Reviewerly/exHarmony huggingface dataset:
Generalized Fixation Invariant Nuclei Detection Through Domain Adaptation Based Deep Learning

Nucleus detection is a fundamental task in histological image analysis and an important tool for many follow up analyses.It is known that sample preparation and scanning procedure of histological slides introduce a great amount of variability to the histological images and poses challenges for automated nucleus detection.Here, we studied the effect of histopathological sample fixation on the accuracy of a deep learning based nuclei detection model trained with hematoxylin and eosin stained images.We experimented with training data that includes three methods of fixation; PAXgene, formalin and frozen, and studied the detection accuracy results of various convolutional neural networks.Our results indicate that the variability introduced during sample preparation affects the generalization of a model and should be considered when building accurate and robust nuclei detection algorithms.Our dataset includes over 67 000 annotated nuclei locations from 16 patients and three different sample fixation types.The dataset provides excellent basis for building an accurate and robust nuclei detection model, and combined with unsupervised domain adaptation, the workflow allows generalization to images from unseen domains, including different tissues and images from different labs.
A Novel Approach in Determining Neural Networks Architecture to Classify Data With Large Number of Attributes

One of the challenges in the successful implementation of deep neural networks (DNN) lies on the determination of its architecture, in terms of the number of hidden layers and neurons for each hidden layer.In this research, a new approach is proposed to determine the neural networks architecture especially in the form of Multi-layer Perceptron (MLP) which will later be used as a machine learning method to classify data with large number of attribute.The new approach is proposed since the previous approaches are no longer applicable as general guidelines to determine the architecture of neural networks.Thus, the proposed approach aims to determine the number of hidden layers by using principal component analysis (PCA), while the number of neurons for each hidden layer is determined by using K-Means clustering.The determined neural network architecture is utilized to classify data with large number of attribute, such as the Gas Sensor Array Drift dataset which has 128 input attributes and six output classes and the Parkinson's Disease Classification dataset which has 754 output attributes and two output classes.The results indicate that the best-performing architecture for the first dataset is the one that uses one hidden layer, with a PCA cumulative variance of 69.7%, while for the second dataset is the one that uses three hidden layers, with a PCA cumulative variance of 38.9%.Increasing the number of hidden layers does not always improve the performance of neural networks.Therefore, it is essential to determine the number of hidden layers and neurons that are appropriate to achieve good performance in neural networks.The use of PCA and K-Means clustering is expected to provide guidelines in determining neural networks architectures with good performance.
Constant depth fault-tolerant Clifford circuits for multi-qubit large block codes

Fault-tolerant quantum computation (FTQC) schemes using large block codes that encode $k>1$ qubits in $n$ physical qubits can potentially reduce the resource overhead to a great extent because of their high encoding rate. However, the fault-tolerant (FT) logical operations for the encoded qubits are difficult to find and implement, which usually takes not only a very large resource overhead but also long $\textit{in-situ}$ computation time. In this paper, we focus on Calderbank-Shor-Steane $[\![ n,k,d ]\!]$ (CSS) codes and their logical FT Clifford circuits. We show that the depth of an arbitrary logical Clifford circuit can be implemented fault-tolerantly in $O(1)$ steps \emph{in-situ} via either Knill or Steane syndrome measurement circuit, with the qualified ancilla states efficiently prepared. Particularly, for those codes satisfying $k/n\sim \Theta(1)$, the resource scaling for Clifford circuits implementation on the logical level can be the same as on the physical level up to a constant, which is independent of code distance $d$. With a suitable pipeline to produce ancilla states, our scheme requires only a modest resource cost in physical qubits, physical gates, and computation time for very large scale FTQC.
Two-level label recovery-based label embedding for multi-label classification with missing labels

Different from single-label learning, multi-label learning has rich semantic information. Label embedding obtains the inherent intelligence of the label space by projecting the label space into a latent one. However, the label space is inevitably incomplete. The missing label data will lead to the label embedding model capturing incomplete inherent information. Therefore, label data recovery becomes particularly important. However, the label correlations recovery mechanism only considers the label correlations but ignores the objective existence of instance-correlation information. The information we obtain from the perspective of label correlations alone will produce inadequate and incorrect phenomena when a large number of labels are lost or wrong. In this paper, a two-level label recovery mechanism is used not only to recover label data from the label perspective but also to take full advantage of the instance correlations in the label space. Although the two-level label recovery mechanism considers both instances and labels, it ignores the label space after recovery to capture its essential information. Therefore, combining the two-level semantic information, we propose a label embedding for multi-label classification based on a two-level label recovery mechanism about missing labels model for incomplete datasets, which is the two-level label recovery. Firstly, the two-level label space is projected to the inherent space, and then the instance and label correlations information are captured in the inherent space. Finally, it is applied to the label prediction of classifiers. The algorithm is trained and tested on several complete and incomplete multi-label datasets. It shows that the proposed algorithm has a good classification effect on the largely missed datasets. The results and statistical hypothesis tests further verify the effectiveness of the proposed algorithm.
MhURI:A Supervised Segmentation Approach to Leverage Salient Brain Tissues in Magnetic Resonance Images

