Reviewerly/exHarmony huggingface dataset:
Dynamic S-Box Design Using a Novel Square Polynomial Transformation and Permutation

New era ciphers employ substitution boxes (S-boxes) which assist in the provision of security for the plaintext in the encryption phase and transforming the ciphertext on the receiver side into original plaintext in the decryption phase.The overall security of a given cipher engaging an S-box greatly depends on the cryptographic forte of the respective S-box.Consequently, many researchers have used different innovative approaches to construct robust S-Boxes.In this article, an innovative and modest square polynomial transformation, the very first time, along with a novel affine transformation and a pioneering permutation approach to construct dynamic S-boxes is proposed.The proposed method has the capability to erect a huge number of robust S-boxes by applying minute changes in the parameters of transformation and permutation processes.An example S-Box is generated, and its recital analysis has been done using typical criteria including bijectivity, strict avalanche criterion, nonlinearity, bit independence criterion, linear probability, differential probability, and fixed-point analysis to check its cryptographic forte.This performance of the proposed S-box is placed side by side against state-of-the-art S-boxes to prove its strength.The performance and comparative analyses authenticate that the projected S-box possesses the true competence for its application in modern-day ciphers.
The invisible made visible through technologies’ agency: a sociomaterial inquiry on emergency remote teaching in higher education

We are used to considering human agency as the most important aspect of the educational process. Technologies are seen as inert matter, subordinated to human intention and design, as if they did not have a role in the eclectic combination of teaching, learning, and knowing about the world. Their agency is invisible until a breakdown occurs, a material moment which shows their doing. In this paper, we make digital action visible by focusing on emergency remote teaching in higher education during the Covid-19 pandemic breakdown. Data were collected/co-created in a graduate course in Education in the first three months of lockdown in Brazil. Through a queer assemblage of teaching-researching-writing, we present a sociomaterial analysis that shows multiple entanglements of bodies, material things, and pedagogic time-spaces. In this exercise, social inequality issues, power structures and ethical problems come to the surface, while students struggle for quality participation in the digitised classes.
A trusted architecture for EV shared charging based on blockchain technology

With the development of the Energy Internet and the support of the subsidy policies of various countries, Electric Vehicles(EVs) have ushered in a golden development period. However, the development of EVs needs to solve the problems of insufficient charging piles(CPs) and difficulty in finding CPs. In order to solve the problem of difficult charging of EVs, the concept of shared charging came into being, in which idle CPs or private CPs are shared to meet the charging needs of more people and improve the utilization rate of CPs. However, the shared charging scheme implemented by third-party platforms faces the issue of trust lacking. This paper proposes a blockchain architecture for shared charging, which can use the blockchain to build a trust environment involving private pile owners, charging pile(CP) operators, Electric Vehicle(EV) users, etc.. The blockchain architecture also contains the block structure where pointer was added for quick search, contract content that can automatically execute multi-party contracts to achieve secure computing and reputation-based incentive mechanism to provide high-quality charging services in detail. This architecture establishes the multi-party trust environment for shared charging from three aspects: secure storage, secure computing, and secure incentives.
Knowledge-Guided Article Embedding Refinement for Session-Based News Recommendation

Personalized news recommendation aims to recommend news articles to customers, by exploiting the personal preferences and short-term reading interest of users. A practical challenge in personalized news recommendations is the lack of logged user interactions. Recently, the session-based news recommendation has attracted increasing attention, which tries to recommend the next news article given previous articles in an active session. Current session-based news recommendation methods mainly extract latent embeddings from news articles and user-item interactions. However, many existing methods could not exploit the semantic-level structural information among news articles. And the feature learning process simply relies on the news articles in training data, which may not be sufficient to learn semantically rich embeddings. This brief presents a context-aware graph embedding (CAGE) approach for session-based news recommendation. It employs external knowledge graphs to improve the semantic-level representations of news articles. Moreover, graph neural networks are incorporated to further enhance the article embeddings. In addition, we consider the similarity among sessions and design attention neural networks to model the short-term user preferences. Extensive results on multiple news recommendation benchmark datasets show that CAGE performs better than some competitive baselines in most cases.
LCDA: Lightweight Continuous Device-to-Device Authentication for a Zero Trust Architecture (ZTA)

