Miracle-dz/UniFAR-FTUs huggingface dataset:
SMASH, SENSE, PILS, GRAPPA: How to Choose the Optimal Method

Fast imaging methods and the availability of required hardware for magnetic resonance tomography (MRT) have significantly reduced acquisition times from about an hour down to several minutes or seconds. With this development over the last 20 years, magnetic resonance imaging (MRI) has become one of the most important instruments in clinical diagnosis. In recent years, the greatest progress in further increasing imaging speed has been the development of parallel MRI (pMRI). Within the last 3 years, parallel imaging methods have become commercially available, and therefore are now available for a broad clinical use. The basic feature of pMRI is a scan time reduction, applicable to nearly any available MRI method, while maintaining the contrast behavior without requiring higher gradient system performance. Because of its faster image acquisition, pMRI can in some cases even significantly improve image quality. In the last 10 years of pMRI development, several different pMRI reconstruction methods have been set up which partially differ in their philosophy, in the mode of reconstruction as well in their advantages and drawbacks with regard to a successful image reconstruction. In this review, a brief overview is given on the advantages and disadvantages of present pMRI methods in clinical applications, and examples from different daily clinical applications are shown.
Prediction of adaptive self-regulatory responses to arthritis pain anxiety in exercising adults: Does pain acceptance matter?

Exercise is recommended to ease arthritis pain; unfortunately, adherence to planned exercise regimens is often very low, particularly among patients with chronic pain. The authors of this study, which involved patients with arthritis who either met exercise recommendations or did not, aimed to elucidate whether pain acceptance is a moderator of the relationship between pain anxiety and self-regulatory responses with regard to exercise.
Assessing Time Offset and Classifier Impacts on Preamble-based Cross-Collection SEI

Internet of Things (IoT) deployments are projected to reach 30.9 billion by 2025, most of which will employ little or no encryption. Specific Emitter Identification (SEI) is being put forward as a lightweight security approach intended to solve the risks of unencrypted or weakly encrypted IoT deployments. SEI exploits waveform distortions that are intrinsic, distinct, and imparted during normal device operations, which permits the employment of SEI without needing to modify the IoT device. However, SEI performs very poorly when the waveforms used to train it are collected at a different time from those it is being tasked with classifying. A solution to this cross-collection problem must be found before SEI can be deployed as a viable IoT security solution. This paper assesses cross-collection SEI when waveform time offset is and is not present, the intentional waveform structure is removed, and SEI is performed using handcrafted and DL-based approaches. The results show removing the intentional waveform structure improves SEI performance by as much as 8% versus using the raw received time domain samples. The results also show that cross-collection SEI accuracy is highest when using a Graphical Neural Network (GNN).
Frequency Dispersion Effects on Time-Varying Magnetic Field-Induced Cellular Transmembrane Potentials: A Simulation Study

In this work, we developed a simulation model to evaluate the effect of frequency dispersion effects on the time-varying magnetic field (TMF)-induced transmembrane potential (TMP) of cells. The simulation results show that the effect of frequency dispersion effect on TMP is not obvious when the frequency of the time-varying field is low, but when the frequency of the time-varying field exceeds 20 MHz, there is a large enhancement of TMP, and the effect of frequency dispersion effect on TMP becomes more and more obvious as the frequency increases. In addition, by comparing the distribution patterns of induced electric field and TMP on the cell membrane and the effect of dispersion effect on both, the feasibility of the TMP calculation model with dispersion effect constructed in this paper is demonstrated.
Experimental Models of Maternal Obesity and Neuroendocrine Programming of Metabolic Disorders in Offspring

Evidence from epidemiological, clinical, and experimental studies have clearly shown that disease risk in later life is increased following a poor early life environment, a process preferentially termed developmental programming. In particular, this work clearly highlights the importance of the nutritional environment during early development with alterations in maternal nutrition, including both under- and overnutrition, increasing the risk for a range of cardiometabolic and neurobehavioral disorders in adult offspring characterized by both adipokine resistance and obesity. Although the mechanistic basis for such developmental programming is not yet fully defined, a common feature derived from experimental animal models is that of alterations in the wiring of the neuroendocrine pathways that control energy balance and appetite regulation during early stages of developmental plasticity. The adipokine leptin has also received significant attention with clear experimental evidence that normal regulation of leptin levels during the early life period is critical for the normal development of tissues and related signaling pathways that are involved in metabolic and cardiovascular homeostasis. There is also increasing evidence that alterations in the epigenome and other underlying mechanisms including an altered gut–brain axis may contribute to lasting cardiometabolic dysfunction in offspring. Ongoing studies that further define the mechanisms between these associations will allow for identification of early risk markers and implementation of strategies around interventions that will have obvious beneficial implications in breaking a programmed transgenerational cycle of metabolic disorders.
On the complexity of training neural networks with continuous activation functions

