Reviewerly/exHarmony huggingface dataset:
First steps in verifying the seL4 Core Platform

We report on our initial effort to formally verify the seL4 Core Platform, an OS framework for the verified seL4 microkernel. This includes a formal specification of the seL4 Core Platform library, an automated proof of its functional correctness, and a verified mapping of the seL4 Core Platform's System Description to the CapDL formalism that describes seL4 access rights and enables verified system initialisation.
Understanding the Security of Linux eBPF Subsystem

Linux eBPF allows a userspace application to execute code inside the Linux kernel without modifying the kernel code or inserting a kernel module. An in-kernel eBPF verifier pre-verifies any untrusted eBPF bytecode before running it in kernel context. Currently, users trust the verifier to block malicious bytecode from being executed.
Sampling with transects inside archaeological sites. A rapid action protocol based on statistical analysis of diagnostic collections and quantitative records

The paper proposes a two stage-sampling strategy for surface surveys. The suggested approach has two stages. First, an intensive survey is undertaken across linear transects, georeferencing the position of each item, and then supplementary diagnostic artefacts between the linear transects are collected. The main objective of the paper is to save time in the first stage of fieldwork. The key claim is that this approach would provide a representative sample of the surface scatter using just one selected transect. The proposed method is applied to the examination of two sites in the Municipium Flavium Baesuccitanum, a Roman municipality from the 1st century CE, located in the upper Guadalquivir valley, in the south of Spain, where a total survey was previously carried out. To assess the reliability of this approach, a simulation study was conducted at these two sites, selected by intensive sampling. These sites were chosen because they are real cases with different singularities, which facilitates analysis of how the proposed protocol would behave in different scenarios. In both cases, the statistical results confirm the plausibility of the hypothesis of similarity between the distributions of the whole site and the candidate transect.
General Boolean Function Benchmark Suite

Just over a decade ago, the first comprehensive review on the state of benchmarking in Genetic Programming (GP) analyzed the mismatch between the problems that are used to test the performance of GP systems and real-world problems. Since then, several benchmark suites in major GP problem domains have been proposed over time, filling some of the major gaps. In the framework of the first review about the state of benchmarking in GP, logic synthesis (LS) was classified as one of the major GP problem domains. However, a diverse and accessible benchmark suite for LS is still missing. In this work, we propose a benchmark suite for LS that covers different types of Boolean functions that are commonly used in the field of GP. We analyze the complexity of the proposed benchmark by using popular complexity measures that are commonly used to classify and characterize Boolean functions and digital circuits.
First Steps Towards a Runtime Analysis of Neuroevolution

We consider a simple setting in neuroevolution where an evolutionary algorithm optimizes the weights and activation functions of a simple artificial neural network. We then define simple example functions to be learned by the network and conduct rigorous runtime analyses for networks with a single neuron and for a more advanced structure with several neurons and two layers. Our results show that the proposed algorithm is generally efficient on two example problems designed for one neuron and efficient with at least constant probability on the example problem for a two-layer network. In particular, the so-called harmonic mutation operator choosing steps of size j with probability proportional to 1/j turns out as a good choice for the underlying search space. However, for the case of one neuron, we also identify situations with hard-to-overcome local optima. Experimental investigations of our neu-roevolutionary algorithm and a state-of-the-art CMA-ES support the theoretical findings.
Flexible unmanned surface vehicles control using probabilistic model-based reinforcement learning with hierarchical Gaussian distribution

This paper focuses on improving the flexibility of probabilistic model-based reinforcement learning (MBRL) in unmanned surface vehicles (USV) against complicated ocean disturbances. Filtered probabilistic model predictive control using hierarchical Gaussian distribution (FPMPC-HG) is proposed to provide a more flexible representation of environmental uncertainties and better control capability against real-time disturbances by integrating hierarchical Gaussian distribution into existing approaches specific to USVs. The proposed approach was evaluated through position-keeping and targets-tracking tasks in a real boat data-driven simulation. The experimental results demonstrated significant improvement in control performance, generalization capability, and task completion compared to the baseline approaches without employing hierarchical Gaussian distribution. The improved expressivity and flexibility of the probabilistic model and the corresponding policy of USVs against unknown ocean disturbances indicate the potential of hierarchical Gaussian distribution in probabilistic MBRL as a growing trend in the USV domain.
Weighted Mutation of Connections To Mitigate Search Space Limitations in Cartesian Genetic Programming

