Reviewerly/exHarmony huggingface dataset:
Arbitrarily Shaped Scene Text Detection With a Mask Tightness Text Detector

Scene text in the environment is complicated. It can exist in arbitrary text fonts, sizes or shapes. Although scene text detection has witnessed considerable progress in recent years, the detection of text with complex shapes, especially curved text, remains challenging. Datasets with adequate samples to overcome the problem presented by curved text (or other irregularly shaped text) have been introduced only recently; however, the performance of the reported methods on these datasets is unsatisfactory. Therefore, detecting arbitrarily shaped text remains a challenging. This motivated us to propose the Mask Tightness Text Detector (Mask TTD) to improve text detection performance. Mask TTD uses a tightness prior and text frontier learning to enhance pixel-wise mask prediction. In addition, it achieves mutual promotion by integrating a branch for the polygonal boundary of each text region, which significantly improves the detection performance of arbitrarily shaped text. Experiments demonstrate that Mask TTD can achieve state-ofthe-art performance on existing curved text datasets (CTW1500, Total-text, and CUTE80) and three common benchmark datasets (RCTW-17, MSRA-TD500, and ICDAR 2015). It is worth mentioning that on CTW1500, our method can outperform previous methods, especially at higher intersection over union (IoU) thresholds (16% higher than the next-best method with an IoU threshold of 0.8), which demonstrates its potential for tight text detection. Moreover, on the largest Chinese-based dataset RCTW-17, Mask TTD outperforms other methods by a large margin in terms of both the Average Precision and F-measure, showing its powerful generalization ability.
Direct Force Feedback Control and Online Multi-Task Optimization for Aerial Manipulators

In this letter we present an optimization-based method for controlling aerial manipulators in physical contact with the environment. The multi-task control problem, which includes hybrid force-motion tasks, energetic tasks, and position/postural tasks, is recast as a quadratic programming problem with equality and inequality constraints, which is solved online. Thanks to this method, the aerial platform can be exploited at its best to perform the multi-objective tasks, with tunable priorities, while hard constraints such as contact maintenance, friction cones, joint limits, maximum and minimum propeller speeds are all respected. An on-board force/torque sensor mounted at the end effector is used in the feedback loop in order to cope with model inaccuracies and reject external disturbances. Real experiments with a multi-rotor platform and a multi-DoF lightweight manipulator demonstrate the applicability and effectiveness of the proposed approach in the real world.
Intra‐pulse modulation radar signal recognition based on CLDN network

Automatic modulation classification of radar signals, which plays a significant role in both civilian and military applications, is researched in this study through a deep learning network. In this study, a novel network combined a shallow convolution neural network (CNN), long short-term memory (LSTM) network and deep neural network (DNN) is proposed to recognise six types of radar signals with different signal-to-noise ratio (SNR) levels from −14 to 20 dB. First, raw signal sequences in the time domain, frequency domain and autocorrelation domain are as input for a shallow CNN. Then the features extracted by CNN will be the input of LSTM network. Finally, DNNs will output the signal modulation types directly. The simulation results demonstrate that the accuracies in autocorrelation domain are all more than 90% at −6 dB and close to 100% when SNR > −2 dB. The recognition performances of the three domains are compared. Compared with other recognition methods, the proposed method has higher average accuracy and better performance under low SNR condition. The measured results show that the proposed method has achieved high accuracies of common four kinds of measured radar signals.
Relation Attention for Temporal Action Localization

Temporal action localization aims to accurately localize and recognize all possible action instances from an untrimmed video automatically. Most existing methods perform this task by first generating a set of proposals and then recognizing each independently. However, due to the complex structures and large content variations in action instances, recognizing them individually can be difficult. Fortunately, some proposals often share information regarding one specific action. Such information, which is ignored in existing methods, can be used to boost recognition performance. In this paper, we propose a novel mechanism, called relation attention, to exploit informative relations among proposals based on their appearance or optical flow features. Specifically, we propose a relation attention module to enhance representation power by capturing useful information from other proposals. This module does not change the dimensions of the original input and output and does not rely on any specific proposal generation methods or feature extraction backbone networks. Experimental results show that the proposed relation attention mechanism improves performance significantly on both Thumos14 and ActivityNet1.3 datasets compared to existing architectures. For example, relying on Structured Segment Networks (SSN), the proposed relation attention module helps to increase the mAP from 41.4 to 43.7 on the Thumos14 dataset and outperforms the state-of-the-art results.
A Group Signature and Authentication Scheme for Blockchain-Based Mobile-Edge Computing

