and capsule networks. Different conventional machine learning models (SVM, LR, NB, RF) and deep neural network based models (CNN, LSTM) are also evaluated on the developed dataset as baselines. Our model (BERT+CNN+GRU+Capsule) outperforms the baselines with overall accuracy, precision, recall and F1-measure values of 79.28%, 78.67%, 81.99% and 80.30%, respectively.
“Subverting the Jewtocracy”: Online Antisemitism Detection Using Multimodal Deep Learning

The exponential rise of online social media has enabled the creation, distribution, and consumption of information at an unprecedented rate. However, it has also led to the burgeoning of various forms of online abuse. Increasing cases of online antisemitism have become one of the major concerns because of its socio-political consequences. Unlike other major forms of online abuse like racism, sexism, etc., online antisemitism has not been studied much from a machine learning perspective. To the best of our knowledge, we present the first work in the direction of automated multimodal detection of online antisemitism. The task poses multiple challenges that include extracting signals across multiple modalities, contextual references, and handling multiple aspects of antisemitism. Unfortunately, there does not exist any publicly available benchmark corpus for this critical task. Hence, we collect and label two datasets with 3,102 and 3,509 social media posts from Twitter and Gab respectively. Further, we present a multimodal deep learning system that detects the presence of antisemitic content and its specific antisemitism category using text and images from posts. We perform an extensive set of experiments on the two datasets to evaluate the efficacy of the proposed system. Finally, we also present a qualitative analysis of our study.
Cryptanalysis of an Oblivious PRF from Supersingular Isogenies

We cryptanalyse the SIDH-based oblivious pseudorandom function from supersingular isogenies proposed at Asiacrypt’20 by Boneh, Kogan and Woo. To this end, we give an attack on an assumption, the auxiliary one-more assumption, that was introduced by Boneh et al. and we show that this leads to an attack on the oblivious PRF itself. The attack breaks the pseudorandomness as it allows adversaries to evaluate the OPRF without further interactions with the server after some initial OPRF evaluations and some offline computations. More specifically, we first propose a polynomial-time attack. Then, we argue it is easy to change the OPRF protocol to include some countermeasures, and present a second subexponential attack that succeeds in the presence of said countermeasures. Both attacks break the security parameters suggested by Boneh et al. Furthermore, we provide a proof of concept implementation as well as some timings of our attack. Finally, we examine the generation of one of the OPRF parameters and argue that a trusted third party is needed to guarantee provable security.
Applications of Blockchain Technology for Digital Marketing

Using blockchain technology in digital marketing is still a budding concept. Hence, this chapter aims to do a systematic review of research published on this topic from 2015 to 2021 (Q1). The chapter will look to uncover the various impacts of using blockchain technology on digital marketing. Impacts include (but are not limited to) improving digital marketing security, countering click fraud, developing trust and transparency, and creating loyalty programs. Moreover, the chapter will also present future research propositions to further investigate blockchain applications in the realm of digital marketing.
A K-means clustering method with feature learning for unbalanced vehicle fault diagnosis

Purpose Vehicle fault diagnosis is a key factor in ensuring the safe and efficient operation of the railway system. Due to the numerous vehicle categories and different fault mechanisms, there is an unbalanced fault category problem. Most of the current methods to solve this problem have complex algorithm structures, low efficiency and require prior knowledge. This study aims to propose a new method which has a simple structure and does not require any prior knowledge to achieve a fast diagnosis of unbalanced vehicle faults. Design/methodology/approach This study proposes a novel K-means with feature learning based on the feature learning K-means-improved cluster-centers selection (FKM-ICS) method, which includes the ICS and the FKM. Specifically, this study defines cluster centers approximation to select the initialized cluster centers in the ICS. This study uses improved term frequency-inverse document frequency to measure and adjust the feature word weights in each cluster, retaining the top τ feature words with the highest weight in each cluster and perform the clustering process again in the FKM. With the FKM-ICS method, clustering performance for unbalanced vehicle fault diagnosis can be significantly enhanced. Findings This study finds that the FKM-ICS can achieve a fast diagnosis of vehicle faults on the vehicle fault text (VFT) data set from a railway station in the 2017 (VFT) data set. The experimental results on VFT indicate the proposed method in this paper, outperforms several state-of-the-art methods. Originality/value This is the first effort to address the vehicle fault diagnostic problem and the proposed method performs effectively and efficiently. The ICS enables the FKM-ICS method to exclude the effect of outliers, solves the disadvantages of the fault text data contained a certain amount of noisy data, which effectively enhanced the method stability. The FKM enhances the distribution of feature words that discriminate between different fault categories and reduces the number of feature words to make the FKM-ICS method faster and better cluster for unbalanced vehicle fault diagnostic.
CoEA: A Cooperative–Competitive Evolutionary Algorithm for Bidirectional Recommendations

In recent decades, recommender systems have been well studied and widely applied. However, most recommenders unilaterally optimize the results from the buying customers’ views without considering expectations of other participants, e.g., merchants. Unfortunately, the expectations of customers and merchants in recommendation are different or even conflicted. Especially for popular group-trading markets, customers and merchants are competing in trading, i.e., customers want to meet their preferences or obtain gains with personal favorite items, while merchants want to recommend wholesale items with setting group-trading terms or conditions. In addition, some practical constraints are not fully considered by prior systems. In this article, we propose a cooperative–competitive evolutionary algorithm (i.e., CoEA) for the bidirectional recommendations in group-trading markets. Specifically, we, respectively, formalize two subproblems with designed objectives for two-sided participants in markets, and integrate the cooperative–competitive optimizations into one framework. Second, CoEA designs a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">binary encoding matrix</i> for individual representation to integrate the two subproblems. Furthermore, by assembling game evolution process, CoEA designs cooperative–competitive evolution operators, i.e., the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cooperative crossover</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">competitive mutation</i> , which guide the solutions to equilibrium by, respectively, bridging communication between two populations of subproblems and optimizing distinctive objective in each population. Finally, we construct two real applications involving bidirectional recommendations, i.e., the group buying and P2P lending, and conduct extensive experiments with the real-world datasets. By comparing CoEA with several representative recommendation algorithms and evolutionary algorithms, the experimental results clearly demonstrate the effectiveness of CoEA.
Application of noise-reduction techniques to machine learning algorithms for breast cancer tumor identification

