Reviewerly/exHarmony huggingface dataset:
Domain generalization in deep learning based mass detection in mammography: A large-scale multi-center study

Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical environments. In this study, we explored the domain generalization of deep learning methods for mass detection in digital mammography and analyzed in-depth the sources of domain shift in a large-scale multi-center setting. To this end, we compared the performance of eight state-of-the-art detection methods, including Transformer based models, trained in a single domain and tested in five unseen domains. Moreover, a single-source mass detection training pipeline was designed to improve the domain generalization without requiring images from the new domain. The results show that our workflow generalized better than state-of-the-art transfer learning based approaches in four out of five domains while reducing the domain shift caused by the different acquisition protocols and scanner manufacturers. Subsequently, an extensive analysis was performed to identify the covariate shifts with the greatest effects on detection performance, such as those due to differences in patient age, breast density, mass size, and mass malignancy. Ultimately, this comprehensive study provides key insights and best practices for future research on domain generalization in deep learning based breast cancer detection.
Blockchain Technology Implementation in the Energy Sector: Comprehensive Literature Review and Mapping

Satisfying the world’s rapidly increasing demands in energy via the optimized management of available resources is becoming one of the most important research trends worldwide. When it comes to energy, it is very important to talk about decentralization, security, traceability and transparency. Thus, over the last few years, numerous research works have presented blockchain technology as the best novel business platform enabling a secure, transparent and tamper-proof energy management solution. In this paper, we conducted a systematic literature review (SLR) using the PRISMA framework of the different existing research studies related to the use of the blockchain technology in the energy sector, published between 2008 and 2021. We identified a total of 769 primary studies after intensive manual analysis and filtering, which we thoroughly assessed using various criteria to address six main research questions that covered the blockchain types, applications and platforms in the energy sector, the energy source types for which blockchain platforms are implemented, the emergent technologies that are combined to blockchain solutions, and the types of consensuses used in energy blockchains. Based on the collected survey data, we built a database to categorize the existing research works, identify research trends, and highlight knowledge gaps and potential areas for additional field study.
Tubule-U-Net: a novel dataset and deep learning-based tubule segmentation framework in whole slide images of breast cancer

Abstract The tubule index is a vital prognostic measure in breast cancer tumor grading and is visually evaluated by pathologists. In this paper, a computer-aided patch-based deep learning tubule segmentation framework, named Tubule-U-Net, is developed and proposed to segment tubules in Whole Slide Images (WSI) of breast cancer. Moreover, this paper presents a new tubule segmentation dataset consisting of 30820 polygonal annotated tubules in 8225 patches. The Tubule-U-Net framework first uses a patch enhancement technique such as reflection or mirror padding and then employs an asymmetric encoder-decoder semantic segmentation model. The encoder is developed in the model by various deep learning architectures such as EfficientNetB3, ResNet34, and DenseNet161, whereas the decoder is similar to U-Net. Thus, three different models are obtained, which are EfficientNetB3-U-Net, ResNet34-U-Net, and DenseNet161-U-Net. The proposed framework with three different models, U-Net, U-Net++, and Trans-U-Net segmentation methods are trained on the created dataset and tested on five different WSIs. The experimental results demonstrate that the proposed framework with the EfficientNetB3 model trained on patches obtained using the reflection padding and tested on patches with overlapping provides the best segmentation results on the test data and achieves 95.33%, 93.74%, and 90.02%, dice, recall, and specificity scores, respectively.
A survey on recent trends in deep learning for nucleus segmentation from histopathology images

Nucleus segmentation is an imperative step in the qualitative study of imaging datasets, considered as an intricate task in histopathology image analysis. Segmenting a nucleus is an important part of diagnosing, staging, and grading cancer, but overlapping regions make it hard to separate and tell apart independent nuclei. Deep Learning is swiftly paving its way in the arena of nucleus segmentation, attracting quite a few researchers with its numerous published research articles indicating its efficacy in the field. This paper presents a systematic survey on nucleus segmentation using deep learning in the last five years (2017–2021), highlighting various segmentation models (U-Net, SCPP-Net, Sharp U-Net, and LiverNet) and exploring their similarities, strengths, datasets utilized, and unfolding research areas.
GRAPE for fast and scalable graph processing and random-walk-based embedding

