Reviewerly/exHarmony huggingface dataset:
Privacy-utility equilibrium data generation based on Wasserstein generative adversarial networks

To solve the contradictory problem of local data sharing and privacy protection, this paper presented the models and algorithms for privacy-utility equilibrium data generation based on Wasserstein generative adversarial networks. First, we introduce the expected estimation error between the discriminant probability of real data and generated data into Wasserstein generative adversarial networks, and we formally construct a basic mathematical model of privacy-utility equilibrium data generation based on computationally indistinguishable. Second, we construct the basic model and the basic algorithm of privacy-utility equilibrium data generation based on Wasserstein generative adversarial networks according to the constructed basic mathematical model, and our theoretical analysis results show that the basic algorithm can achieve the equilibrium between local data sharing and privacy protection. Third, according to the constructed basic model, we construct the federated model and the federated algorithm of privacy-utility equilibrium data generation based on Wasserstein generative adversarial networks using the serialized training method of federated learning, and our theoretical analysis results also show that the federated algorithm can achieve the equilibrium between local data sharing and privacy protection in a distributed environment. Finally, our experimental results show that the proposed algorithms can achieve the equilibrium between local data sharing and privacy protection in this paper. Therefore, the constructed basic mathematical model provides a theoretical basis of achieving the equilibrium between local data sharing and privacy protection. At the same time, the proposed basic model and the federated model of privacy-utility equilibrium data generation provide a concrete method of achieving the equilibrium between local data sharing and privacy protection in centralized and distributed environment respectively.
Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images

The high incidence rates of basal cell carcinoma (BCC) cause a significant burden at pathology laboratories. The standard diagnostic process is time-consuming and prone to inter-pathologist variability. Despite the application of deep learning approaches in grading of other cancer types, there is limited literature on the application of vision transformers to BCC on whole slide images (WSIs). A total of 1832 WSIs from 479 BCCs, divided into training and validation (1435 WSIs from 369 BCCs) and testing (397 WSIs from 110 BCCs) sets, were weakly annotated into four aggressivity subtypes. We used a combination of a graph neural network and vision transformer to (1) detect the presence of tumor (two classes), (2) classify the tumor into low and high-risk subtypes (three classes), and (3) classify four aggressivity subtypes (five classes). Using an ensemble model comprised of the models from cross-validation, accuracies of 93.5%, 86.4%, and 72% were achieved on two, three, and five class classifications, respectively. These results show high accuracy in both tumor detection and grading of BCCs. The use of automated WSI analysis could increase workflow efficiency.
A systematic review of application development in augmented reality navigation research

Augmented Reality (AR) is a technology that has been used extensively in the field of cartography, with one particular application being that of navigation. However even now, AR in navigation is still at an active exploratory stage with multiple different approaches regarding the techniques, methodologies, and development processes. In this work, we present a systematic review of application development in AR navigation research, aiming to fill the gap in the literature by identifying trends in aspects such as hardware, software, and methodologies in AR navigation. We review 107 publications from the past 25 years that present AR navigation software for real-world environments, and analyze their main characteristics regarding intended environments and users, hardware, development platforms, and AR methodologies. We note a rise in AR navigation research interest from 2010 onward, driven in large part by smartphone and mobile device ubiquity, supported further by significant advances and streamlined processes in AR application development. Furthermore, we highlight a rise in the use of increasingly more complex AR methodologies in recent years, signaling that the field is steadily evolving and maturing. At the same time, we also identify significant inconsistencies and omissions regarding the reporting of technical details, with nearly half of reviewed works not reporting one or more of the aspects of interest. After identifying the level of maturity and complexity in the field AR navigation, we provide a list of recommendations and future directions for researchers aiming to develop AR navigation applications.
Immersive University Model: A Tool to Increase Higher Education Competitiveness

The current paper substantiates the effectiveness of immersive learning applications for youth. Emphasis is placed on the fact that the “immersion” of students can be carried out not only through virtual and augmented reality but also in any constructed environment that provides any “immersion” in active and interactive teaching methods. A bibliometric analysis was conducted for the “immersive learning” search query to identify keywords and phrases’ main clusters and their relationships. Based on the bibliometric analysis, the concept of forming an immersive learning model based on a sequential transition between the “dimensions” was formed. The model is developed considering the degree of involvement in the learning scenarios. A multidimensional classification model for immersive learning tools is proposed. A university case of building an immersive learning environment as part of the “electronic university” information system is presented. A technological solution is proposed for evaluating the effectiveness of testing immersive learning tools.
Blockchain-Based Automated Market Makers for a Decentralized Stock Exchange

