Reviewerly/exHarmony huggingface dataset:
Contact Tracing: A Cloud Based Architecture for Safe Covid-19 Mapping

In the recent, Corona Virus epidemic (COVID-19) increases day-to-day from one individual to another because of contact transmission and COVID-19 is created by the SARS-CoV-2. Identifying and stopping the spreading of contagious epidemic such as COVID-19 is important to handling diseases. One major part contracted to find and detect their previous connections so as to then carefully dissociate any persons likely to have been affected and accommodate dispersion is to find out or explore more transmissible persons. These previous connections can be traced using smart machine such as smart watches and smart phones, which can frequently find and collect the connections and location of their infected ones through their embedded transmissions and localization methodologies or technologies, such as Global Positioning System (GPS) location-based navigation rule, Wi-Fi, biological connections, and Bluetooth. Contact Tracing is the one of the best technologies in which we use a methodology for stopping and controlling the COVID19. The main point of this review paper on the methodology of these smart technologies and figure the model of contact tracing accuracy on the flow of the COVID-19, working of contact tracing, algorithms and control of the COVID-19. In this paper, we have determined the role of contact tracing in COVID-19, effective impacts of Contact Tracing and designed a COVID-19 epidemic model that we created to evaluate the number of people quarantined and effectiveness of the steps to be taken, through the smart watches and smart phone contact tracing technique used. In this review paper, our result shows that in order to be accurate for the COVID-19 pandemic, the contact tracing technique must be traced speedily, a valuation ratio of the population must apply the smart devices technique, contact tracing application and this technology must be correct. All these rigid needs make smart device-based contact tracing rather inefficient at accommodating the flow of the virus during the COVID-19. However, in this phase smart machine-based contact tracing could be immensely and enormously effective and recognizing a second section, where a segment of the community will have increased immunity.
Simpler is Better: Spectral Regularization and Up-Sampling Techniques for Variational Autoencoders

Full characterization of the spectral behavior of generative models based on neural networks remains an open issue. Recent research has focused heavily on generative adversarial networks and the high-frequency discrepancies between real and generated images. The current solution to avoid this is to either replace transposed convolutions with bilinear up-sampling or add a spectral regularization term in the generator. We propose a 2D Fourier transform-based spectral regularization loss and evaluate it on the variational autoencoder. We show that it can achieve results equal to, or better than, the current state-of-the-art in frequency-aware losses for generative models. In addition, we experiment with altering the up-sampling procedure in the generator network and investigate how it influences the spectral performance of the model. We include experiments on synthetic and real data sets to demonstrate our results.
Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes.Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks.There are several approaches to identify users' emotions from the conversational text for the English language, however regional or low resource languages have been neglected.The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu language.Therefore, in this paper, we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users' emotions from the text.To accomplish this task, we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis.After that, we have preprocessed the data and selected dialogues with common emotions.Once the dataset is prepared, we have used different deep learning and machine learning techniques for the classification of emotion.We have tuned the algorithms according to the Urdu language datasets.The experimental evaluation has shown encouraging results with 67% accuracy for the Urdu dialogue datasets, more than 10, 000 dialogues are classified into five emotions i.e., joy, fear, anger, sadness, and neutral.We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.
A Framework for Interactive Visual Interpretation of Remote Sensing Data

Machine learning methods have shown tremendous success in understanding earth observation data; however, recently, there is a rising claim toward explainable machine learning approaches. Concerned researchers found interpretable visualizations to be greatly helpful in understanding how a model works. In this research, we propose a framework for interactive and interpretable visualization of remote sensing data using two machine learning models and an Elasticsearch (ES) database. Two explainable machine learning models, namely, bag-of-visual-words (BoVWs) and latent Dirichlet allocation (LDA) are chosen to model the data in an unsupervised manner and give a textual representation. The textualized remote sensing data are stored in an ES database. This framework offers several fast content-based search functionalities exploiting the full-text query capabilities of ES based on the respective representations and also offers an efficient storage mechanism for the data.
Learning General Feature Descriptor for Visual Measurement With Hierarchical View Consistency

