Reviewerly/exHarmony huggingface dataset:
A Novel 2-Step Very Large-Scale Short Text Clustering Algorithm

In recent research, the effectiveness of text clustering is the long-term pursuit goal, because the same clustering documents have the maximum correlation and internal connection, this technology can simplify the user's text processing process. This research study focuses on the very large-scale short text clustering with a case study of legal documents. First, the normal size short text clustering algorithm is studied, the encapsulation method is used, and the Term Frequency-Inverse Document Frequency (TFIDF) algorithm is selected. Then, after finding the shortcomings, the 2-step algorithm for very large short text clustering is proposed. First, the text similarity reduction serves as the pre-processing, and this study applies the iteration function together with the Simhash algorithm to achieve the goal. Second, the class-centric algorithm extension method is designed to effectively cluster massive information. The performance of the algorithm is evaluated by experiment in terms of time and accuracy.
NepaliBERT: Pre-training of Masked Language Model in Nepali Corpus

Recent improvement in the Transformer model has provided us with state-of-the-art architectures like BERT, RoBERTA, and GPT-3. Though, better results are obtained for the real-world NLP problems with the use of these architectures, there is still a lot to achieve in the Nepali domain. Nepali Language, which uses the Devanagari script, has rich semantics and grammatical structure but due to the lack of computational resources, the optimum results are yet to be achieved by using the state-of-the-art architectures . This is why, there is no publicly available NLP Nepali model, which can be used by other researchers. This research study intends to fill this research gap by providing word embeddings for the Nepali language trained on Word2Vec, Doc2Vec and BERT architecture, which can be used as a base for creating benchmark results on different NLP tasks.
Securing Online Quiz Management through Blockchain Technology and Three-Tier Cryptography with Hash-based Authentication

The Online Quiz Management System (OQMS) is a digital platform used by educational institutions for administering and conducting quizzes online. However, traditional OQMS systems have several significant limitations that hinder their performance. It is susceptible to security vulnerabilities and data breaches, putting the sensitive information of students and teachers at risk. Additionally, traditional OQMS depend on centralized systems prone to single-point failures, leading to service disruptions and decreased transparency. The conventional OQMS faces various challenges in online quizzes, such as secure storage of quiz results, non-repudiation, authentication, access control, and preventing result tampering. These problems highlight the need for a more secure and trustworthy OQMS. This study introduces a Blockchain-based Online Quiz Management System (BOQMS) that incorporates Three Tier Cryptography (TTC) and Hash-based Authentication to ensure secure quiz data storage and access control to address these issues. BOQMS implements a combination of Optimized AES algorithm-based image key cryptography, Optimized RSA algorithm-based asymmetric key cryptography, and Optimized RC4 algorithm-based symmetric key cryptography to provide strong security. In addition, the system features role-based access control (RBAC) and hash-based authentication to limit access to quiz management, and smart contracts are used to verify user authenticity during login. BOQMS enables teachers to create quizzes, students to participate, and teachers and administrators to view results securely through an encrypted process using TTC. Only authorized users with appropriate decryption keys can access quiz data, ensuring the confidentiality and integrity of data. Experimental results indicate that BOQMS generates hashes faster, with a time of 98 milliseconds for 100 blocks. TTC cryptography used in BOQMS takes significantly less time for encryption and decryption than other cryptography combinations, with 12.194 seconds for 96-bit data. These results demonstrate the efficiency and effectiveness of BOQMS in providing secure online quiz management.
CE-OST: Contour Emphasis for One-Stage Transformer-based Camouflage Instance Segmentation

Understanding camouflage images at instance level is such a challenging task in computer vision. Since the camouflage instances have their colors and textures similar to the background, the key to distinguish them in the images should rely on their contours. The contours seperate the instance from the background, thus recognizing these contours should break their camouflage mechanism. To this end, we address the problem of camouflage instance segmentation via the Contour Emphasis approach. We improve the ability of the segmentation models by enhancing the contours of the camouflaged instances. We propose the CE-OST framework which employs the well-known architecture of Transformer-based models in a one-stage manner to boost the performance of camouflaged instance segmentation. The extensive experiments prove our contributions over the state-of-the-art baselines on different benchmarks, i.e. CAMO++, COD10K and NC4K.
Novel Weight-Improved Particle Swarm Optimization to Enhance Data Security in Cloud

