Reviewerly/exHarmony huggingface dataset:
Fault-tolerant technology for big data cluster in distributed flow processing system

In order to overcome the problem of poor recovery and stability of traditional big data fault-tolerant technology, a fault-tolerant technology of big data cluster in distributed flow processing system is proposed. Using the target protocol to build the cache data fault-tolerant mechanism of the dis tributed flow processing system and the disk data fault-tolerant mechanism, so as to build the internal data fault-tolerant mechanism of the system. By using spark application framework, a fault-tolerant model of big data cluster is built to realize the fault-tolerant of big data cluster in distributed flow processing system. The experimental results show that compared with the traditional methods, the recovery rate of big data cluster fault-tolerant technology proposed in this paper is higher, the highest recovery efficiency is 99.83%, the stability is stronger, and it is more suitable for big data fault-tolerant processing of distributed flow processing system.
Towards Fully Automatic 2D Us to 3D CT/MR Registration: A Novel Segmentation-Based Strategy

2D-US to 3D-CT/MR registration is a crucial module during minimally invasive ultrasound-guided liver tumor ablations. Many modern registration methods still require manual or semi-automatic slice pose initialization due to insufficient robustness of automatic methods. The state-of-the-art regression networks do not work well for liver 2D US to 3D CT/MR registration because of the tremendous inter-patient variability of the liver anatomy. To address this unsolved problem, we propose a deep learning network pipeline which - instead of a regression - starts with a classification network to recognize the coarse ultrasound transducer pose followed by a segmentation network to detect the target plane of the US image in the CT/MR volume. The rigid registration result is derived using plane regression. In contrast to the state-of-the-art regression networks, we do not estimate registration parameters from multi-modal images directly, but rather focus on segmenting the target slice plane in the volume. The experiments reveal that this novel registration strategy can identify the initial slice phase in a 3D volume more reliably than the standard regression-based techniques. The proposed method was evaluated with 1035 US images from 52 patients. We achieved angle and distance errors of 12.7 ± 6.2° and 4.9 ± 3.1 mm, clearly outperforming state-of-the-art regression strategy which results in 37.0 ± 15.6° angle error and 19.0 ± 11.6 mm distance error.
Semantic analysis of the Oracle Radicals using Similarity Strategy based on the Yolov2 network

Oracle bone inscriptions are the earliest character system in China, and its pattern is similar to the hieroglyphic. This paper selects 14 classes, including six different font evolution forms: oracle bone inscriptions, bronze inscriptions, states inscriptions, seal inscriptions, official scripts and regular scripts. Through the analysis of the combination of oracle radicals and the heads of oracle bones, and use convolutional neural networks to analyze the evolution of the oracle bones structure of the evolution process. In this experiment, this paper uses Yolov2 model to achieve oracle radical detection, and the results show that the oracle radical detection accuracy can reach 95.5%, meanwhile, we prove that some oracle radicals have similar structure relationships between the evolution of characters.
2048-like games for teaching reinforcement learning

2048-like games are a family of single-player stochastic puzzle games, which consist of sliding numbered-tiles that combine to form tiles with larger numbers. Notable examples of games in this family include Threes!, 2048, and 2584. 2048-like games are highly suitable for educational purposes due t o their simplicity and popularity. Numerous machine learning methods have been proposed to play 2048-like games; the application of these techniques can help students gain first-hand experience in implementing machine learning algorithms. This paper proposes a guideline for using 2048-like games for teaching reinforcement learning and computer game algorithms, while also summarizing our experience of using 2584 and Threes! as pedagogical tools that were well received judging by student feedback in two graduate level courses.
Recognition of Chinese finger sign language via gray-level co-occurrence matrix and K-nearest neighbor algorithm

Having a definite expression, Chinese finger sign language obtains vital position in Chinese sign language recognition. In this work, we proposed a method for identifying Chinese finger sign language via gray-level co-occurrence matrix (GLCM) and K-nearest neighbor (KNN) algorithm, which was a vision-based sign language recognition. We captured gesture images and segmented the hand shape, then converted these two-dimensional images into a gray-level co-occurrence matrix and extracted features based on appropriate angle and distance. At the same time, the KNN algorithm was used to classify based on the 10-fold cross-validation mode. We used this model to test 1110 finger images from 30 categories. Finally, the best classification accuracy of 95.3% was achieved. Therefore, our method was considered to be effective for Chinese finger sign language classification.
Density Peaks Spatial Clustering by Grid Neighborhood Search

