Reviewerly/exHarmony huggingface dataset:
Invisible Marker: Automatic Annotation of Segmentation Masks for Object Manipulation

We propose a method to annotate segmentation masks accurately and automatically using invisible marker for object manipulation. Invisible marker is invisible under visible (regular) light conditions, but becomes visible under invisible light, such as ultraviolet (UV) light. By painting objects with the invisible marker, and by capturing images while alternately switching between regular and UV light at high speed, massive annotated datasets are created quickly and inexpensively. We show a comparison between our proposed method and manual annotations. We demonstrate semantic segmentation for deformable objects including clothes, liquids, and powders under controlled environmental light conditions. In addition, we show demonstrations of liquid pouring tasks under uncontrolled environmental light conditions in complex environments such as inside the office, house, and outdoors. Furthermore, it is possible to capture data while the camera is in motion so it becomes easier to capture large datasets, as shown in our demonstration.
Edge-Driven Object Matching for UAV Images and Satellite SAR Images

The task of matching between images acquired by terminal equipment and satellites is important and challenging due to the dramatic viewpoint changes and unknown orientations, especially with different imaging sensors. In this paper, we firstly present the task to match the optical/infrared images acquired by UAVs with satellite SAR images. Many previous works mainly focused on matching with the images of the same modality, and may not perform well to our task. To overcome the difficulties caused by the diversity among the three modalities of data, we mine the common features of them and propose a novel edge-driven matching framework to find the correspondence between the UAV images and SAR images. Experimental results demonstrate the effectiveness and superiority of our method.
Emotion recognition by inclusion of age and gender parameters with a novel hierarchical approach using deep learning

In this paper, the emotion, gender and age recognition has been dealt in real time video stream. This model implements Squeezenet and mini-Xception architectures that are combined in a hierarchical order. In order to train these different networks, different types of large labelled datasets have been utilized which are available publicly through a semi-supervised pipeline to reduce the annotations efforts and time. The results show that the emotion-based system gave better performance than the previous model-based system.
Mining Heterogeneous Data for Formulation Design

Formulated products such as cosmetics, personal care, pharmaceutical products and industrial products such as paints and coatings are a multi-billion dollar industry. Experts carry out designing of new formulations in most of these industries based on their knowledge and basic search from online and offline resources. Reference data for formulation design comes in several formats and from multiple sources with diverse representation. We present an approach to mine the heterogeneous data for formulation design with case studies of cosmetics and steel coating industries. Our contribution is threefold. First, we show data extraction and mining techniques from multi-source and multi-modal text data. Second, we describe how we store and retrieve the data in graph databases. Lastly, we demonstrate the use of extracted and stored data for a simple recommendation system based on data search techniques that aid the experts for the synthesis of new formulation design.
Design and implementation of image kernels using reversible logic gates

This study presents the implementation of image kernels used for filtering and enhancing the images using reversible logic gates, a first in reversible logic literature. Image enhancement/filtering is achieved by performing convolution of an image with a filter kernel. This work proposes reversible logic based design and implementation of six filter kernels. The filter kernels implemented are Gaussian blur, Laplacian outline, Sobel, Emboss, Sharpen and Prewitt edge detection. The kernels are implemented individually using reversible logic gates and the designs are measured in terms of quantum cost, garbage outputs, ancilla inputs and gate count. The functional verification is carried out using 512 × 512 standard images on Kintex 7 FPGA platform. The filtered images from the proposed design have an average structural similarity index of 0.92.
Research on the Design of Multicolored Ceramic Cultural Creative Products Based on Computer VR Technology

Abstract Speaking of my country’s excellent traditional culture, ceramic culture is a very important historical connection point in China’s history. Ceramics are also representative artifacts of Chinese culture. For now, the inheritance and development of ceramic culture is a hot topic in the current academic circles. On this basis, the design of creative products based on multicolored ceramics has also been raised by some ceramic enthusiasts [1] . Now, the rise of computer-based VR technology has also added a lot of color to our lives. On the basis of the design and production process of traditional ceramic products, how to use cutting-edge computer VR technology to assist the design of colorful ceramic cultural creative products to improve the virtual human-computer interaction experience of some ceramic enthusiasts is also some special research. The specific development direction of high-tech companies.
Semantic role labelling using transfer learning model

