Reviewerly/exHarmony huggingface dataset:
Pavo: A RNN-Based Learned Inverted Index, Supervised or Unsupervised?

The booms of big data and graphic processing unit technologies have allowed us to explore more appropriate data structures and algorithms with smaller time complexity.However, the application of machine learning as a potential alternative for the traditional data structures, especially using deep learning, is a relatively new and largely uncharted territory.In this paper, we propose a novel recurrent neural networkbased learned inverted index, called Pavo, to efficiently and flexibly organize inverted data.The basic hash function in the traditional inverted index is replaced by a hierarchical neural network, which makes Pavo be able to well adapt for various data distributions while showing lower collision rate as well as higher space utilization rate.A particular feature of our approach is that a novel unsupervised learning strategy to construct the hash function is proposed.To the best of our knowledge, there are no similar results, in which the unsupervised learning strategy is employed to design hash functions, in the existing literature.Extensive experimental results show that the unsupervised model owns some advantages than the supervised one.Our approaches not only demonstrate the feasibility of deep learning-based data structures for index purpose but also provide benefits for developers to make more accurate decisions on both the design and the configuration of data organization, operation, and parameters tuning of neural network so as to improve the performance of information searching.
Blind quality assessment for screen content images by combining local and global features

Recently, several no-reference image quality assessment (NR-IQA) metrics have been developed for the quality evaluation of screen content images (SCIs). While, most of them are opinion-aware methods, which are limited by the subjective opinion scores of training data. Hence, in this paper, we propose a novel opinion-unaware method to predict the quality of SCIs without any prior information. Firstly, an union feature is proposed by considering the local and global visual characteristics of human visual system simultaneously. Specifically, a local structural feature is extracted from the rough and smooth regions of SCIs by leveraging a sparse representation model. As a supplement, a global feature is obtained by combining the luminance statistical feature and local binary pattern (LBP) feature of entire SCIs. Secondly, to get rid of the limitation of subjective opinion scores, a new large-scale training dataset contained 80,000 distorted SCIs is constructed, and the quality labels of those distorted SCIs are derived by an advanced full-reference IQA metric. Thirdly, a regression model between image features and image quality labels is learned from the training dataset by employing a learning-based framework. And then, the quality scores of test SCIs can be predicted by the pre-trained regression model. The experimental results on two largest SCI-oriented databases show that the proposed method is superior to the state-of-the-art NR-IQA metrics.
Seamline network generation based on foreground segmentation for orthoimage mosaicking

For multiple orthoimages mosaicking, the detection of an optimal seamline in an overlapped region and the generation of a seamline network are two key issues for creating a seamless and pleasant large-scale digital orthophoto map. In this paper, a novel system is proposed to generate the large-scale orthophoto by mosaicking multiple orthoimages via Graph cuts. The proposed system is comprised of two parts. In the first part, to ensure that the detected seamline avoids crossing the obvious objects, a novel foreground segmentation-based approach is proposed to detect the optimal seamline for two adjacent images. The foreground objects are segmented from the overlapped region at the superpixel level followed by the pixel-level seamline optimization. In the second part, we propose a novel seamline network generation approach to produce the large-scale orthophoto by mosaicking multiple orthoimages. The pairwise and junction regions extracted from the initial network are refined using two-label and multi-label Graph cuts, respectively. The key advantage of our proposed seamline network is that junction points can be automatically and optimally found using the multi-label Graph cuts. The experimental results on two groups of orthoimages show that our proposed system can generate high-quality seamline networks with less artifacts, and that it outperforms the state-of-the-art algorithm and the commercial software based on visual comparison and statistical evaluation.
Priority queueing analysis of transaction-confirmation time for Bitcoin

<p style='text-indent:20px;'>In Bitcoin system, a transaction is given a priority value according to its attributes such as the remittance amount and fee, and transactions with high priorities are likely to be confirmed faster than those with low priorities. In this paper, we analyze the transaction-confirmation time for Bitcoin system. We model the transaction-confirmation process as a queueing system with batch service, M/<inline-formula><tex-math id="M1">\begin{document}$ \mbox{G}^B $\end{document}</tex-math></inline-formula>/1. We consider the joint distribution of numbers of transactions in system and the elapsed service time, deriving the mean transaction-confirmation time. Using the result, we derive the recursive formulae of mean transaction-confirmation times of an M/<inline-formula><tex-math id="M2">\begin{document}$ \mbox{G}^B $\end{document}</tex-math></inline-formula>/1 queue with priority service discipline. In numerical examples, we show the effect of the maximum block size on the mean transaction-confirmation time, investigating the accuracy region of our queueing model. We also discuss how the increase in micropayments, which are likely to be given low priorities, affects the transaction-confirmation time.
A joint text mining-rank size investigation of the rhetoric structures of the US Presidents’ speeches

