Reviewerly/exHarmony huggingface dataset:
Universal complementarity between coherence and intrinsic concurrence for two-qubit states

Abstract Entanglement and coherence are two essential quantum resources for quantum information processing. A natural question arises of whether there is a direct link between them. In this work, we propose a definition of intrinsic concurrence for two-qubit states. Although the intrinsic concurrence is not a measure of entanglement, it embodies the concurrence of four pure states which are members of a special pure state ensemble for an arbitrary two-qubit state. And we show that intrinsic concurrence is always complementary to first-order coherence. In fact, this relation is an extension of the complementary relation satisfied by two-qubit pure states. Interestingly, we apply the complementary relation in some composite systems composed by a single-qubit state coupling with four typical noise channels respectively, and discover their mutual transformation relation between concurrence and first-order coherence. This universal complementarity provides reliable theoretical basis for the interconversion of the two important quantum resources.
Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation

Deep neural networks are an increasingly important technique for autonomous driving, especially as a visual perception component. Deployment in a real environment necessitates the explainability and inspectability of the algorithms controlling the vehicle. Such insightful explanations are relevant not only for legal issues and insurance matters but also for engineers and developers in order to achieve provable functional quality guarantees. This applies to all scenarios where the results of deep networks control potentially life threatening machines. We suggest the use of a tiered approach, whose main component is a semantic segmentation model, over an end-to-end approach for an autonomous driving system. In order for a system to provide meaningful explanations for its decisions it is necessary to give an explanation about the semantics that it attributes to the complex sensory inputs that it perceives. In the context of high-dimensional visual input this attribution is done as a pixel-wise classification process that assigns an object class to every pixel in the image. This process is called semantic segmentation. We propose an architecture that delivers real-time viable segmentation performance and which conforms to the limitations in computational power that is available in production vehicles. The output of such a semantic segmentation model can be used as an input for an interpretable autonomous driving system.
Quadmetric Optimized Thumb-to-Finger Interaction for Force Assisted One-Handed Text Entry on Mobile Headsets

Augmented reality head-worn computers often feature small-sized touch interfaces that complicate interaction with content, provide insufficient space for comfortable text input, and can be awkward to use in social situations. This paper presents a novel one-handed thumb-to-finger text entry solution for augmented reality head-worn computers. We design a glove composed of 12 force-sensitive nodes featuring an ambiguous keyboard layout. We first explore the viability of force disambiguation to evaluate the force division within the force spectrum. We select a 3-level force division as it allows to considerably reduce the number of keys while featuring a high (83.9%) accuracy. Following this pilot study, we map the 26 English characters onto the 9 nodes located on the index, middle and ring fingers in a 3-3-3 configuration, and attribute the space, enter and backspace keys to the remaining three nodes. We consider text entry performance as a quadmetric optimization problem considering the following criteria: goodness of character pairs, layout similarity to the QWERTY keyboard, easiness of force interaction, and comfort level of thumb reach. The resulting layout strikes a balance between performance and usability. We finally evaluate the quadmetric optimized layout over 6 sessions with 12 participants. The participants achieve an average text entry rate of 6.47 WPM with 6.85% error rate in the final session, which is significantly faster than existing thumb-to-finger solutions. In addition, our one-handed text entry system enhances the user mobility compared to other state-of-the-art solutions by freeing one hand, while allowing the user to direct his visual attention to other activities.
Research on Smart Contract Optimization Method on Blockchain

The smart contract on the blockchain allows credible transactions without a third party. These transactions are traceable and irreversible. The deployment and implementation of smart contracts in Ethernet will consume some gas, which will directly affect the cost of smart contracts. In order to reduce the consumption of gas during the execution of smart contracts, this article proposes an optimization algorithm for generating business process smart contracts. First, business process modeling notation (BPMN) models are extended to Petri nets. Second, Petri nets are simplified to find nodes in BPMN models that can be considered fusion tasks. Using new mapping rules from the BPMN model to solidity language, BPMN model is generated into Ethereum Smart contract model. In the BPMN models with multilayer fusion task, experimental results show that the proposed algorithm can save 15% gas on average for business processes with multiple fusion tasks.
Likelihood approximations of implied weights parsimony can be selected over the Mk model by the Akaike information criterion

