Reviewerly/exHarmony huggingface dataset:
Multi-scale contrastive learning with attention for histopathology image classification

Whole slide images (WSIs) in histopathology naturally provide multi-scale information. Several previous studies have shown that leveraging such multi-scale information in histopathology image analysis is effective to improve performance. Here, we propose making use of recent advances in contrastive learning and self-attention techniques in multi-scale WSIs for cancer subtype classification using weak labels. The proposed method is based on a Siamese architecture to share a common encoder network for images on different scales to reduce the model size and training cost. In addition, we propose a variant of the self-attention module specifically designed for multi-scale WSIs so that the network can focus on important textural features across different image scales. We assess the efficacy of the proposed method via an ablation study on a real intrahepatic cholangiocarcinoma dataset. The result confirms that our method outperforms conventional multi-scale models with fewer model parameters.
A transformer-based computational approach for H&amp;E to multiplexed immunohistochemistry stain translation

We established a translation dataset that contains pixel-wise registered H&E and multiplexed immunohistochemistry (mIHC) staining images. Deep learning models were trained to translate H&E inputs into their corresponding mIHC image versions. Comparison experiments have been carried out to validate the translation performance between TransUNet, U-Net, and pix2pix models. We also compared the impact of different Losses on model performances. The TransUNet model could achieve 0.862 SSIM score for L1 loss and 0.805 for L2 loss, surpassing U-Net and pix2pix model in both settings. This demonstrates the potential benefit of the Transformer module in stain translation tasks.
Hands-Free Physical Human-Robot Interaction and Testing for Navigating a Virtual Ballbot

&lt;p&gt;This is a submitted (IEEE RO-MAN 2023) draft of a paper on the testing of a hands-free human-robot interaction for navigating a ballbot in a virtual environment. In this study, able-bodied users and manual wheelchair users controlled a virtual ballbot using a hands-free (HF) lean-to-steer control concept that uses torso motions. A custom sensor system (i.e., Torso-dynamics Estimation System (TES)) was utilized to measure and convert the dynamics of the rider’s torso motions into commands to provide HF control of the robot. A simulation study was conducted to explore the efficacy of the HF controller compared to a traditional joystick (JS) controller, and whether there were differences in performance by manual wheelchair users (mWCUs), who may have reduced torso function, compared to able-bodied users (ABUs). Twenty test subjects (10 mWCUs + 10 ABUs) used the subject-specific adjusted TES while wearing a virtual reality headset and were asked to navigate a virtual human rider on the ballbot through obstacle courses replicating seven indoor environment zones. &lt;/p&gt;
Noise Driven Coupled Nonlinear Systems: A “Dynamical Multilayer Perceptron” Demonstrating XOR Functionality

&lt;p&gt;Coupled dynamical systems, especially those comprised of hysteretic elements, possess remarkable behavioral similarities to some aspects of neural activity in living systems. While some features of this similarity have been explored, the results have not yet been leveraged to improve machine learning techniques or artificial neural networks. In this work, a dynamical system of coupled overdamped bistable elements subject to a noise floor assists in information transfer. With the noise allowing the system elements to switch randomly between their stable states, the system response is quantified via a steady state probability distribution. A simple five element realization of this network is used to produce the analog equivalent of an XOR gate, an “XOR transform”, through the proper choice of coupling coefficients and controllable external biases. The demonstration of the XOR transform indicates the ability of the system to synthesize more general functions, a necessary prerequisite for machine learning and computing applications. Finally, a silicon implementation is proposed and simulated using a verified process model that could be fabricated to generate a working analog computing system.&lt;/p&gt;
Noise Driven Coupled Dynamic Systems–Towards Low Power Pattern Recognition and Machine Learning

