Reviewerly/exHarmony huggingface dataset:
Risk early warning management and intelligent real-time system of financial enterprises based on fuzzy theory

The financial market is changing rapidly. Since joining the WTO, our country’s financial companies have faced pressure from dual competition at domestic and abroad. The complex internal and external environment has forced financial enterprise managers to improve risk prevention awareness, early warning and monitoring, so as to responding to emergencies and challenges in the financial market. However, traditional forecasting and analysis methods have problems such as large workload, low efficiency, and low accuracy. Therefore, this article applies intelligent computing to the forecast of financial markets, using related concepts of fuzzy theory and Internet intelligent technology, and proposes to establish a model system for financial enterprise risk early warning management and intelligent real-time monitoring based on fuzzy theory. This article first collected a large amount of data through the literature investigation method, and made a systematic and complete introduction to the related theoretical concepts of fuzzy theory and financial risk early-warning management, has laid a sufficient theoretical foundation for the subsequent exploration of the application of fuzzy theory in financial enterprise risk early warning management and intelligent real-time systems; Then a fuzzy comprehensive evaluation method that combines the analytic hierarchy process and fuzzy evaluation method is proposed, taking a listed company mainly engaged in automobile sales in our province as a case, the company’s financial risk management and modeling experiment of the intelligent real-time system; Finally quoted specific cases again, used the fuzzy comprehensive evaluation method to carry out risk warning and evaluation on the PPP projects of private enterprises in our province, and concluded that the project risk score is between 20-60, which is meet the severe-medium range in the risk level. Research shows that the use of fuzzy theory and modern network technology can make more accurate warnings and assessments of potential and apparent risks of financial enterprises, greatly improving the safety of financial enterprise management and reducing the losses caused by various risks.
Classical vs Quantum Random Oracles

In this paper, we study relationship between security of cryptographic schemes in the random oracle model (ROM) and quantum random oracle model (QROM). First, we introduce a notion of a proof of quantum access to a random oracle (PoQRO), which is a protocol to prove the capability to quantumly access a random oracle to a classical verifier. We observe that a proof of quantumness recently proposed by Brakerski et al. (TQC '20) can be seen as a PoQRO. We also give a construction of a publicly verifiable PoQRO relative to a classical oracle. Based on them, we construct digital signature and public key encryption schemes that are secure in the ROM but insecure in the QROM. In particular, we obtain the first examples of natural cryptographic schemes that separate the ROM and QROM under a standard cryptographic assumption. On the other hand, we give lifting theorems from security in the ROM to that in the QROM for certain types of cryptographic schemes and security notions. For example, our lifting theorems are applicable to Fiat-Shamir non-interactive arguments, Fiat-Shamir signatures, and Full-Domain-Hash signatures etc. We also discuss applications of our lifting theorems to quantum query complexity.
“Look Ma, No Landmarks!” – Unsupervised, Model-Based Dense Face Alignment

In this paper, we show how to train an image-to-image network to predict dense correspondence between a face image and a 3D morphable model using only the model for supervision. We show that both geometric parameters (shape, pose and camera intrinsics) and photometric parameters (texture and lighting) can be inferred directly from the correspondence map using linear least squares and our novel inverse spherical harmonic lighting model. The least squares residuals provide an unsupervised training signal that allows us to avoid artefacts common in the literature such as shrinking and conservative underfitting. Our approach uses a network that is 10 $$\times $$ smaller than parameter regression networks, significantly reduces sensitivity to image alignment and allows known camera calibration or multi-image constraints to be incorporated during inference. We achieve results competitive with state-of-the-art but without any auxiliary supervision used by previous methods.
Targeted Attention Attack on Deep Learning Models in Road Sign Recognition

Real-world traffic sign recognition is an important step toward building autonomous vehicles, most of which highly dependent on deep neural networks (DNNs). Recent studies demonstrated that DNNs are surprisingly susceptible to adversarial examples. Many attack methods have been proposed to understand and generate adversarial examples, such as gradient-based attack, score-based attack, decision-based attack, and transfer-based attacks. However, most of these algorithms are ineffective in real-world road sign attack, because 1) iteratively learning perturbations for each frame is not realistic for a fast moving car and 2) most optimization algorithms traverse all pixels equally without considering their diverse contribution. To alleviate these problems, this article proposes the targeted attention attack (TAA) method for real-world road sign attack. Specifically, we have made the following contributions: 1) we leverage the soft attention map to highlight those important pixels and skip those zero-contributed areas-this also helps to generate natural perturbations; 2) we design an efficient universal attack that optimizes a single perturbation/noise based on a set of training images under the guidance of the pretrained attention map; 3) we design a simple objective function that can be easily optimized; and 4) we evaluate the effectiveness of TAA on real-world data sets. Experimental results validate that the TAA method improves the attack successful rate (nearly 10%) and reduces the perturbation loss (about a quarter) compared with the popular RP2 method. Additionally, our TAA also provides good properties, e.g., transferability and generalization capability. We provide code and data to ensure the reproducibility: https://github.com/AdvAttack/RoadSignAttack.
Toward Verifiable and Privacy Preserving Machine Learning Prediction

