Reviewerly/exHarmony huggingface dataset:
FM-CF: A framework for classifying feature model building approaches

Software product line engineering has emerged as a prominent software engineering paradigm, as it comprises a set of core assets sharing functionality and quality attributes. Feature modelling is one of the most frequently used techniques for modelling the variability within a software product line. There are several proposals for building Feature Models which rely on semi-automated or fully automated means. Unfortunately, automatic feature model construction has been addressed from different viewpoints, so it is not easy to know which is the best approach for automating the building of variability models. In fact, there is no clarity regarding common elements, and the main differences that characterise such approaches. Additionally, the wide variety of terms used to refer to the process of building a Feature Model (e.g. synthesis, location, re-engineering, and weaving) means that approaches are varied and very heterogeneous, making them complex to understand and classify. This paper introduces FM-CF, which is a Conceptual experience-based Framework for classifying approaches for the automatic building of Feature Models. The framework considers a set of categories mainly focused on characterising some aspects, such as input sources, methods and techniques, results, and types of evaluation. A literature review of (semi-) automated Feature Model construction was performed to identify approaches for building Feature Models by (semi-)automatic means, and the main terms used by those approaches. Then the completeness of the framework was evaluated by mapping the set of dimensions and their items, and the terms extracted from the literature. The conceptual framework provides guidance to researchers for choosing the appropriate aspects with which to build Feature Models, and helps in the understanding and clarification of the proposed approaches.
Improved Efficiency of a Linearly Homomorphic Cryptosystem

We present an extended version of the Castagnos and Laguillaumie linearly homomorphic cryptosystem [5] in which the non-maximal imaginary quadratic order is allowed to have conductor equal to a product of prime powers as opposed to a single prime. Numerical results obtained with an optimized C implementation demonstrate that this variation improves performance when large messages and exponents are used. When compared to the cryptosystems of Paillier [11] and Bresson et al. [3] at the same security levels, the basic version of Castagnos and Laguillaumie is the fastest at high security levels for small messages.
Relations between Bohl exponents and general exponent of discrete linear time-varying systems

In the paper, properties of the upper Bohl exponents and senior upper general exponent of discrete linear time-varying systems are investigated. The relation of these exponents to uniform exponential stability is discussed. Moreover, an example of system, which is not uniformly exponentially stable but each trajectory tends uniformly and exponentially to zero is provided.
Intelligent Scheduling for Parallel Jobs in Big Data Processing Systems

The explosive growth of data in various scientific, industrial, and business domains necessitates the use of big data processing systems, such as Hadoop, which are typically deployed in a physical or cloud-based cluster shared by many users running parallel jobs. As the user population and application scale increase, such systems are expanded from time to time with an addition of new nodes of different types, making the cluster highly heterogeneous. Job scheduling in such systems largely determines the performance of big data applications and remains to be a challenging problem. In this paper, we formulate a generic job scheduling problem for parallel processing of big data in heterogeneous clusters and design a k-means based task scheduling algorithm, referred to as KMTS. Simulation results show that KMTS improves execution performance by 25% and 30% on average in single job scheduling and parallel job scheduling, respectively, over existing methods. The performance superiority is also confirmed by real experiments in high-performance computing environments.
Robust Gridless Sound Field Decomposition Based on Structured Reciprocity Gap Functional in Spherical Harmonic Domain

A sound field reconstruction method for a region including sources is proposed. Under the assumption of spatial sparsity of the sources, this reconstruction problem has been solved by using sparse decomposition algorithms with the discretization of the target region. Since this discretization leads to the off-grid problem, we previously proposed a gridless sound field decomposition method based on the reciprocity gap functional in the spherical harmonic domain. Even though this method allows efficient estimation with a closed-form solution while avoiding the off-grid problem, the estimation using a single time-frequency bin can be greatly affected by measurement errors. We formulate an optimization problem using the identical structure of source locations in multiple time-frequency bins and derive an algorithm based on an annihilating filter. Numerical simulation results indicated that robustness against noise can be improved by the proposed method.
Swarm-Based Identification of Animation Key Points from 2D-medialness Maps

