Reviewerly/exHarmony huggingface dataset:
BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation

Existing topic modeling and text segmentation methodologies generally require large datasets for training, limiting their capabilities when only small collections of text are available. In this work, we reexamine the inter-related problems of “topic identification” and “text segmentation” for sparse document learning, when there is a single new text of interest. In developing a methodology to handle single documents, we face two major challenges. First is sparse information : with access to only one document, we cannot train traditional topic models or deep learning algorithms. Second is significant noise : a considerable portion of words in any single document will produce only noise and not help discern topics or segments. To tackle these issues, we design an unsupervised, computationally efficient methodology called Biclustering Approach to Topic modeling and Segmentation (BATS). BATS leverages three key ideas to simultaneously identify topics and segment text: (i) a new mechanism that uses word order information to reduce sample complexity, (ii) a statistically sound graph-based biclustering technique that identifies latent structures of words and sentences, and (iii) a collection of effective heuristics that remove noise words and award important words to further improve performance. Experiments on six datasets show that our approach outperforms several state-of-the-art baselines when considering topic coherence, topic diversity, segmentation, and runtime comparison metrics.
Sky and Ground Segmentation in the Navigation Visions of the Planetary Rovers

Sky and ground are two essential semantic components in computer vision, robotics, and remote sensing. The sky and ground segmentation has become increasingly popular. This research proposes a sky and ground segmentation framework for the rover navigation visions by adopting weak supervision and transfer learning technologies. A new sky and ground segmentation neural network (network in U-shaped network (NI-U-Net)) and a conservative annotation method have been proposed. The pre-trained process achieves the best results on a popular open benchmark (the Skyfinder dataset) by evaluating seven metrics compared to the state-of-the-art. These seven metrics achieve 99.232%, 99.211%, 99.221%, 99.104%, 0.0077, 0.0427, and 98.223% on accuracy, precision, recall, dice score (F1), misclassification rate (MCR), root mean squared error (RMSE), and intersection over union (IoU), respectively. The conservative annotation method achieves superior performance with limited manual intervention. The NI-U-Net can operate with 40 frames per second (FPS) to maintain the real-time property. The proposed framework successfully fills the gap between the laboratory results (with rich idea data) and the practical application (in the wild). The achievement can provide essential semantic information (sky and ground) for the rover navigation vision.
An Analysis and Implementation of the HDR+ Burst Denoising Method

HDR+ is an image processing pipeline presented by Google in 2016. At its core lies a denoising algorithm that uses a burst of raw images to produce a single higher quality image. Since it is designed as a versatile solution for smartphone cameras, it does not necessarily aim for the maximization of standard denoising metrics, but rather for the production of natural, visually pleasing images. In this article, we specifically discuss and analyze the HDR+ burst denoising algorithm architecture and the impact of its various parameters. With this publication, we provide an open source Python implementation of the algorithm, along with an interactive demo.
Multi-view Sensor Fusion by Integrating Model-based Estimation and Graph Learning for Collaborative Object Localization

Collaborative object localization aims to collaboratively estimate locations of objects observed from multiple views or perspectives, which is a critical ability for multi-agent systems such as connected vehicles. To enable collaborative localization, several model-based state estimation and learning-based localization methods have been developed. Given their encouraging performance, model-based state estimation often lacks the ability to model the complex relationships among multiple objects, while learning-based methods are typically not able to fuse the observations from an arbitrary number of views and cannot well model uncertainty. In this paper, we introduce a novel spatiotemporal graph filter approach that integrates graph learning and model-based estimation to perform multi-view sensor fusion for collaborative object localization. Our approach models complex object relationships using a new spatiotemporal graph representation and fuses multi-view observations in a Bayesian fashion to improve location estimation under uncertainty. We evaluate our approach in the applications of connected autonomous driving and multiple pedestrian localization. Experimental results show that our approach outperforms previous techniques and achieves the state-of-the-art performance on collaborative localization.
Relieving pressure from coral reefs: Artificial oyster rocks can replace reef rocks used for biological filtration in marine aquariums