Blockchain-based mobile-edge computing (BMEC) is an innovative architecture, which is essential in the future fifth-generation (5G) network era. BMEC solves the problem of limited computing resources of devices in the mobile blockchain environment while ensuring the distributed deployment of computing resources and the traceable of transaction data. However, the blockchain technology has many security vulnerabilities, among which attacks against consensus algorithms are particularly serious, i.e., double-spend attacks, long-range attacks, and selfish mining. All of these attacks can break the integrity of BMEC, allowing the correct block record to be overwritten with a false one. In this article, we propose a group signature scheme designed for validating blocks of blockchain to address such issues. Each new block will be regarded as a valid block if it obtains a valid group aggregate signature of the group which the block creator belongs to. We describe in detail the process of authentication and key changes when mobile devices join and leave BMEC. We also provide a more efficient authentication scheme of authenticating mobile devices compared to traditional schemes in BMEC. Last, the security analysis is presented to prove that our proposed group signature scheme is effective.
Challenges and Recent Solutions for Image Segmentation in the Era of Deep Learning

Image segmentation has a key role in computer vision and image processing. Superiority of deep learning based segmentation techniques has been shown in various studies in the literature. However, there are challenging issues affecting performances of these methods. Therefore, in this paper, these challenges that are mostly related to architecture and training of deep neural networks are explained. In addition, the state-of-the-art solutions applied in the literature are presented to help researchers to design proper network architectures according to their problems and to be aware of possible challenging issues and recent solutions.
Automatic re-planning of lifting paths for robotized tower cranes in dynamic BIM environments

Computer-Aided Lift Planning (CALP) systems provide smart and optimal solutions for automatic crane lifting, supported by intelligent decision-making and planning algorithms along with computer graphics and simulations. Re-planning collision-free optimal lifting paths in near real-time is an essential feature for a robotized crane operating in a construction environment that is changing with time. The primary focus of the present research work is to develop a re-planning module for the CALP system designed at Nanyang Technological University. The CALP system employs GPU-based parallelization approach for discrete and continuous collision detection as well as for path planning. Building Information Modeling (BIM) is utilized in the system, and a Single-level Depth Map (SDM) representation is implemented to reduce the huge data set of BIM models for usage in discrete and continuous collision detection. The proposed re-planning module constitutes of a Decision Support System (DSS) and a Path Re-planner (PRP). A novel re-planning decision making algorithm using multi-level Oriented Bounding Boxes (OBBs) is formulated for the DSS. A path re-planning strategy via updating the start configuration for the local path is devised for the PRP. Two case studies are carried out with real-world models of a building and a specific tower crane to validate the effective performance of the re-planning module. The results show excellent decision accuracy and near real-time re-planning with high optimality.
Fuzzy Cognitive Map-Based Sustainable Socio-Economic Development Planning for Rural Communities

Every development and production process needs to operate within a circular economy to keep the human being within a safe limit of the planetary boundary. Policymakers are in the quest of a powerful and easy-to-use tool for representing the perceived causal structure of a complex system that could help them choose and develop the right strategies. In this context, fuzzy cognitive maps (FCMs) can serve as a soft computing method for modelling human knowledge and developing quantitative dynamic models. FCM-based modelling includes the aggregation of knowledge from a variety of sources involving multiple stakeholders, thus offering a more reliable final model. The average aggregation method for weighted interconnections among concepts is widely used in FCM modelling. In this research, we applied the OWA (ordered weighted averaging) learning operators in aggregating FCM weights, assigned by various participants/ stakeholders. Our case study involves a complex phenomenon of poverty eradication and socio-economic development strategies in rural areas under the DAY-NRLM (Deendayal Antyodaya Yojana-National Rural Livelihoods Mission) in India. Various scenarios examining the economic sustainability and livelihood diversification of poor women in rural areas were performed using the FCM-based simulation process implemented by the “FCMWizard” tool. The objective of this study was three-fold: (i) to perform a brief comparative analysis between the proposed aggregation method called “OWA learning aggregation” and the conventional average aggregation method, (ii) to identify the significant concepts and their impact on the examined FCM model regarding poverty alleviation, and (iii) to advance the knowledge of circular economy in the context of poverty alleviation. Overall, the proposed method can support policymakers in eliciting accurate outcomes of proposed policies that deal with social resilience and sustainable socio-economic development strategies.
An Adaptive Deep Belief Network With Sparse Restricted Boltzmann Machines

