Reviewerly/exHarmony huggingface dataset:
Delving Deeper Into Pixel Prior for Box-Supervised Semantic Segmentation

Weakly supervised semantic segmentation (WSSS) based on bounding box annotations has attracted considerable recent attention and has achieved promising performance. However, most of existing methods focus on generation of high-quality pseudo labels for segmented objects using box indicators, but they fail to fully explore and exploit prior from bounding box annotations, which limits performance of WSSS methods, especially for fine parts and boundaries. To overcome above issues, this paper proposes a novel Pixel-as-Instance Prior (PIP) for WSSS methods by delving deeper into pixel prior from bounding box annotations. Specifically, the proposed PIP is built on two important observations on pixels around bounding boxes. First, since objects are usually irregularity and tightly close to bounding boxes (dubbed irregular-filling prior), so each row or column of bounding boxes basically have at least one pixel belonging to foreground objects and background, respectively. Second, pixels near the bounding boxes tend to be highly ambiguous and more difficult to classify (dubbed label-ambiguity prior). To implement our PIP, a constrained loss alike multiple instance learning (MIL) and a labeling-balance loss are developed to jointly train WSSS models, which regards each pixel as a weighted positive or negative instance while considering more effective prior (i.e., irregular-filling and label-ambiguity priors) from bounding box annotations in an efficient way. Note that our PIP can be flexibly integrated with various WSSS methods, while clearly improving their performance with negligible computational overload in training stage. The experiments are conducted on most widely used PASCAL VOC 2012 and Cityscapes benchmarks, and the results show that our PIP has a good ability to improve performance of various WSSS methods, while achieving very competitive results.
Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery

The recent advancement in deep Convolutional Neural Network (CNN) has brought insight into the automation of X-ray security screening for aviation security and beyond. Here, we explore the viability of two recent end-to-end object detection CNN architectures, Cascade R-CNN and FreeAnchor, for prohibited item detection by balancing processing time and the impact of image data compression from an operational viewpoint. Overall, we achieve maximal detection performance using a FreeAnchor architecture with a ResNet <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> backbone, obtaining mean Average Precision (mAP) of 87.7 and 85.8 for using the OPIXray and SIXray benchmark datasets, showing superior performance over prior work on both. With fewer parameters and less training time, FreeAnchor achieves the highest detection inference speed of $\sim 13$ fps (3.9 ms per image). Furthermore, we evaluate the impact of lossy image compression upon detector performance. The CNN models display substantial resilience to the lossy compression, resulting in only a 1.1% decrease in mAP at the JPEG compression level of 50. Additionally, a thorough evaluation of data augmentation techniques is provided, including adaptions of MixUp and CutMix strategy as well as other standard transformations, further improving the detection accuracy.
SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings

The monocular depth estimation (MDE) is the task of estimating depth from a single frame.This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems.Approaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e.robot indoor navigation and surveillance).This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms.The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e.edge devices).Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity.The proposed method performs better than other fastthroughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.
Evaluation of Thermal Imaging on Embedded GPU Platforms for Application in Vehicular Assistance Systems

This study is focused on evaluating the real-time performance of thermal object detection for smart and safe vehicular systems by deploying the trained networks on GPU & single-board EDGE-GPU computing platforms for onboard automotive sensor suite testing. A novel large-scale thermal dataset comprising of > 35,000 distinct frames is acquired, processed, and open-sourced in challenging weather and environmental scenarios. The dataset is a recorded from lost-cost yet effective uncooled LWIR thermal camera, mounted stand-alone and on an electric vehicle to minimize mechanical vibrations. State-of-the-art YOLO-V5 networks variants are trained using four different public datasets as well newly acquired local dataset for optimal generalization of DNN by employing SGD optimizer. The effectiveness of trained networks is validated on extensive test data using various quantitative metrics which include precision, recall curve, mean average precision, and frames per second. The smaller network variant of YOLO is further optimized using TensorRT inference accelerator to explicitly boost the frames per second rate. Optimized network engine increases the frames per second rate by 3.5 times when testing on low power edge devices thus achieving 11 fps on Nvidia Jetson Nano and 60 fps on Nvidia Xavier NX development boards.
On Increasing Scalability and Liquidation of Lightning Networks for Blockchains

