Reviewerly/exHarmony huggingface dataset:
Toward All-Optical Layered Encryption: A Feasibility Analysis of Optical Stream Cipher

We study whether the layered encryption and, thus, anonymity of communication can be implemented all-optically, granting ultra-high speed communication and processing. To this end, we evaluate the feasibility of the state-of-the-art all-optical components, including optical linear feedback shift register (oLFSR) and optical XOR (oXOR), for implementing an all-optical stream cipher (oSC). Just like the function of key expansion in today's electronic ciphers, we use pRNGs and oLFSRs to expand short bit sequences of the secret key, thus enabling the transmission and en-/decryption at line rate; oXOR is used for the actual all-optical encryption. Using the principles of information-theoretical entropic security, we evaluate the key uncertainty (entropy and equivocation) and, thus, the computational complexity of various guessing attacks. We also analyze the maximum length of optical key stream and show that oSC can be designed as secure as in today's systems while operating all-optically at higher line rates.
Random Search Enhancement of Incremental Regularized Multiple Hidden Layers ELM

The extreme learning machine (ELM) represents one of the most successful approaches in the field of machine learning recently, especially in solving classification and regression problems. A key advantage of the multiple hidden layers' ELM (MELM) is that the computational time required to train the neural network is significantly lower because it uses random selection and analytical solution, respectively, to determine the weights of the hidden nodes and output nodes. However, due to the use of too many or too few hidden nodes during the training process, the phenomenon of over-fitting or under-fitting may occur in the prediction process. Aiming at the design of MELM neural network architecture, this paper applies the enhanced random search method to the MELM network model and proposes an incremental MELM training algorithm based on the Cholesky decomposition, namely, random search enhancement of incremental regularized MELM (EIR-MELM). The algorithm automatically determines the optimal MELM network structure by increasing the hidden nodes one by one and calculates the output weights by flexibly adopting the Cholesky decomposition method, which effectively reduces the computation burden caused by the incremental process of the hidden layer neurons. However, some hidden nodes added to the network may only have a weak influence on the final output of the network. Adding randomly generated nodes directly to the network only increases the complexity of the neural network structure. Therefore, in the process of adding hidden nodes, EIR-MELM adds a selection phase. According to the principle of structural risk minimization, the optimal node is selected from multiple randomly generated nodes to be added to the network, so that EIR-MELM has a more compact network structure. The experimental researches on the benchmark datasets for classification problems show that EIR-MELM can effectively determine the optimal MELM network structure automatically with high calculation efficiency.
Counting Classical Nodes in Quantum Networks

Quantum networks illustrate the use of connected nodes of quantum systems as the backbone of distributed quantum information processing. When the network nodes are entangled in graph states, such a quantum platform is indispensable to almost all the existing distributed quantum tasks. Unfortunately, real networks unavoidably suffer from noise and technical restrictions, making nodes transit from quantum to classical at worst. Here, we introduce a figure of merit in terms of the number of classical nodes for quantum networks in arbitrary graph states. Such a network property is revealed by exploiting a novel Einstein-Podolsky-Rosen steerability. Experimentally, we demonstrate photonic quantum networks of ${n}_{q}$ quantum nodes and ${n}_{c}$ classical nodes with ${n}_{q}$ up to 6 and ${n}_{c}$ up to 18 using spontaneous parametric down-conversion entanglement sources. We show that the proposed method is faithful in quantifying the classical defects in prepared multiphoton quantum networks. Our results provide novel identification of generic quantum networks and nonclassical correlations in graph states.
Using ArchiMate to model ISO/IEC 29110 standard for very small entities

Paper concentrates on the ISO/IEC 29110 standard aimed at improving systems and software engineering life cycle processes in very small entities, which has been recently developed and started to be implemented worldwide. With the purpose to facilitate development and maintenance of this standard and its implementation in very small entities utilization of ArchiMate is proposed. The ArchiMate is a widely accepted open standard for modeling Enterprise Architecture supported by a variety of modeling tools, which was also successfully used beyond Enterprise Architecture domain. The contribution of this paper lies in (1) the development of the ISO/IEC 29110 Basic Profile Metamodel; (2) the definition of the mapping between the ISO/IEC 29110 Basic Profile Metamodel and ArchiMate; (3) its evaluation using the Bunge-Wand-Weber (BWW) model; (4) the application of the mapping resulting in the development of the ISO/IEC 29110 Basic Profile ArchiMate Model being implemented in a modeling tool that is freely available and can be used by VSEs, assessors and standard developers.
Prior-based Bayesian information criterion

