Reviewerly/exHarmony huggingface dataset:
Recognition of Handwritten Bangla Number Using Multi Layer Convolutional Neural Network

In this paper, a new methodology is proposed to recognize the handwritten Bangla number using multi layer Convolutional Neural Network (CNN). The NumtaDB dataset is used for training the CNN model which consists of 85000+ digits images. After training the model with this vast, standard and unbiased data, the model attains a training accuracy of 99.66% and validation accuracy of 98.96% for recognizing bangla digits which shows that the model is not overfitted or underfitted and ready for real world unseen data. A great emphasis has been given on pre-processing and splitting of digits from a number since multi layer CNN alone is not good enough for attaining a significant accuracy. A small dataset containing 200 number images which has 2 digit to 5 digit numbers is tested against our proposed model. The validation accuracy for this model has been found to be 88%, which is higher than any recently proposed models.
Online State-Time Trajectory Planning Using Timed-ESDF in Highly Dynamic Environments

Online state-time trajectory planning in highly dynamic environments remains an unsolved problem due to the curse of dimensionality of the state-time space. Existing state-time planners are typically implemented based on randomized sampling approaches or path searching on discrete graphs. The smoothness, path clearance, or planning efficiency is sometimes not satisfying. In this work, we propose a gradient-based planner on the state-time space for online trajectory generation in highly dynamic environments. To enable the gradient-based optimization, we propose a Timed-ESDT that supports distance and gradient queries with state-time keys. Based on the Timed-ESDT, we also define a smooth prior and an obstacle likelihood function that are compatible with the state-time space. The trajectory planning is then formulated to a MAP problem and solved by an efficient numerical optimizer. Moreover, to improve the optimality of the planner, we also define a state-time graph and conduct path searching on it to find a better initialization for the optimizer. By integrating the graph searching, the planning quality is significantly improved. Experiments on simulated and benchmark datasets demonstrate the superior performance of our proposes method over conventional ones.
Embedding based learning for collection selection in federated search

Purpose There have been many challenges in crawling deep web by search engines due to their proprietary nature or dynamic content. Distributed Information Retrieval (DIR) tries to solve these problems by providing a unified searchable interface to these databases. Since a DIR must search across many databases, selecting a specific database to search against the user query is challenging. The challenge can be solved if the past queries of the users are considered in selecting collections to search in combination with word embedding techniques. Combining these would aid the best performing collection selection method to speed up retrieval performance of DIR solutions. Design/methodology/approach The authors propose a collection selection model based on word embedding using Word2Vec approach that learns the similarity between the current and past queries. They used the cosine and transformed cosine similarity models in computing the similarities among queries. The experiment is conducted using three standard TREC testbeds created for federated search. Findings The results show significant improvements over the baseline models. Originality/value Although the lexical matching models for collection selection using similarity based on past queries exist, to the best our knowledge, the proposed work is the first of its kind that uses word embedding for collection selection by learning from past queries.
A Novel Ensemble Learning for Defect Detection Method With Uncertain Data

Data which contains noise is termed as uncertain data, and the presence of noise makes a deviation in the correct, intended, or original values. Size and complexity of the software products are the two main reasons for uncertain data set that identifying defective modules in uncertain datasets has become a challenging issue. In this chapter, the authors implemented a multi-learner ensemble model for uncertain datasets for defect detection. In this model, different weak classifiers are optimized to improve the classification rate on uncertain data. They have implemented their proposed model on NASA(PROMISE) metric data program repository. Accuracy is used as performance evaluation metric for our multi-learner ensemble defect detection model and ensemble model outcome achieved higher accuracy rate of 97% and when compared to another classification model.
Preparing for Highly Scalable and Replicable IoT Systems

Interoperability, reusability, and scalability are important design objectives as IoT systems become pervasive and begin to address complex use cases. They underpin the need for open-standard solutions which allow vendors and solution providers to collaborate. A horizontal architecture satisfies the three design objectives while breaking down the barriers between silo IoT applications. oneM2M, the global IoT standard, defines such a horizontal architecture. It consists of a middleware technology layer located between a lower layer that consists of devices and communications technologies, and an upper layer of IoT applications. This article describes the oneTRANSPORT Data Marketplace pilot project, based on the oneM2M standard. It enabled IoT data sharing from over 200 types of connected assets belonging to several public and private sector agencies. Through practical trials, the pilot illustrates how data scientists, operations staff, and facilities managers supervised transportation networks and offered real-time journey planning guidance to the traveling public. The value of the oneM2M standards-based data marketplace is illustrated by reference to other deployments that reuse the horizontal architecture over a larger geographic footprint and to implement other IoT use cases.
High Frame Rate Video Reconstruction and Deblurring Based on Dynamic and Active Pixel Vision Image Sensor

