Reviewerly/exHarmony huggingface dataset:
SuPeR: Milano Retinex implementation exploiting a regular image grid

A spatial color algorithm grounded on the Retinex theory is known as a Milano Retinex. This type of algorithm performs image enhancement by processing spatial and color cues in the neighborhood of each image pixel. Because this local, pixel-wise analysis is time consuming, optimization techniques are needed to expand the use of Milano Retinexes to applications that require fast or even real-time image processing. In this work, we propose SuPeR, an efficient optimization of the Milano Retinex local spatial color processing that exploits superpixels, which are as the regular, rectangular blocks of a grid that partitions the image support. Image enhancement is obtained by reworking channel-wise the intensity of each pixel based on the maximum color intensities of the blocks and on its distance from the blocks. The experiments, carried out on real-world image datasets, show good performance compared to other Milano Retinexes.
A global and local context integration DCNN for adult image classification

With the wide availability of the Internet and the proliferation of pornographic images online, adult image detection and filtering has become very important to prevent young people from reaching these harmful contents. However, due to the large diversity in adult images, automatic adult image detection is a difficult task. In this paper, a new deep convolutional neural network (DCNN) based approach is proposed to classify images into three classes, i.e. porn, sexy, and benign. Our approach takes both the entire picture (global context) and the meaningful region (local context) information into consideration. The proposed network is composed of three parts, i.e. the image characteristics subnet to extract discriminative low-level image features, the sensitive body part detection subnet to detect adult-image related regions, and the feature extraction and fusion subnet to generate high-level features for image classification. A multi-task learning scheme is designed to optimize the network with both the global and local information. Experiments are carried out on two datasets with over 160,000 images. From the experiment results, it was observed that the proposed network achieved high classification accuracies (96.6% in the AIC dataset and 92.7% in the NPDI dataset) and outperformed the other approaches investigated.
Disentropy of the Wigner function

In this work, the usage of the disentropy of the Wigner function as order and quantumness measure is investigated. Furthermore, the relative disentropy of the Wigner function as a distance measure between two Wigner functions is also discussed. The numerical calculation of the dynamic of the disentropy and relative disentropy of the Wigner function are realized considering an initially pure quantum state that evolves to a classical mixture and when an initially coherent state evolves to a Schrödinger cat state. It is shown that in all cases the disentropy is minimal for the quantum states with maximal quantumness, and it is maximal for maximally ordered states (pure states with positive Wigner function).
A novel modified undersampling (MUS) technique for software defect prediction

Abstract Background and aim: Many sophisticated data mining and machine learning algorithms have been used for software defect prediction (SDP) to enhance the quality of software. However, real‐world SDP data sets suffer from class imbalance, which leads to a biased classifier and reduces the performance of existing classification algorithms resulting in an inaccurate classification and prediction. This work aims to improve the class imbalance nature of data sets to increase the accuracy of defect prediction and decrease the processing time . Methodology: The proposed model focuses on balancing the class of data sets to increase the accuracy of prediction and decrease processing time. It consists of a modified undersampling method and a correlation feature selection (CFS) method. Results : The results from ten open source project data sets showed that the proposed model improves the accuracy in terms of F1‐score to 0.52 ∼ 0.96, and hence it is proximity reached best F1‐score value in 0.96 near to 1 then it is given a perfect performance in the prediction process. Conclusion: The proposed model focuses on balancing the class of data sets to increase the accuracy of prediction and decrease processing time using the proposed model.
Fast CNN-Based Object Tracking Using Localization Layers and Deep Features Interpolation

Object trackers based on Convolution Neural Network (CNN) have achieved state-of-the-art performance on recent tracking benchmarks, while they suffer from slow computational speed. The high computational load arises from the extraction of the feature maps of the candidate and training patches in every video frame. The candidate and training patches are typically placed randomly around the previous target location and the estimated target location respectively. In this paper, we propose novel schemes to speedup the processing of the CNN-based trackers. We input the whole region-of-interest once to the CNN to eliminate the redundant computations of the random candidate patches. In addition to classifying each candidate patch as an object or background, we adapt the CNN to classify the target location inside the object patches as a coarse localization step, and we employ bilinear interpolation for the CNN feature maps as a fine localization step. Moreover, bilinear interpolation is exploited to generate CNN feature maps of the training patches without actually forwarding the training patches through the network which achieves a significant reduction of the required computations. Our tracker does not rely on offline video training. It achieves competitive performance results on the OTB benchmark with 8x speed improvements compared to the equivalent tracker.
Multiview Hybrid Embedding: A Divide-and-Conquer Approach

