Reviewerly/exHarmony huggingface dataset:
CaACBIM: A Context-aware Access Control Model for BIM

A building information model (BIM) is of upmost importance with a full life-time cycle in architecture engineering and construction industry. Smart construction relies on BIM to manipulate information flow, data flow, and management flow. Currently, BIM has been explored mainly for information construction and utilization, but there exist few works concerning information security, e.g., audits of critical models and exposure of sensitive models. Moreover, few BIM systems have been proposed to make use of new computing paradigms, such as mobile cloud computing, blockchain and Internet of Things. In this paper, we propose a Context-aware Access Control (CaAC) model for BIM systems on mobile cloud architectures. BIM data can be confidentially accessed according to contexts in a fine-grained manner. We describe functions of CaAC formally by illustrating location-aware access control and time-aware access control. CaAC model can outperform role-based access control for preventing BIM data leakage by distinguishing contexts. In addition, grouping algorithms are also presented for flexibility, in which basic model (user grouping based on user role permissions) and advanced model (user grouping based on user requests) are differentiated. Compared with the traditional role-based access control model, security and feasibility of CaAC are remarkably improved by distinguishing an identical role with multiple contexts. The average efficiency is improved by 2 n / ( 2 n - p - q ) , and time complexity is O ( n ) .
Improved adaptive image retrieval with the use of shadowed sets

Image retrieval algorithms based on the whole image exhibit high complexity due to background interference, low-level description abilities and large storage requirements, while image retrieval algorithms based on the saliency detection have been found to have low accuracy owing to the lack of important information in extracted salient regions caused by the uncertainty of the salient regions of the image. In this paper, we propose a shadowed-set-based image retrieval algorithm, and develop techniques of an automatic selection of two threshold parameters by combining saliency detection and edge detection, which automatically determine shadowed regions. The developed algorithm uses shadowed set theory to divide the image into salient regions, non-salient regions and shadowed regions, in order to extract the useful information of the image and ignore irrelevant one. As a consequence, this leads to the salient regions and the shadowed regions to be jointly involved in the retrieval process. The experimental results reported for several datasets show that the proposed algorithm can effectively improve the retrieval accuracy compared with the existing state-of-the-art algorithms.
A survey on <scp>UML</scp> model smells detection techniques for software refactoring

Abstract Bad smells tend to have a negative impact on software by degrading its quality. It is beneficial to detect model smells to avoid their propagation to later stages of software development. The objective of this paper is to present the state‐of‐the‐art research on techniques for detecting UML model bad smells. The detection techniques are compared and evaluated using a proposed evaluation framework. The framework consists of two parts. The first part of the framework compares the techniques in terms of the implemented approach, the investigated model, and the explored model smells, while the experimental design is explored in the second part of the framework. We found that the detection of bad smells in class and sequence diagrams is accomplished via design patterns, software metrics, and predefined rules, while model smells in use cases are detected using metrics and predefined rules. We also found that the class diagram is the most investigated UML model in the context of model smell detection, whereas there is a lack of work on other UML models. In addition, there is a scarcity of independent studies on sequence diagrams. Furthermore, the studies investigating class diagrams are mostly validated, whereas use case diagrams and sequence diagrams are rarely validated.
Multi-matrix error estimation and reconciliation for quantum key distribution

Post-processing is a significant step in quantum key distribution(QKD), which is used for correcting the quantum-channel noise errors and distilling identical corrected keys between two distant legitimate parties.Efficient error reconciliation protocol, which can lead to an increase in the secure key generation rate, is one of the main performance indicators of QKD setups.In this paper, we propose a multi-low-density parity-check codes based reconciliation scheme, which can provide remarkable perspectives for highly efficient information reconciliation.With testing our approach through data simulation, we show that the proposed scheme combining multi-syndrome-based error rate estimation allows a more accurate estimation about the error rate as compared with random sampling and single-syndrome estimation techniques before the error correction, as well as a significant increase in the efficiency of the procedure without compromising security and sacrificing reconciliation efficiency.
voxel-GAN: Adversarial Framework for Learning Imbalanced Brain Tumor Segmentation

We propose a new adversarial network, named voxel-GAN, to mitigate imbalanced data problem in brain tumor semantic segmentation where the majority of voxels belong to a healthy region and few belong to tumor or non-health region. We introduce a 3D conditional generative adversarial network (cGAN) comprises two components: a segmentor and a discriminator. The segmentor is trained on 3D brain MR or CT images to learn the segmentation label’s in voxel-level, while the discriminator is trained to distinguish a segmentor output, coming from the ground truth or generated artificially. The segmentor and discriminator networks simultaneously train with new weighted adversarial loss to mitigate imbalanced training data issue. We show evidence that the proposed framework is applicable to different types of brain images of varied sizes. In our experiments on BraTS-2018 and ISLES-2018 benchmarks, we find improved results, demonstrating the efficacy of our approach.
Vector space explorations of literary language

