Reviewerly/exHarmony huggingface dataset:
Single image super resolution based on learning features to constrain back projection

Image super-resolution (SR) is an active research point due to its added value for many image processing applications. The classical SR aims to obtain a high resolution (HR) image using multiple low resolution (LR) images. Recently many research works are directed towards obtaining such HR image from a single LR image which is known as single image SR restoration.This paper presents a fast single-image SR approach based on learning the functions that can transfer LR patch into HR features. Then, these features are used to reconstruct the HR image through a process called constrained back-projection. The experimental results show that the proposed approach is capable of providing a high quality super-resolution images.
MARKOV’S PRINCIPLE AND SUBSYSTEMS OF INTUITIONISTIC ANALYSIS

Abstract Using a technique developed by Coquand and Hofmann [3] we verify that adding the analytical form MP 1 : $\forall \alpha (\neg \neg \exists {\rm{x}}\alpha ({\rm{x}}) = 0 \to \exists {\rm{x}}\alpha ({\rm{x}}) = 0)$ of Markov’s Principle does not increase the class of ${\rm{\Pi }}_2^0$ formulas provable in Kleene and Vesley’s formal system for intuitionistic analysis, or in subsystems obtained by omitting or restricting various axiom schemas in specified ways.
Joint spatiograms for multi-modality tracking with online update

Integrating multiple different yet complementary modalities has been proved to be an effective way for boosting tracking performance. Many previous works just consider the fusion of different features from identical image or identical features from images with different spectrum alone, which makes them be quite distinct from each other and be hard to be integrated naturally. In this study, we propose a unified tracking framework to integrate multiple different modalities via innovative use of spatiogram, where the spatiogram is formed by weighting each bin of histogram with the mean and covariance of the locations of the pixels that contribute to that bin. Specifically, each modal target and its candidate are first represented by second-order spatiogram, and their similarity is measured by the weighted Bhattacharayya coefficient. Next, an objective function is built by integrating all modal similarities, then a joint center-shift formula of the target is gained by performing Taylor expansion and gradient minimization on the objective function. Finally, the optimal target location is gained recursively by applying the mean shift procedure. Besides, a fast fuzzy logic system is designed to adaptively adjust the weight of each modality, and a model update scheme based on particle filter is developed to capture the appearance variations. Our framework allows the modality to be original gray of pixel or other extracted feature from single image or different spectral images, and provides the flexibility to arbitrarily add or remove modality. Experimental results on three challenging public datasets demonstrate clearly the robustness and effectiveness of the proposed method.
Managing Structurally Heterogeneous Databases in Software Product Lines

Data variations are prevalent while developing software product lines (SPLs). A SPL enables a software vendor to quickly produce different variants of their software tailored to variations in their clients’ business requirements, conventions, desired feature sets, and deployment environments. In database-backed software, the database of each variant may have a different schema and content, giving rise to numerous data variants. Users often need to query and/or analyze all variants in a SPL simultaneously. For example, a software vendor wants to perform common tests or inquiries over all variants. Unfortunately, there is no systematic approach to managing and querying data variations and users have to use their intuition to perform such tasks, often resorting to repeating a task for each variant. We introduce VDBMS (Variational Database Management System), a system that provides a compact, expressive, and structured representation of variation in relational databases. In contrast to data integration systems that provide a unified representation for all data sources, VDBMS makes variations explicit in both the schema and query. Although variations can make VDBMS queries more complex than plain queries, a strong static type system ensures that all variants of the query are consistent with the corresponding variants of the database. Additionally, variational queries make it possible to compactly represent and efficiently run queries over a huge range of data variations in a single query. This directly supports many tasks that would otherwise be intractable in highly variational database-backed SPLs.
Separable states can be used to realize the fair quantum communication

When Alice and Bob are never entangled with each other, can they share equal amount of information via noiseless channels? When the transfered subsystem B is classical, this basic question concerning communication capacity has a very satisfying answer: the amount of accessible information, as quantified by the classical correlation on B, is always equal. If the subsystem B is in a nonclassical state, the amount of accessible information, like their classical counterparts, is also well quantified by classical correlation, but the similarity ends here: the amount of accessible information from shared states may not be equal, because the subsystem B cannot be accessed perfectly by classical means. In this work, we show how to establish fair quantum communications. Our results give sufficient conditions for fair quantum communications and show that symmetric discord is the essential resource for this task. We further demonstrate that the fair quantum communication is fundamentally the same as the Shannon noisy channel coding theorem.
Communication complexity of byzantine agreement, revisited

