Reviewerly/exHarmony huggingface dataset:
Zoish: A Novel Feature Selection Approach Leveraging Shapley Additive Values for Machine Learning Applications in Healthcare

In the intricate landscape of healthcare analytics, effective feature selection is a prerequisite for generating robust predictive models, especially given the common challenges of sample sizes and potential biases. Zoish uniquely addresses these issues by employing Shapley additive values-an idea rooted in cooperative game theory-to enable both transparent and automated feature selection. Unlike existing tools, Zoish is versatile, designed to seamlessly integrate with an array of machine learning libraries including scikit-learn, XGBoost, CatBoost, and imbalanced-learn.The distinct advantage of Zoish lies in its dual algorithmic approach for calculating Shapley values, allowing it to efficiently manage both large and small datasets. This adaptability renders it exceptionally suitable for a wide spectrum of healthcare-related tasks. The tool also places a strong emphasis on interpretability, providing comprehensive visualizations for analyzed features. Its customizable settings offer users fine-grained control over feature selection, thus optimizing for specific predictive objectives.This manuscript elucidates the mathematical framework underpinning Zoish and how it uniquely combines local and global feature selection into a single, streamlined process. To validate Zoish's efficiency and adaptability, we present case studies in breast cancer prediction and Montreal Cognitive Assessment (MoCA) prediction in Parkinson's disease, along with evaluations on 300 synthetic datasets. These applications underscore Zoish's unparalleled performance in diverse healthcare contexts and against its counterparts.
A Zero-Shot Image Enhancement Method Based on Human Skin Tone Reference

In this work, we introduce a zero-shot method for color enhancement in images containing people. This method is based on photo edition techniques used by professional editors, where these professionals adjust the RGB curves, which affects the image colors globally. The hypothesis adopted in this investigation is that, when there are people present in an image, they become the most important element of the scene, so one of the main aspects of color adjustment is to make their skin tones look more realistic and natural, which should please most of the viewers. The proposed method employs a pipeline of classical algorithms of computer vision to mimic the process applied by professional photo editors. Experiments performed on the Adobe-MIT 5k dataset, a widely used dataset on image enhancement, show that our approach has similar performance compared to supervised data-driven state-of-art methods.
Digital Asset Ownership based on Blockchain: A Literature Review

In the digital age, assets are becoming increasingly prevalent worldwide as they become more digitised and virtual. It is important to understand how to identify the provenance of digital assets. Digital asset ownership encompasses the rights and responsibilities of owning and managing digital assets, such as digital currencies, digital artwork, or virtual properties. Proof of ownership of digital assets is also becoming increasingly important as digital assets become more important. With the advent of blockchain technology, establishing proof of ownership and transferring the ownership of digital assets has become possible, which makes it easier for investors to trade them. Although blockchain has contributed to proving the ownership of digital assets where trust and transparency are required, there have been many efforts to apply it as it is new and is still in the experimentation phase. As few comprehensive studies address the issue of proving digital asset ownership using blockchain technology, this paper highlights and addresses this. This paper aims to fill this gap by reviewing the relevant literature on applying proof of ownership of digital assets to blockchain technology.
Using Support Reading to raise Reading Fluency in a Grade 9 class taking EFL – An Action Research

This action research explores the use of Supported Reading (SR) to improve the reading proficiency of Grade 9 English as a Foreign Language (EFL) students in an Indonesian context. A class of 16 students were involved in this study conducted over 3 weeks. The intervention addressed the students' ability and reluctance to read or speak in English through a structured SR routine, incorporating various reading practices. The research questions focused on the current state of reading proficiency and the effectiveness of SR as an intervention. Baseline and post-intervention reading tests were conducted, and statistical analysis indicated a significant improvement in students' reading scores. The methodology incorporated elements of language testing, using a reading fluency test with a designed rubric. The intervention included vocal warm-ups, silent reading, word attack drills, echo reading, choral reading, and pair/individual reading activities. The results showed a significant increase in fluency, rhythm, expression, and pronunciation skills among the students. Findings suggest that SR, typically considered for lower grades, is a viable intervention even at the upper grades, serving as a form of spiral progression. The study affirms that SR is relevant and useful for EFL teaching and learning. Recommendations include integrating SR into reading instructions for students and in teacher professional development programs. While SR is commonly used in lower primary for reading fluency, educators facing similar challenges in EFL contexts should consider the use of SR to improve reading proficiency and motivation among students in the upper grades.
Chinese Comprehensive Language Knowledge Base

