Reviewerly/exHarmony huggingface dataset:
Devanagari ancient documents recognition using statistical feature extraction techniques

Devanagari ancient document recognition process is drawing a lot of consideration from researchers nowadays. These ancient documents contain a wealth of knowledge. However, these documents are not available to all because of their fragile condition. A Devanagari ancient manuscript recognition system is designed for digital archiving. This system includes image binarization, character segmentation and recognition phases. It incorporates automatic recognition of scanned and segmented characters. Segmented characters may include basic characters (vowels and consonants), modifiers (matras) and various compound characters (characters formed by joining more than one basic characters). In this paper, handwritten Devanagari ancient manuscripts recognition system has been presented using statistical features extraction techniques. In feature extraction phase, intersection points, open endpoints, centroid, horizontal peak extent and vertical peak extent features are extracted. For classification, Convolutional Neural Network, Neural Network, Multilayer Perceptron, RBF-SVM and random forest techniques are considered in this work. Various feature extraction and classification techniques are considered and compared to the recognition of basic characters segmented from Devanagari ancient manuscripts. A data set, of 6152 pre-segmented samples of Devanagari ancient documents, is considered for experimental work. Authors have achieved 88.95% recognition accuracy using a combination of all features and a combination of all classifiers considered in this work by a simple majority voting scheme.
A MapReduce solution for incremental mining of sequential patterns from big data

Sequential Pattern Mining (SPM) is a popular data mining task with broad applications. With the advent of big data, traditional SPM algorithms are not scalable. Hence, many of the researchers have migrated to big data frameworks such as MapReduce and proposed distributed algorithms. However, the existing MapReduce algorithms assume the data as static and do not handle the incremental database updates. Moreover, they use to re-mine the updated database while new sequences are inserted. In this paper, we propose an efficient distributed algorithm for incremental sequential pattern mining (MR-INCSPM) using the MapReduce framework that can handle big data. The proposed algorithm incorporates the backward mining approach that efficiently makes use of the knowledge obtained during the previous mining process. Also, based on the study of item co-occurrences, we propose Co-occurrence Reverse Map (CRMAP) data structure. The issue of combinatorial explosion of candidate sequences is dealt using the proposed CRMAP data structure. Besides, a novel candidate generation and early prune mechanisms are designed using CRMAP to speed up the mining process. The proposed algorithm is evaluated on both the real and synthetic datasets. The experimental results prove the efficacy of MR-INCSPM with respect to processing time, memory and pruning efficiency.
Recognition oriented facial image quality assessment via deep convolutional neural network

Quality of facial images significantly impacts the performance of face recognition algorithms. Being able to predict "which facial image is good for recognition" is of great importance for real application scenarios, where a sequence of facial images are always presented and one should select the image frame with "best quality" for the subsequent matching and recognition task. To this end, we introduce a novel facial image quality automatic assessment framework directly targeting at "selecting better face image for better face recognition". For such as purpose, a deep convolutional neural network (DCNN) is trained to output a general facial quality metric which comprehensively considers various quality factors including brightness, contrast, blurriness, occlusion, and pose etc. Based on this trained facial quality metric network, we are able to sort the input face images accordingly and "select" good face images for recognition. Our method is comprehensively evaluated on Color FERET and KinectFace face datasets. Results show that the proposed facial image quality metric network works end-to-end and it well distinguishes "good" images from "bad" ones, which is highly correlated with the final recognition performance.
A Hybrid Semantic Knowledgebase-Machine Learning Approach for Opinion Mining

Opinion mining tools enable users to efficiently process a large number of online reviews in order to determine the underlying opinions. This paper presents a Hybrid Semantic Knowledgebase-Machine Learning approach for mining opinions at the domain feature level and classifying the overall opinion on a multi-point scale. The proposed approach benefits from the advantages of deploying a novel Semantic Knowledgebase approach to analyse a collection of reviews at the domain feature level and produce a set of structured information that associates the expressed opinions with specific domain features. The information in the knowledgebase is further supplemented with domain-relevant facts sourced from public Semantic datasets, and the enriched semantically-tagged information is then used to infer valuable semantic information about the domain as well as the expressed opinions on the domain features by summarising the overall opinions about the domain across multiple reviews, and by averaging the overall opinions about other cinematic features. The retrieved semantic information represents a valuable resource for modelling a machine learning classifier to predict the numerical rating of each review. Experimental evaluation revealed that the proposed Hybrid Semantic Knowledgebase-Machine Learning approach improved the precision and recall of the extracted domain features, and hence proved suitable for producing an enriched dataset of semantic features that resulted in higher classification accuracy.
Reinforcement learning: bringing together computation and cognition

