Reviewerly/exHarmony huggingface dataset:
A new prediction interval for binomial random variable based on inferential models

The prediction interval (PI) is useful to predict future observations based on the information available in a given sample and has many practical applications. The Score, Bayesian and Fiducial PIs are well-known existing methods. Recently, two PIs based on the inferential model (IM) have been established. However, they have either poor or conservative behavior of the coverage probability in some cases. In this paper, we propose a new PI from the randomized IM for the binomial random variable. Our non-randomized approximation has the better coverage probability and stability, which is confirmed by the simulation studies. Moreover, our ideas can be applied to other discrete populations.
Towards Fully Integrated Real-time Detection Framework for Online Contents Analysis - RED-Alert Approach

Social media is extensively used nowadays and is gaining popularity among the users with the increasing growth in the network capacity, connectivity, and speed. Moreover, affordable prices of data plans, especially mobile data packages, have considerably increased the use of multimedia by different users. This includes terrorists who use social media platforms to promote their ideology and intimidate their adversaries. It is therefore very important to develop automated solutions to semantically analyse online contents to assist law enforcement agencies in the preventive policing of online activities. A major challenge for the social media forensic analysis is to preserve the privacy of citizens who use online social networking platforms. This paper presents results of European H2020 project RED-Alert that aims to enable secure and privacy preserving data processing; hence the malicious content and the corresponding personality can be ethically tracked. We have mined seven social media channels for content and providing support for ten languages for analysis. Our proposed solution is designed to ensure security and policing of online contents by detecting terrorist material. We have used social network analysis, speech recognition, face and object detection besides audio event detection to extract information from online sources that are fed in a complex event processor. We have discussed the challenges and prospects of this work especially the need of analysing online contents while respecting European and national data protection laws notably GDPR.
Affective Video Content Analyses by Using Cross-Modal Embedding Learning Features

Most existing methods on affective video content analyses are dedicated to single media, either visual content or audio content and few attempts for combined analysis of the two media signals are made. In this paper, we employ a cross-modal embedding learning approach to learn the compact feature representations of different modalities that are discriminative for analyzing the emotion attributes of the video. Specifically, we introduce inter-modal similarity constraints and intra-modal similarity constraints to promote the joint embedding learning procedure for obtaining the robust features. In order to capture cues in different grains, global and local features are extracted from both visual and audio signals, thereafter a unified framework consisting with global and local features embedding networks is built for affective video content analyses. Experiments show that our proposed approach significantly outperforms the state-of-the-art methods and demonstrate the effectiveness of our approach.
Advanced Training as a Tool for Innovative Management in Regional Education

In the educational realities of the new millennium, the improvement of professional qualifications and managerial literacy of a teacher, pedagogue, university lecturer is no longer just one of the possible conditions for individual professional trajectory, but is the identification (essential) attribution of the actual human (cultural, civilizational) being. Only in this case, the fact of advanced training is a valid and effective tool of innovation management in regional education, truly coming to the formation of the (self) managerial lifestyle of a teacher, educator, mentor, tutor. The article summarizes the experience of a regional educational institution – the Orenburg State University, including the faculty of advanced training, pedagogical magistracy, the Intersectoral Regional Center for Advanced Studies and Professional Retraining, as well as the Association “Orenburg University (Educational) District” in the aspect of raising the skills of specialists as an instrument of innovation management in regional education.
Using cTAKES to Build a Simple Speech Transcriber Plugin for an EMR

Electronic medical records (EMR) in general provide significant benefits to healthcare organizations and clinicians. However, a major challenge of clinicians who use EMRs is the lowered perceived quality of patient-doctor communication and interaction as a result of doctors being distracted with EMR use during consultations. A unique approach to this problem is through applications that automatically document clinical encounters in real-time. This study aims to develop a speech transcriber plugin for a web-based EMR for real-time clinical encounter documentation. We make use of available speech-to-text services on the web as well as cTAKES for clinical annotation. A draft summary of the clinical encounter is presented to the user in editable SOAP format. Blockchain technology for the speech recording is also explored to secure access to the recording. Internal testings showed that the prototype is able to capture audio conversations into text and parse the transcription for medical concepts. However, after a single formal usability evaluation we found that there is much to be done in terms of the usability of the summarization component.
Classification Method for Tibetan Texts Based on In-depth Learning

