Reviewerly/exHarmony huggingface dataset:
Technical Note: Correction of geometric x‐ray image intensifier distortion based on digital image correlation

X-ray image intensifiers (XRIIs) inevitably produce images suffering from geometric distortion. Presently, various local and global methods exist to correct for these distortions. However, the performance of global methods is limited for dominant local distortions, and local methods tend to suffer from patch discontinuity and are generally sensitive to noise. In this paper, a novel local method is presented based on digital image correlation (DIC), which does not suffer from patch discontinuity and noise.As DIC is a very accurate and robust technique to analyze deformations, it is our candidate of choice to outperform the existing correction methods. The performance of our technique was first validated through distortion simulations. Next, it was validated experimentally for four different orientations of the XRII.A theoretical study on images suffering from a simulated distortion (including noise and blurring) yielded corrections with an average accuracy of (0.20 ± 0.04) pixels. We obtained experimental data with our 14" XRII (292 mm field of view), suffering from a maximum distortion between 9.6 and 12.9 mm, and an average distortion between (4.4 ± 1.3) mm and (6.1 ± 2.5) mm over the image field for the different orientations. For an adequate choice of the facet size in the DIC analysis (greater than 40 pixels), the weighted mean residual error of our method varied between (0.037 ± 0.003) mm and (0.054 ± 0.003) mm, regardless of the XRII orientation. The maximum residual error varied between 0.081 and 0.185 mm.From the simulations, we concluded that the proposed technique is affected by neither Gaussian noise nor blurring. Furthermore, it is shown that our method can reach an accuracy that is on par with or better than the current standard tools. The novel method is fast, requires minimal operator intervention and can be fully automated.
Detection of infected wounds in abdominal surgery images using fuzzy logic and fuzzy sets

Patients undergoing abdominal operations should perform a postoperative follow-up. The subsequent face-to-face consultations have an economic cost to the health system and can affect the quality of life of patients. The follow-up of the patients could be simplified using a smartphone application that, based on an image of the surgical wound and a simple questionnaire, could detect postoperative complications. One of the main complications is the appearance of infection in the wound, signaled by the redness that appears around the wound and the staples. In this paper we present a method to automatically detect infection in surgical wounds. The method has been divided into three steps. First step in this process is to locate and remove the staples that keep the wound closed because they distort the overall color of the image. To do this, after transforming the image to the HSV color space, we apply the Sobel gradient in the vertical direction of the V channel. The discrete fuzzy mathematical morphology process it to yield a binary mask with areas indicating the position of the staples. Second, using a fuzzy mathematical morphology for images in the CIELab color space, we apply an inpainting algorithm to restore the regions detected as staples. Third, using triangular and trapezoidal fuzzy sets, we determine if there are areas of the wound that present a deviation to the red color. Our experiments show that the proposed method can be successfully used to detect the possibility of infection in the wound. In addition, the algorithms have been designed using simple operations so that the method could be introduced in a smartphone application.
Cyclic-controlled quantum operation teleportation in noisy environment

A scheme is proposed for cyclic-controlled quantum operation teleportation (CCQOT) for three sides with EPR and cluster states. Under the control of David, Alice can implement an unknown single-qubit unitary operation on the remote Bob’s quantum system, while Bob can execute a single-qubit unitary operation on Charlie’s quantum system and Charlie can also perform an unknown single-qubit unitary operation on Alice’s quantum system. Our scheme can be generalized to [Formula: see text]) agents involved in the cycle to realize the transmission of single-qubit operations. Moreover, by replacing the quantum channels, we can change the cyclic direction of controlled qunatum operation teleportation (CQOT) from clockwise to counterclockwise. In addition, we discuss our scheme in four types of noisy environments (amplitude-damping, phase-damping, bit-flip and phase-flip noisy environment), and use fidelity to analyze the amount of information lost in the process of CCQOT due to noise. The results show that the fidelity is determined by decoherence rate and amplitude parameters of the final state.
Human Identification through Kinect’s Depth, RGB, and Sound Sensor

Abstract Human identification is a very important subject in computer field and has been researched widely. This paper proposes human identification system based on Kinect’s individual sensor as well as combination of all its available sensors. In the first part of the project, each sensor on the Kinect (i.e IR depth sensor, RGB camera and microphones) was used for skeleton recognition, face recognition and speech recognition respectively. Then, these individual recognition methods are combined as step-by-step process into a multi-sensor recognition system. Few experiments were carried out to test the reliability of the developed human identification systems. The results show that multi-sensor based human identification system is highly efficient compared to the single-sensor system. This is because, multi-sensor recognition system involves many recognition stages as each recognition stage needs particular biometric information of the user.
Exploration of Different Attention Mechanisms on Medical Image Segmentation

