Reviewerly/exHarmony huggingface dataset:
Formalizing common belief with no underlying assumption on individual beliefs

This paper formalizes common belief among players with no underlying assumption on their individual beliefs. Especially, players may not be logically omniscient, i.e., they may not believe logical consequences of their beliefs. The key idea is to use a novel concept of a common basis: it is an event such that, whenever it is true, every player believes its logical consequences. The common belief in an event obtains when a common basis implies the mutual belief in that event. If players' beliefs are assumed to be true, then common belief reduces to common knowledge. The formalization nests previous axiomatizations of common belief and common knowledge which have assumed players' logical monotonic reasoning. Under this formalization, unlike others, if players have common belief in rationality then their actions survive iterated elimination of strictly dominated actions even if their beliefs are not monotonic.
Paraphrase Generation Based on VAE and Pointer-Generator Networks

Paraphrase generation is a challenging task that involves expressing the meaning of a sentence using synonyms or different phrases, either to achieve variations or a certain stylistic response. Most previous sequence-to-sequence (Seq2Seq) models focus on either generating variations or preserving the content. We mainly address the issue of preserving the content in a sentence while generating diverse paraphrases. In this paper, we propose a novel approach for paraphrase generation using variational autoencoder (VAE) and Pointer Generator Network (PGN). The proposed model uses a copy mechanism to control the content transfer, a VAE to introduce variations and a training technique to restrict the gradient flow for efficient learning. Our evaluations on QUORA and MS COCO datasets show that our model outperforms the state-of-the-art approaches and the generated paraphrases are highly diverse as well as consistent with their original meaning.
Efficient Computation of Log-likelihood Function in Clustering Overdispersed Count Data Using Multinomial Beta-Liouville Distribution

In this paper, we present an overdispersed count data clustering algorithm, which uses the mesh method for computing the log-likelihood function, of the multinomial Beta-Liouville distribution (MBLD). In one of the recent research papers, the use of the mesh algorithm, which involves the approximation of the multinomial Dirichlet distribution's (MDD) log-likelihood function, based on the Bernoulli polynomials, has been proposed instead of using the traditional numerical computation of the log-likelihood function which either results in instability, or leads to long run times that make its use infeasible when modeling large-scale data. Therefore, we extend the mesh algorithm approach for computing the log likelihood function of the MBLD, which is a more flexible distribution. A finite mixture model based on MBLD, is optimized by expectation-maximization, and attempts to achieve a high accuracy for count data clustering. We evaluate the performance of the proposed approach, through a set of empirical experiments, that concern natural scenes categorization.
Feedback2Code: A Deep Learning Approach to Identifying User-Feedback-Related Source Code Files

Users frequently raise feedback when using software products. Feedback from users regarding their experiences and expectations and software defects they found adds values to software maintenance and evolution — software managers collect user feedback and then dispatch feedback issues that developers (and/or maintainers) need to track and process. Feedback tracking is often supported by open source platforms and collaborative software systems. Meanwhile, there still exists a gap between feedback issues and source code: since user feedback is usually informal and arbitrary, engineers have to spend much effort on comprehending issues and identifying which source code files need to be improved or fixed. This paper introduces a deep learning approach, Feedback2Code , which facilitates identification of user-feedback-related source code files. The core idea is to (1) explore latent semantics of user feedback and source code using several deep learning techniques such as Multi-Layer Perceptron (MLP), Convolutional Neutral Network (CNN) and skip-gram and (2) establish a multi-correlation model to explore linkages between feedback issues and source code files. Given a feedback issue, the linkages then allow engineers to identify source code files that are highly relevant to the issue. We have implemented Feedback2Code and evaluated it against ChangeAdvisor (a state-of-the-art approach) on 24 open source projects. The evaluation results clearly show the strength of Feedback2Code : for 103793 feedback issues, Feedback2Code successfully established 101190 feedback-code linkages and achieved a precision that is [Formula: see text] higher than that of ChangeAdvisor . Feedback2Code also achieved an MRR and an MAP that are [Formula: see text] and [Formula: see text] higher than those of ChangeAdvisor , respectively. Furthermore, we also found that a Feedback2Code -trained model can be easily transferred, allowing feedback-code linkages to be established in new projects with a little history data.
Classification methods for planar shapes

