Reviewerly/exHarmony huggingface dataset:
Breast Cancer Prediction Using Machine Learning

Abstract: Worldwide, breast cancer is the most common cause of death for women, and increasing survival rates require early identification. Medical scans like mammograms and ultrasounds can be used to predict breast cancer using convolutional neural networks (CNNs). The architecture of CNNs, training data and methods, and performance evaluation criteria are all reviewed in this work as well as the current state of research on CNNs for breast cancer prediction. Also, the benefits and drawbacks of CNNs against conventional techniques for the identification of breast cancer are explored. Although though CNNs have the potential to lower false-positive outcomes and have higher accuracy rates, further testing and study are required to assure their dependability. Also, it’s critical to consider moral concerns like data privacy and bias in machine learning algorithms. Thus, using CNNs to predict breast cancer has enormous potential to increase early detection and, eventually, save lives.So, our objective is to develop a model that can predict breast cancer from mammography images, which will help patients choose between various tests as well as assist medical students in validating their study.
An Improved Model for Breast Cancer Diagnosis by Combining PCA and Logistic Regression Techniques

Breast cancer is weighed one of the most life-threatening illnesses confronting women.It happens when the multiplication of cells in breast tissue is uncontrollable.Several studies have been performed in the healthcare field for early breast cancer diagnosis.However, traditional methods can generate incomplete or misleading outcomes.To overcome these limitations, computer-aided diagnosis (CAD) systems are extensively exploited in the healthcare domain.It is designed to improve accuracy, decrease complexity, and reduce misclassification costs.The goal of this study is to present a breast cancer CAD system based on combining the Principal Component Analysis (PCA) method for feature reduction and Logistic Regression (LR) for BC tumors classification.The experiments have been conducted on Wisconsin Diagnosis Breast Cancer (WDBC) and Wisconsin Original Breast Cancer (WOBC) datasets from UCI repository using different training and testing subsets.Moreover, we carried out extensive comparisons of our approach with other existing approaches.Multiple metrics like precision, F1 score, recall, accuracy, and Area Under Curve (AUC) were used in this study.Experimental results indicate that the proposed approach records a remarkable performance rate with an accuracy of 1.00 and 0.98 for WDBC and WOBC respectively and outperforms the previous works by decreasing the number of features, improving the data quality, and reducing the response time.
Effect of Transportation Infrastructure on Built-up Area Using Prediction of Land Use/Cover Change: Case Study of Yogyakarta International Airport, Indonesia

The development of transportation infrastructure increases the pressure on natural resources, one of which is the increase in the built-up area. The changes do not only happen during the construction of transportation infrastructure but also after its completion. Therefore, this study aims to identify and simulate land use/cover changes in Kulon Progo Regency, Indonesia, to predict the effect of the construction of Yogyakarta International Airport (YIA). A quantitative descriptive method was used with the main data of multitemporal Landsat remote sensing images. Furthermore, the integration of Cellular Automata and Artificial Neural Networks (CA-ANN) was applied to simulate land use/cover change predictions (2035). The results of image classification using the supervised maximum likelihood classification showed an overall accuracy of 85.33% and 86.67% for 2011, and 2015 with 2019 using Landsat 7 and 8 images, respectively. Meanwhile, there was an increase in paddy fields of 1,210.1 ha (2.11%) and built-up area by 2,708.6 ha (4.72%) during 2011 – 2019. Conversely, shrubs and dryland agriculture decreased by 1,594.1 ha (2.78%) and 2,174.1 ha (3.79%). The simulation results indicate that the development of transportation infrastructure further triggers the increase in built-up area, especially around the YIA. Therefore, policymakers and development implementers should adopt and implement appropriate and effective planning for sustainable land use.
Lessons from Developing Multimedia Learning Materials for the Digital Generation

Recognizing that traditional textbooks on need-driven health technology innovation were increasingly misaligned with the needs of today's undergraduate biomedical engineering students and the faculty who teach them, we initiated an effort to develop new learning materials for this audience. To guide our efforts, we conducted literature searches on best practices in the development of online content and engaging digital learners (primarily Gen-Z). We further held a series of discussions with biomedical engineering students and instructors at universities across the United States. This input led us to the development of a set of modular, online, multimedia learning materials specifically designed for the new generation of undergraduate learners. In this article, we present the key decisions that helped shape the project. We also share the results of feedback surveys and focus groups that shed light on how the materials have been preliminarily received. Finally, we reflect on challenges, opportunities, and lessons from this project that may be helpful to other initiatives focused on the creation of multimedia content for the digital generation.
THE USE OF DIGITAL TECHNOLOGIES IN MATHEMATICS AND HIGHER MATHEMATICS CLASSES IN VOCATIONAL PRE-HIGHER EDUCATIONAL INSTITUTIONS