In the application of spatial data clustering, the density-based clustering method can achieve good results. DPC algorithm is a density-based clustering algorithm, which can discover the clustering of irregular shapes. The algorithm is trustworthy of clustering results, simple to implement, and parameter robust. However, the DPC algorithm needs to calculate the distance between the two pairs. It takes a long time to calculate the local density and high-density distance for large-scale spatial data sets. To solve the problem of low efficiency in large datasets, this paper improved the DPC algorithm and proposed a density peak clustering algorithm, DPSCGNS, based on grid neighborhood search. DPSCGNS map raw data to grid cells and redefine the local distance and high-density distance of grid cells. By using the grid to index neighborhood information, the local density and high-density distance of grid cells can be calculated rapidly. Experiments on several data sets demonstrate that the efficiency of DPSCGNS algorithm is improved without decline on clustering effect.
Nonlinear Motion and Occlusion Handling for Pedestrian Tracking: Kalman Filter Approach

This paper addresses the problem of multiple pedestrian tracking in realistic conditions for linear and non-linear motion of pedestrians with and without occlusion. The approach used involves a robust tracking algorithm using Kalman filter that connects tracking with detection to locate and maintain the identity of the pedestrians even during occlusions by taking the input measurement data from detection system for every frame. Kalman Filter is preferred for object tracking as it is flexible in case, motion of object is other than pure translational motion and performs better than other algorithms like mean-shift method in noisy atmosphere and noisy input. The Kalman filter used also acts as error corrector for limited number of erroneous input samples in measurement data. The tracking algorithm designed has been tested and verified for different cases of non-linear motion of pedestrians and occlusion between pedestrians for the custom data set and the comparison is reported.
Higher-order pattern generalization modulo equational theories

Abstract We consider anti-unification for simply typed lambda terms in theories defined by associativity, commutativity, identity (unit element) axioms and their combinations and develop a sound and complete algorithm which takes two lambda terms and computes their equational generalizations in the form of higher-order patterns. The problem is finitary: the minimal complete set of such generalizations contains finitely many elements. We define the notion of optimal solution and investigate special restrictions of the problem for which the optimal solution can be computed in linear or polynomial time.
A Faster Decoding Technique for Huffman Codes Using Adjacent Distance Array

The evaluation of Huffman-based algorithm can be measured in two ways: one in terms of space and another is decoding speed. As memory requirement is going to be reasonable, the time effectiveness of Huffman decoding is more significant. So, this research concentrated on making the decoding speed faster for Huffman codes as well as maintaining the compression ratio for text data. For this, a new technique based on an adjacent distance array is introduced here. Adjacent distance array can save the distances for each adjacent symbol by calculating their ASCII values. Also, we have used a threshold value so that we can skip large codeword for all the adjacent symbols that would be produced by traversing whole Huffman tree. During the decoding process, the adjacent distance array is used to decode adjacent symbols by computing the distances instead of traversing the whole Huffman tree. We have described the encoding and decoding processes for the proposed technique. Besides, we have tested the proposed method on a few renowned corpuses and compared the performance with existing Huffman-based methods. Therefore, we have found that the proposed method performs more effectively in terms of compression–decompression time.
An efficient Algorithm for Frequent Pattern Mining over Uncertain Data Stream

In recent years, due to the increasing demand for real-time data processing, data mining on stream gradually becomes a research hotspots, mining over uncertain data stream is a more practical compare to over precise data stream, which doesn't take uncertainty of data in real world into consideration. Most of the popular algorithms of uncertain data stream mining are based on tree structure, all branches in the tree need be retrieved to find all frequent patterns while is time-consuming. Considering this defect of tree structure, a new structure Uncertain Item-lists (UIT-lists) and a efficient Uncertain Frequent Stream mining algorithm (UFS-mine) is presented. Experiments show our approach has great performance on runtime and with same memory usage compare to an up-to-state uncertain stream data mining algorithm.
Research on SSD Algorithm Based on Occlusion Detection

Pedestrian detection as a basic identification technology, providing technical support for many areas such as security monitoring and automatic driving, and has a wide range of application scenarios. This article is based on the Single Shot Detector(SSD)target detection algorithm, a pedestrian detection system based on the SSD target detection framework was trained using a self-built occlusion pedestrian data set for the specific goal of occluding pedestrians, then the features learned by deep convolution neural network are more robust. For the occlusion pedestrian detection, the network structure of the SSD model has been modified accordingly. The depth can be divided into convolutions to simplify the feature extraction network, reduce the number of network parameters, reduce the training difficulty, and improve the algorithm operation speed. Further, in the prenetwork of the SSD model, the SE-Inception structure was added, and the a priori frame in the network was redesigned to make it easier to match the shape of the pedestrian. In the case of pedestrians who are prone to environmental occlusion and mutual occlusion, data on occlusion pedestrians is added to the training set of the model. The Repulsion Loss enhanced model is used to block the detection of pedestrians, and the experimental results show that the detection performance of the improved SSD model has been greatly improved.
Low-illumination image enhancement algorithm based on multi-feature fusion

