Reviewerly/exHarmony huggingface dataset:
A Copy-Move Image Forgery Detection Using Modified SURF Features and A-KAZE Detector

Copy-move forgery detection (CMFD) is a well-known digital image forgery technique that is used in detecting forgery regions of images.The copy-move forgery in images occurs when a specific section of an image is attached to a new component of the same image to replicate the forged image parts as an original.The forgery appears to be realistic because even after being forged in the objective region, the image acquires all the basic qualities of the original image.Some post-processing functions, such as scaling or rotation invariant, noise handling, hybrid image manipulation, etc., limit the capability of CMFD models.To overcome these limitations, the features are extracted from the Hybrid feature extraction using modified speeded up robust feature (M-SURF) with accelerated KAZE (A-KAZE) feature description and detection algorithm.The main advantages of the SURF descriptor are: the SURF features are resistant to several post-processing attacks and use Laplacian of gaussian to differentiate background and foreground features clearly.The A-KAZE descriptor is robust in detecting scale-invariant of forged regions and poorly localized key points of objects.A-KAZE constructs the invariant scaling levels using nonlinear diffusion filtering.It smooths the image while preserving the edges and decreasing speckle noise.As a result, it captures the precise location of features during the feature extraction process.Following the feature extraction, the matched features are compared, and removed false matches by applying random sample consensus (RANSAC) technique.The performance evaluation of the proposed Hybrid MSURF with A-KAZE CMFD method is validated on MICC-F220, MICC-F2000, CoMoFoD datasets and surpasses the existing CMFD methods.The proposed CMFD achieved better results compared to existing methods such as when CMFD based on DHE-SURF features along with mDBSCAN clustering and CMFD based on SURF, BRISK features along with DBSCAN clustering measured in terms of Precision of 99.93%, Accuracy of 99.46%, F1-score of 98.21%, true positive rate (TPR) of 99.93%, and false positive rate (FPR) of 4.67%.
An Improving Long Short Term Memory-Grid Search Based Deep Learning Neural Network for Software Effort Estimation

One of the main reasons that hinders making software effort estimation remains a most of the unresolved problem due to the heterogeneous nature of software data with complex structures.In processing nonlinear data, the long short-term memory (LSTM) model is often used for the purpose of solving discriminative and generative problems.Taking into account the fact that the LSTM network have low computational efficiency, due to the need to set a large number of hyperparameters.However, training deep learning models requires expensive work in terms of specifying hyperparameter configurations in the model.The grid search (GS) optimization method is used to find the best hyperparameter values for deep learning networks, which have many different hyperparameters that affect how well the network architecture works.In this paper, we proposed the grid search method as a quick step toward optimizing the parameters of the LSTM model.An empirical study was conducted using five datasets.From our results, we have seen that the LSTM-grid search model consistently performs better across datasets in mean absolute error (MAE) and root mean squared error (RMSE) compare to existing work on using machine learning approach for software effort estimation.The main advantage of training the LSTM show that the grid search finds hyperparameters which results in faster convergent, the possibility of generalization, and better coefficient of determination in estimating software effort.
BFCNN: Multi-Disease Classification Bee Inspired CNN Model

An intelligent information technology-based system offering practical health advice can improve the superiority of lifespan for individuals with chronic illnesses.This study develops a BFCNN (Bees featured CNN) model for the classification or prediction of heart, liver, and kidney diseases.The main objective of the proposed classification model is to accurately diagnose a patient's disease.The research model aims to construct a highly efficient deep-learning system for patients with heart, liver, and kidney diseases.A novel approach for detecting multiple diseases in medical datasets using a BEE-featured CNN model.The BEE-featured CNN model is a modified version of the traditional CNN model that incorporates the behavior of bee swarms to enhance the accuracy of the model.The bee-inspired algorithm used in the model involves the use of multiple artificial bees that simulate the behavior of real bees to search for optimal solutions.The proposed model was trained and tested using a dataset consisting of medical datasets of patients with different diseases.The images were collected from various sources and labeled with the corresponding disease type.The dataset was split into training and validation sets, with 80% of the datasets used for training and 20% for validation.The results of the experiments showed that the proposed BEEfeatured CNN model outperformed the traditional CNN model in detecting multiple diseases in medical datasets.The results of the prediction model demonstrate outstanding performance, with an accuracy of 96.7%, specificity of 99%, and Sensitivity of 97%.The research system demonstrates promising results in classification accuracy and holds potential for usage in medical and e-health applications.
Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts

