Reviewerly/exHarmony huggingface dataset:
Deep Reinforcement Learning-Based Optimal Control of Variable Cycle Engine Performance

In order to find the lowest fuel consumption rate in steady-state operation of a variable-cycle engine, and thus increase the flight range as well as the flight radius of the aircraft. In this paper, we design a multivariable control law for variable-cycle engines and adopt a deep reinforcement learning algorithm in machine learning to optimal the control law for variable-cycle engines in real time online, which results in a lower fuel consumption rate compared to the engine fuel consumption rate under conventional optimal algorithms.
A research of target tracking algorithm based on deep learning and kernel correlation filter

As a hot topic in computer vision, target tracking has a vital application in many scientific and technological fields. The tracking method based on correlation filtering transforms the target tracking from the time domain to the frequency domain through the Fourier transform, which can boost the accuracy and success rate. However, in the complex tracking environments, the target tracking process may be affected by deformation, occlusion, and other inferences, which make the traditional target tracking algorithms hardly accommodate the requirements of robustness. Aiming the target tracking in complex scenes, this paper tries to improve the feature extraction based on the Convolution Neural Network, which can learn deep features of the target from different convolution layers with more abstract characteristics. Then these multiple features are fused to enhance the robustness performance of the traditional Kernel Correlation Filter algorithm from the aspects of model characteristics. Furthermore, the accuracy and success rate of the proposed algorithm are verified based on comprehensive comparative experiments in the Object Tracking Benchmark with variant interferences.
Research on Specific Long-term Single Object Tracking Algorithms in the Context of Traffic

A robust object tracking algorithm based on a three-channel Siamese network is proposed for the visual object tracking problem in the context of traffic. By adding the prediction box in the previous frame as the second template, our network obtained the second template to perform secondary prediction on the current frame. Then, in order to make sure whether the model to accurately regress the correct prediction box by the template frame or the previous frame, we design a similarity matching method based on Structure Similarity Index Measure(SSIM). We have also designed a template update method to ensure that the network selects the correct matching box. Our algorithm has a major advantage in robustness over other real-time target tracking algorithms such as SiamRPN.
Computable Consent – From Regulatory, Legislative, and Organizational Policies to Security Policies

Consumer-facing health applications are increasingly requiring flexible approaches for expressing consumer consent preferences for the use of their health data across multiple providers, and across cloud and on-premises systems. This and the recognition of the need for clear governance and legislative rules that specify enforceable policies over how consumer data is used by the nominated and other providers, including AI vendors, increasingly require machine readable, i.e. computable consent expressions. These expressions can be regarded as additional constraints over security policies, applicable to all stakeholders, while accommodating rules from regulatory and legislative policies. Support for both kind of policies contribute to improving consumer trust in the use of their data. This is applicable to both care delivery processes but also research projects, such as clinical trials. This paper proposes a computable consent framework and positions it in the context of the new developments within Health Level Seven (HL7®) Fast Health Interoperability Resources (FHIR®) standard. The proposal is based on the use of precise policy concepts from the ISO/ITU-T RM-ODP (Reference Model for Open Distributed Processing) standard. The aim is to provide general standards-based policy semantics guidance to interoperability/solution architects and implementers involved in digital health applications. The framework is driven by consent requirements, while leveraging broader policy input from medico-legal community.
A Deep Learning-Based Approach for Camera Switching in Amateur Ice Hockey Game Broadcasting

Switching the camera views in ice hockey plays an important role in television broadcasting. The traditional approach is to employ a broadcast director and professional crew responsible for making decisions about switching camera views, which is not an affordable option for amateur games. In this study we propose an automatic switching scheme of camera views that is based on a deep learning-trained model which detects important objects such as players, puck, and net. Our solution uses a Faster-RCNN object detection network which was trained using a dataset of 1000 high-definition (HD)-labeled frames of hockey games. Our Faster-RCNN achieves the average precision (AP) of 0.90, 0.88, and 0.61 for players, net, and puck, respectively, using a vgg-16 pretrained model. Our camera switching approach uses the confidence values of the detected objects to predict the best camera view for the current moment. Performance evaluations showed that our method achieved accuracy of 75% for choosing the most important camera view in real time.
Application of path planning algorithms in the MATLAB environment using Lego Mindstorms

This article is focused on the application of planning algorithms in the Matlab environment using Lego Mindstorms. Describes and analyses the RRT algorithm, the genetic algorithm, and the PRM algorithm, which are used in route planning and solving navigation problems. The article contains practical examples of the use of algorithms in the Matlab environment. When implementing the algorithms, we constructed a robot model for localization, mapping, and route planning purposes.
Implicit Discourse Relation Recognition Based on Multi-granularity Context Fusion Mechanism

