Reviewerly/exHarmony huggingface dataset:
Pyramid-based self-supervised learning for histopathological image classification

Large-scale labeled datasets are crucial for the success of supervised learning in medical imaging. However, annotating histopathological images is a time-consuming and labor-intensive task that requires highly trained professionals. To address this challenge, self-supervised learning (SSL) can be utilized to pre-train models on large amounts of unsupervised data and transfer the learned representations to various downstream tasks. In this study, we propose a self-supervised Pyramid-based Local Wavelet Transformer (PLWT) model for effectively extracting rich image representations. The PLWT model extracts both local and global features to pre-train a large number of unlabeled histopathology images in a self-supervised manner. Wavelet is used to replace average pooling in the downsampling of the multi-head attention, achieving a significant reduction in information loss during the transmission of image features. Additionally, we introduce a Local Squeeze-and-Excitation (Local SE) module in the feedforward network in combination with the inverse residual to capture local image information. We evaluate PLWT’s performance on three histopathological images and demonstrate the impact of pre-training. Our experiment results indicate that PLWT with self-supervised learning performs highly competitive when compared with other SSL methods, and the transferability of visual representations generated by SSL on domain-relevant histopathological images exceeds that of the supervised baseline trained on ImageNet.
Distributed Scalable Edge Computing Infrastructure for Open Metaverse

In order to create and distribute the immense computing power required for the Metaverse, the industry must make strategic investments. AT&T believes that networking and edge computing are among the most essential components of a successful Metaverse ecosystem. AT&T proposes an “end-toend distributed edge computing network infrastructure” using its innovative uCPE [1] Whitebox platform. This innovation includes GPU virtualization, low-latency graphic rendering at the edge, and real-time streaming of 3D immersive content to end users. The groundbreaking design leverages existing wireless/wired home networks to create an overlay network that offloads heavy computing from head-mounted displays (HMDs) to the edge uCPE and distributes the virtual computing power to every end user with high scalability and low costs. In addition, AT&T introduces hardware and software probes to proactively monitor the end-to-end network performance with fault detection and self-healing capabilities, optimizing network paths and providing an uninterrupted immersive experience to users.
FADO: A Federated Learning Attack and Defense Orchestrator

Federated Learning (FL) is a distributed machine learning approach allowing multiple parties to train a model collaboratively without sharing sensitive data. It has gained widespread popularity recently due to its ability to preserve data privacy. However, FL also poses novel security challenges since training relies on data and computations from many entities that a malicious actor might have compromised, as they are usually geographically dispersed and independently managed. Evaluations of current FL security mechanisms in the literature are often based on simplistic testing environments and demand complex programming to integrate new attacks/defenses. Therefore, this work presents an accessible platform that leverages a realistic environment to facilitate the experimentation and evaluation of new solutions in relevant FL scenarios. Comparison with already proposed approaches is also expedited since FADO provides a few out-of-the-box implementations. To demonstrate the platform’s utility, we develop a use case based on a recently published network attack.
rgpdOS: GDPR Enforcement By The Operating System

Currently, it is very hard for companies driven by personal data to make their applications GDPR-compliant, especially if those applications were developed before the GDPR was established. We present rgpdOS, a GDPR-aware operating system that aims to bring GDPR-compliance to every application, while requiring minimal changes to application code.
Popularity-based Aggregation and Bundle-specific Ranking for Bundle Recommendation

Bundle Recommendation aims to recommend a bundle of items to users, which can greatly improve the recommendation efficiency of service providers and satisfy users’ diverse needs. In recent methods, the adoption of graph neural network and contrastive learning has made great progress. However, they ignore the contribution difference of items in a bundle. In addition, they use the conventional BPR(Bayesian Personalized Ranking) to create a user-specific ranking for a set of bundles, but neglect to rank users for each bundle. In this work, we propose a model named PABR(short for Popularity-based Aggregation and Bundle-specific Ranking for Bundle Recommendation). PABR adopts weighted aggregation based on the popularity of items and performs Bayesian Personalized Ranking from user-specific perspective and bundle-specific perspective. Experiments on three real-world datasets demonstrate that our method achieves improvement compared with baselines.
Towards prediction of Quality of Life aspects using wearable data with limited ground truth

