Reviewerly/exHarmony huggingface dataset:
Voice Conversion and Hand Gesture Recognition for Aponic People

Aphonia, a condition resulting in the loss of voice, presents significant challenges in interpersonal interactions. This project proposes a dual-pronged approach involving hand gesture recognition and voice conversion techniques to facilitate effective communication for aphonic individuals. The integration of real-time hand gesture recognition provides an alternative means of expressing ideas and emotions. By capturing and translating hand gestures into textual or auditory output, this approach offers a versatile mode of communication. Additionally, advanced voice conversion algorithms are employed to synthesize natural and intelligible speech from typed or selected text. This innovative coupling of technologies empowers aphonic individuals to engage in fluid conversations, fostering improved social interactions and enhancing their overall quality of life. A webcam is used to communicate with deaf and aphonic people. When there are modalities of communication, such as speech, that are unavailable, the human hand is the preferred option. Hand gestures that transmit concepts utilizing diverse forms and finger alignment enable human-machine interaction. The purpose of this work is to develop a hand gesture detection model and translate the results to text and audio formats. The model also responds to user voice commands and displays hand signs from the database.
A Machine Learning-Based Approach to Identify Hand Gestures

Gesture recognition is an essential and a major factor to build user—friendly technologies. Hand gesture is an important step for human computer interaction. This project aims to create a real-time hand gesture recognition system to bridge the gap for all the people who are yet to learn sign language. In the recent years, this technology received much appreciation and attention due to its manifoldness applications and the unique ability to interact with the systems in an efficient manner, through human and computer interaction. In this paper, the initial aim was to pre-process the input image and threshold is used to remove noise from image and smoothen the image. Then, extra unwanted objects or noise from image were removed. After the pre-processing is over, the image is used as input for the feature extraction phase. Thereafter, the extracted features were normalized and matched with the training dataset. The test image was classified as per the nearest neighbour’s class in the training set.
Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-Shot Logical Reasoning Over Text

Logical reasoning task involves diverse types of complex reasoning over text, based on the form of multiple-choice question answering (MCQA). Given the context, question and a set of options as the input, previous methods achieve superior performances on the full-data setting. However, the current benchmark dataset has the ideal assumption that the reasoning type distribution on the train split is close to the test split, which is inconsistent with many real application scenarios. To address it, there remain two problems to be studied: 1) how is the zero-shot capability of the models (train on seen types and test on unseen types)? and 2) how to enhance the perception of reasoning types for the models? For problem 1, we propose a new benchmark for generalized zero-shot logical reasoning, named ZsLR. It includes six splits based on the three type sampling strategies. For problem 2, a type-aware model TaCo is proposed. It utilizes the heuristic input reconstruction and builds a text graph with a global node. Incorporating graph reasoning and contrastive learning, TaCo can improve the type perception in the global representation. Extensive experiments on both the zero-shot and full-data settings prove the superiority of TaCo over the state-of-the-art (SOTA) methods. Also, we experiment and verify the generalization capability of TaCo on other logical reasoning dataset.
Deep Learning-Based Pipeline for Automatic Identification of Intestinal Regenerating Crypts in Mouse Histological Images

A classical approach for evaluating normal tissue radiation response is to count the number of intestinal regenerating crypts in mouse histological images acquired after abdominal radiation. However, manual counting is time-consuming and subject to inter-observer variations. The goal of this study is to build a deep learning-based pipeline for automatically identifying intestinal regenerating crypts to facilitate high-throughput studies.Sixty-six healthy C57BL/6 female mice underwent 16 MeV whole abdominal electron irradiation. The small bowel was collected from each mouse 4 days post-irradiation, and 9 jejunal cross-sections from each were processed together in a single slide. The slides were stained with hematoxylin and eosin (H&E) and subsequently scanned (x20), providing one electronic histological image per mouse. Regenerating crypts, consisting of more than 10 basophilic crypt epithelial cells, were manually identified using point annotations in histological images. The pipeline was built to take the input of the image containing 9 cross sections and automatically identify the regenerating crypts on each cross section. It mainly consists of two components, cross section segmentation using intensity thresholding and morphological operations and crypt identification using a UNet. The dataset was randomly split into 46, 10, and 10 slide images for UNet training, validation, and testing. Each slide image was split into grid tiles with a voxel size of 200 × 200, and 40 × 40 square masks were placed with centers at manual point annotations on tiles with regenerating crypts. 5203/5198 tiles (w/wo crypt mask) were extracted to train UNet by minimizing dice loss. The mask probability map generated by the UNet was post-processed to identify the crypt position. Postprocessing hyperparameters were tuned using the validation dataset. The model accuracy was evaluated using the testing dataset by computing the mean absolute error (MAE) of the crypt number averaged across all cross sections.The number of regenerating crypts on testing cross sections ranges from 1 to 63. The testing cross-section-wise MAE achieved by the platform is 3.5±4.8 crypts. 81.25% of testing cross sections have absolute number differences less than or equal to 5 crypts.Our established deep learning-based pipeline can accurately count the number of regenerating crypts in mouse intestinal histological images. We have integrated it into an online platform that enables automatic crypt identification and allows users to interactively modify auto-identified crypt annotations. The acquired annotations from the platform will be used to finetune the deep learning model to achieve better identification performance.
IIB-MIL: Integrated Instance-Level and Bag-Level Multiple Instances Learning with Label Disambiguation for Pathological Image Analysis

