Reviewerly/exHarmony huggingface dataset:
Adaptive Streaming Scheme with Reinforcement Learning in Edge Computing Environments

DASH is an effective way to improve the Quality of Experience (QoE) of video streaming. However, existing schemes lack consideration for a multi-client environment, so QoE fairness deteriorates when multiple clients stream video through the same network. In this paper, we propose an adaptive streaming scheme with reinforcement learning in edge computing environments. The proposed scheme learns policy based on reinforcement learning to improve QoE and uses edge computing to improve QoE fairness. We evaluated the performance of the proposed scheme through simulation-based experiments under various network conditions. Through the experimental results, we confirmed that the proposed scheme achieves better performance than the existing schemes in a multi-client environment.
Segmentation of Cerebral Hemorrhage CT Images using Swin Transformer and HarDNet

Segmentation is a technique that divides an image by relevancy into pixel units and is often used to detect diseases in medical imaging analysis. Furthermore, MRI and CT are distinct data types commonly used in medical imaging analysis. The difference is that MRI analyzes 3D images, whereas CT involves 2D images. In other words, it is not certain that a model with a superb performance in MRI images will also be ideal in CT images, as it relates to the generality of the data and the model. In this study, we combined HarDNet and Swin Transformer to describe methods and experimental processes that improve performance compared to using the two individual models separately. In addition, STHarDNet, a combination of HarDNet and Swin Transformer, which performed well in MRI images, was applied to cerebral hemorrhage CT images provided by AIHub. The experiment results showed that STHarDNet exhibited superior performance in cerebral hemorrhage CT images in addition to MRI images compared to seven other models, including U-Net, U-Net++, SegNet, PSPNet, Swin UNet, HarDNet, and Swin Transformer.
A Reference Architecture for Activities-as-Asset Distributed Ledger with Secure Private Computation

This paper describes a reference architecture for a secure Activity-as-Asset (AaA) ecosystem on the Algorand Blockchain system. It has built-in persistent personal ownership of data while allowing the sale of singletime data usage contracts to enterprises. This system would allow users to deny enterprises access to their own personal data at any time, even if they have sold access to that same data in the past.
Face Mask Detection Using SSD-Mobilenet-V2

Mask detection plays a major role in the prevention and control of epidemics after the COVID-19 outbreak as it is the most practical and effective method of prevention. For the appropriate employees, a great automatic real-time face mask identification system based on deep learning can significantly lessen work-related stress. The systems for mask identification that are currently in use, however, are largely resource-intensive and do not strike a reasonable balance between speed and accuracy. In our system, the mask detector is SSD, and to extract the image’s features and decrease a number of parameters, MobileNet takes the role of VGG-16. Pre-trained models from other domains are transferred to our model using transfer learning techniques.
A Comparative Study: Deepfake Detection Using Deep-learning

In recent decades, we have seen significant advancement in fields like Artificial Intelligence, Machine Learning, and Deep Learning, resulting in the developing of new technologies such as deepfake. Deepfakes are a form of digital media that replaces one identity’s likeness with another or creates a synthetic personality: in the form of high-quality realistic fake video, image, or audio. Deepfakes can be helpful in education, art, activism, and self-expression; however, some subjects can use deepfakes to harm the portrayal of people, create pornographic content, and spread misleading information. High-quality deepfakes are easy to build but incredibly difficult to detect, creating a need to explore technologies which can be helpful in deepfake detection. Therefore, we present a comparative study of deep-learning models that can benefit deepfake detection. We have explored four deep-learning models, namely, VGG16, MobileNetV2, XceptionNet, and InceptionV3 and trained these models on the FaceForensics++ dataset. Finally, we evaluate the performance of these models for deepfake detection and conclude the study with our observations and future scope for improvement in this field.
Neural Architectural Nonlinear Pre-Processing for mmWave Radar-based Human Gesture Perception

In modern on-driving computing environments, many sensors are used for context-aware applications. This paper utilizes two deep learning models, U-Net and EfficientNet, which consist of a convolutional neural network (CNN), to detect hand gestures and remove noise in the Range Doppler Map image that was measured through a millimeter-wave (mmWave) radar. To improve the performance of classification, accurate pre-processing algorithms are essential. Therefore, a novel pre-processing approach to denoise images before entering the first deep learning model stage increases the accuracy of classification. Thus, this paper proposes a deep neural network based high-performance nonlinear pre-processing method.
An Empirical Review for Character Recognition in Video Frames using Text Detection Algorithms

