Miracle-dz/UniFAR-FTUs huggingface dataset:
LITERATURE SURVEY OF AUTOMATED DETECTION OF BREAST CANCER

: Breast cancer has become the major cause of the increase in mortality rate among women. The causes of breast cancer can be abnormal growth in the breast cell, dividing the cell faster than usual, and gathering of cells to form a mass. This abnormal growth will affect the cells in the nearby tissue also. The cancer cells in the breast frequently start in the milk- producing ducts. The causes of breast cancer can be a family history of breast cancer, age factor, exposure to radiation, beginning period at an earlier age, menopause at an older age, having first pregnancy at an older age, drinking alcohol etc. The symptoms of breast cancer can be the formation of lumps, discoloration, pain, change in size or shape, etc. This paper discusses the various algorithms and methods available in the literature survey for the detection of breast cancer in detail.
UNDER-RESOURCED OR OVERLOADED ? 1 Running head : UNDER-RESOURCED OR OVERLOADED ? 1 2 3 4 5 Under-resourced or overloaded ? Rethinking working memory deficits in developmental 6 language disorder

26 Dominant theoretical accounts of developmental language disorder (DLD) commonly invoke 27 working memory capacity limitations. In the current report, we present an alternative view: 28 That working memory in DLD is not under-resourced but overloaded due to operating on 29 speech representations with low discriminability. This account is developed through 30 computational simulations involving deep convolutional neural networks trained on spoken 31 word spectrograms in which information is either retained to mimic typical development or 32 degraded to mimic the auditory processing deficits identified among some children with 33 DLD. We assess not only spoken word recognition accuracy and predictive probability and 34 entropy (i.e., predictive distribution spread), but also use mean-field-theory based manifold 35 analysis to assess; (i) internal speech representation dimensionality, and (ii) classification 36 capacity, a measure of the networks’ ability to isolate any given internal speech 37 representation that is used as a proxy for attentional control. We show that instantiating a 38 low-level auditory processing deficit results in the formation of internal speech 39 representations with atypically high dimensionality, and that classification capacity is 40 exhausted due to low representation separability. These representation and control deficits 41 underpin not only lower performance accuracy but also greater uncertainty even when 42 making accurate predictions in a simulated spoken word recognition task (i.e., predictive 43 distributions with low maximum probability and high entropy), which replicates the response 44 delays and word finding difficulties often seen in DLD. Overall, these simulations 45 demonstrate a theoretical account of speech representation and processing deficits in DLD in 46 which working memory capacity limitations play no causal role. 47
Appendices A Latent factors models

where I() is the indicator function and N(0, 1) is the standard univariate Gaussian distribution. The S × r matrix Λ is the factor loading matrix, and the r-dimensional Gaussian random vectors ωi are called latent factors. When the number of latent factors r is smaller than the number of species (usually r S), the dimension of the residual correlation matrix decreases, as we now have R = ΛΛ . Latent variables can be interpreted as ’missing covariates’, and a shared (or opposite) response to these missing covariates (through similar or opposite latent loadings), introduces the residual correlation among taxa.
ENERGY EFFICIENT HEURISTIC FRAMEWORK FOR VIRTUAL MACHINE PLACEMENT IN CLOUD DATA CENTERS

