Reviewerly/exHarmony huggingface dataset:
Lung cancer classification using exponential mean saturation linear unit activation function in various generative adversarial network models

Abstract Nowadays, the mortality rate due to lung cancer increases rapidly worldwide as it can be classified only at the later stages. Early classification of lung cancer will help patients to take treatment and decrease the death rate. The limited dataset and diversity of data samples are the bottlenecks for early classification. In this paper, robust deep learning generative adversarial network (GAN) models are employed to enhance the dataset and to increase classification accuracy. The activation function plays an important feature‐learning role in neural networks. Since the existing activation functions suffer from various drawbacks such as vanishing gradient, dead neurons, output offset, etc., this paper proposes a novel activation function exponential mean saturation linear unit (EMSLU), which aims to speed up training, reduce network running time, and improve classification accuracy. The experiments were conducted using vanilla GAN, Wasserstein generative adversarial network, Wasserstein generative adversarial network with gradient penalty, conditional generative adversarial network, and deep convolutional generative adversarial network. Each GAN is tested with rectified linear unit, exponential linear unit, and proposed EMSLU activation functions. The results show that all the GAN's with EMSLU yields improved precision, recall, F1‐score, and accuracy.
SAR Object Detection Encounters Deformed Complex Scenes and Aliased Scattered Power Distribution

Synthetic aperture radar (SAR) is widely used in terrain classification, object detection, and other fields. Compared with anchor-based detectors, anchor-free detectors remove the anchor mechanism and implement detection box encoding in a more elegant form. However, anchor-free detectors are limited by complex scenes caused by geometric transformations, such as overlaying, shadow, vertex displacement during SAR imaging. And the scattered power distribution of noise is similar to the edge of the object, making it difficult for the detector to locate the edge of the SAR object accurately. In order to alleviate these problems, we propose a high-speed and high-performance SAR image anchor-free detector. First, we propose a shallow feature refinement (SFR) module to effectively extract and retain the detailed information of objects, while coping with deformed complex scenes. Second, we analyze the optimization focus of the detector at different training iterations and propose iteration-aware loss to guide the detector, making the detector more accurately locate the edge of the object disturbed by the noise scattered power distribution. Third, number estimation helps to detect objects with more flexible criteria in box selection without manual labor. Compared with mainstream optical object detectors and SAR dedicated detectors, our method achieves the best speed-accuracy tradeoff on the SAR-ship dataset, with 96.4% average precision when the value of intersection over union is 50% ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$AP_{50}$</tex-math></inline-formula> ) at 64.9 frames per second. The experimental results prove the effectiveness of our method.
The Attitudes of MTUN Students towards m-Learning Usage during COVID-19 Pandemic

Open and Distance Learning (ODL) is an online Teaching and Learning (T&amp;L) method that has been implemented to all educational institutions in Malaysia to curb the spread of COVID-19 pandemic. The purpose of this study is to identify the attitude level of Malaysian Technical University Network (MTUN) students towards the Mobile Learning (M-learning) usage in ODL methods during the COVID-19 pandemic situation. The total number of respondents involved was 481 engineering students from four MTUN institutions. The study design used is quantitative which uses a questionnaire as a research instrument. The questionnaire used measures three elements of students' attitudes in the aspect of affective, behavior and cognitive towards M-learning implementation. Results were analyzed using descriptive statistics involving the use of mean and standard deviation while inferential statistics involved One-way ANOVA analysis. Result shows that attitudes level of MTUN students towards M-learning was at moderate level for two (2) aspects of affective and cognitive while the behavior aspect was rated at high level. Meanwhile, the result of one-way ANOVA showed no significant difference between the attitudes of student towards ODL across year of studies. Therefore, it can be concluded that the students of MTUN institutions had positive attitudes towards the implementation of M-learning with year of study in the university does not have influence on student’s attitude towards M-learning environment. It is advisable that the use of M-learning in the future needs to be enriched with the features of 21st century learning as to enhance students learning experiences.
Comparative Analysis of Algorithms for Recognizing Emotions by Eye Blink

This paper presents the analysis of various algorithms used for eye blinking and emotion recognition and the relation between them. Emotions can also be extracted from the eye which includes eye blinking and also eye movement. Blinking is not only used to clear and lubricate our eyes, but also used to emphasize various emotions and behavior. Study on tiredness showed that drowsy driver tends to blink less which is prone to more accidents. Few conventional methods for eye blink detection like Haar-like features, template matching, EAR are efficient and in use.
A Novel Camera-Based Approach to Increase the Quality, Objectivity and Efficiency of Aeronautical Meteorological Observations

