Reviewerly/exHarmony huggingface dataset:
An enforced block diagonal low-rank representation method for the classification of medical image patterns

Low-rank representation based methods have been used on a variety of medical imaging databases for the segmentation and classification of biomedical images. The subspace segmentation of the data is performed by generating the block diagonal coefficient matrix. Whereas, the data is classified by performing the partitioning of the low-rank representation matrix. There exist several such methods for analysing medical images. The major difference between them lies in the construction of the data dictionary. Most of the time, the input data pattern is used as the dictionary for learning the representation matrix. The direct use of the input data for learning the representation degrades the performance of the model because medical images are subjected to outliers of multiple types, which include environmental lighting, image appearance and varying illumination. These types of errors induce noise in the data. It has been observed that the representation-based model is robust when the training data is clean. If the training data contains corrupted subsamples, the performance of the model drops down. We have addressed the mentioned problem by adopting a class-wise dictionary learning approach. In which the pattern of each class is learnt as the set of tuples in the dictionary. The model has been evaluated on several medical imaging datasets, which includes the Break-his dataset, ALL-IDB, biomedical images, covid CT and chest X-ray. The classification performance of the model is best for the biomedical database (99.16%) followed by the Covid dataset (94%), ALL-IDB database (93.47%) and Break-his dataset (93%).
A novel attention model for salient structure detection in seismic volumes

&lt;abstract&gt;&lt;p&gt;A new approach to seismic interpretation is proposed to leverage visual perception and human visual system modeling. Specifically, a saliency detection algorithm based on a novel attention model is proposed for identifying subsurface structures within seismic data volumes. The algorithm employs 3D-FFT and a multi-dimensional spectral projection, which decomposes local spectra into three distinct components, each depicting variations along different dimensions of the data. Subsequently, a novel directional center-surround attention model is proposed to incorporate directional comparisons around each voxel for saliency detection within each projected dimension. Next, the resulting saliency maps along each dimension are combined adaptively to yield a consolidated saliency map, which highlights various structures characterized by subtle variations and relative motion with respect to their neighboring sections. A priori information about the seismic data can be either embedded into the proposed attention model in the directional comparisons, or incorporated into the algorithm by specifying a template when combining saliency maps adaptively. Experimental results on two real seismic datasets from the North Sea, Netherlands and Great South Basin, New Zealand demonstrate the effectiveness of the proposed algorithm for detecting salient seismic structures of different natures and appearances in one shot, which differs significantly from traditional seismic interpretation algorithms. The results further demonstrate that the proposed method outperforms comparable state-of-the-art saliency detection algorithms for natural images and videos, which are inadequate for seismic imaging data.&lt;/p&gt;&lt;/abstract&gt;
A Trusted Data Storage and Access Control Scheme for Power CPS Combining Blockchain and Attribute-Based Encryption

The traditional data storage method often adopts centralized architecture, which is prone to trust and security problems. This paper proposes a trusted data storage and access control scheme combining blockchain and attribute-based encryption, which allow cyber-physical system (CPS) nodes to realize the fine-grained access control strategy. At the same time, this paper combines the blockchain technology with distributed storage, and only store the access control policy and the data access address on the blockchain, which solves the storage bottleneck of blockchain system. Furthermore, this paper proposes a novel multi-authority attributed-based identification method, which realizes distributed attribute key generation and simplifies the pairwise authentication process of multi-authority. It can not only address the key escrow problem of one single authority, but also reduce the problem of high communication overhead and heavy burden of multi-authority. The analyzed results show that the proposed scheme has better comprehensive performance in trusted data storage and access control for power cyber-physical system.
Active visual mapping system for digital operation environment of bridge crane

Bridge cranes must be able to sense their working environment to achieve autonomous operation. An active visual mapping system is presented in this research to adapt the measurement range based on the crane's operational state. The rotation angles of the two servos are computed based on the crane's speed so that the binocular camera is deflected and a greater field of view is gained in front, improving the crane's safety. An experimental platform is developed to simulate the operation process of the active vision 3D mapping system, and experiments are carried out using the approach proposed in this paper. The experimental results indicate that the technology can successfully enhance the mapping scope of the bridge cranes' digital operating environment and improve crane operation safety.
Logic-Based Neural Network for Image Compression Applications