We present a new approach to model selection and Bayes factor determination, based on Laplace expansions (as in BIC), which we call Prior-based Bayes Information Criterion (PBIC). In this approach, the Laplace expansion is only done with the likelihood function, and then a suitable prior distribution is chosen to allow exact computation of the (approximate) marginal likelihood arising from the Laplace approximation and the prior. The result is a closed-form expression similar to BIC, but now involves a term arising from the prior distribution (which BIC ignores) and also incorporates the idea that different parameters can have different effective sample sizes (whereas BIC only allows one overall sample size n). We also consider a modification of PBIC which is more favourable to complex models.
A Bayesian Joint Decorrelation and Despeckling of SAR Imagery

Despeckling of synthetic aperture radar (SAR) is a known research challenge. A novel solution to this problem has been developed and evaluated via an iterative maximum a posterior estimation incorporating a Bayesian joint decorrelation and despeckling based on a correlation model. This model realistically explores the physical correlation process of SAR speckle noise and is determined automatically via Bayesian estimation in the log-Fourier domain. A patchwise computation is used to account for the spatial nonstationarity associated with SAR image data. The proposed approach is compared to the existing despeckling techniques using both simulated and real SAR data, and the experimental results demonstrate the improvement in preserving the structural details while suppressing speckle noise.
A study of wavelet-based denoising and a new shrinkage function for low-dose CT scans

Wavelet transformation is known as a strong method for signal and image processing tasks in the medical applications. We utilized this transformation to reduce the noise in low-dose x-ray computed tomography (CT) images. The CT images had been acquired with 75% less radiation dose compared to the normal dose scans. In this paper we propose a shrinkage function that outperforms the traditional ones and it doesn't require any selected parameters, if it is tuned for the specific region of the patient volume. In addition, the denoising performances of combinations of wavelet orders, decomposition levels, and thresholding methods were investigated. The results revealed the best combination of wavelet order and decomposition level for low dose CT denoising.
GoCity: Code City for Go

Go is a statically typed and compiled language, which has been widely used to develop robust and popular projects. As other systems, these projects change over time. Developers commonly modify the source code to improve the quality or to implement new features. In this context, they use tools and approaches to support software maintenance tasks. However, there is a lack of tools to support Go developers in this process. To address these challenges, we introduce GoCity, a web-based implementation of the CodeCity program visualization metaphor for Go. The tool extracts source code metrics to create a software visualization in a automated way. We also report usage scenarios of GoCity involving three popular Go projects. Finally, we report the feedback of 12 developers about the GoCity of their projects.
Is Self-Admitted Technical Debt a Good Indicator of Architectural Divergences?

Large software systems tend to be highly complex and often contain unaddressed issues that evolve from bad design practices or architectural implementations that drift from definition. These design flaws can originate from quick fixes, hacks or shortcuts to a solution, hence they can be seen as Technical Debt. Recently, new work has focused on studying source code comments that indicate Technical Debt, i.e., Self-Admitted Technical Debt (SATD). However, it is not known if addressing information left by developers in the form source code comments can give insight about the design flaws in a system and have the potential to provide fixes for bad architectural implementations. This paper investigates the possibility of using SATD comments to resolve architectural divergences. We leverage a data set of previously classified SATD comments to trace them to the architectural divergences of a large open source system, namely ArgoUML. We extract its conceptual and concrete architectures based on available design documentation and source code, and contrast both to expose divergences, trace them to SATD comments, and investigate their resolution. We found 7 high-level divergences in ArgoUML and 22 others among its subsystems, observing that merely 4 out of 29 (14%) divergences can be directly traced to SATD. Although using SATD as an indicator of architectural divergences is viable, the effort of doing so is time-intensive, and in general, will not lend to a significant reduction of architectural flaws in a software system.
TECLA: A temperament and psychological type prediction framework from Twitter data

