Reviewerly/exHarmony huggingface dataset:
A New Method to Construct Strictly Optimal Frequency Hopping Sequences With New Parameters

In order to evaluate the goodness of frequency hopping (FH) sequence design, the periodic Hamming correlation function is used as an important measure. Usually, the length of correlation window is shorter than the period of the chosen FH sequence, so the study of the partial Hamming correlation of FH sequence is particularly important. If an FH sequence or an FH sequence set has an optimal partial Hamming correlation with respect to the partial Hamming correlation bound for all length of correlation window, then the FH sequence or the FH sequence set is said to be strictly optimal. In this paper, we first prove that there is no strictly optimal FH sequence set of family size M and sequence length N over a frequency slot set of size q with respect to the partial Hamming correlation bound derived by Niu et al. when N > q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /M and q ≥ 2, and that by Cai et al. when N > q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /M and q ≥ 2N/(N - 2). Furthermore, we define a special partition-type difference packing (DP) called [N, ∇, H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</sup> ] PDP and give several classes of [N, ∇, H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</sup> ] PDPs. Then, we present a new construction of strictly optimal FH sequences. By choosing different PDPs, the FH sequences constructed can give new and flexible parameters. By utilizing this construction method recursively, we can obtain new [N, ∇, H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</sup> ] PDPs, which lead to infinitely many classes of strictly optimal FH sequences with new parameters. Moreover, based upon an [N, ∇, H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</sup> ] PDP, we present a construction of strictly optimal FH sequence sets. By preceding construction method and recursive construction, we can also obtain infinite classes of strictly optimal FH sequence sets which can give new and flexible parameters.
A Residue-to-Binary Converter with an Adjustable Structure for an Extended RNS Three-Moduli Set

In earlier publications, many researchers have addressed the problem of residue-to-binary conversion for the popular moduli set [Formula: see text], where [Formula: see text] is a positive integer greater than 1. In this paper, we are proposing, potentially, the fastest converter ever for this moduli set with the least hardware requirements. Moreover, the paper revisits the extended three-moduli set [Formula: see text], where [Formula: see text] is a positive integer such that [Formula: see text]. This paper proposes an efficient residue-to-binary converter with an adjustable structure. The proposed structure allows increasing the dynamic range at a cost of two gates per bit. When compared with a similar published work for the extended moduli set, the proposed extended converter showed significant reductions in area by 9.9–13.4%, in delay by 16.9–24.1% and in power consumption by 10.6–16.7%.
High-Speed Scene Flow on Embedded Commercial Off-the-Shelf Systems

Scene flow is an essential part of a stereo-based perception system for autonomous driving and mobile robotics. As in most of these platforms, the computing resource is limited but the computing requirement is high, embedded and parallelized algorithms are of vital importance for real-time tasks. This paper develops a cross-platform embedded scene flow algorithm by using an OpenCL (Open Computing Language) programming. Meanwhile, we propose a method to achieve a good performance by using a novel coarse-grained software pipeline for the embedded stream application. Experimental results show that the proposed algorithm can boost the average processing speed to 50 fps for different commercial off-the-shelf (COTS) hardware, including desktop graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and mobile phone platforms. For certain GPUs, the peak frame rates can also reach 1000 fps. By comparing the efficiency among the serial platform, we illustrate that with the help of OpenCL programming, COTS platforms can provide enough computing resources for the stereo-based perception algorithm.
A Computational Model for Stereoscopic Visual Saliency Prediction

Depth information plays an important role in human vision as it provides additional cues that distinguish objects from their backgrounds. This paper explores depth information for analyzing stereoscopic saliency and presents a computational model that predicts stereoscopic visual saliency based on three aspects of human vision: 1) the pop-out effect; 2) comfort zones; and 3) background effects. Through an analysis of these three phenomena, we find that most of the stereoscopic saliency region can be explained. Our model comprises three modules, each describing one aspect of saliency distribution, and a control function that can be used to adjust the three models independently. The relationship between the three models is not mutually exclusive. One, two, or three phenomena may appear in one image. Therefore, to accurately determine which phenomena the image conforms to, we have devised a selection strategy that chooses the appropriate combination of models based on the content of the image. Our approach is implemented within a framework based on the multifeature analysis. The framework considers surrounding regions, color/depth contrast, and points of interest. The selection strategy can improve the performance of the framework. A series of experiments on two recent eye-tracking datasets shows that our proposed method outperforms several state-of-the-art saliency models.
A brief history of parameterized matching problems

