Reviewerly/exHarmony huggingface dataset:
Unique Identification Authority of India BOT: An Intelligent Application Using Artificial Intelligence Approach

In the modern world, the growth of technology is exponential. Artificial Intelligence (AI) and Machine Learning (ML) are evolving rapidly day by day. The evolving AI applications are super smart and definitely usable for layman. In such application, normal bots are famous application for conversation. This project includes an implementation of Unique Identification Authority of India (UDAI) BOT (mentioned as U-bot) of virtual assistant for the conversation in the natural language with the customer, uniquely identified by their Aadhaar number where the details of the Aadhaar card can be detected by using Quick Responsible (QR) code scanner. According to the details of the Aadhaar card, the virtual assistant tries to satisfy the customer by responding for every appropriate query raised in the form of either voice or the text. Thereby, the customer can have a conversation to the maximum possible level to which it is programmed.
Integration of DDS Based System Using Routing Service

DDS is network middle ware which simplifies application development, deployment and maintenance by providing flexible configuration for integration testing. Software system Integration of distributed real-time applications in Controlled environment, which tries to mimic Actual environment, is a big challenge. This paper studies the integration approach of DDS based system in Controlled environment using the flexibility that DDS provides. Major Issues faced during integration of DDS based software system are identified and solution for the same has also been given. In this paper the proposed DDS Routing Service drastically eases the scaling and integration of real-time systems across Wide Area Net-works as well Local area Net-works.
Is Twitter an Unexploited Potential in Indian Academic Libraries?

Social media have revolutionized today's globalized world including all spheres of modern human being. Among many social networking sites (SNSs), Twitter is one of the most popular. Librarians all over the globe are increasingly using Twitter in their daily routine activities as well as promotion of their systems and services. This study is an attempt to map the Indian libraries' Twitter activity, taking academic libraries as case study. Selected Indian academic library tweets have collected form the Twitter using R programming language. The study further compares few develop countries' academic library tweets. The study observed that Indian academic libraries are very limited activities in Twitter. The sentiment analysis shows that library Tweets are more positive. The study recommends more Twitter activity for Indian academic libraries to attract their users. With the more Twitter activities, library's image will be more friendly and acceptable to the young users particularly the college and university students.
Adaptive Exact Learning in a Mixed-Up World: Dealing with Periodicity, Errors and Jumbled-Index Queries in String Reconstruction

We study the query complexity of exactly reconstructing a string from adaptive queries, such as substring, subsequence, and jumbled-index queries. Such problems have applications, e.g., in computational biology. We provide a number of new and improved bounds for exact string reconstruction for settings where either the string or the queries are “mixed-up”.
System and Methodology to Generate Dynamic Real-time Transparent and Bias-free Feature Modules used to Augment Model Learning Process

Features are the backbone of any Machine Learning(ML) system. During the t <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> epoch of the model learning process, learnt features are the measurable outcomes of the model for a given input function. One of the biggest areas of research has been to try to impose even the slightest amount of control on what features a model learns. Achieving this in-turn poses various advantages, including re-usability of the features. Some of the state-of-the-art reusability techniques work by training the model weights and freeze at a random point k, on the source system and test the accuracy on the target until the ideal k point gives the highest possible accuracy. However, some of the current challenges remain in trying to control what is being sent to the target model and also in sending bias-free content. Thus, if we can aid this by adding a "bias-free control-factor" to the features being re-used in the model we would truly be able to perform many more tasks efficiently and achieve much higher accuracy along with lower consumption of resources required for training a model, thereby enabling ML towards many more industrial applications. We propose to provide a methodology by which we augment dynamic and bias-free feature modules for a model building process by taking an example in a Natural Language Processing(NLP) context.
Decision Surfaces of Localized Classifiers

Local learning algorithms are a very general class of non-parametric lazy learners obtained by stitching together systems of locally weighted parametric models. For a system of locally-learned classifiers, there is currently no convenient method to find points on the decision surface. In this paper, we introduce a generic algorithm for finding the decision surface for systems of localized classifiers using arbitrary model families. The decision surface of a classifier is often useful for obtaining pseudo-probabilistic output from the orthogonal distance of a point to the decision surface. We therefore extend our method to find this orthogonal projection of an arbitrary point onto the decision surface for a broad class of classifier families. We specifically derive the necessary equations for computing the orthogonal projection onto the decision surface of systems of locally linear support vector machines. We demonstrate how this can be used for pseudo-probabilistic calibration, and by extension for multiclass classification strategies such as one-vs-rest or one-vs-one. Lastly, we demonstrate the efficacy of this method to obtain more accurate multi-class classifiers on popular datasets.
A Fuzzy AHP and TOPSIS Approach for Web Service Selection

