Reviewerly/exHarmony huggingface dataset:
The self‐distillation trained multitask dense‐attention network for diagnosing lung cancers based on CT scans

The latest international multidisciplinary histopathological classification of lung cancer indicates that a deeper study of the lung adenocarcinoma requires a comprehensive multidisciplinary platform. However, in the traditional pathological examination or previous computer-vision-based research, the entire lung is not considered in a comprehensive manner.The study aims to develop a deep learning model proposed for diagnosing the lung adenocarcinoma histopathologically based on CT scans. Instead of just classifying the lung adenocarcinoma, the pathological report should be inferred based on both the invasiveness and growth pattern of the tumors.A self-distillation trained multitask dense-attention network (SD-MdaNet) is proposed and validated based on 2412 labeled CT scans from 476 patients and 845 unlabeled scans. Inferring the pathological report is divided into two tasks, predicting the invasiveness of the lung tumor and inferring growth patterns of tumor cells in a comprehensive histopathological subtyping manner with excellent accuracy. In the proposed method, the dense-attention module is introduced to better extract features from a small dataset in the main branch of the MdaNet. Next, task-specific attention modules are utilized in different branches and finally integrated as a multitask model. The second task is a blend of classification and regression tasks. Thus, a specialized loss function is developed. In the proposed knowledge distillation (KD) process, the MdaNet as well as its main branch trained for solving two single tasks, respectively, are treated as multiple teachers to produce a student model. A novel KD loss function is developed to take the advantage of all the models as well as data with labels and without labels.SD-MdaNet achieves an AUC of 98.7±0.4%$98.7\pm 0.4\%$ on invasiveness prediction, and 91.6±1.0%$91.6\pm 1.0\%$ on predominant growth pattern prediction on our dataset. Moreover, the average mean squared error in inferring growth pattern proportion reaches 0.0217±0.0019$0.0217\pm 0.0019$ , and the AUC for predominant growth pattern proportion reaches 91.6±1.0%$91.6\pm 1.0\%$ . The proposed SD-MdaNet is significantly better than all other benchmarking methods ( FDR<0.05$FDR&lt;0.05$ ).Experimental results demonstrate that the proposed SD-MdaNet can significantly improve the performance of the lung adenocarcinoma pathological diagnosis using only CT scans. Analyses and discussions are conducted to interpret the advantages of the SD-MdaNet.
Neural Class Expression Synthesis in $$\mathcal {ALCHIQ(D)}$$

Class expression learning in description logics has long been regarded as an iterative search problem in an infinite conceptual space. Each iteration of the search process invokes a reasoner and a heuristic function. The reasoner finds the instances of the current expression, and the heuristic function computes the information gain and decides on the next step to be taken. As the size of the background knowledge base grows, search-based approaches for class expression learning become prohibitively slow. Current neural class expression synthesis (NCES) approaches investigate the use of neural networks for class expression learning in the attributive language with complement ( $$\mathcal {ALC}$$ ). While they show significant improvements over search-based approaches in runtime and quality of the computed solutions, they rely on the availability of pretrained embeddings for the input knowledge base. Moreover, they are not applicable to ontologies in more expressive description logics. In this paper, we propose a novel NCES approach which extends the state of the art to the description logic $$\mathcal {ALCHIQ(D)}$$ . Our extension, dubbed NCES2, comes with an improved training data generator and does not require pretrained embeddings for the input knowledge base as both the embedding model and the class expression synthesizer are trained jointly. Empirical results on benchmark datasets suggest that our approach inherits the scalability capability of current NCES instances with the additional advantage that it supports more complex learning problems. NCES2 achieves the highest performance overall when compared to search-based approaches and to its predecessor NCES. We provide our source code, datasets, and pretrained models at https://github.com/dice-group/NCES2 .
Lane Line Type Recognition Based on Improved YOLOv5

The recognition of lane line type plays an important role in the perception of advanced driver assistance systems (ADAS). In actual vehicle driving on roads, there are a variety of lane line type and complex road conditions which present significant challenges to ADAS. To address this problem, this paper proposes an improved YOLOv5 method for recognising lane line type. This method can accurately and quickly identify the types of lane lines and can show good recognition results in harsh environments. The main strategy of this method includes the following steps: first, the FasterNet lightweight network is introduced into all the concentrated-comprehensive convolution (C3) modules in the network to accelerate the inference speed and reduce the number of parameters. Then, the efficient channel attention (ECA) mechanism is integrated into the backbone network to extract image feature information and improve the model’s detection accuracy. Finally, the sigmoid intersection over union (SIoU) loss function is used to replace the original generalised intersection over union (GIoU) loss function to further enhance the robustness of the model. Through experiments, the improved YOLOv5s algorithm achieves 95.1% of mAP@0.5 and 95.2 frame·s−1 of FPS, which can satisfy the demand of ADAS for accuracy and real-time performance. And the number of model parameters are only 6M, and the volume is only 11.7 MB, which will be easily embedded into ADAS and does not require huge computing power to support it. Meanwhile, the improved algorithms increase the accuracy and speed of YOLOv5m, YOLOv5l, and YOLOv5x models to different degrees. The appropriate model can be selected according to the actual situation. This plays a practical role in improving the safety of ADAS.
Benchmarking open source and paid services for speech to text: an analysis of quality and input variety

