Reviewerly/exHarmony huggingface dataset:
Image Super-Resolution Using Residual Global Context Network

Recent studies have showed that convolutional neural networks (CNN) can effectively improve the performance of single image super-resolution (SR). However, previous methods rarely considered long-range dependencies between pixels and channel-wise interdependencies at the same time. They ignores the fact that natural images have strong internal data repetition which requires the network to capture long-range dependencies between pixels and considering the interdepen-dencies between channels can better exploit the input information of the network. In addition, although past studies have proved that deep convolutional neural network benefit the performance of image super-resolution, it also means that the network needs more memory consumption and higher computational complexity. To solve these problem,we introduce Global Context block (GCB) and design a comparative shallow network called Residual Global Context Networks (RGC-N). It achieves a better trade-off between the amount of parameter and the quality of image reconstruction. Extensive experiments demonstrate that the proposed method is superior to the state-of-the-art methods.
XMU-TS Systems for NIST SRE19 CTS Challenge

In this paper, we present our submitted XMU-TS system for NIST SRE19 CTS Challenge. The evaluation of this year only offers the open training condition. With the large amounts of data assimilated into training set, the diversity of training data sources inevitably leads to domain mismatch, which becomes a key factor affecting the system performance. In order to solve this problem, we have made a lot of attempts. Based on the x-vector framework, we used different network structures, and tried to modify the performance of factorized time delay deep neural network (F-TDNN) and residual network (ResNet). In addition, in the back-end classifier, we used domain adaption to eliminate the impact of domain mismatch. Finally, we employed Adaptive Symmetric Score Normalization (AS-Norm) for score normalization to adjust the fractional distribution space. These attempts have enriched the diversity of our systems, enabling the fusion system to complement each subsystem and improve the final submission performance.
Eye Movement Planning and Control

This article provides an overview of the control and planning of eye movements to visual stimuli, with a particular focus on the roles of key brain areas involved in guiding saccadic eye movements. We will also cover the processing of vergence eye movements, smooth pursuit eye movements and optokinetic eye movements.
An Approach for Automatic Detection of Quran Quotation (DQQ) in Arabic Texts

Quran language includes the most eloquent Arabic styles, structures, and vocabulary that a language can express. Most brilliant and creative writers have been affected by Quranic styles, vocabularies, structures, and meanings. This paper attempts to measure this effectiveness by matching similar language units with the Quranic language units. This similarity expands all over the linguistic analysis levels (morphological, syntactical, and semantic levels). Then identifies them with their positions in the text and clarifies the similarity automatically. A suggested tool called Detection Quran Quotation (DQQ) is able to accomplish this. DQQ can also be useful in the researches which discover quoted texts, plagiarism, heritage verification, and copyright issues. It will help in many applications, such as text similarities, summarization, and text analysis on all of its levels. In this paper, we explain the architecture of our proposed Quranic quotes detection framework. The DQQ is a framework that takes an Arabic text as an input and outputs the quote ratio results. The framework architecture is the first part of a project and is still under testing.
The Quantum Moonshot

This article reviews the IEEE Computer Society's opportunity and responsibility in the information revolution.
Data Based Construction of Kernels for Classification

This paper is an announcement for our longer paper in preparation. Traditional kernel based methods utilize either a fixed kernel or a combination of judiciously chosen kernels from a fixed dictionary. In contrast, we construct a data-dependent kernel utilizing the components of the eigen-decompositions of different kernels constructed using ideas from diffusion geometry, and use a regularization technique with this kernel with adaptively chosen parameters. In this paper, we illustrate our method using the two moons dataset, where we obtain a zero test error using only a minimal number of training samples.
Kweri ME: A Q&amp;A based model which predicts the accepted answers of questions in CQA sites

An approach to find the best answers from the multiple answer posted for given question in Community Question Answering(CQA) sites is tedious and most time consuming if manual process used. And also the experts are required to do this by analysis all given answers. So we presented an new approach based on topic modeling and classifier. To evaluate correctness of the proposed model, a set of parameters are used, such as Receiver Operating Characteristics Area Under Curve, Precision Recall Area Under Curve, Confussion matrix, F1 score and Accuracy. Results show that the proposed model is effective in predicting the best answer.
Natural Scene Text Detection Based On Multi-level Fusion Proposal Network