Abstract In order to improve the effect of image acquisition under the condition of low illumination, a low illumination image enhancement algorithm based on multi-feature fusion is adopted in this paper. In this algorithm, the illumination information is extracted by bilateral filtering, and the illumination parameters are processed by adaptive gamma correction and limited contrast histogram equalization algorithm. The final illumination parameters are obtained by the feature weighted fusion of the processed parameters and the original illumination parameters. The RGB image is obtained by combining the illumination and reflection parameters, and the enhancement of the image is realized. The experimental results show that the algorithm used in this paper can not only improve the brightness of the image, but also enhance the characteristic parameters of the image, remove the noise, and make the color information of the image fuller and richer
An Integrated Approach for Face Recognition Using Multi-class SVM

According to recent results, the support vector machine (SVM) classifiers have excellent facial recognition accuracy in the pattern recognition compare to other classification methods. Moreover, this method provides high performance in generalization, processing small samples, and tackling high-dimensional data. Undertaking these advantages of Support Vector Machine (SVM), an efficient novel approach is proposed in this paper, using multi-class SVM to recognize face. In this facial recognition approach, the Histogram of Oriented Gradients (HOG) features extraction method is undertaken to extract facial images feature. Then the oneversus-one SVM method is adopted to accomplish multi-class classification on feature vectors of the facial images. The ORL, the YALE face, and along with Self-created databases are used to perform experiments. The experimental results show the accuracy, and that was above 96 percent recognition rate on both databases, and along with Self-created database.
Experimental exploration on loss surface of deep neural network

Abstract The loss function of the deep neural network is high dimensional, nonconvex and complex. So far, the geometric properties of the loss surface of the neural network have not been well understood. Different from most theoretical studies on the loss surface, this article makes the experimental exploration on the loss surface of the deep neural network, including trajectories of various adaptive optimization algorithms, the Hessian matrix of the loss function of the deep neural network, the curvature of the loss surface along the trajectories of the various adaptive optimization algorithms. It is found that the gradient direction of the adaptive optimization algorithms is almost perpendicular to the direction of the maximum curvature of the loss surface, while the gradient directions of the stochastic gradient descent (SGD) algorithm do not have such a rule. The Hessian matrix of the loss surface along the trajectory of the optimization algorithm is degraded, which is inconsistent with the hypothetical that nonsingular of the Hessian matrix in many theoretical studies of deep learning. Besides, this article proposes a new ensemble learning method of the neural network based on the scaling invariance of the ReLu neural network and mode connectivity.
Neural Networks as Classification Mechanisms of Complex Human Activities

Within this paper, we present two neural nets for view-independent complex human activity recognition (HAR) from video frames. For our study here, we reduce the number of frames produced by a video sequence given that we can identify activities from a sparsely sampled sequence of body poses, and, at the same time, we are able to reduce the processing complexity and response while hardly affecting the accuracy, precision, and recall. To do so, we use a formal framework to ensure the quality of data collection and data preprocessing. We utilize neural networks for the classification of single and complex body activities. More specifically, we consider the sequence of body poses as a time-series problem given that they can provide state-of-the-art results on challenging recognition tasks with little data engineering. Deep Learning in the form of Convolutional Neural Network (CNN), Long Short-Term Neural Network (LSTM), and a one-dimensional Convolutional Neural Network Long Short-Term Memory model (CNN-LSTM) are used as benchmarks to classify the activity.
Real-Time Activity Detection of Human Movement in Videos via Smartphone Based on Synthetic Training Data

Current research in the domain of video activity detection focuses on real-time activity detection. This includes multiple approaches in the mobile environment, such as the detection of correct motion sequences in the sports and health area or in safety-relevant environments. Current trends focus on the use of 3D CNNs. This work describes a approach to combine the results of a human skeleton point detector with an LSTM on mobile devices. Frameworks for pose detection are combined with LSTMs for activity detection with sensor data, optimized for the mobile area. The resulting system allows the direct detection of a person pose and the classification of activities in a video recorded with a smartphone. The successful application of the system in several field tests shows that the described approach works in principle and can be transferred to a resource-limited mobile environment by optimization.
Face Recognition Based on Shallow Convolutional Neural Network Classifier