Implicit discourse relation recognition is a NLP task that identifies the semantic relation between arguments without explicit connectives. Existing studies are mostly based on interactive attention. However, if arguments have incomplete semantics, it will be difficult for the model to deeply understand the argument, which negatively impacts the information interaction. In addition, although there is valid information in the context that can mitigate the negative effects caused by the semantic loss, the unfiltered context usually contains much distracting information. We propose a multi-granularity context fusion method for the above problems. Specifically, we first reduce contextual interference by constructing an information extraction mechanism. Then, we fuse the denoised context with the arguments for better learning the semantics. The experiment results in the Penn Discourse Treebank corpus show that, compared with the baseline, our method increases F1 score by 4.11%, 5.46%, 3.26%, and 7.03% in the scenarios of binary classification. For 4-way F1 and 4-way accuracy, we get 65.34% and 71.18% performances. Most of them surpass state-of-the-art systems.
L2C2: Locally Lipschitz Continuous Constraint towards Stable and Smooth Reinforcement Learning

This paper proposes a new regularization technique for reinforcement learning (RL) towards making policy and value functions smooth and stable. RL is known for the instability of the learning process and the sensitivity of the acquired policy to noise. Several methods have been proposed to resolve these problems, and in summary, the smoothness of policy and value functions learned mainly in RL contributes to these problems. However, if these functions are extremely smooth, their expressiveness would be lost, resulting in not obtaining the global optimal solution. This paper therefore considers RL under local Lipschitz continuity constraint, so-called L2C2. By designing the spatio-temporal locally compact space for L2C2 from the state transition at each time step, the moderate smoothness can be achieved without loss of expressiveness. Numerical noisy simulations verified that the proposed L2C2 outperforms the task performance while smoothing out the robot action generated from the learned policy.
Mobile Augmented Reality Design Evaluation

Augmented reality (AR) usage has increased substantially in recent years due to the proliferation of smart phone technology. Retail companies have leveraged augmented reality by allowing users to view products in their space before they buy. Because usability is related to return on investment (Marcus, 2005; Nielsen et al., 2013), ensuring the usability of these products is vital. One way to accomplish this is through heuristic analyses (Tan et al., 2009). While there are many time-tested sets of heuristics (Nielsen, 1994; Schneiderman &amp; Plaisant, 2004), these often fail to address many of the interactions that are unique to AR (Derby &amp; Chaparro, 2021). In this paper, we demonstrate the viability of supplementing traditional heuristic analyses (Nielsen, 1994) with a new heuristic checklist designed specifically for augmented and mixed reality (Derby &amp; Chaparro, In Press) by evaluating a retail AR program.
Design and Development of a Ticket Booking System using Smart Bot

Chatbot is a computer software program, that communicates to users via text or speech. This helps users to get information quickly, guides on something which the user is not aware of, answers the inputs rapidly, provides a better service, and assists online users 24/7. When there is an input, the chatbot will answer the question using the database which is already stored and available. It works based on Artificial intelligence and National Processing Language. This article will help you to develop a chatbot for a website using python inbuilt Libraries like NLTK (Natural Language Toolkit, PyTorch, and other useful dependencies. this application will let you use it in offline mode too. Adding convenience is the main concept of Chabot. The working nature of a chatbot depends on the technology being implemented. Chatbots are widely developed and used in various organizations like Google, Microsoft, Facebook-messenger, Telegram, and more. Python is a simple language, with no or less syntax. It has inbuilt modules, which makes the developer's work easy. PyTorch is one of them. Pytorch helps to create a neural network so that responses to a user can be more accurate. Processing the request by changing verbal language to machine language and in return responding in the verbal language will be trained while developing. Here chatbot development, implementation, and review analysis of various papers are discussed.
Parameter Distribution Ensemble Learning for Sudden Concept Drift Detection

Concept drift is a big challenge in data stream mining (including process mining) since it seriously decreases the accuracy of a model in online learning problems. Model adaptation to changes in data distribution before making new predictions is very necessary. This paper proposes a novel ensemble method called E-ERICS, which combines multiple Bayesian-optimized ERICS models into one model and uses a voting mechanism to determine whether each instance of a data stream is a concept drift point or not. The experimental results on the synthetic and classic real-world streaming datasets showed that the proposed method is much more precise and more sensitive (shown in F1-score, precision, and recall metrics) than the original ERICS models in detecting concept drift, especially a sudden drift.
Smart Contract Vulnerability Detection based on Abstract Syntax Tree

