Reviewerly/exHarmony huggingface dataset:
Hematology and Machine Learning

Substantial improvements in computational power and machine learning (ML) algorithm development have vastly increased the limits of what autonomous machines are capable of. Since its beginnings in the 19th century, laboratory hematology has absorbed waves of progress yielding improvements in both of accuracy and efficiency. The next wave of change in laboratory hematology will be the result of the ML revolution that has already touched many corners of healthcare and society at large.This review will describe the manifestations of ML and artificial intelligence (AI) already utilized in the clinical hematology laboratory. This will be followed by a topical summary of the innovative and investigational applications of this technology in each of the major subdomains within laboratory hematology.Application of this technology to laboratory hematology will increase standardization and efficiency by reducing laboratory staff involvement in automatable activities. This will unleash time and resources for focus on more meaningful activities such as the complexities of patient care, research and development, and process improvement.
A self-similar sine–cosine fractal architecture for multiport interferometers

Abstract Multiport interferometers based on integrated beamsplitter meshes have recently captured interest as a platform for many emerging technologies. In this paper, we present a novel architecture for multiport interferometers based on the sine–cosine fractal decomposition of a unitary matrix. Our architecture is unique in that it is self-similar, enabling the construction of modular multi-chiplet devices. Due to this modularity, our design enjoys improved resilience to hardware imperfections as compared to conventional multiport interferometers. Additionally, the structure of our circuit enables systematic truncation, which is key in reducing the hardware footprint of the chip as well as compute time in training optical neural networks, while maintaining full connectivity. Numerical simulations show that truncation of these meshes gives robust performance even under large fabrication errors. This design is a step forward in the construction of large-scale programmable photonics, removing a major hurdle in scaling up to practical machine learning and quantum computing applications.
A Novel Phishing Website Detection Model Based on LightGBM and Domain Name Features

Phishing attacks have evolved in terms of sophistication and have increased in sheer number in recent years. This has led to corresponding developments in the methods used to evade the detection of phishing attacks, which pose daunting challenges to the privacy and security of the users of smart systems. This study uses LightGBM and features of the domain name to propose a machine-learning-based method to identify phishing websites and maintain the security of smart systems. Domain name features, often known as symmetry, are the property wherein multiple domain-name-generation algorithms remain constant. The proposed model of detection is first used to extract features of the domain name of the given website, including character-level features and information on the domain name. The features are filtered to improve the model’s accuracy and are subsequently used for classification. The results of experimental comparisons showed that the proposed model of detection, which integrates two types of features for training, significantly outperforms the model that uses a single type of feature. The proposed method also has a higher detection accuracy than other methods and is suitable for the real-time detection of many phishing websites.
Learning Lightweight and Superior Detectors with Feature Distillation for Onboard Remote Sensing Object Detection

CubeSats provide a low-cost, convenient, and effective way of acquiring remote sensing data, and have great potential for remote sensing object detection. Although deep learning-based models have achieved excellent performance in object detection, they suffer from the problem of numerous parameters, making them difficult to deploy on CubeSats with limited memory and computational power. Existing approaches attempt to prune redundant parameters, but this inevitably causes a degradation in detection accuracy. In this paper, the novel Context-aware Dense Feature Distillation (CDFD) is proposed, guiding a small student network to integrate features extracted from multi-teacher networks to train a lightweight and superior detector for onboard remote sensing object detection. Specifically, a Contextual Feature Generation Module (CFGM) is designed to rebuild the non-local relationships between different pixels and transfer them from teacher to student, thus guiding students to extract rich contextual features to assist in remote sensing object detection. In addition, an Adaptive Dense Multi-teacher Distillation (ADMD) strategy is proposed, which performs adaptive weighted loss fusion of students with multiple well-trained teachers, guiding students to integrate the learning of helpful knowledge from multiple teachers. Extensive experiments were conducted on two large-scale remote sensing object detection datasets with various network structures; the results demonstrate that the trained lightweight network achieves auspicious performance. Our approach also shows good generality for existing state-of-the-art remote sensing object detectors. Furthermore, by experimenting on large general object datasets, we demonstrate that our approach is equally practical for general object detection distillation.
Unsupervised domain adaptation via re-weighted transfer subspace learning with inter-class sparsity