The ubiquitous needs for extracting insights from data are driving the emergence of service providers to offer predictions given the inputs from customers. During this process, it is important and highly nontrivial for the service providers to generate proofs of honest predictions without leaking the key parameters of their trained models. In addition, the customers are usually unwilling to reveal their sensitive inputs. In this article, we proposed MVP, which enables <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> achine learning prediction in a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</u> erifiable and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> rivacy preserving fashion. MVP features the properties of polynomial decomposition and prime-order bilinear groups to simultaneously facilitate oblivious evaluation and batch outcome verification while maintaining function privacy and input privacy. We further instantiated MVP with Support Vector Machines (SVMs) and extensively evaluated its performance for the spam detection task on three practical Short Message Service (SMS) datasets. Our analysis and evaluation results reveal that MVP achieves the desired properties while incurring low computation and communication overhead.
Deep Convolutional-Neural-Network-Based Channel Attention for Single Image Dynamic Scene Blind Deblurring

The success of convolutional neural network (CNN) based single image dynamic scene blind deblurring (SIDSBD) methods mainly stems from the multi-scale/multi-patch model and the designs of the encoder-decoder architecture, and the residual block structure, which make different contributions to SIDSBD. In this paper, we further exploit the advantages of the multi-scale model, the encoder-decoder module, and the residual block structure, respectively, and propose a novel multi-scale channel attention network (MSCAN) for effective single image dynamic scene blind deblurring. Different from existing multi-scale models, in our proposed network, each scale consists of multiple levels, in which a novel spatial pyramid pooling channel attention (SPPCA) strategy is proposed to adaptively rescale the channel-wise features by using both the global and local feature statistics for more powerful network representation. Extensive experiments on both the synthetic benchmark datasets and the real blurred images show that our method can produce better deblurring results than the state-of-the-art SIDSBD methods in terms of both qualitative evaluation and quantitative metrics.
A User-Centric Framework for Educational Chatbots Design and Development

Increasing frequency of epidemics, such as SARS-CoV, MERS-CoV, Ebola, and the recent COVID-19, have affected various sectors, especially education. As a result, emphasis on e-learning and distance learning has been increasing in recent years. The growing numbers of mobile users and access to the internet across the world has created more favorable conditions for adopting distance learning on a wider scale. However, lessons learnt from current experiments have highlighted poor student engagement with learning processes, hence a user-centric approach to design and develop educational chatbots is presented. A User-centric approach enables developers to consider the following: learners’ and teachers’ technological skills and competencies, attitudes, and perceptions and behaviour; conceptual concerns, such as pedagogical integration on online platforms, assessment procedures, varying learning culture and lifestyles; technical concerns, such as privacy, security, performance, ubiquity; and regulatory concerns, such as policies, frameworks, standards, ethics, roles and responsibilities have been identified in this study. To address these concerns, there is the need for user-centric design and collaborative approaches to the development of distance learning tools. Considering the abovementioned challenges and the growing emphasis on distance learning, we propose chatbot learning as an effective and efficient tool for delivering such learning. In this regard, a user-centric framework for designing chatbot learning applications and a collaborative user-centric design methodology for developing chatbot learning applications is proposed and discussed.
Secure Software Leasing from Standard Assumptions

Secure software leasing (SSL) is a quantum cryptographic primitive that enables users to execute software only during the software is leased. It prevents users from executing leased software after they return the leased software to its owner. SSL can make software distribution more flexible and controllable. Although SSL is an attractive cryptographic primitive, the existing SSL scheme is based on public key quantum money, which is not instantiated with standard cryptographic assumptions so far. Moreover, the existing SSL scheme only supports a subclass of evasive functions. In this work, we present SSL schemes based on the learning with errors assumption (LWE). Specifically, our contributions consist of the following. - We construct an SSL scheme for pseudorandom functions from the LWE assumption against quantum adversaries. - We construct an SSL scheme for a subclass of evasive functions from the LWE assumption against sub-exponential quantum adversaries. - We construct SSL schemes for the functionalities above with classical communication from the LWE assumption against (sub-exponential) quantum adversaries. SSL with classical communication means that entities exchange only classical information though they run quantum computation locally. Our crucial tool is two-tier quantum lightning, which is introduced in this work and a relaxed version of quantum lighting. In two-tier quantum lightning schemes, we have a public verification algorithm called semi-verification and a private verification algorithm called full-verification. An adversary cannot generate possibly entangled two quantum states whose serial numbers are the same such that one passes the semi-verification, and the other also passes the full-verification. We show that we can construct a two-tier quantum lightning scheme from the LWE assumption.
Standardized Variable Distances: A distance-based machine learning method

