Reviewerly/exHarmony huggingface dataset:
Towards Mining Generalized Patterns from RDF Data and a Domain Ontology

Nowadays, linked data (LD) are ubiquitous and mining them for knowledge, e.g. frequent patterns, needs not be argued for.A domain ontology (DO) on top of a LD dataset enables the discovery of abstract patterns, a.k.a. generalized, capturing –rather than identical sub-structures–conceptual regularities in data. Yet with the resulting ontologically-generalized graph patterns (OGP), a miner faces the combined challenges of graph topology and a label hierarchy, which amplifies well-known difficulties with graphs such as support counting or non redundant pattern listing. As OGP mining is yet to be addressed in its generality, we propose a formalization and study two workaround methods that avoid tackling it head-on, i.e. deal with each aspect separately. Both perform pure graph mining with adapted label sets: gSpan-OF merely strips labels of hierarchical structure while Tax-ON first mines frequent graph topologies with only root classes as labels, then successively refines labels on each topology.
Learning Password Modification Patterns with Recurrent Neural Networks

The majority of online services continue their reliance on text-based passwords as the primary means of user authentication. With a growing number of these services and the limited creativity and memory to come up with new memorable passwords, users tend to reuse their passwords across multiple platforms. These factors, combined with the increasing amount of leaked passwords, make passwords vulnerable to cross-site guessing attacks. Over the years, several popular methods have been proposed to predict subsequently used passwords, such as dictionary attacks, rule-based approaches, neural networks, and combinations of the above. In this paper, we work with a dataset of 28.8 million users and their 61.5 million passwords, where there is at least one pair of passwords available for each user. We exploit the correlation between the similarity and predictability of these subsequent passwords. We build on the idea of a rule-based approach but delegate rule derivation, classification, and prediction to a Recurrent Neural Network (RNN). We limit the number of guessing attempts to ten yet get an astonishingly high prediction accuracy of up to 83% in under five attempts in several categories, which is twice as much as any other known models or algorithms. It makes our model an effective solution for real-time password guessing against online services without getting spotted or locked out. To the best of our knowledge, this study is the first attempt of its kind using RNN.
Research on Related Technologies of Natural Language Processing Based on Convolutional Neural Network

Natural language processing as the current science and technology innovation and the main direction of artificial intelligence research and needs to be communication between man and machine as a fusion algorithm of computer science, linguistics, mathematics, and other the premise condition of theoretical knowledge, experience and combined with empirical research scholars at home and abroad to conduct a comprehensive discussion. This paper mainly starts from the convolutional neural network algorithm, and makes an in-depth study of Chinese entity relation extraction in natural language processing. Combined with empirical cases, the final results show that it is feasible to extract Chinese entity relation using convolutional neural network model.
Continuous interval type‐2 fuzzy Q‐learning algorithm for trajectory tracking tasks for vehicles

Abstract Trajectory tracking is a fundamental but challenging task for vehicle automation. In addition to the system nonlinearity, the main difficulties in the trajectory tracking task are due to the environmental noise and the model uncertainties under different driving scenarios. Considering the uncertainties in the environment, the reinforcement learning method with continuous action and noise‐resistance capability could be a promising way to overcome these issues. In this article, a novel continuous interval type‐2 fuzzy Q‐learning (CIT2FQL) algorithm is proposed to deal with the trajectory tracking task. By introducing the n ‐dimensional interval type‐2 fuzzy inference system ( n ‐D IT2FIS) in fuzzy Q‐learning, our proposed method achieves the continuous Q‐learning by combining the action interpolation with IT2FIS for the first time. We also proposed a simplified type‐reduction method for n ‐D IT2FIS to improve the computing efficiency of the proposed method. Moreover, a radial basis function (RBF) layer is chosen as the basis function to achieve the q ‐value interpolation. Finally, a trajectory tracking task in a simulation environment is conducted to verify the effectiveness and robustness of the proposed method under different scenarios. The results demonstrate that the proposed method has better robustness and noise‐resistance capability while maintaining good tracking performance compared with the state‐of‐the‐art baseline algorithms including double deep Q network (DDQN), proximal policy optimization (PPO), and interval type‐2 dynamic fuzzy Q‐learning (IT2DFQL).
Self-adapted Frame Selection Module: Refine the Input Strategy for Video Saliency Detection

Video saliency detection is intended to interpret the human visual system by modeling and predicting while observing a dynamic scene. This method is currently widely used in a variety of devices, including surveillance cameras and Internet-of-Things sensors. Traditionally, each video contains a large amount of redundancies in consecutive frames, while the common practices concentrate on extending the range of input frames to resist the uncertainty of input images. In order to overcome this problem, we propose Self-Adapted Frame Selection (SAFS) module that removes redundant information and selects frames that are highly informative. Furthermore, the module has high robustness and extensive application dealing with complex video contents, such as fast moving scene and images from different scenes. Since predicting the saliency map across multiple scenes is challenging, we establish a set of benchmarking videos for the scene change scenario. Specifically, our method combined with TASED-NET achieves significant improvements on the DHF1K dataset as well as the scene change dataset.
EMD Based Binary Classification of Mammograms with Novel Leader Selection Technique

