Reviewerly/exHarmony huggingface dataset:
A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization

Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research. However, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedures and produces massive data that capture tumor histologic details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nucleus segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nucleus segmentation and TME quantification within image patches. However, existing algorithms are computationally intensive and time consuming for WSI analysis. This study presents Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nucleus segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing WSI analysis methods in nucleus detection, classification accuracy, and computation time. We validated the advantages of the system on 3 different tissue types: lung cancer, liver cancer, and breast cancer. For breast cancer, nucleus features by HD-Yolo were more prognostically significant than both the estrogen receptor status by immunohistochemistry and the progesterone receptor status by immunohistochemistry. The WSI analysis pipeline and a real-time nucleus segmentation viewer are available at https://github.com/impromptuRong/hd_wsi.
Three-Dimensional Path Planning of UAV Based on Improved Particle Swarm Optimization

The traditional particle swarm optimization algorithm is fast and efficient, but it is easy to fall into a local optimum. An improved PSO algorithm is proposed and applied in 3D path planning of UAV to solve the problem. Improvement methods are described as follows: combining PSO algorithm with genetic algorithm (GA), setting dynamic inertia weight, adding sigmoid function to improve the crossover and mutation probability of genetic algorithm, and changing the selection method. The simulation results show that the improved PSO algorithm solves better route results and is faster and more stable.
A Survey on Securing Federated Learning: Analysis of Applications, Attacks, Challenges, and Trends

The growth of data generation capabilities, facilitated by advancements in communication and computation technologies, as well as the rise of the Internet of Things (IoT), results in vast amounts of data that significantly enhance the performance of machine learning models. However, collecting all necessary data to train accurate models is often unfeasible due to privacy laws. Federated Learning (FL) evolved as a collaborative machine learning approach for training models without sharing private data. Unfortunately, several in-design vulnerabilities have been exposed, allowing attackers to infer private data of participants and negatively impacting the performance of the federated model. In light of these challenges and to encourage the development of FL solutions, this paper provides a comprehensive analysis of secure FL proposals that both protect user privacy and enhance the performance of the model. We performed a systematic review using predefined criteria to screen and extract data from multiple electronic databases, resulting in a final set of studies for analysis. Through the systematic review methodology, the paper groups the security vulnerabilities of FL into model-performance and data privacy attacks. It also presents an analysis and comparison of potential mitigation strategies against these attacks. Additionally, the paper conducts a security analysis of state-of-the-art FL applications and proposals based on the vulnerabilities addressed. Finally, the paper outlines the main applications of secure FL and lists future research challenges. The survey highlights the crucial role of security strategies in ensuring the protection of user privacy and model performance in the context of future FL applications.
CADF-CSE: Chaotic map-based authenticated data access/sharing framework for IoT-enabled cloud storage environment

Data is an essential asset of an organization or individual in this information age. Secure and resource-efficient data communication has become paramount in the IoT-enabled cloud storage environment. The users must communicate with the cloud storage servers to access, store, and share the data utilizing the public communication channel, which is exposed to various security threats. Moreover, various security frameworks have been presented to render secure data access, storage, and sharing functionalities for the cloud storage environment. Most of them are complicated and incapacitated of resisting various security attacks. Thus, it is imperative to design a secure and resource-efficient data access, storage, and sharing framework for the cloud storage environment. This paper presents a chaotic map-based authenticated data access/sharing framework for the IoT-enabled cloud storage environment (CADF-CSE). CADF-CSE is designed using the chaotic map, authenticated encryption scheme (AEGIS), and one-way hash function (Esch256). The proposed CADF-CSE comprises three significant phases user access control, data storage, and data sharing. The user access control phase enables the user and cloud server to attain mutual authentication followed by the secret session key establishment. Using the established SK during the access control phase user and cloud server exchange information securely across the public Internet. The data storage phase facilitates the data owner to store the data on a cloud server in encrypted form, where encryption is performed with a secret key derived from the user's biometric. The data-sharing phase enables users to access the data from the cloud server after acquiring mutual permission from the cloud server and the data owner. In addition, an explication of the CADF-CSE through formal and informal analysis shows its resilience to various security attacks. Finally, the performance comparison explicates that CADF-CSE renders better security features while requiring lower computational and communication costs than the related security frameworks.
DHUnet: Dual-branch hierarchical global–local fusion network for whole slide image segmentation