Text classification is a key technology in the field of information retrieval and data mining, which can effectively sort messy information and locate needed information. This paper focuses on the comparative study of depth neural network CNN model, RNN model and LS TM model on the effect of Tibetan text classification. Firstly, we train BiLSTM_CRF model to segment Tibetan categorized text. We construct a word vector space model to get word vectors by removing stop words, calculating word frequency and extracting feature words. Secondly, the word vector is transmitted to the classification model to train the Tibetan text classifier. Finally, we use the Tibetan text classifier to classify Tibetan texts. Experiments show that deep neural network has better classification effect than traditional text classification method when the amount of data is large. Among them, CNN classifier has the best classification effect. When the amount of data is small, the SVM model is effective.
Optimized Hardware Design using Sobel and Median Filters for Lane Detection

In this paper, the image is received from the camera and the lane is sensed. There are various ways to detect lanes. Generally, the method of detecting edges uses a lot of the Sobel edge detection and the Canny edge detection. The minimum use of multiplication and division is used when designing for the hardware configuration. The images are tested using a black box image mounted on the vehicle. Because the top of the image of the used the black box is mostly background, the calculation process is excluded. Also, to speed up, YCbCr is calculated from the image and only the data for the desired color, white and yellow lane, is obtained to detect the lane. The median filter is used to remove noise from images. Intermediate filters excel at noise rejection, but they generally take a long time to compare all values. In this paper, by using addition, the time can be shortened by obtaining and using the result value of the median filter. In case of the Sobel edge detection, the speed is faster and noise sensitive compared to the Canny edge detection. These shortcomings are constructed using complementary algorithms. It also organizes and processes data into parallel processing pipelines. To reduce the size of memory, the system does not use memory to store all data at each step, but stores it using four line buffers. Three line buffers perform mask operations, and one line buffer stores new data at the same time as the operation. Through this work, memory can use six times faster the processing speed and about 33% greater quantity than other methods presented in this paper. The target operating frequency is designed so that the system operates at 50MHz. It is possible to use 2157fps for the images of 640by360 size based on the target operating frequency, 540fps for the HD images and 240fps for the Full HD images, which can be used for most images with 30fps as well as 60fps for the images with 60fps. The maximum operating frequency can be used for larger amounts of the frame processing.
Incorporating Category Taxonomy in Deep Reinforcement Learning Based Image Hashing

Image hashing is critical for large-scale image analytic-based applications, such as image retrieval. Although there have been dozens of hashing approaches, few of them take the hierarchical structure of the image categories into consideration. In this paper, we propose to incorporate the category taxonomy information in a deep reinforcement learning (DRL) model for image hashing. In particular, we learn an agent to predict the hashing codes sequentially under the DRL theme. Each coordinate of the hashing function can take the errors incurred by previous ones into consideration and hence more reliable hashing codes can be obtained than learning them independently. Besides, we design a novel level-specific reward function to gradually refine the hashing function according to the taxonomy information. Extensive experiments on two popular datasets demonstrate effectiveness of the proposed method.
Image saliency prediction by learning deep probability model

Image saliency prediction is an indispensable technique in computer vision, such as video surveillance, image semantic segmentation. However, it is still a challenging task due to the low-resolution images or inaccurate predicted salient regions. With the development of convolutional neural networks (CNNs), deeply-learned feature-based methods have shown impressive performance. Thus, in this paper, we develop a deeply-learned probability model for image saliency prediction. Specifically, a series of candidate regions are generated based on BING feature, where these regions are considered as the salient regions that attract human visual attention. Subsequently, these candidate regions are ranked sequentially based on the designed ranking algorithm. Afterwards, the high-quality patches are fed into CNNs to extract deep representation of the entire image. Finally, a probability model is learned based on deep representation for image saliency prediction. To testify the performance of our method in predicted high-quality salient regions, we also conduct IQA experiment and experimental results show the effectiveness of our method.
Methods and Processors for Image Recognition in a Linear and Quadratic Hamming Space

Image recognition is one of the challenges facing various industries. Image recognition methods based on Euclidean and Hamming distance estimates are the most algorithmically simple and efficient. The paper proposes methods for recognition of analog and digital signals due to the linear and quadratic Euclidean distances in the Hamming space, which are characterized by simplified algorithms and reduced computational complexity. Special-purpose processors for image recognition are designed on the basis of the Hamming distance estimates and structurally optimized components.
Unmanned Ground Vehicle Control System Design Based on Hybrid Architecture

