Reviewerly/exHarmony huggingface dataset:
Improved blur circle detection method for geometric calibration of multifocus light field cameras

Multifocus light field cameras are increasingly gaining attention in the field of computational imaging and machine vision due to their ability to capture spatial and angular information of light rays. Accurate calibration of the geometric parameters of multifocus light field cameras is the prerequisite for implementing computational imaging techniques, such as digital refocusing, depth reconstruction, and three-dimensional reconstruction. We propose an improved blur circle detection method for the calibration of multifocus light field cameras. The circular Hough transform-based circle detection method is used to obtain the subimage centers accurately in white raw images. On this basis, the subimages in the checkerboard images are classified, and the image corners are detected to determine the clusters of image corners corresponding to checkerboard corners. Then, the centers of plenoptic disc features are used as projection points of checkerboard corners on the light field image to calculate the camera pose. Levenberg–Marquardt method is further used to solve the calibration model. Finally, the calibration experiments of multifocus light field cameras are carried out to evaluate the improved blur circle detection method. Experimental results indicated that the improved blur circle detection method has higher calibration accuracy of multifocus light field camera than the blur circle detection method, while the mean reprojection error of the microlens centers and the image corners are 0.12 and 0.33 pixels, and the mean relative distance error between adjacent checkerboards is 2.53%.
An enhanced image quality assessment by synergizing superpixels and visual saliency

• Three limitations between the superpixel-based and VS-based FR-IQA models are found. • We found that the two approaches have a complementary principle. • Base on this principle, a FR-IQA model for synergizing superpixel and VS is proposed. • The proposed method can address the limitations and outperform the state-of-the-art. Superpixel and saliency-based evaluation methods play important roles in full reference image quality assessment (FR IQA). However, we find that these methods have one complementary principle and three limitations: (1) the weighted maps of superpixel-based methods conflict with the perception of the human visual system; (2) saliency-based methods are inefficient in terms of the block distortion; (3) the general two-direction gradient extraction factor must be extended to be multidirectional. To address these limitations, we propose an enhanced image quality assessment by synergizing superpixels and visual saliency. Specifically, the calculation of a newly proposed framework involves three similarities and two strategies: the saliency, superpixel and multidirectional gradient similarities of the neighborhoods, and the saliency pooling strategy, the fusion strategy of these similarities. Theoretical analysis and experimental results show that the proposed method can effectively address the limitations noted above and outperform the existing methods.
Re-presencing Through Artistic Reverie: Implications for Postdigital Theology

This chapter explores art as a means to grounding and reorienting at the ungraspable intersection of theology, technology and humanity in the postdigital. We offer a three-pronged foundation for this consideration: the long history of spiritual art and the divide between art and religion since the mid-nineteenth century; the evolving definition of postdigital art and how that might inform our expectations of art in the postdigital; and a proposed framework for re-presencing through artistic reverie. We then present two examples of postdigital art that engage with biblical imagery and spiritual frames from the ancient to the futuristic and consider whether engagement with postdigital art might catalyze a renewed relationship between the arts and the divine that could inform the emergence of a postdigital theology. The argument of this chapter is that the ungraspability of art, religion and the postdigital invite a return to engagement with art as a means to be present with, present to and present for deeper connection with ourselves and the divine.
Risk verification of stochastic systems with neural network controllers

Motivated by the fragility of neural network (NN) controllers in safety-critical applications, we present a data-driven framework for verifying the risk of stochastic dynamical systems with NN controllers. Given a stochastic control system, an NN controller, and a specification equipped with a notion of trace robustness (e.g., constraint functions or signal temporal logic), we collect trajectories from the system that may or may not satisfy the specification. In particular, each of the trajectories produces a robustness value that indicates how well (severely) the specification is satisfied (violated). We then compute risk metrics over these robustness values to estimate the risk that the NN controller will not satisfy the specification. We are further interested in quantifying the difference in risk between two systems, and we show how the risk estimated from a nominal system can provide an upper bound the risk of a perturbed version of the system. In particular, the tightness of this bound depends on the closeness of the systems in terms of the closeness of their system trajectories. For Lipschitz continuous and incrementally input-to-state stable systems, we show how to exactly quantify system closeness with varying degrees of conservatism, while we estimate system closeness for more general systems from data in our experiments. We demonstrate our risk verification approach on two case studies, an underwater vehicle and an F1/10 autonomous car.
Computer Data Encryption System Based on Nonlinear Partial Differential Equations

