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Ginger infusion increases diet-induced thermogenesis in healthy individuals:A randomized crossover trial

Food Bioscience 2022年第6期50卷 314-319页

作者： Ana Paula da Costa Soares Natália Cristina de Faria Guilherme Fonseca Graciano James Almada da Silva Vitoria Shievila dos Santos Gonçalves Virginia Del Carmen Trancoso Valenzuela Maria Isabel Toulson Davisson Correia Lucilene Rezende Anastácio Post-Graduate Program in Food Science Pharmacy SchoolUniversidade Federal de Minas GeraisBrazil Surgical Post-Graduate Program Alfa Institute of GastroenterologyHospital das ClínicasUniversidade Federal de Minas GeraisBrazil Pharmacy Department Universidade Federal de SergipeBrazil Fundação Ezequiel Dias Brazil

The purpose of this study is to evaluate the acute effect of ingesting ginger infusion on energy metabolism,appetite responses,and food intake in healthy *** is a clinical(RBR-9ZJGQM),randomized,open,and crossover study with a minimum interval of seven days between *** drinks,ginger infusion(2 g)and water(control)were served with a standard *** has 21 volunteers of both sexes(18–40 years old,18.5–26 kg/m^(2)).Consumption of ginger tea increased on average 95.5±27.4 kcal/day the non-nitrogen energy expenditure after 240 min of ingestion(p=0.002).There was a significant effect of ginger on diet-induced thermogenesis(difference between ginger tea treatment ***:113.0±43.0 kcal/day,p=0.016).However,appetitive responses and prospective food intake did not differ between ginger tea and water intake.

关键词： Zingiber officinale Ginger Indirect calorimetry Energy metabolism

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Usefulness of Honeypots Towards Data Security: A Systematic Literature Review

Usefulness of Honeypots Towards Data Security: A Systematic ...

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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Silaen, Kalpin Erlangga Meyliana, Meyliana Warnars, Harco Leslie Hendric Spits Prabowo, Harjanto Hidayanto, Achmad Nizar Anggreainy, Maria Susan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta Indonesia Universitas Indonesia Faculty of Computer Science Department of Information System Depok Indonesia

ISBN: (纸本)9798350382914

The increasing data volume given by the exponential growth of digital devices, cloud platforms, and the Internet of Things (IOT) had become an attractive target for attackers. This makes the search for innovative defense mechanisms intensified leading to a renewed focus on the utilization of honeypots to improve data security. This Systematic Literature Review (SLR) examines the usage and contributions of honeypots in data security field across diverse sectors and environments. Analyzing 38 selected articles from 2018 to 2023, the study highlights the growing adoption of honeypots as tools for detecting and preventing data breaches, exfiltration, and other security incidents. Our finding found that honeypots, with their deceptive capabilities, have proven could be deployed in sectors like healthcare, cloud computing, and industrial control systems and effective in detection and prevention data breaches, exfiltration, and other data breaches, making them as a crucial tool in the data security protection. The future of honeypots lies in their integration with machine learning and AI, to predict and counteract sophisticated. Moreover, evolving honeypots into tools for deterrence and corrective action opens new avenues for cybersecurity strategies. Their ability to act as deterrents by increasing perceived risks for attackers, coupled with their role in aiding recovery from data breaches through detailed forensic analysis, positions honeypots as a cornerstone in the development of resilient data security frameworks. This study paves the way for a more adaptive, proactive, and comprehensive approach to data security, highlighting the indispensable role of honeypots in the ever-evolving landscape of cybersecurity. © 2023 IEEE.

关键词： Machine learning

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Fast Cauchy Sum Algorithms for Polynomial Zeros and Matrix Eigenvalues 13th

Fast Cauchy Sum Algorithms for Polynomial Zeros and Matrix...

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13th International Symposium on Algorithms and Complexity, CIAC 2023

作者： Pan, Victor Y. Go, Soo Luan, Qi Zhao, Liang Department of Computer Science Lehman College of CUNY BronxNY United States Program in Computer Science The Graduate Center of the City University of New York New York United States Program in Mathematics The Graduate Center of the City University of New York New York United States

ISBN: (纸本)9783031304477

Given a black box oracle that evaluates a univariate polynomial p(x) of a degree d, we seek its zeros, aka the roots of the equation. At FOCS 2016, Louis and Vempala approximated within an absolutely largest zero of such a real-rooted polynomial at the cost of the evaluation of Newton’s ratio (formula presented) points x and then extended this algorithm to approximation of an absolutely largest eigenvalue of a symmetric matrix at a record Boolean cost. By applying distinct approach and techniques we obtain much more general results at the same computational cost. Our use of Cauchy integrals and randomization is non-trivial and pioneering in this field. Somewhat surprisingly, the Boolean complexity of the accelerated versions of our algorithms in [25, 26] reached below the known lower bounds on the Boolean complexity of polynomial root-finding. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Approximation algorithms

