Reviewerly/exHarmony huggingface dataset:
End-to-end image dehazing via dual principles of discriminativeness

This work addresses the problem of single image dehazing particularly towards the goal of better visibility restoration. Athough extensive studies have been performed, almost all of them are heavily built on the atmospheric scattering model. What is worse, they usually fail to restore the visibility of dense hazy images convincingly. Inspired by the potentials of deep learning, a new end-to-end approach is presented to restore a clear image directly from a hazy image, while with an emphasis on the real-world weather conditions. In specific, an Encoder-Decoder is exploited as a generator for restoring the dehazed image in an attempt of preserve more image details. Interestingly, it is further found that the performance of the Encoder-Decoder can be largely boosted via our advocated dual principles of discriminativeness in this paper. On the one hand, the dark channel is re-explored in our framework resulting in a discriminative prior formulated specifically for the dehazing problem. On the other hand, a critic is incorporated for adversarial training against the autoencoding-based generator, implemented via the Wasserstein GAN (generative adverarial networks) regularized by the Liptchitiz penalty. The proposed approach is trained on a synthetic dataset of hazy images, while evaluated on both synthetic and real hazy images. The objective evaluation has shown that the proposed approach performs competitively with the state-of-the-art approaches, but outperforms them in terms of the visibility restoration especially in the scenarios of dense haze.
Automatic Curation System Using Multimodal Analysis Approach (MAA)

Extracting sports highlights and summarizing the most exciting moments of the match is an important task for the broadcast media. However, it requires intensive video editing. We propose a Multimodal Analysis Approach (MAA) for auto-curating sports highlights, and use it to create a real-world system for editing aids of soccer highlight reels. MAA fuses information from the players actions (action recognition), the landmarks (image classification), the scores on the scoreboard (OCR) and the commentators tone of the voice (audio detection) to determine the most exciting moments of a match. In addition, we use the face recognition technology to make star highlights. The shot-boundary detection method is developed to accurately identify the start and end frames of highlights for content summaries. The proposed system has performed real-time highlights extraction from the video stream of the FIFA world cup 2018. Moreover, MAA produces highlights with better quality by comprehensive user studies from multiple participants subjects.
A Novel Task-Oriented Text Corpus in Silent Speech Recognition and its Natural Language Generation Construction Method

Millions of people with severe speech disorders around the world may regain their communication capabilities through techniques of silent speech recognition (SSR). Using electroencephalography (EEG) as a biomarker for speech decoding has been popular for SSR. However, the lack of SSR text corpus has impeded the development of this technique. Here, we construct a novel task-oriented text corpus, which is utilized in the field of SSR. In the process of construction, we propose a task-oriented hybrid construction method based on natural language generation (NLG) algorithm. The algorithm focuses on the strategy of data-to-text generation, and has two advantages including linguistic quality and high diversity. These two advantages use template-based method and deep neural networks respectively. In an SSR experiment with the generated text corpus, analysis results show that the performance of our hybrid construction method outperforms the pure method such as template-based natural language generation or neural natural language generation models.
Restoration of Colour Images Using Backward Stochastic Differential Equations with Reflection

Colour image denoising methods based on the chromaticity-brightness decomposition are well-known for their excellent results. We propose a novel approach for chromaticity denoising using advanced techniques of stochastic calculus. In order to solve this problem we use backward stochastic differential equations with reflection. Our experiments show that the new approach gives very good results and compares favourably with deterministic differential equation methods.
Information and Content

Abstract This chapter conceptualizes information in terms of ruling out scenarios. It discusses informative identity statements, which give rise to Frege’s puzzle, and the problem understanding how a valid logical inference can be informative. An analysis of informative logical inferences is given, on which the content of a valid deduction is often indeterminate. A consequence is that it is indeterminate exactly which logical inferences are informative. The chapter then analyses a rather different notion of content, concerning what is said by a speaker in making an utterance.
On the Evaluation of Propagation Force of New Media for Government Affairs Based on the Theory of Information Acceptance Technology

The development of Internet technology and the wide application of new media, such as the Weibo, WeChat or some kinds of new Apps, promoted the construction of new media for government affairs. The decentralized new media for government affairs has weakened the propagation force and influence, therefore, based on the theory of information acceptance, resource integration and cluster communication are the big trends of new media for government affairs. A set of system, which combine with the quantitative and qualitative evaluation, the content supply and user experience assessment, the communication function and efficiency evaluation, can be established to evaluate the clustering propagation force of the new media for government affairs may adjust the communication strategies, accelerate the communication efficiency, enhance the government credibility and promote the construction of service-oriented government.
Accurate scale estimation for correlation filter based visual tracking