The characters in the text image or video is having more information for recovery and indexing applications. The recognition of characters from the video text images is a difficult task due to the complex backgrounds, grey-scale values, illuminations, and sizes. Hence, various text detection algorithms like feature extracting algorithms including texture and region-based, machine learning, and deep learning approaches are implemented. Optical Character Recognition (OCR) is implemented in detecting the characters from the text frames and gives the text information including the location of the frame for text in the video. The Natural Language Processing (NLP) technique results in high performance in detecting the different text languages from the video frames. Convolutional Neural Network (CNN) algorithms are used in the feature selection process for efficient recognition of text characters from the images. The deep learning algorithms are used in textual regions in the frames of the video for detecting the text. The LSTM combined CNN networks are also implemented for extracting the features from the image for efficient and high-quality output text.
Poisoning Attack based on Data Feature Selection in Federated Learning

Federated learning is proposed as a typical distributed AI technique to protect user privacy and data security, and it is based on decentralized datasets that train machine learning models by sharing model gradients rather than sharing user data. However, while this particular machine learning approach safeguards data from being shared, it also increases the likelihood that servers will be attacked. Joint learning models are sensitive to poisoning attacks and can effectively pose a threat to the global model when an attacker directly contaminates the global model by passing poisoned gradients. In this paper, we propose a joint learning poisoning attack method based on feature selection. Unlike traditional poisoning attacks, it only modifies important features of the data and ignores other features, which ensures the effectiveness of the attack while being highly stealthy and can bypass general defense methods. After experiments, we demonstrate the feasibility of the method.
A Comparative study of different approaches for clustering of incomplete medical data

Fuzzy c means is a conventional clustering algorithm that uses complete data sets for the clustering process, making it difficult to deal with incomplete data which is a critical problem in medical research that cannot be avoided. These missing attributes are due to several factors. A variety of imputation and non-imputation-based methods are used to estimate missing data. We reviewed various clustering algorithms used to deal with missing data in the medical sciences in this research work. The four most commonly used non-imputation and iterative clustering strategies and a variety of imputation-based FCM clustering algorithms are thoroughly examined. The Thyroid and Wisconsin breast cancer dataset naturally contain missing values from the UCI repository that are chosen for experimental results.
Tourist reviews summarization and sentiment analysis based on aspects

Reading massive amounts of user-generated text and pulling out the relevant aspects and opinions is a complicated process. Summaries, on the other hand, help busy people who only have a little bit of time to read get the gist of the information quickly. Text summarization is the process of taking the original text and making a shorter version of it that still has all of its informational value and main idea. Humans have a hard time summarizing long texts by hand. Different ways to summarize a text can be put into groups based on the more general techniques of extractive and abstractive summarization. The research paper discusses the need for generating aspect-based summaries and Sentiment analysis. A framework is proposed based on extracting coherent aspects from the reviews and applying the extractive summarization method to generate summaries. In addition, providing insights into the reviews of tourist attractions by using aspect-based sentiment analysis. The results are evaluated using crowdsourcing, Fairsumm, and Centroid method. The crowdsourcing method gives the best result on aspect-based summaries.
Blockchain-Based Service Migration for Multi-Access Edge Computing

Multi-access edge computing (MEC) extends cloud computing capabilities to the edge of the network which enables real-time and low-latency services for mobile users. To ensure that the delay requirement of the services is met even in the event of user mobility, service migration across different edge clusters is needed. Trusted migration and proper destination node selection across domains are two key ingredients for cross-edge migration. To this end, we propose a blockchain-based service migration framework to establish trust among different edge clusters. We use Kubernetes as our cluster orchestrator platform for containerized services. In addition, we model the problem as a Markov Decision Process (MDP) and employ the asynchronous advantage actor-critic (A3C) algorithm to select suitable destinations. The proposed approach is corroborated by a simulation study.
Comparison Analysis of Deep Learning Models In Medical Image Segmentation

Despite U-Net and its variants' notable accomplishments in the field of medical picture segmentation work, its segmentation capabilities intended for little matters are still not good enough. Segmentation in medical imaging has seen widespread use of deep learning. The benefits of precise segmentation of tiny targets and its scalable network design are demonstrated by U-Net. U-Net has received more than 2500 academic citations as a result of the recent increases in the performance requirements for segmentation in medical imaging. The U-Net architecture has been continuously developed by several academics. This paper reviews and categorises the related methodology, summarizes the medical image segmentation machineries based on the U-Net erection alternates in terms of the structure, origination, adeptness, etc., and introduces the loss functions, assessment constraints, and elements typically used for segmentation in medical imaging, which will serve as a useful guide for imminent study. Even though U-Net has confirmed reliable recital in semantic separation, it has been confirmed that U-Net has limits when faced with situations involving more image characteristics and semantic data. To address this, a multistate attention-thick U-net [3] has been created. Here, a comparative analysis of these deep- learning neural network architectures for image processing is done. The detailed comparison of U net and its variants is listed and explained.
Imputation Techniques Analysis for Incomplete Medical Datasets in Case-Based Reasoning System