Despite the general efforts of meteorological services to provide aeronautical observations at all ranges of airports automatically, for some meteorological variables, especially for the ones that highly rely on complex human perception (e.g., prevailing visibility and cloud coverage), reliable, fully automated observations cannot be ensured. This paper introduces novel possibilities to observe prevailing visibility and cloud coverage/height by means of a camera-based observation system that does not necessarily replace, but effectively and synergically amends the standard observations. We present human (and not automated) observations from a remote center that allows for an observer to report meteorological conditions remotely, only using images from cameras installed at the airport. The basic concept of the remote observer was developed within a previous SESAR project. The focus of our methods is set (1) on the quality of information with the occurrence of reduced visibility and enhanced cloud cover in inhomogeneous weather situations and (2) on a comparison of our approaches with those from local human observers. We conclude that for a correct estimation of the prevailing visibility, cloud coverage and cloud types, the automated sensors alone are inadequate; however, the camera-aided remote human approach to observations seems to be a promising supplement to eliminate the sensors’ deficiencies, in terms of the quality (e.g., high quality camera records; no more point measurements), objectivity (e.g., database of archived weather situations) and efficiency (e.g., no need to have an observer physically present at the airport). The possibility to provide observations remotely seems to be advantageous in the COVID-19 and post-COVID-19 era when the society must adapt to different levels of quarantine conditions, affecting and/or disabling standard work and travelling regimes.
Elliptic Curve Cryptography in Cloud Security: A Survey

Nowadays, people are using more cloud services than ever before as it provides more storage, collaborative environment, and more security than any other platform. Public Key Cryptography plays a significant role in securing the cloud applications, particularly, Elliptic Curve Cryptography, as its small key size nature is the most suitable aspect in the Cloud. Although, many contributions have been made in recent years to enhance the security aspect of Elliptic Curve approaches in Cloud service by modifications made in the algorithm or in various algorithm phases, but a review work that integrates recent studies providing research directions is missing in the literature. In this paper, we reviewed recent studies along with the various phases of Elliptic Curve Cryptography, which then follows the data analysis of various approaches and techniques. Several research directions and open research problems are derived that would efficiently assist the future relevant research.
Bone Region Segmentation in Medical Images Based on Improved Watershed Algorithm

Watershed algorithm is widely used in image segmentation, but it has oversegmentation in image segmentation. Therefore, an image segmentation algorithm based on K-means and improved watershed algorithm is proposed. Firstly, Gaussian filter is used to denoise human skeleton image. K-means clustering algorithm is used to segment the denoised image and the connected component with the largest area is extracted as the initial human skeleton region. The initial bone region was morphologically opened and then morphologically closed to eliminate the noise. Morphologically open operation is used to disconnect other human tissues that adhere to the human bone region and eliminate the background noise with small area, while closed operation smoothes the edge of the human bone region and fills the fracture in the contour line. Secondly, the watershed segmentation algorithm is implemented on the image after morphological operation. The similarity degree of two blocks is defined according to the mean difference of gray level of adjacent blocks and the mean value of standard deviation of gray level of pixels in the edge of the block 4-neighborhood. The adaptive threshold T is generated by Otsu method for histogram of gradient amplitude image. If the similarity degree is greater than T, the image blocks will be merged; otherwise, the image blocks will not be merged. The proposed image segmentation algorithm is used to extract and segment the human bone region from 100 medical images containing human bone. The number of blocks segmented by watershed algorithm is 2775 to 3357, but the number of blocks segmented by the proposed algorithm is 221 to 559. The experimental results show that the proposed algorithm effectively solves the oversegmentation problem of watershed algorithm and effectively segments the image target.
Smart Contracts Applications in Real Estate: A Systematic Mapping Study

Recently, smart contracts have emerged as an architectural style for contract design, dealing with how to build and execute transactions. The smart contract provides immutability and integrity of data with no intervention from a trusted third party. Though there have been general researches conducted on smart contracts, however, there is a lack of scientific publications regarding the current research challenges on the use of smart contracts in real estate. This study examines literature focused on the use of smart contracts in real estate while providing a conceptual classification. Based on our conceptual classification. We conducted a systematic mapping study of 68 selected published works on literature released between 2017 and 2021. We sorted and analyzed the selected studies based on a characterization framework. We summarize existing issues, as well as identify gaps, that are available in the literature.
Feature Extraction Algorithm of Basketball Trajectory Based on the Background Difference Method

