Reviewerly/exHarmony huggingface dataset:
TGPrint: Attack fingerprint classification on encrypted network traffic based graph convolution attention networks

Nowadays, most network traffic is encrypted, which protects user privacy but hides attack traces, further hindering identifying attacks to inspect traffic packages. Machine Learning (ML) methods are widely applied to attack classification on encrypted traffic owing to no need for manual analysis. However, existing studies only concentrate on basic statistical features and cannot obtain the crucial attack behaviors hiding in the encrypted traffic. Worse still, attackers constantly update attack vectors to evade detection, which means outdated features extracted from historical traffic fail to recognize unseen attacks. As a solution, we propose an attack classification approach, attack fingerprint based on graphs of time-window (TGPrint). We first filter normal traffic flows using ML models to eliminate the impact of useless, noisy data for attack classification and maintain suspicious traffic. Then, we create attack graphs to depict interaction behaviors of attack-victim hosts from suspicious traffic containing crucial attack behaviors. Besides, we divide a specific duration for each attack to precisely elaborate attack graphs, where temporal, statistical, and aggregate features are extracted to portray attack behaviors. Finally, we utilize Graph Neural Networks (GNNs) to mine and grasp the crucial behavior patterns from attack graphs to generate fingerprints and classify attacks, even unseen attacks. Extensive experiments are conducted on well-known datasets to verify our approach. It achieves a precision of 99% in attack classification on encrypted traffic, an average higher than other ML methods of 50%. Meanwhile, it classifies unseen attacks with an average accuracy of over 80% and has a strong robustness to false positives.
Mining Multiple Class Imbalanced Datasets Using a Specialized Balancing Algorithm and the Adaboost Technique

In this paper, we propose an ensemble classifier extended from a specialized bicriterion balancing algorithm originally proposed by the authors for binary imbalanced classification. The approach uses two specialized criteria for oversampling - classification potential and distance from the borderline between minority and majority instances. For mining multiclass imbalanced datasets the bicriteria oversampling algorithm was adapted to the needs of the multiple class problems using the one-versus-one (OVO) approach and the Adaboost technique. To evaluate the performance of the proposed ensemble classifier we use several state of the art balancing algorithms. The computational experiment shows a very good performance of the proposed approach.
Evaluation of Laser Image Enhancement and Restoration for Underwater Object Recognition

Underwater object recognition is a challenging task due to the degradation of image quality caused by the scattering of light in the water medium. A variety of techniques for image enhancement and restoration (IE/IR) have been proposed over the years; however, these methods have been developed for primarily for imaging of scenes with RGB cameras, not object recognition with laser images, which is the focus of this work. Due to the different radiometric transfer properties of air and water, methods developed for airborne applications are not necessarily as effective on underwater images. Moreover, typical image quality metrics are not necessarily indicative of the efficacy of IE/IR when these techniques are applied prior to object recognition. In contrast, this paper presents an experiment-based, quantitative evaluation of a wide range of image quality metrics and IE/IR algorithms for the purpose of underwater object recognition using laser images by comparing their resulting deep-learning based classification accuracies. A diverse dataset of underwater laser images is acquired for seven different objects positioned at various depths and in water of five different turbidity levels. Our findings thus provide a critical review of current techniques, identify effective methods and metrics, and shed light on ongoing challenges.
Analysis of conditional randomisation and permutation schemes with application to conditional independence testing

Abstract We study properties of two resampling scenarios: Conditional Randomisation and Conditional Permutation schemes, which are relevant for testing conditional independence of discrete random variables X and Y given a random variable Z . Namely, we investigate asymptotic behaviour of estimates of a vector of probabilities in such settings, establish their asymptotic normality and ordering between asymptotic covariance matrices. The results are used to derive asymptotic distributions of the empirical Conditional Mutual Information in those set-ups. Somewhat unexpectedly, the distributions coincide for the two scenarios, despite differences in the asymptotic distributions of the estimates of probabilities. We also prove validity of permutation p -values for the Conditional Permutation scheme. The above results justify consideration of conditional independence tests based on resampled p -values and on the asymptotic chi-square distribution with an adjusted number of degrees of freedom. We show in numerical experiments that when the ratio of the sample size to the number of possible values of the triple exceeds 0.5, the test based on the asymptotic distribution with the adjustment made on a limited number of permutations is a viable alternative to the exact test for both the Conditional Permutation and the Conditional Randomisation scenarios. Moreover, there is no significant difference between the performance of exact tests for Conditional Permutation and Randomisation schemes, the latter requiring knowledge of conditional distribution of X given Z , and the same conclusion is true for both adaptive tests.
A Visual-Vestibular Model to Predict Motion Sickness for Linear and Angular Motion

