Reviewerly/exHarmony huggingface dataset:
Non-linear regression models for behavioral and neural data analysis

Regression models are popular tools in empirical sciences to infer the influence of a set of variables onto a dependent variable given an experimental dataset. In neuroscience and cognitive psychology, Generalized Linear Models (GLMs) -including linear regression, logistic regression, and Poisson GLM- is the regression model of choice to study the factors that drive participant's choices, reaction times and neural activations. These methods are however limited as they only capture linear contributions of each regressors. Here, we introduce an extension of GLMs called Generalized Unrestricted Models (GUMs), which allows to infer a much richer set of contributions of the regressors to the dependent variable, including possible interactions between the regressors. In a GUM, each regressor is passed through a linear or nonlinear function, and the contribution of the different resulting transformed regressors can be summed or multiplied to generate a predictor for the dependent variable. We propose a Bayesian treatment of these models in which we endow functions with Gaussian Process priors, and we present two methods to compute a posterior over the functions given a dataset: the Laplace method and a sparse variational approach, which scales better for large dataset. For each method, we assess the quality of the model estimation and we detail how the hyperparameters (defining for example the expected smoothness of the function) can be fitted. Finally, we illustrate the power of the method on a behavioral dataset where subjects reported the average perceived orientation of a series of gratings. The method allows to recover the mapping of the grating angle onto perceptual evidence for each subject, as well as the impact of the grating based on its position. Overall, GUMs provides a very rich and flexible framework to run nonlinear regression analysis in neuroscience, psychology, and beyond.
Stan: Small tumor-aware network for breast ultrasound image segmentation

Breast tumor segmentation provides accurate tumor boundary, and serves as a key step toward further cancer quantification. Although deep learning-based approaches have been proposed and achieved promising results, existing approaches have difficulty in detecting small breast tumors. The capacity to detecting small tumors is particularly important in finding early stage cancers using computer-aided diagnosis (CAD) systems. In this paper, we propose a novel deep learning architecture called Small Tumor-Aware Network (STAN), to improve the performance of segmenting tumors with different size. The new architecture integrates both rich context information and high-resolution image features. We validate the proposed approach using seven quantitative metrics on two public breast ultrasound datasets. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors. Index
Cartesian Difference Categories: Extended Report

Cartesian differential categories are categories equipped with a differential combinator which axiomatizes the directional derivative. Important models of Cartesian differential categories include classical differential calculus of smooth functions and categorical models of the differential lambda-calculus. However, Cartesian differential categories cannot account for other interesting notions of differentiation such as the calculus of finite differences or the Boolean differential calculus. On the other hand, change action models have been shown to capture these examples as well as more "exotic" examples of differentiation. However, change action models are very general and do not share the nice properties of a Cartesian differential category. In this paper, we introduce Cartesian difference categories as a bridge between Cartesian differential categories and change action models. We show that every Cartesian differential category is a Cartesian difference category, and how certain well-behaved change action models are Cartesian difference categories. In particular, Cartesian difference categories model both the differential calculus of smooth functions and the calculus of finite differences. Furthermore, every Cartesian difference category comes equipped with a tangent bundle monad whose Kleisli category is again a Cartesian difference category.
Combating False Negatives in Adversarial Imitation Learning

In adversarial imitation learning, a discriminator is trained to differentiate agent episodes from expert demonstrations representing the desired behavior. However, as the trained policy learns to be more successful, the negative examples (the ones produced by the agent) become increasingly similar to expert ones. Despite the fact that the task is successfully accomplished in some of the agent's trajectories, the discriminator is trained to output low values for them. We hypothesize that this inconsistent training signal for the discriminator can impede its learning, and consequently leads to worse overall performance of the agent. We show experimental evidence for this hypothesis and that the 'False Negatives' (i.e. successful agent episodes) significantly hinder adversarial imitation learning, which is the first contribution of this paper. Then, we propose a method to alleviate the impact of false negatives and test it on the BabyAI environment. This method consistently improves sample efficiency over the baselines by at least an order of magnitude.
Towards Deep Machine Reasoning: a Prototype-based Deep Neural Network with Decision Tree Inference

