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Web-Based Tools Validation for Antimicrobial Resistance Prediction: An Empirical Comparative Analysis

SN computer science 2024年第1期5卷 147页

作者： Routray, Sweta Padma Sahoo, Swayamprabha Nayak, Debasish Swapnesh Kumar Shah, Sejal Swarnkar, Tripti Center for Biotechnology Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar 751003 India Department of Computer Science and Engineering Institute of Technical Education and Research Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar 751030 India Department of Computer Science and Bioscience Faculty of Technology Marwadi University Gujarat Rajkot 360003 India Department of Computer Application Institute of Technical Education and Research Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar 751030 India

Antimicrobial resistance (AMR) is a serious threat to global public health, necessitating rapid and precise diagnostic tools. The prevalence of novel antibiotic resistance genes (ARGs) has increased due to microbial sequencing, resulting in the need to extract vital information from vast amounts of data. Although many AMR prediction tools exist, only a few are accurate and scalable. We examined 20 widely used AMR prediction tools and chose 4 web-based tools for antimicrobial resistance surveillance over standalone software due to their easy accessibility, portability, and centralized data management, eliminating the need for complex installation and maintenance. CGE (center for Genomic Epidemiology) provides bioinformatics tools and promotes open data sharing. At the same time, CARD (Comprehensive Antibiotic Resistance Database) is a valuable resource for antibiotic resistance gene information, collectively contributing to our understanding and management of antibiotic resistance. We highlighted web-based AMR prediction tools and performed a case study using the Pseudomonas aeruginosa complete plasmid sequence (CPS) to identify strengths and weaknesses in the system. Our study explored four web-based antibiotic resistance gene prediction tools: ResFinder, KmerResistance, ResFinderFG, and RGI. ResFinder excelled at finding acquired antimicrobial resistance genes as well as maintaining a database up to date. KmerResistance identified resistance genes using k-mer analysis. esFinderFG offered a unique perspective, excelling in detecting a broad range of resistant phenotypes, due to its inclusion of sequences discovered through functional metagenomics. RGI was versatile in detecting a wide range of resistance genes and provided extensive resistance mechanism information. researchers must understand the capabilities and trade-offs of these tools to make well-informed choices for efficient resistance gene identification and surveillance as the antibiotic resistance landsca

关键词： Antibiotic resistance genes (ARGs) Antimicrobial resistance (AMR) KmerResistance ResFinder ResfinderFG

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GoT: Effective Graph-of-Thought Reasoning in Language Models

GoT: Effective Graph-of-Thought Reasoning in Language Models

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

ISBN: (纸本)9798891761193

With the widespread use of language models (LMs) in NLP tasks, researchers have discovered the potential of Chain-of-thought (CoT) to assist LMs in accomplishing complex reasoning tasks by generating intermediate steps. However, human thought processes are often non-linear, rather than simply sequential chains of thoughts. Therefore, we propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. By representing thought units as nodes and connections between them as edges, our approach captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. GoT adopts a two-stage framework with an additional GoT encoder for thought graph representation and fuses the graph representation with the original input representation through a gated fusion mechanism. We evaluate GoT's performance on a text-only reasoning task (AQUA-RAT) and a multimodal reasoning task (scienceQA). Our model achieves significant improvement over the strong CoT baseline on the AQUA-RAT test set and boosts accuracy from 85.19% to 87.59% using the T5-base model over the state-of-the-art Multimodal-CoT (Zhang et al., 2023) on the scienceQA test set. Our code is publicly available at https://***/Zoeyyao27/Graphof-Thought. © 2024 Association for Computational Linguistics.

关键词： Rats

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HDCGD-CBAM:Satellite Interference Recognition Algorithm Based on Improved CLDNN and CBAM

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China Communications 2024年第12期21卷 257-274页

作者： Duan Ruifeng Chen Ziyu Meng Wei Wang Xu Yang Guoting Cheng Peng Li Yonghui School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing 100083China Jining Economic and Social Development Research Center Jining 272000China School of Electrical and Information Engineering the University of SydneySydneyNSW 2006Australia Department of Computer Science and Information Technology La Trobe UniversityMelbourneVIC 3086Australia

Satellite communication systems are facing serious electromagnetic interference,and interference signal recognition is a crucial foundation for targeted *** this paper,we propose a novel interference recognition algorithm called HDCGD-CBAM,which adopts the time-frequency images(TFIs)of signals to effectively extract the temporal and spectral *** the proposed method,we improve the Convolutional Long Short-Term Memory Deep Neural Network(CLDNN)in two ***,the simpler Gate Recurrent Unit(GRU)is used instead of the Long Short-Term Memory(LSTM),reducing model parameters while maintaining the recognition ***,we replace convolutional layers with hybrid dilated convolution(HDC)to expand the receptive field of feature maps,which captures the correlation of time-frequency data on a larger spatial ***,Convolutional Block Attention Module(CBAM)is introduced before and after the HDC layers to strengthen the extraction of critical features and improve the recognition *** experiment results show that the HDCGD-CBAM model significantly outper-forms existing methods in terms of recognition accuracy and *** Jamming-to-Signal Ratio(JSR)varies from-30dB to 10dB,it achieves an average accuracy of 78.7%and outperforms the CLDNN by 7.29%while reducing the Floating Point Operations(FLOPs)by 79.8%to ***,the proposed model has fewer parameters with 301k compared to several state-of-the-art methods.