The goal of this paper is to propose supervised learning methods considering two-dimensional landmarks (planar shapes). We introduce a novel method based on a support vector machine (SVM) algorithm, which had to be adapted to complex vectors. We also propose other novel methods based on density estimation, kernel k-means, and hill-climbing. Combinations between the classifiers using the ensemble method are considered as well. Furthermore, we compared the proposed methods to the existing Bayes discriminant approach. We conducted simulation experiments to evaluate the performance of the proposed methods. The numerical results prove that for low concentrated data sets, the SVM algorithm outperforms the other methods. Moreover, four real-world data sets are considered: gorilla skulls, orangutan skulls, mouse vertebrae, and schizophrenia. These data sets present different configurations, such as several landmarks and variability. The proposed SVM method achieved the best performance in three data sets.
Joint Learning of Word and Label Embeddings for Sequence Labelling in Spoken Language Understanding

We propose an architecture to jointly learn word and label embeddings for slot filling in spoken language understanding. The proposed approach encodes labels using a combination of word embeddings and straightforward word-label association from the training data. Compared to the state-of-the-art methods, our approach does not require label embed-dings as part of the input and therefore lends itself nicely to a wide range of model architectures. In addition, our architecture computes contextual distances between words and labels to avoid adding contextual windows, thus reducing memory footprint. We validate the approach on established spoken dialogue datasets and show that it can achieve state-of-the-art performance with much fewer trainable parameters.
Multiple Learning for Regression in Big Data

Regression problems that have closed-form solutions are well understood and can be easily implemented when the dataset is small enough to be all loaded into the RAM. Challenges arise when data are too big to be stored in RAM to compute the closed form solutions. Many techniques were proposed to overcome or alleviate the memory barrier problem but the solutions are often local optima. In addition, most approaches require loading the raw data to the memory again when updating the models. Parallel computing clusters are often expected in practice if multiple models need to be computed and compared. We propose multiple learning approaches that utilize an array of sufficient statistics (SS) to address the aforementioned big data challenges. The memory oblivious approaches break the memory barrier when computing regressions with closed-form solutions, including but not limited to linear regression, weighted linear regression, linear regression with Box-Cox transformation (Box-Cox regression) and ridge regression models. The computation and update of the SS arrays can be handled at per row level or per mini-batch level. And updating a model is as easy as matrix addition and subtraction. Furthermore, the proposed approaches also enable the computational parallelizability of multiple models because multiple SS arrays for different models can be computed simultaneously with a single pass of slow disk I/O access to the dataset. We implemented our approaches on Spark and evaluated over the simulated datasets. Results showed our approaches can achieve exact solutions of multiple models. The training time saved compared to the traditional methods is proportional to the number of models need to be investigated.
Comparative Analysis of Automatic Methods for Measuring Surface of Threads of Oil and Gas Pipes

In this paper a comparative analysis of methods for measuring complex profile surfaces such as threaded surfaces were carried out. The oil and gas pipe and couplings threads were used as an example of a complex surfaces. A theoretical and experimental evaluation of the reliability, performance and accuracy of the geometric measuring systems in shop floor environment was made in cooperation with executive and ordinary staff. Coordinate measurement machine with stylus, profilometer, conoscopic, confocal, interferometric and laser triangulation methods were involved to solve thread measurement problem. The optimal method of thread geometry measurement for a shop floor was tested and identified as a result of the analysis of pipes and couplings threads. The laser 2D triangulation scanning method was verified as the only suitable for thread measuring problem solution. The results of measurement using this method show a possibility to achieve technical requirements for the premium oil and gas pipes thread.
A Hybrid Partitioning Strategy for NewSQL Databases

Several application domains deal with the management of massive data volumes and thousands of OLTP transactions per second. Traditional relational databases cannot cope with these requirements. NewSQL is a new generation of databases that provides both high scalability and availability and ACID properties support. Besides, it is a promising solution to handle these application data management needs. Although data partitioning is an essential feature for tuning relational databases, stills an open issue for NewSQL systems. In this paper, we propose a hybrid partitioning approach for NewSQL databases that allows the user to define the vertical and horizontal data partitions. In order to determine what site will store each data fragment, we propose a hash function that considers schema information and data access statistics. Our experimental evaluation compares our hybrid VoltDB version against the standard VoltDB. The results highlight that our strategy increases the number of single-site transactions from 37% to 76%.
On the grammar of scam: transitivity, manipulation and deception in scam emails

