Reviewerly/exHarmony huggingface dataset:
Comparative Evaluation of FNAC and USG Breast with Core Needle Biopsy in the Diagnosis of Malignant Breast Lesions: A Prospective Observational Study

Background: There is no doubt that breast cancer is the most frequently diagnosed life-threatening cancer in women and a significant cause of cancer death among women in both developing and developed nations. Using breast imaging-reporting and data system (BI-RADS) classification and breast core needle biopsy (CNB) results, we compared the diagnostic efficacy of FNAC and ultrasonography breast in separating malignant from benign breast masses. Methods: After getting the approval from the Institutional Ethical Committee, the present prospective cross-sectional study was conducted in the Department of Pathology, Government Medical College, Srinagar (tertiary care hospital), Kashmir, India over a period of 2 years (July 2019 to June 2021). Results: With a mean age of (45.4±13.4) years, the age incidence among the 108 individuals ranged from 22 to 85 years, with the most common age group for breast lumps to occur being (30-39) years. Both FNAC and USG breast were compared to CNB (gold standard), they both had sensitivity and specificity values of (94.57%, 100%) and (95%, 50%), respectively. Conclusion: When FNAC and USG breast were compared to CNB, the gold standard test, FNAC was found to be more reliable and accurate for the identification of malignant breast lesions. However, when these two tests were utilised in tandem, a better outcome and diagnosis could be achieved. Keywords: Ultrasonography, FNAC, Core needle biopsy, Breast malignancy
City of Smoke

Other| November 01 2022 City of Smoke Jeddie Sophronius Jeddie Sophronius Jeddie Sophronius is a Chinese-Indonesian writer from Jakarta. He received his MFA from the University of Virginia, where he served as the editor of Meridian. His work has appeared in the Cincinnati Review, Prairie Schooner, Iowa Review, and elsewhere. He is currently teaching at the University of Virginia as a lecturer of English. Read more of their work at nakedcentaur.com. Search for other works by this author on: This Site Google the minnesota review (2022) 2022 (99): 10–11. https://doi.org/10.1215/00265667-9992845 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Jeddie Sophronius; City of Smoke. the minnesota review 1 November 2022; 2022 (99): 10–11. doi: https://doi.org/10.1215/00265667-9992845 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll Journalsthe minnesota review Search Advanced Search The text of this article is only available as a PDF. Copyright © 2022 Jeddie Sophronius2022 Article PDF first page preview Close Modal Issue Section: Creative You do not currently have access to this content.
Herbivore Omnivore Vector

Other| November 01 2022 Herbivore Omnivore Vector Abigail Frankfurt Abigail Frankfurt Abigail Frankfurt is a writer and artist. Her prose poetry is coupled with collage. Her writing has appeared in Lit Magazine, Unbroken Literary Journal, and New York Quarterly and will be in the Yale Review in November 2021. Search for other works by this author on: This Site Google the minnesota review (2022) 2022 (99): 7. https://doi.org/10.1215/00265667-9992788 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Abigail Frankfurt; Herbivore Omnivore Vector. the minnesota review 1 November 2022; 2022 (99): 7. doi: https://doi.org/10.1215/00265667-9992788 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll Journalsthe minnesota review Search Advanced Search The text of this article is only available as a PDF. Copyright © 2022 Abigail Frankfurt2022 Article PDF first page preview Close Modal Issue Section: Creative You do not currently have access to this content.
DEVELOPING ENGLISH GUIDE BOOK THROUGH SCIENTIFIC APPROACH FOR THE FIRST GRADE OF MA. MIFTAHUL QULUB POLAGAN GALIS PAMEKASAN

The mastering of English skill has been faced some obstacles for the first students of MA. Mitahul Qulub because some factors, such as: students did not get good guidance to develop their ability, textbook material is not appropriate of students need in the specific institution. Based on the situation, this study aims to develop supplementary English guide book to the student, it includes two English skills both Reading and writing that will be related to the students need and use scientific approach to make a good English book composition. The researcher developed the materials by using some procedures of development that consisted of some steps of need analysis, material development, expert validation, revision, try-out, revision II, final product that used questionnaire and interview to collect data. Based on the result of expert validation and try out of materials, it can be concluded that the result of development English guide book is appropriate with the students need and objectives of English guide program itself, exactly the level of students’ difficulties, interest, and standards for the first grade. The experts’ hopes, the book can give a advantages and contribution as supplementary English guide book for the students to develop their English reading and writing skills. The English guide book development consisted of five chapters that provided the materials both reading and writing skills.
Fast Tuning of Extreme Learning Machine Neural Networks based with Simple Optimization Algorithms

