Reviewerly/exHarmony huggingface dataset:
A Two-Stage Model for Breast Cancer Detection and Classification from Mammograms

A Two-Stage Model for Breast Cancer Detection andClassification from Mammograms IntroductionBreast cancer is one of the major health challenges that is common to women.If detected early, it can reduce the death rate; according to the statistics, 96% of cancers can be cured when detected early.According to the prediction of the World Health Organization (WHO), breast cancer cases will hit 19.3 million by 2025 (Ragab, Sharkas, & Attallah, 2019).Breast cancer is defined as the abnormal growth of certain cells in the breast (Zhou et al., 2020).It appears in the form of tumors in the breast.Cancer tumor spreads across the surrounding tissues in the breast.Benign and malignant are the two classes of tumor (Saber, Sakr, Abo-Seida, Keshk, & Chen, 2021).A benign tumor is made up of non-cancerous cells that grow only in one location and do not spread throughout the body, whereas a malignant tumor is made up of cancerous cells that are capable of spreading faster and faster to various body parts and infecting the tissues.Breast cancer has a high mortality rate of around 17% (Tsochatzidis, Costaridou, & Pratikakis, 2019).Mammography is a common method for detecting breast cancer.It helps in the early detection of breast cancer (Ragab et al., 2019).The mammogram is an x-ray image of the breast that is used for regular early identification and intervention of breast cancer in women (Viegas, Domingues, & Mendes, 2021).The basic views of mammogram image when scanned from various angles are the Medio-lateral Oblique view (MLO) and Craniocaudal view (CC).Masses, clusters of micro-calcifications (MCs), and architectural distortions are some of the signs needed to accurately detect breast cancer in mammographic images (Ragab et al., 2019).Mammogram images analysis is a difficult task for medical personnel including radiologists; hence there is a need to develop a Computer Aided Detection (CAD) method for mammogram image analysis.Computer Aided Detection (CAD) is a machine learning method used to detect, diagnose and classify breast tumors using the mammogram images.It can be used to assist radiologists and medical experts in proper diagnosis and classification of tumors (Ragab et al., 2019).CAD can be used to achieve better diagnostic accuracy and decrease the
Uncertainty quantification for Markov Random Fields

We present an information-based uncertainty quantification method for general Markov Random Fields. Markov Random Fields (MRF) are structured, probabilistic graphical models over undirected graphs, and provide a fundamental unifying modeling tool for statistical mechanics, probabilistic machine learning, and artificial intelligence. Typically MRFs are complex and high-dimensional with nodes and edges (connections) built in a modular fashion from simpler, low-dimensional probabilistic models and their local connections; in turn, this modularity allows to incorporate available data to MRFs and efficiently simulate them by leveraging their graph-theoretic structure. Learning graphical models from data and/or constructing them from physical modeling and constraints necessarily involves uncertainties inherited from data, modeling choices, or numerical approximations. These uncertainties in the MRF can be manifested either in the graph structure or the probability distribution functions, and necessarily will propagate in predictions for quantities of interest. Here we quantify such uncertainties using tight, information based bounds on the predictions of quantities of interest; these bounds take advantage of the graphical structure of MRFs and are capable of handling the inherent high-dimensionality of such graphical models. We demonstrate our methods in MRFs for medical diagnostics and statistical mechanics models. In the latter, we develop uncertainty quantification bounds for finite size effects and phase diagrams, which constitute two of the typical predictions goals of statistical mechanics modeling.
The Role of Supervision on Internal Control Disclosure

The internal control system is an important aspect that is needed in every organization to fulfill the organization's goals and targets.With this importance, Otoritas Jasa Keuangan sets certain rules for public companies to disclose the internal control system that is being applied in their business environment in the annual report.This research analyzed how supervision within the company affects internal control disclosure.The function of supervision is conducted by the board of commissioners and the audit committee in each company.The variables that are chosen in this research are the board of commissioners size, the audit committee size, and the audit committee meetings.The results prove that the board commissioners' size and the audit committee meetings positively impact the internal control disclosure.On the other hand, audit committee meetings were found not to affect the internal control disclosure.
The Impact of Contextual Factors of Silence on Employees’ Satisfaction in the Jordan Ministry of Education

This research aims to determine the impact and relationship direction between organizational silence and employees’ satisfaction in the Jordanian Ministry of Education (MOE). The study used a questionnaire tool that was developed based on previous studies. The questionnaire was distributed to 100 employees randomly. The alpha coefficient was used to test the reliability of the instrument. Also, multi-regression and the One-way ANOVA analysis were used to examine the hypotheses. The results showed that the sample reported a moderate degree of both silence and satisfaction and that the relationship is inverse between the organizational factors of silence and job satisfaction. The results also showed that the “Lack of support” factor was the most influential factor and the “Mistrust” had the lowest impact on satisfaction.
Detecting Financial Statement Fraud Using Fraud Triangle and Board of Director’s Attributes Analyses

