Reviewerly/exHarmony huggingface dataset:
Analysis of Various Noise Reduction Techniques for Breast Ultrasound Image Enhancement

Ultrasound imaging is an essential diagnostic tool for analyzing the body's internal structure to find disease or abnormal tissues. The presence of noise in ultrasound images affects edges and fine details, making diagnosis more difficult by reducing contrast and resolution. It is vital to preserving the feature of the image by reducing the noise level. In this paper, we have explained the types of noises present in the ultrasound image and the filters used to remove the noises. The noise introduced in the breast ultrasound image is impulse noise, Gaussian noise, and speckle noise. Several denoising algorithms have been proposed for image denoising: filter, median filter, Gaussian filter, Butterworth filter, wavelet-based filter, and CNN for efficient noise reduction. A quantitative measure of comparison is provided by the parameters like mean square error (MSE), peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), mean, variance, standard deviation, kurtosis, and skewness of the image.
“From the classroom to the cloud”: Zoom and the platformization of higher education

Videoconferencing platforms like Zoom have been essential to U.S. colleges and universities in migrating from in-person and hybrid classrooms to fully online learning in response to the COVID-19 pandemic. The synchronous connection of cloud-based streaming allows some daily activities of higher-ed to continue; yet, Zoom has faced criticism for surveillance and data extraction, privacy and security issues, platform control of content, and as contributing to infrastructural divides. Drawing from critical media studies and surveillance studies approaches, I examine the issues of videoconferencing platforms like Zoom as compositions of platform, infrastructural, and capitalist surveillance. Despite the oft-promoted benefits of these platforms, critical attention must be paid to the highly problematic inherent surveillant dimensions of partnerships between higher-ed and big tech to ensure the protection of students, employees, and faculty. Specifically, the rapid adoption of Zoom exemplifies the dangers of surveillant platform infrastructure, as well as how technological “solutions” gain traction in moments of crisis.
Genmod: A Generative Modeling Approach for Spectral Representation of Pdes with Random Inputs

We propose a method for quantifying uncertainty in high-dimensional PDE systems with random parameters, where the number of solution evaluations is small. Parametric PDE solutions are often approximated using a spectral decomposition based on polynomial chaos expansions. For the class of systems we consider (i.e., high dimensional with limited solution evaluations) the coefficients are given by an underdetermined linear system in a regression formulation. This implies additional assumptions, such as sparsity of the coefficient vector, are needed to approximate the solution. Here, we present an approach where we assume the coefficients are close to the range of a generative model that maps from a low to a high dimensional space of coefficients. Our approach is inspired be recent work examining how generative models can be used for compressed sensing in systems with random Gaussian measurement matrices. Using results from PDE theory on coefficient decay rates, we construct an explicit generative model that predicts the polynomial chaos coefficient magnitudes. The algorithm we developed to find the coefficients, which we call GenMod, is composed of two main steps. First, we predict the coefficient signs using Orthogonal Matching Pursuit. Then, we assume the coefficients are within a sparse deviation from the range of a sign-adjusted generative model. This allows us to find the coefficients by solving a nonconvex optimization problem, over the input space of the generative model and the space of sparse vectors. We obtain theoretical recovery results for a Lipschitz continuous generative model and for a more specific generative model, based on coefficient decay rate bounds. We examine three highdimensional problems and show that, for all three examples, the generative model approach outperforms sparsity promoting methods at small sample sizes.
A Novel Protection Method of Continuous Location Sharing Based on Local Differential Privacy and Conditional Random Field

At present, the protection of user’s location privacy (especially mobile user’s location privacy) is widely concerned by academia and industry. Many experts and scholars have proposed several secure and efficient solutions to this problem. However, in this context, if a mobile user wants to obtain the services she/he wants, she/he needs to share her/his location information continuously with an untrusted third-party in user’s locations. This will cause privacy and security issue. To tackle this problem, in this paper, we apply local differential privacy (LDP) in supporting continuous location sharing among mobile users. Firstly, we put forward a new idea of using conditional random field (CRF) in model user’s mobility. Then, we combine $$\delta $$ -location set and $$\varepsilon $$ -LDP and advance a mechanism to support continuous location sharing. Finally, we conduct experiments on real data set to evaluate our proposed mechanism. The results show that our mechanism is more effective than planar isotropic mechanism (PIM).
The Impact of Job Satisfaction on Employee Performance: A Case at ABC Manufacturing Company

