Reviewerly/exHarmony huggingface dataset:
A Novel Real-time Intelligent Question Answering Algorithm for English Text Semantic Understanding

As a component of language, vocabulary must first have a certain degree of the stability, so that people can achieve language communication in actual communication and realize the purpose of the mutual understanding and communication. To improve the traditional work, the novel real-time intelligent question answering algorithm for the English text semantic understanding is studied in this research work. The novel similarity measurement, estimation and indexing models are presented to construct the efficient answering algorithm. In addition, the text features are focused to improve the response accuracy. Through the experimental verification, the comparison analysis is conducted and the accuracy testing is conducted. It is obvious that the proposed model has the better accuracy performance.
Path Planning Model of Intelligent Robot based on Computer Vision Recognition Algorithm

This study has designed a path planning model for intelligent robot based on computer vision recognition algorithm. Here, the redundant nodes present in the pre-planned path are removed through the node screening mechanism, and the later the artificial potential field method is introduced to ensure that a safe distance is always maintained from the unknown obstacles present in the re-planning process. At the same time, this research study adopts the method of setting virtual target points to solve the local extreme value problem that may be encountered in the actual movement of the machine. For developing an efficient and intelligent path planning model, the computer vision should be referred. The binocular recognition is equipped with two symmetrical signal acquisition cameras, which can realize the positioning of the status information. The enhanced theoretical vision algorithm is then combined for performing systematic optimization. The performance is validated by testing it on different datasets.
A Novel 3D Image Reconstruction Algorithm for Virtual Smart Design Platform

This paper focuses on the saliency information and systematic optimization to propose the novel 3D image reconstruction algorithm for virtual smart design platform. In general, the estimation of image depth information is realized by using the texture, edge and other features inside the image which will always ignore lots of structure information and pattern model, therefore, in the designed algorithm, visual saliency model is considered as the referring aspect. In the designed algorithm, besides the saliency information, the location feature awareness and detail feature perception are the two other major features. These features are combined to make the 3D reconstruction result efficient. Then, for the hardware side optimization, SLI is considered, and with it, the image 3D rendering efficiency will be improved. Then all the features of the GPU are fully realized by calling the driver. Through the test, the efficiency is proven.
HV-GCN: Hybrid View Feature Fusion for Graph Convolutional Networks

Graph convolution inevitably destroys the original graph space structure in the process of aggregating information about neighboring nodes, which leads to a reduced representation of node feature learning by the graph network model and hinders the execution of the model in downstream tasks. In response to the above mentioned that the graph structure is corrupted in the process of convolution. In this paper, we propose to make full use of the graph structure information. We use the original graph structure node features to regenerate a new graph structure, and employ graph convolution for propagation of graph node features on two views respectively. In this paper, we consider that the result of each layer in graph convolution contains useful information, and use the attention mechanism to learn the importance differences in different graph structures, fuse the node features derived from different graph structures, and finally input them to the classification network for node classification. Also, in order to constrain the discrepancy generated by two graphs during the convolution process, we design a method that minimizes the mutual information between two views and reduces the graph structure being destroyed during the convolution process as much as possible.
Convolutional Neural Networks for Deep Spoken Keyword Spotting

With the increase in biometric security applications, mobile and telephonic communication monitoring and digital assistants, the practical applications of Keyword Spotting (KWS) have increased many folds. The use of Artificial Intelligence in the domain of Keyword Spotting has greatly enhanced its accuracy. In this work, after doing analysis of various feature extraction and Deep Learning techniques, KWS is done both in non-streaming mode and streaming mode. The features of the speech are extracted using Mel-Spectograms and Mel-frequency Cepstral Coefficients (MFCCs). Out of three broad categories of Deep Neural networks, Convolutional Neural Network (CNN) model has been implemented for Keyword Spotting as it out-performs Recurrent Neural Network (RNN) and Feedforward Neural Network (FFNN) due to their lesser complexity and low computational cost. These techniques were used with Google Speech Commands Dataset, provided by Google, online as well as offline.
A study on the prediction model of dam seepage volume based on isolated forest-multiple stepwise linear regression

In the study of hydraulic seepage prediction, it is often necessary to target multiple feature dimensions. In this paper, a variety of traditional machine learning algorithms based on stepwise regression filtering features are used to train a dam seepage prediction model based on isolated forest processing of data outliers. The results show that the algorithm can be applied to predict the seepage data of a reservoir more accurately, and further suggest improvements to the dam's seepage control function and observation facilities. The experimental results show that the correlation coefficient of the linear regression model is significantly improved when the training sample ratio is 0.8 after the isolated forest treatment of outliers and stepwise regression screening features.
Model Inversion Attack with Least Information and an In-depth Analysis of its Disparate Vulnerability