&lt;p&gt;Coupled dynamical systems, especially those comprised of hysteretic elements, possess remarkable behavioral similarities to some aspects of neural activity in living systems. While some features of this similarity have been explored, the results have not yet been leveraged to improve machine learning techniques or artificial neural networks. In this work, a dynamical system of coupled overdamped bistable elements subject to a noise floor assists in information transfer. With the noise allowing the system elements to switch randomly between their stable states, the system response is quantified via a steady state probability distribution. A simple five element realization of this network is used to produce the analog equivalent of an XOR gate, an “XOR transform”, through the proper choice of coupling coefficients and controllable external biases. The demonstration of the XOR transform indicates the ability of the system to synthesize more general functions, a necessary prerequisite for machine learning and computing applications. Finally, a silicon implementation is proposed and simulated using a verified process model that could be fabricated to generate a working analog computing system.&lt;/p&gt;
Synthetic Tabular Data Based on Generative Adversarial Networks in Health Care: Generation and Validation Using the Divide-and-Conquer Strategy (Preprint)

<sec> <title>BACKGROUND</title> Synthetic data generation (SDG) based on generative adversarial networks (GANs) is used in health care, but research on preserving data with logical relationships with synthetic tabular data (STD) remains challenging. Filtering methods for SDG can lead to the loss of important information. </sec> <sec> <title>OBJECTIVE</title> This study proposed a divide-and-conquer (DC) method to generate STD based on the GAN algorithm, while preserving data with logical relationships. </sec> <sec> <title>METHODS</title> The proposed method was evaluated on data from the Korea Association for Lung Cancer Registry (KALC-R) and 2 benchmark data sets (breast cancer and diabetes). The DC-based SDG strategy comprises 3 steps: (1) We used 2 different partitioning methods (the class-specific criterion distinguished between survival and death groups, while the Cramer V criterion identified the highest correlation between columns in the original data); (2) the entire data set was divided into a number of subsets, which were then used as input for the conditional tabular generative adversarial network and the copula generative adversarial network to generate synthetic data; and (3) the generated synthetic data were consolidated into a single entity. For validation, we compared DC-based SDG and conditional sampling (CS)–based SDG through the performances of machine learning models. In addition, we generated imbalanced and balanced synthetic data for each of the 3 data sets and compared their performance using 4 classifiers: decision tree (DT), random forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LGBM) models. </sec> <sec> <title>RESULTS</title> The synthetic data of the 3 diseases (non–small cell lung cancer [NSCLC], breast cancer, and diabetes) generated by our proposed model outperformed the 4 classifiers (DT, RF, XGBoost, and LGBM). The CS- versus DC-based model performances were compared using the mean area under the curve (SD) values: 74.87 (SD 0.77) versus 63.87 (SD 2.02) for NSCLC, 73.31 (SD 1.11) versus 67.96 (SD 2.15) for breast cancer, and 61.57 (SD 0.09) versus 60.08 (SD 0.17) for diabetes (DT); 85.61 (SD 0.29) versus 79.01 (SD 1.20) for NSCLC, 78.05 (SD 1.59) versus 73.48 (SD 4.73) for breast cancer, and 59.98 (SD 0.24) versus 58.55 (SD 0.17) for diabetes (RF); 85.20 (SD 0.82) versus 76.42 (SD 0.93) for NSCLC, 77.86 (SD 2.27) versus 68.32 (SD 2.37) for breast cancer, and 60.18 (SD 0.20) versus 58.98 (SD 0.29) for diabetes (XGBoost); and 85.14 (SD 0.77) versus 77.62 (SD 1.85) for NSCLC, 78.16 (SD 1.52) versus 70.02 (SD 2.17) for breast cancer, and 61.75 (SD 0.13) versus 61.12 (SD 0.23) for diabetes (LGBM). In addition, we found that balanced synthetic data performed better. </sec> <sec> <title>CONCLUSIONS</title> This study is the first attempt to generate and validate STD based on a DC approach and shows improved performance using STD. The necessity for balanced SDG was also demonstrated. </sec>
Identification of molecular cell type of breast cancer on digital histopathology images using deep learning and multiplexed fluorescence imaging

