Reviewerly/exHarmony huggingface dataset:
Spam Email Detection Using Machine Learning Integrated In Cloud

In this project, we focus on electronic mail, one of the most important means of communication among information professionals. As its use and significance among the general populace grows, so does its importance and utility. It has allowed for more adaptability and convenience in communication, both in the private and professional spheres. The increased use of email has led to a rise in spam as well as legitimate messages. An email that is sent to a large number of people without the sender’s knowledge or consent is considered spam. Millions of internet users, both casual and professional, are currently frustrated by the widespread problem of email spam. The purpose of this study is to provide a hybrid approach to machine learning for identifying spam in email. Bagging and boosting of machine learning-based multinomial Decision Tree, Naive Bayes, KNN, Random Forest, and the SVM method are the proposed hybrid techniques. The bagging method uses a concurrent combination of weak classifiers to boost classification accuracy. The standard deviation of misclassifications is decreased by using bagging. Alternatively, by linking the classifiers in a series fashion, the boosting strategy can construct a robust classifier out of two or more relatively weak classifiers. Improved classification results can be achieved through reduced bias and variance thanks to the use of boosting. In order to detect spam in emails, it is necessary to take into account datasets, pre-process those datasets, extract and pick features, and classify the data. In this study, we evaluate the feasibility of conducting experiments using data from the Ling-Spam Corpus and the CSDMC2010 Spam Corpus. According to the stop-word list and lemmatiser, Ling-Spam Corpus’s dataset is split into four different directories: bare, lemm, lemm stop, and stop. In addition, pre-processing consists of converting strings to word vectors (tokenization), stemming words, and removing stop words. Since the Ling Spam Corpus is already organised according to the stop-word list and the lemmatiser, only the CSDMC2010 Spam Corpus undergoes the stemming and stop words removal processes. Features are extracted and selected from the preprocessed data. The feature selection procedure in this work makes use of a correlation-based approach.
People Re-Identification in Service Robots

People Re-Identification (Re-ID) is essential for service robots to have effective human-robot interaction. This article proposes a Re-ID-based multi-people tracker suitable for mobile robots. It combines existing methods such as a people detector, a people localizer, a Re-ID feature extractor and a Kalman filter framework with novel data association and track management approaches. A new and publicly available RGB-D Re-ID multi-people 3D tracking dataset with ground truth is also presented. This dataset was recorded with a moving camera in an environment with obstacles, target occlusions and appearance changes. Experimental evaluation shows that the method achieves good tracking and re-identification performance on the proposed dataset at a high frame rate and outperforms a state-of-the-art method on an open-space dataset. The proposed system is computationally lightweight, robust and suitable for real-world applications, allowing for an improvement in human-robot interaction.
Representing Augmented Reality Applications in Systems Modeling Language

Augmented Reality (AR) devices offer novel capabilities that can be exploited in AR systems to positively impact human-machine interactions in a variety of future-work and education contexts. This paper presents a systems model for a no-code AR systems framework that can be used to create AR applications that present just-in-time informatics to assist and guide users in the completion of complex task sequences while ensuring operator and environment safety. The salient structural and behavioral aspects of the system, and key use cases are modeled using the Systems Modeling Language (SysML). Representative examples of the model are presented using use case, block definition, internal block, activity, and state-machine diagrams. These models offer new insights into how AR capabilities can be integrated with a variety of engineered systems. In the future such SysML models can steer the design of new tools and an ontology to strengthen connections to domain knowledge.
Symbolic Regression with augmented dataset using RuleFit

Symbolic Regression models are often associated with transparency and interpretability. The main motivation is their ability to describe nonlinear models balancing accuracy and conciseness. But, in practice, it may generate models that are hard to understand at the same level as opaque models. From another perspective, linear models are guaranteed to be transparent but fail to model nonlinearities and interactions. The algorithm RuleFit uses a tree-based nonlinear model to create meta-features augmenting the dataset, increasing the accuracy of the linear models while maintaining their transparency. In this paper we test whether this augmented dataset can help Symbolic Regression models to find more transparent models without reducing the overall accuracy. The results indicate that the augmented models have a slightly better accuracy on a class of benchmarks while keeping the expression size small and closer to a linear model. As a caveat, the models also tend to become closer to a step function which limits the interpretability of the studied phenomena.
Decentralized Portfolio Tracking Application On A Polygon Layer 2 Blockchain

