Reviewerly/exHarmony huggingface dataset:
Text Recognition in Real Scenarios with a Few Labeled Samples

Scene text recognition (STR) is still a hot research topic in computer vision field due to its various applications. Existing works mainly focus on learning a general model with a huge number of synthetic text images to recognize unconstrained scene texts, and have achieved substantial progress. However, these methods are not quite applicable in many real-world scenarios where 1) high recognition accuracy is required, while 2) labeled samples are lacked. To tackle this challenging problem, this paper proposes a few-shot adversarial sequence domain adaptation (FASDA) approach to build sequence adaptation between the synthetic source domain (with many synthetic labeled samples) and a specific target domain (with only some or a few real labeled samples). This is done by simultaneously learning each character's feature representation with an attention mechanism and establishing the corresponding character-level latent subspace with adversarial learning. Our approach can maximize the character-level confusion between the source domain and the target domain, thus achieves the sequence-level adaptation with even a small number of labeled samples in the target domain. Extensive experiments on various datasets show that our method significantly outperforms the finetuning scheme, and obtains comparable performance to the state-of-the-art STR methods.
Implementation of decision tree using C5.0 algorithm in preference and electability survey results on regional head election in Aceh

Abstract The decision tree is one of the methods of classification in data mining. There are many algorithms used to construct the tree model; one of them is C5.0 algorithm. The tree model with C5.0 algorithm was carried out based on the survey result dataset of the preference and electability of the regional head selection pre-campaign year 2018 in one of the districts in Aceh. The datasets consisted of 5 predictor variables, i.e. sub-districts, age, main occupations, highest education, and attracting factors from regional head candidate candidates. Variable categories of decisions ranged from candidates A, B, C, and D. The distribution of datasets was divided into training data and testing data using the k-fold cross-validation method. The optimum tree model formation was based on the accuracy value of model and coefficient of Kappa. The result showed that the best tree model was constructed using testing data on S = 10. The accuracy of the model and the Kappa coefficient were 0.8427 and 0.7208, respectively. There were three rules generated with five nodes. The main predictor variable contributing to the optimum model was the attracting factor of candidates and sub-districts.
Three Special Sessions on Quantum Radar at IEEE Radar Conferences

Presents information on the virtual IEEE International Radar Conference that was held on April 30, 2020.
Metadata-based automated IoT device management system

With the development of IoT, it assumes an increasingly important role in our lives. In this context, we provide an automated IoT device management system, which uses devices' metadata as the bridge to instruct the system to administrate IoT devices to construct a scene and provide the user side a top-down designed rule engine. Users can write their rules first and then the system can choose appropriate devices to act their roles.
BCAU-Net: A Novel Architecture with Binary Channel Attention Module for MRI Brain Segmentation

Recently deep learning-based networks have achieved advanced performance in medical image segmentation. However, the development of deep learning is slow in magnetic resonance image (MRI) segmentation of normal brain tissues. In this paper, inspired by channel attention module, we propose a new architecture, Binary Channel Attention U-Net (BCAU- Net), by introducing a novel Binary Channel Attention Module (BCAM) into skip connection of U-Net, which can take full advantages of the channel information extracted from the encoding path and corresponding decoding path. To better aggregate multiscale spatial information of the feature map, spatial pyramid pooling (SPP) modules with different pooling operations are used in BCAM instead of original average-pooling and max-pooling operations. We verify this model on two datasets including IBSR and MRBrainS18, and obtain better performance on MRI brain segmentation compared with other methods. We believe the proposed method can advance the performance in brain segmentation and clinical diagnosis.
Training Real-Time Panoramic Object Detectors with Virtual Dataset

With the rapid development of autonomous driving, real-time object detection on 360° images becomes more and more important. In this paper, we propose a panoramic virtual dataset for training object detectors on 360° images. The most important feature of our dataset includes (1) an auto-generated city scene is created for rendering 360° dataset. (2) annotation work for this dataset is automatic. In addition, we propose a modified YOLOv3 model called Pano-YOLO for real-time panoramic object detection. Compared with YOLOv3, mAP of Pano-YOLO drops 0.39%. While speed is 32.47% faster. Experiments are performed to show that models trained on our virtual dataset can be applied in real world. And Pano-YOLO is capable of real-time object detection task on high-resolution 360° panoramic images and videos.
A correction to make Chao estimator conservative when the number of sampling occasions is finite

Chao estimator is not guaranteed to be asymptotically conservative with finite sampling efforts. A simple correction solves the issue. We illustrate with simulations and examples that the corrected Chao estimator is asymptotically conservative, and has lower standard error.
An evaluation of continuous integration and delivery frameworks for classroom use