Nowadays, medical image segmentation plays an important role in computer-aided medical diagnosis. To realize effective segmentation, Attention Mechanism (AM) is widely adopted. It can be trained to automatically highlight salient features and integrated into convolution neural networks conveniently. However, many researchers choose the attention mechanism without sufficient theoretical interpretability. They ignore the differences and dominant characteristics between various datasets, which causes the failure to select the most appropriate one. In this paper, we explore the implementation and discrimination of four specific attention mechanisms. To evaluate their performances, we incorporate these mechanisms within the U-Net and make a comparison on three medical image datasets. The experimental results show that all these attention mechanisms can improve the value of Mean IoU. More significantly, we find the best AM for each type of dataset and analyze the reasons for different performances from underlying mathematical principles.
Spatial Aggregation of Visual Features for Image Data Search in a Large Geo-Tagged Image Dataset

Two main requirements of searching in a big image database are performance and accuracy. For an accurate similarity search, high-dimensional visual features are preferred while low-dimensional features resulted from dimension reduction techniques are utilized in index structures for performance. Most state-of-the-art indexes utilize low-dimensional visual descriptors to avoid the computing overhead of high-dimensionality in image search, which sacrifices search accuracy. We propose a new descriptor that balances the trade-off between accuracy and performance in image search by extending the representation of an image with the feature set of multiple similar images located in its vicinity (referred to as Spatially-Aggregated Visual Feature Descriptor (SVD)). SVD potentially preserves the visual features of images in both high and low-dimensional spaces better than conventional visual descriptors. Through an empirical evaluation on big datasets, indexing images using SVD provided a significant improvement in search accuracy comparing to using conventional descriptors while maintaining the same performance.
A Multi-objective Approach for Symbolic Regression with Semantic Genetic Programming

This paper proposes a multi-objective approach for solving symbolic regression problems using Geometric Semantic Genetic Programming (GSGP). The proposed method produces models specialized in smaller regions of the semantic search space, where the errors of the models into these different regions are the objectives being optimized. The method incorporates different ways of defining these sub-regions of the semantic space as well as a method to combine the models found intending to produce a unique prediction. Experimental results obtained over 10 real-world datasets show that the proposed method outperforms traditional GSGP in 7 out of 10 datasets.
Fast Detection Based on Customized Complex Valued Convolutional Neural Network for Generalized Spatial Modulation Systems

In this paper, a customized Auto-Encoder Complex Valued Convolutional Neural Network (AE-CV-CNN) that has been developed in a prior work is applied to Single Symbol Generalized Spatial Modulation (SS-GSM) scheme with new extracted features. The achieved reductions in the computational complexity at the receiver is at least 63.64% for M-PSK schemes compared to the complexity of Maximum Likelihood (ML) detection algorithm. This Fast detection algorithm is based on a proposed Low Complexity ML (LC-ML) detector that affords a complexity reduction of at least 40.91%. With these proposed algorithms, the complexity is reduced as the spatial constellation size increases. Furthermore, in comparison to other sub optimal detection algorithms, the computational complexity in terms of real valued multiplications of the AE-CV-CNN applied to LC-ML is independent of the spatial spectrum efficiency which means that the total spectrum efficiency increases with larger spatial constellation size at no additional complexity.
Possibilities of Using Neural Networks in the Investigation of Crimes

The study tested possibilities of using neural networks on the example of different models like, online analytical processing, data mining and knowledge discovery, specific models in the sphere of biology, models which can be used with biometric identification, face recognition systems, ECG recognition models and others. The main point of the study is technical development of scientists in the fields of specific sciences. The final results of the studied technical solutions, if they used correctly, can improve the quality of any investigation. Detailed analysis and comparison of different neural models reveal the strengths and weaknesses of each of them, as well as the their limitations. As a result, the study concludes relevance and perspective of various approaches for different kind of crime investigation
Novel Features Based on Sentence Specificity for Helpfulness Prediction of Online Reviews

The growth of e-commerce websites have provided a fruitful scenario for the increasing availability of user-generated content. Among the different types of data acquired from customers interaction, product reviews have received a special attention since they are a paramount part of the decision making process both for individuals and companies. However, the large volume of reviews that have a low quality hinders the process of gathering helpful information from these reviews. In the present study, we hypothesize that the specificity (in terms of the level of details) of the sentences from a review influences the perception of the helpfulness of a review. Using Amazon reviews as a case study, the findings of this study show that the proposed features extracted from the specificity degree of the sentences improve the performance when comparing against a common baseline model using unigrams and tf-idf weight. These specificity-based features are also ranked higher than unigrams by three different methods that evaluate features goodness. The results show a strong evidence about the relevance of such features in the task of predicting online review helpfulness.
Salient object detection via two-stage absorbing Markov chain based on background and foreground