Abstract Natural scene text detection is a challenging issues. In this work, a multi-level features fusion two-stage text detection network was developed to solve the problem of the insufficient use for feature map and the difference between text and common target. In order to obtain deep semantic feature, the network framework of VGG-16 was improved, and different levels of convolution feature map were confused, meanwhile the fusion approach was partially adjusted. Also, vertical proposal network was used to classify and regress bounding box. Results indicated the accuracy and recall rate of this method are 85.4% and 81.0%, respectively, by evaluating the net on ICDAR2013 dataset. The experimental results suggest that the multi-level features fusion method can improve the efficiency of feature map in natural scene text detection.
A Two stage approach for Detection of Invasive and Cervical Intra-Epithelial Neoplasia using Machine Learning and Image Processing Methodologies

Representing around 6.6% of the cancer cases in women, Cervical Cancer is the fourth most common cancer in women. Cancer is a pernicious disease marked by rapid multiplication and growth of malignant cells in the body. The threat that it poses is evident from the fact that it is the second leading cause of death worldwide and consumes about 1 in 6 individuals worldwide. Of all the types of cancer, Cervical Cancer is the eighth most occurring cancer. Cancer causing infections, such as hepatitis and Human Papilloma Virus (HPV), are accountable for nearly 25% of cancer instances in low- and middle-income countries. Diagnosed in around 122,844 women in India and the cause of death for 67,477 women, this morbid disease can be best tackled when there is early diagnosis. It occurs when there is an aberrant growth of the cells of the cervix which further infect other tissues of the body. The lower part of the uterus that connects it with vagina is the cervix. The abnormal growth of cells mentioned earlier is caused by Human Papilloma Virus (HPV) infection, a sexually transmitted infection.The infection spreads in three stages of Cervical Intra-epithelial Neoplasia (CIN), and finally the most severe stage results in the onset of Cervical Cancer. This paper aims at implementing various Machine Learning Methodologies for first detecting the likelihood of transmission of HPV infection, the leading cause of Cervical Cancer by using a questionnaire involving questions related to the sexual activity of the individuals. Later, it aims at classifying the stage of Cervical Intra-Epithelial Neoplasia which can help in diagnosis and early treatment of the disease, to avert the onset of Cervical Cancer. For achieving this, we use a set of classifiers on the personal and medical detail provided by the user for predicting the likelihood of onset of cervical cancer in stage 1. In stage 2, image processing techniques are used to obtain features which are then given to the classifier to classify them into precancerous stages.
Multiplying Elimination of 8-bit Low-Precision Neural Networks Exploiting Weight and Activation Repetition

Convolutiona1 neural networks (CNNs) have been applied to various applications, such as image recognition and speech recognition, and have even achieved higher prediction accuracy than human eyes. However, the computation complexity of CNN increases rapidly with the increase of network scale, and a huge number of multiplying and accumulating operations may involve for a moderate CNN instance. This may significantly prolong the training and inference process and incur large energy consumption. Thus, a few low-precision CNN acceleration methods are presented to reduce the time complexity with the price of reduced computational accuracy. But they could not essentially reduce the number of calculations. In this backdrop, this paper proposes a table lookup-based multiplication elimination method for low-precision CNNs exploiting their weight and activation repetition. In our method, a table is established in advance to store all the possible multiplying results, and a simple table lookup operation is triggered every time a multiply calculation is encountered. Analysis results have shown that our proposal can greatly reduce the computational time, memory requirement, and energy consumption of low-precision CNNs.
Cooperative Path Planning Based on Pattern Genetic Algorithm

Abstract In order to solve the problem of low “population” effectiveness of genetic algorithm in multi-vehicle route planning, this paper proposes a pattern-based genetic algorithm. The algorithm uses the limited way of “route pattern”, which can greatly improve the effectiveness of population generation, crossover and mutation, thus reducing the iteration times of the algorithm and improving the convergence efficiency. The pattern-based genetic algorithm is applied to the cooperative path planning of multiple aircraft. The algorithm flow of cooperative path planning is designed. An example is given to prove its effectiveness and efficiency.
Accelerated Resolution Recovery Image Reconstruction Using a Neural Network Leapfrogging

Point Spread Function (PSF) is widely modeled in iterative image reconstruction algorithms. However, PSF introduces Gibbs artifact in early stage and reduces it very slowly with further iterations. We have investigated application of neural network to image reconstruction with PSF to leapfrog the iteration gap. One neural network and experiments on brain PET data are presented. Results are promising: artifacts are reduced while quantification is maintained. Use of neural network provides us an alternate, fast way to address this computationally challenging issue.
An improved Dynamic Search Fireworks Algorithm Optimizes Extreme Learning Machine to Predict Virtual Machine Fault

