Reviewerly/exHarmony huggingface dataset:
Exploring Elementary School Teachers’ Selection Process for Social Studies Textbooks: A Contradiction between Standards and Reality

This study sought to understand the gap between the ideal selection criteria and the reality of selection by exploring the selection process of elementary school teachers and to seek alternatives to the realistic selection criteria. The following research results could be derived through questionnaires and FGI interviews with 62 students from 90 elementary schools in Incheon. First, the standard for selecting certified textbooks for elementary school teachers was almost the same as the example standard presented in the manual of the Ministry of Education. Second, apart from the standard proposal for selecting certified textbooks, there was a tendency to prioritize adopted textbooks based on individual teachers’ subjective standards. Third, the Textbook Selection Committee was hurt by receiving negative feedback from fellow teachers about their choices during the textbook selection process. The task became more difficult and stressful, offering fewer rewards in the process of selecting textbooks. Fourth, the proposal for an alternative test textbook standard was presented, encompassing a total of eight questions in three areas: appropriateness, diversity, and usefulness.
XAI for Medicine by ChatGPT Code interpreter

In recent years, with the prevalence of Artificial Intelligence (AI), the interpretability of AI outputs has become a significant issue. Especially the interpretability of large language models (LLMs), including ChatGPT, has emerged as a major challenge. Consequently, there is a growing interest in the research of Explainable Artificial Intelligence (XAI), which seeks to elucidate the decision-making processes of AI in a manner that humans can comprehend. In the medical field, where trust and transparency are important, the use of AI becomes challenging when its decisions are unclear. Therefore, XAI techniques become critically important in the medical field. In this study, we propose the prompt named Code Base Prompt (CBP) to make the ChatGPT's decision-making process on medical texts explainable by using the Python code execution function of Chat GPT Code interpreter. In CBP, the medical decision-making algorithm is rewritten as Python code. Moreover, we propose an explainability evaluation system named Medical Algorithm Presentation Criteria (MAPC) for medical algorithm application tasks to medical text. MAPC is evaluated by five factors to align the human understanding process. To compare CBP with a Text Base Prompt (TBP), we conducted an experiment applying the heart failure classification algorithm to heart failure case report texts in three medical articles. With CBP, the results showed that the ChatGPT Code interpreter executed the Python code in all three cases and met all the five MAPC factors. In contrast, with TBP, no Python code execution was observed in any of the three cases, validating only one factor of MAPC. This study presents a new method for implementing XAI in the use of ChatGPT for medical tasks.
The Application of Mobile APP-based Oral Evaluation in Oral English Teaching at College Level

In the traditional teaching mode, it is hard for oral English teaching to produce good results at various levels, and it is even harder for oral English teachers to accurately control students' learning in a one-to-dozen teaching mode, especially in colleges. At present, most of the oral evaluation platforms are aimed at students' personalized learning, but few offer detailed and effective feedback to teachers for free. This system under discussion has basic oral evaluation functions from students' perspective, and the data analysis function for teachers' sake. It can provide teachers with intuitive and accurate feedback in terms of students' mastery of both sentences and individual words in speech.
Medical Image Joint Deringing and Denoising using Fourier Neural Operator

Gibbs-ringing artifact is a common artifact in medical image processing. It is usually caused by image enhancement procedures, like image deblurring, or physically caused in magnetic resonance imaging (MRI), where the k-space is a finite sampling of underlying signals. Under-sampling in Fourier space is often employed to reduce imaging time for real-time applications. In addition, ringing effect is regularly accompanied by noise boosting due to a low-grade sensor, resulting in lowering image quality to a large extent. In this work we propose hybrid approach for suppressing the stated artifacts, which utilizes recently proposed deep learning Fourier Neural Operator over the classical solvers: Perona-Malik diffusion and Kellner algorithm.
NerveStitcher2.0: Evolution of Stitching Algorithm for Corneal Confocal Microscope Images with Optical Flow

The NerveStitcher demonstrated that a set of in vivo confocal microscopy (IVCM) images can be merged under a framework of graph convolutional neural network. However, the high similarity of the nerval structure on IVCM image results in mis-stitching when the internal images in sequence happen to drop. Particularly on large gap between a pair of adjacent images, NerveStitcher sometimes cannot detected. In this paper, we advance the concept of global optical flow of IVCM image pair and intergrate it to the existing framework. The large improvements in algorithm robustness are caused by that we make a trade of global displacement of the images sequence according to optical flow, and the local corresponding of key points. We firstly analyze the global shifts in pixel intensity and position between consecutive images, which allows us to determine the motion of each pixel. The displacement vector in the optical flow is then used to calculate the displacement distance of the image. After obtaining the displacement distance of each image using the optical flow, we then use NerveStitcher to stitch the same image sequence. By using a feature point matching algorithm, we can calculate the displacement distance of matching feature points in the two images. We then subtract the displacement distances obtained by the two methods and locate and modify the incorrect stitching results based on the difference value range. Experimental results showed that the improved algorithm, named NerveStitcher2.0, decreases the estimation error by more than 25%. The implemented code is available at https://github.com/better-77/nervestitcher2.0.
Development of Bed Exit Alarm via Web Camera Utilizing Image Processing and Artificial Intelligence

