Reviewerly/exHarmony huggingface dataset:
DLTTA: Dynamic Learning Rate for Test-Time Adaptation on Cross-Domain Medical Images

Test-time adaptation (TTA) has increasingly been an important topic to efficiently tackle the cross-domain distribution shift at test time for medical images from different institutions. Previous TTA methods have a common limitation of using a fixed learning rate for all the test samples. Such a practice would be sub-optimal for TTA, because test data may arrive sequentially therefore the scale of distribution shift would change frequently. To address this problem, we propose a novel dynamic learning rate adjustment method for test-time adaptation, called DLTTA, which dynamically modulates the amount of weights update for each test image to account for the differences in their distribution shift. Specifically, our DLTTA is equipped with a memory bank based estimation scheme to effectively measure the discrepancy of a given test sample. Based on this estimated discrepancy, a dynamic learning rate adjustment strategy is then developed to achieve a suitable degree of adaptation for each test sample. The effectiveness and general applicability of our DLTTA is extensively demonstrated on three tasks including retinal optical coherence tomography (OCT) segmentation, histopathological image classification, and prostate 3D MRI segmentation. Our method achieves effective and fast test-time adaptation with consistent performance improvement over current state-of-the-art test-time adaptation methods. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/med-air/DLTTA</uri> .
Multistrategy Improved Whale Optimization Algorithm and Its Application

To address the shortcomings of the whale optimization algorithm (WOA) in terms of insufficient global search ability and slow convergence speed, a differential evolution chaotic whale optimization algorithm (DECWOA) is proposed in this paper. Firstly, the initial population is generated by introducing the Sine chaos theory at the beginning of the algorithm to increase the population diversity. Secondly, new adaptive inertia weights are introduced into the individual whale position update formula to lay the foundation for the global search and improve the optimization performance of the algorithm. Finally, the differential variance algorithm is fused to improve the global search speed and accuracy of the whale optimization algorithm. The impact of various improvement strategies on the performance of the algorithm is analyzed using different kinds of test functions that are randomly selected. The particle swarm optimization algorithm (PSO), butterfly optimization algorithm (BOA), WOA, chaotic feedback adaptive whale optimization algorithm (CFAWOA), and DECWOA algorithm are compared for the optimal search performance. Experimental simulations are performed using MATLAB software, and the results show that the improved whale optimization algorithm has a better global optimization-seeking capability. The improved whale optimization algorithm is applied to the distribution network fault location of IEEE-33 nodes, and the effectiveness and accuracy of the distribution network fault zone location based on the multistrategy improved whale optimization algorithm is verified.
Multimodal markers for technology-independent integration of augmented reality devices and surgical navigation systems

Abstract Augmented reality (AR) permits the visualization of pre-operative data in the surgical field of view of the surgeon. This requires the alignment of the AR device’s coordinate system with the used navigation/tracking system. We propose a multimodal marker approach to align an AR device with a tracking system: in our implementation, an electromagnetic tracking system (EMTS). The solution makes use of a calibration method which determines the relationship between a 2D pattern detected by an RGB camera and an electromagnetic sensor of the EMTS. This allowed the projection of a 3D skull model on its physical counterpart. This projection was evaluated using a monocular camera and an optical see-through device (HoloLens 2) ( https://www.microsoft.com/en-us/hololens/ ) achieving an accuracy of less than 2.5 mm in the image plane of the HoloLens 2 (HL2). Additionally, 10 volunteers participated in a user study consisting of an alignment task of a pointer with 25 projections viewed through the HL2. The participants achieved a mean error of 2.7 1.3 mm and 2.9 2.9 $$^\circ $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> in positional and orientation error. This study showcases the feasibility of the approach, provides an evaluation of the alignment, and finally, discusses its advantages and limitations.
Benchmarking the Second Generation of Intel SGX Hardware

In recent years, trusted execution environments (TEEs) such as Intel Software Guard Extensions (SGX) have gained a lot of attention in the database community. This is because TEEs provide an interesting platform for building trusted databases in the cloud. However, until recently SGX was only available on low-end single socket servers built on the Intel Xeon E3 processor generation and came with many restrictions for building DBMSs. With the availability of the new Ice Lake processors, Intel provides a new implementation of the SGX technology that supports high-end multi-socket servers. With this new implementation, which we refer to as SGXv2 in this paper, Intel promises to address several limitations of SGX enclaves. This raises the question whether previous efforts to overcome the limitations of SGX for DBMSs are still applicable and if the new generation of SGX can truly deliver on the promise to secure data without compromising on performance. To answer this question, in this paper we conduct a first systematic performance study of Intel SGXv2 and compare it to the previous generation of SGX.
“Immuni” and the National Health System: Lessons Learnt from the COVID-19 Digital Contact Tracing in Italy

