Reviewerly/exHarmony huggingface dataset:
LOVE a Pairing

The problem of securely outsourcing the computation of a bilinear pairing has been widely investigated in the literature. Designing an efficient protocol with the desired functionality has, however, been an open challenge for a long time. Recently, Di Crescenzo et al. (CARDIS'20) proposed the first suite of protocols for securely and efficiently delegating pairings with online inputs under the presence of a malicious server. We progress along this path with the aim of LOVE (Lowering the cost of Outsourcing and Verifying Efficiently) a pairing. Our contributions are threefold. First, we propose a protocol (LOVE) that improves the efficiency of Di Crescenzo et al.'s proposal for securely delegating pairings with online, public inputs. Second, we provide the first implementation of efficient protocols in this setting. Finally, we evaluate the performance of our LOVE protocol in different application scenarios by benchmarking an implementation using BN, BLS12 and BLS24 pairing-friendly curves. Interestingly, compared to Di Crescenzo et al.'s protocol, LOVE is up to 29.7% faster for the client, up to 24.9% for the server and requires 23–24% less communication cost depending on the choice of parameters. Furthermore, we note that our LOVE protocol is especially suited for subgroup-secure groups: checking the correctness of the delegated pairing requires up to 56.2% less computations than evaluating the pairing locally (no delegation). This makes LOVE the most efficient protocol to date for securely outsourcing the computation of a pairing with online public inputs, even when the server is malicious.
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation

Automatic and accurate tumor segmentation on medical images is in high demand to assist physicians with diagnosis and treatment. However, it is difficult to obtain massive amounts of annotated training data required by the deep-learning models as the manual delineation process is often tedious and expertise required. Although self-supervised learning (SSL) scheme has been widely adopted to address this problem, most SSL methods focus only on global structure information, ignoring the key distinguishing features of tumor regions: local intensity variation and large size distribution. In this paper, we propose Scale-Aware Restoration (SAR), a SSL method for 3D tumor segmentation. Specifically, a novel proxy task, i.e. scale discrimination, is formulated to pre-train the 3D neural network combined with the self-restoration task. Thus, the pre-trained model learns multi-level local representations through multi-scale inputs. Moreover, an adversarial learning module is further introduced to learn modality invariant representations from multiple unlabeled source datasets. We demonstrate the effectiveness of our methods on two downstream tasks: i) Brain tumor segmentation, ii) Pancreas tumor segmentation. Compared with the state-of-the-art 3D SSL methods, our proposed approach can significantly improve the segmentation accuracy. Besides, we analyze its advantages from multiple perspectives such as data efficiency, performance, and convergence speed.
Constraining the point spread function by using galaxy images

ABSTRACT Inferring the point spread function (PSF) at galaxy positions is one of the crucial steps of the shear measurement. We introduce a novel method to estimate the PSFs at the galaxy positions by using the galaxy images, which could provide additional constrains for the PSF field variations. We construct the PSF for each star image by using Principal-Components-Analysis (PCA) method, which can capture the most significant characteristics of the data. Our method utilizes the image difference of the same object between multi-exposures to probe the coefficients of the principal components, in which the differences are mainly caused by PSFs. We apply our method to the observed data. The results show that the corresponding PSFs can be properly estimated from multiple images of different exposures. We then use the obtained principal components from the observations to mock multi-exposure images, where the PSFs field of each exposure is constructed by bivariate polynomial on coefficients. We find that our method can reproduce the PSFs consistently with mocked data. Our results show that the multi-exposed galaxy images could provide us additional constraints for the PSF fields in PCA scenario. It offers a promising prospect for combing the information of stars and galaxies together to construct the PSF field when the point sources are sparsely sampled.
Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images

Interactive simulation of ultrasound imaging greatly facilitates sonography training. Although ray-tracing based methods have shown promising results, obtaining realistic images requires substantial modeling effort and manual parameter tuning. In addition, current techniques still result in a significant appearance gap between simulated images and real clinical scans. Herein we introduce a novel content-preserving image translation framework (ConPres) to bridge this appearance gap, while maintaining the simulated anatomical layout. We achieve this goal by leveraging both simulated images with semantic segmentations and unpaired in-vivo ultrasound scans. Our framework is based on recent contrastive unpaired translation techniques and we propose a regularization approach by learning an auxiliary segmentation-to-real image translation task, which encourages the disentanglement of content and style. In addition, we extend the generator to be class-conditional, which enables the incorporation of additional losses, in particular a cyclic consistency loss, to further improve the translation quality. Qualitative and quantitative comparisons against state-of-the-art unpaired translation methods demonstrate the superiority of our proposed framework.
Crime Detection Using Sentiment Analysis

