Reviewerly/exHarmony huggingface dataset:
Projective Parallel Single-Pixel Imaging to Overcome Global Illumination in 3D Structure Light Scanning

We consider robust and efficient 3D structure light scanning method in situations dominated by global illumination. One typical way of solving this problem is via the analysis of 4D light transport coefficients (LTCs), which contains complete information for a projector-camera pair, and is a 4D data set. However, the process of capturing LTCs generally takes long time. We present projective parallel single-pixel imaging (pPSI), wherein the 4D LTCs are reduced to multiple projection functions to facilitate a highly efficient data capture process. We introduce local maximum constraint, which provides necessary condition for the location of correspondence matching points when projection functions are captured. Local slice extension method is introduced to further accelerate the capture of projection functions. We study the influence of scan ratio in local slice extension method on the accuracy of the correspondence matching points, and conclude that partial scanning is enough for satisfactory results. Our discussions and experiments include three typical kinds of global illuminations: inter-reflections, subsurface scattering, and step edge fringe aliasing. The proposed method is validated in several challenging scenarios.
A Detail-Preservation Method of Deep Learning One-Step Phase Unwrapping

Phase unwrapping is essential in interferometric synthetic aperture radar (InSAR) data processing. Currently, deep learning is widely used in the phase unwrapping process. For instance, the one-step phase unwrapping method is excellent because of its strong noise adaptability. The method treats the unwrapping process as a regression problem, which uses the l1 or l2 loss function to constrain the reconstructed phase to be close to the ground truth of the absolute phase. However, no matter whether the l1 or l2 loss function is used, the result may lack details in texture, and the details cannot be well preserved. This is because the l1 or l2 smoothens the output greatly, and the texture detail loss is not intentionally considered. Due to the noise of the wrapped phase, there is speckle noise in the valley part of the unwrapping result. To solve these problems, we study the generative adversarial network (GAN) with mixed loss functions. The texture details are preserved with the trained GAN, and the speckle noise in the valley is significantly reduced.
Programmatic Concept Learning for Human Motion Description and Synthesis

We introduce Programmatic Motion Concepts, a hierarchical motion representation for human actions that captures both low-level motion and high-level description as motion concepts. This representation enables human motion description, interactive editing, and controlled synthesis of novel video sequences within a single framework. We present an architecture that learns this concept representation from paired video and action sequences in a semi-supervised manner. The compactness of our representation also allows us to present a low-resource training recipe for data-efficient learning. By outperforming established baselines, especially in the small data regime, we demonstrate the efficiency and effectiveness of our framework for multiple applications.
An Experimental Study on Hyper-parameter Optimization for Breast Cancer Risk Prediction using IBM Snap Machine Learning Techniques

Worldwide approximately 1 in 6 women will succumb to Breast Cancer (BC), making it one of the most lethal illnesses in the female population. To avoid and receive adequate clinical diagnostic therapy, it is imperative that early detection be made possible. Medical applications rely heavily on Machine Learning (ML) for risk prediction, therapy recommendation, and critical decision assistance. With the Wisconsin BC diagnosis dataset from UCI (University of California Irvine) repository, we have evaluated two IBM Watson’s ML algorithms such as Snap Logistic Regression (LR) and Snap SVM Classifier. The algorithm’s goal is to identify the diseased cells as Benign (B) or Malignant (M) that is either non-cancerous or cancerous. Performance metrics such as Accuracy, AROC (Asymptotic Receiver Operating Characteristic), Precision, Recall, F1, Average Precision, and Log Loss are computed to measure the model outperformed the rest of the algorithm in all other measures, with a cross validation score of 98.2 percent.
AdaDrone: Quality of Navigation Based Neural Adaptive Scheduling for Edge-Assisted Drones

Accurate navigation is of paramount importance to ensure flight safety and efficiency for autonomous drones. Recent research starts to use Deep Neural Networks (DNN) to enhance drone navigation given their remarkable predictive capability for visual perception. However, existing solutions either run DNN inference tasks on drones in-situ, impeded by the limited onboard resource, or offload the computation to external servers which may incur large network latency. Few works consider jointly optimizing the offloading decisions along with image transmission configurations and adapting them on the fly. In this paper, we propose AdaDrone, an edge computing assisted drone navigation framework that can dynamically adjust task execution location, input resolution, and image compression ratio in order to achieve low inference latency, high prediction accuracy, and long flight distances. Specifically, we first augment state-of-the-art convolutional neural networks for drone navigation and define a novel metric called Quality of Navigation as our optimization objective which can effectively capture the above goals. We then design a deep reinforcement learning (DRL) based neural scheduler for which an information encoder is devised to reshape the state features and thus improve its learning ability. We finally implement a prototype of our framework wherein a drone board for navigation and scheduling control interacts with edge servers for task offloading and a simulator for performance evaluation. Extensive experimental results show that AdaDrone can reduce end-to-end latency by 28.06% and extend the flight distance by up to 27.28% compared with non-adaptive solutions.
Explainable Artificial Intelligence for Breast Tumour Classification: Helpful or Harmful

