Reviewerly/exHarmony huggingface dataset:
Tumor Segmentation Based on Deeply Supervised Multi-Scale U-Net

Although deep learning has achieved great success in the field of medical image processing, the existing deep learning based medical image segmentation solutions still cannot obtain satisfactory performances for abdominal small organs and lesions due to their small object size and shape-variability. In this work, a Deeply Supervised Multi-Scale U-Net (DSMS U-Net) is proposed for more accurate segmentation performances on abdominal small organs images. DSMS U-Net integrate the existing U-Net model with a restoration decoder module and some multi-scale convolution modules. Our experiment results demonstrate that the proposed DSMS U-Net approach has much better segmentation performances than the state-of-the-art baselines.
Anti-disturbance grabbing of underwater robot based on retinex image enhancement

Underwater Vehicle Manipulator System (UVMS) has the characteristics of weak illumination, strong nonlinearity and many disturbances when operating in underwater environment. It is difficult to control. In this paper, after constructing the mathematical model of manipulator based on task space, a fusion image enhancement algorithm based on Retinex theory is proposed for the problem of low illuminance and blurred target recognition. The bilateral filtering process, color pre-correction, estimated reflectivity and dynamics are proposed. The compensation, color compensation and other processes realize the fine perception of underwater targets; using the extended Kalman filter (EKF) and the spatial feedback linear transformation matrix to construct the robot anti-interference controller to compensate the internal and external uncertainty disturbances and achieve underwater stability operation; The target ranging method is used to solve the joint angles of the manipulators, and the tracking and grasping of the target objects are completed. Underwater experiments show that the algorithm can not only improve the dynamic stability of the UVMS system, but also ensure the accuracy of underwater robot grabbing and optimize system control performance.
Computational Properties of Ising Spin Model on Spin Connection Parameters

Recently, a new computing architecture using Ising spin model is gaining increasing attention. The Ising spin model is considered as an efficient computing method to solve combinatorial optimization problems. Depending on the problems, Ising spin model requires the tuning of spin connection parameters, such as an arbitrary topology and range of interaction coefficient values on the spin connection. In this work, we investigated computational properties of Ising spin model with the various spin connection parameters through solving combinatorial optimization problems. Consequently, the architecture with fully connectable spins makes Ising spin model well suited for solving complex combinatorial optimization problems.
Characterizing coherence with quantum observables

We introduce a procedure based on quantum expectation values of measurement observables to characterize quantum coherence. Our measure allows one to quantify coherence without having to perform tomography of the quantum state and can be directly calculated from measurement expectation values. This definition of coherence allows the decomposition into contributions corresponding to the nonclassical correlations between the subsystems and localized on each subsystem. The method can also be applied to cases where the full set of measurement operators is unavailable. An estimator using the truncated measurement operators can be used to obtain lower bound to the genuine value of coherence. We illustrate the method for several bipartite systems and show the singular behavior of the coherence measure in a spin-1 chain, characteristic of a quantum phase transition.Received 21 January 2019Revised 15 October 2019Accepted 19 December 2019DOI:https://doi.org/10.1103/PhysRevResearch.2.013157Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum coherence & coherence measuresResource theoriesQuantum InformationGeneral Physics
Public Policy Implementation: Study on Educational Budgeting of Palopo

This study aims to analyze the content of the policy in the implementation of the Education Budget in the Palopo City Budget and to analyze the policy context in the implementation of Palopo Regional Educational Budgeting in the. This study uses a qualitative approach through the use of interviews, direct observation in the field and analysis of written materials as the main data source. From the research results obtained, the indicator of interest that is influenced shows the interests of the school become more attentive and more accommodated. Implementation of Free Education in the field where the school benefits in the form of the use of free education funds for additional incentives from teaching hours of teachers outside of school hours and additional operational costs, the use of free education at the elementary level more budget absorbed for intensive teachers and for junior high school budgets more absorbed in the costs of supporting school activities such as stationery for the office and teaching and learning activities. In the implementation of free education, the decision-making process is carried out through deliberations that involve all interested parties (students' parents through the school committee and the school itself). Characteristics that are different from each school makes it difficult for schools to implement free education programs. The existence of this free education program makes the active role of parents in paying attention to the learning of students to be reduced.
A CNN Model Based on Spatial Attention Modules for Casting Type Classification on Pseudo-color Digital Radiography Images

