Reviewerly/exHarmony huggingface dataset:
Mosquito Larvae Detection using Deep Learning

Dengue cases has become endemic in Malaysia. The cost of operation to exterminate mosquito habitats are also high. To do effective operation, information from community are crucial. But, without knowing the characteristic of Aedes larvae it is hard to recognize the larvae without guide from the expert. The use of deep learning in image classification and recognition is crucial to tackle this problem. The purpose of this project is to conduct a study of characteristics of Aedes larvae and determine the best convolutional neural network model in classifying the mosquito larvae. 3 performance evaluation vector which is accuracy, log-loss and AUC-ROC will be used to measure the model’s individual performance. Then performance category which consist of Accuracy Score, Loss Score, File Size Score and Training Time Score will be used to evaluate which model is the best to be implemented into web application or mobile application. From the score collected for each model, ResNet50 has proved to be the best model in classifying the mosquito larvae species.
Assessing Blockchain Adoption in Supply Chain Management, Antecedent of Technology Readiness, Knowledge Sharing and Trading Need

The present research aimed to establish a framework integrating the concept of technology readiness with variables that accomplished the blockchain adoption theory to identify the impact of blockchain adoption on supply chain transparency, blockchain transparency, and supply chain performance. The methodology used was quantitative with PLS-SEM as the analysis method. There were 295 validated datasets used. The procedure of data collection involved questionnaires. The key finding of the research confirmed the six proposed hypotheses. It was also confirmed that technology readiness, knowledge sharing, and trading needs were significant for the profitability of blockchain technology adoption in supply chain management. On the other hand, blockchain adoption played a significant role in supply chain transparency, blockchain transparency, and supply chain performance. The novelty of this research is in the integration of technology readiness into blockchain in the field of supply chain management. This research can be used to improve and analyze the success rate of blockchain adoption in supply chain management systems. The findings of this study contribute to several aspects, namely practical and academic implications, by providing more insights that correlate with blockchain integration into supply chain management systems. Doi: 10.28991/ESJ-2022-06-05-01 Full Text: PDF
Multi-User Semantic Communications for Cooperative Object Identification

In this paper, a multi-user semantic communication system is studied to execute object-identification tasks, where correlated source data among different users is transmitted via a shared channel, and the introduced inter-user data-stream interference (IDI) deteriorates the identification performance severely. Traditional solutions adopt powerful channel codes for individual data protection, e.g., very low coding rate, to guarantee the identification performance, at the cost of sacrificing the real-time requirements. We propose to exploit the data correlation among users to perform cooperative identification. Specifically, by designing a convolutional neural network (CNN) based framework and constructing a combination of loss functions, a deep learning (DL) based multi-user semantic communication system for cooperative object identification, named DeepSC-COl, is proposed to fuse individual semantic features into a global feature through dynamically-tailored weights. In this way, multiple semantic features are jointly leveraged for identification without an extra increase of latency. Evaluation results show that the proposed DeepSC-COI outperforms the non-cooperative scheme with the performance gain of 86.9% at -3 dB, in terms of mean Average Precision (mAP).
RVMOS: Range-View Moving Object Segmentation Leveraged by Semantic and Motion Features

Detecting traffic participants is an essential and age-old problem in autonomous driving. Recently, the recognition of moving objects has emerged as a major issue in this field for safe driving. In this paper, we present RVMOS, a LiDAR Range-View-based Moving Object Segmentation framework that segments moving objects given a sequence of range-view images. In contrast to the conventional method, our network incorporates both motion and semantic features, each of which encodes the motion of objects and the surrounding circumstance of the objects. In addition, we design a new feature extraction module suitably designed for range-view images. Lastly, we introduce simple yet effective data augmentation methods: time interval modulation and zero residual image synthesis. With these contributions, we achieve a 19% higher performance (mIoU) with 10% faster computational time (34 FPS on RTX 3090) than the state-of-the-art method with the SemanticKitti benchmark. Extensive experiments demonstrate the effectiveness of our network design and data augmentation scheme.
High-speed three-dimensional shape measurement with inner shifting-phase fringe projection profilometry

Fringe projection profilometry (FPP) has been extensively studied in the field of three-dimensional (3D) measurement. Although FPP always uses high-frequency fringes to ensure high measurement accuracy, too many patterns are projected to unwrap the phase, which affects the speed of 3D reconstruction. We propose a high-speed 3D shape measurement method using only three high-frequency inner shifting-phase patterns (70 periods), which satisfies both high precision and high measuring speed requirements. Besides, our proposed method obtains the wrapped phase and the fringe order simultaneously without any other information and constraints. The proposed method has successfully reconstructed moving objects with high speed at the camera’s full frame rate (1700 frames per second).
Vertical Federated Learning

