Reviewerly/exHarmony huggingface dataset:
Comparison among Akaike Information Criterion, Bayesian Information Criterion and Vuong's test in Model Selection: A Case Study of Violated Speed Regulation in Taiwan

When doing research on scientific issues, it is very significant if our research issues are closely connected to real applications. In reality, when analyzing data in practice, there are frequently several models that can appropriate to the survey data. Hence, it is necessary to have a standard criterion to choose the most ecient model. In this article, our primary interest is to compare and discuss about the criteria for selecting a model and its applications. The authors provide approaches and procedures of these methods and apply to the traffic violation data where we look for the most appropriate model among Poisson regression, Zero-inflated Poisson regression and Negative binomial regression to capture between number of violated speed regulations and some factors including distance covered, motorcycle engine and age of respondents by using AIC, BIC and Vuong's test. Based on results on the training, validation and test data set, we find that the criteria AIC and BIC are more consistent and robust performance in model selection than the Vuong's test. In the present paper, the authors also discuss about advantages and disadvantages of these methods and provide some of the suggestions with potential directions in future research. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
BIM-Based AR Maintenance System (BARMS) as an Intelligent Instruction Platform for Complex Plumbing Facilities

The traditional architectural design of facilities requires that maintenance workers refer between plumbing layout drawings and the actual facilities in complex, hidden, sealed in-wall, or low illumination environments. The purpose of a Building information modeling-based Augmented Reality Maintenance System (BARMS) in this study was to provide a smartphone-based platform and a new application scenario for cooling tower and pipe shutdown protocol in a real-world old campus building. An intelligent instruction framework was built considering subject, path, and actions. Challenges and solutions were created to monitor the subject and maintenance protocol while moving from inside to outside, between bright and dark environments, and when crossing building enclosures at roof level. Animated instruction using AR was interactive and followed the knowledge and management protocols of associated instruction aids. The results demonstrated a straightforward mapping of in-wall pipes and their connected valves, with practical auxiliary components of walking direction and path guidance. The suggested maintenance routes also ensured a worker’s safety. Statistical analysis showed a positive user response.
Privacy-Preserving Distributed Deep Learning via Homomorphic Re-Encryption

The flourishing deep learning on distributed training datasets arouses worry about data privacy. The recent work related to privacy-preserving distributed deep learning is based on the assumption that the server and any learning participant do not collude. Once they collude, the server could decrypt and get data of all learning participants. Moreover, since the private keys of all learning participants are the same, a learning participant must connect to the server via a distinct TLS/SSL secure channel to avoid leaking data to other learning participants. To fix these problems, we propose a privacy-preserving distributed deep learning scheme with the following improvements: (1) no information is leaked to the server even if any learning participant colludes with the server; (2) learning participants do not need different secure channels to communicate with the server; and (3) the deep learning model accuracy is higher. We achieve them by introducing a key transform server and using homomorphic re-encryption in asynchronous stochastic gradient descent applied to deep learning. We show that our scheme adds tolerable communication cost to the deep learning system, but achieves more security properties. The computational cost of learning participants is similar. Overall, our scheme is a more secure and more accurate deep learning scheme for distributed learning participants.
Size and shape matter: The impact of voxel geometry on the identification of small nuclei

How, and to what extent do size and shape of a voxel measured with magnetic resonance imaging (MRI) affect the ability to visualize small brain nuclei? Despite general consensus that voxel geometry affects volumetric properties of regions of interest, particularly those of small brain nuclei, no quantitative data on the influence of voxel size and shape on labeling accuracy is available. Using simulations, we investigated the selective influence of voxel geometry by reconstructing simulated ellipsoid structures with voxels varying in shape and size. For each reconstructed ellipsoid, we calculated differences in volume and similarity between the labeled volume and the predefined dimensions of the ellipsoid. Probability functions were derived from one or two individual raters and a simulated ground truth for reference. As expected, larger voxels (i.e., coarser resolution) and increasing anisotropy results in increased deviations of both volume and shape measures, which is of particular relevance for small brain structures. Our findings clearly illustrate the anatomical inaccuracies introduced by the application of large and/or anisotropic voxels. To ensure deviations occur within the acceptable range (Dice coefficient scores; DCS > 0.75, corresponding to < 57% volume deviation), the volume of isotropic voxels should not exceed 5% of the total volume of the region of interest. When high accuracy is required (DCS > 0.90, corresponding to a < 19% volume deviation), the volumes of isotropic voxels should not exceed 0.08%, of the total volume. Finally, when large anisotropic factors (>3) are used, and the ellipsoid is orthogonal to the slice axes, having its long axis in the imaging plane, the voxel volume should not exceed 0.005% of the total volume. This allows sufficient compensation of anisotropy effects, in order to reach accuracy in the acceptable range (DCS > 0.75, corresponding to >57% volume deviation).
PAX: Using Pseudonymization and Anonymization to Protect Patients' Identities and Data in the Healthcare System