Continuous integration and delivery (CI/CD) frameworks are a core element of DevOps-based software development. A PHP-based case study assessed the suitability of five such frameworks---JFrog Arti-factory, Bitbucket Pipelines, Jenkins, Azure DevOps, and TeamCity---for instructional use. The five were found to be roughly equivalent in terms of their usability for simple configurations. The effort needed to implement CI/CD substantially increased for more realistic production scenarios, like deployments to cloud and load-balanced platforms. These results suggest a need to limit CI/CD-based academic projects to simple infrastructure and technology stacks: e.g., a web application on a single instance web server.
Human-Centric Parsing Network for Human-Object Interaction Detection

Human-object interactions detection is an essential task of image inference, but current methods can't efficiently make use of global knowledge in the image. To tackle this challenge, in this paper, we propose a Human-Centric Parsing Network (HCPN), which integrates global structural knowledge to infer human-object interactions. In HCPN, a semantic parse graph is first constructed by binding human-object relationships, edge features and node features, where the detected human box in image is regarded as the center node and other detected boxes are linked to it. Second, based on the message passing mechanism, edge features and node features with the relation graph are updated and finally, HCPN predicts human-object interactions and associated locations by a readout function. We evaluate our model on V-COCO dataset, and a great improvement is achieved compared with state-of-the-art methods.
Convolutional neural networks can decode eye movement data: A black box approach to predicting task from eye movements

Previous attempts to classify task from eye movement data have relied on model architectures designed to emulate theoretically defined cognitive processes and/or data that have been processed into aggregate (e.g., fixations, saccades) or statistical (e.g., fixation density) features. Black box convolutional neural networks (CNNs) are capable of identifying relevant features in raw and minimally processed data and images, but difficulty interpreting these model architectures has contributed to challenges in generalizing lab-trained CNNs to applied contexts. In the current study, a CNN classifier was used to classify task from two eye movement datasets (Exploratory and Confirmatory) in which participants searched, memorized, or rated indoor and outdoor scene images. The Exploratory dataset was used to tune the hyperparameters of the model, and the resulting model architecture was retrained, validated, and tested on the Confirmatory dataset. The data were formatted into timelines (i.e., x-coordinate, y-coordinate, pupil size) and minimally processed images. To further understand the informational value of each component of the eye movement data, the timeline and image datasets were broken down into subsets with one or more components systematically removed. Classification of the timeline data consistently outperformed the image data. The Memorize condition was most often confused with Search and Rate. Pupil size was the least uniquely informative component when compared with the x- and y-coordinates. The general pattern of results for the Exploratory dataset was replicated in the Confirmatory dataset. Overall, the present study provides a practical and reliable black box solution to classifying task from eye movement data.
Investigating Sensory Perception as a Material for Fashion Design

The increasing digitalization of fashion design opens up new potentials for designing and experiencing fashion. The study presented in this paper aimed to investigate new potentials for artistic expression by deconstructing the human body into materials for design using technological devices. The senses of sight, hearing, and touch were used as materials for design by creating a distance between these stimuli and the body's surface and relocating them to the surface of a bodysuit that was designed for this purpose. Undergraduate students participated in the study by exploring bodily perception from the perspective of fashion design in a workshop. The findings of the workshop suggest potentials for fashion designers to use technologies as design tools for designing dress beyond the textile surface by turning the human body, in terms of bodily senses, into material for design.
Video Description Based YouTube Comment Classification

A classifier-based tool for segregation of comments in YouTube video sharing website is discussed. Comments are classified into one of the four categories such as relevant, irrelevant, positive and negative based on the relevance of the comments to the video content given by description associated with the video posted. For the comments taken from Natural Language Processing, Sports and Movies domains, we report an average accuracy of 90.58% in recognizing relevant comments and 97.02% in recognizing irrelevant comments. Further, classification accuracy for positive and negative comments are 95.68% and 97.88% respectively in the specified domains.
Dependence-Guided Multi-View Clustering

In this paper, we propose a novel approach called dependence-guided multi-view clustering (DGMC). Our model enhances the dependence between unified embedding learning and clustering, as well as promotes the dependence between unified embedding and embedding of each view. Specifically, DGMC learns a unified embedding and partitions data in a joint fashion, thus the clustering results can be directly obtained. A kernel dependence measure is employed to learn a unified embedding by forcing it to be close to different views, thus the complex dependence among different views can be captured. Moreover, an implicit-weight learning mechanism is provided to ensure the diversity of different views. An efficient algorithm with rigorous convergence analysis is derived to solve the proposed model. Experimental results demonstrate the advantages of the proposed method over the state of the arts on real-world datasets.
Lightweight Cryptography in Cloud-Based IoT