Natural reef rocks have been harvested from the wild for decades to supply the marine aquarium trade. These rocks have been used in home aquariums mostly as substrate to safeguard biological filtration, especially for nitrification. Producing artificial rocks can be a sustainable alternative to the harvest of natural reef rocks from the wild, thus promoting coral reefs conservation. Nonetheless, there is a lack of scientific knowledge on standard formula to prepare artificial rocks, as well as on their potential to promote nitrification. The use of oyster shells, a by-product from oysters’ aquaculture, appears as a promising ecological and economic approach to produce artificial live rocks. Thus, we aimed to develop a standard formula to produce artificial rocks made with oyster shells and test its potential use as a substrate for biological filtration in marine aquariums. For that, we prepared artificial oyster rocks with different sized fragments of oyster shells and compared their ability to promote nitrification of ammonium nitrogen with that of natural reef rocks and fragmented oyster shells. The formula was set at 1 g of dried cement (white Portland cement with limestone) per 3.1 g of dried oyster shell fragments. The volume of water used to prepare this mix was set as 50% of the cement weigh. Our results indicate that artificial rocks made with oyster shells and cement display an ability to promote nitrification similar to that of natural reef rocks, promoting a slight decrease in pH and without releasing PO4–P. All treatments with substrate were able to oxidize up to 21 mg.L−1 of TAN (total ammonia nitrogen, initially added in the form of NH4Cl) within a 48-h interval. In other words, it is possible to safely assume that all tested substrates oxidized an average of ∼4.4 ± 0.1 μg of TAN.g−1.h−1. Thus, our results support that artificial rocks made with white Portland cement and oyster shell fragments are a sustainable option to safeguard a suitable biological filtration in marine aquariums.
A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data Streams

The recent explosive growth in the number of smart technologies relying on data collected from sensors and processed with machine learning classifiers made the training data imbalance problem more visible than ever before. Class-imbalanced sets used to train models of various events of interest are among the main reasons for a smart technology to work incorrectly or even to completely fail. This paper presents an attempt to resolve the imbalance problem in sensor sequential (time-series) data through training data augmentation. An Unrolled Generative Adversarial Networks (Unrolled GAN)-powered framework is developed and successfully used to balance the training data of smartphone accelerometer and gyroscope sensors in different contexts of road surface monitoring. Experiments with other sensor data from an open data collection are also conducted. It is demonstrated that the proposed approach allows for improving the classification performance in the case of heavily imbalanced data (the F1 score increased from 0.69 to 0.72, p<0.01, in the presented case study). However, the effect is negligible in the case of slightly imbalanced or inadequate training sets. The latter determines the limitations of this study that would be resolved in future work aimed at incorporating mechanisms for assessing the training data quality into the proposed framework and improving its computational efficiency.
Adroid: Augmenting Hands-on Making with a Collaborative Robot

Adroid1 enables users to borrow precision and accuracy from a robotic arm when using hand-held tools. When a tool is mounted to the robot, the user can hold and move the tool directly—Adroid measures the user's applied forces and commands the robot to move in response. Depending on the tool and scenario, Adroid can selectively restrict certain motions. In the resulting interaction, the robot acts like a virtual "jig" which constrains the tool's motion, augmenting the user's accuracy, technique, and strength, while not diminishing their agency during open-ended fabrication tasks. We complement these hands-on interactions with projected augmented reality for visual feedback about the state of the system. We show how tools augmented by Adroid can support hands-on making and discuss how it can be configured to support other tasks within and beyond fabrication.
A Multi-view Camera Model for Line-Scan Cameras with Telecentric Lenses

Abstract We propose a novel multi-view camera model for line-scan cameras with telecentric lenses. The camera model supports an arbitrary number of cameras and assumes a linear relative motion with constant velocity between the cameras and the object. We distinguish two motion configurations. In the first configuration, all cameras move with independent motion vectors. In the second configuration, the cameras are mounted rigidly with respect to each other and therefore share a common motion vector. The camera model can model arbitrary lens distortions by supporting arbitrary positions of the line sensor with respect to the optical axis. We propose an algorithm to calibrate a multi-view telecentric line-scan camera setup. To facilitate a 3D reconstruction, we prove that an image pair acquired with two telecentric line-scan cameras can always be rectified to the epipolar standard configuration, in contrast to line-scan cameras with entocentric lenses, for which this is possible only under very restricted conditions. The rectification allows an arbitrary stereo algorithm to be used to calculate disparity images. We propose an efficient algorithm to compute 3D coordinates from these disparities. Experiments on real images show the validity of the proposed multi-view telecentric line-scan camera model.
Spatially Explicit Fuzzy Cognitive Mapping for Participatory Modeling of Stormwater Management