The Dynamic Search Fireworks Algorithm (dynFWA) is an effective algorithm for solving optimization problems. However, dynFWA is easy to fall into local optimal solutions prematurely and it also provides a slow convergence rate. To address these problems, an improved dynFWA (IdynFWA) is proposed in this chapter. In IdynFWA, the population is first initialized based on opposition-based learning. The adaptive mutation is proposed for the core firework (CF) which chooses whether to use Gaussian mutation or Levy mutation for the CF according to the mutation probability. A new selection strategy, namely disruptive selection, is proposed to maintain the diversity of the algorithm. The results show that the proposed algorithm achieves better overall performance on the standard test functions. Meanwhile, IdynFWA is used to optimize the Extreme Learning Machine (ELM), and a virtual machine fault warning model is proposed based on ELM optimized by IdynFWA. The results show that this model can achieve higher accuracy and better stability to some extent.
Federated Classification with Low Complexity Reproducing Kernel Hilbert Space Representations

In federated learning, a centralized model is realized based on information received from a group of agents each collecting data. This setting has two major challenges: the agents observe data over different distributions and they have only limited capabilities of sending data over the network to the centralized unit. Therefore, sending all the training data over the network is impractical. Each agent must train its own model and decide what relevant information it needs to send to the centralized unit. In this work we propose a method for federated learning in which each agent learns a low complexity Reproducing kernel Hilbert space representation. Leveraging the zero duality gap and the fact that each dual variable is associated with a sample, the agent discards samples for which the optimal dual variable is zero and sends only fundamental samples to the centralized unit. The centralized unit then computes the global model. We show that as the sample size grows, the solution obtained by the central unit converges to that obtained by an omniscient classifier which has access to all samples from all agents. We illustrate the performance of our federated learning algorithm and compare it to the omniscient classifier with a simulation.
DPNet: A Dual Path Network for Road Scene Semantic Segmentation

Road scene segmentation has always been regarded as a pixel-wise task in computer vision studies. In this paper, we introduce a practical and new features fusion structure named “Dual Path Network” for road semantic segmentation. This form aims to reduce the gap between low-level and high-level information, thereby improving features fusion. The Dual Path consists of two subpaths: Context Path and Spatial Path. In the Context Path, we select a pre-trained ResNet-101 model as the backbone and use multi-scale convolution blocks comprise the Spatial Path. Then, we create a fusion residual block and channel attention model to further optimize the network. The results of the experiment confirm a state-of-the-art mean intersection-over-union of 68.5% using the CamVid dataset.
On Some Data Pre-processing Techniques For K-Means Clustering Algorithm

Abstract This paper analyzed the performance of the basic K-Means clustering algorithm with two major data pre-processing techniques and superlative similarity measure with automatic initialization of seed values on the dataset. Further experiment was conducted with simulated data sets to prove the accuracy of the new method. The new method presented in this paper gave a good and promising performance for the different types of data sets. The sum of the squares clustering errors reduced significantly for the new method as compared with basic K-Means method whereas inter-distances between clusters are preserved to be as large as possible for better clusters identification.
An Internet Hosted Website Detection Method Based on Network Behavior Analysis for Power Grid Enterprises

Internet hosted websites are big threats to power grid enterprises because they may contain sensitive user data and business secrete while its information security is unregulated. Traditional technique to find Internet hosted websites is to set up web crawler and match key words in webpages, which is expensive and inefficient. In this paper, an Internet hosted website detection method based on network behavior analysis is introduced. Instead of parsing whole webpages, this method only analyzes network packet head information to get visit behavior features such as degree dispersion of source IP addresses and source departments visiting an Internet website. All behavior features of a destination website are integrated to get its probability of being an Internet hosted website. According to testing results on real data, Internet hosted websites are found in over 93 percent of the conducted tests.
Business-intelligence framework for visualization and its associate text narration

Business- Intelligence (BI) tools have become an integral part in data-driven organizations as it can transform raw data into visual reports to support decision makings. While a picture is worth a thousand words, interpreting visualized information sometimes leads to miscommunication between author and readers since an image chart may have many interesting features and readers may perceive different points of view from the author. Text narration can provide additional detailed descriptive on the topics of interest but requires reading time. To convey points of interest to readers without reducing the effectiveness of chart visualization, synergizing between visualization and its associate text narration is required. In this paper, a design of business-intelligence framework for data visualization and its associate text narration is presented. The key idea is to highlight related chart as response to reading (mouse) position on text area and vice versa. The framework is implemented as a JavaScript wrapper which acts as a relationship control between charts and texts. The wrapper can manipulate various software libraries to work together, thus reducing the complexity in the development. The comparisons between web pages from renowned web sites such as WorldBank.org and ones generated from our proposed framework using similar layout and data demonstrates that the design meet expected outcomes.
Rethinking Temporal-Related Sample for Human Action Recognition