Digital pathology plays a pivotal role in the diagnosis and interpretation of diseases and has drawn increasing attention in modern healthcare. Due to the huge gigapixel-level size and diverse nature of whole-slide images (WSIs), analyzing them through multiple instance learning (MIL) has become a widely-used scheme, which, however, faces the challenges that come with the weakly supervised nature of MIL. Conventional MIL methods mostly either utilized instance-level or bag-level supervision to learn informative representations from WSIs for downstream tasks. In this work, we propose a novel MIL method for pathological image analysis with integrated instance-level and bag-level supervision (termed IIB-MIL). More importantly, to overcome the weakly supervised nature of MIL, we design a label-disambiguation-based instance-level supervision for MIL using Prototypes and Confidence Bank to reduce the impact of noisy labels. Extensive experiments demonstrate that IIB-MIL outperforms state-of-the-art approaches in both benchmarking datasets and addressing the challenging practical clinical task. The code is available at https://github.com/TencentAILabHealthcare/IIB-MIL .
Thyroid Nodule Diagnosis in Dynamic Contrast-Enhanced Ultrasound via Microvessel Infiltration Awareness

Dynamic contrast-enhanced ultrasound (CEUS) video with microbubble contrast agents reflects the microvessel distribution and dynamic microvessel perfusion, and may provide more discriminative information than conventional gray ultrasound (US). Thus, CEUS video has vital clinical value in differentiating between malignant and benign thyroid nodules. In particular, the CEUS video can show numerous neovascularisations around the nodule, which constantly infiltrate the surrounding tissues. Although the infiltrative of microvessel is ambiguous on CEUS video, it causes the tumor size and margin to be larger on CEUS video than on conventional gray US and may promote the diagnosis of thyroid nodules. In this paper, we propose a novel framework to diagnose thyroid nodules based on dynamic CEUS video by considering microvessel infiltration and via segmented confidence mapping assists diagnosis. Specifically, the Temporal Projection Attention (TPA) is proposed to complement and interact with the semantic information of microvessel perfusion from the time dimension of dynamic CEUS. In addition, we employ a group of confidence maps with a series of flexible Sigmoid Alpha Functions (SAF) to aware and describe the infiltrative area of microvessel for enhancing diagnosis. The experimental results on clinical CEUS video data indicate that our approach can attain an diagnostic accuracy of 88.79% for thyroid nodule and perform better than conventional methods. In addition, we also achieve an optimal dice of 85.54% compared to other classical segmentation methods. Therefore, consideration of dynamic microvessel perfusion and infiltrative expansion is helpful for CEUS-based diagnosis and segmentation of thyroid nodules. The datasets and codes will be available.
CARL: Cross-Aligned Representation Learning for Multi-view Lung Cancer Histology Classification

Accurately classifying the histological subtype of non-small cell lung cancer (NSCLC) using computed tomography (CT) images is critical for clinicians in determining the best treatment options for patients. Although recent advances in multi-view approaches have shown promising results, discrepancies between CT images from different views introduce various representations in the feature space, hindering the effective integration of multiple views and thus impeding classification performance. To solve this problem, we propose a novel method called cross-aligned representation learning (CARL) to learn both view-invariant and view-specific representations for more accurate NSCLC histological subtype classification. Specifically, we introduce a cross-view representation alignment learning network which learns effective view-invariant representations in a common subspace to reduce multi-view discrepancies in a discriminability-enforcing way. Additionally, CARL learns view-specific representations as a complement to provide a holistic and disentangled perspective of the multi-view CT images. Experimental results demonstrate that CARL can effectively reduce the multi-view discrepancies and outperform other state-of-the-art NSCLC histological subtype classification methods.
Fourier Test-Time Adaptation with Multi-level Consistency for Robust Classification

