Reviewerly/exHarmony huggingface dataset:
Street architecture landscape design based on Wireless Internet of Things and GIS system

An infrastructure that provides geospatial analysis, better understands simulations, great potential for visualizing natural and artificial landscapes, and the universal recognition of the use of the Wireless Internet of Things. Everyday devices, whose platform is the Internet of Things, will be smarter in their daily processing, richer in their daily communication, and smarter. The Wireless Internet of Things still wants its landscape design. Still, its impact is already looking at the scene where the Wireless Internet of Things and GIS (Geographic Information System) systems were connected to make incredible advances. It opens up architecture-specific research and the usual architectural landscape area. It can be used for soil mapping studies and can be used for remote sensing in other engineering areas to obtain a wide range of weather compensation. It encourages the use of geographic information systems for geotechnical applications in soil. Geographic information systems can manipulate georeferenced information to be displayed, store, assemble and create georeferenced data from existing data, and generate multiple missing spatial data based on the attributes attached to it. The system briefly introduces street architecture landscape applications and promotes a street landscape design environment for Wireless Internet of Things and GIS system architecture. The proposed system is described as a thing and GIS as network technology, especially in smart design, street architecture and landscape wireless network connectivity. This is an overview of smart network providers, first to help explore some essential landscapes, equipped with wireless internet and geographic information systems, and how it is used in street architecture.
Predicting Performance and Cost of Serverless Computing Functions with SAAF

Next generation software built for the cloud recently has embraced serverless computing platforms that use temporary infrastructure to host microservices offering building blocks for resilient, loosely coupled systems that are scalable, easy to manage, and extend. Serverless architectures enable decomposing software into independent components packaged and run using isolated containers or microVMs. This decomposition approach enables application hosting using very fine-grained cloud infrastructure enabling cost savings as deployments are billed granularly for resource use. Adoption of serverless platforms promise reduced hosting costs while achieving high availability, fault tolerance, and dynamic elasticity. These benefits are offset by pricing obfuscation, as performance variance from CPU heterogeneity, multitenancy, and provisioning variation obscure the true cost of hosting applications with serverless platforms. Where determining hosting costs for traditional VM-based application deployments simply involves accounting for the number of VMs and their uptime, predicting hosting costs for serverless applications can be far more complex. To address these challenges, we introduce the Serverless Application Analytics Framework (SAAF), a tool that allows profiling FaaS workload performance, resource utilization, and infrastructure to enable accurate performance predictions. We apply Linux CPU time accounting principles and multiple regression to estimate FaaS function runtime. We predict runtime using a series of increasingly variant compute bound workloads that execute across heterogeneous CPUs, different memory settings, and to alternate FaaS platforms evaluating our approach for 77 different scenarios. We found that the mean absolute percentage error of our runtime predictions for these scenarios was just ~3.49% resulting in an average cost error of $6.46 for 1-million FaaS function workloads averaging $150.45 in price.
Distributed Hypothesis Testing with Privacy Constraints

We revisit the distributed hypothesis testing (or hypothesis testing with communication constraints) problem from the viewpoint of privacy. Instead of observing the raw data directly, the transmitter observes a sanitized or randomized version of it. We impose an upper bound on the mutual information between the raw and randomized data. Under this scenario, the receiver, which is also provided with side information, is required to make a decision on whether the null or alternative hypothesis is in effect. We first provide a general lower bound on the type-II exponent for an arbitrary pair of hypotheses. Next, we show that if the distribution under the alternative hypothesis is the product of the marginals of the distribution under the null (i.e., testing against independence), then the exponent is known exactly. Moreover, we show that the strong converse property holds. Using ideas from Euclidean information theory, we also provide an approximate expression for the exponent when the communication rate is low and the privacy level is high. Finally, we illustrate our results with a binary and a Gaussian example.
Self-Supervised Linear Motion Deblurring

Motion blurry images challenge many computer vision algorithms, e.g, feature detection, motion estimation, or object recognition. Deep convolutional neural networks are state-of-the-art for image deblurring. However, obtaining training data with corresponding sharp and blurry image pairs can be difficult. In this paper, we present a differentiable reblur model for self-supervised motion deblurring, which enables the network to learn from real-world blurry image sequences without relying on sharp images for supervision. Our key insight is that motion cues obtained from consecutive images yield sufficient information to inform the deblurring task. We therefore formulate deblurring as an inverse rendering problem, taking into account the physical image formation process: we first predict two deblurred images from which we estimate the corresponding optical flow. Using these predictions, we re-render the blurred images and minimize the difference with respect to the original blurry inputs. We use both synthetic and real dataset for experimental evaluations. Our experiments demonstrate that self-supervised single image deblurring is really feasible and leads to visually compelling results.
Ensembled liver cancer detection and classification using CT images