Deals with computational issues of loading a fixed-architecture neural network with a set of positive and negative examples. This is the first result on the hardness of loading a simple three-node architecture which does not consist of the binary-threshold neurons, but rather utilizes a particular continuous activation function, commonly used in the neural-network literature. The authors observe that the loading problem is polynomial-time if the input dimension is constant. Otherwise, however, any possible learning algorithm based on particular fixed architectures faces severe computational barriers. Similar theorems have already been proved by Megiddo and by Blum and Rivest, to the case of binary-threshold networks only. The authors' theoretical results lend further suggestion to the use of incremental (architecture-changing) techniques for training networks rather than fixed architectures. Furthermore, they imply hardness of learnability in the probably approximately correct sense as well.
Improved Sliding Window Smoothing for Video Temporal Action Segmentation and Recognition

Despite substantial research on human action segmentation in videos to determine the type and timing of activities, the topic is still unresolved because of the dearth of large-scale annotation data in video analysis applications. Supervised video action segmentation employs a number of temporal convolutional network (TCN) models to address this problem. The process is still difficult, because of the intricate temporal duration division of the movements in the videos. In order to create a soft and flexible video partition, we incorporate an improved sliding window smoothing (ISWS) technique into a TCN baseline model in this study. When screening the target video segmentation sequence, our research method carefully selects three discriminative frames and cleverly integrates them into the adaptive sliding window to more specifically optimize the smoothing effect of the entire prediction sequence. It is particularly worth noting that we implement a doubling penalty mechanism when the window slides to the wrong category position. In order to learn the resultants of effective and ineffective segmentation paths, we designed a new loss function to smooth the candidate frames of the segmentation points in the sliding window using the ISWS scheme. So that our method can increase the receptive field of video segmentation effectively to gain the optimal action segmentation. Experiments on the breakfast, 50salads, and GTEA datasets demonstrate that our method significantly improved the frame accuracy for action segmentation in videos when compared to the state-of-the-art techniques.
Heart Rate Variability and the Anxious Client: Cardiac Autonomic and Behavioral Associations With Therapeutic Alliance

Abstract This exploratory study was designed to investigate the link between a client’s heart rate variability (HRV) and the forming of a therapeutic alliance (TA) during psychotherapy. Change in HRV is associated with many psychological and physiological situations, including cardiac mortality. Cardiac effects were evaluated during therapy in 30 symptomatically anxious clients using HRV during six weekly 1-hour therapy sessions (S1–S6). Therapeutic index (TI), a measure of TA, was evaluated using skin conductance resonance between client and therapist. The Working Alliance Inventory provides a subjective measure of TA. State and trait anxiety and mood states were also assessed. Most HRV parameters were highest during S4. The sympathovagal balance was highest in S1 but stabilized after S2. In S4, TI was linked to high HRV parameters. Overall higher anxiety levels seem to be associated to lower HRV parameters. Conversely, in S4, high HRV parameters were linked to higher mood scores. This study found that a subjective measure of TA contradicted the physiological outcome. Results suggest that physiological data collected during therapy are a more accurate barometer of TA forming. These research findings suggest a need for further research identifying physiological markers in clients with a variety of mental health disorders over long-term therapy.
IoT-Enabled Automatic Plant Watering System

In the age of technology, automation has permeated our lives, and the demand for remote control systems continues to grow. This study introduces an IoT-powered Automatic Plant Watering System, driven by the NodeMCU ESP8266 microcontroller and a soil moisture sensor. Users can monitor soil moisture levels through a mobile application, remotely activating the watering process. This system addresses the challenges of manual plant care and the need for efficient solutions, especially in light of the COVID-19 pandemic. The project aims to deliver cost-effective and accessible intelligent farming tools, promoting sustainability and water conservation aligned with the United Nations’ Sustainable Development Goals 2 (SDG-2). By offering precise soil moisture control, customization, and remote capabilities, this innovation empowers users to maintain healthy crops, fostering a recent trend in plant care. The research discusses system design, implementation, and implications, aligning with the demands of modern agriculture.
Scriptless attacks: stealing the pie without touching the sill