The rapid growth of cloud computing has brought numerous benefits and opportunities, but it has raised concerns about data security. To protect sensitive data stored in the cloud is crucial and to ensure data privacy, confidentiality, and integrity for cloud users. The cloud as a platform enables real-time patient monitoring via the use of sensors, which has the potential to revolutionize the healthcare industry. Users transmit sensitive and classified medical information to cloud service providers for centralized storage and processing. This presents cybercriminals with the opportunity to acquire data, interfere with ongoing data processes, and restrict healthcare professionals and patients from accessing confidential information. For a healthcare organization to feel comfortable with and use the cloud computing platform, privacy and security are the two most pressing concerns. Fine-grained access control (FGAC) is used as a tool for controlling who may see what data in a cloud-based healthcare setting. Protects private medical information by limiting who may see it, change it, or even access it at all. Over the last several years, advances in machine learning have made cloud data security one of the most promising areas in the field. In the proposed system the Weight-Improved Particle Swarm Optimization Algorithm (WI-PSO) and machine learning classifiers are implemented to enhance data security in the cloud.
Performance Evaluation and Ranking of Deep Learning Feature Extraction Models for Thyroid Cancer Diagnosis using D-CRITIC TOPSIS

The nodules in the thyroid region can be cancerous or non-cancerous, present even in healthy humans. Early diagnosis of thyroid cancer is helpful for prevention and treatment. Diagnosing thyroid cancer using traditional approaches is a hard-working task due to the considerable burden on the healthcare community. In this paper, we analyzed the performance of the AI models (Swin Transformer, Data Efficient Image Transformer, and Mixer Multi-layer Perceptron) to extract the features from the histopathological and ultrasound images. The Locally Linear Embedding (LLE) is used to reduce the dimensionality of the feature space. These transformed features are utilized for the training five classifiers models (Random Forest classifier, Naive Bayes, Logistic Regression, Support Vector Classifier, and k-nearest neighbors). There is a total of fifteen possible combination models tested using the 5-fold cross-validation technique, and three performance metrics are calculated. The recently proposed TOPSIS technique is used to benchmark the performance of all the models, and based on the TOPSIS scores, rank values are assigned. The distance correlation-based CRITIC (CRiteria Importance Through Intercriteria Correlation) is employed for the weight calculation of different performance criteria. The model with Swin Transformer as a feature extractor and Random forest as a classifier outperformed other models and achieved the highest TOPSIS score. The top-ranked model with a TOPSIS score of 1.0000 achieved an accuracy of 0.9335 and 0.8518 for thyroid ultrasound and histopathological images. The proposed models are simple and can be deployed on resource-constrained remote edge devices. With the help of IoT and 5G/6G communication technologies, either an ensemble model is created, or federated learning techniques can be utilized to transfer the weights in order to train the cloud-based global model.
Trusted Computing Data Transaction Scheme based on Blockchain

A blockchain technology based on encryption algorithm is proposed to address the issue of data theft and leakage in data transactions. Use blockchain to record transaction information and confirm the rights of data assets and sources; Use trusted computing and encryption algorithms to ensure data transmission security; After the data provider and demander complete the trusted calculation, the encrypted data results are returned. This scheme can perform data calculations without exposing the data to the data requester, and ensure transmission security through trusted encryption.
Determine the Degree of Malignancy in Breast Cancer using Machine Learning

It is predicted that 2.1 million women were given a diagnosis of breast cancer for the first time in 2018, and that 626,679 women who had breast cancer passed away because of the disease. Breast cancer is the most common form of cancer among women globally. Even though there is a wide variety of approaches to determining the presence or absence of malignancy, there are several procedures that raise concerns due to their varying degrees of accuracy in diagnosis [3]. As a result of the precision and speed of computer-assisted diagnostics, machine learning is receiving an increasing amount of attention for its potential role in aiding with illness prediction in this field. For this work, we used several different machine learning methods to investigate breast cancer malignancies. To evaluate whether breast tissue cells acquired by fine needle aspiration (FNA) were malignant, these methods were used to a diagnostic dataset of characteristics from those cells. We observed that the k-nearest neighbor (KNN) model had the greatest performance of all the models that were evaluated and scored the maximum accuracy. This was determined by the creation of confusion matrices and the evaluation of several different metrics (97.66 percent). We were also successful in developing a simpler generalized additive model (GAM) that depended on just three characteristics but achieved an accuracy level that was like the original model (95.91 percent). R Studio was used for all the tests that were conducted.
Email Spam Behavioral Sieving Technique using Hybrid Algorithm

Email spam filtering is a critical task in today's digital world due to the significant increase in the volume of spam emails. To address this issue, various machine learning (ML) and statistical techniques have been proposed to classify spam and ham emails accurately. This research study proposes a hybrid algorithm that combines two techniques: The Naive Bayes (NB) and the Support Vector Machine (SVM). The proposed algorithm first preprocesses the email data by removing stop words, stemming, and transforming the text data into a numerical format. Then, the NB algorithm is used to extract the relevant features from the email data. The extracted features are then fed into the SVM algorithm, which uses a nonlinear kernel function to classify the emails. The methods were tested on two datasets: PU, Lingspam. The experimental results show that the proposed hybrid algorithm outperforms the standalone NB and SVM algorithms in terms of precision, and recall.In conclusion, the proposed hybrid algorithm is an effective approach for email spam filtering, and it can be used by email service providers to filter out spam emails from their users' inboxes.
The impact of using morphological operators with enhancement technique

