Reviewerly/exHarmony huggingface dataset:
A Hybrid Deep Learning Based Anomaly Detection Framework dedicated for Big Research Infrastructures

Anomaly detection has been an active artificial intelligence area in research and industrial communities with many unique problem complexities and challenges that require advanced approaches. In this research we concentrate on the analysis of anomalies occurring in big research infrastructures including signal measurement analysis as well as X-ray radiation images classification. The proposed hybrid end-to-end anomaly score learning framework consists of two deep learning based approached for anomaly detection dedicated for two different sources of high-dimensional data. The aim of the system is to identify abnormal status of sensors in certain time steps. In this study, we deploy two transfer learning architectures by examining pre-trained VGG-16, VGG-19, InceptionV3, Xception, as well as EfficientNet CNN models with an adjusted densely-connected classifiers. The system achieved overall accuracy of 94%, what is a state-of-the-art result in such research infrastructures.
On the Role of Data Homogeneity in Multi-Agent Non-convex Stochastic Optimization

This paper studies the role of data homogeneity on multi-agent optimization. Concentrating on the decentralized stochastic gradient (DSGD) algorithm, we characterize the transient time, defined as the minimum number of iterations required such that DSGD can achieve the comparable performance as its centralized counterpart. When the Hessians for the objective functions are identical at different agents, we show that the transient time of DSGD is $\mathcal{O}\left( {{n^{4/3}}/{\rho ^{8/3}}} \right)$ for smooth (possibly non-convex) objective functions, where n is the number of agents and ρ is the spectral gap of connectivity graph. This is improved over the bound of $\mathcal{O}\left( {{n^2}/{\rho ^4}} \right)$ without the Hessian homogeneity assumption. Our analysis leverages a property that the objective function is twice continuously differentiable. Numerical experiments are presented to illustrate the essence of data homogeneity to fast convergence of DSGD.
Efficient Large Scale Stereo Matching based on Cross-Scale

We propose a binocular stereo matching algorithm CS-ELAS. This is a cross-scale ELAS algorithm that improves the accuracy and robustness of parallax in weakly textured regions and edge regions. Our approach focuses on improving the accuracy and number of support point sets. We uniformly sample the stereo images to obtain candidate support point sets and determine robustly matched support point sets based on an adaptive cross skeleton. In this way a richer and more accurate set of support points can be obtained in the weakly textured areas near the edges. In addition, our method uses the parallax and confidence maps of low-resolution images as a priori for high-resolution images and adds high-confidence pixel information to the set of high-resolution support points. In this way, it not only increases the number of support points in weak texture regions, but also narrows the search range of candidate support points and reduces the computational cost.
Motion primitives-based and Two-phase Motion Planning for Fixed-wing UAV

We present an efficient two-phase approach to motion planning for fixed-wing Unmanned Aerial Vehicles (UAV) navigating in complex 3D air slalom environments. Firstly, in discrete 3D workspace, a global planner computer a obstacle-free path roughly which satisfies the kinematic constraints of the UAV. Given a coarse global path, a local planner generate a Dubins curve with collision avoidance based on the UAS's perception constraints, dynamic constraints and the collision perception information received. We also introduce a method of decoupling the horizontal and vertical motion directions of the fixed-wing UAV, realizing the 2D Dubins curve planning in 3D workspace, along with precomputed sets of motion primitives derived from the vehicle dynamics model in order to achieve high efficiency. Finally, the feasibility of two-phase 3D motion planning in appropriate FOV is experimentally demonstrated.
Riemannian Constrained Policy Optimization via Geometric Stability Certificates

In this paper, we consider policy optimization over the Riemannian submanifolds of stabilizing controllers arising from constrained Linear Quadratic Regulators (LQR), including output feedback and structured synthesis. In this direction, we provide a Riemannian Newton-type algorithm that enjoys local convergence guarantees and exploits the inherent geometry of the problem. Instead of relying on the exponential mapping or a global retraction, the proposed algorithm revolves around the developed stability certificate and the constraint structure, utilizing the intrinsic geometry of the synthesis problem. We then showcase the utility of the proposed algorithm through numerical examples.
Minimal resources for exact simulation of quantum walks

Quantum walks are stochastic processes generated by a quantum evolution mechanism, allowing for speed-up in spreading and hitting-time performance with respect to their classical counterparts. Investigating the role of the memory effects for these models, we address the problem of finding the minimal linear system that exactly reproduces the evolution of the output distribution of a quantum walk. After adapting the classical approach to our setting, we investigate analytically and numerically the structural complexity of quantum walks, both in general and in particular examples. Lastly, we focus on Grover’s algorithm, a quantum computing search algorithm that can be re-framed as a quantum walk, showing that it allows for a dramatic reduction in its representation.
Motion Planning for Nonlinear Robotic System based on ADMM and Convex Feasible Set Algorithm