Multi criteria decision making (MCDM) model is proposed for determining the most suitable web service from a collection of functionally-equivalent web services with different non-functional properties. This paper presents an evaluation approach that combines fuzzy analytic hierarchy process (AHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to solve the MCDM selection problems with conflicting criteria. Fuzzy AHP method determines subjective weights by dealing with vagueness and uncertainty in subjective user's judgment while, TOPSIS algorithm ranks the different alternatives. A numerical example is based on a real-world dataset is presented to illustrate the procedural matters of the web service selection model. The numerical results show that the proposed approach can effectively select an appropriate web service based on user preference. WeatherStationService performed better than other web services under the selected QoS requirements.
A Self-Organizing Modular Neural Network for Nonlinear System Modeling

In this paper, a self-organizing modular neural network (SO-MNN) is proposed for nonlinear system modeling, in which the modular structure and sub-networks are optimized to improve the modeling accuracy and efficiency. First, the modular structure is constructed by seeking the maximum modularity degree of the whole neural network and the optimal hub center in each sub-network. Simultaneously, the task is divided into several sub-tasks. Then, according to the assigned sub-tasks, sub-networks are constructed based on an incremental radial basis function (RBF) neural network, whose performance can be guaranteed by a structure growing mechanism and an adaptive second-order learning algorithm. Finally, during the testing or application processes, a winner-take-all strategy is used to integrated all the sub-networks. The effectiveness of the proposed methodology is verified by two benchmark problems and a real-world application.
Exploring an Agent Interaction Modeling System (AIMS) for Human Autonomy Teams: Towards the Development of Intelligent Models of Interaction

This position paper describes an Agent Interaction Model and framework, i.e. the ability to define interaction roles (e.g. professor, student, parent, child, police, civilian) identified by varying amounts of standardized role features (e.g. Curiosity, Responsibility, Deference, Scientific Knowledge) mapped to physical interaction features (e.g. likelihood of interaction, likelihood of knowledge transfer). The framework can be used to enable exploration of amount, types and patterns of knowledge flow through agent interactions based on agent roles. The hypothesis is that in teams of humans and systems there is a distribution of roles that optimizes knowledge transfer. Empathy, i.e. the ability to change roles quickly based on the needs of others, is hypothesized to be an enabler of improved knowledge flow. The framework allows exploration of these hypotheses. In this paper, the authors report on initial development of the model and their proposed next steps to extend the model and the framework for use in human-agent experiments by mapping the features to user-interface components such as text, voice, gesture, appearance, signs and symbols..
Generalizing Deep Models for Overhead Image Segmentation Through Getis-Ord Gi* Pooling.

That most deep learning models are purely data driven is both a strength and a weakness. Given sufficient training data, the optimal model for a particular problem can be learned. However, this is usually not the case and so instead the model is either learned from scratch from a limited amount of training data or pre-trained on a different problem and then fine-tuned. Both of these situations are potentially suboptimal and limit the generalizability of the model. Inspired by this, we investigate methods to inform or guide deep learning models for geospatial image analysis to increase their performance when a limited amount of training data is available or when they are applied to scenarios other than which they were trained on. In particular, we exploit the fact that there are certain fundamental rules as to how things are distributed on the surface of the Earth and these rules do not vary substantially between locations. Based on this, we develop a novel feature pooling method for convolutional neural networks using Getis-Ord Gi* analysis from geostatistics. Experimental results show our proposed pooling function has significantly better generalization performance compared to a standard data-driven approach when applied to overhead image segmentation.
Facial Appearance Modifications using SKPCA-Derived Features Extracted from Convolutional Autoencoder’s Latent Space

The presented paper proposes a method that enables image object appearance editing by modifying its appearance-related high-level attributes. First, attribute-related features get extracted from a latent representation of an image generator and then, their contents gets modified, which results in producing the assumed appearance alterations. Convolutional Autoencoder (CAE) has been adopted as an image manipulation framework and face appearance, characterized by four attributes: age, smile intensity, facial hair intensity and gender, was chosen for modifications. To extract attribute-related features from CAE's latent representation, Supervised Kernel Principal Component Analysis (SKPCA) was used, as this transformation is able to disentangle complex, nonlinear image-to-attribute relationships. The method has been evaluated using large-scale face dataset CelebA. Qualitative results show that realistically-looking appearance modifications can be obtained. To quantify plausibility of introduced modifications, face recognition experiments on altered face images were performed, delivering on average 95% classification accuracy, for twenty-six category dataset.
Conceptual Operations with Semantics for a Companion Robot