This work presents a text mining context and its use for a deep analysis of the messages delivered by politicians. Specifically, we deal with an expert systems-based exploration of the rhetoric dynamics of a large collection of US Presidents’ speeches, ranging from Washington to Trump. In particular, speeches are viewed as complex expert systems whose structures can be effectively analyzed through rank-size laws. The methodological contribution of the paper is twofold. First, we develop a text mining-based procedure for the construction of the dataset by using a web scraping routine on the Miller Center website – the repository site collecting the speeches. Second, we explore the implicit structure of the discourse data by implementing a rank-size procedure over the individual speeches, being the words of each speech ranked in terms of their frequencies. The scientific significance of the proposed combination of text-mining and rank-size approaches can be found in its flexibility and generality, which let it be reproducible to a wide set of expert systems and text mining contexts. The usefulness of the proposed method and of the speeches analysis is demonstrated by the findings themselves. Indeed, in terms of impact, it is worth noting that interesting conclusions of social, political and linguistic nature on how 45 United States Presidents, from April 30, 1789 till February 28, 2017 delivered political messages can be carried out. Indeed, the proposed analysis shows some remarkable regularities, not only inside a given speech, but also among different speeches. Moreover, under a purely methodological perspective, the presented contribution suggests possible ways of generating a linguistic decision-making algorithm.
Multi-tenant provisioning over software defined networking enabled metropolitan area quantum key distribution networks

Quantum key distribution (QKD) has potential to provide long-term security for communications across the Internet. As security-hungry applications dramatically rise nowadays, a growing number of QKD networks are promising to be deployed in the immediate future. Nevertheless, a high-security-demand institution (e.g., a bank) needs to pay a high price to deploy its dedicated QKD network, while a cost-effective way of overcoming this challenge is to make multiple tenants share a QKD network. Each tenant represents a high-security-demand institution and can obtain secret keys on demand from the QKD network infrastructure for security purposes. Hence, how to achieve efficient and flexible multi-tenant provisioning over a QKD network becomes a crucial problem. This work introduces software defined networking (SDN) to address this problem. We experimentally demonstrate multi-tenant provisioning (including tenant establishment, adjustment, and deletion) over SDN-enabled metropolitan area QKD networks. A SDN-enabled metropolitan area QKD network architecture is introduced. We present a workflow, protocol extensions, and an on-demand secret-key resource allocation strategy for multi-tenant provisioning, which are all demonstrated by establishing an experimental testbed in the lab. Experimental results verify the effectiveness and flexibility of our SDN-based approaches for multi-tenant provisioning over metropolitan area QKD networks. Moreover, we conduct the simulation and discover the ways of improving the success probability of multi-tenant provisioning over metropolitan area QKD networks.
Multi-View Spectral Clustering via Integrating Global and Local Graphs

Robust multi-view spectral clustering (RMSC) minimizes the rank of probability matrix to recover a common transition probability matrix from the matrices calculated by each single view and achieves promising performance. However, for the clustering task, the underlying structure of the low-rank probability matrix is readily accessible. Yet, RMSC ignores a priori target rank information, and it does not efficiently depict the complementary information between different views. To address these problems, we propose a novel multi-view Markov chain spectral clustering method with a priori rank information. To be specific, we encourage the target rank constraint by minimizing the partial sum of singular values instead of the nuclear norm and construct a global graph from the concatenated features to exploit the complementary information embedded in different views. The objective function can be optimized efficiently by using the augmented Lagrangian multiplier algorithm. Extensive experimental results on one synthetic and eight benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art approaches.
On-Site Reliable Wheel Size Measurement Based on Multisensor Data Fusion

The real-time monitoring of wheel size is important technology in ensuring the normal and safe operation of railways. Meanwhile, online dynamic measurement equipment based on structured-light sensors is becoming increasingly available. However, such equipment only has an “artificial” measurement mode, collects minimal data, has low reliability, and cannot be used in an on-site complex environment. In this regard, we propose a measurement system based on multisensor data fusion. When a wheel to be measured passes through a rail, two groups of wheel cross-profile acquisition sensors are arranged. Then, two profiles of the measured wheel are reconstructed, aligned together, and segmentation parameterized for a complete profile. On the one hand, they can compensate each other to overcome the data missing in wheel profile. On the other hand, it improves measurement accuracy by resisting error noise data induces. With the complete wheel cross-profile, the reliable and accurate wheel size parameters are obtained. Furthermore, the feasibility of this method is verified by analyzing possible influencing factors. Real experiments demonstrate the robustness and dynamic measurement accuracy. In conclusion, this measurement system is simple and highly robust for an on-site complex environment and has a promising application value.
Experimental Quantum Stochastic Walks Simulating Associative Memory of Hopfield Neural Networks