The aim of this study is to investigate the impact of ‘Job Satisfaction’ on ‘Employee Performance’. The preliminary investigations revealed a researchable area where most of the employees are dissatisfied affecting their job performances. In this study deductive approach has been used to test the existing theories and thus this study involves quantitative analysis. The determinants of independent variable of ‘Job Satisfaction’ included Pay &amp; Benefits, Trainings, Work Condition and Nature of the Job. The dependent variable for the study is ‘Employee Performance’. The sample consists of 66 employees based on simple random sampling technique. Moreover, a structured questionnaire was administered to collect the primary data. The hypotheses were tested using the correlation and regression analysis. The correlation analysis denoted that there is a positive relationship between job satisfaction and employee performance and the regression analysis depicted that the job satisfaction has a statistically significant impact on employee performance at 0.05. This study suggests the organization to maintain salary equality and supervisor relationships to increase the employee performance. Furthermore, it is recommended to recognize the training needs of the employees to boost the performance of the employees. Moreover, based on the findings of this study, the scholars could further investigate the how other factors such as top management support and organization culture impacting on employee performance.
A Novel Educational Video Retrieval System Based on the Textual Information

Video search is an active area of research in the field of information retrieval. The major approaches explored for video retrieval are metadata-based search and content-based video retrieval. In this manuscript, we propose a video retrieval system that will retrieve the videos based on the textual information present in the video frames. The key idea behind the proposed scheme is that the user who needs to retrieve a set of videos will give a keyword text through user consol. The video retrieval system will extract the frames from all the videos in the video pool and an optical character recognition module extracts the text information from each of the frames. The presence of search keywords in each frame will be analysed using the pattern matching technique. The number of times a search keyword is present in a video will decide the rank of that particular video in the final search results. To reduce the time requirement of the video retrieval operation, we have considered one frame that belongs to a scene. A video may consist of several scenes, some of the absolute differences between the reference frame and the frames video will determine the scene changes. The experimental study of the proposed scheme is carried out on the videos downloaded from National Programme on Technology Enhanced Learning (NPTEL) website. The proposed scheme will be useful to academicians to search the videos of a particular topic. The results on a limited video dataset show that the proposed scheme is performing well in real-time situations.
Breast cancer recurrence prediction system using k-nearest neighbor, naïve-bayes, and support vector machine algorithm

Breast cancer is a serious disease and one of the most fatal diseases in the world. Statistics show that breast cancer is the second common cancer worldwide with around two million new cases per year. Some research has been done related to breast cancer, and with the advancements of technology, breast cancer can be detected earlier by using artificial intelligence or machine learning. There are popular machine learning algorithms that can be used to predict the existence or recurrence of breast disease, for example, k-Nearest Neighbor (kNN), Naïve Bayes, and Support Vector Machine (SVM). This study aims to check the prediction of breast cancer recurrence using those three algorithms using the dataset available at the University of California, Irvine (UCI). The result shows that the kNN algorithm gives the best result in terms of accuracy to predict breast cancer recurrence.
1. Introduction to quantum machine learning

Quantum Machine Learning (QML) is popularly known to be an integrative approach to learning of the Quantum Physics (QP) and Machine Learning (ML). In this chapter, an outline of the fundamental ideas and features related to quantum machine learning is laid out. The different facets of quantum algorithms are discussed in this chapter. In addition to this, the basic features of quantum reinforcement learning and quantum annealing are also provided in this chapter. Finally, the chapter deliberates about the advancement of quantum neural networks to through light in the direction of QML.
A Multi-Feature Extraction-Based Algorithm for Stitching Tampered/Untampered Image Classification

With the recent internet connectivity revolution, and the fast-growing prevalence of camera-enabled devices, images play a vital role in several fields of modern life. Photos, which often have been seen as evidence in courts, are nowadays subject to more sophisticated tricky forgery. To detect the image stitching between originally unassociated people/scenes and other combining forgery, an algorithm used to extract multiple specific image features, such as grayscale, complementary color wavelet (CCW) based chroma, sharpness, and natural scene statistics (NSS), is first presented in this paper. It is illustrated that a random forest model can be trained by these extracted features and then be employed to classify the stitching tampered/untampered images. The experimental results show that the proposed algorithm favorably outperforms the techniques reported in the literature, and achieves a state-of-the-art performance with higher accuracy values of 91%, 95.24%, and 88.02% on the Tampering ImageNet, Columbia, and CASIA ITDE V2.0 datasets, respectively. The precision, recall, and F1-score were also improved to a certain extent.
The Cyber-Physical Disruption

