Reviewerly/exHarmony huggingface dataset:
Cooperative Obstacle Avoidance for Underwater Dual-arm Manipulators based on Traversing Adjacent Space Method

Obstacle avoidance is a difficult subject in motion planning of marine robotic systems, especially for control of underwater dual-arm manipulators for underwater interventions, for instance, manipulation for marine biology and underwater archaeology, etc. Complicated ocean environment makes it tough to prevent the manipulator from collision with obstacles in dynamic environment, and collision avoidance between two arms is also complicated in such environment. In this paper, a novel obstacle avoidance control algorithm for underwater dual-arm manipulators is proposed, by using the Traversing Adjacent Space (TAS) method. Combined with the collision detection function, manipulators scan the adjacent work space, identifying the location of end-effector nearest to the target point, and reach the target gradually without collision. Moreover, a master-slave motion control mechanism is studied in this paper. The slave-manipulator moves after the master-manipulator, regarding the master-arm as obstacle, which successfully solves the difficulty in collision avoidance between two arms. The TAS method is computationally efficient, since obtaining a large number of random points is not required in TAS, and the collision-free trajectory to the target is normally the shortest. To avoid manipulator’s trapping in local optimum, generating virtual target is introduced to the paper. Simulations and experiment results show the good effectiveness and efficiency of the TAS obstacle avoidance control algorithm, which performs well both in obstacle avoidance and collision avoidance between two arms. In addition, the prospect of hydrodynamic modeling is presented after the simulation, which is important for future research on the robust control of underwater manipulator.
Power Entity Information Recognition Method Based on Bi-LSTM+CRF

During the long-term operation, power companies have accumulated a large amount of text-based data in the form of natural language. In these text-based data, entities are key semantic units, including faulty equipment, locations, phenomena, etc. The extraction of these entities is the basic work for the construction of electrical knowledge graph and faults diagnosis. However, due to the unstructured form of text-based data, it is difficult to extract entities automatically. This paper introduced a deep learning model for power entity information entity extraction, using each Chinese character as the basic unit, and employing the Word2vec model to convert each character into a vector containing contextual semantic information. Then the vector is introduced into the Bi-LSTM (Bidirectional-Long Short Term Memory) network model. In addition, the transition probability between them are comprehensively considered in the Conditional Random Field (CRF) layer. So the best tag sequence of the sentence are obtained. In the case analysis, the entities of 550 onsite text-based data was extracted. The F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> value of the extraction results was higher than that based on the power dictionary and matching scheme.
Harvesting More Answer Spans from Paragraph beyond Annotation

AutomaticA nswer spanE xtraction (AE) focuses on identifying key information from paragraphs that can be asked. It has been used to facilitate downstream question generation tasks or data augmentation for question answering. Current work of AE heavily relies on the annotated answer spans fromM achineR eadingC omprehension (MRC) datasets. However, these methods suffer from the partial annotation problem due to the annotation protocols of MRC tasks. To tackle this problem, we propose \mymethod, a S tructured Co ntext graph network with P ositive -unlabeled learning. \mymethod first represents the paragraph by constructing a graph with both syntactic and semantic edges, then adopts a unified pointer network for answer span identification. \mymethod narrows the discrenpency between AE and MRC by formulating AE as aP ositive-\textitu nlabeled (PU) learning problem, thus recovering more answer spans from paragraphs. To evaluate newly extracted spans without annotation, we also present an automatic metric from the perspective of question answering and text summarization, which correlates well with human judgments. Comprehensive experiments on both AE and downstream tasks demonstrate the effectiveness of our proposed framework. Our code is available at \urlhttps://github.com/iambabao/SCOPE.
Enhancing the transferability of adversarial black-box attacks

Deep neural networks are especially vulnerable to adversarial example, which could mislead classifiers by adding imperceptible perturbations. While previous researches can effec- tively generate adversarial example in the white-box environment, it is a challenge to produce threatening adversarial example in the black-box environment till now, where attackers only have access to obtain the predicts of models to inputs. To conquer the problem, a feasible solution is harnessing the transferability of adversarial examples and the property makes adversarial examples can successfully attack multiple models simultaneously. Therefore, this paper explores the way to enhance transfer- ability of adversarial examples and then propose a Nadam- based iterative algorithm (NAI-FGM). NAI-FGM can achieve better convergence and effectively correct the deviation so as to boost the transferability of adversarial examples. To validate the effectiveness and transferability of adversarial examples generated by our proposed NAI-FGM, this study conducts the attacks on various single models and ensemble models on open Cifar-10 and Cifar-100. Experiment results exhibit the superiority of NAIFGM that achieves higher transferability than state-of- the-art methods on average against black-box models.
Towards a CIA compliant RSA hybrid built on an Artificial Neural Network

