Reviewerly/exHarmony huggingface dataset:
MEMG: Mobile Encrypted Traffic Classification With Markov Chains and Graph Neural Network

In recent years, user privacy and information Security have attracted widespread attention, encryption ratio of mobile traffic has increased tremendously, which has brought considerable challenges to traditional traffic classification meth-ods. Machine learning methods and deep learning methods have become mainstream methods to solve this problem. However, the existing machine learning methods require manual features and cannot adapt to the newly generated traffic patterns. Deep learning methods are capable of learning features from the raw traffic sequences automatically but will increase the calculation costs. To address these challenges, in this paper, we propose a Mobile Encrypted Traffic Classification with Markov Chains and Graph Neural Network (MEMG). We use the Markov chains to mine the hidden topological information of the flow. Then we build the flow graph structure on this basis, add the sequence information of traffic in the node feature in the graph. We also design a Graph Neural Network-based classifier to learn the topological and sequential features from the graph. The classifier can map the graph structure to the embedding space, and classify different graph structures by embedding vector differences. We have done comprehensive experiments on both the real-world dataset and the public dataset. The real-world dataset contains the traffic of 29 commonly used mobile encrypted applications collected by us recently, and the total number of traffic exceeds 116,000. Our method outperforms the state-of-the-art methods by 6.1&#x0025; and 3.5&#x0025; of the accuracy rate on our dataset and public dataset, respectively. We also lessen the training time overhead and GPU memory usage by 40&#x0025; and 46&#x0025;, respectively.
Ziggurat: A Framework for Providing Scalability and Security in IoT Blockchains

As the internet continues to grow, networked devices are expanding not only in number, but variety as well. The Internet of Things presents an opportunity to connect every-day devices in new and intelligent ways, whether those devices are as mundane as kitchen appliances, or as complex as Autonomous Vehicles. To ensure the security of information in these sometimes public-facing, geographically distributed networks, devices cannot trust one another blindly. A Blockchain protocol may be ideal in that it trades trust in individuals for trust in cryptography, but the performance of such protocols can suffer in densely populated networks. To provide a scalable solution to Blockchain implementations IoT networks, this paper proposes a framework that utilizes a hierarchically distributed Blockchain network with horizontal connections between select devices at each layer to balance traffic without creating a single point of failure.
A study of Augmented Reality performance in web browsers (WebAR)

The extensive development of augmented reality (AR) is ascending excessively, Web-based augmented reality (WebAR) is an advent to users which stamp out the friction of downloading an application based on AR, by simply publishing the 2D/3D content in web browsers to be accessible by customised URL. This paper provides a visionary as well as practical understanding of specialized improvement in the WebAR. We examine current trends which help eliminates the agitation of slacken experience. A practical analysis is done using ar.js, integration of Web graphic library with three.js for overlaying digital content with the help of marker. Furthermore, the browser + cloud solution will provide exclusive network capabilities by conquering the service element provided by web AR from the remote cloud server to the edge server effectively to render 3D model using WebRTC, object recognition algorithm integrated with profound JavaScript libraries which will remove the slacken performance optimization when a digital 2D/3D content is rendered in the user device&#x0027;s camera.
Quantum Machine Learning: A Case Study of Grover’s Algorithm

The complexity of searching algorithms in classical computing is a perpetual researched field. Quantum computers and quantum algorithms can compute these problems faster, and, in addition, machine learning implementation could provide a prominent way to boost quantum technology. We call quantum machine learning to this novel set of tools coming from artificial intelligence and quantum mechanics. To achieve our purpose, we focus on applications on quantum machine learning; in particular, we propose a review and exploration of topics such as variational quantum algorithms, kernel methods, and a review of Grover&#x2019;s algorithm (GA) as a quantum classifier. We start with the GA exploration to achieve this goal, which is a quantum search algorithm that achieves quadratic speedup over optimal classical search implementation. This paper implements a GA exploration that includes a concept summary and implementation, considering only AND, XOR, and OR gates. We also discuss potential in quantum machine learning.
Deep Reinforcement Agent for Failure-aware Job scheduling in High-Performance Computing