Addressing “wicked” problems like urban stormwater management necessitates building shared understanding among diverse stakeholders with the influence to enact solutions cooperatively. Fuzzy cognitive maps (FCMs) are participatory modeling tools that enable diverse stakeholders to articulate the components of a socio-environmental system (SES) and describe their interactions. However, the spatial scale of an FCM is rarely explicitly considered, despite the influence of spatial scale on SES. We developed a technique to couple FCMs with spatially explicit survey data to connect stakeholder conceptualization of urban stormwater management at a regional scale with specific stormwater problems they identified. We used geospatial data and flooding simulation models to quantitatively evaluate stakeholders’ descriptions of location-specific problems. We found that stakeholders used a wide variety of language to describe variables in their FCMs and that government and academic stakeholders used significantly different suites of variables. We also found that regional FCM did not downscale well to concerns at finer spatial scales; variables and causal relationships important at location-specific scales were often different or missing from the regional FCM. This study demonstrates the spatial framing of stormwater problems influences the perceived range of possible problems, barriers, and solutions through spatial cognitive filtering of the system’s boundaries.
Learning Spatial-angular Fusion for Compressive Light Field Imaging in a Cycle-consistent Framework

This paper investigates the 4-D light field (LF) reconstruction from 2-D measurements captured by the coded aperture camera. To tackle such an ill-posed inverse problem, we propose a cycle-consistent reconstruction network (CR-Net). To be specific, based on the intrinsic linear imaging model of the coded aperture, CR-Net reconstructs an LF through progressively eliminating the residuals between the projected measurements from the reconstructed LF and input measurements. Moreover, to address the crucial issue of extracting representative features from high-dimensional LF data efficiently and effectively, we formulate the problem in a probability space and propose to approximate a posterior distribution of a set of carefully-defined LF processing events, including both layer-wise spatial-angular feature extraction and network-level feature aggregation. Through droppath from a densely-connected template network, we derive an adaptively learned spatial-angular fusion strategy, which is sharply contrasted with existing manners that combine spatial and angular features empirically. Extensive experiments on both simulated measurements and measurements by a real coded aperture camera demonstrate the significant advantage of our method over state-of-the-art ones, i.e., our method improves the reconstruction quality by 4.5 dB.
Makespan Optimisation in Cloudlet Scheduling with Improved DQN Algorithm in Cloud Computing

Despite increased cloud service providers following advanced cloud infrastructure management, substantial execution time is lost due to minimal server usage. Given the importance of reducing total execution time (makespan) for cloud service providers (as a vital metric) during sustaining Quality-of-Service (QoS), this study established an enhanced scheduling algorithm for minimal cloudlet scheduling (CS) makespan with the deep Q-network (DQN) algorithm under MCS-DQN. A novel reward function was recommended to enhance the DQN model convergence. Additionally, an open-source simulator (CloudSim) was employed to assess the suggested work performance. Resultantly, the recommended MCS-DQN scheduler revealed optimal outcomes to minimise the makespan metric and other counterparts (task waiting period, resource usage of virtual machines, and the extent of incongruence against the algorithms).
Determining the direction of the local search in topological ordering space for Bayesian network structure learning

Local search in a topological ordering space is an efficient way of learning Bayesian network structures in large-scale problems. However, existing algorithms typically focus on stochastically developing the neighborhood of an ordering without a specific direction and can quickly stop at a local optimum. In this study, a novel approach is proposed to improve the capability of a local search by determining the search direction. The direction of the search step is identified with respect to the priority in a score cache. We also design robust terminal conditions and insertion methods based on the proposed operator. The direction of escaping from local optima is identified by transferring the ordering to a new restart, which implies an equivalent class of the optimal structure. Moreover, we adopt a breadth-first search method for the conversion between structures and orderings. The identification of the direction can accelerate the convergence of the local search and acquire the higher quality structure. Furthermore, our experimental results demonstrated that the proposed methods highly improve the accuracy and efficiency of learning the optimal ordering from the training dataset compared with state-of-the-art algorithms.
Feasibility of using digital confocal microscopy for cytopathological examination in clinical practice

Optical imaging modalities are emerging as digital microscopy tools for tissue examination. The investigation of these techniques for potential applications in anatomic pathology practice has focused primarily on surgical pathology and has not included cytopathological specimens. We evaluated the feasibility of using digital confocal microscopy (CM) to examine cytopathological specimens. Smears and cell suspensions collected in RPMI solution were prepared from tissue scrapes obtained from surgical resections of breast, lung, liver, and kidney. Air-dried smears and cell pellets obtained from centrifugation of the cell suspensions were stained with 0.6 mM acridine orange and imaged with a CM platform. After completion of imaging, the smears were stained with Diff-Quik (DQ), and cell pellets were routinely processed, embedded in paraffin wax, cut, and stained with hematoxylin and eosin (H&E). We evaluated the mean time to acquire digital CM images; quality of images based on the extent of tissue recognition (0%, grade 0; 1-19%, grade 1; 20-50%, grade 2; >50%, grade 3); comparison of images with DQ- and H&E-stained specimens; and ability to make specific diagnoses. We imaged 91 smears and 52 cell pellets and acquired digital CM images within 2-3 min, with 92% and 88% of images, respectively, from smears and cell pellets showing grade 3 quality. On the basis of CM images, 8 smears (9%) and 7 cell pellets (14%) were categorized as benign, and 83 (91%) and 45 (88%), respectively, as malignant. Specific diagnoses were made by using digital CM images of smears and cell pellets that matched accurately with corresponding DQ- and H&E-stained preparations. The results of our first feasibility study clearly indicated the utility of CM as a next-generation digital microscopy tool for evaluating cytology specimens. Prospective clinical studies are warranted for validating our findings for potential incorporation into cytopathological clinical practice.
Label Noise Modeling and Correction via Loss Curve Fitting for SAR ATR