The advancements in communication speeds have enabled the centralized financial market to be faster and more complex than ever. The speed of the order execution has become exponentially faster when compared to the early days of electronic markets. Though the transaction speed has increased, the underlying architecture or models behind the markets have remained the same. These models come with their own disadvantages. The disadvantages are usually faced by non-institutional or small traders. The bigger players, such as financial institutions, have an advantage over smaller players because of factors such as information asymmetry and access to better infrastructure, which give them an advantage in terms of the speed of execution. This makes the centralized stock market an uneven playing field. This paper discusses the limitations of centralized financial markets, particularly the disadvantage faced by non-institutional or small traders due to information asymmetry and better infrastructure access by financial institutions. The authors propose the usage of blockchain technology and the data highway protocol to create a decentralized stock exchange that can potentially eliminate these disadvantages. The data highway protocol is used to generate new blocks with a flexible finality condition that allows for the consensus mechanism to configure security thresholds more freely. The proposed framework is compared with existing frameworks to confirm its effectiveness and identify areas that require improvement. The evaluation of the proposed approach showed that the improved highway protocol boosted the transaction rate compared to the other two mechanisms (PoS and PoW). Specifically, the transaction rate of the proposed model was found to be 2.2 times higher than that of PoS and 12 times higher than that of the PoW consensus model.
Transformer-based Deep Neural Network for Breast Cancer Classification on Digital Breast Tomosynthesis Images

Purpose To develop an efficient deep neural network model that incorporates context from neighboring image sections to detect breast cancer on digital breast tomosynthesis (DBT) images. Materials and Methods The authors adopted a transformer architecture that analyzes neighboring sections of the DBT stack. The proposed method was compared with two baselines: an architecture based on three-dimensional (3D) convolutions and a two-dimensional model that analyzes each section individually. The models were trained with 5174 four-view DBT studies, validated with 1000 four-view DBT studies, and tested on 655 four-view DBT studies, which were retrospectively collected from nine institutions in the United States through an external entity. Methods were compared using area under the receiver operating characteristic curve (AUC), sensitivity at a fixed specificity, and specificity at a fixed sensitivity. Results On the test set of 655 DBT studies, both 3D models showed higher classification performance than did the per-section baseline model. The proposed transformer-based model showed a significant increase in AUC (0.88 vs 0.91, P = .002), sensitivity (81.0% vs 87.7%, P = .006), and specificity (80.5% vs 86.4%, P < .001) at clinically relevant operating points when compared with the single-DBT-section baseline. The transformer-based model used only 25% of the number of floating-point operations per second used by the 3D convolution model while demonstrating similar classification performance. Conclusion A transformer-based deep neural network using data from neighboring sections improved breast cancer classification performance compared with a per-section baseline model and was more efficient than a model using 3D convolutions. Keywords: Breast, Tomosynthesis, Diagnosis, Supervised Learning, Convolutional Neural Network (CNN), Digital Breast Tomosynthesis, Breast Cancer, Deep Neural Networks, Transformers Supplemental material is available for this article. © RSNA, 2023
Multi-scale modeling temporal hierarchical attention for sequential recommendation

Multi-scale modeling of items interacting in a sequence of users' historical behaviors in a sequential recommendation task is crucial. In real scenarios, the user's choice of items depends not only on static preferences (long-term interests) but also on recent dynamic preferences (short-term interests). In recent years, micro-video sharing platforms have been very favorable, and correspondingly, more efficient recommendation method will be needed to support users in finding their interested micro-videos (items). Compared to traditional online videos (such as YouTube), micro-videos are created by grassroots users, shot by smartphones, are short (typically tens of seconds), and have fewer tags or descriptive text, which makes recommending it a challenging task. In this work, we explore how to model users' historical behavior sequences at multiple scales to predict their click-through rates on micro-videos and then determine whether to recommend them to users. We present a novel Multi-scale Modeling Temporal Hierarchical Attention (MMTHA) method for modeling users' behavior sequences inspired by recent deep network-based approaches. Specifically, firstly, we capture users' short-term dynamic interests using temporal windows; secondly, we utilize a category-level attention mechanism to describe the coarse-grained interests of users and an item-level attention mechanism to capture the fine-grained interests of users; thirdly, we employ a forward multi-headed self-attention mechanism to identify and integrate long-term correlations between previously segmented temporal windows. We conducted extensive experiments on two publicly available datasets to verify their effectiveness. The experimental results show that our proposed MMTHA model achieves state-of-the-art performance in all tests.
Experimental study of tensile and flexural performances and failure mechanism of none-felt needled composites