In this article we present the use of dispersive flies optimisation (DFO) for swarms of particles active on a medialness map – a 2D field representation of shape informed by perception studies. Optimising swarms activity permits to efficiently identify shape-based keypoints to automatically annotate movement and is capable of producing meaningful qualitative descriptions for animation applications. When taken together as a set, these keypoints represent the full body pose of a character in each processed frame. In addition, such keypoints can be used to embody the notion of the Line of Action (LoA), a well known classic technique from the Disney studios used to capture the overall pose of a character to be fleshed out. Keypoints along a medialness ridge are local peaks which are efficiently localised using DFO driven swarms. DFO is optimised in a way so that it does not need to scan every image pixel and always tend to converge at these peaks. A series of experimental trials on different animation characters in movement sequences confirms the promising performance of the optimiser over a simpler, currently-in-use brute-force approach.
Conception of a Web Operation System for Processing Petroleum Related Drilling Data: A Focus on Pre-Salt Real-Time Automation and Optimization

Petroleum and Natural Gas still represent a considerable share in terms of energy consumption in the current global matrix, so that its exploration/exploitation is present in the market and driving activities in locations of specific complexities, as the ones along unconventional hydrocarbon resources from the Brazilian pre-salt.The daily cost of well drilling under harsh conditions can exceed US $1 million a day, turning any type of downtime or necessary maintenance during the activities to be very costly, moment in which processes optimization starts to be a key factor in costs reduction.Thus, new technologies and methods in terms of automating and optimizing the processes may be of great advantages, having its impact in total related project costs.In this context, the goal of this research is to allow a computation tool supporting achieving a more efficient drilling process, by means of drilling mechanics parameters choosiness aiming rate of penetration (ROP) maximization and mechanic specific energy (MSE) minimization.Conceptually, driven by the pre-operational drilling test curve trends, the proposed system allows it to be performed with less human influences and being updateable automatically, allowing more precision and time reduction by selecting optimum parame-
The Role of University Teachers in the Assistance to International Students

The idea of providing additional assistance and support to international students is being widely popularized in the global scientific community. This is due to the fact that the number of these students is constantly increasing and they have to encounter numerous problems. There are three main areas of international students’ assistance: didactic assistance focused on the learning process; psychological assistance as the prevention and solution of psychological and emotional problems; sociocultural assistance dealing with adaptation to new environment. This article presents the results of the study directed by the following research questions: whether university teachers are involved in assisting international students, what strategies they prioritize, and what categories of teachers are more focused on providing assistance. The group survey covering 24.2% of teachers of the Interstate Educational Institution of Higher Education “Belarusian-Russian University” resulted in the following conclusions. 1. Most of university teachers are aware of international students’ problems and provide them with additional assistance and support. 2. Teachers with little pedagogical experience, female respondents, and teachers without a scientific degree are more actively involved in assisting international students. 3. The most popular strategies of didactic assistance were explaining details of students’ self-directed work, using lecture presentations and encouraging participation in individual consultations. 4. In order to psychologically support international students, teachers strive to tolerate their speech errors, help them in speaking and create a positive atmosphere in the class. 5. Within the framework of sociocultural assistance, teachers may give students the opportunity to present their homeland, encourage intercultural communication in seminars and workshops, and speak about the country of study.
A genetic-ELM neural network computational method for diagnosis of the Parkinson disease gait dataset

A hybrid computational method based on the extreme learning machine (ELM) neural network for classification and the evolutionary genetic algorithms (GA) for feature selection is presented in this paper. The dimension of the feature space is reduced by the GA in this scheme and only the appointed features are selected. The data is then passed to an ELM neural network for the classification phase. An automated system for the diagnosis of Parkinson’s Disease (PD) based on gait data set is proposed by using the GA-ELM method. PD is a neurodegenerative disease that may cause change in the central nervous causing disturbance to the gait cycle duration. Our hybrid GA-ELM algorithm has produced an optimized diagnosis of PD from healthy subjects given in the gait dataset with accuracy 93.5%, and with five effective features that reduce the original dataset dimension.
Tomographic reconstruction of multiqubit states by phase lift with the Nesterov algorithm

Quantum state tomography is of crucial importance in assessing the quality of quantum information processing devices. Tomography fundamentally requires both experiments and data processing resources, which increase exponentially with the size of qubits. We propose using phase lift to precisely characterize an unknown quantum state using only a small part of the data. We numerically reconstruct the density matrix of multiqubit pure states including the Greenberger-Horne-Zeilinger (GHZ) state, W-class state, cluster state, and random states. The Nesterov algorithm shows high accuracy and high speed for pure states especially for the specific entangled states with a large number of qubits. It shows the highest accuracy among the several specific algorithms, specially for larger number of qubits.
Multi‐sample inference network

