Reviewerly/exHarmony huggingface dataset:
Comparative Analysis for Email Spam Detection Using Machine Learning Algorithms

The prominence of the use of communication over the Internet is increasing progressively. Being economical, faster, and easy user interface, the number of email users is increasing tremendously. These led to the gradually increasing activity of spam. Spam emails are unrequested and unimportant emails in bulk. Due to this, there arise major Internet and email security issues that also include a problem of electronic storing space and waste of time. Thus, the identification of spam emails is very necessary. In this paper, four supervised machine learning algorithms, which are Naïve Bayes, support vector machine (SVM), logistic regression, and random forest classifier, are proposed for spam and ham emails classification. Experiments using these four algorithms are performed on prepared feature sets on two different datasets to select the best model with the highest accuracy and less overfitting or underfitting for spam detection. To automate the workflow of building the model and its evaluation, a machine learning pipeline is used in this project. Experimental results show that the overall accuracy of the random forest classifier model is the highest and also has less complexity.
Subfield Attacks on HSVP in Ideal Lattices

In this paper, we propose two subfield attacks on HSVP in ideal lattices in number fields (not necessarily Galois fields), both under the canonical embedding and the coefficient embedding. These two attacks use the intersection method and the norm method, respectively. In addition,the reduction steps in our attacks are all in polynomial time. By contrast, the former method reduces HSVP in ideal lattices to HSVP in ideal lattices in subfields with larger approximate factor than the latter one, while the latter method has a wider range of applications. Moreover, in Galois number fields, the intersection method performs better in attacking prime ideals with small decomposition fields, whilst the norm method works better in prime ideals with large decomposition fields. Besides, ideals with small norms are vulnerable by both of these two attacks.
IND-CCA Security of Kyber in the Quantum Random Oracle Model, Revisited

In this paper, we answer the open question pointed out by Grubbs et al. (EUROCRYPT 2022) and Xagawa (EUROCRYPT 2022), i.e., the $$\textit{concrete}$$ $$\textsf{IND}$$ - $$\textsf{CCA}$$ security proof of $$\textsf{Kyber}$$ . In order to add robustness, $$\textsf{Kyber}$$ uses a slightly tweaked Fujisaki-Okamoto (FO) transformation. Specifically, it uses a “double-nested-hash” to generate the final key. This makes the proof techniques (Jiang et al., CRYPTO 2018) of proving standard FO transformation invalid. Hence, we develop a novel approach to overcome the difficulties, and prove that $$\textsf{Kyber}$$ is $$\textsf{IND}$$ - $$\textsf{CCA}$$ secure in the quantum random oracle model (QROM) if the underlying encryption scheme is $$\textsf{IND}$$ - $$\textsf{CPA}$$ secure. Our result provides a solid quantum security guarantee for the post-quantum cryptography standard of NIST competition, $$\textsf{Kyber}$$ algorithm.
Optimizing the Depth of Quantum Implementations of Linear Layers

Synthesis and optimization of quantum circuits are important and fundamental research topics in quantum computation, due to the fact that qubits are very precious and decoherence time which determines the computation time available is very limited. Specifically in cryptography, identifying the minimum quantum resources for implementing an encryption process is crucial in evaluating the quantum security of symmetric-key ciphers. In this work, we investigate the problem of optimizing the depth of quantum circuits for linear layers while utilizing a small number of qubits and quantum gates. To this end, we present a framework for the implementation and optimization of linear Boolean functions, by which we significantly reduce the depth of quantum circuits for many linear layers used in symmetric-key ciphers without increasing the gate count.
Griefing Factors and Evolutionary In-Stabilities in Blockchain Mining Games

We revisit the standard game-theoretic model of blockchain mining and identify two sources of instabilities for its unique Nash equilibrium. In our first result, we show that griefing, a practice according to which participants of peer-to-peer networks harm other participants at some lesser cost to themselves, is a plausible threat that may lead cost-efficient miners to allocate more resources than predicted. The proof relies on the evaluation of griefing factors, ratios that measure network losses relative to an attacker’s own losses and leads to a generalization of the notion of evolutionary stability to non-homogeneous populations which may be of independent game-theoretic interest. From a practical perspective, this finding provides explains the over-dissipation of mining resources, consolidation of power and high entry barriers that are currently observed in many mining networks. We, then, turn to the natural question of whether dynamic adjustments of mining allocations may, in fact, lead to the Nash equilibrium prediction. By studying two common learning rules, gradient ascent and best response dynamics, we provide evidence for the contrary. Thus, along with earlier results regarding in-protocol attacks, these findings paint a more complete picture about the various inherent instabilities of permissionless mining networks.
Dissimilar Redundancy in DeFi