Electronic health record (EHR) systems are extremely useful for managing patients' data and are widely disseminated in the health sector. The main problem with these systems is how to maintain the privacy of sensitive patient information. Due to not fully protecting the records from unauthorised users, EHR systems fail to provide privacy for protected health information. Weak security measures also allow authorised users to exceed their specific privileges to access medical records. Thus, some of the systems are not a trustworthy source and are undesirable for patients and healthcare providers. Therefore, an authorisation system that provides privacy when accessing patients' data is required to address these security issues. Specifically, security and privacy precautions should be raised for specific categories of users, doctor advisors, physician researchers, emergency doctors, and patients' relatives. Presently, these users can break into the electronic systems and even violate patients' privacy because of the privileges granted to them or the inadequate security and privacy mechanisms of these systems. To address the security and privacy problems associated with specific users, we develop the Pseudonymization and Anonymization with the XACML (PAX) modular system, which depends on client and server applications. It provides a security solution to the privacy issues and the problem of safe-access decisions for patients' data in the EHR. The~results of theoretical and experimental security analysis prove that PAX provides security features in preserving the privacy of healthcare users and is safe against known attacks.
Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems

Inspired by the behavior of elephants in nature, elephant herd optimization (EHO) was proposed recently for global optimization. Like most other metaheuristic algorithms, EHO does not use the previous individuals in the later updating process. If the useful information in the previous individuals were fully exploited and used in the later optimization process, the quality of solutions may be improved significantly. In this paper, we propose several new updating strategies for EHO, in which one, two, or three individuals are selected from the previous iterations, and their useful information is incorporated into the updating process. Accordingly, the final individual at this iteration is generated according to the elephant generated by the basic EHO, and the selected previous elephants through a weighted sum. The weights are determined by a random number and the fitness of the elephant individuals at the previous iteration. We incorporated each of the six individual updating strategies individually into the basic EHO, creating six improved variants of EHO. We benchmarked these proposed methods using sixteen test functions. Our experimental results demonstrated that the proposed improved methods significantly outperformed the basic EHO.
Data Placement in Era of Cloud Computing: a Survey, Taxonomy and Open Research Issues

In cloud computing, data placement is a critical operation performed as part of workflow management and aims to find the best physical machine to place the data. It has direct impact on performance, cost and execution time of workflows. Number of data placement algorithms is designed in cloud computing environment that aimed to improve various factors affecting the workflows and their execution including the movement of data among data centers. This paper provides a complete survey and analyses of existing data placement schemes proposed in literature for cloud computing. Further, it classifies data placement schemes based on their assess capabilities and objectives. Further objectives and properties of data placement schemes are compared. Finally future research directions are provided with concluding remarks.
Payload Enhancement on Least Significant Bit Image Steganography Using Edge Area Dilation

This research proposes a method to enhance the payload message by embedding messages on the dilated edge areas by the Least Significant Bit (LSB) method. To add security aspects to messages, messages are not embedded directly on the LSB but encrypted with XOR operations with Most Significant Bit (MSB). The experimental results of the test in this study showed that the dilation process to some extent can increase the payload of 18.65% and the average bpp is 1.42 while maintaining the imperceptibilty quality of stego image with an average PSNR value of about 47 dB, SSIM is 0.9977 and MSE is 1.13.
A Symmetric Image Encryption Algorithm Based on a Coupled Logistic–Bernoulli Map and Cellular Automata Diffusion Strategy

In this paper, the properties of the classical confusion-substitution structure and some recently proposed pseudorandom number generators using one-dimensional chaotic maps are investigated. To solve the low security problem of the original structure, a new bit-level cellular automata strategy is used to improve the sensitivity to the cryptosystem. We find that the new evolution effects among different generations of cells in cellular automata can significantly improve the diffusion effect. After this, a new one-dimensional chaotic map is proposed, which is constructed by coupling the logistic map and the Bernoulli map (LBM). The new map exhibits a much better random behavior and is more efficient than comparable ones. Due to the favorable properties of the new map and cellular automata algorithm, we propose a new image-encryption algorithm in which three-dimensional bit-level permutation with LBM is employed in the confusion phase. Simulations are carried out, and the results demonstrate the superior security and high efficiency of the proposed scheme.
Cost-Efficient Quantum Key Distribution (QKD) Over WDM Networks