Today, machine learning algorithms are an important research area capable of analyzing and modeling data in any field. Information obtained through machine learning methods helps researchers and planners to understand and review systematic problems of their current strategies. Thus, it is very important to work fully in every field that facilitates human life, such as early and correct diagnosis, correct choice, fully functioning autonomous systems. In this paper, a novel machine learning algorithm for multiclass classification is presented. The proposed method is designed based on the Minimum Distance Classifier (MDC) algorithm. The MDC is variance-insensitive because it classifies input vectors by calculating their distances/similarities with respect to class-centroids (average value of input vectors of a class). As it is known, real-world data contains certain proportions of noise. This situation negatively affects the performance of the MDC. To overcome this problem, we developed a variance-sensitive model, which we call Standardized Variable Distances (SVD), considering the standard deviation and z-score (standardized variable) factors. To ensure the accuracy of the SVD, we used Wisconsin Breast Cancer Original (WBCO) and LED Display Domain (led7digit) datasets, which we obtained from UCI machine learning repository, with 5-fold cross validation. It was compared and analyzed classification performance of the SVD with Decision Tree (DT), Random Forest (RF), k-Nearest Neighbor (k-NN), Multinomial Logistic Regression (MLR), Naïve Bayes (NB), Support Vector Machine (SVM), and the Minimum Distance Classifier (MDC), which are well-known in the literature. It has also been compared thirteen different studies using the same datasets over the past five years. Our results in the experimental studies have shown that the SVD can classify better than traditional and state-of-the-art methods, compared in this study. The proposed method reached over 97% classification accuracy (CACC), F-measure (FM) and area under the curve (AUC) on the WBCO dataset. On the led7digit dataset, approximately 74% CACC, 75.1% FM and 82.2% AUC scores were obtained. It has been observed that the classification scores obtained with the SVD are higher than other ML algorithms used in the experimental studies.
Learning Success in Immersive Virtual Reality Training Environments: Practical Evidence from Automotive Assembly

Learning success in assembly training using immersive virtual reality technologies depends on multiple factors ranging from quality levels of process and technical documentation, visual 3D rendering quality, maturity of didactic concepts to individual differences and attitudes of workers and trainers. In this paper, we present the results of a field study conducted in an automotive factory to evaluate an immersive virtual reality training environment (VTE). Set up under real training conditions, the VTE was operated by trainers to train novice assembly line workers on a specific task: the assembly of a vehicle center console. Using a between-subject design we compare training performance in terms of skill transfer and retention between workers being trained in the VTE to workers who had been trained conventionally on the physical car. Our results suggest positive transfer of the acquired procedures from the VTE to physical assembly and even performance improvements over time. We discuss learning success in the VTE in contrast to user experience feedback and the implemented sequence steps for mounting assembly parts, based on comprehensive behavioral data. Finally, reflections on trainer feedback lead the way to implications for the adaption of didactic strategies and operational procedures towards increasing overall effectiveness of VTE-supported assembly training.
Permissionless proof‐of‐reputation‐X: A hybrid reputation‐based consensus algorithm for permissionless blockchains

Abstract Over the past years, blockchain technology has become more and more interesting since its ability to carry out transactions without any mediator. To ensure the transactions' reliability, consensus algorithms are adopted by blockchain technology. However, due to the failure of consensus algorithms in managing nodes' identities, blockchain technology is considered inappropriate for many applications. In this article, we propose the permissionless proof‐of‐reputation‐X (PL‐PoRX) that upgrades an existing consensus algorithm, the proof‐of‐reputation‐X (PoRX). PL‐PoRX replaces the trusted identity database in PoRX with a new admission process to make the algorithm suitable for permissionless blockchains, while maintaining PoRX's reputation mechanism. Several experiments are conducted to show the efficiency of our approach under different scenarios. We also debate the security model of the new protocol and study its time complexity. The results show that PL‐PoRX decreases the number of blocks issued by malicious miners, and help benign miners build reputation faster.
Cluster-wise unsupervised hashing for cross-modal similarity search