Deep belief network (DBN) is an efficient learning model for unknown data representation, especially nonlinear systems. However, it is extremely hard to design a satisfactory DBN with a robust structure because of traditional dense representation. In addition, backpropagation algorithm-based fine-tuning tends to yield poor performance since its ease of being trapped into local optima. In this article, we propose a novel DBN model based on adaptive sparse restricted Boltzmann machines (AS-RBM) and partial least square (PLS) regression fine-tuning, abbreviated as ARP-DBN, to obtain a more robust and accurate model than the existing ones. First, the adaptive learning step size is designed to accelerate an RBM training process, and two regularization terms are introduced into such a process to realize sparse representation. Second, initial weight derived from AS-RBM is further optimized via layer-by-layer PLS modeling starting from the output layer to input one. Third, we present the convergence and stability analysis of the proposed method. Finally, our approach is tested on Mackey-Glass time-series prediction, 2-D function approximation, and unknown system identification. Simulation results demonstrate that it has higher learning accuracy and faster learning speed. It can be used to build a more robust model than the existing ones.
Experimental investigation of the tensile test using digital image correlation (DIC) method

The Digitial Image Correlation (DIC) method is a non-contact optical technique to measure the contour deformation, strain and stress values. In order to obtain the stress and strain values, three methods have been used to investigate the tensile properties, which are experimental, strain gauge measurement, and DIC methods. The tensile specimens were fabricated with three types of materials, which were aluminium A1100, glass fiber-reinforced plastics, and pure resin plastics. It was concluded that three methods provided the closed values on Young modulus, and tensile specimen with aluminium material had the highest value with an average 171.22 GPa compared with the other two materials. In addition, it was highlighted that the DIC method had a bit higher value than the experimental method, which is still recommended to approximately predict the tensile properties.
Data Mining-Based Ethereum Fraud Detection

The popularity of blockchain-based currencies, such as Bitcoin and Ethereum, has grown among enthusiasts since 2009. Relying on the anonymity provided by the blockchain, hustlers have adapted offline scams to this new ecosystem. As a result, Ponzi schemes are proliferating on Ethereum, dressed up as secure investment schemes. They reward early investors with funds from the later ones before collapsing, leaving the last investors empty handed. Illegal in the offline world, they are creating thousands of victims on Ethereum, while stealing millions of dollars worth of ether. We use data mining to provide a detection model for Ponzi schemes on Ethereum, improving over prior work. We built a dataset of likely benign Ethereum smart contracts, in addition to known Ponzi scheme smart contracts, and designed features based on their compiled code and transactions. Using Weka to benchmark several classification algorithms, we obtained models that achieve both high precision and high recall. Our 0-day model can be used as soon as a smart contract is uploaded on the blockchain. The full-feature model continued to show high performance for almost 250 days. A detailed analysis on top-strength features provides novel perspectives on Ponzi scheme behavior.
The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences

Abstract Supplementary information: Supplementary data are available at Bioinformatics online.
Brain-Inspired Systems: A Transdisciplinary Exploration on Cognitive Cybernetics, Humanity, and Systems Science Toward Autonomous Artificial Intelligence

Brain-inspired cognitive systems (BCSs) are an emerging field of cybernetics, cognitive science, and system science. BCSs study not only the intelligence science foundations of artificial intelligence (AI) and cognitive systems, but also formal models of the brain embodied by computational intelligence. This article presents the brain and intelligence science foundations of BCS toward hybrid intelligent systems and the symbiotic intelligence of humanity. It explores the transdisciplinary theoretical foundations of system, brain, intelligence, knowledge, cybernetic, and cognitive sciences toward the next generation of knowledge processors beyond classic data processors for autonomous computing systems. A BCS provides an overarching platform for cognitive cybernetics, humanity, and systems to enable emerging hybrid societies shared by humans and intelligent machines.
News Recommendation Systems - Accomplishments, Challenges &amp; Future Directions

News publishers have decreased disseminating news through conventional newspapers and have migrated to the use of digital means like websites and purpose-built mobile applications.It is observed that news recommendation systems can automatically process lengthy articles and identify similar articles for readers considering predefined criteria.The objectives of the current work are to identify and classify the challenges in news recommendation domain, to identify state-of-the-art approaches and classify on the application domain, to identify datasets used for evaluation and their sources, the evaluation approaches used and to highlight the challenges explicitly addressed.The literature is thoroughly studied over the time span of 2001-2019 and shortlisted 81 related studies, broadly classified into six categories and discussed.The analysis showed that 60% of news recommendation system adopted a hybrid approach, 66% studies little talk about datasets, and addresses a few challenges from a long list of challenges in the news domain.This article is the first in the field to draw a comprehensive big picture of news recommendation and explore different dimensions covered in the studies.The last section presents the future research opportunities that lead to improving the recommendation of news articles in the news domain.
A Policy Recommendations Framework To Resolve Global Software Development Issues