A key aspect of human intelligence is our ability to learn very quickly. This ability is still lacking in artificial intelligence. This article will highlight recent research showing how bringing together the fields of artificial intelligence and cognitive science may benefit both. Ideas from artificial intelligence have provided helpful formal theories to account for aspects of human learning. In return, ideas from cognitive science and neuroscience can also inform artificial intelligence research with directions to make algorithms more human-like. For example, recent work shows that human learning can only be understood in the context of multiple separate, interacting memory systems, rather than as a single, complex learner. This insight is starting to show promise in improving artificial agents' learning efficiency.
Outlier Robust Extreme Machine Learning for multi-target regression

The popularity of algorithms based on Extreme Learning Machine (ELM), which can be used to train Single Layer Feedforward Neural Networks (SLFN), has increased in the past years. They have been successfully applied to a wide range of classification and regression tasks. The most commonly used methods are the ones based on minimizing the ℓ2 norm of the error, which is not suitable to deal with outliers, essentially in regression tasks. The use of ℓ1 norm was proposed in Outlier Robust ELM (OR-ELM), which is defined to one-dimensional outputs. In this paper, we generalize OR-ELM to deal with multi-target regression problems, using the error ℓ2,1 norm and the Elastic Net theory, which can result in a more sparse network, resulting in our method, Generalized Outlier Robust ELM (GOR-ELM). We use Alternating Direction Method of Multipliers (ADMM) to solve the resulting optimization problem. An incremental version of GOR-ELM is also proposed. We chose 15 public real-world multi-target regression datasets to test our methods. Our conducted experiments show that they are statistically better than other ELM-based techniques, when considering data contaminated with outliers, and equivalent to them, otherwise.
Privacy protection based on many‐objective optimization algorithm

Summary It is difficult to protect users' privacy and to process private information due to the complexity and uncertainty of such information. To protect private information quickly and accurately, a many‐objective optimization algorithm framework based on the hybrid elite selection strategy is proposed in this paper. First, a mating selection mechanism combined with the achievement scale function and angle information index is used to generate elite offspring of the internal population. Then, the balanceable fitness estimation method is employed to select and update the external archive. To test performance, the proposed algorithm is tested on many‐objective optimization problems (MaOPs) and compared with five state‐of‐the‐art algorithms. Experimental simulation results show that the proposed algorithm is more effective in solving MaOPs and can inspire development of a better privacy protection strategy.
An Evaluation of Atlas Selection Methods for Atlas-Based Automatic Segmentation in Radiotherapy Treatment Planning

Atlas-based automatic segmentation is used in radiotherapy planning to accelerate the delineation of organs at risk (OARs). Atlas selection has been proposed as a way to improve the accuracy and execution time of segmentation, assuming that, the more similar the atlas is to the patient, the better the results will be. This paper presents an analysis of atlas selection methods in the context of radiotherapy treatment planning. For a range of commonly contoured OARs, a thorough comparison of a large class of typical atlas selection methods has been performed. For this evaluation, clinically contoured CT images of the head and neck ( N=316 ) and thorax ( N=280 ) were used. The state-of-the-art intensity and deformation similarity-based atlas selection methods were found to compare poorly to perfect atlas selection. Counter-intuitively, atlas selection methods based on a fixed set of representative atlases outperformed atlas selection methods based on the patient image. This study suggests that atlas-based segmentation with currently available selection methods compares poorly to the potential best performance, hampering the clinical utility of atlas-based segmentation. Effective atlas selection remains an open challenge in atlas-based segmentation for radiotherapy planning.
What Is the Harm of Hate Speech?

In Jeremy Waldron's book, The Harm in Hate Speech, it is not always clear whether he argues that hate speech causes harm or whether it constitutes harm. This article considers this uncertainty, concluding that the best understanding of Waldron's argument is that hate speech tends to cause harm - a weak form of the consequentialist case for its proscription. His argument is not advanced by his apparent reliance on speech-act theory.
Investigating Reduced Path Planning Strategy for Differential Wheeled Mobile Robot