In the motion planning of robot, nonlinear dynamic constraints and obstacle avoidance constraints make the problem highly non-convex and difficult to solve. In this paper, a general algorithmic framework for trajectory optimization is proposed based on alternating direction method of multipliers (ADMM) and convex feasible set algorithm (CFS). The ADMM framework decomposes the original problem into an optimal control sub-problem with only dynamic constraints and a subproblem with only obstacle avoidance constraints, while the sub-problem with obstacle avoidance constraints is solved via CFS based on the geometric properties of the feasible region. The convergence of proposed algorithm is proven, and the simulation results in different cases verify the feasibility and effectiveness of proposed algorithm.
Overcoming Catastrophic Forgetting for Semantic Segmentation Via Incremental Learning

Deep learning based semantic segmentation models have achieved remarkable results in recent years. However, many deep learning based models encounter the problem of catastrophic forgetting, i.e. when the model is required to learn a new task without labels for old objects, its performance drops significantly for the previous tasks. To solve this problem, an incremental learning method, a Combination of Old Prediction and Modified Label (COPML), is developed in this paper. The proposed method utilizes the prediction results of the old model and the modified labels of the new task to create pseudo labels which are close to the ground truths. By using these pseudo labels for training, the model is expected to preserve the knowledge of old tasks. In addition, knowledge distillation, the replay and parameter freezing strategy are also applied to the proposed method to further assist the model in overcoming catastrophic forgetting. The effectiveness of the proposed method is validated on two semantic segmentation models: Unet and Deeplab3 in Pascal- VOC 2012 dataset and a self-made dataset. The experimental results demonstrate that COPML enables the model to maintain most of the old knowledge while obtaining an excellent performance on a new task.
Path Planning and Predictive Control of Autonomous Vehicles for Obstacle Avoidance

This paper investigates path planning/trajectory generation algorithms and Model Predictive Control (MPC) of autonomous vehicles for obstacle avoidance. Two path planning approaches are designed in this work. To demonstrate obstacle avoidance of autonomous vehicles, Prescan and Matlab/Simulink are used to build an integrated model consisting of algorithms, an ego vehicle model, an MPC controller, and obstacle avoidance scenarios. Simulation tests are performed to validate the developed algorithms and compare the performance of two motion planning approaches for two scenarios. The performance comparison is presented in the paper with respect to the smoothness of the reference path/trajectory and the ego vehicle steering angle change while it performs an obstacle avoidance maneuver.
Collision-Free Navigation for Multiple Robots in Dynamic Environment

To aim at the collision-free navigation framework for multi-robot systems in dynamic environment, this work develops a two-layer methodology to implement the obstacle avoidance for multiple robots. A global planner is introduced to construct the global plan at the high layer. Combining the artificial potential field with the pure-pursuit algorithm, a low-layer local planner is designed to generate the control commands for tracking the waypoints obtained from the global plan. Moreover, the rolling windows method, the obstacle filter, and the multi-robot coordination strategy are also introduced to enhance the robustness of the proposed approach implemented on practical robots. Both simulations and experimental results are presented to verify the feasibility of the proposed method.
Peak power prediction method of heat pipe cooled reactor start-up and power-up processes based on ANN

The start-up and power-up processes of the heat pipe cooled reactor are essential parts of the autonomous operations. The rapid power fluctuation in the processes can affect the safety of the heat pipe reactor. The fast and accurate prediction of the peak power is significant for the safe operation of the heat pipe cooled reactor. This paper generates the peak power datasets of heat pipe cooled reactor start-up and power-up processes by coupling Monte Carlo sampling, and system analysis program with heat pipe cooled reactor MegaPoweras the research object. A fast prediction model of peak power was developed based on the artificial neural network and evaluated in terms of cost, accuracy, and interpretability. The results show that the artificial neural network model has high prediction accuracy and is suitable for large datasets with complex non-linear relations. However, the training cost is high, and the interpretability is weak. The above characteristics are explained by theoretical analysis, and the ability of ensemble algorithms to improve the accuracy of the artificial neural networks is discussed.
Bi-Level Implicit Semantic Data Augmentation for Vehicle Re-Identification