Today people live with a variety of diseases and health conditions that can impact their daily lives, from chronic illnesses such as cancer to mental health issues such as anxiety and depression. With the advancement of technology and healthcare, quality of life (QoL) prediction and improvement is becoming increasingly important to help individuals live better. In addition to data collected by questionnaires, machine learning can be employed to predict aspects of QoL, such as fatigue and anxiety, based on data from wearable devices. However, this requires ground truth information, which, in real-world cases is not always present, or is scarce. This paper addresses the problem of fatigue prediction, which is part of the overall QoL prediction. We extend previous literature by viewing the problem as regression, with the ultimate goal of transferring a model trained with a complete dataset of the literature to a real-world dataset with no ground truth, facing the challenge of only a few common features. We compare several regression and classification techniques to select the best-performing ones. We then explore the effect of using only limited important features on accuracy, which allow us to assess the applicability of transferring the models to a real-world dataset. We apply the trained model on data from the ONCORELIEF project and discuss the identified challenges.
A Salient Object Detection Method Based on Boundary Enhancement

Visual saliency refers to the human's ability to quickly focus on important parts of their visual field, which is a crucial aspect of image processing, particularly in fields like medical imaging and robotics. Understanding and simulating this mechanism is crucial for solving complex visual problems. In this paper, we propose a salient object detection method based on boundary enhancement, which is applicable to both 2D and 3D sensors data. To address the problem of large-scale variation of salient objects, our method introduces a multi-level feature aggregation module that enhances the expressive ability of fixed-resolution features by utilizing adjacent features to complement each other. Additionally, we propose a multi-scale information extraction module to capture local contextual information at different scales for back-propagated level-by-level features, which allows for better measurement of the composition of the feature map after back-fusion. To tackle the low confidence issue of boundary pixels, we also introduce a boundary extraction module to extract the boundary information of salient regions. This information is then fused with salient target information to further refine the saliency prediction results. During the training process, our method uses a mixed loss function to constrain the model training from two levels: pixels and images. The experimental results demonstrate that our salient target detection method based on boundary enhancement shows good detection effects on targets of different scales, multi-targets, linear targets, and targets in complex scenes. We compare our method with the best method in four conventional datasets and achieve an average improvement of 6.2% on the mean absolute error (MAE) indicators. Overall, our approach shows promise for improving the accuracy and efficiency of salient object detection in a variety of settings, including those involving 2D/3D semantic analysis and reconstruction/inpainting of image/video/point cloud data.
Hybrid noise protection of logical qubits for universal quantum computation

Quantum computers now show the promise of surpassing any possible classical machine. However, errors limit this ability and current machines do not have the ability to implement error correcting codes due to the limited number of qubits and limited control. Therefore, dynamical decoupling (DD) and encodings that limit noise with fewer qubits are more promising. For these reasons, we put forth a model of universal quantum computation that has many advantages over strategies that require a large overhead such as the standard quantum-error correcting codes. First, we separate collective noise from individual noises on physical qubits and use a decoherence-free subspace that uses just two qubits for its encoding to eliminate collective noise. Second, our bath model is very general as it uses a spin-boson-type bath but without any Markovian assumption. Third, we are able to either use a steady global magnetic field or to devise a set of DD pulses that remove much of the remaining noise and commute with the logical operations on the encoded qubit. This allows removal of noise while implementing gate operations. Numerical support is given for this hybrid protection strategy which provides an efficient approach to deal with the decoherence problems in quantum computation and is experimentally viable for several current quantum computing systems. This is emphasized by a recent experiment on superconducting qubits which shows promise for increasing the number of gates that can be implemented reliably with some realistic parameter assumptions.
Quantum gate algorithm for reference-guided DNA sequence alignment

Reference-guided DNA sequencing and alignment is an important process in computational molecular biology. The amount of DNA data grows very fast, and many new genomes are waiting to be sequenced while millions of private genomes need to be re-sequenced. Each human genome has 3.2 B base pairs, and each one could be stored with 2 bits of information, so one human genome would take 6.4 B bits or about 760 MB of storage (National Institute of General Medical Sciences). Today most powerful tensor processing units cannot handle the volume of DNA data necessitating a major leap in computing power. It is, therefore, important to investigate the usefulness of quantum computers in genomic data analysis, especially in DNA sequence alignment. Quantum computers are expected to be involved in DNA sequencing, initially as parts of classical systems, acting as quantum accelerators. The number of available qubits is increasing annually, and future quantum computers could conduct DNA sequencing, taking the place of classical computing systems. We present a novel quantum algorithm for reference-guided DNA sequence alignment modeled with gate-based quantum computing. The algorithm is scalable, can be integrated into existing classical DNA sequencing systems and is intentionally structured to limit computational errors. The quantum algorithm has been tested using the quantum processing units and simulators provided by IBM Quantum, and its correctness has been confirmed.
Black Swan Theory of Events and the Impact to Hospitality Operators: Literature Review and Proposed Analysis

