Reviewerly/exHarmony huggingface dataset:
Teleportation Fidelity Improvement with Quantum Memory

We show that the fidelity of unheralded state teleportation using SPDC sources and standard linear optical Bell state measurements is limited to 50%. Single photon emissions and non-destructive heralding from quantum memories overcome this limit.
Measuring Waste Recyclability Level Using Convolutional Neural Network and Fuzzy Inference System

This paper presents a hybrid model that is used to measure the waste recyclability level using a convolutional neural network (CNN) and fuzzy inference system (FIS; WRL-CNNFIS). The proposed system uses waste images to train a multilayer convolutional neural network to extract the most relevant features that were used in a rule-based fuzzy system to give an accurate percentage of the recyclability level of these images. The proposed model did overcome many challenges in transfer learning models alone, like overfitting and low accuracy. The use of fuzzy rules, improved the performance even with a small data set, Results have shown the effectiveness of the proposed model in terms of all four metrics: accuracy, precision, recall, and F1 score. The performance was measured under two testing scenarios. For all evaluation measurement, in all experiments the validation was conducted using the cross validation in the last step. The proposed approach is a robust and consistent approach for classifying organic and recyclable waste types. WRL-CNNFIS has achieved accuracy rate of more than 98%.
Driver behavior recognition based on attention module and bilinear fusion network

Due to the small differences in driver distraction actions and the high similarity of some actions, in this paper we propose a distracted driver behavior recognition method (BACNN) based on convolutional neural network (CNN) using bilinear fusion network and combining attention mechanism with current mainstream algorithms of deep learning. In this paper, we use a driver dataset from State Farm for testing, and use 75% of this dataset for training and 25% for testing. The driver behavior pictures in the dataset are extracted using our specific convolutional neural network model for feature extraction and classified using a fully connected layer. Experiments demonstrate that this method has better recognition results compared to single-model network extracted features.
PIMNet: Physics-Infused Neural Network for Human Motion Prediction

Human motion prediction (HMP) predicts future human pose sequences given the past ones. HMP has recently attracted attention in computer vision and the robotics domain as it helps machines understand human behavior, plan target actions and optimize interaction strategies. Existing methods for HMP are based on either purely physics-based models or statistical models. However, each of these methods has its shortcomings. The physics-based techniques are complex and error-prone, while the statistical methods require a large amount of data and lack physical consistency. To overcome their limitations, we propose a physics-infused neural network ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PIMNet</b> ), which combines both physics-based and statistical methods. We first computed the contact forces and joint torques for each pose using the physics-based human dynamical model. Then they are fed into an Encoder-Decoder machine learning architecture to predict future ones. In this way, PIMNet simultaneously obtains computational efficiency and physical consistency. Extensive experimental results on Human 3.6M show that the proposed PIMNet could accurately predict human motion in both short-term and long-term scope. It achieves better or comparable prediction accuracy than the state-of-the-art, even using a basic LSTM as the machine learning model.
Classifying Sport-Related Human Activity from Thermal Vision Sensors Using CNN and LSTM

In this work, we describe a classification of five sport-related human activities which are sensed by a thermal vision sensor. First, we have collected several sport sessions of an inhabitant while developing: push-ups, sit-ups, jumping jacks, squats and planks. Second, we develop an ad-hoc augmentation of data to increase the sturdiness of the data collection and reduce overfitting. Third, a Deep Learning model has been evaluated to compute a sequence of images from the user in order to estimate the activity. A CNN extracts relavant features from spatial domain and LSTM network models the sequence of images to compute the final classification. The results show an encouraging performance and quick learning capabilities.
On tackling infrastructure: the need to learn from marginal cities and populations in the Global South

Due to complex and adverse effects of rapid urbanisation, conventional infrastructure networks in the Global South tend to be stretched in their capacity to deliver. Over the years, different studies have examined how diverse populations manage to operate successfully (albeit with constraints and limitations) despite limits on formal networks. However, most attempts have studied large and central cities at the expense of small and marginal cities. In this article, I make a case for learning from marginal cities and populations in the Global South. I highlight the need to understand better how the urban poor in smaller and marginal cities not only navigate and negotiate the absence and inadequacy of formal infrastructure, but also put together a semblance of viable life through modest, creative and sometimes improvised infrastructural and technological interventions. This, I argue is important for drawing appropriate lessons for tackling infrastructure, particularly in an age where sustainable solutions to urban and infrastructural challenges are bound to emanate not just from technical experts, but also from directly affected populations themselves.
Graph Neural Network Training and Data Tiering

