Reviewerly/exHarmony huggingface dataset:
LOS Rate Estimation Using Extended Kalman Filter

Primary sensor used in any modern air combat tactical aerospace vehicle for engaging moving or fixed targets is a seeker. The on-board sensor i.e. seeker gives the target measurements with which tactical aerospace vehicles are capable of tracking the target and adjust its path to hit the target. Tactical aerospace vehicles are equipped with Radio Frequency (RF) passive seekers in the nose cone to track RF signals from the emitters (ground Radars). Seeker gives target information in terms of two gimbal angles in elevation and azimuth planes. It does not provide closing range, velocity and LOS rates which are required for Proportional Navigation (PN) guidance law for steering the vehicle towards target. The main aim of the research is to estimate the Line of Sight (LOS) rates from the noisy measurement of elevation and azimuth gimbal angles of a seeker. A four state process model is used to implement Extended Kalman Filter (EKF) which estimates unknown LOS rates from the available measurements. The state consists of gimbal angles and LOS rates in elevation and azimuth planes. The vehicle mathematical model is developed along with control, guidance and navigation models to validate the performance of EKF in the closed loop. Influence of initial errors on miss distance is investigated. The design of EKF algorithm is tuned for faster convergence.
Further Results on the Morgan–Mullen Conjecture

Let $$\mathbb {F}_q$$ be the finite field of characteristic p with q elements and $$\mathbb {F}_{q^n}$$ its extension of degree n. The conjecture of Morgan and Mullen asserts the existence of primitive and completely normal elements (PCN elements) for the extension $$\mathbb {F}_{q^n}/\mathbb {F}_q$$ for any q and n. It is known that the conjecture holds for $$n \le q$$ . In this work we prove the conjecture for a larger range of exponents. In particular, we give sharper bounds for the number of completely normal elements and use them to prove asymptotic and effective existence results for $$q\le n\le O(q^\epsilon )$$ , where $$\epsilon =2$$ for the asymptotic results and $$\epsilon =1.25$$ for the effective ones. For n even we need to assume that $$q-1\not \mid n$$ .
A Novel Image Super-Resolution Method Based on Cross Classification Trees and Cascaded Network

In this paper, we propose an efficient and high-quality super-resolution method by combining Cross Classification Trees (CCTs)-based sample classification rule and Deep Forest mapping relationship. Firstly, we use a number of mapping kernels to divide sample space into overlapping subspaces and use decision trees to realize the classification function. In this process, the CCTs are used to classify samples, which can remarkably improve the coverage rate of the sample subspaces and increase the classification accuracy. Then, for each leaf node of CCTs, a Cascade Network (CN) is learned, which simultaneously uses priori and posteriori information to map low-resolution (LR) image patches to high-resolution (HR) image patches for each subspace. This is an ensemble approach in a data-dependent way with a larger sample and fewer hyper-parameters. Extensive experiment results demonstrate that the proposed method achieves better performance and costs less time.
A Reliable Method to Keep Up-to-Date Rights Management Information of Public Domain Images Based on Deep Learning

Most public domain images are exposed to copyright infringement due to different Rights Management Information (RMI) expression system and/or out-of-date RMI. To solve this problem, this paper proposes a reliable method of integrating the RMI representation system and updating the RMI with the up-to-date information based on the most reliable data among information collected from each site through a comparative search technique for public domain images based on deep learning. Through the RMI updating method proposed in this paper, public domain image sites will be able to minimize copyright infringement, since they can provide the most up-to-date RMI and may transform different RMI representation systems into a unified system.
Parametric Display Surface Geometric Calibration Method Based on Parameter Estimation

With the rapid development of computer vision, image processing and projection display technology, multi-projector display has been widely applied in information visualization, industrial design, military simulation, exhibition and so on. But current curved-surface calibration methods still have unsolved issues in practical applications. Here, we proposed a geometric calibration method based on parameter estimation. Camera parameters and surface parameters estimated from the curved surface constraints were used to realize the geometry calibration. Our results demonstrated the validation of this method in calibration accuracy and simplification of calibration process.
A Problem Solving Using Intelligent Social Network

Nowadays, students are becoming familiar with the computer technology at a very early age. Moreover, the wide availability of the internet gives a new perspective to distance education making e-learning environments crucial to the future of education. Intelligent tutoring systems (ITSs) provide sophisticated tutoring systems using artificial intelligence techniques in computer programs to facilitate instruction. ITSs support problem solving by providing adaptive scaffolding in terms of feedback, guidance, problem selection and other types of help. Little attention has been devoted thus far to the difference between novices and advanced students in learning from social network and ITSs. Furthermore, students can benefit from collaboration in social network environments especially when this is combined with an ITS.
Source smartphone identification for digital zoomed images