Natural language processing (NLP) aims to enable computers to understand human language and, moreover, to interact with humans to obtain information and knowledge more efficiently and more effectively. To this end, relevant resources—knowledge bases—can provide assistance and significant support to applications such as machine translation, information extraction, text processing, human-computer dialogue, and even language education. This paper will introduce the rationale for and the process of constructing the Comprehensive Language Knowledge Base (CLKB) by the Institute of Computational Linguistics at Peking University (ICL/PKU), as well as illustrate its importance as the infrastructure for Chinese information processing (CIP) in various domains.
Sense Tagging Unknown Chinese Words with Word Embedding

This chapter will present our proposed new model to predict the senses of unknown Chinese words using a combination of word embedding, part-of-speech (POS) filtering, and suffix filtering. The model first utilized the external feature of contexts to find synonyms of unknown words by training the contexts with word embedding and then detected the semantic categories of these synonyms in Tongyici Cilin, a Chinese thesaurus. Internal features such as POS and suffixes were used to filter out the semantic categories that were inconsistent with the unknown words in terms of POS and suffixes. The model then used voting to select the best sense category. Our experiments showed that the model based on a combination of external features and internal features achieved good precision in sense tagging.
Speech Recognition and Text-to-Speech Synthesis

Automatic speech recognition (ASR) and text-to-speech (TTS) synthesis are two very important modules in human-computer communication. With the development of deep learning, the performance of ASR and TTS has improved significantly. In this chapter, widely used deep models will be introduced, including restricted Boltzmann machines (RBMs), deep belief networks (DBNs) and deep neural networks (DNNs), recurrent neural networks (RNNs), and long short-term memory recurrent neural networks (LSTM-RNNs), as well as their applications in ASR and TTS. The experimental results suggest that the accuracy of ASR and the speech quality of TTS can be improved.
SMLP4Rec: An Efficient all-MLP Architecture for Sequential Recommendations

Self-attention models have achieved the state-of-the-art performance in sequential recommender systems by capturing the sequential dependencies among user-item interactions. However, they rely on adding positional embeddings to the item sequence to retain the sequential information, which may break the semantics of item embeddings due to the heterogeneity between these two types of embeddings. In addition, most existing works assume that such dependencies exist solely in the item embeddings, but neglect their existence among the item features. In our previous study, we proposed a novel sequential recommendation model, i.e., MLP4Rec, based on the recent advances of MLP-Mixer architectures, which is naturally sensitive to the order of items in a sequence because matrix elements related to different positions of a sequence will be given different weights in training. We developed a tri-directional fusion scheme to coherently capture sequential, cross-channel, and cross-feature correlations with linear computational complexity as well as much fewer model parameters than existing self-attention methods. However, the cascading mixer structure, the large number of normalization layers between different mixer layers, and the noise generated by these operations limit the efficiency of information extraction and the effectiveness of MLP4Rec. In this extended version, we propose a novel framework – SMLP4Rec for sequential recommendation to address the aforementioned issues. The new framework changes the flawed cascading structure to a parallel mode, and integrates normalization layers to minimize their impact on the model’s efficiency while maximizing their effectiveness. As a result, the training speed and prediction accuracy of SMLP4Rec are vastly improved in comparison to MLP4Rec. Extensive experimental results demonstrate that the proposed method is significantly superior to the state-of-the-art approaches. The implementation code is available online to ease reproducibility.
On-chip Acceleration of RF Signal Modulation Classification with Short-Time Fourier Transform and Convolutional Neural Network

Automatic Modulation Classification (AMC) is a technique used in wireless communication systems to identify the modulation type of received signals at the receiver. Improving spectrum utilization efficiency is essential for AMC and is carried out by fundamental signal processing methods within the physical layer. Convolutional Neural Networks (CNN) based deep-learning models have recently been employed in AMC systems, demonstrating superior performance. However, the large size of CNN models, floating-point weights, and activations make deploying such systems with limited hardware resources quite complex. In this paper, we propose a hybrid Radio Frequency-based Machine Learning (RFML) model that combines Short-time Fourier Transform and Convolutional Neural Network (STFT-CNN) for AMC. Simulations on RadioML2016.10a demonstrate an average recognition accuracy of 79% for a Signal-to-Noise Ratio of 0dB or higher when using 16-bit fixed-point operations. An on-chip accelerator for the STFT-CNN, designed and synthesized in 28nm CMOS, offers 19 times lower power consumption, 34 times smaller area, 1.3 times higher bandwidth, and 50 times less memory than a time-domain CNN accelerator utilizing 32-bit floating-point operations.
<scp>ChromEDA</scp>: Chromosome classification by ensemble framework based domain adaptation

