Reviewerly/exHarmony huggingface dataset:
The Use and Reuse of Printed Illustrations in 15th-Century Venetian Editions

A presentation of the 15cILLUSTRATION database and website, a searchable database of 15th-century printed illustrations developed by the 15cBOOKTRADE Project in collaboration with the Visual Geometry Group (VGG) at the Department of Engineering Science of the University of Oxford. 15cILLUSTRATION is the first comprehensive and systematic tool to track and investigate the production, use, circulation, and copying of woodblocks, iconographic subjects, artistic styles, within 15th-century printed illustrated editions. The paper illustrates the potential of the 15cILLUSTRATION website as a research support tool for art historians, book historians, philologists and historians of visual and material culture.
Deep Heterogeneous Social Network Alignment

The online social network alignment problem aims at inferring the anchor links connecting the shared users across social networks, which are usually subject to the one-to-one cardinality constraint. Several existing social network alignment models have been proposed, many of which are based on the supervised learning setting. Given a set of labeled anchor links, a group of features can be extracted manually for the anchor links to build these models. Meanwhile, such methods may encounter great challenges in the application on real-world social network datasets, since manual feature extraction can be extremely expensive and tedious for the social networks involving heterogeneous information. In this paper, we propose to address the heterogeneous social network alignment problem with a deep learning model, namely DETA (Deep nETwork Alignment). Besides a small number of explicit features, DETA can automatically learn a set of latent features from the heterogeneous information. DETA models the anchor link one-to-one cardinality constraint as a mathematical constraint on the node degrees. Extensive experiments have been done on real-world aligned heterogeneous social network datasets, and the experimental results have demonstrated the effectiveness of the proposed model compared against the existing state-of-the-art baseline methods.
Grammatical Structure Detection with Intrinsic Plasticity based Echo State Networks for Cognitive Robot

A novel model called Intrinsic plasticity Echo State Network with new weight initialization (IP-ESN) is proposed in this study. The new input weight, which is selected by proposed method called new weight initialization, replaces the input weight of Echo State Network (ESN). Intrinsic plasticity rules is applied to find the gain and bias before training model. These two elements can be used to extend the neurons connection of ESN, which directly impacts on the model performance. According to the results of experiments, comparing with ESN, new weight initialization and intrinsic plasticity rules play a vital roles in decreasing the error rate. Moreover, IP-ESN has the super understanding ability of meaning words and sentence in the different construction numbers of corpus than other compared models. This algorithm also can be applied in human robot applications, because the proposed algorithm can understand the meaning of sentence and predict the grammatical structure in the real time.
Empirical Study of Population-Based Dynamic Constrained Multimodal Optimization Algorithms

There are many dynamic optimization problems in real-world applications. Although many variants of evolutionary algorithms and swarm intelligence have been proposed to solve such problems, little work has been conducted to address the dynamic constrained multimodal optimization problems (DCMMOPs). In DCMMOPs, there exist multiple optimal solutions corresponding to each environment that the algorithm is required to find. Sometimes, it is also necessary to identify the accepted local optima. Therefore, for each environment, the decision maker can select one from among multiple returned solutions according to his/her domain knowledge and/or preferences. The objective of this paper is to test the performance of various combinations of several population-based dynamic multimodal optimization algorithms and popular constraint handling techniques. First, the typical dynamic constrained optimization problems are slightly modified to be in the form of the dynamic constrained multimodal optimization problems. Second, four different population-based dynamic multimodal optimization algorithms, and five different constraint handling techniques, are pairwise tested in the experiments. Experimental results demonstrate that, among the candidates, DCMM-CSA-SR performs most successfully at all accuracy levels.
A new challenging image dataset with simple background for evaluating and developing co-segmentation algorithms

Many image co-segmentation algorithms have been proposed over the last decade. In this paper, we present a new dataset for evaluating co-segmentation algorithms, which contains 889 image groups with 18 images in each and the pixel-wise hand-annotated ground truths. The dataset is characterized by simple background produced from nearly a single color. It looks simple but is actually very challenging for current co-segmentation algorithms, because of four difficult cases in it: easy-confused foreground with background, transparent regions in objects, minor holes in objects, and shadows. In order to test the usefulness of our dataset, we review the state-of-the-art co-segmentation algorithms and evaluate seven algorithms on our dataset. The obtained performance of each algorithm is compared with those previously reported in the datasets with complex background. The results prove that our dataset is valuable for the development of co-segmentation techniques. It is more feasible to solve the four difficulties above on the simple background and then extend the solutions to the complex background problems. Our dataset can be freely downloaded from: http://www.iscbit.org/source/MLMR-COS.zip.
Global Optimal Path Planning for Multi-agent Flocking: A Multi-Objective Optimization Approach with NSGA-III

