Reviewerly/exHarmony huggingface dataset:
A Parallel Framework of Combining Satisfiability Modulo Theory with Indicator-Based Evolutionary Algorithm for Configuring Large and Real Software Product Lines

Multi-objective evolutionary algorithm (MOEA) has been widely applied to software product lines (SPLs) for addressing the configuration optimization problems. For example, the state-of-the-art SMTIBEA algorithm extends the constraint expressiveness and supports richer constraints to better address these problems. However, it just works better than the competitor for four out of five SPLs in five objectives and the convergence speed is not significantly increased for largest Linux SPL from 5 to 30[Formula: see text]min. To further improve the optimization efficiency, we propose a parallel framework SMTPORT, which combines four corresponding SMTIBEA variants and performs these variants by utilizing parallelization techniques within the limited time budget. For case studies in LVAT repository, we conduct a series of experiments on seven real-world and highly-constrained SPLs. Empirical results demonstrate that our approach significantly outperforms the state-of-the-art for all the seven SPLs in terms of a quality Hypervolume metric and a diversity Pareto Front Size indicator.
CARF-Net: CNN attention and RNN fusion network for video-based person reidentification

Video-based person reidentification is a challenging and important task in surveillance-based applications. Toward this, several shallow and deep networks have been proposed. However, the performance of existing shallow networks does not generalize well on large datasets. To improve the generalization ability, we propose a shallow end-to-end network which incorporates two stream convolutional neural networks, discriminative visual attention and recurrent neural network with triplet and softmax loss to learn the spatiotemporal fusion features. To effectively use both spatial and temporal information, we apply spatial, temporal, and spatiotemporal pooling. In addition, we contribute a large dataset of airborne videos for person reidentification, named DJI01. It includes various challenging conditions, such as occlusion, illuminationchanges, people with similar clothes, and the same people on different days. We perform elaborate qualitative and quantitative analyses to demonstrate the robust performance of the proposed model.
Video Object Counting Dataset

Object counting is an important field of research in computer vision. Unlike other areas such as object recognition, there is serious lack of standard object counting datasets, especially videos, for which the object counting algorithms can be compared. This paper addresses such need by bringing together videos with various challenges. It also gives the object count ground truth that was created using a hybrid human-computer approach to maximise accuracy. The dataset is available freely and is structured in an intuitive way to allow for addition of more object classes.
Density Matrix Based Preference Evolution Networks for E-Commerce Recommendation

In e-commerce platforms, mining temporal characteristics in user behavior is conducive to recommend the right product for the user at the right time. Recently, recurrent neural networks (RNNs) based methods have achieved profitable performance in exploring temporal features, however, in complex e-commerce scenarios, user preferences changing over time have not been fully exploited. In order to fill the gap, we propose a novel representation for user preferences with the inspiration of a quantum concept, density matrix. It encodes a mixture of item subspaces and represents distribution of user preferences at one time stamp. Further, such a representation and RNNs are combined to form our proposed Density Matrix based Preference Evolution Networks (DMPENs). Experiments on Amazon datasets as well as real-world e-commerce datasets demonstrate the effectiveness of the proposed methods, which achieve rapid convergence and superior performance compared with the state-of-the-art methods in terms of AUC and accuracy.
EF-Index: Determining number of clusters (K) to estimate number of segments (S) in an image

Estimation of number of segments in an image attracts a formidable interest among the research community. The number of segments in an image is estimated by calculating the number of clusters present in the pixels of that image. The present work offers an unsupervised method, named “Electrostatic Force Index (EF-Index)”, to estimate the number of clusters inherent in an image, reporting of which is very rare in literature. The proposed approach is inspired by Coulomb's law of electrostatics. The EF-Index explores the mutual influence of an arbitrary pixel on another, by considering them similar to point charges. Our proposed cluster indexing method, viz. EF-Index is capable of determining the number of clusters present in an image. It has a strong resemblance to the way the electrostatic force is operative between a pair of static point charges in a closed system as per Coulomb's principle. In order to justify the effectiveness of the proposed approach, we have compared EF-Index of a given image with DB-Index, I-Index, CVNN-Index, DOE-AND-SCA and Sym-Index of the same image. Experimental results show that EF-Index is same as other state-of-the-art indices, whereas EF-Index does not require any clustering algorithm. To establish the applicability of the EF-Index, the same is applied for image segmentation considering Berkeley Segmentation Dataset and Stanford Background Dataset. We observe the results obtained conform to the ground truth and results achieved by applying existing well-established segmentation techniques on the same datasets. The efficacy of the proposed approach is further substantiated in terms of its reduced computational overhead in comparison to the state-of-the-art segmentation algorithms.
Design and Implementation of Virtual-Real Interactive System for Mixed Reality