Abstract Semantic role labelling plays a major role in determining the main predicate and related arguments and its relationship to the predicate. These arguments identify the semantic roles in a sentence such as agent, goal, result, etc. Many approaches have been made to perform the task of semantic role labelling. Many recent works incorporated neural networks making use of syntactic features of the given text such as part of speech tags, POS tagged dependency tree, etc. Other works included the use of BERT along with LSTM and MLP to create semantic role labeling models. In this work, BERT model has been fine tuned to be used as a classifier to detect the main predicate, arguments of the predicate and also the relationship arguments maintain with the predicate in the sentence. Using BERT to build a simple classifier creates an efficient semantic role labelling model. Model performance was assessed using metric accuracy, precision, recall and F1 score. This is achieved by comparing the output of each word with the predicted word and labels as well as the arguments in the prediction of the output.
Exploring deep reuse in winograd CNN inference

Convolutional neural networks (CNNs), as representatives of deep learning, are one of the most commonly used neural networks in applications such as graphic image analysis. However, CNN has heavy computation patterns; network training processes could take several hours even with modern processors. Different from the training process, the inference process is more often executed on devices with low computing power, such as CPUs. Fortunately, a minimal filtering algorithm, Winograd, can reduce the convolution computations by reducing the number of multiplication operations. We find that the Winograd convolution can be further accelerated by reusing the similar data and computation patterns, which is called deep reuse.
TLSmell: Direct Identification on Malicious HTTPs Encryption Traffic with Simple Connection-Specific Indicators

Internet traffic encryption is a very common traffic protection method. Most internet traffic is protected by the encryption protocol called transport layer security (TLS). Although traffic encryption can ensure the security of communication, it also enables malware to hide its information and avoid being detected. At present, most of the malicious traffic detection methods are aimed at the unencrypted ones. There are some problems in the detection of encrypted traffic, such as high false positive rate, difficulty in feature extraction, and insufficient practicability. The accuracy and effectiveness of existing methods need to be improved. In this paper, we present TLSmell, a framework that conducts malicious encrypted HTTPs traffic detection with simple connection-specific indicators by using different classifiers based online training. We perform deep packet analysis of encrypted traffic through data pre-processing to extract effective features, and then the online training algorithm is used for training and prediction. Without decrypting the original traffic, high-precision malicious traffic detection and analysis are realized, which can guarantee user privacy and communication security. At the same time, since there is no need to decrypt the traffic in advance, the efficiency of detecting malicious HTTPs traffic will be greatly improved. Combined with the traditional detection and analysis methods, malicious HTTPs traffic is screened, and suspicious traffic is further analyzed by the expert through the context of suspicious behaviors, thereby improving the overall performance of malicious encrypted traffic detection.
DQN-based Join Order Optimization by Learning Experiences of Running Queries on Spark SQL

In a smart grid, various types of queries such as ad-hoc queries and analytic queries are requested for data. There is a limit to query evaluation based on a single node database engines because queries are requested for a large scale of data in the smart grid. In this paper, to improve the performance of retrieving a large scale of data in the smart grid environment, we propose a DQN-based join order optimization model on Spark SQL. The model learns the actual processing time of queries that are evaluated on Spark SQL, not the estimated costs. By learning the optimal join orders from previous experiences, we optimize the join orders with similar performance to Spark SQL without collecting and computing the statistics of an input data set.
A Multi-syndrome Pathology for Breast Cancer through Intelligent Learning

Abstract The field of data analytics dealt with various methods to analyze the available data and help us to drawn into a point of conclusion about that information. Machine will learn the behavior based on the algorithms applied on those data and provide an appropriate conclusion. When these kinds of data analytic metrics applied to medical diagnosis various symptoms obtained from the patients and then the disease could be detected effectively. When certain optimization methods are applied on the data, the overall efficiency of the system will be improved considerably. To meet the challenges in diagnosing breast cancer based on clinical record sources, different symptoms in descriptions, clinical symptoms, a novel method, which consists of choosing the suitable features, multi-class functions, and multi-label parameters, has been proposed. The proposed work will be implemented as two steps such as discriminative symptoms selection and multi-syndrome learning. Public Breast Cancer Wisconsin (Diagnostic) Data Set has been taken for implementation. The breast cancer data set utilized for this work comprises 699 tumour samples. In that 458(65.5 %) samples belong to Benevolent (non-cancer) tumours and 241(34.5%) belongs to malevolent(cancer) tumours. The overall verisimilitude is 95.23% which will be improved to a greater extent when compared to the existing schemes.
TWGAN: Twin Discriminator Generative Adversarial Networks