In this paper we introduce the DMR -- a prototype-based method and network architecture for deep learning which is using a decision tree (DT)-based inference and synthetic data to balance the classes. It builds upon the recently introduced xDNN method addressing more complex multi-class problems, specifically when classes are highly imbalanced. DMR moves away from a direct decision based on all classes towards a layered DT of pair-wise class comparisons. In addition, it forces the prototypes to be balanced between classes regardless of possible class imbalances of the training data. It has two novel mechanisms, namely i) using a DT to determine the winning class label, and ii) balancing the classes by synthesizing data around the prototypes determined from the available training data. As a result, we improved significantly the performance of the resulting fully explainable DNN as evidenced by the best reported result on the well know benchmark problem Caltech-101 surpassing our own recently published "world record". Furthermore, we also achieved another "world record" for another very hard benchmark problem, namely Caltech-256 as well as surpassed the results of other approaches on Faces-1999 problem. In summary, we propose a new approach specifically advantageous for imbalanced multi-class problems that achieved two world records on well known hard benchmark problems and the best result on another problem in terms of accuracy. Moreover, DMR offers full explainability, does not require GPUs and can continue to learn from new data by adding new prototypes preserving the previous ones but not requiring full retraining.
Explaining Relationships Between Scientific Documents

We address the task of explaining relationships between two scientific documents using natural language text. This task requires modeling the complex content of long technical documents, deducing a relationship between these documents, and expressing the details of that relationship in text. In addition to the theoretical interest of this task, successful solutions can help improve researcher efficiency in search and review. In this paper we establish a dataset of 622K examples from 154K documents. We pretrain a large language model to serve as the foundation for autoregressive approaches to the task. We explore the impact of taking different views on the two documents, including the use of dense representations extracted with scientific IE systems. We provide extensive automatic and human evaluations which show the promise of such models, but make clear challenges for future work.
DESCRIPTION OF FACTORS AFFECTING MEDICAL RECORD FILLING AT THE DENTAL CLINIC IN SEMARANG

Background: A clinic a health service place that provides health services. Based on Peraturan Menteri Kesehatan RI No. 9 tahun 2014, every clinic has an commitment to conduct therapeutic records. Restorative Records could be a record that contains records and reports that contain the personality of the persistent, the comes about of the examination, treatment that has been given, as well as other activities and administrations provided by the doctor to the patient. This study aimed is to look at the description of the factors that affecting medical record filling at the dental clinic in Semarang.Method: This study used a descriptive observational research type of quantitative approach with cross-sectional inquire about plan. Information gotten from questionnaires will be assessed percentage. The research will be processed using SPSS so that it will produce data on the frequency of each factor and the frequency of each question.Result: The results showed that judging from the knowledge factor showed good and less results, motivation and medical record form material showed good results, while the reward method showed good and sufficient results. Conclusion: there were factors that showed results in the category was sufficient, less, and good against filling medical records in the dental clinic of Semarang City.
The Use of Flipbook Learning Media with a Scientific Approach to Improve Prodecure Text Writing Skills