This study explores whether neural networks can classify multiple samples simultaneously in a forward process. Therefore, a multi‐input multi‐prediction network architecture has been proposed. The authors call this method a multi‐sample inference network (MSIN). In addition to maximising the use of network shared parameters, the network can also use multiple samples for training. MSIN allows multiple samples to be randomly combined to act as data augmentation, and the random combination of corresponding labels can regularise the network as a loss regularisation, which makes MSIN have better generalisation performance. In contrast, category expansion is a problem that is difficult to solve because neural networks can only predict a fixed number of categories. The network proposed in this study can solve the category expansion problem by expanding the initial layers and the final layers. It is trained by using samples of multiple domains at the same time to ensure that the network has no significant decline in the predictive performance of the existing categories. The MSIN method can also be applied to the generative adversarial network to enable it to simultaneously generate samples of multiple sample domains.
Recognizing Actions of Distracted Drivers using Inception v3 and Xception Convolutional Neural Networks

In recent years, Deep Convolutional Neural Networks have shown remarkable success in image classification tasks. In our research, we compare the performance of two competing DCNN architectures, viz. Inception v3 and Xception, and use them to recognize ten unique actions of the drivers in the Kaggle's State Farm Distracted Driver Detection challenge. We discuss the performance of both architectures in detail (in terms of loss, accuracy and Kaggle test set scores) under two different weight initialization schemes, i.e. random initialization and transfer learning using ImageNet weights, with the training set split on drivers. Additionally, for comparison, we also used a randomly split training set and trained the models using ImageNet weights. By splitting training dataset on drivers, we find that high top-1 validation accuracy of 85.4% is achieved by training the Xception architecture using transfer learning with ImageNet initialized weights. This accuracy is further increased to 99.3% for the same Xception architecture scheme, when we split training data randomly instead of splitting it on subjects. Our best trained model utilizing the Xception architecture with ImageNet initialized weights ranks at 325th position (out of 1440 entries) on Kaggle's Private Leaderboard with a remarkable test loss of 0.51285.
Virtual reality application for educational interactive media “3 historical monuments of yogyakarta”

Virtual Reality (VR) technology is a technology that able to bring users to immerse into the virtual world. VR is one manifestation of the development of multimedia technologies that can serve as a means of education. Indonesia is a country that has a diversity of cultural and historical attractions. As well as the Special Region of Yogyakarta (DIY) which is famous for its temples and monuments as tourist attractions. Based on statistical data of tourism DIY 2015, the frequently visited historical monuments is Prambanan's Temple Complex and "Kraton Ngayogyakarta" including "TuguJogja". However, visitors or the public in general are less interested in reading a brochure to find out the history of the monument. Therefore, it takes historical educational media that is packed in a modern technology in the form of VR applications in order to attract the interest of the audience. This VR application was created using Luther's method with six stages. This VR application aims to simulate a virtual tour diorama monument with a 2D stereoscopic animated video presentation on the history of the monument. The realization of this VR application is created by using Tools named Unity3D and Google VR SDK.
Robust support vector machine for high-dimensional imbalanced data

In this paper, we consider asymptotic properties of support vector machine (SVM) in high-dimension, low-sample-size (HDLSS) settings. In particular, we investigate the behavior of soft-margin SVM for the regularization parameter C. We show that SVM cannot handle imbalanced classification and SVM is very biased in HDLSS settings. In order to overcome such difficulties, we propose a robust SVM (RSVM). We show that RSVM gives preferable performances in HDLSS settings. Finally, we check the performance of RSVM in actual data analyses.
Three data mining models to predict bank telemarketing