Sensor noise caused by photo response non-uniformity (PRNU) has been widely accepted as a reliable fingerprint for source camera identification (SCI). In the sensor based camera identification methods, the extracted noise from a test image needs to be absolutely synchronized with the camera fingerprint. However, for the images captured under digital zoom, this requirement is not satisfied, which is very common for smartphone shooting environment due to no optical zoom available. Here, through carefully studying about the effect of the digital zoom on the sensor-based SCI, we found several characteristics in this case, and proposed a novel metric to enhance the SCI performance for images taken with digital zoom. Experimental results show that the proposed metric outperforms the benchmark metric for source smartphone identification on digital zoomed images by evaluating the ROC performance.
Modification of chipertext Elgamal algorithm using split merge

Abstract The development of increasingly sophisticated technology with more intensive use of computers facilitates human work activities, so that security problems arise that misuse information that can endanger users. The third parties who commit crimes against users are called cryptanalyst. Cryptanalyst interferes with computer networks in many forms such as data theft. Cryptography is a security solution for cryptanalysts; one of them is by using the El-Gamal algorithm. Researchers have proposed El-Gamal modification with good results, but not all security problems can be solved. The algorithm cannot guarantee protection against the adaptive attack. This article proposes the application of split merge method on the El-Gamal algorithm to improve security. The study describes the system analysis and design. Since the combination uses large prime number calculations, it is expected to be effective to make cryptanalyst more difficult to break the plaintext.
Tree ferns and tea trees in biogeochemical exploration for epithermal Au and Ag in New Zealand

Biogeochemical orientation surveys were undertaken at low sulphidation epithermal Au–Ag occurrences in the Hauraki Goldfield–Coromandel Volcanic Zone and the Taupo Volcanic Zone, and at the Waiotapu active geothermal area in the Taupo Volcanic Zone. Several plant species were sampled, including the foliage of tree ferns and tea trees. The ferns – silver fern ( ponga ), rough tree fern ( wheki ) and black tree fern ( mamaku ) – were ubiquitous and were the easiest species to sample, although tea tree was the dominant genus at Waiotapu. At the Waiotapu geothermal area, significantly higher concentrations of Ag, Au, Sb, As, Cs and Rb were present in samples close to Champagne Pool than elsewhere, confirming its location as the main outflow source of Au, Ag and their pathfinder elements. The fern survey areas at Luck at Last mine, Pine Sinter and Ohui in the Coromandel Volcanic Zone each exhibited biogeochemical anomalies, which successfully highlighted most of the known quartz veins and provided additional anomalies for further investigation. Rough tree fern was the most common species at Goldmine Hill, Puhipuhi (Taupo Volcanic Zone). Although this species absorbs lower concentrations of many elements than the silver fern, the spatial distribution of elements is of greater significance than their absolute concentrations. The highest Au, Ag, As and Al concentrations occurred in samples from a ridge extending WNW from Goldmine Hill. Sb and Bi were at anomalous levels in an area peripheral to the precious metal anomalies, indicating the potential zonation of elements distal from the Au and Ag deposits. Supplementary material: The full datasets on the fern and tea tree chemistry, including quality assurance/quality control and multi-element plots, are available free of charge through the GNS Science website (search for Dunn) at http://shop.gns.cri.nz/publications/science-reports/ .
AN ALGORITHM FOR THE RESTORATION OF PIXELS IMAGE BASED ON THE NEURAL NETWORK

In work the algorithm of restoration of the images damaged as a result of influence of noise of various nature is considered. The advantages and disadvantages of the existing approaches, as well as the prospects of using artificial neural networks, are noted. A double-layer neural network is used as an image restoration tool, and it is assumed that the location of the damaged pixels is known. A neuron is represented as a 3x3 array, where each element of the array has a pixel color value that corresponds to the value of that color in the palette. The neural network is trained on intact images, while the color difference of pixels acts as a learning criterion. For a more accurate restoration, it is ecommended at the training stage to select images similar in color to damaged ones. At the recovery stage, neurons (3x3) are formed around the damaged pixels, so that the damaged pixel is located in the middle of the neuron data array. The damaged pixel is assigned a neuron value depending on the average value of the weights matrix. An algorithm for the restoration of pixels, as well as its software implementation. The simulation was carried out in the RGB palette separately for each channel. To assess the quality of the recovery were selected groups of images with varying degrees of damage. Unlike existing solutions, the algorithm has the simplicity of implementation. The research results show that regardless of the degree of damage (within 50%), about 70% of damaged pixels are restored. Further studies suggest a modification of the algorithm to restore images with enlarged areas of damage, as well as adapting it to restore three-dimensional images.
Application of Cloud Computing Technologies in Academic Library Management