Case based reasoning (CBR) based expert systems are widely accepted for healthcare-related applications. CBR is analogous to how humans solve problems using prior experience. CBR is a lazy learner and maintains the cases in case-base to solve the encountered target case. The accuracy of any case based reasoning based system depends upon the quality of cases in its case base. Nowadays, an ample amount of electronic healthcare data is available, but unfortunately, electronic data come along with a growing amount of missing data. Multiple techniques are available to handle those missing values. One widely used way is to use imputation methods to compute missing values for different attributes, which utilize the total actual values in the given dataset. The study was conducted to find the best suited imputation technique for calculating missing values for constructing case base in case-based reasoning system. This study uses benchmark medical dataset to examine four combination imputation techniques and data preprocessing method. The author proposes a hybrid model that performs the pearson correlation to find the critical feature and then used imputation techniques to fill in missing values in different attributes. Experiment results show that the Pearson correlation and linear regression (LR) outperform the other 3 imputed techniques' and give more accurate imputed values. To justify the implication of data preprocessing methods with imputation technique, the findings are carried to medical dataset collected from UCI.
Radial Median LBP: A novel Local Age Invariant Descriptor in Unconstrained Conditions

This work launches a new variant of LBP Radial Median LBP (RM-LBP). Precisely, age invariant descriptor is proposed in different unconstrained conditions. In RM-LBP, the radial directional pixels are appended with center pixel for developing median value. For forming the code of RM-LBP, the comparison of medians with mean is done. By computing code of RM-LBP in every place forms RM-LBP image and from this the size developed is 256. Further compression and matching steps are achieved by using FLDA and SVMs. The age dataset used for the evaluation is FG-NET. Apart from RM-LBP, the five other descriptors are also evaluated for comparison. RM-LBP conquers accuracy of all compared descriptors. RM-LBP also conquers several literature methods. These numerous literature methods are tested on different settings/protocols. The objective of the proposed work is to introduce the novel age invariant descriptor in the unconstrained conditions. The achieved results proves the effectiveness of the developed method. The novelty of the proposed work is the development of the local descriptor RM-LBP.
Data Security in Cloud Computation

Cloud data protection is the process of safeguarding information stored, sent, or processed by a corporation in a cloud environment, regardless of whether the data is under the firm’s direct control or in the custody of a third party. More and more businesses are moving away from building and operating their own data centers and instead putting their apps and data in the cloud, making it essential for businesses to adopt this strategy. This paper has researched about cloud computation and security of the data stored on clouds. This paper has also concluded the future scope that in the coming years many more companies and users will be drawn towards cloud for cooperative data sharing and the security of these data will be of paramount importance to the global cloud providers. A survey with the objective of finding which cloud provider does different age group people prefer and the various challenges they face and if they think whether the current laws are enough to protect their data privacy is also added and the biggest IF their data is safe when stored in cloud.
An Ai Based Application for Cancer Diagnosis - An Emperical Analysis

Cancer is one of the most dangerous diseases that may strike a person. Global mortality could occur as a result of the late diagnosis. Profuse research now calls for the early detection and start of treatment for the majority of cancer types as it helps with patient medical care. Lung cancer, liver cancer, breast cancer, and cervical cancer are four significant cancers that we have taken into account in this work. It does a comparison study between numerous current algorithms, including Linear Regression, Logistic Regression, Support Vector Machine (SVM), K-nearest neighbors (KNN), Naive Bayes, Gradient Boosting, Xtreme Gradient Boosting (XGBoost), and Random Forest based on all the datasets gathered for the prediction of the sorts of malignancies we've taken into consideration. The indicator that is used for comparing the algorithms is the prediction accuracy value. After performing the comparative analysis, we saw that different algorithms performed better on different datasets. On the breast cancer dataset, Logistic Regression performed best with an accuracy of 99.6%, on the Lung cancer dataset, K-nearest neighbor performed best with an accuracy of 97%, on the Cervical cancer dataset, Random Forest performed best with an accuracy of 99.2% and on Liver Cancer, Random Forest performed best with an accuracy of 73.73%. This will help in further research since to our best knowledge there haven't been many research papers that have compared the most widely used algorithms for cancer prediction models.
Integrated Optimization in Training Process for Binary Neural Network