None-felt needled composite material is a novel type of composite with excellent interlayer connection performance. Nonetheless, the tensile and flexural properties and failure mechanism have not been clarified. In this work, none-felt needled fabrics and reinforced composites were manufactured, which were then characterized structurally using Micro-CT. Moreover, the tensile and flexural performances were researched with digital image correlation (DIC) technology, and the fracture morphology was observed employing SEM. The results showed that more needled fiber bundles were implanted along the thickness direction, and the fiber diameter and length were increased by 127.2% and 148.2% respectively. The tensile strengths and modulus also enhanced by 31.5% and 32.8%, and the flexural strengths and modulus improved by 13.1% and 26.7%. The tensile and flexural failure modes of none-felt needled composites and conventional felt needled composites consisted of matrix fracture, in-plane fiber pull-out and brittle fracture. Besides, the flexural specimens of none-felt needled composites had a shorter delamination length. The none-felt needled composites with high tensile and flexural performances are expected to be used in key core components such as radome, cabin and skin of hypersonic aircraft.
Efficient, economical and energy-saving multi-workflow scheduling in hybrid cloud

Benefiting from the flexible, scalable and secure environment, hybrid cloud can overcome the shortage of limited resources in private cloud to simultaneously execute large-scale scientific workflows. In hybrid cloud, privacy-sensitive tasks are not allowed to be executed on public resources, while non-sensitive tasks are unrestricted. As an NP-Complete problem, it is extraordinarily challenging to schedule multiple workflows efficiently, economically and energy-savingly under quality-of-service constraints. This paper models the hybrid-cloud-based privacy-aware multi-workflow scheduling as a tri-objective optimization problem that optimizes workflow-oriented total tardiness, private-cloud-oriented total energy consumption, and public-cloud-oriented total monetary cost. To the best of authors' knowledge, few studies have been conducted on the tri-objective privacy-aware multi-workflow scheduling in hybrid cloud (PMWS-HC). To solve this problem, we dissect various factors involved during task scheduling and devise a novel Heuristic Scheduling Algorithm based on 9 Factors (HSA9Fs), which dynamically selects the workflows and tasks to be scheduled, and the corresponding VMs to execute them. To optimize the three conflicting objectives simultaneously, we propose a nested algorithm called MSIA, which first employs a Multi-objective Salp swarm algorithm to explore for the Pareto solutions, and then uses an Iterative greedy Algorithm to perform a refined search on individuals to obtain high-quality solutions. Extensive Medium-Small-Scale and Large-Scale simulation experiments show that both HSA9Fs and MSIA outperform state-of-the-art scheduling algorithms in several multi-objective performance metrics.
Affordable federated edge learning framework via efficient Shapley value estimation

Federated Learning (FL), as a privacy-preserving distributed machine learning paradigm, has become a promising privacy computing framework for increasingly complex network systems. To incentivize more data owners to participate, it is important for service providers to fairly evaluate each data owner’s contribution to the FL training process and compensate for the reward. In this context, Shapley value, a classical data evaluation scheme, efficient collaboration with FL, namely Federated Shapley Value (FedSV), becomes an effective solution. Despite its potential, this new approach also faces severe computational overhead, privacy, and fairness challenges in the FL setting, where FL involves thousands of devices and resources are constrained. To this end, in this paper, we propose an affordable federated edge learning framework by designing an efficient Shapley value estimation approach. Furthermore, we survey recent efforts in the collaboration of FL and Shapley value and identify future research directions for researchers and practitioners to further investigate this emerging topic. Last, we conduct a thorough empirical study of FedSV on a series of tasks and integrate the existing benchmark schemes into the open-source function package FedSHAP.
The Arm Morello Evaluation Platform—Validating CHERI-Based Security in a High-Performance System

Memory safety issues are a persistent source of security vulnerabilities, with conventional architectures and the C/C++ codebase chronically prone to exploitable errors. The Capability Hardware Enhanced RISC Instructions (CHERI) research project has explored a novel architectural approach to ameliorate such issues using unforgeable hardware capabilities to implement pointers. Morello is an Arm experimental platform for evaluation of CHERI in the Arm architecture context to explore its potential for mass-market adoption. This article describes the Morello Evaluation Platform, covering the motivation and functionality of the Morello architectural hardware extensions; their potential for fine-grained memory safety and software compartmentalization; formally proven security properties; impact on the microarchitecture of the high-performance, out-of-order multiprocessor Arm Morello processor; and the software-enablement program by Arm, the University of Cambridge, and Linaro. Together, this allows a wide range of researchers in both industry and academia to explore and assess the Morello platform.
Judging facts, judging norms: Training machine learning models to judge humans requires a modified approach to labeling data

