Reviewerly/exHarmony huggingface dataset:
A Multi-Objective Grouping Genetic Algorithm for Server Consolidation in Cloud Data Centers

As the demand for cloud computing services has increased continuously, the number of cloud service providers and cloud applications are increasing dramatically. This has resulted in a significant increase in data center energy consumption. One of the most well-known ways to improve the efficiency of data centers is server consolidation. In our recent study on server consolidation, we presented a grouping genetic algorithm for a single objective that is minimization of the energy consumption in a data center. In order to enhance the performance of the grouping genetic algorithm, this paper presents a multi-objective grouping genetic algorithm for a multi-objective, including minimizing both the energy consumption and resource wastage. The optimal solution for the multi-objective grouping genetic algorithm is determined by a fitness function that considers the two objectives with a trade-off between each objective. The results from the experiments show that the multi-objective grouping genetic algorithm performs well in terms of the resource wastage and number of active physical machines in the data center and is scalable to the number of virtual machines and physical machines.
Large-Scale Application of Fault Injection into PyTorch Models -an Extension to PyTorchFI for Validation Efficiency

Transient or permanent faults in hardware can render the output of Neural Networks (NN) incorrect without user-specific traces of the error, i.e. silent data errors (SDE). On the other hand, modern NNs also possess an inherent redundancy that can tolerate specific faults. To establish a safety case, it is necessary to distinguish and quantify both types of corruptions. To study the effects of hardware (HW) faults on software (SW) in general and NN models in particular, several fault injection (FI) methods have been established in recent years. Current FI methods focus on the methodology of injecting faults but often fall short of accounting for large-scale FI tests, where many fault locations based on a particular fault model need to be analyzed in a short time. Results need to be concise, repeatable, and comparable.To address these requirements and enable fault injection as the default component in a machine learning development cycle, we introduce a novel fault injection framework called PyTorchALFI (Application Level Fault Injection for PyTorch) based on PyTorchFI. PyTorchALFI provides an efficient way to define randomly generated and reusable sets of faults to inject into PyTorch models, defines complex test scenarios, enhances data sets, and generates test KPIs while tightly coupling fault-free, faulty, and modified NN. In this paper, we provide details about the definition of test scenarios, software architecture, and several examples of how to use the new framework to apply iterative changes in fault location and number, compare different model modifications, and analyze test results.
IVSign: Interpretable Vulnerability Signature via Code Embedding and Static Analysis

Software vulnerability detection is evolving from pattern-driven methods to data-driven methods to be more automatic and intelligent due to the emergence of deep learning. However, false positives and coarse-grained detection can not tackle changeable and unknown threats (e.g., Oday vulnerabilities), and also low model interpretability leads to incontinent detection results or maliciously being manipulated. To enhance model confidence and interpretability, in this paper, we propose an Interpretable vulnerability signature (IVSign) scheme to combine pattern-driven and data-driven methods. In IVSign, pattern-driven methods utilize static analysis to provide rich vulnerability trace information extracted from data dependencies and data-driven methods respectively train a transformer-based model to encode these vulnerability traces and a recurrent neural network to generate vulnerability signatures. In the testing stage of IVSign, if a program sample with vulnerabilities is fed into the pipeline, its hit rate in the signature database will be very high. Experiments have shown that when the matching threshold is set appropriately, the evaluation metrics on the testing samples exceed 90.00%.
Towards Rehosting Embedded Applications as Linux Applications

Dynamic analysis of embedded firmware is a necessary capability for many security tasks, e.g., vulnerability detection. Rehosting is a technique that enables dynamic analysis by facilitating the execution of firmware in a host environment decoupled from the actual hardware. Current rehosting techniques focus on high-fidelity execution of the entire firmware. Consequently, these techniques try to execute firmware in an emulated environment, with precise models of hardware (i.e., peripheral) interactions. However, these techniques are hard to scale and have various drawbacks.We propose a novel take on rehosting by focusing on the application components and their interactions with the firmware without the need to model hardware dependencies. We achieve this by rehosting the embedded application as a Linux application. In addition to avoiding precise peripheral modeling, our rehosting technique enables the use of existing dynamic analysis techniques on these embedded applications. We provide an overview of our approach and demonstrate its feasibility on three real-world embedded applications. Our testing of these rehosted applications found 2 previously unknown defects in driver components. We discuss challenges in automating our process and present possible future research directions.
PSP Framework: A novel risk assessment method in compliance with ISO/SAE-21434