This paper presents a new lossy image compression technique using Logic-based Weightless Neural Networks, which underwrite two novel network architectures. The system endorses three processing phases, image optimization, inflation, and skimming. This research demonstrates an untraditional approach of auto-compression network guided by horizontal and vertical pixel intensity wavering trend. The performance of this new approach aligns with human’s perception of singularities in a certain pattern. The potential of trend analysis in image compression incorporates with information storage techniques and knowledge accumulation. The weightless network models generate images underlying enough distinct features that preserve the originality of a particular pattern but give superior levels of compression.
The dramatically changing face of computer vision

This chapter aims to explain the concepts leading up to the recently evolved deep learning milieu, covering aspects such as image processing, feature detection, object recognition, segmentation, and tracking: by providing a useful level of background theory, and an introduction to deep learning, the chapter aims to help prepare readers for the advanced chapters that are to follow. The text is divided into seven parts: Part A, providing an understanding of low-level image processing operators and their use for feature detection; Parts B and C, respectively covering 2-D and 3-D object location and recognition—in the latter case demonstrating the importance of invariance and the achievements of multiple view vision; Part D, discussing the difficulties involved in the tracking of moving objects; Part E, covering texture analysis; Part F, outlining the evolution of artificial neural networks, the explosive development of deep learning methods, and demonstrating how the latter became capable not only of object recognition but also of semantic segmentation and object tracking. Part G summarizes the overall situation.
An Effective Data Clustering System using Weighted K-Means and Firefly Optimization Algorithms

Clustering of data is a standard way used for analyzing the data in several applications such as, data mining, image analysis, pattern recognition, etc. The weighted K-means clustering is one amongst the various data mining techniques used for clustering of the data. The key advantages of weighted k-means clustering are efficient in managing huge amount of data, easy to implement, scalable, simple and easily modifiable. In contrast, the major disadvantage of weighted K-means clustering is the problem with choosing the initial centroids. This clustering technique chooses the initial centroids randomly that leads to a local optimum solution. To address this concern, an effective naturally-inspired optimization algorithm: fire-fly optimization is combined with weighted k-means clustering for obtaining the global optimum solution. In this research paper, weighted k-means clustering along with fire-fly optimization algorithm was developed for enhancing the performance of information sharing and searching efficiency among the population. Here, the proposed system was experimented on dissimilar medical datasets such as, heart disease (original), heart disease (stat-log), liver disease and Indian liver patients. In the practical study, the proposed method enhances the performance up to 0.02-0.4 (label value) as compared to the existing systems by using the concept of precision, recall, and FB-cubed.
Research on the Prediction of Employee Turnover Behavior and Its Interpretability

Employee turnover is a key issue that companies must consider, and predicting employee turnover behavior can help companies reduce losses. However, the current Mechanism-Driven prediction method has some problems such as simplified assumptions and low accuracy. Scholars have begun to use it the Data-Driven method establishes an employee turnover behavior prediction model, but there is a lack of interpretability research on the model, which cannot effectively explain the prediction results, it will hinder the actual implementation of the method. This research establishes the RSGSBoost-SHAP prediction and interpretation framework on the basis of combing relevant literature. The framework can directly predict employee turnover behavior based on input characteristics, and explore the mechanism of the characteristics of the turnover behavior, providing management inspiration for enterprises. In the prediction results, AUC is 0.9224, and Recall is 0.8487, reaching the expected accuracy. The interpretation results elaborated on the mechanism of employee characteristics affecting their turnover behavior.
Novel Approach Transforming Stream Cipher to Block Cipher

In the current time of web, when user establish the communication with another party and user want to share some secrete documents or information in the presence of third party, user need some technique to secure the data from that third party. To protect their data, users are using many methods and day by day improved versions of these methods are coming. At the same time attackers are using new methods to break these new security methods/techniques. A novel approach of providing security to sender & receiver in protecting the data from attackers has been discussed in this article. The proposed approach converts the basic 5x5 play fair stream cipher into the block cipher. Proposed approach is a two-step symmetric key algorithm. First step takes traditional play fair 5x5 matrix of 26 characters and second step takes 25 matrix of (5x5) size having 625 words. So as to change plain content into cipher content first of all see the plain text pair in the first matrix according the position of these characters replace the words from the second matrix by following all rules of traditional play fair algorithm. At the end, a comparative analysis of strength of proposed algorithm has been done with other algorithms.
Reference-free method for investigating classification uncertainty in large-scale land cover datasets