The application of machine learning (ML) techniques to digitized images of biopsied cells for breast cancer diagnosis is an active area of research. We hypothesized that reducing noise in the data would lead to an increase in classification accuracies. To test this hypothesis, we first compared several classification techniques in their ability to discriminate between malignant and benign breast cancer tumors using the Wisconsin Breast Cancer Data Set and subsequently evaluated the effect of noise reduction techniques on model accuracies. We applied two noise-reduction techniques based on Principal Component Analysis – dimensionality reduction and outlier removal – to a comprehensive list of ML algorithms with different learning paradigms including Decision Trees (fine, medium, coarse), dimensionality reduction techniques (Linear Discriminant Analysis, Quadratic Discriminant Analysis, Partial Least Squares-Discriminant Analysis), logistic Regression, Bayesian techniques (Gaussian Naive, Kernel Naive), Support Vector Machines (Linear, Quadratic, Cubic, Gaussian), instance-based techniques (fine, medium, coarse, cosine, cubic, and weighted K-Nearest Neighbors), and Artificial Neural Networks. Results showed that noise removal through dimensionality reduction is most effective when using a cross-validated number of principal components, and accuracies surpassing 99% across all ML models are obtained when both noise-reduction techniques are applied sequentially. Even though such a high accuracy has been demonstrated in few instances for specific algorithms, the methodology proposed herein is the first published report demonstrating the applicability of a technique to a wide range of ML models to achieve high accuracies. We show that dimensionality reduction and outlier analysis can be used as effective approaches to improve discrimination accuracies. Also, dimensionality reduction through a cross-validated number of principal components can provide an effective framework for reducing noise in the data prior to applying a ML algorithm.
High-Frequency Connectedness between Bitcoin and Other Top-Traded Crypto Assets during the COVID-19 Crisis

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Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvatures

Knowledge Graph (KG) completion has been excessively studied with a massive number of models proposed for the Link Prediction (LP) task.The main limitation of such models is their insensitivity to time.Indeed, the temporal aspect of stored facts is often ignored.To this end, more and more works consider time as a parameter to complete KGs.In this paper, we first demonstrate that, by simply increasing the number of negative samples, the recent ATTH model can achieve competitive or even better performance than the state-of-the-art on Temporal KGs (TKGs), albeit its nontemporality.We further propose HERCULES, a time-aware extension of ATTH model, which defines the curvature of a Riemannian manifold as the product of both relation and time.Our experiments show that both HERCULES and ATTH achieve competitive or new state-of-the-art performances on ICEWS04 and ICEWS05-15 datasets.Therefore, one should raise awareness when learning TKGs representations to identify whether time truly boosts performances.
Comparison of Controller-Based Locomotion Techniques for Visual Observation in Virtual Reality

Many virtual reality (VR) applications use teleport for locomotion. The non-continuous locomotion of teleport is suited for VR controllers and can minimize simulator sickness, but it can also reduce spatial awareness compared to continuous locomotion. Our aim was to create continuous, controller-based locomotion techniques that would support spatial awareness. We compared the new techniques, slider and grab, with teleport in a task where participants counted small visual targets in a VR environment. Task performance was assessed by asking participants to report how many visual targets they found. The results showed that slider and grab were significantly faster to use than teleport, and they did not cause significantly more simulator sickness than teleport. Moreover, the continuous techniques provided better spatial awareness than teleport.
DLion

Deep learning (DL) is a popular technique for building models from large quantities of data such as pictures, videos, messages generated from edges devices at rapid pace all over the world. It is often infeasible to migrate large quantities of data from the edges to centralized data center(s) over WANs for training due to privacy, cost, and performance reasons. At the same time, training large DL models on edge devices is infeasible due to their limited resources. An attractive alternative for DL training distributed data is to use micro-clouds---small-scale clouds deployed near edge devices in multiple locations. However, micro-clouds present the challenges of both computation and network resource heterogeneity as well as dynamism. In this paper, we introduce DLion, a new and generic decentralized distributed DL system designed to address the key challenges in micro-cloud environments, in order to reduce overall training time and improve model accuracy. We present three key techniques in DLion: (1) Weighted dynamic batching to maximize data parallelism for dealing with heterogeneous and dynamic compute capacity, (2) Per-link prioritized gradient exchange to reduce communication overhead for model updates based on available network capacity, and (3) Direct knowledge transfer to improve model accuracy by merging the best performing model parameters. We build a prototype of DLion on top of TensorFlow and show that DLion achieves up to 4.2X speedup in an Amazon GPU cluster, and up to 2X speed up and 26% higher model accuracy in a CPU cluster over four state-of-the-art distributed DL systems.
Center pixel weight based on Wiener filter for non-local means image denoising

In non-local means (NLM) method for image denoising, the weight of the center pixel is called the center weight (CW). The CW plays an important role for the performance of NLM. This paper proposes a novel CW by studying the relation between Perona-Malik anisotropic diffusion (PMAD) and NLM. The proposed CW is called the Wiener filter center weight (WFCW) since Wiener filter is introduced in the proposed CW computation. At the same time, the relation between non-local total variation and NLM is further disclosed. Test results show that the proposed method can achieve better results and is very efficient compared with the related NLM methods.