Reviewerly/exHarmony huggingface dataset:
Analysis on the Phenomenon of "A Difficult to Find" in the Study Room for Postgraduate Entrance Examination

With the continuous expansion of undergraduate colleges and universities in recent years, the number of undergraduates is increasing, and the number of graduates is also increasing. The employment problem of college students is highly concerned by the society. Most college students find it difficult to get an ideal offer after graduation. So a new way, "postgraduate examination", "examination public", "examination compilation" and other words began to enter people's attention. In recent years, the number of college students taking the entrance exam has increased dramatically, with the number of applicants exceeding 4.74 million in 2022 and expected to reach 4.91 million in 2024. With the improvement of education level and teaching facilities, students' understanding of academics has also been further developed. Although most students choose to take the postgraduate entrance exam due to employment problems, there are still some students who take the entrance exam for academic research. Therefore, with the prevalence of "research craze", the state also began to adopt the policy of expanding the enrollment of graduate students. In addition, most colleges and universities have opened up special classrooms or self-study rooms for their own candidates, and the surge in the number of students has also led to the "one difficult to find" self-study rooms. Therefore, this paper will start from the analysis of the phenomenon of the entrance examination craze, the analysis of the main motivation of students and the impact of the entrance examination craze. The phenomenon of research craze brings deep thought to individuals and society.
PERANCANGAN GAME PLATFORMER “GRAPPLE” PADA PC

"Grapple" is a platformer game designed using Unity and targeted to be played on PC. Sprites and assets used in this game were created with Pixilart. Players can choose between 2 level packs where each level pack consists of 6 levels. The goal in the game "Grapple" is to pass all available obstacles with the help of a grappling gun and get the highest score possible from each played level. Design &amp; Planning carried out using game design methods. Testing is carried out using black box testing.
Fault detection of high-voltage insulator thermal images based on YOLOv5

Abstract In this paper, we propose a defect detection method based on YOLOv5 for thermal images of high-voltage insulators, in which a large number of insulator photographs are taken by a thermal imager, followed by a YOLOv5 model to detect the captured thermal images. The function Meta-ACON is proposed to replace the original activation function. Finally, the extracted features are used for insulator condition identification and defect detection. Experimental findings demonstrate the efficacy of the suggested approach in successfully accomplishing the feature extraction of insulator faults, thus improving the accuracy and efficiency of insulator fault diagnosis.
AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretation

Given the shortage of cytologists, women in low-resource regions had inequitable access to cervical cytology which plays an pivotal role in cervical cancer screening. Emerging studies indicated the potential of AI-assisted system in promoting the implementation of cytology in resource-limited settings. However, there is a deficiency in evaluating the aid of AI in the improvement of cytologists’ work efficiency. This study aimed to evaluate the feasibility of AI in excluding cytology-negative slides and improve the efficiency of slide interpretation. Well-annotated slides were included to develop the classification model that was applied to classify slides in the validation group. Nearly 70% of validation slides were reported as negative by the AI system, and none of these slides were diagnosed as high-grade lesions by expert cytologists. With the aid of AI system, the average of interpretation time for each slide decreased from 3 minutes to 30 seconds. These findings suggested the potential of AI-assisted system in accelerating slide interpretation in the large-scale cervical cancer screening.
Neuromorphic convolution with a spiking DFB-SA laser neuron based on rate coding

We propose a neuromorphic convolution system using a photonic integrated distributed feedback laser with a saturable absorber (DFB-SA) as a photonic spiking neuron. The experiments reveal that the DFB-SA laser can encode different stimulus intensities at different frequencies, similar to biological neurons. Based on this property, optical inputs are encoded into rectangular pulses of varying intensities and injected into the DFB-SA laser, enabling the convolution results to be represented by the firing rate of the photonic spiking neuron. Both experimental and numerical results show that the binary convolution is successfully achieved based on the rate-encoding properties of a single DFB-SA laser neuron. Furthermore, we numerically predict 4-channel quadratic convolution and accomplish MNIST handwritten digit classification using a spiking DFB-SA laser neuron model with rate coding. This work provides a novel approach for convolution computation, indicating the potential of integrating DFB-SA laser into future photonics spiking neural networks.
Profinite lambda-terms and parametricity