IoT and cloud computing are the novel fields that are rapidly progressing in the world of internet technology. A huge and massive amount of data are communicating via IoT and cloud devices. Along with the highly configured devices, IoT and cloud also empowered many resource-constrained devices to communicate and compute information through network. But the major problem that they face is how to provide data security through conventional cryptographic algorithms in such resource-constrained devices having smaller size, limited memory spaces, low computation capabilities, and limited power. In this scenario, the biggest driver towards the problem is lightweight cryptography (LWC). This chapter discusses thoroughly the LWC, different schemes of LWC, and cryptanalysis of different LWC schemes.
Semi-Supervised Outdoor Image Generation Conditioned on Weather Signals

In recent years, various types of sensors observe the real world. Especially, weather sensors are densely installed all over the world to observe current weather situations at various places. However, weather signals such as the temperature or humidity obtained by weather sensors are intuitively difficult for humans to understand. On the other hand, images captured by typical RGB cameras can tell weather situations at the captured places in a more comprehensible way for humans; however, cameras are only installed at limited places and are not necessarily open to public due to privacy issues. In order to solve this problem, the goal of our work is to generate images which can tell weather situations at arbitrary time and locations. This can be realized by using a conditional generative adversarial network architecture that takes an image and a condition to transform the image accordingly to the condition. Training such image generator requires a large number of image and condition pairs as the training data. Although weather signals can be easily collected from weather sensors, collecting their spatially and temporally synchronized outdoor images is not easy. Thus, we propose a semi-supervised method for training the image generator. A relatively small number of pairs of an outdoor image and weather signals is collected, each from different web services, by considering their semantic consistency. The collected pairs are used to train a predictor for predicting weather signals from a given outdoor image. Then, the image generator is trained by using a large number of pairs of an outdoor image and pseudo weather signals predicted by the predictor as the training data.
Informal learning research based on mobile portable devices

Formal learning and informal learning have always been two ways of learning, but little research has been done on informal learning. It wasn't until the establishment of MOOC in 2012 that this kind of off-campus learning gained traction. This paper first discusses the definition of informal learning and its advantages, then introduces two mobile device-based informal learning methods, and makes an outlook on their prospects and problems.
A Collective Entity Linking Method Based on Graph Embedding Algorithm

Aiming at the poor effect of traditional entity linking method combining context co-occurrence and objective knowledge, this paper proposes a collective entity linking framework based on graph embedding algorithm Node2vec. The framework first uses BiLSTM and CRF to do named entity recognition over a text, aiming to obtain entities related to mention in the text. Then, the Node2vec is used on the sub-graph of the knowledge graph for all entities corresponding to the candidate entity sets and the recognized entities to obtain the vector representation between different entities. The similarity between each entity and the text within a certain range is calculated, and the degree between each pair of entity elements is obtained. The most similar pair is selected as the target entity of the link. Experimental verification shows that F1 in this framework reaches 85.96% on NLPCC2014 data set, and the improved Node2vec algorithm performs better than some other graph embedding algorithms in this framework.
Tourism Forecast Based on Web Search Data and Sentiment Analysis of Social Network

Rapid tourism development promotes the economy, whereas is often accompanied with resource allocation overload and serious environmental problems. One of the solutions is running tourism forecast. Most people now tend to make plans via Internet, which provides a great deal of data to forecast. This essay studies the tourist volume in Sanya, China from January 2012 to December 2019, depending on web search data and social media data. At the beginning, acquire keywords and expand them to a bigger target dataset using diction method and keyword tool. Grab their indexes afterwards. Then calculate Pearson correlation coefficient between tourist volume and search indexes, followed by picking the proper keywords. Next, implement data cleansing and sentiment analysis on texts containing keywords extracted from Sina Weibo, the dominant social media in China, to build sentiment index. Very lastly, build ARIMA model based on search index and sentiment index, to predict monthly tourist volume in the near future. Compared to traditional model not engaging both search index and sentiment analysis, our model shows higher accuracy that MAPE drops from 6.1% to 5.4% and RMSE from 14.4% to 11.1%.
Assessing Semantic Frames to Support Program Comprehension Activities

