Reviewerly/exHarmony huggingface dataset:
Pinyin as a Feature of Neural Machine Translation for Chinese Speech Recognition Error Correction

Text correction after automatic speech recognition (ASR) is an important method to improve the speech recognition system. We regard the speech error correction as a translation task—from the language of bad Chinese to the language of good Chinese. We propose a speech recognition error correction algorithm based on neural machine translation (NMT) model. The algorithm is characterized by Chinese Pinyin coding, using a multilayer convolutional encoder-decoder with attention neural network. In the WeChat speech transcription data set we collected, our model substantially outperforms all prior neural approaches on this data set as well as the strong statistical machine translation-based systems. Our analysis shows the superiority of convolutional neural networks in capturing the local context via attention and thereby improving the coverage in speech transcription errors. By boosting multiple modes, using data augmentation and 3-gram language model tricks, our novel algorithm makes the error rate on the test set decreased by 26.2% on average. Our results show that using a multilayer convolutional encoder-decoder with Pinyin feature is able to achieve state-of-the-art performance in text correction after speech recognition.
Individual Recognition Based on Transfer Learning for Wireless Network Devices

Due to the complexity of the electromagnetic environment and the non-cooperative characteristics of the target in the process of individual recognition of wireless network devices, the amount of data acquired is small, and the data required for training the model cannot be enough. For this small samples situation, a new method of individual recognition of wireless network devices based on transfer learning is proposed. We combine a convenient signal processing method with transfer learning in this paper. The acquired signal is divided into source domain and target domain. We extract the feature of transient signal and get the weight of feature. Then we transfer the knowledge of the source domain to the target domain. The training model is reconstructed by adjusting the weight of the source domain samples, which solves the problem of insufficient training samples in the small samples situation. The results show that in the case where the number of target domain samples is less than 30, the recognition rate has improved obviously. It enriches the application of transfer learning in the field of individual recognition.
A scene classification algorithm of visual robot based on Tiny Yolo v2

When robots and unmanned vehicles are running, they will perform corresponding actions according to different scenes. Traditionally, convolutional neural network is used to classify scene images with low accuracy. This paper proposes a kind of scene classification algorithm (end-to-end multi-object recognition scene classification algorithm, hereinafter referred to as EMORSC algorithm) based on the object recognition algorithm. It is to analyze the main object on the picture and classify it according to the scene in which the object is located. The final accuracy can be more than 80% higher than the classification result of convolutional neural network. In the future, this algorithm can be applied to intelligent logistics, intelligent driving and other fields, and has high practicability in these fields.
ARComposer

Augmented Reality (AR) is rapidly gaining popularity, enhancing human perception of the real world by augmenting digital experiences. Existing tools for authoring AR scenes are either template based or require domain knowledge from experts, and are therefore restrictive. ARComposer is a novel interface that enables easy authoring of AR experiences from free-form text describing the scene. Our proposed interface allows creators to compose varied scenes comprising of multiple objects with diverse relationships to each other as well as human models with animations, starting merely with a textual description. A qualitative evaluation shows that ARComposer provides a good flow experience to its users. Furthermore, a crowd-sourced experiment evaluating various aspects of the rendered AR scenes indicates the viability of the proposed approach.
Dynamics of a Unicycle-Type Wheeled Mobile Manipulator Robot

Mathematical modeling is widely used in robotics to display behaviors and design control algorithms; the dynamic model of a robot describes the movements considering the action of internal and external forces of the system. This paper presents the dynamics of a unicycle mobile manipulator robot using the Euler-Lagrange proposal and conditioning the model to obtain the output velocities in the robot from the desired velocities. Additionally, the dynamic model is complemented by the identification of parameters using experimental data obtained from the AKASHA robot. The results present the signals applied in the identification, the values of the dynamic constants are found and the validation data which indicate a correct performance of the proposal are graphically shown.
Hybrid Solar Tracking System: A Brief Overview

The use of renewable energy source as an alternative source of energy to meet the energy demand has been increasing at a regular pace. The use of solar energy as an alternative source has been increasing due to its availability, and pollution free energy production. The use of Photovoltaic cell (PV) to convert solar energy into electric energy is very less due its less efficiency which is about 12-15%. So to increase the overall output from the PV system, a tracking system is required to track the movement of solar irradiance throughout the day so as to make the solar panel always remain in direct sunlight. A solar tracking though effectively tracks the movement of the sun throughout the day but it suffers when the solar intensity is affected due to critical weather conditions. Hence to overcome such situations a hybrid type racking system can be used which can overcome such situations and can produce better result at its output. Here a basic hybrid solar tracking system along with its control algorithm is describe to give an idea about its functioning and control process. The work presented here shows a tracking system which could effectively produce better result with just the proper utilization of the existing system.
Extracting Ego-Centric Social Networks from Linked Open Data