This chapter examines four emerging areas of technological innovation in which digital technologies are becoming embedded into the physical world. The digitization of everyday objects includes consumer Internet of things and connected objects in smart cities. The Internet of self encompasses cyberphysical systems entangled with the body, such as wearable technologies, implantable chips, biometric identification devices, and digital medical monitoring and delivery systems. The industrial Internet of things, sometimes called the “fourth industrial revolution,” involves restructurings of industries and labor around cyber-physical systems. Finally, emergent embedded systems include those embedded objects that are born digital, such as robotics, 3D printing, and arguably augmented reality systems. Understanding these heterogeneous technical architectures, and the technological affordances and characteristics they all share, is necessary for understanding emerging governance debates.
BMEFIQA: Blind Quality Assessment of Multi-Exposure Fused Images Based on Several Characteristics

A multi-exposure fused (MEF) image is generated by multiple images with different exposure levels, but the transformation process will inevitably introduce various distortions. Therefore, it is worth discussing how to evaluate the visual quality of MEF images. This paper proposes a new blind quality assessment method for MEF images by considering their characteristics, and it is dubbed as BMEFIQA. More specifically, multiple features that represent different image attributes are extracted to perceive the various distortions of MEF images. Among them, structural, naturalness, and colorfulness features are utilized to describe the phenomena of structure destruction, unnatural presentation, and color distortion, respectively. All the captured features constitute a final feature vector for quality regression via random forest. Experimental results on a publicly available database show the superiority of the proposed BMEFIQA method to several blind quality assessment methods.
Digital currencies

Privately issued digital currencies have the capacity to change global payment systems and possibly challenge some official currencies. Arguably cryptocurrencies would not be able to provide liquidity readily in times of crisis as that is not part of their ‘mandate’. This is not unlike the gold standard, where new currency could not be mined in real time and made available to absorb excessive demand. Similarly, deposit guarantees would not be available as a solution in a crypto-financial system. As Claeyset al. have shown the first generation of cryptocurrencies traded in small volumes and therefore never threatened to challenge traditional global currencies, like the euro or the dollar. The second generation of private digital currencies aimed at resolving some of the first generation’s shortcomings. The possibility of a big-scale stablecoin is therefore for the moment not imminent. However, the possibility for future stablecoins to challenge established currencies remains real.
Conceptual Design of a Socially Intelligent Agent with Triadic Empathy and Theory of Mind for Mental Health Support

For socially intelligent agents to become a fully digital therapist to support individual with mental health problem in the future, they need to know how to socially interact with humans. One of the key ingredients to allow this skill to take place is an ability to exhibit empathic behaviours. Despite a number of socially intelligent agents were build with empathic behaviour, they only cover single empathy behaviour, contrary to more complex triadic empathic behaviours. In this article, the conceptual design and model to implement triadic empathy in socially intelligent agents is presented.
Proposal of two facial scales using emojis as tools to understand emotions in research with Brazilian consumers

Emojis are used in digital communication to express feelings and emotions and are commonly used on social media platforms. Research aiming to interpret the meaning of facial emojis is needed and should consider differences among several characteristics as the cultural aspects across different countries. This study aimed to assess the meaning of emojis by Brazilians and create two facial scales that can be used in later research with applications of emojis in consumer studies and sensory analysis of food and beverages. The associations between emotions and emojis were evaluated by 132 participants from Brazil using 39 descriptive terms and 33 facial emojis through a modified Check-All-That-Apply (CATA) questionnaire. The overall average of emoji associations for each CATA term varied from 1.1 to 4.3, demonstrating that the associations varied a lot, depending on the emotion. In this way, it was possible to obtain a group of emotions with a strong association with one emoji (n=15); a second group with a strong association with more than one emoji (n=8), and; a third group with weak associations (n=16). The emojis from the first and second groups were used to make a chart with 14 emojis to be used in consumer research with Brazilian consumers and also a 5-point facial scale of intensity to be used in sensory analysis with food and beverages.
Full-Wave Computation of the Spontaneous Emission Rate of a Transmon Qubit

Understanding the spontaneous emission rate of transmon qubits is an important step in the design of many quantum electromagnetic devices, such as single photon sources and quantum computers. Despite its importance, the spontaneous emission rate is often determined using approximate circuit models or is inferred from measurements on a fabricated device. To improve the accuracy of predictions, it is better to use full-wave numerical methods for this problem. In this work, we show how this can be done with a recently developed field-based description of transmon qubits coupled to an electromagnetic environment. We demonstrate this process by computing the spontaneous emission rate of a single photon source and find qualitative agreement with a similar fabricated device.
Discussion on implementation and application of RSA algorithm in smart card operating system

