Reviewerly/exHarmony huggingface dataset:
Semantic data management in P2P systems driven by self-esteem

Abstract This paper is a contribution to semantic data management in P2P systems. It is based on the previous works of the same authors in which a declarative semantics for P2P systems is defined: under this semantics, only facts not making the local databases inconsistent are imported (Weak Models) and the Maximal Weak Models are those in which peers import maximal sets of facts not violating integrity constraints. The proposal, presented in this paper, stems from the following two observations: (i) the Maximal Weak Model Semantics ensures that locally consistent P2P systems always admit a maximal weak model, but fails for locally inconsistent P2P systems; (ii) the self-esteem degree of a peer should impact on the interaction with other peers in the system so that driving the integration process. From the above-mentioned observations a more general framework is presented. Our proposal is able to manage local inconsistencies and allows to model a peer integration process driven by the self-esteem degree of each peer. Different self-esteem degrees could be considered; in this paper we focus our attention on three different scenarios in which a generic peer can declare an high, low or medium self-esteem degree, stating respectively that it trusts its own knowledge more, less or equally with respect to the knowledge that can be provided from the rest of the system. Three different basic semantics are proposed, High, Low, Medium Self-Esteem Semantics and results about the computational complexity of P2P logic queries are investigated by considering brave and cautions reasoning. The paper also presents an extension of the basic framework of the Self-Esteem Semantics that models a finer-grained self-esteem concept and allows each peer to exhibit different levels of self-esteem each of them defined with respect to a subset of its mapping rules.
Backtracking Hybrid Α* for Resource Constrained Path Planning

This paper considers resource constrained path planning for a Dubins agent. Resource constraints are modeled as path integrals that exert a path-dependent load on the agent that must not exceed an upper bound. A backtracking mechanism is proposed for the Hybrid-A$^*$ graph search algorithm to determine the minimum time path in the presence of the path loading constraint. The new approach is built on the premise that inadmissibility of a node on the graph must depend on the loading accumulated along the path taken to arrive at its location. Conventional hybrid-A* does not account for this fact, causing it to become suboptimal or even infeasible in the presence of resource constraints. The new approach helps ``reset'' the graph search by backing away from a node when the loading constraint is exceeded, and redirecting the search to explore alternate routes to arrive at the same location, while keeping the path load under its stipulated threshold. Backtracking Stopping criterion is based on relaxation of the path load along the search path. Case studies are presented and numerical comparisons are made with the Lagrange relaxation method to solving equivalent resource-constrained shortest path problems.
Private Tabular Survey Data Products through Synthetic Microdata Generation

Abstract We propose two synthetic microdata approaches to generate private tabular survey data products for public release. We adapt a pseudo posterior mechanism that downweights by-record likelihood contributions with weights ∈[0,1] based on their identification disclosure risks to producing tabular products for survey data. Our method applied to an observed survey database achieves an asymptotic global probabilistic differential privacy guarantee. Our two approaches synthesize the observed sample distribution of the outcome and survey weights, jointly, such that both quantities together possess a privacy guarantee. The privacy-protected outcome and survey weights are used to construct tabular cell estimates (where the cell inclusion indicators are treated as known and public) and associated standard errors to correct for survey sampling bias. Through a real data application to the Survey of Doctorate Recipients public use file and simulation studies motivated by the application, we demonstrate that our two microdata synthesis approaches to construct tabular products provide superior utility preservation as compared to the additive noise approach of the Laplace Mechanism. Moreover, our approaches allow the release of microdata to the public, enabling additional analyses at no extra privacy cost.
Learning formulas in finite variable logics

We consider grammar-restricted exact learning of formulas and terms in finite variable logics. We propose a novel and versatile automata-theoretic technique for solving such problems. We first show results for learning formulas that classify a set of positively- and negatively-labeled structures. We give algorithms for realizability and synthesis of such formulas along with upper and lower bounds. We also establish positive results using our technique for other logics and variants of the learning problem, including first-order logic with least fixed point definitions, higher-order logics, and synthesis of queries and terms with recursively-defined functions.
Towards automatic generated content website based on content classification and auto-article generation

In recent years, social media has becoming a battle field, not only for online marketing but also for politic, etc. Such as Facebook, online advertisement is now the main revenue of their company and the main idea is to attract users to particular fans page and to create flow. Flow is king is now the important concept for who want to manage their business online. Thus, in this paper, we intend to develop a website based on the concept of auto-article generation (AAG), which can gather useful information or news from other resources from WWW. The techniques that used for the AAG website including web crawler, cloud storage and computing, content classification, etc. The main idea is to attract users to visit the website and by this to create website flow.
Simframe: A Python Framework for Scientific Simulations