This study aims to (1) describe and explain the quality of the learning process in procedural text writing skills (2) describe and explain the improvement of procedural text writing skills using flipbook learning media with a scientific approach. This study uses the classroom action research method (classroom action research) with 2 cycles, each cycle consisting of 2 meetings. Each cycle consists of planning, implementation, action, observation and reflection. This research was conducted for eight months, from August to March 2022 and was carried out at SMPN 22 Jambi City. The research subjects were Indonesian language teachers and students of class VII G SMP Negeri 22 Jambi City, totaling 28 students consisting of 12 female and 16 male. Data collection techniques consist of test and non-test techniques (observation, interviews, and documentation). The validity of the data in this study used triangulation of data sources. The data analysis technique uses descriptive analysis to describe the increase in student success in each cycle and calculates the average to see the value of student learning completeness. The results showed that 1) The quality of learning to write procedural texts in terms of teacher and student activities has increased. The increase in teacher activity from the learning aspect has increased to reach 100%. The activity of students in the active aspect of listening and receiving teacher explanations increased from 51.79% in the Cycle I to 73.21% in the Cycle II. In the aspect of answering and asking questions, it increased from an average of 44.64% in the Cycle I to 65, 18% in cycle II. In the aspect of ability in discussion increased from an average of 44.64% in the Cycle I to 66.96% in the Cycle II. In the aspect of activeness in carrying out tasks, it increased from 57.14% in the Cycle I to 83.93% in the Cycle II. In the aspect of following the reflection, it increased from 58.93% in the Cycle I to 100% in the Cycle II. 2) The results of students' procedural text writing skills increased as indicated by an increase in grades. In the pre-action, the percentage of completeness was 17.86% in the less category with an average value of 63.25. In the Cycle I, the completeness increased to 67.86% although it was still in the less category with an average value of 71.75. In the Cycle II, the percentage of completeness reached 100% in the very good category, with the average grade achieved was 90.75. This means that flipbook learning media with a scientific approach can improve the quality of learning and writing procedure text skills.
Development of Operating Model for the Design of Stirrer Arms of Slurries: Empirical Evaluation of Stirrer Arms

The “Development of Operating Model for the Design of Stirrer Arms of Slurries: Empirical Evaluation of Stirrer Arms” is reported. Previous work reviewed the “Operating Model for the Design of Stirrer Arms of Slurries” and identified the Two Z and TETE stirrer arms as the most effective. Furthermore, subsequent work “Development of Operating Model for the Design of Stirrer Arms of Slurries: Design and Fabrication of Stirrer Arms” designed and fabricated the Two Z and TETE stirrer arms, evaluated the theoretical (expected) Slurry Mixing Power and Order of Merit Analysis. The current work did the empirical evaluation of the Two Z and TETE stirrer arms and also the hybrid Two Z - TETE stirrer arms. The Objectives of this research were: to do the mixing performance tests in terms of mixing time, power and energy consumed by the Two Z blade, TETE blade and Two Z – TETE hybrid blade using the constituted slurry; to validate the theoretical (predicted or expected) mixing power for the Two Z blade, TETE blade and Two Z – TETE hybrid blade by comparison with the empirical power using the Chi Square; to do the Power, Time and Energy order of merit analysis for the Two Z blade, TETE blade and the Two Z – TETE hybrid blade. Using the Philips HR 1565 mixer with the Akamu (pap) (Viscosity calibrated) slurries, the time and current drawn for complete and consistent slurry mixing by each type of stirrer arm (namely: Two Z, TETE and Two Z – TETE) was recorded. Results obtained were analyzed using the Chi Square and the Order of Merit. There was excellent agreement between the adjusted predicted (expected) slurry mixing power (E’) and Observed slurry mixing power (O) at 5% confidence level. Empirical model was developed to predict expected slurry mixing power. The Order of Merit analysis revealed the TETE stirrer arm as the most energy efficient.
Measurements of Some Meteorological Variables at a Tropical Location in Nigeria

This study observed six meteorological variables at a tropical location from 2016 to 2019. The measurement was performed at the meteorological station (7.55 0N; 4.56 0E) of Obafemi Awolowo University, Ile-Ife, Nigeria. The variables that were measured are: incoming solar radiation, net radiation, air temperature, surface temperature, relative humidity and wind speed. The results obtained showed that the maximum daytime monthly mean values of the incoming solar radiation (S) and net radiation (Rn) were recorded in March (782.0 and 545.4 Wm-2, respectively), which is the transition month from dry to wet season. The minimum daytime monthly mean values of S and Rn were recorded in August (443.9 and 330.4 Wm-2, respectively) which is the cloudiest month of the year at the study location. The daily air and surface temperatures (Ta and Ts, respectively) varied between 16.4 and 37.8 0C; 17.7 and 49.7 0C, respectively. The daytime maximum values peaked at about 15:00 LT with the maximum monthly mean values recorded in February (36.3 and 47.7 0C, respectively) and the daytime minimum monthly mean values recorded in August (28.9 and 33.3 0C, respectively). The daily values of relative humidity (Rh) ranged between 29 % and 98 % with a mean value of 47 %. The diurnal trend of wind speed (u) recorded at the study location ranged between 0.48 ms-1 to 2.10 ms-1.
IAC Yokohama Reporting of Breast Cytology to Assess Risk of Malignancy and Predictive Values

