Reviewerly/exHarmony huggingface dataset:
Communication-Efficient and Byzantine-Robust Distributed Stochastic Learning with Arbitrary Number of Corrupted Workers

Distributed implementations of gradient-based algorithms have been essential for training large machine learning models on massive datasets. However, distributed learning algorithms are confronted with several challenges, including communication costs, straggler issues, and attacks from Byzantine adversaries. Existing works on attack-resilient distributed learning, e.g., the coordinate-wise median of gradients, usually neglect communication and/or straggler issues, and fail to defend against well-crafted attacks. Moreover, those methods are ineffective when more than half of workers are corrupted by a Byzantine adversary. To tackle those challenges simultaneously, we develop a robust gradient aggregation framework that is compatible with gradient compression and straggler mitigation techniques. Our proposed framework requires the parameter server to maintain an honest gradient as a reference at each iteration, thus can compute trust-score and similarity for each received gradient and tolerate arbitrary number of corrupted workers. We also provide convergence analysis of our method for non-convex optimization problems. Finally, experiments of image classification task on Fashion-MNIST dataset are conducted under various Byzantine attacks and gradient sparsification operations, and the numerical results demonstrate the effectiveness of our proposed strategy.
Uncertainty-Aware Multidimensional Scaling

We present an extension of multidimensional scaling (MDS) to uncertain data, facilitating uncertainty visualization of multidimensional data. Our approach uses local projection operators that map high-dimensional random vectors to low-dimensional space to formulate a generalized stress. In this way, our generic model supports arbitrary distributions and various stress types. We use our uncertainty-aware multidimensional scaling (UAMDS) concept to derive a formulation for the case of normally distributed random vectors and a squared stress. The resulting minimization problem is numerically solved via gradient descent. We complement UAMDS by additional visualization techniques that address the sensitivity and trustworthiness of dimensionality reduction under uncertainty. With several examples, we demonstrate the usefulness of our approach and the importance of uncertainty-aware techniques.
New Text Detection Technique Using Machine Learning Architecture

There has been a wide advancement in technology over the past two decades and the extensively usage of camera-embedded mobile phones all around the world has raised the demand of PC vision tasks and offers rise to incalculable open entryways in the field. A hot emerging area of research in this field is the extraction of text embedded in natural scene images. Natural scene images are the photos taken from a camera, where the establishment is self-assertive, and the arrangement of tones used in the image is classy and unique. Exactly when text is accessible in such sort of pictures, it is typically difficult for a machine to recognize and eliminate this due to different limits. This paper deals with a method that uses a blend of the Open Source Computer Vision Library (OpenCV) and the architectures of Machine Learning (ML), to separate English text from images. This paper provides the result by using two Machine Learning architectures, namely, Multi Layer Perceptron (MLP) and TensorFlow (TF). The data is predicted by using both the architectures, to compare their model accuracies and analyze their problems in text detection. The proposed OpenCV execution accomplished a precision rate of 95.2% and a recall rate of 70.6%, proving the efficacy of approach utilized.
Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides

To achieve minimum DNA input requirements for next-generation sequencing (NGS), pathologists visually estimate macrodissection and slide count decisions. Unfortunately, misestimation may cause tissue waste and increased laboratory costs. We developed an artificial intelligence (AI)-augmented smart pathology review system (SmartPath) to empower pathologists with quantitative metrics for accurately determining tissue extraction parameters. SmartPath uses two deep learning architectures, a U-Net based network for cell segmentation and a multi-field-of-view convolutional network for tumor area segmentation, to extract features from digitized H&E-stained formalin-fixed paraffin-embedded slides. From the segmented tumor area, SmartPath suggests a macrodissection area. To predict DNA yield per slide, the extracted features from within the macrodissection area are correlated with known DNA yields to fit a regularized linear model (R = 0.85). Then, a pathologist-defined target yield divided by the predicted DNA yield per slide gives the number of slides to scrape. Following model development, an internal validation trial was conducted within the Tempus Labs molecular sequencing laboratory. We evaluated our system on 501 clinical colorectal cancer slides, where half received SmartPath-augmented review and half traditional pathologist review. The SmartPath cohort had 25% more DNA yields within a desired target range of 100-2000 ng. The number of extraction attempts was statistically unchanged between cohorts. The SmartPath system recommended fewer slides to scrape for large tissue sections, saving tissue in these cases. Conversely, SmartPath recommended more slides to scrape for samples with scant tissue sections, especially those with degraded DNA, helping prevent costly re-extraction due to insufficient extraction yield. A statistical analysis was performed to measure the impact of covariates on the results, offering insights on how to improve future applications of SmartPath. With these improvements, AI-augmented histopathologic review has the potential to decrease tissue waste, sequencing time, and laboratory costs by optimizing DNA yields, especially for samples with scant tissue and/or degraded DNA.
Investigating absorption and scattering effects of turbulent atmosphere on entangled photon pairs propagating through free-space

