Reviewerly/exHarmony huggingface dataset:
Attacks on Wireless Coexistence: Exploiting Cross-Technology Performance Features for Inter-Chip Privilege Escalation

Modern mobile devices feature multiple wireless technologies, such as Bluetooth, Wi-Fi, and LTE. Each of them is implemented within a separate wireless chip, sometimes packaged as combo chips. However, these chips share components and resources, such as the same antenna or wireless spectrum. Wireless coexistence interfaces enable them to schedule packets without collisions despite shared resources, essential to maximizing networking performance. Today’s hardwired coexistence interfaces hinder clear security boundaries and separation between chips and chip components. This paper shows practical coexistence attacks on Broadcom, Cypress, and Silicon Labs chips deployed in billions of devices. For example, we demonstrate that a Bluetooth chip can directly extract network passwords and manipulate traffic on a Wi-Fi chip. Coexistence attacks enable a novel type of lateral privilege escalation across chip boundaries. We responsibly disclosed the vulnerabilities to the vendors. Yet, only partial fixes were released for existing hardware since wireless chips would need to be redesigned from the ground up to prevent the presented attacks on coexistence.
Design of intelligent classroom teaching scheme using artificial intelligence

The study aims to improve the efficiency and quality of teaching, improve students' interest in learning and performance. A design scheme of intelligent classroom is proposed based on artificial intelligence technology, which can be divided into online part of personalised learning recommendation and offline part of classroom quality evaluation. First, a multi-layer Back Propagation (BP) neural network combined with genetic algorithm and an actual entropy model is designed to predict students' performance and analyse their learning behaviour. Second, in the design of offline system, the model of You Only Look Once (YOLOv3) is used to design the model of students' concentration detection. Finally, the online system and offline system are combined to realise the functions of students' learning behaviour feature extraction, students' performance prediction, students' learning rule analysis, and students' personalised learning recommendation.
Adaptive fuzzy neural network-aided progressive Gaussian approximate filter for GPS/INS integration navigation

To address the difficulty of high uncertainty priori information in nonlinear filtering that occurs in tightly coupled navigation and positioning systems, a novel adaptive fuzzy neural network-aided progressive Gaussian filter is proposed in this paper to further improve the robustness and accuracy of the filter. A joint estimation of the step size and measurement noise covariance matrix is performed by a variational Bayesian approach. The estimation accuracy of the progressive Gaussian approximation filter is thereby enhanced. In particular, the measurement noise covariance matrix is adjusted using an adaptive fuzzy algorithm, and the state estimation error covariance matrix is optimized exploiting a neural network model. The proposed filter is experimentally verified to have higher accuracy than the existing state-of-the-art filters.
Primitive idempotents and constacyclic codes over finite chain rings

Let $R$ be a commutative local finite ring. In this paper, we construct the complete set of pairwise orthogonal primitive idempotents of $R[X]/<g>$ where $g$ is a regular polynomial in $R[X]$. We use this set to decompose the ring $R[X]/<g>$ and to give the structure of constacyclic codes over finite chain rings. This allows us to describe generators of the dual code $\mathcal{C}^\bot$ of a constacyclic code $\mathcal{C}$ and to characterize non-trivial self-dual constacyclic codes over finite chain rings.
Adaptive Approximate Bayesian Computational Particle Filters for Underwater Terrain Aided Navigation

To perform long-term and long-range missions, underwater vehicles need reliable navigation algorithms. This paper considers multi-beam Terrain Aided Navigation which can provide a drift-free navigation tool. This leads to an estimation problem with implicit observation equation and unknown likelihood. Indeed, the measurement sensor is considered to be a numerical black box model that introduces some unknown stochastic noise. We introduce a measurement updating procedure based on an adaptive kernel derived from Approximate Bayesian Computational filters. The proposed method is based on two well-known particle filters: Regularized Particle Filter and Rao-Blackwellized Particle Filter. Numerical results are presented and the robustness is demonstrated with respect to the original filters, yielding to twice as less non-convergence cases. The proposed method increases the robustness of particle-like filters while remaining computationally efficient.
Lifestate: Event-Driven Protocols and Callback Control Flow (Extended  Version)