Graph Neural Networks (GNNs) have shown success in learning from graph-structured data, with applications to fraud detection, recommendation, and knowledge graph reasoning. However, training GNN efficiently is challenging because: 1) GPU memory capacity is limited and can be insufficient for large datasets, and 2) the graph-based data structure causes irregular data access patterns. In this work, we provide a method to statistically analyze and identify more frequently accessed data ahead of GNN training. Our data tiering method not only utilizes the structure of input graph, but also an insight gained from actual GNN training process to achieve a higher prediction result. With our data tiering method, we additionally provide a new data placement and access strategy to further minimize the CPU-GPU communication overhead. We also take into account of multi-GPU GNN training as well and we demonstrate the effectiveness of our strategy in a multi-GPU system. The evaluation results show that our work reduces CPU-GPU traffic by 87-95% and improves the training speed of GNN over the existing solutions by 1.6-2.1x on graphs with hundreds of millions of nodes and billions of edges.
Graph-based Representation Learning for Web-scale Recommender Systems

Recommender systems are fundamental building blocks of modern consumer web applications that seek to predict user preferences to better serve relevant items. As such, high-quality user and item representations as inputs to recommender systems are crucial for personalized recommendation. To construct these user and item representations, self-supervised graph embedding has emerged as a principled approach to embed relational data such as user social graphs, user membership graphs, user-item engagements, and other heterogeneous graphs. In this tutorial we discuss different families of approaches to self-supervised graph embedding. Within each family, we outline a variety of techniques, their merits and disadvantages, and expound on latest works. Finally, we demonstrate how to effectively utilize the resultant large embedding tables to improve candidate retrieval and ranking in modern industry-scale deep-learning recommender systems.
Research advances in continuous-variable quantum computation and quantum error correction

Quantum computation presents incomparable advantages over classical computer in solving some complex problems. To realize large-scale quantum computation, it is required to establish a hardware platform that is universal, scalable and fault tolerant. Continuous-variable optical system, which has unique advantages, is a feasible way to realize large-scale quantum computation and has attracted much attention in recent years. Measurement-based continuous-variable quantum computation realizes the computation by performing the measurement and feedforward of measurement results in large-scale Gaussian cluster states, and it provides an efficient method to realize quantum computation. Quantum error correction is an important part in quantum computation and quantum communication to protect quantum information. This review briefly introduces the basic principles and research advances in one-way quantum computation based on cluster states, quantum computation based on optical Schrödinger cat states and quantum error correction with continuous variables, and discusses the problems and challenges that the continuous-variable quantum computation is facing.
Elaboration of Financial Fraud Ontology

Financial Frauds have dynamically changed, the fraudsters are becoming more sophisticated.There has been an estimated global loss of 5.127 trillion dollars each year due to various forms of financial frauds.Industries like banking, insurance, e-commerce and telecommunication are the main targets for financial frauds.Several techniques have been proposed and applied to understand and detect financial frauds.In this paper we propose an ontology to describe financial frauds and related knowledge.The aim of this ontology is to provide a semantic framework for the detection of financial frauds.Theoretical ontology has been elaborated exploring various sources of information.After describing the research objectives, related works and research methodology, this paper presents details of theoretical ontology.It is followed by its validation using real datasets.Discussion of the obtained results gives some perspectives for the future work.
Modernizing the Objectives and Organization of Case Investigation and Contact Tracing for the 21st Century

Abstract Case investigation and contact tracing (CICT) is a longstanding cornerstone of public health disease control efforts for a wide array of communicable diseases, though the content of CICT varies substantially depending on the infection to which it is applied, the epidemiologic circumstances, and interventions available to control an epidemic. In this article, we discuss how CICT is currently used in public health communicable disease, sexually transmitted infection/human immunodeficiency virus, and tuberculosis control programs. We then review how CICT might be modernized, considering issues such as community and health care organization engagement, workforce development, public health program organizational structure, data information systems, case prioritization, and the content to CICT.
Interactive 3D fault prediction using a weighted 2D-CNN and multidirectional 3D-CNN