Automatic medical text simplification can assist providers with patient-friendly communication and make medical texts more accessible, thereby improving health literacy. But curating a quality corpus for this task requires the supervision of medical experts. In this work, we present Med-EASi (Medical dataset for Elaborative and Abstractive Simplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical texts. Its expert-layman-AI collaborative annotations facilitate controllability over text simplification by marking four kinds of textual transformations: elaboration, replacement, deletion, and insertion. To learn medical text simplification, we fine-tune T5-large with four different styles of input-output combinations, leading to two control-free and two controllable versions of the model. We add two types of controllability into text simplification, by using a multi-angle training approach: position-aware, which uses in-place annotated inputs and outputs, and position-agnostic, where the model only knows the contents to be edited, but not their positions. Our results show that our fine-grained annotations improve learning compared to the unannotated baseline. Furthermore, our position-aware control enhances the model's ability to generate better simplification than the position-agnostic version. The data and code are available at https://github.com/Chandrayee/CTRL-SIMP.
Profile Analysis Method Based on Point Cloud Scanning of Blade Profile

Abstract：Aiming at the problems of slow processing speed and poor shape characteristics of optical measurement of massive point cloud on aero-engine blade, a precise analysis algorithm of blade measurement point cloud profile based on profile reconstruction was proposed.This algorithm is able to make full use of the advantage that optical measurement can cover the whole leaf, including point cloud preprocessing, contour extraction and contour analysis.Firstly, Blade height cutting of the point cloud is according to the specifications for blade design.Then, the measured point cloud is processed by using statistical methods to filter rapidly, and the octree data structure is used for voxel sampling of the point cloud.For the point cloud pretreated above, profile reconstruction is carried out by using the regional growth method to acquire profile data.Finally, the method of profile analysis is introduced to calculate the characteristic parameters of blade profile and optimize the solution of profile error.
Visually meaningful image encryption scheme using multi-parameter fractal theory and block synchronous sorting diffusion

Abstract Traditional visually meaningful image encryption (VMIE) works by embedding a secret image (SI) into a visually meaningful carrier image (CAI), thus achieving the dual protection of both data information and appearance features. However, the current VMIE algorithm still suffers from problems of reconstruction quality and transmission efficiency. To address these issues, this paper proposes an innovative VMIE algorithm that utilizes a newly designed two-dimensional hyperchaotic map, multi-parameter fractal matrix (MPFM) theory and compressive sensing. The proposed algorithm achieves dual protection of both the semantic information and appearance image data. First, a newly designed 2D infinite triangle folding map (2D-ITFM) is presented to generate a binary key-controlled measurement matrix to measure and compress a plain image (PI) to generate measured image (MI) and decrease the amount of transmission information. Next, based on the hyperchaotic map 2D-ITFM and MPFM, we present the inter-block scrambling (IBS) algorithm and intra-block synchronous sorting diffusion (IBSSD) algorithm, which are used to process the MI and generate SI. Then, the matrix encoding embedding (MEE) technique is utilized to hide the SI into the CAI to produce a visually secure cipher image (CII). Finally, the experimental results demonstrate that our scheme is effective in improving the anti-attack ability while guaranteeing good imperceptibility and reconstruction performance. This scheme can be employed in the field of information security communication.
Consistency tests for comparing astrophysical models and observations

ABSTRACT In astronomy, there is an opportunity to enhance the practice of validating models through statistical techniques, specifically to account for measurement error uncertainties. While models are commonly used to describe observations, there are instances where there is a lack of agreement between the two. This can occur when models are derived from incomplete theories, when a better-fitting model is not available or when measurement uncertainties are not correctly considered. However, with the application of specific tests that assess the consistency between observations and astrophysical models in a model-independent way, it is possible to address this issue. The consistency tests (ConTESTs) developed in this paper use a combination of non-parametric methods and distance measures to obtain a test statistic that evaluates the closeness of the astrophysical model to the observations. To draw conclusions on the consistency hypothesis, a simulation-based methodology is performed. In particular, we built two tests for density models and two for regression models to be used depending on the case at hand and the power of the test needed. We used ConTEST to examine synthetic examples in order to determine the effectiveness of the tests and provide guidance on using them while building a model. We also applied ConTEST to various astronomy cases, identifying which models were consistent and, if not, identifying the probable causes of rejection.
RMQGS-APS-Kriging-based Active Learning Structural Reliability Analysis Method

