Reviewerly/exHarmony huggingface dataset:
An Evaluation of Time-Series Anomaly Detection in Computer Networks

One critical issue in any network systems is failure detection. Failures not only impact the source network but also propagate through other communicating networks due to the butterfly effect, making root causing of failures even more challenging. Therefore, the necessity to detect failures and anomalies in computer networks is fundamental. Given the nature of computer networks, data is received in a time-series format where each time-point has temporal dependencies on others. As a result, time-series analysis stands out as a potential approach to deal with the task of network anomaly detection. In this paper, we conduct studies on multivariate time series anomaly detection, varying from traditional machine learning techniques to deep learning models. We show that the choice of models is not as important as the choice of pre-processing techniques. Interestingly, non-linear normalization can boost the performance of deep detectors by around 20% in terms of F1 score and balance the preference of deep detectors for specific types of anomalies. We also study the bias of anomaly types to deep detectors, time-performance trade-offs, shortage of data, and effects of weakly labeled data on both synthetic and realworld datasets to fill out the missing insights in the literature.
Improved Generative Adversarial Network for Generating High-Resolution Images from Low-Resolution Images

The procedure of generating a high-resolution (HR) image from a low-resolution (LR) image is known as super-resolution (SR). In this paper, we try to perform SR using Deep Learning techniques. For better performance in medicinal imaging, forensics, pattern recognition, satellite imaging, surveillance, etc., zooming of a particular area of attention in the image is required, making high resolution necessary. We present ISRGAN (Improved Super Resolution Generative Adversarial Network), an improved version of SRGAN (Super Resolution Generative Adversarial Network) for image SR. For training the network, images from the DIV2K dataset have been used. The dataset consists of 800 different images which have been resized to 32x32 and 128x128 pixels to form LR and HR images respectively. A Generative Adversarial Network is based on the idea of Adversarial training, and comprises of 2 parts, a discriminator network, and a generator network. The generator produces a HR image from the LR image, whereas the discriminator network classifies the generated image as fake or real. The presented model produces decent results and our final super-resolved results show that the presented ISRGAN model produces images with enhanced features.
Design of a 3D Scene Reconstruction Network Robust to High-Frequency Areas Based on 2.5D Sketches and Encoders

In this paper, we propose a new 3D scene reconstruction network that is robust to high-frequency areas by extracting multiple 3D feature volumes with accurate and various 3D information. Previous voxel representation-based methods did not perform well in high-frequency areas such as angled drawer parts and desk corners. In addition, the performance is poor even in the low-frequency areas with few feature points such as walls and floors. To solve this problem, we propose various backbone networks by extracting edge surface normal images from RGB images and constructing new branches. Edge images can provide information in the high-frequency areas, and surface normal images can compensate for the lack of information in edge images. As a result, not only 3D information but also the values of the high-frequency areas may be added. Using this as input for a new branch, various backbone networks such as ConvNeXt and Swin Transformer extract 2D image features that retain accurate 3D information. We designed a network that can represent detailed scenes from the entire scene using the hierarchical structure and unprojection of the backbone network to achieve robust performance in the high-frequency areas. We show that the proposed method outperforms the previous methods in quantitative and stereotyped 3D reconstruction results on the ScanNet dataset.
Design of cloud based video sharing platform using Machine Learning

With the growth of the internet came various websites fulfilling a variety of purposes. This new medium for information sharing became broader with each day and in 1997, the world saw the birth of internet’s first video hosting site, "ShareYourWorld.com". Over the course of time, video sharing platforms have evolved into more complex systems that involve more than just video uploads and downloads. Today users can publish, share their own videos to the Internet via online video services which may be intended for specific users on a shared network or for viewing by the broader public. Recent developments have revealed that the comment section of these video sharing platforms have frequently being used to promote spam and vulgar language. In this paper, we have compared different techniques to find out the best one for each identified module of the video sharing platform.
Impact of Word Embedding Methods on Software Vulnerability Severity Prediction Models

Software vulnerability severity prediction has recently gained popularity in software engineering. To perform this task, a prediction model has to be developed. The model’s performance greatly depends on the feature vectors used to train it. These feature vectors are formed using textual data present on software vulnerability and converting it to numerical form. There are various methods for feature vector representation using word embeddings. Unlike traditional methods such as TF-IDF, word embedding methods using deep learning map the words into vectors, preserving the semantic relationships between words as well as doing automatic feature selection, which holds great importance when dealing with textual data. Therefore, word embedding methods using deep learning showed promising results over traditional TF-IDF. Hence, in this paper, we conducted a controlled experiment to examine the effect of various word embedding methods for feature vector representation on the performance of the prediction model. The models developed in this study used TF-IDF, word2vec, and GloVe, coupled with SVM, LSTM, and Bi-LSTM classification algorithms. The classification performance of different word embedding methods is analyzed on five different vulnerability datasets of Mozilla products. The experiments showed that the best performance is achieved by using the GloVe method with the Bi-LSTM algorithm giving an average AUC value of 0.81, which is an improvement of 12.5% from the traditional method of feature vector representation.
Resource Allocation Reinforcement Learning for Quality of Service Maintenance in Cloud-Based Services