Computed tomography (CT) images are commonly used to diagnose liver disease. It is sometimes very difficult to comment on the type, category and level of the tumor, even for experienced radiologists, directly from the CT image, due to the varying intensities. In recent years, it has been important to design and develop computer-assisted imaging techniques to help doctors/physicians improve their diagnosis. The proposed work is to detect the presence of a tumor region in the liver and classify the different stages of the tumor from CT images. CT images of the liver have been classified between normal and tumor classes. In addition, CT images of the tumor have been classified between Hepato Cellular Carcinoma (HCC) and Metastases (MET). The performance of six different classifiers was evaluated on different parameters. The accuracy achieved for different classifiers varies between 98.39% and 100% for tumor identification and between 76.38% and 87.01% for tumor classification. To further, improve performance, a multi-level ensemble model is developed to detect a tumor (liver cancer) and to classify between HCC and MET using features extracted from CT images. The k-fold cross-validation (CV) is also used to justify the robustness of the classifiers. Compared to the individual classifier, the multi-level ensemble model achieved high accuracy in both the detection and classification of different tumors. This study demonstrates automated tumor characterization based on liver CT images and will assist the radiologist in detecting and classifying different types of tumors at a very early stage.
SDP-GAN: Saliency Detail Preservation Generative Adversarial Networks for High Perceptual Quality Style Transfer

The paper proposes a solution to effectively handle salient regions for style transfer between unpaired datasets. Recently, Generative Adversarial Networks (GAN) have demonstrated their potentials of translating images from source domain X to target domain Y in the absence of paired examples. However, such a translation cannot guarantee to generate high perceptual quality results. Existing style transfer methods work well with relatively uniform content, they often fail to capture geometric or structural patterns that always belong to salient regions. Detail losses in structured regions and undesired artifacts in smooth regions are unavoidable even if each individual region is correctly transferred into the target style. In this paper, we propose SDP-GAN, a GAN-based network for solving such problems while generating enjoyable style transfer results. We introduce a saliency network, which is trained with the generator simultaneously. The saliency network has two functions: (1) providing constraints for content loss to increase punishment for salient regions, and (2) supplying saliency features to generator to produce coherent results. Moreover, two novel losses are proposed to optimize the generator and saliency networks. The proposed method preserves the details on important salient regions and improves the total image perceptual quality. Qualitative and quantitative comparisons against several leading prior methods demonstrates the superiority of our method.
Experimental certification of an informationally complete quantum measurement in a device-independent protocol

Minimal informationally complete positive operator-valued measures (MIC-POVMs) are special kinds of measurement in quantum theory in which the statistics of their <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mi>d</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math> -outcomes are enough to reconstruct any <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>d</mml:mi> </mml:math> -dimensional quantum state. For this reason, MIC-POVMs are referred to as standard measurements for quantum information. Here, we report an experiment with entangled photon pairs that certifies, for what we believe is the first time, a MIC-POVM for qubits following a device-independent protocol (i.e., modeling the state preparation and the measurement devices as black boxes, and using only the statistics of the inputs and outputs). Our certification is achieved under the assumption of freedom of choice, no communication, and fair sampling.
SA-CapsGAN: Using Capsule Networks with embedded self-attention for Generative Adversarial Network

Generative Adversarial Network (GAN) based on Convolutional Neural Network (CNN) has been the focus of research in recent years, but CNN is only suitable for detecting objects in images and cannot indicate the position of one part relative to another, losing the spatial feature relationships. In order to solve the above problems, we propose Self-Attention Generative Adversarial Capsule Network (SA-CapsGAN), using Capsule Networks (CapsNets) with an embedded Self-Attention mechanism as the Discriminator. This mechanism can make reasonable and comprehensive use of the information such as features and spatial location. Compared with CNN-based GAN, it effectively solves the lossy compression and long-range dependence of features. It can learn the target data manifold more quickly and has higher stability. Through some comparative experiments and analysis, it demonstrates the superior performance of SA-CapsGAN on MNIST and CelebA datasets, both quantitatively and qualitatively. Additionally, Fashion-MNIST and Rotated-MNIST datasets are used as a supplement to verify its performance.
Nonparallel Voice Conversion With Augmented Classifier Star Generative Adversarial Networks

