Reviewerly/exHarmony huggingface dataset:
A novel three-step deep learning approach for the classification of breast cancer histopathological images

Breast cancer is a widespread and significant health concern among women globally. Accurately categorizing breast cancer is essential for effective treatment, ultimately improving survival rates. Moreover, deep learning (DL) has emerged as a widely adopted approach for precise medical image classification in recent years, showing promise in this domain. However, despite the availability of DL models proposed in the literature for automated classification of breast cancer histopathology images, achieving high accuracy remains challenging. A minor modification to pre-trained models and simple training strategies can further enhance model accuracy. Based on the approach, this paper proposed an anti-aliased filter in a pre-trained ResNet-34 and a novel three-step training process to improve BC histopathology image classification accuracy. The training involves systematically unfreezing layers and imposing additional constraints on the rate of change of learnable parameters. In addition, four-fold on-the-fly data augmentation enhances model generalization. The Ada-Hessian optimizer adjusts learning rates based on first and second-order gradients to improve convergence speed. The training process utilizes a large batch size to minimize the training loss associated with batch normalization layers. Even with the limited GPU size, the gradient accumulation technique achieves a large batch size. Collectively, these strategies minimize training time while maintaining or improving the accuracy of BC histopathology image classification models. In the experimental implementation, the proposed architecture achieves superior results compared to recent existing models, with an accuracy of 98.64%, recall (98.98%), precision (99.35%), F1-Score (99.17%), and MCC (97.36%) for binary classification. Similarly, the model achieves an accuracy of 95.01%, recall (95.01%), precision (94.95%), F1-Score (94.94%), and MCC (93.42%) for the eight-class category of BC images.
An interpretable decision tree ensemble model for imbalanced credit scoring datasets

Credit scoring is a typical example of imbalanced classification, which poses a challenge to conventional machine learning algorithms and statistical classifiers when attempting to accurately predict outcomes for defaulting customers. In this paper, we propose a credit scoring classifier called Decision Tree Ensemble model (DTE). This model effectively addresses the challenge of imbalanced data and identifies significant features that influence the likelihood of credit status. An experiment demonstrates that DTE exhibits superior performance metrics in comparison to well-known based-tree ensemble classifiers such as Bagging, Random Forest, and AdaBoost, particularly when integrated with resampling techniques for handling imbalanced data.
Impact of class imbalance ratio on ensemble methods for imbalance problem: A new perspective

Class imbalance problem (CIP) exists when the class distribution is not uniform. Many real-world scenarios face CIP which attracted the researcher’s attention to this problem. Training machine learning (ML) models with class imbalanced datasets is a challenging problem. Ensemble methods in ML involve training multiple classifiers, combining or averaging their predictions to come to a final prediction. Specifically designed ensemble-based methods can overcome the difficulty faced by traditional classifiers and can handle the CIP. The performance of 19 ensemble methods for 44 unbalanced datasets is assessed in this paper in order to observe the effects of the class imbalance ratio (CIR). For performance evaluation, we divide these datasets into three categories, i.e., Slightly Imbalance (SI), Moderately Imbalance (MI) and Highly Imbalance (HI) based on CIR. With the proposed perspective, we observe that different ensemble methods perform well in different categories suggesting that the percentage of minority or majority class could be a criterion for the selection of ensemble methods for class imbalance datasets. Moreover, visual representations and different non-parametric statistical tests are also used to have more reliable results.
Design of knowledge incorporated VQA based on spatial GCNN with structured sentence embedding and linking algorithm

Visual question Answering (VQA) is a computer vision task that requires a system to infer an answer to a text-based question about an image. Prior approaches did not take into account an image’s positional information or the questions’ grammatical and semantic relationships during image and question processing. Featurization, which leads to the false answering of the question. Hence to overcome this issue CNN –Graph based LSTM with optimized BP Featurization technique is introduced for feature extraction of image as well as question. The position of the subjects in the image has been determined using CNN with a dropout layer and the optimized momentum backpropagation during the extraction of image features without losing any image data. Then, using a graph-based LSTM with loopy backpropagation, the questions’ syntactic and semantic dependencies are retrieved. However, due to their lack of external knowledge about the input image, the existing approaches are unable to respond to common sense knowledge-based questions (open domain). As a result, the proposed Spatial GCNN knowledge retrieval with PDB Model and Spatial Graph Convolutional Neural Network, which recovers external data from Wikidata, have been used to address the open domain problems. Then the Probabilistic Discriminative Bayesian model, based Attention mechanism predicts the answer by referring to all concepts in question. Thus, the proposed method answers the open domain question with high accuracy of 88.30%.
Seeing the Past, Planning the Future: Proudly Celebrating 25 Years of Assisting the Convergence of Process Sciences and Design Science