People are increasingly interested in moving object detection technology as a part of computer vision technology, which has grown in importance in recent years. Moving target detection technology is widely employed in military defense, security monitoring, medical examinations, intelligent transportation, and many other industries at the present time. A moving target detection system relies on rapid and accurate video picture segmentation to identify, locate, and analyze the target. The trajectory of a basketball player’s shooting motion and the extraction of features are important to increase the accuracy of basketball players’ shots. This study proposes an area growth algorithm-based approach to tracing the shooting motion of basketball players. The trajectory of the shot motion is captured using a video sensor image tracking approach. The corner points and the edge contours of the trajectory image are extracted using the edge contour feature extraction method, the feature extraction from the firing motion trajectory image is merged with the regional linear growth approach, and the corner points are then marked. Feature extraction improves basketball players’ shooting accuracy and trajectory control ability. Simulation results show that the prediction accuracy of basketball players’ shooting trajectories using this method can reach up to 100%, which improves the accuracy of motion trajectory extraction and enhances basketball players’ shooting motion control.
FastAdaBelief: Improving Convergence Rate for Belief-Based Adaptive Optimizers by Exploiting Strong Convexity

AdaBelief, one of the current best optimizers, demonstrates superior generalization ability compared to the popular Adam algorithm by viewing the exponential moving average of observed gradients. AdaBelief is theoretically appealing in that it has a data-dependent $O(\sqrt{T})$ regret bound when objective functions are convex, where $T$ is a time horizon. It remains however an open problem whether the convergence rate can be further improved without sacrificing its generalization ability. %on how to exploit strong convexity to further improve the convergence rate of AdaBelief. To this end, we make a first attempt in this work and design a novel optimization algorithm called FastAdaBelief that aims to exploit its strong convexity in order to achieve an even faster convergence rate. In particular, by adjusting the step size that better considers strong convexity and prevents fluctuation, our proposed FastAdaBelief demonstrates excellent generalization ability as well as superior convergence. As an important theoretical contribution, we prove that FastAdaBelief attains a data-dependant $O(\log T)$ regret bound, which is substantially lower than AdaBelief. On the empirical side, we validate our theoretical analysis with extensive experiments in both scenarios of strong and non-strong convexity on three popular baseline models. Experimental results are very encouraging: FastAdaBelief converges the quickest in comparison to all mainstream algorithms while maintaining an excellent generalization ability, in cases of both strong or non-strong convexity. FastAdaBelief is thus posited as a new benchmark model for the research community.
Nature inspired fault tolerant task allocation in cloud computing using neural network model

Cloud computing in the current scenario comes with a large pool of resources, pay-per-use model and reliable infrastructure. Cloud optimization relies on resource optimization to improve the performance and reliability of the cloud. Fault in the cloud places an important role in defining the reliability of the cloud. The identification of fault is a challenging issue in a modular cloud environment. The researchers have developed various methods for the fault-aware scheduling of cloud resources. The fault-aware resource allocation includes static, dynamic, meta-heuristic, and learning-based approaches. In this article, we primarily focused on existing fault-aware resource allocation techniques and then we proposed a model that will primarily focus on fault forecast in tasks allocation. The projected model is based nature-inspired heuristic approach and intelligent artificial neural network. The fault-tolerant aware ANN-based proposed model focuses on performance improvement and reliability testing proactively. The proposed model surpasses the existing state of art methods for proactive and reactive fault-aware scheduling techniques in a large scale datacenter. The results and discussions section support the reliability assertion of the fault-tolerant aware human brain and nature-inspired model.
Design of Auxiliary Teaching System for Preschool Education Specialty Courses Based on Artificial Intelligence

In order to improve the data retrieval accuracy of preschool education major curriculum, this paper designs an auxiliary teaching system for preschool education specialty courses based on artificial intelligence. 2000 preschool education teachers and 3000 students in M city were selected to conduct a questionnaire survey to analyze the application status of artificial intelligence technology, advantages and disadvantages of assisted teaching, obstacles and training intentions of assisted teaching, and the impact on the personal development of teachers and students. Using artificial intelligence technology to improve the shortcomings of existing preschool education professional courses, through the Vue.js technology in the artificial intelligence method to achieve the MVVM mode sharing of preschool education professional courses, using MySQL database to achieve multiuser multithread operation of preschool education system, using MySQL data to complete data storage, C# was used as the programming language to design the learning module of preschool education professional courses, and the artificial intelligence-based preschool education professional course auxiliary teaching system was designed. The experimental results show that when the retrieval quantity of curriculum resources is 600 GB, the response time of resource retrieval of the designed system is 7 s, and the retrieval accuracy of curriculum data of education major can reach 95%. The performance of the system is good.
Iron oxide chemistry supports a multistage hydrothermal genesis of BIF-hosted hematite ore in the Mt. Tom Price and Mt. Whaleback deposits

