Reviewerly/exHarmony huggingface dataset:
An attentive-based generative model for medical image synthesis

Magnetic resonance (MR) and computer tomography (CT) imaging are valuable tools for diagnosing diseases and planning treatment. However, limitations such as radiation exposure and cost can restrict access to certain imaging modalities. To address this issue, medical image synthesis can generate one modality from another, but many existing models struggle with high-quality image synthesis when multiple slices are present in the dataset. This study proposes an attention-based dual contrast generative model, called ADC-cycleGAN, which can synthesize medical images from unpaired data with multiple slices. The model integrates a dual contrast loss term with the CycleGAN loss to ensure that the synthesized images are distinguishable from the source domain. Additionally, an attention mechanism is incorporated into the generators to extract informative features from both channel and spatial domains. To improve performance when dealing with multiple slices, the $K$-means algorithm is used to cluster the dataset into $K$ groups, and each group is used to train a separate ADC-cycleGAN. Experimental results demonstrate that the proposed ADC-cycleGAN model produces comparable samples to other state-of-the-art generative models, achieving the highest PSNR and SSIM values of 19.04385 and 0.68551, respectively. We publish the code at https://github.com/JiayuanWang-JW/ADC-cycleGAN.
POST-PANDEMIC DIGITAL SELF-EFFICACY AND ONLINE LEARNING READINESS OF PRE-SERVICE TEACHERS

The study examined the digital self-efficacy and online learning readiness of pre-service teachers after the pandemic. Using a descriptive correlational research design, the results indicated that pre-service teachers have a high level of digital self-efficacy in several areas of technology integration, including digital usage, digital application, digital-infused learning, technology literacy, and digital-supported assessment. In addition, the results indicate that pre-service teachers are on the verge of being ready for online learning, though they still need to improve their time management and written communication skills. Age was found to be a significant factor in readiness for online learning, with those aged 25-34 demonstrating greater readiness than those aged 18-24 and 35-44. However, there was no significant difference when the participants were grouped by gender. These findings provide valuable insights into the digital self-efficacy and e-learning readiness of pre-service teachers, which can inform the development of strategies and interventions to improve their skills and, ultimately, enhance the quality of education in the post-pandemic world. Recommendations for digital education post-pandemic include providing training and resources for educators, supporting female teachers, incorporating online readiness training for pre-service teachers, tailoring programs to different age groups, and providing additional support for younger pre-service teachers to increase their digital self-efficacy.
A Study on Children’s Play with Media Contents: Focusing on In-depth Interviews with Early Childhood Teachers

This study aims to examine how children play with media contents and how early childhood teachers perceive and guide children's media contents play. To this end, this study conducted an in-depth interview with 10 early childhood teachers and drew the following results. First, young children were familiar with the use of media contents, which were used in various ways as a material for children's play in educational institutions. Teachers perceived that media contents play plays an important role in the growth and development of children, that is, the formation of peer culture, the development of social skills, and the expansion of imagination and creativity. Second, children's media contents play also has negative effects on children's growth such as the formation of prejudice and stereotypes, the learning of violence, and the alienation of children. Third, though teachers recognized that correct guidance is needed, they were experiencing difficulties in teaching due to the lack of guidelines and education for early childhood guidance.
A Study on the Reading Education Method of &lt;ChoiGoWoonCheon(崔孤雲傳)&gt;

This study is to propose the oriented value and activity method of reading education of &lt;ChoiGoWoonCheon&gt; based on the class goal of 'reading and writing' specified in the &lt;2022 Revised Korean Language Curriculum&gt; announced by the Ministry of Education. Choi Chiwon in &lt;ChoiGoWoonCheon&gt; overcomes a national crisis with not the forces but the power of culture. Therefore reading education should be to discover the oriented values of cultural heroes. The method of expression activities was designed to include the ‘visioning the narratives’ as reading of the text in detail, ‘virtual casting’ to integrally recognize the character of the main character, 'tracking the journey of search' to organize the exploration structure of the main character, and 'media production' as a creative expression activity.
Deep learning for blood cells classification and localisation

Nowadays pathologists have to analyse blood cells manually with the aim to diagnose diseases. In order to perform this manual task blood samples must be collected from the patient and then placed on a microscope slide. This slide is studied with the aim to detect the abnormality presence. To automatise this process by helping pathologists, in this paper we propose the adoption of deep learning to automatically count and localise red blood cells, white blood cells and platelets by analysing blood microscopic images. We resort to the YOLO object detection model, able to look at the whole image so its predictions are informed by global context in the image. To show the effectiveness of the proposed method we evaluate our model on a dataset composed by 874 microscopic blood images, obtaining interesting results. Furthermore we show several examples related to how the proposed method can be helpful for the pathologists in their real-world work.
B-CNN: Betadeep Convolutional Neural Network over encrypted data

