Reviewerly/exHarmony huggingface dataset:
The Augmented Homogeneous Coordinates Matrix-Based Projective Mismatch Removal for Partial-Duplicate Image Search

Mismatch removal is a key step in many computer vision problems that involve point matching. The existing methods for checking geometric consistency mainly focus on similarity or affine transformations. In this paper, we propose a novel mismatch removal method that can cope with the projective transformation between two corresponding point sets. Our approach is based on the augmented homogeneous coordinates matrix constructed from the coordinates of anchor matches, whose degeneracy can indicate the correctness of anchor matches. The set of anchor matches is initially all the matches and is iteratively updated by calculating the difference between the estimated matched points, which can be easily computed in a closed form, and the actually matched points and removing those with large differences. Experimental results on synthetic 2D point matching data sets and real image matching data sets verify that our method achieves the highest F -score among all the methods under similarity, affine, and projective transformations with noises and outliers. Our method can also achieve faster speed than all other iterative methods. Those non-iterative methods with slight advantage in speed are not competitive in accuracy when compared with ours. We also show that the set of anchor matches is stable through the iteration and the computation time grows very slowly with respect to the number of matched points. When applied to mismatch removal in partial-duplicate image search, our method achieves the best retrieval precision, and its computing time is also highly competitive.
Implicit Calibration Using Probable Fixation Targets

Proper calibration of eye movement signal registered by an eye tracker seems to be one of the main challenges in popularizing eye trackers as yet another user-input device. Classic calibration methods taking time and imposing unnatural behavior on eyes must be replaced by intelligent methods that are able to calibrate the signal without conscious cooperation by the user. Such an implicit calibration requires some knowledge about the stimulus a user is looking at and takes into account this information to predict probable gaze targets. This paper describes a possible method to perform implicit calibration: it starts with finding probable fixation targets (PFTs), then it uses these targets to build a mapping-probable gaze path. Various algorithms that may be used for finding PFTs and mappings are presented in the paper and errors are calculated using two datasets registered with two different types of eye trackers. The results show that although for now the implicit calibration provides results worse than the classic one, it may be comparable with it and sufficient for some applications.
Rademacher complexity and generalization performance of multi-category margin classifiers

One of the main open problems in the theory of multi-category margin classification is the form of the optimal dependency of a guaranteed risk on the number C of categories, the sample size m and the margin parameter γ. From a practical point of view, the theoretical analysis of generalization performance contributes to the development of new learning algorithms. In this paper, we focus only on the theoretical aspect of the question posed. More precisely, under minimal learnability assumptions, we derive a new risk bound for multi-category margin classifiers. We improve the dependency on C over the state of the art when the margin loss function considered satisfies the Lipschitz condition. We start with the basic supremum inequality that involves a Rademacher complexity as a capacity measure. This capacity measure is then linked to the metric entropy through the chaining method. In this context, our improvement is based on the introduction of a new combinatorial metric entropy bound.
Faster R-CNN and Geometric Transformation-Based Detection of Driver’s Eyes Using Multiple Near-Infrared Camera Sensors

Studies are being actively conducted on camera-based driver gaze tracking in a vehicle environment for vehicle interfaces and analyzing forward attention for judging driver inattention. In existing studies on the single-camera-based method, there are frequent situations in which the eye information necessary for gaze tracking cannot be observed well in the camera input image owing to the turning of the driver’s head during driving. To solve this problem, existing studies have used multiple-camera-based methods to obtain images to track the driver’s gaze. However, this method has the drawback of an excessive computation process and processing time, as it involves detecting the eyes and extracting the features of all images obtained from multiple cameras. This makes it difficult to implement it in an actual vehicle environment. To solve these limitations of existing studies, this study proposes a method that uses a shallow convolutional neural network (CNN) for the images of the driver’s face acquired from two cameras to adaptively select camera images more suitable for detecting eye position; faster R-CNN is applied to the selected driver images, and after the driver’s eyes are detected, the eye positions of the camera image of the other side are mapped through a geometric transformation matrix. Experiments were conducted using the self-built Dongguk Dual Camera-based Driver Database (DDCD-DB1) including the images of 26 participants acquired from inside a vehicle and the Columbia Gaze Data Set (CAVE-DB) open database. The results confirmed that the performance of the proposed method is superior to those of the existing methods.
Underwater terrain-aided navigation method based on improved Gaussian sum particle filtering