This Extended Editorial has been compiled by the members of the Editorial Board to celebrate the 25th anniversary of the establishment of the Journal of Integrated Design and Process Science, which operates as the Transactions of the Society for Process and Design Science. The paper divides in three parts. The first part provides a detailed overview of the preliminaries, the objectives, and the periods of operation. It also includes a summary of the current application-orientated professional fields of interests, which are: (i) convergence mechanisms of creative scientific disciplines, (ii) convergence of artificial intelligence, team and health science, (iii) convergence concerning next-generation cyber-physical systems, and (iv) convergence in design and engineering education. The second part includes invited papers, which exemplify domains within the four fields of interest, and also represent good examples of science communication. Short synopses of the contents of these representative papers are included. The third part takes the major changes in scientific research and the academic publication arena into consideration, circumscribes the mission and vision as formulated by the current Editorial Board, and elaborates on the planned strategic exploration and utilization domains of interest.
Using Z3 to Verify Inferences in Fragments of Linear Logic

Linear logic is a substructural logic proposed as a refinement of classical and intuitionistic logics, with applications in programming languages, game semantics, and quantum physics. We present a template for Gentzen-style linear logic sequents that supports verification of logic inference rules using automatic theorem proving. Specifically, we use the Z3 Theorem Prover [8] to check targeted inference rules based on a set of inference rules that are presumed to be valid. To demonstrate the approach, we apply it to validate several derived inference rules for two different fragments of linear logic: MLL+Mix (Multiplicative Linear Logic extended with a Mix rule) and MILL (Multiplicative Intuitionistic Linear Logic).
Gender Effect on Motion Sickness Susceptibility

The study aimed was to determine whether there was a gender difference in sensitivity to visual stimulation-induced motion sickness (MS). Forty-nine participants (Female: 24, Male: 25) volunteered to join in the study. Participants were exposed to a visual video-recording stimulus to evoke the MS. Simulator Sickness Questionnaire (SSQ) was administered before, after, and 30 min after the MS stimulation to determine MS symptoms. Participants' self-report was used to identify motion sickness. Postural sway (PS) was measured before and immediately after MS stimulation. 58.3% of the female and 48.0% of the male reported that they had MS, while 41.7% of the female and 52.0% of the male reported that they did not have MS. Gender and MS distributions were not significant (p=0.469). Participants with MS before the stimulation had higher PS than those who declared no MS (p=0.008), but PS was not different after the stimulation (p=0.102). Although there was no difference in the pre-test (p=0.231), men with MS had higher PS than women with MS at the post-test (p=0.013). There was a significant increase in PS of men who declared that they had MS after the stimulation (p=0.012). The pre-test (p=0.899) and post-test (p=0.434) SSQ scores of men and women with MS were not different, while women had higher SSQ scores than men at the post-test 30 (p=0.020). Finally, there was no correlation between gender and rates of MS. In terms of symptom severity, females appear to be more susceptible to MS. PS may be a precursor to MS.
A fusion‐attention swin transformer for cardiac MRI image segmentation

Abstract For semantic segmentation of cardiac magnetic resonance image (MRI) with low recognition and high background noise, a fusion‐attention Swin Transformer is proposed based on cognitive science and deep learning methods. It has a U‐shaped symmetric encoding–decoding structure with an attention‐based skip connection. The encoder realizes self‐attention for deep feature representation and the decoder up‐samples global features to the corresponding input resolution for pixel‐level segmentation. By introducing a skip connection between the encoder and decoder based on fusion attention, the remote interaction of global information is realized, and the attention to local features and specific channels is enhanced. A public ACDC cardiac MRI image dataset is used for experiments. The segmentation of the left ventricle, right ventricle, and myocardial layer is realized. The method performs well on a small sample dataset, for example, the pixel accuracy obtained by the proposed model is 93.68%, the Dice coefficient is 92.28%, and HD coefficient is 11.18. Compared with the state‐of‐the‐art models, the segmentation precision has been significantly improved, especially for the low recognition and heavily occluded targets.
Piecewise linear functions representable with infinite width shallow ReLU neural networks

This paper analyzes representations of continuous piecewise linear functions with infinite width, finite cost shallow neural networks using the rectified linear unit (ReLU) as an activation function. Through its integral representation, a shallow neural network can be identified by the corresponding signed, finite measure on an appropriate parameter space. We map these measures on the parameter space to measures on the projective <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="n"> <mml:semantics> <mml:mi>n</mml:mi> <mml:annotation encoding="application/x-tex">n</mml:annotation> </mml:semantics> </mml:math> </inline-formula>-sphere cross <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="double-struck upper R"> <mml:semantics> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="double-struck">R</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">\mathbb {R}</mml:annotation> </mml:semantics> </mml:math> </inline-formula>, allowing points in the parameter space to be bijectively mapped to hyperplanes in the domain of the function. We prove a conjecture of Ongie et al. [<italic>A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case</italic>, arXiv, 2019] that every continuous piecewise linear function expressible with this kind of infinite width neural network is expressible as a finite width shallow ReLU neural network.
Facial Sentiment Recognition using artificial intelligence techniques.