As governments and industry turn to increased use of automated decision systems, it becomes essential to consider how closely such systems can reproduce human judgment. We identify a core potential failure, finding that annotators label objects differently depending on whether they are being asked a factual question or a normative question. This challenges a natural assumption maintained in many standard machine-learning (ML) data acquisition procedures: that there is no difference between predicting the factual classification of an object and an exercise of judgment about whether an object violates a rule premised on those facts. We find that using factual labels to train models intended for normative judgments introduces a notable measurement error. We show that models trained using factual labels yield significantly different judgments than those trained using normative labels and that the impact of this effect on model performance can exceed that of other factors (e.g., dataset size) that routinely attract attention from ML researchers and practitioners.
Quantum Associative Memory with a Single Driven-Dissipative Nonlinear Oscillator

Algorithms for associative memory typically rely on a network of many connected units. The prototypical example is the Hopfield model, whose generalizations to the quantum realm are mainly based on open quantum Ising models. We propose a realization of associative memory with a single driven-dissipative quantum oscillator exploiting its infinite degrees of freedom in phase space. The model can improve the storage capacity of discrete neuron-based systems in a large regime and we prove successful state discrimination between $n$ coherent states, which represent the stored patterns of the system. These can be tuned continuously by modifying the driving strength, constituting a modified learning rule. We show that the associative-memory capacity is inherently related to the existence of a spectral gap in the Liouvillian superoperator, which results in a large timescale separation in the dynamics corresponding to a metastable phase.
Platform supply chain innovations in the blockchain era: the ABCDE framework

Platforms are giants in today's operations. Companies like Amazon and eBay are huge enterprises which belong to top enterprises in the world. In this article, we discuss platform supply chain innovations in the blockchain era. We first search the literature and identify the level of popularity of the topic over the past years. Next, we review some closely related studies in the literature and introduce the featured papers in this IJPR special issue. After that, we identify research gaps, present the ABCDE framework and propose a future research agenda.
Attention-Based Deep Learning System for Classification of Breast Lesions—Multimodal, Weakly Supervised Approach

Breast cancer is the most frequent female cancer, with a considerable disease burden and high mortality. Early diagnosis with screening mammography might be facilitated by automated systems supported by deep learning artificial intelligence. We propose a model based on a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM) classifier able to train under data scarcity effectively. We used a private dataset with 1174 non-cancer and 794 cancer images labelled at the image level with pathological ground truth confirmation. We used feature extractors (ResNet-18, ResNet-34, ResNet-50 and EfficientNet-B0) pre-trained on ImageNet. The best results were achieved with multimodal-view classification using both CC and MLO images simultaneously, resized by half, with a patch size of 224 px and an overlap of 0.25. It resulted in AUC-ROC = 0.896 ± 0.017, F1-score 81.8 ± 3.2, accuracy 81.6 ± 3.2, precision 82.4 ± 3.3, and recall 81.6 ± 3.2. Evaluation with the Chinese Mammography Database, with 5-fold cross-validation, patient-wise breakdowns, and transfer learning, resulted in AUC-ROC 0.848 ± 0.015, F1-score 78.6 ± 2.0, accuracy 78.4 ± 1.9, precision 78.8 ± 2.0, and recall 78.4 ± 1.9. The CLAM algorithm's attentional maps indicate the features most relevant to the algorithm in the images. Our approach was more effective than in many other studies, allowing for some explainability and identifying erroneous predictions based on the wrong premises.
Biologically Inspired Complete Coverage Path Planning Algorithm Based on Q-Learning

Complete coverage path planning requires that the mobile robot traverse all reachable positions in the environmental map. Aiming at the problems of local optimal path and high path coverage ratio in the complete coverage path planning of the traditional biologically inspired neural network algorithm, a complete coverage path planning algorithm based on Q-learning is proposed. The global environment information is introduced by the reinforcement learning method in the proposed algorithm. In addition, the Q-learning method is used for path planning at the positions where the accessible path points are changed, which optimizes the path planning strategy of the original algorithm near these obstacles. Simulation results show that the algorithm can automatically generate an orderly path in the environmental map, and achieve 100% coverage with a lower path repetition ratio.