Reviewerly/exHarmony huggingface dataset:
Image Filter Processing Algorithm Analysis and Comparison

Abstract In the process of capturing images by the camera, various noises will be generated due to the interference of the external environment or the interference of the internal environment. The noise must be filtered to ensure the accuracy of the size measurement. This article takes Gaussian noise and salt and pepper noise as examples. Experimental simulation analysis of image filtering by different filtering methods. Then combined with the image quality evaluation index to analyze the optimal image filtering processing method, finally it is concluded that the most suitable methods for Gaussian noise and salt and pepper noise filtering are adaptive Gaussian filtering and median filtering.
LAUREN - Knowledge Graph Summarization for Question Answering

Besides the challenge that a human can ask one question in many different ways, a key aspect in Question Answering approaches over Knowledge Graphs (KGQA) is to deal with the vast amount of information present in the knowledge graphs. Modern real-world knowledge graphs contain nearly millions of entities and relationships. Additionally, they are enriched with new facts every day. However, not all facts are relevant for answering particular questions, thus fostering several challenges to KGQA systems, which require interpretable and query-able data. One solution to filtering the extra data in knowledge graphs is to rely on graph summarization techniques. Graph-based summarization approaches aim to resize knowledge graphs to be more concise and precise by storing only relevant information. In this paper, we propose a framework named LAUREN that applies different summarization techniques on knowledge graphs to be used in KGQA systems. Our experiments show that LAUREN summarizes large knowledge graphs such as DBpedia by 2 million entities and its summarization still achieves the same performance on both question answering and linking tasks compared to the complete DBpedia.
A Document Image Dataset for Quality Assessment

Abstract Mobile device plays an very important role in capturing document image. However, the quality of the captured image is influenced by many factors, such as device quality and shooting conditions. In this context, it is necessary to automatically assess the quality of captured document image. Although there has a lot of work in the filed of image quality assessment (IQA), insufficient attention has been paid to the establishment of document images dataset. Thus, we propose a large dataset of document images containing 19,943 images which are collected by mobile devices. During the process of image acquisition, many factors such as light intensity, distortion type, document material are considered. After capturing images, multiple volunteers participated in the evaluation and collection of Mean Opinion Score (MOS) of the document images. We use two no-reference image quality assessment algorithms to test the proposed dataset. The experimental results show the validity of our dataset and the reliability of MOS. The proposed dataset can be used in the field of image quality assessment and Optical Character Recognition.
Towards automation of dynamic-gaze video analysis taking functional upper-limb tasks as a case study

Previous studies in motor control have yielded clear evidence that gaze behavior (where someone looks) quantifies the attention paid to perform actions. However, eliciting clinically meaningful results from the gaze data has been done manually, rendering it incredibly tedious, time-consuming, and highly subjective. This paper aims to study the feasibility of automating the coding process of the gaze data taking functional upper-limb tasks as a case study. This is achieved by developing a new algorithm capable of coding the collected gaze data through three main stages; data preparation, data processing, and output generation. The input data in the form of a crosshair and a gaze video are converted into a 25 Hz frame rate sequence. Keyframes and non-key frames are then obtained and processed using a combination of image processing techniques and a fuzzy logic controller. In each trial, the location and duration of gaze fixation at the areas of interest (AOIs) are obtained. Once the gaze data is coded, it can be presented in different forms and formats, including the stacked color bar. The obtained results showed that the developed coding algorithm highly agrees with the manual coding method but significantly faster and less prone to unsystematic errors. Statistical analysis showed that Cohen's Kappa ranges from 0.705 to 1.0. Moreover, based on the intra-class correlation coefficient (ICC), the agreement index between computerized and manual coding methods is found to be (i) 0.908 with 95% confidence intervals (0.867, 0.937) for the anatomical hand and (ii) 0.923 with 95% confidence intervals (0.888, 0.948) for the prosthetic hand. A Bland-Altman plot also showed that all data points are closely scattered around the mean. These findings confirm the validity and effectiveness of the developed coding algorithm. The developed algorithm demonstrated that it is feasible to automate the coding of the gaze data, reduce the coding time, and improve the coding process's reliability.
Studies on image recognition based on VAE and AAE