Considering the need of autonomous maneuvering, a control system of Unmanned Ground Vehicle (UGV) based on hybrid architecture is designed. On the basis of the typical hierarchical control architecture, the system introduced human machine interaction module, GIS module, and environment perception module, in order to combine the autonomous ability of UGV with human intelligence. The system is applied in tracked UGV, and experimental research was made on autonomous maneuvering under the real road environment. The results showed that the proposed system can meet the requirements of autonomous maneuvering tasks.
Dense Deep Joint Image Filter for Upsampling and Denoising

Target images like depth maps often suffer from blurred boundaries or noise when taken from poor quality sensors or upsampled from low-resolution images. Classical methods utilized a target sub-network and a guidance sub-network to extract features independently from the pair-wise target image and guidance image, and then a reconstruction sub-network processed the concatenated features to produce the final result. These methods only took the high-level features from the bottom layers, but neglected the inherited multi-scale attributes of the low-level vision tasks. Research has shown that features from shallow layers can also contribute to the reconstruction accuracy in single image super resolution tasks. Besides, a large receptive field can provide more structure information, which is critical for super-resolution problems. However, most existing methods only took a relative small receptive field. To deal with these problems, this paper proposed a Dense Deep Joint Network (DDJF) based on cascaded small filters and dense skip connections. First, the cascaded small filters were used to enlarge the receptive field and reduce the parameter number. Second, to alleviate the vanishing-gradient problem and strengthen the information flowing through the deep network, the dense skip connections were applied to ensure that the features of both shallow layers and deep layers can directly pass to the reconstruction layer. Finally, a residual learning strategy was applied to improve the training procedure. Experiment results by quantitative and visual comparisons with some state-of-the-art methods show the superiority of the proposed method.
Local Smooth Path Planning for Turning Around in Narrow Environment

In this paper, we introduce a local smooth path planning algorithm for turning around in narrow environment. The algorithm uses a combination of the minimum turning radius arc and the Bezier curve to generate a near optimal smoothing turn-around path in narrow roads or cluttered environments. The algorithm will generate a smooth trajectory that conforms to the kinematic constraints of unmanned ground vehicle (UGV), by adjusting the heading to the target angle through several minimum-radius turns, and smoothly connecting to the global reference path via Bezier curve. We analyze the surrounding congestion and narrowness to choose appropriate turn-around area by integrating obstacle distribution and road edges before path planning. The algorithm was tested at IN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> BOT UGV in our campus and 2018 Cross Dangers & obstacles. The experimental results indicated that the algorithm works efficiently with our previous work and shows good performance in accuracy and computational time.
Towards Accurate Instance-Level Text Spotting with Guided Attention

We tackle the text detection problem from the instance-aware segmentation perspective, in which text bounding boxes are directly extracted from segmentation results without location regression. Specifically, a text-specific attention model and a global enhancement block are introduced to enrich the semantics of text detection features. The attention model is trained with a weakly segmentation supervision signal and enforces the detector to focus on the text regions, while also suppressing the influence of neighboring background clutters. In conjunction with the attention model, a global enhancement block (GEB) is adapted to reason the relationship among different channels with channel-wise weights calibration. Our method achieves comparable performance with the recent state-of-the-arts on ICDAR2013, ICDAR2015, and ICDAR2017-MLT benchmark datasets.
Tech United Eindhoven Middle Size League Winner 2018

The Tech United Eindhoven Middle Size League (MSL) team achieved a first place at RoboCup 2018. This paper presents a short evaluation of the tournament and describes the most notable developments made in preparation of the tournament. One development in the robot’s hardware is presented: the realization of our eight-wheeled soccer player. The following developments in software will be presented: a new approach to ball state estimation and the human-alike dribble. Additionally, research towards the application of artificial intelligence in opponent action prediction and opponent recognition will be presented.
Multiplex graph matching matched filters

Abstract We consider the problem of detecting a noisy induced multiplex template network in a larger multiplex background network. Our approach, which extends the graph matching matched filter framework of Sussman et al. (IEEE Trans Pattern Anal Mach Intell 42(11):2887–2900, 2019) to the multiplex setting, utilizes a multiplex analogue of the classical graph matching problem to use the template as a matched filter for efficiently searching the background for candidate template matches. The effectiveness of our approach is demonstrated both theoretically and empirically, with particular attention paid to the potential benefits of considering multiple channels in the multiplex framework.
A High Efficient Architecture for Convolution Neural Network Accelerator