This paper helps to eradicate a major problem that is occurring in financial services. One of the major issue and cost dependency in financial system is mediatory or third party. The governments,private institutions without the knowledge of the user absorbs a large amount through payment fee,taxes etc. These are the intermediate parties that are misusing the system. To overcome this problem their is a latest technology named Blockchain that helps to prevent this problem. Blockchain is a decentralized peer to peer technology where there will be no involvement of the third parties and helps users to solve the privacy issue. It interacts directly with the sender and receiver without depending on the third party. It provides a user friendly interface where a user can connect his/her decentralized wallet and know the details of his funds. The user can know the price and details of the holdings where he/she can track his/her portfolio. The user can able to send and receive his/her holding without depending on the third party and also can transfer funds without the knowledge or trace of a government or private institutions.
A Blockchain-Enabled Decentralized Gossip Federated Learning Framework

Federated learning (FL) has undergone substantial research and has been used in numerous real-world solutions over the past few years. It shows promising results in addressing the data security and privacy issues present in the traditional centralized machine learning approach. Even though federated learning makes certain of data privacy for each contributing user, the global model and the data are still vulnerable to attacks by compromised clients and servers. Additionally, in the settings for non-independent identical data (Non-IID), federated learning performs significantly less compared to the standard centralized learning mode. To address both the security and performance issues, this paper proposes a blockchain-enabled gossip federated learning framework (BGFL). BGFL replaces the central server with a blockchain-enabled system for global model storage and exchange. Also, to achieve faster training convergence, clients communicate with each other based on a gossip training approach. Then, to evaluate the performance we perform experiments using MNIST and CIFAR datasets in Non-IID settings. The performance and effectiveness of the BGFL framework is demonstrated by the experimental results.
A Novel Query Based Summerizer Model Of Product Reviews Using Modified LDA

In this digital era, there must be a system which can summarize huge lot of data and categorize the documents under specific topic without its semantic meaning being detached. Some important information can be extracted out of these documents as and when it is needed. The system can ease out many cumbersome processes which in other times might require a lot of manual work. Additionally, it becomes easy to navigate through a summarized version of a document rather than investigating a huge lot. The efficiency gets increased and manual work gets decreased. The system is basically an integrated version of both topic modelling and question answering with suitable machine learning algorithms. So, in short, the system works out to ease out some traditional work and can also be a solution to some technical problems related to storage and processing since a summarized version of the document given as input is only stored and further processed to give specific answers to the queries raised by the users.
Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth Maps

Self-supervised monocular depth estimation has been a subject of intense study in recent years, because of its applications in robotics and autonomous driving. Much of the recent work focuses on improving depth estimation by increasing architecture complexity. This paper shows that state-of-the-art performance can also be achieved by improving the learning process rather than increasing model complexity. More specifically, we propose (i) disregarding small potentially dynamic objects when training, and (ii) employing an appearance-based approach to separately estimate object pose for truly dynamic objects. We demonstrate that these simplifications reduce GPU memory usage by 29% and result in qualitatively and quantitatively improved depth maps.
Plenoptic Camera Viewpoints Rotation to Improve 3D Face Reconstruction

Recent advances in plenoptic technology offer new opportunities in the development of video surveillance systems. In this work we assess rotating large-baseline viewpoint cameras for accurate face-reconstruction at large depths. A first study with a 3D plane textured with a smooth gradient shows improvement of reconstruction accuracy by rectifying viewpoints rotation. A second study approaches a surveillance setting of a room with a pan-tilt-zoom camera-array. A human face is synthesized in the middle of the room, allowing to obtain ground truth depth. Results show the array of cameras provides a reconstruction accuracy similar to a near-target plenoptic camera despite of the expected larger face-self-occlusions.
Semantic Navigation Applied to Narrow Passages for Intelligent Wheelchairs

The present paper proposes a method to enhance the indoor navigation of an intelligent wheelchair through the integration of a semantic layer into the navigation framework. Semantic navigation, which refers to a robot’s ability to comprehend its surroundings, is of particular significance in the case of intelligent wheelchairs. To address the problem of negotiating narrow passages, the proposed solution employs a mapping layer that automatically detects and annotates these passages, as well as a planning layer that utilizes the resulting semantic information to guide lower-level planners. The feasibility of the proposed solution was evaluated through a series of experiments in both simulated and real-world scenarios.
A graph-based approach for minimising the knowledge requirement of explainable recommender systems