Mammography is one of the primary radiography techniques that is used for detection of breast lesions which may range from benign to malignant pathologies. However manual analysis of a mammogram can be both time intensive and prone to unwanted error. Engineers off late has been actively contributing to the domain of medical image classification to ease the categorization process and make it efficient. This paper introduces a technique to identify the graveness of breast carcinoma from mammograms using three different sized training sets. In the current study we present an Earth Mover’s Distance (EMD) based binary classification of mammograms containing benign and malignant tumors. To facilitate the classification, a novel Leader Selection (LS) technique is used to identify the leader of each cohort in the training sets. The proposed model achieves a maximum sensitivity of 93.75% while producing a maximum F1 score of 83.33%. It is also observed that increasing the size of the training set improves the relevant performance metric.
Concealing Data in WAVE Audio

Concealing information in audio records is used to hide or insert messages into audio files. The audio files act as a carrier to transfer the concealed message to the receiver. To be specific, the system manages two approaches together one being steganography and other encryption. For encrypting the messages, AES is used. The text called plaintext is encrypted by the help of AES algorithm to produce ciphertext. This ciphertext is then converted into audio signals using steganography by the help of Least significant bit (LSB). To hide and transmit the ciphertext, WAVE format audio record is used. Additionally, the contents of ciphertext are changed over to bit-stream, as the audio record is in ASCII format. The ciphertext audio is inserted behind the original audio by combining the contents using LSB technique. At receiver, the encoded record is separated from the audio file. Once the encoded record is available, the file is decrypted to get the plain text message.
Integration of Blockchain and Supply Chain Management

Supply chain has been the main process of sustainable products/services flow from supplier to customer in various industries. Blockchain has been a revolution in cryptocurrency, and it is the tamperproof, timestamp, consensus-based algorithm with the decentralized ledger. There are emerging applications of blockchain from financials to healthcare and creating various jobs in service industries. Big data and Industry 4.0 with the blockchain ecosystems can bring efficiencies and real-time transactions in the pharmaceutical industry from origin to distribution in the healthcare supply chain. This chapter shall explain the physical aspects of the integration of blockchain and supply chain management in pharmaceutical industries.
An Alternative Fuzzy Logic Inference Method on the Example of a PID Controller

This paper presents a different approach to the inference process of a fuzzy controller. An important trait of the approach is the use of an OFN (ordered fuzzy numbers) property and an arithmetic apparatus. The solutions of inference processing are based on the work by Mamdani or Takagi-Sugeno, which share a common feature, a mechanism of a rule base. The approach postulated in this work does not feature a classic distribution; it only applies characteristic OFN properties using an arithmetic apparatus. The problem of system control is presented as a mathematical formula in which OFN are substituted. The approach simplifies the operations processed in the controller, an important element for which is the mathematical model of the process discussed in the paper. The results are interpreted within the area of interest of the given system. As an example, a discussion of the possible solutions of a PID controller for OFN is provided.
Decoding Customer Opinion for Products or Brands Using Social Media Analytics

This study uses aspect level sentiment analysis using lexicon-based approach to analyse online reviews of an Indian brand called Patanjali, which sells many FMCG products under its name. These reviews have been collected from the microblogging site twitter from where a total of 4961 tweets about ten Patanjali branded products have been extracted and analysed. Along with the aspect level sentiment analysis, an opinion tagged corpora has also been developed. Machine learning approaches - Support Vector Machine (SVM), Decision Tree, and Naïve Bayes have also been used to perform the sentiment analysis and to figure out the appropriate classifiers suitable for such product reviews analysis. Authors first identify customer preferences and / or opinions about a product or brand by analyisng online customer reviews as they express them on social media platform, twitter by using aspect level sentiment analysis. Authors also address the limitations of scarcity of opinion tagged data, required to train supervised classifiers to perform sentiment analysis by developing tagged corpora.
Programming Associative Memories

Ramamurthy, Garimella Swamy, Tata Jagannadha Reddy, YaminidharIn this research paper, the problem of spurious memories in retrieval of stored memories is addressed. By a proper interpretation of associative memory functionality, spurious memories are eliminated in the retrieval process. Programming or synthesis of Hopfield associative memory based on innovative approach is proposed.
Direct Estimation of Appearance Models for Segmentation