Developing interactive applications (apps) against event-driven software frameworks such as Android is notoriously difficult. To create apps that behave as expected, developers must follow complex and often implicit asynchronous programming protocols. Such protocols intertwine the proper registering of callbacks to receive control from the framework with appropriate application-programming interface (API) calls that in turn affect the set of possible future callbacks. An app violates the protocol when, for example, it calls a particular API method in a state of the framework where such a call is invalid. What makes automated reasoning hard in this domain is largely what makes programming apps against such frameworks hard: the specification of the protocol is unclear, and the control flow is complex, asynchronous, and higher-order. In this paper, we tackle the problem of specifying and modeling event-driven application-programming protocols. In particular, we formalize a core meta-model that captures the dialogue between event-driven frameworks and application callbacks. Based on this meta-model, we define a language called lifestate that permits precise and formal descriptions of application-programming protocols and the callback control flow imposed by the event-driven framework. Lifestate unifies modeling what app callbacks can expect of the framework with specifying rules the app must respect when calling into the framework. In this way, we effectively combine lifecycle constraints and typestate rules. To evaluate the effectiveness of lifestate modeling, we provide a dynamic verification algorithm that takes as input a trace of execution of an app and a lifestate protocol specification to either produce a trace witnessing a protocol violation or a proof that no such trace is realizable.
Project Mirabel: XQuery-Based Documentation Reporting and Exploration System

Describes the organic development of a multi-function XQuery-based system, Project Mirabel, for capturing and reporting on the results of applying Schematron validation to large sets of DITA documents, growing from a simple reporting utility to a multi-function platform for general reporting and exploration over large and complex sets of DITA content, representing non-trivial hyperdocuments, all within the span three weeks.
Mitotic Instance Detection in Stain Normalized Histopathological Images using Faster R-CNN

Our method aims to detect and identify the possibility of malignancy based on abnormal mitotic cell division using Artificial Intelligence. In tumour grading, the mitotic activity index is an important prognostic factor. The standard approach entails expert pathologists manually examining H&E stained histopathological sections on glass slides under a microscope, which is time-consuming and error-prone, thereby a need for automation. Due to the lack of pixel-level annotations, the presence of mitotic nuclei in numerous morphological configurations, their sparse representation, and their strong similarity to other cellular and nuclear bodies, automated mitotic nuclei recognition faces many obstacles. Hence, an efficient and accurate automated approach is necessary for early prognosis. This early detection helps the pathologists to find the mitotic cells considerably faster. This research work has used various backbone architectures of the Faster R-CNN model to classify the mitotic nuclei and compare their performance.
Finite-word hyperlanguages

Formal languages are in the core of models of computation and their behavior. A rich family of models for many classes of languages have been widely studied. Hyperproperties lift conventional trace-based languages from a set of execution traces to a set of sets of executions. Hyperproperties have been shown to be a powerful formalism for expressing and reasoning about information-flow security policies and important properties of cyber-physical systems. Although there is an extensive body of work on formal-language representation of trace properties, we currently lack such a general characterization for hyperproperties. We introduce hyperlanguages over finite words and models for expressing them. Essentially, these models express multiple words by using assignments to quantified word variables. Relying on the standard models for regular languages, we propose regular hyperexpressions and finite-word hyperautomata (NFH), for modeling the class of regular hyperlanguages. We demonstrate the ability of regular hyperlanguages to express hyperproperties for finite traces. We explore the closure properties and the complexity of the fundamental decision problems such as nonemptiness, universality, membership, and containment for various fragments of NFH.
Utilizing criminological theories to predict involvement in cyberviolence among the iGeneration

Cyberviolence is a growing concern, leading researchers to explore why some users engage in harmful acts online. This study uses leading criminological theories—the general theory of crime/self-control theory, social control/bonding theory, social learning theory, and general strain theory—to explore why 15–18-year-old American adolescents join ongoing acts of cyberviolence. Additionally, we examine the role of socio-demographic traits and online routines in perpetuating cyberviolence. Results of an ordinal logistic regression indicate that low self-control, online strain, closeness to online communities, and watching others engage in online attacks are associated with joining an ongoing act of cyberviolence. Moreover, an individual’s age and familial relationships are inversely related to joining an online attack. Taken together, all four criminological theories we test help predict engagement in cyberviolence, indicating an integrative theory may be valuable in understanding participation in cyberhate attacks.
Digital Image Steganography

Various applications have various prerequisites of the steganography strategy utilized. As an example, a couple of applications may require outright imperceptibility of the restricted data, while others require a much bigger mystery message to be covered up. During this venture named &#x201C;Image Steganography&#x201D;, we&#x2019;ve zeroed in on least huge piece change methods of spatial area. We&#x2019;ve broke down and analyzed the variations of this calculation regarding viability and concealing limit. Adequacy of the calculation is estimated by client assessments deciding so, all things considered modification to the photographs got evident.
Enhancing Federated Learning with Intelligent Model Migration in Heterogeneous Edge Computing

