Reviewerly/exHarmony huggingface dataset:
Quantum secure learning with classical samples

Studies addressing the question ``Can a learner complete the learning securely?'' have recently been spurred from the standpoints of fundamental theory and potential applications. In the relevant context of this question, we present a classical-quantum hybrid sampling protocol and define a security condition that allows only legitimate learners to prepare a finite set of samples that guarantees the success of the learning; the security condition excludes intruders. We do this by combining our security concept with the bound of the so-called probably approximately correct (PAC) learning. We show that while the lower bound on the learning samples guarantees PAC learning, an upper bound can be derived to rule out adversarial learners. Such a secure learning condition is appealing, because it is defined only by the size of samples required for the successful learning and is independent of the algorithm employed. Notably, the security stems from the fundamental quantum no-broadcasting principle. No such condition can thus occur in any classical regime, where learning samples can be copied. Owing to the hybrid architecture, our scheme also offers a practical advantage for implementation in noisy intermediate-scale quantum devices.
JudithJeyafreedaAndrew@DravidianLangTech-EACL2021:Offensive language detection for Dravidian Code-mixed YouTube comments

Title: JudithJeyafreedaAndrew@DravidianLangTech-EACL2021:Offensive language detection for Dravidian Code-mixed YouTube comments Author: Judith Jeyafreeda Andrew Messaging online has become one of the major ways of communication. At this level, there are cases of online/digital bullying. These include rants, taunts, and offensive phrases. Thus the identification of offensive language on the internet is a very essential task. In this paper, the task of offensive language detection on YouTube comments from the Dravidian lan- guages of Tamil, Malayalam and Kannada are seen upon as a mutliclass classification prob- lem. After being subjected to language spe- cific pre-processing, several Machine Learn- ing algorithms have been trained for the task at hand. The paper presents the accuracy results on the development datasets for all Machine Learning models that have been used and fi- nally presents the weighted average scores for the test set when using the best performing Ma- chine Learning model.
The secret thoughts of social network sites users: a scale for the measurement of online knowledge-hiding in a knowledge exchange (KE) context

Social networking sites (SNSs) are widely used to support online learning and knowledge exchange (KE) in projects that require the coordination of collaborative team-playing, especially in COVID-19...
Automatic traffic surveillance system Utilizing object detection and image processing

In our day and age where the numbers of cars on the road are rapidly increasing, thereby causing traffic. Drivers are becoming more reckless and carefree as the burden on the current human and automated system grows. Drivers and bikers who may wish to save a few minutes may break red lights and avoid wearing helmets but these small actions can have a significant impact and can result in the loss of lives. We propose a system that will intelligently use deep learning-based object detection to identify traffic offenders and provide methods to penalize them by recognizing their number plate. Our system will be able to detect traffic light violators and bikers without helmets. It has been designed in such a way that it is robust enough to work in drastic conditions and intelligent enough to reduce human dependence.
Simultaneous Image Enhancement and Restoration with Non-convex Total Variation

In this article, we propose a novel variational model for the joint enhancement and restoration of low-light images corrupted by blurring and/or noise. The model decomposes a given low-light image into reflectance and illumination images that are recovered from blurring and/or noise. In addition, our approach utilizes non-convex total variation regularization on all variables. This allows us to adequately denoise homogeneous regions while preserving the details and edges in both reflectance and illumination images, which leads to clean and sharp final enhanced images. To solve the non-convex model, we employ a proximal alternating minimization approach, and then an iteratively reweighted $$\ell _1$$ algorithm and an alternating direction method of multipliers are adopted for solving the subproblems. These techniques contribute to an efficient iterative algorithm, with its convergence proven. Experimental results demonstrate the effectiveness of the proposed model when compared to other state-of-the-art methods in terms of both visual aspect and image quality measures.
A new population initialisation method based on the Pareto 80/20 rule for meta‐heuristic optimisation algorithms

In this research, a new method for population initialisation in meta-heuristic algorithms based on the Pareto 80/20 rule is presented. The population in a meta-heuristic algorithm has two important tasks, including pushing the algorithm toward the real optima and preventing the algorithm from trapping in the local optima. Therefore, the starting point of a meta-heuristic algorithm can have a significant impact on the performance and output results of the algorithm. In this research, using the Pareto 80/20 rule, an innovative and new method for creating an initial population in meta-heuristic algorithms is presented. In this method, by using elitism, it is possible to increase the convergence of the algorithm toward the global optima, and by using the complete distribution of the population in the search spaces, the algorithm is prevented from trapping in the local optima. In this research, the proposed initialisation method was implemented in comparison with other initialisation methods using the cuckoo search algorithm. In addition, the efficiency and effectiveness of the proposed method in comparison with other well-known initialisation methods using statistical tests and in solving a variety of benchmark functions including unimodal, multimodal, fixed dimensional multimodal, and composite functions as well as in solving well-known engineering problems was confirmed.
Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers

Training pipelines for machine learning (ML) based malware classification often rely on crowdsourced threat feeds, exposing a natural attack injection point. In this paper, we study the susceptibility of feature-based ML malware classifiers to backdoor poisoning attacks, specifically focusing on challenging clean label attacks where attackers do not control the sample labeling process. We propose the use of techniques from explainable machine learning to guide the selection of relevant features and values to create effective backdoor triggers in a model-agnostic fashion. Using multiple reference datasets for malware classification, including Windows PE files, PDFs, and Android applications, we demonstrate effective attacks against a diverse set of machine learning models and evaluate the effect of various constraints imposed on the attacker. To demonstrate the feasibility of our backdoor attacks in practice, we create a watermarking utility for Windows PE files that preserves the binary's functionality, and we leverage similar behavior-preserving alteration methodologies for Android and PDF files. Finally, we experiment with potential defensive strategies and show the difficulties of completely defending against these attacks, especially when the attacks blend in with the legitimate sample distribution.
Critical Dimensions of Blockchain Technology Implementation in the Healthcare Industry: An Integrated Systems Management Approach

In the digital era, almost every system is connected to a digital platform to enhance efficiency. Although life is thus improved, security issues remain important, especially in the healthcare sector. The privacy and security of healthcare records is paramount; data leakage is socially unacceptable. Therefore, technology that protects data but does not compromise efficiency is essential. Blockchain technology has gained increasing attention as it ensures transparency, trust, privacy, and security. However, the critical factors affecting efficiency require further study. Here, we define the critical factors that affect blockchain implementation in the healthcare industry. We extracted such factors from the literature and from experts, then used interpretive structural modeling to define the interrelationships among these factors and classify them according to driving and dependence forces. This identified key drivers of the desired objectives. Regulatory clarity and governance (F2), immature technology (F3), high investment cost (F6), blockchain developers (F9), and trust among stakeholders (F12) are key factors to consider when seeking to implement blockchain technology in healthcare. Our analysis will allow managers to understand the requirements for successful implementation.
Coherent manipulation of graph states composed of finite-energy Gottesman-Kitaev-Preskill-encoded qubits

Graph states are a central resource in measurement-based quantum information processing. In the photonic qubit architecture based on Gottesman-Kitaev-Preskill (GKP) encoding, the generation of high-fidelity graph states composed of realistic, finite-energy approximate GKP-encoded qubits thus constitutes a key task. We consider the finite-energy approximation of GKP-qubit states given by a coherent superposition of shifted finite-squeezed vacuum states, where the displacements are Gaussian distributed. We present an exact description of graph states composed of such approximate GKP qubits as a coherent superposition of a Gaussian ensemble of randomly displaced ideal GKP-qubit graph states. Using standard Gaussian dynamics, we track the transformation of the covariance matrix and the mean-displacement vector elements of the Gaussian distribution of the ensemble under tools such as GKP-Steane error-correction and fusion operations that can be used to grow large high-fidelity GKP-qubit graph states. The covariance matrix elements capture the noise in the graph state due to the finite-energy approximation of GKP qubits, while the mean displacements relate to the possible absolute shift errors on the individual qubits arising conditionally from the homodyne measurements that are a part of these tools. Our work thus pins down an exact coherent error model for graph states generated from truly finite-energy GKP qubits, which can shed light on their error-correction properties.
Recovery of Surfaces and Functions in High Dimensions: Sampling Theory and Links to Neural Networks

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 26 May 2020Accepted: 10 February 2021Published online: 10 May 2021Keywordslevel set, surface recovery, function representation, image denoising, neural networksAMS Subject Headings94A20, 94A12, 94A08, 42B05, 65T40Publication DataISSN (online): 1936-4954Publisher: Society for Industrial and Applied MathematicsCODEN: sjisbi
Towards Federated Learning with Byzantine-Robust Client Weighting

Federated learning (FL) is a distributed machine learning paradigm where data are distributed among clients who collaboratively train a model in a computation process coordinated by a central server. By assigning a weight to each client based on the proportion of data instances it possesses, the rate of convergence to an accurate joint model can be greatly accelerated. Some previous works studied FL in a Byzantine setting, in which a fraction of the clients may send arbitrary or even malicious information regarding their model. However, these works either ignore the issue of data unbalancedness altogether or assume that client weights are a priori known to the server, whereas, in practice, it is likely that weights will be reported to the server by the clients themselves and therefore cannot be relied upon. We address this issue for the first time by proposing a practical weight-truncation-based preprocessing method and demonstrating empirically that it is able to strike a good balance between model quality and Byzantine robustness. We also establish analytically that our method can be applied to a randomly selected sample of client weights.
A Compact High-Quality Image Demosaicking Neural Network for Edge-Computing Devices