Vehicle re-identification (Re-ID) aims at finding the target vehicle identity from multi-camera surveillance videos, which plays an important role in the intelligent transportation system (ITS). It suffers from the subtle discrepancy among vehicles from the same vehicle model and large variation across different viewpoints of the same vehicle. To enhance the robustness of Re-ID models, many methods exploit additional detection or segmentation models to extract discriminative local features. Some others employ data-driven methods to enrich the diversity of the training data, such as the data augmentation and 3D-based data generation, so that the Re-ID model can obtain stronger robustness against intra-class variations. However, these methods either rely on extra annotations or greatly increase the computational cost. In this paper, we propose the Bi-level Implicit semantic Data Augmentation (BIDA) framework to solve this problem from two aspects. (1) We implicitly augment the images semantically in the feature space according to the identity-level and superclass-level intra-class variations, which can generate more diverse semantic augmentations beyond the intra-identity variations. (2) We introduce the similarity ranking constraints on the augmented training set by extending the sample-wise triplet loss to the distribution-wise one, which can effectively reduce meaningless semantic transformations and improve the discrimination of the feature. We conduct extensive experiments on VeRi-776, VehicleID and Cityflow benchmarks to reveal the effectiveness of our method. And we achieve new state-of-the-art performance on VeRi-776.
Deep Learning Aided Multi-Level Transmit Power Recognition in Cognitive Radio Networks

According to the regulations of the hybrid access strategy in cognitive radio network, the secondary user (SU) needs to identify the primary user’s (PU) specific transmit power level to avoid unacceptable interference with the PU. However, the conventional transmit power recognition methods cannot accurately identify the transmit power in conditions with low signal-to-noise ratio, fading channels and the existence of noise uncertainty, since those methods are based on a fixed statistical theory to model the dynamic electromagnetic environment mathematically. To address these issues, a ResNet-based multi-level transmission power recognition (MTPR) architecture is presented in this paper. Furthermore, the proposed architecture is implemented in two cases with different observation data. In the first case, the received signal’s covariance matrix (CM) containing rich energy information is used as the observation data of CM-MTPR scheme. To further improve the identification accuracy, in the second case, the in-phase and quadrature-phase (IQ) data sampled from the received signal that preserves more original information is configured as the observation data of IQ-MTPR scheme. The IQ-MTPR scheme, however, consumes additional computing resources which forms a trade-off between identification performance and computational consumption with the CM-MTPR scheme. Simulation results demonstrate the identification performance of the proposed schemes.
Edge Distraction-aware Salient Object Detection

Integrating low-level edge features has been proven to be effective in preserving clear boundaries of salient objects. However, the locality of edge features makes it difficult to capture globally salient edges, leading to distraction in the final predictions. To address this problem, we propose to produce distraction-free edge features by incorporating cross-scale holistic interdependencies between high-level features. In particular, we first formulate our edge features extraction process as a boundary-filling problem. In this way, we enforce edge features to focus on closed boundaries instead of those disconnected background edges. Secondly, we propose to explore cross-scale holistic contextual connections between every position pair of high-level feature maps regardless of their distances across different scales. It selectively aggregates features at each position based on its connections to all the others, simulating the “contrast” stimulus of visual saliency. Finally, we present a complementary features integration module to fuse low- and high-level features according to their properties. Experimental results demonstrate our proposed method outperforms previous state-of-the-art methods on the benchmark datasets, with the fast inference speed of 30 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FPS</i> on a single GPU.
Business Review on Amazons Digital Marketing

E-commerce has transformed the way business is done in India. The Indian E-commerce market is expected to grow to US$ 188 billion by 2025 from US$ 46.2 billion as of 2020. By 2030, it is expected to reach US$ 350 billion. In 2022, the Indian e-commerce market is predicted to increase by 21.5%, reaching US$ 74.8 billion. India’s e-commerce market is expected to reach US$ 111 billion by 2024 and US$ 200 billion by 2026. Much of the growth for the industry has been triggered by an increase in internet and smartphone penetration. The number of internet connections in 2021 increased significantly to 830 million, driven by the ‘Digital India’ programme. Out of the total internet connections, ~55% of connections were in urban areas, of which 97% connections were wireless.
CAPTCHA RECOGNITION USING DCNN

Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is an important human-machine distinction technology for websites to prevent the automatic malicious program attack. CAPTCHA recognition studies can find security breaches in CAPTCHA technology By using the concept of deep learning and computer vision, the very purpose of the CAPTCHAs can be defeated. This test can be passed automatically with the help of Convolutional Neural networks(CNN). A CNN is an algorithm of deep learning which takes an image as input and then assigns some value to various features in the image which further helps to differentiate one feature from the other. Its main purpose is to transform the images into a form which is much easier to process, without losing features which are essential for getting an optimized prediction. The proposed system for this project is to expand this CAPTCHA recognition system for larger and more noisy CAPTCHA containing all the symbols possible, a method based on the Deep Convolutional Neural Network (DCNN) model to identify CAPTCHA and avoid the traditional image processing technology such as location and segmentation. The adaptive learning rate is introduced to accelerate the convergence rate of the model, and the problem of over fitting and local optimal solution has been solved. The multi task joint training model is used to improve the accuracy and generalization ability of model recognition. The experimental results show that the model has a good recognition effect on CAPTCHA with background noise and character adhesion distortion. The future scope of this project lies in technologies where more noisier images can be processed such as license plates, handwriting recognition etc.
Predicting total organic carbon from well logs based on deep spatial-sequential graph convolutional network