Smart contracts are computer programs running on the blockchain, and their security issues have received widespread attentions. However, existing vulnerability detection techniques such as static analysis, symbol execution, and fuzz testing could be unable to expose new types of vulnerabilities in time because they rely on extracting vulnerability features manually. On the other hand, vulnerability detection techniques based on machine learning do not require experts to define features manually and can learn vulnerability patterns automatically. In this paper, we propose a vulnerability detection technique for smart contracts, which extracts smart contract features based on the abstract syntax tree (AST) and trains the model to detect smart contract vulnerabilities. Experiment results show that the model achieves an average accuracy of 98.7% and F1-value of 0.885 in detecting three kinds of security vulnerabilities.
Eye Blink to Speech Conversion for Paralyzed (MND) Patients Using ML Based Algorithm

People suffering from Motor Neurone Disease (MND) will not be able to move any body part except eye blink and eye movement. They find it difficult to communicate. This project helps them to overcome the situation by making use of the eye blink. The camera in front of the person captures the eye blink of the patient, analyses the blink pattern and converts it into speech. It uses the Haar Cascade Classifiers for Face and Eye detection in real time video with OpenCV. The system is designed considering the limitations of the existing system, as they employ extra hardware and are expensive.
Development and Implementation of an AI-Embedded and ROS-Compatible Smart Glove System in Human-Robot Interaction

Robotics technology is being widely used for an array of tasks in today's evolving markets. Human-robot collaboration is inevitable which leads to the need for safe, untroublesome, and easy-to-produce products. A smart glove has capabilities to collect data concerning its wearer's movements by the use of sensors. Motivated by this, in this study, we develop an AI-embedded and ROS-compatible smart glove system to realize real-time human-robot interaction in collaborative tasks. To allow the robot to intelligently learn and predict new human intentions for human-robot interaction, we propose an Extreme Learning Machine (ELM)-based human gesture understanding approach using the data from a set of strip and force sensors embedded in the smart glove and effectively run it through ROS. Three typical baseline gestures are conjured for ELM training purposes and fed into the algorithm with an appended label and corresponding sensor data. The developed system and proposed approach are validated in real-world human-robot collaborative tasks with efficiency and success. This work can also serve as a catalyst for the implementation of many important robot-supported applications such as healthcare and daily assistance for senior groups. Future work of this study is also discussed.
Dynamic Key-Based and Context-Aware Internet of Things Authentication Approach

Internet of things can be defined as collection of different physical that can converse with each other without any interruption. The IoT applications have the capability to change the current scenario of various domains such as health service, agriculture, and so on by offering services that can offer luxury to human life and also increases effectiveness. IoT follows open framework. Because of that it presents an opportunity to adversary for easily targeting system by performing various cyber-attacks. Existing well known cryptography solutions cannot be implemented in IoT as it has limitations in terms of resource ownership and also storage capacity. The authors have designed an efficient and secure multi-factor IoT authentication algorithm that is lightweight and provides the protection against different security threats such as MITM, replay, and location spoofing threat. Suggested work is tested on the tool AVISPA for the security validation. Communication and computational cost is low for the suggested work in comparison with the existing work.
Heterogeneous Graph Neural Network with Hypernetworks for Knowledge Graph Embedding

Heterogeneous graph neural network (HGNN) has drawn considerable research attention in recent years. Knowledge graphs contain hundreds of distinct relations, showing the intrinsic property of strong heterogeneity. However, the majority of HGNNs characterize the heterogeneities by learning separate parameters for different types of nodes and edges in latent space. The number of type-related parameters will be explosively increased when HGNNs attempt to process knowledge graphs, making HGNNs only applicable for graphs with fewer edge types. In this work, to overcome such limitation, we propose a novel heterogeneous graph neural network incorporated with hypernetworks that generate the required parameters by modeling the general semantics among relations. Specifically, we exploit hypernetworks to generate relation-specific parameters of a convolution-based message function to improve the model’s performance while maintaining parameter efficiency. The empirical study on the most commonly-used knowledge base embedding datasets confirms the effectiveness and efficiency of the proposed model. Furthermore, the model parameters have been shown to be significantly reduced (from 415M to 3M on FB15k-237 and from 13M to 4M on WN18RR).
HIRO: Heuristics Informed Robot Online Path Planning Using Pre-computed Deterministic Roadmaps

With the goal of efficiently computing collisionfree robot motion trajectories in dynamically changing environments, we present results of a novel method for Heuristics Informed Robot Online Path Planning (HIRO). Dividing robot environments into static and dynamic elements, we use the static part for initializing a deterministic roadmap, which provides a lower bound of the final path cost as informed heuristics for fast path-finding. These heuristics guide a search tree to explore the roadmap during runtime. The search tree examines the edges using a fuzzy collision checking concerning the dynamic environment. Finally, the heuristics tree exploits knowledge fed back from the fuzzy collision checking module and updates the lower bound for the path cost. As we demonstrate in real-world experiments, the closed-loop formed by these three components significantly accelerates the planning procedure. An additional backtracking step ensures the feasibility of the resulting paths. Experiments in simulation and the real world show that HIRO can find collisionfree paths considerably faster than baseline methods with and without prior knowledge of the environment.
FedEWA: Federated Learning with Elastic Weighted Averaging