Abstract A likelihood method that approximates the behaviour of implied weighting is described. This approach provides a likelihood perspective on several aspects of implied weighting, such as guidance for the choice of concavity values, a justification to use different concavities for different numbers of taxa, and a natural basis for extended implied weighting. In this approach, the number of free parameters in the estimation depends on C , the number of characters (in contrast to the standard Mk model, which estimates 2T–3 parameters for T taxa). Depending on the characteristics of the dataset, the likelihood obtained with this approach may in some cases be similar or superior to that of the Mk model, but with fewer parameters being adjusted. Because of that tradeoff, testing against the Mk model by means of the Akaike information criterion on a set of 182 morphological datasets indicated many cases (36) in which the likelihood approximation to implied weighting is the best method, from an information‐theoretic point of view. Given that it is expected to produce (almost) the same results as this maximum‐likelihood approximation, implied weighting can therefore be seen as a valid alternative to the Mk model often used for morphological datasets, on the basis of a criterion for model fit widely advocated by likelihoodists.
Robust regression with deep CNNs for facial age estimation: An empirical study

Recent works have shown that deep Convolutional Neural Networks (CNNs) can be very effective for image-based age estimation. However, the proposed approaches significantly vary, and there are still some open problems. Almost all deep regression networks for age estimation have exploited the Mean Square Error loss only. These deep networks have not considered the influence of aberrant and outlier observations on the final model. In this letter, we introduce the use of robust loss functions in order to learn deep regression networks for age estimation. More precisely, we explore the use of two robust regression functions: (i) the ℓ1 norm error, and (ii) the adaptive loss function that retains the advantages of the ℓ1 and ℓ2 norms. Experimental results obtained on four public databases demonstrate that learning a deep CNN with robust losses can improve the age estimation.
Determinants of corruption in Nigeria: evidence from various estimation techniques

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Secured Fine-Grained Selective Access to Outsourced Cloud Data in IoT Environments

With the vast increase in data transmission due to a large number of information collected by devices, data management, and security has been a challenge for organizations. Many data owners (DOs) outsource their data to cloud repositories due to several economic advantages cloud service providers present. However, DOs, after their data are outsourced, do not have complete control of the data, and therefore, external systems are incorporated to manage the data. Several kinds of research refer to the use of encryption techniques to prevent unauthorized access to data but prove to be deficient in providing suitable solutions to the problem. In this article, we propose a secure fine-grain access control system for outsourced data, which supports read and write operations to the data. We make use of an attribute-based encryption (ABE) scheme, which is regarded as a suitable scheme to achieve access control for security and privacy (confidentiality) of outsourced data. This article considers different categories of data users, and make provisions for distinct access roles and permissible actions on the outsourced data with dynamic and efficient policy updates to the corresponding ciphertext in cloud repositories. We adopt blockchain technologies to enhance traceability and visibility to enable control over outsourced data by a DO. The security analysis presented demonstrates that the security properties of the system are not compromised. Results based on extensive experiments illustrate the efficiency and scalability of our system.
Software Quality Prediction: An Investigation Based on Machine Learning

Irrespective of the type of software system that is being developed, producing and delivering high-quality software within the specified time and budget is crucial for many software businesses. The software process model has a major impact on the quality of the overall system - the longer a defect remains in the system undetected, the harder it becomes to fix. However, predicting the quality of the software in the early phases would immensely assist developers in software maintenance and quality assurance activities, and to allocate effort and resources more efficiently. This paper presents an evaluation of eight machine learning techniques in the context of reliability and maintainability. Reliability is investigated as the number of defects in a system and the maintainability is analyzed as the number of changes made in the system. Software metrics are direct reflections of various characteristics of software and are used in our study as the major attributes for training the models for both defect and maintainability prediction. Among the eight different techniques we experimented with, Random Forest provided the best results with an AUC of over 0.8 during both defect and maintenance prediction.
Hubs, Rebalancing and Service Providers in the Lightning Network

