Reviewerly/exHarmony huggingface dataset:
Zipfian regularities in “non-point” word representations

Being one of the most common empirical regularities, the Zipf's law for word frequencies is a power law relation between word frequencies and frequency ranks of words. We quantitatively study semantic uncertainty of words through non-point distribution-based word embeddings and reveal the Zipfian regularities. Uncertainty of a word can increase due to polysemy, the word having "broad" meaning (such as the relation between broader emotion and narrower exasperation) or a combination of both. Variances of Gaussian embeddings are utilized to quantify the extent a word can be used in different senses or contexts. By using the variance information embedded in the non-point Gaussian embeddings, we quantitatively show that semantic breadth of words also exhibits Zipfian patterns, when polysemy is controlled. This outcome is complementary to Zipf's law of meaning distribution and the related meaning-frequency law by indicating the existence of Zipfian patterns: more frequent words tend to be generic while less frequent ones tend to be specific. Results for two languages, English and Turkish that belong to different language families, are also provided. Such regularities provide valuable information to extract and understand relationships between semantic properties of words and word frequencies. In various applications, performance improvements can be obtained by employing these regularities. We also propose a method that leverages the Zipfian regularity to improve the performance of baseline textual entailment detection algorithms. To the best of our knowledge, our approach is the first quantitative study that uses Gaussian embeddings to examine the relationships between word frequencies and semantic breadth.
Cognitive Computing in Software evaluation

Due to the rapid growth of software field, there is a tremendous increase in the building a software, websites and also applications. Eventually with the increase of the software products, the standards of the product also play a vital role. Based on the good standards of the product, the level of that particular product will always be high. Software evaluation is a method whose main objective is to judge a product and to verify whether the software meets all the prerequisites. Cognitive computing is a combination of several different fields and it imitates the human intellectual capacity in making decisions. The proposed work describes about the software evaluation using cognitive computing which is very inventive with wide range of benefits and provides excellent result. It also improves the efficiency of the company and helps to build advanced software's. The results are very accurate and saves lot of time in performing the software processes.
Application of Sensor Data Information Cognitive Computing Algorithm in Adaptive Control of Wheeled Robot

The four-wheel drive robot has a coupling relationship between the four wheels due to the characteristics of the wheel train distribution. During the operation, there are problems with the stability and control accuracy of the robot’s overall motion. To solve this problem, this paper constructs an adaptive control algorithm based on cognitive computing. We use real-time sensor data as learning samples for online learning, and obtain new sample averages, new feature values, feature vectors, and new subspaces. This information is stored in the semantic long-term memory by projecting the newly read sample into the subspace obtained in the previous calculation. In addition, fuzzy control and partial discharge control are combined to correct the stored information online. Taking into account the distribution characteristics of the wheel system structure, we reasonably allocate the overall error to a single wheel, which will convert the overall error correction into a single wheel error correction. The simulation experiment in the Matlab-Simulink environment shows that after using the fuzzy adaptive controller for error correction, the error value of the robot’s linear velocity and angular velocity is between −1 and 1.5. In addition, the followability and the performance of tracking the target are obviously improved, and the accuracy of robot motion control is improved.
Homography estimation based on common self-polar triangles of sphere images

In the unit viewing sphere imaging model, the projection of a space sphere includes the sphere image and its antipodal sphere image. As the pair of sphere images are separated, they have only one common self-polar triangle. In this study, a method for homography estimation based on the projection of the space sphere is proposed, which involves using a paracatadioptric camera. The proposed method requires two images of the space sphere from different positions to obtain a homography matrix. This homography matrix has eight degrees of freedom, and a line correspondence pair provides two constraints. Hence, using four pairs of line correspondences, the homography matrix can be solved, and the external parameters can be obtained. The effectiveness of the proposed method is verified through simulation and experiments using real images.
Regression PCA for Moving Objects Separation

This work proposed a new approach called regression PCA (RegPCA) for statistical machine learning and big data analyses. One of the potential use cases investigated in this work is to separate the moving objects (foreground) from the background images. This is achieved by performing regression before conducting Robust PCA (RPCA). RegPCA works well in the moving object detection task because the background information can be conceived as the regression portion of the images, while the residual portion of the regression can then be fed into RPCA to fine tune the foreground detection. The experiments show that in moving object detection problems RegPCA provides much better results than applying only RPCA, especially in color videos and when the moving objects are relatively big. Further studies are needed to leverage the interesting features of RegPCA approach and apply it to solve more real world problems.
Scalability achievements for enumerative biclustering with online partitioning: Case studies involving mixed-attribute datasets