Accurate segmentation of critical tissues from a brain MRI is pivotal for characterization and quantitative pattern analysis of the human brain and thereby, identifies the earliest signs of various neurodegenerative diseases. To date, in most cases, it is done manually by the radiologists. The overwhelming workload in some of the thickly populated nations may cause exhaustion leading to interruption for the doctors, which may pose a continuing threat to patient safety. A novel fusion method called U-Net inception based on 3D convolutions and transition layers is proposed to address this issue.A 3D deep learning method called Multi headed U-Net with Residual Inception (MhURI) accompanied by Morphological Gradient channel for brain tissue segmentation is proposed, which incorporates Residual Inception 2-Residual (RI2R) module as the basic building block. The model exploits the benefits of morphological pre-processing for structural enhancement of MR images. A multi-path data encoding pipeline is introduced on top of the U-Net backbone, which encapsulates initial global features and captures the information from each MRI modality.The proposed model has accomplished encouraging outcomes, which appreciates the adequacy in terms of some of the established quality metrices when compared with some of the state-of-the-art methods while evaluating with respect to two popular publicly available data sets.The model is entirely automatic and able to segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) from brain MRI effectively with sufficient accuracy. Hence, it may be considered to be a potential computer-aided diagnostic (CAD) tool for radiologists and other medical practitioners in their clinical diagnosis workflow.
Content Based Phishing Detection with Machine Learning

In recent years due to the inevitable growth of Internet technologies, almost all the real-world systems are transferred to digital platforms. This increases the use of cyberspace in every dimension of our lives especially with mobile devices which enable us to connect to related services in anytime and anywhere concept. However, this ineluctable expansion also brings lots of security breaches especially for standard end users. Phishing is one of the mostly preferred attack types that hackers use by easily hindering themselves. This type attack is initially triggered with a simple e-mail or social media message which mainly forward the victims to a malicious webpage. For security admins, they are really hard attack types to detect. Therefore, in this paper a content based phishing detection mechanism is proposed. In the proposal about six different machine learning models are implemented to select the best training models. Experimental results show that the proposed approaches are very robust and give acceptable accuracies for security admins.
Applying virtual reality for learning woodworking in the vocational training of batch wood furniture production

Taiwan’s vocational wood-furniture schools still focus only on the traditional manual training strategy, and so students’ knowledge of the furniture-manufacturing process is fragmented. Moreover, they possess limited manual skills without learning the workflows and strategies of furniture production, which is inadequate for satisfying the direct work demands of the furniture industry overseas. In response, through virtual reality (VR) technology, this study employed simulations of the furniture production lines in a large Vietnamese furniture-manufacturing factory, enabling students to experience and observe the manufacturing process of furniture production through VR to overcome the limitations in the present teaching environment. In doing so, we recruited 29 freshmen majoring in a furniture-and-woodworking program and divided them into an experimental group (N=15) and a control group (N=14). They were trained with actual furniture production-process cases according to the furniture mass-production process, including paper-based tests, equipment configuration re-draws and production planning table writing. The results showed that the students in the experimental VR-training group had superior judgment concerning the concept of batch furniture production line. This indicates that applying VR technology to the vocational training of batch furniture production effectively enhanced the students’ familiarity of the fast and dynamic production situation of furniture production lines.
Don’t Neglect the Obvious: On the Role of Unambiguous Words in Word Sense Disambiguation

State-of-the-art methods for Word Sense Disambiguation (WSD) combine two different features: the power of pre-trained language models and a propagation method to extend the coverage of such models.This propagation is needed as current sense-annotated corpora lack coverage of many instances in the underlying sense inventory (usually WordNet).At the same time, unambiguous words make for a large portion of all words in WordNet, while being poorly covered in existing senseannotated corpora.In this paper, we propose a simple method to provide annotations for most unambiguous words in a large corpus.We introduce the UWA (Unambiguous Word Annotations) dataset and show how a state-of-theart propagation-based model can use it to extend the coverage and quality of its word sense embeddings by a significant margin, improving on its original results on WSD.
DENAS: automated rule generation by knowledge extraction from neural networks