This research focuses on the development of an innovative system for detecting instances of patients falling out of bed using advanced image processing and artificial intelligence techniques. By analyzing real-time images from a webcam placed in the patient's bed area, the system identifies specific key points on the patient's body to assess fall risks. The study involved testing twelve sleep patterns representing safe sleeping positions and six patterns simulating scenarios with a risk of falling. The system demonstrated remarkable accuracy in detecting fall risks, triggering warnings and alarms when key points associated with falling were not detected. Importantly, the system avoided false alarms caused by incorrect detections. These findings contribute to the improvement of bed exit alarm systems and have significant implications for patient safety.
Faster R-CNN image splicing tampering detection based on multilayer feature refinement fusion

Deep learning-based methods have made significant progress in image tampering detection. However, they often overlook the characteristics of small-size tampered regions, leading to missed or false detections. To address these issues, this paper proposes a novel approach for image splicing tampering detection using Faster R-CNN, which incorporates multi-layer feature refinement fusion. This approach aims to enhance the expression of features by employing a multi-layer feature refinement fusion strategy, thereby improving the accuracy of detecting tampered regions in small areas. The proposed method utilizes the RGB image as the input for the color image channel and employs the SRM filter processed image for the steganographic analysis channel. These two channels are fused in a refinement fusion network, combining deep refinement features with shallow refinement features. This fusion effectively utilizes refined feature information from different levels. The extracted features from the two channels are further fused using a bilinear pooling layer. Additionally, the RGB channels adaptively adjust the anchor frame shapes to enhance the accuracy of detecting splicing tampering in small areas. Experimental results demonstrate that the proposed method achieves im-proved detection accuracy compared to the original approach on publicly available datasets. It effectively detects regions affected by image splicing tampering. The findings highlight the potential of the proposed method in addressing the challenges associated with small-size tampered regions, contributing to the advancement of image tampering detection.
Clustering-Based Cancer Diagnosis Model for Whole Slide Image

Automated classification of Whole Slide Images (WSIs) is of great significance for early diagnosis of cancer. Existing approaches are trained on a specific level which affects the analysis performance due to weak supervision of patches and variants. Additionally, it is difficult to distinguish cancer subtype patches accurately from different magnification levels of WSIs. However, this can be improved by employing artificial intelligence models to address these problems, we propose a novel clustering-based cancer diagnosis (CBCD) method for WSI classification. The CBCD constructs three modules: first, we extracted patches from each magnification level of WSIs with respective cancer sub-types. Second, we employed two features (global and local) to learn discriminative and salient information of each patch. Then we find the meaningful cluster regions based on these features to quantify (select) the best patches of salient cancer subtypes by only relying on the collective characteristics of patches from different magnification levels. The clustering techniques used are k-means, gaussian mixture model, and agglomerative clustering. The quality of each clustering technique was determined using adjusted rand, and calinski harabasz scores. Later we used five state-of-the-art (SOTA) deep learning models to learn and classify cancer subtype regions of WSIs based on two types of features of patches. We also showed the results with no clustering techniques in an end-to-end supervised way by directly extracting patches from WSIs. Our method is evaluated on the public WSI dataset (KBSMC) for cancer sub-types classification and achieves better performance and great interpretability compared with the SOTA methods.
PathScribe: new software to work with whole slide histological images for education and research

Current research in digital pathology uses evermore complex computational and imaging tools to improve diagnostic efficiency and solve a variety of scientific tasks. Also, digital pathology tools are actively used in medical education and can improve the efficiency of training students and residents. Despite the active development of software tools in the field of digital pathology, the existing solutions still require a high entry threshold, are not truly universal and have complex and outdated interfaces.
Saliency Detection Based on Feature Fusion and Weighted Hypergraph