In the casting manufacturing, the dependable automation of classifying casting types on digital radiography (DR) images is a crucial technology to automate the downstream tasks, such as defects detection. Generally, DR images are constructed by single gray-scale information, which constricts feature representations of castings on the DR images. Meanwhile, such the complicated background of DR image acquisition is an undesirable issue for the classification performance. Recently, the neural network, especially convolutional neural network (CNN), has great successes in the conventional tasks. However, CNNs are unable to be applied to the industrial tasks without any additional adjustments. In this paper, an improved pseudo-color processing is first proposed to enhance the original DR images. Then, the query DR images are colorized using pseudo-color processing. Ultimately, we design a novel CNN model based on spatial attention modules which can increase the model capability of focusing on the valid regions of castings. The experiments demonstrate the proposed model is capable of recognizing the casting types precisely on pseudo-color DR images. The superior performance also shows the practical value of these methods in casting type classification.
A Single RGB Camera Based Gait Analysis With A Mobile Tele-Robot For Healthcare

With the increasing awareness of high-quality life, there is a growing need for health monitoring devices running robust algorithms in home environment. Health monitoring technologies enable real-time analysis of users' health status, offering long-term healthcare support and reducing hospitalization time. The propose of this work is twofold, the software focuses on the analysis of gait, which is widely adopted for joint correction and assessing any lower limb, or spinal problem. On the hardware side, a novel marker-less gait analysis device using a low-cost RGB camera mounted on a mobile tele-robot is designed. As gait analysis with a single camera is much more challenging compared to previous works utilizing multi-cameras, a RGB-D camera or wearable sensors, we propose using vision-based human pose estimation approaches. More specifically, based on the out-put of state-of-the-art human pose estimation models, we devise measurements for four bespoke gait parameters: inversion/eversion, dorsiflexion/plantarflexion, ankle and foot progression angles. We thereby classify walking patterns into normal, supination, pronation and limp. We also illustrate how to run the proposed machine learning models in low-resource environments such as a single entry-level CPU. Experiments show that our single RGB camera method achieves competitive performance compared to multi-camera motion capture systems, at smaller hardware costs.
ConfigMiner: Identifying the Appropriate Configuration Options for Config-Related User Questions by Mining Online Forums

While the behavior of a software system can be easily changed by modifying the values of a couple of configuration options, finding one out of hundreds or thousands of available options is, unfortunately, a challenging task. Therefore, users often spend a considerable amount of time asking and searching around for the appropriate configuration options in online forums such as StackOverflow. In this paper, we propose ConfigMiner, an approach to automatically identify the appropriate option(s) to config-related user questions by mining already-answered config-related questions in online forums. Our evaluation on 2,062 config-related user questions for seven software systems shows that ConfigMiner can identify the appropriate option(s) for a median of 83 percent (up to 91 percent) of user questions within the top-20 recommended options, improving over state-of-the-art approaches by a median of 130 percent. Besides, ConfigMiner reports the relevant options at a median rank of 4, compared to a median of 16-20.5 as reported by the state-of-the-art approaches.
Quantum Optimization of Resource Distribution Management for Multi-Task, Multi-Subtasks

This paper proposes a new optimization strategy for resource distribution management based on a quantum algorithm, as a way to reduce the computational complexity in finding the optimum deployment scenario, taking into consideration the required conditions and constraints of the resource distribution system.We show that the quantum method computes the results in minimum time and outperforms on the other classical algorithms in terms of computational complexity.
DATA QUALITY CHECK AND VISUAL ANALYSIS OF CORS STATION BASED ON ANUBIS SOFTWARE

Abstract. Aiming at the observation data of CORS multi-mode data quality and inspection in CORS station, based on the observation data of a CORS station in Guilin, Guangxi, the paper used TEQC and Anubis software to check the data quality from data utilization, cycle slip ratio and multipath. The results show that the three quality inspection indicators of Anubis software are better than TEQC software, which proves that Anubis' data quality inspection results are effective and reliable. On this basis, using Anubis drawing tools to perform quality inspection and visual analysis from satellite constellation, signal band and observation data itself, it shows that Anubis' visual content is more elaborate than TEQC, and users can more intuitively understand and control the data quality of CORS station.
In Defense of Color Names for Small-Scale Person Re-Identification

In this paper, we propose an efficient image representation strategy for addressing the task of small-scale person re-identification. Taking advantages of being compact and intuitively understandable, we adopt color names descriptor (CND) as our color feature. To solve the inaccuracy by comparing color names with image pixels in Euclidean space, we propose a new approach – soft Gaussian mapping (SGM), which uses a Gaussian model to bridge their semantic gap. We further present a cross-view coupling learning method to build a common subspace where the learned features can contain the transition information among different cameras. Experiments on the challenging small-scale benchmark public datasets demonstrate the effectiveness of our proposed method.
A Daubechies DWT Based Image Steganography Using Smoothing Operation