Quantum simulation of the associative memory in Hopfield neural networks is an interesting crossover between quantum information and machine learning. The $q\phantom{\rule{0}{0ex}}u\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}u\phantom{\rule{0}{0ex}}m$ $s\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}h\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}s\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}c$ $w\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}l\phantom{\rule{0}{0ex}}k$ has been proposed for such simulations, yet not realized experimentally. The authors successfully map this scheme to a three-dimensional photonic chip, and achieve quantum stochastic walk evolution. A good match rate between the experimental quantum scheme and the expected result for a Hopfield neural net is attained. This proof of principle, combined with the scalability of low-loss integrated chips and straightforward Hamiltonian engineering, is a primary step toward photonic artificial intelligence.
Infographics as A Learning Tool in Higher Education: The design process and perception of an Instructional Designer

Infographics, as an instructional tool, enable support in the creation of an easy to read platform for learners. Steps involved to develop an infographic, play an essential role in optimizing visual literacy transfers. This study has presented a qualitative analysis of the proposed design strategy, which focuses on the infographic development process. The results have proposed that Infographics assignment design when included in instructional technology disciplines, helps instructional designers to enhance message delivery during visual transfers that eventually leads to increased learning interests among learners. The study also revealed five vital themes that emerged upon analyzing the collected data from participants involved. The present study has provided research scholars, an insight of the developed state-of-the-art tool, which in future research studies might serve as a basis for the development of an Instructional Infographic model. https://doi.org/10.26803/ijlter.18.1.1 Â
Requirements Prioritization and using Iteration Model for Successful Implementation of Requirements

Requirements prioritization is ranking of software requirements in particular order.Prioritize requirements are easy to manage and implement while un-prioritized requirements are costly and consume much time as total estimation time of project can exceed.Because all requirements are depended on each other so total estimation time exceed when requirements wait for pre-requisite requirements.Priority of requirement also increases when other requirements wait for it but assigning low priority to needed requirements will delaying the project.Iteration model is software engineering (SE) process model in which all requirements are not developed at one time but are developed in phases.Only sufficient information or subrequirements of particular user requirement (UR) can be needed for other user requirements (URs) so by implementing only the sufficient requirements in first phase will reduce waiting time.Hence total estimation time of the project will also reduce.In this research work, iteration model approach is used during prioritization to reduce total estimation time of project and to assure timely delivery of project.From the results it is concluded that not all sub-requirements of particular UR get same priority, but there are only few requirements that are important and should be given more priority.
Entanglement of identical particles and coherence in the first quantization language

We suggest a formalism to illustrate the entanglement of identical particles in the first quantization language (1QL). Our 1QL formalism enables one to exploit all the well-established quantum information tools to understand the indistinguishable ones, including the reduced density matrix and familiar entanglement measures. The rigorous quantitative relation between the amount of entanglement and the spatial coherence of particles is possible in this formalism. Our entanglement detection process is a generalization of the entanglement extraction protocol for identical particles with mode splitting proposed by Killoran et al. [Phys. Rev. Lett. 112, 150501 (2014)].
Robot trajectory tracking control using learning from demonstration method

Abstract This paper addresses robot trajectory tracking problem by using the learning from demonstration (LFD) method. Firstly, the trajectory tracking problem is formulated and the related previous works are introduced. Secondly, a trajectory tracking control policy using a three-layer neural network method, i.e., extreme learning machines (ELM), is proposed to minimize the real-time position and velocity errors. In the proposed method, the control algorithms are learnt from demonstrations directly such that the parameter adjusting problem in the traditional model-based methods is avoided. Besides, the trained controller has generalization ability to unseen situations which can be used to track different desired trajectories without any extra re-training. Thirdly, the stability analysis of the proposed control algorithm is provided and the corresponding parameter constraints are derived. Finally, the effectiveness and the generalization ability of the proposed control algorithms are demonstrated and discussed with simulation and experimental examples.
Universal logical gates with constant overhead: instantaneous Dehn twists for hyperbolic quantum codes