Graph representation learning methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise millions of nodes and billions of edges and are beyond the capabilities of current methods and software implementations. We present GRAPE (Graph Representation Learning, Prediction and Evaluation), a software resource for graph processing and embedding that is able to scale with big graphs by using specialized and smart data structures, algorithms, and a fast parallel implementation of random-walk-based methods. Compared with state-of-the-art software resources, GRAPE shows an improvement of orders of magnitude in empirical space and time complexity, as well as competitive edge- and node-label prediction performance. GRAPE comprises approximately 1.7 million well-documented lines of Python and Rust code and provides 69 node-embedding methods, 25 inference models, a collection of efficient graph-processing utilities, and over 80,000 graphs from the literature and other sources. Standardized interfaces allow a seamless integration of third-party libraries, while ready-to-use and modular pipelines permit an easy-to-use evaluation of graph-representation-learning methods, therefore also positioning GRAPE as a software resource that performs a fair comparison between methods and libraries for graph processing and embedding.
Enhanced Tensor Low-Rank and Sparse Representation Recovery for Incomplete Multi-View Clustering

Incomplete multi-view clustering (IMVC) has attracted remarkable attention due to the emergence of multi-view data with missing views in real applications. Recent methods attempt to recover the missing information to address the IMVC problem. However, they generally cannot fully explore the underlying properties and correlations of data similarities across views. This paper proposes a novel Enhanced Tensor Low-rank and Sparse Representation Recovery (ETLSRR) method, which reformulates the IMVC problem as a joint incomplete similarity graphs learning and complete tensor representation recovery problem. Specifically, ETLSRR learns the intra-view similarity graphs and constructs a 3-way tensor by stacking the graphs to explore the inter-view correlations. To alleviate the negative influence of missing views and data noise, ETLSRR decomposes the tensor into two parts: a sparse tensor and an intrinsic tensor, which models the noise and underlying true data similarities, respectively. Both global low-rank and local structured sparse characteristics of the intrinsic tensor are considered, which enhances the discrimination of similarity matrix. Moreover, instead of using the convex tensor nuclear norm, ETLSRR introduces a generalized non-convex tensor low-rank regularization to alleviate the biased approximation. Experiments on several datasets demonstrate the effectiveness of our method compared with the state-of-the-art methods.
Quantum dots for photonic quantum information technology

The generation, manipulation, storage, and detection of single photons play a central role in emerging photonic quantum information technology. Individual photons serve as flying qubits and transmit the quantum information at high speed and with low losses, for example between individual nodes of quantum networks. Due to the laws of quantum mechanics, quantum communication is fundamentally tap-proof, which explains the enormous interest in this modern information technology. On the other hand, stationary qubits or photonic states in quantum computers can potentially lead to enormous increases in performance through parallel data processing, to outperform classical computers in specific tasks when quantum advantage is achieved. Here, we discuss in depth the great potential of quantum dots (QDs) in photonic quantum information technology. In this context, QDs form a key resource for the implementation of quantum communication networks and photonic quantum computers because they can generate single photons on-demand. Moreover, QDs are compatible with the mature semiconductor technology, so that they can be integrated comparatively easily into nanophotonic structures, which form the basis for quantum light sources and integrated photonic quantum circuits. After a thematic introduction, we present modern numerical methods and theoretical approaches to device design and the physical description of quantum dot devices. We then present modern methods and technical solutions for the epitaxial growth and for the deterministic nanoprocessing of quantum devices based on QDs. Furthermore, we present the most promising concepts for quantum light sources and photonic quantum circuits that include single QDs as active elements and discuss applications of these novel devices in photonic quantum information technology. We close with an overview of open issues and an outlook on future developments.
ACRL guidelines for distance learning library services: The final version, approved July 1998

ACRL guidelines for distance learning library services: The final version, approved July 1998
Does Cad Improve Spatial Visualization Ability?

This paper presents a study conducted collaboratively at a two-year community college and a four-year university.Technical drawing skills are fundamental to engineering technology students.It is generally believed that spatial visualization abilities affect a student's performance in graphics courses.Do factors, such as training, age, and gender, contribute to spatial visualization abilities?Do computer-aided design (CAD) courses improve spatial visualization abilities?This study investigates these factors through the Purdue Spatial Visualization Test -Rotation.The statistical results, obtained from two different student populations representing two-year and four-year institutions, are compared.The results from the two-year college also supplement numerous studies done at other four-year universities.
UMLint: Identifying Defects in UML Diagrams

We present UMLint, an automated tool for detecting defects in UML diagrams.This tool is designed to improve object-oriented models developed by students.Standard tools such as IBM Rational Rose provide little feedback on model quality, so students must rely on feedback from instructors.Often there is a significant delay between completing a diagram and getting this feedback, resulting in missed learning opportunities.UMLint addresses this issue by identifying common defects, hopefully encouraging students to look more deeply for other defects.UMLint is available as a web service to allow use by both students and the community at large.This paper presents the checks made by UMLint, suggests possible future directions, and invites discussion about what standards should be expected of student-created models.
An efficient shift rule for the prefer-max De Bruijn sequence