Nowadays, digital images, generated from various devices such as smart phone, surveillance cameras, play an important role in daily life. In order to obtain good photos although in a low light condition, image enhancement is widely addressed in the field of image processing. This paper presents how assembling morphological operators with standard image enhancement techniques will improve the outcomes. Experiments for image enhancement on various benchmarks in comparison with existing methods have validated the interest of our proposal.
Multilingual Sentiment Analysis for Detecting Mental Health Problems using a Hybrid algorithm combining RNN and Bi-LSTM

Sentiment Analysis is gaining traction nowadays owing to its diversified applications, one of them being identifying people’s mental health issues. Researchers have done great work decoding the actual meaning of human sentiments with the help of machine learning and NLP However, multilingual sentiment analysis and detection of depression from the online comments of a user is still demanding the attention of researchers worldwide. This study offers a novel hybrid recurrent neural network (RNN) and bidirectional long short-term memory (Bi-LSTM) architecture for multilingual sentiment analysis to detect mental health issues. RNN captures sequential information, while Bi-LSTM captures bidirectional dependencies, improving performance. The algorithm is comprised of multiple steps. Tokenization and word embeddings are used to preprocess the dataset. Splitting the preprocessed dataset into training and testing sets, sentiment labels are applied to each text sample and encoded numerically. The hybrid RNN and Bi-LSTM model captures sentiment patterns well. The RNN component evaluates sequential information, while the Bi-LSTM component collects forward and backward dependencies to improve contextual knowledge. To train the model, back propagation and gradient descent minimize a loss function like cross-entropy loss.
FoveaBox with Consistent Label Assignment Distillation for Page Object Detection

With the increasing prevalence of digital documents replacing traditional paper documents, there is an urgent need to develop techniques to extract information from them. In particular, image-based documents pose a significant challenge in object detection, where the identification of objects such as figures, tables, and captions can be difficult. The crux of object detection lies in the accuracy of label assignment, and we all know that the human visual system is highly capable and proficient in recognizing objects. In light of this, we propose a novel approach, FoveaLAD, which integrates FoveaBox drawed inspiration from the human eye, and Label Assignment Distillation to enhance the label assignment process. We evaluate FoveaLAD on the UIT-DODV-Ext image document dataset, which comprises three classes: Table, Figure, and Caption. Our findings demonstrate that FoveaLAD effectively selects different scales for objects of varying sizes and improves the accuracy of label assignment in object detection.
Question Answering on Biomedical Research Papers using Transfer Learning on BERT-Base Models

Reading entire research papers for a simple piece of information is very exhausting. Likewise, finding and arranging research papers with a keyword is also exhausting. This paper aims to automate such exhausting processes while working with research papers. This automation is done using a Natural Language Processing (NLP) technique: Question Answering System. The paper introduces benchmark data for developing such a Question Answering system finetuned for biomedical research papers. The data is developed by the annotation of question, context, and answer pairs from biomedical research papers from Pubmed. Applying transfer learning to this benchmark data on several pretrained large language models such as BERT, RoBERTa, PubmedBERT and BioBERT, the paper introduces a state-of-the-art model for such biomedical question-answering data. The model developed by finetuning this custom data on PubmedBERT released by Microsoft achieved an Exact Match of 83.89 and an F-score of 89.67.
Spatio Temporal Feature-based Weapon and Violence Detection from Video Surveillance using Deep Learning

The increasing incidence of violence in public spaces has made developing efficient and accurate weapon detection systems necessary. A Long Short-Term Memory (LSTM) model built on the MobileNet structure has been proposed for the detection of violence and weapons. It examines each frame of a video stream to look for indications of violence or the presence of weapons. The proposed system is trained on a dataset of simulated violent incidents. The dataset includes fights, gunfights, knife attacks, and diverse environmental and lighting conditions. The system is excellent for use in surveillance and security systems and 93.86% accuracy is achieved. It can be implemented on embedded systems or mobile devices due to its high efficiency and minimal computing demands. The proposed method can increase public safety by notably increasing the efficiency of security and surveillance systems.
Performance of VM in SDN-Assisted Cloud Data Center with Working Vacations