Learning-based feature descriptors have been dominantly popular for their notable performance on feature matching tasks along with the rapid development of convolutional neural networks (CNNs). However, existing popular learning-based methods predict discriminative description solely using the high-level features from the last layer of deep CNNs while neglecting the rich complementary clues hidden in intermediary multilevel features, which could further promote the discriminative power by introducing the implicit hierarchical comparison into descriptor space. This hinders the optimization of learned descriptors and limits their performance on real-world visual measurement tasks. In this regard, we propose hierarchical view consistency (HVC) for fully leveraging the complementary information of multilevel features. Specifically, we first present a novel multiviewer neural network (MVNet), which benefits from multiple viewers with local-to-global receptive fields and efficiently generates dense descriptions in a coarse-to-fine manner. Next, we introduce the HVC, i.e., ensuring consistent yet diverse hierarchical features between views, to encourage viewers to encode as hierarchical features as possible while increasing the hierarchical similarity for reliable matches. With our proposed triplet training strategy, MVNet leverages the rich hierarchical complementary clues in multilevel features and efficiently predicts strong discriminative descriptions. Our experiments on feature matching and challenging visual measurement tasks of visual localization and visual 3-D reconstruction demonstrate that our proposed descriptor is efficient and generalizes well to various scenarios.
Study on the Development and Implementation of Ubiquitous Bots for the Discord Interface

Over the past few years, Discord’s popularity has grown tremendously, expanding its target user demographic. Discord, in recent times, has broken the walls that they had previously encapsulated themselves within, and are growing to be much more than an application meant exclusively for gamers. Now, colleges, schools, offices, and groups with common interests have come together to establish communities where all parties are benefited. Discord provides a platform for developers to contribute to the community and various guilds with its publicly available Application Programming Interface (API) - called the Discord API. This paper aims to analyse bot applications, taking our own bot created for student communities, called ‘Kanmani’, written in Python, as an example. Development of a bot involves a multitude of steps right from creating an application on the developers’ portal, writing code for the backend of the bot, ensuring maintenance of security features, and hosting it on a platform that allows the service to be always available to Discord servers. Depending on the requirements of a server and its members, an appropriate bot can be developed and added using Discord's API.
MOFIT: An Efficient Access Control Scheme with Attribute Merging and Outsourcing Capability for Fog-Enhanced IoT

The advancement of technology in the modern era has accelerated the growth of Internet of things (IoT) resulting in an exponential increase in the number of connected devices and the generated data. This encouraged the development of paradigm fog computing, which facilitates data analysis and processing at the edge. Along with fog, cloud co-exists to provide various services such as massive storage, processing resources, etc. However, data storage and computation at multiple levels raise the risk of data security. Ciphertext-policy attribute-based encryption (CP-ABE) is a well-known cryptographic technique for providing fine-grained access control. Unfortunately, the existing CP-ABE schemes do not support the functionality of attribute merging. Therefore, we propose a CP-ABE scheme named MOFIT that solves this long-standing problem. Additionally, expensive encryption and decryption operations are outsourced to fog nodes, which reduces the computation overhead of resource-constrained IoT devices. Further, the task of attribute merging is also outsourced to the third party. The size of the secret key held by the data user is constant and remains unaltered during any updates. According to security and performance analysis, MOFIT is secure and suitable for IoT applications.
Advanced Cryptographic Standards-Based Secure Data Protocol for Distributed Data Storage and Communication in the Cloud

In this research, an ACS-based secure data protocol (ACS-based secure data protocol) is introduced for distributed data storage and communication in cloud. The component is constructed using the Owner’s Machine (OM), Data Processing Section (DPS), Cloud Monitoring Server (CMS), Metadata table, Key Management Server (KMS) and Receiver’s Machine (RM). The session key is generated by exchanging the data between the client and server using the mathematical operations, such as interpolation, hashing, Walsh transform and Elliptic curve cryptography. The Triple Data Encryption Algorithm (3DES) is used for performing the activities, like assigning index, uploading and slicing file and encrypting the sliced files. The fuzzy cluster-based mechanism and the angular distance are performed for indexing and retrieval. The metrics, such as E2E delay, time and detection rate are used for evaluating the developed ACS based secure data protocol in the presence of inside attack, password spoofing attack and Denial-of-Service (DoS) attack.
Bi-attention Modal Separation Network for Multimodal Video Fusion

With the increasing popularity of video sharing websites such as YouTube and Facebook, multimodal video understanding has received increasing attention from the scientific community. Video is usually composed of multimodal signals, such as video, text, image and audio, etc. The main method addressing this task is to develop powerful multimodal fusion techniques. Multimodal data fusion is to transform data from multiple single-mode representations to a compact multimodal representation. Effective multimodal fusion method should contain two key characteristics: the consistency and the difference. Previous studies mainly focused on applying different interaction methods to different modal fusion such as late fusion, early fusion, attention fusion, etc., but ignored the study of modal independence in the fusion process. In this paper, we introduce a fusion approach called bi-attention modal separation fusion network(BAMS) which can extract and integrate key information from various modalities and performs fusion and separation on modality representations. We conduct thorough ablation studies, and our experiments on datasets MOSI and MOSEI demonstrate significant gains over state-of-the-art models.
Transfer Learning Assisted GPHH for Dynamic Multi-Workflow Scheduling in Cloud Computing