Extreme Learning Machine (ELM) is a neural network training paradigm that is characterized by simplicity, speed and high level of accuracy. The tuning of the network parameters is normally carried out with non-linear optimization algorithms that break this principle of simplicity and reduced execution time. This article shows that ELM network tuning can be performed efficiently by simple optimization algorithms, consistent with its basic philosophy. Experiments with 8 optimization algorithms are shown, considering 6 widely used databases in training algorithm benchmarks. The numerical results show that the Golden Section Algorithm dramatically reduces the network hyperparameter search time compared to the search while maintaining a high level of accuracy.
A New Fast Training Algorithm for Autoencoder Neural Networks based on Extreme Learning Machine

Autoencoders are neural networks that are characterized by having the same inputs and outputs. This kind of Neural Networks aim to estimate a nonlinear transformation whose parameters allow to represent the input patterns to the network. The Extreme Learning Machine (ELM-AE) Autoencoders have random weights and biases in the hidden layer, and compute the output parameters by solving an overdetermined linear system using the Moore-Penrose Pseudoinverse. ELM-AE training is based on the Fast Iterative Shrinkage-Thresholding (FISTA). In this paper, we propose to improve the convergence speed obtained by FISTA considering the use of two algorithms of the Shrinkage-Thresholding class, namely Greedy FISTA and Linearly-Convergent FISTA. 6 frequently used public machine learning datasets were considered: MNIST, NORB, CIFAR10, UMist, Caltech256, Stanford Cars. Experiments were carried out varying the number of neurons in the hidden layer of the Autoencoders, considering the 3 algorithms, for all the databases. The experimental results showed that Greedy FISTA and Linearly-Convergent FISTA presented higher convergence speed, increasing the speed of ELM-Autoencoder training, maintaining a comparable generalization error between the three Shrinkage-Thresholding algorithms.
Off-line Writer Verification based on Small Segments of Handwritten Text and Convolutional Neural Networks

This paper proposes a new method for writer identification based on small fragments of handwritten text randomly obtained from a paragraph. The main contribution of this work is to show that small fragments carry enough biometric information for writer identification. A second contribution is the creation of 2 repositories of images of handwritten text from 50 writers. The first one is made up of 4 text paragraphs of 64 words in high resolution per writer. The second one contains more than 700 thousand fragments of text per writer. Experiments were conducted with different Convolutional Neural Networks, considering the VGG-16, VGG19, InceptionV3, ResNet-50 and MobileNetV2m models. 2 classification schemes were implemented. First, the classification of individual fragments and, second, the classification of groups of fragments. The best results were obtained using groups of fragments, achieving accuracy of 96% on the identification of a text with the same content and of 87% on the identification of the writer considering a text with different content.
Analisis Penerimaan Vaksin Covid-19 Berbasis Fuzzy Clustering Machine Learning di Provinsi Riau

Corona Virus Disease 2019 or Covid-19 is called global because it is spreading rapidly around the world, increasing cases and deaths, and lack of treatment and vaccines. Seeing rapid spread of Covid-19 and dangers will arise if not handled immediately, One of the most likely ways to prevent the spread of this virus is develop a vaccine. The Head of the Manpower and Transmigration Office of DKI Jakarta Province Issues Decree (SK) No. 1972 of 2021 concerning the Covid-19 Prevention and Control Protocol in Offices or State-Owned Enterprises. Company leaders only allow the implementation of Work From Office (WFO) to workers who have been vaccinated against Covid-19 at least the first dose. One of the companies that need vaccines for workers is PT. Perkebunan Nusantara V or PTPN V because the number of Covid-19 cases in PTPN V is increasing. Based on this case, this research will model or group Covid-19 vaccine data at PTPN V using the Fuzzy C-Means algorithm. The attributes used in this study were Gender, Age, Unit of Work and Vaccine Status. The best cluster results obtained are 5 clusters, the most cluster in cluster 2 there are 3574 persons, and the cluster which has the least number of products is cluster 4 with 18 person. The result of testing the validity of the Silhouette Index (SI) value is 0.1541, thus the quality of the cluster is still far from reaching the optimal word.
Rancang Bangun Aplikasi Aplikasi Iuran Sampah RT/RW Menggunakan Metode Rapid Application Development Berbasis Android

