Reviewerly/exHarmony huggingface dataset:
3D object classification from point clouds

Artificial intelligence has achieved a breakthrough with the proposal and development of deep learning. Compared with traditional models, deep learning allows machines to extract features and train neural networks by learning weight parameters. Convolutional Neural Networks (CNN), as the top priority of deep learning, have achieved remarkable results in 2D image recognition and classification segmentation. Recently, points cloud is a recent hot 3D data form in the field of deep learning. Point clouds retain better spatial geometric information than other forms of 3D data such as mesh depth maps. Due to the disorder, rotation invariance, the uneven density distribution of 3D point clouds, high sensor noise, and complex scenes, deep learning of 3D point clouds is still in the initial stage, and there are significant challenges. The tasks of deep learning for point clouds are mainly classified into shape classification, instance segmentation, semantic segmentation, etc. This article specifically outlines the development of methods for shape classification tasks and the characteristics and differences of each method. In addition, a comparison of the training accuracy and efficiency of each method on the dataset is provided.
Federated learning for maximum differential choice based on the global perspective

Federated learning has received extensive attention as a new distributed learning framework, which enables joint modeling without data sharing. However, it is still affected by the communication bottleneck, and most clients may not be able to participate in the joint learning modeling at the same time, resulting in slow convergence. To solve the above problems, we propose a federated learning aggregation algorithm based on a global perspective, which considers the data distribution of participating clients. The server builds a feature distribution table according to the data distribution, and each time the server selects a set of clients for training, it will cover more features to a greater extent to learn the global data more fully. Specifically, the selection of these clients is not random. When the server selects, it will construct a set of clients with the largest mutual distribution difference within the range of visible clients, and place it at the end of the selected chain after each training until all clients’ end is selected. We demonstrate the effectiveness of our work through comprehensive experiments and comparisons between the two most popular algorithms. Specifically, our algorithm achieves an average speedup of 40% compared to traditional algorithms.
A comprehensive study on digital video stabilization

Video stabilization is a technology used to improve video stabilization for higher video quality. The traditional video anti-shake technology adopts the 2D optical flow estimation method, but it will produce great errors in the face of complex spatial scenes. In recent years, with the deepening of deep learning in the field of video processing, video stabilization has achieved unprecedented results, and some works are also quite subtle for 3D spatial processing. However, they either fail to account for dynamic parts of the scene, or cause frame clipping. Considering the background of related fields, this article reviews the video anti-shake methods in the past years, puts forward optimization thinking on existing problems, and finally gives an outlook.
Robust audio dual watermarking algorithm based on melody feature

Audio watermarking is an effective scheme for copyright protection. A robust dual watermarking scheme based on melody feature is proposed in this paper. Melody feature can be encoded for integrity verification. And then two robust watermark methods based on discrete cosine transform(DCT) and histogram are adopted to embed the watermark into the audio, which is used to resist different attacks. Tamper can be detected by comparing the melody features in the audio. In the extraction process, two groups of watermarks are extracted, and two images can be obtained. The Brenner gradient values of these two images are calculated, and the image with smaller Brenner value is selected as the final watermark image. The experimental results show that the proposed watermarking scheme has robustness to common signal processing and synchronization attacks. The robustness of the proposed watermark is better than existing algorithms.
Deep Reinforcement Learning for Solving Directed Steiner Tree Problems

The design of approximation algorithms for solving NP-hard Combinatorial Optimization (CO) problems is usually challenging. In recent years, deep learning has demonstrated the power to solve specific CO problems, such as Travelling Salesman problem and Minimum Vertex Cover problem. In this paper, we propose a deep reinforcement learning approach based on graph neural networks (GNN) to tackle Directed Steiner Tree (DST) problem. Simulations are conducted to evaluate the proposed approach compared to benchmarks upon approximation ratios and execution time respectively. The results reveal the potential of our approach in solving DST problems in practice and the scalability that can be smoothly applied to disparate graphs after enough off-line training.
Multi-Wavelength Silicon Photonic Neural Network for WDM Optical Communication Systems

We propose and demonstrate a photonic recurrent neural network which can simultaneously compensate for fiber dispersion and nonlinearity in WDM communication systems in the photonic domain. The resulted Q-factor outperforms some conventional DSP methods.
Block Matching using ResNet Features