Federated Learning (FL) offers a novel distributed machine learning context whereby a global model is collaboratively learned through edge devices without violating data privacy. However, intrinsic data heterogeneity in the federated network can induce model heterogeneity, thus posing a great challenge to the server-side model aggregation performance. Existing FL algorithms widely adopt model-wise weighted averaging for client models to generate the new global model, which emphasizes the importance of the holistic model but ignores the importance of distinctions between internal parameters of various client models. In this paper, we propose a novel parameter-wise elastic weighted averaging aggregation approach to realize the rapid fusion of heterogeneous client models. Specifically, each client evaluates the importance of model internal parameters in the model update and obtains the corresponding parameter importance coefficient vector; the server implements the parameter-wise weighted averaging for each parameter based on their importance coefficient vectors, thereby aggregating a new global model. Extensive experiments on MNIST and CIFAR-10 datasets with diverse network architectures and hyper-parameter combinations show that our proposed algorithm outperforms the existing state-of-the-art FL algorithms on the performance of heterogeneous model fusion.
SWIoTA: Anomaly Detection for Distributed Ledger Technology-Based Internet of Things (IOTA) Using Sliding Window (SW) Technique

IOTA is a Digital Ledger Technology (DLT) prototype for IoT applications that has attracted a rising popularity in recent years. One issue that acts as obstacle to its widespread adoption are the cybersecurity concerns. Some of the security concerns in IOTA include Denial of Service (DoS) double spending, parasite attacks, and DDoS attacks. In this work, we developed a Machine-Learning (ML) approach to create security threat index that can be utilized to proactively provide defenses to the IOTA decentralized infrastructure as well as individual nodes against potential compromises. Our approach is established on the sliding window customized technique to classify the data generated from the DAG-based nodes for cybersecurity anomaly detection. To validate the approach, we implemented “DoS attacks” threat model in the DLT-based IoT environment using Raspberry Pi devices and experimented our security methods and algorithms in this environment. The preliminary experimental results are promising.
DeepPS2: Revisiting Photometric Stereo Using Two Differently Illuminated Images

Estimating 3D surface normals through photometric stereo has been of great interest in computer vision research. Despite the success of existing traditional and deep learning-based methods, it is still challenging due to: (i) the requirement of three or more differently illuminated images, (ii) the inability to model unknown general reflectance, and (iii) the requirement of accurate 3D ground truth surface normals and known lighting information for training. In this work, we attempt to address an under-explored problem of photometric stereo using just two differently illuminated images, referred to as the PS2 problem. It is an intermediate case between a single image-based reconstruction method like Shape from Shading (SfS) and the traditional Photometric Stereo (PS), which requires three or more images. We propose an inverse rendering-based deep learning framework, called DeepPS2, that jointly performs surface normal, albedo, lighting estimation, and image relighting in a completely self-supervised manner with no requirement of ground truth data. We demonstrate how image relighting in conjunction with image reconstruction enhances the lighting estimation in a self-supervised setting (Supported by SERB IMPRINT 2 Grant).
Visual Relationship Detection With Image Position and Feature Information Embedding and Fusion

Visual relationship detection (VRD) is an important direction in the field of image processing, and it is a research task to explore object relationships based on object recognition and localization regression. At the same time, it is also one of the key contents of scene graph generation and construction of multimodal knowledge graph. In order to better improve the visual relationship detection effect, the fusion method of image location and feature information embedding is adopted on the vrd dataset. First, analyze the causes of errors in existing methods, and build a pre-training pre-dataset vrd_P for data enhancement; then, combine the characteristics of the vrd dataset to establish an entities relationship network (Entities-net) to guide target recognition; finally, a visual relationship detection model of LTransE is proposed to achieve the joint representation of target features and target locations for relationship detection. The results show that the method of embedding fusion of image location and feature information can effectively improve the visual relationship detection effect.
Real-Time Face Mask Detection Using Streamlit,TensorFlow, Keras and Open-CV

This paper is based on the protection of our health from coronavirus officially known as COVID-19. Real-time detection of a face mask can help to prevent of the coronavirus, detecting the mask with the help of machine learning and data science algorithms such as Streamlit, MoblieNetV2, OpenCV, etc., are widely used in this ideal methodology. This paper is about the method that provides an accuracy of 99.78% in detecting the mask with live video stream. The method proposes building accurate model and integrating the model with a graphical interface which can improve the experience of the user.