Summary This paper presents a vision-based path planning strategy that aims to reduce the computational time required by a robot to find a feasible path from a starting point to the goal point. The proposed algorithm presents a novel strategy that can be implemented on any well-known path planning algorithm such as A*, D* and probabilistic roadmap (PRM), to improve the swiftness of these algorithms. This path planning algorithm is suitable for real-time scenarios since it reduces the computational time compared to the basis and traditional algorithms. To test the proposed path planning strategy, a tracking control strategy is implemented on a mobile platform. This control strategy consists of three major stages. The first stage deals with gathering information about the surrounding environment using vision techniques. In the second stage, a free-obstacle path is generated using the proposed reduced scheme. In the final stage, a Lyapunov kinematic tracking controller and two Artificial Neural Network (ANN) based-controllers are implemented to track the proposed path by adjusting the rotational and linear velocity of the robot. The proposed path planning strategy is tested on a Pioneer P3-DX differential wheeled mobile robot and an Xtion PRO depth camera. Experimental results prove the efficiency of the proposed path planning scheme, which was able to reduce the computational time by a large percentage which reached up to 88% of the time needed by the basis and traditional scheme, without significant adverse effect on the workability of the basis algorithm. Moreover, the proposed path planning algorithm has improved the path efficiency, in terms of the path length and trackability, challenging the traditional trade-off between swiftness and path efficiency.
A novel FPGA implementation of Hough Transform for straight lane detection

Hough Transform (HT) algorithm is commonly used for linear detection but requires huge memory and computational complexity. In this paper, we design an adapted hardware architecture of HT for straight lane lines detection. Because the lane markings parallel in the 3D world cannot be horizontal or vertical in the image plane, we optimize the architecture and the computational complexity of HT. The proposed architecture uses (Y-intercept, θ) parameterization for the construction of HS and lane orientation, which also simplifies the inverse HT (IHT) operation and reduces the dimension of the accumulator. The efficiency of the proposed architecture is demonstrated by a software-hardware co-simulation on a Xilinx Virtex-5 ML505 platform. Compared to other reported hardware implementation, the proposed method can reduce the circuit resources. The processing speed is 1.47ms and the detection rate is 92%.
Secure Fine-grained Encrypted Keyword Search for e-Healthcare Cloud

E-Healthcare systems are increasingly popular due to the introduction of wearable healthcare devices and sensors. Personal health records (PHRs) are collected by these devices and stored in a remote cloud. Due to privacy concern, these records should not be accessible by any unauthorized party, and the cloud providers should not be able to learn any information from the stored records. To address the above issues, one promising solution is to employ attribute based encryption (ABE) for fine-grained access control and searchable encryption for keyword search on encrypted data. However, most of existing ABE schemes leak the privacy of access policy which may also contain sensitive information. On the other hand, for users' devices with limited computing power and bandwidth, the mechanism should enable them to be able to search the PHRs efficiently. Unfortunately, most existing works on ABE do not support efficient keyword search on encrypted data. In this work, we propose an efficient hidden policy ABE scheme with keyword search. Our scheme enables efficient keyword search with constant computational overhead and constant storage overhead. Moreover, we enhance the recipient's privacy which hides the access policy. As of independent interest, we present a trapdoor malleability attack and demonstrate that some of previous schemes may suffer from such attack.
Towards the Guidelines for Requirements Change Management in Global Software Development: Client-Vendor Perspective

Currently, being deployed by organizations to develop high-quality software at a low cost, global software development (GSD) faces many challenges that make development activities more complex.These GSD challenges are mainly concerned with requirements to change management (RCM).RCM plays a key role in the successful execution of software projects.The objective of this paper is to identify the best practices of the RCM process by adopting a systematic literature review (SLR) and validate them using questionnaire survey with industry experts.A total of 46 best practices were identified through SLR and validated with industry experts.We have further classified the identified practices in the domain of client and vendor GSD organizations with the aim to provide a clear understanding of the RCM best practices in the context of both types of GSD organizations (client, vendor).Moreover, we have conducted a comparison analysis between SLR and questionnaire survey data and found a moderate positive correlation in the ranks of both data sets (r s = 0.522, p=0.003).In addition, the criticality of the identified best practices was assessed using the criteria of a practice having frequency ≥50%.The findings of this paper provide a framework that could help the GSD organizations to address the problems related to RCM in GSD environment.
A Relevance-based approach for Big Data Exploration