Payment channel networks are the most developed proposal to address the well-known issue of blockchain scalability. Currently, the Lightning Network (LN) is the mainstream and most used payment channel network. In a previous work we introduced CLoTH, a payment channel network simulator we developed to analyze capabilities and limitations of such networks. In this work, using CLoTH, we present results of three groups of simulations on a recent snapshot of the LN, aimed to shed a light on the following aspects. Firstly, we investigated how hubs influence the LN performance. Then, we analyzed the effectiveness of two different channel rebalancing approaches, an active and a passive one. Eventually, we studied performance of the LN when a few service-providers nodes receive payments from the other network nodes, which is a typical use case of payment channel networks. We found that the LN is resilient to the removal of hubs, that our passive rebalancing approach reduces of about one fourth the payments failures due to channel unbalancing, and that in the service-providers scenario a consistent part of payments fails because channels directing to the service providers become unbalanced. Our work contributes to prove the strengthen of the Lightning Network when removing hubs and its weakness in the service-provider scenario. In addition, the passive rebalancing approach proposed in this work is a good candidate for the implementation in the Lightning Network protocol to mitigate channel unbalancing.
Orientation- and Scale-Invariant Multi-Vehicle Detection and Tracking from Unmanned Aerial Videos

Along with the advancement of light-weight sensing and processing technologies, unmanned aerial vehicles (UAVs) have recently become popular platforms for intelligent traffic monitoring and control. UAV-mounted cameras can capture traffic-flow videos from various perspectives providing a comprehensive insight into road conditions. To analyze the traffic flow from remotely captured videos, a reliable and accurate vehicle detection-and-tracking approach is required. In this paper, we propose a deep-learning framework for vehicle detection and tracking from UAV videos for monitoring traffic flow in complex road structures. This approach is designed to be invariant to significant orientation and scale variations in the videos. The detection procedure is performed by fine-tuning a state-of-the-art object detector, You Only Look Once (YOLOv3), using several custom-labeled traffic datasets. Vehicle tracking is conducted following a tracking-by-detection paradigm, where deep appearance features are used for vehicle re-identification, and Kalman filtering is used for motion estimation. The proposed methodology is tested on a variety of real videos collected by UAVs under various conditions, e.g., in late afternoons with long vehicle shadows, in dawn with vehicles lights being on, over roundabouts and interchange roads where vehicle directions change considerably, and from various viewpoints where vehicles’ appearance undergo substantial perspective distortions. The proposed tracking-by-detection approach performs efficiently at 11 frames per second on color videos of 2720p resolution. Experiments demonstrated that high detection accuracy could be achieved with an average F1-score of 92.1%. Besides, the tracking technique performs accurately, with an average multiple-object tracking accuracy (MOTA) of 81.3%. The proposed approach also addressed the shortcomings of the state-of-the-art in multi-object tracking regarding frequent identity switching, resulting in a total of only one identity switch over every 305 tracked vehicles.
A Novel Approach for Breast Cancer Investigation and Recognition Using M-Level Set-Based Optimization Functions

Breast cancer identification is the first and foremost step in the journey of proper diagnosis and treatment of this disease. Therefore, many medical examinations and applications are devised and used including different approaches for breast imaging. For medical image analysis, nonparametric algorithm has been used. This work focuses on improving breast cancer recognition in mammogram images using a nonparametric approach based on image pixel intensities (IBCNP). A nonparametric approach is a new expression to deal with uncertainty. In this paper, the nonparametric approach is used to solve the uncertainty in breast cancer detection; uncertainty means no one knows for sure how to detect breast cancer without imaging, which creates an uncertainty for the oncologists. This paper investigates the feasibility of applying the nonparametric approach in mammography (x-ray breast imaging) to detect and hence diagnose and treat breast cancer. Analysing breast images is a very complicated process and requires distinguishing normal and malignant pixel intensities. Maximizing the shared data between image pixel intensities and the desired areas (infected region) is defined as the identification problem, subject to a constraint on the total size of the desired area borders. As part of this research, a comparative study was conducted on the proposed algorithm, Breast Cancer Cells Infestation based on a set of partial differential equations (PDEs), Incoherent Motion in Breast Cancer (IMBC), Global Dynamics of a Breast Cancer Competition Model using five simple differential equations (SDFs), and Estimation of Intensity Using Nonparametric Method (EINP) techniques. These five techniques of medical image segmentation are applied to 156 different mammogram images to obtain the exact measurement of the efficiency of the identification process. This process uses multiple performance metrics such as the overall mammogram image quality (Q), the dice similarity coefficient (DSC), Peak signal-to-noise ratio (PSNR) metric, Haussdorff Distance (HD), and probability of tumor detection (PTD). This paper assumes lack of knowledge of the probability densities in relation to pixel intensities of an image within a given area. It proposes an estimation of non-parametric density that formulates the theoretical data optimization problem by applying curve evolution methods and deriving the correlated gradient flows. It uses level-set techniques to achieve the resulting evolution. The algorithm was applied to 156 sets of different mammogram images from twelve groups with both malignant and/or normal. The experimental findings showed that the proposed algorithm is 94.937% accurately effective in breast cancer detection.
An Experimental Investigation Into the Hash Functions Used in Blockchains