Simframe is a Python framework to facilitate scientific simulations.The scope of the software is to provide a framework which can hold data fields, which can be used to integrate differential equations, and which can read and write data files.
Multi-Domain Deep Convolutional Neural Network for Ancient Urdu Text Recognition System

Deep learning has achieved magnificent success in the field of pattern recognition.In recent years Urdu character recognition system has significantly benefited from the effectiveness of the deep convolutional neural network.Majority of the research on Urdu text recognition are concentrated on formal handwritten and printed Urdu text document.In this paper, we experimented the Challenging issue of text recognition in Urdu ancient literature documents.Due to its cursiveness, complex word formation (ligatures), and context-sensitivity, and inadequate benchmark dataset, recognition of Urdu text from the literature document is very difficult to process compared to the formal Urdu text document.In this work, first, we generated a dataset by extracting the recurrent ligatures from an ancient Urdu fatawa book.Secondly, we categorized and augment the ligatures to generate batches of augmented images that improvise the training efficiency and classification accuracy.Finally, we proposed a multi-domain deep Convolutional Neural Network which integrates a spatial domain and a frequency domain CNN to learn the modular relations between features originating from the two different domain networks to train and improvise the classification accuracy.The experimental results show that the proposed network with the augmented dataset achieves an averaged accuracy of 97.8% which outperforms the other CNN models in this class.The experimental results also show that for the recognition of ancient Urdu literature, well-known benchmark datasets are not appropriate which is also verified with our prepared dataset.
Content-Based Recommendations for Crags and Climbing Routes

Abstract Climbing is a popular sport for active tourists and recreational sportsmen. Alpine climbing areas, such as the Alps, can attract tourists from all over the world. Various websites, mobile applications, and books are used by climbers to obtain information on important aspects of the available climbing routes, including their properties, location, and especially their difficulty. Considering this large amount of information and options, it is in reality difficult for climbers to properly select which routes to climb. Hence, we propose recommendation technologies aimed at supporting climbers in this decision task. The developed system prototype constructs a climber’s profile with preferences derived from climber’s logbook data collected by a mobile app. Then, the system can recommend suitable crags and climbing routes within the selected crags. The designed interface and the basic computational models for such a system prototype are presented. The proposed technology aims at complementing existing electronic climbing guidebooks and providing decision support to climbers.
Cross-Domain Modality Fusion for Dense Video Captioning

Dense video captioning requires localization and description of multiple events in long videos. Prior works detect events in videos solely relying on the visual content and completely ignore the semantics (captions) related to the events. This is undesirable because human-provided captions often also describe events that are visually nonpresent or subtle to detect. In this research, we propose to capitalize on this natural kinship between <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">events</i> and their human-provided descriptions. We propose a semantic contextualization network to encode the visual content of videos by representing it in a semantic space. The representation is further refined to incorporate temporal information and transformed into event descriptors using a hierarchical application of short Fourier transform. Our proposal network exploits the fusion of semantic and visual content enabling it to generate semantically meaningful event proposals. For each proposed event, we attentively fuse its hidden state and descriptors to compute discriminative representation for the subsequent captioning network. Thorough experiments on the standard large-scale ActivityNet Captions dataset and additionally on the YouCook-II dataset show that our method achieves competitive or better performance on multiple popular metrics for the problem.
Custom Convolutional Layer Designs for CNN Based Automatic Modulation Classification Solution

Due to its success in computer vision, deep learning has been rapidly adopted for automatic modulation classification (AMC) in recent years. However, most of the published research work focuses on bringing the lasted innovation in the realm of deep learning into AMC instead of making fundamental change to the algorithm to adapt to the unique challenge provided by AMC. In this paper, a custom convolutional neural network design and a convolutional kernel initialization method is proposed to provide a more intuitive convolutional neural network based solution for AMC applications. The simulated test results show that the proposed scheme is able to achieve superior classification accuracy compared against classic network designs such as LeNet and AlexNet, while requiring much less computational resources.
Cooperative-PHD Tracking Based on Distributed Sensors for Naval Surveillance Area