Temperament and Psychological Types can be defined as innate psychological characteristics associated with how we relate with the world, and often influence our study and career choices. Furthermore, understanding these features help us manage conflicts, develop leadership, improve teaching and many other skills. Assigning temperament and psychological types is usually made by filling specific questionnaires. However, it is possible to identify temperamental characteristics from a linguistic and behavioral analysis of social media data from a user. Thus, machine-learning algorithms can be used to learn from a user's social media data and infer his/her behavioral type. This paper initially provides a brief historical review of theories on temperament and then brings a survey of research aimed at predicting temperament and psychological types from social media data. It follows with the proposal of a framework to predict temperament and psychological types from a linguistic and behavioral analysis of Twitter data. The proposed framework infers temperament types following the David Keirsey's model, and psychological types based on the MBTI model. Various data modelling and classifiers are used. The results showed that Random Forests with the LIWC technique can predict with 96.46% of accuracy the Artisan temperament, 92.19% the Guardian temperament, 78.68% the Idealist, and 83.82% the Rational temperament. The MBTI results also showed that Random Forests achieved a better performance with an accuracy of 82.05% for the E/I pair, 88.38% for the S/N pair, 80.57% for the T/F pair, and 78.26% for the J/P pair.
How Stable Are Eclipse Application Framework Internal Interfaces?

Eclipse framework provides two interfaces: stable interfaces (APIs) and unstable interfaces (non-APIs). Despite the non-APIs being discouraged and unsupported, their usage is not uncommon. Previous studies showed that applications using relatively old non-APIs are more likely to be compatible with new releases compared to the ones that used newly introduced non-APIs; that the growth rate of non-APIs is nearly twice as much as that of APIs; and that the promotion of non-API to APIs happens at a slow pace since API providers have no assistance to identify public interface candidates. Motivated by these findings, our main aim was to empirically investigate the entire population (2,380K) of non-APIs to find the non-APIs that remain stable for a long period of time. We employ cross-project clone detection to identify whether nonAPIs introduced in a given Eclipse release remain stable over successive releases. We provide a dataset of 327K stable nonAPI methods that can be used by both Eclipse interface providers as possible candidates of promotion. Instead of promoting nonAPIs which are too fine-grained, we summarized the non-API methods groups in given classes that are stable together and present class-level non-APIs that possible candidates promotion. We have shown that it is possible to predict the stability of a non-API in subsequent Eclipse releases with a precision of ≥ 56%, a recall of ≥ 96% and an AUC of ≥ 92% and an F-measure of ≥ 81%. We have also shown that the metrics of length of a method and number of method parameters in a nonAPI method are very good predictors for the stability of the non-API in successive Eclipse releases. The results provided can help the API providers to estimate a priori how much work could be involved in performing the promotion.
Macro-scale ore-controlling faults revealed by micro-geochemical anomalies

Whereas the mechanism of fluid flow, and thus structural control, linked with mineral deposit formation is quite understood, the specific structures that likely provided controls on mineralization at certain geographic scales are not readily known for a given region unless it is well-explored. This contributes uncertainty in mineral prospectivity analysis in poorly-explored regions (or greenfields). Here, because the spatial distribution of mineral deposits has been postulated to be fractals (i.e., the patterns of these features are self-similar across a range of spatial scales), we show for the first time that micro-geochemical anomalies (as proxies of micro-scale patterns of ore minerals), from few discrete parts of the Sossego iron-oxide copper-gold (IOCG) deposit in the Carajás Mineral Province (CMP) of Brazil, exhibit trends of macro-scale faults that are known to have controlled IOCG mineralization in the CMP. The methodology described here, which led to this novel finding, would help towards detecting mineral exploration targets as well as help towards understanding structural controls on mineralization in greenfields.
Unsupervised Robust Multiple Kernel Learning via Extracting Local and Global Noises

Kernel-based clustering methods can capture the non-linear structure and identify arbitrarily shaped clusters, so they have been widely used in machine learning tasks. Since the performance of kernel methods critically depends on the choices of kernels, multiple kernel learning methods are proposed to alleviate the effort for kernel designing. The conventional multiple kernel learning methods learn a consensus kernel by linearly combining all candidate kernels, whereas ignoring the influence of the noises. To improve the robustness of multiple kernel learning methods, in this paper, we analyze the local and global noises and design regularized terms to characterize them respectively. Then, we propose a novel local and global de-noising multiple kernel learning method which can explicitly extract the local and global noises to recover the clean kernels for multiple kernel learning. After that, a block coordinate descent algorithm is presented to solve the optimization problem. Finally, the extensive experiments on benchmark data sets will demonstrate the effectiveness of the proposed algorithm.
Multiple Kernel-Based Discriminant Analysis via Support Vectors for Dimension Reduction