Due to their high practical impact, Cross-Site Scripting (XSS) attacks have attracted a lot of attention from the security community members. In the same way, a plethora of more or less effective defense techniques have been proposed, addressing the causes and effects of XSS vulnerabilities. NoScript, and disabling scripting code in non-browser applications such as e-mail clients or instant messengers. As a result, an adversary often can no longer inject or even execute arbitrary scripting code in several real-life scenarios. In this paper, we examine the attack surface that remains after XSS and similar scripting attacks are supposedly mitigated by preventing an attacker from executing JavaScript code. We address the question of whether an attacker really needs JavaScript or similar functionality to perform attacks aiming for information theft. The surprising result is that an attacker can also abuse Cascading Style Sheets (CSS) in combination with other Web techniques like plain HTML, inactive SVG images or font files. Through several case studies, we introduce the so called scriptless attacks and demonstrate that an adversary might not need to execute code to preserve his ability to extract sensitive information from well protected websites. More precisely, we show that an attacker can use seemingly benign features to build side channel attacks that measure and exfiltrate almost arbitrary data displayed on a given website. We conclude this paper with a discussion of potential mitigation techniques against this class of attacks. In addition, we have implemented a browser patch that enables a website to make a vital determination as to being loaded in a detached view or pop-up window. This approach proves useful for prevention of certain types of attacks we here discuss.
Practical Sentiment Analysis for Education: The Power of Student Crowdsourcing

Sentiment analysis provides a promising tool to automatically assess the emotions voiced in written student feedback such as periodically collected unit-of-study reflections. The commonly used dictionary-based approaches are limited to major languages and fail to capture contextual differences. Pretrained large language models have been shown to be biased and online versions raise privacy concerns. Hence, we resort to traditional supervised machine learning (ML) approaches which are designed to overcome these issues by learning from domain-specific labeled data. However, these labels are hard to come by -- in our case manually annotating student feedback is prone to bias and time-consuming, especially in high-enrollment courses. In this work, we investigate the use of student crowdsourced labels for supervised sentiment analysis for education. Specifically, we compare crowdsourced and student self-reported labels with human expert annotations and use them in various ML approaches to evaluate the performance on predicting emotions of written student feedback collected from large computer science classes. We find that the random forest model trained with student-crowdsourced labels tremendously improves the identification of reflections with negative sentiment. In addition to our quantitative study, we describe our crowdsourcing experiment which was intentionally designed to be an educational activity in an introduction to data science course.
Machine Learning for Fake News Detection on Social Media Text

In today's digital age, the swift spreading of information has revolutionized the way for news consumers and makes them informed. However, this convenience comes with a downside – the propagation of fake news, which can spread misinformation, manipulate public opinions, and undermine the credibility of legitimate sources. The term "fake news" refers to intentionally fabricated or misleading information that is frequently presented as news for a variety of cognitive processes, including commercial, social, or political gain. Machine Learning (ML), with its ability to analyze large datasets and discern patterns, has emerged as a promising solution for tackling the issue of fake news. By leveraging techniques such as Natural Language Processing (NLP), classification algorithms, and anomaly detection, ML models can be trained to identify and differentiate between authentic news and fake news. That in turn prevents the news consumer from misleading, prevent a product or service from defame and also helps in political defaming. Machine learning algorithms can be used to analyze historical data and make accurate predictions about whether news fakes or not. In this study, the proposed machine learning-based news analysis model utilizes feature selection technique to categorize the news. The model explores different classification algorithms, including Decision Tree (DT), Passive Aggressive Classifier (PAC), Logistic Regression (LR), and Random Forest (RF), to build the fake news prediction model. The experimental results show that the Passive Aggressive Classifier outperforms other models with an accuracy rate of 93%. The proposed model can help news channels; social media and consumers to distinguish between fake and real news, and minimize the risk of misleading.
A Study on Solving the Data Imbalance Problem for Detecting Heunginjimun Roof Tilt Using Transfer Learning Algorithms

Cultural heritage with high historical value requires continuous management and protection. However, recognizing subtle changes with the naked eye has limitations and requires much time and personnel deployment. To solve this problem, we will automatically detect the tilt of Heunginjimun's roof using Transfer Learning algorithms. In a previous study, among single environments classified into nine types according to season and weather, the ratio of normal and abnormal images in the winter/night and winter/daytime datasets was unbalanced at 9:1 and 8:2. As a result, problems with poor prediction accuracy occurred in some experiments. In this paper, to solve this problem, we adjusted the composition ratio of the dataset and measured the prediction accuracy. When comparing the measurement results with previous studies, the dataset size was reduced by half, but the accuracy was higher. This showed that higher accuracy and performance can be expected by achieving the balance between classes rather than increasing the dataset size.
Optimalization of Tiktok Platform for Green Business Digital Marketing Strategies

