Reviewerly/exHarmony huggingface dataset:
SA Sorting: A Novel Sorting Technique for Large-Scale Data

Sorting is one of the operations on data structures used in a special situation. Sorting is defined as an arrangement of data or records in a particular logical order. A number of algorithms are developed for sorting the data. The reason behind developing these algorithms is to optimize the efficiency and complexity. The work on creating new sorting approaches is still going on. With the rise in the generation of big data, the concept of big number comes into existence. To sort thousands of records either sorted or unsorted, traditional sorting approaches can be used. In those cases, we can ignore the complexities as very minute difference exists in their execution time. But in case the data are very large, where execution time or processed time of billion or trillion of records is very large, we cannot ignore the complexity at this situation; therefore, an optimized sorting approach is required. Thus, SA sorting is one of the approaches developed to check sorted big numbers as it works better on sorted numbers than quick sort and many others. It can also be used to sort unsorted records as well.
Invariant Manifolds and Rate Constants in Driven Chemical Reactions

Reaction rates of chemical reactions under nonequilibrium conditions can be determined through the construction of the normally hyperbolic invariant manifold (NHIM) [and moving dividing surface (DS)] associated with the transition state trajectory. Here, we extend our recent methods by constructing points on the NHIM accurately even for multidimensional cases. We also advance the implementation of machine learning approaches to construct smooth versions of the NHIM from a known high-accuracy set of its points. That is, we expand on our earlier use of neural nets and introduce the use of Gaussian process regression for the determination of the NHIM. Finally, we compare and contrast all of these methods for a challenging two-dimensional model barrier case so as to illustrate their accuracy and general applicability.
Construction of regional informatization ecological environment based on the entropy weight modified AHP hierarchy model

In view of the problem on the construction of the regional informatization ecological environment, a kind of method for the construction of regional informatization ecological environment based on the entropy weight modified AHP hierarchy model is put forward in this paper. In this method, the distributed entropy weight modified AHP hierarchy model is first constructed. Each regional information reports their respective perceptual data to the nearest neighbor AHP hierarchy model, and the AHP hierarchy model carries out the entropy weight correction to construct the single or multiple regional informatization ecological environments that are changing at the same time, so as to obtain the entropy weight correction value for the construction of the regional informatization ecological environment. At the same time, the redundant information is integrated to reduce the amount of data transmission. The simulation results show that the method put forward in this paper has relatively high construction efficiency and that the quality of the ecological environment in the region can be improved and changed.
Vision Sensor-Based Shoe Detection for Human Tracking in a Human–Robot Coexisting Environment: A Photometric Invariant Approach Using DBSCAN Algorithm

Human-tracking problems are considered as one of the major problems in a human-robot coexisting environment. Such systems often employ a sensor fusion mechanism involving a vision sensing-based system along with ultrasonic or IR sensing systems. In this paper, we consider a very important sub-problem, where vision sensing is employed in such a human-robot coexisting environment for shoe detection purposes, by approximating the target person's positions in successive frames captured by the onboard camera of a robot, during human tracking. This paper considers a more challenging scenario of shoe detection under photometric changes and proposes a novel variant of the CFAsT-match algorithm, called photometric-invariant CFAsT-match (PICFAsT-match) algorithm. Both the CFAsT-match and PICFAsT-match have been implemented using the DBSCAN algorithm, a density-based clustering approach. Performance evaluations carried out for real-life tracking of human shoes show the supremacy of the proposed PICFAsT-match algorithm.
A multilevel color image thresholding scheme based on minimum cross entropy and alternating direction method of multipliers

In this paper, we focus on the problem of fast color image segmentation and propose a two-stage image segmentation method using multi-level thresholding algorithm and statistical region merging (SRM) technique. With the help of the alternating direction method of multipliers (ADMM), the optimal threshold values can be determined by the centroid of sub-modes of the image histogram that follow minimum cross entropy (MEC). In the second stage, a modified SRM is used to remove over-segmented regions of thresholded image to bring the final result. Experimental results depicts that the proposed approach selects threshold values more efficiently as compared to other MCE-based thresholding techniques and produces high quality of the segmented color images than the other methods like mean-shift, normalized cuts (Ncuts) and differential evolution (Q=7).
Perception-Enhanced Image Super-Resolution via Relativistic Generative Adversarial Networks