A shift rule for the prefer-max De Bruijn sequence is formulated, for all sequence orders, and over any finite alphabet. An efficient algorithm for this shift rule is presented, which has linear (in the sequence order) time and memory complexity.
Reducing Timing Side-Channel Information Leakage Using 3D Integration

Recently, following the work pioneered by Kocher [1] , using cache behavior as a timing side-channel to leak critical system information has received lots of attentions because of its easy-to-implement nature and amazingly good results. Recent attacks have been demonstrated to successfully leak the full key from many commonly used encryption algorithms including RSA, AES, etc. These attacks pose great threats to applications that depend on these encryption methods such as banking systems, military systems, etc. To mitigate the increasing threat, numerous countermeasures, mostly software patches, have been proposed. Hardware mitigations, however, have been less pursued. 3D integration, which stacks multiple dies vertically, offers shorter wire-length and improves system performance. It may be used to offset the performance overhead these countermeasures incur. In this paper, we investigate several possible ways in which the availability of 3D integration can be exploited to mitigate timing side-channel attacks while still obtaining superior performance over a baseline 2D system. Simulation results using Gem5 simulator show that our techniques can significantly reduce timing information leakage while still achieving 20.43 percent performance gain over a 2D baseline system.
Emerging-Image Motion CAPTCHAs: Vulnerabilities of Existing Designs, and Countermeasures

Based on the notion of “emergence”, Xu et al. (Usenix Security 2012; TDSC 2013) developed the first concrete instantiation of emerging-image moving-object (EIMO) CAPTCHAs using 2D hollow objects (codewords), shown to be usable and believed to be secure. In this paper, we highlight the hidden security weaknesses of such a 2D EIMO CAPTCHA design. A key vulnerability is that the camera projection on 2D objects is constant (unlike 3D objects), making it possible to reconstruct the underlying codewords by superimposing and aggregating the temporally scattered parts of the object extracted from consecutive frames. We design and implement an automated attack framework to defeat this design using image processing techniques, and show that its accuracy in recognizing moving codewords is up to 89.2 percent, under different parameterizations. Our framework can be broadly used to undermine the security of different instances of 2D EIMO CAPTCHAs (not just the current state-of-the-art by Xu et al.), given the generalized and robust back-end theories in our attack, namely the methods to locate a codeword, reduce noises and accumulate objects' contour information from consecutive frames corresponding to multiple time periods. As a countermeasure, we propose a fundamentally different design of EIMO CAPTCHAs based on pseudo 3D objects, and examine its security as well as usability. We argue that this design can resist our attack against 2D EIMO CAPTCHAs, although at the cost of reduced usability compared to the - now insecure - 2D EIMO CAPTCHAs.
Scalable Mining of Contextual Outliers Using Relevant Subspace

In this paper, we propose a scalable mining algorithm to discover contextual outliers using relevant subspaces. We develop the mining algorithm using the MapReduce programming model running on a Hadoop cluster. Relevant subspaces, which effectively capture the local distribution of various datasets, are quantified using local sparseness of attribute dimensions. We design a novel way of calculating local outlier factors in a relevant subspace with the probability density of local datasets; this new approach can effectively reflect the outlier degree of a data object that does not satisfy the distribution of the local dataset in the relevant subspace. Attribute dimensions of a relevant subspace, and local outlier factors are expressed as vital contextual information, which improves the interpretability of outliers. Importantly, the selection of N data objects with the largest local outlier factor value is categorized as contextual outliers in our solution. To this end, our scalable mining algorithm, which incorporates the locality sensitive hashing distributed strategy, is implemented on a Hadoop cluster. The experimental results validate the effectiveness, interpretability, scalability, and extensibility of the algorithm using both synthetic data and stellar spectral data as experimental datasets.
Leakage Resilient Leveled FHE on Multiple Bit Message