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 22 February 2021Accepted: 13 September 2021Published online: 14 February 2022Keywordsimage segmentation, mixture models, Markov random fieldsAMS Subject Headings68U10, 62M05, 62H30, 65C20Publication DataISSN (online): 1936-4954Publisher: Society for Industrial and Applied MathematicsCODEN: sjisbi
A Simple Method for Backscattered Light Estimation and Image Restoration in Turbid Water

In a turbid water environment, scattering and absorption phenomena due to suspended particles in the water affect the propagation of light. This results in the degradation of captured images due to poor visibility and low object contrast. The image restoration process in turbid water involves estimation of the amount of backscattering, once estimated correctly it can be removed from the total radiance of an image to recover the scene radiance. Our study proposes a simple method for backscattered light estimation and image restoration based on the dark channel prior (DCP) [1] and the light channel prior [2]. We show that backscattered light in a degraded underwater image can be estimated by simply computing its light channel image and directly use this as the backscattered light map in the image restoration process. The proposed approach is suitable for uniformly illuminated underwater turbid scenes. We have tested our method using underwater images from the TURBID dataset in [3] and compared it to standard underwater image restoration algorithms such as the underwater dark channel prior (UDCP) method. The experimental results show that the proposed method yields better image restoration results in comparison to state-of-the-art methods at very low computational load. The proposed method has low computational load due to its simplicity in estimating the backscattered light.
AFESSD: Object Detector for Natural Gas Pipeline Construction Scene

Abstract Object detection is a challenging task in computer vision, which aims to classify and locate objects. SSD (Single Shot MultiBox Detector) is one of the classical algorithms in the current object detection field, which uses multiple feature maps with different scales to detect all objects in an image. Based on SSD, in this paper, we propose an improved AFESSD for object detection in natural gas pipeline construction scene: Firstly, the attention mechanism module is introduced; Secondly, Feature Fusion Block and Feature Enhancement Block are designed to achieve high-to-low level feature information fusion and enhancement. Finally, we verify AFESSD on two object detection datasets of natural gas pipeline construction scene. Experimental results on the first dataset demonstrate that SSD and AFESSD achieve 81.35% and 83.13% mean average precision (mAP) respectively. On the second dataset, SSD and AFESSD achieve 50.55% and 50.99% mAP respectively. Therefore, AFESSD improves SSD’s object detection accuracy in natural gas pipeline construction scene.
A comparative study on fully convolutional networks—FCN-8, FCN-16, and FCN-32

In computer vision, semantic segmentation has resulted in powerful visual models called fully convolutional (conv) networks (net). In this network, training is carried out by conv net end-to-end, pixels-to-pixels, with each and every chunk of the image to yield a better and efficient prediction of the image dataset. The deeper layer provides a high-level semantic feature from the initial conv layers. The study involves comparing the mean intersection of the union of FCN-8, FCN-16, and FCN-32 models producing 0.6621, 0.6610, and 0.7931, respectively. The FCN-32 algorithm may be implemented for various tumor segmentations with a slight modification in the algorithm implemented to produce 89% accuracy regarding the tumor.
Calibration and Target Signal-to-Noise Ratio Enhancement Method of Infrared Camera Array Based on Refocus

Abstract The accuracy of infrared (IR) camera calibration directly affects the quality of IR imaging results. Many imaging devices have been utilized in infrared dim and small target detection, which is a major research direction in infrared field. Comparing with single-lens cameras, IR camera array can improve the detection capability by adding more imaging units, and is expected to break the limitations of traditional single-lens imaging system in cost, volume and performance. The calibration methods for camera array nowadays are generally applied in visible spectrum instead of IR band. In this paper, we proposed a calibration method based on refocus for IR camera array. Furthermore, the target signal-to-noise ratio enhancement method become effective after calibration of IR camera array. As the camera parameters obtained can be used to correct image distortion and improve the quality of imaging, the calibrating process can support many subsequent tasks such as target detection. Experiments indicate that our method achieves the expected effect.
Protection of Quantum Information in a Chain of Josephson Junctions

Symmetry considerations are key to our understanding of the fundamental laws of nature. The presence of a symmetry implies that a physical system is invariant under specific transformations, and this invariance may have deep consequences. For instance, symmetry arguments state that a system will remain in its initial state if incentives to actions are equally balanced. Here, we apply this principle to a chain of qubits and show that it is possible to engineer the symmetries of its Hamiltonian in order to keep quantum information intrinsically protected from both relaxation and decoherence. We show that the coherence properties of this system are strongly enhanced relative to those of its individual components. Such a qubit chain can be realized using a simple architecture consisting of a relatively small number of superconducting Josephson junctions.
Round-trip path planning for unmanned aerial vehicle based on improved RRT&amp;lt;bold&amp;gt;*&amp;lt;/bold&amp;gt; algorithm