The results of automatic learning algorithms based on deep neural networks are impressive, and they are extensively used in a variety of fields. However, access to private information, frequently sensitive to confidentiality (financial, medical, etc.), is required in order to use them. This calls for good precision as well as special attention to the privacy and security of the data. In this paper, we propose a novel approach to solve this issue by using Convolutional Neural Network (CNN) model over encrypted data. In order to achieve our contribution, we focus on approximating the often used activation functions that seem to be the key functions in CNN networks which are: ReLu, Sigmoid and Tanh. We start by creating a low-degree polynomial, which is essential for a successful homomorphic encryption (HE). This polynomial which is based on Beta function and its primitive will be used as an activation function. The next step is to build a CNN model using batch normalization to ensure that the data are contained inside a limited interval. Finally, MNIST is used in order to evaluate our methodology and assess the effectiveness of the proposed approach. The experimental results support the efficacy of the proposed approach.
Relative attributes classification via transformers and rank SVM loss

We propose a new model for learning to rank two images with respect to their relative strength of expression for a given attribute. We address this problem – called relative attribute learning — using a vision transformer backbone. The embedded representations of the two images to be compared are extracted and used for comparison with a ranking head, in an end-to-end fashion. The results demonstrate the strength of vision transformers and their suitability for relative attributes classification. Our proposed approach outperforms the state-of-the-art by a large margin, achieving 90.40% and 98.14% mean accuracy over the attributes of LFW-10 and Pubfig datasets.
Learning to detect 3D planes from a single plenoptic image

In this paper, we propose a deep neural network to detect 3D planar surfaces from single plenoptic images captured by a Lytro Illum camera. Different from learning methods based on a single RGB image, we train a network to exploit the light distribution information both in spatial and angular dimensions from multi-sub-aperture images. The features from each sub-aperture image are extracted by using parameter-sharing convolutional layers and then fused to jointly infer the parameters of planes, depths, and segmentation masks. The experiments demonstrate that our approach outperforms the existing state-of-the-art methods with significant margins in the plane detection metrics.
Efficient loss functions for GAN-based style transfer

Style transfer aims to render a new artistic image based on a content image and given artwork style. Recent style transfer techniques often suffer structure distortion and artifact problems that abate the quality of stylized images. Motivated by these observations and the previous works, we introduce a novel GAN framework to enhance the aesthetics, faithfulness and flexibility in the style transfer process. The key factor of our model is the Laplacian Pyramid loss that naturally forces the content preservation and the ResidualStyle discriminator block to capture the artwork’s painting style better. In contrast to existing methods that calculate the Euclidean distance between the features of generated image and content image, our Laplacian Pyramid loss better captures the content representation by different frequency bands of the content image. As evaluated by experimental results, our framework surmounts the unrealistic artifacts to synthesize the photorealistic artworks in real-time, hence attaining striking visual effects.
Classification of imbalanced bioassay data with features learned using stacked autoencoder

Bioassay data classification is an important task in drug discovery. However, the data used in classification is highly imbalanced, leading to inaccuracies in classification for the minority class. We propose a novel approach for classification in which we train separate models by using different features that are derived by training stacked autoencoders (SAE). Experiments are performed on 7 bioassay datasets, in which each data file consists of feature descriptors for every compound along with class label of compound being active, or inactive. We first perform data cleaning using borderline synthetic minority oversampling technique (SMOTE) followed by removing the Tomek links, and then learn different features hierarchically, based on the cleaned data or feature vectors. We then train separate cost-sensitive feed-forward neural network (FNN) classifiers using the hierarchical features in order to obtain the final classification. To increase the True Positive Rate (TPR), a test sample is labeled as active if at least one classifier predicts it as active. In this paper, we demonstrate that by data cleaning and learning separate classifiers one can improve the TPR and F1 score when compared to other machine learning approaches. To the best of our knowledge, the researchers have not yet attempted the use of SAE and FNN for classifying bioassay data.
sTrade: Blockchain based secure energy trading using vehicle-to-grid mutual authentication in smart transportation