Women and girls have been subjected to a great deal of violence and harassment in public locations around the country, ranging from stalking to abuse harassment and assault. This research paper examines the role of social media in improving women's safety in Indian cities, with a focus on the use of social media websites and apps such as Twitter, Facebook, and Instagram. This research also looks at how ordinary Indians can develop a sense of responsibility in Indian society so that we can focus on the protection of women in their surroundings. Tweets on the safety of women in Indian cities, which often include images and text as well as written phrases and quotations, can be used to send a message to the Indian youth culture and encourage them to take harsh action and punish those who harass women. Twitter and other Twitter handles that feature hash tag messages are extensively used throughout the world as a channel for women to share their feelings about how they feel when going to work or travelling by public transportation and what is their mental condition when they are surrounded by unknown males, and do they feel safe or not?
Practical steps for an effective virtual reality course integration

Today’s virtual reality (VR) technologies enable students to traverse immersive learning environments populated with highly engaging course content that closely resembles real-world artifacts and sites, scientific specimens, and otherwise inaccessible training scenarios. The relative affordability of the latest generation of VR hardware has further increased the uptake of VR across academic, but careful consideration must be given to how the technology is deployed for classroom use. This paper draws on a range published research literature, and the authors’ experiences implementing VR in an academic library, to present guidelines and case studies that can be used by library practitioners to craft effective VR course assignments.
Development of a cyberattack response planning method for nuclear power plants by using the Markov decision process model

In an NPP cyberattack situation, not only safety response actions but also security response actions must be taken. However, operators unfamiliar with the cyber security may have difficulties in planning response actions. In this study, a security state-based cyberattack response planning method is developed using the Markov decision process model. Based on the temporal response margin analysis, available response actions are modeled as actions that can increase the available response time or decrease the response time required to secure plant safety. The response reward of an action is quantified as an increase in the response margin time. By modifying the existing action-value function and adopting the Monte-Carlo tree search algorithm, the developed method can help to establish optimal response plans that can maximize the response margin time and minimize the time required for securing plant safety. A case study validated the developed method using a hardware in the loop system.
Sentiment Analysis of Mixed-Case Language using Natural Language Processing

Social Networking has become a platform for people to post their views and opinions about everything. People tend to mix their regional language with other languages that they are familiar with which gives rise to Mixed-Case Languages. These multilingual texts pose a great challenge to conventional Natural Language Processing systems, which currently rely greatly on monolingual resources and cannot handle the combination of multiple languages. The study of people's attitude, opinions, views and to classify them as positive, negative or neutral is called Sentiment Analysis. It is an extremely powerful tool in today's world since it allows businesses, restaurants, internet providers, airlines, etc to quickly detect dissatisfied customers, categorize issues with urgency and prioritize responses. The proposed system focuses on the sentiment analysis of a Hinglish (Hindi-English) text corpus. This paper discusses various approaches such as the Lexicon-Based, Rule-Based and Machine Learning approaches to study the effectiveness of the approach chosen for classifying the text corpus with their appropriate sentiment labels. The proposed Machine Learning techniques are evaluated using parameters Precision, Recall, F1-score and Accuracy and out of all the approaches used, the Support Vector Machine (SVM) and Logistic Regression (LR) approaches have given the best results with both algorithms giving an F1-score of 0.86 and accuracy of 86%.
The Bitcoin: to be or not to be a Real Currency?

The present article focuses on whether the Bitcoin shows sign of increased confidence as a value storage vehicle, a prerequisite to becoming a fully fledged currency. Using Bloomberg data over the period 2008-2017, we find a cointegration relationship between the dollar prices of Bitcoin, Gold and Swiss Franc. The two latter assets are classic safe haven assets, suggesting that at least some Bitcoin buyers are interested in value storage and boding well for its future adoption rate.
Empathetic Response Generation through Graph-based Multi-hop Reasoning on Emotional Causality

