Reviewerly/exHarmony huggingface dataset:
Breast TransFG Plus: Transformer-based fine-grained classification model for breast cancer grading in Hematoxylin-Eosin stained pathological images

Breast cancer is one of the most common malignancies in women, and the pathological grading of breast cancer is very important for the prognosis of breast cancer. But classification of breast Hematoxylin-Eosin (HE) stained pathological images by deep learning for breast cancer grading is difficult due to morphological similarities between different grades. Therefore, it is essential to have an efficient and accurate method of breast cancer grading. In this paper, a transformer-based fine-grained classification model named Breast TransFG Plus is proposed for breast cancer grading. Targeting the widespread distribution of cells in breast HE stained pathological images, this paper proposes part selection module plus, which is a kind of key information extraction method based on matrix addition, and double head classification structure, which is a kind of dual stream network structure. And balanced sampling based on data augmentation is used in the training set. The proposed method has been evaluated on a public dataset and the classification accuracy, precision and recall on the test set are 99.39%, 99.18% and 99.59%, the number of parameters is 93 M. And it is proved that the proposed model is superior to previous studies by comparing with them. Breast TransFG Plus can achieve efficient and accurate breast cancer grading, and has the potential to meet clinical computer-aided diagnosis needs.
ppAURORA: Privacy Preserving Area Under Receiver Operating Characteristic and Precision-Recall Curves

Computing an area under the curve (AUC) as a performance measure to compare the quality of different machine learning models is one of the final steps of many research projects. Many of these methods are trained on privacy-sensitive data and there are several different approaches like $$\epsilon $$ -differential privacy, federated learning and cryptography if the datasets cannot be shared or used jointly at one place for training and/or testing. In this setting, it can also be a problem to compute the global AUC, since the labels might also contain privacy-sensitive information. There have been approaches based on $$\epsilon $$ -differential privacy to address this problem, but to the best of our knowledge, no exact privacy preserving solution has been introduced. In this paper, we propose an MPC-based solution, called ppAURORA, with private merging of individually sorted lists from multiple sources to compute the exact AUC as one could obtain on the pooled original test samples. With ppAURORA, the computation of the exact area under precision-recall and receiver operating characteristic curves is possible even when ties between prediction confidence values exist. We use ppAURORA to evaluate two different models predicting acute myeloid leukemia therapy response and heart disease, respectively. We also assess its scalability via synthetic data experiments. All these experiments show that we efficiently and privately compute the exact same AUC with both evaluation metrics as one can obtain on the pooled test samples in plaintext according to the semi-honest adversary setting.
Security Model for Privacy-Preserving Blockchain-Based Cryptocurrency Systems

Privacy-preserving blockchain-based cryptocurrency systems have become quite popular as a way to provide confidential payments. These cryptocurrency systems differ in their designs, underlying cryptography, and confidentiality level. Some of these systems provide confidentiality for their users or transactions or both. There has been a thriving interest in constructing different privacy-preserving cryptocurrency systems with improved security and additional features. Nevertheless, many of these available systems lack security models which makes it hard to prove the security properties of these systems. Despite the differences in the privacy notions of existing privacy-preserving cryptocurrency systems, in this paper, we present a first attempt to create a general framework for a privacy-preserving blockchain-based bank $$\textsf{PBB}$$ . We present the security properties of this system and model the security experiments for each of the properties. Our $$\textsf{PBB}$$ model can also work for bank-less cryptocurrency systems. Henceforth, we present a brief security analysis for one of the most notable privacy-preserving cryptocurrencies, Monero, using the security model of the $$\textsf{PBB}$$ system. Our analysis proves that our $$\textsf{PBB}$$ system can be easily used to formalise the security of other available privacy-preserving cryptocurrencies.
Cross-stage distillation with inverted bottleneck projectors

Knowledge distillation, which transfers knowledge from a large model (the teacher) to a small model (the student), is a promising way in the field of lightweight model design. Existing distillation methods based on intermediate features mainly focus on knowledge transfer between the same stages of teacher and student networks, which may lead to student networks receiving semantically mismatched knowledge and missing contextual knowledge. To solve this problem, a cross-stage distillation with inverted bottleneck projectors is proposed. A cross-stage connection structure is designed that enables the student network to access the teacher network’s most matched stages in semantics and further obtain rich contextual knowledge from multiple stages of the teacher. After establishing the cross-stage connection between the teacher network and the student network, inverted bottleneck projectors are employed to extract useful knowledge from multiple stages of the teacher. A stack structure with three-layer convolutions is proposed in every projector, which can make the knowledge of the teacher network more easily understood by the student network. The inverted bottleneck structure can reduce information loss, ensuring the integrity of knowledge transmission. Further, ReLU activations are incorporated into the projectors to remove features with lower response values, thereby filtering redundant elements. Extensive experiments on CIFAR100, ImageNet, Tiny-ImageNet, and STL-10 datasets demonstrate the effectiveness of the proposed approach.
T3E: A Practical Solution to Trusted Time in Secure Enclaves

