Reviewerly/exHarmony huggingface dataset:
Probabilistic Modeling Using Tree Linear Cascades

We introduce tree linear cascades, a class of linear structural equation models for which the error variables are uncorrelated but need not be Gaussian nor independent. We show that, in spite of this weak assumption, the tree structure of this class of models is identifiable. In a similar vein, we introduce a constrained regression problem for fitting a tree-structured linear structural equation model and solve the problem analytically. We connect these results to the classical Chow-Liu approach for Gaussian graphical models. We conclude by giving an empirical-risk form of the regression and illustrating the computationally attractive implications of our theoretical results on a basic example involving stock prices.
An Adaptive High-Performance Quantization Approach for Resource-Constrained CNN Inference

Recent convolutional neural network (CNN) development continues to advance the state-of-the-art model accuracy for various applications. However, the enhanced accuracy comes at the cost of substantial memory bandwidth and storage requirements and demanding computational resources. Although in the past the quantization methods have effectively reduced the deployment cost for edge devices, it suffers from significant information loss when processing the biased activations of contemporary CNNs. In this paper, we hence introduce an adaptive high-performance quantization method to resolve the issue of biased activation by dynamically adjusting the scaling and shifting factors based on the task loss. Our proposed method has been extensively evaluated on image classification models (ResNet-18/34/50, MobileNet-V2, EfficientNet-B0) with ImageNet dataset and object detection model (YOLO-V4) with COCO dataset. The results show that our 4-bit integer (INT4) quantization models achieve better accuracy than the state-of-the-art 4-bit models, and in some cases, even surpass the golden full-precision models. The final designs have been successfully deployed onto extremely resource-constrained edge devices for many practical applications.
Disparity Estimation using Light Ray Pair in Stacked 3D Light Field

Light field (LF) is a concept that defines a number of light rays passing through free space, and creates novel views and estimates three-dimensional (3D) information by combining some of the light rays. Among them, the stacked 3D LF is a structure made by stacking several 3D LFs, and it supports free viewpoint movements over a wide range. However, 3D LF is a relatively simple structure and its use is limited due to insufficient vertical light rays. This paper proposes an effective method for estimating vertical disparity between 3D LFs in a stacked 3D LF. This vertical disparity can be used as key information to release the limitation. The contribution of this paper is as follows. First, this paper defines light ray pair in a stacked 3D LF. Light rays corresponding to light ray pair share the same foreground and background, and there is only vertical disparity. Second, based on light ray pair, this paper proposes an effective image reconstruction for disparity estimation. This method reconstructs the reference image by connecting all light rays that are light ray pair with the target image from the neighboring 3D LF. Since this reference image has only vertical disparity, disparity estimation becomes relatively easy. Additionally, the image-based disparity estimation may damage the continuity of 3D LF, so post-processing is applied to compensate for this. In the experimental results, the proposed method shows a noticeable improvement in objects with large disparity that are close to the camera.
Energy Efficient Text Spotting Technique for Mobile Edge Computing

We propose a new scene text spotting system which aims at minimizing the power consumption in mobile edge devices. We focused on preprocessing methods and changes in the processing pipeline. Overall, we removed non-text images from the processing pipeline to reduce power consumption and positioned texts at the center of the images to improve accuracy. Moreover, we were able to achieve a substantial power saving and increased text recognition accuracy. Compared to the baseline method, our proposed method shows an 80% higher performance score. The accuracy score was increased by 17% and the power consumption was reduced by 30% because we could reduce the execution count of the neural network by 40%.
Adaptive Image-based Visual Servoing with Time-varying Learning Rates for Uncertain Robot Manipulators

In this work, we propose a new adaptive image-based visual servoing (IBVS) approach with a time-varying learning rate for robot manipulators with uncertain kinematics. A monocular camera in an eye-to-hand configuration is used to extract the image features of a target object, its centroid coordinates and projected area, allowing visual tracking applications. An indirect adaptive control method based on time-varying adaptation gains and projection operators is then designed to deal with camera model inaccuracies including large misalignment angles which affect the control direction. The Lyapunov stability theory is used to demonstrate the stability and convergence properties of the adaptive IBVS approach. The time-varying gain allows for fast exponential convergence of the system parameters towards a compact set under finite excitation condition. Numerical simulations are included to illustrate the effectiveness and feasibility of the proposed vision-based control methodology.
Answering Binary Causal Questions Using Role-Oriented Concept Embedding

