Reviewerly/exHarmony huggingface dataset:
A novel marker for robust and accurate phase-based 2D motion estimation from noisy image data

Markers with high-contrast patterns are commonly used to improve the accuracy of image intensity-based motion estimation techniques. It has been proven that the phase, which can be extracted by using quadrature pair filters, is more robust to light condition changes than the intensity of the image. However, the marker design for utilizing and stabilizing the phases using phase-based motion estimation has rarely been studied, and errors from the nonlinear phase and sub-pixel motion due to the unique phase extraction process still need to be considered. Herein, we introduce a robust and accurate phase-based 2D motion estimation technique using a novel marker comprising an array of evenly spaced white squares and two complex Gabor filters. The causes of the nonlinear phase error and the sub-pixel motion error and their effects on motion estimation were established. With the novel marker design, the nonlinear phase error can be attenuated by using appropriate filter parameters while the sub-pixel motion error can be controlled by adjusting the marker orientations. The pattern of evenly spaced squares in an array also maximizes the filter response to improve the robustness of phase-based motion estimation against image noise. Experimental demonstrations of the two types of errors were carried out on a building structure by comparing the results from the proposed technique and laser Doppler velocimetry. The anti-noise performances were compared to the other markers, the Kernel-Based Optical Sensor, and the image intensity-based optical flow technique at various noise levels. The proposed technique was also applied to a real gas booster in low-light conditions with a maximum vibrating amplitude of 0.05 mm, which further demonstrated its accuracy and robustness.
The Rise of the Non-Fungible Token (NFT) Market in Turkey: The Effect of Social Media Interaction and the Need for Uniqueness on NFT Purchase Intention<sup>*</sup>

Abstract With the spread of crypto assets, Non-Fungible Token (NFT) technology has become more and more discussed on social media platforms. The idea of uniqueness is in the background of the NFT technology used to make digital assets tradable and registerable. This study investigates the effect of consumers’ social media interaction and the need for uniqueness on NFT purchase intention. The research data was collected from 488 consumers who follow NFT purchasing sites. The results highlight the critical role of the need for uniqueness to leverage the NFT purchase intention. Analyzes were made with SPSS and AMOS statistical package programs. The validity and reliability of the scales used were tested and found to have acceptable values. The study tested possible cause-and-effect relationships between all variables using structural equation modeling. According to the findings, social media interaction, creative choice, unpopular choice, and similarity avoidance positively and significantly affected purchasing intention.
Quantifying coherence in terms of Fisher information

In quantum metrology, the parameter estimation accuracy is bounded by quantum Fisher information. In this paper we present coherence measures in terms of (quantum) Fisher information by directly considering the postselective nonunitary parametrization process. This coherence measure demonstrates the apparent operational meaning by the exact connection between coherence and parameter estimation accuracy. We also discuss the distinction between our coherence measure and the quantum Fisher information subject to unitary parametrization. The analytic coherence measure is given for qubit states.
Low-Cost Human–Machine Interface for Computer Control with Facial Landmark Detection and Voice Commands

Nowadays, daily life involves the extensive use of computers, since human beings are immersed in a technological society. Therefore, it is mandatory to interact with computers, which represents a true disadvantage for people with upper limb disabilities. In this context, this work aims to develop an interface for emulating mouse and keyboard functions (EMKEY) by applying concepts of artificial vision and voice recognition to replace the use of hands. Pointer control is achieved by head movement, whereas voice recognition is used to perform interface functionalities, including speech-to-text transcription. To evaluate the interface's usability and usefulness, two studies were carried out. The first study was performed with 30 participants without physical disabilities. Throughout this study, there were significant correlations found between the emulator's usability and aspects such as adaptability, execution time, and the participant's age. In the second study, the use of the emulator was analyzed by four participants with motor disabilities. It was found that the interface was best used by the participant with cerebral palsy, followed by the participants with upper limb paralysis, spina bifida, and muscular dystrophy. In general, the results show that the proposed interface is easy to use, practical, fairly accurate, and works on a wide range of computers.
Mediating impact of innovative behavior on the relationship between training and turnover intention: A case study of the hospitality industry in Jordan