This research based on business’ need to improve performance and the existence of opportunities on Tiktok platform for marketing. The purpose of the research is to find out the application of digital marketing to sales strategies and to measure the performance of eco-friendly business Earthy Worthy. This research method uses a qualitative approach by doing documentation and triangulation techniques for business performance. The study shows the use of Tiktok as a digital marketing medium has an impact on increasing sales and business performance. The conclusion is the use of Tiktok as digital marketing medium has an impact to better business performance. Utulization of Tiktok can be done through creating creative content, live shopping, and using the features called Tiktok Shop Digital Marketing, Business Performance, Social Media Commerce
Trust-based Service Management of Mobile Devices in Ad Hoc Networks

— With the proliferation of fairly powerful mobile devices and ubiquitous wireless technology, traditional mobile ad hoc networks (MANETs) now migrate into a new era of service oriented MANETs wherein a mobile device can provide and receive service from other mobile devices it encounters and interacts with. We discuss our ongoing research efforts in trust management and trust-based algorithm design for service-oriented MANET applications to answer the challenges of MANET environments, including no centralized authority, dynamically changing topology, limited bandwidth and battery power, limited observations, unreliable communication, and the presence of malicious nodes who act to break the system functionality as well as selfish nodes who act to maximize their own gain. We also highlight key ideas and experiences learned, and provide future research directions.
Transparent Binomially Tapered Series-Fed Patch Array With Circular Polarization for Space Surveillance and Tracking MIMO Radar

In this paper, design of a two transmitter four receiver (2TX-4Rx) MIMO Radar consists with optically transparent binomially tapered series fed patch (BT-SFP) antenna placed above the solar cell is proposed. First, circular polarization (CP) is imposed on the BT-SFP to make its operation space-compatible. Furthermore, the effect of the solar cell on the CP behaviour is critically examined. The proposed antenna provide gain of 9.74 dBic with wide CP beamwidth of 95° and low side lobes. The obtained −10 dB impedance bandwidth (IBW) is 532 MHz and 3 dB axial ratio bandwidth (ARBW) is 57 MHz, which makes the proposed Radar deployable on a small cubesat for on-board space-surveillance and tracking (SST) applications.
In-depth Analysis of Information Overflows and its Role in Shaping Future Knowledge Structures

This study provides a thorough evaluation of data deluges and how they will play a significant role in the development of future knowledge architectures. The suggested methodology is adaptable and interdisciplinary since it combines cutting-edge data mining, NLP, and network analysis methods. Data mining, NLP, network analysis, content analysis, big data analytics, cognitive mapping, SNA, MNA, QR, and IV are all compared to the proposed method, as are machine learning algorithms, qualitative research methods, and data visualization. Accuracy, relevance, depth of analysis, innovation, practical consequences, and clarity are only a few of the areas where the suggested technique shines. Its greater analytical skills are shown by its persistent outperformance of conventional approaches. Additionally, the suggested approach better accords with ethical norms and responsible research procedures, thus they are all considered as well. In summary, this study demonstrates the value of looking at information floods from several angles of study. The suggested technique not only provides cutting-edge analytic instruments for negotiating the intricacies of the digital information ecosystem, but also gives a forward-looking viewpoint by anticipating new patterns and developments. It demonstrates how crucial it is to use cutting-edge methods and evolve with the information age.
IL-7 Licenses Activation of Human Liver Intrasinusoidal Mucosal-Associated Invariant T Cells

Human mucosal-associated invariant T (MAIT) cells are a T cell population characterized by the expression of a semi-invariant TCR capable of recognizing bacterial products in the context of MR1. MAIT cells are enriched in the human liver, which is constantly exposed to bacterial products from the intestine. Whether this specific parenchymal localization influences their function remains unknown. We analyzed MAIT cells resident in the vascular bed of livers and showed that they represented the majority of T cells expressing NK markers and the dominant IL-17A+ T cell subset in the human liver sinusoids. In comparison with MAIT cells purified from peripheral blood, intrasinusoidal MAIT cells expressed markers of T cell activation; however, TCR-mediated cytokine production was equally suppressed in both circulating and intrasinusoidal MAIT cells. MAIT cells also expressed high levels of IL-7R, and we showed that IL-7, a cytokine produced by hepatocytes during inflammation, regulated TCR-mediated activation of MAIT cells, licensing them to dramatically increase Th1 cytokines and IL-17A production. Our quantitative and functional data indicate that MAIT cells are a specialized cell population highly adapted to exert their immune functions in the vascular network of the liver.