The Black Swan theory of events is an incident that deviates beyond what is normally expected of a situation and is extremely difficult to predict (Talib, 2007). Black Swan events impact the hospitality industry in many ways. Weather, widespread illness, civil unrest, terrorism, and loss of utilities are out of the immediate control of the industry’s participants but impact their results. Occurrences at the property/corporate level can also impact performance in ways such as: food contaminants impacting restaurants, cruise ships losing power, running a ground, or sinking, experiencing a widespread Nora virus, hotels face fires, strikes, data breaches, boycotts, as well as events such as amusement parks having ride fatalities and animal welfare protests, etc. All of these events are quickly spread by multiple news sources, and consumers are able to search quickly using Google or other search engines to get more details.Previous studies have looked at the aftereffects of exogenous—or self-inflicted— events, and how management teams reacted or did not react and the impact to operating results (Kosova &amp; Enz, 2012). However, there is a paucity of academic analysis of Black Swan events to determine whether there is a way to reduce the impact.
Random update particle swarm optimizer (RUPSO): A novel robust optimization algorithm

Achieving an optimal design, especially those with numerous variables, is a tedious process. It is regularly achieved by mathematical programming or Metaheuristic algorithm methods. A novel metaheuristic optimization algorithm is presented in this research. The proposed algorithm is based on the well-known Particle Swarm Optimization (PSO) algorithm, in which the updating process has been improved. The efficiency of the new method, named Random Update Particle Swarm Optimizer (RUPSO), has been verified for problems with continuous variables as well as problems with discrete variables. It is shown that RUPSO converges to better optimum solutions than PSO and other existing metaheuristic algorithms. It is for its capability of searching through the entire feasible search spaces and going over the local optima. Random updating process, which has been utilized in RUPSO, decreases the sensitivity of the algorithm and increases its stability in solving optimization problems. High stability of the method is also shown in the paper. The RUPSO algorithm finds the optimized answers with a very high convergence rate for problems with discrete or even continuous variables.
Clustering and Visualising Documents using Word Embeddings

This lesson uses word embeddings and clustering algorithms in Python to identify groups of similar documents in a corpus of approximately 9,000 academic abstracts. It will teach you the basics of dimensionality reduction for extracting structure from a large corpus and how to evaluate your results.
Word memory

When I read a word that I don't know how to pronounce, I can't remember it. Why do I need to be able to pronounce a word for my brain to recall it? (continued)
Multi-Vehicle Dynamic Water Surface Monitoring

Repeated exploration of a water surface to detect objects of interest and their subsequent monitoring is important in search-and-rescue or ocean clean-up operations. Since the location of any detected object is dynamic, we propose to address the combined surface exploration and monitoring of the detected objects by modeling spatio-temporal reward states and coordinating a team of vehicles to collect the rewards. The model characterizes the dynamics of the water surface and enables the planner to predict future system states. The state reward value relevant to the particular water surface cell increases over time and is nullified by being in a sensor range of a vehicle. Thus, the proposed multi-vehicle planning approach is to minimize the collective value of the dynamic model reward states. The purpose is to address vehicles' motion constraints by using model predictive control on receding horizon and fully exploiting the utilized vehicles' motion capabilities. Based on the evaluation results, the approach indicates improvement in a solution to the kinematic orienteering problem and the team orienteering problem in the monitoring task compared to the existing solutions. The proposed approach has been experimentally verified, supporting its feasibility in real-world monitoring tasks.
Mixed reality and the Internet of Things: Bridging the virtual with the real

As Mixed Reality applications penetrate people's daily activities, strong synergies with Geospatial technologies and the Internet of Things are revealed. The first is due to the significance of the spatial reference of all involved actors of a Mixed Reality environment: the end-user, the real and the virtual objects. The second is due to the ever-increasing participation of sensors controlling devices and machines always and everywhere. This paper highlights these synergies and proposes a case proving they crucially empower a Mixed Reality experience. Through this synergy, a novel interaction method for Mixed Reality Environments emerges, the "physical task execution" interaction method. An open-source application prototype was developed to support the case study where we assign a physical task to a virtual man to execute, utilising the Internet of Things in a Mixed Reality Environment.
TEIL III - 9 Computertomografie in der Koordinatenmesstechnik

