Reviewerly/exHarmony huggingface dataset:
On the application of active learning for efficient and effective IoT botnet detection

The active learning approach for machine learning can greatly benefit those environments where a wealth of unlabeled data is available, and the labeling cost of the data can be restrictive. In this regard, security operations centers (SOCs) can take advantage of the human expertise available to improve machine learning-based detection models using the active learning approach. In the context of SOC operations and IoT botnet detection, our study provides a thorough benchmarking of the application of different active learning approaches within the framework of pool-based sampling. The selection of the optimal query instance for learning is evaluated using uncertainty sampling, ranked batch-mode sampling, and query by committee strategies. Our results show that the active learning approach can help to generate better detection models using all the active learning query strategies tested in our benchmarking setup. Leveraging the human–machine interaction can produce high-performance models in the context of IoT botnet detection using significantly less data than the passive approaches traditionally used for the generation of machine learning-based detection systems. Additionally, the impact of wrong-labeled data in the active learning implementation is explored.
Multivariate analysis reveals spatial variability of soil geochemical signals in the area of a medieval manorial farm

This study presents the results of a multi-element and multivariate geochemical analysis of a deserted medieval settlement and its agricultural landscape, including a monastic (Cistercian) manorial farm, situated in the Czech Republic. We used LiDAR survey and historic maps to detect relict landscape features and identify past changes in land use-patterns. Comparing archaeological evidence and soil chemistry, we found that geochemical signals - derived from samples collected in the courtyard of the farm and in the surrounding area and processed by PCA on log-transformed and isometrically log-transformed data - responded to different agricultural activities, which could be linked to the farm and - more broadly - to current and past land-uses. The results generally illuminate the environmental impact of monastic settlement in a marginal landscape, with regard to different economic activities, and the study also demonstrates that this type of analysis allows tracing anthropogenic and natural phenomena over a wider area, beyond the limits of the more narrowly defined archaeological site.
No cost but high performance–An alternative open source solution for 3D‐visualizations in morphology

3D-visualization has become common courtesy in science and also found its way into teaching in schools and universities. Nevertheless, the way to high performance 3D-visualization and analyses remains difficult and is often also a matter of budget. Due to the obvious advantages of presenting morphological and anatomical datasets with the help of 3D-figures, all in one software solutions usually come along with high rental and maintenance fees. For that reason, it is more than overdue to establish and use open source software solutions with all their obvious advantages - as other disciplines already do. Here we provide a modular, highly adaptive and freely available software pipeline for high performance 3D-visualizations of (not only) morphological datasets by combining features of ImageJ, MeshLab and Blender, without any additional costs. Exemplarily using serial-block face SEM data as well as serial AZAN-stained histological sections, the herein presented step-by-step protocol allows for a fast and efficient analysis, visualization and animation of large, anatomical datasets. Regardless which type of serial, morphological datasets needs to be analyzed, our open source guide provides an easy to handle and promptly adaptable solution. Therefore, our pipeline for 3D-visualization represents a valuable alternative to conventional, commercial packages. RESEARCH HIGHLIGHTS: We provide a highly modular and easy to learn open source solution for multiple 3D-visualizations. The step-by-step-guide make it easy to start, and advanced users can replace software, add others and such build your own individual software pipeline.
An Unsupervised Cross-Modal Hashing Method Robust to Noisy Training Image-Text Correspondences in Remote Sensing

The development of accurate and scalable cross-modal image-text retrieval methods, where queries from one modality (e.g., text) can be matched to archive entries from another (e.g., remote sensing image) has attracted great attention in remote sensing (RS). Most of the existing methods assume that a reliable multi-modal training set with accurately matched text-image pairs is existing. However, this assumption may not always hold since the multi-modal training sets may include noisy pairs (i.e., textual descriptions/captions associated to training images can be noisy), distorting the learning process of the retrieval methods. To address this problem, we propose a novel unsupervised cross-modal hashing method robust to the noisy image-text correspondences (CHNR). CHNR consists of three modules: 1) feature extraction module, which extracts feature representations of image-text pairs; 2) noise detection module, which detects potential noisy correspondences; and 3) hashing module that generates cross-modal binary hash codes. The proposed CHNR includes two training phases: i) meta-learning phase that uses a small portion of clean (i.e., reliable) data to train the noise detection module in an adversarial fashion; and ii) the main training phase for which the trained noise detection module is used to identify noisy correspondences while the hashing module is trained on the noisy multi-modal training set. Experimental results show that the proposed CHNR outperforms state-of-the-art methods.
Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

