Reviewerly/exHarmony huggingface dataset:
Enforcing Access Control in Distributed Version Control Systems

Version control systems (VCS), including central VCS (CVCS) and distributed VCS (DVCS), are widely adopted to manage the changes to various types of data. Unlike the CVCS where all the entities obtain the data from the server and the access control is enforced with the cooperation of the server, each entity in the DVCS stores the entire repository, obtains the repository shared by any entity and is free to share its own repository. Therefore, existing access control schemes for CVCS are not suitable for DVCS. In this paper, we present a distributed access control scheme (Disac) for DVCS. Disac makes each entity have the whole control on its data, while the access control is enforced at each entity independently. We adopt Attribute-based Encryption (ABE) and Attribute-based Signature (ABS) to achieve the read and write permission control. The analysis of the Git client demonstrates that Disac is easy to be integrated.
A study on performance of MHDA in training MLPs

Purpose In recent years, the application of metaheuristics in training neural network models has gained significance due to the drawbacks of deterministic algorithms. This paper aims to propose the use of a recently developed “memory based hybrid dragonfly algorithm” (MHDA) for training multi-layer perceptron (MLP) model by finding the optimal set of weight and biases. Design/methodology/approach The efficiency of MHDA in training MLPs is evaluated by applying it to classification and approximation benchmark data sets. Performance comparison between MHDA and other training algorithms is carried out and the significance of results is proved by statistical methods. The computational complexity of MHDA trained MLP is estimated. Findings Simulation result shows that MHDA can effectively find the near optimum set of weight and biases at a higher convergence rate when compared to other training algorithms. Originality/value This paper presents MHDA as an alternative optimization algorithm for training MLP. MHDA can effectively optimize set of weight and biases and can be a potential trainer for MLPs.
A Scalable Framework for Data-Driven Subspace Representation and Clustering

This paper considers the problem of subspace clustering which segments data samples into their underlying subspaces. While existing subspace clustering algorithms have been successfully applied to various problems, they are not applicable for large-scale or streaming data due to their expensive computational cost. As a remedy, we propose a unified scalable pipeline to reduce the complexity of all sub-tasks in subspace clustering. We first present a robust incremental summary representation, assuming that a subspace can be represented by sparse factors. Based on the summary representation, we propose a fully scalable learning pipeline by integrating the affinity learning task with post-processing and spectral clustering, such that the overall time complexity is linear in the number of samples. Moreover, the proposed framework is integrated with kernel methods for nonlinear subspace clustering. An extensive set of experimental studies demonstrate that the proposed framework gives an order-of-magnitude speed-up over existing subspace clustering baselines with competitive clustering performance.
Displaying Time Series, Spatial, and Space-Time Data with R, 2nd ed

"Displaying Time Series, Spatial, and Space-Time Data with R, 2nd ed." The American Statistician, 73(3), pp. 310–311
Real-Time Grasp Type Recognition Using Leap Motion Controller

The recognition of grasp type is essential for a more detailed analysis of human action. In this paper, we propose a novel method for real-time grasp type recognition using Leap motion controller (LMC). Our proposal is based on the tracking data provided by the LMC sensor and a series of feature descriptors are introduced and extracted from LMC data. Combining the feature descriptors of relative positions of thumb, finger joint angles and finger directions lead to the best representation of the arrangement of the fingers. And then the grasp type classification can be achieved by using a SVM classifier. An experimental study of our approach is addressed and we show that recognition rate could be improved. The current implementation is also can satisfy the real-time requirements.
Two Datasets of Questions and Answers for Studying the Development of Cross-Platform Mobile Applications using Xamarin Framework

A cross-platform mobile application is an application that runs on multiple mobile platforms (Android, iOS). Several frameworks have been proposed to simplify the development of cross-platform mobile applications and, therefore, to reduce development and maintenance costs. Between them, cross-compiler mobile development frameworks, such as Xamarin from Microsoft, transform the application's code written in intermediate (aka non-native) language to native code for each target platform (Android, iOS). However, to our best knowledge, there is not much research about the advantages and disadvantages of the use of those frameworks during the development and maintenance phases of mobile applications. The objective of this paper is to present two datasets of questions and answers (Q&A) related to the development of mobile applications using Xamarin. Our datasets have 85,908 questions mined from the Xamarin Forum and 44,434 from Stack Overflow. To show their usefulness, we present two experiments. First, we replicate a study for discovering the main discussion topics of Xamarin development present in the two datasets using Latent Dirichlet Allocation (LDA), and we compare them with topics previously reported about mobile development. Between the main topics discovered from those questions, we found that some of them are exclusively related to Xamarin and Microsoft technologies such as the design pattern 'MVVM'. Secondly, we aim at improving the official Xamarin documentation, which does not provide a complete description of the error codes that the Xamarin platform throws during development. We use our datasets of Q&A for complementing the error codes with information mined from accepted answers. We could complement the documentation of 23,9% of the error codes.
A Video Post-Filter Deblocking Method Based on Temporal Boosting Residual Networks