A basic question in the theory of fault-tolerant quantum computation is to understand the fundamental resource costs for performing a universal logical set of gates on encoded qubits to arbitrary accuracy. Here we consider qubits encoded with constant space overhead (i.e. finite encoding rate) in the limit of arbitrarily large code distance<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>d</mml:mi></mml:math>through the use of topological codes associated to triangulations of hyperbolic surfaces. We introduce explicit protocols to demonstrate how Dehn twists of the hyperbolic surface can be implemented on the code through constant depth unitary circuits, without increasing the space overhead. The circuit for a given Dehn twist consists of a permutation of physical qubits, followed by a constant depth local unitary circuit, where locality here is defined with respect to a hyperbolic metric that defines the code. Applying our results to the hyperbolic Fibonacci Turaev-Viro code implies the possibility of applying universal logical gate sets on encoded qubits through constant depth unitary circuits and with constant space overhead. Our circuits are inherently protected from errors as they map local operators to local operators while changing the size of their support by at most a constant factor; in the presence of noisy syndrome measurements, our results suggest the possibility of universal fault tolerant quantum computation with constant space overhead and time overhead of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi class="MJX-tex-caligraphic" mathvariant="script">O</mml:mi></mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>d</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mi>log</mml:mi><mml:mo>⁡</mml:mo><mml:mi>d</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math>. For quantum circuits that allow parallel gate operations, this yields the optimal scaling of space-time overhead known to date.
Semisupervised Discriminant Multimanifold Analysis for Action Recognition

Although recent semisupervised approaches have proven their effectiveness when there are limited training data, they assume that the samples from different actions lie on a single data manifold in the feature space and try to uncover a common subspace for all samples. However, this assumption ignores the intraclass compactness and the interclass separability simultaneously. We believe that human actions should occupy multimanifold subspace and, therefore, model the samples of the same action as the same manifold and those of different actions as different manifolds. In order to obtain the optimum subspace projection matrix, the current approaches may be mathematically imprecise owe to the badly scaled matrix and improper convergence. To address these issues in unconstrained convex optimization, we introduce a nontrivial spectral projected gradient method and Karush-Kuhn-Tucker conditions without matrix inversion. Through maximizing the separability between different classes by using labeled data points and estimating the intrinsic geometric structure of the data distributions by exploring unlabeled data points, the proposed algorithm can learn global and local consistency and boost the recognition performance. Extensive experiments conducted on the realistic video data sets, including JHMDB, HMDB51, UCF50, and UCF101, have demonstrated that our algorithm outperforms the compared algorithms, including deep learning approach when there are only a few labeled samples.
An Integrated Deep Learning Framework for Occluded Pedestrian Tracking

Numerous object-tracking and multiple-person-tracking algorithms have been developed in the field of computer vision, but few trackers can properly address the issue of when a pedestrian is partially or fully occluded by other objects or persons. In order to achieve efficient pedestrian tracking in various occlusion conditions, a pedestrian tracking framework is proposed and developed based on the deep learning networks. First, a pedestrian detector is trained as a tracking mechanism based on the Faster R-CNN, which narrows the search range and efficiently improves accuracy, as compared with the traditional gradient descent algorithm. Second, in the process of target matching, a color histogram and scale-invariant feature transform are combined to provide the target model expression, and a full convolution network (FCN) is trained to extract the pedestrian information in the target model, based on an FCN image semantic segmentation algorithm that can remove background noise effectively. Finally, the extensive experiments on a commonly used tracking benchmark show that the proposed method achieves better performance than the other state-of-the-art trackers in various occlusion situations.
Germination, harvesting stage, antioxidant activity and consumer acceptance of ten microgreens

The Ceylon Journal of Science is a peer-reviewed journal published quarterly by the University of Peradeniya, Sri Lanka in March, June, September and December. It is aimed at publishing high quality research articles on topics related to different disciplines in Science. The journal accepts original research articles, book reviews, reviews and mini-reviews, short communications, opinions, research notes, and commentaries and notes. The Journal has its own website https://site.pdn.ac.lk/cjs/. The journal strictly adheres to publication ethics as emphasized by the Committee on Publication Ethics (COPE). It is indexed in Sri Lanka Journals Online (SLJOL), Directory of Open Access Journals (DOAJ), Google Scholar and Zoological Records. According to the Google Scholar;H5-Index: 12H5-Median: 15According to the Exaly (1970 – 2021);Impact Factor: 0.6 (top 19%)Extended IF: 0.6 (top 19%)H-Index: 8 (top 28%)Citations/paper: 1.42