Since the early stage of the current pandemic, digital contact tracing (DCT) through mobile phone apps, called “Immuni”, has been introduced to complement manual contact tracing in Italy. Until 31 December 2021, Immuni identified 44,880 COVID-19 cases, which corresponds to less than 1% of total COVID-19 cases reported in Italy in the same period (5,886,411). Overall, Immuni generated 143,956 notifications. Although the initial download of the Immuni app represented an early interest in the new tool, Immuni has had little adoption across the Italian population, and the recent increase in its download is likely to be related to the mandatory Green Pass certification for conducting most daily activities that can be obtained via the application. Therefore, Immuni failed as a support tool for the contact tracing system. Other European experiences seem to show similar limitations in the use of DTC, leaving open questions about its effectiveness, although in theory, contact tracing could allow useful means of “proximity tracking”.
Assessment of the Relevance and Reliability of Reaction Time Tests Performed in Immersive Virtual Reality by Mixed Martial Arts Fighters

Immersive virtual reality (VR) is increasingly applied in various areas of life. The potential of this technology has also been noticed in recreational physical activity and sports. It appears that a virtual environment can also be used in diagnosing certain psychomotor abilities. The main aim of this study consisted of assessing the relevance and reliability of VR-implemented tests of simple and complex reaction time (RT) performed by mixed martial arts (MMA) fighters. Thirty-two professional MMA fighters were tested. The original test developed in the virtual environment was applied for RT assessment. The fighters' task consisted of reacting to the lighting up of a virtual disc situated in front of them by pushing a controller button. The relevance of the test task was estimated by juxtaposing the obtained results with the classic computer test used for measuring simple and complex reactions, while its reliability was assessed with the intraclass correlation procedure. Significant relationships found between the results of VR-implemented tests and computer-based tests confirmed the relevance of the new tool for the assessment of simple and complex RT. In the context of their reliability, RT tests in VR do not differ from tests conducted with the use of standard computer-based tools. VR technology enables the creation of tools that are useful in diagnosing psychomotor abilities. Reaction time tests performed by MMA fighters with the use of VR can be considered relevant, and their reliability is similar to the reliability obtained in computer-based tests.
Selecting Optimal Context Sentences for Event-Event Relation Extraction

Understanding events entails recognizing the structural and temporal orders between event mentions to build event structures/graphs for input documents. To achieve this goal, our work addresses the problems of subevent relation extraction (SRE) and temporal event relation extraction (TRE) that aim to predict subevent and temporal relations between two given event mentions/triggers in texts. Recent state-of-the-art methods for such problems have employed transformer-based language models (e.g., BERT) to induce effective contextual representations for input event mention pairs. However, a major limitation of existing transformer-based models for SRE and TRE is that they can only encode input texts of limited length (i.e., up to 512 sub-tokens in BERT), thus unable to effectively capture important context sentences that are farther away in the documents. In this work, we introduce a novel method to better model document-level context with important context sentences for event-event relation extraction. Our method seeks to identify the most important context sentences for a given entity mention pair in a document and pack them into shorter documents to be consume entirely by transformer-based language models for representation learning. The REINFORCE algorithm is employed to train models where novel reward functions are presented to capture model performance, and context-based and knowledge-based similarity between sentences for our problem. Extensive experiments demonstrate the effectiveness of the proposed method with state-of-the-art performance on benchmark datasets.
Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation

Federated Learning (FL) is a machine learning paradigm where local nodes collaboratively train a central model while the training data remains decentralized. Existing FL methods typically share model parameters or employ co-distillation to address the issue of unbalanced data distribution. However, they suffer from communication bottlenecks. More importantly, they risk privacy leakage risk. In this work, we develop a privacy preserving and communication efficient method in a FL framework with one-shot offline knowledge distillation using unlabeled, cross-domain, non-sensitive public data. We propose a quantized and noisy ensemble of local predictions from completely trained local models for stronger privacy guarantees without sacrificing accuracy. Based on extensive experiments on image classification and text classification tasks, we show that our method outperforms baseline FL algorithms with superior performance in both accuracy and data privacy preservation.
FedInv: Byzantine-Robust Federated Learning by Inversing Local Model Updates

Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that enables multiple clients to collaboratively train statistical models without disclosing raw training data. However, the inaccessible local training data and uninspectable local training process make FL susceptible to various Byzantine attacks (e.g., data poisoning and model poisoning attacks), aiming to manipulate the FL model training process and degrade the model performance. Most of the existing Byzantine-robust FL schemes cannot effectively defend against stealthy poisoning attacks that craft poisoned models statistically similar to benign models. Things worsen when many clients are compromised or data among clients are highly non-independent and identically distributed (non-IID). In this work, to address these issues, we propose FedInv, a novel Byzantine-robust FL framework by inversing local model updates. Specifically, in each round of local model aggregation in FedInv, the parameter server first inverses the local model updates submitted by each client to generate a corresponding dummy dataset. Then, the server identifies those dummy datasets with exceptional Wasserstein distances from others and excludes the related local model updates from model aggregation. We conduct an exhaustive experimental evaluation of FedInv. The results demonstrate that FedInv significantly outperforms the existing robust FL schemes in defending against stealthy poisoning attacks under highly non-IID data partitions.
MuMu: Cooperative Multitask Learning-Based Guided Multimodal Fusion

Multimodal sensors (visual, non-visual, and wearable) can provide complementary information to develop robust perception systems for recognizing activities accurately. However, it is challenging to extract robust multimodal representations due to the heterogeneous characteristics of data from multimodal sensors and disparate human activities, especially in the presence of noisy and misaligned sensor data. In this work, we propose a cooperative multitask learning-based guided multimodal fusion approach, MuMu, to extract robust multimodal representations for human activity recognition (HAR). MuMu employs an auxiliary task learning approach to extract features specific to each set of activities with shared characteristics (activity-group). MuMu then utilizes activity-group-specific features to direct our proposed Guided Multimodal Fusion Approach (GM-Fusion) for extracting complementary multimodal representations, designed as the target task. We evaluated MuMu by comparing its performance to state-of-the-art multimodal HAR approaches on three activity datasets. Our extensive experimental results suggest that MuMu outperforms all the evaluated approaches across all three datasets. Additionally, the ablation study suggests that MuMu significantly outperforms the baseline models (p
Survey in Classical Database and Quantum database With Logical Design To Memory Management , File Structure and Authentication Module For Quantum Database

&lt;p&gt; This is a survey for the comparison between existing model which is used in classical database and quantum computing. Theoretically it is possible to converge both of this quantum database in classical database as we will always have Hamiltonian of the signal which are written in binary.Quantum computing holds immense amount of capabilities to solve any classical problems those are unresolved.This report mostly identifies the gap between these classical database and quantum database but it shows no result rather it proposes only the hypothesis of existing mechanism / architecture with new perspective. &lt;/p&gt;
Estimation of Heavy Metal Content in Soil Based on Machine Learning Models

Heavy metal pollution in soil is threatening the ecological environment and human health. However, field measurement of heavy metal content in soil entails significant costs. Therefore, this study explores the estimation method of soil heavy metals based on remote sensing images and machine learning. To accurately estimate the heavy metal content, we propose a hybrid artificial intelligence model integrating least absolute shrinkage and selection operator (LASSO), genetic algorithm (GA) and error back propagation neural network (BPNN), namely the LASSO-GA-BPNN model. Meanwhile, this study compares the accuracy of the LASSO-GA-BPNN model, SVR (Support Vector Regression), RF (Random Forest) and spatial interpolation methods with Huanghua city as an example. Furthermore, the study uses the LASSO-GA-BPNN model to estimate the content of eight heavy metals (including Ni, Pb, Cr, Hg, Cd, As, Cu, and Zn) in Huanghua and visualize the results in high resolution. In addition, we calculate the Nemerow index based on the estimation results. The results denote that, the simultaneous optimization of BPNN by LASSO and GA can greatly improve the estimation accuracy and generalization ability. The LASSO-GA-BPNN model is a more accurate model for the estimate heavy metal content in soil compared to SVR, RF and spatial interpolation. Moreover, the comprehensive pollution level in Huanghua is mainly low pollution. The overall spatial distribution law of each heavy metal content is very similar, and the local spatial distribution of each heavy metal is different. The results are of great significance for soil pollution estimation.
Quantifying the effect of color processing on blood and damaged tissue detection in Whole Slide Images

Histological tissue examination has been a longstanding practice for cancer diagnosis where pathologists identify the presence of tumors on glass slides. Slides acquired from laboratory routine may contain unintentional artifacts due to complications in surgical resection. Blood and damaged tissue artifacts are two common problems associated with transurethral resection of the bladder tumor. Differences in histotechnical procedures among laboratories may also result in color variations and minor inconsistencies in outcome. A digitized version of a glass slide known as a whole slide image (WSI) holds enormous potential for automated diagnostics. The presence of irrelevant areas in a WSI undermines diagnostic value for pathologists as well as computational pathology (CPATH) systems. Therefore, automatic detection and exclusion of diagnostically irrelevant areas may lead to more reliable predictions. In this paper, we are detecting blood and damaged tissue against diagnostically relevant tissue. We gauge the effectiveness of transfer learning against training from scratch. Best models give 0.99 and 0.89 F1 scores for blood and damaged tissue detection. Since blood and damaged tissue have subtle color differences, we assess the impact of color processing methods on the binary classification performance of five well-known architectures. Finally, we remove the color to understand its importance against morphology on classification performance.
Multi-Stage Prompting for Knowledgeable Dialogue Generation