This study investigates the improvement of the RSA algorithm by incorporating a neural network to learn data integrity and data authenticity towards creating a CIA-compliant hybrid RSA model. This is the first time a neural network has been proposed for improving the RSA model towards CIA compliance. Experimental results indicate that a neural network can learn data integrity and data authenticity in RSA encrypted messages. Data analysis affirmed that the neural network learning can be generalized. A conclusion that the RSA algorithm can be upgraded towards CIA compliance when a neural network is incorporated was arrived at. These findings have implications for the commercial standing of the RSA algorithm as well as for the body of knowledge in the cryptography domain.
Resource-Conscious High-Performance Models for 2D-to-3D Single-View Reconstruction

We propose two transfer learning-based deep neural network architectures for 2D-to-3D single-view image reconstruction, with an emphasis on low computational resources for training and high reconstruction performance. The proposed models, namely AE-Dense and 3D-SkipNet use DenseNet and ResNet architectures in the encoder, with additional skip connections. Through extensive experimental study on the 3D ShapeNets database, we show that the proposed models outperform state-of-the-art models, namely Pix2Vox and 3D-R2N2, in terms of intersection over union (IoU) metric. In particular, the AE-Dense offers the highest IoU, while the 3D-SkipNet yields a significant reduction in memory and training time, compared to Pix2Vox and 3D-R2N2.
Generalization of Blocks Linked with Query Response Using Meta-heuristic Approach

The term blockchain can be practically viewed as a database as storage and retrieval of information can be performed within blocks. But database cannot be termed as blockchain. In the simplest definition, it is the time stamped series of data along with heterogeneity which is managed by a cluster of systems with security implementing the cryptographic applications. Practically, it uniformly stores the information in blocks, where each block contains the hashed information from the previous block to provide cryptographic security. The coded symbol representing the data links to the desired blocks and process to address the previous blocks. This implementation mechanism is absolutely decentralized supporting peer-to-peer storage of data to all the access points. Accordingly, it captures the prerequisite and relevant information logically structured in blocks. Each block is attached to its preceding blocks by applying the cryptographic generic function where the details of the initial block are required to be focussed. Based upon the non-recursive processing of data, it cannot repeat the data already validated in the block. In general, there are some basic aspects of this technology to ascertain decentralization, maintain transparency as well as immutability and scalability in the system. In case of decentralization, rather having faith on single entity, it is essential to focus on other linked entities and to maintain integrity of data during the application. After that as soon as the data is associated with the blockchain, the information can be stored permanently, and the particular block will be associated with the succeeding blocks to update the information. In this paper, it is intended to generalize the response of queries linked with blocks implementing abstraction as well as meta-heuristic approach.
Unveiling Real-Life Effects of Online Photo Sharing

Social networks give free access to their services in exchange for the right to exploit their users’ data. Data sharing is done in an initial context which is chosen by the users. However, data are used by social networks and third parties in different contexts which are often not transparent. In order to unveil such usages, we propose an approach which focuses on the effects of data sharing in impactful real-life situations. Focus is put on visual content because of its strong influence in shaping online user profiles. The approach relies on three components: (1) a set of visual objects with associated situation impact ratings obtained by crowdsourcing, (2) a corresponding set of object detectors for mining users’ photos and (3) a ground truth dataset made of 500 visual user profiles which are manually rated per situation. These components are combined in LERV UP, a method which learns to rate visual user profiles in each situation. LERV UP exploits a new image descriptor which aggregates object ratings and object detections at user level and an attention mechanism which boosts highly-rated objects to prevent them from being overwhelmed by low-rated ones. Performance is evaluated per situation by measuring the correlation between the automatic ranking of profile ratings and a manual ground truth. Results indicate that LERV UP is effective since a strong correlation of the two rankings is obtained. A practical implementation of the approach in a mobile app which raises user awareness about shared data usage is also discussed.
The Classification and Detection of Malicious URLs Using a Machine Learning Based Approach