Deep neural networks (DNNs) have been widely applied in the software development process to automatically learn patterns from massive data. However, many applications still make decisions based on rules that are manually crafted and verified by domain experts due to safety or security concerns. In this paper, we aim to close the gap between DNNs and rule-based systems by automating the rule generation process via extracting knowledge from well-trained DNNs. Existing techniques with similar purposes either rely on specific DNNs input instances or use inherently unstable random sampling of the input space. Therefore, these approaches either limit the exploration area to a local decision-space of the DNNs or fail to converge to a consistent set of rules. The resulting rules thus lack representativeness and stability.
LAW: Learning Automatic Windows for Online Payment Fraud Detection

The rapid development of internet finance has caused increasing concern in online payment fraud due to its great threat. It is typical to employ rule systems or machine learning-based techniques to detect frauds. For the most significant features of such fraudulent transactions are exhibited in a sequential form, the sliding time window is a widely-recognized effective tool for this problem. With a sliding time window, features about the transaction characteristics can be extracted, and the latent patterns hidden in transaction records can be captured. However, the adaptive setting of sliding time window is really a big challenge, since the transaction patterns in real-life application scenarios are often too elusive to be captured. As a matter of fact, the practical setting usually needs to be updated and refined with manual intervention regularly. This is time-consuming indeed. In this article, we pursue an adaptive learning approach to detect fraudulent online payment transactions with automatic sliding time windows. Accordingly, we make efforts on optimizing the setting of windows and improving the adaptability. We design an intelligent window, called learning automatic window (LAW). It utilizes the learning automata to learn the proper parameters of time windows and adjust them dynamically and regularly according to the variation and oscillation of fraudulent transaction patterns. By the experiments over a real-world dataset of the online payment service from a commercial bank, we validate the gain of LAW in terms of detection effectiveness and robustness. To the best of our knowledge, this is the first work to make a sliding time window for fraud detection capable of learning its proper size in changing situations.
Learning-Based Hyperspectral Imagery Compression through Generative Neural Networks

Hyperspectral images (HSIs), which obtain abundant spectral information for narrow spectral bands (no wider than 10 nm), have greatly improved our ability to qualitatively and quantitatively sense the Earth. Since HSIs are collected by high-resolution instruments over a very large number of wavelengths, the data generated by such sensors is enormous, and the amount of data continues to grow, HSI compression technique will play more crucial role in this trend. The classical method for HSI compression is through compression and reconstruction methods such as three-dimensional wavelet-based techniques or the principle component analysis (PCA) transform. In this paper, we provide an alternative approach for HSI compression via a generative neural network (GNN), which learns the probability distribution of the real data from a random latent code. This is achieved by defining a family of densities and finding the one minimizing the distance between this family and the real data distribution. Then, the well-trained neural network is a representation of the HSI, and the compression ratio is determined by the complexity of the GNN. Moreover, the latent code can be encrypted by embedding a digit with a random distribution, which makes the code confidential. Experimental examples are presented to demonstrate the potential of the GNN to solve image compression problems in the field of HSI. Compared with other algorithms, it has better performance at high compression ratio, and there is still much room left for improvements along with the fast development of deep-learning techniques.
Ubiquitous healthcare on smart environments: A systematic mapping study

The number of elderly is increasing in recent years. According to the United Nations (UN), in 2050 there will be 2.1 billion people above 60 years of age worldwide. In addition, the World Health Organization (WHO) reported that more than 1 billion people live with some form of disability, the leading causes of which are chronic diseases and accidents. Thus, many opportunities for the application of smart environments to support ubiquitous healthcare are emerging, the benefits of which may be reflected in reduced medical costs and increased convenience of patients and families. This systematic mapping study aims to identify how smart environments have been applied to support ubiquitous healthcare, what techniques and technologies are being used, and what research gaps are still left unexplored. Eight scientific repositories were used to search for papers in the area of ubiquitous healthcare, and a filtering process was used to remove bias. Of an initial sample of 1706 studies, 49 were reviewed entirely, analyzed, and categorized. Among these, we highlight those oriented to monitoring, detection, notification, and action on situations that may cause illnesses or promote the improvement of people’s health and wellness. Technologies to support ubiquitous healthcare were categorized into three groups: ambient sensors, wearables, and social robotics. These technologies have been applied most frequently to support the elderly and disabled. The diseases most commonly cited were dementia, diabetes, Alzheimer’s, autism, obesity, mental stress, sleep disorders, asthma, epilepsy and chronic diseases. We found only three papers that used prediction models. Finally, we observed a trend of using social robotics to improve the intelligence of ambient, aggregating mobility, and acting.
Comparing Different Diffie-Hellman Key Exchange Flavors for LDACS

