Reviewerly/exHarmony huggingface dataset:
Privacy Masking Stochastic Subgradient-Push Algorithm for Distributed Online Optimization

This article investigates the problem of distributed online optimization for a group of units communicating on time-varying unbalanced directed networks. The main target of the set of units is to cooperatively minimize the sum of all locally known convex cost functions (global cost function) while pursuing the privacy of their local cost functions being well masked. To address such optimization problems in a collaborative and distributed fashion, a differentially private-distributed stochastic subgradient-push algorithm, called DP-DSSP, is proposed, which ensures that units interact with in-neighbors and collectively optimize the global cost function. Unlike most of the existing distributed algorithms which do not consider privacy issues, DP-DSSP via differential privacy strategy successfully masks the privacy of participating units, which is more practical in applications involving sensitive messages, such as military affairs or medical treatment. An important feature of DP-DSSP is tackling distributed online optimization problems under the circumstance of time-varying unbalanced directed networks. Theoretical analysis indicates that DP-DSSP can effectively mask differential privacy as well as can achieve sublinear regrets. A compromise between the privacy levels and the accuracy of DP-DSSP is also revealed. Furthermore, DP-DSSP is capable of handling arbitrarily large but uniformly bounded delays in the communication links. Finally, simulation experiments confirm the practicability of DP-DSSP and the findings in this article.
Transfer Learning and Fine Tuning in Breast Mammogram Abnormalities Classification on CBIS-DDSM Database

Breast cancer has an important incidence in women mortality worldwide.Currently, mammography is considered the gold standard for breast abnormalities screening examinations, since it aids in the early detection and diagnosis of the illness.However, both identification of mass lesions and its malignancy classification is a challenging problem for artificial intelligence.In this work, we extend our previous research in mammogram classification, where we studied NasNet and MobileNet in transfer learning to train a breast abnormality malignancy classifier, and include models like: VGG, Resnet, Xception and Resnext.However, training deep learning models tends to overfit.This problem is also carried out in this work.Our results show that Fine Tuning achieves the best classifier performance in VGG16 with AUC value of 0.844 in the CBIS-DDSM dataset.
Task complexity and learning styles in situated virtual learning environments for construction higher education

This paper contributes to the ongoing discussion and research on the role of 3D virtual learning environments in teaching and learning. It specifically focuses on the use of video games as an enabling technology in construction higher education. As such, it investigates whether task complexity has any influence on an individual's preferred learning style while learning through virtual reality (VR) technology. To answer this question and address relevant issues, an educational experiment has been designed and conducted. An experimental virtual learning environment, the Situation Engine, was set up as a virtual construction site for undergraduate construction students to experience construction work in progress. The design and development of the Situation Engine has drawn on powerful pedagogical theories such as situated and experiential learning. 253 undergraduate students participated in the educational experiment. Three tasks of different complexity levels were designed as the experimental environments; with level of complexity being the independent variable. The hypothesis that students would adopt different learning styles when engaged in learning tasks of different complexities was rejected. No significant difference in the preferred learning styles was identified among the three experimental groups. It was concluded, therefore, that when using virtual reality technology for construction education there is no evidence to suggest the level of task complexity has significant influence on how people learn. The study presented in this paper is the first empirical study in the construction higher education field that reveals the relationship between the task complexity and students learning styles when Virtual Learning Environments are engaged.
Robust nuclei segmentation in histopathology using ASPPU-Net and boundary refinement

Automated nuclear segmentation in histopathological images is a prerequisite for a computer-aided diagnosis framework. However, it remains a challenging problem due to the nucleus occlusion or overlapping, shape variation, and image background complexity. Recently, deep learning techniques are widely used in analyzing digital histopathology. We present a computerized image-based method for automatically segmenting nuclei using an integration of a deep learning model and an improved concave point detection algorithm. A modified atrous spatial pyramid pooling U-Net (ASPPU-Net) is derived to capture multi-scale nuclei features and obtain nuclei context information without reducing the spatial resolution of feature map. A weighted binary cross entropy loss function with Dice loss function is used to better handle the data unbalance problem. An accelerated concave point detection method allows to effectively and accurately segmenting highly overlapping nuclei. Our ASPPU-Net based method was tested on four independent data cohorts and achieved the highest Dice similarity coefficient of 0.83, and pixel wise accuracy of 0.95. The experimental results suggested that the combination of ASPPU-Net model and concave point detection method was able to gain improved performance in separating both isolated and touching clustered nuclei in histopathology.
Sample imbalance disease classification model based on association rule feature selection