In view of the common problems in image saliency detection, such as inaccurate positioning of saliency objects and easy loss of detail information in complex scenario. This paper proposes a saliency detection based on feature fusion and weighted hypergraph. Firstly, SLIC superpixel segmentation is performed to extract the color features, spatial features and deep features of the image, and a weighted hypergraph model is constructed. Then the saliency score of each superpixel block is obtained by using the random walk algorithm, the primary saliency map is generated according to the order of each superpixel score. Compared with seven advanced algorithms on three challenging datasets (ECSSD, HKU-IS, PASCAL-S), the proposed model significantly improves the performance of saliency map details and salient object localization under complex scenario.
Non-Dominated Sorting Bidirectional Differential Coevolution

Constrained multiobjective optimization problems (CMOPs) are commonly found in real-world applications. CMOP is a complex problem that needs to satisfy a set of equality or inequality constraints. This paper proposes a variant of the bidirectional coevolution algorithm (BiCo) with differential evolution (DE). The novelties in the model include the DE differential mutation and crossover operators as the main search engine and a non-dominated sorting selection scheme. Experimental results on two benchmark test suites and eight real-world CMOPs suggested that the proposed model reached better overall performance than the original model.
Assessing the Vulnerability of Self-Supervised Speech Representations for Keyword Spotting Under White-Box Adversarial Attacks

Self-supervised speech pre-training has emerged as a useful tool to extract representations from speech that can be used across different tasks. While these models are starting to appear in commercial systems, their robustness to so-called adversarial attacks have yet to be fully characterized. This paper evaluates the vulnerability of three self-supervised speech representations (wav2vec 2.0, HuBERT and WavLM) to three white-box adversarial attacks under different signal-to-noise ratios (SNR). The study uses keyword spotting as a downstream task and shows that the models are very vulnerable to attacks, even at high SNRs. The paper also investigates the transferability of attacks between models and analyses the generated noise patterns in order to develop more effective defence mechanisms. The modulation spectrum shows to be a potential tool for detection of adversarial attacks to speech systems.
Integrating Bidirectional Long Short-Term Memory with Subword Embedding for Authorship Attribution

The problem of unveiling the author of a given text document from multiple candidate authors is called authorship attribution. Manifold word-based stylistic markers have been successfully used in deep learning methods to deal with the intrinsic problem of authorship attribution. Unfortunately, the performance of word-based authorship attribution systems is limited by the vocabulary of the training corpus. Literature has recommended character-based stylistic markers as an alternative to overcome the hidden word problem. However, character-based methods often fail to capture the sequential relationship of words in texts which is a chasm for further improvement. The question addressed in this paper is whether it is possible to address the ambiguity of hidden words in text documents while preserving the sequential context of words. Consequently, a method based on bidirectional long short-term memory (BLSTM) with a 2-dimensional convolutional neural network (CNN) is proposed to capture sequential writing styles for authorship attribution. The BLSTM was used to obtain the sequential relationship among characteristics using subword information. The 2-dimensional CNN was applied to understand the local syntactical position of the style from unlabeled input text. The proposed method was experimentally evaluated against numerous state-of-the-art methods across the public corporal of CCAT50, IMDb62, Blog50, and Twitter50. Experimental results indicate accuracy improvement of 1.07%, and 0.96%, on CCAT50 and Twitter, respectively, and produce comparable results on the remaining datasets.
Run Away From the Original Example and Towards Transferability

Transfer-based attacks against black-box neural network models have received increasing attention because they are more realistic scenarios, but how to produce highly transferable adversarial examples on the surrogate model becomes critical. In this work, we find that if the attack direction of the original example is controlled from the beginning, the produced adversarial examples will be more transferable. Specifically, we propose the Output Direction Controller (ODC) to initialize the example direction so that the example starts off with a deviation from the true direction or toward the target direction. ODC is a simple and extensible component that can be combined with various transfer-based attack methods and significantly improve the transferability of the adversarial examples. On the ImageNet dataset, we optimize the baseline method by ODC to improve the success rate of untargeted attacks by an average of 11.79% and targeted attacks by an average of 3.38%. Code is available at https://github.com/yangrongbo/ODC.
Dense Depth Estimation for Monocular Endoscope Robot with an Adaptive Baseline

Depth information is useful to surgeons and surgical assistance systems. However, it is a challenging task to estimate the depth of various surgical scenes based on a monocular endoscope. We propose a depth estimation approach for a monocular endoscope with a stereo matching algorithm. The monocular endoscope is moved horizontally by a robotic endoscope holder to simulate a stereo vision system. The main challenge is how to obtain a proper baseline for better depth information generation as the depth range of a surgical scene is unknown beforehand. We design a baseline evaluation and selection algorithm to search for suitable baselines for surgical scenes with different depth ranges. Experimental results show that our approach improves the average accuracy of different depth scenarios by 10.8% when the error range is 2mm.
Global-Aware Attention Network for Multi-modal Sarcasm Detection