Block matching is one of the popular methods for motion estimation problems. This block-based technique divides an image into macroblocks and finds the movement of each macroblock within a specified search window. This technique uses the pixel values of an image to obtain the motion vectors. In this paper, features extracted using a Convolutional Neural Network (CNN) feature extractor are used instead of the direct pixel values. Compared to pixels, features extracted with a CNN are less affected by small changes in lighting and position within an image. Modern CNNs, such as the ResNet, capture different sizes of features, some finer and some coarser. Compared to directly using pixel values, this method improves the accuracy of matched blocks while reducing the complexity of the block matching algorithm.
Kaizen: Computer Vision Based Interactive Karate Training Platform

All types of martial arts consist of several forms of combat used in self-defense, which are deeply rooted in many countries. Of all the martial art types, karate is considered the most well-known out of them all. Due to the pandemic situation in Sri Lanka, karate enthusiasts have lost the opportunity to train in a well-guided environment. As a result, even though virtual training came into play, it has continuously proved its ineffectiveness in evaluating the performance and accuracy of the trainees. The main objective of this proposed system is to virtualize the processes of a physical karate dojo. Kaizen - A Computer Vision-Based Interactive Karate Training Platform is a web-based application that functions as a virtual instructor. The proposed system consists of two main core components for Training and Assessments. The karate training component evaluates the techniques against a set of predefined joint angles. The BlazePose model is used for keypoint detection, and Analytic Geometry is used to extract joint angles. It is also integrated with Amazon Polly, a Deep Learning-based Text-To-Speech (TTS) service to produce real-time audio feedback. The assessment component has the capability to evaluate the trainees through a built-in Smart Evaluator based on a Recurrent Neural Network (RNN). Additionally, the capability to manage the assessments supports the instructors in conducting all the assessments virtually, overcoming the barriers in physical training.
F-GAN: Real-Time Color Correction Model of Underwater Images

In recent years, color correction of underwater images is becoming a hot research field due to the wide application of Autonomous Underwater Vehicles and Remotely Operated Vehicles in monitoring marine environments. Many kinds of deep learning models have been presented by researchers to correct the color of the underwater images to facilitate the work of both people and machines. However, these models emphasize the correction effect more, and they can't be applied to real-time scenarios, especially for high-resolution images (480*640). Therefore, we propose a more concise generative adversarial network-based model to achieve real-time color correction of underwater images. To supervise the training process better, we design a multiterm loss function including adversarial loss, content loss, and structural similarity loss to estimate the generated image quality. Experiments on the EUVP (Enhancing Underwater Visual Perception) dataset show that our approach produces visually satisfactory results for high-resolution images under the real-time requirement. Besides, our method can even outperform some state-of-the-art models.
Handwashing Action Detection System for an Autonomous Social Robot

Young children are at an increased risk of contracting contagious diseases such as COVID-19 due to improper hand hygiene.An autonomous social agent that observes children while handwashing and encourages good hand washing practices could provide an opportunity for handwashing behavior to become a habit.In this article, we present a human action recognition system, which is part of the vision system of a social robot platform, to assist children in developing a correct handwashing technique.A modified convolution neural network (CNN) architecture with Channel Spatial Attention Bilinear Pooling (CSAB) frame, with a VGG-16 architecture as the backbone is trained and validated on an augmented dataset.The modified architecture generalizes well with an accuracy of 90% for the WHO-prescribed handwashing steps even in an unseen environment.Our findings indicate that the approach can recognize even subtle hand movements in the video and can be used for gesture detection and classification in social robotics.
Cloud Segmentation, Denoising, and Compression Techniques for use on Sentinel-3 Satellite Data

Satellite images of the Earth's surface have a multitude of uses. However., this data comes with some inherent issues. One such issue is cloud coverage. We propose the use of image segmentation to identify clouds in images. Another problem is the size of the data., which must be transferred over the satellite's limited connection bandwidth. We propose several compression methods for reducing data size effectively. The data is of varying quality, so the use of noise removal techniques can improve the accuracy and usability of the data.
Dual attention-guided state controller mechanism for Text-to-Image generation

The main challenge of text-to-image (Txt2Img) synthesis is how to enrich the image details while preserving the semantic consistency between text descriptions and generated images. To tackle this challenge, we propose a dual attention-guided state controller (DASC) mechanism for Txt2Img synthesis in this paper. Different from the conventional approaches that use an average pooling for extracting global semantic information, the proposed approach computes a new word-to-visual (W2V) attention together with the conventional visual-to-word (V2W) attention to form the dual attention. It extracts local semantic information, which is more relevant to each word at each image generation stage, in a recurrent manner by controlling the subsequent image generation states. Furthermore, guided by the local semantic information extracted in dual attention, a state controller is proposed to perform dynamic importance boosting for the mismatched words, and control the states of image generation by refining the generated image with rich details. Experiments are carried out to demonstrate the superior performance of the proposed approach in Txt2Img synthesis on the benchmark CUB and MS-COCO datasets.
An FPGA-based Accelerator for Local Bundle Adjustment of Stereo Visual Odometry