Biclustering is a powerful data analysis technique and its concept is appealing in many domains, such as natural sciences and market basket analysis. To exemplify the wide range of biclustering applications, we can also mention recommender systems, educational data mining, emerging topic detection and counterfeit product detection. In this paper, we further extend RIn-Close_CVC, a biclustering algorithm capable of performing, in numerical datasets, an efficient, complete, correct and non-redundant enumeration of maximal biclusters with constant values on columns. By avoiding a priori partitioning and itemization of the dataset, RIn-Close_CVC implements an online partitioning, which is demonstrated here to guide to more informative biclustering results. The improved algorithm, called RIn-Close_CVC3, is characterized by: a drastic reduction in memory usage; a consistent gain in runtime; additional ability to handle datasets with missing values; and new skills to operate with attributes characterized by distinct distributions or even mixed data types. Moreover, RIn-Close_CVC3 keeps those four attractive properties of RIn-Close_CVC, as formally proved here. The experimental results include synthetic and real-world datasets used to perform scalability and sensitivity analyses, besides a comparative inquiry involving a priori and online partitioning. As a practical case study, a parsimonious set of relevant and interpretable mixed-attribute-type rules is obtained in the context of supervised descriptive pattern mining.
Embedded Feature Selection Based on Relevance Vector Machines With an Approximated Marginal Likelihood and its Industrial Application

Feature selection is of great importance to make prediction for process variables in industrial production. An embedded feature selection method, based on relevance vector machines with an approximated marginal likelihood function, is proposed in this study. By setting hierarchical prior distributions over the model weights and the parameters of the automatic relevance determination kernel function, respectively, a Gaussian approximation method is designed to approximate the intractable exact marginal likelihood by using the law of the total expectation and the total covariance. Furthermore, in this study, the joint posterior distribution over the model weights and the kernel parameters is estimated by combining the Gibbs sampling with a Laplace approximation. Thus, feature selection is performed by examining the posterior over the kernel parameters. To verify the performance of the proposed method, a series of benchmark datasets and two practical industrial datasets are employed. The experimental results demonstrate that the proposed method not only produces higher prediction accuracy than other methods but also performs better in feature selection, especially in industrial case.
I-Vector and DNN Hybrid Method for Short Utterance Speaker Recognition

The paper proposes a hybrid model method of I-Vector and DNN to enhance the recognition accuracy of the short utterance speaker recognition system. Firstly, In order to improve the recognition accuracy of I-Vector model, this paper adopts the probability linear discriminant analysis method to avoid channel interference in speech. As a critical method, the DNN makes it possible to extract deep features by training speech features in deep neural networks, which has achieve the effective training and recognition with a small amount of speech data. Finally, a dual discrimination mechanism based on the hybrid model is proposed to effectively overcome the shortcomings of small training samples and easy interference, thus improving the recognition rate of short speech speaker recognition system. The speaker recognition experiment based on TIMIT database indicate that the presented method can effectively improve identification accuracy of the speaker recognition system in short utterance environment.
A general model-based filter initialization approach for linear and nonlinear dynamic systems

Tracking performance significantly relies on the quality of filter initialization, including accuracy and consistency, especially in a nonlinear filtering system. Most existing methods are built on assumptions that the target position varies linearly with time or the dynamic model is linear. In practical applications where the assumptions are violated, these methods may suffer from performance degradation or cannot be applied. To address this limitation, a general model-based initialization method for both linear and nonlinear dynamic systems is proposed. According to the dynamic model, the relationships between the first several measurements and the state to be initialized are analyzed and several equations are constructed to solve the initial state estimate. When transcendental equations are involved, numeric root-finding methods are employed to find numeric solutions. In cases with linear analytic solutions, the initial state covariance can be derived explicitly. In other cases, the unscented transformation is employed to calculate the initial covariance. Two commonly used linear dynamic models and three representative nonlinear dynamic models are used as examples to illustrate the procedure of the proposed algorithm. Monte Carlo simulation results demonstrate the effectiveness of the proposed filter initialization algorithm.
Multi-level dictionary learning for fine-grained images categorization with attention model