We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multiview subspace learning (MvSL) that aims to learn a latent subspace shared by multiview data. Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multiview data is sampled from nonlinear manifolds or suffers from heavy outliers. To circumvent this drawback, motivated by the Divide-and-Conquer strategy, we propose multiview hybrid embedding (MvHE), a unique method of dividing the problem of cross-view classification into three subproblems and building one model for each subproblem. Specifically, the first model is designed to remove view discrepancy, whereas the second and third models attempt to discover the intrinsic nonlinear structure and to increase the discriminability in intraview and interview samples, respectively. The kernel extension is conducted to further boost the representation power of MvHE. Extensive experiments are conducted on four benchmark datasets. Our methods demonstrate the overwhelming advantages against the state-of-the-art MvSL-based cross-view classification approaches in terms of classification accuracy and robustness.
Ultra Lightweight Multiple-Time Digital Signature for the Internet of Things Devices

Digital signatures are basic cryptographic tools to provide authentication and integrity in the emerging ubiquitous systems in which resource-constrained devices are expected to operate securely and efficiently. However, existing digital signatures might not be fully practical for such resource-constrained devices (e.g., medical implants) that have energy limitations. Some other computationally efficient alternatives (e.g., one-time/multiple-time signatures) may introduce high memory and/or communication overhead due to large private key and signature sizes. In this paper, our contributions are two-fold: First, we develop a new lightweight multiple-time digital signature scheme called Signer Efficient Multiple-time Elliptic Curve Signature ( <inline-formula><tex-math notation="LaTeX">$\mathtt {SEMECS}{}$</tex-math></inline-formula> ), which is suitable for resource-constrained embedded devices. <inline-formula><tex-math notation="LaTeX">$\mathtt {SEMECS}{}$</tex-math></inline-formula> achieves optimal signature and private key sizes for an EC-based signature <i>without</i> requiring any EC operation (e.g., EC scalar multiplication or addition) at the signer. We prove <inline-formula><tex-math notation="LaTeX">$\mathtt {SEMECS}{}$</tex-math></inline-formula> is secure (in random oracle model) with a tight security reduction. Second, we fully implemented <inline-formula><tex-math notation="LaTeX">$\mathtt {SEMECS}{}$</tex-math></inline-formula> on 8-bit AVR microprocessor with a comprehensive energy consumption analysis and comparison. Our experiments confirm up to 19× less battery-consumption for <inline-formula><tex-math notation="LaTeX">$\mathtt {SEMECS}{}$</tex-math></inline-formula> as compared to its fastest (full-time) counterpart, SchnorrQ, while offering significant performance advantages over its multiple-time counterparts in various fronts. We open-source our implementation for public testing and adoption.
Limit Distributions for Euclidean Random Permutations

We study the length of cycles in the model of spatial random permutations in Euclidean space. In this model, for given length L, density $$\rho $$ , dimension d and jump density $$\varphi $$ , one samples $$\rho L^d$$ particles in a d-dimensional torus of side length L, and a permutation $$\pi $$ of the particles, with probability density proportional to the product of values of $$\varphi $$ at the differences between a particle and its image under $$\pi $$ . The distribution may be further weighted by a factor of $$\theta $$ to the number of cycles in $$\pi $$ . Following Matsubara and Feynman, the emergence of macroscopic cycles in $$\pi $$ at high density $$\rho $$ has been related to the phenomenon of Bose–Einstein condensation. For each dimension $$d\ge 1$$ , we identify sub-critical, critical and super-critical regimes for $$\rho $$ and find the limiting distribution of cycle lengths in these regimes. The results extend the work of Betz and Ueltschi. Our main technical tools are saddle-point and singularity analysis of suitable generating functions following the analysis by Bogachev and Zeindler of a related surrogate-spatial model.
Real-time visual detection and correction of automatic screw operations in dimpled light-gauge steel framing with pre-drilled pilot holes