This study examines the language of scam, focusing on one key lexicogrammatical system for representing experience, the system of transitivity. The study is informed by systemic functional linguistics theory and is based on a clause-by-clause analysis of forty scam email messages, comprising 860 clauses. The frequency distribution of process types shows that scammers mimic the everyday taken-for-granted construction of experience in discourse in producing scam, thereby concealing the motive of the scammer. Second, scammers favor three sub-types of material processes, namely communication-oriented clauses, clauses of transfer of possession and use-oriented clauses. In addition, scam emails are shown to be interpersonally rich in the use of personal pronouns to index and position scammers relative to their target email recipients in manipulative ways. Also, the possessive determiners my, your and our are used in nominal groups functioning as participants to position the scammer and target recipients differently. Notably, the pronoun my (representing the scammer) normally collocates with social relationship/kinship terms or a noun denoting the condition of the scammer, your (representing the recipient) collocates with nouns denoting material possessions or semiotic activities, while our often collocates with nouns that evoke some institutional commitment, locating the scammer within a network of relations.
Parameter Adaptation in the Imperialist Competitive Algorithm Using Generalized Type-2 Fuzzy Logic

In this paper we propose the use of generalized type-2 fuzzy systems to dynamic adjustment the parameters of the imperialist competitive algorithm (ICA), and we take a type-1 fuzzy system as a basis to extend our proposal using generalized type-2 fuzzy logic. The ICA algorithm is based on the concept of imperialism in which the strongest countries try to take control of the weakest countries. In order to measure the performance of our proposed method different benchmark functions were used and finally, a comparison was made between the variants to observe their behavior applied to benchmark mathematical functions.
DC Designer: Software System of Scaling Multiprovider Data Centers Networks

Currently, large enterprises are striving to create a sustainable network structure based on data centers (DC), served by various communication providers, to which it is easy to connect new segments taking into account the specifics of the company. The task of structures reconfiguration of multiprovider DC networks is a difficult task, especially in the case of dynamic network scaling. Creating new high-speed communication links leads to an increase of total costs. High load of routers in DC and frequent updating of routing information about all changes in the network increase the complexity of the computational process of building routing tables. In addition, it is necessary to analyze all possible solutions based on the number of connected providers in the DC, as well as the types of user traffic. The aim of the work is developing software system DC Designer and new algorithm for scaling multiprovider DC networks that allow to reduce formation time of optimal routes and reduce the total costs of maintenance in comparison with the known analogues. The results of experimental research show their effectiveness in reconfiguring the structures of multiprovider DC.
Semantic Image Completion Through an Adversarial Strategy

Image completion or image inpainting is the task of filling in missing regions of an image. When those areas are large and the missing information is unique such that the information and redundancy available in the image is not useful to guide the completion, the task becomes even more challenging. This paper proposes an automatic semantic inpainting method able to reconstruct corrupted information of an image by semantically interpreting the image itself. It is based on an adversarial strategy followed by an energy-based completion algorithm. First, the data latent space is learned by training a modified Wasserstein generative adversarial network. Second, the learned semantic information is combined with a novel optimization loss able to recover missing regions conditioned by the available information. Moreover, we present an application in the context of face inpainting, where our method is used to generate a new face by integrating desired facial attributes or expressions from a reference face. This is achieved by slightly modifying the objective energy. Quantitative and qualitative top-tier results show the power and realism of the presented method.
Fast and memory efficient de-hazing technique for real-time computer vision applications

Some features of an image may spoil due to fog or haze, smoke. These images lose their brightness due to air-light scattering. It offers troublesomeness to the people lives in hill and fog regions of the world. This paper proposed two key aspects. One is a modified dark-channel method based on the median for eliminating the refine the transmission map as well as halos and artifacts, another important aspect is a memory-efficient row-based arrangement of the pixels for real-time applications. The advantage of this method is air-light can be predicted directly from the modified dark channel and also accurate transmission map can be estimated. This method is compared with other existing four algorithms. Our proposed method analyzed in terms of Peak Signal to Noise Ratio (PSNR), Average Time cost (ATC), percentage of haze improvement (PHI), average contrast of output image (ACOI), Mean Squared Error (MSE) and Structural Similarity Index (SSIM). The quality of the output de-haze image of our algorithm over existing algorithms is more. It has taken less computation time, equal MSE, with higher SSIM and has more percentage of haze improved over existing methods.
A Multi-Dimensional, Counter-Based Pseudo Random Number Generator as a Standard for Monte Carlo Simulations

As decisions models involving Monte Carlo simulations become more widely used and more complex, the need to organize and share components of models increases. Standards have already been proposed which would facilitate the adoption and quality control of simulations. I propose a new pseudo-random number generator (PRNG) as part of those standards. This PRNG is a simple, multi-dimensional, counter-based equation which compares favorably to other widely used PRNGs in statistical tests of randomness. Wide adoption of this standard PRNG may be helped by the fact that the entire algorithm fits in a single cell in an Excel spreadsheet. Also, quality control and auditability will be helped because it will produce the same results in any common programming language regardless of differences in precision.
End-to-End Xception Model Implementation on Carla Self Driving Car in Moderate Dense Environment