Fine-grained image categorization is a challenging task due to the difficulty of localizing the discriminative regions for different sub-categories. Previous works mainly focus on using the manual annotations or the attention algorithm to localize these regions, which is demanding and complex in practical applications. This paper proposes a method of using a multi-level attention model (MLA-CNN) which has been trained on the full-size image train set of current tasks to localize the most discriminative regions. Intuitively, three typical receptive field sizes are selected for the multi-level attention maps. Then, multi-level dictionary learning is introduced to extract discriminative features from these localized regions. Our method explores a new thought about how to use the neural activations to generate multi-scale regions which are helpful for the fine-grained categorization. The method can be achieved in two steps. The first step is to select the neurons that have the max activation in the selected three feature maps. These feature maps are the outputs of the pre-trained CNN model by feeding the full-size images into the model. Then, we generate the discriminative regions according to the receptive field size of the selected neurons. The second step is to train the subtle networks with these multi-scale regions. One scaled discriminative region can be regarded as one typical dictionary feature. Then these results are integrated for final prediction. We evaluate our method on three challenging fine-grained image datasets, CUB-200-2011, Stanford Dogs, and Stanford Cars. The experimental results demonstrate that our method outperforms many state-of-the-art methods, using extra object/parts annotations and attention-based methods.
Developing deep LSTM model for real-time path planning in unknown environments

This paper proposed a real-time path planning approach based on deep LSTM model, in which the model was constructed by Long Short-Term Memory (LSTM). By collecting various scenarios and paths generated by the existing static path planning algorithm, we form the dataset including the UAV's optimal behaviors and the current observed threats at each time step, which is utilized for training the deep LSTM model. Thus, through learning the training dataset, the model can make optimal decisions in real-time according to the threat around at the current time. Experimental results show that the proposed approach has the ability to fulfill the real-time path planning requirements, including environment unknown, navigation to the target and collision-free. Moreover, our real-time path planning approach can improve the global optimization quality of the path.
Cross-Blockchain Databases for Governments: The Technology for Public Registries and Smart Laws

This paper presents a concept of the protocol for public registries based on block-chain. The proposed mechanism allows creating a standard database over a bun-dle of distributed ledgers. It ensures a blockchain agnostic approach and utilizes the benefits of various blockchain technologies in one ecosystem. In this scheme, blockchains play the role of journal storages (immutable log), while the overlaid database is the indexed storage. The distinctive feature of such a system is that in blockchain, users can perform peer-to-peer transactions directly in the ledger us-ing blockchain native mechanism of user access management with public-key cryptography (blockchain does not require to administrate its database). The pro-tocol is designed for public property registries, i.e., land titles, cars, boats, corpo-rate rights, etc. Users can create and manage their rights using the full power of blockchain technologies and smart contracts. The governance component in this protocol is introduced as Smart Laws and Digital Authorities, the algorithms to manage the overlaid system and address legal issues with property rights and law enforcement.
A Flexible Highly Configurable System Architecture for Geographical Information System

In the current environment, the functionality and the usage of Geographic Information Systems (GIS) have grown steadily, resulting in many diversified and complex artifacts. Although there is a different GIS used for other regions with different goals, they have versatile features and requirements. So, for intensive software reuse, such as Highly Configurable System Engineering (HCSE), different techniques can be applied. Despite extensive research on Highly Configurable Systems Engineering in recent years, the same has not yet been defined in the field of GIS. In this research work, we identified the requirements and capabilities for a standard product of Web-based GIS. We shared them jointly, which allowed us to regenerate many of the different objects and software variants, which will enable software engineers to configure his application. We then define the functionality and technical architecture of a highly configurable system that uses current technology to develop web-based applications. Lastly, we designed a tool to configure and assemble components to generate the desired products. Subsequently, the proposed platform is flexible to create its GIS product as per each customer's specific needs.
Ensemble Learning Algorithm-based Artificial Neural Network for Predicting Solar Radiation Data

Trustworthy acquaintance and accurate solar radiation measurements are a condition for designing and managing solar energy systems. Frequently, there are substantial spatial and temporal lacks of measures so that predictive approaches become of interest. In the present paper, an ensemble learning approach is proposed based on the deep neural network technique. The suggested approach is applied to forecast the hourly time series of global solar radiation related to Marrakech, Morocco. For that purpose, the developed forecasting model was trained using seven years of measurements with an hour resolution via the efficient optimizer of Levenberg-Marquardt. Five exogenous inputs are involved for performing the prediction task, including air temperature, relative humidity, precipitation, wind speed, and time feature. The achieved outcomes prove the proposed learning approach's consistency and accuracy to generate synthetic solar radiation data in case of need.
Script Identification for Printed and Handwritten Indian Documents: An Empirical Study of Different Feature Classifier Combinations