Brazil has an extensive coastline and Exclusive Economic Zone (EEZ) area, which are of high economic and strategic importance. A Maritime Surveillance System becomes necessary to provide information and data to proper authorities, and target tracking is crucial. This paper focuses on a multitarget tracking application to a large-scale maritime surveillance system. The system is composed of a sensor network distributed over an area of interest. Due to the limited computational capabilities of nodes, the sensors send their tracking data to a central station, which is responsible for gathering and processing information obtained by the distributed components. The local Multitarget Tracking (MTT) algorithm employs a random finite set approach, which adopts a Gaussian mixture Probability Hypothesis Density (PHD) filter. The proposed data fusion scheme, utilized in the central station, consists of an additional step of prune & merge to the original GM PHD filter algorithm, which is the main contribution of this work. Through simulations, this study illustrates the performance of the proposed algorithm with a network composed of two stationary sensors providing the data. This solution yields a better tracking performance when compared to individual trackers, which is attested by the Optimal Subpattern Assignment (OSPA) metric and its location and cardinality components. The presented results illustrate the overall performance improvement attained by the proposed solution. Moreover, they also stress the need to resort to a distributed sensor network to tackle real problems related to extended targets.
An Algorithm of Fall Detection Based on Vision

The Y olov5 algorithm is optimized to improve the detection speed of the vision-based fall detection algorithm and the detection accuracy of overlapping targets. The loss function CIOU_Loss is used instead of GIOU_Loss, and the aspect ratio information is added to improve the speed and accuracy of the prediction box regression. The prediction box screening method is improved from the traditional NMS to DIOU_NMS, adding the center point of the boundary, which makes the detection effect of the occluded target better. The improved algorithm was trained and the verification showed that the improved Y olov5 has a detection accuracy of 97.45%, and the fastest detection speed is 30fps. A comparison with the detection results of the existing algorithm shows that the improved algorithm has better detection accuracy and detection speed, which can meet the real-time and accuracy requirements of fall detection.
Enhancing ICT for Female Economic Participation in Sub-Saharan Africa

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Assessing Blockchain-Based Innovation for the “Right to Education” Using MCDA Approach of Value-Focused Thinking and Fuzzy Cognitive Maps

Education forms the basic fabric of human life, and nations invest heavily in providing free basic education. However, these programs face serious issues, including financial mismanagement, fraudulent teaching practices, lack of actionable feedback mechanisms, and diversion of benefits. Thus, there is an urgent need for these programs to innovate with respect to their requirements through better alternatives or solutions. We propose a cognitive analytics management (CAM) framework-based assessment for blockchain-based technological innovation in the government-aided free basic education system in India. Value-focused thinking, a multicriteria decision analysis aid for the creation of optimal decision alternatives, is used here as a cognitive tool of the CAM process for interacting with key stakeholders. Fuzzy cognitive maps are used as an analytics tool for scenario planning to assess blockchain innovation. On the basis of the convergence of the cognitive and analytics phases, we present management-based actionable insights. The practical contributions of this article are 1) an approach that models the cognitive premises of stakeholders into quantified actionable insights for blockchain innovation, 2) a clarification of the importance of phasewise and selective adoption of blockchain innovation in facilitating optimal success scenarios, and 3) a demonstration that the CAM framework is a valuable tool for assessing innovation systems in varied contexts.
Detection of fraudulent credit card transactions: A comparative analysis of data sampling and classification techniques

Abstract Every year there is an increasing loss of a huge amount of money due to fraudulent credit card transactions. Recently there is a focus on using machine learning algorithms to identify fraud transactions. The number of fraud cases to non-fraud transactions is very low. This creates a skewed or unbalanced data, which poses a challenge to training the machine learning models. The availability of a public dataset for this research problem is scarce. The dataset used for this work is obtained from Kaggle. In this paper, we explore different sampling techniques such as under-sampling, Synthetic Minority Oversampling Technique (SMOTE) and SMOTE-Tomek, to work on the unbalanced data. Classification models, such as k-Nearest Neighbour (KNN), logistic regression, random forest and Support Vector Machine (SVM), are trained on the sampled data to detect fraudulent credit card transactions. The performance of the various machine learning approaches are evaluated for its precision, recall and F1-score. The classification results obtained is promising and can be used for credit card fraud detection.
Learning-Driven Exploration for Reinforcement Learning

Effective and intelligent exploration remains an unresolved problem for reinforcement learning. Most contemporary reinforcement learning relies on simple heuristic strategies which are unable to intelligently distinguish the well-explored and the unexplored regions of state space, which can lead to inefficient use of training time. We introduce entropy-based exploration (EBE) that enables an agent to explore efficiently the unexplored regions of state space. EBE quantifies the agent's learning in a state using state-dependent action values and adaptively explores the state space, i.e. more exploration for the unexplored region of the state space. We perform experiments on a diverse set of environments and demonstrate that EBE enables efficient exploration that ultimatelyresults in faster learning withouthaving totune anyhyperparameter. The code to reproduce the experimentsisgiven at https://github.com/Usama1002/EBE-Exploration and the supplementary video is given at https://youtu.be/nJggIjjzKic.
Text Analytic Research Portals: Supporting Large-Scale Social Science Research

