Reviewerly/exHarmony huggingface dataset:
A new platform for machine-learning-based network traffic classification

This study provides a new platform for classifying encrypted network traffic based on machine learning (ML) techniques. The architecture of the platform is designed for real-world network traffic classification problems with performance-oriented, practical, and up-to-date software technologies. In addition, this study introduces a new feature extraction method to the literature. The proposed platform applies ML techniques with flow-based statistical features of encrypted network traffic and new feature extraction. It takes network traffic packets as input and passes them through feature extraction, data preparation, and ML stages. In the feature extraction stage, network flows are extracted from the network traffic data by calculating their features with the NFStream tool. During the data preparation stage, the dataset is transformed into a processable state for the ML algorithm with the Apache Spark framework. This stage also includes the feature selection operation. The ML stage runs GBTree, LightGBM, and XGBoost algorithms. Moreover, we use the MLflow framework in the proposed process management to observe the ML lifecycle, including experimentation, reproducibility, and deployment. The experimental results show that the XGBoost algorithm achieves the best result with an F1 score of above 99%.
Document-Level Relation Extraction with Cross-sentence Reasoning Graph

Relation extraction (RE) has recently moved from the sentence-level to document-level, which requires aggregating document information and using entities and mentions for reasoning. Existing works put entity nodes and mention nodes with similar representations in a document-level graph, whose complex edges may incur redundant information. Furthermore, existing studies only focus on entity-level reasoning paths without considering global interactions among entities cross-sentence. To these ends, we propose a novel document-level RE model with a GRaph information Aggregation and Cross-sentence Reasoning network (GRACR). Specifically, a simplified document-level graph is constructed to model the semantic information of all mentions and sentences in a document, and an entity-level graph is designed to explore relations of long-distance cross-sentence entity pairs. Experimental results show that GRACR achieves excellent performance on two public datasets of document-level RE. It is especially effective in extracting potential relations of cross-sentence entity pairs. Our code is available at https://github.com/UESTC-LHF/GRACR .
Data encryption for bank management system

Data breaches are perhaps the single most significant risk that banks face today. In the financial industry. Aside from the direct costs involved in these breaches, like lost funds, increased insurance expenses and fines, and penalties, institutions also must contend with the indirect costs. Loss of reputation and consumer trust are not easy to quantitatively measure, but it’s guaranteed the price is very high.&#x0D; Unfortunately, banks need to face an ever-evolving set of threats because of the nature of their business. They hold sensitive financial records that bad actors are known to target, and with methods that are always shifting. Consequently, security cannot be static to meet these threats. It must be a continuous process to monitor and respond to new risks as they emerge. Also, compliance needs to be built into the overall process to meet the stringent privacy regulations for the financial industry. Data security for banks is undoubtedly a challenge, but a holistic approach may make it easier to manage.
Systematic analysis of automated threat modelling techniques: Comparison of open-source tools

Abstract Companies face increasing pressure to protect themselves and their customers from security threats. Security by design is a proactive approach that builds security into all aspects of a system from the ground up, rather than adding it on as an afterthought. By taking security into account at every stage of development, organizations can create systems that are more resistant to attacks and better able to recover from them if they do occur. One of the most relevant practices is threat modelling, i.e. the process of identifying and analysing the security threat to an information system, application, or network. These processes require security experts with high skills to anticipate possible issues: therefore, it is a costly task and requires a lot of time. To face these problems, many different automated threat modelling methodologies are emerging. This paper first carries out a systematic literature review (SLR) aimed at both having an overview of the automated threat modelling techniques used in literature and enumerating all the tools that implement these techniques. Then, an analysis was carried out considering four open-source tools and a comparison with our threat modelling approach using a simple, but significant case study: an e-commerce site developed on top of WordPress.
Pulse repetition interval modulation recognition using deep CNN evolved by extreme learning machines and IP-based BBO algorithm

