Reviewerly/exHarmony huggingface dataset:
Key Opportunities and Challenges of Data Migration in Cloud: Results from a Multivocal Literature Review

Cloud data migration is the procedure of moving information, localhost applications, services, and data to the distributed cloud computing infrastructure. The success of this data migration process is depending on several aspects like planning and impact analysis of existing enterprise systems. One of the most common operations is moving locally stored data in a public cloud computing environment. This paper, through a multivocal literature review, identifies the key advantages and consequences of migrating data into the cloud. There are five different cloud migration strategies and models prescribed to evaluate the performance, identifying security requirements, choosing a cloud provider, calculating the cost, and making any necessary organizational changes. The results of this research paper can give a road map for the data migration journey and can help decision makers towards a safe and productive migration to a cloud computing environment.
Binarization of Degraded Document Images using Convolutional Neural Networks Based on Predicted Two-Channel Images

Due to the poor condition of most of historical documents, binarization is difficult to separate document image background pixels from foreground pixels. This paper proposes Convolutional Neural Networks (CNNs) based on predicted two-channel images in which CNNs are trained to classify the foreground pixels. The promising results from the use of multispectral images for semantic segmentation inspired our efforts to create a novel prediction-based two-channel image. In our method, the original image is binarized by the structural symmetric pixels (SSPs) method, and the two-channel image is constructed from the original image and its binarized image. In order to explore impact of proposed two-channel images as network inputs, we use two popular CNNs architectures, namely SegNet and U-net. The results presented in this work show that our approach fully outperforms SegNet and U-net when trained by the original images and demonstrates competitiveness and robustness compared with state-of-the-art results using the DIBCO database.
Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification

In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner. Therefore, TP-DPL can unify the procedures of salient feature representation and classification. The twin-incoherence constraint on coefficients and features can explicitly ensure high intra-class compactness and inter-class separation over them. TP-DPL also integrates the adaptive weighting to preserve local neighborhood of both coefficients and salient features within each class explicitly. For efficiency, TP-DPL selects the Frobenius-norm and abandons the costly l0/l1-norm for group sparse representation. Another byproduct is that TP-DPL can directly apply the class-specific twin-projective reconstruction residual to compute the label of data. Extensive results on public databases show that TP-DPL can deliver the state-of-the-art performance.
Permutation Strategies for Mining Significant Sequential Patterns

The identification of significant patterns, defined as patterns whose frequency significantly deviates from what is expected under a suitable null model of the data, is a key data mining task with application in several areas. We present PROMISE, an algorithm for identifying significant sequential patterns while guaranteeing that the probability that one or more false discoveries are reported in output (i.e., the Family-Wise Error Rate - FWER) is less than a user-defined threshold. PROMISE employs the Westfall-Young method to correct for multiple hypothesis testing, a more powerful method than the commonly used Bonferroni correction. PROMISE crucially hinges on the generation of (random) permuted datasets with features similar to the input dataset, for which we provide two efficient strategies. We also provide a rigorous analysis of one of such strategies, which is based on a properly defined swap operation, proving a rigorous bound on the number of swaps it requires. The results of our experimental evaluation show that PROMISE is an efficient method that allows the discovery of statistically significant sequential patterns from transactional datasets while properly controlling for false discoveries.
Test coverage criteria for software product line testing: Systematic literature review

In software product line testing (SPLT), test coverage criterion is an important concept, as it provides a means of measuring the extent to which domain testing has been performed and redundant application testing can be avoided based on the test coverage level achieved in domain testing. However, no previous literature reviews on SPLT have addressed test coverage criterion in SPLT. The objectives of this paper are as follows: (1) to clarify the notions of test basis and test coverage criterion for SPLT; (2) to identify the test coverage criteria currently used for SPLT; (3) to investigate how various SPLT aspects, such as the SPLT method, variability implementation mechanism, and variability management approach, affect the choice of test coverage criterion for SPLT; and (4) to analyze the limitations of test coverage criteria currently used for SPLT. This paper conducts a systematic review of test coverage criteria in SPLT with 78 selected studies. We have several findings that can guide the future research on SPLT. One important finding is that choice of test coverage criterion in SPLT is independent from variability implementation mechanism, variability management, SPL approach, and binding time but is dependent on the variability representation used in development artifacts. Another that is easily overlooked is that SPL test coverage criteria with the same test coverage criterion names of single system testing neither adequately convey what should be covered by the test methods applying them, nor can they be more generally regarded as extensions or generalizations for SPLT of their corresponding test coverage criteria of single system testing. This study showed that SPL test coverage criteria should be defined or redefined so that they can clearly deliver the target properties to be satisfied by SPLT.
An autonomous exploration algorithm using environment-robot interacted traversability analysis

