Reviewerly/exHarmony huggingface dataset:
Feasibility of Automatic Seed Generation Applied to Cardiac MRI Image Analysis

We present a method of using interactive image segmentation algorithms to reduce specific image segmentation problems to the task of finding small sets of pixels identifying the regions of interest. To this end, we empirically show the feasibility of automatically generating seeds for GrowCut, a popular interactive image segmentation algorithm. The principal contribution of our paper is the proposal of a method for automating the seed generation method for the task of whole-heart segmentation of MRI scans, which achieves competitive unsupervised results (0.76 Dice on the MMWHS dataset). Moreover, we show that segmentation performance is robust to seeds with imperfect precision, suggesting that GrowCut-like algorithms can be applied to medical imaging tasks with little modeling effort.
Genetic Programming with Noise Sensitivity for Imputation Predictor Selection in Symbolic Regression with Incomplete Data

This paper presents a feature selection method that incorporates a sensitivity-based single feature importance measure in a context-based feature selection approach. The single-wise importance is based on the sensitivity of the learning performance with respect to adding noise to the predictive features. Genetic programming is used as a context-based selection mechanism, where the selection of features is determined by the change in the performance of the evolved genetic programming models when the feature is injected with noise. Imputation is a key strategy to mitigate the data incompleteness problem. However, it has been rarely investigated for symbolic regression on incomplete data. In this work, an attempt to contribute to filling this gap is presented. The proposed method is applied to selecting imputation predictors (features/variables) in symbolic regression with missing values. The evaluation is performed on real-world data sets considering three performance measures: imputation accuracy, symbolic regression performance, and features' reduction ability. Compared with the benchmark methods, the experimental evaluation shows that the proposed method can achieve an enhanced imputation, improve the symbolic regression performance, and use smaller sets of selected predictors.
Achieving Privacy-Preserving Group Recommendation with Local Differential Privacy and Random Transmission

Group activities on social networks are increasing rapidly with the development of mobile devices and IoT terminals, creating a huge demand for group recommendation. However, group recommender systems are facing an important problem of privacy leakage on user’s historical data and preference. Existing solutions always pay attention to protect the historical data but ignore the privacy of preference. In this paper, we design a privacy-preserving group recommendation scheme, consisting of a personalized recommendation algorithm and a preference aggregation algorithm. With the carefully introduced local differential privacy (LDP), our personalized recommendation algorithm can protect user’s historical data in each specific group. We also propose an Intra-group transfer Privacy-preserving Preference Aggregation algorithm (IntPPA). IntPPA protects each group member’s personal preference against either the untrusted servers or other users. It could also defend long-term observation attack. We also conduct several experiments to measure the privacy-preserving effect and usability of our scheme with some closely related schemes. Experimental results on two datasets show the utility and privacy of our scheme and further illustrate its advantages.
Image Recognition Based on Multiscale Pooling Deep Convolution Neural Networks

Depth neural network (DNN) has become a research hotspot in the field of image recognition. Developing a suitable solution to introduce effective operations and layers into DNN model is of great significance to improve the performance of image and video recognition. To achieve this, through making full use of block information of different sizes and scales in the image, a multiscale pooling deep convolution neural network model is designed in this paper. No matter how large the feature map is, multiscale sampling layer will output three fixed-size character matrices. Experimental results demonstrate that this method greatly improves the performance of the current single training image, which is suitable for solving the image generation, style migration, image editing, and other issues. It provides an effective solution for further industrial practice in the fields of medical image, remote sensing, and satellite imaging.
On the tonehole lattice cutoff frequency of conical resonators: applications to the saxophone

The tonehole lattice cutoff frequency is a well-known feature of woodwind instruments. However, most analytic studies of the cutoff have focused on cylindrical instruments due to their relative geometric simplicity. Here, the tonehole lattice cutoff frequency of conical instruments such as the saxophone is studied analytically, using a generalization of the framework developed for cylindrical resonators. First, a definition of local cutoff of a conical tonehole lattice is derived and used to design “acoustically regular” resonators with determinate cutoff frequencies. The study is then expanded to an acoustically irregular lattice: a saxophone resonator, of known input impedance and geometry. Because the lattices of real instruments are acoustically irregular, different methods of analysis are developed. These methods, derived from either acoustic (input impedance) or geometric (tonehole geometry) measurements, are used to determine the tonehole lattice cutoff frequency of conical resonators. Each method provides a slightly different estimation of the tonehole lattice cutoff for each fingering, and the range of cutoffs across the first register is interpreted as the acoustic irregularity of the lattice. It is shown that, in contrast with many other woodwind instruments, the cutoff frequency of a saxophone decreases significantly from the high to low notes of the first register.
Symmetric Nonnegative Matrix Factorization Based on Box-Constrained Half-Quadratic Optimization

