Reviewerly/exHarmony huggingface dataset:
Joint Graph-Based Depth Refinement and Normal Estimation

Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings. These scenarios are characterized by a prevalence of human made structures, which in most of the cases are either inherently piece-wise planar or can be approximated as such. With these settings in mind, we devise a novel depth refinement framework that aims at recovering the underlying piece-wise planarity of those inverse depth maps associated to piece-wise planar scenes. We formulate this task as an optimization problem involving a data fidelity term, which minimizes the distance to the noisy and possibly incomplete input inverse depth map, as well as a regularization, which enforces a piece-wise planar solution. As for the regularization term, we model the inverse depth map pixels as the nodes of a weighted graph, with the weight of the edge between two pixels capturing the likelihood that they belong to the same plane in the scene. The proposed regularization fits a plane at each pixel automatically, avoiding any a priori estimation of the scene planes, and enforces that strongly connected pixels are assigned to the same plane. The resulting optimization problem is solved efficiently with the ADAM solver. Extensive tests show that our method leads to a significant improvement in depth refinement, both visually and numerically, with respect to state-of-the-art algorithms on the Middlebury, KITTI and ETH3D multi-view datasets.
Graph refinement based airway extraction using mean-field networks and graph neural networks

Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications. In this work, we extract trees or collection of sub-trees from image data by, first deriving a graph-based representation of the volumetric data and then, posing the tree extraction as a graph refinement task. We present two methods to perform graph refinement. First, we use mean-field approximation (MFA) to approximate the posterior density over the subgraphs from which the optimal subgraph of interest can be estimated. Mean field networks (MFNs) are used for inference based on the interpretation that iterations of MFA can be seen as feed-forward operations in a neural network. This allows us to learn the model parameters using gradient descent. Second, we present a supervised learning approach using graph neural networks (GNNs) which can be seen as generalisations of MFNs. Subgraphs are obtained by training a GNN-based graph refinement model to directly predict edge probabilities. We discuss connections between the two classes of methods and compare them for the task of extracting airways from 3D, low-dose, chest CT data. We show that both the MFN and GNN models show significant improvement when compared to one baseline method, that is similar to a top performing method in the EXACT'09 Challenge, and a 3D U-Net based airway segmentation model, in detecting more branches with fewer false positives.
Optimizing Earthquake Early Warning Alert Distance Strategies Using the July 2019 Mw 6.4 and Mw 7.1 Ridgecrest, California, Earthquakes

ABSTRACT The ShakeAlert earthquake early warning system aims to alert people who experience modified Mercalli intensity (MMI) IV+ shaking during an earthquake using source estimates (magnitude and location) to estimate median-expected peak ground motions with distance, then using these ground motions to determine median-expected MMI and thus the extent of MMI IV shaking. Because median ground motions are used, even if magnitude and location are correct, there will be people outside the alert region who experience MMI IV shaking but do not receive an alert (missed alerts). We use 91,000 “Did You Feel It?” survey responses to the July 2019 Mw 6.4 and Mw 7.1 Ridgecrest, California, earthquakes to determine which ground-motion to intensity conversion equation (GMICE) best fits median MMI with distance. We then explore how incorporating uncertainty from the ground-motion prediction equation and the GMICE in the alert distance calculation can produce more accurate MMI IV alert regions for a desired alerting strategy (e.g., aiming to alert 95% of people who experience MMI IV+ shaking), assuming accurate source characterization. Without incorporating ground-motion uncertainties, we find MMI IV alert regions using median-expected ground motions alert fewer than 20% of the population that experiences MMI IV+ shaking. In contrast, we find &amp;gt;94% of the people who experience MMI IV+ shaking can be included in the MMI IV alert region when two standard deviations of ground-motion uncertainty are included in the alert distance computation. The optimal alerting strategy depends on the false alert tolerance of the community due to the trade-off between minimizing missed and false alerts. This is especially the case for situations like the Mw 6.4 earthquake when alerting 95% of the 5 million people who experience MMI IV+ also results in alerting 14 million people who experience shaking below this level and do not need to take protective action.
Butterfly Transform: An Efficient FFT Based Neural Architecture Design