Abstract Traditionally, recommender systems use collaborative filtering or content-based approaches based on ratings and item descriptions. However, this information is unavailable in many domains and applications, and recommender systems can only tackle the problem using information about interactions or implicit knowledge. Within this scenario, this work proposes a novel approach based on link prediction techniques over graph structures that exclusively considers interactions between users and items to provide recommendations. We present and evaluate two alternative recommendation methods: one item-based and one user-based that apply the edge weight , common neighbours , Jaccard neighbours , Adar/Adamic , and Preferential Attachment link prediction techniques. This approach has two significant advantages, which are the novelty of our proposal. First, it is suitable for minimal knowledge scenarios where explicit data such as ratings or preferences are not available. However, as our evaluation demonstrates, this approach outperforms state-of-the-art techniques using a similar level of interaction knowledge. Second, our approach has another relevant feature regarding one of the most significant concerns in current artificial intelligence research: the recommendation methods presented in this paper are easily interpretable for the users, improving their trust in the recommendations.
Science and technology foresight in the system of engineering education

The development of information and communication technologies makes adjustments to the learning activities of students. Formation of the ability in scientific and technical forecasting increases the professional skills of future specialists. Performance of design works provides each of them with the design and implementation of the model of academic discipline. As an example, we consider the process of designing and modeling the system of monitoring the condition of dam structures in real-time mode. In recent years, various natural disasters have become more frequent in the Republic of Kazakhstan, including accidents caused by the destruction of dam structures. The consequences of such disasters have not only material but also human losses for the state. The reason for such accidents is that most of the dam structures in the country are old and not properly maintained. Therefore, the control and management of dam structures by various technical devices is an important research area of this paper. During the review and analysis of modern technological solutions, it was substantiated that the following equipment is needed to create complex monitoring of hydraulic structures: a personal computer, a microcomputer, a smartphone. The main advantage of the system of remote control of technical condition of dam structures is the availability of supersensitive sensor. To start the Raspberry Pi microcomputer, it is necessary to have a high-capacity SD card with the Raspbian operating system installed on it. The microcomputer must be output to a monitor or TV via an HDMI cable to make initial setups. In particular, the dam is powered by a 12-volt current source of the system for remote control of the technical condition of the structures, so there is a need to make a special board.
Im-SMART: Developing Immersive Student Participation in the Classroom Augmented with Mobile Telepresence Robot

The advent of the Novel Coronavirus brought about a multitude of challenges and the educational sector was severely impacted. The entire academic community had to adjust to the changed scenario and the online mode of delivery of educational content has been a mainstay, particularly in higher education. The concept of Telepresence provided a promise of meeting the challenges encountered and Mobile Telepresence Robots have donned the avatar for Telepresence. The prime challenge for these systems is being able to reproduce the ‘sense of being there’. This is a loaded term and encompasses many aspects like connectedness, immersive experiences, social presence, interaction effectiveness and communication effectiveness. Continuous development with incorporation of new technologies for Mobile Telepresence Robots is a pervasive phenomenon and therefore building a prototype that can continuously provide a test bed for experimental research is a need. With these drivers as a motivation, we present the development of a Mobile Telepresence Robot that is useful in the environment of educational institutions. The aim is to address aspects of telepresence and measure the effectiveness of some of the recent technologies which are deployed on the platform. We apportion functions to hardware and software, formulate a system architecture, and also describe the system usage scenarios. Some results from the experiments conducted with the platform are also presented and set the stage for future work with the platform.
Challenges and Opportunity for Salient Object Detection in COVID-19 Era: A Study

Coronavirus disease-2019 (COVID-19) is a pandemic that has spread all over the world. A rapidly transmittable viral infection is caused due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The virus is dangerous for human life and considerably affects human lifestyle. In this paper, we focus on the effect of COVID-19 on salient object detection performance due to certain factors such as humans wearing a face mask that may degrade the performance of salient object detection methods. The decrease in performance is due to features similarity of face mask with face skin. Salient object detection has been applied on computer vision tasks to improve their effectiveness in terms of accuracy and computational time. In COVID-19, both factors play a vital role for success of computer vision applications for detection of the virus. Therefore, we analyze and present the challenges posed and opportunities for the researchers working in this research area. Further, we propose a dataset for salient object detection, which contains images with features of COVID-19 conditions.
Improving object counting in aerial images with density-based threshold shifting