Image demosaicking has been an essential and challenging problem among the most crucial steps of image processing behind image sensors. Due to the rapid development of intelligent processors based on deep learning, several demosaicking methods based on a convolutional neural network (CNN) have been proposed. However, it is difficult for their networks to run in real-time on edge computing devices with a large number of model parameters. This paper presents a compact demosaicking neural network based on the UNet++ structure. The network inserts densely connected layer blocks and adopts Gaussian smoothing layers instead of down-sampling operations before the backbone network. The densely connected blocks can extract mosaic image features efficiently by utilizing the correlation between feature maps. Furthermore, the block adopts depthwise separable convolutions to reduce the model parameters; the Gaussian smoothing layer can expand the receptive fields without down-sampling image size and discarding image information. The size constraints on the input and output images can also be relaxed, and the quality of demosaicked images is improved. Experiment results show that the proposed network can improve the running speed by 42% compared with the fastest CNN-based method and achieve comparable reconstruction quality as it on four mainstream datasets. Besides, when we carry out the inference processing on the demosaicked images on typical deep CNN networks, Mobilenet v1 and SSD, the accuracy can also achieve 85.83% (top 5) and 75.44% (mAP), which performs comparably to the existing methods. The proposed network has the highest computing efficiency and lowest parameter number through all methods, demonstrating that it is well suitable for applications on modern edge computing devices.
Digitalization of higher education in Europe and Russia: benefits and risks

The article analyzes the attitude to the digitalization of higher education in the developed countries of the world and especially in Europe. The benefits and risks that arise from the use of digital technologies in the educational process are defined. The impact of the pandemic on accelerating the adoption of virtual platforms and online courses and related problems of teaching quality is being studied. The peculiarities of the use of digital technologies in the field of higher education of the Russian Feleration are considered.
Body Centered Cubic Grid - Coordinate System and Discrete Analytical Plane Definition

We define a new compact coordinate system in which each integer triplet addresses a voxel in the BCC grid, and we investigate some of its properties. We propose a characterization of 3D discrete analytical planes with their topological features (in the Cartesian and in the new coordinate system) such as the interrelation between the thickness of the plane and the separability constraint we aim to obtain.
Faster Motif Counting via Succinct Color Coding and Adaptive Sampling

We address the problem of computing the distribution of induced connected subgraphs, aka \emph{graphlets} or \emph{motifs}, in large graphs. The current state-of-the-art algorithms estimate the motif counts via uniform sampling, by leveraging the color coding technique by Alon, Yuster and Zwick. In this work we extend the applicability of this approach, by introducing a set of algorithmic optimizations and techniques that reduce the running time and space usage of color coding and improve the accuracy of the counts. To this end, we first show how to optimize color coding to efficiently build a compact table of a representative subsample of all graphlets in the input graph. For $8$-node motifs, we can build such a table in one hour for a graph with $65$M nodes and $1.8$B edges, which is $2000$ times larger than the state of the art. We then introduce a novel adaptive sampling scheme that breaks the "additive error barrier" of uniform sampling, guaranteeing multiplicative approximations instead of just additive ones. This allows us to count not only the most frequent motifs, but also extremely rare ones. For instance, on one graph we accurately count nearly $10.000$ distinct $8$-node motifs whose relative frequency is so small that uniform sampling would literally take centuries to find them. Our results show that color coding is still the most promising approach to scalable motif counting.
External control of qubit-photon interaction and multi-qubit reset in a dissipative quantum network

A quantum network is a promising quantum many-body system because of its tailored geometry and controllable interaction. Here, we propose an external control scheme for the qubit-photon interaction and multiqubit reset in a dissipative quantum network, which comprises superconducting circuit chains with microwave drives and filter-filter couplings. The traditional multiqubit reset of the quantum network requires physically disconnected qubits to prevent their entanglement. However, we use an original effect of dissipation, i.e., consuming the entanglement generated by qubits’ interaction, to achieve an external control of the multiqubit reset in an always-connected superconducting circuit. The reset time is independent of the number of qubits in the quantum network. Our proposal can tolerate considerable fluctuations in the system parameters and can be applicable to higher-dimensional quantum networks.
Evaluation of Child–Computer Interaction Using Fitts’ Law: A Comparison between a Standard Computer Mouse and a Head Mouse