Block-based hybrid coding is widely used in video compression. As the bit rate decreases, the quantization becomes lossy resulting in unacceptable blocking artifacts. Although advanced video encoder with loop filter has achieved promising results, the problem still remains unsolved. Most of the previous solutions regard a video sequence as a group of independent frames, without considering their temporal relationships. To address the above issue, this paper designs a temporal boosting residual network aiming to integrate the temporal information and the structural information. And the information is represented as the residual values of previous frames which are extracted by the deep residual network. The entire framework is a cascading architecture that implies a coarse-to-fine processing. The proposed solution does not modify any module of the codec, it only takes the lossy frames as input, and outputs the enhanced frames, which is a typical end-to-end mapping. Experimental results validate that our framework has achieved 0.6-1.0 dB improvement on average based on HEVC software and outperformed the state-of-the-art methods in both objective and perceptual quality.
Usability testing of SOLAP for Indonesia agricultural commodity

Spatial Online Analytical Processing (SOLAP) for Indonesia Agricultural Commodities has successfully developed in our previous study. The application is integrated into a data warehouse that manages historical agricultural commodities data in four sectors: food crop, horticulture, plantation, and livestock. The main features of SOLAP are providing historical data as the results of roll up and drill down operation and visualization of the data in crosstab tables, graphs, and map. In order to evaluate the SOLAP implementation, this study conducted usability testing by involving respondents as potential users of SOLAP. We use the Post-Study System Usability Questionnaire (PSSUQ) which tests the system based on efficiency, effectivity, and user's satisfaction. There are three testing aspects in PSSUQ, namely system usefulness, information quality, and interface quality with the Likert scale from 1 (low) to 7 (high). The measurement was done by involving 13 respondents who are postgraduate students of Bogor Agricultural University (IPB), researchers in Agrarian Study Center IPB, data analysis and data management staff of Data and Information Center, Ministry of Agriculture Indonesia. The results show that SOLAP for Indonesia Agricultural Commodities has high usability measures which reach 5.424. The testing results of each aspect are system usefulness of 5.54, information quality of 5.258, and interface quality of 5.433. In addition to PSSUQ, this study performed usability testing based on tasks completion. The result shows that the respondents are able to complete the OLAP operations and data visualization in an acceptable time.
Permanent Connectivity: From Modes of Restrictions to Strategies of Resistance

The current level of permanent connectivity can be conceptualized as a new dispositif of communication defined by a new status of individual integration into the technological infrastructures of digital networks. On the one hand, new potentials and options for everyday life are offered at the level of digital connectivities. At the same time, numerous risks and disruptions are arising out of the techno-economic impact forces of the Internet. This leads to various imbalances, like new power structures of surveillance or erosions of privacy, impelling us to develop different ways of integrating digital technologies into everyday life. Against this background we suggest adopting Hall’s model of encoding/decoding of communication within the context of digital network structures, in order to differentiate between various modes of network ‘behavior’—reaching from unreflected and full adoption of power structures to modes of rejection of and resistance to network structures in different ways. In most of the cases, we observed that it is challenging for users to develop new ‘technologies of the self’ to strengthen their skills and literacies to meet the dominant impact forces in network structures. In addition, the ethical implications of current developments are considerable and require concepts of digital ethics and resilience to be further developed in digital network environments.
A Methodology for Multi-goal Trajectory Planning in Welding

In this paper, a methodology is proposed for multi-goal trajectory planning in welding, which is divided into effective movements and supporting movements. For effective movements, four objective functions are defined to describe the task requirements. A non-dominated sorting genetic algorithm (NSGA-II) is used to get the optimum solution. For supporting movements, the classical algorithm RRT-Connect is adopted to find a minimum-time trajectory and a smoothing algorithm is proposed to remove the redundant vertices. A simulation is presented to show that the proposed algorithms are effective and an experiment is conducted to illustrate that the trajectories meet the welding requirements.
De–randomized Meta-Differential Evolution for Calculating and Predicting Glucose Levels