Facial emotion recognition technology is used to analyze and recognize human emotions based on facial expressions. This technology uses deep learning models to classify facial expressions, eyes, eyebrows, mouth, and other facial expressions to determine a person's emotions. The application of facial emotion recognition in the field of education is a potential way to evaluate the level of student absorption after each class period. Using cameras and emotion recognition technology, the system can record and analyze students' facial expressions during class. In this paper, we use the Convolutional Neural Network (CNN) algorithm combined with the linear regression analysis method to build a model to predict students' facial emotions over a period of time camera recorded.
Multi-view emotional expressions dataset using 2D pose estimation

Abstract Human body expressions convey emotional shifts and intentions of action and, in some cases, are even more effective than other emotion models. Despite many datasets of body expressions incorporating motion capture available, there is a lack of more widely distributed datasets regarding naturalized body expressions based on the 2D video. In this paper, therefore, we report the multi-view emotional expressions dataset (MEED) using 2D pose estimation. Twenty-two actors presented six emotional (anger, disgust, fear, happiness, sadness, surprise) and neutral body movements from three viewpoints (left, front, right). A total of 4102 videos were captured. The MEED consists of the corresponding pose estimation results (i.e., 397,809 PNG files and 397,809 JSON files). The size of MEED exceeds 150 GB. We believe this dataset will benefit the research in various fields, including affective computing, human-computer interaction, social neuroscience, and psychiatry.
Explanation of the views and opinions regarding the education strategies for using 3D hologram technology as an educational media

ABSTRACTThis article seeks the opinions, perceptions, and attitudes of faculty members about the strategies for using hologram technology in the learning environment. The research method was qualitative and based on this, 17 faculty members from Farhangian University Centers (CFU) and public universities in Iran were interviewed. The data obtained were analyzed using the content analysis method. The results, while providing valuable strategies in this field, show that the major strategies for using hologram technology in the learning environment included are “Promoting the efficiency of the curriculum in order to take advantage of hologram technology,” “Sharing international experiences through hologram technology in the field of education” “Social, cultural and professional context building for the use of hologram technology” “Creating strategic planning in the field of hologram technology in education” and “Changing traditional approaches and adopting correct management actions in order to make optimal use of hologram technology”. The results, show that this technology can definitely be improved and according to the trend it has adopted, it will be one of the main technologies in the field of education. This paper, provides the necessary strategies for the government and the private sector to use this technology in the school curriculum.KEYWORDS: Learning environmenthologram technologyuniversitieseducation Disclosure statementNo potential conflict of interest was reported by the author(s).
Assessing the Practicality of Practical Courses in Online Education

The delivery of education and learning experiences using the internet or other digital technologies is referred to as online education, often known as e-learning. It enables students to access learning resources, communicate with teachers, and take part in schoolwork from anywhere with an internet connection, usually from the comfort of their own homes. During the corona pandemic, there was a huge increase in enrolment in online courses. In most fields, it was established and accepted as a replacement for traditional education, but in courses involving practical topics, laboratory work, or experimental procedures, flaws were found in the methods used to convey the lessons, the practical work, and the evaluation procedures. This paper is an attempt to understand the limitations involved in online education viz technical and practical subjects. Authors have prepared an assessment on various practical based courses in different domains of education which can be delivered and evaluated using online mode. This paper also focusses on challenges involved in delivery and assessment of subjects which authors believe are not suitable for online model.
Application of the Metaverse in Product Engineering – A Workshop for Identification of Potential Field of Action