The lightning network (LN) is a layer-two solution in Bitcoin for support scalability. LN uses offchain micropayment channels to scale the blockchain's capability to perform instant transactions without a global block confirmation process. However, micropayment scalability in a large LN is still limited by its relatively large searching space for a suitable route. Liquidation for small nodes still remains major challenges for the LN as the amount of transactions along a channel is predetermined by the channel capacity defined by two end nodes of the channel. In this paper, we introduce the notion of supernodes and the corresponding supernodes-based pooling to address these challenges. In order to meet the high adaptivity and low maintenance cost in the dynamic LN where users join and leave, supernodes are constructed locally to avoid global information or label propagation. Each supernode, together with a subset of (non-supernodes) neighbors, forms a supernode-based pool. These pools constitute a partition of the LN. Additionally, supernodes are self-connected. Micropayment scalability is supported through node set reduction as only supernodes are involved in searching and in payment with other supernodes. Liquidation is enhanced through pooling to redistribute funds within a pool to external channels of its supernode. Extensive simulations using LN simulator CLoTH have been conducted to validate the improvement in routing scalability and liquidation of the proposed architecture under different settings.
Wide Range Head Pose Estimation Using a Single RGB Camera for Intelligent Surveillance

Head pose estimation is one of the sensing systems needed for some intelligent surveillance, such as human behavior analysis, intelligent driver assistance, visual attention, and monitoring. These systems require accurate alignment and head movement direction prediction. The previous methods are greatly dependent on the facial landmarks and depth information. Usually, the head pose is measured by estimating several keypoints that require a correct head pose mapping to get accurate results. Moreover, facial landmarks have a detrimental effect on performance when the face is occluded or not adequately visualized. This paper proposes a method for head pose estimation of various facial conditions, such as occlusion and challenging viewpoints. We present a combination of coarse and fine feature maps classification to train a multi-loss deep Convolutional Neural Networks (CNN) to get precise Euler angles (yaw, pitch, roll) of the head position without keypoints and landmarks. Our proposed method uses more quantization units for angle classification to learn coarse and fine structure mapping for better spatial clustering features on an RGB image of a single camera. The experiments are performed on benchmark datasets and some head poses in real cases. The mean average error of prediction is 5.06°, 4.06°, and 2.96°, for the AFLW2000, AFLW, and BIWI datasets, which significantly improves the head pose estimation performance compared to the previous methods. Additionally, the proposed method outperforms previous approaches in computation time of 11 frames per second that is beneficial for real-life applications.
The Coming Battle of Digital Currencies

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Data sharing platforms: instruments to inform and shape science policy on data sharing?

Data sharing platforms are being constructed to make clinical cohort data more findable, accessible, interoperable, and reusable. Their primary purpose is to enhance the sharing of data. However, the lack of incentives for data sharing has been conceptualized in both scientific literature and policy documents as a problem of science policy. As platforms can only facilitate data sharing through technical means, they may not be able of fully resolving the data sharing problem. In this article, it is shown how the design of platforms may help in addressing policy barriers to data sharing in the long-term. In essence, platforms can be made into policy instruments that generate information on the data sharing process and the functionality of data access committees. This allows platforms to be used to inform science policy development, to monitor data sharing practices and to steer funding prioritization for cohorts and data infrastructures themselves. In this way, the creation of data infrastructures is closely connected to the policy evolutions in the context of open science.
Structure-from-motion based image unwrapping and stitching for small bore pipe inspections

Visual inspection is one of the most ubiquitous forms of non-destructive testing, being widely used in routine pipe inspections. For small bore pipes (centimetre diameter), inspectors often have a restricted field of view limiting overall image and inspection quality. Stitching multiple unwrapped images is a common inspection technique to provide a full view inspection image by combining multiple video frames together. A key challenge of this method is knowing the camera pose of each frame. Consequently, mechanical centralisers are often utilised to ensure the camera is located centrally. For the inspection of small-bore pipes, such mechanical centralisers are often too large to fit. This paper presents a post-processing, Structure-from-Motion (SfM) based approach to unwrap and stitch inspection images, captured by a manually deployed commercial videoscope. It advances state-of-the-art approaches which rely on the projection of a laser pattern into the field of view, thus reducing the equipment size. The process consists of camera pose estimation, preliminary point cloud generation, secondary fitting, images unwrapping and stitching to form an undistorted view of the pipe interior. Two industrial focussed demonstrators verified the successful implementation for small-bore pipe inspections. Whereby the new approach does not rely on image features to create the surface texture and is less sensitive to the image quality, more areas can be retrieved from inspections. The reconstructed area was increased by up to 87% using the new approach versus the conventional 3D model.
Combinatorial optimization with photonics-inspired clock models