Most of the correlation filter-based trackers learn a 1D correlation filter for scale search. The scale searching space of 1D scale filter is discrete, which results in high computation complexity and low scale estimation accuracy. In this paper, we present an interpolation model for accurate scale estimation for correlation filter-based tracking. Experiments are performed on selected images from the OTB (Object Tracking Benchmark) datasets. Compared to the standard 1D correlation filter for scale search, our approach has better average distance precision and overlap precision. Additionally, the center distance error is even less than half of the standard 1D correlation filter. Meanwhile, our method is computationally efficient, operating at a 20.7% higher frame rate.
An improved technique for verification of high-speed impact simulation

The risk of strange objects impacts that can decrease local strength properties must be taken into account in the design of primary airframe structures in the aeronautics, energy and space industries. Damage that may occur as a result of high-speed impacts can affect the structure load carrying capacity of the structure. Testing all impact scenarios on real structures is expensive and impractical. This paper integrates numerical analyses with high-speed impact tests of hail stones on flat metallic and composite panels. A new technique for displacement measurements using a laser triangulation sensor during impacts were proposed and used for a quantitative analysis and for precision verification of a numerical model results. A comparison between the testing and simulation results reveals consistent predictions of damage initialization on the composite material and plastic deformation on the metallic material. The application of flat test panels for verification and tuning of the numerical models allows an optimized final design of a real structure and decreases the risk of non-compliance of the structure during verification tests.
Using Low-Level Sensory Data to Recognize Events in a Smart Home

The recent growth of smart, low-cost sensor devices combined with the ubiquity of WiFi and cellular networks have given rise to new opportunities for smart home applications in support of independent living of elderly people. Homes that are fitted with smart devices provide new conveniences, comfort, and safety for its inhabitants. To provide the needed support for independent living, the smart devices must be able to make predictions about the activities in the home and the residents’ needs and they must also be able to easily integrate into a home without costly alterations. This paper discusses the implementation and evaluation of a new sensor device that collects unobtrusively acoustic and other low-level sensory data and applies machine learning techniques to recognize events in the home. The device integrates into a prototype smart home system built in our lab to perform activity recognition in an effort to assist and improve the well-being of residents in a home. Results of our new sensor device suggest that events in the home can be recognized with high accuracy but that improving the accuracy for some events requires additional data.
The Problem isn’t the Database…

Abstract Ron Ballard, author of Relational Databases for Agile Developers, examines why many application builders are finding that their database applications take too long to build, have performance and reliability issues and are hard to change.
Edu-ACoCM

Online learning repositories are the heart of learning content management systems. This article proposes a model that utilizes the educational content of learning repositories, to create and display multiple learning paths to the students. When a student specifies a topic to study, the model creates the learning paths in form of a tree, with student specified learning concepts as the root node and its co-existing concepts as the child nodes. The model also proposes to automatically extract three co-existing concepts: prerequisites, subsequent topics and features. Use of pattern-based mining and a rule-based classification approach is proposed for the extraction of co-existing concepts. Automatically extracted results are checked for meaningfulness and usefulness against expert generated results. Evaluation of the authors' model on various learning materials shows the appropriate generation of learning paths depicting the co-exiting concepts. The average F1 score obtained for automatic prerequisite extraction is 78%, automatic subsequent topic extraction is 83% and automatic feature extraction is 88%.
On the Computation of the Euler Characteristic of Binary Images in the Triangular Grid

Apart from the widely used square grid, other regular grids (hexagonal and triangular) are gaining prominence in topological data analysis and image analysis/processing communities. One basic but important integer-valued topological descriptor of binary images in these grids is the Euler characteristic. We extend two algorithms for the computation of the Euler characteristic from the square to the triangular grid, taking into account specific properties of the triangular grid. The first algorithm is based on simple cell counting, and the second is based on critical point approach. Both algorithms iterate over the grid vertices. We extend also their improvement based on reusing information common to the previous and the next vertex in the scan order. Our experiments show that the critical point based algorithms outperform the naive cell-counting ones, with the improved versions reducing the average runtime further.
Micronucleus image recognition based on feature-map spatial transformation

