Reviewerly/exHarmony huggingface dataset:
Trademark image retrieval via transformation-invariant deep hashing

Trademark images are usually used to distinguish goods due to their uniqueness, and the amount becomes too huge to search these images accurately and fast. Most existing methods utilize conventional dense features to search visually-similar images, however, the performance and search time are not satisfactory. In this paper, we propose a unified deep hashing framework to learn the binary codes for trademark images, resulting in good performance with less search time. The unified framework integrates two types of deep convolutional networks (i.e., spatial transformer network and recurrent convolutional network) for obtaining transformation-invariant features. These features are discriminative for describing trademark images and robust to different types of transformations. The two-stream networks are followed by the hashing layer. Network parameters are learned by minimizing a sample-weighted loss, which can leverage the hard-searched images. We conduct experiments on two benchmark image sets, i.e., NPU-TM and METU, and verify the effectiveness and efficiency of our proposed approach over state-of-the-art.
Mobile Forms of Gold and Pathfinder Elements in Surface Sediments at the Novye Peski Gold Deposit and in the Piilola Prospecting Area (Karelia Region)

The success of prospecting for gold deposit in overburdened areas based on the using of secondary dispersion haloes mostly depends on the chosen method of geochemical survey (sampling horizon, sample preparation for analysis, etc.). At the same time, the geochemistry of gold in the supergene zone is insufficiently studied, especially it’s migration and concentration in association with other elements in surface sediments due to weathering of gold-bearing ore. The main aim of the study presented in this paper is the determination of mobile forms of gold and pathfinder elements (As, Cu, Ni, Ag, Zn, Pb, Se, Sb, Mo, Bi, and Te) in podzol soil and moraine in the areas of Karelia region with known gold mineralization. As a result of conducted experiments it was determined that the main mobile forms of gold are water-soluble and bound to organic matter, while pathfinder elements bound preferably to Fe and Mn(hydr)oxides and to organic matter. As gold and some pathfinders bind with organic matter, this form was considered in more detail, and the elements’ interaction with humic and fulvic acids was investigated. In addition, it was determined that the studied elements are quite “mobile” because the percentage of the mobile form in their total content was mostly more than 50%. The main features of the elements’ migration and concentration were identified in surface sediments of the study areas.
Stroke order normalization for improving recognition of online handwritten mathematical expressions

We present a technique based on stroke order normalization for improving recognition of online handwritten mathematical expressions (ME). The stroke order dependent system has less time complexity than the stroke order free system, but it must incorporate special grammar rules to cope with stroke order variations. The stroke order normalization technique solves this problem and also the problem of unexpected stroke order variations without increasing the time complexity of ME recognition. In order to normalize stroke order, the X–Y cut method is modified since its original form causes problems when structural components in ME overlap. First, vertically ordered strokes are located by detecting vertical symbols and their upper/lower components, which are treated as MEs and reordered recursively. Second, unordered strokes on the left side of the vertical symbols are reordered as horizontally ordered strokes. Third, the remaining strokes are reordered recursively. The horizontally ordered strokes are reordered from left to right, and the vertically ordered strokes are reordered from top to bottom. Finally, the proposed stroke order normalization is combined with the stroke order dependent ME recognition system. The evaluations on the CROHME 2014 database show that the ME recognition system incorporating the stroke order normalization outperforms all other systems that use only CROHME 2014 for training while the processing time is kept low.
Detection of prospective areas for mineralization deposits using image analysis technique of aeromagnetic data around Marsa Alam-Idfu road, Eastern Desert, Egypt

Area around Marsa Alam-Idfu road is considered as one of the most prospective regions for gold and base metals mineralization in Egypt. The total intensity aeromagnetic reduced to northern magnetic pole (RTP) data of such area revealed that a set of parallel continuous linear features taken NNW-SSE trend with variable magnetic amplitudes, which are related to faults, dykes and shear zones. Also, this map showed strong negative and positive magnetic anomalies generated in contact zones, which may be considered with the linear features as prospective areas for mineralization. The application of centre for exploration targeting (CET) grid image analysis technique was applied for the aeromagnetic data of such area for rapidly locating regions perceived to be most favorable for gold and base metals deposits. The technique generated a series of high structural complexity maps which are combined to create an overall prospectively map that defined regions of high potentially for mineral deposits. Integration of the obtained results from the resulted maps and the RTP magnetic map, aided by the geological information revealed that a good correlation between the known gold and base metals locations and the regions of mineralization that are generated by the proposed technique. Besides, additionally locations of gold and base metals deposits were found on the generated map. These new locations are suggestive of where the mineralization may occur. The derived locations of these mineralization are aligned along definite trends, which are arranged according to their magnitude and importance as NNE-SSW, NW-SE, NNW-SSE, E-W and NE-SW directions. Serpentine rocks and their contacts with the surrounding rocks could be considered valuable as gold mineralization zones of economic importance and are considered as significant targets for follow-up and ground investigations.
Unsupervised Deep Hashing With Adaptive Feature Learning for Image Retrieval