As more cars connect to the internet and other devices, the automotive market has become a lucrative target for cyberattacks. This has made the industry more vulnerable to security threats. As a result, car manufacturers and governments are working together to reduce risks and prevent cyberattacks in the automotive sector. However, existing attack feasibility models derived from the information technology field may not always provide accurate assessments of the potential risks faced by Vehicle Electronic Control Units in different operating conditions and domains. To address this issue, This paper introduces the PUNCH Softronix and Politecnico di Torino (PSP) framework. This framework is designed to provide accurate assessments compatible with the attack feasibility models defined by the automotive product security standards. The PSP framework utilizes social sentiment analysis to evaluate the real threat risk levels.
Tutorial: Using the seL4 Microkernel

The seL4 microkernel [3] is the first general-purpose operating system (OS) kernel with a formal proof of implementation correctness. By now, its verification covers functional correctness to the binary (taking the compiler out of the trust chain), proofs of security enforcement and worst-case execution-time bounds, and span three architectures (32-bit Arm and 64-bit RISC-V and x86) [1]. All this while featuring unbeaten performance. seL4 is now being deployed in safety- and security-critical systems around the world, and is backed by the non-profit seL4 Foundation [2].
Aerial Image Stitching Method Based on Feature Transfer and Tile Image

For the problems of slow stitching speed and large memory resource consumption, which caused by the high pixel, high precision and large size of UAV image. In this paper, a method of feature information transfer and image slicing is proposed to improve the stitching speed of images and reduce the memory usage in the stitching process. First, the image is corrected through POS data and reduced image size. Then, the SURF algorithm is used to calculate the feature points and feature vectors of the image, the image stitching is completed by gradually entering and leaving, and the feature points and feature vectors of the previous one is assigned to the stitched image after the stitching is completed. After every N images are stitched, the images are sliced and stored, freeing up system memory. The result is a good performance between speed and memory.
A Dynamic Trajectory Planning Algorithm for Urban Ultra-Low Altitude UAVs Based on a Fusion Heuristic Algorithm

This paper presents a dynamic trajectory planning method for ultra-low altitude UAV flights in urban multi-building environments and conducts simulations of multiple UAVs dynamic trajectory planning in urban environments. The initial UAV trajectory is obtained by adding a time dimension to the traditional $\mathrm{A}^{\star}$ algorithm, and the environment is monitored during flight, and local path re-planning is carried out by an improved ant colony algorithm when unexpected obstacles are encountered. Simulation experiments show that the dynamic trajectory planning algorithm proposed in this paper improves the usability of the planned path compared with the traditional path planning method, and the combination of pre-planned path and dynamic corrected trajectory is beneficial to the UAVs efficient flight in the complex urban environment.
Exploring the Effect of Various Maintenance Activities on the Accumulation of TD Principal

One of the most well-known laws of software evolution suggests that code quality deteriorates over time. Following this law, recent empirical studies have brought evidence that Technical Debt (TD) Principal tends to increase (in absolute value) as the system grows, since more technical debt issues are added than resolved over time. To shed light into how technical debt accumulation occurs in practice, in this paper we explore specific maintenance activities (i.e., feature addition, bug fixing, and refactoring) and explore the balance between the technical debt that they introduce or resolve. To achieve this goal, we rely on studying Pull Requests (PR), which are the most established way to contribute code to an open-source project. A Pull Request is usually comprised by more than one commits, corresponding to a specific development / maintenance activity. In our study, we categorized Pull Requests, based on their labels, to find the effect that the different maintenance activities have on the accumulation of technical debt across evolution. In particular, we have analysed more than 13.5K pull requests (mined from 10 OSS projects), by calculating the TD Principal (calculated through SonarQube) before and after the Pull Requests. The results of the study suggested that several labels are used for tagging Pull Requests, out of which the most prevalent ones are new features, bug fixing, and refactoring. The effect of these activities on TD Principal accumulation is statistically different, and: (a) the addition of features tends to increase TD Principal; (b) refactoring is having an almost consistent positive effect (reducing TD Principal); and (c) bug fixing activity has undecisive impact on TD Principal. These results are compared to existing studies, interpreted, and various useful implications for researchers and practitioners have been drawn.
Measuring Improvement of F<sub>1</sub>-Scores in Detection of Self-Admitted Technical Debt