This study aims to examine the employee intention to leave their organizations and discover how employee training could affect employee turnover intention. Furthermore, this paper intends to determine the relationship between the variables to present the idea of the impact of training on the ability of organizations to retain their employees. The research hypotheses were tested using the data obtained through a questionnaire. The sample included 160 employees working in 20 Jordanian five-star hotels in Amman, Jordan. The collected data were analyzed using the Smart-PLS software, where all necessary statistical techniques were applied. The results showed that training significantly affects the employees’ intention to leave their jobs. Moreover, the findings indicated that innovative behavior positively mediates the relationship between training and turnover intention. This requires human resource managers in this category of hotels to enhance investment in training and continue to hold training programs that meet the real needs of employees in addition to providing an environment that stimulates and enhances the innovative behavior of employees in the hospitality sector.
On the boundary problems in diagnostic classification models

In diagnostic classification models, parameter estimation sometimes provides estimates that stick to the boundaries of the parameter space, which is called the boundary problem and may lead to extreme values of standard errors. However, the relationship between the boundary problem and irregular standard errors has not been analytically explored. In addition, prior research has not shown how maximum-a-posteriori estimates avoid the boundary problem and affect the standard errors of estimates. To analyze these relationships, the expectation–maximization algorithm for maximum-a-posteriori estimates and a complete data Fisher information matrix are explicitly derived for a mixture formulation of saturated diagnostic classification models. Theoretical considerations show that the emptiness of attribute mastery patterns causes both the boundary problem and the inaccurate standard error estimates. Furthermore, unfortunate boundary problem without emptiness causes shorter standard errors. A simulation study shows that the maximum-a-posteriori method prevents boundary problems. Moreover, this method with monotonicity constraint estimation improves standard error estimates more than unconstrained maximum likelihood estimates do.
Animatomy: an Animator-centric, Anatomically Inspired System for 3D Facial Modeling, Animation and Transfer

We present Animatomy, a novel anatomic+animator centric representation of the human face. Present FACS-based systems are plagued with problems of face muscle separation, coverage, opposition, and redundancy. We, therefore, propose a collection of muscle fiber curves as an anatomic basis, whose contraction and relaxation provide us with a fine-grained parameterization of human facial expression. We build an end-to-end modular deformation architecture using this representation that enables: automatic optimization of the parameters of a specific face from high-quality dynamic facial scans; face animation driven by performance capture, keyframes, or dynamic simulation; interactive and direct manipulation of facial expression; and animation transfer from an actor to a character. We validate our facial system by showing compelling animated results, applications, and a quantitative comparison of our facial reconstruction to ground truth performance capture. Our system is being intensively used by a large creative team on Avatar: The Way of Water. We report feedback from these users as qualitative evaluation of our system.
The Study of Decentralized Autonomous Organization (DAO)in Social Network

In the past, the term "Decentralized Autonomous Organization (DAO)" was used to refer to a loosely organized network of autonomous entities, such as a corporation, a cooperative, or a non-profit party. Since Ethereum pioneered smart contracts in the blockchain, the Ethereum Virtual Machine (EVM) can connect public nodes scattered throughout the network into a virtual machine to execute Turing-complete programs. The EVM can also run other applications such as decentralized autonomous organization (DAO), which is a new type of blcokchain-based organization that uses smart contracts to perform tasks on a distributed scale. The smart contracts have organizational rules and operational logic embedded and automatically execute organizational decisions and proposals making it highly decentralized and more democratic. The DAOs are organizations that are not subject to legal, physical, or economic constraints, as well as the ability to operate outside the control of a single central authority or a single governing body. Because DAO is formed by many smart contract that operate together, which makes it highly decentralized and has a strong democratic system. This paper discusses the pros and cons of DAO, and the potential of DAO in community development, especially in non-profit organizations. It is hope to provide an overall picture of DAO, and the roadmap for future development of DAO on the right track.
A New X-ray Medical-Image-Enhancement Method Based on Multiscale Shannon–Cosine Wavelet

Because of noise interference, improper exposure, and the over thickness of human tissues, the detailed information of DR (digital radiography) images can be masked, including unclear edges and reduced contrast. An image-enhancement algorithm based on wavelet multiscale decomposition is proposed to address the shortcomings of existing single-scale image-enhancement algorithms. The proposed algorithm is based on Shannon-Cosine wavelets by taking advantage of the interpolation, smoothness, tight support, and normalization properties. Next a multiscale interpolation wavelet operator is constructed to divide the image into several sub-images from high frequency to low frequency, and to perform different multi-scale wavelet transforms on the detailed image of each channel. So that the most subtle and diagnostically useful information in the image can be effectively enhanced. Moreover, the image will not be over-enhanced and combined with the high contrast sensitivity of the human eye's visual system in smooth regions, different attenuation coefficients are used for different regions to achieve the purpose of suppressing noise while enhancing details. The results obtained by some simulations show that this method can effectively eliminate the noise in the DR image, and the enhanced DR image detail information is clearer than before while having high effectiveness and robustness.
Getting Smarter about Smart Cities: Improving Data Security and Privacy through Compliance