Fast and accurate user identification and verification is always desirable. Face recognition, which is machine recognization of person face by analysing patterns on facial features is becoming important for security and validation. Less interaction from user contributes high enrolment as well as easily applicable for current technology further adds its importance. In this regard, we propose a Convolutional Neural Network (CNN) based face recognition technique previously done with eigenfaces[8] but CNN has better accuracy. Entire process is divided into four phases: capturing the image, features extraction, classification and matching. At&t faces dataset is used in this paper. Input images are first fed for face detection. Face detection in input images are performed using Viola Jones algorithm. Convolutional Neural Network (CNN) is applied for feature extraction and classification. The result obtained in this paper shows that - recall is 0.992, precision is 99.4, f1 score is 99.1 and f1 beta score is 0.992 for 70-30 split of dataset, i.e, 70% dataset used as training dataset and 30% as testing dataset.
Negotiating human engagement and the fixity of computational design: Toward a performative design space for the differently-abled bodymind

Computational design affords agency: the ability to orchestrate the material, spatial, and technical architectural system. In this specific case, it occurs through enhanced, authored means to facilitate making and performance—typically driven by concerns of structural optimization, material use, and responsivity to environmental factors—of an atmospheric rather than social nature. At issue is the positioning of this particular manner of agency solely with the architect auteur. This abruptly halts—at the moment in which fabrication commences—the ability to amend, redefine, or newly introduce fundamentally transformational constituents and their interrelationships and, most importantly, to explore the possibility for extraordinary outcomes. When the architecture becomes a functional, social, and cultural entity, in the hands of the idealized abled-bodied user, agency—especially for one of an otherly body or mind—is long gone. Even an empathetic auteur may not be able to access the motivations of the differently-abled body and neuro-divergent mind, effectively locking the constraints of the design process, which creates an exclusionary system to those beyond the purview of said auteur. It can therefore be deduced that the mechanisms or authors of a conventional computational design process cannot eradicate the exclusionary reality of an architectural system. Agency is critical, yet a more expansive terminology for agent and agency is needed. The burden to conceive of capacities that will always be highly temporal, social, unpredictable, and purposefully unknown must be shifted far from the scope of the traditional directors of the architectural system. Agency, and who it is conferred upon, must function in a manner that dissolves the distinctions between the design, the action of designing, the author of design, and those subjected to it.
Volumetric Registration of Brain Cortical Regions by Automatic Landmark Matching and Large Deformation Diffeomorphisms

A well-known challenge in fMRI data analysis is the excessive variability in the MR signal and the high level of random and structured noise. A common solution to deal with such high variability/noice is to recruit a large number of subjects to enhance the statistical power to detect any scientific effect. To achieve this, the morphologies of the sample brains are required to be warped into a standard space. However, human's cerebral cortices are highly convoluted, with large inter-subject morphological variability that renders the task of registration challenging. Currently, the state-of-the-art non-linear registration methods perform poorly on brains' cortical regions particularly on aging and clinical populations. To alleviate this issue, we propose a landmark-guided and region-based image registration method. We have evaluated our method by warping the brain cortical regions of both young and older participants into the standard space. Compared with the state-of-the-art method, we showed our method significantly (t = 117; p = 0) improved the overlap of the cortical regions (Dice increased by 57%). We concluded our method can significantly improve the registration accuracy of brain cortical regions.
Image Edge Detection Based on Interval Type-2 Fuzzy Similarity

This paper presents a new method for edge detection of color images that is based on interval type-2 fuzzy similarity. Firstly, the noise of the image is estimated, and the image with noise higher than the threshold value is filtered out. Then this paper combines the type-2 fuzzy set theory to calculate the adjacent pixel similarity of each channel pixels in the image. After type reduction, it is divided into the edge and non-edge pixels by threshold. Compare to Sober operator, Canny operator, LOG operator and type-1 fuzzy similarity, the proposed method has a great balance noise resistance and edge detection accuracy.
Multi-Level Attentional Network for Aspect-based Sentiment Analysis

Aspect-based Sentiment Analysis (ABSA) has attracted much attention due to its capacity to determine the sentiment polarity of the certain aspect in a sentence. In previous works, great significance of the interaction between aspect and sentence has been exhibited in ABSA. In consequence, a Multi-Level Attentional Networks (MLAN) is proposed. MLAN consists of four parts: Embedding Layer, Encoding Layer, Multi-Level Attentional (MLA) Layers and Final Prediction Layer. Among these parts, MLA Layers including Aspect Level Attentional (ALA) Layer and Interactive Attentional (ILA) Layer is the innovation of MLAN, and its function is to focus on the important information and obtain multiple levels' attentional weighted representation of aspect and sentence, which makes MLAN superior than the most of deep learning models for ABSA. In the experiments, MLAN is compared with classical TD-LSTM, MemNet, RAM, ATAE-LSTM, IAN, AOA, LCR-Rot and AEN-Globe on SemEval 2014 Dataset. The experimental results show that MLAN outperforms those state-of-the-art models greatly. And in case study, the works of ALA Layer and ILA Layer have been proven to be effective and interpretable.