Background: IAC Yokohama system has 5 categories that may be stratified by their risk of malignancy (ROM) and supply guidance within a management algorithm for every category. The main objectives were to categorize the Breast FNAC samples according to new system of reporting and to assess the Risk of malignancy (ROM), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy for all categories. Material and Methods: A total of 174 cases were prospectively studied over a period of one year from 1st November 2019 to 31st October 2020 in GMC, Jammu. All the FNAC received was reported routinely according to the newly proposed Yokohama system of reporting breast cytology. The ROM, sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and accuracy of Breast FNAC were calculated. Results: ROM is 0% for category 1, 2.27% for category 2, 50% for category 3, 50% for category 4 and 100% for category 5. Sensitivity, specificity, and diagnostic accuracy were, respectively, 100%, 100% and 99.11% for category A (only C5 category cases considered positive test results), 88.2%, 93.6%, and 93.6% for category B, (both C4 and C5 categories considered positive test results), and 94.1%, 91.48%, and 92.1% for category C (C3, C4, and C5 category cases grouped as positive test results). PPV and NPV were also calculated. Conclusion: Categorization of the Breast FNAB cytology according to IAC Yokohama system of reporting helps pathologist in the diagnostic clarity and guides clinician in the appropriate patient management.
Identifikasi Sinyal Congestive Heart Failure dengan Metode Convolutional Neural Network 1D

ABSTRAKPenyakit jantung merupakan salah satu penyebab utama kematian di dunia. Salah satu penyakit jantung yang perlu diperhatikan adalah congestive heart failure (CHF). CHF adalah suatu kondisi di mana jantung tidak mampu memompa darah ke seluruh tubuh. Penyakit ini dapat didiagnosis dengan EKG. Oleh karena itu, pada penelitian ini dibuat sebuah sistem yang dapat mengidentifikasi penyakit CHF secara otomatis menggunakan metode convolutional neural network (CNN) dengan 4 hidden layer dan 16 output channel, fully connected layer, dan aktivasi Softmax. Data yang digunakan dalam penelitian ini diambil dari MITBIH dan BIDMC. Penlitian ini memberikan akurasi 100%, sehingga deteksi penyakit CHF otomatis membantu staf medis mendiagnosis pasien untuk menerima perawatan yang tepat.Kata kunci: Elektrokardiogram (EKG), Convolutional Neural Network (CNN), Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF)ABSTRACTHeart disease is one of the leading causes of death in the world. One of the heart diseases that need to be considered is congestive heart failure (CHF). CHF is a condition in which the heart is unable to pump blood throughout the body. ECG can diagnose this disease. Therefore, this study created a system that can automatically identify CHF disease using the convolutional neural network (CNN) method with four hidden layers and 16 output channels, a fully connected layer, and Softmax activation. The data used in this study were taken from MIT-BIH and BIDMC. In this study provides 100% accuracy. Automated CHF disease detection helps medical staff diagnose patients to receive appropriate treatment.Keywords: Electrocardiogram (ECG), Convolutional Neural Network (CNN), Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF)
Survey the bacterial types in breast tissue and CA13-5 for women with breast disease in Babylon province