Answering binary causal questions is a challenging task, and it requires rich background knowledge to answer such questions. Extracting useful causal features from the background knowledge base and applying them effectively in a model is a crucial step to answering binary causal questions. The state-of-the-art approaches apply deep learning techniques to answer binary causal questions. In these approaches, candidate concepts are often embedded into vectors to model causal relationships among them. However, a concept may play the role of a cause in one question, but it could be an effect in another question. This aspect has not been extensively explored in existing approaches. Role-oriented causal concept embeddings are proposed in this article to model causality between concepts. We also propose leveraging semantic concept similarity to extract causal information from concepts. Finally, we develop a deep learning framework to answer binary causal questions. Our approach yields accuracy that is comparable to or better than the benchmark approaches.
Hybrid Truck-Drone Delivery Under Aerial Traffic Congestion

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Hybrid Truck-Drone Delivery Under Aerial Traffic Congestion 27 Pages Posted: 6 Sep 2022 See all articles by Ruifeng SheRuifeng SheUniversity of Illinois at Urbana-ChampaignYanfeng OuyangUniversity of Illinois at Urbana-Champaign - Department of Mathematics Date Written: August 13, 2022 Abstract This paper focuses on a hybrid truck-drone delivery system, in which a truck carries goods and a fleet of drones around the neighborhoods of customers, while the drones are dispatched from the truck to perform the last-mile delivery. We formulate a continuous traffic equilibrium model in the form of partial differential equations (PDEs) to describe the optimal drone routing and truck-drone synchronization strategies when low-altitude aerial traffic congestion arises in large-scale steady-state operations. A customized solution algorithm is then developed, using a physics-informed neural network framework and various enhancement techniques, to efficiently solve the PDEs. The PDE solution is then used to evaluate the operational cost of a truck-drone delivery system, through a dimensionless surrogate model, which further provides the basis for optimizing several service design decisions, such as truck speed, truck routing plan and delivery headway. Numerical experiments are conducted to show the applicability of the proposed modeling framework, and to draw managerial insights for logistics carriers. Keywords: Unmanned aerial vehicle, drone, vehicle routing, congestion, continuous traffic equilibrium, physics-informed neural network Suggested Citation: Suggested Citation She, Ruifeng and Ouyang, Yanfeng, Hybrid Truck-Drone Delivery Under Aerial Traffic Congestion (August 13, 2022). Available at SSRN: https://ssrn.com/abstract=4189367 Ruifeng She University of Illinois at Urbana-Champaign ( email ) Yanfeng Ouyang (Contact Author) University of Illinois at Urbana-Champaign - Department of Mathematics ( email ) Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 1 Abstract Views 12 PlumX Metrics Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo5 in 0.246 seconds
Making Maximally Ethical Decisions via Cognitive Likelihood and Formal Planning

This chapter attempts to give an answer to the following question: Given an obligation and a set of potentially-inconsistent, ethically-charged beliefs, how can an artificially-intelligent agent ensure that its actions maximize the likelihood that the obligation is satisfied? Our approach to answering this question is in the intersection of several areas of research, including automated planning, reasoning with uncertainty, and argumentation. We exemplify our reasoning framework in a case study based on the famous, heroic ditching of US Airways Flight 1549, an event colloquially known as the “Miracle on the Hudson.”
Big Data Sharing Model and Key Mechanism of International Trade Based on Blockchain

COVID-19 has made the world realize that the world is a whole, and international trade is the main way to connect countries. With the increase of international trade, the trade data has also become gradually huge. However, the current international trade data is still transmitted using the traditional Internet, which not only is inefficient, but also has poor security. Therefore, this paper aimed to use blockchain technology to explore the big data sharing model and key mechanism of international trade. This paper mainly introduced the design and implementation of a blockchain-based IoT data security sharing system. Firstly, the functional architecture of the prototype system was comprehensively introduced. According to the division of different functional modules, the data interaction design of different modules and the design of related smart contracts were expounded. According to the characteristics of international trade big data, the encryption and decryption algorithms are designed for the transmission of international trade big data. Finally, the prototype system was introduced from data acquisition, experimental environment deployment, system-related functional verification, and performance testing. The experimental test results showed that the encryption and decryption of transaction data by the data security sharing prototype system were more than 2 times faster than those of the traditional algorithm, and the transmission efficiency of the system at different nodes fluctuated within 10%, which showed that the system could perform security interactive sharing of data.
Uncertainty Measured Active Client Selection for Federated Learning in Smart Grid