Combining ideas coming from Stone duality and Reynolds parametricity, we formulate in a clean and principled way a notion of profinite lambda-term which, we show, generalizes at every type the traditional notion of profinite word coming from automata theory. We start by defining the Stone space of profinite lambda-terms as a projective limit of finite sets of usual lambda-terms, considered modulo a notion of equivalence based on the finite standard model. One main contribution of the paper is to establish that, somewhat surprisingly, the resulting notion of profinite lambda-term coming from Stone duality lives in perfect harmony with the principles of Reynolds parametricity. In addition, we show that the notion of profinite lambda-term is compositional by constructing a cartesian closed category of profinite lambda-terms, and we establish that the embedding from lambda-terms modulo beta-eta-conversion to profinite lambda-terms is faithful using Statman's finite completeness theorem. Finally, we prove that the traditional Church encoding of finite words into lambda-terms can be extended to profinite words, and leads to a homeomorphism between the space of profinite words and the space of profinite lambda-terms of the corresponding Church type.
Underwater image re-enhancement with Simplest Color Balance, CLAHE Algorithm, De-noising Convolutional Neural Network and Unsharp Masking

Restoring Underwater images is quiet challenging task. In an attempt to resolve this, a proposal of fusion algorithm that consists of a Simple Color Balancing algorithm, Contrast Limited Adaptive Histogram Equalization, De-noising Convolutional Neural Network, and Unsharp Masking. To minimize the impact of color cast in underwater images, use of a simple color balance algorithm to stretch the r, g, and b values to their maximum range [0,255] to get a similar distribution of rgb channels in the histogram. To improve the perceptibility of underwater images, increase of the contrast of underwater images using the CLAHE algorithm. De-noising Convolutional Neural Network is used to remove the noise present in underwater images. An unsharp masking approach is implemented to eliminate the blurriness in output denoising images and sharpen the output images. PSNR values show that the resulting images of the proposed system have the best image quality. Some gave the best results based on their SSIM values compared to the MSRCR model. The UCIQE values of the resulting images dominate the results of other models. The efficiency of the fusion algorithm is proved by the results obtained and then compared with the results of current models.
PEMBELAJARAN KRIYA TEKSTIL MENGGUNAKAN KAIN PERCA BATIK DI SMK NEGERI 8 PADANG

The research entitled “Learning Textile Crafts Using Batik Patchwork at SMK Negeri 8 Padang” is a descriptive study of how the creativity of students and students in developing textile crafts using batik patchwork and the factors causing the low creativity of students and students. This research is a qualitative research where the researcher acts as an instrument that goes directly to the field using data collection methods in the form of observation, interviews, documentation and literature study. The results of this study indicate that the process of making textile crafts using batik patchwork in the form of functional objects is carried out by students and students in various ways. The process or steps for making batik patchwork are: 1) preparing the materials and tools used such as batik patchwork, thread, sewing machine, sewing needle, scissors, pen and paper; 2) Making designs; 3) Selection of batik patchwork; and 4) Making works according to the design. The batik patchwork created in the form of a one-layer mask, a two-layer mask, a tote bag and a sling bag. Based on the results of this study, the factors causing the low creativity of students are influenced by internal factors and external factors.
Center-Focused Affinity Loss for Class Imbalance Histology Image Classification

Early-stage cancer diagnosis potentially improves the chances of survival for many cancer patients worldwide. Manual examination of Whole Slide Images (WSIs) is a time-consuming task for analyzing tumor-microenvironment. To overcome this limitation, the conjunction of deep learning with computational pathology has been proposed to assist pathologists in efficiently prognosing the cancerous spread. Nevertheless, the existing deep learning methods are ill-equipped to handle fine-grained histopathology datasets. This is because these models are constrained via conventional softmax loss function, which cannot expose them to learn distinct representational embeddings of the similarly textured WSIs containing an imbalanced data distribution. To address this problem, we propose a novel center-focused affinity loss (CFAL) function that exhibits 1) constructing uniformly distributed class prototypes in the feature space, 2) penalizing difficult samples, 3) minimizing intra-class variations, and 4) placing greater emphasis on learning minority class features. We evaluated the performance of the proposed CFAL loss function on two publicly available breast and colon cancer datasets having varying levels of imbalanced classes. The proposed CFAL function shows better discrimination abilities as compared to the popular loss functions such as ArcFace, CosFace, and Focal loss. Moreover, it outperforms several SOTA methods for histology image classification across both datasets. The paper code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Taslim-M/CFAL-max-margin-keras</uri> .
1002 km twin-field quantum key distribution with finite-key analysis

Abstract Quantum key distribution (QKD) holds the potential to establish secure keys over long distances. The distance of point-to-point QKD secure key distribution is primarily impeded by the transmission loss inherent to the channel. In the quest to realize a large-scale quantum network, increasing the QKD distance under current technology is of great research interest. Here we adopt the 3-intensity sending-or-not-sending twin-field QKD (TF-QKD) protocol with the actively-odd-parity-pairing method. The experiment demonstrates the feasibility of secure QKD over a 1002 km fibre channel considering the finite size effect. The secure key rate is $3.11\times 10^{-12}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mn>3.11</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>12</mml:mn> </mml:mrow> </mml:msup> </mml:math> per pulse at this distance. Furthermore, by optimizing parameters for shorter fiber distances, we conducted performance tests on key distribution for fiber lengths ranging from 202 km to 505 km. Notably, the secure key rate for the 202 km, the normal distance between major cities, reached 111.74 kbps.
CAT-Seg: cascaded medical assistive tool integrating residual attention mechanisms and Squeeze-Net for 3D MRI biventricular segmentation