Pulse repetition interval modulation (PRIM) recognition is a critical task in electronic intelligence (ELINT) and electronic support measure (ESM) systems for detecting radar threats accurately. However, PRI recognition is a complex issue due to missing and spurious pulses, resulting in noisy PRI pattern changes in real environments. To address this problem, this paper proposes a novel approach that recognizes the five common types of PRIM through a four-phase process. In the first phase, a deep convolutional neural network (DCNN) is used as a feature extractor. Then, extreme learning machines (ELMs) are used for real-time recognition of the PRIM patterns in the second phase. In the third phase, we employ the biogeography-based optimizer (BBO) to enhance the network’s robustness by optimizing the connection weights and biases. To address the increasing complexity of the model, we introduce an optimized variable-length internet protocol-based BBO (VBBO) in the fourth phase. In this approach (i.e., DCNN-VBBO-ELM), each layer of DCNN is encoded by an IP address into a habitat of VBBO in the same sequence as the DCNN layers. To evaluate the proposed method, we develop a real experimental dataset consisting of five common PRI patterns. Our approach achieves a final accuracy of 97.05%, which is better than other ELM-based benchmark models. Moreover, the proposed model requires only 27 s of training time to process 50,000 training images, confirming its real-time capabilities. In conclusion, our proposed approach improves PRI recognition by leveraging DCNN, ELM, and VBBO, resulting in a more accurate and robust real-time radar PRI classifier.
Optimization method of power grid material warehousing and allocation based on multi-level storage system and reinforcement learning

Businesses may save money and make better use of their resources by exercising reasonable control over the storage of power materials. Poor timeliness and ineffectiveness are the result of the old model's inability to fully use a significant volume of acquired data and information. This research presents a reinforcement learning-based material storage control model for the power grid to solve these issues. In order to achieve zero storage, real-time tracking of consumables is implemented. Material for large disasters need manufacturer coordination for storage. The reinforcement learning approach is used to dynamically stock and distribute emergency supplies. The three-tiered dynamic storage approach based on reinforcement learning that is suggested in this study is shown to be very efficient and significantly cuts operational expenses for power grid firms by verifying particular data.
Target Detection Algorithm Incorporating Visual Expansion Mechanism and Path Syndication

Because the lack of semantic information exchange between characteristic layers, the SSD (Single Shot multibox Detector) algorithm has insufficient detection performance.To address this problem, a detection algorithm called VPE-SSD (Visual Path Enhancement SSD) by incorporating a visual expansion mechanism and path syndication proposed in this paper.Firstly, a visual expansion mechanism is added to the shallow characteristic layer to increase the perceptual field.This enables the semantic information in the shallow layer to be more fully utilized by the network.It can also achieve the purpose of enhancing the expressiveness of the shallow feature layer.Then, the processed deep and shallow characteristic layers are fed into the path syndication module for bi-directional fusion.This improves the global information of the feature layers and generates multi-scale global feature maps.Next, to enhance the detailed information of deep characteristics and improve their expression, the deep characteristic enhancement module is applied to the last three characteristic maps.Finally, using the blended attention module to reduce the negative interference and improve the expression of characteristic maps during target detection.The experimental analysis of the VPE-SSD algorithm is conducted on VOC and COCO, and the mAP is 83.4% and 48.4%.From the result, VPE-SSD algorithm can make better use of the different size characteristic information which helps to improve the performance of the algorithm.
Extracting perfect GHZ states from imperfect weighted graph states via entanglement concentration

Photonic Greenberger-Horne-Zeilinger (GHZ) states serve as the central resource for a number of important applications in quantum information science, including secret sharing, sensing, and fusion-based quantum computing. The use of photon-emitter entangling gates is a promising approach to creating these states that sidesteps many of the difficulties associated with intrinsically probabilistic methods based on linear optics. However, the efficient creation of high-fidelity GHZ states of many photons remains an outstanding challenge due to both coherent and incoherent errors during the generation process. Here, we propose an entanglement concentration protocol that is capable of generating perfect GHZ states using only local gates and measurements on imperfect weighted graph states. We show that our protocol is both efficient and robust to incoherent noise errors.
Minimizing incident response time in real-world scenarios using quantum computing

Abstract The Information Security Management Systems (ISMS) are global and risk-driven processes that allow companies to develop their cybersecurity strategy by defining security policies, valuable assets, controls, and technologies for protecting their systems and information from threats and vulnerabilities. Despite the implementation of such management infrastructures, incidents or security breaches happen. Each incident has associated a level of severity and a set of mitigation controls, so in order to restore the ISMS, the appropriate set of controls to mitigate their damage must be selected. The time in which the ISMS is restored is a critical aspect. In this sense, classic solutions are efficient in resolving scenarios with a moderate number of incidents in a reasonable time, but the response time increases exponentially as the number of incidents increases. This makes classical solutions unsuitable for real scenarios in which a large number of incidents are handled and even less appropriate for scenarios in which security management is offered as a service to several companies. This paper proposes a solution to the incident response problem that acts in a minimal amount of time for real scenarios in which a large number of incidents are handled. It applies quantum computing, as a novel approach that is being successfully applied to real problems, which allows us to obtain solutions in a constant time regardless of the number of incidents handled. To validate the applicability and efficiency of our proposal, it has been applied to real cases using our framework (MARISMA).
A Decade of Cryptocurrency Investment Literature: A Cluster-Based Systematic Analysis