To solve the nonlinear Bayesian estimation problem in underwater terrain-aided navigation, a terrain-aided navigation method based on improved Gaussian sum particle filter is proposed. This method approximates the Bayesian function using multiple Gaussian components, and the components can be obtained by radial basis function neural network. This method has no resampling process, the particle depletion of particle filtering is eliminated in principle. The simulation shows that the proposed method has good matching performance, which is suitable for autonomous underwater vehicle navigation.
A Generalized Attack on Some Variants of the RSA Cryptosystem

Let $$N=pq$$ be an RSA modulus with unknown factorization. The RSA cryptosystem can be attacked by using the key equation $$ed-k(p-1)(q-1)=1$$ . Similarly, some variants of RSA, such as RSA combined with singular elliptic curves, LUC and RSA with Gaussian primes can be attacked by using the key equation $$ed- k\left( p^2-1\right) \left( q^2-1\right) =1$$ . In this paper, we consider the more general equation $$eu-\left( p^2-1\right) \left( q^2-1\right) v=w$$ and present a new attack that finds the prime factors p and q in the case that u, v and w satisfy some specific conditions. The attack is based on Coppersmith’s technique and improves the former attacks.
Chinese Character Boxes: Single Shot Detector Network for Chinese Character Detection

This paper proposes a deep learning-based Chinese character detection network which is important for character recognition and translation. Detecting the correct character area is an important part of recognition and translation. Previous studies have focused on methods using projection through image pre-processing and recognition methods based on segmentation and methods using hand-crafted features such as analyzing and using features. Unfortunately, the results are vulnerable to noise. Recently, recognition or translation systems based on deep learning were dealt with as a single step from detection to translation but they failed to consider the inaccurate localization problem that arises in detectors. This paper proposes a Chinese character boxes (CCB) network that deals with a method to detect the character area more accurately using the single-shot multibox detector (SSD) as the baseline and called CCB-SSD. The proposed CCB-SSD network has a single prediction layer structure in which unnecessary layers are removed from the feature-pyramid structure. The augmentation method for training is introduced and the problem caused by the use of default boxes is solved by using the proposed non-maximum suppression (NMS). The experimental results revealed a 96.1% detection rate and 0.89 performance against the false positives per character (FPPC) which is the proposed false positive index for the character data-set and caoshu data-set used in this paper. This method showed better performance than the conventional SSD with 69.4% and 6.57 FPPC.
Selecting Appropriate Metrics for Evaluation of Recommender Systems

The abundance of information on the web makes it difficult for users to find items that meet their information need effectively.To deal with this issue, a large number of recommender systems based on different recommender approaches were developed which have been used successfully in a wide variety of domains such as e-commerce, e-learning, e-resources, and egovernment among others.Moreover, in order for a recommender system to generate good quality of recommendations, it is essential for a researcher to find the most suitable evaluation metric which best matches a given recommender algorithm and a recommender's task.However, with the availability of several recommender tasks, recommender algorithms, and evaluation metrics, it is often difficult for a researcher to find their best combination.This paper aims to discuss various evaluation metrics in order to help researchers to select the most appropriate metric which matches a given task and an algorithm so as to provide good quality of recommendations.
Automatic Fusion of Segmentation and Tracking Labels

Labeled training images of high quality are required for developing well-working analysis pipelines. This is, of course, also true for biological image data, where such labels are usually hard to get. We distinguish human labels (gold corpora) and labels generated by computer algorithms (silver corpora). A naturally arising problem is to merge multiple corpora into larger bodies of labeled training datasets. While fusion of labels in static images is already an established field, dealing with labels in time-lapse image data remains to be explored. Obtaining a gold corpus for segmentation is usually very time-consuming and hence expensive. For this reason, gold corpora for object tracking often use object detection markers instead of dense segmentations. If dense segmentations of tracked objects are desired later on, an automatic merge of the detection-based gold corpus with (silver) corpora of the individual time points for segmentation will be necessary. Here we present such an automatic merging system and demonstrate its utility on corpora from the Cell Tracking Challenge. We additionally release all label fusion algorithms as freely available and open plugins for Fiji ( https://github.com/xulman/CTC-FijiPlugins ).
A Survey: Prospects of Internet of Things (IoT) Using Cryptography Based on its Subsequent Challenges

In Today’s era of information data plays vital role in communication, Security has been a ponderous affair for the professionals in any field. Security is not about only protection of assets but privacy and authority to access in legitimate terms too. Cryptography provides the functionality that protects and secures the data through its encryption algorithms and authentication to its end users. IoT has been most attractive topic/area in recent era. Privacy and security is an inseparable part of this technology. Which could affect in multi fold, so, the paper majorly focuses on four major challenges involving Security, Privacy, Compatibility and Connectivity of IOT and apprehension of IoT with its provided solution of using cryptographic algorithms.
Proximal Splitting Meets Variance Reduction