Explainable Artificial Intelligence (XAI) is the field of AI dedicated to promoting trust in machine learning models by helping us to understand how they make their decisions. For example, image explanations show us which pixels or segments were deemed most important by a model for a particular classification decision. This research focuses on image explanations generated by LIME, RISE and SHAP for a model which classifies breast mammograms as either benign or malignant. We assess these XAI techniques based on (1) the extent to which they agree with each other, as decided by One-Way ANOVA, Kendall's Tau and RBO statistical tests, and (2) their agreement with the diagnostically important areas as identified by a radiologist on a small subset of mammograms. The main contribution of this research is the discovery that the 3 techniques consistently disagree both with each other and with the medical truth. We argue that using these off-shelf techniques in a medical context is not a feasible approach, and discuss possible causes of this problem, as well as some potential solutions.
Moderating the Fediverse: Content Moderation on Distributed Social Media

Current approaches to content moderation generally assume the continued dominance of “walled gardens”: social media platforms that control who can use their services and how. But an emerging form of decentralized social media—the "Fediverse"—offers an alternative model, one more akin to how email works and that avoids many of the pitfalls of centralized moderation. This essay, which builds on an emerging literature around decentralized social media, seeks to give an overview of the Fediverse, its benefits and drawbacks, and how government action can influence and encourage its development.Part I describes the Fediverse and how it works, beginning with a general description of open versus closed protocols and then proceeding to a description of the current Fediverse ecosystem, focusing on its major protocols and applications. Part II looks at the specific issue of content moderation on the Fediverse, using Mastodon, a Twitter-like microblogging service, as a case study to draw out the advantages and disadvantages of the federated content-moderation approach as compared to the current dominant closed-platform model. Part III considers how policymakers can encourage the Fediverse, whether through direct regulation, antitrust enforcement, or liability shields.
Dynamic Proposal Generation for Oriented Object Detection in Aerial Images

Current two-stage oriented object detectors for aerial images have achieved remarkable progress. However, they still suffer from some drawbacks. Firstly, most of them place redundant anchors or utilize complicated transformation to generate oriented proposals, which are inefficient. Secondly, the generation of proposals is static, which cannot adapt to the extremely nonuniform distribution of objects. To address these issues, we propose a Dynamic Proposal Generation Network (DPGN) which can generate high-quality oriented proposals directly and estimate the upper limit of proposals adaptively. To be specific, with Guided Anchor Regression (GAR), we obtain the coarse oriented anchors and utilize them to align the features. After this, we make further classification and regression to produce final oriented proposals. Meanwhile, we design Maximum Number Estimation (MNE) for predicting an approximate value to remain the proposals adaptively. Without tricks, our method can achieve competitive detection accuracy compared with other mainstream methods on DOTA dataset.
Learning Time Delay Systems with Neural Ordinary Differential Equations

A novel way of using neural networks to learn the dynamics of time delay systems from sequential data is proposed. A neural network with trainable delays is used to approximate the right hand side of a delay differential equation. We relate the delay differential equation to an ordinary differential equation by discretizing the time history and train the corresponding neural ordinary differential equation (NODE) to learn the dynamics. An example on learning the dynamics of the Mackey-Glass equation using data from chaotic behavior is given. After learning both the nonlinearity and the time delay, we demonstrate that the bifurcation diagram of the neural network matches that of the original system.
Applying Answer Set Optimization to Preventive Maintenance Scheduling for Rotating Machinery

Preventive maintenance (PM) of manufacturing units aims at maintaining the operable condition of the production line while optimizing the maintenance timing and the loss of productivity during maintenance operations. The lesser studied type of preventive maintenance understands a production line as a single machine with multiple components of different maintenance needs. This is relevant when rotating machinery is deployed, e.g., in the paper and steel industries, in the mass production of raw materials consumed by other businesses. A failure in any stage of the production line has the potential of making the entire machine inoperable and enforcing a shutdown and corrective maintenance costs. This work gives an abstract formalization of PM scheduling for multi-component machines as an optimization problem. To provide a lower bound for the complexity of the optimization problem, we prove that the underlying decision problem is NP-complete for varying-size multi-component machines and scheduling timelines. Besides the formalization, the second main contribution of the paper is due to the practical need to solve the problem in industrial applications: the work gives the first encoding of the PM scheduling problem using Answer Set Optimization (ASO). Some preliminary experiments are conducted and reported to set the scene for further algorithm development.
Secure UHF RFID Authentication With Smart Devices