Time is used in secure systems to validate security properties. Consequently, it is vital to protect the integrity of time information. Intel SGX enables building secure applications inside a Trusted Execution Environment (TEE), called an enclave, isolated from the untrusted OS. However, accessing time information from the enclave remains challenging as the OS controls the system time. Previous versions of the SGX SDK provided the sgx_get_trusted_time function as an alternative to OS time. However, Intel removed the API in 2020, without providing an alternative. This paper examines trusted time challenges in SGX and presents TPM-based Trusted Time Extensions (T3E), a novel solution that builds on readily available hardware. T3E leverages TPM functionality to provide trusted time services in enclaves while protecting against common attacks. It offers better time granularity and lower latency than Intel's sgx_get_trusted_time implementation. Unlike related work, it does not rely on deprecated features or hardware/firmware modifications.
A computationally efficient mixture innovation model for time-varying parameter regressions

The mixture innovation (MI) model places a spike-and-slab mixture distribution for the innovations of time-varying regression coefficients and permits flexible time variation patterns while allowing for dynamic shrinkage. Despite its appeal, the standard Bayesian algorithm to block sample the vector of 0/1 mixture indicators at each time t needs to evaluate the model likelihood over all its 2K scenarios for a regression model with K regressors and becomes impractical when K grows. As an alternative, a new specification of the MI model is proposed in which the 0/1 mixture indicators in the original MI model are approximated by a logistic function of latent continuous variables. As such the model likelihood only needs to be evaluated twice in an Metropolis-Hastings step to block update the latent variables and hence the approximated mixture indicators at each time t, offering large improvement in computational efficiency while keeping the benefits of the MI model. An efficient MCMC algorithm is developed to estimate the new model. A simulation study shows that the new model can achieve the same level of estimation accuracy as the original MI model but at a much smaller computation cost. The new model is further tested in two empirical applications where block sampling the mixture indicators at each time t in the original MI model is practically infeasible.
CAMID: An assuasive approach to reveal source camera through inconspicuous evidence

Identifying the source camera of digital images is an important problem due to the abundance of such images nowadays. Although photo response non-uniformities (PRNUs) can accurately identify the source camera, seam-carving techniques have been introduced to hide the cameras’ identity by invalidating the positional correspondence assumption in the PRNU. Especially, the seam carving techniques create irregular carving patterns, which makes source camera identification from seam-carved images challenging. In this paper, we propose a feature-based method called CAM1D, to identify the source camera of seam-carved images. CAM1D constructs a series of camera signatures to enhance robustness and reliability, rather than relying on a single PRNU for identification. Useful features are extracted from the correlation between a series of camera signatures and seam-carved photos, giving high source camera identification accuracy. Experimental results using the confusion matrix and F1 score confirm the effectiveness of CAM1D.
The nonarithmeticity of the predicate logic of primitive recursive realizability

The notion of primitive recursive realizability was introduced by S. Salehi as a kind of semantics for the language of basic arithmetic using primitive recursive functions. It is of interest to study the corresponding predicate logic. D. A. Viter proved that the predicate logic of primitive recursive realizability by Salehi is not arithmetical. The technically complex proof combines the methods used by the author of this article in the study of predicate logics of constructive arithmetic theories and the results of M. Ardeshir on the translation of the intuitionistic predicate logic into the basic predicate logic. The purpose of this article is to present another proof of Viter's result by directly transferring the methods used earlier in proving the nonarithmeticity of the predicate logic of recursive realizability.
Diagnosis model of noise-type defects for dry-type transformer based on time–frequency-space tensors and improved prototypical network under small sample conditions

This paper presents a diagnostic method for dry-type transformer noise-type defects using time–frequency-space tensors and an improved prototypical network. The method converts single-channel time domain sound signals into time–frequency matrices using a parameter-optimized variational mode decomposition algorithm and the Hilbert-Huang transform, and arranges the matrices in space. Sample enhancement via sound source virtual rotation and dimensionality reduction through hyper-principal component analysis are employed to enhance the model's generalization capability, computational speed, and recognition accuracy. The improved prototypical network, fortified with a custom residual network as the encoder, improves the model's ability to learn complex patterns and simultaneously diminishes the model's computational complexity. Experimental results show that the proposed method has good recognition accuracy, computation speed, and stability with few samples. The recognition accuracy is 96.0% ± 2.1% with 15 samples per type of defect. This method demonstrates good applicability to sound signals collected by microphone arrays with rotational symmetry structure.
A unified framework for visual domain adaptation with covariance matching