The hashing method is widely used for large-scale image retrieval due to its low time and space complexity. However, the existing deep hashing methods are mainly designed for labeled datasets. Without supervised information, retrieval performance on unlabeled datasets is not guaranteed. In this letter, we propose a novel deep hashing approach for unsupervised image retrieval applications. The contributions are two-fold. First, the pseudolabels are generated using their global features aggregated from the pretrained network and employed as self-supervised information to optimize the objective function of training. Second, adaptive feature learning is used in this deep hashing framework to perform simultaneous hash function learning and global features learning in an unsupervised manner. The experimental results validated the effectiveness of the proposed method, obtaining state-of-the-art performances on several public datasets such as CIFAR-10, Holidays, and Oxford5k.
A Generalized Enhanced Quantum Fuzzy Approach for Efficient Data Clustering

Data clustering is a challenging task to gain insights into data in various fields. In this paper, an Enhanced Quantum-Inspired Evolutionary Fuzzy C-Means (EQIE-FCM) algorithm is proposed for data clustering. In the EQIE-FCM, quantum computing concept is utilized in combination with the FCM algorithm to improve the clustering process by evolving the clustering parameters. The improvement in the clustering process leads to improvement in the quality of clustering results. To validate the quality of clustering results achieved by the proposed EQIE-FCM approach, its performance is compared with the other quantum-based fuzzy clustering approaches and also with other evolutionary clustering approaches. To evaluate the performance of these approaches, extensive experiments are being carried out on various benchmark datasets and on the protein database that comprises of four superfamilies. The results indicate that the proposed EQIE-FCM approach finds the optimal value of fitness function and the fuzzifier parameter for the reported datasets. In addition to this, the proposed EQIE-FCM approach also finds the optimal number of clusters and more accurate location of initial cluster centers for these benchmark datasets. Thus, it can be regarded as a more efficient approach for data clustering.
Calibration Method Based on the Image of the Absolute Quadratic Curve

In this paper, a new camera calibration method based on the image of the absolute quadratic curve (IAC) is proposed, and a new target is designed for this method, which is both convenient and flexible. It first extracts the characteristic points and the characteristic lines of the target and finds out the vanishing point and the vanishing line. The radial and tangential distortion coefficients are obtained by using the cross ratio invariance to correct the target image distortion. Then, the four internal parameters of the camera are obtained by IAC. The influence of the skew parameters is ignored. The rotation matrix is then calculated by the orthogonal characteristic of the coordinate system, and the translation vector is calculated by the center coordinates of the camera. In this way, the internal and external parameters of the camera can be obtained. The internal and external parameters are taken as initial values, and the optimal results are obtained by nonlinear optimization using the reprojection method. Finally, the relative position between different target images can be obtained by using the fundamental matrix, namely, the rotation angle. In the process of solving, the normalization method is used to improve the accuracy of data processing. Not requiring any prior information of the camera, the method has a wide range of applications.
Fast BGP Simulation of Large Datacenters

Frequent configuration churn caused by maintenance, upgrades, hardware and firmware failures regularly leads to costly outages. Preventing network outages caused by misconfigurations is important for ensuring high network availability. Dealing with production datacenters with thousands of routers is a major challenge. Network verification inspects the forwarding tables of routers. These tables are determined by the so-called control plane, which is given by the steady state of the routing protocols. The ability to simulate routing protocols given router configuration files and thus obtain the control plane is a key enabling technology. In this paper, we present FastPlane, an efficient BGP simulator. BGP support is mandated by modern datacenter designs, which choose BGP as the routing protocol. The key to FastPlane's performance is our insight into the routing policy of cloud datacenters that allows the usage of a generalized Dijkstra's algorithm. The insight reveals that these networks are monotonic, i.e., route advertisements decrease preference when propagated through the network. The evaluation on real world, production datacenters of a major cloud provider shows that FastPlane (1) is two orders of magnitude faster than the state-of-the-art on small and medium datacenters, and (2) goes beyond the state-of-the-art by scaling to large datacenters. FastPlane was instrumental in finding several production bugs in router firmware, routing policy, and network architecture.
Classification and Analysis of Facebook Metrics Dataset Using Supervised Classifiers