Mixed reality (MR) combines real and virtual worlds to create an interactive bridge between the real world, the virtual world and users. In order to make the interaction between the user and the virtual world intelligent, a virtual-real interaction system for mixed reality scheme is proposed. This paper mainly discusses the technical solutions for implementing the system and demonstrates the interaction between the user and virtual characters in the interactive system. The virtual characters of the interactive system are stimulated by real and virtual scenes, and the behavior of real people changes [1]. The modules used in this study include: 1) Unreal Engine 4 builds virtual scenes and characters, 2) Motion capture system captures real-world information, aligns real and virtual worlds. Alignment refers to the same position information and orientation information of the real world and the virtual world. 3) Graphics workstation synthesizes scenes captured by optical cameras and virtual scenes.
Deep Wavelet Extreme Learning Machine for Data Classification

Recently, Extreme Learning Machine (ELM) has drawn an increasing attention, Due to its fast and good generalization ability. This paper proposes a new learning method for Extreme Learning Machine based wavelet and deep architecture. We have applied a composite wavelet activation function at the hidden nodes of ELM and the learning is done by a Deep Extreme Learning Machine. To evaluate the performance of our approach we have used a standard benchmark dataset for multi-class image classification (MNIST). Results show that our approach offers a significantly better performance relative to others approaches.
Composite pattern to handle variation points in software architectural design of evolving application systems

The variation points in software architecture arise as a result of the availability of large number of filters and component libraries. An integration of different architectural styles is crucial and necessary in the development of large-scale software application systems to handle the variation points. This article proposes a composite software architectural style for building application systems involving data streams, user interactivity, and dynamic mode. It uses a pattern within a pattern approach for combining the architectural styles. This approach provides flexibility to add or delete any filter or component at run time. In addition, the changes in the order of processing of the different filters or components can also be incorporated. The software architectural specification for any combination of input components and their order of processing is generated automatically. This specification acts as a baseline for the subsequent design and implementation phases of the application system. This model is generic and has been successfully validated for a prototype application system involving all the three modes of operation.
Retroactive Packet Sampling for Traffic Receipts

Is it possible to design a packet-sampling algorithm that prevents the network node that performs the sampling from treating the sampled packets preferentially? We study this problem in the context of designing a "network-transparency'' system. In this system, networks emit receipts for a small sample of the packets they observe, and a monitor collects these receipts to estimate each network's loss and delay performance. Sampling is a good building block for this system, because it enables a solution that is flexible and combines low resource cost with quantifiable accuracy. The challenge is cheating resistance: when a network's performance is assessed based on the conditions experienced by a small traffic sample, the network has a strong incentive to treat the sampled packets better than the rest. We contribute a sampling algorithm that is provably robust to such prioritization attacks, enables network performance estimation with quantifiable accuracy, and requires minimal resources. We confirm our analysis using real traffic traces.
A complete classification of the complexity and rewritability of ontology-mediated queries based on the description logic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi mathvariant="script">EL</mml:mi></mml:math>

We provide a fine-grained analysis of the data complexity and rewritability of ontology-mediated queries (OMQs) based on an EL ontology and a conjunctive query (CQ). Our main results are that every such OMQ is in , -complete, or -complete and that containment in coincides with rewritability into linear Datalog (whereas containment in coincides with rewritability into first-order logic). We establish natural characterizations of the three cases in terms of bounded depth and (un)bounded pathwidth of certain minimal ABoxes on which the OMQ yields an answer. We also show that each of the associated meta problems such as deciding whether a given OMQ is rewritable into linear Datalog is ExpTime-complete. We also give a way to construct linear Datalog rewritings when they exist and prove that there is no constant bound on the arity of IDB relations in linear Datalog rewritings.
Balancing Multiple Roles of Repositories: Developing a Comprehensive Repository at Carnegie Mellon University