The collection, organisation and analysis of large amount of data (Big Data) in different application domains still require the involvement of experts for the identification of relevant data only, without being overwhelmed by volume, velocity and variety of collected data. According to the “Human-In-the-Loop Data Analysis” vision, experts explore data to take decisions in unexpected situations, based on their long-term experience. In this paper, the IDEAaS (Interactive Data Exploration As-a-Service) approach is presented, apt to enable Big Data Exploration (BDE) according to data relevance. In the approach, novel techniques have been developed: (i) an incremental clustering algorithm, to provide summarised representation of collected data streams; (ii) multi-dimensional organisation of summarised data, for data exploration according to different analysis dimensions; (iii) data relevance evaluation techniques, to attract the experts’ attention on relevant data only during exploration. The approach has been experimented to apply BDE for state detection in the Industry 4.0 domain, given the strategic importance of Big Data management as enabling technology in this field. In particular, a stream of numeric features is collected from a Cyber Physical System and is explored to monitor the system health status, supporting the identification of unknown anomalous conditions. Results of an extensive experimentation in the Industry 4.0 domain are presented in the paper and demonstrated the effectiveness of developed techniques to attract the attention of experts on relevant data, also beyond the considered domain, in presence of disruptive characteristics of Big Data, namely volume (millions of collected records), velocity (measured in milliseconds) and variety (number and heterogeneity of analysis dimensions).
Deep human activity recognition using wearable sensors

This paper addresses the problem of classifying motion signals acquired via wearable sensors for the recognition of human activity. Automatic and accurate classification of motion signals is important in facilitating the development of an effective automated health monitoring system for the elderlies. Thus, we gathered hip motion signals from two different waist mounted sensors and for each individual sensor, we converted the motion signal into spectral image sequence. We use these images as inputs to independently train two Convolutional Neural Networks (CNN), one for each of the generated image sequences from the two sensors. The outputs of the trained CNNs are then fused together to predict the final class of the human activity. We evaluate the performance of the proposed method using the cross-subjects testing approach. Our method achieves recognition accuracy (F1 score) of 0.87 on a publicly available real-world human activity dataset. This performance is superior to that reported by another state-of-the-art method on the same dataset.
Evaluating cryptocurrency laundering as a complex socio-technical system

Purpose The purpose of this paper is to present the findings from a literature review, which aimed to identify previous studies evaluating cryptolaundering from a systems thinking perspective. The aim of this paper is to first confirm that cryptolaundering systems can indeed be defined as complex socio-technical systems and second to present the findings from a systematic review of the literature to determine the extent to which previous research has adopted a systems thinking perspective. Design/methodology/approach The study involved a SLR of studies published in the peer-reviewed literature between 2009 and 2018. Rasmussen’s risk management framework (Rasmussen, 1997) was used to evaluate the extent to which a systems thinking perspective had been adopted. Findings The cryptolaundering process is considered to be a complex socio-technical system. The review demonstrates that no previous studies have defined cryptolaundering as a complex socio-technical system or used systems thinking framework approach to evaluate how criminals, regulatory bodies or law enforcement entities understand processes and assess risk within cryptolaundering systems. It is argued that using such an approach to the cryptolaundering process would likely improve assessing criminal risk analyses of cryptolaundering and assist law enforcement and regulatory bodies with understanding risk management during the laundering of cryptocurrencies. Originality/value Future assessments of cryptolaundering using socio-technical system analytical processes may afford law enforcement and regulatory bodies the opportunity to improve intervention techniques and identify gaps in regulations and enforcement.
Exploration of Youth’s Digital Competencies: A Dataset in the Educational Context of Vietnam

The recent surge of the Fourth Industrial Revolution has set forth demands for a new generation of the labor force with a comprehensive set of skills to meet the standards of the global market. Despite widespread concerns about educational reforms and renovations to enhance the workforce capacity in terms of information and communication technology (ICT) skills, research into the digital proficiencies of students has been limited in Vietnam. This dataset contains 1061 observations on the digital competency level of 10th-grade students in 20 surveyed schools from five provinces in Vietnam. The investigation, joining frequentist and Bayesian analyses, aims to provide valuable insights into the current state of children’s attitudes, behaviors, competency levels, and use of ICT within the Vietnamese educational context. The values of the dataset lie in its proposed scientific framework for replication in multiple regions and contexts as well as the feasibility of categorical regression techniques together with Bayesian statistics for hierarchical regression analysis.
Enhancing Cuckoo Search Algorithm by using Reinforcement Learning for Constrained Engineering optimization Problems

Cuckoo Search Algorithm (CSA) has been successfully applied to a range of various fields such as engineering, medical, and image processing. However, it typically suffers from a lack of effective exploration, loose diversity, and premature convergence. This paper aspires to develop a new version of CSA that is based on the features of Reinforcement Learning (RL) to enhance the research technique of CSA, which will be called CSARL. The performance of CSARL is evaluated by applying set of unimodal and multimodal benchmark functions. The results demonstrate that the CSARL outperforms the basic CSA, genetic, harmony search and krill heard algorithms, in terms of convergence speed, the diversity, and exploration search.