Unsupervised domain adaptation is intended to respond to a challenging scenario where the training and testing samples are related yet distributed differently. A common technique to realize the alignment of distributions is feature transformation, which is gaining increasing attention in the transfer learning community. Many of existing studies concentrate on learning domain-invariant feature representations in order to transfer classifiers from the source to the target domain. However, the class discriminability of features is also crucial for the cross-domain learning, which could lead to decreased performance if ignored. To extract both the transferable and discriminative feature representation, a novel unsupervised domain adaptation method termed re-weighted transfer subspace learning with inter-class sparsity (ICS-RTSL) is proposed. Central to the proposed approach is the introduction of a class-wise sparsity regularization that aims to reduce the intra-class distances by maintaining the structural consistency of the source samples, thus making it possible to learn a more discriminative representation. In addition, a residual term through the least absolute criterion is constructed to mitigate the negative impact possibly brought by outliers. This is followed by the proposal of an efficient algorithm with iteratively re-weighted least squares to optimize the learning model. Supported by a set of comparative experiments, the effectiveness of the proposed ICS-RTSL is demonstrated through cross-domain image recognition tasks.
TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution With Applications to Imaging Through Turbulence

We present a self-supervised and self-calibrating multi-shot approach to imaging through atmospheric turbulence, called TurbuGAN. Our approach requires no paired training data, adapts itself to the distribution of the turbulence, leverages domain-specific data priors, and can generalize from tens to thousands of measurements. We achieve such functionality through an adversarial sensing framework adapted from CryoGAN 1, which uses a discriminator network to match the distributions of captured and simulated measurements. Our framework builds on CryoGAN by (1) generalizing the forward measurement model to incorporate physically accurate and computationally efficient models for light propagation through anisoplanatic turbulence, (2) enabling adaptation to slightly misspecified forward models, and (3) leveraging domain-specific prior knowledge using pretrained generative networks, when available. We validate TurbuGAN on both computationally simulated and experimentally captured images distorted with anisoplanatic turbulence.
Russian university bureaucracy - terra incognita for sociology of education

The authors argue that in contemporary Russian universities, the bureaucracy has turned into the most influential corporation, and insist on the sociological analysis of its status, positive and negative consequences of formation and evolution. Despite many studies of this phenomenon, the university bureaucracy remains terra incognita for sociology of education - many fundamental issues remain unclear: why in the meritocratic and initially humanitarian university, practices of formal rationality with the focus on normative acts and instructions (rationality for its own sake) prevail; how to explain that even the obvious inconsistency of many bureaucratic practices does not lead to discrediting the bureaucratic system, but, on the contrary, contributes to its extended reproduction; why many specialists find the results of the university management optimization based on a bureaucratic approach doubtful. The majority of sociological works on the university bureaucratization provide answers to these questions in the form of admitting the current situation, but there is no causal analysis of the university bureaucracy development and evolution. The article considers possible causes of the current situation: the lack of a systematic approach to the university bureaucratization, its study mainly in the context of the higher education reforms; uncritical reproduction of the classical theories of bureaucracy, which need changes according to the contemporary realities; the lack of works on the status of the university bureaucracy under the digital transformation of society; the barrier of institutionalized hypocrisy; servility relationships between sociologists and university managers. The identified problems require empirical verification and discussion in the professional sociological community.
Quantum Resources Required to Block-Encode a Matrix of Classical Data

We provide modular circuit-level implementations and resource estimates for several methods of block-encoding a dense $N\times N$ matrix of classical data to precision $\epsilon$; the minimal-depth method achieves a $T$-depth of $\mathcal{O}{(\log (N/\epsilon))},$ while the minimal-count method achieves a $T$-count of $\mathcal{O}{(N\log(1/\epsilon))}$. We examine resource tradeoffs between the different approaches, and we explore implementations of two separate models of quantum random access memory (QRAM). As part of this analysis, we provide a novel state preparation routine with $T$-depth $\mathcal{O}{(\log (N/\epsilon))}$, improving on previous constructions with scaling $\mathcal{O}{(\log^2 (N/\epsilon))}$. Our results go beyond simple query complexity and provide a clear picture into the resource costs when large amounts of classical data are assumed to be accessible to quantum algorithms.
Personalized Online Federated Learning for IoT/CPS: Challenges and Future Directions