Cross-modal hashing similarity retrieval plays dual roles across various applications including search engines and autopilot systems. More generally, these methods also known to reduce the computation and memory storage in a training scheme. The key limitation of current methods are that: (i) they relax the discrete constrains to solve the optimization problem which may defeat the model purpose, (ii) projecting heterogenous data into a latent space may encourage to loss the diverse representations in such data, (iii) transforming real-valued data point to the binary codes always resulting in a loss of information and producing the suboptimal continuous latent space. In this paper, we propose a novel framework to project the original data points from different modalities into its own low-dimensional latent space and finds the cluster centroid points in its a low-dimensional space, using Cluster-wise Unsupervised Hashing (CUH). In particular, the proposed clustering scheme aims to jointly learns the compact hash codes and the corresponding linear hash functions. A discrete optimization framework is developed to learn the unified binary codes across modalities under of the guidance cluster-wise code-prototypes. Extensive experiments over multiple datasets demonstrate the effectiveness of our proposed model in comparison with the state-of-the-art in unsupervised cross-modal hashing tasks.
Constructions for Optimal Ferrers Diagram Rank-Metric Codes

Optimal rank-metric codes in Ferrers diagrams can be used to construct good subspace codes. Such codes consist of matrices having zeros at certain fixed positions. This paper generalizes the known constructions for Ferrers diagram rank-metric (FDRM) codes. Via a criteria for linear maximum rank distance (MRD) codes, an explicit construction for a class of systematic MRD codes is presented, which is used to produce new optimal FDRM codes. By exploring subcodes of Gabidulin codes, if each of the rightmost $\delta-1$ columns in Ferrers diagram $\cal F$ has at least $n-r$ dots, where $r$ is taken in a range, then the conditions that an FDRM code in $\cal F$ is optimal are established. The known combining constructions for FDRM code are generalized by introducing the concept of proper combinations of Ferrers diagrams.
Stability and Scalability of Blockchain Systems

The blockchain paradigm provides a mechanism for content dissemination and distributed consensus on Peer-to-Peer (P2P) networks. While this paradigm has been widely adopted in industry, it has not been carefully analyzed in terms of its network scaling with respect to the number of peers. Applications for blockchain systems, such as cryptocurrencies and IoT, require this form of network scaling. In this paper, we propose a new stochastic network model for a blockchain system. We identify a structural property called \emph{one-endedness}, which we show to be desirable in any blockchain system as it is directly related to distributed consensus among the peers. We show that the stochastic stability of the network is sufficient for the one-endedness of a blockchain. We further establish that our model belongs to a class of network models, called monotone separable models. This allows us to establish upper and lower bounds on the stability region. The bounds on stability depend on the connectivity of the P2P network through its conductance and allow us to analyze the scalability of blockchain systems on large P2P networks. We verify our theoretical insights using both synthetic data and real data from the Bitcoin network.
Pure Single Photons From Scalable Frequency Multiplexing

We demonstrate multiphoton interference using a resource-efficient frequency multiplexing scheme, suitable for quantum information applications that demand multiple indistinguishable and pure single photons. In our source, frequency-correlated photon pairs are generated over a wide range of frequencies by pulsed parametric down conversion. Indistinguishable single photons of a predetermined frequency are prepared using frequency-resolved detection of one photon to control an electro-optic frequency shift applied to its partner. Measured photon statistics show multiplexing increases the probability of delivering a single photon, without a corresponding increase to multiphoton events. Interference of consecutive outputs is used to bound the modal purity and demonstrate the nonclassical nature of the emitted light.
Privacy-preserving data mining for open government data from heterogeneous sources

Open data is a global movement with the potential to generate significant social and economic benefits. Policies on open government data (OGD) inspire the development of new and innovative services that government agencies may lack. The International Open Data Charter adequately describes the importance of data mining. Governments that have signed this charter should focus on the following areas—(i) data mining, (ii) linkage, and (iii) in-depth analysis, i.e., distribution of open data that is freely accessible for elaborate analysis using machine reading. However, a series of practical difficulties is observed in connection with the data mining of OGD for in-depth analysis. First, most OGD do not have identifiers to prevent privacy disclosure. Second, owing to the nature of siloed data, the data sharing and collection methods vary with respect to heterogeneous OGD, and administrative or institutional barriers need to be overcome. This has created a demand for a novel technical solution that applies micro-aggregation and distance-based record linkage to address the aforementioned issues. Thus, in this study, a method capable of integrating two or more de-identified OGDs into one dataset to enable OGD data mining is proposed. In addition, the proposed method allows users to adjust the privacy threshold level to determine an appropriate balance between privacy disclosure risk and data utility. The effectiveness of the method is evaluated in terms of several metrics via extensive experimentation. This study emphasizes the importance of the research on efficient utilization of already-published OGDs, which has been relatively neglected in the past. Further, it broadens the research area for privacy-preserving data mining by proposing a method capable of mining heterogeneous data even in the absence of identifiers.