The article examines the peculiarities of the use of various digital technologies in mathematics and higher mathematics classes. The use of certain digital technologies is carried out according to different schemes in accordance with the needs of a specific lesson or topic, the level of mastery of various software products.&#x0D; The author suggests using certain digital technologies when teaching mathematics, which will help to process information effectively, perform appropriate mathematical calculations, build graphs and diagrams, constantly maintain contact with students, form students' technical skills in solving mathematical problems, monitor the implementation of independent students’ work, check the educational achievements of students.&#x0D; It is emphasized on the advantages of using these or other software products which form cognitive interest in mathematical disciplines and contribute to the assimilation of the material, motivate and interest students in learning, contribute to the training of future specialists who are able to use information technologies in their future professional activities.
Analyzing the Importance of Age-biased Data in Recognizing Emotions from Facial Expressions Using Custom Data Sets and CNN Algorithm

This paper explores the importance of age-biased data in recognizing emotions from facial expressions. A custom data set was created by separating existing data sets into adults and kids. Three CNN architectures were tested, and the SE-ResNeXt50(32×4d) achieved the highest accuracy at 79.42%. The age-based model outperformed the non-age-based model by 22.24%. The study highlights the impact of age-biased learning data and algorithm types on emotion recognition accuracy, particularly for fear and neutral emotions.
TGSNET: A FRACTAL NEURAL NETWORK FOR ACTION RECOGNITION

In the study of action recognition based on optical flow, improving the recognition speed of two-stream neural networks is challenging. In this paper, a new network structure Teacher Guided Student Network (TGSNet) which is based on two-stream and teacher–student architecture is proposed to judge the category of action rapidly in the application. There are two sub-networks with optical flow and RGB frame stream in the network, the optical flow sub-network is assigned as the teacher and the RGB frame stream sub-network as the student. In the training stage, the optical flow sub-network computes the optical flow of the video frame and trains the sub-network then transmits the feature to the RGB frame stream sub-network. The RGB frame stream sub-network uses the RGB frame to mimic the optical flow to train the sub-network. In the test stage, there is only RGB frame stream sub-network existing for action recognition rapidly without computing optical flow. The experimental results show that the TGSNet feeds only by RGB frame stream get a competitive accuracy of 56.7% and a better run-time on HMDB51.
Distance Learning in the System of Professional Retraining (Based on the Experience of Implementing an Additional Professional Program of Professional Retraining of Military Specialists at Omsk Tank-Automotive Engineering Institute)

The article attempts to present some results of a study of the implementation quality of an additional professional program for the professional retraining of military specialists in the civilian specialty “Pedagogy of Higher Education” on the basis of the Omsk Tank-Automotive Engineering Institute in a remote format. The data obtained can be used to further improve the process of distance learning for trainees of professional retraining in the conditions of a military higher educational institution.
On the Diophantine Equation 15x – 13y = z2

In this article, we prove that the Diophantine equation 15x – 13y = z2 has nonnegative integer solution. The result reveals that the solution (x, y, z) = (0, 0, 0). Keywords: Diophantine equation; factoring method; modular arithmetic method
AN ANALYTICAL REVIEW ON HIGHER EDUCATION INSTITUTIONS UNDER NEW NATIONAL EDUCATION POLICY 2020

In India, the New Education Policy (NEP) -2020 is expected to bridge a gap in education quality standards.It will help in internationalization and e-learning through education in the country and inculcate innovativeness.NEP-2020 is a strong focus to reach comprehensive targets and more prominently, this is attributable to the inevitability of fulfilling the government's goals of overhauling India's education system taking into consideration the extensive changes that have taken place in terms of scientific, economic, and social, areas stint last twenty years.India has seen a transformation in the education system from Gurukul to Macaulay.Deprived results in higher education can be seen in poor employability and outflow of students to universities internationally.The study portrays an overview of NEP-2020, discusses the virtue and Stumbling Blocks of the policy in higher education, insights into the implementation guidelines specified in the policy to accomplish its objectives grounded on focus group discussions.
Efficient content-based image retrieval using integrated dual deep convolutional neural network