As Byzantine Agreement (BA) protocols find application in large-scale decentralized cryptocurrencies, an increasingly important problem is to design BA protocols with improved communication complexity. A few existing works have shown how to achieve subquadratic BA under an adaptive adversary. Intriguingly, they all make a common relaxation about the adaptivity of the attacker, that is, if an honest node sends a message and then gets corrupted in some round, the adversary cannot erase the message that was already sent—henceforth we say that such an adversary cannot perform “after-the-fact removal”. By contrast, many (super-)quadratic BA protocols in the literature can tolerate after-the-fact removal. In this paper, we first prove that disallowing after-the-fact removal is necessary for achieving subquadratic-communication BA. Next, we show new subquadratic binary BA constructions (of course, assuming no after-the-fact removal) that achieve near-optimal resilience and expected constant rounds under standard cryptographic assumptions and a public-key infrastructure (PKI) in both synchronous and partially synchronous settings. In comparison, all known subquadratic protocols make additional strong assumptions such as random oracles or the ability of honest nodes to erase secrets from memory, and even with these strong assumptions, no prior work can achieve the above properties. Lastly, we show that some setup assumption is necessary for achieving subquadratic multicast-based BA.
Addition of feldspart minerals as a substitution material on ceramics clay manufacture

The use of clay minerals as the main raw material for pottery and sand additives has many weaknesses, among others, easy to crack, have high water absorption and low compressive strength. Therefore it is necessary to develop a better method of making traditional ceramics by providing additional materials such as mineral feldspar. Mineral feldspart can be used as a ceramic raw material because it is functional as filler and glass forming phases. Clay and mineral feldspart raw materials were analyzed using XRF method, followed by mixing with the composition of clay soil mixture as much as 65% compared to feldspart by 35%, printed with the shape of cube size 50x50x50mm. Drying in oven with temperature 110°C for 8 hours. Combustion in muflle furnace at temperature variation is 800°C, 900°C, 950°C and 1000°C and holding time is 2 hours, 3 hours, 4 hours and 5 hours. Test of compressive strength and porosity is done to see the physical properties of pottery ceramics and characterization by using XRF and XRD. The result of compressive strength test shows the temperature of 1000°C with 3 hours reaches the optimum value of compressive strength which is 9.52 Mpa, while the porosity value reaches the optimum point at heating temperature 1000°C with time of 5 hours that is equal to 16,19%. Characterization of XRF shows that ceramic pottery contains SiO2 of 64.074%, and Al2O3 of 25.287% according to XRD characterization indicates the dominant phase formed by mineral quartz or silica oxside (SiO2).
An improved electronic medical record system (IEMRS) using oblivious transfer

With the development of cloud service, electronic medical record systems enable hospitals and doctors to capture, transmit and analyze patients’ medical and health information more efficiently, which provides very useful and economic services for patients, medical institutions and government. However, security and privacy issues have not been well addressed in the existing schemes. This article presents an improved electronic medical record system (IEMRS) with two desirable functionalities, where a mutual authentication is used to guarantee authorized requests, and the privacy of a request query is achieved to resist the intruder. Performance analysis demonstrates that the IEMRS scheme outperforms other schemes.
Hybrid image super-resolution using perceptual similarity from pre-trained network

The goal of super-resolution (SR) is to recover a high-resolution (HR) image from its corresponding low-resolution (LR) image. It is an ill-posed problem. Most recent methods are based on external training data. They can reconstruct pleasing HR results, especially when the input patch has a similar counterpart within the training dataset. Other methods are driven by self-similarity and are called internal methods. They can produce visually plausible HR images when the input images contain abundant regular structures. In this paper, we propose a hybrid method for image SR that exploits the complementary advantages of external and internal SR methods. Each input LR patch is first super-resolved using convolutional neural network (CNN) for external SR and self-similarity for internal SR. Then, we calculate the perceptual similarity between the feature representations from the pre-trained VGG network to learn an adaptive weight. Finally, our algorithm automatically selects the optimal method on the basis of the calculated adaptive weight. The experimental results of our visual and quantitative evaluations verify the effectiveness of the proposed method, by comparing it with state-of-the-art methods.
Dual Approaches for Elliptic Hough Transform: Eccentricity/Orientation vs Center Based