Commodity ultra-high-frequency (UHF) RFID authentication systems only provide weak user authentication, as RFID tags can be easily stolen, lost, or cloned by attackers. This paper presents the design and evaluation of SmartRFID, a novel UHF RFID authentication system to promote commodity crypto-less UHF RFID tags for security-sensitive applications. SmartRFID explores extremely popular smart devices and requires a legitimate user to enroll his smart device along with his RFID tag. Besides authenticating the RFID tag as usual, SmartRFID verifies whether the user simultaneously possesses the associated smart device with both feature-based machine learning and deep learning techniques. The user is considered authentic if and only if passing the dual verifications. Comprehensive user experiments on commodity smartwatches and RFID devices confirmed the high security and usability of SmartRFID. In particular, SmartRFID achieves a true acceptance rate of above 97.5% and a false acceptance rate of less than 0.7% based on deep learning. In addition, SmartRFID can achieve an average authentication latency of less than 2.21 s, which is comparable to inputting a PIN on a door keypad or smartphone.
GreenBIQA: A Lightweight Blind Image Quality Assessment Method

Deep neural networks (DNNs) achieve great success in blind image quality assessment (BIQA) with large pre-trained models in recent years. Their solutions cannot be easily deployed at mobile or edge devices, and a lightweight solution is desired. In this work, we propose a novel BIQA model, called GreenBIQA, that aims at high performance, low computational complexity and a small model size. GreenBIQA adopts an unsupervised feature generation method and a supervised feature selection method to extract quality-aware features. Then, it trains an XGBoost regressor to predict quality scores of test images. We conduct experiments on four popular IQA datasets, which include two synthetic-distortion and two authentic-distortion datasets. Experimental results show that GreenBIQA is competitive in performance against state-of-the-art DNNs with lower complexity and smaller model sizes. Code is available at: https://github.com/zhanxuanm/GreenBIQA
Weakly Supervised High-Fidelity Clothing Model Generation

The development of online economics arouses the demand of generating images of models on product clothes, to display new clothes and promote sales. However, the expensive proprietary model images challenge the existing image virtual try-on methods in this scenario, as most of them need to be trained on considerable amounts of model images accompanied with paired clothes images. In this paper, we propose a cheap yet scalable weakly-supervised method called Deep Generative Projection (DGP) to address this specific scenario. Lying in the heart of the proposed method is to imitate the process of human predicting the wearing effect, which is an unsupervised imagination based on life experience rather than computation rules learned from supervisions. Here a pretrained StyleGAN is used to capture the practical experience of wearing. Experiments show that projecting the rough alignment of clothing and body onto the StyleGAN space can yield photo-realistic wearing results. Experiments on real scene proprietary model images demonstrate the superiority of DGP over several state-of-the-art supervised methods when generating clothing model images.
Shift-Tolerant Perceptual Similarity Metric

Existing perceptual similarity metrics assume an image and its reference are well aligned. As a result, these metrics are often sensitive to a small alignment error that is imperceptible to the human eyes. This paper studies the effect of small misalignment, specifically a small shift between the input and reference image, on existing metrics, and accordingly develops a shift-tolerant similarity metric. This paper builds upon LPIPS, a widely used learned perceptual similarity metric, and explores architectural design considerations to make it robust against imperceptible misalignment. Specifically, we study a wide spectrum of neural network elements, such as anti-aliasing filtering, pooling, striding, padding, and skip connection, and discuss their roles in making a robust metric. Based on our studies, we develop a new deep neural network-based perceptual similarity metric. Our experiments show that our metric is tolerant to imperceptible shifts while being consistent with the human similarity judgment. Code is available at https://tinyurl.com/5n85r28r .
WSRC: Weakly Supervised Faster RCNN Toward Accurate Traffic Object Detection