In the field of Machine Learning, it is widely acknowledged that training and test data should ideally stem from the same source and distribution. However, in the real world, this is not always feasible. Domain Adaptation (DA) techniques address this issue by adapting a classifier trained on annotated source domain data on unannotated target domain data while minimizing the impact of domain shift. Many recent DA approaches concentrate on learning a latent feature space that remains invariant to domain shift by mitigating various statistical and geometrical divergences. Although these methods have demonstrated effectiveness, they frequently overlook a crucial aspect of learning a domain-invariant discriminative latent feature space across diverse domains. To address this issue, we propose a novel framework called Unified Framework for Visual Domain Adaptation with Covariance Matching (UDACM) that aims to learn a domain invariant and class discriminative latent feature space by incorporating multiple objectives such as maximizing target domain variance, minimizing distribution and subspace divergence, performing manifold learning, preserving discriminative information of both the source and target domains, and measuring and matching covariance simultaneously. In our proposed framework, the measuring and matching of covariance play a crucial role in ensuring the learning of discriminative latent feature space by aligning the within-class and between-class covariance matrices of the source and target domains simultaneously. Detailed experiments demonstrate the outperformance of UDACM on baseline datasets such as CMU-PIE, Office+Caltech10, USPS+MNIST, VLCS, and Office–Home, compared to various established primitive, shallow, and deep domain adaptation methods on several image classification tasks.
AKGF: Automatic Kernel Generation for DNN on CPU-FPGA

Abstract While tensor accelerated compilers have proven effective in deploying deep neural networks (DNN) on general-purpose hardware, optimizing for FPGA remains challenging due to the complex DNN architectures and the heterogeneous, semi-open compute units. This paper introduces the Automatic Kernel Generation for DNN on CPU-FPGA (AKGF) framework for efficient deployment of DNN on heterogeneous CPU-FPGA platforms. AKGF generates an intermediate representation (IR) of the DNN using TVM’s Halide IR, annotates the operators of model layers in the IR to compute them on the corresponding hardware cores, and further optimizes the operator code for CPU and FPGA using ARM’s function library and the polyhedral model to enhance model inference speed and power consumption. The experimental tests conducted on a CPU-FPGA board validate the effectiveness of AKGF, demonstrating significant acceleration ratios (up to 6.7x) compared to state-of-the-art accelerators while achieving a 2x power optimization. AKGF effectively leverages the computational capabilities of both CPU and FPGA for high-performance deployment of DNN on CPU-FPGA platforms.
Knowledge-Aware Dual-Channel Graph Neural Networks For Denoising Recommendation

Abstract Knowledge graph (KG) is introduced as side information into recommender systems, which can alleviate the sparsity and cold start problems in collaborative filtering. Existing studies mainly focus on modeling users’ historical behavior data and KG-based propagation. However, they have the limitation of ignoring noise information during recommendation. We consider that noise exists in two parts (i.e. KG and user-item interaction data). In this paper, we propose Knowledge-aware Dual-Channel Graph Neural Networks (KDGNN) to improve the recommendation performance by reducing the noise in the recommendation process. Specifically, (1) for the noise in KG, we design a personalized gating mechanism, namely dual-channel balancing mechanism, to block the propagation of redundant information in KG. (2) For the noise in user-item interaction data, we integrate personalized and knowledge-aware signals to capture user preferences fully and use personalized knowledge-aware attention to denoise user-item interaction data. Compared with existing KG-based methods, we aim to propose a knowledge-aware recommendation method from a new perspective of denoising. We perform performance analysis on three real-world datasets, and experiment results demonstrate that KDGNN achieves strongly competitive performance compared with several compelling state-of-the-art baselines.
Pengaplikasian Matlab pada Perhitungan Matriks

This study aims to determine the Application of Matlab in the Matrix. The development and presence of computer technology have made it easy for various parties to use it in life, including education, to support education. Computers are increasingly providing significant benefits in education, especially in learning. Hundreds or even thousands of computer application programs or software can be used for learning mathematics, especially the Matrix. One software that can be used in Matrix learning is the Matrix Laboratory (MATLAB). This program can increase speed and accuracy in mathematical calculations in Matrix learning so that the time needed to work is more efficient. The results obtained are more accurate than estimates done manually. It can also visualize graphs in 2 and three dimensions, for example, in completing the Matrix, so it is expected to increase understanding of the material provided.
Augmented reality sickness questionnaire (ARSQ): A refined questionnaire for augmented reality environment