Generative Adversarial Networks (GAN) has become more and more popular these years. However, it is difficult to train and suffers from the training instability problem. To tackle this difficulty, this paper proposes a novel approach. Our idea is intuitive but proven to be very useful. In essence, it combines saturating loss and non-saturating loss into the loss function. Thus it will exploit the complementary statistical properties from two kinds of loss functions to effectively improve the training stability. We term our method twin discriminator Generative Adversarial Networks (TWGAN), which, unlike GAN, has a generator and a twin discriminator. The twin discriminator consists of two discriminators with identical architecture and both of them aim to distinguish whether the samples are from real data or fake data. We develop theoretical analysis to show that, given the optimal discriminators, optimizing the generator of TWGAN reduces to minimizing the Kullback-Leibler (KL) divergence between the distribution of generated data ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$P_g$</tex-math></inline-formula> ) and the distribution of real data ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$P_data$</tex-math></inline-formula> ), hence effectively addressing the training instability problem. Extensive experiments on MNIST, Fashion MNIST, CIFAR-10/100 and STL-10 datasets demonstrate that the competitive performance of our TWGAN in generating good quality and diverse samples over baselines. The obtained highest inception score (IS) and lowest Fr <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\acute{e}$</tex-math></inline-formula> chet Inception Distance (FID), compared with other state-of-the-art GANs, show the superiority of our TWGAN.
The interplay between gaze and consistency in scene viewing: Evidence from visual search by young and older adults

Abstract Searching for an object in a complex scene is influenced by high-level factors such as how much the item would be expected in that setting (semantic consistency). There is also evidence that a person gazing at an object directs our attention towards it. However, there has been little previous research that has helped to understand how we integrate top-down cues such as semantic consistency and gaze to direct attention when searching for an object. Also, there are separate lines of evidence to suggest that older adults may be more influenced by semantic factors and less by gaze cues compared to younger counterparts, but this has not been investigated before in an integrated task. In the current study we analysed eye-movements of 34 younger and 30 older adults as they searched for a target object in complex visual scenes. Younger adults were influenced by semantic consistency in their attention to objects, but were more influenced by gaze cues. In contrast, older adults were more guided by semantic consistency in directing their attention, and showed less influence from gaze cues. These age differences in use of high-level cues were apparent early in processing (time to first fixation and probability of immediate fixation) but not in later processing (total time looking at objects and time to make a response). Overall, this pattern of findings indicates that people are influenced by both social cues and prior expectations when processing a complex scene, and the relative importance of these factors depends on age.
Transmission line routing optimization using rapid random trees

After publishing the auction conditions for the transmission facilities, concessionaires must assess whether the proposed condition is advantageous for them, taking into account various cost factors, directly financial or not. Given the short time for making this decision, it is important that there is a tool to assist them in this process. In this work, we present an optimization methodology for obtaining the optimal routing of transmission lines between two points, taking into account several financial, social and environmental factors. This problem has been discussed in many previous works, but the most significant difference of the proposed methodology is that it is an adaptation of a preeminent technique for the motion planning of robots: (Optimal) Rapid Random Trees, since there is a strong analogy between the problem of optimal routing of transmission lines and that of optimal motion planning of robots. The algorithm is detailed and results for three scenarios with moderate complexity are presented. These results are compatible with the experience of professionals from the field who supervised this research.
Two-direction self-learning super-resolution propagation based on neighbor embedding

Neighbor embedding (NE) is a widely used super-resolution (SR) algorithm, but the one-to-many problem always degrades the performance of NE. The simplest way to avoid this performance degradation is to extract image features from low-resolution (LR) patches, that correctly reflect the features in the corresponding high-resolution (HR) patches. In this paper, we propose several feature extraction methods to extract patch features in LR space, and use coarse-to-fine patch matching methods to select matching patches for each test patch and find the best matching patch candidate to update the matching patch. Since finding matching patches using a training set is exhaustive work in NE, we explore the traditional local/non-local similarity prior and propose vertical similarity in the image pyramid to accelerate the matching patch search process. To accomplish this, we propose a random oscillation+horizontal propagation+vertical propagation strategy to update the matching patches. The experimental results show that the proposed method is superior to many existing self-learning-based methods, but it is inferior to many external-learning-based methods. These results show that the proposed feature extraction method and oscillation propagation method are useful for finding proper matching patches in NE.
Generate Realistic Traffic Sign Image using Deep Convolutional Generative Adversarial Networks