We study the features crucial for a companion robot conceptual processing and suggest a practical implementation of a cognitive architecture that support these features while operating a real F-2 companion robot. The robot is designed to react to incoming speech and visual events, guide a person in a problem space and accumulate knowledge from texts and events in memory for further dialogue support. We show how a conceptual representation system designed for a companion robot deals with several types of conceptual representations: text semantics, sets of emotions and reactions, operations in a problem space and in semantic memory. We also suggest a conceptual representation based on linguistic valency structures (semantic predication) that is suitable to link the processing components. The general processing architecture is based on the production approach: it may trigger several scripts and combine speech and behavioral outputs of these scripts on the robot. The system performs conceptual operations with semantics while processing texts on a server in a standalone mode, or while controlling the robot in a dialogue mode, or assisting a user in solving Tangram puzzle.
Algebraic Bayesian Networks: the Complexity of Consistency Checking

One of the primary tasks arising in the theory of algebraic Bayesian networks is the task of checking and maintaining the consistency of a knowledge pattern. Now, there are algorithms for solving this problem, but in computer science and programming, it is extremely important to understand the computational complexity of the methods used. This paper is devoted to estimating the number of steps of these algorithms.
MAM: Mixed Attention Module with Random Disruption Augmentation for Image Classification

Visual attention mechanism extracts features by establishing a mask branch characterizing the distribution regions we are interested in. With the thought that eliminating useless features helps in the process of extracting significant features, in this paper we design a novel visual attention mechanism structure called the mixed attention module (MAM), which is capable of distilling significant patterns as well as suppressing unimportant features. The generic MAM constructed by the min-pooling and the tanh function is embedded into a mixed attention network for image classification. Moreover, a new image enhancement method named the random disruption (RandomDisrupt) is developed by slicing an image and then scrambling it. RandomDisrupt destroys the positional information between the features and the structural information of the object to accomplish data augmentation. The combination of the MAM and RandomDisrupt has improved the image classification performance significantly. Benchmark experiments are conducted to show the superiority of the proposed algorithm over a state-of-the-art method. Source code would be available at https://github.com/weiyu-zeng/MAM.
Monitoring of Potential Safety Hazards of Transmission Lines Based on Object Detection

Power transmission line safety monitoring is one of the important tasks to maintain the security of national power grid. In this paper, the object detection method based on computer vision is applied to automatically monitor the potential safety risk of transmission line. We firstly create a potential safety risk object dataset. Secondly we analyze most state-of-the-art object detection model. Thirdly according to the specific dataset, an object detection model was trained, which uses training tricks to get high performance. Fourthly, we built a monitoring system that feeds the discriminant results back to the display terminal, which can comprehensively grasp the situation of the whole safe area and ensure the safe operation of the transmission network. Our experiments show the excellent results are Cascade R-CNN detection framework based on deep learning and backbone based on high resolution representations network. It gains 81.5 mAP on 26 kinds of objects datasets at IOU threshold 0.5, and show hidden danger detection algorithm based on deep learning can accurately discriminate the dangerous sources. The monitoring system feeds the discriminant results back to the display terminal, which can comprehensively grasp the situation of the whole safe area and ensure the safe operation of the transmission network.
Image Query-Based Tablet Identification System by Examining Various Text Recognition Classifiers

Classification is the most important and frequently used technique in image processing. Detecting text region in images is helpful in computer vision applications, like searching, analyzing, and retrieving image. Text detection and extraction (TDE) is an important step of image query-based tablet identification system, as it is used as an important feature during tablet identification. The key purpose of this work is to improve the effectiveness of text detection and extraction process. This research work deals with the approach based on a binarization method that uses canny edge detection, Otsu thresholding, enhanced connected component labeling with automatic threshold procedure. Experimental results show that the support vector machine (SVM) classifier is efficient than the other three classifiers with respect to accuracy, speed, precision, recall, and F-measure in both consumer and reference images.
Cardinality Based Rate Limiting System for Time-Series Data