Abstract Energy efficiency is a critical issue in the management and operation of cloud data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy-efficiency improvement for virtualized data centers. Among various methods to solve the VM-placement problem, the genetic algorithm (GA) has been well accepted for the quality of its solution. However, GA is also computationally demanding, particularly in the computation of its fitness function. This limits its application in large-scale systems or specific scenarios where a fast VM-placement solution of good quality is required. Our analysis in this paper reveals that the execution time of the standard GA is mostly consumed in the computation of its fitness function. Therefore, this paper designs a data structure extended from a previous study to reduce the complexity of the fitness computation from quadratic to linear one with respect to the input size of the VM-placement problem. Incorporating with this data structure, an alternative fitness function is proposed to reduce the number of instructions significantly, further improving the execution-time performance of GA. Experimental studies show that our approach achieves 11 times acceleration of GA computation for energy-efficient VM placement in large-scale data centers with about 1500 physical machines in size.
B-SSMD: A Fine-Grained Secure Sharing Scheme of Medical Data Based on Blockchain

The application of blockchain technology in the medical information system is gradually triggering profound changes, which has heterogeneous, cross-domain, and open network characteristics. However, today’s electronic medical systems fall short of ensuring the confidentiality and safe sharing of medical data, which poses serious threats to their authenticity and accuracy. Moreover, most of the current blockchain medical systems are under great transmission and storage pressure. Based on the aforementioned security and performance considerations, this paper proposes a fine-grained secure sharing scheme of medical data based on blockchain (B-SSMD). We design a three-chain model to store patient information, medical staff information, and medical records hierarchically. The integration of IPFS technology and the encryption algorithm ensures secure and efficient off-chain data storage. The user classification is realized by assigning attributes, and the attribute encryption technology is adopted for secondary encryption of the key and ciphertext path. Meanwhile, through hierarchical encryption, the risk of a system attack is greatly reduced. Our scheme not only solves the problems of low throughput and poor stability in the single chain model but also improves the data confidentiality and enables medical data to be managed more safely and efficiently in the sharing process. We provide the security and performance analysis and it is confirmed that our scheme has higher security and controllability.
LATIC–A linguistic analyzer for text and item characteristics

Analyzing texts and items regarding their linguistic features might be important for researchers to investigate the effects of the linguistic presentation as well as for practitioners to estimate the readability of a text or an item. The Linguistic Analyzer for Text and Item Characteristics (LATIC) is a software that enables users to analyze texts and items more efficiently. LATIC offers a multitude of features at three different reading levels and can be used for texts and items in four different languages: English, French, German, and Spanish. It is open source, free to use and designed to be user-friendly. In this study, we investigated LATIC’s performance: LATIC achieves highly accurate results, while being extremely time saving compared to human raters. While developing LATIC, the respective features are tested continuously to ensure a high accuracy of results in the future.
CrowdRep: A Blockchain-based Reputation System for Crowdsourced HD Map Update

Crowdsourced vehicles are expected to be the answer to an efficient yet economic solution for maintaining an HD (High-Definition) map for autonomous vehicles. It is the building block that helps keep the autonomous vehicle safe and sound. However, in realizing this idea, there comes problem such as the reliability of the data provided by crowdsourced vehicles and malicious entities that can compromise map database security. In this paper, we propose a novel idea of implementing blockchain technology to solve the reliability problem and eliminate the effect of malicious intent from unknown entities. We explore the idea of using reputation to value the contribution given by crowdsourced vehicles and update it according to the consensus generated by the peer-to-peer members in the area of update. Essentially, these two key elements are combined to solve the problem faced by crowdsourced solutions for HD map updates. We create a numerical simulation to show that our method can group vehicles into categories according to the quality of data it provides. Furthermore, we show that the system can handle a significant amount of attackers (not a majority) with only a slight decrease in the map element localization accuracy.
A Novel Probabilistic V2X Data Fusion Framework for Cooperative Perception

The paper addresses the vehicle-to-X (V2X) data fusion for cooperative or collective perception (CP). This emerging and promising intelligent transportation systems (ITS) technology has enormous potential for improving efficiency and safety of road transportation. Recent advances in V2X communication primarily address the definition of V2X messages and data dissemination amongst ITS stations (ITS-Ss) in a traffic environment. Yet, a largely unsolved problem is how a connected vehicle (CV) can efficiently and consistently fuse its local perception information with the data received from other ITS-Ss. In this paper, we present a novel data fusion framework to fuse the local and V2X perception data for CP that considers the presence of cross-correlation. The proposed approach is validated through comprehensive results obtained from numerical simulation, CARLA simulation, and real-world experimentation that incorporates V2X-enabled intelligent platforms. The real-world experiment includes a CV, a connected and automated vehicle (CAV), and an intelligent roadside unit (IRSU) retrofitted with vision and lidar sensors. We also demonstrate how the fused CP information can improve the awareness of vulnerable road users (VRU) for CV/CAV, and how this information can be considered in path planning/decision making within the CAV to facilitate safe interactions.
Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network