Steganography is a capability which conceals the top-secret information into cover media (e.g., digital images, sound files etc.,). This Paper presents a secure, higher embedding capacity Discrete Wavelet Transformation (DWT) based technique. Before embedding correlation in between cover and the secret image is increased by multiplying some variable (i.e., 1/k) to the secret image. In embedding process, the Daubechies DWT of both Arnold transformed secret and cover images are taken followed by alpha blending operation. Arnold is a type of scrambling process which increases the confidentiality of secret image and alpha blending is a type of mixing operation of two images, the alpha value indicates the amount of secret image is embedded into the cover image. Daubechies Inverse Discrete Wavelet Transformation (IDWT) of the resulting image is performed to obtain the stego image. Smoothing operation inspired by the Genetic Algorithm (GA) is used to improve the quality of stego-image by minimizing Mean square error and morphological operation is used to extract the image component from the extracted secret image. Simulation results of the proposed steganography technique are also presented. The projected method is calculated on different parameters of image visual quality measurements
Projection based weight normalization: Efficient method for optimization on oblique manifold in DNNs

Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned problem. We observe that the scaling based weight space symmetry (SBWSS) in rectified nonlinear network will cause this negative effect. Therefore, we propose to constrain the incoming weights of each neuron to be unit-norm, which is formulated as an optimization problem over the Oblique manifold. A simple yet efficient method referred to as projection based weight normalization (PBWN) is also developed to solve this problem. This proposed method has the property of regularization and collaborates well with the commonly used batch normalization technique. We conduct comprehensive experiments on several widely-used image datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet for supervised learning over the state-of-the-art neural networks. The experimental results show that our method is able to improve the performance of different architectures consistently. We also apply our method to Ladder network for semi-supervised learning on permutation invariant MNIST dataset, and our method achievers the state-of-the-art methods: we obtain test errors as 2.52%, 1.06%, and 0.91% with only 20, 50, and 100 labeled samples, respectively.
Notes on the Symmetries of 2-Layer ReLU-Networks

Symmetries in neural networks allow different weight configurations leading to the same network function. For odd activation functions, the set of transformations mapping between such configurations have been studied extensively, but less is known for neural networks with ReLU activation functions. We give a complete characterization for fully-connected networks with two layers. Apart from two well-known transformations, only degenerated situations allow additional transformations that leave the network function unchanged. Reduction steps can remove only part of the degenerated cases. Finally, we present a non-degenerate situation for deep neural networks leading to new transformations leaving the network function intact.
Virtual Situated Learning of Spoken English Based on Computer Simulation Technology

This paper aims to effectively improve the learning effect of spoken English, which is difficult to learn or practice. For this purpose, the 3D space was constructed by computer simulation and virtual reality (VR) technology. Then, the author designed a computer simulation software to create a 3D virtual learning environment, which is almost indistinguishable from the real world, for immersive learning of spoken English. A total of 100 students were selected to learn spoken English for 12 weeks in the virtual environment. The learning outcomes were collected and analysed by statistical and induction methods. The analysis results show that the virtual environment greatly stimulated the students’ enthusiasm for spoken English learning, and improved the learning effect by 26~32%; the delay of the proposed software falls in the allowable range, but needs to be further improved. The research findings shed new light on the methods and theories of virtual situated learning of spoken English.
Improving speaker diarization for naturalistic child-adult conversational interactions using contextual information

While deep learning has driven recent improvements in audio speaker diarization, it often faces performance issues in challenging interaction scenarios and varied acoustic settings such as between a child and adult (caregiver/examiner). In this work, the role of contextual factors that affect diarization performance in such interactions is analyzed. Factors that affect each type of diarization error are identified. Furthermore, a DNN is trained on diarization outputs in conjunction with the factors to improve diarization performance. The results demonstrate the usefulness of incorporating context in improving diarization performance of child-adult interactions in clinical settings.
Dynamic Deformation Measurement of Specular Surface with Deflectometry and Speckle Digital Image Correlation