The meteoric rise of Decentralized Finance (DeFi) has been accompanied by a number of financially devastating attacks on its protocols. There have been over 70 exploits of DeFi protocols, with the total of lost funds amounting to approximately 1.5bn USD. In this paper, we introduce a new approach to minimizing the frequency and severity of such attacks: dissimilar redundancy for smart contracts. In a nutshell, the idea is to implement a program logic more than once, ideally using different programming languages. Then, for each implementation, the results should match before allowing the state of the blockchain to change. This is inspired by and has clear parallels to the field of avionics, where on account of the safety-critical environment, flight control systems typically feature multiple redundant implementations. We argue that the high financial stakes in DeFi protocols merit a conceptually similar approach, and we provide a novel algorithm for implementing dissimilar redundancy for smart contracts.
Experimental investigation to determine necking of commercially pure titanium sheets using a time-of-flight camera and Heaviside-digital image correlation

The plastic instability evolution in polycrystalline metallic materials is a key to understanding the deformation processes during mechanical loading. Plasticity assessments of commercially pure titanium (CP-Ti) can be performed to enhance its mechanical properties. The aim of the present investigation is to determine the plastic deformation during the metal forming processes of CP-Ti sheets at room temperature. This localized deformation might affect the ductility of the sheets’ materials during the forming processes due to their complex strain paths. Therefore, this study is focused on the determination of Forming Limit Diagrams (FLDs) of CP-Ti sheets in different strain paths based on experimental Nakazima test. The latter is employed to determine the complete FLD using several specimens with various geometries to achieve different strain paths in tensile tests. For this purpose, the strain field measurements before cracking are carried out by the Digital Image Correlation (DIC) procedure. The detection of macroscopic cracks is determined by the Heaviside-DIC (H-DIC). During experimental tests, several images are taken with an integrated camera. The captured images are analyzed by DIC and H-DIC. In this work, a second depth camera is accurately calibrated. Thus, depth and standard cameras address most error sources inherent in the time-of-flight (ToF) technology within a unified calibration framework. Therefore, the automatic calibration method of the consumer ToF camera is used and developed for the first time with H-DIC technique, in the framework of material formability, to better determine the ductility limit of CP-Ti.
Improved Image Quality Assessment by Utilizing Pre-Trained Architecture Features with Unified Learning Mechanism

The purpose of the no-reference image quality assessment (NR-IQA) is to measure perceived image quality based on subjective judgments; however, due to the lack of a clean reference image, this is a complicated and unresolved challenge. Massive new IQA datasets have facilitated the creation of deep learning-based image quality measurements. We present a unique model to handle the NR-IQA challenge in this research by employing a hybrid strategy that leverages from pre-trained CNN model and the unified learning mechanism that extracts both local and non-local characteristics from the input patch. The deep analysis of the proposed framework shows that the model uses features and a mechanism that improves the monotonicity relationship between objective and subjective ratings. The intermediary goal was mapped to a quality score using a regression architecture. To extract various feature maps, a deep architecture with an adaptive receptive field was used. Analyses of this biggest NR-IQA benchmark datasets demonstrate that the suggested technique outperforms current state-of-the-art NR-IQA measures.
Improving Active Learning Performance through the Use of Data Augmentation

Active learning (AL) is a well-known technique to optimize data usage in training, through the interactive selection of unlabeled observations, out of a large pool of unlabeled data, to be labeled by a supervisor. Its focus is to find the unlabeled observations that, once labeled, will maximize the informativeness of the training dataset, therefore reducing data-related costs. The literature describes several methods to improve the effectiveness of this process. Nonetheless, there is a paucity of research developed around the application of artificial data sources in AL, especially outside image classification or NLP. This paper proposes a new AL framework, which relies on the effective use of artificial data. It may be used with any classifier, generation mechanism, and data type and can be integrated with multiple other state-of-the-art AL contributions. This combination is expected to increase the ML classifier’s performance and reduce both the supervisor’s involvement and the amount of required labeled data at the expense of a marginal increase in computational time. The proposed method introduces a hyperparameter optimization component to improve the generation of artificial instances during the AL process as well as an uncertainty-based data generation mechanism. We compare the proposed method to the standard framework and an oversampling-based active learning method for more informed data generation in an AL context. The models’ performance was tested using four different classifiers, two AL-specific performance metrics, and three classification performance metrics over 15 different datasets. We demonstrated that the proposed framework, using data augmentation, significantly improved the performance of AL, both in terms of classification performance and data selection efficiency (all the codes and preprocessed data developed for this study are available at https://github.com/joaopfonseca/publications/).
A Tool for Visualization and Analysis of Neighbourhoods, Clusters, and Indicators during the COVID-19 Pandemic

