Reviewerly/exHarmony huggingface dataset:
Nonlinear edge-preserving diffusion with adaptive source for document images binarization

This paper proposes a nonlinear edge-preserving diffusion equation with an adaptive source term for binarization of degraded document images. The role of nonlinear diffusion term is to smooth images with preservation of text edges and corners, while the source term is responsible for the desired binarization. Unlike other binarization techniques (such as clustering-based and threshold-based), the idea behind the proposed method is that a sequence of gradually binarized images is obtained by solving the evolution equation starting with the image to be binarized, and tends to the slightly smoothed version of the desired binary image at infinity. A semi-implicit parallel splitting-up method is developed for solving the proposed model effectively. The proposed model with algorithm is tested on the DIBCO (Document Image Binarization Competitions) series datasets. The results show that it has generally the best performance, compared to four PDE (partial differential equation)-based binarization models, and six recent and benchmark binarization algorithms (non-PDE based).
Libraries as social institutions in the knowledge society

The author offers a new paradigm of the library as a social institution in the period of the civilization transition from the information society to the knowledge society. The library functions and goals are defined, in particular encouraging and administering of free exchange of opinions and ideas between social strata and groups to solve their vital problems; providing space and tools (including acess to chargeable information content) to implement creative initiatives and to solve user everyday problems; designing training (educational) programs to adapt residents to continuously emerging innovations. Taking into account these new functions and tasks, the author defines the library as a social institution supporting cognitive and creative activities of the community being served through intellectual collaboration, educational and recreational programs, providing space, equipment hard- and software for public use and access to information sources. The author specifies the measures that would facilitate the RF libraries’ transition. He emphasizes that, to ensure success, the transformations are needed to be made both at the local (libraries) and at the federal level.
Real-time object tracking via least squares transformation in spatial and Fourier domains for unmanned aerial vehicles

This paper addresses the problem of real-time object tracking for unmanned aerial vehicles. We consider the task of object tracking as a classification problem. Training a good classifier always needs a huge number of samples, which is always time-consuming and not suitable for real-time applications. In this paper, we transform the large-scale least-squares problem in the spatial domain to a series of small-scale least-squares problems with constraints in the Fourier domain using the correlation filter technique. Then, this problem is efficiently solved by two stages. In the first stage, a fast method based on recursive least squares is used to solve the correlation filter problem without constraints in the Fourier domain. In the second stage, a weight matrix is constructed to prune the solution attained in the first stage to approach the constraints in the spatial domain. Then, the pruned classifier is used for tracking. To evaluate proposed tracker’s performance, comprehensive experiments are conducted on challenging aerial sequences in the UAV123 dataset. Experimental results demonstrate that proposed approach achieves a state-of-the-art tracking performance in aerial sequences and operates at a mean speed of beyond 40 frames/s. For further analysis of proposed tracker’s robustness, extensive experiments are also performed on recent benchmarks OTB50, OTB100, and VOT2016.
Two-Stage Bagging Pruning for Reducing the Ensemble Size and Improving the Classification Performance

Ensemble methods, such as the traditional bagging algorithm, can usually improve the performance of a single classifier. However, they usually require large storage space as well as relatively time-consuming predictions. Many approaches were developed to reduce the ensemble size and improve the classification performance by pruning the traditional bagging algorithms. In this article, we proposed a two-stage strategy to prune the traditional bagging algorithm by combining two simple approaches: accuracy-based pruning (AP) and distance-based pruning (DP). These two methods, as well as their two combinations, “AP+DP” and “DP+AP” as the two-stage pruning strategy, were all examined. Comparing with the single pruning methods, we found that the two-stage pruning methods can furthermore reduce the ensemble size and improve the classification. “AP+DP” method generally performs better than the “DP+AP” method when using four base classifiers: decision tree, Gaussian naive Bayes, K-nearest neighbor, and logistic regression. Moreover, as compared to the traditional bagging, the two-stage method “AP+DP” improved the classification accuracy by 0.88%, 4.06%, 1.26%, and 0.96%, respectively, averaged over 28 datasets under the four base classifiers. It was also observed that “AP+DP” outperformed other three existing algorithms Brag, Nice, and TB assessed on 8 common datasets. In summary, the proposed two-stage pruning methods are simple and promising approaches, which can both reduce the ensemble size and improve the classification accuracy.
BlockQuicksort