Artificial Intelligence and Machine Learning have witnessed rapid, significant improvements in Natural Language Processing (NLP) tasks. Utilizing Deep Learning, researchers have taken advantage of repository comments in Software Engineering to produce accurate methods for detecting Self-Admitted Technical Debt (SATD) from 20 open-source Java projects’ code. In this work, we improve SATD detection with a novel approach that leverages the Bidirectional Encoder Representations from Transformers (BERT) architecture. For comparison, we re-evaluated previous deep learning methods and applied stratified 10-fold cross-validation to report reliable F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -scores. We examine our model in both cross-project and intra-project contexts. For each context, we use re-sampling and duplication as augmentation strategies to account for data imbalance. We find that our trained BERT model improves over the best performance of all previous methods in 19 of the 20 projects in cross-project scenarios. However, the data augmentation techniques were not sufficient to overcome the lack of data present in the intra-project scenarios, and existing methods still perform better. Future research will look into ways to diversify SATD datasets in order to maximize the latent power in large BERT models.
Resolving Security Issues via Quality-Oriented Refactoring: A User Study

Software quality is crucial in software development: if not addressed in early phases of the software development life cycle, it may even lead to technical bankruptcy, i.e., a situation in which modifications cost more than redeveloping the application from scratch. In addition, code security must also be addressed to reduce software vulnerabilities and to comply with legal requirements. In this work, we aim to investigate the relationship between refactoring code quality and software security, with the purpose of understanding whether and to what extent improving software quality could have a positive impact on software security as well. Specifically, we investigate to what extent rule violations of a software quality tool such as SonarQube overlap with rule violations of a software vulnerability tool like Fortify Static Code Analyzer. We first compared the rules encoded in the quality models of both tools, to discover possible overlapping cases. Later, we compared the issues raised by both tools on a set of open source Java projects; we also investigated the cases in which a quality refactoring process impacts over software security (thus removing one or more vulnerabilities). We furthermore validated our results statistically. Our results show that resolving software quality issues might also resolve security issues but only in part: many security issues still persist in the source code; also, some quality aspects are more likely to be improved in respect to others. In addition, this empirical study uncovers rule co-occurrences between the two tools. This study confirms the need for using a security-oriented static analysis tool to enforce software security instead of relying only on a quality-oriented one. Results have highlighted important insights for practitioners.
Practical Algorithm Substitution Attacks on Real-World Public-Key Cryptosystems

The revelations about massive surveillance have created significant interest in algorithm substitution attack (ASA), where an honest implementation of a cryptographic primitive is replaced by a subverted one which can help “big brother" to break cryptographic security while generating output indistinguishable from the honest output. The current known ASAs on public-key cryptography are either dedicated for a type of concrete constructions with specific internal, or restrictive when applying to the real-word cryptographic standards (Ateniese et al., ACM CCS’15; Russell et al., ACM CCS’17; Chen et al., ASIACRYPT’20). In this paper, we first present a practical undetectable substitution for a general randomized algorithm with certain structure such that the randomness can be revealed to the big brother. Then, instantiating this randomized algorithm, we present a series of ASAs on core primitives in public-key cryptography including public-key encryption, key encapsulation mechanism, key exchange, and digital signature. In particular, our ASAs are universal in the sense that they do not rely on the internal description of the underlying cryptographic algorithm. Moreover, our ASAs are also practical since they can affect not only the widely deployed cryptographic standards, but also the ongoing NIST post-quantum standards.
Modeling of Reptile Search Algorithm With Deep Learning Approach for Copy Move Image Forgery Detection

Copy-move (CM) forgery is a common type of image manipulation that involves copying and pasting a region within an image to conceal or duplicate content.Detection of such forgeries acts as an important part of digital image forensics.Deep learning techniques, such as convolutional neural networks (CNNs), are employed to extract informative features from images.CNNs are known for their ability to capture complex patterns and structures, making them well-suited for image-related tasks like forgery detection.This paper introduces a reptile search algorithm with a deep transfer learning-based CM forgery detection (RSADTL-CMFD) approach.The presented model uses Neural Architectural Search Network (NASNet) for feature extraction in forgery detection which allows the network to effectively capture relevant and discriminative features from the input images.To enhance the performance of the NASNet model, we employ the reptile search algorithm (RSA) for hyperparameter tuning.This algorithm optimizes the network's hyperparameters, enabling the model to quickly adapt to different forgery detection tasks and achieve superior performance.Finally, extreme gradient boosting (XGBoost) effectively utilizes the extracted features from the deep learning network to classify regions within the image as genuine or manipulated/forged.The experimental result analysis of the RSADTL-CMFD model is tested using benchmark datasets.An extensive comparative study highlighted the enhanced outcomes of the RSADTL-CMFD method over recent techniques.
Secure Communication Guarantees for Diverse Extended-Reality Applications: A Unified Statistical Security Model