基于 RMQGS
A New Image Encryption Scheme Based on 6D Hyperchaotic System and Random Signal Insertion

With the development of communication techniques and the Internet, how to protect private image transmission on the Internet has been a research highlight.Scholars have proposed different kinds of image encryption algorithms.Some of them are not secure enough.A novel cryptosystem is presented which is based on a 6D hyperchaotic system and random signal insertion.To enhance the dynamic performance of the hyperchaotic system, insert some random signals into the system variables during iteration.The sum value of all plaintext pixels is used to produce the initial values of the system.Thus, the proposed cryptosystem is closely related to the plain image.Split a pixel into two equal parts and form a larger matrix.Scrambling, cycle shift, and diffusion are operated on the new matrix.Finally, we obtain the encrypted image.The simulation results reveal that the proposed method has a very large key space, high key sensitivity and robustness, and is able to resist many different attacks.It is more secure than many other image encryption schemes.
Interactive Information Visualization Models: A Systematic Literature Review

Interactive information visualization models aim to make dimensionality reduction (DR) accessible to non-expert users through interactive visualization frameworks. This systematic literature review explores the role of DR and information visualization (IV) techniques in interactive models (IM). We search relevant bibliographic databases, including IEEE Xplore, Springer Link, and Web of Science, for publications from the last five years. We identify 1448 scientific articles, which we then narrow down to 52 after screening and selection. This study addresses three research questions, revealing that the number of articles focused on interactive DR-oriented models has been in the minority in the last five years. However, related topics such as IV techniques or RD methods have increased. Trends are identified in the development of interactive models, as well as in IV techniques and RD methods. For example, researchers are increasingly proposing new DR methods or modifying existing ones rather than relying solely on established techniques. Furthermore, scatter plots have emerged as the predominant option for IV in interactive models, with limited options for customizing the display of raw data and details in application windows. Overall, this review provides insights into the current state of interactive IV models for DR and highlights areas for further research.
GENIUS APPLICATION MOBILE LEARNING ON LISTENING SKILLS AND ATTITUDES OF EFL ADULT LEARNERS

This study aimed to investigate the effect of using Genius Application mobile learning on listening skills and attitudes of EFL adult learners in the first semester of the English Department. The study is quasi-experimental consisted of two groups, an experimental group (n=20) and a control group (n=21). The research data included the results of two sets of listening tests and learners’ responses on an attitude questionnaire. Data were collected through a survey and the results were analyzed by using SPSS 12.0. The results were found that it can enhance motivation, increase exposure, expand vocabulary repertoire, and provide easy access to any time and everywhere.
Exploring a Research Agenda for Design Knowledge Capture in Meetings

Meetings are a frequent part of life for a software developer. Software design is often performed, discussed, and reviewed in these meetings. This means that meetings may contain important design information that could be captured for later use. Meeting design tools may be a way to capture design information as a byproduct of discussion that arises in these meetings. In this paper, we identify a list of key meeting support tool features that could support the capture and retrieval of design information and compare these to features currently offered in commercial meeting support tools.
Multi-view clustering via label-embedded regularized NMF with dual-graph constraints

Nonnegative matrix factorization (NMF) methods have achieved remarkable performances in multi-view clustering due to their effectiveness and efficiency. To better obtain a low-dimensional common representation, the limited labels and the geometric structure of the multi-view data should be fully utilized in clustering. In this work, we introduce a novel multi-view learning approach, dubbed label-embedded regularized NMF with dual-graph constraints (LeNMF-DC), for clustering. Our proposed LeNMF-DC approach mainly utilizes matrix factorization to obtain a low-dimensional common representation of the multi-view data, in which the prior knowledge hidden in data can be fully explored. Specifically, we construct three graph regularization terms to preserve the manifold structure in the data, feature and label space, respectively. Moreover, we take advantage of the labels of the labeled samples without additional parameters. In addition, we develop an alternate iterative optimization scheme to solve the model of LeNMF-DC and then show its convergence rate. Compared with traditional multi-view clustering approaches, the labels of unlabeled samples in our proposed LeNMF-DC approach are assigned by the label constraint matrix rather than the clustering algorithm, and thus it avoids performance loss during the clustering. Experimental results on four benchmark datasets manifest that our LeNMF-DC approach can achieve superior performances than several state-of-the-art approaches in multi-view clustering.
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Minimal codes are a special type of linear codes, which have nice applications in secret sharing and secure two-party computation. How to construct new infinite family of minimal codes has been a hot topic in coding theory and cryptography. In this paper, by employing exponential sums, we study the Lee-weight distributions of several classes of linear codes from defining sets over the finite chain ring Fp+uFp and determine exactly the complete weight enumerator of their Gray images under the Gray map. Furthermore, we prove that the Gray images of these linear codes are new infinite families of minimal five-weight linear codes with wminwmax<p−1p.
Modelling Differential Diagnosis of Febrile Diseases with Fuzzy Cognitive Map