With the advent of computing and communication era, social network websites become inevitable in every home, office, or in the day-to-day life of our society. Generally, people who are used to online, devote at least a chunk of their time on social media sites. It is no doubt a fact that social networks have rapidly changed the way people meet, communicate, and express their emotions. Due to the overdependency of society on social networks, social network analysis has recently become the center point of research among many researchers. Social networking websites like Facebook has become a popular communication medium for online users. It allows the users to post and share their opinions, viz., comments, links, images, and videos. Classifying these user posts reveals the users' sentiments, moods, and other social networking habits and type of contents the users are mostly interested in. This article makes a sincere attempt to classify the users' posts using Facebook metrics and predicts the same using various test cases. Initially, 21 popular classifiers of seven different groups, viz., Bayes, Functions, Lazy, Meta, Rules and Tree, and others have been evaluated on Facebook metrics dataset provided UCI machine learning repository across four popular performance metrics accuracy, misclassification rate, precision, and mean absolute error. The classifier having the highest performance in all sectors of performance metrics is taken into consideration for further classification and prediction.
Location-based personalized recommender system in the internet of cultural things

With the recent developments in digital technologies, the cultural heritage domain has attracted new researchers to drive technological trends for offering interactive and pleasant experience to users. The emergences of location-based services, recommendation systems, and the internet of things ha ve created a smart research space to support cultural heritage visitors. In this paper, we present an intelligent cultural space through the development of proximity detection method and the deployment of smart sense bricks. The presented intelligent cultural space is supported by the CHXplorer-Information Management System to present multimedia content to the users through our CHXplorer mobile decision-support tool. The CHXplorer is designed to enhance the satisfaction and enjoyment of the user visiting the cultural heritage sites, and it is capable of generating personalized recommendations to the active target user. The effectiveness and efficiency of the presented CHXplorer are experimentally evaluated and user study made establishes the users’ positive feedback on the system.
An Intelligent Context-Aware Management Framework for Cold Chain Logistics Distribution

Contextual information can accurately describe the user's time, location, environment, activities, the use of devices, and so on. The recommender systems with contextual information have better prediction accuracy, coverage, and user satisfaction. However, the application of conventional context-aware framework in some special fields has some drawbacks. For example, in the field of cold chain logistics distribution, the conventional context-aware framework cannot adapt to the multi-dimensional, complex and dynamic contexts of cold chain logistics, the lack of context-aware risk management, and the lack of traceability of cargo contexts. In this paper, we propose an intelligent context-aware management framework for cold chain logistics (CMFCCL) distribution that contains acquisition framework, recommender systems framework, risk management framework, tracing back framework, and user portrait framework of cold chain logistics distribution. Cold chain info data set is used for simulation, and three other recommendation approaches are considered in comparison. The experimental results indicate that CMFCCL can reduce the mean absolute error and root mean squared error of the data set by about 8.37%-19.99%. Thus, the evaluation presents encouraging results, indicating that CMFCCL would be useful in the context-aware management of cold chain logistics distribution.
A spatiotemporal Markov-chain mixture distribution model of the clear-sky index

This study presents a spatiotemporal Markov-chain mixture distribution model of the clear-sky index for an arbitrary number of locations, and is particularly suited for simulations of small-scale spatial networks with a span of 10 km or less. The model is statistical, but in practice data-driven and based on clear-sky index input from an arbitrary number of locations to generate synthetic time-series for the same locations. When trained on clear-sky index data based on the NREL Hawaii network radiometer solar irradiance data, dispersed within 1 km × 1.2 km, the model is shown to have high goodness-of-fit compared with test data from the network in terms of probability distributions, autocorrelations, location pair-correlations and k-lag correlations between locations. It is also shown to perform comparably to state of the art temporal, spatial and spatiotemporal clear-sky index generators. All measures of model goodness-of-fit are shown to improve with increased number of bins, up to a certain limit of N>4, where the performance improvements reaches a plateau. The results are also shown to be insensitive with respect to choice of training and test data sets as well as number of output time-steps.
Blind modulation format identification using decision tree twin support vector machine in optical communication system