In this paper, we study model inversion attribute inference (MIAI), a machine learning (ML) privacy attack that aims to infer sensitive information about the training data given access to the target ML model. We design a novel black-box MIAI attack that assumes the least adversary knowledge/capabilities to date while still performing similarly to the state-of-the-art attacks. Further, we extensively analyze the disparate vulnerability property of our proposed MIAI attack, i.e., elevated vulnerabilities of specific groups in the training dataset (grouped by gender, race, etc.) to model inversion attacks. First, we investigate existing ML privacy defense techniques- (1) mutual information regularization, and (2) fairness constraints, and show that none of these techniques can mitigate MIAI disparity. Second, we empirically identify possible disparity factors and discuss potential ways to mitigate disparity in MIAI attacks. Finally, we demonstrate our findings by extensively evaluating our attack in estimating binary and multi-class sensitive attributes on three different target models trained on three real datasets.
A Novel Pose Recognition Algorithm based on Multi-Feature Point Fusion for Training Images

Efficiently implementing accurate human pose estimation is one of the most fundamental and challenging tasks in computer vision, then, the novel pose recognition algorithm based on multi-feature point fusion for the training images is proposed in this research work. This study considers 3-step framework to achieve the goal of efficient estimation. Step 1: non-maximum suppression of the gradient magnitude is considered in the improved Canny algorithm, the direction of the gradient can be defined as one of the four regions, and the edge information will be collected. Step 2: The Gabor feature and Haar feature are combined together to achieve the fused feature. Step 3: the convolutional neural network is used for pose recognition. y analyzing the bottom layer of the convolution operation, it can be seen that the convolution operation can only linearly transform the input and the efficient estimation result can be obtained. The proposed model is tested on the collected database, and the pose recognition performance is tested.
Enhancing Encryption Mechanisms using SHA-512 for user Authentication through Password &amp; Face Recognition

Authentication is a process that is used to verify the identities of users, before allowing them access to the system. Authentication technology controls access to systems by comparing a user's credentials to those stored in a database of authorized users or on a data authentication server. Authentication ensures secure business processes, secure systems, and secure corporate data. There are a lot of authentication mechanisms employed by many technological entities and enterprises to validate their customers or employees. Every entrepreneurial, educational businesses like multi-national companies, institutions etc., uses web applications or websites for their business, marketing and growth of their enterprise and user authentication has become a must and should component in all their web applications, for their ERPs, with at least the basic level of authentication like passwords. But, the vastly growing technology also has its own negative side of people, who are a threat to the information of the companies, which made them to employ safer security measures. Inspite of increas ing their s ecuri ty mechanis ms in websites, there is still a lot of data being stolen by the people, who are a threat-hackers or attackers and increased their ability to adopt the newer and advanced security measures. This study has conducted some research in these areas and found out that, when a user data or an employee's data is procured by the hackers that almost compromises the security of the system and we've thought of a solution that can be used to increas e the security at the us er end. To sustain any data breach upon the attacks on the database we planned to use SHA-512 encryption for their databas es in order to increas e the data security and a second level of authentication, face recognition using Machine Learning models. As per the user data encryption done using SHA-512 algorithm, better encryption standards are provided.
An Ensemble Method to Classify Telugu Idiomatic Sentences using Deep Learning Models

Text classification is a requirement for every text processing application because the web contains a vast amount of text data. Intent detection, information extraction, sentiment analysis, and spam detection involves text categorization. Since text classification uses idioms, metaphors, and polysemic words, intent detection can be difficult. It is challenging to automatically identify idioms in Natural Language Processing applications such as Information Retrieval, Machine Translation, and chatbots. In all these applications, automatic idiom recognition is crucial. In this work, idiomatic and literals sentences are being classified. Idioms are typical expressions with new meanings. This research proposes an ensemble model using pretrained deep learning models to make model with more predictive nature. The models are trained and tested using in-house dataset. Moreover, an in-house dataset that contains 1040 idiomatic and literal sentences is suggested. The experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy of 86% on the test dataset.
PolyKervNets: Activation-free Neural Networks For Efficient Private Inference