Growth of civil air traffic worldwide poses a great challenge for the supporting Communication, Navigation and Surveillance (CNS) infrastructure. Analogue systems have to be replaced by digital means to optimize spectrum efficiency and automation is becoming much more important to be able to handle the amount of participants in the air traffic system. As safety and security are strongly intertwined in aviation, cybersecurity is one key enabler for digitalization in civil aviation. As such we investigate mutual authentication and key agreement methods for the digital aeronautical ground-based communications system L-band Digital Aeronautical Communication System (LDACS). Thereby, we compare the suitability of three different Diffie-Hellmann (DH) key exchange flavors used in a modified version of the Station-To-Station (STS) protocol, for digital aeronautical communication in terms of latency and security data overhead. We conclude, the STS protocol based on a central Public Key Infrastructure (PKI) trust solution with Supersingular Isogeny Diffie-Hellman (SIDH) for post-quantum security to be best suited for long term security. However, due to the smaller key sizes, Elliptic Curve Diffie-Hellman (ECDH) is the more resource efficient candidate and may play a role in low resource authentication scenarios for LDACS.
Natural language processing systems for pathology parsing in limited data environments with uncertainty estimation

Abstract Objective Cancer is a leading cause of death, but much of the diagnostic information is stored as unstructured data in pathology reports. We aim to improve uncertainty estimates of machine learning-based pathology parsers and evaluate performance in low data settings. Materials and methods Our data comes from the Urologic Outcomes Database at UCSF which includes 3232 annotated prostate cancer pathology reports from 2001 to 2018. We approach 17 separate information extraction tasks, involving a wide range of pathologic features. To handle the diverse range of fields, we required 2 statistical models, a document classification method for pathologic features with a small set of possible values and a token extraction method for pathologic features with a large set of values. For each model, we used isotonic calibration to improve the model’s estimates of its likelihood of being correct. Results Our best document classifier method, a convolutional neural network, achieves a weighted F1 score of 0.97 averaged over 12 fields and our best extraction method achieves an accuracy of 0.93 averaged over 5 fields. The performance saturates as a function of dataset size with as few as 128 data points. Furthermore, while our document classifier methods have reliable uncertainty estimates, our extraction-based methods do not, but after isotonic calibration, expected calibration error drops to below 0.03 for all extraction fields. Conclusions We find that when applying machine learning to pathology parsing, large datasets may not always be needed, and that calibration methods can improve the reliability of uncertainty estimates.
Programmable Coherent Linear Quantum Operations with High-Dimensional Optical Spatial Modes

A simple and flexible scheme for high-dimensional linear quantum operations is demonstrated on optical discrete spatial modes in the transverse plane. Quantum-state tomography via symmetric informationally complete positive-operator-valued measures and quantum Fourier transformation are implemented with dimensionality of 15. The statistical fidelity of symmetric informationally complete positive-operator-valued measures and the fidelity of quantum-state tomography are approximately 0.97 and up to 0.853, respectively, while the matrix fidelity of quantum Fourier transformation is 0.85. We believe that our approach has the potential for further exploration of high-dimensional spatial entanglement provided by spontaneous parametric down-conversion in nonlinear crystals.
Bayesian Active Learning for Collaborative Task Specification Using Equivalence Regions

Specifying complex task behaviours while ensuring good robot performance may be difficult for untrained users. We study a framework for users to specify rules for acceptable behaviour in a shared environment such as industrial facilities. As non-expert users might have little intuition about how their specification impacts the robot's performance, we design a learning system that interacts with the user to find an optimal solution. Using active preference learning, we iteratively show alternative paths that the robot could take on an interface. From the user feedback ranking the alternatives, we learn about the weights that users place on each part of their specification. We extend the user model from our previous work to a discrete Bayesian learning model and introduce a greedy algorithm for proposing alternative that operates on the notion of equivalence regions of user weights. We prove that with this algorithm the revision active learning process converges on the user-optimal path. In simulations on realistic industrial environments, we demonstrate the convergence and robustness of our approach.