Dynamic Multi-Workflow Scheduling (DWS) in cloud is a challenging optimisation due to the dynamic nature of the problem. It requires complex mapping decisions to be made under unknown dynamic events. Genetic Programming Hyper-Heuristic (GPHH) has been successfully applied to generate scheduling heuristics for DWS due to its flexible representation. However, the simulation-based evaluation is computationally expensive due to calculations required by individuals to make decisions in the simulation. To improve efficiency, this paper proposes a novel Transfer Learning (TL) Assisted GPHH (TL-GPHH). Specifically, TL-GPHH is designed and compared with Non-Assisted GPHH to generate effective heuristic rules in less time. The results show that the proposed algorithm generates heuristics that can minimise makespan in more scenarios than Non-Assisted GPHH and other state-of-the-art heuristics. Moreover, the proposed algorithm can reduce the computational costs of GP without sacrificing the performance.
MaxDropoutV2: An Improved Method to Drop Out Neurons in Convolutional Neural Networks

In the last decade, exponential data growth supplied the machine learning-based algorithms' capacity and enabled their usage in daily life activities. Additionally, such an improvement is partially explained due to the advent of deep learning techniques, i.e., stacks of simple architectures that end up in more complex models. Although both factors produce outstanding results, they also pose drawbacks regarding the learning process since training complex models denotes an expensive task and results are prone to overfit the training data. A supervised regularization technique called MaxDropout was recently proposed to tackle the latter, providing several improvements concerning traditional regularization approaches. In this paper, we present its improved version called MaxDropoutV2. Results considering two public datasets show that the model performs faster than the standard version and, in most cases, provides more accurate results.
Classification of Multi-Frame Human Motion Using CNN-based Skeleton Extraction

Human pose estimation has been a major concern in the field of computer vision.The existing method for recognizing human motion based on twodimensional (2D) images showed a low recognition rate owing to motion depth, interference between objects, and overlapping problems.A convolutional neural network (CNN) based algorithm recently showed improved results in the field of human skeleton detection.In this study, we have combined human skeleton detection and deep neural network (DNN) to classify the motion of the human body.We used the visual geometry group network (VGGNet) CNN for human skeleton detection, and the generated skeleton coordinates were composed of three-dimensional (3D) vectors according to time changes.Based on these data, we used a DNN to identify and classify human motions that were most similar to the existing learned motion data.We applied the generated model to the data set that could occur in general closed circuit television (CCTV) to check the accuracy.The configured learning model showed effective results even with two-dimensional continuous image data composed of red, green, blue (RGB).
Obstacle Avoidance System Design of Intelligent Sweeping Robot Based on Improved Genetic Algorithm

There are still some deficiencies in the design of obstacle avoidance function of intelligent cleaning robot. Therefore, an obstacle avoidance system of intelligent cleaning robot based on improved genetic algorithm is designed in this paper. Through the information detected by the sensor system, the real-time obstacle avoidance path planning is carried out. The obstacle avoidance module is designed to describe the obstacle avoidance path planning under the improved genetic algorithm, the grid steering obstacle avoidance instruction design is obtained, and the software design of the system is completed. The final system test results show that compared with the obstacle avoidance system without improved genetic algorithm, the repetition rate of the obstacle avoidance system with improved genetic algorithm is relatively low, indicating that the obstacle avoidance structure of the cleaning robot covers all comers of the environment, which greatly avoids some unclean areas. The walking control error of the robot is small, and the motion control error will be greatly reduced. The design of the system is effective and feasible, and has certain application significance.
Robust region-based active contour models via local statistical similarity and local similarity factor for intensity inhomogeneity and high noise image segmentation