Continuous Authentication (CA) has been proposed as a potential solution to counter complex cybersecurity threats posed against conventional static authentication mechanisms that merely authenticate at ingress points of a platform. However, widely researched CA mechanisms that rely on user’s behavioural characteristics cannot be extended to continuously authenticate Internet of Things (IoT) devices. Challenges are exacerbated with the increased adoption of device-to-device (d2d) communication in critical infrastructures. Existing d2d authentication protocols proposed in the literature are either prone to subversion or are computationally infeasible to be deployed on constrained IoT devices. In view of these challenges, we propose a novel, L ightweight C ontinuous D evice-to-Device A uthentication (LCDA) protocol that leverages communication channel properties and a tunable mathematical function to generate dynamically changing session keys for continuous device authentication. Our extensive informal and formal analysis confirms the efficacy of the proposed LCDA protocol in terms of its resilience to known attack vectors, thereby demonstrating its strong potential for deployment in critical and resource-constrained scenarios for secure d2d communication.
Capacity-Driven Autoencoders for Communications

The autoencoder concept has fostered the reinterpretation and the design of modern communication systems.It consists of an encoder, a channel and a decoder block that modify their internal neural structure in an end-to-end learning fashion.However, the current approach to train an autoencoder relies on the use of the cross-entropy loss function.This approach can be prone to overfitting issues and often fails to learn an optimal system and signal representation (code).In addition, less is known about the autoencoder ability to design channel capacity-approaching codes, i.e., codes that maximize the input-output mutual information under a certain power constraint.The task being even more formidable for an unknown channel for which the capacity is unknown and therefore it has to be learnt.In this paper, we address the challenge of designing capacity-approaching codes by incorporating the presence of the communication channel into a novel loss function for the autoencoder training.In particular, we exploit the mutual information between the transmitted and received signals as a regularization term in the cross-entropy loss function, with the aim of controlling the amount of information stored.By jointly maximizing the mutual information and minimizing the cross-entropy, we propose a theoretical approach that a) computes an estimate of the channel capacity and b) constructs an optimal coded signal approaching it.Theoretical considerations are made on the choice of the cost function and the ability of the proposed architecture to mitigate the overfitting problem.Simulation results offer an initial evidence of the potentiality of the proposed method.
Deep Learning Can Predict Laboratory Quakes From Active Source Seismic Data

Abstract Small changes in seismic wave properties foretell frictional failure in laboratory experiments and in some cases on seismic faults. Such precursors include systematic changes in wave velocity and amplitude throughout the seismic cycle. However, the relationships between wave features and shear stress are complex. Here, we use data from lab friction experiments that include continuous measurement of elastic waves traversing the fault and build data‐driven models to learn these complex relations. We demonstrate that deep learning models accurately predict the timing and size of laboratory earthquakes based on wave features. Additionally, the transportability of models is explored by using data from different experiments. Our deep learning models transfer well to unseen datasets providing high‐fidelity models with much less training. These prediction methods can be potentially applied in the field for earthquake early warning in conjunction with long‐term time‐lapse seismic monitoring of crustal faults, CO 2 storage sites and unconventional energy reservoirs.
Temporal Solar Photovoltaic Generation Capacity Reduction From Wildfire Smoke

Wildfire smoke and other particulate matter can substantially inhibit solar photovoltaic (PV) generation production. While solar PV facilities may not be located in areas with a high fire risk, smoke from wildfires can travel hundreds of kilometers impacting a large number of facilities. This paper proposes a geospatial wildfire PV capacity model to quantify the anticipated temporal reduction in PV capacity due to wildfire smoke. A case study using data for two time periods from the 2020 California wildfires and real utility scale solar generation data evidences the model's high accuracy. Results argue that wildfire smoke can cause significant temporal solar generation capacity reductions over wide geographic regions. Application of the proposed model to inform power system resiliency planning is demonstrated for two use cases: generation scheduling and siting. With meteorological service providers beginning to release smoke forecasts, our geospatial wildfire PV capacity model enables balancing authorities to make use of this information to proactively schedule generation to compensate for reductions in PV capacity. The trained model also produces geospatial derate maps that can enable generation developers to consider historical capacity derates due to smoke when making siting or planning decisions.
Denoising of magnetic resonance imaging using Bayes shrinkage based fused wavelet transform and autoencoder based deep learning approach