In recent years, federated learning (FL) has emerged as a powerful paradigm for distributed learning thanks to its privacy-preserving capabilities. With the use of FL, a network of edge devices can make intelligent decisions without exposing their data to others. Despite its success, the traditional FL is not well suited to many practical applications such as those that involve the internet-of-things (IoT) or cyber-physical systems (CPS), where data access can be intermittent, and edge devices are semi-independent with device-specific dynamic behavior characteristics. Those devices are referred to here as semi-independent devices since they need to make decisions based on their own data and device characteristics, often independent of other devices and the information obtained from other devices in the network. Additionally, as new information becomes available, traditional FL must repeat the entire learning process and may not be able to provide timely and tailored solutions to participants. Personalized online FL, on the other hand, retains the collaborative and privacy-preserving aspects while learning in real time from intermittent data. It further enables devices to learn models customized to the device and the specific tasks it performs. In light of these reasons, personalized Online-FL is ideal for applications where the learning relies on heterogeneous data streams, and local optimization is beneficial. In this work, we want to bring attention to this new learning paradigm, present a few of the applications that could benefit from it, and highlight the principal challenges the research community faces in developing successful personalized Online-FL.
Defocused calibration for large field-of-view binocular cameras

In tunnel boring machine construction sites, on-site calibration of large field-of-view binocular cameras is still a challenging task due to factors such as the narrow spaces of the machines' mechanical structures, high pressure, or falling rocks. Therefore, a calibration method using a small plate in proximity to the camera (a defocused position safe for workers) is proposed in this paper to deal with the above challenges. First, a defocus-insensitive feature point encoding pattern is developed. Then feature points are extracted through a phase recovery network based on the Swin Transformer, and finally camera parameters are optimized based on virtual symmetric transfer errors. The relevant simulation and experimental data show that the relative error between the proposed method and the phase-shift method in feature extraction is 0.15 pixels, while the relative error between the proposed method and a high-precision laser tracker in three-dimensional measurement is 1.3%. The results demonstrate the feasibility of the method in a large field-of-view binocular camera for tunnel boring machine construction sites. Increasing the diversity of data sets and lightweight models will be the focus of future research.
Deep-PANTHER: Learning-Based Perception-Aware Trajectory Planner in Dynamic Environments

This letter presents Deep-PANTHER, a learning-based perception-aware trajectory planner for unmanned aerial vehicles (UAVs) in dynamic environments. Given the current state of the UAV, and the predicted trajectory and size of the obstacle, Deep-PANTHER generates multiple trajectories to avoid a dynamic obstacle while simultaneously maximizing its presence in the field of view (FOV) of the onboard camera. To obtain a computationally tractable real-time solution, imitation learning is leveraged to train a Deep-PANTHER policy using demonstrations provided by a multimodal optimization-based expert. Extensive simulations show replanning times that are two orders of magnitude faster than the optimization-based expert, while achieving a similar cost. By ensuring that each expert trajectory is assigned to one distinct student trajectory in the loss function, Deep-PANTHER can also capture the multimodality of the problem and achieve a mean squared error (MSE) loss with respect to the expert that is up to 18 times smaller than state-of-the-art (Relaxed) Winner-Takes-All approaches. Deep-PANTHER is also shown to generalize well to obstacle trajectories that differ from the ones used in training.
Systematic literature review: The use of virtual reality as a learning media

With current technological developments, Virtual Reality can be one of the learning media that can make users interested and interact while learning. The use of VR in learning such as simulating soldiers' battlefields, learning fashion design, learning to assemble things (buildings, vehicles, etc.), conducting medical simulations, reducing anxiety/phobia, and so on. This study aims to determine whether virtual reality can be applied as a learning media using the systematic literature method (SLR) method by collecting various journal databases. The research problem formulation is: What are the benefits of using VR as a media? Does the use of VR for learning have any drawbacks or effects on users? Is the use of VR much more efficient than conventional learning methods? Based on the experience that has been done, it is concluded that there are two approaches that can be applied such as the entertaintment or game approach and the simulation approach. The learning method using VR has many benefits for students, such as increasing motivation, skills, and students. Then, it can overcome various anxiety/phobias that can interfere with a person's learning, such as learning to swim, drive, and health. Also, VR in this learning method is much more effective and efficient than learning in general, which is monotonous in class.
Resource Optimization of MAB-Based Reputation Management for Data Trading in Vehicular Edge Computing