In the research of computer-aided diagnosis, the shortage of disease feature dimension curse and the imbalance of medical samples have always been the focus of research on diagnostic decision support systems. For these two problems, we propose a feature selection algorithm based on association rules and an integrated classification algorithm based on random equilibrium sampling. We extracted and cleaned the electronic medical record text obtained from the hospital to obtain a diabetes data set. The proposed algorithm was verified in this data set and the public data set UCI. Experimental results show that the feature selection algorithm based on association rules is better than the CART, ReliefF and RFE-SVM algorithms in terms of feature dimension and classification accuracy. The proposed integrated classification algorithm based on random equalization sampling is superior to the comparative SMOTE-Boost and SMOTE-RF algorithms in macro precision, macro-full rate and macro F1 value, which embodies the robustness of the algorithm.
Review of Stereo Matching Algorithms Based on Deep Learning

Stereo vision is a flourishing field, attracting the attention of many researchers. Recently, leveraging on the development of deep learning, stereo matching algorithms have achieved remarkable performance far exceeding traditional approaches. This review presents an overview of different stereo matching algorithms based on deep learning. For convenience, we classified the algorithms into three categories: (1) non-end-to-end learning algorithms, (2) end-to-end learning algorithms, and (3) unsupervised learning algorithms. We have provided a comprehensive coverage of the remarkable approaches in each category and summarized the strengths, weaknesses, and major challenges, respectively. The speed, accuracy, and time consumption were adopted to compare the different algorithms.
Image analysis and artificial intelligence in infectious disease diagnostics

BackgroundMicrobiologists are valued for their time-honed skills in image analysis, including identification of pathogens and inflammatory context in Gram stains, ova and parasite preparations, blood smears and histopathologic slides. They also must classify colony growth on a variety of agar plates for triage and assessment. Recent advances in image analysis, in particular application of artificial intelligence (AI), have the potential to automate these processes and support more timely and accurate diagnoses.ObjectivesTo review current AI-based image analysis as applied to clinical microbiology; and to discuss future trends in the field.SourcesMaterial sourced for this review included peer-reviewed literature annotated in the PubMed or Google Scholar databases and preprint articles from bioRxiv. Articles describing use of AI for analysis of images used in infectious disease diagnostics were reviewed.ContentWe describe application of machine learning towards analysis of different types of microbiologic image data. Specifically, we outline progress in smear and plate interpretation as well as the potential for AI diagnostic applications in the clinical microbiology laboratory.ImplicationsCombined with automation, we predict that AI algorithms will be used in the future to prescreen and preclassify image data, thereby increasing productivity and enabling more accurate diagnoses through collaboration between the AI and the microbiologist. Once developed, image-based AI analysis is inexpensive and amenable to local and remote diagnostic use.
Learning to Classify: A Flow-Based Relation Network for Encrypted Traffic Classification

As the size and source of network traffic increase, so does the challenge of monitoring and analyzing network traffic. The challenging problems of classifying encrypted traffic are the imbalanced property of network data, the generalization on an unseen dataset, and overly dependent on data size. In this paper, we propose an application of a meta-learning approach to address these problems in encrypted traffic classification, named Flow-Based Relation Network (RBRN). The RBRN is an end-to-end classification model that learns representative features from the raw flows and then classifies them in a unified framework. Moreover, we design “hallucinator” to produce additional training samples for the imbalanced classification, and then focus on meta-learning to classify unseen categories from few labeled samples. We validate the effectiveness of the RBRN on the real-world network traffic dataset, and the experimental results demonstrate that the RBRN can achieve an excellent classification performance and outperform the state-of-the-art methods on encrypted traffic classification. What is more interesting, our model trained on the real-world dataset can generalize very well to unseen datasets, outperforming multiple state-of-art methods.
Face Detection in Security Monitoring Based on Artificial Intelligence Video Retrieval Technology

With the rapid development of video monitoring, the massive information of the monitoring image has far exceeded the effective processing range of human resources. Intelligent video retrieval technology has become an increasingly indispensable part of video monitoring system. Intelligent video retrieval technology integrates video processing, computer vision and artificial intelligence, which greatly improves the efficiency of monitoring and the accuracy and linkage of monitoring system. Face recognition and other emerging technologies continue to rise and apply to the security monitoring system. Based on deep learning theory and face detection neural network, this paper proposes a video oriented cascaded intelligent face detection algorithm, which builds deep learning network by cascading multiple features, from edge features, contour features, local features to semantic features, and advances layer by layer. According to the last semantic features, the information of the input data is obtained to accurately realize the face detection under the non ideal condition. Simulation results show that the algorithm has good detection performance for single face and multi face images, and has strong robustness for rotating face. At the same time, the algorithm is fast and can basically meet the requirements of real-time face detection.
A filter-based feature construction and feature selection approach for classification using Genetic Programming