Job scheduling is crucial in high-performance computing (HPC), which is dedicated to deciding when and which jobs are allocated to the system and placing the jobs on which resources, by considering multiple scheduling goals. Along with the incremental of various resources and dazzling deep learning training (DLT) workloads, job failure becomes a quite common issue in HPC, which will affect user satisfaction and cluster utilization. To alleviate the influence of hardware and software errors as much as possible, in this paper, we aim to tackle the problem of failure-aware job scheduling in HPC clusters. Inspired by the success of previous studies of deep reinforcement learning-driven job scheduling, we propose a novel HPC scheduling agent named FARS (Failure-aware RL-based scheduler) by considering the effects of job failures. On the one hand, a neural network is applied to map the information of raw cluster and job states to job placement decisions. On the other hand, to consider the influence of job failure for user satisfaction and cluster utilization, FARS leverages make-span of the entire workload as the training objective. Additionally, effective exploration and experience replay techniques are applied to obtain effectively converged agent. To evaluate the capability of FARS, we design extensive trace-based simulation experiments with the popular DLT workloads. The experimental results show that, compared with the best baseline model, FARS obtains 5.69% improvement of average make-span under different device error rates. Together, our FARS is an ideal candidate for failure-aware job scheduler in HPC clusters.
A Case Study of Credential Stuffing Attack: Canva Data Breach

In May 2019, the hacker known as GnosticPlayers attacked Canva, an Australian tech giant, and was able to obtain data from 139 million users from this one attack alone. Overall, GnosticPlayers has data from nearly one billion users from attacking different platforms and companies. The technique they used to take all of this user data is called credential stuffing and credential cracking. Credential cracking is using brute force to find the passwords of users which could lead to finding the password to an admin account and being able to get information from all users. Credential stuffing is very similar, but it uses past information from previous breaches to be able to guess passwords more easily. This attack had a major impact on many people and companies who&#x2019;s data was stolen adn can be prevented by multiple strategies including Multi. In response, Canva and other companies sent an email urging using to change their passwords for Canva, and if they use the same password for other sites, to change those as well. This paper analyzes the attack methodology, impact and possible defense solutions in detail.
Neighborhood Base Matched Morphological Filters: Cross-fertilization with Linear Lowpass Filtering

The manuscript introduces Neighborhood Base Matched Morphological Filters (NBM-MF) by fusion of linear lowpass filtering process into the mathematical morphology structure of the operators. This internal cross-fertilization is gained via the deployment of a dynamic structuring element that adaptively matches itself to the neighborhood base of the signal/image. The neighborhood base is indeed the base-line/base-surface of the signal/image approximated by linear lowpass filtering called hereafter &#x2018;base&#x2019;. This cross-fertilization enables NBM-MF for modifying geometrical features of the signal/image more adaptively to the local geometry, and thereof having stronger filtering efficiency and less side effects of disturbing the original structure of the signal/image. The morphological smoothing is deployed for the efficiency evaluation of NBM-MF compared to the classical MF. Using three different numerical evaluation criteria, the morphological smoothing of signals with different noise and different base structure approves the higher efficiency of NBM-MF with respect to the classical one.
A Scheme for Data Deduplication Using Advance Machine Learning Architecture in Distributed Systems

In a distributed architecture, data as a resource has its own value, but continuous integration of large amounts of data across several locations without cross-verification to preserve a single instance data pattern appears impossible. As a result, systems have encountered hurdles that have a direct influence on the efficiency and performance of distributed workforces. Users need high-quality data or information in order to continue working as improved data services in order to find future trends. However, duplicate data entries in storage repositories are considered a major flaw or stumbling block in the data analysis and query operations processes. As a result, businesses have invested significant resources in detecting duplicate data throughout the duplicate entry detection process. We&#x0027;ve introduced a cutting-edge machine learning framework for detecting duplicate data on both current and new data entries. Textual data inputs or queries are imported into memory, preprocessed, and transformed to a vector space model using this technique. To arrange data in groups with equal capacity, a clustering K-means approach is used. To save time and money during the detection phase, similarity computations were done cluster-by-cluster rather than on a huge dataset. The suggested technique performs better than existing deduplication algorithms, with an optimal accuracy of 99.7&#x0025;. If the result-test and gt-test outcomes are determined to be same during comparison, the accuracy performance parameter of the deduplication process is greater.
Developing Cybersecurity Workforce: Introducing CyberSec Labs for Industry Standard Cybersecurity Training

In recent years, widespread internet usage, increasing numbers of IoT devices, and vast data on social media have increased cyber attack vectors. Parallel to this trend, the demand for the cyber security workforce has augmented; however, employers could not fill these positions due to the shortage in the field. This anomaly, in turn, has placed companies in a vulnerable situation by being exposed to an increased level of cyber security threats/incidents. Relevant authorities underscore the importance of a comprehensive cybersecurity workforce framework to overcome this problem. Given this context, this study offers a CyberSec Labs framework to improve the skills of potential cybersecurity actors. The framework extensively benefits from hands-on exercises like cloud computing and networking, OSINT, data-driven cybersecurity approach, vulnerability and penetration test analysis, and volatility analysis. The overall goal of this open-source material is to prepare students for their future cybersecurity roles, which contributes to overcoming the shortage of skilled human capital in the cybersecurity field.
Towards Network-accelerated ML-based Distributed Computer Vision Systems