RT047/RW008 or Jl. Aries is a Neighborhood Association located in Sungai Besar Village, Banjarbaru City, South Kalimantan province. Within this RT environment, weekly and monthly activities are often held. One of the activities in this area is the monthly waste fee. This activity has been around for a long time when this environment was formed. However, in this monthly waste fee activity, we still use the recording method by recording data on a physical citizen membership fee card. In addition, the manufacture of this garbage contribution card still uses the service of making a contribution card which requires traveling and also incurs some costs. In the process of monthly waste contribution activities, it often results in the loss or damage of the citizen membership dues card, in which the treasurer has to make another card at the contribution card making service. Based on the problems found, a solution to the problem that can be found is to design an Android-based application that can run on Android Smartphone devices. This application will be designed using the Rapid Application Development method which will go through the process of data collection to application design. This application can perform the administrative process of digital waste dues. In addition, in the application there is also a reminder feature that will be sent to Whatsapp for every registered member of the group. Because this application runs on Android Smartphone devices, it can make it easier for users to input data and change data anywhere and anytime. The method used for designing this application is to analyze to find out what is needed in an application that you want to build. The final result of the implementation process in the form of Java programming and test results by users. By using the white box testing and black box testing methods, the result is that the application can run well and in accordance with the needs required by the user. With this android application, the RT environment can be more focused and become an RT that can be in line with current technological developments
PELATIHAN PERAKITAN DAN INSTALASI KOMPUTER UNTUK SANTRIWAN/SANTRIWATI DI DAYAH TERPADU SYAMSUDDHUHA ACEH UTARA

Along with development of globalization era, world of Information and Communication Technology is also growing rapidly and it has become a primary need for many people, including students. By applying information and Communication Technology, a process and activity can be carried out more quickly, easily and efficiently. Therefore, mastery of communication technology devices needs to be taught to students in Islamic boarding schools or dayah. Community service is an effort of Malikussaleh University to contribute in science and technology to the students. Purpose of the service is to provide training on computer assembly and installation to Santriwan/Santriwati at Syamsuddhuha Integrated Dayah. This training is expected to provide students with provisions regarding computer assembly and installation. Outcomes of this activity are: (1) The student is able to know each function of computer assembling and installing, and (2) participants have the ability to understand computer and its components.
State of Library Awareness Service in the IT Era and Users Response: A Study of JNN College of Engineering Library-Shimoga

Information service is the emerging motto of all information centers. To tweet the expected level of the current users, the center should do needful to include certain nascent tools and techniques in their operation. JNN College of Engineering Library and Information Center has advanced and up-to-date information of the resources in the library. And to effectively distribute the preserved papers to its users, the library organizer must properly organize the library in the direction of being well-equipped. The head of the library must understand the nature of the library, the nature of the users of the library, and also change the pattern as per the users' likes and dislikes. Since today the mindset of the users' community is completely changed, because of the time constraints and accuracy in the information, users of the library lean towards IT-based services. Not only services, but relevant service awareness is also critical. In that direction, every information organizer and administrator should put an effort into conducting well-planned and required methods of library awareness programs. Keeping in mind massive coverage of all kinds of user communities, an organization must think over common methods and techniques in conducting awareness programs. Especially in the trend of today, massive numbers of the users' community are on social media like Facebook, Twitter, WhatsApp, and Web page, etc. Keeping these aspects in the mind, information centers of today must be alert and think over the reachable media and implement them in making effective awareness about the library and its activities to the current users' community. This article attempted to make an understanding of how the users’ community is making use of the library resources effectively and in making effective use, what are the efforts put by the library organizer to make awareness of the library to its users are the main goals of the study.
Confronting the Intersection of COVID-19 and Privacy

W hen I first proposed this special issue in the spring of 2021, I had hoped the resulting articles would be a retrospective—looking back at how we had met the challenge of COVID-19, conquered it, and taken the lessons that it gave us into the future. Writing this editorial now, it is clear that is not what has happened. COVID-19 and its variants of concern, most recently BQ.1, continue to wreak havoc on our public institutions. This is, of course, most notably felt in the healthcare system, but all public services must grapple with balancing serving their customers with ensuring the safety of their employees—and the privacy of both.
Deconstructing the Calculus of Relations with Tape Diagrams