This study examines whether fraud triangle and board of director's attributes analyses can detect financial statement fraud in Indonesian listed companies.Financial statement fraud is proxied by Dechow 's fraud score model.The fraud triangle analysis includes financial stability, effective monitoring, and rationalization whereas the board of director's attributes analysis covers number of independent board and board of director's gender diversity.The unit of analysis is companies listed in KOMPAS100, an index that comprises 100 companies listed in the Indonesian Stock Exchange that have sound liquidity and large market capitalization.The period of the study is 2016 to 2019.The results of the study show that neither of the three aspects fraud triangle nor the two aspects of the board of director's attributes analysis can detect the financial statement fraud.Such results evidence the extent to which large and liquid Indonesian listed companies have fairly reported their financial statements.
Risk Management in Public Educational Institution (Case Study at XYZ Education and Training Center)

This study aims to evaluate the risk management implementation and provide suggestions for improvements to the XYZ Education and Training Center as the research object, following regulations and best practice implementation.XYZ Education and Training Center is an educational institution in the field of sea transportation that opens its facilities to the public.Research on the risk management implementation in educational institutions is not widely carried out in Indonesia.Educational institutions also face internal and external risks, so risk management is needed so that organizations can mitigate risks that hinder the achievement of organizational goals.This study used a qualitative descriptive method with data instruments such as documentation analysis, interviews, and FGDs.This study used a conceptual framework for risk management ISO 31000[1] and a benchmarking method by comparing risk management at similar educational institutions as best practices.The study results indicated that the XYZ Education and Training Center had carried out the risk identification and analysis process but had not completed the risk evaluation and mitigation process.The proposed improvements included risk likelihood scale improvement, recalculating risk scores, setting risk priorities, determining risk appetite, and compiling risk mitigation.
Group work as an effective way to develop some individual activity of higher education students in the study of chemistry

International scientific conference “Natural resource potential, ecology, and sustainable development of administrative units of the Republic of Latvia and Ukraine amidst EU legislative requirements” : conference proceedings (August 30–31, 2022. Riga, the Republic of Latvia). Riga, Latvia : “Baltija Publishing”, 2022. 100 pages.
TextPruner: A Model Pruning Toolkit for Pre-Trained Language Models

Pre-trained language models have been prevailed in natural language processing and become the backbones of many NLP tasks, but the demands for computational resources have limited their applications. In this paper, we introduce TextPruner, an open-source model pruning toolkit designed for pre-trained language models, targeting fast and easy model compression. TextPruner offers structured post-training pruning methods, including vocabulary pruning and transformer pruning, and can be applied to various models and tasks. We also propose a self-supervised pruning method that can be applied without the labeled data. Our experiments with several NLP tasks demonstrate the ability of TextPruner to reduce the model size without re-training the model.
Visualizing the Relationship Between Encoded Linguistic Information and Task Performance

Probing is popular to analyze whether linguistic information can be captured by a well-trained deep neural model, but it is hard to answer how the change of the encoded linguistic information will affect task performance. To this end, we study the dynamic relationship between the encoded linguistic information and task performance from the viewpoint of Pareto Optimality. Its key idea is to obtain a set of models which are Pareto-optimal in terms of both objectives. From this viewpoint, we propose a method to optimize the Pareto-optimal models by formalizing it as a multi-objective optimization problem. We conduct experiments on two popular NLP tasks, i.e., machine translation and language modeling, and investigate the relationship between several kinds of linguistic information and task performances. Experimental results demonstrate that the proposed method is better than a baseline method. Our empirical findings suggest that some syntactic information is helpful for NLP tasks whereas encoding more syntactic information does not necessarily lead to better performance, because the model architecture is also an important factor.
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Estimators for Reinforcement Learning

Gradient-based methods for optimisation of objectives in stochastic settings with unknown or intractable dynamics require estimators of derivatives. We derive an objective that, under automatic differentiation, produces low-variance unbiased estimators of derivatives at any order. Our objective is compatible with arbitrary advantage estimators, which allows the control of the bias and variance of any-order derivatives when using function approximation. Furthermore, we propose a method to trade off bias and variance of higher order derivatives by discounting the impact of more distant causal dependencies. We demonstrate the correctness and utility of our objective in analytically tractable MDPs and in meta-reinforcement-learning for continuous control.
Kajian Meta Analisis: Meningkatkan Hasil Belajar Siswa sekolah dasar Dengan Model Problem Based Learning