Flocking is a multi-objective operation performed by multiple agents in uncertain environments. Objectives of flocking include reaching target for each agent, avoiding collision with obstacles and other agents, as well as maintaining certain pattern among all agents. Multi-objective optimization can be performed in priori methods, posteriori methods and scalarizing methods. Pareto front optimization is the best way to optimize multiple objectives simultaneously. To date, flocking has been performed with summation of objective values. In this paper, Pareto front optimization is adopted for the first time for flocking simulation for multi-agents in uncertain environments. For a team of agents, e.g. rovers, in an uncertain environment, Cooperative Co-Evolutionary Algorithm (CCEA) performs well for both exploration and exploitation. CCEAs coupled with Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and NSGA-III are performed to achieve flocking for multi-agents. A new reward structure is introduced for CCEA. In order to check the reward structure, flocking is performed in different environment, open and closed. In addition, the performances of NSGA-II and NSGA-III are compared for various cases of flocking with different numbers of objectives. Towards the end, the effectiveness of the developed methods is demonstrated through numerical simulations.
Uranium anomalies detection through Random Forest regression

Airborne geophysics provides one of the most relevant data for uranium exploration. However, the application of radiometric surveys is decreasing considerably as the depth of exploration is increasing. Notwithstanding, there is still a large potential for radiometric data, especially using recent data processing techniques such as machine learning methods. In this work, we propose a new method to detect uranium anomalies through regression using the Random Forest machine learning algorithm (RF). The RF regression allows combining airborne geophysical data to predict the expected uranium content, which represents the uranium content generated by environmental effects such as lithology and pedogenesis. Therefore, the deviation (Ud) between the measured uranium and the expected uranium represents the secondary effects such as weathering, soil alteration, hydrothermal alteration or mineralisation process. We evaluated the relevance of the geophysical parameters proposed by previous authors in the prediction of the expected uranium (thorium, thorium potassium ratio, uranium potassium ratio, and Total Gradient Amplitude). Randomly selecting only 10% of the database as training data, we estimate the expected uranium with an R2 = 0.99 concerning the measured uranium. To assess the reliability of the Ud anomalies, we employed the proposed methodology in the Carajás Mineral Province (CMP), Brazil. In the CPM, the Ud anomalies showed a clear correlation with the several Iron Oxide Copper–Gold deposits (IOCG) and some IOCG-related and granite-related prospects. In situ measurements with a portable gamma-ray spectrometer in the Salobo mine supported the uranium anomalies. The Ud map also highlighted contrasts within granites that correlate with previously reported granitic facies. Therefore, the Ud map generated by RF regression is useful in setting exploration targets for conventional and unconventional uranium resources, as well as in high-detail granitic facies mapping.
Word Sense Disambiguation Using Wikipedia Link Graph

In this paper, we present a observation that a word with some special sense should appear in specific category of articles, and this appearance will “take” the word to its Wiki sense [1] by Wikipedia link graph. This property can help to solve word sense disambiguation problem using Wikipedia.
A multivocal study to improve the implementation of global requirements change management process: A client‐vendor prospective

Abstract Software development is a complex task, and the introduction of multi‐site development teams spread across the globe makes it even harder. Requirement changes during the software development process are inevitable, and failing to manage evolving requirements is one of the contributors to project failures. Requirements change management (RCM) becomes difficult in global software development (GSD) projects due to the need to communicate and coordinate between stakeholders in a distributed environment. This research work aims to identify the factors that have positive impacts on RCM activities in GSD. We conducted a multi‐vocal literature review (MLR) and a questionnaire survey study to identify the RCM success factors. The results of the t ‐test (ie, t = 0.0347 and P = .700 &gt; .05) and correlation (r s (25) = 0.573, P = .003) indicates that there is no significant difference between the findings of MLR and the questionnaire survey. The identified factors were further analyzed in the context of client and vendor organizations to better understand the RCM success factors in both types of GSD firms. The findings of this study can provide a useful framework for tackling problems associated with RCM in a GSD environment that is significant to the progression of GSD firms.
A Novel Multi-objective Approach to Fuzzy Clustering