Parameterized pattern matching is a string searching variant that was initially defined to detect duplicate code but later proved to support several other applications. In particular, two equal-length strings X andY are a parameterized-match if there exists a bijective function g for which every text symbol in X is equal to g(Y). Baker was the first researcher to have addressed this problem (Baker, 1993) and, since then, many others have followed her work. She did, indeed, open up a wide field of extensive research. Over the years, many variants and extensions that have been pursued include: parameterized matching under edit and Hamming distances, parameterized multi-pattern matching, two dimensional parameterized matching, structural matching, function matching, and the very recent developments in succinct and streaming models. This accelerated research could only be justified by the usefulness of its practical applications such as in software maintenance, image processing and bioinformatics to name some. Even though the problem was posed about 25 years ago, research on parameterized matching is still very active. Its extensive study over the years and its current relevance motivate us to review the most notorious contributions as road map for current and future research.
Smell Bees Optimization for new embedding steganographic scheme in spatial domain

In this work, we are interested in the steganography domain where the problem of security represents the basic key in the dissimulation of relevant information. The paper highlights two facets of research. The first facet represents a novel proposition of a naturally-inspired metaheuristic based on the smell sense of a honeybee insect. Herein, the collective foraging behavior is not explicitly intended but the search driven by smell sense is critical. The second facet highlights the proposition of two steganographic embedding schemes. The main idea behind the 2-Bit embedding scheme focus on the embedding rate of two secret bits by pixel. Compared to other steganographic algorithms, the intended bit that will support the elementary secret information can be anyone in the pixel byte provided that its content matches with the secret bit to embed. The proposed steganographic algorithm named HIBE takes advantage from transform domain since it presents more robustness against image processing attacks. And eventually, takes advantage from spatial domain since the lower distortion results achieved in comparison with other well-knew algorithm like HUGO, and WOW gives it more efficiency. Moreover, to evaluate its performance against steganalyzers methods based machine learning and classifiers some experiments were carried out on the BOSSbase dataset images. The reached results have shown that the used detectors, also well-knew, failed to classify the learned images as stego since their achieved detection accuracies approaches the random detection.
Lessons Learned Developing a Visual Analytics Solution for Investigative Analysis of Scamming Activities

The forensic investigation of communication datasets which contain unstructured text, social network information, and metadata is a complex task that is becoming more important due to the immense amount of data being collected. Currently there are limited approaches that allow an investigator to explore the network, text and metadata in a unified manner. We developed Beagle as a forensic tool for email datasets that allows investigators to flexibly form complex queries in order to discover important information in email data. Beagle was successfully deployed at a security firm which had a large email dataset that was difficult to properly investigate. We discuss our experience developing Beagle as well as the lessons we learned applying visual analytic techniques to a difficult real-world problem.
Forecasting Financial Markets Using High-Frequency Trading Data: Examination with Strongly Typed Genetic Programming

Market regulators around the world are still debating whether high-frequency trading (HFT) plays a positive or negative role in market quality. We develop an artificial futures market populated with high-frequency traders (HFTs) and institutional traders using Strongly Typed Genetic Programming (STGP) trading algorithm. We simulate real-life futures trading at the millisecond time frame by applying STGP to E-Mini S&P 500 data stamped at the millisecond interval. A direct forecasting comparison between HFTs and institutional traders indicate the superiority of the former. We observe that the negative implications of high-frequency trading in futures markets can be mitigated by introducing a minimum resting trading period of less than 50 milliseconds. Overall, we contribute to the e-commerce literature by showing that minimum resting trading order period of less than 50 milliseconds could lead to HFTs facing a queuing risk resulting in a less harmful market quality effect. One practical implication of our study is that we demonstrate that market regulators and/or e-commerce practitioners can apply artificial intelligence tools such as STGP to conduct trading behavior-based profiling. This can be used to detect the occurrence of new HFT strategies and examine their impact on the futures market.
A scalable and distributed dendritic cell algorithm for big data classification