Cloud data centers are growing rapidly in both number and capacity to meet the increasing demands for highly-responsive computing and massive storage. Cloud is a virtual infrastructure that is accessed or delivered with a local network or accessing the remote location through internet. As a cloud is realized on large-scale usually distributed data-centers, it consumes an enormous amount of energy. Several researches have been conducted on Virtual Machine (VM) consolidation is an emerging solution for energy saving. Among the proposed VM consolidations, Open Stack Neat is notable for its practicality. OpenStack Neat is an open-source VM consolidation framework that can seamlessly integrate to OpenStack, it can be configured to use custom VM consolidation algorithms and transparently integrates with existing OpenStack deployment without the necessity of modifying their configuration. The framework has components for deciding when to migrate VMs and selecting suitable hosts for VM placement. It focuses on minimizing the number of servers. However, the solution is not only less energy efficient but also increases Service Level Agreement (SLA) violation and consequently cause more VM migrations. Therefore, in this research work we proposed energy efficient heuristic framework for VM placement to address the problem of allocation and consolidation of Virtual Machines by modifying the bin-packing heuristics with the power-efficiency parameter. In addition to that, we introduced two solutions: First, in the overloaded host decision step, the algorithm check whether a host is overloaded with SLA violation or not based on the overload threshold and specification of the active hosts. Second, in the underloaded VM migration step, this study puts forward a minimum power policy then power off the target host. Finally, to evaluate the proposed framework we have conducted experiments using CloudSim on three cloud data-center scenarios: default, heterogeneous and homogeneous. The workload that run in the data-center scenarios are generated from traces of PlanetLab and Bitbrains clouds. The experimental evaluation shows that our framework minimizes the energy consumption by 62.3% and reduce SLA violation and number of VM migrations by 75.73% and 68.73% respectively compared to the existing framework.
Deep Neural Network Approaches for Video Based Human Activity Recognition

:- In this paper we explained, tried, and tested methods of Human Action recognition for the application of video surveillance. This paper provides a method for automatically recognizing human activities included in video sequences captured by a single large view camera in outdoor locations. The elaboration of the dataset which are videos are taken with the resolution of 720x480, precisely explained. The methods we implement are CNN-VGG16 model and the Single-frame CNN model. We demonstrated our techniques using real-world video data to automatically distinguish normal behaviors from suspicious ones in a playground setting, films of continuous performances of six different types of human-human interactions: handshakes, pointing, hugging, pushing, kicking, and punching. As per the observation, we concluded that the Single frame CNN model shows much better results as compared to CNN VGG16. The implementation was done in python. This paper consist of how the convolution neural networks' simple classification method proved to be efficient for the prediction of the activity by using a single frame method. The working of this method is briefly mentions in the methodology. The difference and the drawback of these methods for human activity recognition can be clearly seen in the output and results of the respective.
A semi-analytical refrigeration cycle modelling approach for a heat pump hot water heater

ABSTRACT The use of heat pump systems in applications like the production of hot water or space heating makes important the modelling of the processes for the evaluation of the performance of existing systems, as well as for design purposes. The proposed semi-analytical model offers the opportunity to estimate the performance of a heat pump system producing hot water, without using detailed geometrical or any performance data. This is important, as for many commercial systems the type and characteristics of the involved subcomponents can hardly be detected, thus not allowing the implementation of more analytical approaches or the exploitation of the manufacturers’ catalogue performance data. The analysis copes with the issues related with the development of the models of the subcomponents involved in the studied system. Issues not discussed thoroughly in the existing literature, as the refrigerant mass inventory in the case an accumulator is present, are examined effectively.
Regional ombrian curves: a methodology for diverse hydrometeorological regimes

Theano Iliopoulou* , Nikolaos Malamos and Demetris Koutsoyiannis Original Research, Front. Clim. Climate Services Received on: 22 Sep 2021, Edited by: Giuseppe Mascaro Manuscript ID: 781539 Research Topic: A Quest to Fully Understand Precipitation: Novel Methods to Characterize, Model, and Detect Precipitation Processes
Logic and memory characteristics of an inverter comprising a feedback FET and a MOSFET

In this study, we design an inverter comprising a p-channel feedback field-effect transistor (p-FBFET) and an n-channel metal-oxide-semiconductor field-effect transistor and examine its logic and memory characteristics. For the transition of inverter from the logic ‘0’ (‘1’) state to ‘1’ (‘0’) state, the gain is 2001.6 V/V (1992.4 V/V). The steep switching characteristics and high on/off current ratio of the p-FBFET contribute to the high inverter gains. For an inverter with zero static power consumption, the logic states remain for more than 500 s. The long retention time allows the inverter proposed in this study to be applicable to logic-in-memory.
A new effective denoising filter for high density impulse noise reduction