&lt;p&gt;Content-based image retrieval (CBIR) uses the content features for retrieving and searching the images in a given large database. Earlier, different hand feature descriptor designs are researched based on cues that are visual such as shape, colour, and texture used to represent these images. Although, deep learning technologies have widely been applied as an alternative to designing engineering that is dominant for over a decade. The features are automatically learnt through the data. This research work proposes integrated dual deep convolutional neural network (IDD-CNN), IDD-CNN comprises two distinctive CNN, first CNN exploits the features and further custom CNN is designed for exploiting the custom features. Moreover, a novel directed graph is designed that comprises the two blocks i.e. learning block and memory block which helps in finding the similarity among images; since this research considers the large dataset, an optimal strategy is introduced for compact features. Moreover, IDD-CNN is evaluated considering the two distinctive benchmark datasets the oxford dataset considering mean average precision (mAP) metrics and comparative analysis shows IDD-CNN outperforms the other existing model.&lt;/p&gt;
Optimized load balancing mechanism in parallel computing for workflow in cloud computing environment

Cloud computing gives on-demand access to computing resources in metered and powerfully adapted way; it empowers the client to get access to fast and flexible resources through virtualization and widely adaptable for various applications. Further, to provide assurance of productive computation, scheduling of task is very much important in cloud infrastructure environment. Moreover, the main aim of task execution phenomena is to reduce the execution time and reserve infrastructure; further, considering huge application, workflow scheduling has drawn fine attention in business as well as scientific area. Hence, in this research work, we design and develop an optimized load balancing in parallel computation aka optimal load balancing in parallel computing (OLBP) mechanism to distribute the load; at first different parameter in workload is computed and then loads are distributed. Further OLBP mechanism considers makespan time and energy as constraint and further task offloading is done considering the server speed. This phenomenon provides the balancing of workflow; further OLBP mechanism is evaluated using cyber shake workflow dataset and outperforms the existing workflow mechanism.
Video saliency-detection using custom spatiotemporal fusion method

There have been several researches done in the field of image saliency but not as much as in video saliency. In order to increase precision and accuracy during compression, reduce coding complexity and time consumption along with memory allocation problems with our proposed solution. It is a modified high-definition video compression (HEVC) pixel based consistent spatiotemporal diffusion with temporal uniformity. It involves taking apart the video into groups of frames, computing colour saliency, integrate temporal fusion, pixel saliency fusion is conducted and then colour information guides the diffusion process for the spatiotemporal mapping with the help of permutation matrix. The proposed solution is tested on a publicly available extensive dataset with five global saliency valuation metrics and is compared with several other state-of-the-art saliency detection methods. The results display and overall best performance amongst all other candidates.
PFDCT: An Enhanced Transport Layer Protocol for Precise Flow Control in Data Centers

This paper proposes an enhanced transport layer protocol for precise flow control in data centers, referred to as PFDCT, combining explicit rate control (priority scheduling) and implicit rate control (marking algorithm with dynamic threshold) innovatively, while satisfying the transmission requirements of each data flow. It enables multiple data flows to share the bandwidth fairly, which is rarely achieved by previous data transmission protocols. Our design stems from the observation that different packets in a data center network need to be processed differentially and that the accuracy of the feedback of the indication information to the link state will directly affect the effectiveness of the congestion control mechanism. Our tests show that PFDCT has lower network jitter, better fairness, and significant performance gains over existing proposals (DCTCP, ICTCP, and L2DCT) in terms of Flow Completion Time (FCT) and throughput on average. Furthermore, we discussed the impact of the marking algorithm with dynamic thresholds on the performance of PFDCT, and the results of the experiments show that the addition of the marking algorithm will further improve the performance of PFDCT in terms of packet loss rate, latency, and FCT.
Analisis Gap Evaluasi Kualitas Sistem E-Learning di Universitas Ibn Khaldun Bogor

A GAP analysis has been conducted on the evaluation of E-Learning systems of LMS UIKA Bogor. Five (5) subjects of discussion in this study, namely include Structured Learning Methods, Unstructured Learning Methods, Population and Samples, E-learning User Activity Record, Evaluation of Results. Process of evaluating the results of a calculation of the System Usability Scale (SUS). Judging from usability or usefulness the e-learning system is feasible. With the following details: a. Based on acceptability ranges, the e-learning falls into the accepted category, b. Based on the grade scale, included in grade C where the SUS score produced is 79, c. Based on adjective ratings, the value is between a score of 70-80 which means it falls into the range of good categories. The results of the usability evaluation of LMS UIKA Bogor products stated that overall, were acceptable or feasible.
On the razor’s edge

Most often, the concept of illusion has been interpreted in a negative way as a synonym for deception. However, a second, positive meaning has gained sometimes prominence according to which illusion does not necessarily imply any cognitive failure or distortion. As such, it can even play an important role in eliciting genuine aesthetic enjoyment. This introduction focuses on crucial aspects in the history and theory of aesthetic illusion, a notion that has resurfaced recently as a key aspect of the phenomenon of immersion, being regarded as a goal to be pursued by both the creators and the experiencers of virtual environments.
Forecasting Indonesian Oil, Non-Oil and Gas Import Export with Fuzzy Time Series