Introduction Speech to text (STT) technology has seen increased usage in recent years for automating transcription of spoken language. To choose the most suitable tool for a given task, it is essential to evaluate the performance and quality of both open source and paid STT services. Methods In this paper, we conduct a benchmarking study of open source and paid STT services, with a specific focus on assessing their performance concerning the variety of input text. We utilizes ix datasets obtained from diverse sources, including interviews, lectures, and speeches, as input for the STT tools. The evaluation of the instruments employs the Word Error Rate (WER), a standard metric for STT evaluation. Results Our analysis of the results demonstrates significant variations in the performance of the STT tools based on the input text. Certain tools exhibit superior performance on specific types of audio samples compared to others. Our study provides insights into STT tool performance when handling substantial data volumes, as well as the challenges and opportunities posed by the multimedia nature of the data. Discussion Although paid services generally demonstrate better accuracy and speed compared to open source alternatives, their performance remains dependent on the input text. The study highlights the need for considering specific requirements and characteristics of the audio samples when selecting an appropriate STT tool.
PE-YOLO: Pyramid Enhancement Network for Dark Object Detection

Current object detection models have achieved good results on many benchmark datasets, detecting objects in dark conditions remains a large challenge. To address this issue, we propose a pyramid enhanced network (PENet) and joint it with YOLOv3 to build a dark object detection framework named PE-YOLO. Firstly, PENet decomposes the image into four components of different resolutions using the Laplacian pyramid. Specifically we propose a detail processing module (DPM) to enhance the detail of images, which consists of context branch and edge branch. In addition, we propose a low-frequency enhancement filter (LEF) to capture low-frequency semantics and prevent high-frequency noise. PE-YOLO adopts an end-to-end joint training approach and only uses normal detection loss to simplify the training process. We conduct experiments on the low-light object detection dataset ExDark to demonstrate the effectiveness of ours. The results indicate that compared with other dark detectors and low-light enhancement models, PE-YOLO achieves the advanced results, achieving 78.0 $$\%$$ in mAP and 53.6 in FPS, respectively, which can adapt to object detection under different low-light conditions. The code is available at https://github.com/XiangchenYin/PE-YOLO .
Error Selection Based Training of Fully Complex-Valued Dendritic Neuron Model

In this paper, an efficient training algorithm is proposed for fully complex-valued dendritic neuron model (FCDNM), where the weights and bias are tuned by complex-valued Levenberg-Marquardt (CLM) algorithm with error selection. Firstly, compared with complex-valued first-order algorithms, the CLM algorithm overcomes the problem of slow convergence for training FCDNM. Secondly, for practical applications, the choice of optimization algorithms is over-emphasized, while the effect of complex-valued Hessian matrix is usually ignored. Indeed, not all errors contribute equally to the parameter modification during the optimization process. Therefore, an error selection strategy is introduced to reduce the computational complexity during the training process. Experimental results show the effectiveness of the proposed algorithm.
An image steganography using improved hyper-chaotic Henon map and fractal Tromino

&lt;span lang="EN-US"&gt;Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. First, the cover image is converted into a wavelet environment using the integer wavelet transform (IWT), which protects the cover images from false mistakes. The grey wolf optimizer (GWO) is used to choose the pixel’s image that would be utilized to insert the hidden image in the cover image. GWO effectively selects pixels by calculating entropy, pixel intensity, and fitness function using the cover images. Moreover, the secret image was encrypted by utilizing a proposed hyper-chaotic improved Henon map and fractal Tromino. The suggested method increases computational security and efficiency with increased embedding capacity. Following the embedding algorithm of the secret image and the alteration of the cover image, the least significant bit (LSB) is utilized to locate the tempered region and to provide self-recovery characteristics in the digital image. According to the findings, the proposed technique provides a more secure transmission network with lower complexity in terms of peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC), structural similarity index (SSIM), entropy and mean square error (MSE). As compared to the current approaches, the proposed method performed better in terms of PSNR 70.58% Db and SSIM 0.999 respectively.&lt;/span&gt;
Online Training Application Design with Website-Based Blended Learning System Method