Federated learning is a hot machine learning research direction, its goal is to train a high quality central model while protecting the privacy of all parties, and it has a broad application prospect in smart grid and other fields. However, in federated learning with massive client participation, it is impossible to have all clients participate in training and model aggregation every time due to the limitation of communication and computing resources. Usually the method of selecting clients for federated learning is random, some studies have studied this problem from aspects of client data quality, model training effect, communication and computing resources, etc. In this paper, we propose an active client selection algorithm from the perspective of model uncertainty, this algorithm is called uncertainty measured active client selection in FL (UCS-FL). The server actively selects a subset of clients to participate in the FL training, and the unselected clients do not need to train in this round, saving computing and communication resources. Perform a thorough empirical analysis of the image classification task to demonstrate the excellent performance of UCS-FL against baseline in the context of monitored FL settings. Finally, we describes the real-world application of the proposed architecture, especially in smart grid scenarios.
Combating spatial redundancy with spectral norm attention in convolutional learners

There is an inherent and longstanding challenge for vision learners to exploit informative features from digital images with spatial redundancy. Given pre-processing image methods require task-specific customization and may rise unanticipated poor performance due to redundancy removal, we explore improving vision learners to combat spatial redundancy during vision learning, a task-agnostic and robust solution. Among popular vision learners, vision transformers with self-attention can mitigate pixel redundancy by capturing global dependencies, while convolutional learners fall into locality via a limited receptive field. To this end, based on investigating inter-pixel spatial redundancy of images, in this work, we propose spectral norm attention (SNA), a novel yet efficient attention block to help convolutional neural networks (CNNs) highlight informative features. We can seamlessly plug SNA into off-the-shelf CNNs to suppress the contributions of redundant features by globally differentiating and weighting. In particular, SNA performs singular value decomposition (SVD) on intermediate features of each image within a mini-batch to obtain its spectral norm. The features in the direction of the spectral norm are most informative, while the discriminative features in other directions leave less. Hence, we apply the rank-one approximation of the spectral norm direction as attention weights to enhance informative features. Besides, we adopt the power iteration algorithm to approximate the spectral norm to significantly reduce the matrix computation overhead during training, thus keeping inference speed on par with vanilla CNNs. We extensively evaluate our SNA on four mainstream natural datasets to demonstrate the effectiveness and favourability of our SNA against its counterparts. In addition, the experimental results of image classification and object detection show our SNA can bring more gains to medical images with heavy redundancy than other state-of-the-art attention modules.
Hierarchical multi-view metric learning with HSIC regularization

As the information era develops rapidly, it’s common to utilize multiple features from different sources to represent one object. Measuring the similarity between multi-view objects is the fundamental task in multi-view learning. To effectively measure the similarity between multi-view samples, multi-view metric learning has gained extensive attention recently. Nevertheless, most existing methods merely focus on the closeness of similar pairs and the separability of dissimilar ones inside each view, so that rich consensus properties existing in multi-views data might be ignored to some extent. To mitigate this issue, we come up with a novel method entitled Hierarchical Multi-view Metric learning with HSIC regularization (HM2H). HM2H aims to simultaneously maintain the closeness of similar points and the separability of dissimilar ones in intra-view and inter-view. Since multiple views depict different perspectives of the same object, the shared metric is introduced to capture the consensus information among those views. Moreover, we take advantage of the Hilbert–Schmidt Independence Criterion to seek the maximum distribution agreement of the multi-view dataset. Correspondingly, an algorithm based on Alternating Direction Method is provided to solve the proposed HM2H. Finally, various experimental results on five visual recognition datasets confirm the effectiveness and feasibility of our proposed method.
COVID-19 Impacts in the New Technological Era: Cross-Border Privacy Issues With Emphasis on AI

Apart from the impacts on the economy, health, and society, the systematic, essential, and massive processing of personal data during the management of the global pandemic of COVID-19, demands the analysis of specific legal issues regarding privacy. Many issues affecting data protection have been emerged as a result of this global crisis and are been analyzed in this research work, including the proper balance between public interest and data protection, the classification of privacy impacts and the analysis of cross-border data flows, considering current technology. In particular, this paper focuses on the privacy impacts of COVID-19 by examining the type and the context of these impacts, the cross-border data flows tools from a European law perspective, taking into consideration the contribution of artificial intelligence in scientific research during the global pandemic.
Strategies for Variational Quantum Compiling of a Zero-Phase Beam Splitter on the Xanadu X8 Processor