Linked Open Data (LOD) refers to freely available data on the WWW that are typically represented using Resource Description Framework (RDF). LOD is an invaluable source of rich and structured information, and enables a wide range of new applications, such as Social Network Analysis (SNA). In this paper, we address the extraction of social networks from LOD using SPARQL language, and we present various patterns to extract ego-centric networks. We also present two case studies: i) influence networks of intellectuals, and ii) co-acting networks, to demonstrate the applicability and usefulness of the approach.
Hand Gesture Based Human-Computer Interaction Using Arduino

In this era of evolving technologies, most of the human interactions with the electronic devices are becoming smart. For elderly and blind people, it may be difficult to use mouse and keyboard for every operation especially when watching videos in computer, increasing or decreasing of volume and play pause, and when using web browsers, scrolling up or down and swapping of the taps. This paper has tried to provide solutions to the above-mentioned problem by reducing the usage of mouse and keyboard using IOT technologies. Gesture recognition is one of the essential techniques to build user-friendly interface. This paper deals with Human Computer Interaction(HCI) consists of hardware components such as Arduino UNO and the ultrasonic sensors with the software components such as Arduino and Python IDLE for exchanging the information or communication between the user and machine.
SKNN Algorithm for Filling Missing Oil Data Based on KNN

Abstract Along with the rapid development of science and technology information in contemporary society, massive data has become a common phenomenon in information processing in various industries, and various data quality problems have also followed. Among them, data loss is a common problem. In the process of oilfield production, the dynamic data of production wells is increasing continuously every day, and data missing problems often occur. Aiming at the missing data, this paper proposes an improved data filling algorithm SKNN based on KNN. Based on the KNN algorithm, the algorithm uses the SMOTE algorithm to randomly generate the fill data range, and uses the multi-filling idea to find the weighted average as the fill data. The experiment with the production data of a well area in Daqing oilfield as the sample set verified that the SKNN algorithm could not only fill the data, but also improve the accuracy.
Augmented Reality Based Visualization Method for Spacecraft Cable Assembly Process

Abstract At present, the spacecraft cable assemble process has problems such as inconvenient process reading and great difficulty for process understanding. Based on the advantages of augmented reality technology, this paper proposes a visualization method for spacecraft cable assembly process to solve these problems. Firstly, the tracking method based on Vuforia and SLAM technology is used to improve the accuracy of registration while expanding the tracking range. Secondly, this paper adjusts and lightweights the spacecrafts design model, uses Unity3D to create visual guidance, and then deploys the prototype system to HoloLens. Finally, verifies the application in the cable assembly process of a certain type of spacecraft, which proves the advantage of the method based on the augmented reality.
Ardestan

This paper describes Ardestan, a visual programming language (VPL) for Arduino currently under development. The language is designed with art and design students in mind as novice programmers. Although multitasking and event scheduling are essential for interactive prototypes, novices often face difficulty in implementing these features. By borrowing the language design from Pure Data, a VPL for interactive music, Ardestan facilitates the implementation of multitasking and event scheduling, while generating C++ code for a standalone Arduino system. Such features would be beneficial to support prototyping activity by art and design students in undergraduate interaction design courses.
Causal reasoning of emergency cases based on Fuzzy Cognitive Map

Emergency case reasoning is essential to emergency management. In this paper, we propose a novel emergency case reasoning method based on fuzzy cognitive map (FCM), to model the inherent causal relationships in emergency cases. Specifically, we first obtain emergency domain elements and mine their association rules, by leveraging natural language processing technology and FT-Growth dada mining algorithm. We then design an effective algorithm to learn causal knowledge links from the gathered association rules. Finally, we construct an FCM regarding emergency events and show the reasoning process. Experiments on the gas explosion demonstrate that the proposed method can successfully model the internal causal relationships of emergency elements, and the development of the emergency event can be reflected by the reasoning process of the proposed method according to its varying variables of state. The proposed method can effectively inference and predict the tendency of emergency cases based on the reasoning process, which can further provide valuable decision supports to emergency responders.
Estimating Focused Object using Smooth Pursuit Eye Movements and Interest Points in the Real World