Education is a very important thing for humans which aims to humanize humans and make the abilities and qualities possessed by humans to be superior and advance the life of the nation. The purpose of this study is to determine whether the Problem Based Learning Model from several published meta-analytical studies can have an effect on improving the learning outcomes of elementary school students. This study uses a type of meta-analysis research. The results of the Effect Size test prove that the Problem Based Learning model has a small effect on improving the learning outcomes of elementary school students. In addition, the results of the Ancova Problem Based Learning model have an average value of 80,0800. So it can be concluded that the Problem Based Learning learning model is effective if applied in improving the learning outcomes of elementary school students.
A New Large Scale SVM for Classification of Imbalanced Evolving Streams

Classification from imbalanced evolving streams possesses a combined challenge of class imbalance and concept drift (CI-CD). However, the state of imbalance is dynamic, a kind of virtual concept drift. The imbalanced distributions and concept drift hinder the online learner’s performance as a combined or individual problem. A weighted hybrid online oversampling approach,”weighted online oversampling large scale support vector machine (WOOLASVM),” is proposed in this work to address this combined problem. The WOOLASVM is an SVM active learning approach with new boundary weighing strategies such as (i) dynamically oversampling the current boundary and (ii) dynamic weighing of the cost parameter of the SVM objective function. Thus at any time step, WOOLASVM maintains balanced class distributions so that the CI-CD problem does not hinder the online learner performance. Over extensive experiments on synthetic and real-world streams with the static and dynamic state of imbalance, the WOOLASVM exhibits better online classification performances than other state-of-the-art methods.
FedDAR: Federated Domain-Aware Representation Learning

Cross-silo Federated learning (FL) has become a promising tool in machine learning applications for healthcare. It allows hospitals/institutions to train models with sufficient data while the data is kept private. To make sure the FL model is robust when facing heterogeneous data among FL clients, most efforts focus on personalizing models for clients. However, the latent relationships between clients' data are ignored. In this work, we focus on a special non-iid FL problem, called Domain-mixed FL, where each client's data distribution is assumed to be a mixture of several predefined domains. Recognizing the diversity of domains and the similarity within domains, we propose a novel method, FedDAR, which learns a domain shared representation and domain-wise personalized prediction heads in a decoupled manner. For simplified linear regression settings, we have theoretically proved that FedDAR enjoys a linear convergence rate. For general settings, we have performed intensive empirical studies on both synthetic and real-world medical datasets which demonstrate its superiority over prior FL methods.
Functional dimension of feedforward ReLU neural networks

It is well-known that the parameterized family of functions representable by fully-connected feedforward neural networks with ReLU activation function is precisely the class of piecewise linear functions with finitely many pieces. It is less well-known that for every fixed architecture of ReLU neural network, the parameter space admits positive-dimensional spaces of symmetries, and hence the local functional dimension near any given parameter is lower than the parametric dimension. In this work we carefully define the notion of functional dimension, show that it is inhomogeneous across the parameter space of ReLU neural network functions, and continue an investigation - initiated in [14] and [5] - into when the functional dimension achieves its theoretical maximum. We also study the quotient space and fibers of the realization map from parameter space to function space, supplying examples of fibers that are disconnected, fibers upon which functional dimension is non-constant, and fibers upon which the symmetry group acts non-transitively.
Study of Underwater Fruit Object Detection Using Deep Learning Model

Underwater autonomous vehicle operations are becoming progressively important in order to avoid the hazardous high-pressure deep-sea environment, and the relevance of underwater study and utilisation of marine resource is also rising. Computer vision is noteworthy technology for underwater autonomous vehicles study. In this research work, underwater raw data set is used for training, validating, and testing using YOLO v5 deep learning model to detect the one class (fruit) object. As the underwater images are blurry and hazy, detecting underwater objects without pre-processing is very challenging. In this study, we utilised raw data as underwater dataset to train the yolo model. The raw underwater dataset is difficult to acquire, so in the laboratory Raspberry-pi camera is used to capture the object at different angles, thereafter, data is augmented, yolo model is trained and performance parameters such as accuracy, precision, sensitivity and F1 score are analysed.
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation

We consider training models on private data that are distributed across user devices. To ensure privacy, we add on-device noise and use secure aggregation so that only the noisy sum is revealed to the server. We present a comprehensive end-to-end system, which appropriately discretizes the data and adds discrete Gaussian noise before performing secure aggregation. We provide a novel privacy analysis for sums of discrete Gaussians and carefully analyze the effects of data quantization and modular summation arithmetic. Our theoretical guarantees highlight the complex tension between communication, privacy, and accuracy. Our extensive experimental results demonstrate that our solution is essentially able to match the accuracy to central differential privacy with less than 16 bits of precision per value.
Walk-Through MR World Approach for Students Using Mixed Reality