Recently, in order to improve the service quality of cloud-based services, research on a reinforcement learning model that predicts an appropriate amount of cloud resources by identifying patterns of user demands is being conducted. Reinforcement learning Q-learning algorithms rely on building a table (Q-table) for Q values, so if the state space and action space are vastly larger, they do not obtain optimal policies. In addition, learning errors in false experiences from the correlation of successive data in reinforcement learning may exist. In this paper, we study reinforcement learning modeling techniques that achieve higher accuracy than existing models by reducing the state definition space of hardware resources arising from services. It is possible to maximize service quality by allocating cloud resources or returning unnecessary resources with accurate resource demand prediction. For performance analysis, the service request prediction results according to the number of learnings were confirmed, and the service request prediction accuracy of three different models according to the neural network was compared. In the experiment, the model applying the proposed Convolutional Neural Network(CNN) neural network modeling technique is found to predict the amount of cloud resources in close proximity to the actual service request as the number of learning increases. We also compare the average of service request prediction accuracy of different models applying three neural networks, Deep Neural Network(DNN), Long Short-Term Memory(LTSM), and CNN, and find that the proposed technique has 3.36% higher prediction accuracy than LSTM-based models, and 40.2% higher than DNN-based models. In the future, additional research is needed, such as building various learning datasets or applying other reinforcement learning algorithms. Further research is also needed on cloud resource rental costs and provisioning latency.
Multi-Person 3D Pose Estimation in Mobile Edge Computing Devices for Real-Time Applications

In the last few years, real-time 3D pose estimation from RGB monocular images on Mobile Edge Computing devices has drawn immense attraction due to the ability to estimate, infer and transfer 3D motion and pose in VR, AR, gaming, animation and so on. However, estimating motion under occlusion is very challenging. Though a number of effective and efficient approaches have been proposed to deal with this issue, there is still a demand for robust occlusion-aware multi-person 3D pose and motion estimation under occlusion in real-world scenarios. In this paper, we propose a one-shot occlusion-aware real-time 3D pose estimation and inference approach called RRMP. Our proposed RRMP performs both 2D and 3D pose estimation and is composed of three sequential stages: 1) the residual to render a multi-level perspective for each individual people, 2) the initial stage, and 3) the refinement stages. As our goal is to estimate the pose in real-time for mobile edge computing devices, the RRMP is designed using Depthwise Separable Convolutions (DSCs) that perform with an average of 40 fps in real-time execution. Our extensive results and analysis depict that the proposed RRMP improves the performances of the existing state-of-the-art methods. Our RRMP technique can be deployed into any existing state-of-the-art works for further improving the robustness in terms of occlusion.
Reinforcement Learning in Process Scheduling

An efficient scheduling algorithm for a CPU has always been on the lookout by researchers and engineers since the invention of computers. Today most of the tasks are running online facilitated by servers. So, the requirement for fast, efficient, and reliable scheduling algorithms is the need of the hour. However, the traditional heuristic algorithms are having issues like complexity and uncertainty, so it is important to apply innovative technology to improve the task scheduling to ensure the efficient performance of these heavyduty systems. This study aims at developing a new scheduling model that integrates both optimized task scheduling and resource utilization using deep reinforcement learning algorithms.
Scalable Channel Allocation in Downlink NOMA Using Parallel Array of Laser Chaos Decision-Maker

Non-orthogonal multiple access (NOMA) is a technology that multiplexes multiple users on the same channel at transmitters through appropriate channel allocation, and the signal detection of multiple users at receivers is conducted based on successive interference cancellation. Real-time processing requires an ultra-fast channel allocation scheme. Previous studies have demonstrated that a laser chaos decision-maker is ultra-fast and effective in solving the decision-making problem. This study demonstrates a scalable channel allocation using a laser chaos decision-maker. The possible approaches to channel allocation increase abruptly with the number of users, which is difficult to handle with the previously proposed NOMA principle using a single laser chaos decision-maker in which its decision corresponds to the channel allocation for all users. Here, the proposed principle adopts an array of laser chaos decision-makers, each of which decides a channel for a particular user. Using this architecture, the proposed scheme can handle an increasing number of users or realize a scalable and ultrafast channel allocation in NOMA. The obtained numerical results indicate that the proposed approach obtains a higher throughput than conventional-NOMA and uniformed channel gain difference-NOMA in several wireless communication environments with differences in density and distance decay.
GDFed: Dynamic Federated Learning for Heterogenous Device Using Graph Neural Network