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Exploring Recurrent Neural Network Models for Depression Detection Through Facial Expressions: A Systematic Literature Review 6

Exploring Recurrent Neural Network Models for Depression Det...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Rumahorbo, Brilyan Nathanael Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9798350345162

Major Depressive Disorder (MDD) is a prevalent mental disorder, affecting a significant number of individuals, with estimates reaching 300 million cases worldwide. Currently, the diagnosis of this condition relies heavily on subjective assessments based on the experience of medical professionals. Therefore, researchers have turned to deep learning models to explore the detection of depression. The objective of this review is to gather information on detecting depression based on facial expressions in videos using deep learning techniques. Overall, this research found that RNN models achieved 7.22 MAE for AVEC2014. LSTM models produced 4.83 MAE for DAIC-WOZ, while GRU models achieved an accuracy of 89.77% for DAIC-WOZ. Features like Facial Action Units (FAU), eye gaze, and landmarks show great potential and need to be further analyzed to improve results. Analysis can include applying feature engineering techniques. Aggregation methods, such as mean calculation, are recommended as effective approaches for data processing. This Systematic Literature Review found that facial expressions do have relevant patterns related to MDD. © 2023 IEEE.

关键词： deep learning facial expression GRU LSTM major depressive disorder

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News-Driven Price Movement Forecasting with Label-Prior Graph Attention 24

News-Driven Price Movement Forecasting with Label-Prior Grap...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Liou, Yi-Ting Chen, Chung-Chi Huang, Hen-Hsen Chen, Hsin-Hsi Graduate Program of Data Science National Taiwan University Academia Sinica Taiwan AIST Japan Institute of Information Science Academia Sinica Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan

ISBN: (纸本)9798400701726

This paper introduces a novel approach to stock movement prediction using multi-label classification, leveraging the interconnections between news articles and related company stocks. We present the Label-Prior Graph Attention (LPGA) model, which significantly enhances the performance of news-driven stock price movement forecasting. This model is comprised of a unique graph attention architecture, incorporating a label encoder and a text encoder, designed to effectively capture and utilize the relationships between labels in a graph-based context. Our model demonstrates superior performance over several benchmark models. The LPGA model’s efficacy is further validated through experiments on two multilabel datasets. The model outperforms established baseline models across various evaluation metrics. The success of the LPGA model in both stock movement prediction and general multi-label classification tasks indicates its potential as a versatile tool in the realm of machine learning and financial analysis. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graphic methods

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Analysis of the Cyber Attacks in Indonesia's Health Sector in Industry 4.0 Era 8

Analysis of the Cyber Attacks in Indonesia's Health Sector i...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Hidayat, Arif Mantoro, Teddy Susandi, Armi Indonesian State Intelligence College Medical Intelligence Post-Graduate Program Bogor Indonesia Sampoema University Media-Tech Lab Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665453899

At this time the world has undergone considerable changes, where the world has entered the era of the industrial revolution 4.0. With these changes, there are many positive impacts or opportunities that can support human activities in the economic, political, socio-cultural, health, and other fields. And alsothe author tries to analyze about industry 4.0 era for Indonesia's health sector with SWOT Analysis to know about Strenght, Weaknesses, Opportunities, and Threats. However, the challenges of technological advances can pose a threat to countries that are not ready to facethem, one of which is cyber attacks that can threaten data security. Therefore, it is necessary to be prepared in terms of data security by cooperating with various parties involved in it. © 2022 IEEE.

关键词： Industry 4.0

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NoSQL Injection Detection Using Deep Neural Network and Principal Component Analysis of Injection Feature Vectors

NoSQL Injection Detection Using Deep Neural Network and Prin...

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International Conference on computer, Control, Informatics and Its Applications (IC3INA)

作者： Irwin Santoso Soegiharto Abba Suganda Girsang Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Binus Graduate Program - Master of Computer Science Binus Nusantara University Jakarta Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

NoSQL database has gained popularity in Big Data and other various applications for its simplicity and flexibility. The non-relational nature of NoSQL database such as MongoDB proves to improve development lifecycles and resources efficiency. However, security challenges arise along with increasing usage of NoSQL database, and NoSQL database is no exception to injection attacks. Machine learning proved to be an efficient method, as much has been researched. However, in the future there may be an increasing complexity of features that may prove costly to the model’s performance. Therefore, this research aims to utilize principal component analysis as dimensionality reduction and deep neural network as the classification method, to improve the security of NoSQL database. The text query is converted to feature vectors then further processed to reduce the input dimension of the deep neural network using PCA. The features used are based on previous research and various sources, and some are added after analyzing the dataset.10-fold cross validation is also applied to ensure that the model does not overfit the data, attempting to reduce bias to the result. The 10-fold cross validation model accuracy result is in average 97.44% with a standard deviation of 1.7%, and the testing results are 97.5% in accuracy,95.65% in precision, 91.67% in recall, and 93.61% in F1 score. Thus, it can be concluded that the usage of PCA on injection feature vectors can reduce complexity of the model.