Ellipse matching is the process of extracting (detecting and fitting) elliptic shapes from digital images. This process typically requires the determination of 5 parameters, which can be obtained by using an Elliptic Hough Transform (EHT) algorithm. In this paper, we focus on Elliptic Hough Transform (EHT) algorithms based on two edge points and their associated image gradients. For this set-up, it is common to first reduce the dimension of the 5D EHT by means of some geometrical observations, and then apply a simpler HT. We present an alternative approach, with its corresponding algebraic framework, based on the pencil of bi-tangent conics, expressed in two dual forms: the point or the tangential forms. We show that, for both forms, the locus of the ellipse parameters is a line in a 5D space. With our framework, we can split the EHT into two steps. The first step accumulates 2D lines, which are computed from planar projections of the parameter locus (5D line). The second part back-projects the peak of the 2D accumulator into the 5D space, to obtain the three remaining parameters that we then accumulate in a 3D histogram, possibly represented as three separated 1D histograms. For the point equation, the first step extracts parameters related to the ellipse orientation and eccentricity, while the remaining parameters are related to the center and a sizing parameter of the ellipse. For the tangential equation, the first step is the known center extraction algorithm, while the remaining parameters are related to the ellipse half-axes and orientation.
Do We Need (Digital) Archeology Literacy?

The field of archeology with its traditional and digital environment presents a complex field that is not only influenced by the advancement of technology but also dependent on the cultural context. Although technology made a huge difference with the time needed for excavations and data gathering it has created a whole array of the new challenges. As new challenges are created, new solutions required. The question of digital archeology literacy has emerged as one of the solutions. This pilot study was conducted with field archeologists in order to identify their positions in digital archeology. The results show that there were no digital archeologists in Croatia. The majority of archaeologist were still in the digitization period, juggling between traditional and digital archeology. They are still struggling with the digital technology and the abundance of data created with it. The lack of data literacy and information management as well as the emergence of digital autodidactics show that there was a lack of structural integration of information literacy in the education of digital archeologists.
DVSNet: deep variance‐stabilised network robust to spatially variant characteristics in imaging

In real imaging systems, unwanted noises or artefacts can be caused by an image reconstruction error during photon-to-digital conversion. The generated noises typically tend to have spatially variant characteristics in an acquired image due to their signal dependencies. The noise characteristics via a preliminary study are analytically introduced and an image restoration scheme based on deep variance-stabilised network is proposed. Specifically, to improve the robustness of restoration performance to the noise properties, variance-stabilising transformation and binning priors are properly combined with a deep neural network as a layer structure particularly designed for denoising. Experimental results show the suggested network model outperforms existing state-of-the-art denoising methods based on convolutional neural network.
Secure Quantum Data Communications Using Classical Keying Material

We put together recent results on quantum cryptography and random generation of quantum operators. In a synthetic way, we review the work on quantum message authentication by Aharonov et al. and Broadbent and Wainewright. We also outline the work on asymmetric and symmetric-key encryption of Alagic et al. and St-Jules. Quantum operators, i.e., Cliffords and Paulis, play the role of keys in their work. Using the work of Koenig and Smolin on the generation of symplectics, we examine how classical key material, i.e., classical bits, can mapped to quantum operators. We propose a classical key mapping to quantum operators used in quantum cryptography. It is a classical cryptography interface to quantum data cryptography. The main advantage is that classical random key generation techniques can be used to produce keys for quantum data cryptography.
Logics for Belief as Maximally Plausible Possibility

We consider a basic logic with two primitive uni-modal operators: one for certainty and the other for plausibility. The former is assumed to be a normal operator (corresponding—semantically—to a binary Kripke relation), while the latter is merely a classical operator (corresponding—semantically—to a neighborhood function). We then define belief, interpreted as “maximally plausible possibility”, in terms of these two notions: the agent believes $$\phi $$ if (1) she cannot rule out $$\phi $$ (that is, it is not the case that she is certain that $$\lnot \phi $$ ), (2) she judges $$\phi $$ to be plausible and (3) she does not judge $$\lnot \phi $$ to be plausible. We consider four interaction properties between certainty and plausibility and study how these properties translate into properties of belief (e.g. positive and negative introspection and their converses). We then prove that all the logics considered are minimal logics for the highlighted theorems. We also consider a number of possible interpretations of plausibility, identify the corresponding logics and show that some notions considered in the literature are special cases of our framework.
Validating Partial Configurations of Product Lines

Configuring a new variant of a product line is not always a one-time task. In some cases, many stakeholders are involved in the configuration process. This is needed for example, if different stakeholders are responsible for different parts of the product line and they are not allowed to see specific parts of the product line already configured in previous steps. Thus, a partial derivation can be performed, where a part of the configuration process can be done by some stakeholders, while finishing the rest of the configuration process can be achieved by others.
Smart Social Development Key for Smart African Cities

