Reviewerly/exHarmony huggingface dataset:
CrossFuNet: RGB and Depth Cross-Fusion Network for Hand Pose Estimation

Despite recent successes in hand pose estimation from RGB images or depth maps, inherent challenges remain. RGB-based methods suffer from heavy self-occlusions and depth ambiguity. Depth sensors rely heavily on distance and can only be used indoors, thus there are many limitations to the practical application of depth-based methods. The aforementioned challenges have inspired us to combine the two modalities to offset the shortcomings of the other. In this paper, we propose a novel RGB and depth information fusion network to improve the accuracy of 3D hand pose estimation, which is called CrossFuNet. Specifically, the RGB image and the paired depth map are input into two different subnetworks, respectively. The feature maps are fused in the fusion module in which we propose a completely new approach to combine the information from the two modalities. Then, the common method is used to regress the 3D key-points by heatmaps. We validate our model on two public datasets and the results reveal that our model outperforms the state-of-the-art methods.
D magnetic model of the Earth’s crust of the White Sea and adjacent territories

An important task for the White Sea region, Russia’s second largest diamond-producing province, is the search for magmatic bodies overlapped by sedimentary cover via magnetometer survey. The models, linking local and magnetic anomalies with their sources, are essential for interpretation of search results. The aim of the study is to build a 3D magnetic model of the Earth’s crust for the White Sea region using aeromagnetic data and the modeling technologies of the Integro software package. The simulation is basing on a digital map of the pole-reduced anomalous magnetic field. The sources of magnetic anomalies are believed to be located in the Earth’s crust. The researchers obtained 3D distribution of the relative magnetic susceptibility of rocks by solving the inverse problem of magnetic prospecting. To separate the magnetic sources by spatial frequencies and depth, the model magnetic field was recalculated upward, as well as the TDR derivatives, which determine the lateral boundaries of the sources of positive magnetic field anomalies, were calculated. The researchers further analyzed 2D distributions of the magnetic sources of the model for vertical and horizontal sections with depths of 10, 15 and 20 km, thus proving the relationship between the surface and deep structures of the magnetic sources of the Earth’s crust in the region.
Towards Incremental Transformers: An Empirical Analysis of Transformer Models for Incremental NLU

Incremental processing allows interactive systems to respond based on partial inputs, which is a desirable property e.g. in dialogue agents.The currently popular Transformer architecture inherently processes sequences as a whole, abstracting away the notion of time.Recent work attempts to apply Transformers incrementally via restart-incrementality by repeatedly feeding, to an unchanged model, increasingly longer input prefixes to produce partial outputs.However, this approach is computationally costly and does not scale efficiently for long sequences.In parallel, we witness efforts to make Transformers more efficient, e.g. the Linear Transformer (LT) with a recurrence mechanism.In this work, we examine the feasibility of LT for incremental NLU in English.Our results show that the recurrent LT model has better incremental performance and faster inference speed compared to the standard Transformer and LT with restartincrementality, at the cost of part of the nonincremental (full sequence) quality.We show that the performance drop can be mitigated by training the model to wait for right context before committing to an output and that training with input prefixes is beneficial for delivering correct partial outputs.
Adversarial Bone Length Attack on Action Recognition

Skeleton-based action recognition models have recently been shown to be vulnerable to adversarial attacks. Compared to adversarial attacks on images, perturbations to skeletons are typically bounded to a lower dimension of approximately 100 per frame. This lower-dimensional setting makes it more difficult to generate imperceptible perturbations. Existing attacks resolve this by exploiting the temporal structure of the skeleton motion so that the perturbation dimension increases to thousands. In this paper, we show that adversarial attacks can be performed on skeleton-based action recognition models, even in a significantly low-dimensional setting without any temporal manipulation. Specifically, we restrict the perturbations to the lengths of the skeleton's bones, which allows an adversary to manipulate only approximately 30 effective dimensions. We conducted experiments on the NTU RGB+D and HDM05 datasets and demonstrate that the proposed attack successfully deceived models with sometimes greater than 90% success rate by small perturbations. Furthermore, we discovered an interesting phenomenon: in our low-dimensional setting, the adversarial training with the bone length attack shares a similar property with data augmentation, and it not only improves the adversarial robustness but also improves the classification accuracy on the original data. This is an interesting counterexample of the trade-off between adversarial robustness and clean accuracy, which has been widely observed in studies on adversarial training in the high-dimensional regime.
Secure Real-Time Computational Intelligence System Against Malicious QR Code Links