Kernel-based discriminant analysis is an effective nonlinear mechanism for pattern analysis. Conventional kernel-based discriminant analysis mainly based on a single kernel function may be insufficient when dealing with datasets with complicated geometric structures. A combination of multiple kernels is able to represent the complementary information of the original data from multiple views and thereby improves recognition performance. However, the discriminant analysis methods based on the combination of multiple kernels face the challenges of optimizing the weights of the “base kernels” and the heavy computational burden. To address these challenges, this paper proposes a novel multi-kernel discriminant analysis method based on support vectors (MKDASV) to represent the data structure more effectively by incorporating the between-class and within-class information. First, the multi-kernel SVM algorithm is utilized to obtain the weight of each “base kernel” and the support vectors; and then the criteria of discriminant analysis method are constructed by taking into account both the margin maximizing classification theory of SVM and the expression of the within-class scatter in LDA algorithm; and finally, to effectively reduce the amount of computation, only the support vectors are used as the training samples to participate in the dimensionality reduction operation. The experimental results on six standard databases validated that our proposed method outperformed the other five methods in terms of classification accuracy and the computational efficiency as well.
Containers vs Virtual Machines for Auto-scaling Multi-tier Applications Under Dynamically Increasing Workloads

Multi-tier architecture is widely used to develop large-scale applications. Virtual Machines (VM) are commonly used to deploy and manage multi-tier applications where each tier of the application is deployed on a separate VM instance. However, recent advancements in OS-level virtualization, known as a Container, is compelling to use as an alternate to VM mainly due to it eliminates the need of a guest operating system and ensures a good level of isolation. In this paper, we compare the performance of VMs and Containers to deploy, manage, and auto-scale multi-tier applications under dynamically increasing workloads. We used OpenNebula-based private testbed cloud to provision VMs and Docker Swarm to provision Containers to a multi-tier application. Our experimental evaluation shows that Containers provide comparable performance to serve a large number of concurrent requests whereas a significant reduction in the rejected requests can be achieved. We observed 46.48% and 70.23% fewer request rejections using Container-based deployment and auto-scaling for multi-tier web applications.
Sentiment Analysis of Arabic and English Tweets

Due to the continuous and rapid growth of daily posted data on the social media sites in many different languages, the automated classification of this huge amount of data has become one of the most important tasks for handling, managing, and organizing this huge amount of textual data. There exist many examples of social media sites, but Twitter is considered to be one of the most popular and commonly used, as users are able to communicate with each other, share their opinions, and express their emotions (sentiments) in the form of convenient short blogs using less than 140 words. Accordingly, many companies and organizations may analyze these sentiments in order to evaluate the users’ thoughts, and determine their polarity from the content of the text. For this process, natural language processing techniques, statistics, or machine learning algorithms are being used to identify and extract the sentiment of the text. In practice, many data mining techniques and algorithms are being applied to observe patterns and correlation among that huge amount of data. This paper proposes an efficient approach in handling Tweets, in both Arabic and English languages, with different processing techniques applied. This approach is based on using the Vector Space Model (VSM) to represent text documents and Tweets, and the Term Frequency Inverse Document Frequency (TFIDF) in a term weighting process to generate the feature vector for classification process. The proposed approach has been evaluated using several experiments with different classifiers on five datasets: Decision trees, Naive-Bayes, kNN, Logistic Regression, Perceptron, and Multilayer Perceptron. The experimental results reveal the effectiveness of our proposed approach when comparing classification results with the published work in [1–3].
A Compiler for Agnostic Programming and Deployment of Big Data Analytics on Multiple Platforms

To run proper Big Data Analytics, small and medium enterprises (SMEs) need to acquire expertise, hardware and software, which often translates to relevant initial investments for activities not directly connected to the company's business. To reduce such investments, the TOREADOR project proposes a Big Data Analytics framework which supports users in devising their own Big Data solutions by keeping the inherent costs at a minimum, and leveraging pre-existent knowledge and expertise. Among the objectives of the TOREADOR framework is supporting developers in parallelizing and deploying their Big Data algorithms, in order to develop their own analytics solutions. This paper describes the Code-Based approach, adopted within the TOREADOR framework to parallelize users' algorithms and deploy them on distributed platforms, via the annotation of parallelizable code portions with parallelization primitives. The approach, which relies on the guidance of Parallel Patterns to implement the parallelization, and on Skeletons to automatically build execution and deployment templates, is realized through a source-to-source Compiler, also described in the present paper.