Sarcasm detection is crucial for natural language processing in various applications, such as affective computing and opinion mining. Multi-modal sarcasm detection, which combines information from different modalities, has attracted increasing attention in recent years. However, many current methods concatenate image and text features directly without considering the contextual information between the cross-modal alignment and single-modal features simultaneously. Inspired by this observation, we propose a novel Global-Aware Attention Network (GAAN) for multi-modal sarcasm detection. Specifically, we investigate a cross-modal multi-granularity alignment module that captures align context features through coarse-grained and fine-grained attention. More importantly, considering the complementary effects of single-modal contextual information in sarcasm detection, we fuse textual, visual context features and alignment context features to obtain the global context features. We conducted extensive experiments on public datasets, and the results compared to the baselines illustrate that our proposed model obtains state-of-the-art performance in multi-modal sarcasm detection.
Cooperative Knowledge Elicitation for Formal Ontology Design: An Exploratory Study Applied in Industry for Knowledge Management

Building formal ontologies remains a complex process for companies. In the literature, this process is based on the technical knowledge and expertise of domain experts, without further details on the used methodologies. Possible problems of disagreements between experts, expression of implicit knowledge related to high level know-how rarely verbalized, qualification of results by use cases, or simply adhesion of the group of experts, remain currently unsolved. This paper proposes a methodological approach based on knowledge elicitation for the conception of formal, consensual, and shared ontologies. The proposed approach is experimentally tested on industrial collaboration projects in the field of manufacturing (associating knowledge sources from multinational companies) and in the field of viticulture (associating explicit knowledge and implicit knowledge acquired through observation).
DA-MTAD: Capturing Intra- and Inter-Metric Dependencies for Multivariate Time Series Anomaly Detection

Multivariate time series anomaly detection is a challenging task due to the intricate intra- and inter-metric dependencies present in the data. Previous methods have mainly focused on capturing intra-metric dependency, while neglecting the utilization of the dependency information across metrics. More recent approaches have considered inter-metric dependency, but have not been able to capture the complex inter-metric dynamics accurately and adaptively. To address these challenges, we propose a novel dual-attentional multivari-ate time series anomaly detection framework. Our approach improves on the original Dot-Product Attention to robustly capture intra-metric dependency. Additionally, we introduce automatic graph structure learning and graph attention mechanism to adaptively capture inter-metric dependency and utilize it effectively. Experiments on five public datasets from different domains demonstrate that comprehensive and accurate modeling of both dependencies enables our proposed method to outperform baseline methods in accurately detecting anomalies.
Online Sparse Streaming Feature Selection via Decision Risk

Online streaming feature selection (OSFS) is an effective approach to addressing high-dimensional data. In real big data-related applications, streaming features commonly have massive missing data due to various uncertain factors. The missing data may cause some uncertain relationships between sparse features and labels. However, existing OSFS methods tend to select sparse streaming features based on certain relevance and redundancy analysis, which may erroneously discard some weak relevant but irredundant features. As a result, some essential information is discarded. Motivated by this, this paper proposes a Decision- Risk-incorporated OSFS (DRO) algorithm. Its main idea is two-fold: 1) the missing data of sparse streaming features are pre-estimated by using the Latent Factor Analysis (LFA), and 2) the decision risk of relevance and redundancy analysis is minimized on the estimated complete streaming features via the three-way decision (3WD). Extensive empirical studies are conducted on eight real-world datasets. The results show that DRO significantly outperforms five state-of-the-art competitors.
Helping Language Models Learn More: Multi-Dimensional Task Prompt for Few-shot Tuning

Large language models (LLMs) can be used as accessible and intelligent chatbots by constructing natural language queries and directly inputting the prompt into the large language model. However, different prompt' constructions often lead to uncertainty in the answers and thus make it hard to utilize the specific knowledge of LLMs (like ChatGPT). To alleviate this, we use an interpretable structure to explain the prompt learning principle in LLMs, which certificates that the effectiveness of language models is determined by position changes of the task's related tokens. Therefore, we propose MTPrompt, a multi-dimensional task prompt learning method consisting based on task-related object, summary, and task description information. By automatically building and searching for appropriate prompts, our proposed MTPrompt achieves the best results on few-shot samples setting and five different datasets. In addition, we demonstrate the effectiveness and stability of our method in different experimental settings and ablation experiments. In interaction with large language models, embedding more task-related information into prompts will make it easier to stimulate knowledge embedded in large language models.