Temporal-related samples always have huge intra-class appearance variation, on which lots of existing action recognition algorithms have poor performance. In this paper, our motivation is to address this issue by utilizing temporal information more effectively. A novel light-weight Voting-based Temporal Correlation module (VTC) is proposed to enhance temporal cues. VTC integrates sparse temporal sampling strategy into feature sequences, so it mitigates the effect of redundant information and focuses more on temporal modeling. Furthermore, we propose a simple and intuitive Similarity Loss (SL) to guide the training procedure for VTC. Introducing confusion in the predicted vector intentionally, SL eases intra-class variation by discovering class-specific common motion pattern rather than sample-specific discriminative information. Combining VTC and SL with complementary advances in this field, we clearly outperform state-of-the-art results on HMDB51, UCF101, and Something-something-v1 dataset. The code has been made publicly available on https://github.com/FingerRec/TRS.
Towards a New Understanding of the Training of Neural Networks with Mislabeled Training Data

We investigate the problem of machine learning with mislabeled training data. We try to make the effects of mislabeled training better understood through analysis of the basic model and equations that characterize the problem. This includes results about the ability of the noisy model to make the same decisions as the clean model and the effects of noise on model performance. In addition to providing better insights we also are able to show that the Maximum Likelihood (ML) estimate of the parameters of the noisy model determine those of the clean model. This property is obtained through the use of the ML invariance property and leads to an approach to developing a classifier when training has been mislabeled: namely train the classifier on noisy data and adjust the decision threshold based on the noise levels and/or class priors. We show how our approach to mislabeled training works with multi-layered perceptrons (MLPs).
Simultaneous image matching for person re‐identification via the stable marriage algorithm

Abstract Recently, multiperson tracking across non‐overlapping camera views is gaining increasing attention in the video surveillance field due to its importance for public security. Most existing methods find a correspondence of person images individually across each pair of adjacent two cameras. However, in such a naive approach, redundant matching where one of the person images is selected for the matching pair for several times, often occurs. It leads to tracking failure. To overcome this problem, in this paper, by considering the matching problem as an instance of the “marriage problem” that is well known in Economics, we propose a simultaneous image matching method for multiple persons based on the stable marriage algorithm. We confirmed that the proposed method outperforms some of the existing state‐of‐the‐art methods on several well‐known public datasets. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
Intellectual Property Rights Protection in Nigeria

Intellectual property right protection is of growing importance in most countries of the world due to its role in the development of any nation. However, intellectual property rights protection in Nigeria has not yielded any positive results as expected due to the problem of weak enforcement and non-implementation of protection laws. In view of this, the main purpose of this article is to unravel the challenges militating against the protection of intellectual property rights of creators and inventors in Nigeria. This article further discusses the benefits of intellectual property rights protection. Challenges contributing to weak enforcement of intellectual property rights were also identified in this article. In conclusion, intellectual property rights protection in Nigeria should be given the highest priority by government to aid national development as well as promoting creativity and innovation on the part of creators and inventors.
The Collection and Service Optimization of China's Academic Library ILL Based on Bipartite Matching

The interlibrary loan (ILL) service is essential to a multi-campus university library. This article builds a dynamic collection optimization model based on readers' needs using ILL data. This article first examined the status quo and methods of ILL. Then, refers to some existing algorithms of bipartite matching and recommendation such as network-based inference (NBI). Based on the above analysis, this article builds a model to optimize university library collection. With the loaning data of Soochow Academic Library from 2013 as the training data, an optimizing case was performed. The 2469 interlibrary loan records including readers and book they borrow from other library in 2014 was used for testing the effects of the model. The model is able to identify potential ILL demands and improve the efficiency of book circulation.
Research on Digital Watermarking Technology Based on MATLAB

With the rapid development of digital multimedia technology and Internet technology, the copyright protection of multimedia products has become a very important and challenging research topic at present. Digital watermarking technology is an effective method to realize the copyright protection of digital products, and it has become a hotspot in the field of information protection and information hiding. In this paper, DWT and LSB algorithm are adopted to realize the embedding and extraction of digital watermark through Matlab, after comparing the digital watermarking image under the Gaussian noise, salt and pepper noise, cutting and rotating attacks, the effect of two algorithms to extract digital watermarking image, the results show that: NC values (0.5067, 0.7584, 0.2301, 0.1330) and subjective ratings (3, 4, 2, 2) of DWT algorithm are superior to LSB algorithms (NC values are 0.0965, 0.7005, 0.5507, 0.0567, and subjective ratings are 1, 4, 3, 1). The DWT algorithm has better attack resistance and provides reference for the application of digital watermarking.