This paper considers a deep Generative Adversarial Networks (GAN) based method referred to as the Perception-Enhanced Super-Resolution (PESR) for Single Image Super Resolution (SISR) that enhances the perceptual quality of the reconstructed images by considering the following three issues: (1) ease GAN training by replacing an absolute with a relativistic discriminator, (2) include in the loss function a mechanism to emphasize difficult training samples which are generally rich in texture and (3) provide a flexible quality control scheme at test time to trade-off between perception and fidelity. Based on extensive experiments on six benchmark datasets, PESR outperforms recent state-of-the-art SISR methods in terms of perceptual quality. The code is available at https://github.com/thangvubk/PESR .
Complete weight enumerators of a class of linear codes with two or three weights

We construct a class of linear codes by choosing a proper defining set and determine their complete weight enumerators and weight enumerators. The results show that they have at most three weights and they are suitable for applications in secret sharing schemes. This is an extension of the results raised by Wang et al. (2017).
Mitigating cyber attacks through the measurement of non-IT professionals’ cybersecurity skills

Purpose Users’ mistakes due to poor cybersecurity skills result in up to 95 per cent of cyber threats to organizations. Threats to organizational information systems continue to result in substantial financial and intellectual property losses. This paper aims to design, develop and empirically test a set of scenarios-based hands-on tasks to measure the cybersecurity skills of non-information technology (IT) professionals. Design/methodology/approach This study was classified as developmental in nature and used a sequential qualitative and quantitative method to validate the reliability of the Cybersecurity Skills Index (CSI) as a prototype-benchmarking tool. Next, the prototype was used to empirically test the demonstrated observable hands-on skills level of 173 non-IT professionals. Findings The importance of skills and hands-on assessment appears applicable to cybersecurity skills of non-IT professionals. Therefore, by using an expert-validated set of cybersecurity skills and scenario-driven tasks, this study established and validated a set of hands-on tasks that measure observable cybersecurity skills of non-IT professionals without bias or the high-stakes risk to IT. Research limitations/implications Data collection was limited to the southeastern USA and while the sample size of 173 non-IT professionals is valid, further studies are required to increase validation of the results and generalizability. Originality/value The validated and reliable CSI operationalized as a tool that measures the cybersecurity skills of non-IT professionals. This benchmarking tool could assist organizations with mitigating threats due to vulnerabilities and breaches caused by employees due to poor cybersecurity skills.
Principal characteristic networks for few-shot learning

Few-shot learning aims to build a classifier that recognizes unseen new classes given only a few samples of them. Previous studies like prototypical networks utilized the mean of embedded support vectors to represent the prototype that is the representation of class and yield satisfactory results. However, the importance of these different embedded support vectors is not studied yet, which are valuable factors that could be used to push the limit of the few-shot learning. We propose a principal characteristic network that exploits the principal characteristic to better express prototype, computed by distributing weights based on embedded vectors’ different importance. The high-level abstract embedded vectors are extracted from our eResNet embedding network. In addition, we proposed a mixture loss function, which enlarges the inter-class distance in the embedding space for accurate classification. Extensive experimental results demonstrate that our network achieves state-of-the-art results on the Omniglot, miniImageNet and Cifar100 datasets.
RuleCOSI: Combination and simplification of production rules from boosted decision trees for imbalanced classification

In the field of machine learning, the problem of imbalanced classification arises when the class percentage on the data is unevenly distributed. Different strategies using boosting ensemble algorithms have shown improved results over the imbalanced classification problem by combining weak learners to produce a single strong learner. In particular, decision trees are often used as base learners in ensemble learning for classification or regression. However, boosting ensemble algorithms sometimes generate a large number of decision trees that could grow too large to be understandable and interpretable. Additionally, the use of weights adds more complexity to the final result. For this reason, in this paper, we present RuleCOSI, a novel method for combining and simplifying the output of an ensemble of binary decision trees into a single set of production rules. The proposed method takes into account the weight of each decision tree and using a combination matrix generates a single set of simplified production rules with performance comparable to that of the original boosting ensemble. In order to measure the performance and prove the applicability of the proposed method, we carried out an empirical validation using three different boosting algorithms over several well-known machine learning datasets as well as real-life data collected from a manufacturing company. The results of the algorithm are acceptable in most of the experiments reducing the complexity of the boosting ensemble output while maintaining a similar performance.
Unconstrained Offline Handwritten Word Recognition by Position Embedding Integrated ResNets Model