In this paper, we show that extending the butterfly operations from the FFT algorithm to a general Butterfly Transform (BFT) can be beneficial in building an efficient block structure for CNN designs. Pointwise convolutions, which we refer to as channel fusions, are the main computational bottleneck in the state-of-the-art efficient CNNs (e.g. MobileNets). We introduce a set of criterion for channel fusion, and prove that BFT yields an asymptotically optimal FLOP count with respect to these criteria. By replacing pointwise convolutions with BFT, we reduce the computational complexity of these layers from O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O(n log n) with respect to the number of channels. Our experimental evaluations show that our method results in significant accuracy gains across a wide range of network architectures, especially at low FLOP ranges. For example, BFT results in up to a 6.75% absolute Top-1 improvement for MobileNetV1, 4.4 % for ShuffleNet V2 and 5.4% for MobileNetV3 on ImageNet under a similar number of FLOPS. Notably, ShuffleNet-V2+BFT outperforms state-of-the-art architecture search methods MNasNet, FBNet and MobilenetV3 in the low FLOP regime.
Self-adaptive feature learning based on a priori knowledge for facial expression recognition

Conventional feature extraction methods generally focus on extracting global and local features from the original data or converting a high dimensional space to a lower dimensional one. However, they tend to overlook the discriminative information of pixel values hidden in the original data. Pixel values in some local parts of a face, such as the mouth, eyebrows and eyes, provide extremely useful information for expression recognition, as they reveal the correlation between these local parts. While this information can be learned manually, being able to automatically identify important location information in this context is highly desirable. Given this, we propose a self-adaptive feature learning approach based on a priori knowledge for facial expression recognition in this paper. The proposed approach aims to adaptively select active features. It first generates an intra-class, low-rank dictionary that can decouple the original space from the expression subspace and mitigate the dependence on individual facial identities. Next, the active feature dictionary is formed, taking both global and local importance into account simultaneously. After that, the principal component of the active feature dictionary is extracted to address the influence of redundant features and reduce the dimension. We also introduce an active feature learning model as the final classification framework to make the features more discriminative and reduce the computation time. Results of comprehensive experiments on public facial expression datasets confirm the efficacy of the proposed approach, in terms of accuracy and computation time, compared to some state-of-the-art feature extraction and dictionary learning methods.
Zero-VIRUS: Zero-shot Vehicle Route Understanding System for Intelligent Transportation

Nowadays, understanding the traffic statistics in real city-scale camera networks takes an important place in the intelligent transportation field. Recently, vehicle route understanding brings a new challenge to the area. It aims to measure the traffic density by identifying the route of each vehicle in traffic cameras. This year, the AI City Challenge holds a competition with real-world traffic data on vehicle route understanding, which requires both efficiency and effectiveness. In this work, we propose Zero-VIRUS, a Zeroshot VehIcle Route Understanding System, which requires no annotation for vehicle tracklets and is applicable for the changeable real-world traffic scenarios. It adopts a novel 2D field modeling of pre-defined routes to estimate the proximity and completeness of each track. The proposed system has achieved third place on Dataset A in stage 1 of the competition (Track 1: Vehicle Counts by Class at Multiple Intersections) against world-wide participants on both effectiveness and efficiency, with a record of the top place on 50% of the test set.
Quadratic speedup for finding marked vertices by Quantum walks

A quantum walk algorithm can detect the presence of a marked vertex on a graph quadratically faster than the corresponding random walk algorithm (Szegedy, FOCS 2004). However, quantum algorithms that actually find a marked element quadratically faster than a classical random walk were only known for the special case when the marked set consists of just a single vertex, or in the case of some specific graphs. We present a new quantum algorithm for finding a marked vertex in any graph, with any set of marked vertices, that is (up to a log factor) quadratically faster than the corresponding classical random walk, resolving a question that had been open for 15 years.
EOG-Based HCI System for Quadcopter Navigation

Human-computer interface (HCI) systems are extending their boundaries in our daily life and becoming an important subject in biomedical engineering. Electrooculogram (EOG) signal as an input for such systems stems from the corneoretinal standing potential, which can be used for monitoring human eye rotation. Higher amplitude, better signal-to-noise ratio, and much easier recording conditions compared with electroencephalography, make it an important input modality for HCI systems. In this article, real-time processing and cost-effective (<; 100$) HCI system was designed and developed based on the EOG signals. The required electrodes were embedded in updated eyeglasses for easy electrode placement over the subject's face. EOG signals were acquired by the subject's eye movement toward the four middle parts of the screen edges of a laptop placed in front of them. The system training for each subject ameliorated system withstanding against the blink and wrinkle artifact. Finally, the required commands for quadcopter navigation (up, down, left, and right) generated with 0.6-s total delay and 94.8% of system accuracy in detecting the correct eye movements. A real quadcopter navigation experiment based on the standard navigation interface and the developed HCI system represented the system accuracy, robustness, and usefulness.
Cloud-Assisted Attribute-Based Data Sharing with Efficient User Revocation in the Internet of Things