Denoising of medical images is of great concern as it plays a significant role in performance of computer aided diagnosis (CAD) systems. In real life scenarios, various conditions like vibration of magnetic coils due to rapid pulses of electricity contribute to noise during the procurement of medical images such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound. The use of imaging modality depends on the type of disease and its severity, and MRI is the commonly employed imaging modality for diagnosis of second most common dreadful cancers in men known as prostate cancer. However, MRI is prone to certain noises as Gaussian and Rician making denoising one of the important steps in the CAD system. Traditional approaches used for denoising of MRI were prone to certain issues such as loss of data due to compression and preservation of edge details. Hence, this paper presents Bayes shrinkage based fused wavelet transform (BSbFWT) and Block based autoencoder network (BBAuto-Net) for removal of noise from MRI. Further, the performance analysis of the denoising approaches are performed using different metrics. Thus, the values of peak signal to noise ratio (PSNR), mean squared error (MSE), structural similarity index metric (SSIM) and mean absolute error (MAE) for proposed BB-Autonet is found to be 28.029, 89.354, 0.581 and 21.802 for combined Gaussian and Rician noise. Whereas, the values of PSNR, MSE, SSIM and MAE for proposed BSbFWT are found to be 29.028, 81.33, 0.747 and 21.962 for combined Gaussian and Rician noise.
High-Fidelity Neural Human Motion Transfer from Monocular Video

Video-based human motion transfer creates video animations of humans following a source motion. Current methods show remarkable results for tightly-clad subjects. However, the lack of temporally consistent handling of plausible clothing dynamics, including fine and high-frequency details, significantly limits the attainable visual quality. We address these limitations for the first time in the literature and present a new framework which performs high-fidelity and temporally-consistent human motion transfer with natural pose-dependent non-rigid deformations, for several types of loose garments. In contrast to the previous techniques, we perform image generation in three subsequent stages: synthesizing human shape, structure, and appearance. Given a monocular RGB video of an actor, we train a stack of recurrent deep neural networks that generate these intermediate representations from 2D poses and their temporal derivatives. Splitting the difficult motion transfer problem into subtasks that are aware of the temporal motion context helps us to synthesize results with plausible dynamics and pose-dependent detail. It also allows artistic control of results by manipulation of individual framework stages. In the experimental results, we significantly outperform the state-of-the-art in terms of video realism. The source code is available at https://graphics.tu-bs.de/publications/kappel2020high-fidelity.
Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point detection methods extract IFI from an input image, we propose a taxonomy of the IFI extraction techniques for interest point detection. According to this taxonomy, we discuss different types of IFI extraction techniques for interest point detection. Furthermore, we identify the main unresolved issues related to the existing IFI extraction techniques for interest point detection and any interest point detection methods that have not been discussed before. The existing popular datasets and evaluation standards are provided and the performances for fifteen state-of-the-art approaches are evaluated and discussed. Moreover, future research directions on IFI extraction techniques for interest point detection are elaborated.
Combinatorial Testing Metrics for Machine Learning

This paper defines a set difference metric for comparing machine learning (ML) datasets and proposes the difference between datasets be a function of combinatorial coverage. We illustrate its utility for evaluating and predicting performance of ML models. Identifying and measuring differences between datasets is of significant value for ML problems, where the accuracy of the model is heavily dependent on the degree to which training data are sufficiently representative of data encountered in application. The method is illustrated for transfer learning without retraining, the problem of predicting performance of a model trained on one dataset and applied to another.
Balancing Consistency and Disparity in Network Alignment

Network alignment plays an important role in a variety of applications. Many traditional methods explicitly or implicitly assume the alignment consistency which might suffer from over-smoothness, whereas some recent embedding based methods could somewhat embrace the alignment disparity by sampling negative alignment pairs. However, under different or even competing designs of negative sampling distributions, some methods advocate positive correlation which could result in false negative samples incorrectly violating the alignment consistency, whereas others champion negative correlation or uniform distribution to sample nodes which may contribute little to learning meaningful embeddings. In this paper, we demystify the intrinsic relationships behind various network alignment methods and between these competing design principles of sampling. Specifically, in terms of model design, we theoretically reveal the close connections between a special graph convolutional network model and the traditional consistency based alignment method. For model training, we quantify the risk of embedding learning for network alignment with respect to the sampling distributions. Based on these, we propose NeXtAlign which strikes a balance between alignment consistency and disparity. We conduct extensive experiments that demonstrate the proposed method achieves significant improvements over the state-of-the-arts.
Breast Lesions Detection and Classification via YOLO-Based Fusion Models