: Today, thanks to the rapid development of technology, the importance of digital images is increasing. However, sensor errors that may occur during the acquisition, interruptions in the transmission of images and errors in storage cause noise that degrades data quality. Salt and pepper noise, a common impulse noise, is one of the most well-known types of noise in digital images. This noise negatively affects the detailed analysis of the image. It is very important that pixels affected by noise are restored without loss of image fine details, especially at high level of noise density. Although many filtering algorithms have been proposed to remove noise, the enhancement of images with high noise levels is still complex, not eﬀicient or requires very long runtime. In this paper, we propose an effective denoising filter that can restore the image effectively in terms of quality and speed with less complexity for high density noise level. In the experimental studies, we compare the denoising results of the proposed method with other state-of-the-art methods and the proposed algorithm is quantitatively and visually comparable to these algorithms when the noise intensity is up to 90%.
Training CNN to Denoise Images Corrupted by Mixed Poisson-Gaussian Noise Without Ground Truth Data

In this project, we explore the use of unbiased risk estimators as an objective function to train CNN-based denoiser for images corrupted by Poisson and mixed Poisson-Gaussian noise. Our result shows the unbiased risk estimator can be used for the dataset without ground truth. The difference between unbiased risk estimator and standard mean square error (MSE) loss during training procedure is only little. Furthermore, the model trained on unbiased risk estimator shows only small decrease in the performance on test set, compared to model trained with MSE with ground truth. Lastly, we demonstrate training model with unbiased risk estimator can be used in transfer learning to improve performance on domain-speciﬁc application, such as astrophotography.
Impact of Internet of Things Governance on Productivity in Agriculture Sector with AI-aided Agriculture Knowledge Managers

: The use of IoT in intelligent agriculture is now very common among farmers, and with the use of drones and sensors, advanced agriculture is rapidly becoming a growing global standard. The use of IoT requires infrastructure that is defined within an IoT governance framework. This paper proposes new approaches to knowledge management, Artificial Intelligence, and IoT governance and their impact on productivity in the agriculture sector by hiring specialized people who are called AI-aided Agriculture Knowledge Managers. Given the importance of all three topics, knowledge management, IoT, and agriculture, we have tried to show the impact of the presence of AI-aided Agriculture Knowledge Managers and IoT in reducing water consumption as one of the most important requirements of agriculture in a simulation by Matlab Software. The data was obtained from the Eurostat database. We also provide a framework for the presence of AI-aided Agriculture Knowledge Managers in fields that are specialized in agricultural science and knowledge management. Finally, due to the importance of governance in this sector, a framework for the governance of artificial intelligence in the field of agriculture with the presence of knowledge managers has been proposed.
Reply to Interactive comment on “Chemical Transformation of α -Pinene derived Organosulfate via Heterogeneous OH Oxidation: Implications for Sources and Environmental Fates of Atmospheric Organosulfates” by

