Reviewerly/exHarmony huggingface dataset:
NATIVE: Network Aggregation based Tiled Live Video Streaming

The COVID-19 pandemic has forced most interactions to move to online space, starting from online lectures, conferences in hybrid mode, and work-from-home office works. Hence, it is essential for live video streaming to be reliable and provide a good quality of experience (QoE) to users. However, in large sections of the world, the cellular network is not reliable enough to be used for online participation in such events. To quantify this problem, we first measure the QoE in terms of lag, video resolution, and dropped calls on a popular video conferencing platform Google Meet over three different cellular ISPs in New Delhi, India. We observe significantly worse quality of experience metrics compared to a study recently performed in the US. To mitigate this problem, we propose NATIVE (Network Aggregation-based Tiled lIve Video strEaming), a system of aggregating the cellular network connectivity using a secondary or helper device in the vicinity trusted by the user. The implementation of NATIVE uses tiled encoding of video, where the video frames are divided into rectangular units known as tiles. All the tiles are divided into two subsets which are scheduled independently via the available network interfaces depending on its importance. The receiver device receives video segments from the two network paths and stitches back the tiles in them to play. We show the demo of NATIVE using two laptops and a cloud server where the server acts as a streamer.
Robust Multi-person Reconstruction in Right Spatial Arrangement from In-the-Wild Scenes

Research on monocular 3D human reconstruction has great progress recent years. Existing methods struggled when input with in-the-wild scenes dues to inherent depth ambiguity, occlusion and interaction. Most existing multiple people reconstruction researches follow two-stage paradigm that first implement body detect in monocular scenes and then estimate multiple people 3D body meshes independently, which result in ignoring the relative depth and interaction between human in the scene. To solve this problem, we present a end-to-end paradigm to directly regress multiple people 3D pose and shape, as well as their relative spatial arrangement from monocular in-the-wild scenes. We present a multi-task feature extractor framework that efficiently comprising 2D pose estimation and monocular depth estimation, a decoupling module that predicts and optimizes human body mesh parameters. Our method simultaneously predicts 2D joint heatmap and scene depth map, which can jointly extract image feature to drive the 3D reconstruction task. We validate our method on challenging scenes with multiple people and interaction, showing that our method obtain good results both quantitatively and qualitatively.
Fake AP Detection Technology Based on Improved DBSCAN Algorithm

With the rapid development of the mobile Internet, mobile users’ access to the Internet through wireless networks has become one of the main access methods for obtaining information. However, the openness of wireless networks makes them vulnerable to fake AP attacks. Currently, detection research for such attacks is often based on whitelisting, however it is not always feasible to register device fingerprints in advance in practice. Aiming at such situation, this paper proposes a pseudo AP detection method based on DBSCAN algorithm. Using the nonlinear phase error feature extracted from the channel state information (CSI), the method can cluster the fingerprint data of different devices into different clusters, and then can judge the existence of the fake AP attack behavior. At the same time, this paper improves the algorithm and introduces the K-means nearest neighbor idea into the DBSCAN algorithm, so that the parameters can be adaptively selected according to the characteristics of the sample itself. The experimental results show that the method proposed in this paper can divide different wireless devices, and then can accurately detect the fake AP attack behavior in this scenario.
Research on Blind Restoration Algorithm of Motion Blurred Remote Sensing Image

The reconstruction of motion blur is a significant subject in remote sensing image processing. It has a great effect on the follow-up processes of target detection and recognition. To meet the needs of on-board intelligent processing, a new blind restoration method based on Radon transform and autocorrelation function is proposed to process remote sensing image blurred by motion. The method adopt frequency spectrum diagram and Radon transform to calculate the direction of motion blur image. Autocorrelation function is quoted to calculate the blur scale. After the point spread function is estimated, Wiener filter is applied to realize the restoration of motion blurred images. By rotating blurred image to the horizontal axis direction, the two-dimensional restoration problem is converted into one dimension. Test results indicate that the method can obtain satisfactory recovering results of remote sensing image blurred by linear motion.
Achieving Privacy-Preserving Diagnosis with Federated Learning in LEO Satellite Constellation