Objective This study proposed a model to predict passenger motion sickness under the presence of a visual-vestibular conflict and assessed its performance with respect to previously recorded experimental data. Background While several models have been shown useful to predict motion sickness under repetitive motion, improvements are still desired in terms of predicting motion sickness in realistic driving conditions. There remains a need for a model that considers angular and linear visual-vestibular motion inputs in three dimensions to improve prediction of passenger motion sickness. Method The model combined the subjective vertical conflict theory and human motion perception models. The proposed model integrates visual and vestibular sensed 6 DoF motion signals in a novel architecture. Results Model prediction results were compared to motion sickness data obtained from studies conducted in motion simulators as well as on-road vehicle testing, yielding trends that are congruent with observed results in both cases. Conclusion The model demonstrated the ability to predict trends in motion sickness response for conditions in which a passenger performs a task on a handheld device versus facing forward looking ahead under realistic driving conditions. However, further analysis across a larger population is necessary to better assess the model’s performance. Application The proposed model can be used as a tool to predict motion sickness under different levels of visual-vestibular conflict. This can be leveraged to design interventions capable of mitigating passenger motion sickness. Further, this model can provide insights that aid in the development of passenger experiences inside autonomous vehicles.
Assessing the Effectiveness of Fraud Detection Models

Our research seeks to determine whether the detection of potential fraud in China-listed U.S. companies could be measured by the U.S. M-Score and F-Score models. Then, we group the data for 18 pairs of companies to make a t-test defined as the average difference between firms' M-scores or F-scores before and after the fraud. Moreover, more detailed information about these companies, financial fraud methods and motives, and ways to prevent financial fraud. Through our preliminary research, these two U.S. detection models can be used to detect financial fraud for Chinese-listed U.S. companies in statistical significance. With this premise, we also do an additional t-test to prove the standing adverse effects of committing fraud on firms.
Research and Analysis of VR Technology in the Field of Education

Virtual reality (VR) is an emerging medium that has the potential to be a brand-new visual form with uses in a variety of industries, including but not limited to education, the arts, and entertainment. The educational application study is combed through and examined in this work from three angles: ideological education, artistic education, and educational video games. The use of virtual reality technology in ideological and ideological instruction is first implemented, starting with the educational sector. The use of virtual reality technology in the sphere of art is then introduced. This section illustrates the significant contribution that virtual reality technology has made to the study of art. It basically explains how VR can be used in the classroom, how valuable it is for teaching art design, and what VR is used for in the new art design curriculum. The author concludes by explaining how virtual reality education is founded on the teaching principle of games and uses educational goals as the design idea. The use of VR in these three areas is compiled and discussed at the conclusion of this essay.
Regional Consistency for Semi-Supervised Segmentation of 3D Medical Images

Semi-supervised medical image segmentation (SSMIS)is a research hotspot.However,existing consistency regular-ization methods do not adequately consider the robustness gains obtained by the model overcoming perturbations in the network structure and the spatial context. To address this problem, we propose a regional consistency strategy for SSMIS. Specifically, we construct network structure perturbations by making the two networks use different downsampling strategies. As for spatial contextual perturbations, we perform two random crops for each 3D medical image and feed different sub-image to different networks. We introduce entropy minimization to encourage both networks to produce consistent, high-confidence predictions for intersecting regions. A weighted combination of supervised and unsupervised losses optimizes the networks. We conducted extensive experiments on two datasets, and the results show that introducing network structure perturbations and spatial environmental perturbations can improve various metrics and demonstrate the effectiveness of our method
Data-quality Aware Incentive Mechanism based on Stackelberg Game in Mobile Edge Computing

Crowdsourcing becomes a popular data-collection method to relieve the burden of high cost and latency for data-gathering. Since the involved users in crowdsourcing are volunteers, need incentives to encourage them to provide data. However, the current incentive mechanisms mostly pay attention to the data quantity, while ignoring the data quality. In this paper, we design a Data-quality awaRe IncentiVe mEchanism (DRIVE) for collaborative tasks based on the Stackelberg game to motivate users with high quality, the highlight of which is the dynamic reward allocation scheme based on the proposed data quality evaluation method. In order to guarantee the data quality evaluation response in real-time, we introduce the mobile edge computing framework. Finally, one case study is given and its real-data experiments demonstrate the superior performance of DRIVE.
Evaluating and Increasing Segmentation Robustness in CARLA