Data encryption is to convert plaintext data into ciphertext through a data encryption algorithm and then transmit the ciphertext. After the recipient receives the ciphertext, the ciphertext is restored to plaintext, which provides protection and technical support for information security. The main purpose of this article is to design a computer data encryption system based on nonlinear partial differential equations. This paper uses the DES encryption algorithm to encrypt data and implements an onion encryption system that encrypts the outer layer of the database and tests and analyzes the encryption efficiency and additional overhead of the database encryption system on a general database to verify the design application prospects of ideas. In addition, the overall scheme of the encryption system, the hardware, and software of the system are designed in detail, the system is debugged, the overall test is tested, and the data encryption and decryption are effective and feasible. The experimental results of this paper show that after the construction of a computer data encryption system based on nonlinear partial differential equations, the overall security of the data is increased by 25%. In addition, after comparison, the security performance of the onion-type data encryption system is higher than that of the MySQL-type data. The performance of the encryption system is 21% lower. It has certain practical value and significance to apply it to the computer data encryption system.
High-accuracy low-cost privacy-preserving federated learning in IoT systems via adaptive perturbation

With the rapid development of the Internet of Things (IoT), federated learning (FL) has been widely used to obtain insights from collected data while preserving data privacy. Differential privacy (DP) is an additive noise scheme that has been widely studied as a privacy-preserving approach on FL. However, privacy protection under DP usually comes at the cost of model accuracy for the underlying FL process. In this paper, we propose a novel low-cost (for both communication and computational overhead) adaptive noise perturbation/masking scheme to protect FL clients’ privacy without degrading the global model accuracy. In particular, we set the magnitude of the additive noise to adaptively change with the magnitude of the local model updates. Then, a direction-based filtering scheme is used to accelerate the convergence of the FL model. A maximum tolerable noise bound for local clients is derived using the central limit theorem (CLT). The designed noise maximizes privacy protection for clients while preserving the accuracy and convergence rate of the FL model , as a result of the noise cancelling out and forming a more concentrated distribution after the aggregation operation on the server. We theoretically prove that FL with the proposed noise perturbation scheme retains the same accuracy and convergence rate (O(1/T) for convex loss functions and O(1/T) for non-convex loss functions) as that of non-private FL with SGD. We also evaluate the performance of the proposed scheme in terms of convergence behavior, computational efficiency, and privacy protection against state-of-the-art privacy inference attacks on real-world datasets. Experimental results show that FL with our proposed perturbation scheme outperforms DP in the accuracy and convergence rate of the FL model in both client dropout and non-client dropout scenarios. Compared with DP, our proposed scheme does not incur additional computational and communication overhead. Our approach provides DP-comparable or better effectiveness in defending against privacy attacks under the same global model accuracy.
KD‐GAN: An effective membership inference attacks defence framework

Over the past few years, a variety of membership inference attacks against deep learning models have emerged, raising significant privacy concerns. These attacks can easily infer whether a sample exists in the training set of the target model with little adversary knowledge, and the inference accuracy is often much higher than random guessing, which causes serious privacy leakage. To this end, defenses against membership inference attacks have attracted great interest. However, the current available defense methods such as regularization, differential privacy, and knowledge distillation are unable to balance the trade-off between privacy and utility well. In this paper, we combine knowledge distillation and generative adversarial networks to propose a novel training framework that can effectively defend against membership inference attacks, called KD-GAN. Extensive experiments show that our method implements an attack success rate of nearly 0.5 (random guesses) which can successfully defend against membership inference attacks without causing significant damage to model utility, and consistently outperforming other defense methods in the balance of privacy and utility.
Educational Leadership as a Process in Today's Changing Society

The authors propose the necessity to consider educational leadership as a process within complex, unexpected, and continuous changes of 21st century society. They suggest that the source of the educational leadership process consists of expanding learning and personal authority. The process is situated and develops within human relationships, and its main elements are synergy, shared power in terms of empowerment and shared responsibility, and transformation. They studied two long-term educational leadership processes within two large Western educational organizations through qualitative content analysis. They determined that the most influential factor between the educational leadership process and the societal change process is tensions which result in either blocks or solutions. The kind of educational leadership process that is capable of producing solutions was found to entail reciprocity of expanding learning and personal authority. Importantly, within a successful educational leadership process, there is a tight interconnection between synergy, shared power, and transformation.
Segmentation of Pectoral Muscle in Mammogram Images Using Gaussian Mixture Model-Expectation Maximization