Privacy and security assurance over wireless transmissions is one of critical issues for future extended reality (XR) communication systems expected to be supported by the sixth generation of mobile communications networks (6G). In light of the strong anti-eavesdropping capability, physical layer security (PLS) techniques have been recognized as a competitive candidate to provide secure transmissions for XR services. However, existing key performance evaluation metrics, such as the security capacity and the outage security capacity, cannot well capture diverse features of quality-of-security (QoSec) requirements raised by XR services. To overcome the problem, in this article we propose a unified statistical security model that can characterize fine-grained security requirements for various XR applications. Specifically, the eavesdropping process at the eavesdropper is modeled by a queuing system. The arrival process represents the legitimate user's streaming data correctly-captured by the eavesdropper. The departure process represents data that are outdated and moved out of the queue, fitting the essential time-sensitive nature of XR applications. Yet storing the overheard data in the queue is not equivalent to data recovery, the eavesdropper has to accumulate a sufficient of amount correctly-captured data in the queue to successfully decipher some data each time. Under this framework, leveraging the effective bandwidth theory in statistically queuing analyses, we develop the concept of statistical security capacity, which is used to evaluate the legitimate user's throughput with the constrained information level leaked to the eavesdropper. The statistical security model is featured with a parameter called QoSec exponent, quantitatively indicating the fine-grained level of security requirement. Following this model, we formulate the nonconvex statistical-security-capacity maximization problems with the internal and external eavesdroppers, respectively, associated with the cases with and without eavesdropper's CSI known at the legitimate transmitter. Solving the two problems, we derive the corresponding optimal resource schemes over the time-varying fading channels. Simulation results demonstrate our proposal as an effective model for security requirements, and our scheme can significantly improve security-constrained throughput in XR communications compared to the baseline schemes.
ICNN-Ensemble: An Improved Convolutional Neural Network Ensemble Model for Medical Image Classification

Deep learning (DL) classification has become a major research topic in the areas of cancer prediction, image cell classification, and image classification in medicine.Furthermore, DL classification is the core of other subfields.Owing to various forms of ensemble models, DL models have achieved state-of-theart performances in fields such as medicine.However, the existing models cannot solve the problem of generalization perfectly and proposed solutions only for tasks with specific datasets.Most state-of-the-art classification models presented their results in ImageNet dataset, and models elaborate on the insights of the dataset.Nonetheless, model architectures or pretrained models cannot provide the same accurate results for datasets with different classes than ImageNet.Hence, this research proposes an improved convolutional neural network ensemble (ICNN-Ensemble) based on the representation of high-resolution image channels (RHRIC) and a systematic model dropout ensemble (SMDE).ICNN-Ensemble exploits image channels after applying RHRIC and RGB images in their original forms, which accesses more residual feature connections and represents more insight into image channels.Furthermore, SMDE is applied to choose ensemble members, considering the changes of the accurate prediction field (APF) in the ICNN-Ensemble model.In addition, the proposed model executes ensembling during the test set prediction, which allows the model to be trained with larger batches and images compared to ensemble model's final results during training, allowing maximal effective usage of the graphics processing unit (GPU).Despite the small size of the model, the results of benchmarking for Malaria cell images dataset clearly illustrated that the ICNN-Ensemble model achieved significantly more accurate results than other base state-of-the-art models.
Deep Learning-Based Multi-Modal Ensemble Classification Approach for Human Breast Cancer Prognosis

Ensemble models based on deep learning have made significant contributions to the medical field, particularly in the area of disease prediction.Breast cancer is a highly aggressive disease with a high mortality rate.Timely and effective prediction of breast cancer can reduce the risk of it progressing to later stages and the need for unnecessary medications.While previous studies have focused on predicting breast cancer using single-modal datasets, multi-modal datasets that include gene expression (gene exp), clinical, and copy number variation (CNV) data have become available in recent years for predictive model development.However, despite multiple studies using multi-modal data for disease prediction, models designed for breast cancer are typically homogeneous neural networks.This article proposes a heterogeneous deep learning-based ensemble model for effective breast cancer prediction using multi-modal data.The model consists of three phases: feature extraction, stacked feature set creation, and using extracted features as input for a stacked-based model using a random forest algorithm for effective prediction.For feature extraction, convolutional neural networks (CNNs) are used for clinical and gene expression data, and deep neural networks (DNNs) are used for CNV data.The extracted features from CNNs and DNNs are stacked to create a comprehensive feature set.The simulation results demonstrate the superiority of the proposed framework in terms of accuracy compared to uni-modal and homogeneous model-multi-modal frameworks.