It is well known that Quicksort -- which is commonly considered as one of the fastest in-place sorting algorithms -- suffers in an essential way from branch mispredictions. We present a novel approach to addressing this problem by partially decoupling control from dataflow: in order to perform the partitioning, we split the input into blocks of constant size. Then, all elements in one block are compared with the pivot and the outcomes of the comparisons are stored in a buffer. In a second pass, the respective elements are rearranged. By doing so, we avoid conditional branches based on outcomes of comparisons (except for the final Insertionsort). Moreover, we prove that when sorting n elements, the average total number of branch mispredictions is at most ϵ n log n + O ( n ) for some small ϵ depending on the block size. Our experimental results are promising: when sorting random-integer data, we achieve an increase in speed (number of elements sorted per second) of more than 80% over the GCC implementation of Quicksort (C++ std::sort). Also, for many other types of data and non-random inputs, there is still a significant speedup over std::sort. Only in a few special cases, such as sorted or almost sorted inputs, can std::sort beat our implementation. Moreover, on random-input permutations, our implementation is even slightly faster than an implementation of the highly tuned Super Scalar Sample Sort, which uses a linear amount of additional space. Finally, we also apply our approach to Quickselect and obtain a speed-up of more than 100% over the GCC implementation (C++ std::nth_element).
Machine Learning and Meta-heuristic Algorithms for Renewable Energy: A Systematic Review

The demand for energy is become essential due to industrial activities and increasing agricultural of any nation. According to the aforementioned, the renewable energy resources available are very suitable to meet the ever-growing requirement of energy by the humanity rather causing any harmful effects to nature. Therefore, several research studies have been introduced in the renewable energy field such as solar, wind, biomass, and biogas due to the clean and sustainability. To better scheme and utilize this energy resource, good forecasting and optimization are necessary and intrinsic. So, this review introduces an overview of the renewable energy forecasting techniques that have been utilized in this field based on meta-heuristic optimization algorithms and machine learning (ML). In addition, several challenges have been addressed, recommendations for future research are provided, and a comprehensive bibliography is conducted. Eventually, in general speaking, this comprehensive review of renewable energy resources may help the researchers, energy planners, and policymakers.
Deep Networks for Image-to-Image Translation with Mux and Demux Layers

Image processing methods using deep convolutional networks have achieved great successes on quantitative and qualitative assessments in many tasks, such as super–resolution, style transfer and enhancement. Most of these solutions use many layers, many filters and complex architectures. It is difficult to implement them on mobile devices, e.g. smart phones, because of the limited resources. Many applications need to deploy these methods on mobile devices. But it is difficult because of limited resources. In this paper we present a lightweight end–to–end deep learning approach for image enhancement. To improve the performance, we present mux layer and demux layers, which could perform up–sampling and down–sampling by shuffling the pixels without losing any information of feature maps. For further higher performance, denseblocks are used in the models. To ensure the consistency of the output and input, we use weighted L1 loss to increase PSNR. To improve image quality, we use adversarial loss, contextual loss and perceptual loss as parts of the objective functions during training. And NIQE is used for validation to get the best parameters for perceptual quality. Experiments show that, compared to the state–of–the–art, our method could improve both the quantitative and qualitative assessments, as well as the performance. With this system, we get the third place in PIRM Enhancement–On–Smartphones Challenge 2018 (PIRM–EoS Challenge 2018).
An automated data verification approach for improving data quality in a clinical registry

The quality of data is crucial for clinical registry studies as it impacts credibility. In the regular practice of most such studies, a vulnerability arises from researchers recording data on paper-based case report forms (CRFs) and further transcribing them onto registry databases. To ensure the quality of data, verifying data in the registry is necessary. However, traditional manual data verification methods are time-consuming, labor-intensive and of limited-effect. As paper-based CRFs and electronic medical records (EMRs) are two sources for verification, we propose an automated data verification approach based on the techniques of optical character recognition (OCR) and information retrieval to identify data errors in a registry more efficiently.Three steps are involved to develop the automated verification approach. First, we analyze the scanned images of paper-based CRFs with machine learning enhanced OCR to recognize the checkbox marks and hand-writing. Then, we retrieve the related patient information from the EMRs using natural language processing (NLP) techniques. Finally, we compare the retrieved information in the previous two steps with the data in the registry, and synthesize the results accordingly. The proposed automated method has been applied in a Chinese registry study and the difference between automated and manual approach has been evaluated.The automated approach has been implemented in The Chinese Coronary Artery Disease Registry. For CRF data recognition, the accuracy of recognition for checkboxes marks and hand-writing are 0.93 and 0.74, respectively. For EMR data extraction, the accuracy of information retrieval from textual electronic medical records is 0.97. The accuracy, recall and time consumption of the automated approach are 0.93, 0.96 and 0.5 h, better than the corresponding values of the manual approach, which are 0.92, 0.71 and 7.5 h.Compared to the manual data verification approach, the automated approach enhances the recall of identify data errors and has a higher accuracy. The time consumed is far less. The results show that the automated approach is more effective and efficient for identifying incomplete data and incorrect data in a registry. The proposed approach has potential to improve the quality of registry data.
Nuclear-norm based 2DLDA with application to face recognition