This article investigates how choosing a different hash function might affect the overall performance of a blockchain. We focus on the selection of hash function for Ethereum and carry out extensive experiments to evaluate the performance change after the replacement of hash function. Our findings indicate that some performance metrics of a blockchain might be significantly affected by the hash function used. This suggests that selection of a specific hash function may not be a trivial decision for designing a blockchain.
Tightening Mutual Information Based Bounds on Generalization Error

A mutual information based upper bound on the generalization error of a supervised learning algorithm is derived in this paper. The bound is constructed in terms of the mutual information between each individual training sample and the output of the learning algorithm, which requires weaker conditions on the loss function, but provides a tighter characterization of the generalization error than existing studies. Examples are further provided to demonstrate that the bound derived in this paper is tighter, and has a broader range of applicability. Application to noisy and iterative algorithms, e.g., stochastic gradient Langevin dynamics (SGLD), is also studied, where the constructed bound provides a tighter characterization of the generalization error than existing results.
Symmetric Private Information Retrieval with Mismatched Coded Messages and Randomness

The capacity of symmetric private information retrieval (PIR) with N servers and K messages, each coded by an (N, M)-MDS code has been characterized as C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDS-SPIR</sub> = 1- M/N . A critical assumption for this result is that the randomness is similarly coded by an (N, M)-MDS code, i.e., the code parameters of the messages and randomness are matched. In this work, we are interested in the mismatched case, and as a preliminary result, we establish the capacity of the mismatched MDS coded symmetric PIR (SPIR) problem under an extreme setting, where the messages are coded by an (N, M)-MDS code and the randomness is replicated (i.e., coded by an (N,1)MDS code). The capacity is shown to be C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mis-MDS-SPIR</sub> = (1 - 1/N) · (1+M-1/N (1 + M/N + ⋯ + (M/N) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-2</sup> )) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . Interestingly, C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mis-MDS-SPIR</sub> > C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDS-SPIR</sub> , so mismatched coded randomness (with more redundancy) is strictly beneficial. Further, mismatched SPIR exhibits properties that are similar to PIR.
An Innovative Approach of Applying Knowledge Management in M-Learning Application Development

The development of mobile learning (m-learning) applications is not a new research topic. However, the development of m-learning applications from the perspective of knowledge management (KM) processes is a new research trend that has never been tackled in the existing literature. The main objective of this study is to develop an m-learning application that enables KM processes (acquisition, sharing, application, and protection) to evaluate the students' perceptions and acceptance towards its usage. A pilot study was conducted in two different universities in two developing countries, namely Universiti Malaysia Pahang (UMP) in Malaysia and Al Buraimi University College (BUC) in Oman. A questionnaire survey is used to collect data from 51 IT undergraduate students. The findings provided empirical evidence that the developed m-learning application has raised the students' acceptance towards the application and enhanced their intent to use such applications in the future.
On The Stability of Interpretable Models

Interpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process. Bias in data collection and preparation, or in model's construction may severely affect the accountability of the design process. We conduct an experimental study of the stability of interpretable models with respect to feature selection, instance selection, and model selection. Our conclusions should raise awareness and attention of the scientific community on the need of a stability impact assessment of interpretable models.
Deeper Capsule Network For Complex Data

Capsule Network (CapsNet) addresses the problem of Convolutional Neural Network (CNN) by introducing dynamic routing between capsules. Our work further develops CapsNet in depth and performance. By introducing Convolutional Capsule Layer (Conv-Caps Layer), we deepen CapsNet, which greatly improves the performance. We also propose Capsule Pool (Caps-Pool), a new pooling operation, to reduce the number of parameters. This pooling operation preserves the full representation of features. In our experiment, our DeeperCaps model received so far the strongest result of CapsNet on the bigger dataset (Cifar-10). Our Caps-Pool reduces half of the parameters between layers while maintaining the performance. It also influences the output distribution to be intuitively more reasonable.