Abstract NP-hard combinatorial optimization problems are in general hard problems that their computational complexity grows faster than polynomial scaling with the size of the problem. Thus, over the years there has been a great interest in developing unconventional methods and algorithms for solving such problems. Here, inspired by the nonlinear optical process of q -photon down-conversion, in which a photon is converted into q degenerate lower energy photons, we introduce a nonlinear dynamical model that builds on coupled single-variable phase oscillators and allows for efficiently approximating the ground state of the classical q -state planar Potts Hamiltonian. This reduces the exhaustive search in the large discrete solution space of a large class of combinatorial problems that are represented by the Potts Hamiltonian to solving a system of coupled dynamical equations. To reduce the problem of trapping into local minima, we introduce two different mechanisms by utilizing controlled chaotic dynamics and by dynamical formation of the cost function through adiabatic parameter tuning. The proposed algorithm is applied to graph- q -partitioning problems on several complex graphs.
PoseContrast: Class-Agnostic Object Viewpoint Estimation in the Wild with Pose-Aware Contrastive Learning

Motivated by the need for estimating the 3D pose of arbitrary objects, we consider the challenging problem of class-agnostic object viewpoint estimation from images only, without CAD model knowledge. The idea is to leverage features learned on seen classes to estimate the pose for classes that are unseen, yet that share similar geometries and canonical frames with seen classes. We train a direct pose estimator in a class-agnostic way by sharing weights across all object classes, and we introduce a contrastive learning method that has three main ingredients: (i) the use of pre-trained, self-supervised, contrast-based features; (ii) pose-aware data augmentations; (iii) a pose-aware contrastive loss. We experimented on Pascal3D+, ObjectNet3D and Pix3D in a cross-dataset fashion, with both seen and unseen classes. We report state-of-the-art results, including against methods that additionally use CAD models as input.
Smart-thing design by children at a distance: How to engage them and make them learn

In recent years, research in Child–ComputerInteraction has shifted the focus from design with children, giving them a voice in the design process, to design by children to bring child participants different benefits, such as engagement and learning. However, design workshops, encompassing different stages, are challenging in terms of engagement and learning, e.g., they require prolonged commitment and concentration. They are potentially more challenging when held at a distance, as in recent years due to the COVID-19 pandemic. This paper explores at-a-distance smart-thing design by children, how it can engage different children and support their learning in programming. The paper reports a series of design workshops with 20 children, aged from 8 to 16 years old, all held at a distance. They were all organised with the DigiSNaP design framework and toolkit. The first workshop enabled children to explore what smart things are, to start ideating their own smart things and to scaffold their programming. The other workshops enabled children to evolve their own smart-thing ideas and programs. Data were gathered in relation to children’s engagement and learning from different sources. Results are promising for future editions of smart-thing design at a distance or in a hybrid modality. They are discussed along with guidelines for smart-thing design by children at a distance.
Learning Discriminant Spatial Features With Deep Graph-Based Convolutions for Occluded Face Detection

The use of face masks has become a widespread non-pharmaceutical practice to mitigate the transmission of COVID-19. However, achieving accurate facial detection while people wear masks or similar face occlusions is a major challenge. This paper introduces a model to detect occluded or masked faces based on fused convolutional graphs. This model includes a deep neural architecture with two spatial-based graphs that rely on a set of key facial features. First, a distance graph is used to identify geographical similarity between the facial nodes that represent certain key face parts. Second, a correlation graph is formulated to compute the correlations between every two nodes that represent two different augmented facial modalities. Transfer learning is then performed using a pretrained deep architecture as a baseline to map the abstract semantic information into multiple feature filters. Then, discriminant graph convolutions are constructed based on the fusion of distance and correlation graphs. This model evaluates two tasks of facial detection, which are the binary detection of masked or unmasked faces, and multi-category detection of masked, unmasked, or occluded face with no mask. The experimental results on two benchmarking real-world datasets show that the proposed deep learning model is highly effective with an accuracy of 98% achieved in binary detection. Even with high variance in image occlusions, our proposed model has great promise in detecting and distinguishing between types of facial occlusion with an accuracy of 86% reported in multi-category detection.
Block term decomposition with distinct time granularities for temporal knowledge graph completion