Two-dimensional Linear Discriminant analysis (2DLDA) has been widely used for dimensionality reduction and feature extraction of data. However, most existing 2DLDA based methods cannot well characterize the spatial geometric structure of image. To solve this problem, inspired by the fact that nuclear-norm well reveals spatial relationship among pixels in each image and is robust to illumination and noise, we propose a nuclear-norm based two-dimensional Linear Discriminant analysis (2DLDA-nuclear), which employs nuclear-norm to characterize within-class and between-class scatters. Compared with most existing robust 2DLDA methods, our method not only helps suppress noise and illumination but also preserves spatial geometric structure of image. We also provide an effective iterative algorithm to solve 2DLDA-nuclear. Experiments on five public image databases show the effectiveness of our model.
Blended memory: A framework for understanding distributed autobiographical remembering with photography

This article offers a framework for understanding how different kinds of memory work together in interaction with people, photographs and other resources. Drawing on evidence from two qualitative studies of photography and memory, as well as literature from cognitive psychology, distributed cognition and media studies, I highlight complexities that have seldom been taken into account in cognitive psychology research. I then develop a ‘blended memory’ framework in which memory and photography can be interdependent, blending together as part of a wider activity of distributed remembering that is structured by interaction and phenomenology. In contrast to studies of cued recall, which commonly feature isolated categories or single instances of recall, this framework takes account of people’s histories of photographic practices and beliefs to explain the long-term convergence of episodic, semantic and inferential memory. Finally, I discuss implications for understanding and designing future memory research.
SOM-based information maximization to improve and interpret multi-layered neural networks: From information reduction to information augmentation approach to create new information

The present paper aims to show the necessity of information augmentation to cope with the natural decrease in information content in multi-layered neural networks. It is natural to make an effort to collect as much information as possible, because it is impossible to know which information is necessary or useful before learning. Thus, the present paper tries to force neural networks to form new network configurations to have as much information as possible, contrary to the conventional approach of information reduction, such as many types of regularization. For information augmentation, we use the self-organizing map (SOM), which can over-represent inputs and produce as many similar weights as possible. The method was applied to two data sets: the banknote authentication data set and the character recognition data set. In both experimental results, it was confirmed that redundant and excessive information generation in terms of the excessive number of connection weights was connected with improved generalization.
Closed-Loop Optimization of DIC Speckle Patterns Based on Simulated Experiments

Stereo digital image correlation (DIC) spreads widely in the last years as one of the most flexible and accurate full-field displacement-strain measurement techniques. The development of novel applications based on DIC makes the design of 3-D setups a challenging task, given the complex and nonlinear nature of stereophotogrammetric methods. The present literature allows the design and optimization of DIC experimental setup only with approximated uncertainty models or upon image quality metrics that are linked loosely with the actual value of uncertainty. In this paper, a closed-loop optimization method based on 3-D experiment simulation is presented. The approach may be used to optimize several parameters (from the camera setup to the DIC processing parameters). This paper features a case study on the problem of optimizing a regular speckle pattern for different measurement tasks. The whole approach is validated experimentally in Section V.
Key Frame Extraction Using Rough Set Theory for Video Retrieval

Key frame is a representative frame which contains the entire information of the shot. It is used for indexing, classification, analysis and retrieval of video. The existing algorithms generate relevant key frames but they also generate a few redundant key frames. Some of them are not able to represent the entire shot since relevant key frames are not extracted. We have proposed a more effective algorithm based on DC coefficients and Rough Sets to prevail over the rest. It extracts the most relevant key frames by eliminating the vagueness of the selection of key frames. It can be applied for compressed MPEG videos hence decompression is not required. The performance of this algorithm shows its effectiveness.
Key Reuse Attack on NewHope Key Exchange Protocol

In recent years, Ring Learning with Errors (RLWE) key exchange has been recognized for its efficiency and is considered a potential alternative to Diffie-Hellman (DH) key exchange protocol. We focus on RLWE key exchange protocols in the context of key reuse. In 2016 (ePrint 085), Fluhrer firstly presented an attack aiming at the case where party B reuse his secret key. In key reuse attack, the adversary plays the role of A with the abilities to initiate any number of key exchange sessions with party B. The adversary initiates a sequence of key exchange sessions with a malformed key, then looks for the signal variations sent by party B. In this work, we describe a new key reuse attack against the NewHope key exchange protocol proposed by Alkim et al. in 2016. We give a detailed analysis of the signal function of the NewHope and describe a new key recovering technique based on the special property of NewHope's signal.
A New Framework to Train Autoencoders Through Non-Smooth Regularization