Abstract Image recognition is applied to all aspects of life, such as brush face authentication and defect detection. Traditional convolutional neural network trains network through a large number of data sets. However, in practice, it takes a lot of time and effort to collect training data. In the case of less training data and larger verification data set, the accuracy is relatively low. Therefore, this paper proposes two methods this method can improve the accuracy and loss function fluctuation of the case with less training data and more verification sets. The lenet5 convolutional network structure is used as the main body to form a new convolutional neural network by combining variational self coding and counter self coding. The experimental results show that in the cifar-10 data set and cifar-100 data set, the combination of variational self coding and combined counter self coding can increase by up to 4 percentage points compared with the traditional convolutional neural network, which is effective in the case of insufficient training data.
On Subspaces of Kloosterman Zeros and Permutations of the Form $$L_1(x^{-1})+L_2(x)$$

Permutations of the form \(F(x)=L_1(x^{-1})+L_2(x)\) with linear functions \(L_1,L_2\) are closely related to several interesting questions regarding CCZ-equivalence and EA-equivalence of the inverse function. In this paper, we show that F cannot be a permutation on binary fields if the kernel of \(L_1\) or \(L_2\) is large. A key step of our proof is an observation on the maximal size of a subspace V of \(\mathbb {F}_{2^n}\) that consists of Kloosterman zeros, i.e. a subspace V such that \(K_n(v)=0\) for every \(v \in V\) where \(K_n(v)\) denotes the Kloosterman sum of v.
Reform of Teaching Management Based on the Perspective of “Deep Learning’’

Over the past few years, the demand for social skills has been increasing every day, which is important in the education system of schools and universities, in order to develop and move their talents. The traditional teaching model, the high quality and inclusive skills of colleges and universities, which leads to a serious challenge for future students. This article started a study on the reform of the teaching direction of the “deep learning” perspective. In the study, this article uses questionnaire methods to analyse the status of teaching administrations at universities in China, to solve the problems in university management, and to propose reforms in education management from the “deep learning” perspective to these problems. In addition, from the “deep learning” perspective, we chose ways of teaching frequently as a comparative analysis to improve the effectiveness of reform changes. The results show that the score of teaching management in deep learning is 81 points in teaching method, 85 points in teaching concept, 83 points in teaching mode and 90 points in teaching evaluation method, which is far greater than that in shallow level learning. Therefore, the teaching management reform measures proposed in this paper based on the perspective of “deep learning” are feasible.
A Mobile Vision-based System for Gap and Flush Measuring between Planar Surfaces using ArUco Markers

The dimensional inspection of the gap and flush (GF) created by two different surfaces has been a longstanding and challenging field of research. In this paper, a novel mobile vision-based measurement system has been developed with the goal to detect and measure the gap accurately. ArUco markers have been attached to two planar surfaces. A sequence of images is captured, and the 3D information of markers are estimated. The flush is the difference between the depth information obtained from the markers, while the width can be converted from the image using the pixel resolution computed from the marker's physical size. A working prototype of the proposed method has been implemented and extensively evaluated on synthetic gaps. The experimental results validate the robustness and applicability of the proposed method in a real-world environment.
A unified framework for attribute extraction in electronic medical records

research-article Share on A unified framework for attribute extraction in electronic medical records Authors: Ming Du Donghua University, China Donghua University, ChinaView Profile , Wenkun Wang Donghua University, China Donghua University, ChinaView Profile , Sufen Wang Donghua University, China Donghua University, ChinaView Profile , Bo Xu Donghua University, China Donghua University, ChinaView Profile Authors Info & Claims ACAI 2020: 2020 3rd International Conference on Algorithms, Computing and Artificial IntelligenceDecember 2020 Article No.: 83Pages 1–7https://doi.org/10.1145/3446132.3446410Online:09 March 2021Publication History 0citation53DownloadsMetricsTotal Citations0Total Downloads53Last 12 Months33Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
XferNAS: Transfer Neural Architecture Search

The term Neural Architecture Search (NAS) refers to the automatic optimization of network architectures for a new, previously unknown task. Since testing an architecture is computationally very expensive, many optimizers need days or even weeks to find suitable architectures. However, this search time can be significantly reduced if knowledge from previous searches on different tasks is reused. In this work, we propose a generally applicable framework that introduces only minor changes to existing optimizers to leverage this feature. As an example, we select an existing optimizer and demonstrate the complexity of the integration of the framework as well as its impact. In experiments on CIFAR-10 and CIFAR-100, we observe a reduction in the search time from 200 to only 6 GPU days, a speed up by a factor of 33. In addition, we observe new records of 1.99 and 14.06 for NAS optimizers on the CIFAR benchmarks, respectively. In a separate study, we analyze the impact of the amount of source and target data. Empirically, we demonstrate that the proposed framework generally gives better results and, in the worst case, is just as good as the unmodified optimizer.
Visual Perception: How Better Imaging Can Make Things Worse