We learned from Chapter 4 that horizontal federated learning (HFL) is applicable to scenarios where participants’ datasets share the same feature space but differ in sample spaces. Thus, HFL is convenient to be applied to build applications powered by a massive amount of mobile devices [McMahan et al., 2016a, McMahan and Ramage, 2017]. In those cases, the targets being federated are the individual consumers of applications, which can be considered as B2C (business-to-consumer) paradigm. However, in many practical scenarios, the participants of federated learning are organizations that collected different data features for the same group of people for pursuing different business goals. Those organizations often have strong motivations to cooperate to improve business efficiency, which can be considered as B2B (business-to-business) paradigm.
Semi-supervised vision transformer with adaptive token sampling for breast cancer classification

Various imaging techniques combined with machine learning (ML) models have been used to build computer-aided diagnosis (CAD) systems for breast cancer (BC) detection and classification. The rise of deep learning models in recent years, represented by convolutional neural network (CNN) models, has pushed the accuracy of ML-based CAD systems to a new level that is comparable to human experts. Existing studies have explored the usage of a wide spectrum of CNN models for BC detection, and supervised learning has been the mainstream. In this study, we propose a semi-supervised learning framework based on the Vision Transformer (ViT). The ViT is a model that has been validated to outperform CNN models on numerous classification benchmarks but its application in BC detection has been rare. The proposed method offers a custom semi-supervised learning procedure that unifies both supervised and consistency training to enhance the robustness of the model. In addition, the method uses an adaptive token sampling technique that can strategically sample the most significant tokens from the input image, leading to an effective performance gain. We validate our method on two datasets with ultrasound and histopathology images. Results demonstrate that our method can consistently outperform the CNN baselines for both learning tasks. The code repository of the project is available at https://github.com/FeiYee/Breast-area-TWO .
A Digital Twin for the 5G Era: the SPIDER Cyber Range

Service providers, 5G network operators and, more generally, vertical industries face today a dangerous shortage of highly skilled cybersecurity experts. Along with the escalation and growing sophistication of cyber-attacks, 5G networks require the training of skilled and highly competent cyber forces. To meet these requirements, the SPIDER cyber range focuses specifically on 5G, and is based on three pillars, (i) cyber security assessment, (ii) training cyber security teams to defend against complex cyber-attack scenarios, and (iii) evaluation of cyber risk. The SPIDER cyber range replicates a customized 5G network, enabling the execution of cyber-exercises that take advantage of hands-on interaction in real time, the sharing of information between participants, and the gathering of feedback from network equipment, as well as the development and adaptation of advanced operational procedures. This aims to help 5G security professionals improve their ability to collaboratively manage and predict security incidents, complex attacks, and propagated vulnerabilities. The SPIDER cyber range is validated in two relevant use case scenarios aimed at demonstrating, in a realistic, measurable, and replicable way the transformations SPIDER will bring to the cybersecurity industry.
Experimental Investigation of Quantum Correlations in a Two-Qutrit Spin System

We report an experimental investigation of quantum correlations in a two-qutrit spin system in a single nitrogen-vacancy center in diamond at room temperatures. Quantum entanglement between two qutrits was observed at room temperature, and the existence of nonclassical correlations beyond entanglement in the qutrit case has been revealed. Our work demonstrates the potential of the NV centers as the multiqutrit system to execute quantum information tasks and provides a powerful experimental platform for studying the fundamental physics of high-dimensional quantum systems in the future.
Image copy-move forgery detection and localization based on super-BPD segmentation and DCNN

Abstract With the increasing importance of image information, image forgery seriously threatens the security of image content. Copy-move forgery detection (CMFD) is a greater challenge because its abnormality is smaller than other forgeries. To solve the problem that the detection results of the most image CMFD based on convolutional neural networks (CNN) have relatively low accuracy, an image copy-move forgery detection and localization based on super boundary-to-pixel direction (super-BPD) segmentation and deep CNN (DCNN) is proposed: SD-Net. Firstly, the segmentation technology is used to enhance the connection between the same or similar image blocks, improving the detection accuracy. Secondly, DCNN is used to extract image features, replacing conventional hand-crafted features with automatic learning features. The feature pyramid is used to improve the robustness to the scaling attack. Thirdly, the image BPD information is used to optimize the edges of rough detected image and obtain final detected image. The experiments proved that the SD-Net could detect and locate multiple, rotated, and scaling forgery well, especially large-level scaling forgery. Compared with other methods, the SD-Net is more accurately located and robust to various post-processing operations: brightness change, contrast adjustments, color reduction, image blurring, JPEG compression, and noise adding.
Immersive Virtual Environments’ Impact on Individual and Collective Creativity