This study aims to systematically analyze and synthesize the literature produced thus far on cryptocurrency investment. We use a systematic review process supported by VOSviewer bibliographic coupling to review 482 papers published in the ABS 2021 journal list, considering all different areas of knowledge. This paper contributes an in-depth systematic analysis on the unconsolidated topic of cryptocurrency investment through the use of a cluster-based approach grounded in a bibliographic coupling analysis, revealing complex network associations within each cluster. Four literature clusters emerge from the cryptocurrency investment literature, namely, investigating investor behavior, portfolio diversification, cryptocurrency market microstructure, and risk management in cryptocurrency investment. Additionally, the study delivers a qualitative analysis that reveals the main conclusions and future research venues by cluster. The findings provide researchers with cluster-based information and structured networking for research outlets and literature strands.
KNN-Based Machine Learning Classifier Used on Deep Learned Spatial Motion Features for Human Action Recognition

Human action recognition is an essential process in surveillance video analysis, which is used to understand the behavior of people to ensure safety. Most of the existing methods for HAR use computationally heavy networks such as 3D CNN and two-stream networks. To alleviate the challenges in the implementation and training of 3D deep learning networks, which have more parameters, a customized lightweight directed acyclic graph-based residual 2D CNN with fewer parameters was designed from scratch and named HARNet. A novel pipeline for the construction of spatial motion data from raw video input is presented for the latent representation learning of human actions. The constructed input is fed to the network for simultaneous operation over spatial and motion information in a single stream, and the latent representation learned at the fully connected layer is extracted and fed to the conventional machine learning classifiers for action recognition. The proposed work was empirically verified, and the experimental results were compared with those for existing methods. The results show that the proposed method outperforms state-of-the-art (SOTA) methods with a percentage improvement of 2.75% on UCF101, 10.94% on HMDB51, and 0.18% on the KTH dataset.
Security and privacy concerns in cloud-based scientific and business workflows: A systematic review

Today, the number of data-intensive and compute-intensive applications like business and scientific workflows has dramatically increased, which made cloud computing more popular because of its ability to deliver a large number of computing resources on-demand. Security is a critical issue affecting the wide adoption of cloud technologies, especially for workflows that are mostly dealing with sensitive data and tasks. In this paper, we carry out a review of the state-of-the-art on how security and privacy concerns in scientific and business workflows in cloud environments are being addressed and identify the limitations and gaps in the current body of knowledge in this area. In this extensive literature review, we first present the state-of-the-art security solutions organized according to the phases of the workflow life cycle they target for both business and scientific workflows. The analysis shows that most of the existing literature focuses on the modeling and execution phases, while the monitoring and adaptation phases are not covered adequately by a scarce amount of publications thus leaving a huge gap in the body of knowledge relevant to detection, prevention of and reaction to security violations in cloud-based workflows.
An Ensembled Framework for Human Breast Cancer Survivability Prediction Using Deep Learning

Breast cancer is categorized as an aggressive disease, and it is one of the leading causes of death. Accurate survival predictions for both long-term and short-term survivors, when delivered on time, can help physicians make effective treatment decisions for their patients. Therefore, there is a dire need to design an efficient and rapid computational model for breast cancer prognosis. In this study, we propose an ensemble model for breast cancer survivability prediction (EBCSP) that utilizes multi-modal data and stacks the output of multiple neural networks. Specifically, we design a convolutional neural network (CNN) for clinical modalities, a deep neural network (DNN) for copy number variations (CNV), and a long short-term memory (LSTM) architecture for gene expression modalities to effectively handle multi-dimensional data. The independent models' results are then used for binary classification (long term > 5 years and short term < 5 years) based on survivability using the random forest method. The EBCSP model's successful application outperforms models that utilize a single data modality for prediction and existing benchmarks.
Pulmonary Nodule Detection and Classification Using All-Optical Deep Diffractive Neural Network