To solve the incompleteness problem in temporal knowledge graphs (TKGs) and to discover the new knowledge, TKG completion remains an essential task always solved by graph embedding technology. Existing TKG completion methods encode time at only single granularity, which is insufficient in exploiting the rich information of distinct time granularities. Furthermore, most of them lack a comprehensive consideration of the characteristic of both time points and time periods, resulting in the inability to handle the two types of facts with different time forms, namely the discrete facts and continuous facts, simultaneously. In this paper, we propose a novel TKG embedding model which introduces the block term tensor decomposition and utilizes the core tensor and factor matrices to capture information presented by facts under distinct time granularities. By focusing on moments included in the time period and treating the discrete fact as a special case of the continuous fact, the model manages the processing of different types of facts in a unified manner. Besides, we explicitly design the static properties of entities and relations as well as their interactions to conform to reality. Experiments on 3 real datasets of different types verify the effectiveness of our proposed method compared with most state-of-the-art methods.
Little data is often enough for distance-based outlier detection

Many real-world use cases benefit from fast training and prediction times, and much research went into speeding up distance-based outlier detection methods to millions of data points. Contrary to popular belief, our findings suggest that little data is often enough for distance-based outlier detection models. We show that using only a tiny fraction of the data to train distance-based outlier detection models often leads to no significant reduction in predictive performance and detection variance over a wide range of tabular datasets. Furthermore, we compare a data reduction based on random subsampling and clustering-based prototypes and show that both approaches yield similar outlier detection results. Simple random subsampling, thus, proves to be a useful benchmark and baseline for future research on speeding up distance-based outlier detection.
EMK-ABSE: Efficient Multikeyword Attribute-Based Searchable Encryption Scheme Through Cloud-Edge Coordination

Cloud storage and edge computing provide the possibility to address the tremendous storage and computing pressure caused by the explosive growth of traffic at the edge of the networks. In this scene, as data is outsourced to the cloud or edge servers, data privacy can be leaked. For enhancing security and privacy, attribute-based searchable encryption (ABSE), as an effective technical approach, achieves controllable search of ciphertext. Aiming at addressing the issues of the low search efficiency in a single-keyword ABSE scheme and the large computing overhead of the existing multikeyword ABSE schemes, we propose a novel multikeyword ABSE scheme (EMK-ABSE) through cloud-edge coordination. The huge amounts of encrypted data is stored to cloud server (CS), while the corresponding encrypted index is uploaded to the nearest edge node (EN) to perform multikeyword search and assisted decryption. To further release the computational burden of clients, a hybrid online/offline mechanism is adopted in encryption. Security analysis indicates that the multikeyword index in EMK-ABSE has secure indistinguishability under chosen keyword attack (IND-CKA). The comprehensive evaluation proves that EMK-ABSE achieves not only encrypted multikeyword retrieval but also fine-grained access control, with lower computation complexity in the three stages of encryption, trapdoor generation, and decryption. We show that the proposed scheme has higher efficiency and practicability than the selected relative works.
Flexible Cross-Modal Hashing

Hashing has been widely adopted for large-scale data retrieval in many domains due to its low storage cost and high retrieval speed. Existing cross-modal hashing methods optimistically assume that the correspondence between training samples across modalities is readily available. This assumption is unrealistic in practical applications. In addition, existing methods generally require the same number of samples across different modalities, which restricts their flexibility. We propose a flexible cross-modal hashing approach (FlexCMH) to learn effective hashing codes from weakly paired data, whose correspondence across modalities is partially (or even totally) unknown. FlexCMH first introduces a clustering-based matching strategy to explore the structure of each cluster and, thus, to find the potential correspondence between clusters (and samples therein) across modalities. To reduce the impact of an incomplete correspondence, it jointly optimizes the potential correspondence, the cross-modal hashing functions derived from the correspondence, and a hashing quantitative loss in a unified objective function. An alternative optimization technique is also proposed to coordinate the correspondence and hash functions and reinforce the reciprocal effects of the two objectives. Experiments on public multimodal data sets show that FlexCMH achieves significantly better results than state-of-the-art methods, and it, indeed, offers a high degree of flexibility for practical cross-modal hashing tasks.