Breast tumor may be malignant and benign so these tumor and breast diseases are harboring bacteria ,but types of bacteria may differ among breast tumors so the current study was aimed to detect type of bacteria in breast tissue . Specimens( Breast tissue and blood) was collected from 50 female (aged ranged between 14 to 60 ) undergoing breast surgery at AL-Hilla Teaching Hospital and Al-Sadiq Hospital in Babylon Province during period from November 2021 - March 2022 and blood sample also collected from healthy women as control . tissue obtained was combined outside the marginal zone, approximately 5 cm away from the neoplasm. After enucleation, the new tissue was immediately placed in a sterile plane tube or urine cup it contain a normal saline solution and it was cut , homogenized by using a sterile surgical scalpel and wooden sticks within 30 min of collection.2 it was cultivated on blood agar &amp;MacConkey media ,and then on selective media .and Identification for bacteria by microscopic ,biochemical test and confirmed by Vitec 2Compact System . The results appeared the percentage of malignant tumor (17.14%) and benign (82.85%) , The growth percentage (88.57%) while no growth was (11.42%).
APPLICATION OF CLUSTERING ANALYSIS TO DATA DISTRIBUTION OF COVID-19 IN BENGKULU PROVINCE

Bengkulu Province is one of the provinces in Indonesia. Based on the results of the Population Census (SP) in September 2020, carried out by BPS, there were 2,010,670 inhabitants in Bengkulu Province. The area of ​​Bengkulu Province is 19,813 km2, consisting of 10 regencies/cities. The large area and population encourage an effort to anticipate the transmission of COVID-19 that is soaring high in Bengkulu Province. One is by grouping regencies/cities in Bengkulu Province based on several variables that characterize objects using the Clustering method. This study aimed to group districts/cities in Bengkulu Province based on several variables that characterize objects related to the spread of COVID-19 in Bengkulu Province. The method used was the clustering method. The data used in this study was secondary data about the variable of the spread of COVID-19 in Bengkulu Province from January 1, 2021, to May 31, 2021. It is accessed through the official website of the Bengkulu Province government to convey information to the public regarding the increase of COVID-19 Cases in Bengkulu Province. The grouping using the Hierarchical Clustering method obtained the best model as complete linkage, with the number of clusters K = 2 and the K-Means method with K = 2. The results obtained are good because it has relatively tiny variability within the cluster, and the value of variability in both clusters is relatively large.
Experimental interfacing with the IPhone 6s through its PCIe communication bus

One of the issues to perform hardware attacks on wired communication protocol is the physical access to the bus. Considering the iPhones, PCIe is used as a communication bus between the SoC and its non-volatile memory. This communication can be targeted by a MitM attack in order to access personal information. In this paper, we propose a physical way of interfacing between the motherboard and the non-volatile memory of an iPhone 6s. This was achieved by reverse-engineering the memory in order to retrieve the footprint and the pinout, defining a protocol to safely extract and re-solder the memory, without altering its content, and multiple tests to validate the approach. The results obtained allow us to consider the fabrication of an interposer in order to realize a real hardware MitM and to exploit a large possibility of attacks such as real-time data analysis, data-replay, shadow-copy, and so on.
MATHEMATICS PRE-SERVICE TEACHERS’ DIGITAL LITERACY AND THEIR READINESS TOWARDS 21ST CENTURY LEARNING: A MIXED METHOD STUDY

The rapid advancement of technology in Indonesia seems not in line with its utilization, especially in the educational field. According to studies, Indonesian teachers are not ready to implement ICT in their classrooms. Furthermore, most elderly teachers are not sensitive enough to technology changes. When it comes to emergency teaching and learning in the pandemic era, teachers as predicted do not have enough capacity to maximize the use of ICT as the only source. This study aims to find out: (1) if the lack of digital literacy skills already happened in college, and (2) pre-service teachers' readiness to face 21st century education. As the result, this study could not prove that the lack of digital skills occurs in college. The digital literacy skills of pre-service are good (77,6%) and they have received multimedia learning subject from university. Related to their readiness toward 21st century learning, most of them claimed not yet ready because of the lack of skills and knowledge in implementing ICT and because of the lack of ICT facility support.