Model robustness is a crucial property in safety-critical applications such as autonomous driving and medical diagnosis. In this paper, we use the CARLA simulation environment to evaluate the robustness of various architectures for semantic segmentation to adverse environmental changes. Contrary to previous work, the environmental changes that we test the models against are not applied to existing images, but rendered directly in the simulation, enabling more realistic robustness tests. Surprisingly, we find that Transformers provide only slightly increased robustness compared to some CNNs. Furthermore, we demonstrate that training on a small set of adverse samples can significantly improve the robustness of most models. The code and supplementary results for our experiments are available online ( https://github.com/venkatesh-thiru/weather-robustness ).
Data Classification of Dark Web using SVM and S3VM

Abstract: There are many issues regarding the dark web structural Type. It also increases the number of cybercrimes like illegal trade, forums, Terrorist activity. By understanding online criminal’s actions are challenging because the data is available in a very great extent amount. In a recent day the Online crimes are increasing all over the world. The data related to different types of frauds and scams, such as phishing schemes, identity theft etc. The data and discussion related to the act of hacking (hacktivist) activities, this often involve political or social causes. In some parts of dark web might be used for anonymous communication and the losing of sensitive information to explore wrong doing by governments or corporations. But in some countries the dark web might be used as a means to access information and content that is hardly restricted. The primary focus of this research is to develop a hybrid classification model that combines the strengths of deep learning and natural language processing algorithms. The model leverages a curated dataset of Dark Web content, meticulously labeled by content category, ranging from illegal commerce to cyber threats. By extracting relevant features from the textual and visual components of the data, the model demonstrates superior accuracy in distinguishing between different content categories
gym-saturation: Gymnasium Environments for Saturation Provers (System description)

Abstract This work describes a new version of a previously published Python package — : a collection of OpenAI Gym environments for guiding saturation-style provers based on the given clause algorithm with reinforcement learning. We contribute usage examples with two different provers: Vampire and iProver. We also have decoupled the proof state representation from reinforcement learning per se and provided examples of using a known Python code embedding model as a first-order logic representation. In addition, we demonstrate how environment wrappers can transform a prover into a problem similar to a multi-armed bandit. We applied two reinforcement learning algorithms (Thompson sampling and Proximal policy optimisation) implemented in Ray RLlib to show the ease of experimentation with the new release of our package.
OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature Ranking

The design of modern recommender systems relies on understanding which parts of the feature space are relevant for solving a given recommendation task. However, real-world data sets in this domain are often characterized by their large size, sparsity, and noise, making it challenging to identify meaningful signals. Feature ranking represents an efficient branch of algorithms that can help address these challenges by identifying the most informative features and facilitating the automated search for more compact and better-performing models (AutoML). We introduce OutRank, a system for versatile feature ranking and data quality-related anomaly detection. OutRank was built with categorical data in mind, utilizing a variant of mutual information that is normalized with regard to the noise produced by features of the same cardinality. We further extend the similarity measure by incorporating information on feature similarity and combined relevance. The proposed approach’s feasibility is demonstrated by speeding up the state-of-the-art AutoML system on a synthetic data set with no performance loss. Furthermore, we considered a real-life click-through-rate prediction data set where it outperformed strong baselines such as random forest-based approaches. The proposed approach enables exploration of up to 300% larger feature spaces compared to AutoML-only approaches, enabling faster search for better models on off-the-shelf hardware.
Poster: Improving Customer Service Through Rapid Prototyping of Data Exploration Chatbots

Companies are increasingly using relational databases to store their business data. In most cases, they would benefit from adopting conversational user interfaces to explore this data, allowing non-technical users to perform complex queries without the need to involve customer service or developers. This poster presents the details on the re-engineering of CHATIDEA (CHATbots for Interactive Data ExplorAtion), a novel no-code framework that supports the rapid prototyping of chatbots to explore information extracted from structured data sources.
Morphologically-Aware Consensus Computation via Heuristics-based IterATive Optimization (MACCHIatO)

The extraction of consensus segmentations from several binary or probabilistic masks is important to solve various tasks such as the analysis of inter-rater variability or the fusion of several neural network outputs. One of the most widely used methods to obtain such a consensus segmentation is the STAPLE algorithm. In this paper, we first demonstrate that the output of that algorithm is heavily impacted by the background size of images and the choice of the prior. We then propose a new method to construct a binary or a probabilistic consensus segmentation based on the Fréchet means of carefully chosen distances which makes it totally independent of the image background size. We provide a heuristic approach to optimize this criterion such that a voxel’s class is fully determined by its voxel-wise distance to the different masks, the connected component it belongs to and the group of raters who segmented it. We compared extensively our method on several datasets with the STAPLE method and the naive segmentation averaging method, showing that it leads to binary consensus masks of intermediate size between Majority Voting and STAPLE and to different posterior probabilities than Mask Averaging and STAPLE methods. Our code is available at &lt;a href='https://gitlab.inria.fr/dhamzaou/jaccardmap'&gt;https://gitlab.inria.fr/dhamzaou/jaccardmap&lt;/a&gt;
An Introductory Synthetic Data Tool