With the advent of cloud computing, machine learning as a service (MLaaS) has become a growing phenomenon with the potential to address many real-world problems. In an untrusted cloud environment, privacy concerns of users is a major impediment to the adoption of MLaaS. To alleviate these privacy issues and preserve data confidentiality, several private inference (PI) protocols have been proposed in recent years based on cryptographic tools like Fully Homomorphic Encryption (FHE) and Secure Multiparty Computation (MPC). Deep neural networks (DNN) have been the architecture of choice in most MLaaS deployments. One of the core challenges in developing PI protocols for DNN inference is the substantial costs involved in implementing non-linear activation layers such as Rectified Linear Unit (ReLU). This has spawned a search for accurate, but efficient approximations of the ReLU function and neural architectures that operate on a stringent ReL U budget. While these methods improve efficiency and ensure data confidentiality, they often come at a significant cost to prediction accuracy. In this work, we propose a DNN architecture based on polynomial kervolution called PolyKervNet (PKN), which completely eliminates the need for non-linear activation and max pooling layers. PolyKervNets are both FHE and MPC- friendly - they enable FHE- based encrypted inference without any approximations and improve the latency on MPC-based PI protocols without any use of garbled circuits. We demonstrate that it is possible to redesign standard convolutional neural networks (CNN) architectures such as ResNet-18 and VGG-16 with polynomial kervolution and achieve up to 30 x improvement in latency of MPC-based PI with minimal loss in accuracy on many image classification tasks.
Secured Web Application using Artificial Intelligence with Enhanced Methodology

Web application with Artificial Intelligence is spreading its wings in the marketing industry, secured authenticated systems are transitioning into a new technological route. Deep learning technologies or cognitive computing enable the deployment of more trust worthy, efficient, and user-friendly authentication procedures on login sites. This research study aims to offer an authentication system where artificial intelligence is integrated with the existing secured methodologies resulting a better application.
Flight controller design and Geometric Parameter Optimization for a Tandem Wing UAV

This work focuses on flight controller design and aircraft geometric parameter optimization without explicitly using the aircraft mathematical model and its transfer functions. The design and testing of flight controller is performed using MATLAB and X-Plane. The tandem-wing aircraft used here was designed using Plane Maker, and the same is loaded in the X-Plane environment. A PID control algorithm implemented in MATLAB/Simulink is used to control the aircraft in the X-Plane environment. The control loop is closed by sending the aircraft state parameters from X-Plane to MATLAB. A Universal Datagram Protocol (UDP) communication is established for the exchange of data between MATLAB and X-Plane. A methodology for implementing waypoint navigation is proposed, and the same is tested in the X-Plane simulation environment. To test the performance of the designed flight controller, an autonomous flight of a tandem-wing aircraft using waypoint navigation in X-Plane software is performed. The simulation is performed with and without atmospheric disturbance, and the outcomes are used to optimize the aircraft’s geometric parameters.
FaShapley: Fast and Approximated Shapley Based Model Pruning Towards Certifiably Robust DNNs

Despite the great success achieved by deep neural networks (DNNs) recently, several concerns have been raised regarding their robustness against adversarial perturbations as well as large model size in resource-constrained environments. Recent studies on robust learning indicate that there is a tradeoff between robustness and model size. For instance, larger smoothed models would provide higher robustness certification. Recent works have tried to weaken such a tradeoff by training small models via optimized pruning. However, these methods usually do not directly take specific neuron properties such as their importance into account. In this paper, we focus on designing a quantitative criterion, neuron Shapley, to evaluate the neuron weight/filter importance within DNNs, leading to effective unstructured/structured pruning strategies to improve the certified robustness of the pruned models. However, directly computing Shapley value for neurons is of exponential computational complexity, and thus we propose a fast and approximated Shapley (FaShapley) method via gradient-based approximation and optimized sample-size. Theoretically, we analyze the desired properties (e.g, linearity and symmetry) and sample complexity of FaShapley. Empirically, we conduct extensive experiments on different datasets with both unstructured pruning and structured pruning. The results on several DNN architectures trained with different robust learning algorithms show that FaShapley achieves state-of-the-art certified robustness under different settings.
Real-time object detection and 3D scene perception in self-driving cars

Reliable autonomous urban driving hinges upon the vehicle's ability to perceive and navigate the environment. This research paper emphasizes designing and implementing a vision-based perception system for NUSTAG self-driving car. The primary task is the implementation of 3D bounding box estimation and depth perception using a stereo camera feed to estimate the positions of cars, bikes, and pedestrians. Moreover, road signs and traffic lights are detected using 2D object detection and classification. The major challenge to implement all these deep learning algorithms in parallel in the NVIDIA Jetson Xavier development kit is achieved by optimizing the models to perform inference in real-time. This is accomplished using the TensorRT framework employing ROS interface. The models have been trained for our requirements to yield efficient results within our operational design domain.
Performance Seismic Design Method of Highway Tunnel Structure Based on Simulated Annealing Algorithm