ER, PR (estrogen, progesterone receptor), and HER2 (human epidermal growth factor receptor 2) status are assessed using immunohistochemistry and reported in standard clinical workflows as they provide valuable information to help treatment planning. The protein Ki67 has also been suggested as a prognostic biomarker but is not routinely evaluated clinically due to insufficient quality assurance. The routine pathological practice usually relies on small biopsies, such that the reduction in consumption is necessary to save materials for special assays. For this purpose, we developed and validated an automatic system for segmenting and identifying the (ER, PR, HER2, Ki67) positive cells from hæmatoxylin and eosin (H&E) stained tissue sections using multiplexed immunofluorescence (MxIF) images at cellular level as a reference standard. In this study, we used 100 tissue-microarray cores sampled from 56 cases of invasive breast cancer. For ER, we extracted cell nucleus images (HoverNet) from the H&E images and assigned each cell nucleus as ER positive vs. negative based on the corresponding MxIF signals (whole cell segmentation with DeepCSeg) upon H&E to MxIF image registration. We trained a Res-Net 18 and validated the model on a separate test-set for classifying the cells as positive vs. negative for ER, and performed the same experiment for the other three markers. We obtained area-under-the- receiver-operating-characteristic-curves (AUCs) of 0.82 (ER), 0.85 (PR), 0.75 (HER2), 0.82 (Ki67) respectively. Our study demonstrates the feasibility of using machine learning to identify molecular status at cellular level directly from the H&E slides.
Designing a Framework for Conversational Interfaces

Wherever possible, business logic should be described by code rather than training data. This keeps our system's behavior principled, predictable, and easy to change. Our approach to conversational interfaces allows them to be built much like any other application, using familiar tools, conventions, and processes, while still taking advantage of cutting-edge machine-learning techniques.
Monte Carlo Simulations on 2D LRF Based People Tracking using Interactive Multiple Model Probabilistic Data Association Filter Tracker

Consistency of tracking filter such as Interactive Multiple Model Probabilistic Data Association Filter (IMMPDAF) is the most important factor in targets tracking. Inaccurate tracking capability will lead to poor tracking performance when dealing with multiple people's interactions and occlusions. In order to validate the consistency, Normalized Estimation Error Squared (NEES) and Normalized Innovation Squared (NIS) were evaluated and tested using Monte Carlo experiments for 50 runs. These simulations has proven that the tracker is conditionally consistent on targets tracking despite the fact that it has difficulties on handling occlusions and maneuvering people. NEES requires ground truth of tracking data and predicted data, whereas NIS requires observation and predicted data for Monte Carlo simulations. In NEES simulations, the result emphasizes that state estimation errors of IMMPDAF tracker are inconsistent with filter-calculated covariances especially when dealing with sudden turns in zig-zag motion where quite a large number of points fall outside 95\% probability region. In NIS simulations, IMMPDAF tracker is confirmed to have difficulties to handle multiple targets with a short period of occlusion although a small number of points falls outside of 95\% probability region. Filter tracker is considered mismatched when dealing with zig-zag motion; however, it deemed to be optimistic when dealing with occlusions. As a result, the IMMPDAF tracker has limited capability in monitoring sharp turns under occlusion conditions, although it is acceptable when dealing with occlusions only.
Gaussian Processes-BayesFilters with Non-Parametric Data Optimization for Efficient 2D LiDAR Based People Tracking