Convolutional neural networks in deep learning models have dominated the recent image recognition works. But the lack of capacity to maintain spatial invariance makes identification of micronucleus cells as a classic task in digital pathology still a challenge task. In this paper, a novel convolutional neural network for feature maps spatial transformation (FSTCNN) is proposed, which incorporates a Spatial Transformer Network. Our model allows the spatial manipulation of data within the network, provides the ability of active spatial transformation for neural network without any extra supervision. We compared the results of inserting STN into different convolutional layers and found that such a network can transform the input image more steadily, correct the image to one certain position, make it fill the whole screen to create a better environment for image recognition. The results show a distinct advantage over other convolutional neural networks for medical image recognition.
A Keyword Extraction Scheme from CQI Based on Graph Centrality

Recently, most of the universities in Korea is doing a lecture evaluation survey every semester. The continuous quality improvement (CQI) report is one of the most popular lecture evaluation service systems, which able to summaries and analysis the mean of evaluation reports. Since 2016, education office allows CQI system to begin uploads and analysis the CQI report in all subjects. To improve the school and support to students, the school has to do a lecture evaluation after midterm and final exam every semester. The problems are the school getting so long to make the report on students lecture evaluation. In this paper, we propose a summary keywords extraction method form CQI and represented as graph tools based on centrality. We expected that this method can be efficiently extracted the most important relation keywords from huge CQI data of each lecture evaluations to summaries for the report.
Transfer Learning for Driving Pattern Recognition

Driving pattern recognition based on driving status features (GPS, gear, and speed etc.) is of central importance in the development of intelligent transportation. While it is expensive and labor intensive to obtain a large amount of labeled driving data in real applications. It makes the driving pattern recognition particularly difficult for those domains without labeled data. In this paper, to tackle this challenging recognition task, we propose a novel and robust Transfer Learning method for Driving Pattern Recognition (TLDPR) that can transfer knowledge from other related source domains with labeled data to the target domain. Compared to the traditional supervised learning, one of the major difficulties of transfer learning is that the data from different domains may have distinct distributions. The proposed TLDPR is able to reduce the distribution difference in RKHS between the samples in target and source domain with the same driving pattern, and it can preserve the local manifold structure simultaneously. In addition, an iterative ensemble strategy is implemented to make the model more robust using the pseudo-labels. To evaluate the performance of TLDPR, comprehensive experiments have been conducted on parking lots datasets. The results show TLDPR can substantially outperform the state-of-the-art methods.
Predictive Runtime Simulation for Building Trust in Cooperative Autonomous Systems

Future autonomous systems will also be cooperative systems. They will interact with each other, with traffic infrastructure, with cloud services and with other systems. In such an open ecosystem trust is of fundamental importance, because cooperation between systems is key for many innovation applications and services. Without an adequate notion of trust, as well as means to maintain and use it, the full potential of autonomous systems thus cannot be unlocked. In this paper, we discuss what constitutes trust in autonomous cooperative systems and sketch out a corresponding multifaceted notion of trust. We then go on to discuss a predictive runtime simulation approach as a building block for trust and elaborate on means to secure this approach.
Selective Ensemble Method Based on Spectral Clustering

Currently, spectral clustering has attracted wide attention in clustering analysis domain. Compared with traditional partition-based clustering algorithm, spectral clustering is more adaptable to data distribution and less computational complexity for the large-scale data sets. Nevertheless, a single spectral clustering algorithm is affected by initialization, which results in unstable clustering results, especially when the data type and structure are more complicated. This paper proposes a novel approach of selective ensemble method based on spectral clustering to solve this issue. First of all, We obtain multiple spectral clustering results by random initialization parameters and generate candidate base clustering members. The normalized mutual information (NMI) is used to select high quality base clusterings from these candidate members as the input of clustering ensemble. Secondly, a new method of distance decision is devised as a consistency to evaluate the base clusterings. Finally, we introduce the cumulative matrix obtained by distance decision into the density peaks clustering method to gain the results of clustering ensemble. We implement the proposed method in UCI machine learning database that is commonly used in the field of clustering analysis, and experimental results demonstrate that our method can effectively improve the accuracy of single clustering algorithm.
A Unified Probabilistic Matrix Factorization Recommendation Fusing Dynamic Tag