Large-scale organic data generated from newspapers, social media, television, and radio require an expertise in infrastructure management, data collection, and data processing in order to gain research value from them. We have developed text analytic research portals to help social science researchers who do not have the resources necessary to collect, store, and process these large-scale data sets. Our portals allow researchers to use an intuitive point and click interface to generate variables from large, dynamic data sets using state of the art text mining and learning methods. These timely variables constructed from noisy text can then be used to advance social science research in areas such as political science, economics, public health, and psychology research.
Optimization of fuzzy similarity by genetic algorithm in user‐based collaborative filtering recommender systems

Abstract The most important subjects in the memory‐based collaborative filtering recommender system (RS) are to accurately calculate the similarities between users and finally finding interesting recommendations for active users. The main purpose of this research is to provide a list of the best items for recommending in less time. The fuzzy‐genetic collaborative filtering (FGCF) approach recommends items by optimizing fuzzy similarities in the continuous genetic algorithm (CGA). In this method, first, the crisp values of user ratings are converted to fuzzy ratings, and then the fuzzy similarities are calculated. Similarity values are placed into the genes of the genetic algorithm, optimized, and finally, they are used in fuzzy prediction. Therefore, the fuzzy system is used twice in this process. Experimental results on RecSys, Movielens 100 K, and Movielens 1 M datasets show that FGCF improves the collaborative filtering RS performance in terms of quality and accuracy of recommendations, time and space complexities. The FGCF method is robust against the sparsity of data due to the correct choice of neighbours and avoids the users' different rating scales problem but it not able to solve the cold‐start challenge.
EMDQ: Removal of Image Feature Mismatches in Real-Time

This paper proposes a novel method for removing image feature mismatches in real-time that can handle both rigid and smooth deforming environments. Image distortion, parallax and object deformation may cause the pixel coordinates of feature matches to have non-rigid deformations, which cannot be represented using a single analytical rigid transformation. To solve this problem, we propose an algorithm based on the re-weighting and 1-point RANSAC strategy (R1P-RNSC), which operates under the assumption that a non-rigid deformation can be approximately represented by multiple rigid transformations. R1P-RNSC is fast but suffers from the drawback that local smoothing information cannot be considered, thus limiting its accuracy. To solve this problem, we propose a non-parametric algorithm based on the expectation-maximization algorithm and the dual quaternion-based representation (EMDQ). EMDQ generates dense and smooth deformation fields by interpolating among the feature matches, simultaneously removing mismatches that are inconsistent with the deformation field. It relies on the rigid transformations obtained by R1P-RNSC to improve its accuracy. The experimental results demonstrate that EMDQ has superior accuracy compared to other state-of-the-art mismatch removal methods. The ability to build correspondences for all image pixels using the dense deformation field is another contribution of this paper.
AffRankNet+: Ranking Affect Using Privileged Information

Many of the affect modelling tasks present an asymmetric distribution of information between training and test time; additional information is given about the training data, which is not available at test time. Learning under this setting is called Learning Under Privileged Information (LUPI). At the same time, due to the ordinal nature of affect annotations, formulating affect modelling tasks as supervised learning ranking problems is gaining ground within the Affective Computing research community. Motivated by the two facts above, in this study, we introduce a ranking model that treats additional information about the training data as privileged information to accurately rank affect states. Our ranking model extends the well-known RankNet model to the LUPI paradigm, hence its name Af-fRankNet+. To the best of our knowledge, it is the first time that a ranking model based on neural networks exploits privileged information. We evaluate the performance of the proposed model on the public available Afew-VA dataset and compare it against the RankNet model, which does not use privileged information. Experimental evaluation indicates that the AffRankNet+ model can yield significantly better performance.
Document-Level Event Factuality Identification Using Negation and Speculation Scope

Document-level Event Factuality Identification (DEFI) is to identify the factuality of an event in document. DEFI is an important foundation for many Natural Language Understanding (NLU) tasks, such as information extraction and text understanding. The negation and speculation scope refers to the continuous segment of text, which is controlled by the words with negative or speculative semantics. In this paper, we explore the importance of negation and speculation scope for DEFI, and propose two methods to use the scope features. The model of detecting scope is trained from cross-domain corpus, and applied to the Document-Level Event Factuality (DLEF) corpus. Experimental results show that our DEFI model is superior to several baselines.