The precise identification of chromosomal abnormalities based on microscopy images in clinics heavily relies on manual analysis by karyotype experts, which is expensive and labor-intensive. Therefore, there is an urgent need for automatic classification technologies. However, current deep learning-based methods require a large amount of annotated data to achieve satisfactory performance. Additionally, there are domain shifts between datasets obtained from different instruments and clinical agencies, which hinders the utilization of existing public data resources. To overcome these challenges, we propose a novel ensemble learning framework called ChromEDA. This method incorporates soft pseudo-label learning, adversarial learning, and angle classification learning strategies to mitigate the domain shift. To evaluate its performance, we conducted cross-domain classification experiments using both public and private datasets. The results demonstrate that ChromEDA effectively addresses the domain shift issue and outperforms existing methods in cross-domain chromosome classification tasks. RESEARCH HIGHLIGHTS: The research transferred an unsupervised cross-domain algorithm into the chromosome microscopic image processing area, which can support fast and efficient deployment in equipment such as microscopies. To our knowledge, it is the first domain adaption application in chromosome image analysis. The research designed an ensemble learning framework-based chromosome classification model to alleviate the domain shift between different domains. It is a combination of soft pseudo-label learning, adversarial learning, and self-supervised angle recognition, which can significantly reduce the annotation requirements for chromosomal image processing. The results find that for chromosomes sharing the same centromere position category, banding information may contribute more to within-group classification. Thus, banding pattern information is encouraged to be considered for more precise classification of chromosomes.
ThaiSpoof: A Database for Spoof Detection in Thai Language

Many applications and security systems have widely applied automatic speaker verification (ASV). However, these systems are vulnerable to various direct and indirect access attacks, which weakens their authentication capability. The research in spoofed speech detection contributes to enhancing these systems. Unfortunately, the study in spoofing detection is limited to only some languages due to the need for various datasets. This paper focuses on a Thai language dataset for spoof detection. The dataset consists of genuine speech signals and various types of spoofed speech signals. The spoofed speech dataset is generated using text-to-speech tools for the Thai language, synthesis tools, and tools for speech modification. To showcase the utilization of this dataset, we implement a simple spoof detection model based on a convolutional neural network (CNN) taking linear frequency cepstral coefficients (LFCC) as its input. We trained, validated, and tested the model on our dataset referred to as ThaiSpoof. The experimental result shows that the accuracy of model is 93%, and equal error rate (EER) is 6.78%. The result shows that our ThaiSpoof dataset has the potential to develop for helping in spoof detection studies.
A half jaw panoramic stitching method of intraoral endoscopy images based on dental arch arrangement

&lt;abstract&gt; &lt;p&gt;To address the challenges of repetitive and low-texture features in intraoral endoscopic images, a novel methodology for stitching panoramic half jaw images of the oral cavity is proposed. Initially, an enhanced self-attention mechanism guided by Time-Weighting concepts is employed to augment the clustering potential of feature points, thereby increasing the number of matched features. Subsequently, a combination of the Sinkhorn algorithm and Random Sample Consensus (RANSAC) is utilized to maximize the count of matched feature pairs, accurately remove outliers and minimize error. Last, to address the unique spatial alignment among intraoral endoscopic images, a wavelet transform and weighted fusion algorithm based on dental arch arrangement in intraoral endoscopic images have been developed, specifically for use in the fusion stage of intraoral endoscopic images. This enables the local oral images to be precisely positioned along the dental arch, and seamless stitching is achieved through wavelet transformation and a gradual weighted fusion technique. Experimental results demonstrate that this method yields promising outcomes in panoramic stitching tasks for intraoral endoscopic images, achieving a matching accuracy of 84.6% and a recall rate of 78.4% in a dataset with an average overlap of 35%. A novel solution for panoramic stitching of intraoral endoscopic images is provided by this method.&lt;/p&gt; &lt;/abstract&gt;
A Semi-supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images

Throughout the world, breast cancer is one of the leading causes of female death. Recently, deep learning methods are developed to automatically grade breast cancer of histological slides. However, the performance of existing deep learning models is limited due to the lack of large annotated biomedical datasets. One promising way to relieve the annotating burden is to leverage the unannotated datasets to enhance the trained model. In this paper, we first apply active learning method in breast cancer grading, and propose a semi-supervised framework based on expectation maximization (EM) model. The proposed EM approach is based on the collaborative filtering among the annotated and unannotated datasets. The collaborative filtering method effectively extracts useful and credible datasets from the unannotated images. Results of pixel-wise prediction of whole-slide images (WSI) demonstrate that the proposed method not only outperforms state-of-art methods, but also significantly reduces the annotation cost by over 70%.
A Parallel Gannet Optimization Algorithm for Image Segmentation