Despite the raise to fame of stochastic variance reduced methods like SAGA and ProxSVRG, their use in non-smooth optimization is still limited to a few simple cases. Existing methods require to compute the proximal operator of the non-smooth term at each iteration, which, for complex penalties like the total variation, overlapping group lasso or trend filtering, is an iterative process that becomes unfeasible for moderately large problems.In this work we propose and analyze VRTOS, a variance-reduced method to solve problems with an arbitrary number of non-smooth terms. Like other variance reduced methods, it only requires to evaluate one gradient per iteration and converges with a constant step size, and so is ideally suited for large scale applications.Unlike existing variance reduced methods, it admits multiple non-smooth terms whose proximal operator only needs to be evaluated once per iteration.We provide a convergence rate analysis for the proposed methods that achieves the same asymptotic rate as their full gradient variants and illustrate its computational advantage on 4 different large scale datasets.
Social relationships classification using social contextual features and SVDD-based metric learning

Family relationship is an important concern in image-based social relationships recognition, and there are very limited attempts to tackle diverse social relationships in the literature. In this paper, we propose the problem of social relationships classification in which we aim to model three types of social relationships( e.g., family, colleagues and friends) in the images. To this end, we introduce two types of social contextual features to capture detailed information( e.g., geometry or appearance) in images. Moreover, we present a new Support Vector Data Description-based metric learning( SML) method for social relationships classification. Motivated by the fact that the images are unavoidably degraded by noise due to some variation factors such as illumination and pose, we aim to learn a robust distance metric to suppress noise and model the spatial structure among multiple entities, such that more discriminative information can be exploited for classification. We also extend our method to multiview version-MSML, which helps to exploit multiple features to improve the social relationships classification performance. Extensive experiments on our newly released social relationships database demonstrate the feasibility and effectiveness of our proposed methods.
Attention-Based Joint Entity Linking with Entity Embedding

Entity linking (also called entity disambiguation) aims to map the mentions in a given document to their corresponding entities in a target knowledge base. In order to build a high-quality entity linking system, efforts are made in three parts: Encoding of the entity, encoding of the mention context, and modeling the coherence among mentions. For the encoding of entity, we use long short term memory (LSTM) and a convolutional neural network (CNN) to encode the entity context and entity description, respectively. Then, we design a function to combine all the different entity information aspects, in order to generate unified, dense entity embeddings. For the encoding of mention context, unlike standard attention mechanisms which can only capture important individual words, we introduce a novel, attention mechanism-based LSTM model, which can effectively capture the important text spans around a given mention with a conditional random field (CRF) layer. In addition, we take the coherence among mentions into consideration with a Forward-Backward Algorithm, which is less time-consuming than previous methods. Our experimental results show that our model obtains a competitive, or even better, performance than state-of-the-art models across different datasets.
Acceleration Estimation Using Imperfect Incremental Encoders in Automotive Applications

We address the problem of rotational velocity and acceleration estimation from incremental encoders in the presence of sensor imperfections, with a particular orientation toward automotive applications such as traction and brake control. In this area, measurements of the wheel speed and acceleration are often affected by large disturbances with a period of one revolution that arise from sensor imperfections and degrade the performance of most control designs. We present an algorithm to identify and remove such periodic perturbations online, without the need of error compensation look-up tables, and without assuming constant velocity. Experimental tests prove that the method is able to greatly reduce the impact of perturbations without introducing an important phase lag, as opposed to the results obtained using a notch filter.
Hide and Speak: Deep Neural Networks for Speech Steganography

Steganography is the science of hiding a secret message within an ordinary public message, which is referred to as Carrier. Traditionally, digital signal processing techniques, such as least significant bit encoding, were used for hiding messages. In this paper, we explore the use of deep neural networks as steganographic functions for speech data. We showed that steganography models proposed for vision are less suitable for speech, and propose a new model that includes the short-time Fourier transform and inverse-short-time Fourier transform as differentiable layers within the network, thus imposing a vital constraint on the network outputs. We empirically demonstrated the effectiveness of the proposed method comparing to deep learning based on several speech datasets and analyzed the results quantitatively and qualitatively. Moreover, we showed that the proposed approach could be applied to conceal multiple messages in a single carrier using multiple decoders or a single conditional decoder. Lastly, we evaluated our model under different channel distortions. Qualitative experiments suggest that modifications to the carrier are unnoticeable by human listeners and that the decoded messages are highly intelligible.
Content-based image retrieval: a deep look at features prospectus