Federated learning has made it possible to learn models using distributed computing resources. The most commonly used, Fedavg method has a simple structure and shows good performance. However, Fedavg has the disadvantage of not considering the performance of each user device in real-time. To solve this problem, we propose GDFed. It is a federated learning structure that understands the performance and real-time status of each user device during proceeds learning. GNN learns from the graph-type dataset and can perform tasks such as node clustering or node classification through the feature of each node (in this case, the device). GDFed is an architecture that clusters devices using pre-trained GNN models and proceeds with federated learning, taking into account the current capabilities of each device. In the experiment, we show the GDFed method outperforms the Fedavg method by 43.3% in reducing the delay time.
Abnormal Client Detection Federated Learning Using Image Vectors

Federated learning is a distributed machine learning system that can learn AI models in cooperation with each other without directly sharing data stored in multiple locations. Since federated learning requires training the model without direct access to the client data, AI models can be trained while protecting the client’s data. In the presence of clients with relatively different data distributions from other clients, this can lead to poor model learning performance in federated learning. In this paper, we propose a method to obtain cosine similarity by computing the vector inner product based on the vector for the client’s image data, and to improve the performance of federated learning by eliminating clients with low similarity. Compared to the case of conducting federated learning without detecting abnormal clients, the performance improvement of 6% was confirmed when the proposed method was applied.
Classification performance evaluation of latent vector in encoder-decoder model

This paper compares and analyzes the classification performance of latent vectors in the encoder-decoder model. A typical encoder-decoder model, such as an autoencoder, transforms the encoder input into a latent vector and feeds it into the decoder. In this process, the encoder-decoder model learns to produce a decoder output similar to the encoder input. We can consider that the latent vector of the encoder-decoder model is well preserved by abstracting the characteristics of the encoder input. Further, it is possible to apply to unsupervised learning if the latent vector guarantees a sufficient distance between clusters in the feature space. In this paper, the classification performance of latent vectors is analyzed as a basic study for applying latent vectors in encoder-decoder models to unsupervised and continual learning. The latent vectors obtained by the stacked autoencoder and 2 types of CNN-based autoencoder are applied to 6 kinds of classifiers including KNN and random forest. Experimental results show that the latent vector using the CNN-based autoencoder with a dense layer shows superior classification performance by up to 2% compared to the result of the stacked autoencoder. Based on the results in this paper, it is possible to extend the latent vector obtained by using a CNN-based auto-encoder with dense layer to unsupervised learning.
Implementing Topic Modelling For Document Clustering

The most up-to-date method in text mining is called topic modeling. It is a method for quantitatively figuring out the underlying semantic framework of a collection of documents. About twenty scholarly articles on topic modeling were reviewed to compile the information presented here. Methods for topic modeling, as well as several topic modeling evolution models and their respective applications, are included. Methods for assessing topic modeling, such as Latent Semantic Analysis and Latent Dirichlet Allocation, are also described at length to help readers better grasp the concept of topic modeling. Extensive discussion on subject modeling challenges is included at the end, which will definitely give academics some direction for more fruitful investigation.
Exaggerated Advertisement Inspection System for Judging the Suitability of Advertisements in Social Media Environment

Recently, as the social media markets are expanding, the amount of health functional food advertisements posted by individual users such as influencers and social media promoters is increasing. Therefore, users need a system that supports them to post false advertisements after inspecting them. In this paper, we propose an exaggerated advertisement inspection system that judges the suitable of advertisements and presents the grounds for disqualification. The proposed system consists of a module that classifies advertisements and explainable artificial intelligence(XAI). The system provides a rationale for judging the results of advertising classification and exaggerated advertisements. Therefore, the user may know why his or her writing is classified as exaggerated advertisement. The language model and embedding model, used in the exaggerated advertisement classification step, check the accuracy of the confusion matrix through the evaluation data. The XAI model checks performance by inputting data designated as exaggerated advertisement by health functional food-related institutions.
Traffic reduction for out-of-band network management over LPWA