Software developers often rely on natural language text that appears in software engineering artifacts to access critical information as they build and work on software systems. For example, developers access requirements documents to understand what to build, comments in source code to understand design decisions, answers to questions on Q&A sites to understand APIs, and so on. To aid software developers in accessing and using this natural language information, soft-ware engineering researchers often use techniques from natural language processing. In this paper, we explore whether frame semantics, a general linguistic approach, which has been used on requirements text, can also help address problems that occur when applying lexicon analysis based techniques to text associated with program comprehension activities. We assess the applicability of generic semantic frame parsing for this purpose, and based on the results, we propose SEFrame to tailor semantic frame parsing for program comprehension uses. We evaluate the correctness and robustness of the approach finding that SEFrame is correct in between 73% and 74% of the cases and that it can parse text from a variety of software artifacts used to support program comprehension. We describe how this approach could be used to enhance existing approaches to identify meaning on intention from software engineering texts.
A Neuro-Fuzzy Classifier Based on Evolutionary Algorithms

Neuro-fuzzy systems have been proved effective in training classifiers, especially when it comes to noisy, inaccurate or incomplete datasets. For this reason, and due to their simple comprehensible nature, these systems have become popular in designing classifiers. One of the major challenges in designing a neuro-fuzzy classifier is achieving the optimum system parameters such as the type and position of the membership function as well as its training method. These factors could affect the function of the classifier significantly. In this paper, a novel method based on evolutionary algorithms such as inclined planes optimization algorithm (IPO), particle swarm optimizer (PSO) and genetic algorithm (GA) is introduced to design a neuro-fuzzy classifier in such a way that the accuracy is increased and the error rate is minimized. To prove the efficiency of the proposed method, several experiments are conducted on well-known datasets with different number of classes and different feature vector lengths. Results indicate that the proposed evolutionary-based neuro-fuzzy classifier is superior to a normal neuro-fuzzy classifier in terms of accuracy. In addition, experiments showed that the proposed method is able to properly classify the data with a relatively high stability.
Secret Key-Based Image Steganography in Spatial Domain

Day by day, the requirement for secure communication among users is rising in a digital world, to protect the message from the undesirable users. Steganography is a methodology that satisfies the user’s necessity of secure communication by inserting a message into different formats. This paper proposes a secret key-based image steganography to secure the message by concealing the grayscale image inside a cover image. The proposed technique shares the 20 characters long secret key between two clients where the initial eight characters of a secret key are utilized for bit permutation of characters and pixels while the last 12 characters of secret key decide the encryption keys and position of pixels of a grayscale image into the cover. The grayscale image undergoes operation such as encryption and chaotic baker followed by its hiding in a cover to form a stego image. The execution of the proposed strategy is performed on Matlab 2018. It shows that the proposed approach manages to store the maximum message of size 16[Formula: see text]KB into the cover of size [Formula: see text]. The image quality of stego images has been evaluated using PSNR, MSE. For a full payload of 16[Formula: see text]KB, PSNR is around 51[Formula: see text]dB to 53[Formula: see text]dB which is greater than satisfactory PSNR.
Accurate Cell Segmentation in Digital Pathology Images via Attention Enforced Networks

Automatic cell segmentation is an essential step in the pipeline of computer-aided diagnosis (CAD), such as the detection and grading of breast cancer. Accurate segmentation of cells can not only assist the pathologists to make a more precise diagnosis, but also save much time and labor. However, this task suffers from stain variation, cell inhomogeneous intensities, background clutters and cells from different tissues. To address these issues, we propose an Attention Enforced Network (AENet), which is built on spatial attention module and channel attention module, to integrate local features with global dependencies and weight effective channels adaptively. Besides, we introduce a feature fusion branch to bridge high-level and low-level features. Finally, the marker controlled watershed algorithm is applied to post-process the predicted segmentation maps for reducing the fragmented regions. In the test stage, we present an individual color normalization method to deal with the stain variation problem. We evaluate this model on the MoNuSeg dataset. The quantitative comparisons against several prior methods demonstrate the superiority of our approach.
Robust Spatial-Temporal Correlation Model for Background Initialization in Severe Scene

Scene background initialization is an important step as one low-layer method for high-layer applications in computer vision. However, this process is always affected by practical challenges such as illumination changes, back-ground motion, camera jitter, intermittent movement and bad weather outdoors, etc. In this work, we develop a novel method called co-occurrence pixel-block (CPB) model via spatial-temporal correlation for robust back-ground initialization. This work first introduces the CPB method for foreground extraction. And then, background information in spatial-temporal features are utilized to recover an adaptive background for the current frame. Experimental results obtained from the dataset of the challenging benchmark (SBMnet) validate it’s performance under various challenges.