The work by Xu et al. investigated the heterogeneous OH oxidation of one α -Pinene derived organosulfate (i.e., C 10 H 17 O 5 SNa, α pOS-249), and both reaction kinetics and mechanisms are well studied and discussed. This is an interesting work that uses a structure–activity relationship (SAR) to 10 assess which carbon site is more favorable for hydrogen abstraction in reactions of OH radical with α pOS-249, further confirmed by the products measured. The experiments, model predictions, results, and conclusions are sound and well described. Therefore, this work could be published nearly as is. Xu presents laboratory studies where they investigated the heterogenous oxidation of an alpha-pinene organosulfate surrogate, sodium 2-hydroxy-2,6,6-trimethylbicyclco[3.1. 1]heptan-3-yl sulfate, 10 or apOS-249, using an oxidation flow reactor. The reaction and products were measured with an UHPLC-ESI-QToFMS, HPLC-ESI-QTRAP-MS, and IC. The authors observed various products they provided chemical formula and compared against ambient and lab studies where these compounds had been observed before. Further, they determined the heterogenous OH oxidation of apOS-249 and found the lifetime to be comparable to the lifetime of aerosol (~10 days). They explored potential reaction mechanisms. Finally, they investigated the potential amount of inorganic sulfate (SO4) produced from the heterogenous reaction of apOS-249 with OH. The paper is generally well written with interesting results that would be of importance for the community that reads ACP. I recommend publication after the authors address some of the comments below.
ON THE ESTIMATION OF CURE FRACTION USING POWER GOMPERTZ DISTRIBUTION UNDER BAYESIAN APPROACH

The cure rate models have demonstrated, beyond doubt, their utilitarian value for analysis of data pertaining to long term survivors in diseases like cancer, HIV et al.In the present paper, we have estimated cure fraction using Power Gompertz distribution, in the presence of covariates and censoring under Bayesian framework.Using the developed model, Bayesian analysis of a data set related to patients with breast cancerhas been done.The standard MCMC techniques in OpenBUGS Software have been used to analyze the data.
A Review on Machine Learning Approaches in COVID-19 Pandemic Prediction and Forecasting

Novel COVID-19 Coronavirus disease, namely SARS-CoV-2, is a global pandemic and has spread to more than 200 countries. The sudden rise in the number of cases is causing a tremendous effect on healthcare services worldwide. To assist strategies in containing its spread, machine learning (ML) has been employed to effectively track the daily infected and mortality cases as well as to predict the peak growth among the states or/and country-wise. The evidence of ML in tackling previous epidemics has encouraged researchers to reciprocate with this outbreak. In this paper, recent studies that apply various ML models in predicting and forecasting COVID-19 trends have been reviewed. The development in ML has significantly supported health experts with improved prediction and forecasting. By developing prediction models, the world can prepare and mitigate the spread and impact against COVID-19.
Manuscript Title : How I Managed to Integrate—An analysis of the protective factors in determining good life events and turning points of three former prisoners in Malaysia

Imprisonment impacts the life events and turning points of former prisoners upon release. The challenges faced during societal re-integration are contributing factors that shape the life events of former prisoners upon release and subsequently determine their turning point. This study aims to prove that positive life events will cause former prisoners to shape positive turning points after release. Criminal justice studies have since strived to understand the factors that cause former prisoners to repeat their crimes after being released from prison. However, in Malaysia, not many works have explored the factors that cause former prisoners to cease committing a crime. Besides, the successful reintegration of these prisoners is still understudied. The authors conducted a qualitative study in Kuala Lumpur with 19 former prisoners. The results revealed that 16 informants had repeatedly committed crimes, while only three managed to desist. In this study, the researcher will describe the views and experiences of the three former prisoners (desisters) only on how they desist from crime during re-integration. This study only considered three desisters and did not include 16 other recidivist informants since the objective of this study was to identify the success factors for re-integration and to understand the protective factors of former prisoners during re-integration into society. The crucial f indings were also discussed, including family acceptance, the presence of a supportive spouse, Mohd Alif Jasni et al. employment, avoiding negative peers, no drug addiction, and being healthy. These factors managed to prevent the former prisoners from committing crimes again.
A novel fault detection approach based on multilinear sparse PCA: application onthe semiconductor manufacturing processes