This work presents and evaluates a novel modification to existing mutation operators for Cartesian Genetic Programming (CGP). We discuss and highlight a so far unresearched limitation of how CGP explores its search space which is caused by certain nodes being inactive for long periods of time. Our new mutation operator is intended to avoid this by associating each node with a dynamically changing weight. When mutating a connection between nodes, those weights are then used to bias the probability distribution in favour of inactive nodes. This way, inactive nodes have a higher probability of becoming active again. We include our mutation operator into two variants of CGP and benchmark both versions on four Boolean learning tasks. We analyse the average numbers of iterations a node is inactive and show that our modification has the intended effect on node activity. The influence of our modification on the number of iterations until a solution is reached is ambiguous if the same number of nodes is used as in the baseline without our modification. However, our results show that our new mutation operator leads to fewer nodes being required for the same performance; this saves CPU time in each iteration.
Reducing Attack Surface with Container Transplantation for Lightweight Sandboxing

Containers, which have evolved in Linux primarily, have become a significant trend in the cloud due to their lightweight virtualization and growing convenient ecosystem. However, the laxer isolation of containerization also introduces attack surfaces on the underlying Linux kernel. Unfortunately, combining other virtualizations, such as the traditional VM and interposition by application kernel, for sandboxing could spoil the lightweight and scalable nature of the containers. In this study, we propose another approach to lightweight sandboxing that focuses on the fact that such attackers have mostly assumed containers rely on Linux. It can avert major vulnerability exploits derived from Linux by transplanting Linux containers onto the FreeBSD kernel. Furthermore, it can fortify the isolation by transparently applying "Capsicum," a unique sandbox mechanism that is nonstandard in Linux, to the transplanted containers. This paper analyzes vulnerabilities faced by Linux containers, identifies technical issues in transplanting Linux containers onto FreeBSD, and designs a mechanism to transparently apply the Capsicum sandbox to Linux applications to explore the feasibility of our approach.
First Complexity Results for Evolutionary Knowledge Transfer

The field of evolutionary knowledge transfer (EKT) has recently begun to systematically develop algorithms that exploit a number of related problem instances to accelerate problem solving on difficult optimization tasks. EKT has the potential to have a major impact on evolutionary computation practice, comparable to the role that neural network pretraining has had on machine learning. But the community has only scratched the surface of the theoretical hurdles that the knowledge-transfer workflow raises. We introduce a three-part collect-select-exploit framework for understanding EKT, which we use to highlight the need for better evaluation and benchmarking approaches for transfer. We then present some of the first analytical results for EKT, proving no-free-lunch theorems for transfer, and proving what is (to our knowledge) the first asymptotic runtime result for transfer optimization. These results can serve as the basis for future research into the strengths and limitations of knowledge transfer as an optimization paradigm, and they serve to emphasize the need for more comprehensive benchmarks to hone progress in the field.
Curing ill-Conditionality via Representation-Agnostic Distance-Driven Perturbations

The objective value of an ill-conditioned function may significantly change with a minor shift of the argument in the search space. Ill-conditioned functions do not have at all or exhibit very few hints towards better solutions and, thus, they are usually difficult to optimize with randomized search heuristics. However, problems that emerge in practical applications are likely to be formulated as ill-conditioned functions, as often Euclidean metric is used to measure distance in the search space. At the same time, it may be possible to use domain-specific knowledge to define such a metric in the search space so that the function stops being ill-conditioned. We consider finite search spaces and propose two mutation operators that leverage such metric to optimize the function more efficiently. The first operator assumes prior knowledge about the distance, the second operator uses the distance as a black box. Those operators apply an estimation of distribution algorithm to find the best mutant according to the defined function, which employs the given metric. For pseudo-Boolean and integer optimization problems, we experimentally show that both mutation operators speed up the search on most of the functions when applied in considered evolutionary algorithms and random local search. Moreover, those operators can be applied in any randomized search heuristic which uses perturbations. However, our mutation operators increase wall-clock time and so are helpful in practice when distance is (much) cheaper to compute than the real objective function.
Cultivating Creativity of High School Students in Cross-Cultural Learning Project Based on VR Technology

The cross-cultural learning activity was carried out using virtual reality (VR) created by 360-degree video technology. The study focused on improvements of students’ creativity. Students from China and Indonesia participated in the activity. Students created VR panoramic tours using 360-degree video technology, exchanged them and were involved in discussions about cultural artifacts and created tours. We investigated whether the cross-cultural learning activity supported by VR technology positively impacted students’ creativity. The data was collected through the questionnaire, rubric, and interview. The results demonstrated that the cross-cultural learning activity helped improve students’ creativity. Based on the results some suggestions were put forward for educators and researchers in the field.
Improving Throughput-oriented Generative Inference with CPUs