Rig categories with finite biproducts are categories with two monoidal products, where one is a biproduct and the other distributes over it. In this work we present tape diagrams, a sound and complete diagrammatic language for these categories, that can be intuitively thought as string diagrams of string diagrams. We test the effectiveness of our approach against the positive fragment of Tarski's calculus of relations.
Qunity: A Unified Language for Quantum and Classical Computing

We introduce Qunity, a new quantum programming language designed to treat quantum computing as a natural generalization of classical computing. Qunity presents a unified syntax where familiar programming constructs can have both quantum and classical effects. For example, one can use sum types to implement the direct sum of linear operators, exception-handling syntax to implement projective measurements, and aliasing to induce entanglement. Further, Qunity takes advantage of the overlooked BQP subroutine theorem, allowing one to construct reversible subroutines from irreversible quantum algorithms through the uncomputation of "garbage" outputs. Unlike existing languages that enable quantum aspects with separate add-ons (like a classical language with quantum gates bolted on), Qunity provides a unified syntax and a novel denotational semantics that guarantees that programs are quantum mechanically valid. We present Qunity's syntax, type system, and denotational semantics, showing how it can cleanly express several quantum algorithms. We also detail how Qunity can be compiled into a low-level qubit circuit language like OpenQASM, proving the realizability of our design.
Continuous Control With Swarm Intelligence Based Value Function Approximation

Value function approximation, such as Q-learning, is widely used in the discrete control rather than the continuous one because the optimal action in the discrete setting is more easily selected. Optimizing the action is a non-convex optimization problem with respect to the complex value function. Some notable studies simplify the non-convex optimization problem by assuming the value function as quadratic in the actions or by discretizing the action space. However, the performance of the output policy will decline if these studies’ premises do not hold. In order to address the problem, we propose a framework that combines swarm intelligence algorithms with value-based Reinforcement Learning, where the swarm intelligence algorithms are employed to search for the optimal action with respect to the state and the value function. To ensure the correctness of this framework, we conditionally claim the convergence rate of swarm intelligence algorithms with high probability. We then implement it by searching the batch optimal actions to various states on the GPU platform for the batch training. Furthermore, we employ the population-based atomic actions for the compatibility with the existing related work about solving discrete control problems. Four classical control models and four robot simulation environments are utilized in the comparisons. According to empirical results, our framework outputs a policy comparable with that of the policy-based algorithms by 10% timesteps in the continuous control. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the exploration-exploitation dilemma of Reinforcement Learning to solve continuous control tasks. To balance the exploration and exploitation, the stochastic exploration and the prioritized exploration are roughly two feasible ways, where the prioritized one is a better choice due to the higher data efficiency than the stochastic one, e.g. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon$</tex-math> </inline-formula> -greedy. Normally, the prioritized exploration works well in the value-based Reinforcement Learning algorithms rather than the policy-based ones; meanwhile, the policy-based algorithms are more suitable to continuous control tasks than the value-based ones. To tackle this conflict, we especially design a particle swarm optimization to maximize the Q-value of action in Q-learning. Our design can be hybridized by various swarm intelligence and value-based Reinforcement Learning algorithms. Also, it can be embedded in most intelligent control systems easily. The aim of this study is to solve the continuous control tasks by value-based algorithms as the first step of applying the prioritized exploration. The simulative results verify the effectiveness and efficiency of our design.
Simulation of IoT-oriented Fall Detection Systems Architectures for In-home Patients

Fall detection (FD) systems enable rapid detection and intervention for people who experience falls, a leading threat to the elderlys health and autonomy. Most of these systems conform to an IoT reference architecture which may include multiple sensing mechanisms to balance the advantages and drawbacks of each alternative. However, developing such a heterogeneous system may be costly and quite resource and time-demanding. This paper presents a Discrete Event System Specification (DEVS) simulation model for FD systems that compares the accuracy of nine different systems architectures that combine traditional wearable and non-wearable sensing devices in the acquisition layer. We perform simulations for each architectural arrangement using four public datasets of FD systems, totaling 36 simulations. Results reveal that an FD accuracy of 96.67% is possible with an investment of almost 6,000 US. Besides, spending 36 times less (around 150 US), designers and clients could acquire an FD system composed of wearable and non-wearable devices with an accuracy of 91%, i.e., only 5% less than the most expensive alternative.