This paper proposes an FPGA design and implementation of Local Bundle Adjustment (LBA) for Stereo Visual Odometry. In order to implement the LBA module on FPGA, binary Cauchy weight is designed. The LBA module is generated by Vivado HLS and implemented on the FPGA (XCZU3EG). Compared with the performance running on the FPGA and that on the Intel and ARM Processor, the LBA accelerator has achieved the highest speed which could speed up the process by 2.5 times than that on the Intel CPU, and 7.5 times than that on the ARM CPU. Based on improved LBA method on FPGA, the localization deviation is constrained within 10mm.
The Effect of Noisy and Blurry Data on Deep Learning: Application in Brain Image Classification

Convolutional Neural Networks (CNN) is one of the best Deep Learning algorithms commonly used for computer vision tasks, including medical image analysis. CNN can learn the representational features from images starting from the lower to complex features. However, noisy data can affect the generalization of the networks, which we can often find in medical images, such as Magnetic Resonance Imaging (MRI). In this paper, we intend to find the correlation between noisy data and the performance of CNN models. We build automatic CNN-based classifiers for normal brain MR images based on axial view by setting up three different data scenarios to train the classifiers: 1) original data, 2) blurred data, and 3) noisy data. We also evaluate the relationship between the prediction accuracy and kernel size of the convolutional layers. Based on our investigation, deeper layers and smaller kernels in the CNN models give better generalization.
Denoising for Photon-limited Imaging via a Multi-level Pyramid Network

Imaging under photon-scarce situations introduces challenges to many applications as the captured images are with low signal-to-noise ratio. Here, we target on the denoising under photon-limited imaging. We develop a multi-level pyramid denoising network (MPDNet), which employs the idea of Laplacian pyramid to learn the small-scale noise map and larger-scale high-frequency details at different levels. Feature extractions are conducted on the multi-scale input images to encode richer contextual information. The major component of MPDNet is the multi-skip attention residual block, which integrates multi-scale feature fusion and attention mechanism for better feature representation. Experimental results have demonstrated that our MPDNet can achieve superior denoising performance on the images with low photon counts.
Facial Expression Recognition Based on Data Augmentation and Swin-Transformer

We propose a Transformer-based facial expression detection framework, combining different data enhancement methods with pre-trained model, achieves the best performance compared with other baselines.
Optimization of Dew Point, Humidity, and Temperature in Data Centers using Genetic Algorithm with Multiple Linear Regression

This study provides help in the Department of Public Works & Highways - Information Management Service Technology Support Division Systems Administration Section of the Philippines in bringing optimization analysis and metric standardization in their current data centers. The researchers proposes that the relationship between dew point, humidity and temperature be known in order to produce standards in maintaining the server reliability of their data centers. The researchers use a MATLAB program to create a genetic algorithm code in producing the best preferred optimization values with the help of the multiple linear regression as its fitness function. The results in the multiple linear regression analysis proved that there is a strong significant relationship between dew point (negative relationship) and humidity (positive relationship) to temperature based on its multiple r (correlation), r-squared and ANOVA- significance F. The generated optimized results from the MATLAB genetic algorithm code are the following; dew point be 12 degrees Celsius, humidity be in the range of 58 to 61 percent, and temperature be in the range of 20.027 to 20.253 degrees Celsius.
A Technique for Constrained and Quantized Control of Nonlinear Systems Using Second-Order Cone Programming

Abstract This paper presents a novel technique for control of systems with bounded nonlinearity, convex state constraints, and control constraints. The technique is particularly useful for problems whose control constraints may be written as convex sets or the union of convex sets. The problem is reduced to finding bounding solutions associated with linear systems, and it is shown that this can be done with efficient second-order cone program (SOCP) solvers. The nonlinear control may then be interpolated from the bounding solutions. Three engineering problems are solved. These are the Van der Pol oscillator with bounded control and with quantized control, a pendulum driven by a DC motor with bounded voltage control, and a lane change maneuver with bounded rotational control acceleration. For each problem, the resulting second-order cone program solves in approximately 0.1 s or less. It is concluded that the technique provides an efficient means of solving certain control problems with control constraints.
The art of augmented memory: Remembering with augmented reality application in the case of the Temple of Artemis