Nonnegative Matrix Factorization (NMF) based on half-quadratic (HQ) functions was proven effective and robust when dealing with data contaminated by continuous occlusion according to the half-quadratic optimization theory. Nonetheless, state-of-the-art HQ NMF still cannot handle symmetric data matrices, and this caused problems when applications require processing symmetric matrices, e.g., similarity matrices. In view of such, this study presents HQ Symmetric NMF along with HQ optimization algorithms that can solve symmetric matrix decomposition and avoid oscillations during alternating updates. Moreover, variants of HQ Symmetric NMF are proposed and examined in this study - The direct method and the asymmetry penalty constrained method. Both show their distinct advantages. Experiments on open datasets were conducted for comparison with other methods. Experimental results showed that the proposed HQ Symmetric NMF was more robust against continuous occlusion than the baselines. Both root-mean-squared errors and coefficients of determination were better than those of the baselines, thereby demonstrating effectiveness of the proposed method.
Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes

High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task. In this work, we propose the first unsupervised abstractive dialogue summarization model for tete-a-tetes (SuTaT). Unlike standard text summarization, a dialogue summarization method should consider the multi-speaker scenario where the speakers have different roles, goals, and language styles. In a tete-a-tete, such as a customer-agent conversation, SuTaT aims to summarize for each speaker by modeling the customer utterances and the agent utterances separately while retaining their correlations. SuTaT consists of a conditional generative module and two unsupervised summarization modules. The conditional generative module contains two encoders and two decoders in a variational autoencoder framework where the dependencies between two latent spaces are captured. With the same encoders and decoders, two unsupervised summarization modules equipped with sentence-level self-attention mechanisms generate summaries without using any annotations. Experimental results show that SuTaT is superior on unsupervised dialogue summarization for both automatic and human evaluations, and is capable of dialogue classification and single-turn conversation generation.
Stepwise local stitching ultrasound image algorithms based on adaptive iterative threshold Harris corner features

Abstract Herein, a Harris corner detection algorithm is proposed based on the concepts of iterated threshold segmentation and adaptive iterative threshold (AIT–Harris), and a stepwise local stitching algorithm is used to obtain wide-field ultrasound (US) images. Cone-beam computer tomography (CBCT) and US images from 9 cervical cancer patients and 1 prostate cancer patient were examined. In the experiment, corner features were extracted based on the AIT–Harris, Harris, and Morave algorithms. Accordingly, wide-field ultrasonic images were obtained based on the extracted features after local stitching, and the corner matching rates of all tested algorithms were compared. The accuracies of the drawn contours of organs at risk (OARs) were compared based on the stitched ultrasonic images and CBCT. The corner matching rate of the Morave algorithm was compared with those obtained by the Harris and AIT–Harris algorithms, and paired sample t tests were conducted ( t = 6.142, t = 31.859, P &lt; .05). The results showed that the differences were statistically significant. The average Dice similarity coefficient between the automatically delineated bladder region based on wide-field US images and the manually delineated bladder region based on ground truth CBCT images was 0.924, and the average Jaccard coefficient was 0.894. The proposed algorithm improved the accuracy of corner detection, and the stitched wide-field US image could modify the delineation range of OARs in the pelvic cavity.
XuperChain: A blockchain system that supports smart contracts parallelization

Nowadays, blockchain technology is not only used for cryptocurrency transactions, but also plays an important role in a wide range of fields, such as evidence preservation and commodity traceability. However, many existing blockchain systems perform poorly and fail to meet the needs of the business, because smart contracts are executed sequentially. We've created a new blockchain system that supports the parallel execution & verification of smart contracts.
Latent Unexpected Recommendations

Unexpected recommender system constitutes an important tool to tackle the problem of filter bubbles and user boredom, which aims at providing unexpected and satisfying recommendations to target users at the same time. Previous unexpected recommendation methods only focus on the straightforward relations between current recommendations and user expectations by modeling unexpectedness in the feature space, thus resulting in the loss of accuracy measures to improve unexpectedness performance. In contrast to these prior models, we propose to model unexpectedness in the latent space of user and item embeddings, which allows us to capture hidden and complex relations between new recommendations and historic purchases. In addition, we develop a novel Latent Closure (LC) method to construct a hybrid utility function and provide unexpected recommendations based on the proposed model. Extensive experiments on three real-world datasets illustrate superiority of our proposed approach over the state-of-the-art unexpected recommendation models, which leads to significant increase in unexpectedness measure without sacrificing any accuracy metric under all experimental settings in this article.
Adaptive Fringe Projection for 3D Shape Measurement with Large Reflectivity Variations by Using Image Fusion and Predicted Search