Web attackers aim to propagate malicious links using various techniques to deceive users. They attempt to control victims’ devices or obtain their passwords remotely, thereby acquiring access to bank accounts, financial transactions, or private and sensitive information they trade via the Internet. QR codes are accessible, free, easy to use, and can be scanned through several free apps on smartphones. As there is no standard structure or authentication phase in QR code generation, such codes are vulnerable to suspicious online content embedding, i.e., phishing, Cross-Site Scripting (XSS), and malware. Many studies have highlighted the attacks that may be perpetrated using barcodes, and there are some security countermeasures. Several of these solutions are limited to malicious link detection methods or require knowledge of cryptographic techniques. This study’s main objective is to detect malicious URLs embedded in QR codes. A dataset of 90 000 benign and malicious URLs was collected from various resources, and their lexical properties were extracted. Two computational intelligence models, fuzzy logic and multilayer perceptron artificial neural network (MLP-ANN), were applied and compared. An MLP-ANN was identified as the best classifier for detecting malicious URLs, and a proactive, secure, real-time computational intelligence barcode scanner implementation (BarCI ) against malicious QR code links was proposed based on this classifier. The results demonstrate that this approach enables efficient real-time attack detection with 82.9% accuracy
1, 2, 3, Fork: Counter Mode Variants based on a Generalized Forkcipher

A multi-forkcipher (MFC) is a generalization of the forkcipher (FC) primitive introduced by Andreeva et al. at ASIACRYPT’19. An MFC is a tweakable cipher that computes s output blocks for a single input block, with s arbitrary but fixed. We define the MFC security in the ind-prtmfp notion as indistinguishability from s tweaked permutations. Generalizing tweakable block ciphers (TBCs, s = 1), as well as forkciphers (s = 2), MFC lends itself well to building simple-to-analyze modes of operation that support any number of cipher output blocks.Our main contribution is the generic CTR encryption mode GCTR that makes parallel calls to an MFC to encrypt a message M. We analyze the set of all 36 “simple and natural” GCTR variants under the nivE security notion by Peyrin and Seurin rom CRYPTO’16. Our proof method makes use of an intermediate abstraction called tweakable CTR (TCTR) that captures the core security properties of GCTR common to all variants, making their analyses easier. Our results show that many of the schemes achieve from well beyond birthday bound (BBB) to full n-bit security under nonce respecting adversaries and some even BBB and close to full n-bit security in the face of realistic nonce misuse conditions.We finally present an efficiency comparison of GCTR using ForkSkinny (an MFC with s = 2) with the traditional CTR and the more recent CTRT modes, both are instantiated with the SKINNY TBC. Our estimations show that any GCTR variant with ForkSkinny can achieve an efficiency advantage of over 20% for moderately long messages, illustrating that the use of an efficient MFC with s ≥ 2 brings a clear speed-up.
Construction of Multimedia-Assisted English Teaching Mode in Big Data Network Environment

Oral English teaching is the weakest link in multimedia English teaching at this stage. English teachers are constantly exploring effective approaches to improve oral English Teaching in their own educational practice. The big data multimedia English teaching mode conforms to embark on the historical stage. Firstly, this paper constructs the big data architecture of English teaching model mining and divides the construction of the teaching model into three parts: data mining, teaching model evaluation, and improvement optimization. Data mining uses the advanced DBN network to send data into the DBN-DELM network, which significantly improves the accuracy of the multimedia assisted English construction model. The simulation results show that teaching mode construction based on big data can effectively improve students’ interest in English learning; attitude; and oral English level including pronunciation, pronunciation and intonation, dialogue and communication, and oral expression and improve students’ group cooperation and communication ability, autonomous learning ability, evaluation consciousness, and ability.
Multitask adversarial attack with dispersion amplification

Abstract Recently, adversarial attacks have drawn the community’s attention as an effective tool to degrade the accuracy of neural networks. However, their actual usage in the world is limited. The main reason is that real-world machine learning systems, such as content filters or face detectors, often consist of multiple neural networks, each performing an individual task. To attack such a system, adversarial example has to pass through many distinct networks at once, which is the major challenge addressed by this paper. In this paper, we investigate multitask adversarial attacks as a threat for real-world machine learning solutions. We provide a novel black-box adversarial attack, which significantly outperforms the current state-of-the-art methods, such as Fast Gradient Sign Attack (FGSM) and Basic Iterative Method (BIM, also known as Iterative-FGSM) in the multitask setting.
Image Source Identification Using Convolutional Neural Networks in IoT Environment