LEO satellite constellations can support the federated learning-based diagnosis model construction for epidemics occurring in remote less developed areas, like Malaria. However, the model trained directly from the patients’ medical data may leak their physical conditions, and LEO satellite constellations also suffer from the long propagation delay and limited energy supply. To tackle the challenges, we suggest a new privacy-preserving model update framework in federated learning, which adapts to the highly dynamic topology of the LEO satellite constellation. To protect each individual piece of model update, we propose this privacy-preserving scheme which combines a symmetric homomorphic cryptosystem and a verifiable secret sharing scheme, where the security goals of privacy preservation and authentication can be achieved. We demonstrate the feasibility and evaluate the effectiveness of our proposed privacy-preserving diagnosis model on the real dataset, namely the Malaria Cell Image Dataset, and simulation results demonstrate that our proposed privacy-preserving scheme mostly improves the computational complexity in contrast to a scheme exploiting the Paillier cryptosystem.
Big Data Processing Mode Based on Cross Data Source SQL Engine

With the advent of the 5G era, the volume of global data is increasing, and big data processing has become one of the current hot technologies, and many mature and excellent big data processing technologies have emerged. However, for an enterprise, a single technology stack is difficult to meet the needs of complex big data processing. Therefore, based on Docker, with Presto and Hive as the core, this paper uses Alluxio to preheat big files, and constructs a big data processing model based on cross data source SQL engine. By adding data fusion layer and agent layer, the unified interface is exposed to the outside world, and the cross-data source operation, resource elasticity expansion, programming language interface and other black boxes are black-boxed, so as to provide data analysts with universal SQL form and scalable big data analysis capabilities.
A Hybrid Adaptive Image Retrieval Approach by Using Clustering and Neural Network Techniques

In the current era, an accurate image retrieval system is required to resolve the problem of enormous image information in applications. Importance of image content is quite relevant in case of variety of applications such as human face, signature identification, etc. A hybrid adaptive approach incorporates data mining and artificial neural network (ANN) techniques to retrieve image information in response of query. A feedback-based scheme is also integrated to provide better response of user query in the manner of system intelligent. A 3-tier system is implemented to utilize trained dataset from ANN as an input clustered image which obtains user suggestions as feedback to recluster the images. The image similarity score assigns an input image to the category of having highest probability. This rank-based criterion helps to discriminate irrelevant images from dataset. The highly acclaimed rank provides a relevant feedback to this proposed approach for estimate global approximations.
Ship Detection from Satellite Images with Advanced Deep Learning Model (Single Shot Detector (SSD))

Effectual monitoring system for maritime transport is essential for every country military/navy security system. The demand is very high for security and commercial areas to detect unknown objects (or) ships. There are many reasons to implement this idea such as secured monitoring of ships in narrow canals, oil theft control, sea pollution monitoring, attacks of pirates, terrorism activities, identification of debris and lost ships, and identification of submarines. There are many ship detection methodologies which are available like vessel monitoring system, automatic identification system, and long-range identification and tracking, but these methods require very high complex hardware equipment, especially high-frequency transponders are required to find the ship location. With these traditional methods, there may be more chance of getting attacked by pirates (or) thieves. In this paper, we proposed a new fast learning method for ship detection by using satellite images (which captured by satellite). Deep learning models were used to implement secured ship detection, i.e., single shot detector (SSD). There are many methods like HOG, RCNN, and YOLO which were implemented by using machine learning algorithms, but quick and accurate detection is not possible in these methods. In our proposed method by using SSD, we may get accuracy up to 99%. Our modeling results will show that ship detection method for satellite images by using deep learning SSD model is best and accurate method.
A Comparative Analysis of Sentiment Analysis and Emotion Detection Problems on Texts

In natural language processing, sentiment analysis and emotion detection are two common tasks. Sentiment analysis seeks to anticipate a text’s sentimental orientation, whereas emotion detection aims to distinguish basic emotions in the text. In this paper, we demonstrate the relation between sentiment analysis and emotion detection, whether these two classification problems are similar or not, and after expanding the ISEAR dataset, create a new dataset where both sentiment and emotion are labeled together. We use four machine learning classifiers to forecast dataset performance in terms of different evaluation metrics including accuracy, and f-measure. Finally, for emotion detection and sentiment analysis, the top-performing classifier is recommended.
Comparative Analysis of Software Defect Prediction Using Dimensionality Reduction

Software defect prediction (SDP) is a dynamic research issue in the field of software development life cycle. It is very helpful in the testing phase of the life cycle of software development. It helps ensure the quality of the software being generated. In this particular paper, we have done a comparative analysis of various dimensionality reduction techniques such as principal component analysis (PCA), kernel PCA, incremental PCA, and sparse PCA, with random forests (RF) and artificial neural networks (ANN) as classifiers. We have collected the data from the android git repository and extracted metrics from 2 different versions of android. For comparing results, we have used three different metrics for a total of 10 experiments run on a dataset combined from 2 different versions of android. The result metrics we used are F1 score, area under receiver operating curve, and accuracy.
Hand Gesture Recognition on Skeletal Data Using Multi-head Neural Network