Electric vehicles are gaining popularity in the Vehicle-to-Grid network due to recharge batteries during off-peak electricity hours and discharge during peak hours. It allows for accomplishing electricity requirements in periods of high demand and additional storage in case of surplus generation from the grid. Electric vehicles also earn reward points for energy trading. The significant trading data volume processed by the underlying Vehicle-to-Grid infrastructure results in numerous security, privacy and scalability challenges. Researchers suggested various energy trading schemes but suffered from anonymity, large overheads, and dependency on a centralized system, which may cause single-point failure. As a solution, new design principles are desirable to enable the next generation of Vehicle-to-Grid networks. Decentralized energy information networks are a crucial aspect of future power grids, and blockchain technology offers promising potential to support local energy trading and decentralized power generation. Thus, a proposed framework uses the blockchain-based decentralized, secure, and privacy vehicle-to-grid network mutual authentication and energy trading system using elliptic curve cryptography with a scheduling feature. The Proposed scheme is divided into four steps (1) Registration, (2) Scheduling, (3) Mutual authentication and (4) Consensus and energy trading. Distributed ledger blockchain technology makes every transaction valid and authentic, possibly minimizing or eliminating mediators in energy trading, while lightweight elliptical curve cryptography is used for mutual authentication. Further, the performance of our scheme is justified by using the popular AVISPA simulation tool. Our analysis illustrates that the proposed model is secure, privacy-preserving, and supports minimal communicational and computational overhead of 1750 bits and 7.027 ms compared to state-of-the-art work.
Confidence-aware calibration and scoring functions for curriculum learning

Despite the great success of state-of-the-art deep neural networks, several studies have reported models to be over-confident in predictions, indicating miscalibration. Label Smoothing has been proposed as a solution to the over-confidence problem and works by softening hard targets during training, typically by distributing part of the probability mass from a ‘one-hot’ label uniformly to all other labels. However, neither model nor human confidence in a label are likely to be uniformly distributed in this manner, with some labels more likely to be confused than others. In this paper we integrate notions of model confidence and human confidence with label smoothing, respectively <i>Model Confidence LS </i>and <i>Human Confidence LS</i>, to achieve better model calibration and generalization. To enhance model generalization, we show how our model and human confidence scores can be successfully applied to curriculum learning, a training strategy inspired by learning of ‘easier to harder’ tasks. A higher model or human confidence score indicates a more recognisable and therefore easier sample, and can therefore be used as a scoring function to rank samples in curriculum learning. We evaluate our proposed methods with four state-of-the-art architectures for image and text classification task, using datasets with multi-rater label annotations by humans. We report that integrating model or human confidence information in label smoothing and curriculum learning improves both model performance and model calibration. The code are available at https://github.com/AoShuang92/Confidence Calibration CL.
Improvements to code2vec: Generating path vectors using RNN

Source code analysis has many application scenarios, such as code plagiarism detection and software vulnerability search. Source code analysis can benefit from machine learning, but it typically requires a standard vector representation and cannot be directly applied to the source code. Thus, we are required to embed source code into vector representation while maintaining the semantics of the code as much as possible. Code2vec proposes a code embedding method that converts source code into code vector through Abstract Syntax Tree(AST). However, we found that code2vec uses a hashing algorithm to generate the identifier for the path in the path context, which leads to the loss of node information in the path and also causes the model training parameters to be very large. Therefore, we present a new path representation which utilizes RNN to generate vectors for paths. We also proposed alternative model designs and evaluated their impact on the model in the experiments. The results we obtained in a challenging source code classification task suggest that, compared to code2vec, the RNN-based paths representation can produce a better embedding model with fewer training parameters.
View‐graph key‐subset extraction for efficient and robust structure from motion

Abstract Structure from motion (SfM) is used to recover camera poses and the sparse structure of real scenes from multiview images. SfM methods construct a view‐graph from the matching relationships of images. Redundancy and incorrect edges are usually observed in it. Redundancy inhibits the efficiency and incorrect edges result in the misalignment of structures. In addition, the uneven distribution of vertices usually affects the global accuracy. To address these problems, we propose a coarse‐to‐fine approach in which the poses of an extracted key‐subset of images are first computed and then all remaining images are oriented. The core of this approach is view‐graph key‐subset extraction, which not only prunes redundant data and incorrect edges but also obtains properly distributed key‐subset vertices. The extraction approach is based on a replaceability score and an iteration‐update strategy. In this way, only vertices with high SfM importance are preserved in the key‐subset. Different public datasets are used to evaluate our approach. Due to the absence of ground‐truth camera poses in large‐scale datasets, we present new datasets with accurate camera poses and point clouds. The results demonstrate that our approach greatly increases the efficiency of SfM. Furthermore, the robustness and accuracy can be improved.
The Interactive Impact of Immersive Virtual Reality Experience on the SensesTake the VR Project "Unlocking Perception in Virtual Reality" as an Example