Quantum entanglement is critical to build the backbone of all quantum technologies. Quantum networks, quantum computations, and quantum communication networking is based on long-range distribution of entangled photons and teleportation of photon qubit states. In order to understand quantum entanglement, characterization of atmospheric turbulence and its effects on propagating quantum states in free-space is essential. One method of photon entanglement is using a photon’s polarization. In this paper, we report results using polarization entangled signal and idler photons. The results may be applicable to support various quantum computing, encryption, and other qubit based high-performance communication protocols. Classically, the degradation of beam quality occurs due to many factors but primarily due to the distortion of spatial and temporal fields of refractive index. However, behavior of single photons through similar turbulent media creates a different set of challenges pointing to integrity of quantum states during propagation. We study this behavior by analyzing quantum states and the degree of entanglement in real-time and correlating it to known atmospheric models, (refractive index structure parameter), and relevant propagation path parameters. This experimental study was performed initially in a controlled laboratory environment, and then devised to be implemented outdoors over a 100-meter free space communication link.
Intelligent splicing technology of off-axis aspheric mirrors based on Zygo interferometer

With the increasing size of the aspheric, manufacturing and testing technologies face enormous challenges. A Zygo interferometer-based intelligent splicing technology for off-axis aspherical mirrors is proposed in this paper, which provides a "small to big" manufacturing strategy for large aspherical mirrors. In the proposed method, a Zygo interferometer, a splicing adjustment platform, and a computer-integrated algorithm are combined to achieve closed-loop adjustment of the off-axis aspherical mirrors. Taking any one of the off-axis aspherical sub-mirrors as a reference, the point cloud information on the surfaces of all off-axis sub-mirrors is obtained by combining the optimal spherical wavefront data with the Zygo interferometer data. The splicing adjustment platform performs positional corrections to the to-be-adjusted off-axis aspherical mirrors using the calculated point cloud registration parameters as the adjustment amount. The iterative operation of point cloud alignment and splicing platform adjustment is done until the positional deviation of the adjusted off-axis aspherical mirror surface reaches a predetermined threshold value, at which point the adjustment stops. The experimental verification employed two off-axis aspherical sub-mirrors with a 100 mm aperture that belonged to the same aspherical surface. The result shows that large aspherical mirrors can be made quickly and effectively with intelligent splicing technology.
Blockchain Technology: A Panacea for IoT Security Challenge

The Internet of Things (IoT) platforms, despite the wide range of application is not without loop holes of which cyberattackers can take advantage. In order to improve the platform's security while also increasing other features, it has been proposed that blockchain technology be implemented in any IoT system. However, while blockchain technology has many advantages, it is important to consider other options because they all have their own drawbacks that may not be ideal for every use case situation. IoT network devices have limited computer power, storage space, and bandwidth. As a result, these systems are easily prone to assault than other network connected devices, such PCs, cell phones and tablets. With focus on IoT security challenges and the countermeasures offered by the blockchain technology, consensus algorithm, data encryption and smart contracts were discovered to be the common and effective algorithm employed by the blockchain technology in securing Iot systems over time.
Semantic segmentation of low magnification effusion cytology images: A semi-supervised approach

Cytopathologists examine microscopic images obtained at various magnifications to identify malignancy in effusions. They locate the malignant cell clusters at a low magnification and then zoom in to investigate cell-level features at a high magnification. This study predicts the malignancy at low magnification levels such as 4X and 10X in effusion cytology images to reduce scanning time. However, the most challenging problem is annotating the low magnification images, particularly the 4X images. This paper extends two semi-supervised learning (SSL) models, MixMatch and FixMatch, for semantic segmentation. The original FixMatch and MixMatch algorithms are designed for classification tasks. While performing image augmentation, the generated pseudo labels are spatially altered. We introduce reverse augmentation to compensate for the effect of the spatial alterations. The extended models are trained using labelled 10X and unlabelled 4X images. The average F-score of benign and malignant pixels on the predictions of 4X images is improved approximately by 9% for both Extended MixMatch and Extended FixMatch respectively compared with the baseline model. In the Extended MixMatch, 62% sub-regions of low magnification images are eliminated from scanning at a higher magnification, thereby saving scanning time.
Data augmentation for disruption prediction via robust surrogate models