Breast cancer is one of the significant issues in medical sciences today. Specifically, women are suffering most worldwide. Early diagnosis can result to control the growth of the tumor. However, there is a need of high precision of diagnosis for right treatment. This chapter contributes toward an achievement of a computer-aided diagnosis (CAD) system. It deals with mammographic images and enhances their quality. Then, the enhanced images are segmented for pectoral muscle (PM) in the Medio-Lateral-Oblique (MLO) view of the mammographic images. The segmentation approach uses the tool of Gaussian Mixture Model-Expectation Maximization (GMM-EM). A standard database of Mini-MIAS with 322 images has been utilized for the implementation and experimentation of the proposed technique. The metrics of structural similarity measure and DICE coefficient have been utilized to verify the quality of segmentation based on the ground truth. The proposed technique is quite robust and accurate, it supersedes various existing techniques when compared in the same context.
An Accuracy Based Comparative Study on Different Techniques and Challenges for Sentiment Analysis

The use of social media has gained more popularity in the modern society. People provide their own opinions on different subjects such as, social issues, news, events and products, on the web. Sentiments are generated by large number of people in social media. The user’s sentiments are useful in uplifting a society in educational, commercial and business aspects. Sentiment analysis is the procedure to analyze the sentiment of textual matter in social networks. Sentiment Analysis constitutes a tool to take out the requisite content from the web. This analysis is also performed to collect the opinion for the assessment. Various techniques exist to execute this job. Different challenges are observed when the sentiment analysis is computed. This paper surveys on the respective methods for Sentiment Analysis and the different challenges faced by each technique.
An Effective Eye-Blink-based Cyber Secure PIN Password Authentication System

Cyberattack is an intentional and malicious attempt by a person to hack other person’s system, organizational information. The use of a pin for user authentication is a common method used in most of the systems such as locking and unlocking of private devices and doors. With an ever increasing network standards like 5G, new processes and procedures having less controlled environments, it is becoming an easy opportunity for a hacker to carry out the malicious attacks on the credentials of a person like bank details, organizational confidential information which use password-based system and are easy to hack and leak the confidential information. Password-based attack is where a person tries to hack a person’s password such as phishing attack, wherein a trustworthy person sends a fraudulent email or message, man-in-the-middle attack, brute force attack. Hence, a stronger password-based system needs to be designed to avoid such cybercrimes, which motivates for the implementation of this proposed work. This work proposes an eyeblink-based cyber-secure PIN password authentication system that uses blinking eye in the successive frames of an image captured for creating a PIN password. Virtual keypad is used by the user to enter his/her sensitive input; this virtual keypad uses orientation of eyes, gazing position on the virtual keypad screen. Captured information has been processed using machine learning and image processing algorithms for effective authentication. Implementation results have shown the strength of this proposed password system and its effective application avoiding the most frequent slippery slopes and follow-up attacks.
Use Virtual Reality to Enhance Intercultural Sensitivity: A Randomised Parallel Longitudinal Study

Prior studies suggest that emotional empathy is one of the components of intercultural sensitivity - the affective dimension under the concept of intercultural communication competence. Based on existing theories and findings, this paper reports a randomised parallel longitudinal study investigating the use of virtual reality (VR) exposure to enhance intercultural sensitivity. A total of 80 participants (36 females and 44 males) joined the study and were included in the data analysis. The participants were randomly assigned to the VR group, the video group, and the control group. Their intercultural sensitivity was measured three times: one week before the exposure ($T_{1}$), right after the exposure ($T_{2}$), and three weeks after the exposure ($T_{3}$). The results suggested that (1) the intercultural sensitivity of the VR group was significantly enhanced in both within-subject comparisons and between-subject comparisons, (2) there were no significant differences in intercultural sensitivity between the VR group and the video group at $T_{2}$, but the VR group retained the enhancement better at $T_{3}$, and (3) the sense of presence and emotional empathy well predicted the change in intercultural sensitivity of the VR group. The results, together with the participants' feedback and comments, provide new insights into the practice of using VR for intercultural sensitivity training and encourage future research on exploring the contributing factors of the results.
A Context-Aware Blockchain-Based Crowdsourcing Framework: Open Challenges and Opportunities