Global software development (GSD) is basically a development which is done through low cost in given time frame by sitting in remote areas within cities, countries and around the globe. The global software development is facing major challenges such as, time zone differences, language barrier, cultural differences, geographical distance, communication gap, coordination problems, lack of project guidance, lack of experienced software testers, customer dissatisfaction, poor project management and so on. These challenges has major impact on software quality in GSD and decreases the acceptability among its users. In recent years, some strategies had been applied to reduce quality related issues that exist in GSD, however there is still a need to develop effective and efficient methods, techniques and best practices that can lead to enhance the quality of software development. The main objective of this article is to highlight the existing issues of GSD and to provide the policy recommendations to mitigate them. Therefore, a quantitative analysis have been performed that provides detailed statistical analysis on data by analyzing and applying nonparametric test (rejected the null hypothesis with less than 0.5 significance) and one sample T-test (with 2-tailed significance difference 0.00) through SPSS. Hence, this work contributes by making strong policy recommendations which is explained how GSD project should be handled by selecting best process, and how to achieve maximum quality of software product for customers to satisfy them, improve the acceptance among its users, bring their trust back to gain more business, and overall development in (software) industry.
Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations

Abstract The aim of this critical review paper is threefold: (a) to provide an insight on the impact of ontology engineering methodologies (OEMs) to the evolution of living and reused ontologies, (b) to update the ontology engineering (OE) community on the status and trends in OEMs and of their use in practice and (c) to propose a set of recommendations for working ontologists to consider during the life cycle of living, evolved and reused ontologies. The work outlined in this critical review paper has been motivated by the need to address critical issues on keeping ontologies alive and evolving while these are shared in wide communities. It is argued that the engineering of ontologies must follow a well-defined methodology, addressing practical aspects that would allow (sometimes wide) communities of experts and ontologists to reach consensus on developments and keep the evolution of ontologies ‘in track’. In doing so, specific collaborative and iterative tool-supported tasks and phases within a complete and evaluated ontology life cycle are necessary. This way the engineered ontologies can be considered ‘ shared, commonly agreed and continuously evolved “live” conceptualizations ’ of domains of discourse. Today, in the era of Linked Data and Knowledge Graphs, it is more necessary than ever not to neglect to consider the recommendations that OEMs explicitly and implicitly introduce and their implications to the evolution of living ontologies. This paper reports on the status of OEMs, identifies trends and provides recommendations based on the findings of an analysis that concerns the impact of OEMs to the status of well-known, widely used and representative ontologies.
Adversarial Examples in RF Deep Learning: Detection and Physical Robustness

While research on adversarial examples (AdExs) in machine learning for images has been prolific, similar attacks on deep learning (DL) for radio frequency (RF) signals and corresponding mitigation strategies are scarcely addressed in the published work, with only a handful of recent publications in the RF domain. With minimal waveform perturbation, RF adversarial examples (AdExs) can cause a substantial increase in misclassifications for spectrum sensing/ survey applications (e.g. ZigBee mistaken for Bluetooth). In this work, two statistical tests for AdEx detection are proposed. One statistical test leverages the peak-to-average-power ratio (PAPR) of the RF samples. The second test uses the softmax outputs of the machine learning model, which is proportional to the likelihoods the classifier assigns to each of the trained classes. The first test leverages the RF nature of the data while the latter is universally applicable to AdExs regardless of the domain. Both solutions are shown as viable mitigation methods to subvert adversarial attacks against RF waveforms, and their effectiveness is analyzed as function of the propagation channel and type of waveform.
Selective medical image encryption using DNA cryptography

In the medical field, advanced techniques like e-health, smart health, and telemedicine applications are in use. These techniques transmit a digital medical image via open-source networks. The digital medical image contains confidential and sensitive information of patients. The transmitted digital medical images are used for diagnosis in the remote center. Hence, providing security and maintaining the confidentiality of the medical image is a major apprehension. In this paper, DNA cryptography and dual hyperchaotic map techniques are proposed to provide high-level security for a digital medical image. The digital medical images are very large in size and require more computational time. To reduce computational time, the selective digital medical image encryption algorithm is proposed. In the proposed cryptosystem, the permutation and diffusion process are performed on selected pixels of digital medical images. To construct theDNA structure for digital medical images, all DNA encoding rules based on the pixel position of the digital medical image are used. The cipher image is attained by using all DNA decoding rules based on the pixel value of the digital medical image. The proposed cryptosystem is resistant to different types of attacks.