Modular and panelized construction have been promoted and recognized globally as advanced construction techniques for residential and commercial industries alike. Light-Gauge Steel (LGS) panels have become more popular for commercial buildings and high-rise residential buildings in the last decades. When constructing such panels, for ease of manufacturing and assembling, a common practice in the construction industry is the use of dimples and pre-drilled pilot holes. Current automatic LGS machinery, however, is not designed to operate with such constraints. In this study, a real-time vision-based approach is proposed to enable current machinery to use dimpled studs with pre-drilled pilot holes. An algorithm designed for hole detection inside the dimples on LGS steel studs, based on edge detection and ellipse fitting is proposed. Finally, an adaptive approach is proposed to adjust the screw driving manipulators to ensure that the drilling operation occurs accurately, avoiding any possible damage to the LGS studs or failure of the screwing operation. The proposed algorithm is validated on a real steel assembly and a comparison is provided with other well-known algorithms for ellipse detection to demonstrate the effectiveness of the proposed method. This real-time algorithm gives real-time results for pilot hole detection and screwing location estimation within 3 mm tolerance. When compared with other well-known approaches in the literature, the proposed approach is 59% more accurate than the fastest available algorithm.
Pretty good quantum state transfer in asymmetric graphs via potential

We construct infinite families of graphs in which pretty good state transfer can be induced by adding a potential to the nodes of the graph (i.e. adding a number to a diagonal entry of the adjacency matrix). Indeed, we show that given any graph with a pair of cospectral nodes, a simple modification of the graph, along with a suitable potential, yields pretty good state transfer between the nodes. This generalizes previous work, concerning graphs with an involution, to asymmetric graphs.
PUlasso: High-Dimensional Variable Selection With Presence-Only Data

In various real-world problems, we are presented with classification problems with positive and unlabeled data, referred to as presence-only responses. In this article we study variable selection in the context of presence only responses where the number of features or covariates p is large. The combination of presence-only responses and high dimensionality presents both statistical and computational challenges. In this article, we develop the PUlasso algorithm for variable selection and classification with positive and unlabeled responses. Our algorithm involves using the majorization-minimization framework which is a generalization of the well-known expectation-maximization (EM) algorithm. In particular to make our algorithm scalable, we provide two computational speed-ups to the standard EM algorithm. We provide a theoretical guarantee where we first show that our algorithm converges to a stationary point, and then prove that any stationary point within a local neighborhood of the true parameter achieves the minimax optimal mean-squared error under both strict sparsity and group sparsity assumptions. We also demonstrate through simulations that our algorithm outperforms state-of-the-art algorithms in the moderate p settings in terms of classification performance. Finally, we demonstrate that our PUlasso algorithm performs well on a biochemistry example. Supplementary materials for this article are available online.
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Let p be any odd prime number, m and s be arbitrary positive integers, and let Fpm be the finite field of cardinality pm. Existing literature only determines the number of all (Euclidean) self-dual cyclic codes of length ps over finite chain ring R=Fpm+uFpm (u2=0), such as Dinh et al. (2018). Using some combinatorial identities, we obtain certain properties for Kronecker product of matrices over Fp with a specific type. On that basis, we give an explicit representation and enumeration for all distinct self-dual cyclic codes of length ps over R. Moreover, we provide an efficient method to construct every self-dual cyclic code of length ps over R precisely.
Foreground Segmentation Using Adaptive 3 Phase Background Model

An extensive collection of algorithms have been proposed over the years to identify the foreground from a video scene, but none of them considers the classification history of previous frames for discovering moving objects. All the existing algorithms focus on a single background model to cope with all types of challenging and complex video environments. In this paper, a real-time pixel level classification method is proposed that uses its previous output history to update its parameters. The model has three components designed to handle various types of challenging background environments. For each pixel, three sub-parts, namely the Adaptive Background Model, the Neighborhood Background Model, and the Change Detection Background Model are constructed to detect various types of complex background changes. A pixel level model updating method uses the previous foreground/background binary classification results to periodically refresh all the three background models. This update method helps to recognize the foreground accurately by adjusting the algorithmic parameters to efficiently detect complex background changes. This novel method shows a significant improvement in performance for a variety of complex video scenes.
Aspect-based Sentiment Analysis on mobile phone reviews with LDA

With the maturation of e-commerce platform, online shopping has become an easy and preferable mode of shopping. As one of the largest e-commerce platforms worldwide, Amazon enjoy numerous user communities. Volumes of user-generated data of users' preferences and opinions towards products, usually for specific aspects of a commodity, popped up every day. Although loaded with information, these texts are often unstructured data that requires a thorough analysis for both consumers and manufactures to extract meaningful and relevant information. Traditional lexicon-based sentiment analysis considers polarity score of words but ignores the differences among aspects. Document level topic modeling help overcome these lacunae. In this paper, we claim that the aspects should also be weighted for highlighting significance of various aspects appropriate to a domain. Thus, manufacturers can understand what potential consumers may want as improvement in the forthcoming products. To showcase our framework, more than 400,000 Amazon unlocked phone reviews were collected as training data. LDA models were used to cluster topic words with their corresponding probability values. Based on the machine learning framework results, a corpus of nearly 1,000 Amazon reviews of a new mobile phone mode, iPhone X, was tested using this framework to perform topic labeling and sentiment analysis. Performance analysis was done using Confuse Matrix and F-measure.
Study on the Anti-Theft Technology of Museum Cultural Relics Based on Internet of Things