In the era of big data, scaling evolution up to large-scale data sets is a very interesting and challenging task. The application of standard biological systems in such data sets is not straightforward. Therefore, a new class of scalable biological systems that embraces the huge storage and processing capacity of distributed platforms is required. In this work, we focus on the Dendritic Cell Algorithm (DCA), a bio-inspired classifier, and its limitation when coping with very large data sets. To overcome this limitation, we propose a novel distributed DCA version for data classification based on the MapReduce framework to distribute the functioning of this algorithm through a cluster of computing elements. Our experimental results show that our proposed distributed solution is suitable to enhance the performance of the DCA enabling the algorithm to be applied over big data classification problems.
Study of monthly mean daily diffuse and direct beam radiation estimation with MODIS atmospheric product

Reliable diffuse and direct beam radiation data are critical for many scientific researches and engineering applications. While knowledge of radiation components is not sufficient due to the cost and maintenance and calibration of the measuring equipments. Exploring novel methods for estimating radiation components have therefore considerable significance. In this paper, twenty satellite-based models for estimating radiation components were developed using MOD08-M3 atmospheric product, and evaluated using measured radiation data at 15 sites across China. The best site-specific models for diffuse and direct beam radiation were proposed, with the average RMSE of 0.642 MJ m−2 (9.299%), and 0.736 MJ m−2(10.69%), respectively. General models for radiation components were also recommended for widespread applications. The study provides a simple but efficient alternative to obtaining solar radiation components data at regional scale using MODIS satellite data. For other places of interest, the coefficients of the proposed models can be calibrated following the guidelines provided in this study with relative ease, making our models appropriate for widespread applications.
Learning principal orientations and residual descriptor for action recognition

In this paper, we propose an unsupervised representation method to learn principal orientations and residual descriptor (PORD) for action recognition. Our PORD aims to learn the statistic principal orientations and to represent the local features of action videos with residual values. The existing hand-crafted feature based methods require high prior knowledge and lack of the ability to represent the distribution of features of the dataset. Most of the deep learned feature based methods are data adaptive, but they do not consider the projection orientations of features nor the loss of locally aggregated descriptors of the quantization. We propose a method of principal orientations and residual descriptor considering that the principal orientations reflect the distribution of local features in the dataset and the residual of projection contains discriminative information of local features. Moreover, we propose a multi-modality PORD method by reducing the modality gap of the RGB channels and the depth channel at the feature level to make our method applicable to RGB-D action recognition. To evaluate the performance, we conduct experiments on five challenging action datasets: Hollywood2, UCF101, HMDB51, MSRDaily, and MSR-Pair. The results show that our method is competitive with the state-of-the-art methods.
Manga face detection based on deep neural networks fusing global and local information

As more and more digitized manga (Japanese comics) books are available, efficient and effective access to manga is urgently needed. Among various elements of manga, character’s face plays one of the most important roles in access and retrieval. We propose a deep neural network method to do manga face detection, which is a challenging but relatively unexplored topic. Given a manga page, we first find candidate regions based on the selective search scheme. Three convolutional neural networks are then proposed to detect manga faces of various appearance. We extract information from the entire object region and several local regions, and integrate multi-scale information in an early fusion manner or a late fusion manner. The proposed methods are evaluated based on a large-scale benchmark. Convincing performance compared to the state-of-the-art face detection modules designed for human faces is demonstrated.
Object detection and tracking under Complex environment using deep learning‐based LPM

Object detection and tracking under complex environment are challenging because of the disturbances induced by background clutter, illumination changes, occlusions and other factors. The bulk of traditional algorithms basically rely on hand‐crafted features, which are not sufficiently robust to a complex environment. Moreover, the processes of detection and tracking are separated, which leads to the overall efficiency not high. In this study, a novel local probability model (LPM)‐based mean shift (MS) algorithm is proposed to integrate object detection and tracking. The main contributions include: (i) a new framework based on the combination of LPM and MS is established for the integration of object tracking and detection. (ii) For object detection, the training and prediction of LPM are built by stacked denoising autoencoders based deep learning. (iii) For object tracking, an MS tracking algorithm leveraging LPM is modified to improve the tracking efficiency under a complex environment. Experimental results demonstrate that the proposed method is superior to the colour histograms based MS and histograms of oriented gradients based MS in terms of robustness and tracking accuracy.
Give Me a Hint! Navigating Image Databases Using Human-in-the-Loop Feedback