Final Version: . 2022 Abstract: Batch processes are extremely important to researchers since they are widely used in many fields such as biochemistry, pharmacy, and semiconductors. The powerful batch detection method is critical to increase the performance of the overall equipment and to reduce the use of check wafers. Many techniques have been used in batch process monitoring. Among them, the multivariate statistical process control (MSPC) is very useful in batch process monitoring because of the large number of records data. Therefore, batch processes have certain characteristics, such as multimodal batch nonlinearity trajectories, which were challenged by these MSPCs. In this paper, a novel process monitoring methods based on multilinear sparse PCA (MSPCA) are proposed to overcome these shortcomings. MSPCA handle batch data as a matrix (second order), although most of the other multivariate statistical analysis approaches handles batch data as a vector (first order). Vectorization of batch data tends to ignore parts of the information. Furthermore, The MSPCA can extract more useful data from the batch data with less storage requirements and computational complexity compared to current multivariate statistical analysis approaches. The eﬀiciency of the monitoring technique is implemented in the numerical example and Lam 9600 metal etcher process. The performance of MPSCA is characterized by a fault detection rate of 100%, as well as a false alarm rate higher than 83%. Simulation results show that MSPCA outperforms
Distributional Consequences of Monetary Policy Across Races: Evidence from the U.S. Credit Register

We examine the consequences of monetary policy on racial disparities, focusing on the role of bank lending to ﬁrms through collateral and selection channels. Leveraging comprehensive loan-level data from the U.S. credit register (Y-14Q) of the Federal Reserve, we show that ﬁrms in Black communities obtain business loans that are more expensive and have a shorter maturity. These ﬁrms are also more likely to experience adverse credit supply shocks, controlling for ﬁrm risk and investment opportunities, as well as geographic and cultural covariates. We also study the eﬀects of monetary policy across racial groups and document that, following a monetary policy tightening, banks extend loans to ﬁrms in Black communities at disproportionately higher interest rates. Furthermore, banks pass a monetary tightening through to loan rates for borrowers who have no collateral, have prior defaults, and have a shorter banking relationship, but even more to loan rates for ﬁrms in Black communities. Our ﬁndings suggest that monetary policy has distributional consequences in the form of tightened selectivity for Black minorities through lending conditions. Our analysis calls for place-based policies that target certain minority groups. This table shows OLS regression estimates for a speciﬁcation linking racial demographics to loan and monetary policy data at the (loan) 2013:Q1-2019:Q4 County This table shows OLS regression estimates for a speciﬁcation linking racial demographics to loan rates and monetary policy shocks, focusing on periods of easing (negative monetary policy surprise) and tightening (positive monetary policy surprise). The data are at the bank-ﬁrm-quarter (loan) level during 2013:Q1-2019:Q4 and comprises loan renewals and originations. County variables include macroeconomic variables (unemployment rate and log-median household income), and a dummy variable for urban MSAs. Firm characteristics include size (log-assets), cash ratio, tangible ratio, age, proﬁtability (return on assets), sales growth, and ﬁrm risk (a dummy variable for speculative grade ﬁrms). Loan-level controls include dummy variables for credit lines and syndicated loans. Monetary policy shocks G¨urkaynak, Sack and Swanson (2005)) are lagged by one quarter. Standard errors clustered the county level.
A Review of Feature Selection Techniques for Opinion Mining Systems

— Opinion Mining or Sentiment analysis is a subfield of Natural Language Processing that is used to categorize documents, reviews, social media posts etc into different categories of opinion based on sentiment expressed in the text. Large datasets often collected from the worldwide web using different sources like social media, blogs, articles, news reports are used by various organizations for sentiment analysis, Thus better-performing models in terms of accuracy and speed are required which can be achieved through feature reduction. For the research, we have used supervised machine learning algorithms, a learning model that uses mutual information, chi-square and genetic algorithm for feature selection techniques and ensemble model for classification. We have done a comparative study on the feature selection techniques like mutual information, chi-square and union of mutual information gain and chi-square. The comparative study has been done using different feature selection techniques, accuracy and results using these feature selection techniques with different supervised learning algorithms are then compared. We have then used genetic algorithms for feature selection and classification and compared its result with above-mentioned feature selection techniques.