As the central nerve of the intelligent vehicle control system, the in-vehicle network bus is crucial to the security of vehicle driving. One of the best standards for the in-vehicle network is the Controller Area Network (CAN bus) protocol. However, the CAN bus is designed to be vulnerable to various attacks due to its lack of security mechanisms. To enhance the security of in-vehicle networks and promote the research in this area, based upon a large scale of CAN network traffic data with the extracted valuable features, this study comprehensively compared fully-supervised machine learning with semi-supervised machine learning methods for CAN message anomaly detection. Both traditional machine learning models (including single classifier and ensemble models) and neural network based deep learning models are evaluated. Furthermore, this study proposed a deep autoencoder based semi-supervised learning method applied for CAN message anomaly detection and verified its superiority over other semi-supervised methods. Extensive experiments show that the fully-supervised methods generally outperform semi-supervised ones as they are using more information as inputs. Typically the developed XGBoost based model obtained state-of-the-art performance with the best accuracy (98.65%), precision (0.9853), and ROC AUC (0.9585) beating other methods reported in the literature.
CASwin Transformer: A Hierarchical Cross Attention Transformer for Depth Completion

LiDARs and RGB cameras are commonly used sensors in autonomous driving vehicles. However, the high-resolution LiDAR is too expensive, limiting its large-scale application in commercial autonomous vehicles. The low-resolution LiDAR is more affordable and it can approximate the perception level of high-resolution LiDAR combined with corresponding images. In this paper, we propose a hierarchical cross-attention Transformer in dual-branch to predict a dense depth map. The hierarchical architecture builds a feature pattern at all scales and the cross-attention modules fuse the features from different modalities at multiple feature levels. Furthermore, we develop a depth refinement stage to amend the dense depth map predicted by the fusion stage. The proposed method is evaluated on the indoor NYUDepthV2 dataset and outdoor KITTI Odometry dataset. The experiments demonstrate its effectiveness and accuracy compared with the present state-of-the-art methods.
Predicting Vulnerabilities in Web Applications Based on Website Security Model

Web sites and services are probably the most used digital channels today, from ordinary web-sites to cloud services that enable many aspects of our digital lives. Due to the popularity of the web, it is also a very common target of cyber attacks that typically focus either on web application itself or on the underlying server infrastructure. Regarding the highest level of the stack - the web application - there are many available frameworks and content management systems (CMS) for rapid web development, from the ones more oriented to developers (e.g. Spring, Django) to the ones that focus on end users (e.g. Wordpress, Joomla). Typical problem with using a framework or a CMS is the need for constant care of its security, which is done by regular patching of the systems. When going a bit lower towards the web server, one can observe the security related features that might or might not be implemented on the server, such as header security (e.g. cookie related flags, force of encryption etc.). The state of all the mentioned parameters can well be obtained by web crawlers that can browse the web and collect specific information about web applications, sites and servers that run them. In this paper, we propose a model for estimating the possibility of web compromise based on the historical crawler collected data. Due to large amounts of data that can be gathered from the web sites and, especially, indication of compromise of particular web sites, we can determine what factors might lead to a compromise in near future. In this sense, we propose a method for analyzing web site data with respect to known compromises from historical data. We build a model that describes a web site's security state and use the method for estimating how secure the modeled web is and how likely it would become a victim of compromise.
Benchmarking Resource Usage for Efficient Distributed Deep Learning

Deep learning (DL) workflows demand an ever-increasing budget of compute and energy in order to achieve outsized gains. As such, it becomes essential to understand how different deep neural networks (DNNs) and training leverage increasing compute and energy resources-especially specialized computationally-intensive models across different domains and applications. In this paper, we conduct over 3,400 experiments training an array of deep networks representing various domains/tasks-natural language processing, computer vision, and chemistry-on up to 424 graphics processing units (GPUs). During training, our experiments systematically vary compute resource characteristics and energy -saving mechanisms such as power utilization and GPU clock rate limits to capture and illustrate the different trade-offs and scaling behaviors each representative model exhibits under various resource and energy-constrained regimes. We fit power law models that describe how training time scales with available compute resources and energy constraints. We anticipate that these findings will help inform and guide high-performance computing providers in optimizing resource utilization, by selectively reducing energy consumption for different deep learning tasks/workflows with minimal impact on training.
Neurodynamics-driven holistic approaches to semi-supervised feature selection