Many academic and research institutions today maintain multiple types of institutional repositories operating on different systems and platforms to accommodate the needs and governance of the materials they house. Often, these institutions support multiple repository infrastructures, as these systems and platforms are not able to accommodate the broad range of materials that an institution creates. Announced in 2017, the Carnegie Mellon University (CMU) Libraries implemented a new repository solution and service model. Built upon the Figshare for Institutions platform, the KiltHub repository has taken on the role of a traditional institutional repository and institutional data repository, meeting the disparate needs of its researchers, faculty, and students. This paper will review how the CMU Libraries implemented the KiltHub repository and how the repository services was redeveloped to provide a more encompassing solution for traditional institutional repository materials and research datasets. Additionally, this paper will summarize how the CMU University Libraries surveyed the current repository landscape, decided to implement Figshare for Institutions as a comprehensive institutional repository, revised its previous repository service model to accommodate the influx of new material types, and what needed to be developed for campus engagement. This paper is based upon a presentation of the same title delivered at the 2018 Open Repositories Conference held at Montana State University in Bozeman, Montana.
Unsupervised Dimension Reduction for Image Classification Using Regularized Convolutional Auto-Encoder

Unsupervised dimension reduction has gained widespread attention. Most of previous work performed poorly on image classification due to taking no account of neighborhood relations and spatial localities. In this paper, we propose the ‘regularized convolutional auto-encoder’, which is a variant of auto-encoder that uses the convolutional operation to extract low-dimensional representations. Each auto-encoder is trained with cluster regularization terms. The contributions of this work are presented as follows: First, we perform different sized filter convolution in parallel and abstract a low-dimensional representation from images cross scales simultaneously. Second, we introduce a cluster regularized rule on auto-encoders to reduce the classification error. Extensive experiments conducted on six publicly available datasets demonstrate that the proposed method significantly reduces the classification error after dimension reduction.
Improvement of PSNR in Image Denoising by using DWT Algorithm

Expelling clamor from the first flag is as yet a testing subject for predictors. There have been a couple of dispersed estimations and each methodology has its suppositions, focal points, and restrictions. This paper introduces an audit of some huge work in the zone of picture de noising. After a short presentation, some famous methodologies are characterized into various gatherings and a review of different calculations and examination is given. Wavelet calculations are extremely helpful device for flag handling, for example, picture pressure and picture de noising. The principle point demonstrate the aftereffect of Mathematical parameter new premise, clamor with depends limit expelled information. Experiences and potential future patterns in the region of de noising are likewise examined.
A wearable imaging system for recording the game tactics in martial arts using swords

The intermediate level players in Tankendo may use the images of the gaze of the high-ranking players during the match or training as references to learn the gamesmanship and game tactics from the skilled players. However, for example, even a miniature camera, which is installed at the position of eye on a face protector with no close consideration, is likely to block the sight of the high-ranking player who wears the face protector with the camera or to be damaged when being hit or thrusted by the opponent. We propose therefore the configuration that a camera of the width equal to or smaller than that of the longitudinal metal bar “Tategane” of the face protector is installed “around the nose position” of the Tategane. This enables the scenes of playing matches to be captured through the wearer's gaze without blocking his/her sight. In addition, the camera and interface components are unlikely to be destroyed when attacked by the opponent.
A Comprehensive Review on Speckle Denoising Techniques in Satellite I|mages

Noise removals from satellite images are challenging assignment for the researchers. There are so many image de-noising techniques used for noise removal in satellite images. Speckle noise is originated in satellite image, Synthetic Aperture Radar (SAR) and medical images. There are various filters having both linear and non-linear characteristics for the reduction of noise in image processing applications. The impulse noise can't be reduced using linear filters and the edges are blurred. The non-linear filters are beat this problems. This research paper put forward a methodical review of various noise removal filters along with their performance analysis factors like SNR. MSE, PSNR, APSNR, BPP & CoC and compared their advantages and disadvantages.
Temporal Querying of Faces in Videos Using Bitmap Index

Bitmap indexing has recently been used for big data systems especially for column-based retrieval. In this paper, we study bitmap indexing for temporal querying of faces that appear in videos. Although bitmap index is suitable for finding records that satisfy a condition, its application to databases with temporal context is not straightforward. We consider temporal operators such as next and eventually, as well as queries for co-appearance. After recognizing faces and indexing them, our method can be used to filter and extract intervals of appearance and can also be used to compute additional conditions using bitwise operations. We have applied our method on a sample video database to show how bitmap index can be used for temporal querying of faces for retrieving relevant intervals and videos.
Efficient Secure Ridge Regression from Randomized Gaussian Elimination