A clustering algorithm is an unsupervised method, which aims to divide data points into two groups or more. These algorithms generally rely on the optimization of a single criterion to find optimal cluster structures. This choice might lead to cluster structures of poor quality, and does not reflect how humans generally rely on multiple (possibly conflicting) criteria when grouping similar elements together. In this paper, we apply an different approach based on multi-objective optimization to solve the problem of fuzzy clustering. Specifically, we combine the objective function of the popular fuzzy c-means algorithm with a second objective function, which aims at maximizing the number of data points having a high degree of membership to one of the clusters. The rationale is that data points close to a cluster center have a high membership value, while data points in between cluster centers share their membership between the different clusters: by optimizing the second criterion we expect an improvement of the quality of the resulting clustering structure. We perform the multi-objective optimization by means of the Non-dominated Sorting Genetic Algorithm (NSGA-II), a multi-objective, evolutionary global optimization algorithm. Our results show that a multi-objective approach to fuzzy clustering can generate solutions of higher quality than classic fuzzy C-means, on both synthetic and real world data sets.
Pi-Sigma Neural Network: Survey of a Decade Progress

From the past few years due to the rapid development in neural networks, it has been emerging as the most prominent research area in scientific community. To overcome the limitations of neural networks such as large training time and incapability to cope with nonlinear data, researchers have been attracted toward higher-order neural networks. Nonlinear mapping capability of higher-order neural network using fewer number of processing units leads to major enhancement in the productivity and performance of several classification methods. In this paper, an extensive analytical study of a higher-order neural network called Pi-sigma neural network and its variants in various application domains such as classification, prediction, function approximation, pattern recognition has been illustrated. Further, this paper also focuses on challenges and several issues of research area to describe the supremacy of Pi-sigma neural network in various application areas.
A Semantic Framework with Humans in the Loop for Vulnerability-Assessment in Cyber-Physical Production Systems

Critical manufacturing processes in smart networked systems such as Cyber-Physical Production Systems (CPPSs) typically require guaranteed quality-of-service performances, which is supported by cyber-security management. Currently, most existing vulnerability-assessment techniques mostly rely on only the security department due to limited communication between different working groups. This poses a limitation to the security management of CPPSs, as malicious operations may use new exploits that occur between successive analysis milestones or across departmental managerial boundaries. Thus, it is important to study and analyse CPPS networks' security, in terms of vulnerability analysis that accounts for humans in the production process loop, to prevent potential threats to infiltrate through cross-layer gaps and to reduce the magnitude of their impact. We propose a semantic framework that supports the collaboration between different actors in the production process, to improve situation awareness for cyberthreats prevention. Stakeholders with different expertise are contributing to vulnerability assessment, which can be further combined with attack-scenario analysis to provide more practical analysis. In doing so, we show through a case study evaluation how our proposed framework leverages crucial relationships between vulnerabilities, threats and attacks, in order to narrow further the risk-window induced by discoverable vulnerabilities.
Improved auto-extrinsic calibration between stereo vision camera and laser range finder

This study identifies a way to accurately estimate extrinsic calibration parameters between stereo vision camera and 2D laser range finder (LRF) based on 3D reconstruction of monochromatic calibration board and geometric co-planarity constraints between the views from these two sensors. It supports automatic extraction of plane-line correspondences between camera and LRF using monochromatic board, which is further improved by selecting optimal threshold values for laser scan dissection to extract line features from LRF data. Calibration parameters are then obtained by solving co-planarity constraints between the estimated plane and line. Furthermore, the obtained parameters are refined by minimising reprojection error and error from the co-planarity constraints. Moreover, calibration accuracy is achieved because of extraction of reliable plane-line correspondence using monochromatic board which reduces the impact of range-reflectivity-bias observed in LRF data on checkerboard . As the proposed method supports to automatically extract feature correspondences, it provides a major reduction in time required from an operator in comparison to manual methods. The performance is validated by extensive experimentation and simulation, and estimated parameters from the proposed method demonstrate better accuracy than conventional methods.
A Methodology for Cross-Platform, Event-Driven Big Data Analytics-as-a-Service

The advent of Big Data has revolutionized the way in which data are collected, analyzed, and processed, becoming a pre-requisite for each enterprise that competes in the global market. In this respect, the commodization of Big Data analytics is an essential goal to be faced in the near future. Recently, some preliminary approaches have been presented mostly focusing on distributing Big Data platforms as a service, while less has been done on cross-platform Big Data analytics. In this paper, we propose a model-based methodology for Big Data Analytics-as-a-Service that extends existing techniques by supporting cross-communication between batch and stream processing, deployment on multiple platforms, and end-to-end verification against users' requirements.
Efficient Weighted Semantic Score Based on the Huffman Coding Algorithm and Knowledge Bases for Word Sequences Embedding