Literary novels are said to distinguish themselves from other novels through conventions associated with literariness. We investigate the task of predicting the literariness of novels as perceived by readers, based on a large reader survey of contemporary Dutch novels. Previous research showed that ratings of literariness are predictable from texts to a substantial extent using machine learning, suggesting that it may be possible to explain the consensus among readers on which novels are literary as a consensus on the kind of writing style that characterizes literature. Although we have not yet collected human judgments to establish the influence of writing style directly (we use a survey with judgments based on the titles of novels), we can try to analyze the behavior of machine learning models on particular text fragments as a proxy for human judgments. In order to explore aspects of the texts associated with literariness, we divide the texts of the novels in chunks of 2–3 pages and create vector space representations using topic models (Latent Dirichlet Allocation) and neural document embeddings (Distributed Bag-of-Words Paragraph Vectors). We analyze the semantic complexity of the novels using distance measures, supporting the notion that literariness can be partly explained as a deviation from the norm. Furthermore, we build predictive models and identify specific keywords and stylistic markers related to literariness. While genre plays a role, we find that the greater part of factors affecting judgments of literariness are explicable in bag-of-words terms, even in short text fragments and among novels with higher literary ratings. The code and notebook used to produce the results in this paper are available at https://github.com/andreasvc/litvecspace.
Comparing Lexical Bundles across Corpora of Different Sizes: The Zipfian Problem

Formulaic sequences in language use are often studied by means of the automatic identification of frequently recurring series of words, often referred to as ‘lexical bundles’, in corpora that contrast different registers, academic disciplines, etc. As corpora often differ in size, a critically important assumption in this field states that the use of a normalized frequency threshold, such as 20 occurrences per million words, allows for an accurate comparison of corpora of different sizes. Yet, several researchers have argued that normalization may be unreliable when applied to frequency threshold. The study investigates this issue by comparing the number of lexical bundles identified in corpora that differ only in size. Using two complementary random sampling procedures, subcorpora of 100,000 to two million words were extracted from five corpora, with lexical bundles identified in them using two normalized frequency thresholds and two dispersion thresholds. The results show that many more lexical bundles are identified in smaller subcorpora than in larger ones. This size effect can be related to the Zipfian nature of the distribution of words and word sequences in corpora. The conclusion discusses several solutions to avoid the unfairness of comparing lexical bundles identified in corpora of different sizes.
Asymmetric hybrid encryption scheme based on modified equal modulus decomposition in hybrid multi-resolution wavelet domain

In this paper, an asymmetric hybrid encryption scheme, using coherent superposition and modified equal modulus decomposition in a hybrid multi-resolution wavelet, is proposed. The hybrid multi-resolution wavelet is generated using fractional Fourier transform of multiple orders and Walsh transform. The fractional orders of the fractional Fourier transform increase the key space and hence provide additional strength to the cryptosystem. The designed scheme has a large key space to avoid brute-force attack and is non-linear in nature. The scheme is validated on grey-scale images. Computer-based simulations have been performed to verify the validity and performance of the proposed scheme against various attacks. Scheme's robustness to the special attack is also checked. Results show that single equal modulus decomposition with fractional Fourier transform and hybrid transform are vulnerable to the special attack, whereas the proposed scheme endures the special attack.
Scalable statistical inference for averaged implicit stochastic gradient descent

Abstract Stochastic gradient descent (SGD) provides a scalable way to compute parameter estimates in applications involving large‐scale data or streaming data. As an alternative version, averaged implicit SGD (AI‐SGD) has been shown to be more stable and more efficient. Although the asymptotic properties of AI‐SGD have been well established, statistical inferences based on it such as interval estimation remain unexplored. The bootstrap method is not computationally feasible because it requires to repeatedly resample from the entire data set. In addition, the plug‐in method is not applicable when there is no explicit covariance matrix formula. In this paper, we propose a scalable statistical inference procedure, which can be used for conducting inferences based on the AI‐SGD estimator. The proposed procedure updates the AI‐SGD estimate as well as many randomly perturbed AI‐SGD estimates, upon the arrival of each observation. We derive some large‐sample theoretical properties of the proposed procedure and examine its performance via simulation studies.
Collision Avoidance of Arbitrarily Shaped Deforming Objects Using Collision Cones