This paper proposes a method of blind modulation format identification using decision tree twin support vector machine classifier trained with the features extracted from the high-order cumulant and cyclic spectrum after compressed sensing in order to solve the problem of identification efficiency and computing speed and reduce the performance requirements of the sampling system in low optical signal to noise ratio. By reconstructing the feature parameters of fourth-order, eighth-order cumulants and cyclic spectrum under the theory of compressed sensing, and introducing the decision tree twin support vector machine classifier to achieve high-precision classification, the different modulation formats of amplitude shift keying, multiple phase-shift keying and multiple quadrature amplitude modulation are effectively identified. Simulation analysis of the influence of identification accuracy and identification time improves the identification performance and achieves the purpose of identifying more signals with fewer feature parameters. The results indicate that the average identification accuracy of the optical modulation format signals can be achieved over 94% when the OSNR is -5dB, and the identification time is 4.3 times higher than the standard SVM. Owing to its excellent performance, this method can be employed in the next generation optical transport network for auto-adaption real-time modulation format identification.
Post-Quantum Enabled Cyber Physical Systems

Cyber physical systems (CPSs) will be deployed for decades, thus they should be secure against long-term attacks. Most CPSs adopt the datagram transport layer security (DTLS), as the de facto security protocol. By using public key cryptography (PKC) based on traditional RSA or elliptic-curves, DTLS establishes secured communication channels between multiple parties. However, the foreseeable breakthrough of quantum computers represents a risk for many PKC ecosystems. Traditional PKC will no be longer considered secure. Therefore, the integration of post-quantum security is mandatory. Due to their limited resources, tight performance requirements, and long-term life-cycles, this is especially challenging for CPSs. In this letter we propose, implement and evaluate for the first time a post-quantum enhanced DTLS, able to establish secure communications of CPSs, even in the presence of quantum computers. An NTRU post-quantum solution was used to perform the key transport among the CPSs entities. We show that is feasible to integrate our post-quantum enhanced DTLS, together with the full Internet engineering task force protocol stack in highly constrained environments, such as the CPSs.
Radar Image Series Denoising of Space Targets Based on Gaussian Process Regression

We address the problem of image series denoising for high-resolution radar in a nonparametric Bayesian framework. By exploiting the characteristics of amplitude variation at different pixels in the image series, we impose the Gaussian process (GP) model to the corresponding time series of each pixel and achieve effective image series denoising by GP regression. Particularly, the model parameters are solved conveniently by the maximum likelihood estimation. Compared with available denoising techniques in the data domain, spatial domain, and image frequency domain, the proposed method has exhibited more flexibility in data description and better performance in structure preserving and denoising, especially in low signal-to-noise ratio scenarios.
Novel fruit fly algorithm for global optimisation and its application to short-term wind forecasting

Fruit fly optimisation algorithm is a new swarm intelligence algorithm, which is simple and efficient. However, it is easy to get premature convergence in solving high-dimensional complex continuous functions. In order to overcome the shortcoming and improve the precision of solution, we propose a new fruit fly optimisation algorithm (SEDMFOA) based on spatial expansion and dynamic mutation. It is featured with changing the original constant step size to a focused search method, and embedding the dynamic mutation strategy in the evolution of the algorithm. Furthermore, we employed gauss mapping operation on the best individual to generate new individuals to substitute for those trans-boundary individuals. Finally, the inverse solution to expand the space was designed to develop the durative search ability in the later stage of the algorithm. According to the experimental results of eighteen well-known benchmark functions, the SEDMFOA is efficient and effective. The precision and stability of the approximate solution of SEDMFOA are superior the algorithms proposed in some related literatures. In wind energy research, the new algorithm is applied to optimise extreme learning machines for short-term wind forecasting. Simulation results show that SEDMFOA has better prediction effect than traditional algorithms.
Minding the Gaps in a Video Action Analysis Pipeline

We present an event detection system, which shares many similarities with standard object detection pipelines. It is composed of four modules: feature extraction, event proposal generation, event classification and event localization. We developed and assessed each module separately by evaluating several candidate options given oracle input using intermediate evaluation metric. This particular process results in a mismatch gap between training and testing when we integrate the module into the complete system pipeline. This results from the fact that each module is trained on clean oracle input, but during testing the module can only receive system generated input, which can be significantly different from the oracle data. Furthermore, we discovered that all the gaps between the different modules can contribute to a decrease in accuracy and they represent the major bottleneck for a system developed in this way. Fortunately, we were able to develop a set of relatively simple fixes in our final system to address and mitigate some of the gaps.