Weakly supervised object detection has attracted increasing interest in recent years as driven by data annotation bottleneck in deep network training.On the other hand, the performance of weakly supervised models still lags far behind due to the missing of object location information during training.This paper presents a Weakly Supervised Faster RCNN (WSRC) that aims to train accurate traffic object detectors without using any box annotations, addressing the phenomenon that the traffic object detector trained by traditional method cannot be applied in the real traffic scene due to the lack of data annotation.The WSRC designs an innovative proposal evaluation network (PEN) that is trained by using a large amount of proposals that are scored according to their Intersection-over-Union (IoU) overlap with pseudo ground-truth boxes.Novel loss functions are designed to teach the PEN and WSRC to identify good proposals and regress to accurate object boxes under the guidance of loss dynamics and proposal scores.The proposed WSRC achieves superior detection performance over the datasets Pascal VOC2007, MS COCO and Cityscape, demonstrating its effectiveness while short of annotation boxes.
Research and Application of Intelligent Vehicle Cybersecurity Threat Model

Threat analysis and risk assessment are important activities in the cybersecurity life cycle of intelligent vehicles, which provide guarantee for the formation of correct cybersecurity requirements for automotive. Based on some research on threat analysis and risk assessment technologies used in traditional information systems as well as some practical experience in the automotive field, this paper proposes a structured and systematic threat model for intelligent vehicles, based on which the threat analysis and risk assessment process is realized, with the aim of improving some existing methods. Taking a typical vehicle-level security-related business OTA as the example, the model and the analysis process proposed in this paper are practically applied, in which the methods of data-flow-diagram and attack-tree are combinedly used to analyze possible threats to assets of an intelligent vehicle. The workload of threat modeling and risk analysis is evaluated, and the practical significance of improving the analysis efficiency via a software tool is analyzed. The result shows that this model is more comprehensive to achieve a complete analysis, and it is also convenient to be implemented as a software tool.
Personalized Federated Learning with Robust Clustering Against Model Poisoning

Recently, federated Learning (FL) has been widely used to protect clients’ data privacy in distributed applications, and heterogeneous data and model poisoning are two critical challenges to attack. To tackle the first challenge that data of each client is usually not independent or identically distributed, personalized FL (PFL) or clustered FL, which can be seen as a cluster-wise PFL method to learn multiple models across clients or clusters. To detect the anomaly clients or outliers, local outlier factor is a popular method based on the density of data points. Therefore, a nested bi-level optimization objective is constructed, and an algorithm of PFL with robust clustering called FedPRC is proposed to detect outliers and maintain state-of-the-art performance. The breakdown point of FedPRC can be at least 0.5. Our experimental analysis has demonstrated effectiveness and superior performance in comparison with baselines in multiple benchmark datasets.
Meta Path-Aware Recommendation Method Based on Non-Negative Matrix Factorization in LBSN

Location-based social networks (LBSN) is a new type of heterogeneous information network (HIN). The check-in data usually has the characteristics of a large amount of data and high sparsity. It is a problem worth studying how to effectively discover its complex community structure and accurately recommend it to users. Most HIN-based recommendation methods rely on path-based similarity, which cannot fully mine latent structure features of LBSN users and items. This paper proposes a meta-path-aware common clustering recommendation method MPNMF (meta-path-aware non-negative matrix factorization), based on non-negative matrix tri-factorization. By establishing the objective function based on non-negative matrix tri-factorization and second-order meta-path method, LBSN users and points of interest are integrated with their multi-dimensional heterogeneous relationships. The interrelated user clusters and interest point clusters can be obtained, effectively alleviating the influence of data sparsity. To solve the initial value problem of the model, this method uses the spectral cluster method, which provides a good initial value for the construction of the prediction model. It improves the operational efficiency of the model and the precision of model recommendations. Experiments on the real LBSN datasets show that the proposed method has a high recommendation precision and recall.
CrystalBLEU

Recent work has focused on using machine learning to automate software engineering processes, such as code completion, code migration, and generating code from natural language description. One of the challenges faced in these tasks is evaluating the quality of the predictions, which is usually done by comparing the prediction to a reference solution. BLEU score has been adopted for programming languages as it can be easily computed for any programming language and even incomplete source code, while enabling fast automated evaluation. However, programming languages are more verbose and have strict syntax when compared to natural languages. This feature causes BLEU to find common n-grams in unrelated programs, which makes distinguishing similar pairs of programs from dissimilar pairs hard. This work presents CrystalBLEU, an evaluation metric based on BLEU, that mitigates the distinguishability problem. Our metric maintains the desirable properties of BLEU, such as handling partial code, applicability to all programming languages, high correlation with human judgement, and efficiency, in addition to reducing the effects of the trivially matched n-grams. We evaluate CrystalBLEU on two datasets from previous work and a new dataset of human-written code. Our results show that CrystalBLEU differentiates similar and unrelated programs better than the original BLEU score and also a variant designed specifically for source code, CodeBLEU.