In order to improve the anti-seismic performance and detection ability of highway tunnel, a performance-based anti-seismic design method of highway tunnel based on simulated annealing algorithm is proposed. Taking model material, design parameters of highway tunnel structure and counterweight of frame structure under earthquake excitation as constraint index parameter set, the seismic structural performance test model of highway tunnel is constructed. The parameter adaptive estimation method is used to estimate the influence coefficient of seismic strength and heavy load parameters of highway tunnel structure. The characteristic equation of seismic tensile stress is established by simulated annealing algorithm, and the seismic design of highway tunnel structure is realized by parameter optimization. The test results show that the seismic performance detection ability of this method is good, and the tension prediction accuracy can reach 99.3%, which improves the stability of highway tunnel structure.
PRI: PCH-based privacy-preserving with reusability and interoperability for enhancing blockchain scalability

Blockchain systems, one of the most popular distributed systems, are well-applied in various scenarios, e.g., logistics and finance. However, traditional blockchain systems suffer from scalability issues. To tackle this issue, Payment Channel Hubs (PCHs) are proposed. Recent efforts, such as A2L (SP'21) and Teechain (SOSP'19), enhance the privacy, reusability, and interoperability properties of PCHs. Nevertheless, these solutions have intrinsic limitations: they rely on trusted hardware or suffer from the deposit lock-in problem. Furthermore, the functionalities of some of these solutions are restricted to fixed-amount payments and do not support multi-party participation. These aforementioned problems limit their capabilities to alleviate blockchain scalability issues. In this paper, we propose PRI, a novel PCH solution that simultaneously guarantees transaction Privacy (i.e., relationship unlinkability and value confidentiality), deposit Reusability, and blockchain Interoperability, which can mitigate the aforementioned problems. PRI is constructed by several new building blocks, including (1) an atomic deposit protocol that enforces user and hub to deposit equivalent assets in a shared address for building a fair payment channel; (2) a privacy-preserving deposit certification scheme that leverages the Pointcheval and Sanders signature and non-interactive zero-knowledge proof to resolve the deposit lock-in issue in maintaining payment channels; (3) a range proof which ensures the legality and confidentiality of transaction values. We conduct extensive experimental evaluations of PRI, demonstrating that it improves the state-of-the-art approaches in terms of performance.
A Study on Artificial Intelligence-based Natural Language Processing for Online Shopping Services

As a result of the developments in the digital age, communication has become the primary route for modern business. Business discussions, profiles, seminars, transactions, and agreements were all conducted in person in the past, but today practically everything is done digitally. The goal of this work is to investigate how text-object connections made by artificial intelligence help NLP and AI better meet human requirements. Additionally, this work considers how modern applications like NLP and AI integrated with IoT services have altered online shopping environments. Online shopping service providers have created sophisticated NLP systems that take human demands and instantaneously satisfy them through text, phone conversations, smart records, and chatbots as communication takes center stage in the commercial environment. This work modeled the artificial intelligence-based natural language processing framework for online shopping services and evaluated using support vector machine (SVM) classification algorithm and a hybrid Naive Bayes - Hidden Markov model (NB-HMM) algorithm. The experimental analysis shows the better performance when the model use SVM for classification over NB-HMM with the average accuracy of 94.3% and F-score of 97.5%.
Application of Big Data Analysis for Fault Diagnostics in Maintenance

Since recent advancements in bigdata seek to extract data, information, and knowledge from huge records, they have attracted a lot of attention. The development of Information & Communication Technologies (ICT) and sensor technology in the industrial sector has allowed for the capture and storage of enormous volumes of streaming, and nonlinear records to keep decision making. Identification of issues to records is an essential component of e-Maintenance systems since it can aid in support result. Advanced system fault detection can help to reduce the risk of unexpected breakdowns and assure the dependability and safety of industrial systems. Far above the ground dimensionality may result in the infamous “curse of dimensionality” and worsen the precision of defect finding systems, according to the standpoint of data modeling. Algorithms must respond to the arrival of fresh samples in close to real time in order to handle fast-moving data streams. High nonlinearity necessitates that fault detection methods have adequate expressive capacity and steer clear of over fitting or under fitting issues. In this study, an ABSAD (Angle based Subspace-Anomaly Identification) method to detect in data of high-dimension is developed. Analytical analyses and numerical examples illustrate how effective the approach is. The come up to expanded to an available type constructed on the downhill pane strategy in order to find errors in high-dimensional data streams. The online extension is able to adapt to the time-varying behavior, according to experiments on synthetic datasets.