Smart cities assure the masses a higher quality of life through digital interconnectivity, leading to increased efficiency and accessibility in cities. In addition, a huge amount of data is being exchanged through smart devices, networks, cloud infrastructure, big data analysis and Internet of Things (IoT) applications in the various private and public sectors, such as critical infrastructures, financial sectors, healthcare, and Small and Medium Enterprises (SMEs). However, these sectors require maintaining certain security mechanisms to ensure the confidentiality and integrity of personal and critical information. However, unfortunately, organizations fail to maintain their security posture in terms of security mechanisms and controls, which leads to data breach incidents either intentionally or inadvertently due to the vulnerabilities in their information management systems that either malicious insiders or attackers exploit. In this paper, we highlight the importance of data breaches and issues related to information leakage incidents. In particular, the impact of data breaching incidents and the reasons contributing to such incidents affect the citizens' well-being. In addition, this paper also discusses various preventive measures such as security mechanisms, laws, standards, procedures, and best practices, including follow-up mitigation strategies.
A critical comparison on six static analysis tools: Detection, agreement, and precision

Developers use Static Analysis Tools (SATs) to control for potential quality issues in source code, including defects and technical debt. Tool vendors have devised quite a number of tools, which makes it harder for practitioners to select the most suitable one for their needs. To better support developers, researchers have been conducting several studies on SATs to favor the understanding of their actual capabilities. Despite the work done so far, there is still a lack of knowledge regarding (1) what is their agreement, and (2) what is the precision of their recommendations. We aim at bridging this gap by proposing a large-scale comparison of six popular SATs for Java projects: Better Code Hub, CheckStyle, Coverity Scan, FindBugs, PMD, and SonarQube. We analyze 47 Java projects applying 6 SATs. To assess their agreement, we compared them by manually analyzing – at line – and class-level — whether they identify the same issues. Finally, we evaluate the precision of the tools against a manually-defined ground truth. The key results show little to no agreement among the tools and a low degree of precision. Our study provides the first overview on the agreement among different tools as well as an extensive analysis of their precision that can be used by researchers, practitioners, and tool vendors to map the current capabilities of the tools and envision possible improvements.
ROOM: Adversarial Machine Learning Attacks Under Real-Time Constraints

Advances in deep-learning have enabled a wide range of promising applications. However, these systems are vulnerable to adversarial attacks; adversarially crafted pertur-bations to their inputs could cause them to misclassify. Most state-of-the-art adversarial attack generation algorithms focus primarily on controlling the noise magnitude to make it undetectable. The execution time is a secondary consideration for these attacks and the underlying assumption is that there are no time constraints. However, just-in-time adversarial attacks where an attacker opportunistically generates adversarial examples on-the-fly represent an even more critical threat, especially against real-time applications. Therefore, this paper introduces a new problem: how to systematically generate adversarial noise under real-time constraints? Understanding this problem improves our understanding of the threat these attacks pose to real-time systems and provides security evaluation benchmarks for future defenses. Therefore, first, we conduct a run-time analysis of adversarial generation algorithms. Our analysis show that universal attacks produce a general attack offline, with no online overhead. However, their success rate is limited because of their generality. In contrast, online algorithms, which target a specific input, are computationally expensive, making them inappropriate under time constraints. Thus, we propose ROOM, a novel Real-time Online-Offline attack construction Model where an offline component warms up the online algorithm, making it possible to generate highly successful attacks under time constraints. Our results show that ROOM can achieve high attack success rates under real-time constraints with up to 90x faster adversarial attack generation than state-of-the-art methods. For example, ROOM achieves 100% adversarial attack success rate on MNIST with a throughput of up to 1250 frame per second (FPS), more than 60% success rate with 200 FPS on CIFAR-10 and 60% with 16 FPS on ImageNet.
A novel dropout mechanism with label extension schema toward text emotion classification