Deep Neural Networks (DNNs) have recently become larger and deeper to keep up with more complex applications, resulting in high power and memory consumption. Due to simplicity in computation and storage, Binary Neural Networks (BNNs) have been one of the potential approaches to overcome these challenges. Previous works proposed many techniques to mitigate the accuracy degradation because of less bit-width representation. However, each technique follows different optimization directions, while the combination can gain better results. In addition, the padding value which is an essential factor directly affecting the accuracy and inference implementation has not been touched on in the state-of-the-art solutions. In this paper, based on the previous works, an integrated approach is applied in the training process for BNNs to improve accuracy and training stability. In particular, to increase the probability of changing weights’ sign, the ReCU function proposed in related work is used to transform full-precision weight to binary weight, while to make the gradient mismatch of the sign function closer to the real one, the training-aware approximation function is used to replace the sign function. Besides, to make the BNNs compatible with post-XNOR implementation, the padding value for convolution is proposed to change to minus one from the default zero. The integrated method is implemented on the Cifar-10 dataset with VGG-small model shows that the training process is more stable with higher accuracy, compared to the baseline, while the model architecture and training algorithm are preserved.
DCGit: Decentralized Internet Hosting for Software Development

Git has been the de-facto version control system for the Software Development industry. Although Git is distributed, developers’ tools for collaboration, such as GitHub, are centralized entities owned by large corporations such as Microsoft. The centralization creates trust and privacy issues for software development companies (preserving their intellectual property), along with a significant" single point of failure" issue. In addition, such centralized systems are susceptible to Sybil and distributed denial of service (DDoS) attacks due to the presence of malicious individuals. Blockchain technology has many key characteristics (such as decentralization, transparency, immutability, and audibility), solving these centralization issues. However, the requirement of having a storage system to store the user’s repositories over the blockchain creates a scalability issue (in terms of storage). Most importantly, it makes data (code) privacy more severe due to its open nature. In this paper, we propose a privacy-preserving decentralized alternative solution and framework named "DCGit" powered by Web3 technologies such as the Ethereum Blockchain and InterPlanetary File System (IPFS) to provide security and scalability yet user-friendly collaboration for software development.
Optimised Load Balancing for Green Cloud Computing

Consumption of energy accounts for a significant portion of the costs associated with carrying out any activity. Cloud computing has seen spectacular development in network technology because of the advancements in communication technology, the expansion of Internet usage, and the solution of issues on a bigger scale. The NP-hard challenge of scheduling and allocating resources to meet the needs of users in the Cloud has been referred to as a scheduling problem. For this reason, balancing the load is critical to overcoming all the issues listed above. Researchers have compared a wide range of load balancing techniques in the cloud to improve different performance metrics and help load balancing. The study also describes relation between green cloud computing and load balancing.
Scalability and Sustainability in Chatbot and Mobile Application Development

A primary concern in the development of mobile applications is ensuring that the apps can manage a rising user base without degrading the user experience or performance. Therefore, the application’s architecture needs to be able to handle several requests per minute. Considering the scalability aspect, we developed a multilingual Chatbot using RASA as the Natural Language Processing (NLP) library, flutter as the cross-platform application development framework, and Django framework for doing server-side manipulations. Our application showcases the best architectural and security practices in application development to make the model scalable over time and keep it free from any security threats. The entire work is divided into two main components: a mobile application for the chatbot and a desktop website where users can enter data in several languages to train a deep learning-based model for intent detection. The developed chatbot, designed to be a university bot, will automatically identify the user’s preferred language and provide responses in that language. We aimed to make the application user-friendly and as safe as possible.
Optimizing Forensic Data Availability and Retention of SDN Forensic Logs by Using Bloom Filter

To perform network forensic analysis on an incident in software defined networking (SDN), logs are of the utmost importance. Without any logs, an investigator is not be able to complete and justify the forensic analysis. With the advancement of network and communication technologies, the volume of logs that needs to be collected and retained is growing exponentially big. The network administrators face the problems in maintaining such huge logs for longer period of time for forensic analysis. Network Providers rely on purging data based on a certain stipulated duration expected by the local rules for forensics evidence. SDN providers have limitations in managing such big data since the expense involved is commensurate with the duration of data retention. Here, in this work we propose a novel idea to reduce and summarize large forensic data sets for faster querying as well as reducing its space complexity by using bloom filters. Through this work we aim to propose a system which can deliver more optimized forensic data availability in SDN platform compared to existing systems.