Nowadays, the development of the Internet of Things (IoT) has received much attention from both industry and academia. Sensors and devices connected to the IoT network can conveniently gather and collect information for further usage and analysis by IoT users. However, a large quantity of data produced by IoT devices contain sensitive information, which leads to many challenging security issues in IoT systems. The most important one is how to efficiently and securely share IoT data with valid IoT users while forbidding others from obtaining the data. In this article, we propose a cryptographic method to protect the privacy of IoT data while maintaining the functionality of efficient data sharing and user revocation. Our solution relies on a revocable attribute-based encryption (ABE) scheme to encrypt IoT data. The ABE technique makes fine-grained access control available on the encrypted IoT data, while the revocation technique makes invalid users unable to access future encrypted IoT data. To alleviate the issue of resource limitation of IoT devices, we involve a cloud-assisted data sharing and user revocation technique. Finally, we experimentally tested our scheme, and the performance evaluation results demonstrate the practice of our solution scheme.
Building Continuous Integration Services for Machine Learning

Continuous integration (CI) has been a de facto standard for building industrial-strength software. Yet, there is little attention towards applying CI to the development of machine learning (ML) applications until the very recent effort on the theoretical side. In this paper, we take a step forward to bring the theory into practice.
Dynamic Fluid Surface Reconstruction Using Deep Neural Network

Recovering the dynamic fluid surface is a long-standing challenging problem in computer vision. Most existing image-based methods require multiple views or a dedicated imaging system. Here we present a learning-based single-image approach for 3D fluid surface reconstruction. Specifically, we design a deep neural network that estimates the depth and normal maps of a fluid surface by analyzing the refractive distortion of a reference background image. Due to the dynamic nature of fluid surfaces, our network uses recurrent layers that carry temporal information from previous frames to achieve spatio-temporally consistent reconstruction given a video input. Due to the lack of fluid data, we synthesize a large fluid dataset using physics-based fluid modeling and rendering techniques for network training and validation. Through experiments on simulated and real captured fluid images, we demonstrate that our proposed deep neural network trained on our fluid dataset can recover dynamic 3D fluid surfaces with high accuracy.
Viewpoint-aware Channel-wise Attentive Network for Vehicle Re-identification

Vehicle re-identification (re-ID) matches images of the same vehicle across different cameras. It is fundamentally challenging because the dramatically different appearance caused by different viewpoints would make the framework fail to match two vehicles of the same identity. Most existing works solved the problem by extracting viewpoint-aware feature via spatial attention mechanism, which, yet, usually suffers from noisy generated attention map or otherwise requires expensive keypoint labels to improve the quality. In this work, we propose Viewpoint-aware Channel-wise Attention Mechanism (VCAM) by observing the attention mechanism from a different aspect. Our VCAM enables the feature learning framework channel-wisely reweighing the importance of each feature maps according to the "viewpoint" of input vehicle. Extensive experiments validate the effectiveness of the proposed method and show that we perform favorably against state-of-the-arts methods on the public VeRi-776 dataset and obtain promising results on the 2020 AI City Challenge. We also conduct other experiments to demonstrate the interpretability of how our VCAM practically assists the learning framework.
Fall detection algorithm for the elderly based on human posture estimation

With the acceleration of the global aging process, the safety and health of the elderly has become a widespread concern, and falls have become the main health threat of the elderly. In this paper, a fall detection model based on OpenPose human posture estimation algorithm is proposed by using the fall detection method based on machine vision. On the basis of OpenPose human key point detection, combined with SSD MobileNet object detection framework can remove the non human key points detected by OpenPose algorithm, reduce the false detection rate of the algorithm, improve the robustness of the algorithm in complex environment, and then extract the features of human joint points, use SVDD classification algorithm to classify, experiments show that this method can effectively detect the occurrence of falls, and the accuracy rate can reach more than 93%.
On the need for large Quantum depth

Near-term quantum computers are likely to have small depths due to short coherence time and noisy gates. A natural approach to leverage these quantum computers is interleaving them with classical computers. Understanding the capabilities and limits of this hybrid approach is an essential topic in quantum computation. Most notably, the quantum Fourier transform can be implemented by a hybrid of logarithmic-depth quantum circuits and a classical polynomial-time algorithm. Therefore, it seems possible that quantum polylogarithmic depth is as powerful as quantum polynomial depth in the presence of classical computation.
Sampled to Death? The Rise and Fall of Probability Sampling in Archaeology