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyInteractive 3D fault prediction using a weighted 2D-CNN and multidirectional 3D-CNNAuthors: Jesse LomaskSamuel ChambersJesse LomaskS&P GlobalSearch for more papers by this author and Samuel ChambersS&P GlobalSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3746971.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWe present an interactive 2D Convolutional Neural Network (CNN) with a weighted loss function. The interpreter is required to pick a minimal number of “fault” and “no fault” picks. These picks are used to weight the loss function of a 2D-CNN. The 2D-CNN is updated, and quickly predicted to maintain tight interactive feedback with the interpreter. Once the result is deemed sufficiently accurate, predictions of the 2D-CNN are fed into a pretrained 3D-CNN to create a novel solution for 3D fault prediction. This new method allows the interpreter to influence the prediction of the CNN by integrating any number of manually drawn picks. This method provides a 3D fault interpretation solution with the crispness and quality of a synthetically pretrained 3D-CNN solution, combined with the added value of interactivity, flexibility, and continual training and updating of a 2D-CNN solution.Keywords: seismic, interpretation, interactivePermalink: https://doi.org/10.1190/image2022-3746971.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Jesse Lomask and Samuel Chambers, (2022), "Interactive 3D fault prediction using a weighted 2D-CNN and multidirectional 3D-CNN," SEG Technical Program Expanded Abstracts : 1307-1311. https://doi.org/10.1190/image2022-3746971.1 Plain-Language Summary KeywordsseismicinterpretationinteractivePDF DownloadLoading ...
Adversarial Graph Regularized Deep Nonnegative Matrix Factorization for Data Representation

This work proposes a novel unsupervised deep non-negative matrix factorization (NMF) model called AGDNMF by deep exploration of the structure of the original data. Compared with the existing NMF research results, the model explores the deep association of data and constructs accurate spatial structure from a new perspective. Specifically, for the purpose of effectively learning low-dimensional representations of data. This work constructs a deep NMF method to approximate the coding structure of the original data, which enables AGDNMF to learn the hierarchical mapping relationship between the categories of the original data, and learn the hidden association information of the data from low-to-high in the middle layer. Moreover, the model gives an adversarial graph regularization representation for the manifoldization of data structures to push away the spatial distances between different sample clusters. Finally, based on the mathematical model of the proposed algorithm, the optimization strategy and convergence proof are given. Extensive experiments and data analysis demonstrate that the proposed model outperforms state-of-the-art unsupervised NMF methods.
Filipino Sign Language Recognition Using Long Short-Term Memory and Residual Network Architecture

Filipino sign language (FSL) has improved communication among deaf people. However, the majority of the population in the Philippines does not understand FSL. This study explored computer vision in obtaining the data and deep learning in training the continuous SLR model. The model has been trained using ResNet and LSTM model using the features extracted using MediaPipe Holistic from video files of Filipino phrases performed by three (3) FSL signers. The SLR system developed can recognize (15) continuous Filipino phrases. Based on the comparison of both deep learning models, the best performing model is from the LSTM setup, wherein the accuracy achieved is 94%. In comparison, ResNet produced 87% accuracy on the test set. The feature importance analysis has ranked the facial component in this order (eyebrows, eyes, and mouth). The analysis shows that the exclusion of such features has negatively affected the performance of the SLR model compared to the model with full facial features. Based on the analysis of the experimentation results, the developed SLR system is robust, time-efficient, signer-independent, and can detect both manual and non-manual features of the gesture.
A Small Maximal Sidon Set in ${\mathbb{Z}}_2^n$

A Sidon set is a subset of an Abelian group with the property that each sum of two distinct elements is distinct. We construct a small maximal Sidon set of size $O((n \cdot 2^n)^{1/3})$ in the group ${\mathbb{Z}}_2^n$, generalizing a result of Ruzsa concerning maximal Sidon sets in the integers.
Performance of Low-Latency DASH and HLS Streaming in Mobile Networks

Reducing end-to-end streaming latency is critical to Hypertext Transfer Protocol (HTTP)-based live video streaming. There are currently two technologies in this domain: 1) Low-Latency HTTP Live Streaming (LL-HLS) and 2) Low-Latency Dynamic Adaptive Streaming over HTTP (LL-DASH). The latter is sometimes also referred to as Low-Latency Common Media Application Format (LL-CMAF), but effectively it is the same architecture. Several existing implementations of streaming players, as well as encoding and packaging tools, support both technologies. Well-known examples include Apple’s AVplayer, Shaka player, HLS.js, DASH.js, FFmpeg, and so on. In this article, we conduct a performance analysis of such streaming systems. We perform a series of live streaming experiments, repeated using identical video content, encoders, encoding profiles, and network conditions, emulated by using traces of 4G LTE mobile networks from two major operators. We capture several performance metrics, such as average stream bitrate, the amounts of downloaded media data, streaming latency, buffering, frequency of stream switching, and so on. Subsequently, we analyze the captured data and describe the observed differences in the performance of LL-HLS and LL-DASH-based systems.
Voice Guidance System for Color Recognition Based on IoT