Active image sensors can only record scenes at low frame rates. The dynamic vision sensor (DVS) can capture high-speed scenes under challenging lighting conditions. However, DVS discards absolute light intensity information. The dynamic and active pixel vision image sensor (DAVIS) can output events and low-frame-rate grayscale images simultaneously. This paper proposes a high frame rate video reconstruction and deblurring algorithm based on events and low-speed blurred image sequences from DAVIS. First, we visualized events as a sequence of event images and performed image denoising process. Then, we used the extended 3D partial recursive search (E-3DPRS) method to generate high-performance motion vector field (MVF) between adjacent denoising event images with low computational complexity. MVF reflects the object motion trajectory with high time resolution. Furthermore, MVF is used as a priori information to restore potentially sharp images, which alleviates the highly ill-posed deblurring process. At last, we reconstructed a high frame rate sharp video based on a sharp image sequence and MVFs. Extensive experiments show that our proposed algorithm outperforms the existing state-of-the-art methods and achieves better average feature similarity and structural similarity.
Optimization of Fuzzy Trajectory Tracking in Autonomous Mobile Robots Based on Bio-inspired Algorithms

In this paper a comparison between the Bee Colony Optimization (BCO) and the Chicken Search Optimization (CSO) algorithms in optimal fuzzy control design is presented. The comparison is based on solving the problem to find the optimal distribution on the Membership Functions (MFs) of fuzzy controllers for mobile robots. Optimization in the structure and parameters for designing for a fuzzy tracking controller is presented. A new CSO algorithm for the optimization in Fuzzy Logic Controllers (FLC) is presented. The main aim is to use the two bio-inspired algorithms individually to improve of trajectories of an Autonomous Mobile Robot. CSO algorithm shows better results when compared to BCO in the simulation results for this control problem.
Virtual Reality for Understanding Multidimensional Spatiotemporal Phenomena in Neuroscience

Neuroscientists traditionally use information representations on 2D displays to analyze multivariate spatial and temporal datasets for an evaluation stage before neurosurgery. However, it is challenging to mentally integrate the information from these datasets. Our immersive tool aims to help neuroscientists to understand spatiotemporal phenomena inside a brain during the evaluation. We refined our tool through different phases by gathering feedback from the medical experts. Early feedback from neurologists suggests that using virtual reality for epilepsy presurgical evaluation can be useful in the future.
Extracting Chinese Domain-specific Open Entity and Relation by Using Learning Patterns

Nowadays, Chinese domain-specific relation extraction faces a major challenge, that is the lack of annotation data. To cope with this challenge, the distant supervision which can automatically label large-scale training data was proposed. However, the distant supervision can produce noisy data which will hinder the performance of a model trained on such noisy data. Although significant progress has been made in filtering noisy data, the distant supervision method extracts the relation which already exists in the knowledge base. However, another major challenge in the extraction of domain-specific entities and relations is the diversity of entities and relation, which makes it difficult to accurately predefine relations in the knowledge base. Therefore, the distant supervision does not apply to domain-specific. In order to overcome the above challenges on specific domain, this paper proposes a Chinese Domain-specific Open Entity Relation Extraction Model (DOERE) which learns patterns from a small number of annotated data, and applies extraction patterns to the new domain-specific corpus for extracting entities and relations. Then, this paper proposes a method for automatically labeling data based on patterns. The experimental results show that the model has achieved better precision and recall in large-scale specific domain. And the method of automatically labeling data based on patterns has a good effect on data labeling in specific domain.
An Argumentation-Based Approach for Explaining Goals Selection in Intelligent Agents

During the first step of practical reasoning, i.e. deliberation or goals selection, an intelligent agent generates a set of pursuable goals and then selects which of them he commits to achieve. Explainable Artificial Intelligence (XAI) systems, including intelligent agents, must be able to explain their internal decisions. In the context of goals selection, agents should be able to explain the reasoning path that leads them to select (or not) a certain goal. In this article, we use an argumentation-based approach for generating explanations about that reasoning path. Besides, we aim to enrich the explanations with information about emerging conflicts during the selection process and how such conflicts were resolved. We propose two types of explanations: the partial one and the complete one and a set of explanatory schemes to generate pseudo-natural explanations. Finally, we apply our proposal to the cleaner world scenario.
Interview with a Robot: How to Equip the Elderly Companion Robots with Speech?