There is always a great challenge for the structured light technique that it is difficult to deal with the surface with large reflectivity variations or specular reflection. This paper proposes a flexible and adaptive digital fringe projection method based on image fusion and interpolated prediction search algorithm. The multiple mask images are fused to obtain the required saturation threshold, and the interpolated prediction search algorithm is used to calculate the optimal projection gray-level intensity. Then, the projection intensity is reduced to achieve coordinate matching in the unsaturated condition, and the adaptive digital fringes with the optimal projection intensity are subsequently projected for phase calculation by using the heterodyne multifrequency phase-shifted method. The experiments demonstrate that the proposed method is effective for measuring the high-reflective surface and unwrapping the phase in the local overexposure region completely. Compared with the traditional structured light measurement methods, our method can decrease the number of projected and captured images with higher modulation and better contrast. In addition, the measurement process only needs two prior steps and avoids hardware complexity, which is more convenient to apply to the industry.
Efficient Certification of Spatial Robustness

Recent work has exposed the vulnerability of computer vision models to vector field attacks. Due to the widespread usage of such models in safety-critical applications, it is crucial to quantify their robustness against such spatial transformations. However, existing work only provides empirical robustness quantification against vector field deformations via adversarial attacks, which lack provable guarantees. In this work, we propose novel convex relaxations, enabling us, for the first time, to provide a certificate of robustness against vector field transformations. Our relaxations are model-agnostic and can be leveraged by a wide range of neural network verifiers. Experiments on various network architectures and different datasets demonstrate the effectiveness and scalability of our method.
A Comprehensive Description of a Low-Cost Wireless Dynamic Real-Time Data Acquisition and Monitoring System

Nowadays, low-cost sensors and acquisition devices have been emerging as an obvious solution to many innovative applications such as Structural Health Monitoring (SHM) systems.In this paper, issues regarding dynamic data acquisition, as well as their respective solutions, are presented.Moreover, a comprehensive description through-out an inexpensive sensor network system using opensource hardware for a real-time acceleration data acquisition has been presented.The platform consists of an accelerometer, an Arduino board, and a computer as a data recorder and presenter.Data is recorded through an efficient microcontroller code that can provide a considerable reading frequency (up to 300Hz).Using Python, instant acceleration recording for all the three axes has been done.It is shown how the performance of the proposed system is efficient for the field of SHM systems.
Stylized Dialogue Response Generation Using Stylized Unpaired Texts

Generating stylized responses is essential to build intelligent and engaging dialogue systems. However, this task is far from well-explored due to the difficulties of rendering a particular style in coherent responses, especially when the target style is embedded only in unpaired texts that cannot be directly used to train the dialogue model. This paper proposes a stylized dialogue generation method that can capture stylistic features embedded in unpaired texts. Specifically, our method can produce dialogue responses that are both coherent to the given context and conform to the target style. In this study, an inverse dialogue model is first introduced to predict possible posts for the input responses. Then this inverse model is used to generate stylized pseudo dialogue pairs based on these stylized unpaired texts. Further, these pseudo pairs are employed to train the stylized dialogue model with a joint training process. A style routing approach is proposed to intensify stylistic features in the decoder. Automatic and manual evaluations on two datasets demonstrate that our method outperforms competitive baselines in producing coherent and style-intensive dialogue responses.
Semantically Scalable Image Coding With Compression of Feature Maps

In this paper, we consider a novel image coding paradigm, termed semantically scalable coding. In the new paradigm, coded bitstream serves for multiple different semantic analysis tasks, and different tasks require different semantic granularities of the image. Thus, the bitstream is designed to be scalable in the sense that progressive decoding of the bitstream provides coarse-to-fine semantic granularities. As a concrete example, we consider the task of coarse-grained and fine-grained image classification. We present a method to compress the multiple deep feature maps that are intermediate representations of an image passing a trained deep network. The deep-layer feature maps can serve for coarse-grained image classification while the shallow-layer feature maps can serve for fine-grained image classification. Experimental results demonstrate the feasibility of the proposed method, as well as the advantage of the semantically scalable coding paradigm.
An Approach for Data Treatment of Solar Photovoltaic Generation