Gestures can act as a strong communication medium as they can work in both information delivery as well as interaction. For expressing any gesture, face and hands are the most commonly used expression mediums. Hand gestures are a particularly strong tool in both these fields as they can be used to convey any information with movement and also for interacting with any system. Today various applications in the fields of smart devices, healthcare, sign language communication, etc., rely on hand gesture recognition technology. This paper introduces the concept of hand gesture detection and provides a brief about the work done in this field till now. Moreover, it will elaborate on our new methodology to implement a parallel multi-channel Convolutional Neural Network (CNN) to classify depth-based input. Our proposed model provides a high accuracy while using less parameters as compared to the other similar approaches and is fit for real-time applications.
Mammography Image Classification and Detection by Bi-LSTM with Residual Network Using XG-Boost Approach

Early identification of breast cancer is crucial to increase the odds of a positive therapy, thus contributing decrease in mortality rate. Screen-based mammography and digitized mammography are two methods of mammography, out of which digitized mammography one is more secure. The proposed approach is based on ResNet-50 and Bi-LSTM network with extreme gradient boost (XG-boost), which resolves the problem of vanishing and exploding gradient, and contains the memory units in bi-direction making predictions easier than the existing approaches and provides an accuracy of 97.17% on the test set which is much higher than the accuracy of the existing approaches. It is vital to note that feature engineering is critical in this industry. Due to long short-term memory network (LSTM) economical memory, this research attempts to extract useful features using attention layers and to decrease error using the Bi-LSTM technique.
Study of Class Incremental Learning Strategies for Intrusion Detection System

In today’s digital world, the Internet connects many apps and gadgets. This advancement is beneficial in many aspects, but on the other hand, it provides more chances for attackers to explore and exploit vulnerabilities. There is a rapid increase in cyberattack; due to this, there is a need for a self-adaptable defense mechanism system that evolves continually. The classic machine learning-based model fails to meet this condition since it is incapable of continuous learning. Specifically, neural network (NN)-based model suffers from catastrophic forgetting (CF). In this work, we implemented continual learning (CL) algorithm on the network dataset for a class incremental learning scenario. We have implemented three CL approaches, namely regularization (EWC, SI), rehearsal (replay, GEM), and hybrid on the benchmark dataset. Our findings suggest that rehearsal-based methods have comparable performance.
AlexNet Model for Sign Language Recognition

In recent years, for recognizing sign language, several hardware approaches have been developed using the leap motion controller and Kinect sensors. The sensor-based approaches were costly, and the complexity of designing was high. In machine learning approaches, it was found that less accuracy of prediction occurs as compared to other approaches. As a solution, machine learning-based approaches using image processing evolved as an approach with better prediction and accuracy. In this paper, deep learning approach for sign language recognition (SLR) system has been proposed using AlexNet model. AlexNet is pretrained model, and it is being tested on Indian Sign Language (ISL) Dataset pf Stanford University comprising of alphabets (A–Z) and numerals (0–9). The proposed model has yielded an accuracy of 99.6%.
Handwritten Mathematical Character Recognition Using Machine Learning and Deep Learning

Writing a mathematical expression on computer is a tough task as require prior knowledge of LATEX, while writing a mathematical character on paper is much easier. Our machine learning-based recognition system helps to convert a mathematical character written on paper into corresponding LATEX form. The six successive phases of system development include preprocessing, data segmentation, feature extraction, and classification. This paper aims at implementation of different learning algorithms using Python to recognise handwritten mathematical characters. Around 100,000 images with different styles of 1000 mathematical symbols have been used as data set for model training. Logistic Regression, SVM, Naive Bayes, and Random Forest classifiers have been used for classification purpose which resulted in 86.13, 96.26, 89.48, and 98.45% accuracy. Random Forest algorithm proved to be most efficient method amongst all for handwritten mathematical character recognition.
A Novel Approach for Bug Triaging Using TOPSIS