The success of deep learning in synthetic aperture radar (SAR) automatic target recognition (ATR) relies on a large number of labeled samples; however, there are often wrong (noisy) labels in a large-scale dataset. In this article, we propose a loss curve-fitting-based method, which can identify the noisy labels and train the classification network effectively. We propose to model label noise by unsupervised clustering via fitting loss curve to identify whether the sample’s label is clean or noisy. Then, we train the network using augmented samples with clean labels to correct noisy labels further. The experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset prove that our proposed method can deal with the situation when training a network with different ratios of noisy labels and correct noisy labels effectively. When the noise ratio is small (40%) in the training dataset, our method can correct 97.9% of noisy labels and train the classification network with 98.8% classification accuracy. While the noise ratio is large (80%), our method can correct 78.1% of noisy labels and train the classification network with 79.6% classification accuracy.
Semantic Scalable Image Compression with Cross-Layer Priors

In an intelligent society, image compression needs to serve both human vision and machine vision. Traditional image compression schemes only consider visual quality for humans. In addition, the bitstream needs to be fully decoded to images before performing semantic analysis (e.g., by deep neural networks). These two factors make traditional image compression schemes semantically inefficient. To better serve the needs of both human vision and machine vision, it is more reasonable to compress and transmit image signals and features simultaneously. In this paper, we propose a novel end-to-end semantic scalable image compression method, which progressively compresses coarse-grained semantic features, fine-grained semantic features, and image signals. To utilize the cross-layer correlation between features and image signals, we propose a cross-layer context model to reduce the information redundancy, which takes higher-layer features as cross-layer priors to predict the probability distribution parameters for the entropy model of lower-layer features or images. Furthermore, we adopt a Region of Interest (ROI) compression scheme. The objects with rich semantic information and the background are compressed separately, to further improve the compression efficiency. Experimental results on the CUB-200-2011 and FGVC-Aircraft datasets demonstrate the effectiveness of our proposed scheme compared to separate compression of image signals and features.
A Transfer Learning Scheme for Time Series Forecasting Using Facebook Prophet

We describe our methodology to support time-series forecasts over spatial datasets using the Prophet library. Our approach underpinned by our transfer learning scheme ensures that model instances capture subtle regional variations and converge faster while using fewer resources. Our benchmarks demonstrate the suitability of our methodology.
Development of a New Image-Guided Neuronavigation System: Mixed-Reality Projection Mapping Is Accurate and Feasible

Image-guided systems improve the safety, functional outcome, and overall survival of neurosurgery but require extensive equipment.To develop an image-guided surgery system that combines the brain surface photographic texture (BSP-T) captured during surgery with 3-dimensional computer graphics (3DCG) using projection mapping.Patients who underwent initial surgery with brain tumors were prospectively enrolled. The texture of the 3DCG (3DCG-T) was obtained from 3DCG under similar conditions as those when capturing the brain surface photographs. The position and orientation at the time of 3DCG-T acquisition were used as the reference. The correct position and orientation of the BSP-T were obtained by aligning the BSP-T with the 3DCG-T using normalized mutual information. The BSP-T was combined with and displayed on the 3DCG using projection mapping. This mixed-reality projection mapping (MRPM) was used prospectively in 15 patients (mean age 46.6 yr, 6 males). The difference between the centerlines of surface blood vessels on the BSP-T and 3DCG constituted the target registration error (TRE) and was measured in 16 fields of the craniotomy area. We also measured the time required for image processing.The TRE was measured at 158 locations in the 15 patients, with an average of 1.19 ± 0.14 mm (mean ± standard error). The average image processing time was 16.58 min.Our MRPM method does not require extensive equipment while presenting information of patients' anatomy together with medical images in the same coordinate system. It has the potential to improve patient safety.