Visual search is critical to daily life and to socially important tasks — from cancer screening to airport security. New research shows how a technological advancement can interact with the human visual system to improve search for one type of target while making matters worse for another. Part of the problem is that we are surprisingly bad at knowing where we have looked.
On CCZ-Equivalence of the Inverse Function

The inverse function x → x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> on \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2<sup>n</sup></sub> is one of the most studied functions in cryptography due to its widespread use as an S-box in block ciphers like AES. In this paper, we show that, if n ≥ 5, every function that is CCZ-equivalent to the inverse function is already EA-equivalent to it. This confirms a conjecture by Budaghyan, Calderini and Villa. We also prove that every permutation that is CCZ-equivalent to the inverse function is already affine equivalent to it. The majority of the paper is devoted to proving that there is no permutation polynomial of the form L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> (x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> )+L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (x) over \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2<sup>n</sup></sub> if n ≥ 5, where L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> are nonzero linear functions. In the proof, we combine Kloosterman sums, quadratic forms and tools from additive combinatorics.
4W1H Keyword Extraction based Summarization Model

In this internet era, with rapidly growing online information, there is a need for automatic summarization of textual documents from plethora of available information, making it an interesting area of research. Automatic keyword extraction and text summarization are Natural Language Processing (NLP) tasks for extracting relevant information from the large text documents. 4W1H (Who, When, Where, What, How) keywords are crucial for sentence generation. Despite the potential of 4W1H keywords, there have not been approaches that utilize the keywords in NLP tasks, particularly summarization. In this paper, we propose a new summarization method based on 4W1H keywords extraction which extracts the answer to a question corresponding to each event in QA format. We apply our methods to BERT and ELECTRA models to generate a summary, which are well-known pre-trained Language Models (LMs) in NLP domain, as a baseline. In experiments, our 4W1H keyword extraction method shows promising performance on AI Hub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">**</sup> https://www.aihub.or.kr/aidata/86 Machine Reading Comprehension (MRC) dataset, recording an extraction performance of an F1-score as 84.93%. Moreover, we show the results of generating a rule-based summarization using keywords extracted with 4W1H.
Coordinating Multi-Agent Deep Reinforcement Learning in Wargame

The successful application of deep reinforcement learning in RTS games such as StarCraft II has inspired people to apply multi-agent deep reinforcement learning(MADRL) to more fields. In the field of wargame, hexagonal maps are often used for simulation, which can't adapt to the rapid development of wargame. In continuous space of wargame, we construct a ship-defense scenario that includes multiple aircraft and ships. We apply deep Q network(DQN) method to MADRL, CNN to extract the features of multiple entities, and a centralized and distributed decision-making training architecture to control the aircraft's fixed-wing module components. Experiment results demonstrate the effectiveness of the proposed formulation, which show that the CNN-based feature extraction model can effectively defeat the built-in rule bot with multiple levels, and the training effect of CNN-based is better than the feature extraction method by full connection.
Deep Learning for Control of Digital Systems

Abstract Classically industrial systems apply a number of techniques to control their components, including the control system, which modify the relationship between input and output signals to configure the system to provide the required response. In most practical systems these signals are continuous, hence it is important to convert them into digital signals to be processed by digital systems. Despite the great development in technology, given the importance of the control system in relation to dynamic systems to achieve optimal performance, but classical control suffers from some important problems. The complexity of the control system represented by the program implementation algorithms and the loss of most information during the process of converting the system to digital and not adapting to external variables or with new updates. In this research classical control is replaced by deep neural networks, which is a thriving field with practical and medical applications and is characterized by its ability to learn and train as it is a branch of machine learning and artificial intelligence. The results proved that the functioning of the neural networks and their performance is similar to classical control systems, with the advantage of simplicity and adaptability.
Flow‐based frame interpolation networks combined with occlusion‐aware mask estimation