The goal of this work is to generate large statistically representative data sets to train machine learning models for disruption prediction provided by data from few existing discharges. Such a comprehensive training database is important to achieve satisfying and reliable prediction results in artificial neural network classifiers. Here, we aim for a robust augmentation of the training database for multivariate time series data using Student $t$ process regression. We apply Student $t$ process regression in a state space formulation via Bayesian filtering to tackle challenges imposed by outliers and noise in the training data set and to reduce the computational complexity. Thus, the method can also be used if the time resolution is high. We use an uncorrelated model for each dimension and impose correlations afterwards via colouring transformations. We demonstrate the efficacy of our approach on plasma diagnostics data of three different disruption classes from the DIII-D tokamak. To evaluate if the distribution of the generated data is similar to the training data, we additionally perform statistical analyses using methods from time series analysis, descriptive statistics and classic machine learning clustering algorithms.
Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

Human–robot interactions require the ability of the system to determine if the user is paying attention. However, to train such systems, massive amounts of data are required. In this study, we addressed the issue of data scarcity by constructing a large dataset (containing ~120,000 photographs) for the attention detection task. Then, by using this dataset, we established a powerful baseline system. In addition, we extended the proposed system by adding an auxiliary face detection module and introducing a unique GAN-based data augmentation technique. Experimental results revealed that the proposed system yields superior performance compared to baseline models and achieves an accuracy of 88% on the test set. Finally, we created a web application for testing the proposed model in real time.
A deep learning‐based framework to identify and characterise heterogeneous secure network traffic

Abstract The evergrowing diversity of encrypted and anonymous network traffic makes network management more formidable to manage the network traffic. An intelligent system is essential to analyse and identify network traffic accurately. Network management needs such techniques to improve the Quality of Service and ensure the flow of secure network traffic. However, due to the usage of non‐standard ports and encryption of data payloads, the classical port‐based and payload‐based classification techniques fail to classify the secured network traffic. To solve the above‐mentioned problems, this paper proposed an effective deep learning‐based framework employed with flow‐time‐based features to predict heterogeneous secure network traffic best. The state‐of‐the‐art machine learning strategies (C4.5, random forest, and K‐nearest neighbour) are investigated for comparison. The proposed 1D‐CNN model achieved higher accuracy in classifying the heterogeneous secure network traffic. In the next step, the proposed deep learning model characterises the major categories (virtual private network traffic, the onion router network traffic, and plain encrypted network traffic) into several application types. The experimental results show the effectiveness and feasibility of the proposed deep learning framework, which yields improved predictive power compared to the state‐of‐the‐art machine learning techniques employed for secure network traffic analysis.
Multi‐scale cascaded networks for synthesis of mammogram to decrease intensity distortion and increase model‐based perceptual similarity

Synthetic digital mammogram (SDM) is a 2D image generated from digital breast tomosynthesis (DBT) and used as a substitute for a full-field digital mammogram (FFDM) to reduce the radiation dose for breast cancer screening. The previous deep learning-based method used FFDM images as the ground truth, and trained a single neural network to directly generate SDM images with similar appearances (e.g., intensity distribution, textures) to the FFDM images. However, the FFDM image has a different texture pattern from DBT. The difference in texture pattern might make the training of the neural network unstable and result in high-intensity distortion, which makes it hard to decrease intensity distortion and increase perceptual similarity (e.g., generate similar textures) at the same time. Clinically, radiologists want to have a 2D synthesized image that feels like an FFDM image in vision and preserves local structures such as both mass and microcalcifications (MCs) in DBT because radiologists have been trained on reading FFDM images for a long time, while local structures are important for diagnosis. In this study, we proposed to use a deep convolutional neural network to learn the transformation to generate SDM from DBT.To decrease intensity distortion and increase perceptual similarity, a multi-scale cascaded network (MSCN) is proposed to generate low-frequency structures (e.g., intensity distribution) and high-frequency structures (e.g., textures) separately. The MSCN consist of two cascaded sub-networks: the first sub-network is used to predict the low-frequency part of the FFDM image; the second sub-network is used to generate a full SDM image with textures similar to the FFDM image based on the prediction of the first sub-network. The mean-squared error (MSE) objective function is used to train the first sub-network, termed low-frequency network, to generate a low-frequency SDM image. The gradient-guided generative adversarial network's objective function is to train the second sub-network, termed high-frequency network, to generate a full SDM image with textures similar to the FFDM image.1646 cases with FFDM and DBT were retrospectively collected from the Hologic Selenia system for training and validation dataset, and 145 cases with masses or MC clusters were independently collected from the Hologic Selenia system for testing dataset. For comparison, the baseline network has the same architecture as the high-frequency network and directly generates a full SDM image. Compared to the baseline method, the proposed MSCN improves the peak-to-noise ratio from 25.3 to 27.9 dB and improves the structural similarity from 0.703 to 0.724, and significantly increases the perceptual similarity.The proposed method can stabilize the training and generate SDM images with lower intensity distortion and higher perceptual similarity.
SEMMI: Multi-party Security Decision-making Scheme Under the Internet of Medical Things