In this paper, we introduce an attribute-based interactive image search which can leverage human-in-the-loop feedback to iteratively refine image search results. We study active image search where human feedback is solicited exclusively in visual form, without using relative attribute annotations used by prior work which are not typically found in many datasets. In order to optimize the image selection strategy, a deep reinforcement model is trained to learn what images are informative rather than rely on hand-crafted measures typically leveraged in prior work. Additionally, we extend the recently introduced Conditional Similarity Network to incorporate global similarity in training visual embeddings, which results in more natural transitions as the user explores the learned similarity embeddings. Our experiments demonstrate the effectiveness of our approach, producing compelling results on both active image search and image attribute representation tasks.
A learning framework for shape retrieval based on multilayer perceptrons

With the rapid development of 3D technology, the demand to use and retrieve 3D models has become increasingly urgent. In this paper, we present a framework that consists of a sketch-based local binary pattern (SBLBP) feature extraction method, a learning algorithm for the best view of a shape based on multilayer perceptrons (MLPs) and a learning method for shape retrieval based on two Siamese MLP networks. The model is first projected into many multiview images. A transfer learning scheme based on graphic traversal to identify Harris key points is proposed to build relations between view images and sketches. In addition, an MLP classifier is used for classification to obtain the best views of each model. Moreover, we propose a new learning method for shape retrieval that simultaneously uses two Siamese MLP networks to learn SBLBP features. Furthermore, we build a joint Bayesian method to fuse the outputs of the views and sketches. Based on training with many samples, the MLP parameters are effectively fit to perform shape retrieval. Finally, an experiment is conducted to verify the feasibility of the approach, and the results show that the proposed framework is superior to other approaches.
A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements

We study the problem of inferring readers’ identities and estimating their level of text comprehension from observations of their eye movements during reading. We develop a generative model of individual gaze patterns (scanpaths) that makes use of lexical features of the fixated words. Using this generative model, we derive a Fisher-score representation of eye-movement sequences. We study whether a Fisher-SVM with this Fisher kernel and several reference methods are able to identify readers and estimate their level of text comprehension based on eye-tracking data. While none of the methods are able to estimate text comprehension accurately, we find that the SVM with Fisher kernel excels at identifying readers.
Unsupervised Multi-task Learning with Hierarchical Data Structure

Unsupervised multi-task learning exploits the shared knowledge to improve performances by learning related tasks simultaneously. In this paper, we propose an unsupervised multi-task learning method with hierarchical data structure. It strengthens similarities between instances in the same cluster, and increases diversities of instances by utilizing instances from related clusters. Firstly, we introduce Representative Dual Features (RepDFs) that possess representative capabilities in the feature space and the sample space for each cluster concurrently. Secondly, we explore hierarchical structural similarities between clusters in related tasks from the topological perspective: 1) feature basis matrix, which learns compact representations for features in the feature space; and 2) sample refined matrix, which preserves local structures in the sample space. Thirdly, we adopt RepDFs to measure correlations between clusters and incorporate hierarchical structural similarities to conduct knowledge transfer among tasks. Experimental results on real-world data sets demonstrate the effectiveness and superiority of the proposed method over existing multi-task clustering methods.
Validating (MNF) transform to determine the least inherent dimensionality of ASTER image data of some uranium localities at Central Eastern Desert, Egypt

One of the main determinations of geological remote sensing is to develop and improve different image processing algorithms and methods for mineral mapping and rock type discrimination. This paper investigates one of those sophisticated algorithms; the validity of Minimum Noise Fraction (MNF) components to improve the spectral separability of The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor image data of Terra satellite, and to develop a standard method of noise removal and image enhancement. Previous studies that used in image processing to eliminate MNF eigenvalues, eliminate values less than the unit. However, removing MNF eigenvalues more than this value and its effect on the data, has not been examined. Therefore, in this paper, we demonstrate that we can eliminate more than the unit up to 2 eigenvalue without a negative inherent effect on the origin data structure. A comparison study using different Image Quality Measures (IQMs) methods, has been accomplished on data distribution before and after MNF transformation of the original and atmospherically corrected data. Matched Filter (MF) classification was performed on data before and after MNF to map a chosen known alteration mineral (Kaolinite) by matching the image endmember spectra and its spectra from USGS library. By examining the spectral trends of specific mineral alteration type across these transformed channels, areas of interest that are unique by the related previous well-known uranium mineralization may become superficial apparent. This examination also showed how the use of MNF as a pre-processing technique could improve the capability to extract information from ASTER data of El-Missikat, El-Eridya and Kab Amiri areas in the Central Eastern desert as a case study district for uranium mineralization occurrences.