The deformation measurement of a specular surface is of great importance during the quality inspection and installation of optical elements or wafers, especially those with large apertures. We propose a deflectometry method with speckle digital image correlation (DeSDIC) to realize the dynamic and high-accuracy measurement of the deformation on specular surfaces, with a simple system structure and robustness to noises and environmental vibrations. Random speckle pattern displayed on liquid crystal display is reflected by the original surface under test (SUT), and the distorted pattern is recorded by a camera. This originally distorted pattern is taken as the reference image, and the patterns captured afterwards are digitally correlated with the reference image to calculate the gradient change and deformation of the SUT. The theoretical relationships and an experimental one-step calibration scheme are proposed. Both static and dynamic deformations of a deformable mirror were experimentally measured to demonstrate the feasibility and accuracy of DeSDIC, which is comparable to phase-measuring deflectometry and interferometry.
THE TEACHING OF WRITING RECOUNT TEXTS BY UTILIZING PADLET

Writing has always been beneficial for those who master it. Albeit its virtues, it is subject to investigation due to issues concerning its derisory tasks, its complexity, and its arid learning activities. Based upon the issues, this study is aimed at portraying how English teachers teach students writing recount texts by integrating Padlet into their classroom. Involving one English teacher and a class of 25 students, this study obtained the data through observation of four class meetings. The data were then analyzed qualitatively to depict thoroughly the teaching practice of the teacher as Padlet was deployed in the classroom. The analysis resulted in findings vis-�-vis the integration of Padlet and the teacher�s ways of integrating Padlet, that the integration of technology is categorized into some levels. The findings to some extent conform to previous studies that the integration of technology has been prevalent among English teaching in general. It is therefore suggested that teachers maintain the integration of technology while keep on fostering the accompanying capability of integrating it.�Keywords: writing; recount texts; Padlet; technology integration; digital teaching media.
Memory Efficient Implementation of Modular Multiplication for 32-bit ARM Cortex-M4

In this paper, we present scalable multi-precision multiplication implementation and scalable multi-precision squaring implementation for 32-bit ARM Cortex-M4 microcontrollers. For efficient computation and scalable functionality, we present optimized Multiplication and ACcumulation (MAC) techniques for the target microcontrollers. In particular, we present the 64-bit wise MAC operation with the Unsigned Long Multiply with Accumulate Accumulate (UMAAL) instruction. The MAC is used to perform column-wise multiplication/squaring (i.e., product-scanning) with general-purpose registers in an optimal way. Second, the squaring algorithm is further optimized through an efficient doubling routine together with an optimized product-scanning method. Finally, the proposed implementations achieved a very small memory footprint and high scalability to cover algorityms ranging from well-known public key cryptography (i.e., Rivest–Shamir–Adleman (RSA) and Elliptic Curve Cryptography (ECC)) to post-quantum cryptography (i.e., Supersingular Isogeny Key Encapsulation (SIKE)). All SIKE round 2 protocols were evaluated with the proposed modular reduction implementations. The results demonstrate that the scalable implementation can achieve the smallest code size together with a reasonable performance.
SYMOG: Learning symmetric mixture of Gaussian modes for improved fixed-point quantization

Deep neural networks (DNNs) have been proven to outperform classical methods on several machine learning benchmarks. However, they have high computational complexity and require powerful processing units. Especially when deployed on embedded systems, model size and inference time must be significantly reduced. We propose SYMOG (symmetric mixture of Gaussian modes), which significantly decreases the complexity of DNNs through low-bit fixed-point quantization. SYMOG is a novel soft quantization method such that the learning task and the quantization are solved simultaneously. During training the weight distribution changes from an unimodal Gaussian distribution to a symmetric mixture of Gaussians, where each mean value belongs to a particular fixed-point mode. We evaluate our approach with different architectures (LeNet5, VGG7, VGG11, DenseNet) on common benchmark data sets (MNIST, CIFAR-10, CIFAR-100) and we compare with state-of-the-art quantization approaches. We achieve excellent results and outperform 2-bit state-of-the-art performance with an error rate of only 5.71% on CIFAR-10 and 27.65% on CIFAR-100.
The modified ASHRAE model based on the mechanism of multi-parameter coupling

In recent years, serious air pollution weakened the solar radiation received near the ground, which leads to a remarkable error between the calculated value of ASHRAE model and the measured data. Therefore, based on the day of year (D) of the existing model, this paper identifies air temperature (T), humidity ratio (d), air quality index (AQI) and relative humidity (φ) as correction parameters to obtain the modified ASHRAE model. Then ASHRAE model and its modified model are compared and analyzed based on the measured data. The results show that the accuracy of modified ASHRAE model is significantly improved compared with the ASHRAE model, and the calculated values of modified ASHRAE model are in good agreement with the measured data. The Nash-Sutcliffe Equation (NSE) is improved by 27.17%, and the root mean square error (RMSE) is reduced by 55.99%.