Quantum key distribution (QKD) uses the laws of quantum mechanics to distribute secret keys with information-theoretic security. To realize QKD for secure communications among multiple distant users, fiber-based QKD networks can be deployed with today's available technologies. Incorporating QKD into existing wavelength-division multiplexing (WDM) network infrastructure provides a practical way to reduce the difficulty and cost of QKD networking. Nonetheless, the costs of QKD nodes and QKD links are still considered as the major barriers to long-distance QKD network deployment in practice. In this work, we study how to minimize the cost of deploying QKD-over-WDM backbone networks. We introduce a general architecture of QKD-over-WDM backbone networks. A novel cost-oriented model is defined to elaborate the cost-minimized problem, in which various QKD network components including QKD transceivers, QKD auxiliary equipment for QKD backbone nodes and trusted repeater nodes (TRNs), and QKD links are considered. The physical-layer parameters such as secret-key rate, physical distance, and the layout of TRNs are also incorporated in our defined model. We develop an integer linear programming (ILP) model and a novel cost-efficient QKD networking (CEQN) heuristic algorithm to address the cost-minimized problem. The extensive simulations indicate that our proposed approaches are effective in reducing the cost of deploying QKD-over-WDM backbone networks, where the designed CEQN algorithm is efficient to demonstrate similar to the ILP model. Finally, some open issues in future work are discussed, given that this work only presents an early highlevel analysis as opposed to a definitive detailed model.
DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery

Recent deep-learning counting techniques revolve around two distinct features of data—sparse data, which favors detection networks, or dense data where density map networks are used. Both techniques fail to address a third scenario, where dense objects are sparsely located. Raw aerial images represent sparse distributions of data in most situations. To address this issue, we propose a novel and exceedingly portable end-to-end model, DisCountNet, and an example dataset to test it on. DisCountNet is a two-stage network that uses theories from both detection and heat-map networks to provide a simple yet powerful design. The first stage, DiscNet, operates on the theory of coarse detection, but does so by converting a rich and high-resolution image into a sparse representation where only important information is encoded. Following this, CountNet operates on the dense regions of the sparse matrix to generate a density map, which provides fine locations and count predictions on densities of objects. Comparing the proposed network to current state-of-the-art networks, we find that we can maintain competitive performance while using a fraction of the computational complexity, resulting in a real-time solution.
QUANTUM INFORMATICS: OVERVIEW OF THE MAIN ACHIEVEMENTS

The urgency of conducting research in the field of quantum informatics is grounded. Promising areas of research are highlighted. For foreign and Russian publications and materials, a review of the main scientific results that characterize the current state of research in quantum computer science is made. It is noted that knowledge and funds are invested most intensively in the development of the architecture of a quantum computer and its elements. Despite the fact that today there is no information on the creation of a physical implementation of a quantum computer comparable in functionality to a classical digital computer, the development of quantum algorithms is one of the popular areas of research. An advantage of quantum algorithms is the fact that they reduce the time required to solve the problem due to the parallelization of operations by generating entangled quantum states and their subsequent use. This advantage (quantum acceleration) is most important when solving the problem of modeling the dynamics of complex systems and enumerated mathematical problems. (The general case of enumeration is the Grover scheme and its variants; the tasks of searching for hidden periods: Shor's scheme of using the fast quantum Fourier transform and its analogues.) The demand for cybersecurity developments (search for vulnerabilities in smart spaces, secure storage and use of big data, quantum cryptography) is noted. More than a dozen articles are devoted to quantum algorithms of key search, key distribution on optical fibers of various lengths, and the analysis of quantum resources necessary for conducting a cyber attack. In the field of artificial quantum intelligence, attention is paid, first of all, to the “search” for a model of a quantum neural network that is optimal from the point of view of using all the advantages presented by quantum computing and neural networks, as well as machine learning algorithms. Examples of the use of quantum computing in cognitive and social sciences for studying the decision-making mechanism with incomplete data are given. It is concluded that quantum informatics is promising for the simulation of complex natural and artificial phenomena and processes.
Unobtrusive Monitoring the Daily Activity Routine of Elderly People Living Alone, with Low-Cost Binary Sensors