&lt;p style='text-indent:20px;'&gt;In this paper, we design a novel variational segmentation method for two types of segmentation problems, namely, global segmentation (all objects /features in a given image are aimed to be segmented) and selective/ interactive segmentation (an objects /feature of interest in a given image is aimed to be segmented) for inhomogeneous and severe additive noisy images. The proposed segmentation models implement a local denoising constraint, capable to tackle efficiently noise/outliers and coping with intensity inhomogeneity issues, combined with local similarity factor based on spatial distances and intensity differences in the local region that guides accurately the level set function to distinguish between outliers and minute important details. Furthermore, to exhibit the accuracy of the proposed models, an experimental comparison is inducted and shown comparisons with state-of-art models on synthetic images, outdoor images, and medical images.&lt;/p&gt;
Identifying transient and persistent errors in aircraft cruise trajectory prediction using Bayesian state estimation

• Bayesian error estimation in non-intrusive trajectory prediction model. • Partitioning model error into transient and persistent components. • Data-driven correction for persistent error in trajectory prediction model. • Validation using historical trajectory data. Recorded aircraft trajectory data may vary significantly from predictions based on physics-informed models. This discrepancy may be attributed to inadequacies in the trajectory prediction models, including errors in modeling aircraft dynamics, and omission of inputs such as weather data, equipment malfunctions, and pilot errors. In this work, we represent model errors as additional external inputs to the trajectory prediction model, evaluate them using Bayesian state estimation, and update the trajectory prediction model to address these errors. In doing so, we distinguish between two types of model errors – transient and persistent errors – of which only persistent errors are considered for correction. In this study, we estimate the model error for the completed portion of ongoing flights, separate out the persistent error and account for it in the system model, which allows for an improved prediction of aircraft trajectory for the remaining flight duration. We perform verification and validation of the proposed methodology using both synthetically generated data as well as historically recorded data corresponding to flights affected by adverse weather conditions and engine failure.
Bayesian Analysis of Rank Data with Covariates and Heterogeneous Rankers

Data in the form of ranking lists are frequently encountered, and combining ranking results from different sources can potentially generate a better ranking list and help understand behaviors of the rankers. Of interest here are the rank data under the following settings: (i) covariate information available for the ranked entities; (ii) rankers of varying qualities or having different opinions; and (iii) incomplete ranking lists for nonoverlapping subgroups. We review some key ideas built around the Thurstone model family by researchers in the past few decades and provide a unifying approach for Bayesian Analysis of Rank data with Covariates (BARC) and its extensions in handling heterogeneous rankers. With this Bayesian framework, we can study rankers’ varying quality, cluster rankers’ heterogeneous opinions, and measure the corresponding uncertainties. To enable an efficient Bayesian inference, we advocate a parameter-expanded Gibbs sampler to sample from the target posterior distribution. The posterior samples also result in a Bayesian aggregated ranking list, with credible intervals quantifying its uncertainty. We investigate and compare performances of the proposed methods and other rank aggregation methods in both simulation studies and two real-data examples.
Access Control Enforcement in IoT: state of the art and open challenges in the Zero Trust era

Zero Trust (ZT) is a security paradigm which is nowadays finding application in different domains. One of the domain that can most benefit from ZT is represented by Internet of Things (IoT), where huge quantity of personal and sensitive data are continuously generated by IoT devices, which, in the recent years, have been the target of several security attacks. In this paper, we identify a set of access control requirements which are expected to enable the adoption of ZT in the IoT domain. Through the analysis of state of the art access control approaches currently employed in IoT ecosystems, we observe the lack of solutions capable of addressing all the identified requirements. We therefore discuss significant open challenges that still need to be addressed in this area.
Creating Semi-supervised learning-based Adaptable Object Detection Models for Autonomous Service Robot

Autonomous robots are now widely used in smart factories as they have led to improvements in productivity and reduction in long-term operating costs. New smart factories have also been designed to integrate the use of robots and include static and close environment where objects and routes are completely predefined. This is a usual requirement for mobile robots since their perception capability is usually limited to a predefined number of objects. Most detection models are trained beforehand with a huge amount of annotated data and are difficult to adapt to new objects or change in existing definition or classes. Therefore, visionbased robotic navigation and path planning are also limited and any adaptation or a change of environment is often a challenge.Semi-supervised detection models have been introduced to tackle this problem. With the use of a limited amount of labelled data together with unlabelled data, models can still be trained and achieve a better performance than fully supervised models. However, they are still affected by data bias, noisy labels and are usually applied on limited datasets. In this paper, we first demonstrate how data distribution and change in robot sensors significantly reduce the accuracy of existing detection models. We discuss a preselection strategy for selecting data to be labelled and then introduce a semi-supervised approach that outperforms fully supervised approach.We successfully apply it to a real-world robot delivery scenario in a hospital environment and demonstrate that our model improves the detection accuracy compared to fully supervised methods.