Massive monitoring systems that require high availability and performance for both ingestion and retrieval of data are often encountered with rogue streams of data having a high cardinality. The management of such high cardinality data sets for time-series data and a performance sensitive system is challenging. The challenges primarily arise as the time-series data sets, typically needs to be loaded onto a limited memory space before results can be returned to the client. This affects the number of incoming queries that can be supported simultaneously. Too many time-series can potentially degraded read performance and thereby affect user experience. Our proposed rate-limiting system described herein seeks to address a key availability issue on a high-volume, time-series system by using a dynamic cardinality computation in combination with a central assessment service to detect and block high cardinality data streams. As a result of this technical improvement, anomalous logging behavior is detected quickly, affected tenants are notified, and hardware resources are used optimally.
ParaCA: A Speculative Parallel Crawling Approach on Apache Spark

The World Wide Web today is growing at a phenomenal rate. The crawling approach is of vital importance to improve the efficiency of crawling the web. The existing sequent crawling algorithms are mostly time consuming and do not support large data well. In order to improve parallelism and efficiency of crawler on distributed network environments, based on the software thread-level speculation technique, this paper raises a speculative parallel crawler approach (ParaCA) on Apache Spark. By analyzing the process of web crawler, the ParaCA firstly hires a function to divide a crawling process into several subprocesses which can be implemented independently and then spawns a number of threads to speculatively crawl in parallel. At last, the speculative results are merged to form the final outcome. Comparing with the conventional parallel approach on multicore platform, ParaCA is very efficiency and obtains a high parallelism degree by making the best of the resources of the cluster. Experiments show that the proposed approach could achieve a significant speedup improvement with compare to the traditional approach in average. In addition, with the growing number of working nodes, the execution time decreases gradually, and the speedup scales linearly. The results indicate that the crawling efficiency can be significantly enhanced by adopting this speculative parallel algorithm.
A Developer Recommendation Method Based on Code Quality

During the development cycle of a project, it is common for software requirements and functionality to change and for code errors to occur. To deal with these unforeseen changes, the artifact known as change request, which is a formal proposal to alter a system, is used. Its assignment is an important step in the development process. Projects can receive a very high number of requests daily, which makes the automation of this process compelling. This work proposes a method for assigning unresolved requests, based on developer's profiles. The proposed method consists of three steps. The first step is to extract code quality metrics, commit data and previously resolved requests, in order to model developers through the mining of repositories. The second step concerns with the selection of the profile of potential developers through the application of natural language processing and information retrieval techniques. And finally, in the third step the appropriate developers are selected based on the quality of their code and the impact of their commits. Results from experimental evaluation show that the method is able to recommend more developers with a positive impact on the repository quality if compared to the iMacPro method.
The Robustness of the VSSD Watermarking Algorithm to UDFF Image Deformations

The first part of the paper describes the Variable Steps on the Schur Decomposition (VSSD) algorithm for watermark insertion and extraction. After that, the User-Defined Force Fields (UDFF) algorithm for image deformation is described. The second part of the paper describes an experiment within which robustness of the watermarked image (inserted by the VSSD algorithm,) that had been deformed by UDFF algorithm, was tested. Objective quality measures (MSE, PSNR, AMBE, SSIM, and NC) between the inserted and extracted watermark were calculated. The results are presented graphically. At the end, watermark robustness was determined by analyzing the results.
Semantic Core Building of a Site Based on Clustering Algorithms

Websites are the main sources of information in the Internet. The ability to increase the ranking of the site for the search engine is particularly relevant in these circumstances. Engaging users with a demonstrating of a landing page on a search engine is the goal of search engine optimization. One of the steps of such optimization is a semantic core building. Semantic core is the most complete list of keywords that describes the theme and orientation of the site and matches its content. This paper presents semantic core building techniques for a site based on clustering algorithms.
A Hybrid Firefly Algorithm Based on Orthogonal Opposition

Firefly Algorithm (FA) may suffer from lower convergence accuracy when solving high-dimensional and complex optimization problems. To solve this problem, a completely new strategy named Hybrid Firefly Algorithm Based on Orthogonal Opposition (OHFA) is proposed. In OHFA, we perform differential evolution (DE) on brighter fireflies (j) and orthogonal opposition-based learning (OOBL) on globally optimal firefly to improve the search ability of the population. Besides, in high-dimensional and large-scale search space, there is an obvious long Euclidean distance between fireflies, which reduces attraction in movement. Therefore, OHFA adopts a new movement to improve the application of the firefly algorithm in high-dimensional space. Computational results show the effectiveness of OOBL and DE. Our findings suggest that OHFA achieves better solutions than other proposed algorithms on most of the test functions.