Computer vision is a crucial component in many modern applications (e.g., medical image analysis, environmental monitoring and self-driving cars). However, their stringent computational, latency and bandwidth requirements still pose a huge challenge to system architects, which must seek for alternatives to both the limited resources (e.g., low-end CPU) on client devices and the hurdles of moving data from clients to cloud/edge servers for analysis. In this work, we advocate for the usage of emerging programmable network devices to speed up ML-based computer vision tasks, particularly image classification, on resource constrained environments. To take the first step towards this new paradigm, we propose NetPixel, a framework that enables P4-programmable switches to classify images in realtime, accurately and at scale. We implemented a prototype of NetPixel in a software switch to show its feasibility and conducted a preliminary evaluation on widely adopted datasets. Our results show that NetPixel can classify images with an accuracy within 8% that of a server-based implementation even for shallow classifiers and low-resolution images.
Containerized Workflow Builder for Kubernetes

Docker and Kubernetes have recently dominated the whole cloud-native technology ecosystem and speeded up the containerization process of workflows. By optimizing the task scheduling order, workflow scheduling algorithms signifi-cantly improve the execution efficiency of workflows. However, existing works do not support workflow scheduling following the optimized task scheduling order on Kubernetes. How to ensure the consistency of workflow scheduling algorithms and Kubernetes scheduler in task scheduling order is challenging. In this paper, we propose a Containerized Workflow Builder (CWB) for Kubernetes, a framework able to implement con-tainerization of workflows in a two-level scheduling scheme and connect workflow scheduling algorithms to Kubernetes, ensuring the consistency of the task scheduling order. It uses the Informer component to watch the underlying resource events of Kubernetes, providing the event trigger mechanism to respond to event callback in real-time and implement containerization of workflows by the Client-go package. Experimental results show that our proposed CWB ensures the consistency of the workflow scheduling algorithms and Kubernetes scheduler in the task scheduling order. Compared with the state-of-the-art, the CWB achieves better performance in terms of average task pod execution time, average workflow lifecycle, and resource usage rate.
Deep Learning-Based Semantic Segmentation in Autonomous Driving

Perception is the first and most important task of any autonomous driving system. It extracts visual information about the surrounding environment of the vehicle. The perception data is then fed to a decision-making system to provide the optimum decision given a specific scenario to avoid potential collisions. In this paper, we have developed variants of the U-Net model to perform semantic segmentation on urban scene images to understand the surroundings of an autonomous vehicle. The U-N et model and its variants are adopted for semantic segmentation in this project to account for the power of the UNet in handling large and small datasets. We have also compared the best-performing variant with other commonly used semantic segmentation models. The comparative analysis was performed using three well-known models, including FCN-16, FCN-8, and SegNet. After conducting sensitivity and comparative analysis, it is concluded that the U-Net variants performed the best in terms of the Intersection over Union (IoU) evaluation metric and other quality metrics.
Digital Report Application Audit Using The COBIT 5 Framework

MTs is one of the schools that realizes the importance of the role of IT in education, they have used one of the ARD applications in processing its values. Judging from this, it is very necessary to have an audit on the application, especially in terms of users because it is to provide maximum service for these users. In this study, the framework used is the COBIT 5 framework, especially the DSS (Deliver Support Service) domain, the domain used is the DSS01 (manage operations) domain, DSS02 (manage service requests and incidents), DSS03 (manage problems), DSS04 (manage continuity)., and DSS05 (manage security service) obtained from problem mapping. In the DSS01 domain it reaches 3.7625 or with a percentage of 75%, in the DSS02 domain it reaches 3.5875 or with a percentage of 72%, in the domain DSS03it reaches 4.0875 or with a percentage of 820%, in the DSS04 domain it reaches 3.975 or with percentage 80%, in the DSS05 domain it reaches 4.15 or 83%. Based on the results of the assessment, the Digital Report Application at MTs is running well enough which must be improved to achieve maximum results in order to increase user satisfaction of the application.
5-Fold Cross Validation on Supporting K-Nearest Neighbour Accuration of Making Consimilar Symptoms Disease Classification

Classification is a technique in machine learning that is used to built the group of data. The data that consist of class and target are grouped based on the data attachment to the sample data, so that the data group can be used as a basis for making decisions at a later stage. This study aims to find a classification of two diseases, dengue fever and typhus which have similar symptoms using a combination of two techniques, 5-Fold Cross Validation and K-Nearest Neighbor. The combination of these techniques is done to improve the accuracy of the classification, feature selection is done in preprocessing phase to collect verified feature to get information gain to measure which feature is kept and continue to next stage of classification process. A confusion matrix will be applied to test the performance in terms of the accuracy of the constructed classification. K-Nearest Neighbor (KNN) is a classification method that applies a supervised algorithm, while 5-Fold Cross Validation is a statistical method that is applied to test the performance of a method or technique. This investigation resulted in an increase in the value of accuracy and recall, with a score of 1.79 points and a recall value of 96.43%.
Image Processing based UR5E Manipulator Robot Control in Pick and Place Application for Random Position and Orientation of Object