关键词： Technological innovation NoSQL databases Artificial neural networks Vectors Complexity theory Security Informatics Standards Principal component analysis Testing

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AI in EEG-Based BCI for the Diagnosis of Mild Cognitive Impairment: A Mini Review 13

AI in EEG-Based BCI for the Diagnosis of Mild Cognitive Impa...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Zaitsev, Vasilii Wei, Chun-Shu EECS International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9798331521929

Mild Cognitive Impairment (MCI) is a condition that often precedes dementia, making early diagnosis critical for delaying cognitive decline. Electroencephalography (EEG) has emerged as a non-invasive, cost-effective tool for monitoring brain activity and detecting MCI. This paper overviews recent advancements in machine learning (ML) and deep learning (DL) models for EEG-based MCI diagnosis. Traditional ML approaches, such as support vector machines (SVM) and K-nearest neighbors (KNN), have been widely used but rely on manually extracted features and face challenges with the complex nature of EEG signals. In contrast, DL models like convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and transformers have shown promise in automatically learning features and capturing temporal and spatial information from EEG data. Despite these advancements, issues such as small dataset sizes and variability in EEG recordings remain barriers to clinical application. This paper discusses these challenges and highlights potential future directions for improving the diagnosis of MCI. © 2025 IEEE.

关键词： Convolutional neural networks

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Implementation of Lightweight PRNG on PLC for Industrial Control Systems 3

Implementation of Lightweight PRNG on PLC for Industrial Con...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Sama, Andi Gaol, Ford Lumban Harso Supangkat, Suhono Ranti, Benny Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Institute of Technology Bandung Faculty of Digital Communication and Hotel & Tourism Bandung Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350394559

The Information Technology/Operational Technology convergence towards Industry 4.0 opens the opportunity to leverage recent advancements in Information Technology for Operational Technology, such as Cloud, Internet of Things, and Artificial Intelligence. Meanwhile, cyber-attacks are increasing for Operational Technology. The security aspect in Operational Technology systems has traditionally been a low priority, in contrast with speed. This introduces challenges as Industrial Control Systems such as programmable Logic Controller in Operational Technology have been traditionally optimized for speed rather than security due to limited computing power. As computations need to be as efficient as possible, security in Operational Technology has yet to be managed as robustly as in Information Technology. Operational Technology communication protocols for securing data transfer, for example, have no or just a few security capabilities, even for basic authentication and encryption. Common security algorithms rely on random numbers. However, Random Number Generator is not usually part of standard functions in programmable Logic Controllers, the core control component in Industrial Control Systems. In Industrial Control Systems, the Random Number Generator is mostly implemented as a software-based Pseudo Random Number Generator. This paper shows how to apply a Pseudo Random Number Generator in a Siemens Compact PLC S7-1200 using a modified lightweight XORshift algorithm. The XORshift algorithm can generate better randomness than the system's clock-based implementation in the Siemens Library of Generic Functions. © 2023 IEEE.

关键词： Internet of things

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The Influence of Well-Dressed NPCs on Player Perception, Immersion and Decision-Making in Gaming 5

The Influence of Well-Dressed NPCs on Player Perception, Imm...

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5th Annual IEEE Conference on Games, CoG 2023

作者： Paradeda, Raul B. Freitas, Georgy Alves Filho, Sebastiao E. Abner Souza, Anderson State University of Rio Grande do Norte Department of Computer Science Natal Brazil State University of Rio Grande do Norte Computer Science Graduate Program UERN-UFERSA Department of Informatics Mossoró Brazil

ISBN: (纸本)9798350322774

This empirical study involved volunteers who played a game featuring NPCs specially developed for the research. The research investigated the influence and behaviour of NPC appearance on some factors regarding the players. Results suggest that NPC appearance can significantly affect the player's emotions, immersion, perception, and decision-making. Poorly dressed NPCs negatively influenced the player's emotional state, while well-dressed NPCs generated higher trustworthiness and positively influenced the player's decision-making. This study may help develop more efficient strategies for creating characters that influence the player's decision-making in video games and encourage the development of new research in the field of human-agent interaction and game development. © 2023 IEEE.

关键词： Decision making

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