Visual analytics tools for spatiotemporal analysis can be used to manage and monitor the propagation of an epidemic. The problem is that dashboards encountered in the literature do not take into consideration how the geolocation characteristics, such as socioeconomic indicators, influence the infection risk or other epidemic variables. This analysis can support health officials in managing the outbreak to consider information about indicators in compartment models for propagation prediction and intervention simulation. The objective of this work was to bring widgets that offer a more profound exploration and analysis of the impact of the pandemic on socioeconomic indicators. In our approach, the association of epidemic variables and indicators can be explored with commonly adopted visualization plots. Also, we propose a way to gather the specialist’s risk perception, and as a result, a risk heatmap is produced, allowing the reduction of time series data cluttering. Finally, it is possible to use the risk heatmap information to compare neighbourhoods and socioeconomic indicators by ranking them according to a severity score. Some use cases were performed to demonstrate the use and capability of the proposed widgets.
COPMA: Compact and Optimized Polynomial Multiplier Accelerator for High-Performance Implementation of LWR-Based PQC

The rapid progress in quantum computing has initiated a new round of cryptographic innovation, that is, developing postquantum cryptography (PQC) to resist attacks from well-established quantum computers. In this brief, we propose a novel compact and optimized polynomial multiplier accelerator (COPMA) for high-performance implementation of learning-with-rounding (LWR)-based PQC. As not many LWR-based PQC schemes are available in the literature, we have just used Saber, the National Institute of Standards and Technology (NIST) third-round PQC standardization finalist, as a typical case study example. First of all, we have formulated the polynomial multiplication, the major component of Saber, into a novel “subpolynomial”-based processing format for compact computation (yet has the potential for fast operation). Then, we have designed the proposed algorithm into an area-efficient polynomial multiplication hardware accelerator with high-frequency operational capability. Finally, we have verified the efficiency of the developed COPMA and have deployed it to build a cryptoprocessor. The implementation and analysis demonstrate the superior performance of the proposed COPMA. The proposed strategy is highly efficient and can be extended to build other PQC hardware accelerators.
Subband Differentiated Learning Network for Rain Streak Removal

Due to the adverse effects of far more complex rain streaks on the performance of outdoor vision systems, it is critical to accurately eliminate rain streaks from a single rainy image. Recently, many algorithms have made significant progress in rain streak removal. However, these approaches do not give enough consideration to the established fact that rain streaks reflected in different wavelet subbands may have distinct spatial and intensity distributions, resulting in residual degraded components in subbands if not addressed properly. To deal with this problem, we propose a subband differentiated learning network (SDLNet) for rain streak removal. First, we put forward a wavelet subband mask module (WSMM) to produce a wavelet mask, in which different concentrations of rain streak features are provided for different subbands. Second, we advance a WSMM-driven repetitive learning resblock (WSMM-driven RLR) with a novel repetitive learning mechanism (RLM). This RLM enables the WSMM-driven RLR to further probe rain streak features. Third, we propose an adaptive wavelet subband loss (AWSL) function, with the same loss structure but a different loss weight for each subband, exploiting unique error priors in subbands. Finally, the proposed SDLNet employs both a UNet-like architecture with the novel WSMM-driven RLR and the AWSL function, generating a derained image. Compared with state-of-the-art methods, experimental results show that our proposed SDLNet achieves better rain streak elimination performance, where the average PSNR of derained images is improved up to 1.86 dB, while recovering more texture details of clean images with better subjective quality in recovered images.
Steerability criteria based on Heisenberg–Weyl observables

Abstract Einstein–Podolsky–Rosen (EPR) steering is an intermediate form of nonlocality which interpolates between entanglement and Bell nonlocality. It has been shown that EPR steerable states are fundamental resources for one-sided device-independent quantum information tasks. Nevertheless, distinguishing quantum steerable states from the unsteerable ones in general cases remains a big challenge under active studies. The generalized Bloch representation of density matrices in terms of the Heisenberg observables is a useful tool for analyzing characteristics of quantum systems and plays an important role in quantum information. In this work, the role of this representation in detection of EPR steering is studied. More specifically, based on the correlation matrices of the Heisenberg–Weyl observables, a family of steerability criteria for arbitrary dimensional bipartite systems are proposed. A large class of Hermitian operators, which can conveniently witness steering in some scenarios, are further provided. Finally, these criteria are illustrated through several examples, are compared with some existing ones, and their power and advantages are exhibited in certain cases.
End-to-end learning of representations for instance-level document image retrieval