Cloud computing is an umbrella term used to describe a category of sophisticated on-demand computing services. Libraries generally have been adopting cloud-based solutions for different services such as electronic journal access management, integrated library system (ILS) hosting, electronic databases, digital library hosting, and cloud OPAC. This chapter discusses how cloud computing technology tools can be deployed to ensure uniformity in library services and standard of information resources available to students at all the study centers spread across the six geo-political zones. The chapter concludes that cloud computing offers many opportunities for academic libraries, and especially for the NOUN Library.
Unemployment, Migration and Cyber Criminality in Nigeria

It could only have been a pleasant surprise if the densely populated virtual world of the Internet is crime-free. While it is understandable that crime is inevitable in every human community, including the Internet, because humans are not angels, it is mind boggling that the level of this species of crime in Nigeria is gargantuan. There are variegated versions of cyber-criminality in the world, but the majority of the versions of the crime that originate from Nigeria or by Nigerians elsewhere are economic; hence, this chapter discusses cyber-criminality in Nigeria, querying the roles of the economic realities of the country, especially unemployment and economic migration in promoting the e-crimes. This study is qualitative in design, and it adopts the explanatory capability of McClelland’s Needs Theory in the content analysis of data collected from secondary sources such as textbooks, journals, newspapers and the Internet. The study identified the motivation to satisfy needs for achievement, affiliation and power, which could not be satisfied due to unemployment or underemployment, as the root of the intimidating level of cyber-criminality in Nigeria. It recommended value reorientation, job creation and the implementation of the letters and spirits of the laws against cyber-criminality in Nigeria as the solutions to the ravaging and image-battering crime in the country.
Pixel and Channel Attention Network for Person Re-identification

The combination of global and partial features has been an effective method to improve the precision for Person Re-identification. However, illumination, camera angle and pedestrian pose, etc. still have adverse effects on the retrieval results. In particular, a lot of background and other redundant information is contained in the boundingbox. Meanwhile, the part-based solutions are imprecise on account of unbalanced partitioning. In order to minimize the impact of these factors on the retrieval results, we introduced the pixel, channel attention modules and middle layer supervision into the ReID system to aggregate person features. In this paper, we propose a novel architecture for Person Re-Identification, with the pixel and channel attention modules that are beneficial for feature extraction. Comprehensive experiments results on the mainstream datasets including Market-1501, DukeMTMC-ReId, CUHK03-labeled and CUHK03-detected show that our method achieves better results.
p-Adic measures associated with zeta values and p-adic log multiple gamma functions

We study a relation between two refinements of the rank one abelian Gross–Stark conjecture. For a suitable abelian extension [Formula: see text] of number fields, a Gross–Stark unit is defined as a [Formula: see text]-unit of [Formula: see text] satisfying certain properties. Let [Formula: see text]. Yoshida and the author constructed the symbol [Formula: see text] by using [Formula: see text]-adic [Formula: see text] multiple gamma functions, and conjectured that the [Formula: see text] of a Gross–Stark unit can be expressed by [Formula: see text]. Dasgupta constructed the symbol [Formula: see text] by using the [Formula: see text]-adic multiplicative integration, and conjectured that a Gross–Stark unit can be expressed by [Formula: see text]. In this paper, we give an explicit relation between [Formula: see text] and [Formula: see text] and prove that two refinements are consistent.
Matching Graph, a Method for Extracting Parallel Information from Comparable Corpora

Comparable corpora are valuable alternatives for the expensive parallel corpora. They comprise informative parallel fragments that are useful resources for different natural language processing tasks. In this work, a generative model is proposed for efficient extraction of parallel fragments from a pair of comparable documents. The core of the proposed model is a graph called the Matching Graph. The ability of the Matching Graph to be trained on a small initial seed makes it a proper model for language pairs suffering from the scarce resource problem. Experiments show that the Matching Graph performs significantly better than other recently published models. According to the experiments on English-Persian and Arabic-Persian language pairs, the extracted parallel fragments can be used instead of parallel data for training statistical machine translation systems. Results reveal that the extracted fragments in the best case are able to retrieve about 90% of the information of a statistical machine translation system that is trained on a parallel corpus. Moreover, it is shown that using the extracted fragments as additional information for training statistical machine translation systems leads to an improvement of about 2% for English-Persian and about 1% for Arabic-Persian translation on BLEU score.
Framework for Evaluation of Swarm-Based Chemical Reaction Optimization Algorithm