Indonesia is active in export and import activities. Some of the commodities traded are oil and gas, as well as food and other industrial materials. Export and import activities play a role in determining the stability of the country's economy seen from its trade balance. According to the Central Statistics Agency, Indonesia experienced a deficit of USD 864 million due to a decline in exports at the beginning of 2020. Based on the state of the trade balance, the government needs to make policies in order to maintain the stability of the Indonesian economy. The right decision-making must be supported by accurate information, therefore, through this research, the value of Indonesia's exports and imports will be forecasted in the oil and gas and non-oil and gas sectors for the next period using the Fuzzy Time Series (FTS). FTS was chosen as the forecasting method because it is able to predict free real time data with arbitrary patterns. The data used is data on the value of exports and imports of oil and gas and non-oil and gas sectors for 2011-2020. To overcome the problem of stationary data variance and reduce the error value, a Box Cox transformation will be applied. The research stages include data transformation with Box Cox, forming universe and linguistic sets, determining interval length, fuzzification, forming FLR and FLR, defuzzification and forecasting. The final forecast results estimate that exports and imports in the oil and gas sector in 2021 will decline, while for the non-oil and gas sector will fluctuate and increase from the previous year. Forecasting with Box Cox transform data is more accurate with MAPE 19.56% and RMSE 121.52 compared to forecasting with original data with MAPE 74.89% and RMSE 132.09.
Vision Based Anomalous Human Behaviour Detection Using CNN and Transfer Learning

Abstract: With the advent of the Internet of Things (IoT), there have been significant advancements in the area of human activity recognition (HAR) in recent years. HAR is applicable to wider application such as elderly care, anomalous behavior detection and surveillance system. Several machine learningalgorithms have been employed to predict the activities performed by the human in an environment. However, traditional machine learning approaches have been outperformed by feature engineering methods which can select an optimal set of features. Onthe contrary, it is known that deep learning models such as Convolutional Neural Networks (CNN) can extract features and reduce the computational cost automatically. In this paper, we use CNN model to detect human activities from Image Dataset model. Specifically, we employ transfer learning to get deep image features and trained machine learning classifiers. Our experimental results showed the accuracy of 96.95% using VGG16. Our experimental results also confirmed the high performance of VGG-16 as compared to rest of the applied CNN models
Pattern-based detection, extraction and analysis of code lists in ontologies and vocabularies

While the early phase of the Semantic Web put emphasis on conceptual modeling through ontology classes, and the recent years saw the rise of loosely structured, instance-level knowledge graphs (used even for modeling concepts), in this paper, we focus on a third kind of concept modeling: via code lists, primarily those embedded in ontologies and vocabularies. We attempt to characterize the candidate structures for code lists based on our observations in OWL ontologies. Our main contribution is then an approach implemented as a series of SPARQL queries and a lightweight web application that can be used to browse and detect potential code lists in ontologies and vocabularies, in order to extract and enhance them, and to store them in a stand-alone knowledge base. The application allows inspecting query results coming from the Linked Open Vocabularies catalog dataset. In addition, we describe a complementary bottom-up analysis of potential code lists. We also provide in this paper a demonstration of the dominant nature of embedded codes from the aspect of ontological universals and their alternatives for modeling code lists.
Depth estimation from SEM images using deep learning and angular data diversity

There is a growing need for accurate depth measurements of on-chip structures. Since Scanning Electron Microscopes (SEMs) are already regularly being used for fast and local 2D imaging, it is attractive to explore the 3D capabilities of SEMs. This paper presents a comprehensive study of depth estimation performance when single- or multi-angle data is available. The research starts with an analytical line-scan model to show the major contributors of the signal change with increasing height and angle. We also analyze Monte-Carlo scattering simulations for height sensitivity on similar structures. Next, we validate the depth estimation performance with a supervised machine learning model and show correlation with the previous studies. As predicted by the sensitivity studies, we show that the height prediction greatly improves with increasing tilt angle. Even with a small angle of 3 degrees, a threefold average performance improvement is obtained (RMSE of 16.06 nm to 5.28 nm). Finally, we discuss a preliminary proof-of-concept of a self-supervised algorithm, where no ground-truth data is needed anymore for height retrieval. With this work we show that a data-driven tilted-beam approach is a leap forward in accurate height prediction for the semiconductor industry.