The Ministry of Health provides training to employees to improve their skills and level of service to the people of Indonesia. In the health service training activities carried out, employee training is carried out through seminars. The seminar was conducted in the form of application presentations and hardcopy manuals. The implementation that has been implemented is still lacking because there is no online application that helps the facilitator measure participants' understanding. Therefore, the researchers built an application that aims to produce a website-based online training application. The system development method uses Rapid Application Development (RAD), which is modeled using Unified Modeling Language (UML) tools. Using the PHP programming language and MySQL as a Relational Database Management system, this research produces a training application that can assist the Ministry of Health, especially the Center for Data and Information, in conducting training.
Correlation Between Self-Concept and Anxiety of Communicative Competence in English Foreign Language Learner

This research entitled “Correlation Between Self-Concept and Anxiety of Communicative Competence in English as Foreign Language Learner”. Common problem in this research is to find 1) Whether any correlation between Self-Concept and Anxiety toward communicative competence or not?, 2) How high a level of positive self-concept and anxiety does the student have?.&#x0D; The subjects of this study were students of Muhammadiyah Sidenreng Rappang University, Consisting of 21 Students as samples from fourth semester of English department. The approach used in this reaserch is quantitative by using correlation method. Data collection techniques used are questionnaire, interview and vocabullary test.Based on questionnaire and interviews with case subjects concluded that the identification of student problems are as follows: 1) There are correlation between self-concept and anxiety toward communicative competence, 2) Researcher find that student had high anxiety and mildly positive Self-concept.&#x0D;
An improved ANN-based global-local approximation for small medical data analysis

INTRODUCTION: The task of approximation of complex nonlinear dependencies, especially in the case of short datasets, is important in various applied fields of medicine. Global approximation methods describe the generalized behavior of the model, while local methods explain the behavior of the model at specific data points. Global-local approximation combines both approaches, which makes such methods a powerful tool for processing short sets of medical data that can have both broad trends and local variations.OBJECTIVES: This paper aims to improve the method of sequential obtaining global and local components of the response surface to increase the accuracy of prediction in the case of short sets of medical data.METHODS: In this paper, the authors developed a new method that combined two ANNs: a non-iterative SGTM neural-like structure for obtaining the global component and GRNN as a powerful tool of local approximation in the case of short datasets.RESULTS: The authors have improved the method of global-local approximation due to the use of a General Regression Neural Network instead of RBF ANN for obtaining the local component, which ensured an increase in the accuracy of the body fat prediction task. The authors optimized the operation of the method and investigated the efficiency of the sequential obtaining global and local components of the response surface in comparison with the efficiency using a number of existing methods.CONCLUSION: The conducted experimental studies for solving the body fat prediction task showed the high efficiency of using the improved method in comparison with a number of existing methods, including ensemble methods.
Emerging Technologies: A Catalyst for Sustainable Business Model Innovation

Grand Challenges are global problems requiring coordinated effort from stakeholders at different levels. For-profit businesses, as loci of innovation, can play a pivotal role in advancing Grand Challenges by fostering social impact collaboration. Business Model Innovation is a useful mean to combine the possibilities offered by emerging technologies with the needs of sustainable challenges. Blockchain is widely recognized as an emerging technology, rapidly growing, highly novel and with a prominent impact on different sectors. We adopt a Comparative Case Study in the field of the Voluntary Carbon Market, unregulated markets where organizations can offset their impact by compensating their negative externalities on the environment by buying carbon credits generated through certified mitigation projects following international standards. As climate change is a main Grand Challenges and a threat to humanity, as recognized by the United Nations Sustainable Development Goals, this research aims to explore the role of blockchain as a catalyst for Sustainable Business Model Innovation. This study makes two significant contributions to the field of business model innovation, emerging technologies and Grand Challenges. First, it highlights the importance of Business Model Innovation to address Grand Challenges and overcome their managerial problems, including valuating, coordination, trust, access and reach. Our study presents a novel conceptual model anchored in the Business Model as an activity system view, revealing three actionable characteristics of blockchain that can help to design novel activities configurations able to advance managerial problems encountered by for-profit organizations while advancing effort towards Grand Challenges: asset enabler, trust machine and coordinated and collaborative action enhancer. Second, from a technology-oriented perspective, it offers insights into how an emerging technology can be leveraged to foster complementarity by converging with existing assets and technologies, thereby generating new value. For practice, our findings provide a framework for both startups and established companies to leverage blockchain technologies to create meaningful and lasting impact.
An Efficient Binary Sand Cat Swarm Optimization for Feature Selection in High-Dimensional Biomedical Data