Metaverse will not only change our interpersonal interaction in the future, but also how we develop future products, systems and services. This paper presents five potential fields of action in product engineering regarding the application of the metaverse in product engineering, which were identified in an overarching workshop. For this purpose, different research profiles were determined in the workshop with people of different expertise in product engineering to identify initial research questions and challenges. Research profiles, based on the product profile in product engineering, revealed the following topics of the five potential fields of action: creativity, validation and verification, cross-generational development, Product-Production-CoDesign and education. Human creativity is an important foundation for product development, as it can generate new ideas. This is precisely where potential fields of action exist, for example, in how far the metaverse enables the execution of creativity methods in virtual environment. The metaverse shows potential in validation and verification, as several relevant stakeholders can be integrated with early virtual, physical or hybrid prototypes. Furthermore, products can be represented across generations to ensure circular economy, upgradeability. Here, the metaverse shows potential to access multi-generational products. In addition, the production system can be developed simultaneously with the product itself to correct errors or problems at an early stage. In addition to the direct support of product development, the developers themselves can also be trained in the metaverse by being introduced to new methods, processes and tools by experts. These five potential fields of action need to be further explored in order to better develop or enable services in the metaverse in product development.
CBDC – An Alternative to Cryptocurrency in the Metaverse: An Indian Perspective

The purpose of this paper is to understand the link between Meta-verse and the Central Bank Digital Currencies (CBDCs) which are issued by some of the Central Governments around the world. Cryptocurrencies are an integral part of the Metaverse ecosystem. The governments are looking at CBDC as an alternative to fight the Cryptocurrencies, prominently because of the security concerns it has raised in the recent past. The study focuses on the Indian perspective of CBDC (e₹). This study is conducted through a review of the literature published in research papers, newspaper articles, and publications by Indian regulatory authorities. The authors have also included their observations from using the CBDC App launched by one of the leading commercial banks in India. The study tries to find a link between Metaverse and CBDC via Cryptocurrency. Since CBDC is still in its pilot stage in India, it is to be seen as to how successful it is in the Indian financial system. Further studies can be conducted at a later stage when and if India’s central bank (the Reserve Bank of India) takes CBDC to its full implementation stage.
Defeating deep learning based de-anonymization attacks with adversarial example

Deep learning (DL) technologies bring new threats to network security. Website fingerprinting attacks (WFA) using DL models can distinguish victim’s browsing activities protected by anonymity technologies. Unfortunately, traditional countermeasures (website fingerprinting defenses, WFD) fail to preserve privacy against DL models. In this paper, we apply adversarial example technology to implement new WFD with static analyzing (SA) and dynamic perturbation (DP) settings. Although DP setting is close to a real-world scenario, its supervisions are almost unavailable due to the uncertainty of upcoming traffics and the difficulty of dependency analysis over time. SA setting relaxes the real-time constraints in order to implement WFD under a supervised learning perspective. We propose Greedy Injection Attack (GIA), a novel adversarial method for WFD under SA setting based on zero-injection vulnerability test. Furthermore, Sniper is proposed to mitigate the computational cost by using a DL model to approximate zero-injection test. FCNSniper and RNNSniper are designed for SA and DP settings respectively. Experiments show that FCNSniper decreases classification accuracy of the state-of-the-art WFA model by 96.57% with only 2.29% bandwidth overhead. The learned knowledge can be efficiently transferred into RNNSniper. As an indirect adversarial example attack approach, FCNSniper can be well generalized to different target WFA models and datasets without suffering fatal failures from adversarial training.
MobileRaT: A Lightweight Radio Transformer Method for Automatic Modulation Classification in Drone Communication Systems

Nowadays, automatic modulation classification (AMC) has become a key component of next-generation drone communication systems, which are crucial for improving communication efficiency in non-cooperative environments. The contradiction between the accuracy and efficiency of current methods hinders the practical application of AMC in drone communication systems. In this paper, we propose a real-time AMC method based on the lightweight mobile radio transformer (MobileRaT). The constructed radio transformer is trained iteratively, accompanied by pruning redundant weights based on information entropy, so it can learn robust modulation knowledge from multimodal signal representations for the AMC task. To the best of our knowledge, this is the first attempt in which the pruning technique and a lightweight transformer model are integrated and applied to processing temporal signals, ensuring AMC accuracy while also improving its inference efficiency. Finally, the experimental results—by comparing MobileRaT with a series of state-of-the-art methods based on two public datasets—have verified its superiority. Two models, MobileRaT-A and MobileRaT-B, were used to process RadioML 2018.01A and RadioML 2016.10A to achieve average AMC accuracies of 65.9% and 62.3% and the highest AMC accuracies of 98.4% and 99.2% at +18 dB and +14 dB, respectively. Ablation studies were conducted to demonstrate the robustness of MobileRaT to hyper-parameters and signal representations. All the experimental results indicate the adaptability of MobileRaT to communication conditions and that MobileRaT can be deployed on the receivers of drones to achieve air-to-air and air-to-ground cognitive communication in less demanding communication scenarios.