No AccessHandbuch Messtechnik in der industriellen ProduktionAug 2023TEIL III - 9 Computertomografie in der Koordinatenmesstechnikhttps://doi.org/10.3139/9783446466265.022SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2023Pages: 459-498Print ISBN: 978-3-446-46559-6eISBN: 978-3-446-46626-5 PDF downloadLoading ...
Voice Guard: Protecting Voice Privacy with Strong and Imperceptible Adversarial Perturbation in the Time Domain

Adversarial example is a rising tool for voice privacy protection. By adding imperceptible noise to public audio, it prevents tampers from using zero-shot Voice Conversion (VC) to synthesize high quality speech with target speaker identity. However, many existing studies ignore the human perception characteristics of audio data, and it is challenging to generate strong and imperceptible adversarial audio. In this paper, we propose the Voice Guard defense method, which uses a novel method to advance the adversarial perturbation to the time domain to avoid the loss caused by cross-domain conversion. And the psychoacoustic model is introduced into the defense of VC for the first time, which greatly improves the disruption ability and concealment of adversarial audio. We also standardize the evaluation metrics of adversarial audio for the first time, combining multi-dimensional metrics to define the criteria for defense. We evaluate Voice Guard on several state-of-the-art zero-shot VC models. The experimental results show that our method can ensure the perceptual quality of adversarial audio while having a strong defense capability, and is far superior to previous works in terms of disruption ability and concealment.
A model for evaluating the correctness of the choice and efficiency of use according to the specified criterion of means of observation and control of objects of various purposes

The informational approach to solving the problems of observation and control is not used until now. The theory of measurements is based on the concept of entropy of random variables as a measure of their uncertainty (a set of sets of their possible values). The greater the number of values that a discrete random variable can take, or the greater the range of a continuous random variable, the greater their entropy. The amount of information obtained during measurement, control, research corresponds to the reduction of entropy from the value that characterizes the uncertainty that remains after obtaining the result of measurement of some parameters of the objective, phenomena, etc. We are talking about the relationship between accuracy, energy consumption and speed of measuring equipment. The issue of determining the threshold of the maximum possible accuracy of measurements of actual values, as well as obtaining generalizing information-energy ratios, which allow optimizing the procedure for choosing the main quality indicators of measuring equipment (MT) are highlighted. At the same time, making an attempt to more accurately determine the value of a measurement quantity, at some stage we will inevitably encounter the fundamental impossibility of their further clarification, which is ultimately related to the physically possible degree of certainty (in contrast to entropy, which characterizes uncertainty, and which is called non-entropy) of any measurement value, which is determined either by its personal discreteness (for example, when measuring the number of atoms of any substance in a mixture, it is meaningless to talk about the accuracy of the count, which is equal to 0.1 or 0.01 atom) or by its thermal (molecular) fluctuations . This micro-scale uncertainty limit is known as Heisenberg's Uncertainty Rule. The article proposes a model of evaluation and effective use of observation and control of objects of various nature. An "informational approach" to solving the problems of choosing and using measuring equipment in the conditions of the transition from traditional metrological indicators to informational ones is proposed. Let's give an example of the choice of FTA and the number of measurements by this FTA.
Senses and Sensors

Virtual reality is known as dependent on specific hardware. This chapter explains why this hardware is needed. Users in VR should be able to perceive the virtual world. This means that the user of a VR system should have a way of feeling the virtual world through his or her senses, which is only possible with the use of sensors, which are hardware dependent. This chapter presents the various senses that exist and should be ideally perceived in a virtual world.
Tuberculosis classification using random forest with K-prototype as a method to overcome missing value

Tuberculosis is a disease that attacks the core of the respiratory organs, which affects many people. This disease is one of the contributors to high mortality cases, especially in Indonesia. Based on its anatomical location, tuberculosis is divided into two classes, namely pulmonary for tuberculosis detected in lung parenchymal tissue and extrapulmonary for tuberculosis detected in organs other than the lungs. Detecting the location of the infection in the lungs requires some analysis of laboratory results for the triggering parameters where the analysis process is still done manually, so it takes longer, and because the input process is still done manually, patient data which causes the possibility of human error to be very high. Therefore, the solution offered and the aim of this study is the ease of patient diagnosis in determining the classification of TB disease. The method used in this study is k-prototype imputation to repair missing values that have different data types, then for tuberculosis data classification methods and medical record data processing using the Random Forest, Support Vector Machine, and Backpropagation methods. Of the three classification methods proposed in this study, all three have an excellent level of accuracy. However, the Random Forest method performs more than other methods, reaching 98.8%.