Recent breakthroughs are making a significant contribution to big data in biomedicine which are anticipated to assist in disease diagnosis and patient care management. To obtain relevant information from this data, effective administration and analysis are required. One of the major challenges associated with biomedical data analysis is the so-called “curse of dimensionality”. For this issue, a new version of Binary Sand Cat Swarm Optimization (called PILC-BSCSO), incorporating a pinhole-imaging-based learning strategy and crossover operator, is presented for selecting the most informative features. First, the crossover operator is used to strengthen the search capability of BSCSO. Second, the pinhole-imaging learning strategy is utilized to effectively increase exploration capacity while avoiding premature convergence. The Support Vector Machine (SVM) classifier with a linear kernel is used to assess classification accuracy. The experimental results show that the PILC-BSCSO algorithm beats 11 cutting-edge techniques in terms of classification accuracy and the number of selected features using three public medical datasets. Moreover, PILC-BSCSO achieves a classification accuracy of 100% for colon cancer, which is difficult to classify accurately, based on just 10 genes. A real Liver Hepatocellular Carcinoma (TCGA-HCC) data set was also used to further evaluate the effectiveness of the PILC-BSCSO approach. PILC-BSCSO identifies a subset of five marker genes, including prognostic biomarkers HMMR, CHST4, and COL15A1, that have excellent predictive potential for liver cancer using TCGA data.
Robust Descriptor Algorithm Considering the Changing Gray Value Trends Inside Ground Objects for Heterogeneous Optical Image Matching

Differences in sensor types, resolutions, and imaging conditions can lead to considerable spectral differences in heterogeneous optical remote sensing images and the similarity of scale-invariant feature transform (SIFT) or local self-similarities (LSS) feature descriptors of the same point can be poor. Consequently, we proposed a robust descriptor construction algorithm considering the changing gray values inside ground objects. The main contributions of this study include the following. First, based on the stability of the internal gray value changes of ground objects, we suggest that the change orientations and degrees of gray values of pixels, which can be used to express the stability of the same area of heterogeneous images, providing the basis for image matching; second, unlike many existing methods that use gradient information to calculate feature orientation and descriptors, the proposed algorithm uses change orientation and degree to calculate the feature orientation and descriptor, enabling it to obtain stable descriptors in image matching with large illumination changes. Experimental analysis of homologous and heterogeneous optical remote sensing images demonstrated the superior stability and capability of the proposed algorithm over commonly used algorithms, including the radiation-invariant feature transform, adaptive binning SIFT, gradient orientation modification SIFT, and LSS algorithms.
Mitigating the Impact of Work Overload on Cybersecurity Behavior: The Moderating Influence of Corporate Ethics—A Mediated Moderation Analysis

This present study examines how work overload affects cybersecurity behavior, considering job stress as a mediator and corporate ethics as a moderator. Using a time-lagged survey design, 377 South Korean employees were surveyed. The results of SEM analysis (structural equation model) show that work overload leads to higher job stress, which in turn worsens cybersecurity behavior. High levels of corporate ethics buffer this negative relationship. This research fill gaps in the existing literature by linking work overload, job stress, and corporate ethics to cybersecurity behavior, offering valuable insights for both theory and practice. Future research directions are also discussed.
Making Friends in the Dark: Ad Hoc Teamwork Under Partial Observability

This paper introduces a formal definition of the setting of ad hoc teamwork under partial observability and proposes a first-principled model-based approach which relies only on prior knowledge and partial observations of the environment in order to perform ad hoc teamwork. We make three distinct assumptions that set it apart previous works, namely: i) the state of the environment is always partially observable, ii) the actions of the teammates are always unavailable to the ad hoc agent and iii) the ad hoc agent has no access to a reward signal which could be used to learn the task from scratch. Our results in 70 POMDPs from 11 domains show that our approach is not only effective in assisting unknown teammates in solving unknown tasks but is also robust in scaling to more challenging problems. Supplementary material is available at https://github.com/jmribeiro/adhoc-teamwork-under-partial-observability.
Deep Hypersphere Feature Regularization for Weakly Supervised RGB-D Salient Object Detection

We propose a weakly supervised approach for salient object detection from multi-modal RGB-D data. Our approach only relies on labels from scribbles, which are much easier to annotate, compared with dense labels used in conventional fully supervised setting. In contrast to existing methods that employ supervision signals on the output space, our design regularizes the intermediate latent space to enhance discrimination between salient and non-salient objects. We further introduce a contour detection branch to implicitly constrain the semantic boundaries and achieve precise edges of detected salient objects. To enhance the long-range dependencies among local features, we introduce a Cross-Padding Attention Block (CPAB). Extensive experiments on seven benchmark datasets demonstrate that our method not only outperforms existing weakly supervised methods, but is also on par with several fully-supervised state-of-the-art models. Code is available at https://github.com/leolyj/DHFR-SOD.