We often need to identify the colors of such objects as buildings, clothing, photos, and traffic signs in everyday life.Some people cannot distinguish among such colors.This study proposes a method to help people, including colorblind people, identify colors.We establish a platform for color identification that is connected by the Internet of Things (IoT).The Espressif Systems 32 (ESP32) single chip is connected to a Wi-Fi communication network to transmit data to the color identification platform.The system interface is displayed in the form of color code and color name display.The speech synthesis module Speech Synthesizer Node 6288 (SYN6288) is used to broadcast color-related information.Since there are many color conversion technologies in modern times, or other color recognition methods are not applicable to the guide-blind function.Therefore, the authors use the method of reading out color-related information to directly enable colorblind people to conveniently identify the colors of objects.After experiments: the accuracy of various colors, the accuracy of environmental impact, the comparison of the original sensing experiment and the effect of adding interference light sources, the time required for color sensing to identify various colors, and the interference test results of any color light source, it is proved that the research mentioned in this research.The proposed method can not only improve the rate of recognition of the system in different environments, but can also accurately identify a variety of colors.The platform is integrated with the IoT to allow users to quickly monitor the displayed data.
A Structure Preserving Numerical Scheme for Fokker--Planck Equations of Structured Neural Networks with Learning Rules

In this work, we are concerned with a Fokker--Planck equation related to the nonlinear noisy leaky integrate-and-fire model for biological neural networks which are structured by the synaptic weights and equipped with the Hebbian learning rule. The equation contains a small parameter $\varepsilon$ separating the time scales of learning and reacting behavior of the neural system, and an asymptotic limit model can be derived by letting $\varepsilon\to 0$, where the microscopic quasi-static states and the macroscopic evolution equation are coupled through the total firing rate. To handle the endowed flux-shift structure and the multiscale dynamics in a unified framework, we propose a numerical scheme for this equation that is mass conservative, unconditionally positivity preserving, and asymptotic preserving. We provide extensive numerical tests to verify the schemes' properties and carry out a set of numerical experiments to investigate the model's learning ability, and we explore the solution's behavior when the neural network is excitatory.
Multi-Model Fusion of Encoding Methods-Based Visual Words for Target Recognition in Infrared Images

The limited information contained in infrared images present a serious problem, therefore it is necessary to form a powerful feature descriptor that allows extracting the maximum information and describing the image efficiently. To address this challenge, we propose a novel approach named multi-model fusion of encoding methods (MMFEM). First, several encoding methods for Bag Of Visual Words (BOVW) model were evaluated. Then, we fuse the best encoding methods obtained using three levels of fusion: feature-level fusion, decision-level fusion and hybrid-level fusion. Finally, the outputs of the fusion process were used to form a final decision for target recognition in infrared images. Two infrared datasets were employed to evaluate the performance of the proposed approach. The first one is Visible and Infrared Spectrum (VAIS) dataset comprising six categories of ships and the second dataset is a subset of Forward-Looking InfraRed (FLIR) thermal dataset comprising two object categories, vehicles and pedestrians. The proposed approach has exceed the state of the art for both datasets and we have reached 96.96% for FLIR and 71.26% for VAIS in overall classification accuracy.
An interactive practice platform of English mobile teaching in colleges and universities based on open API

In order to overcome the problems of long response time, high CPU and memory usage, this paper proposes a new interactive platform based on open API. On the basis of open API protocol, the whole framework of B/S structure design platform is introduced. The platform hardware mainly includes three main components: microprocessor, memory, programmer, etc. through feedback elite teaching optimisation algorithm and open API protocol program, students are graded to complete the design of open API College English mobile teaching interactive practice platform. The experimental results show that the maximum response time is reduced to 0.85, and the CPU utilisation and memory utilisation are reduced to 56.4 and 64.2 when the number of users is the largest, so the evaluation of platform learning investment effect is better.
Chaotic-DNA system for efficient image encryption

In order to prevent unwanted access to sensitive data by unauthorized individuals, color images are encoded. Because chaotic-deoxyribonucleic acid (chaotic-DNA) encoding can make information highly secure, it is often employed in image encryption. In this study, a new image encryption technique has been proposed based on a new 4D-chaotic system and DNA computing. The algorithm consists of two phases: in the first phase, the pixel positions are permuted by chaotic sequences. In the second phase, according to the concept of DNA cryptography, a set of operations (like DNA addition, DNA XOR, DNA subtraction, shift right, and shift left) are performed on the DNA encoding sequence. The performance of the suggested algorithm is evaluated through analyses like correlation coefficient, entropy, histogram, and key space. The results show that the encryption method that was exhibited has good encryption performance and high security. For encrypted images, the histogram is fairly uniform, the correlation values between adjacent pixels are very small and close to zero, and the entropy is near to the ideal value of eight. In addition, the proposed system has a very large key space that is equal to 2627 keys, which makes it resistant to brute-force, differential, and statistical attacks.