In this paper, we present the UKKO dialogue system which is intended to be implemented in humanoid robots, namely robots designed to help the elderly living in semi-hospital environments. First, we explain the UKKO’s approach, in comparison with other man-machine dialogue systems. UKKO takes advantage of works carried out as part the linguistic intelligence. The system includes, inter alia, a natural language understanding module, a natural language generation module and a dialogue manager module. These modules use linguistic resources. Second, we discuss the language facts analysis on which the man-machine interaction model relies. This analysis takes into account the following principles. Firstly, any statement is interpreted as a propositional content. Secondly, the minimal unit of any dialogue is a pair of successive statements. Thirdly, any dialogue is structured thanks to rules which govern turn-taking and the way statement pairs are linked. Finally, we lay out the system main technological aspects. We present the UKKO modular architecture and describe its principal components.
Responding to the need for online collective intelligence facilitation: A framework for systems thinking facilitators

Systems Research and Behavioral ScienceEarly View RESEARCH NOTE Responding to the need for online collective intelligence facilitation: A framework for systems thinking facilitators Michael Hogan, Corresponding Author michael.hogan@nuigalway.ie orcid.org/0000-0001-6227-0530 School of Psychology, NUI, Galway, Ireland Correspondence Michael Hogan, School of Psychology, NUI, Galway, Ireland. Email: michael.hogan@nuigalway.ieSearch for more papers by this authorOwen Harney, School of Psychology, NUI, Galway, IrelandSearch for more papers by this authorRobert Razzante, Hugh Downs School of Human Communication, Arizona State University, Tempe, AZ, USASearch for more papers by this author Michael Hogan, Corresponding Author michael.hogan@nuigalway.ie orcid.org/0000-0001-6227-0530 School of Psychology, NUI, Galway, Ireland Correspondence Michael Hogan, School of Psychology, NUI, Galway, Ireland. Email: michael.hogan@nuigalway.ieSearch for more papers by this authorOwen Harney, School of Psychology, NUI, Galway, IrelandSearch for more papers by this authorRobert Razzante, Hugh Downs School of Human Communication, Arizona State University, Tempe, AZ, USASearch for more papers by this author First published: 05 November 2020 https://doi.org/10.1002/sres.2752Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Global Best Guided Crow Search Algorithm for Optimization Problems

Crow search algorithm (CSA) is a nature-inspired metaheuristic algorithm that is emboldened by the social activity of intelligent bird. Crow preserves their surplus food from other crows which gives information regarding local search and limits to the solution in search space. The behavior of crow to search the food is formulated as CSA for solving optimization problems. However, it suffers from poor exploitation to the local search. Inspired from artificial bee colony (ABC), the search equation of CSA is modified in order to improve the exploitation guided by global best solution obtained so far in the search equation. The superiority of the global best guided CSA (G-CSA) is realized by a comparative analysis with DE, PSO, SCA, CSA algorithms on various benchmark equations.
On the Intention to Use the Pepper Robot as Communication Channel in a Business Context

This paper presents the results of a user acceptance survey (based on the Technology Acceptance Model ? TAM) of the humanoid social robot Pepper as a communication channel in different information retrieval scenarios in a business context. In total, 239 passers-by participated in the survey, which had a specific focus on the impact of perceived safety and security aspects. The results revealed a positive assessment of its perceived ease of use and perceived usefulness. It also showed how safety, data protection, and privacy concerns impact the intention to use the robot. The study findings are supported by the outcomes of eight expert interviews.
Unified Performance Measure for Binary Classification Problems

Different performance measures are used to inspect, compare and evaluate the behaviour of classifiers in Machine Learning (ML). ML researchers employ one or several of these performance measures in their classification studies to report their success. However, no widespread consensus has been reached on a unified chosen measure. In this work, we introduce a reliable and informative measure, the Unified Performance Measure (UPM), by modifying the $$F_1$$ -score to avoid its undesired behaviour in imbalanced classification problems. UPM is compared with alternative performance measures, like the $$F_1$$ -score or Accuracy, in both simulated confusion matrices and real datasets. The proposed measure outperforms the alternatives, providing a promising new research line.
Research on Data Sharing Architecture for Ecological Monitoring Using Iot Streaming Data