Abstract Cardiac image segmentation is a critical step in the early detection of cardiovascular disease. The segmentation of the biventricular is a prerequisite for evaluating cardiac function in cardiac magnetic resonance imaging (CMRI). In this paper, a cascaded model CAT-Seg is proposed for segmentation of 3D-CMRI volumes. CAT-Seg addresses the problem of biventricular confusion with other regions and localized the region of interest (ROI) to reduce the scope of processing. A modified DeepLabv3+ variant integrating SqueezeNet (SqueezeDeepLabv3+) is proposed as a part of CAT-Seg. SqueezeDeepLabv3+ handles the different shapes of the biventricular through the different cardiac phases, as the biventricular only accounts for small portion of the volume slices. Also, CAT-Seg presents a segmentation approach that integrates attention mechanisms into 3D Residual UNet architecture (3D-ResUNet) called 3D-ARU to improve the segmentation results of the three major structures (left ventricle (LV), Myocardium (Myo), and right ventricle (RV)). The integration of the spatial attention mechanism into ResUNet handles the fuzzy edges of the three structures. The proposed model achieves promising results in training and testing with the Automatic Cardiac Diagnosis Challenge (ACDC 2017) dataset and the external validation using MyoPs. CAT-Seg demonstrates competitive performance with state-of-the-art models. On ACDC 2017, CAT-Seg is able to segment LV, Myo, and RV with an average minimum dice symmetry coefficient (DSC) performance gap of 1.165%, 4.36%, and 3.115% respectively. The average maximum improvement in terms of DSC in segmenting LV, Myo and RV is 4.395%, 6.84% and 7.315% respectively. On MyoPs external validation, CAT-Seg outperformed the state-of-the-art in segmenting LV, Myo, and RV with an average minimum performance gap of 6.13%, 5.44%, and 2.912% respectively.
Application of Computer Vision to Laboratory Experiments

Computer vision has been applied in many fields. We demonstrate some simple applications of computer vision to improve the accuracy of laboratory experiments. The techniques used require only a camera in a mobile phone. Individual frames can be extracted from the video using PYTHON/MATLAB. Further processing of the images can be used to accurately measure the time period of oscillation or rotational time periods. The techniques described can be easily extended to a variety of fields.
Sign Language Recognition for Low Resource Languages Using Few Shot Learning

Sign Language Recognition (SLR) with machine learning is challenging due to the scarcity of data for most low-resource sign languages. Therefore, it is crucial to leverage a few-shot learning strategy for SLR. This research proposes a novel skeleton-based sign language recognition method based on the prototypical network [20] called ProtoSign. Furthermore, we contribute to the field by introducing the first publicly accessible dynamic word-level Sinhala Sign Language (SSL) video dataset comprising 1110 videos over 50 classes. To our knowledge, this is the first publicly available SSL dataset. Our method is evaluated using two low-resource language datasets, including our dataset. The experiments show the results in 95% confidence level for both 5-way and 10-way in 1-shot, 2-shot, and 5-shot settings.
Threshold Key Storage via Fuzzy Extractors With Applications

We propose a novel threshold key storage scheme that relies on biometric fuzzy extractors for the derivation of user keys. This approach builds upon the existing framework of password-protected secret sharing constructions, offering a potential improvement in security and user convenience. Usually in such schemes, users are required to generate and remember passwords, which in practice can introduce vulnerabilities and usability issues. By removing this reliance on traditional passwords, our scheme may enhance the overall security of threshold key storage solutions. Moreover, in situations where password recovery is not a viable option, our mechanism provides a dependable solution for online private key storage, ensuring users can access their keys securely and reliably.
A mixed-methods study on the use of a nongynecological virtual microscopy platform in cytology education