A good solar power photovoltaic generation forecast depends on good quality time series data from measurements of Global Horizontal Irradiance and Solar Power Generation. However, measurement system failures and errors in data handling can corrupt data records with gaps and outliers that undermine forecasting accuracy. Therefore, it is important that the fitting of solar energy prediction models must be preceded by a data analysis in order to detect and correct measurement errors. Given that Global Horizontal Irradiance and Solar Power Generation are correlated variables, this paper aims to present the main characteristics of an offline approach developed for the joint treatment of hourly data values of both variables in a photovoltaic plant. In the proposed methodology, the measurements of Global Horizontal Irradiance and Solar Power Generation are analyzed by using reanalysis data and statistical and data mining techniques for the correction of outliers and the filling of data gaps. The application of the approach is illustrated by the analysis of measurements from a real solar PV system.
Deep Selection: A Fully Supervised Camera Selection Network for Surgery Recordings

Recording surgery in operating rooms is an essential task for education and evaluation of medical treatment. However, recording the desired targets, such as the surgery field, surgical tools, or doctor's hands, is difficult because the targets are heavily occluded during surgery. We use a recording system in which multiple cameras are embedded in the surgical lamp, and we assume that at least one camera is recording the target without occlusion at any given time. As the embedded cameras obtain multiple video sequences, we address the task of selecting the camera with the best view of the surgery. Unlike the conventional method, which selects the camera based on the area size of the surgery field, we propose a deep neural network that predicts the camera selection probability from multiple video sequences by learning the supervision of the expert annotation. We created a dataset in which six different types of plastic surgery are recorded, and we provided the annotation of camera switching. Our experiments show that our approach successfully switched between cameras and outperformed three baseline methods.
Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising

Hyperspectral images (HSIs) are susceptible to various noise factors leading to the loss of information, and the noise restricts the subsequent HSIs object detection and classification tasks. In recent years, learning-based methods have demonstrated their superior strengths in denoising the HSIs. Unfortunately, most of the methods are manually designed based on the extensive expertise that is not necessarily available to the users interested. In this paper, we propose a novel algorithm to automatically build an optimal Convolutional Neural Network (CNN) to effectively denoise HSIs. Particularly, the proposed algorithm focuses on the architectures and the initialization of the connection weights of the CNN. The experiments of the proposed algorithm have been well-designed and compared against the state-of-the-art peer competitors, and the experimental results demonstrate the competitive performance of the proposed algorithm in terms of the different evaluation metrics, visual assessments, and the computational complexity.
Exploring the Security Awareness of the Python and JavaScript Open Source Communities

Software security is undoubtedly a major concern in today's software engineering. Although the level of awareness of security issues is often high, practical experiences show that neither preventive actions nor reactions to possible issues are always addressed properly in reality. By analyzing large quantities of commits in the open-source communities, we can categorize the vulnerabilities mitigated by the developers and study their distribution, resolution time, etc. to learn and improve security management processes and practices.
Two Classes of Linear Codes From Weil Sums

In this article, we consider two classes of p-ary linear codes. This article is a generalization of the recent construction methods given by Jian, Lin and Feng (2019). By choosing different defining sets, two classes of two-weight or three-weight linear codes over finite fields are constructed and their weight distributions are determined based on Weil sums. We also give some examples and some of the linear codes are almost optimal with respect to the Griesmer bound which can be directly employed to obtain democratic secret sharing schemes. Additionally, all nonzero codewords are minimal and they are crucial to apply in association schemes, strongly regular graphs, weakly regular plateaued functions and authentication codes.
Convolutional Neural Networks and Long Short-Term Memory Networks for Textual Classification of Information Access Requests

The e-sic system aims to centralize requests for access to information addressed to the Brazilian Federal Executive. However, the volume of requests received can be an impediment to responses to those requests. The purpose of this article is to create an automatic classifier for these requests. For that, they were analyzed as architectures of the Convolutional Neural Network (CNN) and of Long Short Term Memory (LSTM), as well as a combination of these two architectures in order to identify the best architectures to this problem. The metrics used to evaluate the results were the area under curve roc and accuracy, and the error function used was cross entropy. The study concluded that the CNN network performed the best. Thus, the main contribution of this article is the identification of the most appropriate network architecture for classifying texts of interaction between citizens and government written in Portuguese.
Voice Recognition and Marking Using Mel-frequency Cepstral Coefficients

A real-time voice recognition and marking system was developed in this study to automatically identify different voices of speakers.A microphone array was installed for audio reception.Pre-emphasis, framing and Hamming window, fast Fourier transform, melfrequency, and mel-frequency cepstral coefficients with processing times of 0.001, 0.305, 0.205, 0.049, and 0.546 s, respectively, were used in the system.The total processing time was less than 1.5 s.Unique eigenvalues were obtained for each sound.The results indicated that the proposed system, which is an example of intelligent recording, can be used to automatically record speech in meetings or during classes.