The rapid development of Internet of Things (IoT) technology and the widespread deployment of various sensors around the world have produced a large number of data streams. Thus, current computing systems face the challenge of quickly receiving and managing these large-scale streaming data. This study builds an efficient distributed database based on Greenplum (GP) and focuses on solving the problem of the low efficiency of structured data queries for observed ecological data collected from fragile areas in Northwest China's desert oasis. First, a distributed database is designed and deployed at the physical storage structure level. A database table structure is then established based on the characteristics of the streaming data. On this basis, the data storage strategy is optimized at the data table level. Additionally, the query efficiency of the distributed database is compared with the query efficiency of traditional standalone databases. The results show that the distributed database significantly improves the data query efficiency. The greater the amount of data stored, the better the improvement in efficiency. Finally, based on the optimized distributed database, we develop a data sharing system for streaming data from ecologically fragile areas in the desert oasis in Northwest China, which provides a new approach for the efficient sharing of massive amounts of IoT streaming data for ecological monitoring. Our storage system is still currently working normally, which is highly important to both data managers and users.
Upoma: A Dynamic Online Price Comparison Tool for Bangladeshi E-commerce Websites

The e-commerce industry is rapidly emerging all over the world. E-commerce sites provide the independence to choose and buy things over the internet. However, different websites put different prices on the exact same product, and this leads people to spend extra money on products which could be bought with less. Although there are several e-commerce sites available in Bangladesh, no price comparison tool yet developed. Thus, we have designed and developed a price comparison tool for Bangladeshi websites named `Upoma (Comparison)' which supports comparison even if the titles and prices of products are written English, Bangla, and Transliterated Bangla. We have fetched over 90,53,015 product details from various websites to enrich our result based on the user's query. In order to make our system dynamic and to keep pace with real-time changes occurring on the sites, our database is automatically updated in every 12 hours. Our system displays the result with 93.06% accuracy according to the user's query.
Classification of Underrepresented Text Data in an Imbalanced Dataset Using Deep Neural Network

Text classification is a well researched, much-explored topic for burgeoning researchers in the field of data mining. Yet, all the inadequacies that actual text data present during practical applications of these models in real life calls forth the need for further study. Imbalanced datasets, in particular, provide a roadblock to common high-performance algorithms. Thus, our research explores text classification concerning imbalanced datasets containing underrepresented categories, using the favored machine learning algorithms of today - neural networks. We have looked to explore how neural network classification models can improve upon inadequate datasets. Our research showcases why deep learning is the preferred classification algorithm, and proves, with hard evidence, that it outperforms other machine learning algorithms in text classification.
Security of Streaming Encryption in Google's Tink Library

We analyze the multi-user security of the streaming encryption in Google's Tink library via an extended version of the framework of nonce-based online authenticated encryption of Hoang et al. (CRYPTO'15) to support random-access decryption. We show that Tink's design choice of using random nonces and a nonce-based key-derivation function indeed improves the concrete security bound. We then give two better alternatives that are more robust against randomness failure. In addition, we show how to efficiently instantiate the key-derivation function via AES, instead of relying on HMAC-SHA256 like the current design in Tink. To accomplish this we give a multi-user analysis of the XOR-of-permutation construction of Bellare, Krovetz, and Rogaway (EUROCRYPT'98).
Use of firefly optimization algorithm for fractional fourier transform based reversible data hiding

This paper presents a Fractional Fourier transform based reversible data hiding technique to secure the data transmitted over communication network. The proposed algorithm modifies the cover image to improve the robustness of data hiding technique. The cover image is transformed using Fractional Fourier Transform (FrFT) into a time-frequency domain and the optimal pixel locations for hiding the secret data are found using firefly algorithm. Firefly algorithm uses multi-objective function, which is a combination of Structural Similarity Index Measure (SSIM) and Bit Error rate (BER). The histogram shifting technique embeds secret data in the optimal pixel locations. The quality of test images is analyzed under varying payload as well as under varying fractional order. Experimental results conclude that this scheme produces good quality stego image. It has also been found from the simulation results that the proposed algorithm is more robust and reversible against various attacks as it provides lower bit error rate and higher normalization coefficient.
A HEVC Steganography Method Based on QDCT Coefficient

This paper presents a HEVC video steganography algorithm based on QDCT coefficient without intra-frame distortion drift. We embed the secret message into the multi-coefficients of the selected 8 × 8 luminance QDCT blocks which meet our conditions to avert the distortion drift. With the use of three multi-coefficients in each row or column of the 8 × 8 luminance QDCT block, the embedded capacity is considerably larger than using single multi-coefficients. The experimental results show that this video steganography algorithm can effectively avert intra-frame distortion drift and get good visual quality.