In the field of software development life cycle, the maintenance phase is one of the focused steps. Normally, thousands of bugs are reported daily by testers. So, it is very important to fix that bug as soon as possible. In the current era, various project are there that work on the same project and if bug not fixed timely the other product can easily overtake the company business, so to fix the newly arrived bug the project manager finds the best developer to fix it and assign to the developer, and this process is called bug triaging. For bug triaging task automation, various methods had been carried out by various researchers machine learning, information retrieval, deep learning, etc., but the cons with that method were that they were not able to fix the problem simultaneously like bug tossing, load balancing, and developer availability. Hence to overcome that we have proposed a method called technique for order of preference by similarity to ideal solution (TOPSIS) which is based on multi-criteria decision-making (MCDM), which will consider the developer metadata with various criteria to automate the task of bug triaging. Based on the criteria, the parameter (closeness ratio) will be calculated, and based on the parameter value, the developer will be ranked for bug triaging.
Disjunction in a Predictive Theory of Anaphora

In this paper I develop a dynamic semantics for a first-order fragment, which incorporates insights from work on anaphora in logic, and the trivalent approach to presupposition projection. The resulting system—EDS—has interesting features which set it apart, both conceptually and empirically, from earlier iterations of dynamic semantics. Conceptually, the meanings of the logical connectives are derived by systematically generalizing the Strong Kleene connectives into a dynamic setting—the system is thereby predictive, drawing a tight connection between the logic of presupposition projection and patterns of anaphoric accessibility. On the empirical side, EDS diverges sharply from earlier proposals. In this paper, I focus mainly on disjunction, arguing that EDS provides a simple and elegant account of the dynamics of disjunction, including traditionally problematic cases such as Partee disjunctions and program disjunctions.
An Innovative Cloud Security and Data Sharing Across Multi-clouds Through PACM Techniques

Cloud is providing virtual environment in order to share the data across multiple owners and users across pervasive computing environments. The security among virtual environment is the key challenge of the research in providing role-based access control policies and providing security to the confidential data of the cloud owners. Cloud generally provides low-cost services and provides the end-to-end solutions through managing the documents and various files of the data owner, and data of the owners is available in various formats. Key management and cryptographic techniques are widely used to provide security to the data transmitted across insecure networks. The secret key management is providing the valid keys to authenticate through the cloud-based services and access with valid keys and providing security without any loopholes in the transmission and sharing the data through owners and cloud users. A symmetric key management algorithm is used to provide a unique to each and every cloud user and a group key to access the permissions and allows particular used to gain access to the cloud services using key pairs for valid authentication process. Providing security across multi-cloud environment through PACM techniques and implementing most advanced secured key management algorithms to overcome the existing data leakages across cloud computing environment.
Exploring Enterprise Architecture Knowledge Graphs in Archi: The EAKG Toolkit

This paper presents the EAKG Toolkit that entails a new Knowledge Graph-based representation of enterprise architecture (EA) models and further enables reasoning on EA knowledge. Our developed EAKG Toolkit is unique in the sense that it i) transforms ArchiMate models into a KG representation – the Enterprise Architecture Knowledge Graph (EAKG), ii) visualizes the EAKG for interactive exploration, and iii) extends the EAKG with additional nodes and edges to visually represent detected EA smells.
A Hybrid Feature Selection Framework for Breast Cancer Prediction Using Mutual Information and AdaBoost-RFE

Every year, the number of deaths from breast cancer rises drastically. It is the most common kind of cancer and the biggest reason for mortality in women throughout the world. In real-world applications like breast cancer classification, it is difficult to cope with high-dimensional and unbalanced datasets. To counter this problem, feature selection is used as a strategy for preprocessing, with the classification performance and computing efficiency taken into account. This research introduces a hybrid feature selection framework that incorporates filter and wrapper techniques to obtain optimal feature subset. In the first stage, the filter technique mutual information (MI) was used to arrange the features according to their relevance due to its computational efficiency. It cannot, however, discard less important features. Therefore, a wrapper method recursive feature elimination (RFE) employed to eliminate redundant features in the second phase. For RFE, Adaptive Boosting (AdaBoost) is used as the estimator. By using a random forest classifier, the suggested feature selection approach is compared to existing feature selection methods. With a 79.41% feature reduction ratio, the suggested MI-AdaBoost-RFE technique selects seven features out of 32. The suggested method has a 97.36% accuracy and an AUROC of 0.974, which is higher than other recent studies.
LightHARs: A Light Human Activity Recognition System

Human activity recognition (HAR) is the method to identify an activity of a human by rendering images, video, and sensor data collectively. While performing HAR and working with images, the video dataset is not secure because these datasets reveal the user identity, but the sensor data do not. Therefore, in this paper, we proposed a cost-efficient deep neural network model i.e. light human activity recognition system (LightHARs) which takes less parameters and computational time as compared to existing HAR models. It outperformed existing models by achieving 4.5% more accuracy.