Auto-exploration is a task for self-driving robots to explore unknown environments, which becomes much complicated when they move on irregular outdoor terrains. To improve the situation, a new frontier-based exploration algorithm is presented in this paper. It starts from original 3D cloud points of the environment to analyze the traversability of the scanned area, and further provides a reachability map to mark all map grid cells as reachable, dangerous or unknown. Frontier candidates are obtained from the reachable map, then clustered and reduced using an improved K-means. Finally, the target of next exploration step is selected from the frontiers left by evaluating their travel cost. The algorithm is validated on an irregular outdoor terrain and shows the capability for a field robot to explore on an irregular terrain.
A comparison of n-ary simple code and n-ary gray code phase unwrapping in high-speed fringe projection profilometry

Binary defocusing technique has been widely used in fringe projection profilometry for high-speed measurement. This technique projects designed binary patterns with a defocused projector to generate fringes for wrapped phase calculation, and n-ary simple codes (nSC) or n-ary gray codes (nGC) for phase unwrapping. The unwrapped phase reflecting the object surface profile is calculated by a phase-shifting algorithm with nSC/nGC phase unwrapping. However, it remains unclear which phase unwrapping method, nSC or nGC, is more appropriate for high-accuracy and high-speed measurement. This paper comprehensively compares nSC and nGC with the consideration of various factors, including the strategies for error removal, the base n, the step heights and invalid regions on the measured objects, defocusing level and noise level of the system. From simulations and experiments, we conclude that, (i) in general, nSC with a proposed identification and binary classification (IBC) method has a similar result as nGC; (ii) when accuracy is critical, binary simple code (nSC with n = 2) and binary gray code (nGC with n = 2) methods are recommended; (iii) when speed is critical, quaternary simple code (nSC with n = 4) method with continuity/geometry constraints is recommended.
A survey on the service interoperability in cloud computing: Client‐centric and provider‐centric perspectives

Summary The vendor lock‐in is a prominent issue in cloud computing. It is caused by cloud providers who offer proprietary services, which hinders the cloud interoperability. Client‐centric interoperability enables the migration of the data and applications across clouds; it gives the clients control over their workloads and a wider range of service choices. Whereas, provider‐centric interoperability allows the providers to collaborate. Thus, providers, who have spare resources, can lend them to other providers who lack computational or storage capabilities to overcome the limitations of their local resources. In this article, we conduct a survey to differentiate between client‐ and provider‐centric interoperability solutions. We aim to provide an up‐to‐date analysis of the current tendencies and the neglected areas of the cloud interoperability field. Thus, we study the cloud service interoperability evolution through the years. Furthermore, we propose definitions for the intra‐cloud and inter‐cloud interoperability. Moreover, we propose a taxonomy to classify the cloud interoperability approaches into client‐centric and provider‐centric categories. Then, for each category, we classify the approaches based on their interoperability environment into single cloud or interconnected clouds. Finally, we analyze and compare the approaches based on multiple criteria. The study reveals the focus on the client‐centric solutions and the interoperability in interconnected clouds. We notice more interest in the data and application levels interoperability, mainly, in infrastructure as a service model. We also find that client‐centric solutions are, mostly, semantic technologies and brokers. However, provider‐centric solutions are middleware, protocols, and standards. We conclude that a generic cloud service interoperability model is needed.
Artificial intelligence based power consumption estimation of two-phase brushless DC motor according to FEA parametric simulation

In this study, Artificial Neural Networks, Extreme Machine Learning and Support Vector Machine (SVM) are used to estimate power consumption of Brushless DC motor in Unmanned Aerial Vehicle (UAV). Durafly 3648 Brushless DC motor of UAV is modelled with the Finite Element Analysis software. Then it is simulated with Ansys-Rmxprt according to pulse degree, speed and battery voltage of the UAV. The training times and Mean Absolute Percentage Errors (MAPE) of the Artificial Intelligence Techniques (AITs) are calculated for motor input power estimation. The best result among the implemented AITs is achieved with the MAPE of 0.269% by the SVM model. Then the graphical user interface (GUI) is developed to easily obtain information about how efficient the battery can be used, and the flight time of UAVs. Thanks to the proposed GUI software, the input power of the motor can be estimated via input parameters without the long-time consuming simulations.
Optimal Temporal Logic Planning for Multi-Robot Systems in Uncertain Semantic Maps