The state-of-the-art methods usually integrate with linguistic knowledge in the recognizer, which makes models more complicated and hard for resource-lacking languages. This letter proposes a new method for unconstrained offline handwritten word recognition by combining position embeddings with residual networks (ResNets) and bidirectional long short-term memory (BiLSTM) networks. At first, ResNets are used to extract abundant features from the input image. Then, position embeddings are used as indices of the character sequence corresponding to a word. By combining the ResNets features with each position embedding, the model generates different inputs for the BiLSTM networks. Finally, the state sequence of the BiLSTM is used to recognize corresponding characters. Without additional language resource, the proposed model achieved the best result on two public corpora, i.e., the 2017 ICDAR word-level information extraction in historical handwritten records competition and the RIMES public dataset on character error rate.
Learning Binary Hash Codes for Fast Anchor Link Retrieval across Networks

Users are usually involved in multiple social networks, without explicit anchor links that reveal the correspondence among different accounts of the same user across networks. Anchor link prediction aims to identify the hidden anchor links, which is a fundamental problem for user profiling, information cascading, and cross-domain recommendation. Although existing methods perform well in the accuracy of anchor link prediction, the pairwise search manners on inferring anchor links suffer from big challenge when being deployed in practical systems. To combat the challenges, in this paper we propose a novel embedding and matching architecture to directly learn binary hash code for each node. Hash codes offer us an efficient index to filter out the candidate node pairs for anchor link prediction. Extensive experiments on synthetic and real world large-scale datasets demonstrate that our proposed method has high time efficiency without loss of competitive prediction accuracy in anchor link prediction.
An Interpolative AMBTC-based high-payload RDH scheme for encrypted images

A reserving room before encryption (RRBE) framework is proposed to provide separable reversible data hiding for encrypted images in this paper. A combination of Interpolative AMBTC and Huffman coding is used to enhance the hiding capacity. In our scheme, a receiver with only the hiding key can extract the secret data without knowing about the content. If the receiver has only the encryption key, it is not possible to extract the hidden secret data, but it is possible to decrypt an image similar to the original image. Only when the receiver has both the hiding key and the encryption key, it is possible to extract the secret data and completely recover the original content without any error. The experimental results demonstrated the efficiency of our proposed high payload reversible data hiding scheme for encrypted images (HP-RDHEI), especially with respect to the data embedding rate.
Optimal Trajectory Generation for Quadrotor Teach-and-Repeat

In this letter, we propose a novel motion planning framework for quadrotor teach-and-repeat applications. Instead of controlling the drone to precisely follow the teaching path, our method converts an arbitrary jerky human-piloted trajectory to a topologically equivalent one, which is guaranteed to be safe, smooth, and kinodynamically feasible with an expected aggressiveness. Our proposed planning framework optimizes the trajectory in both spatial and temporal aspects. In the spatial layer, a flight corridor is found to represent the free space that is topologically equivalent with the teaching path. Then, a minimum-jerk piecewise trajectory is generated within the flight corridor. In the temporal layer, the trajectory is re-parameterized to obtain a minimum-time temporal trajectory under kinodynamic constraints. The spatial and temporal optimizations are both formulated as convex programs and are done iteratively. The proposed method is integrated into a complete quadrotor system and is validated to perform aggressive flights in challenging indoor and outdoor environments.
A novel word-independent gesture-typing continuous authentication scheme for mobile devices

In this study, we produce a new continuous authentication scheme for gesture-typing on mobile devices. Our scheme is the first scheme that authenticates gesture-typing interactions in a word-independent format. The scheme relies on groupings of features extracted from the word gesture after it has been reduced to parts common to all gestures. We show that movement sensors are also important in differentiating between users. We describe the feature extraction processes and analyse our proposed feature set. The unique process of our authentication scheme is presented and described. We collect our own gesture typing dataset including data collected during sitting, standing and walking activities for realism. We test our features against state-of-the-art touch-screen interaction features and compare feature extraction times on real mobile devices. Our scheme authenticates users with an equal error rate of 3.58% for a single word-gesture. The equal error rate is reduced to 0.81% when 3 word-gestures are used to authenticate.
A Graph is Worth a Thousand Words: Telling Event Stories using Timeline Summarization Graphs

Story timeline summarization is widely used by analysts, law enforcement agencies, and policymakers for content presentation, story-telling, and other data-driven decision-making applications. Recent advancements in web technologies have rendered social media sites such as Twitter and Facebook as a viable platform for discovering evolving stories and trending events for story timeline summarization. However, a timeline summarization structure that models complex evolving stories by tracking event evolution to identify different themes of a story and generate a coherent structure that is easy for users to understand is yet to be explored. In this paper, we propose StoryGraph, a novel graph timeline summarization structure that is capable of identifying the different themes of a story. By using high penalty metrics that leverage user network communities, temporal proximity, and the semantic context of the events, we construct coherent paths and generate structural timeline summaries to tell the story of how events evolve over time. We performed experiments on real-world datasets to show the prowess of StoryGraph. StoryGraph outperforms existing models and produces accurate timeline summarizations. As a key finding, we discover that user network communities increase coherence leading to the generation of consistent summary structures.
Facets of quantum information under non-Markovian evolution