Chemical Reaction Algorithm (CRO) is a metaheuristic for optimization inspired by the nature of chemical reactions. A chemical reaction is a natural process of transforming the unstable substances to the stable ones. In this research paper a hybrid chemical reaction optimization algorithm based on local search and global search with an intuitive graphical evaluation framework is presented, which combines the advantages of Chemical Reaction Optimization and Particle Swarm Optimization.
A conditional-probability zone transformation coding method for categorical features

It has been a key issue for solving problems efficiently by machine learning models with code categorical features. The state-of-the-art one-hot coding is a widely accepted method to convert the categorical features into numerical values. However, it attracts a sparse space and meaningless value after coding. We come up with a novel coding method based on conditional probability after dividing the features into zones, which is called Conditional-probability-based Zone Transformation (CZT) coding. CZT coding calculates the conditional probability of each feature, then divides the features into several zones according to the probability and finally codes the features in each zone. We mathematically prove that compared with the state-of-the-art method, CZT coding reduces the code length by at least the mean of feature space and the issue becomes into an easier one after CZT coding for the following machine learning model. Finally, using the same neuron network as the classifier, we compare the performance of CZT coding and one-hot coding by using the titanic dataset, where most of the features are categorical, and the result is that CZT coding makes the classifier performs better both on the accuracy and steadiness.
Verifiable Stream Computation and Arthur--Merlin Communication

In the setting of streaming interactive proofs (SIPs), a client (verifier) needs to compute a given function on a massive stream of data, arriving online, but is unable to store even a small fraction of the data. It outsources the processing to a third party service (prover) but is unwilling to blindly trust answers returned by this service. Thus, the service cannot simply supply the desired answer; it must convince the verifier of its correctness via a short interaction after the stream has been seen. In this work we study “barely interactive” SIPs. Specifically, we show that one or two rounds of interaction suffice to solve several query problems---including index, median, nearest neighbor search, pattern matching, and range counting---with polylogarithmic space and communication costs. Such efficiency with $O(1)$ rounds of interaction was thought to be impossible based on previous work. On the other hand, we initiate a formal study of the limitations of constant-round SIPs by introducing a new hierarchy of communication models called online interactive proofs (OIPs). The online nature of these models is analogous to the streaming restriction placed upon the verifier in a SIP. We give upper and lower bounds that (1) characterize, up to quadratic blowups, every finite level of the OIP hierarchy in terms of other well-known communication complexity classes, (2) separate the first four levels of the hierarchy, and (3) reveal that the hierarchy collapses to the fourth level. Our study of OIPs reveals marked contrasts and some parallels with the classic Turing machine theory of interactive proofs, establishes limits on the power of existing techniques for developing constant-round SIPs, and provides a new characterization of (nononline) Arthur--Merlin communication in terms of an online model.
Long term activity prediction in first person viewpoint

Predicting future activities from an egocentric viewpoint is of particular interest for assisted living arena. More importantly, knowing how future activities evolve with time (i.e., what will happen first, second, and etc.) enables lots of potential applications, e.g. usage in intelligent robot to forecast humans next movement and occurrence time to prepare for assisting human being with the movement in advance. However, state-of-the-art techniques are mostly NOT capable of predictive tasks, as their synchronous processing architecture performs poorly in either modeling long-term event dependency or pruning temporal redundant features. In this work, we propose an asynchronous gaze-event driven activity evolving curve prediction network. This network features two modules (sub-networks): 1) a gaze-event extraction module which models the asynchronous occurrence/ending of a certain activity; and 2) an asynchronous reasoning module which generates event-modulated activity occurring probability functions via deriving long-temporal dependency from various events. Extensive experimental results on several egocentric video benchmarks well demonstrate the effectiveness of the proposed method in long-term activity evolving curve prediction.
Video computing across trust domains based on blockchain

Smart city construction brings great development opportunities for the video surveillance industry, but it also brings significant challenges. With the development of Internet of things and artificial intelligence technology, video computing across different trust domains has become important and necessary for ensuring public safety and promoting smart city construction. With the continuous increasing of video front-end equipment and massive video data, trust and security problems across different domains, network bandwidth and computing resources are gradually becoming bottlenecks of video computing. We propose a solution based on integration of blockchain and Internet of things which brings together and distributes video data and computing resources to enhance the edge video computing capability. Blockchain provides a distributed solution for trust video computing across different trust domains. We evaluate the performance of blockchain-based video computing solution across different trust domains and the results show that this solution could provide trust video computing across trust domains feasibly and efficiently. This solution implies a new trigger of trust distributed applications and innovative trust economic ecosystem.