ObjectivesSynthetic data reproduces features of a dataset without disclosing sensitive information, allowing researchers to explore data structures and test code without requiring access to real, potentially sensitive, data. We produced a low-fidelity synthetic data generation tool, accompanied by extensive documentation, allowing novice and expert users to produce such data.&#x0D; MethodsOur tool, consisting of a Python notebook and a user guide, takes a dataset as input, and produces ‘low-fidelity’ synthetic copy of this dataset, recreating the data fields (or columns) of a dataset, as well as the data types and statistical relationships within these fields, but not between them. It has been tested using real-world administrative data sets and with several users, looking at the quality of the data generated, inspecting whether the data is indeed low-fidelity (i.e. statistical relationships between fields are not recreated) and the usability of the tool.&#x0D; ResultsOur tool successfully created synthetic datasets from administrative datasets. Users were positive about its usability and the generated data. Tests indicated that computational memory is a main constraint on the size of datatable that can be read in by the tool. We have since implemented improvements to the memory efficiency of the tool to partially address this and have also added procedures that allow for using subsets instead of complete datasets, allowing for the use of datasets which would have otherwise been too large to be used. Testing further indicated that, while the tool by design does not preserve any relationships between fields, they can be reproduced by coincidence, and a limited disclosure process may be required when correlations from the original data are reproduced.&#x0D; ConclusionsThe tool is easy to use and therefore a useful introduction to synthetic data, providing users with a foundation before using more sophisticated synthetic data tools like Synthpop. Future work could include the development of a Python library and extension of the tool to handle linked datatables.
Meta-analisis Literasi Digital pada Pembelajaran

Digital literacy has a significant role in the learning process. Several articles reveal that the use of digital literacy can improve the quality and results of learning. The search for the extent to which digital literacy influences learning can be analyzed by meta-analysis. This study aims to analyze digital literacy articles on learning that are published in national journals indexed sinta 1-6. The research design used is meta-analysis. The research sample is 16 digital literacy articles on learning which are published nationally and meet the sample selection criteria. The research data was obtained from an analysis of digital literacy articles in learning. The data were analyzed by looking at the effect size of each article based on the results of the t-test analysis. The results of the study show that the average effect size is 0.373 in the large effect category which states that digital literacy has a large influence on learning. The region with the most use of digital literacy in learning is the island of Java. Digital literacy has been widely tested on learning outcome variables and has a positive influence. So it can be concluded that digital literacy affects the learning process.
A Memory-Efficient Edge Inference Accelerator with XOR-based Model Compression

Model compression is widely adopted for edge inference of neural networks (NNs) to minimize both costly DRAM accesses and memory footprints. Recently, XOR-based model compression has demonstrated promising results to maximize compression ratio and minimize accuracy drop. However, XOR-based decompression alone produces bit errors and requires auxiliary data for error correction. To minimize model size and hence DRAM traffic, we propose an enhanced decompression algorithm and a low-cost hardware accelerator for it. Since not all errors are equal, our algorithm selects only important errors to correct with no accuracy drop. Compared with the baseline XOR compression scheme correcting all errors, the compressed model size of ResNet-18 and VGG-16 is reduced by 23% and 27% respectively. We also present a low-cost hardware implementation of on-line XOR decompression and error-correction logic built on Gemmini, an open-source systolic array accelerator, at the cost of only a 0.39% and 0.46% increase in area and power.
PTStore: Lightweight Architectural Support for Page Table Isolation

Page tables are critical data structures in kernels, serving as the trust base of most mitigation solutions. Their integrity is thus crucial but is often taken for granted. Existing page table protection solutions usually provide insufficient security guarantees, require heavy hardware, or introduce high overheads. In this paper, we present a novel lightweight hardware-software co-design solution, PTStore, consisting of a secure region storing page tables and tokens verifying page table pointers. Evaluation results on FPGA-based prototypes show that PTStore only introduces <0.92% hardware overheads and <0.86% performance overheads, but provides strong security guarantees, showing that PTStore is efficient and effective.