Deep structures consisting of many layers of nonlinearities have a high potential of expressing complex relations if properly initialized. Autoencoders play a complementary role in training a deep structure by initializing each layer in a greedy unsupervised manner. Due to the high capacity presented by autoencoders, these structures need to be regularized. While mathematical regularizers (based on weight decay, sparsity, etc.) and structural ones (by way of, e.g., denoising and dropout) have been well studied in the literature, quite a few papers have addressed the problem of training autoencoder with non-smooth regularization. In this paper, we address the problem of training autoencoder with non-smooth regularization. We propose an efficient algorithm and mathematically prove that it is convergent, where the regularizer needs to be proximable. As one of major applications of the proposed method, we get focused on the problem of sparse autoencoders and show that the new training method leads to better disentangling of factors of variation.
Building A Size Constrained Predictive Models for Video Classification

Herein we present the solution to the $$2^\mathrm{nd}$$ YouTube-8M video understanding challenge which placed $$1^\mathrm{st}$$ . Competition participants were tasked with building a size constrained video labeling model with a model size of less than 1 GB. Our final solution consists of several submodels belonging to Fisher vectors, NetVlad, Deep Bag of Frames and Recurrent neural networks model families. To make the classifier efficient under size constraints we introduced model distillation, partial weights quantization and training with exponential moving average.
Alphabetic or Contributor Author Order. What Is the Norm in Danish Economics and Political Science and Why?

Researchers have different ways of deciding on the author order, and how they do it often depends on the culture of their field. Some fields are well known for using alphabetic author order, while others put a great emphasis on the meaning of the author order and place authors according to contribution. This article is the first to use mixed method to examine the extent of alphabetic author order and to examine why researchers adopt a certain author order norm in the fields of economics and political science. The article finds that alphabetic authorship has been and is the norm in economics, while some tendency towards it exists in political science. The differences in the intellectual and social organization of the fields seem to be a factor in the extent that these researchers will adopt a certain norm. Furthermore, the increasing number of authors per article and the publish‐or‐perish culture seems to put pressure on the alphabetic norm because it creates greater attention to the reputational advantages of being first‐author.
ISI/ITI Turbo Equalizer for TDMR Using Trained Local Area Influence Probabilistic Model

In this paper, a local area influence probabilistic (LAIP) detector for estimating magnetic grain interactions with coded data bits in two-dimensional magnetic recording is combined with a 2-D Bahl-Cocke-Jelinek-Raviv (BCJR)-based detector for joint removal of intertrack interference (ITI) and intersymbol interference (ISI). The LAIP detector sends log-likelihood ratio estimates of coded bits and an estimate of the local ISI/ITI convolution mask to a BCJR-based ISI/ITI detector followed by an irregular-repeat-accumulate decoder. Simulation results on a random Voronoi grain media model with ISI and ITI show that the concatenated LAIP/BCJR system, which detects three tracks simultaneously, achieves user information bit areal densities competitive or higher than results reported in a previous paper that employed the LAIP detector alone on a Voronoi grain channel without ISI/ITI. Simulation results on a grain-flipping probability media model based on micromagnetic simulations show that the proposed detector achieves an 11.3% bit error rate reduction compared to a recently proposed system with a 2-D linear equalizer followed by a two-track BCJR detector with 2-D pattern-dependent noise prediction.
A Novel Design of a Multiplier Using Reversible Ternary Gates

Reversible ternary circuit design is now being taken into consideration for its application within the quantum computer and other technologies associated with the nanotechnology domain, in addition to the advantages of the ternary logic application as opposed to binary logic. Multiplier circuit is one of the most important computing circuit in the ALU. Therefore, the circuit optimization for the multiplication will lead to efficient processor. In this paper, the reversible circuit for multiplication of two unsigned two-digit ternary numbers has been proposed. To construct reversible ternary multiplier, a new component termed TPPG with the quantum cost of 13 for partial product generation was proposed. In addition, we have introduced four new blocks of reversible ternary adder with the quantum cost of 11, 10, 7, 5 and one constant input, which can be used in the proposed multiplier circuit. We attempted to optimize the design of the circuits in terms of quantum cost, a number of primitive gates, constant inputs, and garbage outputs as far as possible. To realize all proposed circuits, one-qutrit shift gates and two-qutrit Muthukrishnan-Stroud gates were used.