Digital image forensics is a key branch of digital forensics that based on forensic analysis of image authenticity and image content. The advances in new techniques, such as smart devices, Internet of Things (IoT), artificial images, and social networks, make forensic image analysis play an increasing role in a wide range of criminal case investigation. This work focuses on image source identification by analysing both the fingerprints of digital devices and images in IoT environment. A new convolutional neural network (CNN) method is proposed to identify the source devices that token an image in social IoT environment. The experimental results show that the proposed method can effectively identify the source devices with high accuracy.
Macroscopic and deterministic quantum feature generation via phase basis quantization in a cascaded interferometric system

Abstract Quantum entanglement is the quintessence of quantum information science governed by quantum superposition mostly limited to a microscopic regime. For practical applications, however, macroscopic entanglement has an essential benefit for quantum sensing and metrology to beat its classical counterpart. Recently, a coherence approach for entanglement generation has been proposed and demonstrated in a coupled interferometric system using classical laser light, where the quantum feature of entanglement has been achieved via phase basis superposition between identical interferometric systems. Such a coherence method is based on the wave nature of a photon without violating quantum mechanics under the complementarity theory. Here, a method of phase basis quantization via phase basis superposition is presented for macroscopic entanglement in an interferometric system, which is corresponding to the energy quantization of a photon.
Generic resources are what you need: Style transfer tasks without task-specific parallel training data

Style transfer aims to rewrite a source text in a different target style while preserving its content.We propose a novel approach to this task that leverages generic resources, and without using any task-specific parallel (source-target) data outperforms existing unsupervised approaches on the two most popular style transfer tasks: formality transfer and polarity swap.In practice, we adopt a multistep procedure which builds on a generic pretrained sequence-to-sequence model (BART).First, we strengthen the model's ability to rewrite by further pre-training BART on both an existing collection of generic paraphrases, as well as on synthetic pairs created using a general-purpose lexical resource.Second, through an iterative back-translation approach, we train two models, each in a transfer direction, so that they can provide each other with synthetically generated pairs, dynamically in the training process.Lastly, we let our best resulting model generate static synthetic pairs to be used in a supervised training regime.Besides methodology and state-of-the-art results, a core contribution of this work is a reflection on the nature of the two tasks we address, and how their differences are highlighted by their response to our approach.
Printed and e-book: problems of choice of modern students of the university

The article highlights the urgency of the problem of digitalization of education, substantiates the need to develop digital competence of young people in the context of using various electronic resources, technologies, the ability to be critical of the accuracy of information, competently use digital media to achieve personal, professional or social goals. The need for the development of reading competence in university students is emphasized. The study used methods of conceptual and comparative analysis, as well as psychodiagnostic and statistical methods. The results of an empirical study conducted in Ukrainian universities on the researched problem are presented. The positive and negative sides in the use of printed and electronic publications by modern students are given. Their preferences for using these sources to solve educational problems are revealed. The peculiarities of the development of readers’ interests of future specialists and the prospects of competition of the printed book with the electronic one are determined. According to the survey, it was found that respondents prefer e-books, they have more advantages in terms of practical application. It is investigated that for study they more often choose printed books, textbooks, which help them to focus more deeply on scientific information and to comprehend it critically (more than 70% of respondents). Emphasis on the importance in modern conditions of a successful combination in educational (professional) activities of electronic and printed publications.
Composing Modeling and Simulation with Machine Learning in Julia