Hematoxylin and eosin stained whole slide images (WSIs) are the gold standard for pathologists and medical professionals for tumor diagnosis, surgery planning, and postoperative examinations. In recent years, due to the rapidly emerging field of deep learning, there have been many convolutional neural networks (CNNs) and Transformer based models applied to computational pathology for accurate segmentation. However, the generalization ability and robustness of models often affect the diagnosis and prognosis of cancer, and we attempt to effectively combine the advantage of CNN which excels in sparse WSI segmentation while Transformer excels in dense cases. In this paper, We propose a novel feature fusion strategy, DHUnet, which utilizes Swin Transformer and ConvNeXt modules with a dual-branch hierarchical U-shaped architecture to fuse global and local features for WSI segmentation. Firstly, a WSI is divided into small patches, which are put into the global and local encoders to generate hierarchical features in parallel. Then, with the help of global–local fusion modules and skip connections, the decoder can fully obtain the global coarse and local fine-grained information during the upsampling process. The proposed Cross-scale Expand Layer can make the patches with the same center but different scales recover the input resolution gradually at each stage. Finally, all the projected pixel-level patch masks are merged to restore the final WSI tumor segmentation. Extensive experiments demonstrate that DHUnet has excellent performance and generalization ability for WSI segmentation, achieving the best segmentation results on three datasets with different types of cancer, densities, and target sizes. The code and pre-processed datasets will be publicly available at https://github.com/pengsl-lab/DHUnet.
Automated defect identification for cell phones using language context, linguistic and smoke-word models

Product defects are a widespread concern for manufacturers when conducting quality and customer relationship management. Prior approaches addressed many electronic products however cell phones are still unexplored. Moreover, prior work mainly focused on the lexicon, probabilistic graphic, failure mode, and effect analysis models but the utilization of word embeddings and language models are not explored. State-of-the-art contextual word embeddings and language models generate automated features and both are very effective in capturing the semantic and contextual dimensions of the text. This paper addresses these dimensions and proposes a novel approach that integrates word2vec and Bidirectional Encoder Representations from Transformers (BERT) models with smoke and domain-specific lexicon approaches to detect product defects for cell phones. Compared to existing studies, smoke and domain-specific approaches surpass the traditional sentiment methods. Moreover, BERT and word2vec presented the best performance with the logistic regression model by achieving an accuracy of 90% on training data and 84% on validation data. A case study in the Apple iPhone and Samsung cell phone industry proves the predominant performance of our approach and provides great potential in defect and customer relationship management.
VRArchEducation: Redesigning building survey process in architectural education using collaborative virtual reality

Architectural education requires students to work as a group under the supervision of a teacher in the same physical environment since they need interaction to learn how to do a set of measurements in practice. However, a number of obstacles, such as pandemic situations, weather conditions, and the crowdedness of working sites, do not allow them to be and work together in a physical environment. Existing digital solutions, such as online and distance education, do not provide the required immersive and collaborative learning environment to assist students to practice in the same virtual environment. Therefore, the main aim of this study is to develop an immersive architectural educational environment, named VRArchEducation, using Virtual Reality (VR). Specifically, we design and implement a system that allows students and teachers to enjoy direct and simultaneous interaction through their virtual avatars regardless of their current physical location. Furthermore, the proposed VRArchEducation will enable users to have hands-on immersive experience while performing a set of measurement tasks. VRArchEducation presents four virtual fundamental measurement tools: water level hose, plumb, measurement tape, and a sketching board. Using these tools, students can perform a building survey process together in the VRArchEducation system just like in a traditional class environment. We conduct a user study to evaluate the system's effectiveness, success, accuracy, and usability. The proposed VRArchEducation has a great potential to be used in architectural education as an alternative to the traditional environment.
Exploiting one-dimensional improved Chebyshev chaotic system and partitioned diffusion based on the divide-and-conquer principle for 3D medical model encryption