针对无人机(Unmanned Aerial Vehicle, UAV)在低空复杂未知环境下的航迹规划问题, 充分考虑去程与返程过程中信息的不对称特性, 本文提出一种UAV往返航迹规划算法. 去程规划通过UAV机载设备实时探测不断更新环境信息, 并利用改进的B-RRT*(Bidirectional Rapidly-exploring Random Tree*)算法结合滚动规划生成去程航迹. 返程规划根据去程航迹与全局环境信息, 限制在由航迹起始点和目标点确定的椭圆子集中进行集中采样, 并采用三次B样条插值算法对规划航迹进行平滑处理生成返程航迹. 仿真结果表明所研究的改进算法能有效实现去程与返程的航迹规划. 最后, 搭建四旋翼UAV平台进行UAV避障飞行实验, 验证了本文所提算法的有效性.
Naïve Bayes Classifier for depression detection using text data

Depression is a prevalent medical illness that affects one’s emotions. Despite the large demography of people suffering from depression, It goes unnoticed more of ten than it should be. Timely detection and treatment of this illness can prevent further complications. With the outburst of social media, it has been noticed that people express their feelings on the plat form rather than seeking professional help. In this work Naïve Bayes Classifier, Logistic Regression Model, Random Forest Classifier and Support Vector Machine Classifier were used for classification.TheresultsindicatedthatNaïveBayesClassifierperfor medbetterwhencomparedtootherclassifiers.
Learning to Generate the Unknowns as a Remedy to the Open-Set Domain Shift

In many situations, the data one has access to at test time follows a different distribution from the training data. Over the years, this problem has been tackled by closed-set domain adaptation techniques. Recently, open-set domain adaptation has emerged to address the more realistic scenario where additional unknown classes are present in the target data. In this setting, existing techniques focus on the challenging task of isolating the unknown target samples, so as to avoid the negative transfer resulting from aligning the source feature distributions with the broader target one that encompasses the additional unknown classes. Here, we propose a simpler and more effective solution consisting of complementing the source data distribution and making it comparable to the target one by enabling the model to generate source samples corresponding to the unknown target classes. We formulate this as a general module that can be incorporated into any existing closed-set approach and show that this strategy allows us to outperform the state of the art on open-set domain adaptation benchmark datasets.
Assisting Wheelchair: Assist W

Traditional wheelchairs used by disabled people are required to be controlled manually. Hence, continuous monitoring and assistance of a caretaker is a mandatory requirement. This paper introduces an autonomous assisting wheelchair - Assist W, which would facilitate disabled people to do their day-to-day activities independently in a very safe manner, thereby managing their mental and physical health. Assist W can scan the location and design a 2D map of the house using SLAM algorithm and LIDAR sensor. After generating the map, Assist W is able to move automatically according to the commands (Voice and touch) given by the user, with the help of the map data. There is an AR (Augmented Reality) chat-bot that acts as a good companion to manage the mental health of the disabled person. Assist W is also able to manage the security and physical health of the disabled person by providing a fall detection system and automatic lifting system, and sending emergency alerts to the caretakers. This system was tested using simulation.
DPView: Differentially Private Data Synthesis Through Domain Size Information

The use of differentially private synthetic data has been adopted as a common security measure for the public release of sensitive data. However, the existing solutions either suffer from serious privacy budget splitting or fail to fully automate the generation procedures. In this study, we propose an automated system for synthesizing differentially private synthetic tabular data, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DPView</i> . Our key insight is that high-dimensional data synthesis can be accomplished by utilizing the domain sizes of attributes, which are public information, whereas identifying the correlation among attributes is necessary but leads to severe privacy budget splitting. In addition, we analytically optimize both the privacy budget allocation and consistency procedures of the proposed method through mathematical programming. We further propose two novel methods, including iterative non-negativity and consistency-aware normalization, to postprocess the synthetic data. An extensive set of experimental results demonstrates the superior utility of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DPView</i> .
Metamorphic Testing for Reliability in System of Systems

System of Systems (SoS), refers to a class of systems that are comprised of independent constituent systems (CS) interacting with one another to form a larger system, thus resulting in a common goal being achieved through the set of interactions between the CSs. In conventional systems software testing, the problem of identifying correct behaviour given a set of test inputs is called the oracle problem. In terms of SoS, the oracle problem is exacerbated due to each CS in an SoS being sufficiently complex systems themselves. We address the oracle problem in the domain of the software testing in System of Systems (SoS) by applying Metamorphic Testing (MT). In order to derive concrete Metamorphic Relations (MR) for SoS, we first borrow the concept of creating MRs from abstract Metamorphic Relation Patterns (MRPs), where the represented MRPs themselves describe characteristics that are used in the modeling and analysis of SoS. They are then applied to two SoS simulator types, the Smart Grid SoS and Mass Casualty Incident (MCI) Response SoS in order to test for reliability in SoS and to also determine the viability of MT in SoS. It is shown through the experiments conducted that the derived concrete MRs are able to find faults in both the systems under test.