Researchers have been aware that emotion is not one-hot encoded in emotion-relevant classification tasks, and multiple emotions can coexist in a given sentence. Recently, several works have focused on leveraging a distribution label or a grayscale label of emotions in the classification model, which can enhance the one-hot label with additional information, such as the intensity of other emotions and the correlation between emotions. Such an approach has been proven effective in alleviating the overfitting problem and improving the model robustness by introducing a distribution learning component in the objective function. However, the effect of distribution learning cannot be fully unfolded as it can reduce the model's discriminative ability within similar emotion categories. For example, "Sad" and "Fear" are both negative emotions. To address such a problem, we proposed a novel emotion extension scheme in the prior work (Li, Chen, Xie, Li, and Tao, 2021). The prior work incorporated fine-grained emotion concepts to build an extended label space, where a mapping function between coarse-grained emotion categories and fine-grained emotion concepts was identified. For example, sentences labeled "Joy" can convey various emotions such as enjoy, free, and leisure. The model can further benefit from the extended space by extracting dependency within fine-grained emotions when yielding predictions in the original label space. The prior work has shown that it is more apt to apply distribution learning in the extended label space than in the original space. A novel sparse connection method, i.e., Leaky Dropout, is proposed in this paper to refine the dependency-extraction step, which further improves the classification performance. In addition to the multiclass emotion classification task, we extensively experimented on sentiment analysis and multilabel emotion prediction tasks to investigate the effectiveness and generality of the label extension schema.
TW-Net: Transformer Weighted Network for Neonatal Brain MRI Segmentation

Accurate neonatal brain MRI segmentation is valuable for investigating brain growth patterns and tracking the progression of neurodevelopmental disorders. However, it is a challenging task to use intensity-based methods to segment neonatal brain structures because of small contrast differences between brain regions caused by the inherent myelination process. Although convolutional neural networks offer the potential to segment brain structures in an intensity-independent manner, they suffer from lack of in-plane long-range dependency which is essential for the segmentation. To solve this problem, we propose a novel Transformer-Weighted network (TW-Net) to incorporate in-plane long-range dependency information. TW-Net employs a conventional encoder-decoder architecture with a Transformer module in the middle. The Transformer module uses a rotate-and-flip layer to better calculate the similarity between two patches in a slice to leverage similar patterns of geometrical and texture features within brain structures. In addition, a deep supervision module and squeeze-and-excitation blocks are introduced to incorporate boundary information of brain structures. Compared with state-of-the-art deep learning algorithms, TW-Net outperforms these methods for multiple-label tasks in 2D and 2.5D configurations on two independent public datasets, demonstrating that TW-Net is a promising method for neonatal brain MRI segmentation.
Rethinking normalization methods in federated learning

Federated learning (FL) is a popular distributed learning framework that can reduce privacy risks by not explicitly sharing private data. In this work, we explicitly uncover external covariate shift problem in FL, which is caused by the independent local training processes on different devices. We demonstrate that external covariate shifts will lead to the obliteration of some devices' contributions to the global model. Further, we show that normalization layers are indispensable in FL since their inherited properties can alleviate the problem of obliterating some devices' contributions. However, recent works have shown that batch normalization, which is one of the standard components in many deep neural networks, will incur accuracy drop of the global model in FL. The essential reason for the failure of batch normalization in FL is poorly studied. We unveil that external covariate shift is the key reason why batch normalization is ineffective in FL. We also show that layer normalization is a better choice in FL which can mitigate the external covariate shift and improve the performance of the global model. We conduct experiments on CIFAR10 under non-IID settings. The results demonstrate that models with layer normalization converge fastest and achieve the best or comparable accuracy for three different model architectures.
Functional simulation of real-time quantum control software

Modern quantum computers rely heavily on real-time control systems for operation. Software for these systems is becoming increasingly more complex due to the demand for more features and more real-time devices to control. Unfortunately, testing real-time control software is often a complex process, and existing simulation software is not usable or practical for software testing. For this purpose, we implemented an interactive simulator that simulates signals at the application programming interface level. We show that our simulation infrastructure simulates kernels 6.9 times faster on average compared to execution on hardware, while the position of the timeline cursor is simulated with an average accuracy of 97.9% when choosing the appropriate configuration.
Privacy and Explainability: The Effects of Data Protection on Shapley Values

There is an increasing need to provide explainability for machine learning models. There are different alternatives to provide explainability, for example, local and global methods. One of the approaches is based on Shapley values. Privacy is another critical requirement when dealing with sensitive data. Data-driven machine learning models may lead to disclosure. Data privacy provides several methods for ensuring privacy. In this paper, we study how methods for explainability based on Shapley values are affected by privacy methods. We show that some degree of protection still permits to maintain the information of Shapley values for the four machine learning models studied. Experiments seem to indicate that among the four models, Shapley values of linear models are the most affected ones.