A physiological model improves delivered healthcare, when constructing a medical device. Such a model comprises a number of parameters. While an analytical method determines model parameters, an evolutionary algorithm can improve them further. As evolutionary algorithms were designed on top of random-number generators, their results are not deterministic. This raises a concern about their applicability to medical devices. Medical-device algorithm must produce an output with a minimum guaranteed accuracy. Therefore, we applied de-randomized sequences to Meta-Differential Evolution instead of using a random-number generator. Eventually, we designed an optimization method based on zooming with derandomized sequences as an alternative to the Meta-Differential Evolution. As the experimental setup, we predicted glucose-level signal to cover a blind window of glucose-monitoring signal that results from a physiological lag in glucose transportation. Completely de-randomized differential evolution exhibited the same accuracy and precision as completely non-deterministic differential evolution. They produced 93% of glucose levels with relative error less than or equal to 15%.
Representation Learning and Dissimilarity for Writer Identification

Writer identification by using manuscripts became a very important research topic in forensic analysis of documents. That is because the writing can be considered as an identifying characteristic of a person. By analyzing the challenges and the resources available for the research development in this field of investigation, one can find different databases of writers manuscripts. In this work, we aim to evaluate the performance of deep learning techniques in the process of writer identification by using manuscripts. For this, a convolutional neural network (CNN) was developed to address the writer identification task. We have also evaluated the performance of features obtained with CNN when submitted to a support vector machine (SVM) classifier, considering the traditional classification approach. To assert the objectives of this proposal, CVL and BFL databases were used. The experiments were conducted by using a texture generation approach, starting from the original documents. Feature learning was accomplished using CNN on the texture obtained from the manuscripts. Finally, we have also evaluated the impact of the classification using both classification scenarios with or without using the dissimilarity approach. The best results were achieved using the SVM classifier and dissimilarity approach on the features obtained with CNN. The performance demonstrated the robustness of the proposed method with performance similar or superior to that described in the literature.
LOGIC THRESHOLD FOR MICRORING RESONATOR-BASED BDD CIRCUITS: PHYSICAL AND OPERATIONAL ANALYSES

Moore’s Law has been the fuel of expansive innovation in computing. The chip industry kept the Moore’s law extant for almost four decades. However, the halt of the rapid progress of the silicon technology is incipient by reason of the physical limitations. Emerging computing proposals suggest several alternatives to current computing paradigms and technologybases. The photonic circuitry is one of the most promising candidates with its high operation speed, energy efficient passive components, low crosstalk and appropriateness for parallel computation. Among various approaches to photonic logic, microring resonator-based Binary-Decision Diagram (BDD) architectures have a special place due to their small circuit footprint. However, the physical limitations imposed on their logic implementation has not been studied in depth to enable design of efficient circuits. In this paper, we study the physical structure and operational details of a microring resonatorbased Half-Adder (HA) circuit and outline the conditions under which the performance and accuracy of information processing is compromised due to its physical characteristics. Our analyses significantly contribute to determining key physical features and operations concerning logic implementation of microring resonator based BDD HA, which informs the future design and operational optimization of the microring resonator-based BDD logic circuits.
A Novel Solution to the Curse of Dimensionality in Using KNNs for Image Classification

k-Nearest Neighbors (KNN) is one of the simplest and widely used algorithms in Machine Learning applications such as Image Classification. Being based on the Euclidean distance the algorithm is quite simple and effective in most cases. However, it suffers from the problem of The Curse of Dimensionality as the Euclidean distance becomes meaningless when the dimension of data becomes significantly high. In this paper we present a novel solution to this problem by making use of the Convolutional Neural Network (CNN) which can extract the most important features automatically from the images. These features extracted by the CNN are of reduced dimensions and can effectively be used by the KNN to recognize the images. results and comparisons show that the proposed method is also seen to reduce the time taken for testing while retaining high accuracy. proposed technique achieved an accuracy of 96.92% on MNIST, 85.09% on Fashion MNIST and 95.17% on the A-Z Alphabets databases respectively.
Architecture Modeling of Industrial IoT Systems Using Data Distribution Service UML Profile

The adoption of Internet of Things (IoT) in the industrial sector, that is the IIoT, has led to a dramatic increase in the volume of data that is usually distributed over multiple devices. To realize the distributed execution and management of IIoT systems, various requirements and quality factors must be satisfied. To reduce the effort for developing IIoT-based systems, a middlewareMiddleware seems to be a feasible solution. A middleware that is directly related to data-intensive systems in which quality of service parameters are explicitly considered is the Data Distribution ServiceDistribution service (DDSDDS ) software. The DDS has been applied for the development of high-performance distributed systems such as in the defense, finance, automotive, and simulation domains. Yet, for modeling the DDS-based IIoT systems, the specific modeling abstractions are missing which impedes the overall design. To overcome this problem, we provide a UMLUniversal Modelling Language (UML) DDS profile that can be used for modeling the architecture of DDS-based IIoT systems. Along with the profile, we propose a systematic method for applying the profile. We illustrate the application of the profile for modeling the architecture of a smart traffic systemSmart Traffic System (STS) .
Sentiment classification improvement using semantically enriched information