Deep classifiers may encounter significant performance degradation when processing unseen testing data from varying centers, vendors, and protocols. Ensuring the robustness of deep models against these domain shifts is crucial for their widespread clinical application. In this study, we propose a novel approach called Fourier Test-time Adaptation (FTTA), which employs a dual-adaptation design to integrate input and model tuning, thereby jointly improving the model robustness. The main idea of FTTA is to build a reliable multi-level consistency measurement of paired inputs for achieving self-correction of prediction. Our contribution is two-fold. First, we encourage consistency in global features and local attention maps between the two transformed images of the same input. Here, the transformation refers to Fourier-based input adaptation, which can transfer one unseen image into source style to reduce the domain gap. Furthermore, we leverage style-interpolated images to enhance the global and local features with learnable parameters, which can smooth the consistency measurement and accelerate convergence. Second, we introduce a regularization technique that utilizes style interpolation consistency in the frequency space to encourage self-consistency in the logit space of the model output. This regularization provides strong self-supervised signals for robustness enhancement. FTTA was extensively validated on three large classification datasets with different modalities and organs. Experimental results show that FTTA is general and outperforms other strong state-of-the-art methods.
Fusion-Based Edge and Color Recovery Using Weighted Near-Infrared Image and Color Transmission Maps for Robust Haze Removal

Various haze removal methods based on the atmospheric scattering model have been presented in recent years. Most methods have targeted strong haze images where light is scattered equally in all color channels. This paper presents a haze removal method using near-infrared (NIR) images for relatively weak haze images. In order to recover the lost edges, the presented method first extracts edges from an appropriately weighted NIR image and fuses it with the color image. By introducing a wavelength-dependent scattering model, our method then estimates the transmission map for each color channel and recovers the color more naturally from the edge-recovered image. Finally, the edge-recovered and the color-recovered images are blended. In this blending process, the regions with high lightness, such as sky and clouds, where unnatural color shifts are likely to occur, are effectively estimated, and the optimal weighting map is obtained. Our qualitative and quantitative evaluations using 59 pairs of color and NIR images demonstrated that our method can recover edges and colors more naturally in weak haze images than conventional methods.
IMPLEMENTASI KLASIFIKASI KEHAMILAN BERESIKO DENGAN METODE NAIVE BAYES PADA PUSKESMAS KELURAHAN MALAKA JAYA

Pregnancy risk is classified into three categories: Low Risk Pregnancy (KRR), High Risk Pregnancy (KRT), and Very High Risk Pregnancy (KRST). Examination data recorded in the Maternal and Child Health (MCH) book at Puskesmas is used to categorize pregnancy risk. Although the maternal mortality rate has decreased, recent surveys show a significant increase. The causes include lack of counseling on obstetric and gynecological health, lack of emergency obstetric and neonatal care, and the use of ineffective traditional medicine. To overcome this problem, it is necessary to improve quality maternal health services. Efforts to accelerate the reduction of maternal mortality rate (MMR) include access to appropriate health services, postpartum care, and family planning services. Data processing uses data mining techniques with the Naïve Bayes classification method, which has high accuracy and short execution time. This study aims to develop a Naïve Bayes classification model in early detection of pregnancy risk, so that it can help diagnose pregnant women with low or high risk.
RANCANG BANGUN APLIKASI REKAM MEDIS ELEKTRONIK DI KLINIK MEDIKA LESTARI JAKARTA PUSAT

The implementation of Electronic Medical Records (RME) in all health service facilities is a digital transformation target in Indonesia. Clinics are health service facilities as the spearhead of primary level health services. However, there are still many clinics whose operations are still paper-based. The purpose of this study is to design an RME Clinical information system that produces precise and accurate data and information and uses RME variable guidelines and metadata to be ready to be integrated into the SATUSEHAT platform. The research design used in this research is qualitative. The system development method uses the System Development Life Cycle (SDLC) with the prototype method. The results of data collection found several problems in the implementation of medical records including paper-based, not standardized and structured diagnoses, medical record card storage was not efficient, report preparation was less effective. Based on this, a web-based clinical RME prototype was built. The resulting prototype makes clinical administration more efficient without a lot of paper/cards, standardized diagnostics, structured and safer data storage, and more effective report preparation because it is done through the system. Reports generated by the prototype can be used as a basis for clinical policies such as providing drug stock based on patient disease reports, as well as routine reports that are reported to the Puskesmas. This Clinical RME prototype can also be the forerunner of a clinical information system that wants to integrate RME with the SATUSEHAT platform.
IMPLEMENTASI WEB SERVICE PADA SISTEM LAPORAN ARUS KAS DENGAN METODE RESTFUL API STUDI KASUS YAYASAN RUMAH DERMAWAN INDONESIA