In the context of variational compiling of a continuous-variable (CV) unitary operation, the architecture and parameter space of the Xanadu X8 processor constrain both the set of feasible compiling problems and the allowed cost functions. In this paper, we motivate a faithful cost function for variational compiling of a two-mode, continuous-variable beamsplitter gate with real matrix elements (i.e., a "zero-phase" beamsplitter) that complies with the constraints of the X8 processor. This cost function is then computed on the X8. Despite the noise in the processor, we find that the cost function exhibits optimum parameter resilience and, therefore, that this variational compiling problem is feasible on the X8. The intent of the paper is partly to report a proof-of-principle cost function calculation on near-term CV hardware, and partly to present methods that may be relevant for CV variational compiling problems in more complicated, large scale settings.
Image recognition by FFT, artificial intelligence and k-nearest neighbors approach

Abstract This paper presents procedures of processing and recognition of color images by means of artificial intelligence and computer aided training. Two stages of processing have been applied with regard to extraction of features. Processing involves conversion of RGB images in grayscale and binary; and conversion of RGB, grayscale and binary into index formats. Spectral analysis is also applied in here with Fourier’s algorithm for fast conversion concerning indicated graphic formats and segmentation of the real and imaginary complex parts. Activities related with training, assessment and synthesis of feed-forward neural networks and probabilistic neural networks, based on Accuracy and Mean-Squared Error analysis. Special emphasis is made on a method for computer aided training k-nearest neighbors with Euclidean, Euclidean squared, Cityblock and Chebychev metric distances in relation to Cross-validation accuracy value. Some positive indications have been achieved in recognition of assigned graphic images by means of the employed mathematical apparatus.
Privacy preserving knowledge graph for healthcare applications

Abstract Knowledge graphs in healthcare applications help all the stakeholders in the healthcare domain. The doctors can consult across various hospitals for a particular scenario or can query the knowledge graph for the history of the patients. Similarly, the patients can query the knowledge graph through well-defined interface or a Chabot system for location of the nearest doctor with a high rating. The physicians can query the knowledge graph for a generic drug family or detecting salt of a particular drug. The paper proposes architecture for knowledge graph in healthcare domain. Further, privacy preserving for a medical domain is an essential requirement. The paper proposes privacy preserving schemes for knowledge graph for healthcare applications.
Analysis of Tree-Family Machine Learning Techniques for Risk Prediction in Software Requirements

Risk prediction is the most sensitive and critical activity in the Software Development Life Cycle (SDLC). It might determine whether the project succeeds or fails. To increase the success probability of a software project, the risk should be predicted at the early stages. This study proposed a novel model based on the requirement risk dataset to predict software requirement risks using Tree-Family-Machine-Learning (TF-ML) approaches. Moreover, the proposed model is compared with the state-of-the-art models to determine the best-suited methodology based on the nature of the dataset. These strategies are assessed and evaluated using a variety of metrics. The findings of this study may be reused as a baseline for future studies and research, allowing the results of any proposed approach, model, or framework to be benchmarked and easily checked.
Intervention &amp; Interaction Federated Abnormality Detection with Noisy Clients

Federated learning (FL), which trains a shared global model by collaboration between distributed clients (e.g. medical institutions) and preserves the privacy of local data, has been widely deployed in the medical field to benefit abnormality diagnosis. However, it is inevitable that local data contains noise across clients, resulting in notably performance deterioration in the global model. To this end, a practical yet challenging FL problem is studied in this paper, namely Federated abnormality detection with noisy clients (FADN). We represent the first effort to reason the FADN task as a structural causal model, and identify the main issue that leads to the performance deterioration, namely recognition bias. To tackle the problem, an Intervention & Interaction FL framework (FedInI) is proposed, comprising two key strategies: (1) Intervention: considering the data distribution heterogeneity caused by different noisy levels within each client, we use the global model to intervene the training of local models, by shuffling and mixing features extracted from different models and suppress the noise gradually; (2) Interaction: we devise an adaptive sample-wise weighting strategy that jointly considers the local training statuses and global noisy levels with a shared interactive layer. Extensive experiments on class-conditional noise and instance-dependant noise settings are conducted, FedInI outperforms state-of-the-arts by a remarkable margin. Code is available at github.com/CityU-AIM-Group/FedInI .