This chapter focuses on the Social Development dimensions of smart cities, composed of elements of health and education. Healthy workers are more productive, and bring greater income to families and higher levels of economic growth for nations, and, in turn, enhance smart economy. First the chapter focuses on health considering that a healthy population is critical to realizing any social and economic development. Then the chapter concentrates on Education, which is critical to meeting the challenges of smart city, as it connects people to new approaches, solutions and technologies that enable them to identify, clarify and tackle local and global problems. When education and health are combined, undoubtedly they contribute significantly to human development. In both these critical dimensions, African cities have made significant progress during these past twenty years, and continue to do so as we progress through the 21st century.
Failure Modeling of a Propulsion Subsystem: Unsupervised and Semi-Supervised Approaches to Anomaly Detection

In this work, the sensor data related to a diesel engine system and specifically its turbocharger subsystem were analyzed. An incident where the turbocharger seized was recorded by dozens of standard turbocharger-related sensors. By training models to distinguish between normal healthy operating conditions and deteriorated conditions, there is an opportunity to develop prognostic and predictive tools to ideally help prevent a similar occurrence in the future. Analysis of this event provides an opportunity to identify changes in equipment indicators with a known outcome. A number of data analysis tools were used to characterize the healthy and deteriorated states of the turbocharger system, including various supervised classification as well as semi-supervised and unsupervised anomaly detection techniques. The leader clustering algorithm was also implemented to reduce the amount of data to train and develop the models. This paper describes the results of this modeling process, validated by testing on healthy data from the same propulsion system and a second distinct one. Although this problem posed challenges due to the severely imbalanced class distribution, the supervised classifiers, in particular Support Vector Machine (SVM) and Random Forest (RF), performed very well in all metrics while the unsupervised anomaly detection models achieved near-perfect accuracy for identifying healthy turbocharger states.
Deep Neural Networks and Explainable Machine Learning

From a general perspective, the most impressive results [1, 6] in Machine Learning have been recently obtained via black-box models, being Deep Neural Networks (DNNs) the major player in the game.
Combined Preference Ranking Algorithm for Comparing and Initial Ranking of Cloud Services

Background: With the immense significance of cloud computing over the decade, different IT companies offer varieties of cloud services. Objective: The selection of cloud services from the expanding range of Cloud Service Providers (CSPs) makes it difficult for the Cloud Consumers (CCs) to choose a CSP based on their preferences. Methods: In this context, this paper proposes an efficient trust management architecture for cloud service selection and put forward Combined Preference Ranking Algorithm (CPRA) for initial ranking of CSPs and their services before doing any transaction in the past based on CCs requirements. Results: The proposed trust management architecture prompts the CSPs to improve the Quality of Service (QoS) by adhering to Service Level Agreement (SLA). Conclusion: The experimental results show that compared with other ranking approaches CPRA generates the accurate ranking list of CSPs with minimal execution time.
Coherent reconstruction of pump beams through recombination of entangled photon pairs

In theoretical and experimental work, we consider the process in which an entangled photon pair recombines into a single photon in a nonlinear crystal, with particular interest in the amplitude distribution and coherence properties of the up-converted light. It is found that the up-converted state has perfect mutual coherence with the pump that originally produced the photon pair in another nonlinear crystal and that the amplitude has the spatiotemporal structure of the original pump mode, filtered by a linear transfer function. These conclusions are drawn from leading-order calculations performed in the interaction picture, with the multimodal nature of all fields being fully accounted for. In our experimental results, we observe the spatial similarity between the up-converted light and the original pump mode; their mutual coherence is demonstrated through observation of fringes with near-unit visibility. The effects studied here have potential applications in quantum information processing and also confirm that the principle of path indistinguishability holds even under unusually extreme conditions.
An Approach to Generate Virtual Tutors for Game Programming Classes

Nowadays, researchers are looking for ways to adapt education to include technology and more engaging methods. For computer science courses, the use of problem-based learning, virtual tutors, learning style personalization and game programming are possible ways to achieve this goal. In this context, this work proposes and evaluates an approach to generate different kinds of virtual tutors (with various levels of interaction) for problem-based game programming classes. The system was evaluated in an experiment where two types of virtual tutors were proposed and generated considering two virtual game programming classes. Results showed that the use of virtual tutors promoted efficient learning and that the availability of different kinds of tutors can optimize individual engagement.
Exploiting the Power of Group Differences: Using Patterns to Solve Data Analysis Problems

This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differenc