As the digital age progresses, 3D models have become increasingly popular in various fields, such as medicine, engineering, and the metaverse. One of the main benefits of using 3D models is their ability to provide more realistic and concrete representations. In the medical field specifically, 3D medical models have proven to be very useful in assisting with diagnosis and treatment. However, this also increases the risk of tampering during transmission, making the protection of 3D model data crucial. In this paper, an encryption algorithm for 3D medical models is proposed. First, a one-dimensional improved Chebyshev chaotic system (1D-ICCS) is designed for generating pseudo-random sequences based on the Chebyshev chaotic system and memristor model, and experimental analyses prove that it has better chaotic performance in several aspects. Further, the chaotic system is used to design a permutation-diffusion framework for 3D models. Therein, a permutation based on combinatorial chaotic indexes (PBCCI) is presented to disrupt data correlation, followed by a partitioned diffusion based on the divide-and-conquer principle (PDBDCP) that separates the floating-point 3D model data into integer and decimal parts and diffuses them using different methods. Experimental results show that the proposed method can resist typical attacks for encrypting 3D medical models and that the ciphertext information entropy reaches 7.999.
Compressed Interaction Graph based Framework for Multi-behavior Recommendation

Multi-types of user behavior data (e.g., clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users’ multi-faceted preferences. However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data “as features” and gradient conflict in multi-task learning when treating multi-behavior data “as labels”. In this paper, we propose CIGF, a Compressed Interaction Graph based Framework, to overcome the above limitations. Specifically, we design a novel Compressed Interaction Graph Convolution Network (CIGCN) to model instance-level high-order relations explicitly. To alleviate the potential gradient conflict when treating multi-behavior data “as labels”, we propose a Multi-Expert with Separate Input (MESI) network with separate input on the top of CIGCN for multi-task learning. Comprehensive experiments on three large-scale real-world datasets demonstrate the superiority of CIGF.
Knowledge-infused Contrastive Learning for Urban Imagery-based Socioeconomic Prediction

Monitoring sustainable development goals requires accurate and timely socioeconomic statistics, while ubiquitous and frequently-updated urban imagery in web like satellite/street view images has emerged as an important source for socioeconomic prediction. Especially, recent studies turn to self-supervised contrastive learning with manually designed similarity metrics for urban imagery representation learning and further socioeconomic prediction, which however suffers from effectiveness and robustness issues. To address such issues, in this paper, we propose a Knowledge-infused Contrastive Learning (KnowCL) model for urban imagery-based socioeconomic prediction. Specifically, we firstly introduce knowledge graph (KG) to effectively model the urban knowledge in spatiality, mobility, etc., and then build neural network based encoders to learn representations of an urban image in associated semantic and visual spaces, respectively. Finally, we design a cross-modality based contrastive learning framework with a novel image-KG contrastive loss, which maximizes the mutual information between semantic and visual representations for knowledge infusion. Extensive experiments of applying the learnt visual representations for socioeconomic prediction on three datasets demonstrate the superior performance of KnowCL with over 30% improvements on R2 compared with baselines. Especially, our proposed KnowCL model can apply to both satellite and street imagery with both effectiveness and transferability achieved, which provides insights into urban imagery-based socioeconomic prediction.
Interaction-level Membership Inference Attack Against Federated Recommender Systems

The marriage of federated learning and recommender system (FedRec) has been widely used to address the growing data privacy concerns in personalized recommendation services. In FedRecs, users’ attribute information and behavior data (i.e., user-item interaction data) are kept locally on their personal devices, therefore, it is considered a fairly secure approach to protect user privacy. As a result, the privacy issue of FedRecs is rarely explored. Unfortunately, several recent studies reveal that FedRecs are vulnerable to user attribute inference attacks, highlighting the privacy concerns of FedRecs. In this paper, we further investigate the privacy problem of user behavior data (i.e., user-item interactions) in FedRecs. Specifically, we perform the first systematic study on interaction-level membership inference attacks on FedRecs. An interaction-level membership inference attacker is first designed, and then the classical privacy protection mechanism, Local Differential Privacy (LDP), is adopted to defend against the membership inference attack. Unfortunately, the empirical analysis shows that LDP is not effective against such new attacks unless the recommendation performance is largely compromised. To mitigate the interaction-level membership attack threats, we design a simple yet effective defense method to significantly reduce the attacker’s inference accuracy without losing recommendation performance. Extensive experiments are conducted with two widely used FedRecs (Fed-NCF and Fed-LightGCN) on three real-world recommendation datasets (MovieLens-100K, Steam-200K, and Amazon Cell Phone), and the experimental results show the effectiveness of our solutions.
HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer

Recent studies have highlighted the limitations of message-passing based graph neural networks (GNNs), e.g., limited model expressiveness, over-smoothing, over-squashing, etc. To alleviate these issues, Graph Transformers (GTs) have been proposed which work in the paradigm that allows message passing to a larger coverage even across the whole graph. Hinging on the global range attention mechanism, GTs have shown a superpower for representation learning on homogeneous graphs. However, the investigation of GTs on heterogeneous information networks (HINs) is still under-exploited. In particular, on account of the existence of heterogeneity, HINs show distinct data characteristics and thus require different treatment. To bridge this gap, in this paper we investigate the representation learning on HINs with Graph Transformer, and propose a novel model named HINormer, which capitalizes on a larger-range aggregation mechanism for node representation learning. In particular, assisted by two major modules, i.e., a local structure encoder and a heterogeneous relation encoder, HINormer can capture both the structural and heterogeneous information of nodes on HINs for comprehensive node representations. We conduct extensive experiments on four HIN benchmark datasets, which demonstrate that our proposed model can outperform the state-of-the-art.
What innovation paths for AI to become a GPT?

Abstract Early commercial applications of artificial intelligence technologies (AITs) were narrow but extremely profitable. Comparable uses of those technologies throughout the economy would lead to a growth boom. Firms which emulated the early applications successfully would make tremendous strategic gains. This is a situation familiar from earlier rounds of information and communication technology. However, for AITs to become a general‐purpose technology across many commercial applications sectors will require some new innovations. This paper examines the innovation paths that could lead to that desirable outcome, the ones that have stalled, the ones in the process now, and the ones that might occur in the future. Strikingly, early AIT use, both commercial and with technical customers, occurred where Digital Transformation was not required for it to succeed. The innovation paths all require Digital Transformation as key steps.
Making the metaverse real

Key stakeholders across different industries with competing visions, motivations and technology bets are investing significant financial resources in developing the building blocks of the metaverse. The purpose of this opinion piece aimed primarily at practitioners is to explore the key question: What is needed to make the metaverse real? What are the key uncertainties that must be resolved? We are motivated by shedding light on whether the metaverse is just a new marketing buzzword or a new transformative trend. We have identified six areas with key uncertainties, which require agreement and solutions from industry players – the space, the interface, the IT infrastructure, the monetary infrastructure, the digital ID and the energy. These uncertainties will be resolved in a series of cooperative and competitive games among the key stakeholders who will need to work on being aligned on a set of standards. The practical implication is that the metaverse cannot be ignored, but there are non-trivial challenges that need to be resolved first. Our paper provides a framework for practitioners to think through these issues in a more systematic fashion.
Breast cancer diagnosis using the fast learning network algorithm

The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosis. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multiclass classification, and c) perform like a kernel-based support vector machine with a structure of the neural network. In this study, two BC databases (Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC)) were used to assess the performance of the FLN algorithm. The results of the experiment demonstrated the great performance of the suggested FLN method, which achieved an average of accuracy 98.37%, precision 95.94%, recall 99.40%, F-measure 97.64%, G-mean 97.65%, MCC 96.44%, and specificity 97.85% using the WBCD, as well as achieved an average of accuracy 96.88%, precision 94.84%, recall 96.81%, F-measure 95.80%, G-mean 95.81%, MCC 93.35%, and specificity 96.96% using the WDBC database. This suggests that the FLN algorithm is a reliable classifier for diagnosing BC and may be useful for resolving other application-related problems in the healthcare sector.