In the last decade, land cover products are produced at a global scale and updated with an unprecedent speed with the development of earth observation and mapping techniques. However, assessing large-scale land cover datasets is always a challenging task because of lack of ground truth and high dependency on manual inspection. To promote the efficiency of assessment, this study introduces a generic framework that identifies potential land cover classification errors without traditional reference data. Depending on rich features in remote sensing data, the overall procedure of can be regarded as a multi-class anomaly detection problem. To improve the performance in dealing with complex classification scheme, a pairing strategy is firstly proposed for pairwise analysis on each two classes, so that the multi-class problem is decomposed into multiple smaller binary-class problems. Secondly, the proximity matrix of each class pair is generated on the basis of extra trees model, which is applicable to mixed numerical and categorical data and provides robust proximity measurement by including multiple levels of randomness. Finally, an improved proximity-based anomaly detection algorithm is applied, and the detection results of each class pair are ensembled to obtain the final anomaly score of each land instance. Experiments show that the overall area under the receiver operating characteristic curve score reaches approximately 0.9 for synthetic datasets and 0.8 for real-life datasets. The proposed method is expected to provide users valuable data for a more efficient assessment in the absence of ground truth in practice.
Unsupervised Keyword Combination Query Generation from Online Health Related Content for Evidence-Based Fact Checking

False information in the domain of online health related articles is of great concern, which can be witnessed in the current pandemic situation of Covid-19. It is markedly different from fake news in the political context as health information should be evaluated against the most recent and reliable medical resources such as scholarly repositories. However, one of the challenges with such an approach is the retrieval of the pertinent resources. In this work, we formulate a new unsupervised task of generating queries using keywords extracted from a health-related article which can be further applied to retrieve relevant authoritative and reliable medical content from scholarly repositories to assess the article's veracity. We propose a three-step approach for it and illustrate that our method is able to generate effective queries. We also curate a new dataset to aid the evaluation for this task which will be made available upon request.
Object Servoing of Differential-Drive Robots

Due to possibly changing pose of a movable object and nonholonomic constraint of a differential-drive robot, it is challenging to design an object servoing scheme for the differential-drive robot to asymptotically park at a predefined relative pose to the movable object. In this paper, a novel object servoing scheme is designed for the differential-drive robots. Each on-line relative pose is first estimated by using feature points of the moveable object and it serves as the input of an object servoing friendly parking controller. The linear velocity and angular velocity are then determined by the parking controller. Experimental results validate the performance of the proposed object servoing scheme. Due to its low on-line computational cost, the proposed scheme can be applied for last mile delivery of differential-drive robots to movable objects.
Strategies for safeguarding examiner objectivity and evidence reliability during digital forensic investigations

The paper investigates how digital forensic (DF) practitioners approach examiner objectivity and evidence reliability during DF investigations. Fifty-three DF practitioners responded to a questionnaire concerning these issues after first receiving a scenario, contextual information, and a task description and then examining an evidence file. The survey showed that hypotheses played an important role in how the DF practitioners handled contextual information before the analysis and safeguarding objectivity during the analysis. Dual tool verification was the most frequently referred measure for controlling evidence reliability. The analysis of responses revealed three significant causes of concern. First, 45% started the analysis without an innocence hypothesis in mind. Second, 34% applied no techniques to maintain their objectivity during the analysis. Third, 38% did not use any techniques to examine or control evidence reliability. The paper provides insights into how DF investigations are carried out in practice. These insights are essential for the DF community to guide the development of procedures that safeguard fair investigations and effective error mitigation strategies.
Depth-First Coupled Sensor Configuration and Path-Planning in Unknown Static Environments

We address path-planning for a mobile agent in an unknown static environment. The environment is observed by a sensor network where each sensor has a configurable location and field of view. We propose a depth-first coupled sensor configuration and path-planning (DF-CSCP) iterative method, which iteratively finds an optimal sensor configuration (location and FoV), applies Gaussian Process Regression to construct a threat field estimate, and then finds a candidate optimal path with minimum expected threat exposure. The DF-CSCP method uses a two stage procedure, (1) Explore and (2) Exploit, to drive the uncertainty of the candidate path cost variance below a prespecified threshold. To maintain tractability of GPR with increasing number of measurements, we present a sparse-update scheme. The proposed method relies on novel task-driven information gain (TDIG) metrics, the maximization of which provides sensor configurations. The TDIG metric quantifies the importance of acquiring sensor data of highest relevance to the path-planning task. Through numerical studies, we demonstrate the technical results that the DF-CSCP algorithm finds near-optimal paths with significantly fewer sensor measurements compared to traditional information-maximization methods.
Research on Flipped Classroom of University Curriculum Using Eye Movement Analysis and LSTM Neural Network