The emergence of new and challenging text mining applications is demanding the development of novel text processing and knowledge extraction techniques. One important challenge of text mining is the proper treatment of text meaning, which may be addressed by incorporating different types of information (e.g., syntactic or semantic) into the text representation model. Sentiment classification is one of the challenging text mining applications. It may be considered more complex than the traditional topic classification since, although sentiment words are important, they may not be enough to correctly classify the sentiment expressed in a document. In this work, we propose a novel and straightforward method to improve sentiment classification performance, with the use of semantically enriched information derived from domain expressions. We also propose a superior scheme for generating these expressions. We conducted an experimental evaluation applying different classification algorithms to three datasets composed by reviews of different products and services. The results indicate that the proposed method enables the improvement of classification accuracy when dealing with reviews of a narrow domain.
A Feature-Based Approach for Light Field Video Enhancement

Light field imaging is increasingly spreading and its applications are reaching a larger number of users. The quality of the rendered content as perceived by the viewers is a key factor for the future development of this technology. Many factors can cause a degradation of the Light Field quality, from data acquisition/creation to the final rendering. To increase the perceived quality of the Light Field content, as usually performed for 2D and 3D content, processing techniques can be applied. In this work, an approach for enhancing Light Field videos is presented. The proposed approach is based on the characteristics of the human visual system and relies on the estimation of depth, motion and saliency. Those features guide a selective enhancement that is implemented in the Laguerre-Gauss Transform domain. The performed tests show the effectiveness of the proposed method.
Parallelization of Algorithms for Mining Data from Distributed Sources

We suggest an approach to optimize data mining in modern applications that work on distributed data. We formally transform a high-level functional representation of a data-mining algorithm into a parallel implementation that performs as much as possible computations locally at the data sources, rather than accumulating all data for processing at a central location as in the traditional MapReduce approach. Our approach avoids the main disadvantages of the state-of-the-art MapReduce frameworks in the context of distributed data: increased run time, high network traffic, and an unauthorized access to data. We use the popular data-mining algorithm – Naive Bayes – for illustrating our approach and evaluating it experimentally. Our experiments confirm that the implementation of Naive Bayes developed by using our approach significantly outperforms the traditional MapReduce-based implementation regarding the run time and the network traffic.
Maximum Physically Consistent Trajectories

Trajectories are usually collected with physical sensors, which are prone to errors and cause outliers in the data. We aim to identify such outliers via the physical properties of the tracked entity, that is, we consider its physical possibility to visit combinations of measurements. We describe optimal algorithms to compute maximum subsequences of measurements that are consistent with (simplified) physics models. Our results are output-sensitive with respect to the number k of outliers in a trajectory of n measurements. Specifically, we describe an O(n log n log2 k) time algorithm for 2D trajectories using a model with unbounded acceleration but bounded velocity, and an O(nk) time algorithm for any model where consistency is "concatenable": a consistent subsequence that ends where another begins together form a consistent sequence. We also consider acceleration-bounded models which are not concatenable. We show how to compute the maximum subsequence for such models in O(nk2 log k) time, under appropriate realism conditions. Finally, we experimentally explore the performance of our algorithms on several large real-world sets of trajectories. Our experiments show that we are generally able to retain larger fractions of noisy trajectories than previous work and simpler greedy approaches. We also observe that the speed-bounded model may in practice approximate the acceleration-bounded model quite well, though we observed some variation between datasets.
Self-Paced Subspace Clustering

Subspace clustering aims to segment data sampled from a union of subspaces in visual data tasks. Structured Sparse Subspace Clustering (SSSC) model is a unified optimization framework, which proves successful in learning both the self representation of the data and their subspace segmentation. However, SSSC involves solving non-convex subproblems and hence it may be stuck into bad local minima such that clustering performance degrades. In this paper, we propose a self-paced subspace clustering algorithm to tackle this problem, which learns subspace segmentation of data by progressing from 'easy' to 'complex' examples under a novel self-paced regularizer. Experiments on the real-world human face datasets verify the effectiveness of the proposed algorithm.
QR code’s maximum scanning distance investigation

The present paper discuses dependences between QR code size, distance, angle of view, type (printed/screen displayed), content of the code, and resolution of the scanning device. Experiments were made with change of each of the mentioned factors, the results were summarized. Factors such as luminance change and technical differences between scanning devices other than resolution are not included.