In this letter, the problem of collision avoidance of objects, which can deform by changing their shape as a function of time, is considered. There are several scenarios involving such deforming objects-examples include environments with shape shifting robots, such as snake robots, boundaries of swarms of vehicles, and boundaries of oil spills. There is very limited work in the literature that considers dynamic environments comprising such shape changing entities. To develop conditions that predict the onset of collision for deformable objects, this letter uses the notion of collision cones in environments involving engagements between a point object and a deforming object, a circular object and a deforming object, and finally, an arbitrarily shaped object and a deforming object. The collision cone equations are subsequently embedded in a Lyapunov framework and used to develop nonlinear analytical guidance laws for collision avoidance in such environments. Simulations are performed to demonstrate the efficacy of the guidance laws.
Deep Supervised Hashing Based on Stable Distribution

Recently, the convolutional neural network (CNN)-based hashing method has achieved its promising performance for image retrieval. However, tackling the discrepancy between quantization error minimization and discriminability maximization of the network outputs simultaneously still remains unsolved. Distinguished from the previous works, which only can search an equilibrium point within the discrepancy, we propose a novel deep supervised hashing based on stable distribution (DSHSD) to eliminate the discrepancy with distribution consistency guarantee. First, we utilize a smooth projection function, in which the amount of smoothing is adaptable, to relax the discrete constraint instead of any quantization regularizer. Second, a mathematical connection between the smooth projection and the feature distribution is made to maintain distribution consistency. A relaxed multi-semantic information fusion method is implemented to make hash codes learned to preserve more semantic information and accelerate the training convergence. According to stable distribution, we propose a novel hashing framework to eliminate the discrepancy and support fast image retrieval. The extensive experiments on the CIFAR-10, NUS-WIDE, and ImageNet datasets show that our method can outperform the state-of-the-art methods from various perspectives.
Speckle-interferometric phase fringe patterns de-noising by using fringes’ direction and curvature

We propose an adaptive filter method based on a deformable window shape to remove the noise of wrapped phase fringe patterns in electronic speckle pattern interferometry (ESPI). The filter kernel automatically adjusts its size, shape, and direction according to the direction and curvature of the phase fringe pattern. The direction of a phase fringe pattern is determined using a gradient-based method. Fringe curvature is estimated based on the calculated direction and fitted phase contours. Simulations were performed to evaluate the proposed method, and the results were compared with those of the 2-D windowed Fourier transform filtering (WFTF) algorithm and the medial filter technique. Simulated and experimental results show that the new approach can provide more effective de-noising for phase fringe patterns compared to the other two methods, using few parameters.
Correlation identification in multimodal weibo via back propagation neural network with genetic algorithm

The rapid development of social media services has spawned abundant user generated contents (UGC), such as Sina Weibo, which is one of the biggest Chinese microblogging platforms. In order to enhance the quality and popularity of the posted weibo (the microblog), Weibo users usually embed some social information and images or micro-videos, namely the multimodal weibo, and we assume that there is a close correlation among the multimodal weibo data, especially between the visual data (image/micro-video) and its corresponding text, for a multimodal weibo of high quality. Hence, we try to evaluate the quality of multimodal weibo via analyzing the correlation in the multimodal weibo. This paper constructs the classification model based on back propagation (BP) neural network with genetic algorithm (GA), to automatically identify the correlation within the multimodal weibo, and investigates three kinds of features from multimodal weibo to uncover their contributions to the correlation. The experimental results verify the superiority of the GA-BP based classification model over the traditional BP neural network.
2D and 3D Medical Image Analysis by Discrete Orthogonal Moments

The 2D and 3D medical imaging play a very important role in medical diagnosis to detect and isolate various pathologies. The treatment, exploitation and the interpretation of the obtained images are valuable tools to help establish a medical diagnosis. In this context, we will present in this paper an effective method for the analysis of different types of 2D and 3D medical image based on the discrete orthogonal moments of Tchebichef, Krawtchouk, Hahn, Charlier and Meixner. By this method, the 2D and 3D medical images are described by moment descriptors. The use of these moment descriptors has allowed us to significantly reduce the storage space of 2D and 3D medical images and speed up the image transfer process. The results of simulations on different medical images by the five types of moments have shown the ability of these types to improve and speed up the medical image reconstruction.
CaMeLOT: An educational framework for conceptual data modelling