Currently, rapid growth of digital images on the internet is observed, accordingly, the need for content-based image retrieval systems are in high demand. Content-based image retrieval (CBIR) is an image search technique that does not depend on manually assigned annotations; rather, CBIR uses discriminative features to search an image. By refining features, an efficient retrieval mechanism could be achieved. The aim of this research is to review features extraction and selection that have an impact on content-based image retrieval (CBIR) and information extraction from images using global and local features such as shape, texture and colour. In order to extract most appropriate features for content-based image retrieval (CBIR), several feature extraction and selection techniques are analysed and their efficiency is compared. Additionally, shortcomings of current content-based image retrieval techniques are addressed and possible solutions are suggested to enhance accuracy.
Montgomery Algorithm over a Prime Field

New formulae for point addition and point doubling on elliptic curves over prime fields are presented. Based on these formulae, an improved Montgomery algorithm is proposed. The theoretical analysis indicates that it is about 13.4% faster than Brier and Joye's Montgomery algorithm. Experiments on the elliptic curve over a 256-bit prime field recommended by the National Institute of Standards and Technology and over a 256-bit prime field in Chinese elliptic curve standard SM2 support the theoretical analysis.
The social value of anonymity on campus: a study of the decline of Yik Yak

This paper considers the social value of anonymity in online university student communities, through the presentation of research which tracked the final year of life of the social media application Yik Yak. Yik Yak was an anonymous, geosocial mobile application launched in 2013 which, at its peak in 2014, was used by around two million students in the US and UK. The research we report here is significant as a mixed method study tracing the final year of the life of this app in a large UK university between 2016 and 2017. The paper uses computational and ethnographic methods to understand what might be at stake in the loss of anonymity within university student communities in a datafied society. Countering the most common argument made against online anonymity – its association with hate speech and victimisation – the paper draws on recent conceptual work on the social value of anonymity to argue that anonymity online in this context had significant value for the communities that use it. This study of a now-lost social network constitutes a valuable portrait by which we might better understand our current predicament in relation to anonymity, its perceived value and its growing impossibility.
USE OF COMPUTER ALGEBRA SYSTEM MAXIMA IN THE PROCESS OF TEACHING FUTURE MATHEMATICS TEACHERS

The article is devoted to the research of a problem of the application of computer algebra systems in the course of skill training of future mathematics teachers. The possibility of study organization for students of pedagogical institutions of higher education receiving "014 secondary education (mathematics)" speciality with the use of computer algebra systems is considered. The term "computer algebra system" is understood as the software which is intended for the solving of various mathematical tasks by means of a computer with a possibility of visualization of all stages of calculations. Proceeding from the fact that the computer algebra systems are actively used in educational process all over the world and informational educational environments in the course of teaching mathematical disciplines are created on their basis, it is possible to make a conclusion that the computer algebra systems are very important components of the process of training future experts. The current state of the problem of introduction and application of the computer algebra systems in the training of future mathematics teachers is investigated. The role of the computer algebra systems in teaching mathematics subjects and general natural sciences in pedagogical establishments of higher education is defined. Such computer algebra systems as MathCAD, MatLab, Maple, Mathematica, Maxima, SAGE, and Axiom are marked out among the most modern and successfully used systems aimed at solving mathematical tasks. Arguments in favour of the use of the free computer algebra system Maxima in educational and scientific activity of students are adduced in the article. Its main advantages and disadvantages are presented. The main functional of this system is considered which allows to optimize the time of the solution of various mathematical tasks, to adjust a conclusion of the received result, to perform staged verification of correctness of calculations.
Capital Controls, Entrepreneurship and Economic Growth in Selected Developing Countries

This paper presents the role of capital controls in the relationship between entrepreneurship and economic growth in developing countries. Data of forty-four (44) developing countries for the period of 2005-2014 were sourced and analysed using panel generalized method of moments and two stage least square. The result revealed that entrepreneurship had a robust, positive and significant effect on economic growth. In addition, the study confirmed that the intensity of capital controls matter could further strengthen the relationship between entrepreneurship and economic growth in developing countries. These results were not only consistent with intuition and experience, but also with empirical findings of the previous studies. Theoretically, it confirmed the view of Keynesian economists that promote the use of capital controls in an economy.