To approach the challenges of non-IID data and limited communication resource raised by the emerging federated learning (FL) in mobile edge computing (MEC), we propose an efficient framework, called FedMigr, which integrates a deep reinforcement learning (DRL) based model migration strategy into the pioneer FL algorithm FedAvg. According to the data distribution and resource constraints, our FedMigr will intelligently guide one client to forward its local model to another client after local updating, rather than directly sending the local models to the server for global aggregation as in FedAvg. Intuitively, migrating a local model from one client to another is equivalent to training it over more data from different clients, contributing to alleviating the influence of non-IID issue. We prove that FedMigr can help to reduce the parameter divergences between different local models and the global model from a theoretical perspective, even over local datasets with non-IID settings. Extensive experiments on three popular benchmark datasets demonstrate that FedMigr can achieve an average accuracy improvement of around 13%, and reduce bandwidth consumption for global communication by 42% on average, compared with the baselines.
Retrieval-Based Gradient Boosting Decision Trees for Disease Risk Assessment

In recent years, machine learning methods have been widely used in modern electronic health record (EHR) systems, and have shown more accurate prediction performance on disease risk assessment tasks than traditional methods. However, most of the existing machine learning methods make the assessment solely based on features of the target case but ignore the cross-sample feature interactions between the target case and other similar cases, which is inconsistent with the general practice of evidence-based medicine of making diagnoses based on existing clinical experience. Moreover, current methods that focus on mining cross-sample information rely on deep neural networks to extract cross-sample feature interactions, which would suffer from the problems of data insufficiency, data heterogeneity and lack of interpretability in disease risk assessment tasks. In this work, we propose a novel retrieval-based gradient boosting decision trees (RB-GBDT) model with a cross-sample extractor to mine cross-sample information while exploiting the superiority of GBDT of robustness, generalization and interpretability. Experiments on real-world clinical datasets show the superiority and efficacy of RB-GBDT on disease risk assessment tasks. The developed software has been deployed in hospital as an auxiliary diagnosis tool for risk assessment of venous thromboembolism.
Application and Prospect Analysis of Blockchain Technology in Water Resources Protection

As a very popular technology, blockchain technology has become more and more widely used in various fields due to its advantages in distributed storage, data transparency, and traceability. Starting from the existing problems of water resources protection, this article comprehensively discusses the characteristics of blockchain technology, the scenarios and modes that blockchain can be used in the field of water resources protection, such as water body data storage, multi-sectoral cooperation, and expanded public participation Degree and so on. Furthermore, the reasons why the current blockchain cannot be applied on a large scale and in the field of water protection is discussed, for example: different standards, resource consumption, and insufficient technical foundation of the people. All these will provide ideas for the development of blockchain in the field of water protection.
Privacy Risk Assessment of Training Data in Machine Learning

With the wide application of machine learning technology, more and more sensitive data were used to develop the machine learning model. Unfortunately, machine learning systems have been shown to be vulnerable to various privacy leakage attacks, such as membership inference attacks and model inversion attacks. These privacy threats break down the confidentiality of the training data; what&#x2019;s more, the leakage of sensitive privacy will reduce the enthusiasm of data owners for sharing data with the machine learning system. Without enough training data, more seriously, it will hinder the application of machine learning. Therefore, it requires analyzing strategies for privacy risks to help data owners pre-assess the candidate dataset and assist data owners in implementing reasonable privacy control. However, the systematic privacy risk assessment is still absent from the data owner&#x2019;s perspective.This paper investigates and analyzes machine learning privacy risks to understand the relationship between training data properties and privacy leakage. Based on this recognition, we introduce a privacy risk assessment scheme based on the clustering distance of training data. Our clustering distance-based method can reflect the privacy risk level of a different individual data record. And then, we combine both existing privacy analysis based on data features and our clustering distance-based method to investigate privacy risks systematically. Our experiments showed that our clustering distance method and other set properties are tightly related to privacy leakage. And data owners can pre-assess the privacy risks of candidate datasets before uploading or sharing them to the machine learning model. If needed, decide to re-choose the dataset to reduce privacy risks to an acceptable level.
Transfer Learning for Feature Dimensionality Reduction