Rumah Dermawan Indonesia Foundation is a foundation engaged in the humanitarian field in Tangerang. In its activities, the Foundation encounters a problem in recording cash flow, because incoming and outgoing cash flows are still recorded on only one platform, making it less effective. The problem of recording cash flows will be more effective if you use a web service system to facilitate recording of incoming and outgoing cash flows. The model method used to record cash flows on the right web service system for this problem is the REST-Ful API which can be applied to the foundation. Therefore, the researcher proposed an idea that would be applied to the Foundation with the theme of a cash system report using the REST-Ful API method. From the method that the author uses, namely the REST-Ful API method, it shows that it can be used on more than one platform. So that in the future if there is development on other platforms it can be reused. Because the output generated from the REST-Ful API is JSON (Javascript Object Notation). To create a REST-Ful API, researchers will use the Golang language program, this is because the language is light when compiling and processing data.
PENERAPAN ERP PADA SISTEM INFORMASI MANAJEMEN PERUSAHAAN DI BIDANG KEUANGAN MENGGUNAKAN APLIKASI ZAHIR ACCOUNTING PADA TOKO ABC

UMKM are one of the economic actors whose support national development. However, some of them experience problems in accounting reporting so it’s difficult to assess business success. ABC Stores are trading sector UMKM in Padang City which still record and process financial data manually so they are prone to errors. Technological developments enable company’s business process optimalization. This research carried out implementation of Zahir Accounting ERP version 6.0 which has simple design, uses Indonesian, and automation of accounting so it’s easy for non-accounting users to understand. Implementation on Toko ABC is applied to process of buying, selling, recording and financial reporting. Research method begins with data collection through observation, interviews, and literature studies. Then data from Toko ABC is implemented in Zahir Accounting. Based on the implementation, Zahir Accounting can be a solution for UMKM in recording and reporting finances because integrated system between business processes and financial reports.
NOSpcimen: A First Approach to Unsupervised Discarding of Empty Photo Trap Images

A key tool in wildlife conservation is the observation and monitoring of wildlife using photo-trapping cameras. Every year, thousands of cameras around the world take millions of images. A large proportion of these are empty – they do not show any animals. Sorting out these blank images requires considerable effort from biologists, who spend hours on the task. It is therefore of particular interest to automate this task. So far, systems have been proposed which are based on the use of supervised learning models. In order to learn, these systems require the annotation of images to indicate where animals are located within them. NOSpcimen (NOn-SuPervised disCardIng of eMpty images based on autoENcoders) system takes a different approach. It relies on unsupervised learning mechanisms. Thus, no prior annotation work is required to automate the process of discarding empty images.
Integrating State-of-the-Art Face Recognition and Anti-Spoofing Techniques into Enterprise Information Systems

Face Recognition Technology and Face Anti-Spoofing became a necessity during the Covid 19 pandemic, Monkeypox Virus etc. In the current era, the use of contactless technology has become crucial and highly beneficial for individuals. Vietnam is experiencing a significant digital transformation across various sectors including culture, education, tourism, finance, industry, and entertainment. However, most Enterprise Information System institutions in Vietnam lack facial recognition. To address this issue, we have undertaken research to devise a secure anti-spoofing method and determine an effective approach for face recognition processing. Our aim is to develop a comprehensive solution that can be implemented to establish a complete system that we researched and assessed at each stage. To construct a Facial Recognition application, we implemented a Convolutional Neural Network (CNN) as a core to recognize faces in real-time. To identify whether the faces are genuine or counterfeit, we utilized Landmark68 during the anti-spoofing phase. We applied our findings and developed an application AILib during the Covid-19 outbreak, when it was challenging for people to physically visit and login with their IDs at the counter. People can now login without physically being there by logging in using their faces on the AILib. According to the findings of our research, the system functions satisfactorily, with an ideal level of accuracy of 98.42%. Furthermore, we discovered that the optimal threshold value for identifying Asian faces in our face recognition test was determined to be 0.4, while varying threshold values were determined for different face types. For anti-spoofing, during the facial anti-spoofing test, the best threshold value for left, right and front was d < −50, d < −150 and d > −50 respectively, and the right value is d > −50 and in comparison, with state-of-the-art methods is pretty good. The program has a high level of practicality, significantly advancing the groundbreaking application of artificial intelligence to enhance people’s quality of life and safety.
Prototype of Automatic Tractor Control Navigation System Using ESP 32 Microcontroller

A prototype of an automatic tractor control navigation system using a microcontroller has been designed. This study aims to design a prototype of a tractor control navigation system automatically. The design begins with initializing ESP 32, followed by reading GPS (Ublox Neo M8N) and compass (QMC5883L). After that, waiting for commands from the wifi network, followed by reading waypoints, moving the motor, then the motor will drive the tractor prototype. The tests carried out included GPS and compass testing, control response testing, and waypoint navigation testing. The test results show that the accuracy of the coordinates of the GPS UBlox Neo M8N is 0.04 m from the Android smartphone GPS, the QMC5883L compass test shows the difference between the analog compass and the android smartphone compass by 0.4 degrees and 1.0 degrees. Testing the navigation system using the waypoint method, it was found that it was able to map an area or location with a distance of less than 5 meters in following the waypoint route and provide accurate coordinate information to the server during the trip as a source of map information.