Gannet optimization algorithm (GOA) is a meta-heuristic algorithm based on habits of gannet proposed by Zhang et al. In this paper, we propose a Gannet optimization algorithm using parallel strategy (PGOA). Since the GOA algorithm has the risk of easily falling into local optimality, the use of the parallel strategy can largely avoid falling into local optimality. Therefore, we use the parallel strategy to improve the GOA algorithm, which greatly improves the performance and efficiency of the algorithm. The improved algorithm is applied to image segmentation, and the processed images are evaluated using PSNR, SSIM, and FSIM as evaluation metrics. The experimental results show that the improved GOA algorithm can achieve higher quality image segmentation compared to other algorithms on image segmentation,
Research on the Innovation of English Teaching in Colleges and Universities under the Background of Internet

Abstract The intelligent development of society has brought new impetus to English teaching in universities, which has led education workers to update their education philosophy and realize the integration and innovation of teaching methods. This paper takes advantage of the Internet’s advantages in blended teaching as the entry point and constructs a blended learning model for English teaching. To verify the innovation and practicability of the model in teaching, the evaluation index system of the blended learning model of English teaching is constructed on the basis of the CIPP model, and the variance maximization idea is used to combine the weights of principal component analysis and entropy weights, which guarantees the scientificity and objectivity of the weights of the evaluation indexes. The blended learning model of teaching was quantitatively analyzed in terms of teaching objectives, programs, processes, and effects using examples. The results show that the blended learning model of English teaching is conducive to improving the level of English teaching in colleges and universities, and its overall evaluation score is 3.59, which is a good level. Each first-level index in the evaluation index system has a score range of 3.2 to 3.7, which is a good level overall. Under the background of the Internet, the introduction of blended teaching can effectively innovate English teaching, and on the basis of focusing on students’ learning behaviors, we should pay attention to the guiding role of teachers so as to boost the quality of English teaching.
Modeling Intra- and Inter-Modal Alignment with Optimal Transport for Visual Dialog

Visual dialog aims to address a sequence of questions by effectively reasoning over both the dialog history and image content. While existing methods primarily focus on devising various attention mechanisms to capture interactions between different modalities, explicit signals encouraging semantic alignment in the visual dialog are seldom utilized. In this paper, we present a novel approach that leverages Optimal Transport to provide explicit and interpretable training signals to guide intra- and inter-modal alignment for the text and image in the visual dialog. Specifically, our approach consists of two kinds of alignment modules, Word-Word Alignment (WWA) and Region-Word Alignment (RWA). The WWA module learns latent relationships between a given question and a dialog history to align different concepts or pronouns that represent the same entity. As for the RWA module, it models the internal structures of text and images with graphs and performs graph matching for region-word alignment. We perform experiments on the benchmark dataset Visdial v1.0, and the experimental results show that our proposed approach achieves new state-of-the-art performance with respect to most metrics.
Using the TypeScript compiler to fix erroneous Node.js snippets

Most online code snippets do not run. This means that developers looking to reuse code from online sources must manually find and fix errors. We present an approach for automatically evaluating and correcting errors in Node.js code snippets: Node Code Correction (NCC). NCC leverages the ability of the TypeScript compiler to generate errors and inform code corrections through the combination of TypeScript’s builtin codefixes, our own targeted fixes, and deletion of erroneous lines. Compared to existing approaches using linters, our findings suggest that NCC is capable of detecting a larger number of errors per snippet and more error types, and it is more efficient at fixing snippets. We find that 73.7% of the code snippets in NPM documentation have errors; with the use of NCC’s corrections, this number was reduced to 25.1%. Our evaluation confirms that the use of the TypeScript compiler to inform code corrections is a promising strategy to aid in the reuse of code snippets from online sources.
Belnap-Dunn Logic and Query Answering in Inconsistent Databases with Null Values

This paper concerns an expansion of first-order Belnap-Dunn logic, called BD^{\supset,F}_{\bot}, and an application of this logic in the area of relational database theory. The notion of a relational database, the notion of a query applicable to a relational database, and several notions of an answer to a query with respect to a relational database are considered from the perspective of this logic, taking into account that a database may be an inconsistent database and/or a database with null values. The chosen perspective enables among other things the definition of a notion of a consistent answer to a query with respect to a possibly inconsistent database without resort to database repairs. For each of the notions of an answer considered, being an answer to a query with respect to a database of the kind considered is decidable.