User calibration is a significant problem in eye-based interaction. To overcome this, several solutions, such as the calibration-free method and implicit user calibration, have been proposed. Pursuits-based interaction is another such solution that has been studied for public screens and virtual reality. It has been applied to select graphical user interfaces (GUIs) because the movements in a GUI can be designed in advance. Smooth pursuit eye movements (smooth pursuits) occur when a user looks at objects in the physical space as well and thus, we propose a method to identify the focused object by using smooth pursuits in the real world. We attempted to determine the focused objects without prior information under several conditions by using the pursuits-based approach and confirmed the feasibility and limitations of the proposed method through experimental evaluations.
Color Image Segmentation Via Wavelet Frames

Image segmentation aims at partitioning an image into its constituent parts, which plays a crucial role in practical applications. In this paper, we present a wavelet frame-based model for color images segmentation, which can be regarded as a discretization to the classical Chan-Vese (C-v) model. The advantage of the wavelet frame-based approach is that it has fast algorithm and is able to extract important features of the input images. We then apply the alternating direction method of multipliers (ADMM) algorithm to solve the model. The experiments on some color image segmentation tasks indicate that our algorithm performs favorably against several existing methods.
Detecting outlier and poor quality medical images with an ensemble-based deep learning system

There are numerous reasons why inappropriate data can occur in a database. It is essential to detect and eliminate these elements for getting accurate results and conclusions. The process of filtering anomalies generated in the database is called outlier detection. The outliers, that are extreme values deviating from other observations on data, indicate a variability in a measurement, experimental errors or a novelty. In this paper, an ensemble-based outlier detection method is presented, where the members of the ensemble are convolutional neural networks (CNNs) combined with a Support Vector Machine (SVM) classifier. Ensemble-based methods are highly popular approaches that increase the accuracy of a decision by aggregating the opinions of individual voters. From hybrid models constructed by the neural networks and the SVM classifier, we have created an ensemble system that makes decision based on the conception of majority voting and results in very accurate outlier filtering. It actually serves as a pre-filter that can be integrated into a more exhaustive image analysis process with rejecting images that fall outside the domain or have poor quality. We have tested the performance of the proposed method for filtering databases consisting of retinal and skin lesion images, respectively. Our results show that the proposed ensemble system improved the effectiveness of the member hybrid CNN-SVMs in the outlier detection for retinal images and for skin lesions, as well.
Learning Multilingual Sentence Embeddings from Monolingual Corpus

Learning multi-lingual sentence embeddings usually requires large scale of parallel sentences which are difficult to obtain. We propose a novel self-learning approach which is capable of learning multi-lingual sentence embeddings from monolingual corpora. Our assumption is that, irrelevant to languages, sentences appearing in similar contexts are similar. Thus, we first train monolingual sentence embeddings of different languages with shared parameters as initialization. Then we iteratively extract similar sentence pairs and exchange their positions regardless of languages. Through their relations to their new contexts we predict the similarities between a similar sentence pair. Our experiments show that the proposed approach outperforms existing unsupervised approaches and is competitive to supervised approaches.
Semi-NMF network for image classification

Semi non-negative matrix factorization (Semi-NMF) is an algorithm that gets a low-dimensional representation of a database. In this paper, we propose an efficient convolutional neural network (CNN) based on Semi-NMF to address the image classification problem. Unlike the traditional learning approach of CNN, convolutional filters are achieved by the Semi-NMF on input images. In the output layer, binary hashing and blockwise histograms are used to obtain the feature maps. To get better classification results, we introduce the weakly supervised Semi-NMF network which incorporates known attribute information for computing the convolutional filters in first layer. Experiments on MNIST dataset show our methods perform better than the state-of-the-art methods.
Overview of the Current State of Quantum-Based Technologies

Abstract In this paper, we review the current state and discuss prospects for quantum technologies relating to computation, communications, and sensing. We also consider defense-related applications, especially in the maritime domain.
Compare the accuracy of different classfiers using Breast Cancer in Big Data