In the remote management of network equipment, network administrators use the command-line interactive interface to troubleshoot problems and modify configurations. A dedicated network for out-of-band network management is required to ensure remote control of network equipment even in the event of system failures, increasing the cost of network management. LPWA is a possible candidate for constructing a network for out-of-band management due to its low-cost and long-distance communication performance. However, its low bandwidth would degrade the performance of the interactive management. This paper solves this problem by reducing the size of interactive commands sent to network equipment. We employ Huffman coding-based compression by exploiting biases and operator characteristics of used commands for network management. Performance evaluation results show that the proposed method can reduce the size of operation commands to 3.6%, which is quite better than the traditional deflate algorithm.
Sionna: Introduction to Embedded Open-Source Semantic Communication Platforms

Recently, in the wireless communication protocol, the machine learning (ML) technique has emerged as the most promising approach for designing the entirely model-free end-to-end protocol or the partly model-free protocol by replacing only a few parts. Yet, its main challenge is the consistency of the simulator for each functional block in wireless communication. Even though many research outputs shed light on the ML for wireless communication topic, most of the results do not provide a reasonable baseline or are incompatible with conventional systems, such as 4G-LTE, and 5G-NR. This paper introduces the overview of the python-based open-source library, Sionna [1], enabling us to easily employ the 4G-LTE and 5G-NR compatible functional blocks in CUDA-GPU ML framework. Besides, one exemplary result, "trainable QAM constellation with Polar coding," is presented, which motivates us to study more practical research on ML for wireless communication.
Collaborative Computation Offloading Scheme Based on Deep Reinforcement Learning

Deep Reinforcement Learning (DRL)-based computation offloading scheme has been proposed to improve the Quality of Experience (QoE). However, the existing DRL does not consider the temporal states because of the fully connected layer. Also, DRL learns the policy regardless of the importance of experience. To solve these problems, we propose a collaborative computation offloading scheme with DRL. First, we define the objective function about task service time and load balance. Second, we utilize the Least Absolute Shrinkage and Selection Operator (LASSO) regression in the backbone network for considering temporal states. Finally, we prioritize the experience according to the Temporal Difference (TD) error and learning loss. The simulation results show that the proposed scheme achieves high QoE due to low task service time and high load balance.
Accelerating convergence in wireless federated learning by sharing marginal data

Deploying federated learning (FL) over wireless mobile networks can be expensive because of the cost of wireless communication resources. Efforts have been made to reduce communication costs by accelerating model convergence, leading to the development of model-driven methods based on feature extraction, model-integrated algorithms, and client selection. However, the resulting performance gains are limited by the dependence of neural network convergence on input data quality. This work, therefore, investigates the use of marginal shared data (e.g., a single data entry) to accelerate model convergence and thereby reduce communication costs in FL. Experimental results show that sharing even a single piece of data can improve performance by 14.6% and reduce communication costs by 61.13% when using the federated averaging algorithm (FedAvg). Marginal data sharing could therefore be an attractive and practical solution in privacy-flexible environments or collaborative operational systems such as fog robotics and vehicles. Moreover, by assigning new labels to the shared data, it is possible to extend the number of classifying labels of an FL model even when the initial input datasets lack the labels in question.
On Phishing: Proposing a Traffic Behavior-Based Model to Detect, Prevent, and Classify Webpage Suspicious and Malicious Activities

Phishing is a criminal act in which a Phisher sends a well- counterfeit Webpage, using its Lexical, Host-Based, and Content (LHC) features, containing unseen security threats and stealthy attacks to unwary victims tickling them to disclose sensitive credentials such as financial data, address, etc. The Webpage will probably pass under anti-phishing techniques (APT) because they mainly focus on detecting and classifying Webpages as either Malicious or Benign, neglecting Webpage traffic behavior (TB). In this research, we propose the detection, prevention, and classification (DPC) of Webpages' (W) suspicious and malicious activities based on their TB model, namely DPC based on the B - WTB or L2 model, as the second line of defense against a classified Benign Webpage has passed under the APT line of defense undetected. The L2 model is encapsulated in a sandbox to avoid system failure and keep attacks from spreading around the network, which will classify L1 Webpages as Benign, Suspicious, or Malicious based on their TB when they attempt to access unauthorized resources. Using 10369 records from ISCX - URL2016 dataset, the L2 model achieves an accuracy of 90.07%, 91.85%, and 92.62%, using KNN, LR, and SVM machine learning algorithms. In addition, the implementation of the proposed L2 model shows a significant observation regarding classified Webpages' attempt to access restricted resources based on their maximum number of access violation attempts for each of the restricted resources and an accumulative number of access attempts over time for each violation access attempts on the restricted resources. The experimental results show the precision score, the recall score, and the F1 score for each model.