Abstract Objectives Expanding the virtual microscopy (VM) slide collection for nongynecological cytology is important to teaching. In a mixed-methods approach, this study evaluated VM’s performance and user experience to determine its feasibility and usage in an educational setting. Methods From September through October 2022, the students reviewed 44 whole-slide imaged nongynecological slides. The concordance rate with reference diagnosis was compared with that from light microscopy (LM) from 4 months earlier. In addition to assessing the overall performance from VM, imaged urinary cytology’s accuracy was reviewed for both urinary and nonurinary cytology. Finally, the students’ weekly feedback logs were analyzed to gain insights for improving the digital screening experience. Results The overall nongynecological diagnostic accuracy was significant between the 2 screening platforms (P &amp;lt; .001), favoring LM over the VM platform. Light microscopy also performed better than VM in urine cytology cases, with 84.2% concordance against reference diagnosis, compared with 61.1% for the VM platform (P = .03). As for the accuracy of nonurinary cases, its glass slide (LM) agreement with the reference diagnosis was also superior at 84.8%, compared with 58.8% for VM (P = .03). Finally, the overarching theme discerned from reviewing the user logs was concern over image quality, which was mentioned 76 times. Conclusions The VM results were poorer compared with LM in our validation. Its use seems promising, but more focus is needed to improve the VM screening platform.
A BIGRU-Based Stacked Attention Network for Biomedical Named Entity Recognition with Chinese EMRs

Biomedical named entity recognition (BNER) is an effective method to structure the medical text data. It is an important basic task for building the medical application services such as the medical knowledge graphs and the intelligent auxiliary diagnosis systems. Existing medical named entity recognition methods generally leverage the word embedding model to construct text representation, and then integrate multiple semantic understanding models to enhance the semantic understanding ability of the model to achieve high-performance entity recognition. However, in the medical field, there are many professional terms that rarely appear in the general field, which cannot be represented well by the general domain word embedding model. Second, existing approaches typically only focus on the extraction of global semantic features, which generate a loss of local semantic features between characters. Moreover, as the word embedding dimension becomes much higher, the standard single-layer structure fails to fully and deeply extract the global semantic features. We put forward the BIGRU-based Stacked Attention Network (BSAN) model for biomedical named entity recognition. Firstly, we use the large-scale real-world medical electronic medical record (EMR) data to fine-tune BERT to build the proprietary embedding representations of the medical terms. Second, we use the Convolutional Neural Network model to extract semantic features. Finally, a stacked BIGRU is constructed using a multi-layer structure and a novel stacking method. It not only enables comprehensive and in-depth extraction of global semantic features, but also requires less time. Experimentally validated on the real-world datasets in Chinese EMRs, the proposed BSAN model achieves 90.9% performance on F1-values, which is stronger than the BNER performance of other state-of-the-art models.
Incorporating Annotator Uncertainty into Representations of Discourse Relations

Annotation of discourse relations is a known difficult task, especially for non-expert annotators. In this paper, we investigate novice annotators’ uncertainty on the annotation of discourse relations on spoken conversational data. We find that dialogue context (single turn, pair of turns within speaker, and pair of turns across speakers) is a significant predictor of confidence scores. We compute distributed representations of discourse relations from co-occurrence statistics that incorporate information about confidence scores and dialogue context. We perform a hierarchical clustering analysis using these representations and show that weighting discourse relation representations with information about confidence and dialogue context coherently models our annotators’ uncertainty about discourse relation labels.
Towards Topology-Hiding Computation from Oblivious Transfer

Topology-Hiding Computation (THC) enables parties to securely compute a function on an incomplete network without revealing the network topology. It is known that secure computation on a complete network can be based on oblivious transfer (OT), even if a majority of the participating parties are corrupt. In contrast, THC in the dishonest majority setting is only known from assumptions that imply (additively) homomorphic encryption, such as Quadratic Residuosity, Decisional Diffie-Hellman, or Learning With Errors. In this work we move towards closing the gap between MPC and THC by presenting a protocol for THC on general graphs secure against all-but-one semi-honest corruptions from constant-round constant-overhead secure two-party computation. Our protocol is therefore the first to achieve THC on arbitrary networks without relying on assumptions with rich algebraic structure. As a technical tool, we introduce the notion of locally simulatable MPC, which we believe to be of independent interest.
Peningkatan Kematangan Pilihan Karir Peserta Didik Melalui Layanan Informasi Bermuatan Internet

The purpose of this study are: 1) to describe the implementation of Internet-based information services in an effort to increase the consolidation of students' career choices. 2) Obtaining Increased Consolidation of Student Career Choices through Internet-Based Information ServicesThe results of research on Strengthening Career Choices Students in the Pre-Cycle obtained a score of 14 (47%) in the Poor category, in Cycle I obtained an average score of 20 (67%) in the Enough category and in Cycle II obtained an average score of 2 (87%) in the Very Good category.The conclusions of this study are: "1. Internet-Based Information Services can Increase the Consolidation of Student Career Choices. 2. Consolidating Student Career Choices can be increased through Internet-Based Information Services, Consolidation of Student Career Choices in Cycle I obtained a score of 77 (76%) in the Enough category. Cycle II obtained a score of 91 (79%) in the Good category.