By reversing the arrangement of knowledge transfer and knowledge internalisation, FC (flipped classroom) changed the roles of teachers and students in traditional teaching and replanned the use of classroom time, realizing the innovation of traditional teaching mode. This research uses a network teaching platform to create a network platform supporting environment suitable for FC teaching in universities, allowing for the reform of university classroom teaching modes. An LSTM (Long Short-Term Memory) eye movement analysis algorithm is proposed to solve this problem. By analyzing the sequence of eye movements, this algorithm can predict students’ learning behavior and complete the current learning state detection. The research shows that the design and application of this research have achieved a good teaching effect in practice, which provides an idea for flipped classroom teaching design and curriculum resource design and development.
Indian Sign Language Recognition using Deep Learning

Sign language is used by people having speaking and hearing disabilities. It generally has a set of words, where each word is represented by one or more hand gestures in sequence and may contain facial expressions. In order to address the interpretation/translation from sign language to English Language, we present our sign recognition approach for Indian sign language which aims to provide a method for interpreting signs in Indian sign language to words in English language translation. The approach is to have a vision based system in which the sequence of images representing a word in ISL is translated to equivalent English word. The translation would be done by means of Deep learning algorithms namely convolutional neural nets and recurrent neural nets. The system will be analyzing sequence of images, hence CNNs will analyze each image and their sequence is analyzed by LSTM (which is an implementation of RNN). We divided dataset into training dataset and testing dataset, which obtained 73.60% accuracy. The image distributions are kept fairly different in training and testing datasets.
Generative Adversarial Networks based method for Generating Photo-Realistic Super Resolution Images

Since the word picture was coined, resolution has always been a challenge. Many studies have been conducted to generate high-resolution photographs, but none have been able to develop a process that is both time and quality effective. As a result, the super resolution issue is discussed in this paper using single-processing techniques. Deep learning methods are used to solve the same problem. The method suggested here will transform a low-resolution image into a high-resolution image of a pleasant and satisfactory quality. This can be accomplished using GANs (Generative Adversarial Networks) with significant up scaling factors.
Efficient Variational Bayes Learning of Graphical Models With Smooth Structural Changes

Estimating a sequence of dynamic undirected graphical models, in which adjacent graphs share similar structures, is of paramount importance in various social, financial, biological, and engineering systems, since the evolution of such networks can be utilized for example to spot trends, detect anomalies, predict vulnerability, and evaluate the impact of interventions. Existing methods for learning dynamic graphical models require the tuning parameters that control the graph sparsity and the temporal smoothness to be selected via brute-force grid search. Furthermore, these methods are computationally burdensome with time complexity <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(NP^3)$</tex-math></inline-formula> for <inline-formula><tex-math notation="LaTeX">$P$</tex-math></inline-formula> variables and <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> time points. As a remedy, we propose a low-complexity tuning-free Bayesian approach, named BASS. Specifically, we impose temporally dependent spike and slab priors on the graphs such that they are sparse and varying smoothly across time. An efficient variational inference algorithm based on natural gradients is then derived to learn the graph structures from the data in an automatic manner. Owing to the pseudo-likelihood and the mean-field approximation, the time complexity of BASS is only <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(NP^2)$</tex-math></inline-formula> . To cope with the local maxima problem of variational inference, we resort to simulated annealing and propose a method based on bootstrapping of the observations to generate the annealing noise. We provide numerical evidence that BASS outperforms existing methods on synthetic data in terms of structure estimation, while being more efficient especially when the dimension <inline-formula><tex-math notation="LaTeX">$P$</tex-math></inline-formula> becomes high. We further apply the approach to the stock return data of 78 banks from 2005 to 2013 and find that the number of edges in the financial network as a function of time contains three peaks, in coincidence with the 2008 global financial crisis and the two subsequent European debt crisis. On the other hand, by identifying the frequency-domain resemblance to the time-varying graphical models, we show that BASS can be extended to learning frequency-varying inverse spectral density matrices, and further yields graphical models for multivariate stationary time series. As an illustration, we analyze scalp EEG signals of patients at the early stages of Alzheimer’s disease (AD) and show that the brain networks extracted by BASS can better distinguish between the patients and the healthy controls.