After a heyday in the 1970s and 1980s, probability sampling became much less visible in archaeological literature as it came under assault from the post-processual critique and the widespread adoption of “full-coverage survey.” After 1990, published discussion of probability sampling rarely strayed from sample-size issues in analyses of artifacts along with plant and animal remains, and most textbooks and archaeological training limited sampling to regional survey and did little to equip new generations of archaeologists with this critical aspect of research design. A review of the last 20 years of archaeological literature indicates a need for deeper and broader archaeological training in sampling; more precise usage of terms such as “sample”; use of randomization as a control in experimental design; and more attention to cluster sampling, stratified sampling, and nonspatial sampling in both training and research.
An Android Application for Campus Information System

The availability of an online campus information system enables the students to get a better layout of education planning. It helps the university regarding well managed and strategic information management. In this paper, we describe and contextualize the model for an online campus information system for undergraduate students as well as university faculty. Information gathered employing structured interviews from a recognized University. The collected data enables us to prepare an android application for the campus information system, aims to help students as well as faculty in the best possible way.
The Role of Machine Learning in Digital Forensics

Digital forensics, as a branch of the forensic sciences, is facing new challenges from the aspect that potential digital evidence is growing and expanding. Rapid development in the fields of computer science and information technology provides innovative techniques for digital investigations. In this paper, the important role of machine learning is explained as an application of artificial intelligence, and how it can be used to analyse large amounts of diverse datasets in order to reveal any criminal behaviour and intent through learning from previous and historical activities to predict criminal behaviours in the future.
Challenges and Issues Integrating Smartphones into Teacher Support Programs in India

Current ICTD best practices call on practitioner organizations that deploy technology interventions with marginalized communities to commit to long-term engagements and provide continuous support. This paper describes a qualitative study that examines the challenges and issues that arise for organizations that have tried to answer this call: a set of education-focused non-profit organizations in India that have invested heavily in building long-term relationships with low-income schools and that are deeply committed to providing ongoing support, both in-person and via technology. Interviews and observations with 71 participants (51 from eight organizations, and 15 teachers and five principals from 12 schools) reveal (1) the challenges and issues that arise as organizations integrate smartphones into teacher support programs, and (2) the strategies that teachers and organizations use to ease smartphone adoption in teacher support programs. Our findings uncover the effects of organizations' smartphone-oriented support programs on teachers' workloads, efficacy, and stress, and highlight opportunities for organizations to improve their programs and support structures.
A survey on Adopting Agile Software Development: Issues &amp; Its impact on Software Quality

Agile development method has engrossed extensive communal and academic consideration as the restrictions of conventional software development techniques become apparent. A frequent observation in the software development industry is that by making use of agile project development style, projects are expected to be successful. Agile use to keep track of the constant changing development environment [1]. For a traditional software development organization, highly effective techniques are required to adopt agile methodology. Adopting agile signifies shoring more cooperative environments and organizational policies that enable self-organization and team efficiency. The ability to meet client needs, improved software quality/fewer defects, reduced time and costs and the timely delivery of quality product are tremendous advantages of using agile development [2]. For a successful implementation of agile in an organization, transformation model needs to be used in an efficient manner. While adopting agile development, numbers of organizations come across issues and challenges along with their own projects success and failure stories. In order to identify the issues related to agile development adoption and its impact over the quality of end product, we have conducted a survey. In this survey, people from 50 different organizations interviewed. Some highly occurring issues, in adopting the agile development methodology and affect of agile over the quality of the end product were identified.
Image encryption algorithm based on the finite fields in chaotic maps

With the advancement of computer and communication technologies in data processing, storage, and increasing bandwidth of data transmission, the amount of multimedia data generation and sharing has increased exponentially. Therefore, the security of multimedia data becomes more important in transmission, processing, and storing the day-to-day. Cryptography is one of the important mechanisms in preserving the confidentiality, integrity, and availability of multimedia data such as images. In this research, a digital encryption method is presented based on Galois fields, consisting of two main stages of diffusion and permutation. At the Diffusion stage, using matrix multiplication operations in the GF (256), the overlapping rows and columns of image pixels are mixing. The permutation stage changes image pixels position by using The 2D chaotic map in GF (2n) or The 3D chaotic map in the GF field (2k). The proposed method, with a maximum of two rounds repetition of the main steps, reaches the optimal values of the parameters of the performance evaluation. By performing standard security tests, the proposed method is resistant to differential and statistical attacks and has yielded relatively good performance compared to similar digital image encryption methods.