Feature construction and feature selection are two common pre-processing methods for classification. Genetic Programming (GP) can be used to solve feature construction and feature selection tasks due to its flexible representation. In this paper, a filter-based multiple feature construction approach using GP named FCM that stores top individuals is proposed, and a filter-based feature selection approach using GP named FS that uses correlation-based evaluation method is employed. A hybrid feature construction and feature selection approach named FCMFS that first constructs multiple features using FCM then selects effective features using FS is proposed. Experiments on nine datasets show that features selected by FS or constructed by FCM are all effective to improve the classification performance comparing with original features, and our proposed FCMFS can maintain the classification performance with smaller number of features comparing with FCM, and can obtain better classification performance with smaller number of features than FS on the majority of the nine datasets. Compared with another feature construction and feature selection approach named FSFCM that first selects features using FS then constructs features using FCM, FCMFS achieves better performance in terms of classification and the smaller number of features. The comparisons with three state-of-art techniques show that our proposed FCMFS approach can achieve better experimental results in most cases.
Pair-wise Preference Relation based Probabilistic Matrix Factorization for Collaborative Filtering in Recommender System

Matrix Factorization (MF) is one of the most popular techniques used in Collaborative Filtering (CF) based Recommender System (RS). Most of the MF methods tend to remove sparsity or predict missing ratings. However, the prime objective of any RS is to generate recommendation of items, where items having higher preferences should be placed at higher positions in the recommendation list. Further, the integration of user and item features ( i.e. Side Information) in any RS model, increases the quality of recommendation. Keeping these in mind, we propose a Probabilistic MF (PMF) model that takes Preference Relation as input (instead of ratings) for generating efficient ranking of items. The user and item side information are integrated into the model using matrix co-factorization technique. Also, user and item neighborhood information (known as second-order interaction among similar users or items) are integrated into the model. The use of PMF enables the proposed method to capture user-item interaction of higher-order. Hence, the proposed RS model can capture all the desired user-item interaction information. Experimental results obtained using two ‘MovieLens’ datasets indicate the superiority in recommendation quality of the proposed model over the recent state-of-the-art MF methods.
Achieving a Covert Channel over an Open Blockchain Network

Blockchain technology is an immutable database that cannot be censored, tampered with, or erased, which can increase credibility and reduce interaction costs. Therefore, blockchain technology is expected to become the next generation Internet infrastructure, with broad application prospects in many industries, such as medicine, energy, and finance. The open network and publicly stored data in a blockchain system, however, raise serious risks of data theft and user privacy leakage, which has become a core issue that restricts the blockchain technology from becoming practical. This article uses kleptography technology to design a blockchain covert data transmission scheme to achieve high-concealment and high-performance data transmission under open network conditions. Experiments conducted in the Bitcoin network prove that the covert data transmission mechanism of this article has high compatibility and high performance, and can be practically applied to many popular blockchain systems.
TAPER: Time-Aware Patient EHR Representation

Effective representation learning of electronic health records is a challenging task and is becoming more important as the availability of such data is becoming pervasive. The data contained in these records are irregular and contain multiple modalities such as notes, and medical codes. They are preempted by medical conditions the patient may have, and are typically recorded by medical staff. Accompanying codes are notes containing valuable information about patients beyond the structured information contained in electronic health records. We use transformer networks and the recently proposed BERT language model to embed these data streams into a unified vector representation. The presented approach effectively encodes a patient's visit data into a single a distributed representation, which can be used for downstream tasks. Our model demonstrates superior performance and generalization on mortality, readmission and length of stay tasks using the publicly available MIMIC-III ICU dataset.
Laser Stripe Center Detection Under the Condition of Uneven Scattering Metal Surface for Geometric Measurement