Learning text representation is forming a core for numerous natural language processing applications. Word embedding is a type of text representation that allows words with similar meaning to have similar representation. Word embedding techniques categorize semantic similarities between linguistic items based on their distributional properties in large samples of text data. Although these techniques are very efficient, handling semantic and pragmatics ambiguity with high accuracy is still a challenging research task. In this article, we propose a new feature as a semantic score which handles ambiguities between words. We use external knowledge bases and the Huffman Coding algorithm to compute this score that depicts the semantic relatedness between all fragments composing a given text. We combine this feature with word embedding methods to improve text representation. We evaluate our method on a hashtag recommendation system in Twitter where text is noisy and short. The experimental results demonstrate that, compared with state-of-the-art algorithms, our method achieves good results.
Improving the effectiveness of teaching the course “Data structure and algorithms” based on structuration and integration of the discipline

In didactic, a system of empirical pedagogical principles has long existed, following which, it would seem, you can select, organize and structure the training material, accordingly. But in reality, the structuring of the content of the discipline is in no way connected with the information base of the discipline. Information and computer support, distance learning, automated knowledge control are not provided, since the principles of structuring are very general, thus, need to be improved and made more technologically advanced. In accordance with the developed set of didactic requirements for the structuring of educational materials, a practical structuring of the part “Sorting of numerical arrays” of the course “Data Structure” is considered as a running example in this paper. In the experiments, the pedagogical effectiveness of the developed technologies was experimentally tested and practical instructions for the use of this technology were given to the teachers of higher education.
Method for Computing Emotions of Tweets with an Emoticon

In text-based message exchange services, non-verbal expressions, such as emoticons, are typically used. However, their usage is intuitive, and many questions persist as to how emoticons affect the emotional aspect of messages. Therefore, we formulate the effect of emoticons on emotions of tweets with an emoticon and propose a method for computing emotion values of tweets with an emoticon. Initially, emotion values of emoticons and those of tweets with an emoticon are determined based on results of questionnaires. Subsequently, emotion values of tweets are calculated via two sentiment analysis tools. We then apply multiple regression analysis to the three types of emotion values, and thus we obtain multiple regression equations to compute emotion values of tweets with an emoticon. Our performance evaluation indicates that the proposed method is effective in terms of computing values of the following nine emotions: "Sorrow," "Disgust," "Relief," "Fear," "Liking," "Joy," "Surprise," "Anger," and "Shame."
TEDM-PU: A Tax Evasion Detection Method Based on Positive and Unlabeled Learning

Tax evasion detection plays a crucial role in reducing tax revenue loss and many efforts have been made to develop detection models based on machine learning techniques. To train an effective model to detect tax evaders, a large amount of data is required, especially sufficient labeled data. However, the expensive and time-consuming annotation process results in small amount of labeled data being available, which makes the development of detection models difficult. To address this issue, we propose a tax evasion detection method based on positive and unlabeled learning (TEDM-PU), to identify tax evasion by utilizing limited annotated tax evasion taxpayers and a large amount of unlabeled data. The TEDM-PU framework consists of three stages: a preprocessing stage extracting taxpayer features based on random forest, a pseudo labeling stage assigning pseudo labels to unlabeled samples based on PUAdapter, and a model training stage based on LightGBM method. To evaluate the effectiveness of our proposed TEDM-PU, we conduct experimental tests on real-world tax data. The results demonstrate that TEDM-PU method can detect tax evaders with higher accuracy and better interpretability than state-of-the-art methods.
A Mission Planning and Task Allocation Framework For Multi-UAV Swarm Coordination

This paper presents a multi-agent mission planning and task allocation framework designed to coordinate autonomous aerial vehicles engaged in a competition scenario. The development was a part of an inter-university UAV Swarm competition that was supported by BAE Systems. The proposed centralised system was developed with the main objectives of robustness and scalability. The system consists of a general mission planning module which decomposes the overall mission into identified sub-stages to achieve the overall mission goal. In order to enable autonomous defence actions a dynamic task allocation approach is proposed. The dynamic task allocation is using received information of detected enemies and utilises the information for a further combinatorial optimisation problem. In this work, we discuss the structure of the framework and present results obtained in a high-fidelity simulation environment. Moreover, a comparative study of the performance of three different optimization algorithms for the given combinatorial problem, namely Kuhn-Munkres, Jonker-Volgenant and Gale-Shapley, implemented in the system is included. The results demonstrate that the best allocation result performances, in terms of minimal costs, are obtained with utilising, both Kuhn-Munkres or Jonker-Volgenant methods, while the Gale-Shapley algorithms have benefits in terms of time efficiency for cases in which minimal costs are not the highest priority.