Abstract. This paper explores the recent advances in research concerning the impact of immersive virtual environments affordances on the expression of users’ creativity at individual and team levels. While the top virtual reality (VR) application areas are entertainment and gaming, simulation and training for professionals, research in the domain of the psychology of creativity and VR is advancing rapidly in Europe. Indeed, between 2014 and 2021, 72% of publications in this domain resulted from European research in diverse fields such as engineering, design, music composition, art-making, and so forth. These studies took advantage of advanced VR affordances, such as head and hand motion trackers to synchronize an avatar in real-time, live streaming of a video into a VR headset screen to create artwork, etc. Four main topics were explored: (a) new creativity techniques involving a virtual upgrade of traditionally used techniques, virtual sketching and prototyping, as well as sophisticated interactive virtual menus and motion tracking systems, (b) the right digital self-representation for enhancing creativity and the degree to which users identify with the “persona avatar” in the context of user-centered innovations, (c) the impact of physical and social virtual contextual cues on creative performance, and (d) the perception of virtual reality by creativity and innovation professionals. Our review confirms that VR supports greater creative performance at individual and collaborative levels as well as enjoyment and fun. However, as rich and varied as this literature has become, it presents major methodological limitations that should be addressed in future research.
Can (A)I Give You a Ride? Development and Validation of the CRUISE Framework for Autonomous Vehicle Services

Advances in artificial intelligence (AI) are increasingly enabling firms to develop services that utilize autonomous vehicles (AVs). Yet, there are significant psychological barriers to adoption, and insights from extant literature are insufficient to understand customer emotions regarding AV services. To allow for a holistic exploration of customer perspectives, we synthesize multidisciplinary literature to develop the Customer Responses to Unmanned Intelligent-transport Services based on Emotions (CRUISE) framework, which lays the foundation for improved strategizing, targeting, and positioning of AV services. We subsequently provide empirical support for several propositions underpinning the CRUISE framework using representative multinational panel data ( N = 27,565) and an implicit association test ( N = 300). We discover four distinct customer segments based on their preferred degree of service autonomy and service risk. The segments also differ in terms of the valence and intensity of emotional responses to fully autonomous vehicle services. Additionally, exposure to positive information about AV services negatively correlates with the likelihood of membership in the two most resistant segments. Our contribution to service research is chiefly twofold; we provide: 1) a formal treatise of AV services, emphasizing their uniqueness and breadth of application, and 2) empirically validated managerial directions for effective strategizing based on the CRUISE framework.
An introduction to machine learning for classification and prediction

Abstract Classification and prediction tasks are common in health research. With the increasing availability of vast health data repositories (e.g. electronic medical record databases) and advances in computing power, traditional statistical approaches are being augmented or replaced with machine learning (ML) approaches to classify and predict health outcomes. ML describes the automated process of identifying (“learning”) patterns in data to perform tasks. Developing an ML model includes selecting between many ML models (e.g. decision trees, support vector machines, neural networks); model specifications such as hyperparameter tuning; and evaluation of model performance. This process is conducted repeatedly to find the model and corresponding specifications that optimize some measure of model performance. ML models can make more accurate classifications and predictions than their statistical counterparts and confer greater flexibility when modelling unstructured data or interactions between covariates; however, many ML models require larger sample sizes to achieve good classification or predictive performance and have been criticized as “black box” for their poor transparency and interpretability. ML holds potential in family medicine for risk profiling of patients’ disease risk and clinical decision support to present additional information at times of uncertainty or high demand. In the future, ML approaches are positioned to become commonplace in family medicine. As such, it is important to understand the objectives that can be addressed using ML approaches and the associated techniques and limitations. This article provides a brief introduction into the use of ML approaches for classification and prediction tasks in family medicine.
TransCUNet: UNet cross fused transformer for medical image segmentation