Instance-level document image retrieval plays a vital role in many document image processing systems. An appropriate image representation is of paramount importance for effective retrieval. To this end, we propose an image representation that is well-suited for the instance-level document image retrieval task. A novel end-to-end three-stream Siamese network is presented to learn the image representation, which accepts a triplet: a query image, its matching image and its non-matching image. The network is trained to jointly minimize two types of loss: ranking loss and classification loss. By employing the ranking loss, the distance between the representations of the query image and its matching image can be explicitly forced to be smaller than that between the query image and its non-matching image. Besides, each stream of the network is further extended as a classification model to fully exploit the supervised information of each individual image. The cross-entropy loss is then employed for the classification model. After training, an arbitrary image can be fed to either stream of the network to generate its representation. Extensive comparison and ablation experiments on three datasets have demonstrated the effectiveness of the proposed image representation. The two types of loss have been shown to complement each other.
Digital maturity as an indicator of the success of the university’s digital transformation

The article highlights the problems of achieving digital maturity by higher education organizations. The relationship between the categories “digital transformation” and “digital maturity” is shown. An overview of methods for determining the level of digital maturity of organizations is presented. It is argued that digital transformation is associated with a clear understanding of the goals of any digital transformation and the ability to assess the effectiveness of changes. On the example of the Kutafin University (MSAL), the experience of digital transformation of the university is comprehended. It is shown how, through the adoption of local regulations and the formation of an innovative infrastructure, the transition from digital literacy to digital maturity is being made in MSAL. It is emphasized that in order to carry out successful digitalization, it is necessary to implement a set of interrelated measures, such as the introduction of advanced information and telecommunications technologies, the development of digital infrastructure, the revision of approaches to education management, the change of higher school employees themselves. Based on a sociological study, the degree of awareness of students about the normative consolidation of the category “digital maturity”, its content and scope of application was revealed.
Combating errors in quantum communication: an integrated approach

Near-term quantum communication protocols suffer inevitably from channel noises, whose alleviation has been mostly attempted with resources such as multiparty entanglement or sophisticated experimental techniques. Generation of multiparty higher dimensional entanglement is not easy. This calls for exploring realistic solutions which are implementable with current devices. Motivated particularly by the difficulty in generation of multiparty entangled states, in this paper, we have investigated error-free information transfer with minimal requirements. For this, we have proposed a new information encoding scheme for communication purposes. The encoding scheme is based on the fact that most noisy channels leave some quantities invariant. Armed with this fact, we encode information in these invariants. These invariants are functions of expectation values of operators. This information passes through the noisy channel unchanged. Pertinently, this approach is not in conflict with other existing error correction schemes. In fact, we have shown how standard quantum error-correcting codes emerge if suitable restrictions are imposed on the choices of logical basis states. As applications, for illustration, we propose a quantum key distribution protocol and an error-immune information transfer protocol.
Symphony: Expressive Secure Multiparty Computation with Coordination

Context: Secure Multiparty Computation (MPC) refers to a family of cryptographic techniques where mutually untrusting parties may compute functions of their private inputs while revealing only the function output. Inquiry: It can be hard to program MPCs correctly and efficiently using existing languages and frameworks, especially when they require coordinating disparate computational roles. How can we make this easier? Approach: We present Symphony, a new functional programming language for MPCs among two or more parties. Symphony starts from the single-instruction, multiple-data (SIMD) semantics of prior MPC languages, in which each party carries out symmetric responsibilities, and generalizes it using constructs that can coordinate many parties. Symphony introduces **first-class shares** and **first-class party sets** to provide unmatched language-level expressive power with high efficiency. Knowledge: Developing a core formal language called $\lambda$-Symphony, we prove that the intuitive, generalized SIMD view of a program coincides with its actual distributed semantics. Thus the programmer can reason about her programs by reading them from top to bottom, even though in reality the program runs in a coordinated fashion, distributed across many machines. We implemented a prototype interpreter for Symphony leveraging multiple cryptographic backends. With it we wrote a variety of MPC programs, finding that Symphony can express optimized protocols that other languages cannot, and that in general Symphony programs operate efficiently. [ full abstract at https://doi.org/10.22152/programming-journal.org/2023/7/14 ]