This paper addresses a multi-robot motion planning problem in probabilistic maps obtained by semantic simultaneous localization and mapping (SLAM). The goal of the robots is to accomplish complex collaborative high level tasks captured by global temporal logic specifications in the presence of uncertainty in the workspace. Specifically, the robots operate in an unknown environment modeled as a semantic map determined by Gaussian distributions over landmark positions and arbitrary discrete distributions over landmark classes. We extend Linear Temporal Logic by including information-based predicates allowing us to incorporate uncertainty and probabilistic satisfaction requirements directly into the task specification. We propose a new highly scalable sampling-based approach that synthesizes paths that satisfy the assigned task specification while minimizing a user-specified motion cost function. Finally, we show that the proposed algorithm is probabilistically complete, asymptotically optimal and supported by convergence rate bounds. We provide extensive simulation results that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale planning tasks.
Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking

Sensor-based sorting is a machine vision application that has found industrial application in various fields. An accept-or-reject task is executed by separating a material stream into two fractions. Current systems use line-scanning sensors, which is convenient as the material is perceived during transportation. However, line-scanning sensors yield a single observation of each object and no information about their movement. Due to a delay between localization and separation, assumptions regarding the location and point in time for separation need to be made based on the prior localization. Hence, it is necessary to ensure that all objects are transported at uniform velocities. This is often a complex and costly solution. In this article, we propose a new method for reliably separating particles at nonuniform velocities. The problem is transferred from a mechanical to an algorithmic level. Our novel advanced image processing approach includes equipping the sorter with an area-scan camera in combination with a real-time multiobject tracking system, which enables predictions of the location of individual objects for separation. For the experimental validation of our approach, we present a modular sorting system, which allows comparing sorting results using a line-scan and area-scan camera. Results show that our approach performs reliable separation and hence increases sorting efficiency.
Integrating Collocation as TF-IDF Enhancement to Improve Classification Accuracy

The motivation of the study is to address the weakness of Term Frequency - Inverse Document Frequency (TF-IDF) in dealing with single terms because single terms can sometimes be vague. That is, a single term when used for indexing, could convey several interpretations. A single term can also be too general, in which, it doesn't have a discriminating power to differentiate terms such as from two individual terms such as "junior" and "college." It is not enough to distinguish "junior college" from "college junior". Thus, this study aims to enhance TF-IDF by integrating collocation as a term feature. The collocated terms are extracted based on the determination of part-of-speech (POS) that forms specific patterns such as adjective + noun, noun + noun, noun + verb, etc. There are three (3) document classifiers which had been considered in this study. These classifiers will be subjected to traditional and modified TF-IDF are RandomForest, MultinomialNB (MultiNB), and SVM. The result of this experiment shows that integrating collocation as part of the enhancement of the TF-IDF process outperforms the traditional TF-IDF by an increase of up to 10 percent.
Hate Speech Detection in Indonesian Language on Instagram Comment Section Using K-Nearest Neighbor Classification Method

Instagram is a social media that is widely used by people in the world, especially Indonesia. Instagram has a feature in delivering opinions in the Instagram photo comment section. Comments given can be positive or negative comments. Negative comments on Instagram include hate speech. Hate speech is one of the big problems and very difficult to overcome by the authorities. Because of that, this research will create a system that will detect hate speech or not on the Instagram comment section with the K-Nearest Neighbor classification method. Accuracy generated from this research is 98.13%, 98% of precision, recall and f1-score using K-Nearest Neighbor with K=3.
Performance of Bifacial Photovoltaic Modules on a Dual-Axis Tracker in a High-Latitude, High-Albedo Environment

Bifacial photovoltaic dual-axis tracker systems have the potential to out-perform other module/mounting configurations at high latitudes, where the reflectivity of snow in winter boosts bifacial performance and the low solar angle-of-incidence favors dual-axis tracking. Two years of empirical data from dual-axis experimental systems in Vermont support this assertion, demonstrating that bifacial modules on a dual-axis tracker produced 14 percent more electricity in a year than their monofacial counterparts and as much as 40 percent during the peak winter months. These bifacial gains are in addition to the estimated 35-40 percent energy gains of a dual-axis tracker relative to a fixed-tilt system. Such findings suggest that bifacial two-axis tracker systems could be economically attractive in northern latitudes, with high-efficiency modules compensating for the trackers' installation and maintenance costs, and future design improvements enabling further performance gains.
Fabricatable Machines