The total organic carbon (TOC) is a key geologic parameter for unconventional reservoirs. Conventional empirical [Formula: see text] methods cannot handle the nonlinear relationships between the characteristics of TOC and its well-log responses. Increased data availability has the potential to speed up deep learning applications, which can reasonably propagate the integrated information from well logs to indirectly observable geologic properties, such as TOC. Although the existing convolutional neural network (CNN) has found superior performance to [Formula: see text] for predicting TOC, CNNs feature-learning capability is still constrained by the fact that it can only extract log-specific sequential features of the input logs. However, the cross-log topological association features are potentially essential for the nonlinear mapping between well logs and TOC. Thus, we introduce a novel deep spatial-sequential graph convolutional network (SSGCN) for predicting the TOC by jointly leveraging the cross-log topological association features and log-specific sequential features. Through further use of the previously unaccounted topological interactions, our SSGCN dramatically outperforms the sequence-based CNN. In the southeast Sichuan Basin, SSGCN exhibits beneficial mapping not demonstrated previously: its models achieve a better cross-validation performance within the same gas field wells and a greater generalizability in another gas field well. Our SSGCN method can predict TOC of shale gas field well with the best [Formula: see text] being 0.87 within 1 s on the CPU of a desktop computer, which increases the efficiency of obtaining the TOC parameter. From this study, we recommend graph and sequential convolutions for designing deep learning architectures in the well-log analysis.
Spatial variability of petrofacies using supervised machine learning and geostatistical modeling: Sycamore Formation, Sho-Vel-Tum Field, Oklahoma, USA

The Sycamore Formation at the Sho-Vel-Tum Field primarily consists of clay-rich mudstones (Mdst) and quartz-rich siltstones. The clay-rich Mdst are mainly composed of clays, quartz grains, some allochems, and detrital organic matter. The siltstones are structureless and are divided into two petrofacies: high porosity and permeability massive calcareous siltstones and low porosity and permeability massive calcite-cemented siltstones. Core and well-log data provide mineralogical, lithologic, and porosity information that is useful to define petrophysical facies (petrofacies) and to create facies logs within the Sycamore Formation. We used the data to establish the Sycamore Formation stratigraphic architecture and to map its spatial variability and reservoir properties. To classify the Sycamore Formation petrofacies in noncored wells, we developed a machine learning-based workflow that compares more than 1800 classification models and selects the best combination of well logs, algorithms, and hyperparameters to predict defined petrofacies. The process includes combinations of well logs that were optimized in four classification algorithms: artificial neural network, K-nearest neighbor, support vector machine, and random forest. To adjust each classifier, we used a grid search and a fivefold cross validation to find the best combination of three hyperparameters to improve results of each algorithm. This workflow allows for the efficient extraction of information from cores at a low cost. After we generated the petrofacies logs in noncored wells, we combined them with multiple constraints to create a 3D petrofacies model for the Sycamore Formation at the Sho-Vel-Tum Field and analyze the stratigraphic and diagenetic controls on petrofacies and its impact in reservoir quality.
An Efficient Graph Accelerator with Distributed On-Chip Memory Hierarchy

Graph processing has evolved and expanded swiftly with artificial intelligence and big data technology. High-Bandwidth Memory (HBM), which delivers terabyte-level memory bandwidth, has opened up new development possibilities for FPGA-based graph accelerators. However, despite the tremendous expansion of underlying hardware capabilities, existing graph accelerators have not benefited too much. In this paper, we observe that the uniformed on-chip memory hierarchy is the key to the low scalability of existing graph accelerators. We present a novel graph accelerator with a distributed on-chip memory hierarchy called GraphS. The on-chip memory of GraphS is divided into numerous tiny blocks, each of which is assigned to only one Processing Element (PE). Different PEs are connected through a scalable network-on-chip (NoC). For realistic graphs with power-law properties, a degree-aware preprocessing method is designed to balance the workload among different PEs. Our results with various graph algorithms demonstrate that GraphS can outperform state-of-the-art ForeGraph by up to 21.84 $$\times $$ .