We developed a two-dimensional motion sickness measurement index for augmented reality (AR) environments. Studies on the side effects of AR devices, such as motion sickness, are needed. There is no sickness measuring tool for AR environments; therefore, we modified the conventional simulator sickness questionnaire (SSQ) for use in AR environments. Twenty-four participants performed target selection tasks using Microsoft HoloLens to measure motion sickness in an AR environment. The SSQ data were collected after each task in a Latin square balanced design experiment. The existing three-component SSQ was revised to develop a two-component AR sickness questionnaire (ARSQ) through exploratory and confirmatory analyses. The button size and distance from the target virtual objects to eyes were found to be significant factors affecting motion sickness in the AR environment. The obtained results can be used for measuring motion sickness in AR devices and designing AR applications.
Unified shape and appearance reconstruction with joint camera parameter refinement

In this paper, we present an inverse rendering method for the simple reconstruction of shape and appearance of real-world objects from only roughly calibrated RGB images captured under collocated point light illumination. To this end, we gradually reconstruct the lower-frequency geometry information using automatically generated occupancy mask images based on a visual hull initialization of the mesh, to infer the object topology, and a smoothness-preconditioned optimization. By combining this geometry estimation with learning-based SVBRDF parameter inference as well as intrinsic and extrinsic camera parameter refinement in a joint and unified formulation, our novel method is able to reconstruct shape and an isotropic SVBRDF from fewer input images than previous methods. Unlike in other works, we also estimate normal maps as part of the SVBRDF to capture and represent higher-frequency geometric details in a compact way. Furthermore, by regularizing the appearance estimation with a GAN-based SVBRDF generator, we are able to meaningfully limit the solution space. In summary, this leads to a robust automatic reconstruction algorithm for shape and appearance. We evaluated our algorithm on synthetic as well as on real-world data and demonstrate that our method is able to reconstruct complex objects with high-fidelity reflection properties in a robust way, also in the presence of imperfect camera parameter data.
Detect and Correct Errors In Chat GPT With Peter Chew Mental Calculation

This article discusses the Errors of Chat GPT specifically in terms of their restricted knowledge and their inability to calculate certain higher digit multiplication calculation. Chat GPT's knowledge is confined to data up until 2021. However, these Errors can be Detect and Correct With Peter Chew Mental Calculation.The Peter Chew Mental Calculation allows us quickly get exact answers to certain higher digit multiplication calculations. The Peter Chew Mental Calculation provides a promising solution to Detect and Correct Errors In Chat GPT. Therefore, Peter Chew Mental Calculation (Multiplication) Method provide correct, faster, and better than Chat GPT for certain higher digit multiplication calculations.Mental math brings higher learning, increased usage allows students to live healthier, smarter lifestyles. According to a research paper published on the U.S. National Institutes of Healthâ€™s National Library of Medicine, students with strong number sense obtain greater overall mathematical success and often perform better at both written calculations and applied problem solving.Peter Chew Mental Calculation demonstrates the potential of leveraging mathematical concepts to Detect and Correct the Errors posed by restricted knowledge in AI systems like Chat GPT. This can be effective in let student interest in using Peter Chew Mental Calculation especially when analogous covid -19 issues arise in the future.
FastAiAlloc: A real-time multi-resources allocation framework proposal based on predictive model and multiple optimization strategies

In cloud platforms, a common task is to allocate computational resources, e.g., memory and CPU, requested by applications and users. The allocation of these resources, which is an optimal load-balancing task, is considered an NP-Hard problem, being a challenging research area. There are many works proposed in the literature to address this problem. They use several strategies to deal with this optimization problem such as evolutionary algorithms, exact programming and also heuristics. However, some steps of the allocation process are not considered by these works, which are sometimes treated separately and not in an integrated manner. These steps include the applications and users resource consumption request profile as part of the optimization process and defining adequate metrics to check the optimized allocation. To integrate these steps, this work proposes a framework based on the following strategies, widely used in the literature: Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Linear Programming, besides our Heuristic approach. Furthermore, we restricted the resource allocation optimization to a real-time scenario, facilitating its use in an industrial process. In addition, Key Performance Indicators (KPIs) are proposed to carry out a comparative study in a public dataset. Our experiments showed that our proposed framework achieved better results than the baseline one, e.g., using a random allocation. In some cases, we observed an increase of almost 60% in performance compared with the baseline. In addition, when trying to balance memory and CPU consumption in the cluster, the linear approach performed the best, while the GA achieved the best result in allocating different user profiles across the cluster.