This paper presents an approach to reduce the negative effects of object density when objects are counted via detection in aerial images. A novel image dataset was generated and used to fine-tune an existing object detection architecture. A novel video dataset was also generated and used for the development and testing of the proposed approach. The datasets consist of aerial images and videos of sheep with scenarios of tightly clustered and isolated individuals. This ensures both sparse and dense object distributions within our dataset. The proposed approach is compared to a proven detection-based counting baseline. In this work, it is explicitly shown that object density and classification probability have a linearly inverse relationship and that reductions in classification probabilities, caused by high densities, has a significantly negative impact on counting performance. The proposed approach uses density-based threshold shifting to improve counting performance, i.e. by dynamically adjusting the counting threshold, based on the density, we are able to improve counting performance. The proposed approach reduces the overall mean absolute error by 78.51% compared to the baseline on an unseen test dataset. It seamlessly integrates with most existing object detection or instance segmentation frameworks without any modifications. The proposed approach also reduces the sensitivity of the counting threshold selection, implying that competitive results can be achieved with minimal tuning. The code is made publicly available.11. https://github.com/David-Biggs/LDTS.
Recovering Images Using Image Inpainting Techniques

Image inpainting is the task of reconstructing corrupted images. This challenging task has been tackled by various approaches. Due to recent advancements in image processing techniques more sophisticated approaches are available for image inpainting. Deep Convolutional Neural Networks and Generative Adversarial Networks are proven to be effective for this task. Image inpainting is used to restore images damaged due to scratches, remove unwanted objects while editing, recover the style of old photographs, effectively encode images for transmission, and many more. This work identifies the methods available for the image inpainting tasks and analyzes their effectiveness on established metrics. Navier–Stokes and Telea algorithms achieve PSNR 32 between 34 and SSIM above 0.96 for smaller inpainting tasks. Telea algorithm performs better as compared to Navier–Stokes method.
3D Obstacle Detection and Path Planning for Aerial Platform Using Modified DWA Approach

This paper presents a novel approach for drones to reach the goal while avoiding obstacles autonomously using an RGB-D camera. Our algorithm first detects the immediate obstacles through a CNN on the feed of the RGB-D camera. Further, we divide the FOV of the RGB-D Camera into several grids through convolution and treat each grid as a possible path the drone can take. Then we calculate the cost for each path. The cost depends on the distance from the goal destination, path smoothness, and distance from obstacles. We finally choose and traverse the path with minimum cost. Based on the obstacle, we can follow different maneuvering strategies. This paper presents our dataset to train object detection in AirSim, model for object detection, and our novel approach and cost function for obstacle avoidance. We have performed and tested obstacle detection and avoidance in Microsoft AirSim, an open-source simulator.
Architecture and Algorithms for a Pixhawk-Based Autonomous Vehicle

This paper presents our approach to developing a low-cost autonomous electric vehicle research platform. The first part of the paper elaborates on our experiments across perception, planning, and control modules. We briefly explain the algorithms and also present our implementation on CARLA, an open-source SOA Urban Driving Simulator Simulator. This is followed by our proposed architecture which combines the localization and control capabilities of Pixhawk, a popular flight controller in the UAV industry, with our perception and planning modules. Our approach is highly-modular and customizable, using ROS(Robot Operating System), which allows the researcher to focus on his area of interest while using the rest of the automation stack. We also share our approach to scale this work on an actual golf cart as a further testbed for autonomous driving research.
Human Activity Recognition Using Deep Learning

The coupling of real-time video feeds with convolutional neural networks gives rise to multiple interdisciplinary applications ranging from general statistics to security-based surveillance. In this work, we have trained CNNs like ResNet50, ResNet101, InceptionV3, and InceptionResNetV2 on a common human activity dataset (Stanford 40) and achieved an accuracy of 80.41% with ResNet101 on image data and an accuracy of 54.16% with ResNet101 on video data. The creation of a responsive end-to-end application allows the user to select the type of inference (image/video) and would enable the common users to interface with the backend with ease either on PC or on mobile devices.