Teaching conceptual data modelling (CDM) remains a challenging task for educators. Despite the fact that CDM is an integral part of software engineering curricula, there is no generally accepted educational framework for the subject. Moreover, the existing educational literature shows significant gaps when it comes to pursued learning outcomes and their assessment. In this paper, we propose an educational framework for conceptual data modelling, based on the revised Bloom's taxonomy of educational objectives, and provide necessary examples of systemized learning outcomes. We utilized the revised Bloom's taxonomy to develop an adapted framework specifically for learning outcomes related to CDM. We validated the framework by mapping learning outcomes distilled from the existing course material to the framework, by presenting the framework for feedback to the experts in the field and further elaborating and refining it based on the feedback and experiences from these validation activities. CaMeLOT is an adaptation of the Bloom's taxonomy specifically for learning outcomes related to CDM. We identified different content areas and indicated the necessary scaffolding. Based on the framework, we worked out 17 example tables of learning outcomes related to content areas at different levels of scaffolding, exemplifying the different knowledge and cognitive levels. We clarify the differences in learning outcomes related to different knowledge and cognitive levels and thereby provide a domain specific clarification of the classification guidelines. CaMeLOT gives educators an opportunity to enhance the CDM part of software engineering curricula with a systemized set of learning outcomes to be pursued, and open the path for creating more complete, useful and effective assessment packages. The adoption of our educational framework may reduce the time spent on designing educational material and, at the same time, improve its quality.
When a robot teaches humans: Automated feedback selection accelerates motor learning

Automated feedback selection focusing on the dominant error accelerated motor learning in robot-assisted rowing training.
A Lightweight and Efficient Secure Hybrid RSA (SHRSA) Messaging Scheme With Four-Layered Authentication Stack

For virtual private network and LAN-to-LAN tunnel sessions, End-to-End security is the main constraint in private messaging scenarios.Internet Protocol Security can negotiate new keys for every communication, but when the key is compromised, it is a problem.Perfect Forward Secrecy is the resolution.In addition, the messaging scheme should be efficient and lightweight for keeping parity with daily needs.The popular Rivest-Shamir-Adleman (RSA) cipher is omnipresent in secure communications but has many scientific problems.So here, in this paper, a lightweight and efficient Secure Hybrid RSA (SHRSA) messaging scheme with four-layered authentication stack is implemented and analyzed.The scheme is resolving the problem of asymptotic very low speed of decryption of RSA, the computational modular exponentiation complexity and partial key exposure vulnerability issues of RSA, and many more.The four-layered authentication stack have eliminated the need of the use of any password, external digital certificates, and a third party for authentication with its own four techniques in a staked way.We have found that in evolutions and analysis of the scheme, it is not only resolving various scientific problems of RSA but also occupying 2%-4% less CPU than main RSA and occupying 1%-3% less memory than main RSA.Its decryption average time has gained 8.858 times compared to the main RSA and gained 2.248 times compared to CRT RSA.We have found that the RSA, CRT-RSA, and SHRSA's encryption throughput are alike-all are around 6 KB/Sec but decryption throughput of SHRSA has gained 8.5345 times than main RSA and gained 2.1174 times than CRT-RSA.The results obtained have shown us the relevancies of the SHRSA messaging scheme to be integratable as a cipher in Blockchain architectures, cyber-physical systems, and the Internet of Everything.
Foreground detection based on co-occurrence background model with hypothesis on degradation modification in dynamic scenes

This work presents a Hypothesis on Degradation Modification (HoD) based on Co-occurrence Pixel-Block Pairs (CPB, which is proposed in our previous work) to further resist background changes for foreground detection, such as illumination changes and background motion. HoD provides CPB with a model update strategy that can be used for a long time. While further improving the robustness of CPB, it also stabilizes the efficiency of CPB over time. A key contribution of this work is it offers a robust background subtraction for foreground detection in dynamic scenes. The observation is robust to illumination changes and background motion and demonstrates the ability of HoD. Experimental results obtained from the datasets under different challenges of PETS 2001, AIST-Indoor, SBMnet and CDW-2012 databases prove that our algorithm has a good effectiveness for foreground detection.
New fusion of SVD and Relevance Weighted LDA for face recognition

Biometrics has become fashionable in areas that require a high level of security and control. Among all the technologies that exist, face recognition is one of the most used and adapted technologies. In this work, we propose a new fusion of two projection based face recognition algorithms in Discrete wavelet transform domain(DWT).Those two algorithms are Relevance Weighted Linear Discriminant Analysis(RW-LDA) and singular value decomposition (SVD) using the left and right singular vectors. Our experimental work conducted on two well known facial databases indicate that the application of the Min- Max, Z-score normalization schemes followed by a fusion method demonstrates improvement in terms of recognition rate and training time.