This paper proposes a saliency detection method via two-stage absorbing Markov chain based on background and foreground for detecting salient objects in images. Firstly, image preprocessing is performed, followed by convex hull construction and superpixel segmentation, to prepare for subsequent processing. Secondly, according to the boundary connectivity, the superpixels with lower background probability value in the candidate boundary background set B0 are deleted, and the boundary background set B1 is obtained. With the saliency values of the nodes in the boundary-prior saliency map Sbg1, the background seeds are added appropriately in the region outside the candidate boundary background set B0 and the convex hull H, and the background seed set B is obtained after update. Then, the background-absorbing Markov chain is constructed to generate background-absorbing saliency map Sbg2. By fusing the saliency maps Sbg1 and Sbg2, the first-stage background-based saliency map Sbg is obtained. Thirdly, in the range of the convex hull H, the foreground seed set F is determined according to the saliency map Sbg. Then, the foreground-absorbing Markov chain is constructed, to obtain the second-stage foreground-absorbing saliency map Sfg. Finally, the saliency maps Sbg and Sfg of the two stages are combined to obtain a fused saliency map S, and the final saliency map S∗ is obtained after optimization through smoothing mechanism. Compared with the traditional methods, the performance of the proposed method is significantly improved. The proposed method is tested on three public image datasets, and it shows great accuracy in detecting salient objects.
Designing and Implementing an e-Course Using Adobe Captivate and Google Classroom: A Case Study

Nowadays, Learning Management Systems (LMS) are widely spread across academic institutions. They are not restricted to online and distant courses but are also useful during or in addition to face-to-face learning sessions. This Study took place at The University of Jordan as an attempt to evaluate the acceptance level of Google Classroom which is one of the most trending LMS platforms. The experiment used a course designed on Adobe Captivate 2019, this course teaches LATEX, a high-quality typesetting system that includes features designed for the production of technical and scientific documentation to help researchers focus on the content of their research and worry less about the structure of the document. To measure the satisfaction level of learners and teachers with Google classroom, a modified and extended version of Technology Acceptance Model(TAM) has been used. The results showed that participants felt comfortable taking a course using Google Classroom, and they agreed upon the high effectiveness of Google Classroom as a learning management system.
Risk assessment method based on factor analysis and cloud model and its application

Abstract Aiming at the problems of excessive risk factors and uncertainties in risk assessment, a risk assessment method based on factor analysis and cloud model is proposed. Firstly, the dimension reduction effect of factor analysis (FA) method and its implementation process are introduced to simplify the risk assessment indicators. Then, cloud model is adopted to replace membership function. A risk assessment method based on cloud model is proposed, which solves the problem of fuzziness and randomness of indicators. Finally, the effectiveness of the proposed method is verified by an example.
Optimization of Two Degree of Freedom PID Controller for Quadrotor with Stochastic Fractal Search Algorithm

A new metaheuristic algorithm, Stochastic Fractal Search (SFS), is proposed to optimize the two degree of freedom PID controller parameters to ensure an optimal control action. This alternative controll technique is able to deal with quadrotor complexity quite well. For comparison purposes, this paper also use the Particle Swarm Optimization (PSO) algorithm to optimize the controller parameters. The results show the superiority of SFS algorithm as an optimization algorithm, especially in this particular control scheme.
Designing Deep Neural Network with Chicken Swarm Optimization for Violence Video Classification Using VSD2014 Dataset

In this paper, a violence video classification model based on deep neural network (DNN) with chicken swarm optimization (CSO) is proposed. Violence is a self-sufficient attribute, the contents that one would not be favorable to see in movies or web videos. This is a challenging problem due to strong content variations among the positive instances of violence. Currently, deep neural network has shown its efficiency in various field that relevant to its implementation and attracts plenty of researchers in awe. However, conservative deep neural network has limitations and a tendency to easily fall into local minima. Regardless of the conventional methods applied to overcome this issue, but these techniques seem insufficiently accurate and does not adopt well to certain webs or user needs. Therefore, the purpose of this study is to assess the classification performances on violence video using Deep Neural Network with Chicken Swarm Optimization (DNNCSO). Hence, in this paper different architectures of hidden layers in DNN have been implemented using the try-error method and the importance of the parameters to examine the effect of the number of hidden layers to the classification performance. The algorithm is evaluated based on error convergence and accuracy. The results have proved the effectiveness of the proposed method up to 77–79% as compared to the conventional DNN which holds 63%. Based on the promising outcome of proposed method, in future the study intended to work on improvised bio-inspired algorithm possibly with different domains and relevant features.
Singapore Report: Data Protection in the Internet