Recently, with hardware limitation, many autonomous car developers using a simulator to test their network model or solve some self-driving car issues. With that in mind, Carla simulator provides an open platform with many different varieties of maps and real environment parameter, which indicate multiple challenges to be accomplished. There are many approaches to solve these problems, ranging from a complex model such as imitation learning followed by inverse reinforcement learning to a simple model adopting spatial or time-based network with performance-based oriented putting computational time aside. Pertaining this matter, we look into a light-weight model for spatial classification which can reduce computational time with a slight trade back.
Clustered Hierarchical Entropy-Scaling Search of Astronomical and Biological Data

Both astronomy and biology are experiencing explosive growth of data, resulting in a “big data” problem that stands in the way of a “big data” opportunity for discovery. One common question asked of such data is that of approximate search (ρ–nearest neighbors search). We present a hierarchical search algorithm for such data sets that takes advantage of particular geometric properties apparent in both astronomical and biological data sets, namely the metric entropy and fractal dimensionality of the data. We present CHESS (Clustered Hierarchical Entropy-Scaling Search), a search tool with virtually no loss in specificity or sensitivity, demonstrating a 13.6 × speedup over linear search on the Sloan Digital Sky Survey’s APOGEE data set and a 68 × speedup on the GreenGenes 16S metagenomic data set, as well as asymptotically fewer distance comparisons on APOGEE when compared to the FALCONN locality-sensitive hashing library. CHESS demonstrates an asymptotic complexity not directly dependent on data set size, and is in practice at least an order of magnitude faster than linear search by performing fewer distance comparisons. Unlike locality-sensitive hashing approaches, CHESS can work with any user-defined distance function. CHESS also allows for implicit data compression, which we demonstrate on the APOGEE data set. We also discuss an extension allowing for efficient k-nearest neighbors search.
Managing Multimedia Big Data: Security and Privacy Perspective

Multimedia Big Data (MMBD) is growing day by day because data is created by everyone and for everything from mobile devices, digital camera, digital game, multimedia sensor, video lectures, and social networking sites, etc. It is observed that various organizations and businesses moving toward it and taking out the benefits from it. MMBD has its applications in various fields that containing social networking sites, science and research, business enterprises, surveillance image and video, health care industries, etc. Huge size of data is being transfered from the Internet in each second. Due to increase in MMBD, it is necessary to protect this data from external threats or malicious attacks which can affect privacy, integrity, and genuineness of information systems. Therefore, to protect the integrity, identity, and security of individual’s data from these unwanted malicious attacks, an effective security mechanism is much needed. In this paper, the author discussed about the current issues in MMBD and security approaches to deal with these issues and also, focus on multimedia big data security and privacy. It also enumerated the directions to be taken while using MMBD along with security measures.
Effective Approach for Sentiment Analysis of Food Delivery Apps

Sentiment analysis is used as a tool from a decade by various companies to understand the trends and react to customer opinion and to stand in the competitive world by analysing the data about their product quality and features that are collected from social apps like Facebook, Twitter and App Store where people post their opinions about the product features, quality, benefits and other values of the products, which help companies to identify the problem and work upon it in time to improve and target the right people. This paper presents the steps for sentiment extraction from structured data derived by pre-processing of unstructured data collected from Twitter by using tools.
A Comparison of Deep Learning Vehicle Group Detection in Satellite Imagery

Object detection is a challenging but important task for computer vision, and this is especially true in the remote sensing domain where data collections may bring billions of pixels in a single image. There are many methods for object detection, but in recent years the You Only Look Once (YOLO) algorithm has become a leading technique, gaining popularity due to its ability to perform real-time object detection. While YOLO and its successors have shown excellent results in realtime detection, there are many object detection tasks that require better precision, and do not require real-time detection. In this paper, YOLOv3 is compared to other deep neural networks (DNN) for detecting Vehicle Groups in very high resolution remote sensing imagery (VHR-RSI). A unique centerpoint-based dataset is developed by leveraging a novel data framework and combining quality assured chips with regions of interest in the XView Challenge Dataset. This dataset is then used to train state of the art models including two Neural Architecture Search (NAS) variant DNN for object detection. Additionally, a blind test set is developed to further compare our methods with the YOLOv3 algorithm. The results shows that our method detects vehicle groups with a lower false positive rate (FPR) and better true positive rate (TPR) than state-of-the-art YOLOv3 models for the blind test set; achieving a reduction in error rate of 26.70% over YOLOv3 in F1 Score on the blind test set.