Convolutional neural networks (CNNs) are widely used in modern Artificial Intelligence (AI) systems. Compared with other classical methods, CNNs have superior performance in image classification, speech recognition and object detection. However the computational load of CNNs is very heavy and a large amount of data movement are expected. An efficient way of data movement is critical for both performance and power efficiency for an accelerator design. In this paper we propose a novel CNN accelerator architecture with unique parallel loading scheme and smart memory addressing solution. Our solution is 30% faster than others [1] on Alexnet. Our proposal can achieve high efficiency for FC layer without using image batching. This will make our solution very suitable for edge applications.
Improving regional cyberinfrastructure services through collaboration

Advances in the volume, diversity, and complexity of research data and associated workflows requires enhanced capabilities to access, secure, reuse, process, analyze, understand, curate, share, and preserve data. To address this need at the regional level the University of Colorado, Colorado State University, and the University of Utah formed a "Cyberteam" in 2017 to provide cyberinfrastructure (CI) support to researchers at institutions in the Rocky Mountain Advanced Computing Consortium (RMACC) encompassing states across the Intermountain West. The Cyberteam is comprised of CI professionals across the three institutions who collaborate closely, sharing expertise and resources. Since its establishment, the Cyberteam has worked to broaden accessibility and options for computing, storage, and data publishing for RMACC researchers; enhance training on data- and workflow-oriented topics; improve engagement with researchers using CI; and better understand user needs and challenges. One key accomplishment has been the development of a series of focus group and survey instruments to achieve better understanding the CI needs and challenges of researchers across a diverse spectrum of disciplines. This paper provides an overview of the RMACC Cyberteam's objectives, accomplishments, challenges, and future direction.
A Study on the Refractive Effect of Glass in Vision Systems

Vision systems are going to be ever-present in many different applications and studying the factors which influence their accuracy is of high interest in the computer vision community. In most of the applications of camera-based 3D scanners, the medium between the camera and the object is air, whereas in some specific applications such as sole scanning, the medium is a flat see-through glass. A piece of glass can influence the accuracy of the scanner due to the refraction of light it causes; therefore, in this work, the effect of glass on the camera calibration step, which is intended to recognize the parameters of a vision system, is studied. For this purpose, a setup is developed and the calibration object is captured through pieces of glass with different thicknesses and distances from the camera and the calibration algorithm is run for different series of images. Reprojection Error, as a common criterion for evaluating the accuracy of the camera calibration process, is used for analyzing the effect of glass. The results demonstrate that the effect of the studied thicknesses of glass is very small keeping the average reprojection error less than two pixels and can be interpreted as ignorable in most applications.
Configurable FPGA-Based Outlier Detection for Time Series Data

An outlier detection technique based on joint estimation of model parameters and outlier effects in time series is implemented in FPGA-based configurable real-time outlier detection hardware. The hardware models time series data with an autoregressive-moving-average (ARMA) process and identifies the outliers based on test statistics using unbiased parameters. A configurable hardware implementation was written in Verilog, simulated to verify its correctness, and synthesized on an Altera FPGA device from the Stratix V family. The design is configurable by adjusting the number of iterations for the optimization process, the number of samples in the time series data, and the critical value. A reported configuration of this design has a total power dissipation of 1.14W, while processing 35 million data points per second, giving an energy usage of 32 nJ per processed data point. The implemented hardware is capable of detecting multiple additive outliers in time series data with a detection accuracy of 99% and has a type I error rate of 1.05%. Compared to a general purpose CPU running a software implementation of the outlier detection algorithm, the FPGA implementation reduces the power consumption by 89% at similar rates of data throughput.
Intelligent Elastic Scheduling Algorithms for PaaS Cloud Platform Based on Load Prediction

According to the historical load data of PaaS cloud platform, a resource scheduling prediction model is established, and the periodic and non-periodic laws of load are analyzed. A combined forecasting model based on Fourier and Markov chains is proposed to realize intelligent forecasting of resource scheduling, which can realize load forecasting based on historical running data and intelligent scalable scheduling of PaaS cloud platform.