With recent breakthroughs in artificial intelligence, the use of deep learning models achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare.Particularly, numerous applications provided efficient solutions to assist radiologists for medical imaging analysis.For instance, automatic lesion detection and classification in mammograms is still considered a crucial task that requires more accurate diagnosis and precise analysis of abnormal lesions.In this paper, we propose an end-to-end system, which is based on You-Only-Look-Once (YOLO) model, to simultaneously localize and classify suspicious breast lesions from entire mammograms.The proposed system first preprocesses the raw images, then recognizes abnormal regions as breast lesions and determines their pathology classification as either mass or calcification.We evaluated the model on two publicly available datasets, with 2907 mammograms from the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and 235 mammograms from INbreast database.We also used a privately collected dataset with 487 mammograms.Furthermore, we suggested a fusion models approach to report more precise detection and accurate classification.Our best results reached a detection accuracy rate of 95.7%, 98.1% and 98% for mass lesions and 74.4%, 71.8% and 73.2% for calcification lesions, respectively on CBIS-DDSM, INbreast and the private dataset.
Dual-view hypergraph neural networks for attributed graph learning

Graph embedding analyzes network data by learning the vector representation of each vertex in the network, and has attracted widespread attention in recent years. In many real-world networks, considering the topology and attributes of nodes comprehensively has potential value in achieving more effective graph embedding. However, the existing methods do not fully utilize and integrate these two kinds of information. The main challenges include three aspects: sparseness and noise in structure information; insufficient modeling of non-linear relationship between nodes in attribute semantic space; heterogeneity of structure and attribute information. To address these issues, based on graph neural network and hypergraph, we propose a D ual-view H yper G raph N eural N etwork (DHGNN) model for attributed graph learning. First, we unify the expression form of different information sources of nodes by hypergraph, and construct dual hypergraphs according to topology and attributes of nodes. Secondly, we propose a dual-view hypergraph neural network for graph embedding. The central idea is that we model and integrate different information sources by shared and specific hypergraph convolutional layer, and use the attention mechanism to adequately combine dual node embeddings. Finally, we train the model through semi-supervised node classification task. Extensive experiments have been carried out on four real world public datasets, demonstrating the performance of the proposed model DHGNN has always been superior to that of state-of-the-art graph embedding methods.
An empirical study of data intrinsic characteristics that make learning from imbalanced data difficult

Learning from data stemming from real-world problems is inherently challenging and difficult due to the numerous intrinsic characteristics present in datasets. The problem of class imbalance is known to significantly impair classification performance and has attracted increasing attention from researchers. On the other hand, some studies suggest that the detrimental effects of class imbalance occur only when the dataset encompasses other intrinsic characteristics such as small disjuncts, class overlapping, noise or data rarity. However, the literature is often ambiguous in terms of understanding and distinguishing the influence of these characteristics on the behaviour of standard classification algorithms. This paper provides a contemporary empirical study of the behaviour and performance of five well-known classifiers on a large number of imbalanced datasets exhibiting numerous combinations of the stated characteristics. The aim of the study is to identify and rank difficulty factors when learning from imbalanced data, depending on the type of classification algorithm used. In general, the obtained results suggest that if classifiers conceptually have no problem with class separation into sub-concepts, noise is the characteristic that most impairs their performance, closely followed by class overlapping and class imbalance. To alleviate these problems, oversampling and undersampling procedures were tested and directions are given for selecting appropriate techniques when dealing with the problem of class imbalance.
A new model for forensic data extraction from encrypted mobile devices

In modern criminal investigations, mobile devices are seized at every type of crime scene, and the data on those devices often becomes critical evidence in the case. Various mobile forensic techniques have been established and evaluated through research in order to extract possible evidence data from devices over the decades. However, as mobile devices become essential tools for daily life, security and privacy concerns grow, and modern smartphone vendors have implemented multiple types of security protection measures - such as encryption - to guard against unauthorized access to the data on their products. This trend makes forensic acquisition harder than before, and data extraction from those devices for criminal investigation is becoming a more challenging task. Today, mobile forensic research focuses on identifying more invasive techniques, such as bypassing security features, and breaking into target smartphones by exploiting their vulnerabilities. In this paper, we explain the increased encryption and security protection measures in modern mobile devices and their impact on traditional forensic data extraction techniques for law enforcement purposes. We demonstrate that in order to overcome encryption challenges, new mobile forensic methods rely on bypassing the security features and exploiting system vulnerabilities. A new model for forensic acquisition is proposed. The model is supported by a legal framework focused on the usability of digital evidence obtained through vulnerability exploitation.