A model for expressing and describing human motion patterns must be able to improve tracking accuracy. However, Conventional Bayesian Filters such as Kalman Filter (KF) and Particle Filter (PF) are vulnerable to failure when dealing with highly maneuverable targets and long-term occlusions. Gaussian Processes (GP) is then used to adapt human motion patterns and integrate the model with Bayesian Filters. In GP, all samples in training phase need to be included and periodically, new samples will be added into training samples whenever it is available. Larger amount of data will increase the computational time to produce the learned GP models due to data redundancies. As a result, Mutual Information (MI) based technique with Mahalanobis Distance (MD) is developed to keep only the informative data. This method is used to process data which is collected by a robot equipped with a LiDAR. Experiments have demonstrated that reducing data does not raise Average Root Mean Square Error (ARMSE) considerably. EKF, PF, GP-EKF and GP-PF are utilised as a tool for tracking people and all techniques have been analyzed in order to distinguish which method is more efficient. The performance of GP-EKF and GP-PF are then compared to EKF and PF where it proved that GP-BayesFilters performs better than Conventional Bayesian Filters. The proposed approach has reduced data points up to more than 90\% while keeping the ARMSE within acceptable limits. This data optimization technique will save computational time especially when deal with periodically accumulative data sets. Comparing on four tracking methods, both GP-PF and GP-EKF have achieved higher tracking performance when dealing with highly maneuverable targets and occlusions.
Analisa Kepatuhan Minum Obat Anti Tuberculosis: Perbandingan Penggunaan Layanan Pesan Singkat dengan Pengawas Minum Obat

The Directly Observed Treatment Short-course (DOTS) strategy is the best strategy recommendation for pulmonary tuberculosis (TB) intervention at the international level and has been implemented in Indonesia. However, the bored may also occur among Medication Supervisor (PMO) resulted medication non-adherence to the TB patients. This study aims to determine differences in the level of patient medication adherence through the use of Short Message Service (SMS) with PMO at Puskesmas Dinoyo, Malang. The post-test only control group design was used on a population of pulmonary TB patients who were registered at the Puskemas Dinoyo Malang during July to December 2012 with a total of 33 patients. The sample were recruited using simple random sampling. The Self Reported Questionnaire and observation form used as study instrument. Fisher exact test was used in hypothesis testing. The results showed non- significant difference between the drug adherence among respondents who received SMS and respondents who received reminders from PMO only (p-value 0.50 &gt; 0.05). Providing reminders at the same time every day, either by SMS or PMO only, is equally beneficial in the success of treatment. Together will increase compliance and reduce the negligence to take medication. Longitudinal design is recommended for further study.
CLOUDMOAP: Multilayer Security by Online Encryption and Auditing Process in Cloud

Each image is divided into squares by this module, from which the user selects one to serve as the pass-square. A 7 x 11 grid is used to split a picture. The password space increases as the discretized image size decreases. Yet, an excessively focused division might make it harder to operate the user interface on palm-sized mobile devices and cause problems with object detection. As 60 pixels is the optimal size for precisely selecting certain items on touch displays, a division was set at 60-pixel intervals in both the horizontal and vertical axes in our solution. Index Terms— Online Encryption, Hash Code, Shoulder Surfing, Randomized Image Selection, Cipher Key, Data Security.
OBJECT DETECTION AND ESTIMATING THE DISTANCE BETWEEN DETECTED OBJECTS USING DEEP LEARNING ALGORITHMS

Object detection is a computer vision technique that allows us to identify and locate objects in an image. Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Object detection involves a bounding box around each object in the image and distance should be estimated between objects. It is used to identify the objects in a given image like person, bicycle, dog etc. With this kind of identification and localization, object detection can be used to count objects in a scene and determine and track their precise locations, all while accurately labeling them. In our project we are using the COCO dataset and YOLOv3 using a deep learning method. Key Words: Deep learning-YOLO, a monocular camera, distance calculation, and object identification
Effects of schooling examined through medium of instruction on cognitive, behavioural, and academic achievement of students

This research compares the cognitive, behavioural, and academic achievements of students in Odia and English medium schools to determine the impact of schooling as examined by the medium of instruction.Thirty Grade-VII students from both institutions make up the sample.They are evaluated using the MFFT, RPM, Depression Questionnaire, and TCBC, which are provided to the instructor to determine whether any behavioural issues exist among the students.The data gathered from the tests were used to determine the mean and standard deviation, and One-way ANOVA was then used to assess for significance.The ANOVA table's findings indicate that the results are not significant.Thus, there is no significant difference between the cognitive, behavioural, and academic achievement of Odia and English medium students, demonstrating that the medium of instruction is not a strong predictor of children's competencies.
A New Computer Science Academic Word List