We consider two non-Markovian models: Random Telegraph Noise (RTN) and non-Markovian dephasing (NMD). The memory in these models is studied from the perspective of quantum Fisher information flow. This is found to be consistent with the other well known witnesses of non-Markovianity. The two noise channels are characterized quantum information theoretically by studying their gate and channel fidelities. Further, the quantum coherence and its balance with mixedness is studied. This helps to put in perspective the role that the two noise channels can play in various facets of quantum information processing and quantum communication.
Exploiting the Local Optima in Genetic Algorithm using Tabu Search

Objectives: To explores the process of selecting retrieval schemes along with their weights, and fusion function for data fusion in information retrieval. Methods/Statistical Analysis: This has been carried out using the hybrid Genetic Algorithm. The fusion function, retrieval schemes and their weights lead to a tremendous combination. Finding an optimal solution from this great combination is entirely based on the exploration. Findings: We used, odd and even point crossover as an exploration tool. This exploration tool suffers a setback of slow convergence. The convergence rate can be improved by merging Tabu search, a best local search, with the genetic algorithm. This Tabu GA is used to select the retrieval schemes, weights and fusion function. The outcome of the experiments conducted over the test data sets namely: 1. adi, 2. cisi, and 3. cranlooks promising. We achieved 6.89% of improvement in performance, and the significance of the result is tested statistically. The convergence rate is also improved. Application/Improvements: We achieved 6.89% of improvement in performance, and the significance of the result is tested statistically. The convergence rate is also improved. Keywords: Genetic Algorithm, Information Retrieval, Odd and Even Point Crossover, Tabu GA, Tabu Search
MODAL LOGIC WITHOUT CONTRACTION IN A METATHEORY WITHOUT CONTRACTION

Abstract Standard reasoning about Kripke semantics for modal logic is almost always based on a background framework of classical logic. Can proofs for familiar definability theorems be carried out using a nonclassical substructural logic as the metatheory? This article presents a semantics for positive substructural modal logic and studies the connection between frame conditions and formulas, via definability theorems. The novelty is that all the proofs are carried out with a noncontractive logic in the background. This sheds light on which modal principles are invariant under changes of metalogic, and provides (further) evidence for the general viability of nonclassical mathematics.
Time-Bin and Polarization Superdense Teleportation for Space Applications

To build a global quantum-communication network, low-transmission, fiber-based communication channels can be supplemented by using a free-space channel between a satellite and a ground station on Earth. We construct a system that generates hyperentangled photonic ``ququarts'' and measures them to execute multiple quantum-communication protocols of interest. We successfully execute and characterize superdense teleportation, a modified remote-state preparation protocol that transfers more quantum information than standard teleportation, for the same classical information cost, and moreover, is in principle deterministic. Our measurements show an average fidelity of $0.94\ifmmode\pm\else\textpm\fi{}0.02$, with a phase resolution of approximately ${7}^{\ensuremath{\circ}}$, allowing reliable transmission of $&gt;{10}^{5}$ distinguishable quantum states. Additionally, we demonstrate the ability to compensate for the Doppler shift, which would otherwise prevent sending time-bin encoded states from a rapidly moving satellite, thus allowing the low-error execution of phase-sensitive protocols during an orbital pass. Finally, we show that the estimated number of received coincidence counts in a realistic implementation is sufficient to enable faithful reconstruction of the received state in a single pass.
A Memory Model Based on the Siamese Network for Long-Term Tracking

We propose a novel memory model using deep convolutional features for long-term tracking to handle the challenging issues, including visual deformation or target disappearance. Our memory model is separated into short- and long-term stores inspired by Atkinson-Shiffrin Memory Model (ASMM). In the tracking step, the bounding box of the target is estimated by the Siamese features obtained from both memory stores to accommodate changes in the visual appearance of the target. In the re-detection step, we take features only in the long-term store to alleviate the drift problem. At this time, we adopt a coarse-to-fine strategy to detect the target in the entire image without the dependency of the previous position. In the end, we employ Regional Maximum Activation of Convolutions (R-MAC) as key criteria. Our tracker achieves an F-score of 0.52 on the LTB35 dataset, which is 0.04 higher than the performance of the state-of-the-art algorithm.