At present, breast cancer in women is most is the prominently discovered life-threatening cancer in women and took over too many life's of women all around the world. This project deals with the Breast Cancer Wisconsin dataset to compare the accuracy of various machine learning algorithm in predicting the breast cancer in women. The various classification model are built and trained with the Wisconsin dataset. The different classifiers that are used to construct the model are Naive Bayes, Support Vector Machine, Regression Tree, Random Forest and K-Nearest Neighbor. The efficient working of these model are assessed by estimating the accuracy score of each model with the usage of unstandardized and standardized dataset. Once the performance of the model are evaluated, the optimal working algorithm are used to identify the type of breast cancer in the patient entry.
See Through the Windshield from Surveillance Camera

This paper attempts to address the challenging task of seeing through the windshield images captured by surveillance cameras in the wild. Such images usually have very low visibility due to heterogeneous degradations caused by blur, haze, reflection, noise etc., which makes existing image enhancing methods inapplicable. We propose a windshield image restoration generative adversarial network (WIRE-GAN) to restore and enhance the visibility of windshield images. We adopt the weakly supervised framework based on the generative model, which has effectively released the request of paired training data for a specific type of degradation. To generate more semantically consistent results even in extreme lighting conditions, we introduce a novel content-preserving strategy into the proposed weakly-supervised framework. To make the image restoration more reliable, the WIRE-GAN network constructs a sort of content-aware embedding space and enforces the constraint of the restored windshield images being closer to the original input in the embedding space. Moreover, we collect a large-scale windshield image dataset (WIRE dataset) to validate the advantage of our method in improving the image quality, and further evaluate the impact of windshield restoration on the vehicle ReID performance.
Tracing and implementation of IMM Kalman filtering feed-forward compensation technology based on neural network

UAV has great varieties, large controllable velocity and angular velocity, which makes high requirements on automatically identifying capability and tracking accuracy of ground search and tracking system. It happens frequently that servo feed-forward compensation technology is added in the search and tracking system to improve the tracking accuracy. However, accurate estimations of target velocity and acceleration becomes the difficult point of controlling feed-forward compensation technology. This paper proposes to adopt IMM Kalman filter technology based on neural network to estimate velocity and acceleration of the moving targets, which served as input variable of the servo feed-forward compensation system to eliminate the error of the missing distance caused by velocity and acceleration. Neural network can identify target types and makes self-adaptive adjustment on IMM Kalman filter parameters, which is helpful to improve estimation accuracy. Experimental results show that IMM Kalman filter feed-forward compensation technology based on neural network in the search and tracking system can improve the tracking accuracy of the system by more than 3 times than the conventional Kalman filter compensation, and the model verification is effective.
Light field image compression using Versatile Video Coding

Light field cameras (or plenoptic cameras) made it possible to not only capture information about the light intensity in a scene, but also the light direction traveling in space. The resulting light field images added two extra spatial dimensions to conventional 2D images by capturing a scene from different angles. Light field technology is expected to deliver the needed six-degrees-of-freedom in virtual reality, and it is a cornerstone technology in holography. Due to the vast amount of data a light field needs to capture for representing high-resolution images, the need of new coding solutions has been considered. While coding standards aiming at plenoptic images are something to be expected in the near future, video coding standards are one of the most efficient solutions to compress light fields. There are countless scanning topologies for restructuring a light field image into a 2D video, and similarly, numerous prediction structures to be used for inter-prediction. In this paper, we provide a comparison of light field image coding performance using the three latest generations of video compression standards. Moreover, we assess a set of coding structures aiming for optimal compression rates as well as random access capabilities. Results show the necessary trade-off between bandwidth and potential random access between light field views at decoding level. Bitrate savings up to 25% were achieved at certain cases where the proposed prediction structure is used over a common scheme used in the literature. The findings of this overview provide a useful benchmark for future development of light field image coding solutions using video coding standards.
Layer-Wise Invertibility for Extreme Memory Cost Reduction of CNN Training

Convolutional Neural Networks (CNN) have demonstrated state-of-the-art results on various computer vision problems. However, training CNNs require specialized GPU with large memory. GPU memory has been a major bottleneck of the CNN training procedure, limiting the size of both inputs and model architectures. Given the ubiquity of CNN in computer vision, optimizing the memory consumption of CNN training would have wide spread practical benefits. Recently, reversible neural networks have been proposed to alleviate this memory bottleneck by recomputing hidden activations through inverse operations during the backward pass of the backpropagation algorithm. In this paper, we push this idea to extreme and design a reversible neural network with minimal training memory consumption. The result demonstrated that we can train CIFAR10 dataset on Nvidia GTX750 GPU only with 1GB memory and achieve 93% accuracy within 67 minutes.