This study evaluates and compares the suitability for child–computer interaction (CCI, the branch within human–computer interaction focused on interactive computer systems for children) of two devices: a standard computer mouse and the ENLAZA interface, a head mouse that measures the user’s head posture using an inertial sensor. A multidirectional pointing task was used to assess the motor performance and the users’ ability to learn such a task. The evaluation was based on the interpretation of the metrics derived from Fitts’ law. Ten children aged between 6 and 8 participated in this study. Participants performed a series of pre- and post-training tests for both input devices. After the experiments, data were analyzed and statistically compared. The results show that Fitts’ law can be used to detect changes in the learning process and assess the level of psychomotor development (by comparing the performance of adults and children). In addition, meaningful differences between the fine motor control (hand) and the gross motor control (head) were found by comparing the results of the interaction using the two devices. These findings suggest that Fitts’ law metrics offer a reliable and objective way of measuring the progress of physical training or therapy.
Secure Online Voting System using Visual Cryptography

The development of a secure online voting system using visual cryptography is highly essential for present voting systems. Based on the current requirements and design aspects of an existing online voting system, emerging technologies are required in online voting schemes, and these are examined in this work. The emerging cryptographic techniques which are suitable for secure online voting systems are analyzed. Techniques like password hashed-based schemes, visual cryptography, and threshold decryption cryptosystem are highlighted for secure online voting systems. Visual cryptography (VC) is a technique where visual information can be encrypted on the user side, with the information decrypted on the admin side, which can be helpful in allowing participation in voting systems securely and ensuring fast vote counting and monitoring of the voting process to achieve high accuracy while being scam-free. The proposed secure online voting system using visual cryptography is efficiently developed using Python and achieves better performance on minimum software and hardware configuration systems.
Cryptanalysis of Modular Exponentiation Outsourcing Protocols

Abstract Public-key cryptographic primitives are time consuming for resource-constrained devices. A classical problem is to securely offload group exponentiations from a (comparatively) weak device—the client—to an untrusted more powerful device—the server. A delegation protocol must usually meet two security objectives: privacy—the exponent or the base should not be revealed to a passive adversary—and verifiability—a malicious server should not be able to make the client accept an invalid value as the result of the delegated computation. Most proposed protocols relies on a secret splitting of the exponent and the base, and a considerable amount of literature has been devoted to their analysis. Recently, Su et al. (Su, Q., Zhang, R. and Xue, R. (2020) Secure outsourcing algorithms for composite modular exponentiation based on single untrusted cloud. Comput. J., 63, 1271.) and Rangasamy and Kuppusamy (Rangasamy, J. and Kuppusamy, L. (2018) Revisiting Single-Server Algorithms for Outsourcing Modular Exponentiation. In Chakraborty, D. and Iwata, T. (eds), Progress in Cryptology - INDOCRYPT 2018: 19th International Conference in Cryptology in India, New Delhi, India, December 912, Vol. 11356, Lecture Notes in Computer Science. Springer, Heidelberg, Germany, pp. 320. proposed outsourcing protocols for modular exponentiations. They claim that their protocols achieve security (privacy and verifiability). We show that these claims are flawed and that their schemes are broken beyond repair. They remain insecure even if one increases significantly the proposed parameters (and consequently the protocols computational and communication complexities). Our attacks rely on standard lattice-based cryptanalytic techniques, namely the Coppersmith methods to find small integer zeroes of modular multivariate polynomials and simultaneous Diophantine approximation methods for the so-called approximate greatest common divisor problem.
Comparative Analysis of Semantic Similarity Word Embedding Techniques for Paraphrase Detection

Most of the state-of-the-art plagiarism detection tools focus on verbatim reproduction of a document for plagiarism identification (PI), not keeping into account its semantic properties. Recently, deep learning models have shown considerable performance in identifying paraphrases using word embedding approach. This paper gives an overview and comparison of the performances of five word-embedding based deep learning models in the field of semantic similarity detection, such as TF-IDF, Word2Vec, Doc2Vec, FastText, and BERT on two publicly available corpora: Quora Question Pairs (QQP) and Plagiarized Short Answers (PSA). After extensive literature review and experiments, the most appropriate text preprocessing approaches, distance measures, and the thresholds have been settled on for detecting semantic similarity/paraphrasing. The paper concludes on FastText being the most efficient model out of the five, both in terms of evaluation metrics, i.e., accuracy, precision, recall, F1-score, receiver operating characteristic (ROC) curve, and resource consumption. It also compares all the models with each other based on the above-mentioned metrics.