The report of the World Health Organization (WHO) about the poor accessibility of people living in low-to-middle-income countries to medical facilities and personnel has been a concern to both professionals and nonprofessionals in healthcare. This poor accessibility has led to high morbidity and mortality rates in tropical regions, especially when such a disease presents itself with confusable symptoms that are not easily differentiable by inexperienced doctors, such as those found in febrile diseases. This prompted the development of the fuzzy cognitive map (FCM) model to serve as a decision-support tool for medical health workers in the diagnosis of febrile diseases. With 2465 datasets gathered from four states in the febrile diseases-prone regions in Nigeria with the aid of 60 medical doctors, 10 of those doctors helped in weighting and fuzzifying the symptoms, which were used to generate the FCM model. Results obtained from computations to predict diagnosis results for the 2465 patients, and those diagnosed by the physicians on the field, showed an average of 87% accuracy for the 11 febrile diseases used in the study. The number of comorbidities of diseases with varying degrees of severity for most patients in the study also covary strongly with those found by the physicians in the field.
Task-Space Clustering for Mobile Manipulator Task Sequencing

Mobile manipulators have gained attention for the potential in performing large-scale tasks which are beyond the reach of fixed-base manipulators. The Robotic Task Sequencing Problem for mobile manipulators often requires optimizing the motion sequence of the robot to visit multiple targets while reducing the number of base placements. A two-step approach to this problem is clustering the task-space into clusters of targets before sequencing the robot motion. In this paper, we propose a task-space clustering method which formulates the clustering step as a Set Cover Problem using bipartite graph and reachability analysis, then solves it to obtain the minimum number of target clusters with corresponding base placements. We demonstrated the practical usage of our method in a mobile drilling experiment containing hundreds of targets. Multiple simulations were conducted to benchmark the algorithm and also showed that our proposed method found, in practical time, better solutions than the existing state-of-the-art methods.
Light-Weight Pointcloud Representation with Sparse Gaussian Process

This paper presents a framework to represent high-fidelity pointcloud sensor observations for efficient communication and storage. The proposed approach exploits Sparse Gaussian Process to encode pointcloud into a compact form. Our approach represents both the free space and the occupied space using only one model (one 2D Sparse Gaussian Process) instead of the existing two-model framework (two 3D Gaussian Mixture Models). We achieve this by proposing a variance-based sampling technique that effectively discriminates between the free and occupied space. The new representation requires less memory footprint and can be transmitted across limited-bandwidth communication channels. The framework is extensively evaluated in simulation and it is also demonstrated using a real mobile robot equipped with a 3D LiDAR. Our method results in a 70~100 times reduction in the communication rate compared to sending the raw pointcloud. We have provided a demonstration video <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Video: https://youtu.be/BQZzXiCFGrM and open-sourced our code <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Code: https://github.com/mahmoud-a-ali/vsgp_pcl.
Towards Open-World Interactive Disambiguation for Robotic Grasping

Language-based communications are essential in human-robot interaction, especially for the majority of non-expert users. In this paper, we present SeeAsk, an open-world interactive visual grounding system to grasp specified targets with ambiguous natural language instructions. The main contribution of SeeAsk is that it can robustly handle open-world scenes in terms of both open-set objects and open-vocabulary interactions. Specifically, our SeeAsk is built upon modern large-scale vision-language pre-trained models and traditional decision-making process, and shows promising results to be deployed in real-world scenarios. SeeAsk outperforms previous state-of-the-art algorithms with a clear margin in terms of not only success rate but also asking smarter and more informative questions. User studies also demonstrate its advantages over previous works.