Despite recent attempts to reduce the number of parameters of large language models (LLMs), their parameter data is still too large to fit into a single GPU. With the emergence of throughput-oriented tasks, high-throughput generative inference frameworks for LLMs on a single commodity GPU leverage GPU, DRAM, and NVMe to run inference on large models with terabytes of data. Our analysis of the technique shows that the runtime is dominated by data transfers of the weights, leading to a low utilization of both the GPU and the CPU. In this paper, we increase the throughput and decrease the total latency of state-of-the-art frameworks by including the CPU as a compute device and overlapping computations on the CPU with GPU data transfers. Our work shows a promising improvement of around 40% in throughput and total latency, with potential room for further improvements.
Trusted Heterogeneous Disaggregated Architectures

The rising performance demands and increasing heterogeneity in cloud data centers lead to a paradigm shift in the cloud infrastructure, from monolithic servers to a disaggregated architecture. In a multi-tenant cloud, users should be able to leverage trusted computing to protect their applications from untrusted parties. While Trusted Execution Environments (TEEs) are a well-known technique to realize trusted computing on monolithic servers, we cannot adopt existing TEE technologies to the disaggregated architecture due to their distributed nature and heterogeneity of devices. To address these challenges, we propose trusted heterogeneous disaggregated architectures, which allows cloud users to construct virtual TEEs (vTEEs): TEE-based, secure, isolated environments assembled with any combination of disaggregated components.
Reliable prediction of software defects using Shapley interpretable machine learning models

Predicting defect-prone software components can play a significant role in allocating relevant testing resources to fault-prone modules and hence increasing the business value of software projects. Most of the current software defect prediction studies utilize traditional supervised machine learning algorithms to predict defects in software applications. The software datasets utilized in such studies are imbalanced and therefore the reported results cannot be reliably used to judge their performance. Moreover, it is important to explain the output of machine learning models employed in fault-predication techniques to determine the contribution of each utilized feature to the model output. In this paper, we propose a new framework for predicting software defects utilizing eleven machine learning classifiers over twelve different datasets. For feature selection, we employ four different nature-inspired search algorithms, namely, particle swarm optimization, genetic algorithm, harmony algorithm, and ant colony optimization. Moreover, we make use of the synthetic minority oversampling technique (SMOTE) to address the problem of data imbalance. Furthermore, we utilize the Shapley additive explanation model for highlighting the highest determinative features. The obtained results demonstrate that gradient boosting, stochastic gradient boosting, decision trees, and categorical boosting outperform others tested model with over 90% accuracy and ROC-AUC. Additionally, we found that the ant colony optimization technique outperforms the other tested feature extraction techniques.
Detecting accurate parametric intrusions using optical fiber sensors for long-distance data communication system

The intrusion detectors and warning devices have been aided by advancements in the fields of optical fiber, integrated circuit fabrication technology, and devices. This paper describes a system of long-distance intrusion and a warning system for monitoring third-party physical intrusion in long-distance sensitive Areas. It provides around-the-clock third-party intrusion detection. The signals of the external sensors are influenced by vegetation and external natural conditions, but not in the case of optical cable sensors. A workflow algorithm has been developed in this research paper where Fast Fourier transform (FFT) is used and mathematical interpretation is made. Long-distance data communication for optical perimeter intrusion detection and warning systems is demonstrated using a block diagram. Intrusion detection system range of detection has been demonstrated pictorial also. The higher the magnitude of vibration of equipment or human being, the higher will be the range of the detection by the system. Real-Time intrusion simulation performed during the research work is depicted for a better understanding. We have conducted three simulations namely manual digging, vehicle traversing the fiber and mechanical digging which is depicted with amplitude vs. channel graph and also by waterfall display. The controller employs the distributed acoustic sensing (DAS) principle which is based on interrogating the scattering of light from nanoparticles on the optical fiber in response to changes in refractive index or molecular binding events in the surrounding environment.
Quasi opposite-based learning and double evolutionary QPSO with its application in optimization problems

Although quantum-behaved particle swarm optimization (QPSO) algorithm has the advantages of few parameters and simple implementation, it suffers from these problems of low precision while calculating high-dimensional complex problems and being caught in local optimum during the later stage of iteration. To tackle these shortages effectively, a modified QPSO (QDQPSO) algorithm is presented. In order to improve the search efficiency and convergence speed of the algorithm, the idea of quasi opposite-based learning is used in the initialization stage. For enhancing the overall performance of the algorithm, the double evolutionary mechanism is applied to update the individual location during the iterative process. Furthermore, perturbation at global optimum position and bound constraint handling are considered to help the algorithm to escape from local optimum and maintain the diversity of population. According to the results obtained by QDQPSO and other nine optimization algorithms on 28 benchmark functions under different dimensions, it is found that QDQPSO performs better on the accuracy and stability of the optimal solution. Subsequently, Wilcoxon rank-sum test and Friedman test demonstrate significant advantages of the improved algorithm. Finally, QDQPSO algorithm displays superior performance in solving five practical optimization problems compared to several optimization methods.
Liquid