Singapore’s data protection regime is an evolving one. Its Personal Data Protection Act (PDPA), which applies to both electronic and non-electronic data, recognises the need to strike a reasonable balance between the need for organisations to collect, use and disclose personal data, with individuals’ right to protection of their personal data. As such, the data protection authorities are constantly reviewing the PDPA and its impact, with the results seen through new or updated advisory guidelines, and potentially even changes to the PDPA itself. Other laws, such as the Computer Misuse Act and the Criminal Procedure Code, also affect Singapore’s data protection regime in significant ways.
Behaviour-Driven Requirements Engineering for Agile Product Line Engineering

Due to the need of the software development community to integrate the flexibility and the high responsiveness of agile software development with the economic advantages, of reuse and mass customisation, offered by product lines, agile product line engineering has been gaining a momentum of attention from both researchers and practitioners. Developing and managing the different requirements engineering phases of product lines present is a dilemma that the efforts done in the literature was not able to effectively and efficiently resolve. To realise the full potential of agile product line engineering in practice, a complete, consistent, correct, and unambiguous user requirements must be produced without investing that huge amount of time in the upfront domain engineering phase of a traditional software product line. In this proposal, a behaviour-driven cloud-based framework is introduced to develop and maintain software product lines in an agile-based environment. The approach relies on upfront automated testing to derive and manage core assets and variations of an agile-based software product line. The model is validated through an empirical study and the preliminary results are very promising.
A Comparative Study of Reversible Video Watermarking Using Automatic Threshold Adjuster and Non-feedback-Based DE Method

For authentication and content protection, the act of hiding information in frames is done by reversible video watermarking. An automatic threshold adjuster-based reversible video watermarking (ATAVW) method using difference expansion (DE) for gray-scale video processing system is provided in this paper. To present the best watermarked video frames of an input video stream in terms of peak-signal-to-noise ratio (PSNR), the maximum payload size and the embedded threshold with respect to the embedding capacity is automatically calculated by the proposed algorithm. The main feature of ATAVW method is that the payload size and the embedding threshold are not needed to be specified by the user, like other feedback-based reversible video watermarking algorithms to restore the accurate original video. Software implementation results for an input video having 30 frames are explained based on their quality of services parameters. The ATAVW method is then compared with the non-feedback-based reversible watermarking algorithms. These results clearly demonstrated that the ATAVW method provides higher PSNR and embedding capability with respect to the DE method for reversible watermarking.
Automatically detecting rigidly and nonrigidly deformed facial landmarks from coarseness to fineness

We propose a coarse-to-fine method automatically detecting facial landmarks for both rigid and nonrigid facial deformations. For an input 3-D face, first, we roughly detect the nose by employing 3-D local shape descriptors and further detect the tip of the nose with the tip features, e.g., the symmetry of the face. Second, we localize the eyes and mouth according to the distribution of human facial features and use a convolutional neural network to minimize the combined loss and to provide candidates for the corners of the eyes and mouth. Finally, for accurately detecting the landmarks of the eyes and mouth, we iteratively update the candidates by maximizing the similarity of the candidates and the landmarks based on the features of the candidate and its neighbors. Over Bosphorus and CASIA datasets, we evaluate the proposed method. Experiments show that compared with the state-of-the-art methods, our method detects the corners of eyes and mouth more accurately and robustly.
An Overview on Privacy Preservation and Public Auditing on Outsourced Cloud Data

Privacy preservation is the process of providing the solution for maintaining safety of outsourced data in cloud and the integrity of the outsourced data is checked with the help of public auditing. Outsourced cloud data offers big relief in terms of data storage in local machine, and security related to outsourced data is an important issue in the field of data management. This paper discusses the available encryption techniques to ensure privacy for a particular level in cloud. Based on the study of various literatures, no privacy preservation system is completely available in the present situation. The performance of available systems is evaluated based on security characteristics, that is, privacy, low computation cost, key exchange and management and so on. Still, there is a scope of improvement in designing a system to secure outsourced data storage with the help of privacy preservation and public auditing in terms of huge computation required to ensure integrity of data storage and reducing auditing time.