In this paper we introduce JuliaSim, a high-performance programming environment designed to blend traditional modeling and simulation with machine learning.JuliaSim can build accelerated surrogates from component-based models, such as those conforming to the FMI standard, using continuous-time echo state networks (CTESN).The foundation of this environment, ModelingToolkit.jl, is an acausal modeling language which can compose the trained surrogates as components within its staged compilation process.As a complementary factor we present the Ju-liaSim model library, a standard library with differentialalgebraic equations and pre-trained surrogates, which can be composed using the modeling system for design, optimization, and control.We demonstrate the effectiveness of the surrogate-accelerated modeling and simulation approach on HVAC dynamics by showing that the CTESN surrogates accurately capture the dynamics of a HVAC cycle at less than 4% error while accelerating its simulation by 340x.We illustrate the use of surrogate acceleration in the design process via global optimization of simulation parameters using the embedded surrogate, yielding a speedup of two orders of magnitude to find the optimum.We showcase the surrogate deployed in a co-simulation loop, as a drop-in replacement for one of the coupled FMUs, allowing engineers to effectively explore the design space of a coupled system.Together this demonstrates a workflow for automating the integration of machine learning techniques into traditional modeling and simulation processes.
Crop Planning in Synchronized Crop-Demand Scenarios: A Biobjective Optimization Formulation

Crop planning problems have been extensively studied from different perspectives (profit maximization, optimizing available water use, sustainability, etc.). In this paper, a new approach is proposed that considers new forms of customer-producer relationship, involving long-term cooperation agreements where the product volumes are agreed, and the demand is guaranteed in advance. In this context, typical of manufacturing production systems, crop planning must guarantee a given production level on specific dates, thus becoming deterministic in nature. In that context, this paper introduces a lexicographic biobjective optimization approach that, in addition to cost minimization, aims at minimizing the risk of not meeting the agreed demands. The latter is done by maximizing the geographic dispersion of the crops so that weather risk is mitigated. A number of experiments have been carried out to test the proposed approach, showing the high complexity of the solution and opening the door to new solution procedures for a problem that results from interest given to the new type of relationships in the food logistics chain.
Organisational Information Security Management Maturity Model

Information Security Management (ISM) is a systematic initiative in managing the organisation's information security.ISM can also be defined as a strategic approach to addressing information security (IS) risks, breaches, and incidents that could threaten the confidentiality, integrity, and availability of information.Although organisations have complied with ISM requirements, security incidents are still afflicting numerous organisations.This issue shows that the current implementation of ISM is still ineffective.The ineffective ISM implementation illustrates the low maturity level.To achieve a higher level of maturity, organisations should always evaluate their ISM practices.Several maturity models have been developed by international organisations, consultants, and researchers to assist organisations in assessing their ISM practices.However, the current models do not evaluate ISM practices holistically.The measurement dimensions in current models are more focused on assessing certain factors only.This caused the maturity assessment to be not executed comprehensively.Therefore, this study aims to address this shortcoming by proposing a comprehensive maturity assessment model that takes into account ISM success factors to evaluate the effectiveness of the implementation.This study adopted a mixedmethod approach, which comprises qualitative and quantitative studies to strengthen the research finding.The qualitative study analyses the existing literature and conducts interviews with nine industry practitioners and six experts while the quantitative study involves a questionnaire survey.The data obtained from the qualitative study were analysed using content analysis while the quantitative data employed statistics analysis.The study identified fourteen success factors and fifty-seven maturity dimensions, which each contains five maturity levels.The proposed model was evaluated through experts' reviews to ensure its accuracy and suitability.The evaluation shows that the model can identify the ISM maturity level systematically and comprehensively.This model will ultimately help the organisations to improve the weaknesses in the implementations thus diminishing security incidents.
Practical considerations for a library’s research data management services: the case of the National Institutes of Health Library

This study investigates research data management (RDM) services using a crosstab framework with the National Institutes of Health (NIH) Library as a case study to provide practical considerations for libraries seeking to improve their RDM services.We conducted semistructured interviews with four librarians who provide data services at the NIH Library regarding library user characteristics, RDM services provided, RDM infrastructure, and collaboration experiences. Through the analysis of interview transcripts, we identified and analyzed the NIH Library's RDM services according to Online Computer Library Center (OCLC)'s three categories of RDM services and the six stages of the data lifecycle.The findings show that the two models' crosstab framework can provide an overview of an institution's current RDM services and identify service gaps. The NIH Library tends to take more responsibility in providing education and expertise services while relying more on information technology departments for curation services. The library provides significant support for data creation, analysis, and sharing stages to meet biomedical researchers' needs, suggesting areas for potential expansion of RDM services in the less supported stages of data description, storage, and preservation. Based on these findings, we recommend three key considerations for libraries: identify gaps in current services, identify services that can be supported via partnerships, and get regular feedback from users.These findings provide a deeper understanding of RDM support on the basis of RDM service categories and the data lifecycle and promote discussion of issues to be considered for future improvements in RDM services.