Today's deep neural network (DNN) training pipeline utilizes hardware resources holistically, including host CPUs and storage devices for preprocessing the input data and accelerators like GPUs for computing gradients. As the performance of the accelerator scales rapidly, the frontend data preparation stages are becoming a new performance bottleneck to yield suboptimal training throughput. Since the bottleneck in the pipeline may vary depending on hardware configurations, DNN models, and datasets, overprovisioning hardware resources for data preparation such as CPU cores and disk bandwidth is not a cost-effective solution. Instead, we make a case for leveraging multiple data formats, possibly with opposing characteristics in resource utilization, to balance the training pipeline. This idea is realized by Liquid, a new system for building an efficient training pipeline with multi-format datasets. Our evaluation on three distinct execution environments demonstrates that Liquid achieves up to 3.05x and 1.54x higher data preparation throughput on Cityscapes/CityPersons (PNG) and ImageNet (JPEG) datasets, respectively, over the baseline single-format pipeline. This leads up to 2.02x and 1.25x higher end-to-end geomean training throughput with no accuracy drop.
Reviving the Arabian Souk using Mobile Augmented Reality Technology: A Qualitative Thematic Analysis

This article discusses the outcomes of a Focus Group thematic analysis that revolved around the topic of using mobile Augmented Reality (AR) technology to help revive the Arabian Souk. The Old Souk of the city of Abu Dhabi was utilized as a case study for revival, using AR for a new form of smart tourism for the city. Major themes revealed both intangible and tangible elements through a variety of shops, products and personal experiences from those who have visited the Old Souk. The themes were then turned into an AR experience, in the form of three-dimensional (3D) computer-aided models, to aid in experiencing the once existing Old Souk on its original site today. This article points to the potential of recreating the Arabian Souk using AR technology as part of the Arabian Gulf’s smart tourism experience.
Determination of bending and lateral-torsional buckling displacements in castellated and circular opening steel beams by image processing

The points in high-web steel beams deviate irregularly due to lateral-torsional buckling, so it’s a challenging task to accurately measure displacements perpendicular to the loading and precise measurements cannot be made with fixed gauging devices. This study aimed to determine all displacements in high-web beams including those caused by buckling using an image processing technique. One castellated and one circular opening steel beam were produced and exposed to four-point bending tests. The vertical displacements were measured by LVDTs and transducers, and buckling displacements in the web and flanges at points spaced at certain intervals were measured by a total station device. Aside from that, both vertical and lateral deformations all over the beam were determined by a novel image processing technique using video recordings taken by two cameras (front and side). By the image processing technique, vertical displacements were detected with an accuracy of 98%, and deformations and displacements arising from lateral-torsional buckling on the web were detected with an accuracy of 99%. In conclusion, it is shown that the image processing technique can be easily and efficiently used for precise detection of both in-plane and out-of-plane deformations of castellated steel beams caused by bending moment and lateral torsional buckling.
Runtime Analysis of a Co-Evolutionary Algorithm

Co-evolutionary algorithms have found several applications in game-theoretic applications and optimisation problems with an adversary, particularly where the strategy space is discrete and exponentially large, and where classical game-theoretic methods fail. However, the application of co-evolutionary algorithms is difficult because they often display pathological behaviour, such as cyclic behaviour and evolutionary forgetting. These challenges have prevented the broad application of co-evolutionary algorithms.
Evasive subspaces, generalized rank weights and near MRD codes

We revisit and extend the connections between Fqm-linear rank-metric codes and evasive Fq-subspaces of Fqmk. We give a unifying framework in which we prove in an elementary way how the parameters of a rank-metric code are related to special geometric properties of the associated evasive subspace, with a particular focus on the generalized rank weights. In this way, we can also provide alternative and very short proofs of known results on scattered subspaces. We then use this simplified point of view in order to get a geometric characterization of near MRD codes and a clear bound on their maximal length. Finally we connect the theory of quasi-MRD codes with h-scattered subspaces of maximum dimension, extending to all the parameters sets the already known results on MRD codes.
Retracted: College English Multimodal Teaching Based on Digital Information Technology

[This retracts the article DOI: 10.1155/2022/2911215.].