Fully Homomorphic Encryption ( <inline-formula><tex-math notation="LaTeX">$\mathsf {FHE}$</tex-math></inline-formula> ) allows computing over encrypted data without decrypting the corresponding ciphertexts, and it constitutes a promising cryptographic primitive to preserve data privacy in the big data computing environments. In general, <inline-formula><tex-math notation="LaTeX">$\mathsf {FHE}$</tex-math></inline-formula> schemes can be constructed by using the standard Learning with Errors ( <inline-formula><tex-math notation="LaTeX">$\mathsf {LWE}$</tex-math></inline-formula> ) assumption, and the current crux lies in how to achieve efficient multi-bit <inline-formula><tex-math notation="LaTeX">$\mathsf {FHE}$</tex-math></inline-formula> encryption while being leakage-resistent against attackers who may capture the information of cryptographic secret keys via side channel attacks. Based on Berkoff-Liu’s work at TCC’14, we aim to address this issue by giving a new structure of public key matrix with any number of <inline-formula><tex-math notation="LaTeX">$\mathsf {LWE}$</tex-math></inline-formula> instances, thereby avoiding the use of a straightforward composition to achieve multi-bit <inline-formula><tex-math notation="LaTeX">$\mathsf {FHE}$</tex-math></inline-formula> encryption under standard <inline-formula><tex-math notation="LaTeX">$\mathsf {LWE}$</tex-math></inline-formula> . Particularly, our scheme attains provable security.
Copula Ordinal Regression Framework for Joint Estimation of Facial Action Unit Intensity

Joint modeling of the intensity of multiple facial action units (AUs) from face images is challenging due to the large number of AUs (30+) and their intensity levels (6). This is in part due to the lack of suitable models that can efficiently handle such a large number of outputs/classes simultaneously, but also due to the lack of suitable data the models on. For this reason, majority of the methods resort to independent classifiers for the AU intensity. This is suboptimal for at least two reasons: the facial appearance of some AUs changes depending on the intensity of other AUs, and some AUs co-occur more often than others. To this end, we propose the Copula regression approach for modeling multivariate ordinal variables. Our model accounts for ordinal structure in output variables and their non-linear dependencies via copula functions modeled as cliques of a conditional random fields. The copula ordinal regression model achieves the joint learning and inference of intensities of multiple AUs, while being computationally tractable. We demonstrate the effectiveness of our approach on three challenging datasets of naturalistic facial expressions and we show that the estimation of target AU intensities improves especially in the case of (a) noisy image features, (b) head-pose variations and (c) imbalanced training data. Lastly, we show that the proposed approach consistently outperforms (i) independent modeling of AU intensities and (ii) the state-of-the-art approach for the target task and (iii) deep convolutional neural networks.
On Composite Quantum Hypothesis Testing

We extend quantum Stein's lemma in asymmetric quantum hypothesis testing to composite null and alternative hypotheses. As our main result, we show that the asymptotic error exponent for testing convex combinations of quantum states $\rho^{\otimes n}$ against convex combinations of quantum states $\sigma^{\otimes n}$ can be written as a regularized quantum relative entropy formula. We prove that in general such a regularization is needed but also discuss various settings where our formula as well as extensions thereof become single-letter. This includes an operational interpretation of the relative entropy of coherence in terms of hypothesis testing. For our proof, we start from the composite Stein's lemma for classical probability distributions and lift the result to the non-commutative setting by using elementary properties of quantum entropy. Finally, our findings also imply an improved recoverability lower bound on the conditional quantum mutual information in terms of the regularized quantum relative entropy -- featuring an explicit and universal recovery map.
Online Heterogeneous Face Recognition Based on Total-Error-Rate Minimization

In this paper, we propose a recursive learning formulation for online heterogeneous face recognition (HFR). The main task is to compare between images which are acquired from different sensing spectrums for identity recognition. Using an extreme learning machine, the proposed recursive formulation seeks a direct optimization to the classification error goal where the solution converges exactly to the batch mode solution. Due to the nonlinear nature of the classification error objective function, formulation of a recursive solution that converges is an important and nontrivial task. Based on this recursive formulation, an online HFR system is designed. The system is evaluated using two challenging heterogeneous face databases with images captured under visible, near infrared and infrared spectrums. The proposed system shows promising performance which is comparable with that of competing state-of-the-arts.
Redesigning Bitcoin’s Fee Market

The Bitcoin payment system involves two agent types: users that transact with the currency and pay fees and miners in charge of authorizing transactions and securing the system in return for these fees. Two of Bitcoin’s challenges are (i) securing sufficient miner revenues as block rewards decrease, and (ii) alleviating the throughput limitation due to a small maximal block size cap. These issues are strongly related as increasing the maximal block size may decrease revenue due to Bitcoin’s pay-your-bid approach. To decouple them, we analyze the “monopolistic auction” [ 16 ], showing (i) its revenue does not decrease as the maximal block size increases, (ii) it is resilient to an untrusted auctioneer (the miner), and (iii) simplicity for transaction issuers (bidders), as the average gain from strategic bid shading (relative to bidding one’s value) diminishes as the number of bids increases.