Convolutional Neural Networks (CNNs) achieve excellent in traffic sign detection and recognition with sufficient annotated training data. The quality of the whole vision system based on neural networks depend on the dataset. However, it is complicated to find traffic sign datasets from most of the countries of the world. In this context, Deep Convolutional Generative Adversarial Networks (DCGAN) can synthesize realistic and diverse additional training images to fill the data lack in the real image distribution. This paper mainly discusses how is the quality of the pictures generated by the DCGAN with various parameters. We use an image with a different number and size for training. Further, the Structural Similarity Index (SSIM) and MSE were used to evaluate the quality of the image. Our work measured SSIM values between generated images and corresponding real images. The generated images show high similarity with the actual image while using more images for training. The highest SSIM values reached when using 200 total images as input and images size 32×32.
A Hybrid Model based on Fused Features for Detection of Natural Disasters from Satellite Images

Earthquakes, floods, tsunami, and other natural disasters are appearing as worldwide threats because of their widespread destruction that results in thousands of human and economic losses. It is vital for first responders to know the root cause of damages in a region so that the emergency response activities can be planned accordingly and more effectively. We proposed a framework for the detection and recognition of natural disasters from satellite images. In this work, satellite images of six different types of disasters are considered, namely earthquake, volcanic eruption, flood, tsunami, and hurricane. The framework relies on the fusion of wavelet image scattering features and local binary pattern features for constructing the final feature vector. We also compared the accuracy of our framework with the existing state-of-the-art hand-crafted and machine learning models. Simulation results confirm that the proposed framework is able to recognize the type of natural disaster from the satellite images with an accuracy of 99.59% (Kappa coefficient 98.54% and F-Score 99.40%). The proposed approach results in less computational cost while achieving better accuracy compared to the deep convolutional neural network. We believe that the proposed model can be integrated with satellite imagery for locating the geographical regions affected by the multiple natural disaster events at the same time or at short intervals.
A Study on the EDA-based Classification of News Text

Abstract At present, the commonly used word vector methods for news text classification mostly adopt Word2Vec, Glove, Bert and other word vector models, ignoring the remote context connections of Chinese text itself. TextCNN, RNN, BiLSTM and other neural network classification models lack the extraction of important information features of the text, and the word dependence inside the text is not strong, resulting in inaccurate classification results. To solve the above problems, this paper proposes the DPCNN-Attention news text classification model based on ERNIE’s pre-training model(EDA for short, the following general). The DPCNN neural network model with Mish() activation function is adopted to obtain the maximum length of semantic association between long distance texts in news texts. By adding attention mechanism into EDA model, in the feature extraction process, according to the importance of words to the classification results, different weights are assigned to them to enhance the word dependence relationship within the text, thus greatly improving the classification accuracy. The EDA model was experimentally verified on the THUCNews dataset. The results showed that the EDA model improved by about 6% compared with BERT’s pre-training model, and 0.4% compared with ERNIE’s pre-training model. The loss rate decreased by 0.2 compared with BERT and 0.01 compared with ERNIE’s.
Safe and Effective Picking Paths in Clutter given Discrete Distributions of Object Poses

Picking an item in the presence of other objects can be challenging as it involves occlusions and partial views. Given object models, one approach is to perform object pose estimation and use the most likely candidate pose per object to pick the target without collisions. This approach, however, ignores the uncertainty of the perception process both regarding the target's and the surrounding objects' poses. This work proposes first a perception process for 6D pose estimation, which returns a discrete distribution of object poses in a scene. Then, an open-loop planning pipeline is proposed to return safe and effective solutions for moving a robotic arm to pick, which (a) minimizes the probability of collision with the obstructing objects; and (b) maximizes the probability of reaching the target item. The planning framework models the challenge as a stochastic variant of the Minimum Constraint Removal (MCR) problem. The effectiveness of the methodology is verified given both simulated and real data in different scenarios. The experiments demonstrate the importance of considering the uncertainty of the perception process in terms of safe execution. The results also show that the methodology is more effective than conservative MCR approaches, which avoid all possible object poses regardless of the reported uncertainty.