In the Internet of Medical Things, the intelligent auxiliary decision-making system uses machine learning algorithms to analyze medical data for disease diagnosis, auxiliary intervention, and analysis and early warning. However, in the process of medical data transmission, processing, and storage, a large amount of private information is also at risk of leakage. Therefore, this article proposes a smart classification and decision-making program in the Internet of Medical Things scenario-SEMMI, which can effectively deal with the risk of data leakage in the process of medical data processing. At the same time, it reduces the huge computing and storage pressure caused by encryption and decryption operations in medical institutions. In the scheme, data collection, processing, transmission, storage and calculation are completed by ciphertext. In addition, in view of the relatively weak computing and storage capabilities of sensor nodes, we use chaos theory to construct a stream cipher algorithm to ensure the security of transmission from sensor to user; the homomorphic encryption algorithm is used to ensure the computability of the ciphertext and the security of storage. Through security analysis, it can be concluded that this scheme can resist attacks from adversaries; at the same time, the experimental results show that the scheme has good performance in terms of calculation, storage overhead, accuracy, and so on.
Effect of Virtual Intervention Technology in Virtual Vocational Training for People with Intellectual Disabilities: Connecting Instructor in the Real World and Trainee in the Virtual World

There are various social support services to help people with intellectual disabilities (ID) find jobs for their independent social and economic activities in life. With the advancement of virtual reality (VR) technologies, vocational training can take place in a virtual environment (VE) without temporal and spatial limitations. Therefore, opportunities for people with ID to receive professional vocational training in a VE continue to expand. Accordingly, this study proposes that virtual intervention (VI) technology can improve the effects of virtual training and learning transfer from the virtual to the real world. We defined VI as a supportive activity that assists trainees in a VE according to their individual ability and training situation through an instructor's control in the real world. We designed the virtual intervention content (VIC) as intervening virtual objects and applied them to a virtual barista job training program. We developed the virtual intervention interface (VII) for smoothly supporting the interaction between the trainee in the VE and the instructor in the real world, especially for people with ID or beginner trainees. In this study, we derived statistically significant findings that demonstrated the effects of VI technology through two experiments conducted with 39 participants with ID. Firstly, VIC showed an effective intervention function that helped the participants solve problems during virtual vocational training. Also, we observed differences in the intervention effect according to the sensory perception characteristics of the participants. Secondly, trainees' barista job performance rates improved by 37.43% after receiving virtual training. Moreover, the effects of training were largely maintained in the job performance assessment after three weeks in an actual cafe situation. This suggests that the effects of VI-based virtual training are meaningful not only within the VE but also in the real world for the transfer of learning. Furthermore, the VI method was significantly more effective and efficient than the conventional method using the human instructor's verbal and physical intervention in terms of time, frequency, and success rate.
Smart Student Attendance System Based on Facial Recognition and Machine Learning

The traditional technique of taking attendance is time-consuming for teachers because it is done by hand. The time it takes to take the full class's attendance increases as the class size expands. To address this issue, a smart attendance system is being implemented. Automatic face recognition technology has advanced significantly in the ever-changing environment. Real-time face recognition for class attendance is a realistic technique to keep track of students’ attendance on a regular basis. Face recognition, a sub-field of machine learning, is used to track attendance in this research. The algorithm was written in Python, a programming language. The algorithm is trained and evaluated using a set of student pictures. Face detection is a method of determining a person's facial expression.
Dynamic Search and Integration of Web Services

This paper is focused on search and integration of the Web services dynamically according to the user request. Due to the dynamicity and similarity of Web services, the search and automatic integration of Web services are required to satisfy the user request. The unavailability of UDDI is motivated this system to use search engine to discover the required Web services. The methods used here are retrieval of the WSDL of services using search engine’s search technique, processing the WSDL links to extract operation elements and integration of Web services which generates more than one integration plan. This paper is also focused on selection of suitable integration plan from similar integration plans.