The bank telemarketing dataset was collected by a Portuguese retail bank when selling a bank long-term deposits product. In order to predict the success of bank telemarketing, an more accurate approach was proposed. Firstly, the dataset was analyzed and processed. The features value of the dataset could be divided into two kinds: continuous variable and categorical variable. The two kinds of variable had been processed with different methods. The continuous variables were transformed to discrete features and then were normalized. The categorical variables were coded with two different methods according to whether they were belong to the ordered categories. The missing value of features had to been processed with two different methods depending on the examples number of the missing value. Secondly, the three data mining models which were support vector machine, neural network and decision trees were adopted to predict the result of the processed bank telemarketing dataset. The classification and regression trees algorithm was adopted in the decision trees model. The BFGS algorithm had been used when the neural network model was programming. The simple sequential minimal optimization (SMO) algorithm was adopted in the support vector machine model. All the three models were executed using python 2.7 and conducted in a Windows7 PC, with an Intel Cerelon G1630 2.80 GHz processor. The results of three models were analyzed and compared. The decision trees presented the best results and cost the least time, and its accuracy, precision, recall value and AUC area of the were all equal to 1.0. It showed that decision trees could predict 100% correct rate. The accuracy, recall value and AUC area of the neural network were better than those obtained by the support vector machine (improvement of 1.5 pp, 2.7 pp and 5 pp respectively), but the neural network cost almost 4.4 times more time than the support vector machine. It showed that not only choosing the data mining models but also processing datasets in advance played a key role in obtaining better prediction results when datasets needed classifying.
Predicting Academic Digital Library OPAC Users' Cross-device Transitions

With more and more users using different devices, such as personal computers, iPads, and smartphones, they can access OPAC (online public access catalog) services and other digital library services in different contexts. This leads to the phenomenon that user's behavior can be transferred to different devices, which leads to the richness and diversity of user's behavior data in digital libraries. A large number of user data challenge digital libraries to analyze user's behavior, such as search preferences and borrowing habits. In this study, we study the user's cross-device transition behavior when using OPAC. Based on the large-scale OPAC transaction log, the online activities between device transitions in the process of using OPAC are studied. In order to predict the follow-up activities that users may take, and the next device that users may use, we detect features from several perspectives and analyze the feature importance. We find that the activity and time interval on the first device are more important for predicting the user's next activity and the next device. In addition, features of operating system help to better predict the next device. The next device used is more likely to predict the next activity after the device transition. This study examines the cross-device transition prediction in library OPAC, which can help libraries provide smart services for users when accessing OPAC on different devices.
Constructing and Compressing Frames in Blockchain-based Verifiable Multi-party Computation

In previous work, we proposed a scalable multi-party verification scheme for expensive iterative computations on a Blockchain substrate by appropriate storage and endorsement of frames of iterates. In this work, we extend the framework to verify sets of complete computations with different unordered hyperparameters and develop frame ordering and compression algorithms to enable scalability in the system. We illustrate the efficacy of the proposed approach by verifying the OpenMalaria epidemiological simulation.
COVER: A Cluster-based Variance Reduced Method for Online Learning

In this paper, we develop a stochastic-gradient learning algorithm for situations involving streaming data that arise from an underlying clustered structure. In such settings, the variance of gradient noise can be decomposed into the in-cluster variance σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> plus the between-cluster variance σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bet</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . We develop a cluster-based online variancereduced method (COVER) to eliminate σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bet</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and improve the MSD performance of stochastic-gradient descent (SGD) to the order of O(σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). We establish the convergence property of COVER and derive a tight closed-form mean-square deviation (MSD) performance expression. Our simulations illustrate the improved performance of COVER in terms of steady-state performance.
A Topology-aware Coding Framework for Distributed Graph Processing

This paper proposes a coded distributed graph processing framework to alleviate the communication bottleneck in large-scale distributed graph processing. In particular, we propose a topology-aware coded computing (TACC) algorithm that has two salient features. First, we propose a topology-aware graph allocation strategy. Second, we propose a coded aggregation scheme that combines the intermediate computations for graph processes while constructing coded messages. The proposed setup builds on a trade-off between computation and communication, in that increasing the computation load at the distributed parties can in turn reduce the communication load. We demonstrate the effectiveness of the TACC algorithm by comparing the communication load with existing setups on a Google web graph for PageRank computations. In particular, we show that the proposed coding strategy can lead up to 82% improvement in reducing the communication load when compared to the state-of-the-art.
DSNET: Accelerate Indoor Scene Semantic Segmentation

In this paper, we address the problem of real-time image semantic segmentation for indoor scene. As for scene parsing, both accuracy and speed are equally important. However, most of existing methods mainly focus on improving accuracy rather than speed. How to find a balance between accuracy and speed is crucial for real-time semantic segmentation tasks. To tackle this problem, we propose a lightweight framework with depthwise dilation residual module and multi-scale information integration module. A single DSNet yields the performance of mIoU accuracy 32.12% on SUN RGB-D dataset and accuracy 26.32% on ADE20K dataset, which is the most challenge scene parsing dataset. Besides, our system yields real-time inference on a single NVIDIA GPU.