In existing social tagging systems, user interests changing is not considered. In order to solve this problem, in this paper we propose a unified probabilistic matrix factorization (TTUPMF) recommendation algorithm which combines social tags and time factor. In the proposed approach, A user-item rating matrix, a user-tag tagging matrix, an item-tag correlation matrix and a unified probabilistic matrix factorization are constructed to obtain the latent feature vectors of three matrices to be recommended to users by optimizing the training parameters. The experimental results demonstrate that the proposed model uses the tags semantics effectively and improves the recommendation quality.
National Research and Knowledge Systems

Strong national research and knowledge systems are important in national development. If libraries are well integrated in national research and knowledge systems vibrant spaces of intellectual interrogation, research synthesis and translation can be created. Such an institutional landscape is key in linking research and policy, and this is critical for national development. Research and knowledge systems connect and facilitate sharing of lessons learned, what works, where, when, and how among researchers, practitioners, and policymakers thereby strengthening strategic collaborations to drive vital national development initiatives. Research and knowledge systems where libraries are an integral component create robust communities of practice where there is effective use of research and knowledge in policy and practice. This chapter focuses on how libraries can be part of a national research and knowledge system through institutional collaborations and partnerships and how such a system contributes to sustainable development through promotion of evidence informed policy and practice.
Image Retrieval with Similar Object Detection and Local Similarity to Detected Objects

Commercial image search applications like eBay and Pinterest allow users to select the focused area as bounding box over the query images, which improves the retrieval accuracy. The focused area image retrieval strategy motivated our research, but our system has three main advantages over the existing works. (1) Given a query focus area, our approach localizes the most similar region in the database image and only this region is used for computing image similarity. This is done in a unified network whose weights are adjusted both for localization and similarity learning in an end-to-end manner. (2) This is achieved using fewer than five proposals extracted from a saliency map, which speedups the pairwise similarity computation. Usually hundreds or even thousands of proposals are used for localization. (3) For users, our system explains the relevance of the retrieved results by locating the regions in the database images that are the most similar to the query object. Our method achieves significantly better retrieval performance than the off-the-shelf object localization-based retrieval methods and end-to-end trained triplet method with a region proposal network. Our experimental results demonstrate 86% retrieval rate as compared to 73% achieved by the existing methods on PASCAL VOC07 and VOC12 datasets. Extensive experiments are also conducted on the instance retrieval databases Oxford5k and INSTRE, where we exhibit competitive performance. Finally, we provide both quantitative and qualitative results of our retrieval method demonstrating its superiority over commercial image search systems.
A Robotic Hand for Arabic Sign Language Teaching and Translation

Deaf and deaf-blind individuals face many challenges in communicating with others and understanding the world surrounding them. To overcome their isolation, deaf persons use sign language to converse with others. On the other hand, deaf-blind persons are unable to see so they rely on tactile sign language by touching other persons’ hands while they perform the gestures in order to read sign language. Deaf and deaf-blind people experience more difficulties connecting with their local community when other individuals are not familiar with sign language or are not comfortable with the tactile sign language. Moreover, learning sign language is fundamental to deaf people and those who live with deaf people, as this will be a mean for them to communicate with their environment. Teaching sign language with 2D tools such as videos and pictures are not as effective as teaching it with 3D tools, which offer full demonstration of the sign language gestures. In this paper, we present the design and construction of a robotic hand that translates written Arabic texts into Arabic sign language alphabet. The aim of this robotic hand is to serve as an assistive technology device that acts as a mean of communication between the deaf or deaf-blind person and the world. In addition, it can be used as a tool to aid in teaching sign language for deaf people and those who want to learn sign language. The robotic hand was tested through three experimental studies on three different types of volunteers, which include non-deaf/non-deaf-blind, deaf and deaf-blind individuals. The results were very promising; the success ratios for gestures recognition were 86%, 81%, and 67%, respectively.
Effective Pre-processing of Genetic Programming for Solving Symbolic Regression in Equation Extraction

Estimating a form of equation that explains data is very useful to understand various physical, chemical, social, and biological phenomena. One effective approach for finding the form of an equation is to solve the symbolic regression problem using genetic programming (GP). However, this approach requires a long computation time because of the explosion of the number of combinations of candidate functions that are used as elements to construct equations. In the present paper, a novel method to effectively eliminate unnecessary functions from an initial set of functions using a deep neural network was proposed to reduce the number of computations of GP. Moreover, a method was proposed to improve the accuracy of the classification using eigenvalues when classifying whether functions are required for symbolic regression. Experiment results showed that the proposed method can successfully classify functions with over 90% of the data created in the present study.