Most expert projections indicate that in 2030, there will be over one billion people aged 60 or over. The vast majority of them prefer to spend their last years at home, and almost a third of them live alone. This creates a growing need for technology-based solutions capable of helping older people to live independently in their places. Despite the wealth of solutions proposed for this general problem, there are very few support systems that can be reproduced on a larger scale. In this study, we propose a method to monitor the activity of the elderly living alone and detect deviations from the previous activity patterns based on the idea that the residential living environment can be modeled as a collection of behaviorally significant places located arbitrarily in a generic space. Then we use virtual pheromones-a concept defined in our previous work-to create images of the pheromone distribution maps, which describe the spatiotemporal evolution of the interactions between the user and the environment. We propose a method to detect deviations from the activity routines based on a simple statistical analysis of the resulting images. By applying this method on two public activity recognition datasets, we found that the system is capable of detecting both singular deviations and slow-deviating trends from the previous activity routine of the monitored persons.
Approx-SVP in Ideal Lattices with Pre-processing

We describe an algorithm to solve the approximate Shortest Vector Problem for lattices corresponding to ideals of the ring of integers of an arbitrary number field K. This algorithm has a pre-processing phase, whose run-time is exponential in $$\log |\varDelta |$$ with $$\varDelta $$ the discriminant of K. Importantly, this pre-processing phase depends only on K. The pre-processing phase outputs an “advice”, whose bit-size is no more than the run-time of the query phase. Given this advice, the query phase of the algorithm takes as input any ideal I of the ring of integers, and outputs an element of I which is at most $$\exp (\widetilde{O}((\log |\varDelta |)^{\alpha +1}/n))$$ times longer than a shortest non-zero element of I (with respect to the Euclidean norm of its canonical embedding). This query phase runs in time and space $$\exp (\widetilde{O}( (\log |\varDelta |)^{\max (2/3, 1-2\alpha )}))$$ in the classical setting, and $$\exp (\widetilde{O}((\log |\varDelta |)^{1-2\alpha }))$$ in the quantum setting. The parameter $$\alpha $$ can be chosen arbitrarily in [0, 1 / 2]. Both correctness and cost analyses rely on heuristic assumptions, whose validity is consistent with experiments. The algorithm builds upon the algorithms from Cramer et al. [EUROCRYPT 2016] and Cramer et al. [EUROCRYPT 2017]. It relies on the framework from Buchmann [Séminaire de théorie des nombres 1990], which allows to merge them and to extend their applicability from prime-power cyclotomic fields to all number fields. The cost improvements are obtained by allowing precomputations that depend on the field only.
Asymmetric Residual Neural Network for Accurate Human Activity Recognition

Human activity recognition (HAR) using deep neural networks has become a hot topic in human–computer interaction. Machines can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem but also has many real-world practical applications. Based on the success of residual networks in achieving a high level of aesthetic representation of automatic learning, we propose a novel asymmetric residual network, named ARN. ARN is implemented using two identical path frameworks consisting of (1) a short time window, which is used to capture spatial features, and (2) a long time window, which is used to capture fine temporal features. The long time window path can be made very lightweight by reducing its channel capacity, while still being able to learn useful temporal representations for activity recognition. In this paper, we mainly focus on proposing a new model to improve the accuracy of HAR. In order to demonstrate the effectiveness of the ARN model, we carried out extensive experiments on benchmark datasets (i.e., OPPORTUNITY, UniMiB-SHAR) and compared the results with some conventional and state-of-the-art learning-based methods. We discuss the influence of networks parameters on performance to provide insights about its optimization. Results from our experiments show that ARN is effective in recognizing human activities via wearable datasets.
Efficiently Searching In-Memory Sorted Arrays

In this paper, we focus on the problem of searching sorted, in-memory datasets. This is a key data operation, and Binary Search is the de facto algorithm that is used in practice. We consider an alternative, namely Interpolation Search, which can take advantage of hardware trends by using complex calculations to save memory accesses. Historically, Interpolation Search was found to underperform compared to other search algorithms in this setting, despite its superior asymptotic complexity. Also, Interpolation Search is known to perform poorly on non-uniform data. To address these issues, we introduce SIP (Slope reuse Interpolation), an optimized implementation of Interpolation Search, and TIP (Three point Interpolation), a new search algorithm that uses linear fractions to interpolate on non-uniform distributions. We evaluate these two algorithms against a similarly optimized Binary Search method using a variety of real and synthetic datasets. We show that SIP is up to 4 times faster on uniformly distributed data and TIP is 2-3 times faster on non-uniformly distributed data in some cases. We also design a meta-algorithm to switch between these different methods to automate picking the higher performing search algorithm, which depends on factors like data distribution.