Empathetic response generation aims to comprehend the user emotion and then respond to it appropriately. Most existing works merely focus on what the emotion is and ignore how the emotion is evoked, thus weakening the capacity of the model to understand the emotional experience of the user for generating empathetic responses. To tackle this problem, we consider the emotional causality, namely, what feelings the user expresses (i.e., emotion) and why the user has such feelings (i.e., cause). Then, we propose a novel graph-based model with multi-hop reasoning to model the emotional causality of the empathetic conversation. Finally, we demonstrate the effectiveness of our model on EMPATHETICDIALOGUES in comparison with several competitive models.
A College Music Teaching System Designed Based on Android Platform

Today’s rapid evolution in information and communication technologies affects all sectors, including education, and has a positive impact. As a result, teachers need to use technology effectively and keep up with innovation to meet the needs of the next generation. The college music educational system was always an important part of higher education in China, and the corresponding music curriculum system has gradually been established. This curriculum system has been instrumental in the systematization and standardization of China’s music industry, and it has produced a large number of outstanding musical talents for the country. The goal of this research is to discover advanced and dependable teaching methods. With the growing popularity of mobile intelligent terminal devices and the expanding application of the Android platform, using a mobile intelligent terminal for university courses’ learning has become a more powerful technical feature. Through the development of an intelligent learning application using the Android intelligent platform, students can practice exercises and evaluate themselves, allowing them to analyze their weak points based on their evaluation value, whether in class or after class, and then facilitate their learning. In this paper, I present my original perspectives and proposals on music education in colleges and universities, based on literature, analysis, and study, as well as my years of practical experience. To assure stability of the suggested framework, scalability, and sustainability, I have used the Model View Controller (MVC) architecture. This framework is based on the mobile client of Android that teaches and queries college music remotely and controls smart music. According to the experimental data, online music teaching has a greater learning effect on music skills and enhances traditional music performance by 25%. In terms of increasing interest in musical courses, this online college teaching information platform has the ability to raise 74% of students’ awareness.
Demo: Cloak: A Framework For Development of Confidential Blockchain Smart Contracts

In recent years, as blockchain adoption has been expanding across a wide range of domains, e.g., digital asset, supply chain finance, etc., the confidentiality of smart contracts is now a fundamental demand for practical applications. However, while new privacy protection techniques keep coming out, how existing ones can best fit development settings is little studied. Suffering from limited architectural support in terms of programming interfaces, state-of-the-art solutions can hardly reach general developers. In this paper, we proposed the CLOAK framework for developing confidential smart contracts. The key capability of Cloak is allowing developers to implement and deploy practical solutions to multi-party transaction (MPT) problems, i.e., transact with secret inputs and states owned by different parties by simply specifying it. To this end, CLOAK introduced a domain-specific annotation language for declaring privacy specifications and further automatically generating confidential smart contracts to be deployed with trusted execution environment (TEE) on blockchain. In our evaluation on both simple and real-world applications, developers managed to deploy business services on blockchain in a concise manner by only developing CLOAK smart contracts whose size is less than 30% of the deployed ones.
A Deep Learning-based Surrogate for the XRF Approximation of Elemental Composition within Archaeological Artefacts before Restoration

The restoration of archaeological artefacts is naturally utterly important for preserving the cultural heritage. The first step that is undertaken in this process is the chemical analysis of the object, in order to decide the best procedures for its restoration. The gold standard in approximating the concentration of the elements in its composition (in percentages, between 0 and 100) is performed through an X-ray fluorescence (XRF) machine. While this is a non-invasive approach, it comes at substantial financial and training costs, and possible radiation exposure of the investigator. In this context, the present paper explores the potential of a deep learning regression model to give an estimate on the concentration of a given element from stereo microscopy slides of historical artefacts, as an alternative means to the XRF. Two problems with different degrees of complexity are examined in turn. The first one is represented by the consideration of iron objects, where the metal is strongly dominant in the chemical structure. The second comes both as a complement to the other, in order to expose the model also to non-iron items, and as a more difficult task of identifying the degree of copper that is present only as part of an alloy constitution. While for iron the one absolute value prediction of the model is always very close to the XRF approximation, copper has a wider distribution of its concentration among objects, which is more challenging to learn; hence, performance for a singular absolute estimation can rise only with the increase in the amount of data. A window of error acceptability was also implemented and it allows for an approximation that is sufficient for grasping the degree of the metal in the composition that is necessary for the restoration procedures. The findings therefore provide a first step in putting forward a computational support tool that represents a less expensive and less dangerous alternative for approximating the elemental analysis before artefact reinstatement.
Light Field Image Quality Assessment Using Contourlet Transform