This study demonstrates the great value of iron oxide chemistry in low-temperature systems. Hematite records elemental signatures of structural control, partial inheritance of pre-existing alteration, and allows chemical fingerprinting of multistage BIF-hosted ore. Mt. Whaleback and Mt. Tom Price are Western Australia's largest banded iron formation (BIF) hosted hematite ore deposits. The mechanisms and timing of iron enrichment from BIF (with ∼20–40 wt% Fe) to high-grade iron ore (with up to 69 wt% Fe) are still controversially debated. In-situ iron oxide chemistry support post-metamorphic, structurally controlled, hydrothermal hematite ore formation via multiple stages of hydrothermally altered BIF. Iron oxides in two hydrothermal alteration zones in BIF at Mt. Tom Price (distal alteration assemblages: magnetite-siderite-stilpnomelane ± pyrite and intermediate alteration assemblages: hematite-magnetite-ankerite-chlorite) show similar trace element patterns that are distinct from metamorphic magnetite in least-altered magnetite-quartz ± hematite ± Fe-carbonate ± Fe-silicates BIF. At Mt. Whaleback, the hydrothermal alteration is completely eradicated by subsequent microplaty-martite hematite ore formation, and solely recorded in hematite composition by the inheritance of various conservative element abundances. Petrographically indistinct zones within a given orebody are chemically distinct and demonstrate formation via contrasting structurally controlled hydrothermal fluid flow: At Mt. Whaleback, specifically in the vicinity of Central Fault and Whaleback Fault, systematic trace element group abundance trends are recorded: major controls are fO2, pH, and acquired country rock signatures, i.e., evolved (Al, Mn, Mo, Pb, U) at Central Fault, and primitive (Al, Cr, Ti, V, Cu, Ni, Co) at Whaleback fault. Complex zoned growth textures and element distribution in microplaty hematite support precipitation during protracted hydrothermal fluid/rock interaction. In combination, our results falsify the importance of alternative hematite crystallisation models, including goethite dehydration and coalescent nanoparticles.
GAN-Based Information Leakage Attack Detection in Federated Learning

Federated learning (FL) has been a popular distributed learning framework to reduce privacy risks by keeping private data locally. However, recent work (Hitaj 2017) has demonstrated that sharing model’s parameter updates still leaves FL vulnerable to internal attacks in its training phase. Existing works cannot detect such attacks well. To address this problem, we propose a novel and lightweight detection scheme which selects and analyzes just a few parameter updates of the last convolutional layer in the FL model. Extensive experiments demonstrate that our proposed detection scheme can accurately and efficiently detect the malicious participant in near real time for a scenario with a malicious participant.
MetaGeo: A General Framework for Social User Geolocation Identification With Few-Shot Learning

Identifying the geolocation of social media users is an important problem in a wide range of applications, spanning from disease outbreaks, emergency detection, local event recommendation, to fake news localization, online marketing planning, and even crime control and prevention. Researchers have attempted to propose various models by combining different sources of information, including text, social relation, and contextual data, which indeed has achieved promising results. However, existing approaches still suffer from certain constraints, such as: 1) a very few samples are available and 2) prediction models are not easy to be generalized for users from new regions-which are challenges that motivate our study. In this article, we propose a general framework for identifying user geolocation-MetaGeo, which is a meta-learning-based approach, learning the prior distribution of the geolocation task in order to quickly adapt the prediction toward users from new locations. Different from typical meta-learning settings that only learn a new concept from few-shot samples, MetaGeo improves the geolocation prediction with conventional settings by ensembling numerous mini-tasks. In addition, MetaGeo incorporates probabilistic inference to alleviate two issues inherent in training with few samples: location uncertainty and task ambiguity. To demonstrate the effectiveness of MetaGeo, we conduct extensive experimental evaluations on three real-world datasets and compare the performance with several state-of-the-art benchmark models. The results demonstrate the superiority of MetaGeo in both the settings where the predicted locations/regions are known or have not been seen during training.
PKNN-MIFS: A Parallel KNN Classifier over an Optimal Subset of Features

Feature selection help select an optimal subset of features from a large feature space to achieve better classification performance. The performance of KNN classifier can be improved significantly using an appropriate subset of features from a large feature space. Recent development in General Purpose Graphics Processing Units (GPGPU) has provided us a low cost yet high performance computing support for wide range of applications. This paper presents a parallel KNN classifier powered by a mutual information based feature selection called PKNN-MIFS for effective classification of real life data. It selects an optimal subset of features from the original feature set by exploiting the mutual information concept for the estimation of feature-class and feature-feature relevance. It selects a non-redundant feature by giving higher priority on feature-class relevance. The performance of the proposed PKNN-MIFS has been evaluated over several datasets and has been found to be superior to its closed counterpart.