Abstract This article focuses on the effects of different representation modes of architectural heritage in augmented reality (AR) applications on remembering. Deterioration of the tangible evidence of architectural heritage compromises not only its visibility in the heritage site, but also its presence in memory. Converging survived features and digitally produced representations of the heritage, AR applications in mobile devices provide the memory of the site with endurance, however what is remembered immensely depends on how the heritage is digitally represented on screen. Conceived as a case study for the method of analysis derived from classical memorizing technique of the art of memory, the ‘[AR]temis’ project, reported in this article, aimed to get insights into the effects of the representational qualities of augmented architectural heritage on remembering and also into future AR projects developed for architectural heritage sites with its original method of analysis to inform design decisions. The research project involved the development of the method of art of augmented memory, the AR application, as well as questionnaires and interviews with the respondents’ on-site tests of the application. The results of this analysis show that the decisions regarding the digital representation of architectural heritage in AR applications entail not only the visual qualities of the heritage per se, but also how the actual site of memory is visualized on screen.
Uncertainty‐aware scheduling of real‐time workflows under deadline constraints on multi‐cloud systems

Summary The elasticity and pay‐as‐you‐go features of cloud computing are popular with customers, and more and more workflow applications are migrating to cloud platforms. Many workflow scheduling algorithms aim to obtain minimal rental costs. However, most of the existing research assumes that task execution time is deterministic. In fact, due to the performance fluctuations of VMs, the task execution time is uncertain before scheduling. Furthermore, many works ignore the cost savings given by multi‐cloud systems. To this end, this paper provides a scheduling framework for real‐time workflows. The framework includes four main components: the workflow analyzer, task pool, task allocation controller, and resource manager. Then based on the framework, we propose the RWSMC heuristic algorithm. The algorithm's goal is to minimize the total rental cost while satisfying the deadline constraints and ensuring the reliability of task execution. The RWSMC algorithm reduces the cost by selecting the appropriate billing mechanism based on the task's execution time and mitigates the impact of uncertain execution time by scheduling the task to the VM with the shortest predicted start time. Simulation experiments demonstrate that our proposed algorithm outperforms three recent state‐of‐the‐art scheduling algorithms in the total rental cost, deadline violation rate, and VM resource utilization.
Image Classification and Text Identification in Inspecting Military Aircrafts Logos: Application of Convolutional Neural Network

Object detection and inspection using images or videos have been receiving increased attention in many applications such as traffic control, brand monitoring, trademark compliance, and product authentication. A particular application that is currently a topic of interest is aircraft logo detection, which aims at automating the visual inspection carried out manually by aircraft engineers. Aircraft logos should meet a large set of requirements that include geometric constraints on the logo elements and patterns, and constraints on the position and orientation with respect to specific references. This work considers the design of a high accuracy convolutional neural network to detect and classify aircraft logos as either adequate or inadequate based on specified criteria. The performance of the developed network is compared to a number of classical machine learning algorithms to demonstrate its effectiveness. Adequate logos are then processed further by extracting them from a frame using robust features extraction algorithm and determining their orientation angle with respect to the horizontal reference axis. Afterward, a text detection technique using a character region awareness for text detection algorithm implemented on a pre-trained network is carried out, along with optical character recognition tool to detect and extract the text from the logos for further processing in other applications. The developed network is tested on actual aircraft logos, captured from the field, where satisfactory results are obtained.
Golden Eye

File systems are important sources of intelligence information and digital evidence. They have long attracted the interest of researchers in recovering files that are deleted from a hard disk. Existing file recovery studies rely heavily on an operating system (OS). However, it is often encountered that OS services are not available, making existing file recovery approaches unusable. To address this issue, the authors design and implement an OS-independent file recovery algorithm named Golden Eye (GE) by targeting the EXT4 file system. Fed the raw image obtained from a (sanitized) hard disk, GE can automatically recover any designated file or even the whole EXT4 file system. GE is based on the understanding of the file disk layout of EXT4 and does not need any support from additional hardware or software. Experimental results prove the feasibility and correctness of GE. This work not only solves the OS dependency problem that most existing file recovery work suffers from but also reveals the fact that even sanitized hard disks are still at risk of leaking sensitive data.