Feature selection is a crucial part of machine learning and pattern recognition, which aims at selecting a subset of informative features from the original dataset. Because of label information, supervised feature selection performs better than unsupervised feature selection without label information. However, in the presence of a small number of labeled data and a large number of unlabeled data, it is challenging for supervised feature selection methods to select relevant features. In this paper, we propose three neurodynamics-driven holistic approaches to semi-supervised feature selection via semi-supervised feature redundancy minimization and semi-supervised feature relevancy maximization. We first define information-theoretic semi-supervised similarity coefficient matrix and semi-supervised feature relevancy vector based on multi-information, unsupervised symmetric uncertainty, and entropy to measure feature redundancy and relevancy. We then formulate a fractional programming problem and an iteratively weighted quadratic programming problem based on the semi-supervised similarity coefficient matrix and semi-supervised feature relevancy vector for semi-supervised feature selection. To solve the formulated problems, we delineate three neurodynamic optimization approaches based on two projection neural networks. We elaborate on the experimental results on six benchmark datasets to demonstrate the superior classification performance of the proposed neurodynamic approaches against six existing supervised and semi-supervised feature selection methods.
Computer-Assisted Annotation of Digital H&amp;E/SOX10 Dual Stains Generates High-Performing Convolutional Neural Network for Calculating Tumor Burden in H&amp;E-Stained Cutaneous Melanoma

Deep learning for the analysis of H&E stains requires a large annotated training set. This may form a labor-intensive task involving highly skilled pathologists. We aimed to optimize and evaluate computer-assisted annotation based on digital dual stains of the same tissue section. H&E stains of primary and metastatic melanoma (N = 77) were digitized, re-stained with SOX10, and re-scanned. Because images were aligned, annotations of SOX10 image analysis were directly transferred to H&E stains of the training set. Based on 1,221,367 annotated nuclei, a convolutional neural network for calculating tumor burden (CNNTB) was developed. For primary melanomas, precision of annotation was 100% (95%CI, 99% to 100%) for tumor cells and 99% (95%CI, 98% to 100%) for normal cells. Due to low or missing tumor-cell SOX10 positivity, precision for normal cells was markedly reduced in lymph-node and organ metastases compared with primary melanomas (p < 0.001). Compared with stereological counts within skin lesions, mean difference in tumor burden was 6% (95%CI, -1% to 13%, p = 0.10) for CNNTB and 16% (95%CI, 4% to 28%, p = 0.02) for pathologists. Conclusively, the technique produced a large annotated H&E training set with high quality within a reasonable timeframe for primary melanomas and subcutaneous metastases. For these lesion types, the training set generated a high-performing CNNTB, which was superior to the routine assessments of pathologists.
CheXPrune: sparse chest X-ray report generation model using multi-attention and one-shot global pruning

Automatic radiological report generation (ARRG) smoothens the clinical workflow by speeding the report generation task. Recently, various deep neural networks (DNNs) have been used for report generation and have achieved promising results. Despite the impressive results, their deployment remains challenging because of their size and complexity. Researchers have proposed several pruning methods to reduce the size of DNNs. Inspired by the one-shot weight pruning methods, we present CheXPrune, a multi-attention based sparse radiology report generation method. It uses encoder-decoder based architecture equipped with a visual and semantic attention mechanism. The model is 70% pruned during the training to achieve 3.33× compression without sacrificing its accuracy. The empirical results evaluated on the OpenI dataset using BLEU, ROUGE, and CIDEr metrics confirm the accuracy of the sparse model viz-a`-viz the dense model.
Improving the efficiency of graph algorithm executions on high‐performance computing

Summary The growing need for extracting information from large graphs has been pushing the development of parallel graph algorithms. However, the highly irregular structure of the real‐world graphs limits the performance and energy improvements of graph applications. In this paper, we show that, in most cases, using all the available cores of the multiprocessor is not the best option in terms of the aforementioned non‐functional requirements. Based on that, we propose GraphKat , a framework that enables the simultaneous processing of several algorithms/graphs instead of executing them serially (i.e., one after another), increasing efficiency in terms of performance and energy. GraphKat works in two steps: (i) it characterizes the graph applications with a specific number of threads based on their efficiency levels; and (ii) it defines the execution order of all graph applications in the target system. Experimental results on three multicore processors (Intel and AMD) show that GraphKat improves the overall system's efficiency related to performance (up to ) and energy‐saving (up to 245.21), and reduces the graph applications' execution time (up to ) and energy consumption (up to 6.64) compared to the default execution of parallel applications on HPC systems.