Many industries are required to increase output and productivity. Manipulator Robot is chosen as a solution, especially for pick and place applications. An intelligent system is needed to reduce uncertainty and decision-making for some varying object or workpieces. In this paper, the UR5e manipulator robot can automatically detect the object using computer vision through the camera as a sensor and OpenCV computer program. Geometrical transformations and statistical approaches transform picture data into position and orientation data for robot control. The system detects the center point coordinates and the orientation angle of the detected object. The coordinates obtained by the camera perception are transformed into robot perception. At the same time, the value of the orientation degree is converted into a quaternion form which becomes the rotational movement parameter for the end effector of the robot. Position and orientation data are sent to the robot controller from the ROS framework computer program via ethernet. The robotic arm can move towards the object, and the end effector can rotate accordingly. The experimental results prove successful recognition of object position and orientation. The system has an accuracy in detecting object positions at x-axis direction = 99.0%, at y-axis direction = 98.05%, and the system orientation accuracy is 99.11%.
Improved pattern recognition of various traditional clothes with Convolutional neural network

This Study would identify regional fabric patterns throughout Indonesia. Traditional clothes patterns come from the handicrafts of remote communities in the regions of Indonesia. A Convolutional neural network is used for Resnet50 V2, VGG16, VGG19, MobileNetV2, and Inception V3 models to support this research. This research was conducted on five types of experiments with supporting parameters: the number of training images and test images, rotation and scale of each image, image class, and CNN parameters. After we conducted experiments on a collection of images found in various scales and degrees, the average classification accuracy using VGG16 was 79.2% in condition 2, but the classification accuracy was between 84.2%-92.5%. Meanwhile, if using VGG19 the classification accuracy is 79.9%, with condition 1 for the highest accuracy between 92%-96%. If we used mobilenetV2, the average classification accuracy is 83.4% for 87%-96%. If we used inceptionV3, the average classification accuracy is 83.7% for the accuracy between 72%-98%, and the last if we used Resnet50 V2, the average classification accuracy is 79.2% for the accuracy between 71%-94%. Traditional clothes pattern recognition research using Deep CNN has succeeded in recognizing some traditional clothes patterns up to more than 97%. We continue the study to recognize other traditional clothes patterns more optimally.
Towards a Multi-Output Kaizen Programming Algorithm

A model obtained from solving a symbolic regression problem is a surrogate model that represent a system with high accuracy. In the area of process system engineering, surrogate models substitute rigorous models in optimization and design process problems. As chemical processes have several outputs with a common physical-chemical phenomena, it is expected that the surrogate models generated for the outputs share terms or function basis. Kaizen Programming (KP) is a novel technique to solve symbolic regression problems, which do not assume any supposition of the form of the model in advance. This technique has shown a better performance than Genetic Programming on benchmarking functions. In this work, we propose an extension of Kaizen Programming, Multi-Output KP (MO-KP), to construct multi-output models in a single execution.The experimental evaluation was conducted on an extension of three classical benchmarking functions to multi-output scenarios, considering three different schemes of function basis sharing. The experimental results shown that MO-KP builds well fitted models, and it is even able to construct better models than single-output KP in some scenarios. The results also confirm that MO-KP favors the sharing of terms between the generated models. Finally, we found that the median execution time of MO-KP is in general shorter than the equivalent executions of single-output KP, but with larger variability in the distribution of the runtimes.
Sentiment Analysis on Brazilian Portuguese User Reviews

Sentiment Analysis is one of the most classical and primarily studied natural language processing tasks. This problem had a notable advance with the proposition of more complex and scalable machine learning models. Despite this progress, the Brazilian Portuguese language still disposes only of limited linguistic resources, such as datasets dedicated to sentiment classification, especially when considering the existence of predefined partitions in training, testing, and validation sets that would allow a more fair comparison of different algorithm alternatives. Motivated by these issues, this work analyzes the predictive performance of a range of document embedding strategies, assuming the polarity as the system outcome. This analysis includes five sentiment analysis datasets in Brazilian Portuguese, unified in a single dataset, and a reference partitioning in training, testing, and validation sets, both made publicly available through a digital repository. A cross-evaluation of dataset-specific models over different contexts is conducted to evaluate their generalization capabilities and the feasibility of adopting a unique model for addressing all scenarios.