Transfer learning is a machine learning methodology by which a model developed for achieving a task is exploited for another related job. Many pre-trained image classification models trained on ImageNet are used for transfer learning. These pre-trained networks could also be used for classifying out of domain images by retraining them. This paper, along with the existing application for these pre-trained models, is also being exploited for feature dimensionality reduction. Many dimensionality reduction methods are available; the pre-trained image models will help us perform both image feature extraction and dimensionality reduction in a single go using the same network. The fine-tuning of the fully connected layers of the pre-trained network is done to extract the image features; along with this fine-tuning, some more tweaking is done on the fully connected layers of these models to reduce the image feature dimensionality. Here, VGG-16 and VGG-19 are the pre-trained models considered for feature vector generation and dimensionality reduction. An analysis of the efficiency of features generated by these pre-trained networks in classifying the out-of-domain images is done. Three different variants of VGG-16 and VGG-19 are analysed. All the three variants developed gave an AUC value above 0.8, which is considered good.
Overview for Parallel Particle Swarm Optimization Algorithms (PPSO)

The increasing computational cost in complex optimization problems that have a large size resulted in the development of optimization algorithms in parallelization. PSO's basic architecture inherits parallelism of natural and the acceptance of quick handling machine has made this mission pretty convenient. The emergence of parallel PSO (PPSO) as an algorithm has been well accepted by the researchers. Parallelizing algorithm PSO has been used in a number studies so far. This proposed work provides an overview of the parallelism of the PPSO.
New Spam Filtering Method with Hadoop Tuning-Based MapReduce Na飗e Bayes

As the importance of email increases, the amount of malicious email is also increasing, so the need for malicious email filtering is growing.Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques, we used a Hadoop MapReduce framework and Naïve Bayes among machine learning methods for malicious email filtering.Naïve Bayes was selected because it is one of the top machine learning methods(Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbor(KNN), and Decision Tree) in terms of execution time and accuracy.Malicious email was filtered with MapReduce programming using the Naïve Bayes technique, which is a supervised machine learning method, in a Hadoop framework with optimized performance and also with the Python program technique with the Naïve Bayes technique applied in a bare metal server environment with the Hadoop environment not applied.According to the results of a comparison of the accuracy and predictive error rates of the two methods, the Hadoop MapReduce Naïve Bayes method improved the accuracy of spam and ham email identification 1.11 times and the prediction error rate 14.13 times compared to the non-Hadoop Python Naïve Bayes method.
The Race for Quantum-Resistant Cryptography [quantum - cyber security]

That large-scale universal quantum computers could break widely used encryption methods is well known, but what was once seen as a distant, even theoretical, problem is now driving the latest technology race. Current developments in advanced quantum cryptographic solutions are discussed, including the use of public and private/secret keys, random number generation (RNG), quantum key distribution (QKD), QuantumCloud and chip-based quantum cryptography communication systems along with protocols and security/trustless computing issues. Some possible applications such as quantum-resistant communication systems for defence aircraft/drones, blockchain, Internet of Things (IoT) and smart city are mentioned. Particular focus is given to satellite-based QKD software architecture and QKD over optical fibre, along with appropriate standards, allowing over globe-spanning distances, to achieve end-to-end secure communication.
Enhanced beetle antennae search algorithm for complex and unbiased optimization

Beetle Antennae Search algorithm is a kind of intelligent optimization algorithms, which has the advantages of few parameters and simplicity. However, due to its inherent limitations, BAS has poor performance in complex optimization problems. The existing improvements of BAS are mainly based on the utilization of multiple beetles or combining BAS with other algorithms. The present study improves BAS from its origin and keeps the simplicity of the algorithm. First, an adaptive step size reduction method is used to increase the usability of the algorithm, which is based on an accurate factor and curvilinearly reduces the step size; second, the calculated information of fitness functions during each iteration are fully utilized with a contemporary optimal update strategy to promote the optimization processes; third, the theoretical analysis of the multi-directional sensing method is conducted and utilized to further improve the efficiency of the algorithm. Finally, the proposed Enhanced Beetle Antennae Search algorithm is compared with many other algorithms based on unbiased test functions. The test functions are unbiased when their solution space does not contain simple patterns, which may be used to facilitate the searching processes. As a result, EBAS outperformed BAS with at least 1 orders of magnitude difference. The performance of EBAS was even better than several state-of-the-art swarm-based algorithms, such as Slime Mold Algorithm and Grey Wolf Optimization, with similar running times. In addition, a WSN coverage optimization problem is tested to demonstrate the applicability of EBAS on real-world optimizations.