This corpus-based vocabulary study aimed to develop a new computer science academic word list across ten sub-disciplines of computer science defined by Association for Computing Machinery (hereafter ACM). A corpus of Computer Science containing 2,500,990 running words was developed from 300 Computer Science Research Articles (hereafter CSRAC) as a database of this study. Drawing on and combining procedures and methods from Coxhead (2000), Gardner and Davies (2014) and other previous studies, this study developed a New Computer Science Academic Word List (hereafter NCSAWL), containing the most frequently-used computer words in computer research articles from the corpus. The NCSAWL contains 444 words, which accounts for approximately 20.33% of the coverage in the CSRAC, the NCSAWL has a much better coverage of computer English. The result of this study has numerous implications for computer science learners, English teachers, researchers, as well as material writers and course syllabus designers. For examples, English computer teachers should focus on teaching learners the most high-frequent words which have a dispersed coverage and have special meaning and use in the discipline of computer science. Teachers could also raise the awareness of learners that some words have different meanings and uses in general English. The material designers for English for academic and special purposes could incorporate the NCSAWL vocabulary into their academic reading and writing materials for computer science students. Researchers and English language teachers who are interested in expanding their computer science academic vocabulary could also use this NCSAWL.
Investigating ESL Learners’ Perception and Problem towards Artificial Intelligence (AI) -Assisted English Language Learning and Teaching

The contemporary language learning strategy, Artificial Intelligence (AI) Assisted Language Learning (AI-ALL), incorporates AI-powered applications to support learners' learning activities. Many scholars have been experimenting with AI applications concerning activities relevant to education. The major objectives of this study are 1) The ESL learners' perspectives concerning AI-assisted English language learning and teaching; 2). ESL learners’ problems concerning artificial AI-assisted English language learning and teaching. The present investigation employed a quantitative methodology utilizing survey instruments to accumulate distinct information from 81 engineering stream students including essential primary research objects. A survey with a 5-point Likert Scale was administered to collect the data. According to the study, most of the students had favorable perceptions toward using AI-powered tools, particularly while learning English. The major problem is the lack of quality in AI-powered language-learning apps on smartphones. However, it is envisaged that AI-powered apps in language learning would be deployed as one of the instructional media that might help learners learn English as a Second Language efficiently. The present study recommends further research to investigate thoroughly how experienced language instructors use AI-powered applications in their classrooms to build best practices for utilizing AI in teaching and learning in ESL environments.
ANALISIS PERAN ORANG TUA TERHADAP MOTIVASI BELAJAR PESERTA DIDIK KELAS II SD NEGERI PANGGUNG KIDUL SEMARANG

The background that drives this research is that there are some students who have low reading, writing, and numeracy skills. This has resulted in some students getting learning outcomes scores that are below the minimum completion criteria. The goal to be achieved in this study is to describe and analyze the role of parents and the obstacles experienced by parents to the student learning motivation of second grade of Public Elementary School Panggung Kidul Semarang. This research method is qualitative. Data collection techniques use observation, interviews and documentation. There are 3 stages of data analysis used, namely, data reduction, data presentation and drawing conclusions. The data of this study was obtained through subjects consisting of 10 parents students of second grade, and the teacher of second grade. Then for testing the validity of the data using triangulation. The results showed the role of parents in children's learning motivation, namely 1) Providing learning attention, 2) Guiding and accompanying children, 3) Providing advice and support, 4) providing learning facilities, 5) Providing gifts and punishments. However, some of the parents are also still lacking in providing learning attention. The obstacles experienced by parents in increasing learning motivation are 1) The condition of children who are lazy in learning, 2) The busyness of parents who cannot divide time because of work matters. It can be concluded that parents of grade II students have played a role in providing motivation for children's learning, but in providing motivation for learning there are several obstacles experienced by parents.