With the advancement of society, the museum has exhibited more and more cultural relics, the number of visitors has also increased rapidly, and more and more criminals have stolen cultural relics. The traditional anti-theft methods cannot completely block their pace. This paper proposes a museum anti-theft scheme based on the Internet of Things (IoT) technology, which identifies whether the cultural relics are within the safe range through the passive RFID readers/writers. Once stolen, the cultural relics will leave the effective RFID identification range, which results in immediately alarming, then the system starts the anti-theft plan. The method is free from the drawbacks of the traditional infrared anti-theft, door magnetic detection and the like, the proposed anti-theft method monitoring has the immediacy and the safety factor is higher. Finally, in this paper, hardware circuit designs, software development and a series of tests are carried out to achieve the desired results.
Real-time video super-resolution via motion convolution kernel estimation

The goal of video super-resolution (SR) is to generate a high-resolution (HR) video frame from multiple consecutive low-resolution (LR) frames. This task is challenging because it considers not only the spatial relationship but also the temporal one. Recently, lots of video SR methods emerged, especially the ones using convolutional neural networks (CNN). Most of these methods align reference frames with the predicted motion and send LR current and reference frames to a CNN to learn a mapping function to generate a corresponding HR frame. However, the performance of these methods is limited by inaccurately predicted motion and inappropriate CNN architecture. Therefore, we propose a novel real-time video SR method by addressing the above-mentioned problems from two aspects. First, we align each reference frame with a pair of 1D motion convolution kernels, which are predicted from our motion convolution kernel estimation network. Second, we improve the gated residual unit (GEU), which can combine input and output signals with trainable weights, and integrate GEU to our network architecture. Our proposed method is verified by benchmark datasets both qualitatively and quantitatively. Experimental results demonstrate that our method is able to provide a more than 1.0 dB gain in real-time manner compared to state-of-the-art methods.
Language model adaptation for language and dialect identification of text

Abstract This article describes an unsupervised language model (LM) adaptation approach that can be used to enhance the performance of language identification methods. The approach is applied to a current version of the HeLI language identification method, which is now called HeLI 2.0. We describe the HeLI 2.0 method in detail. The resulting system is evaluated using the datasets from the German dialect identification and Indo-Aryan language identification shared tasks of the VarDial workshops 2017 and 2018. The new approach with LM adaptation provides considerably higher F1-scores than the basic HeLI or HeLI 2.0 methods or the other systems which participated in the shared tasks. The results indicate that unsupervised LM adaptation should be considered as an option in all language identification tasks, especially in those where encountering out-of-domain data is likely.
Toward Identifying Features for Automatic Gender Detection: A Corpus Creation and Analysis

The current paper aims to construct an inventory of stylometric and psychometric features for the automatic identification of the author's gender. These features are derived from an analysis of a manually developed Saudi Dialect Twitter Corpus (SDTwittC), consisting of four million words. Given that the study seeks to provide machine learning algorithms with the accurate set of features in solving the gender identification problem, word-based, character-based, syntactic, and function words are all considered during the selection stage. The word-based features constitute the largest category and they represent the possible gender discriminators from sociological, psychological and lexical perspectives. The results show that Saudi males use different styles that separate them from their female counterparts in terms of politeness (greeting, thanking, apology, congratulation, encouragement, best wishes etc), impoliteness (profanity and sarcasm), uses of intensifiers, hedges, color, emotion, reason, emoji among many others.
Rank-Metric Codes Over Finite Principal Ideal Rings and Applications

In this paper, it is shown that some results in the theory of rank-metric codes over finite fields can be extended to finite commutative principal ideal rings. More precisely, the rank metric is generalized and the rank-metric Singleton bound is established. The definition of Gabidulin codes is extended and it is shown that its properties are preserved. The theory of Gröbner bases is used to give the unique decoding, minimal list decoding, and error-erasure decoding algorithms of interleaved Gabidulin codes. These results are then applied in space-time codes and in random linear network coding as in the case of finite fields. Specifically, two existing encoding schemes of random linear network coding are combined to improve the error correction.