The research background of this paper will revolve around the VR immersive experience project "Unlocking perception in virtual reality". The main motivation of this analysis is to combine virtual reality projects with human perception unlocking as an entry point, and focus on analyzing the impact on the experiencer's immersion. The experimental conclusion found that "unlocking perception in virtual reality" has a good immersive effect on human vision and hearing. Although "Unlocking Perception in Virtual Reality" lacks story background and game hints compared to other game projects, and requires players to explore by themselves, it also increases the sense of immersion because of this design. This project demo brings a special VR experience to the experiencer by unlocking the main game concept of perception.
Sec2vec: Anomaly Detection in HTTP Traffic and Malicious URLs

In this paper, we show how methods known from Natural Language Processing (NLP) can be used to detect anomalies in HTTP requests and malicious URLs. Most of the current solutions focusing on a similar problem are either rule-based or trained using manually selected features. Modern NLP methods, however, have great potential in capturing a deep understanding of samples and therefore improving the classification results. Other methods, which rely on a similar idea, often ignore the interpretability of the results, which is so important in machine learning. We are trying to fill this gap. In addition, we show to what extent the proposed solutions are resistant to concept drift. In our work, we compare three different vectorization methods: simple BoW, fastText, and the current state-of-the-art language model RoBERTa. The obtained vectors are later used in the classification task. In order to explain our results, we utilize the SHAP method. We evaluate the feasibility of our methods on four different datasets: CSIC2010, UNSW-NB15, MALICIOUSURL, and ISCX-URL2016. The first two are related to HTTP traffic, the other two contain malicious URLs. The results we show are comparable to others or better, and most importantly - interpretable.
A Theoretical Framework for Self-Adaptive Systems: Specifications, Formalisation, and Architectural Implications

In the last two decades, the popularity of self-adaptive systems has significantly increased in the literature across various research domains. However, despite the extensive work in this research field and the vibrant research community, the literature still needs a more precise understanding of self-adaptive systems, including clear specifications and usable definitions. So far, the terminology related to self-adaptive systems is still used liberally, which can result in different entities having a different understanding of what these systems are. Having an ambiguous description of the terminology and the lack of a precise understanding of these systems adds complexity to an already complex field of research. As a result, the overarching objective of this paper is to broaden the understanding of self-adaptive systems. For improved clarity and coherence--- potentially yielding to even higher prominence of this domain---we consider as essential to set a common terminology and gain a better comprehension of the semantics of that terminology. In response, in this paper, we propose a theoretical framework for self-adaptive systems, in which we specify the process of self-adaptation, elicit the minimum requirements for self-adaptive systems, formalise these systems, and discuss various architectural implications from our theoretical contributions. Ideally, this work 1) sets a foundation for more constructive scientific debates, 2) complements the already existing works in this field, and 3) sets the foundation for future engineering approaches for self-adaptive systems.
Show, Prefer and Tell: Incorporating User Preferences into Image Captioning

Current image captioning models produce fluent captions, but they rely on a one-size-fits-all approach that does not take into account the preferences of individual end-users. We present a method to generate descriptions with an adjustable amount of content that can be set at inference-time, thus providing a step toward a more user-centered approach to image captioning.*
A fully automated and scalable Parallel Data Augmentation for Low Resource Languages using image and text analytics

Linguistic diversity across the world creates a disparity with the availability of good quality digital language resources thereby restricting the technological benefits to majority of human population. The lack or absence of data resources makes it difficult to perform NLP tasks for low-resource languages. This paper presents a novel scalable and fully automated methodology to extract bilingual parallel corpora from newspaper articles using image and text analytics. We validate our approach by building parallel data corpus for two different language combinations and demonstrate the value of this dataset through a downstream task of machine translation and improve over the current baseline by close to 3 BLEU points.
Emerging Techniques and Algorithms Used in Soft Computation

Machines’ conceptual intelligence is built on the foundation of soft computing. The goal is to take use of tolerance for imprecision, ambiguity, approximate reasoning, and partial truth in order to create a close likeness to human decision-making. Soft computing is the study of reasoning, thinking, recognizing, and evaluating problems in the real world using biologically inspired methodologies. There are no mathematical models accessible in many applications, and they are impossible to use. Mathematical models don’t always provide the most accurate or precise results. Soft computing, on the other hand, establishes techniques such as neural networks, evolutionary computing, fuzzy logic, genetic algorithms, probabilistic reasoning, and statistics that are ideal for those situations. This chapter focuses on various soft computing approaches and their applications.