Accurate segmentation of medical images is crucial for clinical diagnosis and evaluation. However, medical images have complex shapes, the structures of different objects are very different, and most medical datasets are small in scale, making it difficult to train effectively. These problems increase the difficulty of automatic segmentation. To further improve the segmentation performance of the model, we propose a multi-branch network model, called TransCUNet, for segmenting medical images of different modalities. The model contains three structures: cross residual fusion block (CRFB), pyramidal pooling module (PPM) and gated axial-attention, which achieve effective extraction of high-level and low-level features of images, while showing high robustness to different size segmentation objects and different scale datasets. In our experiments, we use four datasets to train, validate and test the models. The experimental results show that TransCUNet has better segmentation performance compared to the current mainstream segmentation methods, and the model has a smaller size and number of parameters, which has great potential for clinical applications.
PFDN: Pyramid Feature Decoupling Network for Single Image Deraining

Restoring images degraded by rain has attracted more academic attention since rain streaks could reduce the visibility of outdoor scenes. However, most existing deraining methods attempt to remove rain while recovering details in a unified framework, which is an ideal and contradictory target in the image deraining task. Moreover, the relative independence of rain streak features and background features is usually ignored in the feature domain. To tackle these challenges above, we propose an effective Pyramid Feature Decoupling Network (i.e., PFDN) for single image deraining, which could accomplish image deraining and details recovery with the corresponding features. Specifically, the input rainy image features are extracted via a recurrent pyramid module, where the features for the rainy image are divided into two parts, i.e., rain-relevant and rain-irrelevant features. Afterwards, we introduce a novel rain streak removal network for rain-relevant features and remove the rain streak from the rainy image by estimating the rain streak information. Benefiting from lateral outputs, we propose an attention module to enhance the rain-irrelevant features, which could generate spatially accurate and contextually reliable details for image recovery. For better disentanglement, we also enforce multiple causality losses at the pyramid features to encourage the decoupling of rain-relevant and rain-irrelevant features from the high to shallow layers. Extensive experiments demonstrate that our module can well model the rain-relevant information over the domain of the feature. Our framework empowered by PFDN modules significantly outperforms the state-of-the-art methods on single image deraining with multiple widely-used benchmarks, and also shows superiority in the fully-supervised domain.
An effective explainable food recommendation using deep image clustering and community detection

In food diet communication domain, images convey important information to capture users' attention beyond the traditional ingredient content, making it crucial to influence user-decision about the relevancy of a given diet. By using a deep learning-based image clustering method, this paper proposes an Explainable Food Recommendation system that uses the visual content of food to justify their recommendations. n the recommendation system. Especially, a new similarity score based on a tendency measure that quantifies the extent to which user community prefers a given food category is introduced and incorporated in the recommendation. Finally, a rule-based explainability is introduced to enhance transparency and interpretability of the recommendation outcome. Our experiments on a crawled dataset showed that the proposed method enhances recommendation quality in terms of precision, recall, F1, and Normalized Discounted Cumulative Gain (NDCG) by 7.35%, 6.70%, 7.32% and 14.38%, respectively, when compared to other existing methodologies for food recommendation. Besides ablation study is performed to demonstrate the technical soundness of the various components of our recommendation system.
A generalizable and robust deep learning algorithm for mitosis detection in multicenter breast histopathological images

Mitosis counting of biopsies is an important biomarker for breast cancer patients, which supports disease prognostication and treatment planning. Developing a robust mitotic cell detection model is highly challenging due to its complex growth pattern and high similarities with non-mitotic cells. Most mitosis detection algorithms have poor generalizability across image domains and lack reproducibility and validation in multicenter settings. To overcome these issues, we propose a generalizable and robust mitosis detection algorithm (called FMDet), which is independently tested on multicenter breast histopathological images. To capture more refined morphological features of cells, we convert the object detection task as a semantic segmentation problem. The pixel-level annotations for mitotic nuclei are obtained by taking the intersection of the masks generated from a well-trained nuclear segmentation model and the bounding boxes provided by the MIDOG 2021 challenge. In our segmentation framework, a robust feature extractor is developed to capture the appearance variations of mitotic cells, which is constructed by integrating a channel-wise multi-scale attention mechanism into a fully convolutional network structure. Benefiting from the fact that the changes in the low-level spectrum do not affect the high-level semantic perception, we employ a Fourier-based data augmentation method to reduce domain discrepancies by exchanging the low-frequency spectrum between two domains. Our FMDet algorithm has been tested in the MIDOG 2021 challenge and ranked first place. Further, our algorithm is also externally validated on four independent datasets for mitosis detection, which exhibits state-of-the-art performance in comparison with previously published results. These results demonstrate that our algorithm has the potential to be deployed as an assistant decision support tool in clinical practice. Our code has been released at https://github.com/Xiyue-Wang/1st-in-MICCAI-MIDOG-2021-challenge.