Abstract The world wide web has become the most cardinal and salient source of information, for which in today's technological era, web pages are incessantly being utilized to broadcast pernicious activity over the web. Hence, the perpetual technological advancements have enthralled cybercriminals to easily exploit the web environment by embedding malicious code on these web pages which permits oblivious and innocent users into becoming victims by visiting these harmful pages with a click of a button. Such detrimental and malicious web pages are created by cyber-attackers to promote and host viruses and exploit frauds, attacks, and scams. Consequently, within an organization, emails containing Uniform Resource Locators (URLs) cause a considerable amount of risk towards an organization as these links become instrumental in terms of giving systematic control to the cyber-attackers. There is a critical need of filtering employees’ incoming emails in accordance with the maliciousness of the URL to protect the web environment users from such threats. Accordingly, the premier objective of this proposed paper is to constructively classify and detect malicious URLs while utilizing a machine learning based approach. This will lead towards sufficiently decreasing the number of URL-based attacks that may target organizations via email, protecting the users of the web from phishing, scam, defacement, and malware web attacks. Hence, the conceptualization behind the proposed AI framework is to construct a multi-classification model to effectively thwart and protect organizations from detrimental and malicious web-based attacks by classifying a URL as either a defacement, benign, malware, spam, or a phishing-based URL. The general framework for the proposed malicious URL detection system is illustratively displayed in Figure 1.
Detection of Malignancy in Mammogram by Modified Convolution Neural Network

In recent decades, cases on breast cancer are increasing rapidly and there are many awareness campaigns conducted in order to ensure the timely diagnosis of the same. Mammogram is a medical imaging technique by X-Ray for the breast. There are two views of the mammogram. They are namely mediolateral oblique view and craniocaudal view. The mammogram images for research are provided by various data banks like mammographic image analysis society (MIAS) and many others. Engineers play a vital role in enhancing, processing and classifying medical mammogram images. Support from engineer to radiologist increases the precision in detection of tumor. The malignant tumor detection in the digital mammogram involves the stages starting with preprocessing followed by segmentation then feature extraction and finally classification. Target in the field of mammogram tumor research is to identify the positive of the disease in early stage and to improve accuracy of detecting diseased patient and healthy person from digital mammogram. The proposed Marker controlled watershed Algorithm with AlexNet (MCWAAN) mechanism shows its better performance in reducing False Positive and increasing accuracy of detection.
Motion Planning for Tractor-Trailer System

Motion planning for a tractor-trailer system is a challenging problem due to the nonholonomic constraints and highly nonlinear nature of the system. In this paper, we present a motion planning approach based on the Hybrid A* algorithm which produces smooth, collision-free, and kinematically feasible paths for a tractor-trailer system in a known environment. Kinematic model based on the on-axle hitching configuration of the tractor-trailer system is used for node expansion. Voronoi graph is used to compute sub-goals that guide the search towards the goal faster. A pure pursuit controller is employed to track the generated trajectory. The planning approach is evaluated over a number of Monte-Carlo simulation runs, and the experimental results indicate a perfect accuracy.
Linguistic Analysis of Stack Overflow Data: Native English vs Non-native English Speakers

Collaborative projects in software engineering span globally. English is the primary language for interaction in software development. Native English speakers can adapt to the rigors of software engineering projects because of their familiarity with the language in code development and communications related to software development. We analyze the communications related to software collaboration using linguistic features. We mine data from online question-answer forum, Stack Overflow and evaluate machine learning approaches to classify contributors as native English speakers vs non-native English speakers.
API parameter recommendation based on language model and program analysis

APIs are extensively and frequently used in source code to leverage existing libraries and improve programming productivity. However, correctly and effectively using APIs, especially from unfamiliar libraries, is a non-trivial task. Although various approaches have been proposed for recommending API method calls in code completion, suggesting actual parameters for such APIs still needs further investigating. In this paper, we introduce FLUTE, an efficient and novel approach combining program analysis and language models for recommending API parameters. With FLUTE, the source code of programs is first analyzed to generate syntactically legal and type-valid candidates. Then, these candidates are ranked using language models. Our empirical results on two large real-world projects Netbeans and Eclipse indicate that FLUTE achieves 80&#x0025; and +90&#x0025; in Top-1 and Top-5 Precision, which means the tool outperforms the state-of-the-art approach.
A Study on Korean Students’ Semantic Acquisition of Possible Complement Structure “V de/bu shang”: Based on Corpus