The Internet of Things (IoT)'s exponentially growing network traffic has a significant impact on energy utilization and network performance. A recommended power-saving traffic control approach for cloud data centers uses virtual machines (VM) vacation. The essential idea is that the (VM) in the cloud system may go on vacation through the utilization of software-defined network (SDN) paradigm. The reduction of active VMs while upholding QoS standards is the aim for the QoS optimization. The M/M/1 queue model with working vacations (WV) and customer requests impatience behavior is examined in this research study. This study considers the possibility that the customer's impatience is caused by a WV. The requests to be served at the cloud data center by the VMs are at slow rate than normal during WVs and are prone to get impatient. An "impatience timer" that follows exponential distribution is activated whenever customer’s request reached to the cloud system and discovers that the VM is on WV. A customer leaves the queue and does not come back if their service is not finished before their timer runs out. The system's numerous performance parameters such as average system size, the sojourn time, the percentage of customer’s request being served, and the abandonment frequency are calculated. Finally, some numerical findings are obtained to show how some specific parameters affect the system performance indicators.
PBVR: Physically Based Rendering in Virtual Reality

Virtual Reality (VR) is a rapidly growing domain that requires high-fidelity graphics for immersion. To understand and improve the VR architecture, an open-source, end-to-end platform for VR research was recently proposed. However, studying the stereo rendering aspect of VR applications remains challenging due to the lack of infrastructure.In this work, we augment the aforementioned open-source platform, ILLIXR, by integrating a high-end, physically based 3D rendering engine, Filament. This upgrade, named PBVR, enables developers to render high-quality graphics in a completely open-source VR platform.As a case study, we leverage our proposed PBVR to look into gaze-tracked foveated rendering and profile three different scenes. We show that a handful of renderpasses consume the most time and that readily available foveated rendering solutions, such as Variable Rate Shading, might not provide significant advantages. Moreover, our results reveal that eye tracking can incur a significant overhead on the graphics processing unit (GPU).
Network Traffic Classification Using Distributed ML-Based Data Parallelization Approach

The large datasets related to network traffic flow classification in the internet benefit machine learning (ML) and deep learning (DL) models for more accurate classification, which is used in many applications such as in detecting traffic anomaly for prevention of potential cyber-attacks. Data Parallelization allows for faster training times on large datasets as shown in our results, and it is also beneficial in the cloud-edge environment by allowing efficient distribution of computation and data across multiple nodes. We deployed Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), advanced hybrid Convolutional LSTM (ConvLSTM), Convolutional GRU (ConvGRU), and XGBoost algorithm using Data Parallelization approach. The experimental setup was implemented in the cloud and parallel training was executed using Nvidia Tesla Graphics Processing Units (GPUs). Lastly comparison of the performance metric results is presented between the non-parallel centralized (single node) and data parallel distributed approaches.
HTC-BI: Hybrid Task Cascade with Boundary Information for Instance Segmentation

Hybrid Task Cascade (HTC) is an effective cascading architecture for instance segmentation. By fully utilizing the interdependent relationship between detection and segmentation, it has shown remarkable performance. However, exploring the potential of boundary information remains open. In this work, we propose a novel method to integrate multi-level boundary information from global to local context into the HTC framework, which entirely supports segmentation tasks at all levels and produces more accurate predictions. Our method consists of two major components: (eq1) at the image level, learning the boundary representation and combining it with the semantic segmentation branch for better spatial context information; (eq2) at the instance level, adopting boundary-preserving mask head replacing the basic mask head that jointly learns object mask and boundary. By additionally learning the boundary representation, our method achieves 39.2% AP and outperforms the original HTC on MS COCO dataset. Code and models are available at https://github.com/thuyltncs/HTCBI.
Making Sense of Using a SmartNIC to Reduce Datacenter Tax from SLO and TCO Perspectives

The speed of network interfaces has rapidly increased, while the performance and energy efficiency of CPUs have not, due to the demise of Dennard scaling. As a result, functions processing network packets have become responsible for a rapidly increasing portion of the datacenter tax. To tackle this problem, the industry has developed SmartNICs (SNICs) integrating conventional NICs with inexpensive and energy-efficient processors that can efficiently execute functions widely used by network-intensive datacenter applications. With such processors, the SNICs promise to reduce the total cost of ownership (TCO) for datacenters by increasing energy efficiency of servers and/or decreasing the number of expensive server CPU cores. In this paper, to make sense of using SNICs, we focus on analyzing energy efficiency of a server with an SNIC, especially under service level objective (SLO) constraints which matter for many datacenter applications. To this end, we not only measure the system-wide power consumption of a server but also devise a custom hardware setup that allows us to isolate the power consumption of the SNIC from that of the server. This helps us better understand the impact of using SNICs on energy efficiency of servers. Second, we take popular TCP/UDP, DPDK, and RDMA-based functions, and prepare them to run on an SNIC processor and a server CPU. Then, we measure maximum throughput, tail latency, system-wide energy efficiency of executing the functions on the SNIC processor and the server CPU, respectively. Lastly, based on analyses of the measurements, we make five key observations and three strategies to better use and design SmartNICs in the future.