Knowledge is prodigious, and learning has no boundary. The curiosity of the one to learn, discover and invent decides the future of the world. Educational Institutions’ main objective is to provide qualitative knowledge to the students and supporting tools used for learning play a major role. The conventional education system is arduous to be used in the current situation of the global pandemic of COVID-19. New methods and tools are required to make learning and imparting knowledge more effective. Applications like Google Meet, Zoom, Cisco WebEx are being used in schools and colleges. When it comes to simulations in technical education, for instance, to develop any electrical circuits, robots, buildings, etc., software like MATLAB, 3Ds MAX, GNU Octave is being used. These methods neither are interactive nor provide an immersive experience to the user. To subdue this problem, Mixed Reality (MR) technology can be utilized as a boon by developing an application where students can have interactive classes, submerging themselves, and gaining the required knowledge. Also, the technical students can simulate their experiments onto the real world, providing an idea of how the world may look when new things are adopted and can undergo a walk-through experience in the MR world.
A Fourfold Refined Enumeration of Alternating Sign Trapezoids

Alternating sign trapezoids have recently been introduced as a generalisation of alternating sign triangles. Fischer established a threefold refined enumeration of alternating sign trapezoids and provided three statistics on column strict shifted plane partitions with the same joint distribution. In this paper, we are able to add a new pair of statistics to these results. More precisely, we consider the number of $-1$s on alternating sign trapezoids and introduce a corresponding statistic on column strict shifted plane partitions that has the same distribution. More generally, we show that the joint distributions of the two quadruples of statistics on alternating sign trapezoids and column strict shifted plane partitions, respectively, coincide. In addition, we provide a closed-form expression for the $2$-enumeration of alternating sign trapezoids.
Impact of noise to edge detection on images of different complexity

In this paper, an analysis of the edge detection over images of different complexity affected by Salt and Pepper, Gaussian and Speckle noise is performed. An analysis was performed for three noise levels, 0.01, 0.05 and 0.1. Over 100 images from the BSD database were used for analysis and each image has a GroundTruth with which an objective assessment of the detected edges was performed using PR and F measures. Five edge detectors Canny, LoG, Sobel, Prewitt and Roberts operator were used. The results are presented graphically. The obtained results show that noise significantly affects the detection of edges. When it comes to Salt and Pepper noise, Canny detector has achieved the best results for all levels of noise and image complexity. With the Speckle noise type for high and medium number of details in the image, Canny also gave the best results, while for low number of details in the image it is the Prewitt operator. When it comes to Gaussian noise, for all three categories of image complexity the best operator is Prewitt.
Multi-factor Authentication for the Internet of Things

The Internet of Things (IoT) is integrated and applied in various domains such as Smart Home, Healthcare, Industrial IoT (IIoT), and others. IoT allows physical objects to be able to create, receive and exchange huge amounts of data at any time. The goal of such applications is to automate physical objects that will be able to function without human intervention or with minimal intervention depending on the purpose of the application. Because all domains involved involve the use of sensitive data and that data is usually transmitted over insecure channels, security and privacy issues arise due to potential cyber-attacks. This paper presents various IoT security techniques as well as the main security goals and characteristics. Three types of authentication schemes, one-factor, two-factor, and threefactor are then explained. Since authentication is one of the most important security goals, a special focus is on two-factor authentication schemes.
Social-Cultural Quantum Optics at a Predominantly White University: Refusing, Rebuffing and Undoing Racism through Collective Experiential Observation

Racism is commonly defined as prejudice plus power, but in some contexts it can be practiced as a lack of diversity, equity and inclusion. These issues have been at the forefront of popular culture and academic research for some time now, but one could argue, interest has exploded in the U.S. consciousness since May of 2020 and the video recording of the extra-judicial killing of George Floyd in Minneapolis, mirroring that of Philando Castile four years earlier in the same city. In this paper we document the ways diversity, equity and inclusion are perceived, and how those perceptions render racism (un)visible. Within this context, we introduce a framework we are calling social-cultural quantum optics (SQO). With data from two large diversity symposia at a university on the east coast that is more diverse than many coupled with our experiences at three art exhibits, we begin to construct a path unlocking small scale, quantum, determinants of racism. We argue that understanding SQO can undo its links to racism on everyday life in cities and communities where universities are located, while at the same time urging the use collective experiential observation of artistic expression to aid in data analysis.