The uneven reflective metal surface has great influence on the high-precision measurement of the structured light 3-D shape. By analyzing the laser scattering and reflection models, a set of light stripe center's subpixel extraction methods which has strong robustness is proposed. The method first cuts off the influence of the uneven fixed background by using the difference image method to process the bright field and the dark field. Then, the regional growth statistics method is used to eliminate the influence of random laser speckle noise, and then the gray-gravity method is used to obtain the coarse center of the laser stripe. The Sobel operator is used to obtain the gradient vector of the stripe pixel point, and then the normal direction field of the light stripe is obtained. The normal direction field vector is taken as the direction to find the $5\times5$ neighborhood of the coarse light stripe's center, and then the gray-gravity method is reused to determine the center position of the laser stripe in the normal line direction; the pixel coordinates of the subpixel level are obtained by using bilinear interpolation. The experimental results show that the method can effectively eliminate the interference of flash point noise and has strong robustness. The detection error of the stripe is less than 0.1 pixels, which ensures the system measurement accuracy. Compared with other methods, the system's resolution can reach 0.02 mm.
Vulnerabilities Mapping based on OWASP-SANS: A Survey for Static Application Security Testing (SAST)

The delivery of a framework in place for secure application development is of real value for application development teams to integrate security into their development life cycle, especially when a mobile or web application moves past the scanning stage and focuses increasingly on the remediation or mitigation phase based on static application security testing (SAST). For the first time, to the author's knowledge, the industry-standard Open Web Application Security Project (OWASP) top 10 vulnerabilities and CWE/SANS top 25 most dangerous software errors are synced up in a matrix with Checkmarx vulnerability queries, producing an application security framework that helps development teams review and address code vulnerabilities, minimise false positives discovered in static scans and penetration tests, targeting an increased accuracy of the findings. A case study is conducted for vulnerabilities scanning of a proof-of-concept mobile malware detection app. Mapping the OWASP/SANS with Checkmarx vulnerabilities queries, flaws and vulnerabilities are demonstrated to be mitigated with improved efficiency.
Applying Deep Neural Networks over Homomorphic Encrypted Medical Data

In recent years, powered by state-of-the-art achievements in a broad range of areas, machine learning has received considerable attention from the healthcare sector. Despite their ability to provide solutions within personalized medicine, strict regulations on the confidentiality of patient health information have in many cases hindered the adoption of deep learning-based solutions in clinical workflows. To allow for the processing of sensitive health information without disclosing the underlying data, we propose a solution based on fully homomorphic encryption (FHE). The considered encryption scheme, MORE (Matrix Operation for Randomization or Encryption), enables the computations within a neural network model to be directly performed on floating point data with a relatively small computational overhead. We consider the well-known MNIST digit recognition problem to evaluate the feasibility of the proposed method and show that performance does not decrease when deep learning is applied on MORE homomorphic data. To further evaluate the suitability of the method for healthcare applications, we first train a model on encrypted data to estimate the outputs of a whole-body circulation (WBC) hemodynamic model and then provide a solution for classifying encrypted X-ray coronary angiography medical images. The findings highlight the potential of the proposed privacy-preserving deep learning methods to outperform existing approaches by providing, within a reasonable amount of time, results equivalent to those achieved by unencrypted models. Lastly, we discuss the security implications of the encryption scheme and show that while the considered cryptosystem promotes efficiency and utility at a lower security level, it is still applicable in certain practical use cases.
The State of the Uniform: Attacks on Encrypted Databases Beyond the Uniform Query Distribution

Recent foundational work on leakage-abuse attacks on encrypted databases has broadened our understanding of what an adversary can accomplish with a standard leakage profile.Nevertheless, all known value reconstruction attacks succeed under strong assumptions that may not hold in the real world.The most prevalent assumption is that queries are issued uniformly at random by the client.We present the first value reconstruction attacks that succeed without any knowledge about the query or data distribution.Our approach uses the searchpattern leakage, which exists in all known structured encryption schemes but has not been fully exploited so far.At the core of our method lies a support size estimator, a technique that utilizes the repetition of search tokens with the same response to estimate distances between encrypted values without any assumptions about the underlying distribution.We develop distributionagnostic reconstruction attacks for both range queries and knearest-neighbor (k-NN) queries based on information extracted from the search-pattern leakage.Our new range attack follows a different algorithmic approach than state-of-the-art attacks, which are fine-tuned to succeed under the uniformly distributed queries.Instead, we reconstruct plaintext values under a variety of skewed query distributions and even outperform the accuracy of previous approaches under the uniform query distribution.Our new k-NN attack succeeds with far fewer samples than previous attacks and scales to much larger values of k.We demonstrate the effectiveness of our attacks by experimentally testing them on a wide range of query distributions and database densities, both unknown to the adversary.