A deep diffractive neural network (D2NN) is a fast optical computing structure that has been widely used in image classification, logical operations, and other fields. Computed tomography (CT) imaging is a reliable method for detecting and analyzing pulmonary nodules. In this paper, we propose using an all-optical D2NN for pulmonary nodule detection and classification based on CT imaging for lung cancer. The network was trained based on the LIDC-IDRI dataset, and the performance was evaluated on a test set. For pulmonary nodule detection, the existence of nodules scanned from CT images were estimated with two-class classification based on the network, achieving a recall rate of 91.08% from the test set. For pulmonary nodule classification, benign and malignant nodules were also classified with two-class classification with an accuracy of 76.77% and an area under the curve (AUC) value of 0.8292. Our numerical simulations show the possibility of using optical neural networks for fast medical image processing and aided diagnosis.
Artificially Intelligent Readers: An Adaptive Framework for Original Handwritten Numerical Digits Recognition with OCR Methods

Advanced artificial intelligence (AI) techniques have led to significant developments in optical character recognition (OCR) technologies. OCR applications, using AI techniques for transforming images of typed text, handwritten text, or other forms of text into machine-encoded text, provide a fair degree of accuracy for general text. However, even after decades of intensive research, creating OCR with human-like abilities has remained evasive. One of the challenges has been that OCR models trained on general text do not perform well on localized or personalized handwritten text due to differences in the writing style of alphabets and digits. This study aims to discuss the steps needed to create an adaptive framework for OCR models, with the intent of exploring a reasonable method to customize an OCR solution for a unique dataset of English language numerical digits were developed for this study. We develop a digit recognizer by training our model on the MNIST dataset with a convolutional neural network and contrast it with multiple models trained on combinations of the MNIST and custom digits. Using our methods, we observed results comparable with the baseline and provided recommendations for improving OCR accuracy for localized or personalized handwritten text. This study also provides an alternative perspective to generating data using conventional methods, which can serve as a gold standard for custom data augmentation to help address the challenges of scarce data and data imbalance.
A Convex Optimization Approach to Multi-Robot Task Allocation and Path Planning

In real-world applications, multiple robots need to be dynamically deployed to their appropriate locations as teams while the distance cost between robots and goals is minimized, which is known to be an NP-hard problem. In this paper, a new framework of team-based multi-robot task allocation and path planning is developed for robot exploration missions through a convex optimization-based distance optimal model. A new distance optimal model is proposed to minimize the traveled distance between robots and their goals. The proposed framework fuses task decomposition, allocation, local sub-task allocation, and path planning. To begin, multiple robots are firstly divided and clustered into a variety of teams considering interrelation and dependencies of robots, and task decomposition. Secondly, the teams with various arbitrary shape enclosing intercorrelative robots are approximated and relaxed into circles, which are mathematically formulated to convex optimization problems to minimize the distance between teams, as well as between a robot and their goals. Once the robot teams are deployed into their appropriate locations, the robot locations are further refined by a graph-based Delaunay triangulation method. Thirdly, in the team, a self-organizing map-based neural network (SOMNN) paradigm is developed to complete the dynamical sub-task allocation and path planning, in which the robots are dynamically assigned to their nearby goals locally. Simulation and comparison studies demonstrate the proposed hybrid multi-robot task allocation and path planning framework is effective and efficient.
Multi-stage distribution correction: A promising data augmentation method for few-shot fault diagnosis

Benefiting from the excellent capability of data processing, deep learning-based methods have been well applied in fault diagnosis. However, these methods may perform poorly due to lack of labeled data for training in real-world applications In few-shot learning settings, these methods may be trapped in overfitting since the data distribution estimated from a small number of labeled samples may be overly biased, which cannot cover the ground-truth data distribution. Given this problem, we propose a data augmentation method named Multi-Stage Distribution Correction (MSDC) for few-shot fault diagnosis. The proposed method can be divided into four training stages. In the first stage, unlabeled query features are clustered into several groups via an unsupervised Fuzzy C-means algorithm. Secondly, according to cosine similarity, the few labeled support features are assigned to the cluster with the highest similarity. Then, the Gaussian statistics of each cluster are extracted to correct the distributions of support features. Specifically, Our proposed method assumes that each dimension of the feature representation from the same class follows Gaussian distribution. New labeled features are generated by sampling from the corrected distributions. These generated features, along with the labeled support features, are used to train the task-specific classifier in the last stage. By joint training of the four stages, the scale of the labeled training dataset is effectively expanded, and the classification performance of the proposed method can be enhanced. The experimental results reveal that the proposed method outperforms the selected comparative methods in two case studies.