Based on the Chinese inter-language corpus of Korean students, this paper examines the Korean students’ acquisition of the structure ‘V de/bu shang(V得/不上)’. The conclusions are as follows:<BR> Firstly, the number of use cases of ‘V bu shang(V不上)’ is higher than that of ‘V de shang(V得上)’, but the error rate of ‘V de shang(V得上)’ is higher than that of ‘V bu shang(V不上)’. In the process of acquisition, there are five types of errors, such as misorder, confusion of ‘V de/bu shang(V得/不上)’ with other complement, confusion of ‘V de/bu shang(V得/不上)’ with other possible complement, other confusion, misuse and omission.<BR> Secondly, the error characteristics and rules of Korean learners in acquiring the possible expression ‘V de/bu shang(V得/不上)’ can be roughly sorted out as follows: 1) The use case proportion of Korean students using the term ‘realistic upward displacement’ is much lower than that of Chinese native speakers, indicating that Korean students may avoid using the construction ‘V de/bu shang(V得/不上)’ when expressing the meaning of ‘upward movement’; 2) ‘reach a certain standard’ is the most common meaning in Korean students’ use cases, and also the most frequent type of errors, including omission, confusion, misuse and misorder; 3) ‘achieve a certain goal’ has the highest error rate and is the most difficult meaning for Korean students to learn; 4) The error rate of each meaning of the positive expression ‘V shang(V上)’ is slightly higher than that of ‘V bushang(V不上)’, which may be related to the asymmetry of ‘possible/impossible’ expressions in Chinese and Korean. 5) Overall, when Korean learners acquire the possible expression ‘V de/bu shang(V得/不上)’, there are confusion, misorder, misuse, omission, among which confusion is most frequent type, which occurs in the acquisition process of all meanings and runs through the whole learning stage of learners.<BR> Finally, according to the results of error analysis, this paper puts forward some advices for teaching ‘V de/bu shang’ to Korean students.
Model Compression Using Optimal Transport

Model compression methods are important to allow for easier deployment of deep learning models in compute, memory and energy-constrained environments such as mobile phones. Knowledge distillation is a class of model compression algorithms where knowledge from a large teacher network is transferred to a smaller student network thereby improving the student’s performance. In this paper, we show how optimal transport-based loss functions can be used for training a student network which encourages learning student network parameters that help bring the distribution of student features closer to that of the teacher features. We present image classification results on CIFAR-100, SVHN and ImageNet and show that the proposed optimal transport loss functions perform comparably to or better than other loss functions.
Issues of Knowledge Management in Deep Web and Its Graph-Based Analysis

There has been an enormous evolution of the Internet and network technologies since the past few decades, alongside the increase in the count of users and their ever increasing demand for preservation of identity and privacy. In order to meet the demand of the users, scientists have come up with the novel thoughts, thereby leading to the evolution of the large portion of the Internet, i.e., the deep Web. Although the demand of the privacy preservation has been solved by the deep Web for some gentle users, but alongside, it has opened up the arena for the accommodation of several unlawful activities, leading to the generation of dark Web. This necessitates in the search of automatic solutions in support of the law and parallel assisting the security agencies for collection of information from dark Web in order to disclose similar activities. In this paper, the authors will present a brief overview of the deep Web and present its features. The main point of focus shall be the issue of knowledge management and how it will be utilized for making a graph-based analysis. The paper concludes with an example of a proposed system for extraction of knowledge.
Probability, Choice and Reason

Journal of the Royal Statistical Society: Series A (Statistics in Society)Early View BOOK REVIEW Probability, Choice and Reason , Williams, Leighton Vaughan, 2022 Boca Raton, CRC Press ISBN 978-0-367-53893-4, DOI: 10.1201/9781003083610 Simon French, Simon French simon.french.50@gmail.com Alliance Manchester Business School, Manchester, UKSearch for more papers by this author Simon French, Simon French simon.french.50@gmail.com Alliance Manchester Business School, Manchester, UKSearch for more papers by this author First published: 15 February 2022 https://doi.org/10.1111/rssa.12829Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
FMS (Federated Model as a service) for healthcare: an automated secure-framework for personalized recommendation system

The Healthcare sector has been emerging on the platform ofdata science. And data scientists are often using machine learningtechniques based on historical data to create models, makepredictions or recommendations. This paper aims to providebackground and information for the community on the benefitsand variants of Federated Learning (F.L.) with other technologiesfor medical applications and highlight key considerationsand challenges of F.L. implementation in the digital health background.With this FMaaS, we envisage a future for digital federatedhealth. We hope to empower and raise awareness aboutthe environment and fog computing to provide a more secureand better-analyzing environment. The AutoML framework isused to generate and optimize machine learning models usingautomatic engineering tools, model selection, and hyperparameteroptimization on fog nodes. Thus, making the systemmore reliable and secure for each individual by preserving privacyat their end devices. And this will lead to a personalizedrecommendation system for each individual associated withthis framework by deploying the Model to their devices foron-device inferences through the concept of differential privateModel averaging. With this framework, users don’t haveto compromise with privacy, and all their sensitive data will besecure on their end devices.