Existing knowledge-grounded dialogue systems typically use finetuned versions of a pretrained language model (LM) and large-scale knowledge bases.These models typically fail to generalize on topics outside of the knowledge base, and require maintaining separate potentially large checkpoints each time finetuning is needed.In this paper, we aim to address these limitations by leveraging the inherent knowledge stored in the pretrained LM as well as its powerful generation ability.We propose a multi-stage prompting approach to generate knowledgeable responses from a single pretrained LM.We first prompt the LM to generate knowledge based on the dialogue context.Then, we further prompt it to generate responses based on the dialogue context and the previously generated knowledge.Results show that our knowledge generator outperforms the state-of-the-art retrieval-based model by 5.8% when combining knowledge relevance and correctness.In addition, our multi-stage prompting outperforms the finetuning-based dialogue model in terms of response knowledgeability and engagement by up to 10% and 5%, respectively.Furthermore, we scale our model up to 530 billion parameters and show that larger LMs improve the generation correctness score by up to 10%, and response relevance, knowledgeability and engagement by up to 10%.
Memorisation versus Generalisation in Pre-trained Language Models

State-of-the-art pre-trained language models have been shown to memorise facts and perform well with limited amounts of training data.To gain a better understanding of how these models learn, we study their generalisation and memorisation capabilities in noisy and low-resource scenarios.We find that the training of these models is almost unaffected by label noise and that it is possible to reach near-optimal results even on extremely noisy datasets.However, our experiments also show that they mainly learn from high-frequency patterns and largely fail when tested on lowresource tasks such as few-shot learning and rare entity recognition.To mitigate such limitations, we propose an extension based on prototypical networks that improves performance in low-resource named entity recognition tasks.
Neural Architecture Search Net-Based Feature Extraction With Modular Neural Network for Image Classification of Copper/ Cobalt Raw Minerals

Image processing is one of the most quickly evolving technologies today and it is an approach for applying operations on an image to improve it or extract relevant information from it. It is a critical research field in engineering and computer science. However, analyzing a high number of variables demands a lot of memory and processing resources, and it can cause a classification algorithm to overfit on training samples and underfit on test samples. As a result, various strategies, such as extraction can be used to reduce the number of features in a dataset by producing new ones from old ones. In this paper, we firstly propose a deep learning-based feature extraction approach with a modular neural network where we employ a pre-trained network, neural architecture search net (NASNet), as a feature extractor on a custom dataset of raw minerals of copper and cobalt images. It allows the input image to be feed-forwarded while performing feature learning and feature map, then stopping at a pooling layer before the fully connected (FC) layer in the NASNet to extract and save the outputs of that layer in dumped files. Secondly, the extracted features are then used as training data to build a deep neural network and machine learning algorithms for the image classification of copper and cobalt raw minerals. The experimental results show that the NASNet extracts the features efficiently, and the proposed modular neural network performs well with the boosting-decision tree as a classifier, which gives higher accuracy of 91% than 90% of the deep neural network; moreover, the precision is 1 higher than 0.98 for the deep neural network.
An Analysis of Negation in Natural Language Understanding Corpora

This paper analyzes negation in eight popular corpora spanning six natural language understanding tasks.We show that these corpora have few negations compared to generalpurpose English, and that the few negations in them are often unimportant.Indeed, one can often ignore negations and still make the right predictions.Additionally, experimental results show that state-of-the-art transformers trained with these corpora obtain substantially worse results with instances that contain negation, especially if the negations are important.We conclude that new corpora accounting for negation are needed to solve natural language understanding tasks when negation is present.
Vulnerability in Massive API Scraping: 2021 LinkedIn Data Breach

This paper analyzes the data breach of Linkedin in the summer of 2021. An adversary utilized LinkedIn&#x2019;s overly invasive API in order to scrape a massive amount of personal information data. Connecting this data with other API sources allowed the adversary to create a super-list of data that would be maliciously sold through the internet. The attack exposed 90% of users&#x2019; data in LinkedIn and forced the company to re-evaluate its API scheme. This huge amount of data containing personal information enabled other bad actors to launch social engineering attacks on targeted users. In addition to providing the detailed attack methodology, this paper inspects the impact of this breach and outlines possible defense strategies such as proper authentication and authorization, limiting the data scraping, and anomaly detection techniques.