In this paper, a new full-reference image quality assessment (IQA) method for performing perceptual quality evaluation on the light field (LF) image is proposed, called the contourlet transform-based model (CTM). The LF image consists of a set of sub-aperture images (SAIs) that have similar image content but with small angular deviations due to different viewing angles. Hence, the abundant image details can be extracted from the SAIs. To fulfil this goal, the contourlet transform is used to extract the multi-scale spatial features of the reference and distorted SAIs, respectively. Based on our proposed IQA metric, the degree of similarity can be computed based on the above-measured quantities for arriving at the final IQA score of the distorted LF image under evaluation. Experimental simulation results obtained from the dense light fields datasets clearly show that the proposed CTM algorithm is more in line with the quality assessment of the LF images perceived by the human visual system (HVS) when compared with that of using other state-of-the-art IQA algorithms.
A Dual Blockchain Framework to Enhance Data Trustworthiness in Digital Twin Network

Data are the basis in Digital Twin (DT) to set up bidirectional mapping between physical and virtual spaces, and realize critical environmental sensing, decision making and execution. Thus, trustworthiness is a necessity in data content as well as data operations. A dual blockchain framework is proposed to realize comprehensive data security in various DT scenarios. It is highly adaptable, scalable, evolvable, and easy to be integrated into Digital Twin Network (DTN) as enhancement.
A Knowledge Graph Entity Disambiguation Method Based on Entity-Relationship Embedding and Graph Structure Embedding

The purpose of knowledge graph entity disambiguation is to match the ambiguous entities to the corresponding entities in the knowledge graph. Current entity ambiguity elimination methods usually use the context information of the entity and its attributes to obtain the mention embedding vector, compare it with the candidate entity embedding vector for similarity, and perform entity matching through the similarity. The disadvantage of this type of method is that it ignores the structural characteristics of the knowledge graph where the entity is located, that is, the connection between the entity and the entity, and therefore cannot obtain the global semantic features of the entity. To improve the Precision and Recall of entity disambiguation problems, we propose the EDEGE (Entity Disambiguation based on Entity and Graph Embedding) method, which utilizes the semantic embedding vector of entity relationship and the embedding vector of subgraph structure feature. EDEGE first trains the semantic vector of the entity relationship, then trains the graph structure vector of the subgraph where the entity is located, and balances the weights of these two vectors through the entity similarity function. Finally, the balanced vector is input into the graph neural network, and the matching between the entities is output to achieve entity disambiguation. Extensive experimental results proved the effectiveness of the proposed method. Among them, on the ACE2004 data set, the Precision, Recall, and F1 values of EDEGE are 9.2%, 7%, and 11.2% higher than baseline methods.
Neural Language Models for Nineteenth-Century English

We present four types of neural language models trained on a large historical dataset of books in English, published between 1760 and 1900, and comprised of ≈5.1 billion tokens. The language model architectures include word type embeddings (word2vec and fastText) and contextualized models (BERT and Flair). For each architecture, we trained a model instance using the whole dataset. Additionally, we trained separate instances on text published before 1850 for the type embeddings, and four instances considering different time slices for BERT. Our models have already been used in various downstream tasks where they consistently improved performance. In this paper, we describe how the models have been created and outline their reuse potential.
System Modeling and Motion Control of a Cable-Driven Parallel Platform for Underwater Camera Stabilization

Underwater camera platform’s low image stabilization accuracy and poor waterproofness seriously restrict the quality of photos. In order to better cope with underwater camera work, this paper proposes a cable-driven underwater camera stabilized platform. It is a mobile platform driven in parallel by four flexible cables. To improve image stabilization accuracy and anti-interference performance, the system’s dynamic model is established in a non-inertial reference frame. And the random water wave interference is modeled. Moreover, a novel double-loop integral-type global fast terminal sliding mode control strategy is designed. Lyapunov stability theory is used to analyze the stability of the strategy. Finally, by comparing with the existing global fast terminal sliding mode controller and traditional sliding mode controller, the designed controller is simulated and verified. The results show that the proposed control strategy not only has the advantages of fast response and robustness, but also has the characteristics of rapid convergence in a finite time and high accuracy. This method can provide a valuable reference for the development of underwater camera stabilized platforms.