This article is based on RSA algorithm analyzes the basic content, combining with the RSA algorithm in the file system design, auxiliary work component design, safety management component design, the concrete application in the design of business functions to study an example RSA algorithm implementation process in the smart card operating system, its aim is to improve the cognitive level of the role of RSA algorithm, meet the operating requirements of the smartcard operating system.
Research and analysis of infrared image enhancement algorithm based on fractional differentiation

Abstract Due to the inherent defects of infrared imaging systems and the influence of the external complex environment, infrared images have low contrast, blurred edge details, low signal-to-noise ratio and poor visual effects compared with visible images, which have a great impact on the subsequent feature extraction, detection and identification and target tracking, and cannot meet the requirements in military, medical and civilian fields. The current technical deficiencies of infrared imaging devices at the hardware level cannot fundamentally solve these problems, so it is especially necessary to enhance infrared images from the perspective of algorithms. We propose an improved fractional differentiation algorithm that enhances the contrast of infrared images and the contrast of the images is controlled by the fractional order. Experiments and analysis show that the proposed method has good feedback for enhancing the contrast of dark images, and it can effectively enhance the edge information and detail information.
Internet of Things Integrated with Multi-level Authentication for Secured IoT Data Stream Through TLS/SSL Layer

With the advancement in recent technology, the world is connected to multiple devices or peripherals through the Internet of things (IoT). Therefore, there is a need for proper configuration with the required authentication and secured data is required to transfer from a particular IoT device to the respective IoT cloud server configured to a specific application and IoT platform. Hence, this paper intends to configure any IoT device that integrates with a multi-level authentication for a secure IoT data stream over TLS/SSL. The multi-level authentication includes the first stage that includes cryptography-based fingerprint authentication to verify whether or not a particular IoT device is paired with the particular IoT cloud platform. In the second stage, each IoT device is configured with an API Key (Application Programming Interface) to enable data transfer from API enabled IoT device to the appropriate API Key activation of the IoT cloud platform. Multi-level authentication, activated with fingerprint authentication and an API key, thus provide a secure IoT data stream between every IoT device and the respective IoT cloud platform. To accomplish secure data transfer, we use a hashing algorithm such as Message Direct (MD5) cryptography for fingerprint authentication.
Quantum Computing and Quantum Cryptography

Relativity and quantum mechanics are the pillars of modern physics. Relativity deals with the observable phenomenon in macroscopic level, while quantum mechanics engages in the sub-atomic particles. The quantum properties of sub-atomic particles such as electron behave both like wave and particle at the same time. The same properties can be applied in computing by using a concept called qubits. In digital computers, we have 0s and 1s as the input, whereas, in quantum computing, we have qubits, the state of the bit in superposition as both 0s and 1s at the same time. Using this superposition bits, the extremely difficult calculations can be cracked by the power of quantum computers. One such extreme case is breaking the cryptography. This is a piece of bad news for government agencies, militaries, and financial institutions. To overcome this problem, researchers are coming with the idea of quantum cryptography, which theoretically acts as a secure solution to the existing cryptography problems.
EFL Students’ Writing Anxiety in Online Learning Environment during the Covid-19 Pandemic

Research on writing anxiety has been widely done in an offline setting for many years.The results informed that students were at various levels of writing anxiety.Due to the global pandemic, language classrooms have been shifted into emergency remote learning.It is susceptible that students also experience writing anxiety in an online learning environment.This study investigates the students' writing anxiety in an online setting.More specifically, it investigates the the levels and types of writing anxiety across gender.This study involved 215 EFL students from three universities in Indonesia.The SLWAI questionnaire was distributed via Google.The obtained data were analyzed using an independent sample t-test to figure out the difference and levels and types of writing anxiety across gender.The findings showed that EFL students experience writing anxiety in an online setting.The results showed that both groups had moderate anxiety levels.Female students' average score (63.81) was slightly higher than male students (62.01).Statistical analysis showed that no statistical difference was observed in levels of anxiety across gender.In addition, male and female students experienced somatic anxiety, cognitive, and avoidance behavior.Cognitive anxiety and avoidance behavior anxiety were at moderate levels; however, the two groups experienced higher somatic anxiety in online writing activities.This indicates that students had unpleasant feelings as a result of the anxiety experienced.