Digital fabrication is changing the way we design and manufacture the objects around us. Digital fabrication machines enable mass-customisation. However, customising the machines themselves requires a high amount of expertise, which prevents even advanced users from taking part in the creation of bespoke fabrication tools. We present Fabricatable Machines, an open-source toolkit for designing custom fabrication machines. We designed a linear motion module, The Fabricatable Axis, that provides robust automated linear motion. The Fabricatable Axis can be resized, adjusted, and fabricated from different materials. Users can build machines by combining multiple axes. We optimised the design of the axis to be manufactured using a CNC mill, with few externally sourced parts. We observed users creating machines including portable milling machines, 3D printers, and pipe inspection robots using the Fabricatable Machines Toolkit.
Monitoring Misuse for Accountable 'Artificial Intelligence as a Service'

AI is increasingly being offered 'as a service' (AIaaS). This entails service providers offering customers access to pre-built AI models and services, for tasks such as object recognition, text translation, text-to-voice conversion, and facial recognition, to name a few. The offerings enable customers to easily integrate a range of powerful AI-driven capabilities into their applications. Customers access these models through the provider's APIs, sending particular data to which models are applied, the results of which returned.
Model-Based Clustering with Measurement or Estimation Errors

Model-based clustering with finite mixture models has become a widely used clustering method. One of the recent implementations is MCLUST. When objects to be clustered are summary statistics, such as regression coefficient estimates, they are naturally associated with estimation errors, whose covariance matrices can often be calculated exactly or approximated using asymptotic theory. This article proposes an extension to Gaussian finite mixture modeling—called MCLUST-ME—that properly accounts for the estimation errors. More specifically, we assume that the distribution of each observation consists of an underlying true component distribution and an independent measurement error distribution. Under this assumption, each unique value of estimation error covariance corresponds to its own classification boundary, which consequently results in a different grouping from MCLUST. Through simulation and application to an RNA-Seq data set, we discovered that under certain circumstances, explicitly, modeling estimation errors, improves clustering performance or provides new insights into the data, compared with when errors are simply ignored, whereas the degree of improvement depends on factors such as the distribution of error covariance matrices.
CFOF

We present a novel notion of outlier, called the Concentration Free Outlier Factor, or CFOF. As a main contribution, we formalize the notion of concentration of outlier scores and theoretically prove that CFOF does not concentrate in the Euclidean space for any arbitrary large dimensionality. To the best of our knowledge, there are no other proposals of data analysis measures related to the Euclidean distance for which it has been provided theoretical evidence that they are immune to the concentration effect. We determine the closed form of the distribution of CFOF scores in arbitrarily large dimensionalities and show that the CFOF score of a point depends on its squared norm standard score and on the kurtosis of the data distribution, thus providing a clear and statistically founded characterization of this notion. Moreover, we leverage this closed form to provide evidence that the definition does not suffer of the hubness problem affecting other measures in high dimensions. We prove that the number of CFOF outliers coming from each cluster is proportional to cluster size and kurtosis, a property that we call semi-locality. We leverage theoretical findings to shed lights on properties of well-known outlier scores. Indeed, we determine that semi-locality characterizes existing reverse nearest neighbor-based outlier definitions, thus clarifying the exact nature of their observed local behavior. We also formally prove that classical distance-based and density-based outliers concentrate both for bounded and unbounded sample sizes and for fixed and variable values of the neighborhood parameter. We introduce the fast-CFOF algorithm for detecting outliers in large high-dimensional dataset. The algorithm has linear cost, supports multi-resolution analysis, and is embarrassingly parallel. Experiments highlight that the technique is able to efficiently process huge datasets and to deal even with large values of the neighborhood parameter, to avoid concentration, and to obtain excellent accuracy.
Quantum Queuing Delay

Queuing delay is an essential topic in the design of quantum networks. This paper introduces a tractable model for analyzing the queuing delay of quantum data, referred to as quantum queuing delay (QQD). The model employs a dynamic programming formalism and accounts for practical aspects such as the finite memory size. Using this model, we develop a cognitive-memory-based policy for memory management and show that this policy can decrease the average queuing delay exponentially with respect to memory size. Such a significant reduction can be traced back to the use of entanglement, a peculiar quantum phenomenon that has no classical counterpart. Numerical results validate the theoretical analysis and demonstrate the near-optimal performance of the developed policy.