We previously proposed a method that allows for nonparallel voice conversion (VC) by using a variant of generative adversarial networks (GANs) called StarGAN. The main features of our method, called StarGAN-VC, are as follows: First, it requires no parallel utterances, transcriptions, or time alignment procedures for speech generator training. Second, it can simultaneously learn mappings across multiple domains using a single generator network and thus fully exploit available training data collected from multiple domains to capture latent features that are common to all the domains. Third, it can generate converted speech signals quickly enough to allow real-time implementations and requires only several minutes of training examples to generate reasonably realistic-sounding speech. In this article, we describe three formulations of StarGAN, including a newly introduced novel StarGAN variant called “Augmented classifier StarGAN (A-StarGAN)”, and compare them in a nonparallel VC task. We also compare them with several baseline methods.
RFRN: A recurrent feature refinement network for accurate and efficient scene text detection

Scene text detection plays a vital role for scene text understanding, but arbitrary-shaped text detection remains a significant challenge. To extract discriminative features, most recent state-of-the-art methods adopt heavy networks, resulting in parameter redundancy and inference inefficiency. For accurate and efficient scene text detection, in this paper we propose a novel recurrent feature refinement network (RFRN). RFRN, as a recurrent segmentation framework, contains a recurrent path augmentation that refines the previous feature maps as inner states, which not only helps improve the segmentation quality, but also fully facilitates the reuse of parameters and low computational cost. During testing, RFRN discards redundant prediction procedures for efficient inference, and achieves a good balance between speed and accuracy of inference. We conduct experiments on four challenging scene text benchmarks, CTW1500, Total-Text, ICDAR2015 and ICDAR2017-MLT, which include curved texts and multi-oriented texts with complex background. The results show that the proposed RFRN achieves competitive performance on detection accuracy while maintaining computational efficiency.
Social capital for flood disaster management: Case study of flooding in a village of Bengawan Solo Riverbank, Tuban, East Java Province

This study aims to describe the effectiveness of social capital in the management of flood disasters in Bengawan Solo Riverbank area in Plumpang, Tuban and the problems faced in managing the flood. The flood occurs every year during the rainy season from October to April due to the rising water level that overtops the riverbank. Though occurs annually, the timing cannot be predicted precisely. The impacts of the flooding are: a) submersion of farm lands and houses; b) damage of public facilities (schools, village roads, places of worship, village offices, etc.); and c) disruption of social and economic activities of the villagers. Dealing with the flood and its impacts would have to be a collaborative endeavor of multiple stakeholders. One of the important strategies for managing flood is strengthening social capital. For this reason, a qualitative study was conducted from January to July 2019 in a village of which the river runs through and is severely affected by the flood. The research data were collected through observation, interviews, focused group discussions, and a review of documents related to the problem of flooding. The study finds that social capital in flood disaster management includes a) social capital for preventing flood impacts; b) moment of flooding, and c) recovery from the impacts of the flood. Social capital in each stage of flood disaster management involves several stakeholders, including the villagers, people from outside the village, government, and private sector. Social capital for managing flood is necessary to strengthen the collaboration between stakeholders and researchers on flood management evaluation.
Hardware-based Always-On Heap Memory Safety

Memory safety violations, caused by illegal use of pointers in unsafe programming languages such as C and C++, have been a major threat to modern computer systems. However, implementing a low-overhead yet robust runtime memory safety solution is still challenging. Various hardware-based mechanisms have been proposed, but their significant hardware requirements have limited their feasibility, and their performance overhead is too high to be an always-on solution. In this paper, we propose AOS, a low-overhead always-on heap memory safety solution that implements a novel bounds-checking mechanism. We identify that the major challenges of existing bounds-checking approaches are 1) the extra instruction overhead for memory checking and metadata propagation and 2) the complex metadata addressing. To address these challenges, using Arm PA primitives, we leverage unused upper bits of a pointer to store a key and have it propagated along with the pointer address, eliminating propagation overhead. Then, we use the embedded key to index a hashed bounds table to achieve efficient metadata management. We also introduce a micro-architectural unit to remove the need for memory checking instructions. We show that AOS overcomes all the aforementioned challenges and demonstrate its feasibility as an efficient runtime memory safety solution. Our evaluation for SPEC 2006 workloads shows an 8.4% performance overhead on average.
Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded

Decision trees usefully represent sparse, high-dimensional, and noisy data. Having learned a function from these data, we may want to thereafter integrate the function into a larger decision-making problem, for example, for picking the best chemical process catalyst. We study a large-scale, industrially relevant mixed-integer nonlinear nonconvex optimization problem involving both gradient-boosted trees and penalty functions mitigating risk. This mixed-integer optimization problem with convex penalty terms broadly applies to optimizing pretrained regression tree models. Decision makers may wish to optimize discrete models to repurpose legacy predictive models or they may wish to optimize a discrete model that accurately represents a data set. We develop several heuristic methods to find feasible solutions and an exact branch-and-bound algorithm leveraging structural properties of the gradient-boosted trees and penalty functions. We computationally test our methods on a concrete mixture design instance and a chemical catalysis industrial instance.
Subdivided Phase Oracle for NISQ Search Algorithms

Because noisy, intermediate-scale quantum (NISQ) machines accumulate errors quickly, we need new approaches to designing NISQ-aware algorithms and assessing their performance. Algorithms with characteristics that appear less desirable under ideal circumstances, such as lower success probability, may in fact outperform their ideal counterparts on existing hardware. We propose an adaptation of Grover's algorithm, subdividing the phase flip into segments to replace a digital counter and complex phase flip decision logic. We applied this approach to obtaining the best solution of the MAX-CUT problem in sparse graphs, utilizing multi-control, Toffoli-like gates with residual phase shifts. We implemented this algorithm on IBM Q processors and succeeded in solving a 5-node MAX-CUT problem, demonstrating amplitude amplification on four qubits. This approach will be useful for a range of problems, and may shorten the time to reaching quantum advantage.
The MBPEP: a deep ensemble pruning algorithm providing high quality uncertainty prediction

Machine learning algorithms have been effectively applied into various real world tasks. However, it is difficult to provide high-quality machine learning solutions to accommodate an unknown distribution of input datasets; this difficulty is called the uncertainty prediction problems. In this paper, a margin-based Pareto deep ensemble pruning (MBPEP) model is proposed. It achieves the high-quality uncertainty estimation with a small value of the prediction interval width (MPIW) and a high confidence of prediction interval coverage probability (PICP) by using deep ensemble networks. In addition to these networks, unique loss functions are proposed, and these functions make the sub-learners available for standard gradient descent learning. Furthermore, the margin criterion fine-tuning-based Pareto pruning method is introduced to optimize the ensembles. Several experiments including predicting uncertainties of classification and regression are conducted to analyze the performance of MBPEP. The experimental results show that MBPEP achieves a small interval width and a low learning error with an optimal number of ensembles. For the real-world problems, MBPEP performs well on input datasets with unknown distributions datasets incomings and improves learning performance on a multi task problem when compared to that of each single model.
What Is Learned in Deep Uncalibrated Photometric Stereo?

This paper targets at discovering what a deep uncalibrated photometric stereo network learns to resolve the problem's inherent ambiguity, and designing an effective network architecture based on the new insight to improve the performance. The recently proposed deep uncalibrated photometric stereo method achieved promising results in estimating directional lightings. However, what specifically inside the network contributes to its success remains a mystery. In this paper, we analyze the features learned by this method and find that they strikingly resemble attached shadows, shadings, and specular highlights, which are known to provide useful clues in resolving the generalized bas-relief (GBR) ambiguity. Based on this insight, we propose a guided calibration network, named GCNet, that explicitly leverages object shape and shading information for improved lighting estimation. Experiments on synthetic and real datasets show that GCNet achieves improved results in lighting estimation for photometric stereo, which echoes the findings of our analysis. We further demonstrate that GCNet can be directly integrated with existing calibrated methods to achieve improved results on surface normal estimation. Our code and model can be found at https://guanyingc.github.io/UPS-GCNet .
Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin

Histological evaluation plays a major role in cancer diagnosis and treatment. The appearance of H&E-stained images can vary significantly as a consequence of differences in several factors, such as reagents, staining conditions, preparation procedure and image acquisition system. Such potential sources of noise can all have negative effects on computer-assisted classification. To minimize such artefacts and their potentially negative effects several color pre-processing methods have been proposed in the literature-for instance, color augmentation, color constancy, color deconvolution and color transfer. Still, little work has been done to investigate the efficacy of these methods on a quantitative basis. In this paper, we evaluated the effects of color constancy, deconvolution and transfer on automated classification of H&E-stained images representing different types of cancers-specifically breast, prostate, colorectal cancer and malignant lymphoma. Our results indicate that in most cases color pre-processing does not improve the classification accuracy, especially when coupled with color-based image descriptors. Some pre-processing methods, however, can be beneficial when used with some texture-based methods like Gabor filters and Local Binary Patterns.