Vehicles are hesitant to upload data to edge servers in vehicle edge computing (VEC) as many vehicle data collected and perceived by various on-board sensors contain sensitive and personal information and lack economic incentive. Instead of free access to shared data, encrypted data trading will alleviate security and privacy concerns and provide an incentive for vehicle owners to share their data. The edge server needs to pay the price in data trading, and reputation management is a great method to help it trade with reliable and available vehicles. In this paper, we propose a multi-armed bandit (MAB)-based reputation management scheme, so the edge servers can select the high reputation vehicles for data trading, which can ensure the credibility and reliability of the data. The encryption scheme is applied to achieve the required transmission security level and defend the rights and interests of the edge server. On the other hand, implementing security measures will consume the computation and communication resources of the vehicles. We formulate an optimization problem that maximizes the revenue of vehicles in data trading under the constraints of time delay, energy consumption, and security level. Simulation results demonstrate that the proposed scheme is effective and efficient for vehicle reputation management, data trading selection, and resource allocation.
A Thorough Benchmark and a New Model for Light Field Saliency Detection

Compared with current RGB or RGB-D saliency detection datasets, those for light field saliency detection often suffer from many defects, e.g., insufficient data amount and diversity, incomplete data formats, and rough annotations, thus impeding the prosperity of this field. To settle these issues, we elaborately build a large-scale light field dataset, dubbed PKU-LF, comprising 5,000 light fields and covering diverse indoor and outdoor scenes. Our PKU-LF provides all-inclusive representation formats of light fields and offers a unified platform for comparing algorithms utilizing different input formats. For sparking new vitality in saliency detection tasks, we present many unexplored scenarios (such as underwater and high-resolution scenes) and the richest annotations (such as scribble annotations, bounding boxes, object-/instance-level annotations, and edge annotations), on which many potential attention modeling tasks can be investigated. To facilitate the development of saliency detection, we systematically evaluate and analyze 16 representative 2D, 3D, and 4D methods on four existing datasets and the proposed dataset, furnishing a thorough benchmark. Furthermore, tailored to the distinct structural characteristics of light fields, a novel symmetric two-stream architecture (STSA) network is proposed to predict the saliency of light fields more accurately. Specifically, our STSA incorporates a focalness interweavement module (FIM) and three partial decoder modules (PDM). The former is designed to efficiently establish long-range dependencies across focal slices, while the latter aims to effectively aggregate the extracted coadjutant features in a mutual-enhancement way. Extensive experiments demonstrate that our method can significantly outperform the competitors.
Federated Learning for IoT Devices With Domain Generalization

Federated learning (FL) is a distributed machine learning (ML) technique that allows numerous Internet of Things (IoT) devices to jointly train an ML model using a centralized server for help. Local data never leaves each IoT device in FL, so the local data of IoT devices are protected. In FL, distributed IoT devices usually collect their local data independently, so the data set of each IoT device may naturally form a distinct source domain. In real-world applications, the model trained over multisource domains may have poor generalization performance on unseen target domains. To address this issue, we propose federated adversarial domain generalization (FedADG) to equip FL with domain generalization capability. FedADG employs the federated adversarial learning approach to measure and align the distributions among different source domains via matching each distribution to a reference distribution. The reference distribution is adaptively generated (by accommodating all source domains) to minimize the domain shift distance during alignment. Therefore, the learned feature representation tends to be universal, and, thus, it has good generalization performance over the unseen target domains while protecting local data privacy. Intensive experiments on various data sets demonstrate that FedADG has comparable performance with the state-of-the-art.
Augmented reality for studying hands on the human body for elementary school students

Augmented Reality (AR) media has become a utilization or an intermediary tool in everyday life. Augmented Reality has become a medium to contribute to human life, such as games, virtual tourism objects, and learning media. However, the application of Augmented Reality media in the world of education as a learning medium is still rarely used. Therefore, the holding of this research is as a form of contribution to the development of Augmented Reality media as a learning medium. The form of contribution from this research is a simple Augmented Reality created by using qualitative method to collect and analyze the elementary students data and Marker Based Tracking method resulting an application that has a function as a hand-shaped prop teaching aid for learning the body parts of the hands in elementary school students. With this learning media, it is hoped that it would help teachers and elementary school students in studying the parts of human body. All elementary school teachers can use the Aplikasi AR Belajar Bagian Tangan as a teaching medium in elementary school laboratories on their elementary school students or as a prop while students listening to their teacher explanations while seeing which parts of the hand are pointed out by elementary school teachers through the Aplikasi AR Belajar Bagian Tangan. This Aplikasi AR Belajar Bagian Tangan may provide elementary school students with their study and experience in using AR as a learning medium that can improve students’ analytical skills.