Frame interpolation is one of the most challenging tasks in the video processing field. Recent advances have demonstrated that the deep learning-based frame interpolation methods are promising. However, the experiments show that most existing deep learning-based methods have the same problem as traditional methods. When these algorithms handle severe occlusions, they will produce distortions, especially around the motion boundaries. To better synthesise the image of the motion areas, the authors design a mask-guided frame synthesis model, which consists of multiple components, based on deep convolutional neural networks. The proposed model first estimates the asymmetric bi-directional optical flows from the intermediate frame to the input frames. Then it estimates the occlusion-aware masks, which can compensate for the optical flow inaccuracy based on optical flows and correlation information. Finally, the warped frames are adaptively fused under the guidance of the masks to generate a high-quality intermediate frame. Furthermore, to generate more realistic video frames, they train the network model with the pixel-based loss and the feature-based loss in a step-by-step way. In the experiment, they analyse the proposed model and compare it with the high-performance methods, both qualitative and quantitative results show that their method performs better.
Distortion-aware Panoramic Image Resizing Using Seam Carving

We propose a solution for scattered information and distorted contours found in panorama images. Our weight map, mixed with the original forward energy obtained through dynamic programming, is used to produce a resized image with less panoramic distortion. The proposed method retargets a given image into the standard ratio of 16:9 for widescreen displays without losing aesthetic quality of the scene, as reported by human raters. The method could be applied to various situations where optical distortion and dispersed visual information are unavoidable.
Separating Left and Right Images in Unsupervised Monocular Depth Estimation

Monocular depth estimation based on unsupervised learning methods have recently shown promising results. Given stereo image pairs, these models utilize photometric reprojection errors as self-supervised signals. However, one drawback of most existing methods is that they mix the left and right images to optimize a single network. This degrades the model performance due to the different border-artifact effects and information distributions between the left and right images. To solve this problem, we introduce three novel methods to separate the left and right images during training, including (1) using two independent decoders for the left and right images, respectively, (2) utilizing two novel feature-processing modules and (3) adopting two different codes (01,10) to guide the training. The experiments on KITTI benchmark show that our methods can produce higher quality depth maps and outperform existing state-of-the-art approaches.
Review on Offline Signature Verification: Datasets, Methods and Challenges

Signature verification is one of the major field of biometrics for authentication of human beings. Biometrics refers to the metrics related to human characteristics. From the last decade, the research on signature verification is going vigorously, but still, the research problem is being explored. Due to multiple challenges present in automated signature verification and identification, the study is open and inspiration for researchers. The major objective of the offline signature verification system is to discriminate between genuine(original) and forged signatures. There are many factors that make the processing of offline signature very complex. This paper describes what kind of approaches used in previous research attempts. In those attempts, some of the methodologies achieved a significant improvement. We will analyze how the problem is handled to improve the verification and identification task in the existing literature.
Towards a methodology for maturity models development: an exploratory study in software systems interoperability domain

Maturity models have been used in several domains to evaluate system maturity according to specific aspects. Despite their popularity, maturity models have been criticized due to lack of empirical validation and effective methods to aid in their definition. This paper presents our efforts to systematize the development of maturity models towards a methodology. In this direction, tasks, artifacts, methods, and tools related to maturity model definition were proposed and organized as an initial methodology to support developers. In addition, a maturity model, named Amortisse was developed applying the proposed methodology. The results of this investigation show the Amortisse Maturity Model and the methodology are feasible. We hope this methodology can help the definition of maturity models in different domains contributing to maturity models standardization.
Multi Objective Task Scheduling Algorithm in Cloud Computing Using the Hybridization of Particle Swarm Optimization and Cuckoo Search

Rapid growth has been occurred in the IT industry with the emergence of Cloud computing in terms of the resources provisioned to the users in a seamless and flexible way. Task Scheduling is a prodigious challenge in the Cloud Computing. It is difficult to schedule the continuously varying requests to schedule on continuously varying resources. The existing approaches haven’t considered all the metrics while considering only the metrics like makespan and waiting time. In this paper, our focus is to formulate a Multi objective approach which is used to optimally map and load balance the tasks in the cloud by calculating the task priority and VM priority based on the electricity price per unit cost while minimizing the makespan, migration time and the power cost in the datacenters. The proposed algorithm is modeled using the hybridized approach by combining PSO and Cuckoo search algorithms. It is simulated on cloudsim simulator and it is compared against the basic ACO, GA, PSO and CS algorithms and our algorithm is outperformed against these basic algorithms with concerned parameters such as makespan, Migration time and the Total Power cost in the datacenters.