In this paper we present practical protocols for secure ridge regression. We develop the necessary secure linear algebra tools, using only basic arithmetic over prime fields. In particular, we will show how to solve linear systems of equations and compute matrix inverses efficiently, using appropriate secure random self-reductions of these problems. The distinguishing feature of our approach is that the use of secure fixed-point arithmetic is avoided entirely, while circumventing the need for secure rational reconstruction at any stage as well. In fact, in recent follow-up works, our results have already been applied and extended to several other settings. We demonstrate the potential of our protocols in a standard setting for information-theoretically secure multiparty computation, tolerating a dishonest minority of passively corrupt parties. Using the MPyC framework, which is based on threshold secret sharing over finite fields, we show how to handle large datasets efficiently, achieving practically the same root-mean-square errors as Scikit-learn. Moreover, our protocols are designed with the outsourcing scenario in mind, which makes our protocols much more versatile than existing solutions. In the outsourcing scenario one does not assume that (any part of) the dataset is held privately by any of the parties performing the multiparty computation—in contrast to federated learning, for instance, where the dataset is partitioned either horizontally or vertically between these parties.
Introduction to Yogyakarta Icons in The Game of Running Challenge

Yogyakarta's strong culture makes Yogyakarta one of the tourist areas frequented by tourists, both foreign tourists and local tourists. The Visitors come from various groups, for example visitors who come with family or individuals. A game model is needed that can facilitate the child in remembering the tourist icons in Yogyakarta through a Game Running Challenge that is made as attractive as possible and can run on the Windows operating system. The game running challenge application can provide convenience to users who are children aged 6-10 years to get to know and know the most famous icons in Yogyakarta through a game. So that it can be an educational medium that has a pattern of learning by doing learning.
Word Sense Disambiguation with Massive Contextual Texts

Word sense disambiguation is crucial in natural language processing. Both unsupervised knowledge-based and supervised methodologies try to disambiguate ambiguous words through context. However, they both suffer from data sparsity, a common problem in natural language. Furthermore, the supervised methods are previously limited in the all-word WSD tasks. This paper attempts to collect all publicly available contexts to enrich the ambiguous word’s sense representation and apply these contexts to the simplified Lesk and our M-IMS systems. Evaluations performed on the concatenation of several benchmark fine-grained all-word WSD datasets show that the simplified Lesk improves by 9.4% significantly and our M-IMS has shown some improvement as well.
A Probabilistic Approach for Inferring Latent Entity Associations in Textual Web Contents

Latent entity associations (EA) represent that two entities associate with each other indirectly through multiple intermediate entities in different textual Web contents (TWCs) including e-mails, Web news, social network pages, etc. In this paper, by adopting Bayesian Network as the framework to represent and infer latent EAs as well as the probabilities of associations, we propose the concept of entity association Bayesian Network (EABN). To construct EABN efficiently, we employ self-organizing map for TWC dataset division to make the co-occurrence-based dependence of each pair of entities concern just a small set of documents. Using probabilistic inferences of EABN, we evaluate and rank EAs in all possible entity pairs, by which novel latent EAs could be found. Experimental results show the effectiveness and efficiency of our approach.
Access Control based Efficient Hybrid Security Mechanisms for Cloud Storage

Providing security to the cloud data is a highly complicated and demanding task nowadays. For this purpose, different existing works provide encryption and key generation based techniques during cloud data sharing. But, it has some major drawbacks such as, inefficient computation, increased time consumption, and requires more storage space. The cost of maintenance of data over the cloud could be considered and hence this paper presents a cost efficient and reliable data hosting scheme called, Hybrid cryptography which provides systematic architecture with distributed data hosting in the heterogeneous cloud infrastructure SMS to transit the data files to optimize both bandwidth and speed. The proposed system considers data migration within the cloud system with respect to the cost of migration and the data access patterns. For experimental results, the performance of the suggested technique is evaluated based on the measures of encryption time, decryption time, and key generation time.
User Intention-Based Document Summarization on Heterogeneous Sentence Networks

Automatic extraction-based document summarization is a difficult Natural Language Processing task. Previous approaches have usually generated the summary by extracting the top K salient sentences on graph-based ranking algorithms, but sentence feature representation only captures the surface relationship between the objects, hence the results may not accurately reflect the user's intentions. Therefore, we propose a method to address this challenge, and: (1) obtain deeper semantic concepts among candidate sentences using meaningful sentence vectors combining word vectors and TF-IDF; (2) rank the sentences considering both relationships between sentences and the user's intention for each sentence to identify significant sentences, and apply these to a heterogeneous graph; (3) generate the result sentence by sentence to ensure summary semantics are properly related to the original document. We verified the proposed approach experimentally using English summarization benchmark datasets DUC2001 and DUC2002; the large Chinese summarization data set, LCSTS. We also collected news data and produced a reference summary using a group of bank auditor experts that we compared to the proposed approach using ROUGE evaluation.