The identification of script in a document page image is the first step for an OCR system processing multi-script documents. In this multilingual/multiscript world, document processing systems relying on the OCR that need human involvement to select the appropriate OCR package is definitely undesirable and inefficient. The development of robust and efficient methods for automatic script identification of a document is a subject of major importance for automatic document processing in a multilingual/multiscript environment. Thus, the basic objective is to come up with some intuitive methods having straightforward implementation without compromising with efficiency. The aim of this work is to evaluate state-of-the-art feature extraction and classification techniques in the field of automatic script identification of printed and handwritten documents and to propose the best combination for the same.
Enhanced Genetic Algorithm for Optimized Classification

Genetic algorithm (GA) is a well-known adaptive nature inspired technique that utilized in various applications and research domains. This research proposes an Enhanced Genetic Algorithm (EGA) inspired by Genetic Engineering. In EGA, the process of chromosome generation is interfered based on the intercorrelation between genes such that the highly correlated genes are treated. The proposed EGA were employed to optimize the classification results using Support Vector Machine (SVM) on the “Spambase” popular dataset. Experimental results show that classification results were more optimized using EGA as compared to basic GA optimization. The classification results were enhanced using the proposed Enhanced Genetic Algorithm (EGA).
Improved IMM algorithm based on XGBoost

Abstract Based on Markov hypothesis, IMM uses multiple motion models to match the moving states of the target, and assumes the transfer probability of each model according to the prior knowledge, which has strong adaptability to tracking maneuvering target. However, it is not direct enough to obtain prior knowledge from source data statistics and then make decision according to maximum likelihood, and the information of source data is not fully utilized. Therefore, we use XGBoost in machine learning algorithm to replace this process. We propose XGBoost-IMM model algorithm. XGBoost can fully learn the information of the source data and make decision on the target motion model, and then IMM can perform multi-filter filtering based on the decision. Experimental results show that our algorithm has good performance.
A Study of User Story in Practice

The focus of agile methodology is on customer satisfaction through the early and continuous delivery of valuable software. In agile, requirements are represented in the form of user stories. And the user stories are represented from the user's point of view. The user stories are effective only when the software practitioners are aware of the user story quality and customer value delivery. In this research work, we studied how well are the user stories used in practice. And are agile software practitioners using user stories and using them correctly? Furthermore, are they aware of the quality issues in the user stories? It was observed from our study that all agile practitioners do not use user stories. The results show that only 45% of agile practitioners used user stories. And only 14.81% of these practitioners had a sound understanding of user stories. This suggests that most of the agile software practitioners may not be using user stories correctly. Additionally, the results also show that backend developers do not seem to focus much on user stories when compared to frontend and most importantly the full-stack developers. Nonetheless, there is still a significant knowledge gap. Therefore, we see it as a general need to train and educate agile professionals about user stories so that they can be used in an effective way to generate customer value.
A comparison of reverse projection and PhotoModeler for suspect height analysis

The purpose of this study was to compare the accuracy and precision between the Reverse Projection and PhotoModeler methods for suspect height analysis. Thirty analysts were assigned to measure the heights of three different suspects, one for each method, with the suspects having been recorded standing at three different distances in a scene. For Reverse Projection, the analysts were provided with height scales to place and video-record at the same positions their suspects stood in at the test scene, so that frames could be extracted from the video and overlaid onto frames of the suspects to measure height. For PhotoModeler, analysts calibrated frames of the suspects using 3D point cloud data obtained from a laser scan of the scene, so that measurements could be made in PhotoModeler software. Errors were calculated for the measurements and compared using the Mann-Whitney U-test and Kruskal-Wallis H-test, which indicated significant differences for errors between the two methods (p = 0.0025 and p = 0.008). Reverse Projection yielded a greater range of error and tended to have higher standard deviations than PhotoModeler, but the overall accuracy between the two methods was found to be comparable. The majority of absolute measurement errors for both methods were less than 2 cm.