Crowdsourcing is a rapidly growing paradigm that commercial platforms such as Amazon MTurk and UpWork are adopting for allocating tasks to workers. Such frameworks typically employ a centralized infrastructure to implement required mechanisms such as task allocation, submission evaluation, and payment computation. However, centralized deployment comes with unresolved challenges in terms of trust, reliability, and transparency. Blockchain technology has been embraced for the deployment of crowdsourcing frameworks to enable trusted and autonomous execution. Each of the existing Blockchain-based crowdsourcing/ crowdsensing framework targets a specific application context due to the constraint capabilities of Blockchain. In this paper, we propose a context-aware Blockchain-based crowdsourcing framework where the context is defined by task requirements and workers’ availability. The proposed framework is developed upon the review of existing works integrating Blockchain and crowdsourcing where the challenges and future directions are identified. The proposed framework has two classes of components: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">core components</i> implementing the basic framework functionalities, and 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">advanced components</i> which are context and data managers that help improve the framework performance. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Advanced Context Manager</i> is designed to monitor the current context and select the mechanisms to run for the core components accordingly. The core components are implemented as smart contracts on Blockchain for autonomous and trusted execution, while the advanced components are implemented spanning Blockchain and the cloud for flexibility and scalability. A case study demonstrating the performance of context-aware task allocation algorithms is presented. It shows how capturing the current system context can help achieve better overall performance based on the objective of the sensing application under consideration.
Detecting Overlapping Areas in Unbalanced High-Dimensional Data Using Neighborhood Rough Set and Genetic Programming

Unbalanced classification has attracted widespread interest because of its broad applications. However, due to mainly the uneven class distribution, constructed classifiers are usually biased toward the majority class, and thereby perform terribly on the minority class. Unfortunately, the minority class is often the class of interest in many real-world applications. High dimensionality often further degrades the classification performance, making it more complicated to address the class imbalance issue. Genetic programming (GP) has been applied to construct classifiers, which can simultaneously select good-quality features to improve the classification performance. To handle the class imbalance issue, cost-sensitive GP classifiers treat the minority class as being more important than the majority class, but this may cause an accuracy decrease in overlapping areas where the prior probabilities of the two classes are almost the same. To date, most cost-sensitive classification methods have not been specifically investigated how the impacts of overlapping areas on cost-sensitive classifiers can be avoided. In this study, we propose a new cost-sensitive GP method, where rough set theory is employed to detect overlapping areas before training cost-sensitive classifiers for classification with unbalanced high-dimensional data. The proposed method is compared with 46 popular classification methods, including 10 GP methods and 36 non-GP methods on 14 datasets that are unbalanced and high dimensional. The experimental results indicate that the proposed method performs better than the compared methods in almost all cases.
Development of Blockchain and Machine Learning System in the Process of Construction Planning Method of the Smart Building to Save Cost and Time

Achieving project objectives that are effective, efficient, and safe requires careful planning of construction methods. The construction method of a project may vary depending on the description and design of the project. Planning for construction methods generally takes a long time to review, involves many parties, and requires consideration in terms of costs, resources, and safety. And the need for construction planning characteristics for smart buildings recently. Machine learning combined with the blockchain system is an instrument that can accommodate processes in the construction method. Start from planning to integration-evaluation with a historical database. This instrument is expected to be able to provide suggestions and evaluation of methods, to be able to assist the planning process of construction methods so that the application of work methods will be more effective and efficient.
Application of Fuzzy Inference System Mamdani at Pelican Crossing

Fuzzy logic is one of the soft computing methodologies which aims to analyze the system that contains fuzziness. Fuzzy inference system (FIS) defines as a decision-making process based on fuzzy set theory, fuzzy rules, and fuzzy reasoning. In this research, we use FIS Mamdani which has been used in several applications, especially transportation. Pedestrians are the weakest mode of transportation that requires an infrastructure that is safe and comfortable when crossing the road. Pelican crossing is one of the pedestrian crossing types. Pelican crossing in Indonesia is based on fixed time where the pedestrian time is fixed. It is not effective because the pedestrian time is not based on the volume of traffic conditions. To overcome this problem, the FIS Mamdani method is used. Mamdani method is composed of three steps to obtain an output, namely pedestrian time. Fuzzification with two input variables, namely pedestrian and vehicle volumes, and the output variable is pedestrian time. Inference engine which executes all logic with the if–then rule. Apply defuzzification with the Centroid method to obtain the output result. The purpose of this research is to determine the pedestrian time on pelican crossing using FIS Mamdani. The result shows the pedestrian time change according to the volume of pedestrians and vehicles. FIS Mamdani can adjust the traffic light signal time to the traffic conditions that occur on a pelican crossing.