关键词： attention mechanism CLDNN HDC interference recognition satellite communication

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Electromagnetic Functions Modulation of Recycled By-Products by Heterodimensional Structure

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Nano-Micro Letters 2025年第6期17卷 264-278页

作者： Ze Nan Wei Wei Zhenhua Lin Ruimei Yuan Miao Zhang Jincheng Zhang Jianyong Ouyang Jingjing Chang Hejun Li Yue Hao State Key Laboratory of Wide-Bandgap Semiconductor Devices and Integrated Technology School of MicroelectronicsXidian UniversityXi’an 710071People’s Republic of China State Key Laboratory of Solidification Processing Shaanxi Province Key Laboratory of Fiber Reinforced Light Composite MaterialsCarbon/Carbon Composites Research CenterNorthwestern Polytechnical UniversityXi’an 710072People’s Republic of China Advanced Interdisciplinary Research Center for Flexible Electronics Xidian UniversityXi’an 710071People’s Republic of China Department of Materials Science and Engineering National University of Singapore7 Engineering Drive 1Singapore 117574Singapore

One of the significant technological challenges in safeguarding electronic devices pertains to the modulation of electromagnetic(EM)wave jamming and the recycling of defensive *** synergistic effect of heterodimensional materials can effectively enable the manipulation of EM waves by altering the *** we propose a novel approach for upcycling by-products of silver nanowires that can fabricate shape-tunable aerogels which enable the modulation of its interaction with microwaves by heterodimensional structure of ***-product heterodimensionality was used to design EM-wave-jamming-dissipation structures and therefore two typical tunable aerogel forms were *** first tunable form was aerogel film,which shielded EM interference(EMI shielding effectiveness(EMI SE)>89 dB)and the second tunable form was foam,which performed dual EM functions(SE>30 dB&reflective loss(RL)<-35 dB,effective absorption bandwidth(EAB)>6.7 GHz).We show that secondary recycled aerogels retain nearly all of their EM protection properties,making this type of closed-loop cycle an appealing *** findings pave the way for the development of adaptive EM functions with nanoscale regulation in a green and closed-loop cycle,and they shed light on the fundamental understanding of microwave interactions with heterodimensional structures.

关键词： Silver nanowires Electromagnetic shielding Microwave absorption Recycled aerogels

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Secrecy Capacity Analysis with Imperfect Channel State Information and Varying Interference for 6G C-V2X Communication 10

Secrecy Capacity Analysis with Imperfect Channel State Infor...

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10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023

作者： Ayaz, Hina Abbas, Ghulam Abbas, Ziaul Haq Waqas, Muhammad Gik Institute of Engineering Sciences and Technology Facutly of Computer Science and Engineering Topi23460 Pakistan Research Center Topi23460 Pakistan University of Bahrain College of Information Technology Computer Engineering Department 32038 Bahrain

ISBN: (纸本)9798350329674

It has been observed that the use of radio-frequency fingerprinting (RF-FP) for location estimation (LE) of vehicles can significantly improve secrecy capacity (SC) for urban scenarios in 6th generation cellular vehicle-to-everything (6G C-V2X) communication. However, in most of the literature, interference is considered constant for simplicity. Thus, there is a need to investigate the impact of varying levels of interference and incomplete channel state information (CSI) on the performance of SC. This study examines the impact of shadowing and interference in the presence of heavy traffic and obstructions. We have proposed a technique that analyzes the effects of varying levels of interference for LE via RF-FP to improve SC with incomplete CSI. Simulation results demonstrate that interference has a significant impact on SC which depends on the distance and location of the eavesdropping vehicle. However, a decrease in SC is not solely caused by the increase in interference, since other factors, such as the speed and relative position of the illegitimate vehicles as well as incomplete CSI, can also significantly affect the SC performance, as demonstrated through simulations. © 2023 IEEE.

关键词： Vehicle to Everything

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EMO-Mamba: Multimodal Selective Structured State Space Model for Depression Detection

EMO-Mamba: Multimodal Selective Structured State Space Model...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xing, Tao Dou, Yutao Xie, Xiaolan Zhou, Jiansong Chen, Xianliang Peng, Shaoliang College of Computer Science and Engineering Guilin University of Technology Guilin541006 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The Second Xiangya Hospital Central South University Department of Psychiatry National Clinical Research Center for Mental Disorders National Center for Mental Disorders Changsha410011 China

ISBN: (纸本)9798350386226

Depression is a severe mental disorder that significantly affects health and quality of life, making early detection critical for effective treatment and management. In traditional clinical diagnostics, doctors and mental health professionals typically assess emotional states by observing patients' facial expressions and listening to their speech. Changes in facial expressions can reflect emotional responses, while vocal features such as tone and pace may indicate underlying emotional distress or psychological conditions. Artificial intelligence technologies are adept at efficiently extracting and analyzing these multimedia signals. However, existing methods often overlook critical information when processing long-time series data and struggle to handle multiple modalities simultaneously. To address these issues, we propose the EMO-Mamba, which utilizes selective mechanisms and state space models (SSM) to filter important features and effectively maintain memory over long time-series. Additionally, we introduced a multimodal data fusion framework to integrate key features from various modals, thereby enhancing model performance. Evaluations on the multimodal public datasets D-Vlog showed that our method achieved accuracy of 75.54%, demonstrating its effectiveness across diverse data environments and optimizing detection outcomes. © 2024 IEEE.

关键词： Data fusion

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Adversarial learning for Mirai botnet detection based on long short-term memory and XGBoost

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 153-160页

作者： Vajrobol, Vajratiya Gupta, Brij B. Gaurav, Akshat Chuang, Huan-Ming Institute of Informatics and Communication University of Delhi India International Center for AI and Cyber Security Research and Innovations. Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Ronin Institute MontclairNJ United States Department of Information Management National Yunlin University of Science and Technology Yunlin Taiwan Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Dehradun India

In today's world, where digital threats are on the rise, one particularly concerning threat is the Mirai botnet. This malware is designed to infect and command a collection of Internet of Things (IoT) devices. The use of Mirai attacks has intensified in recent times, thus threatening the smooth operation of numerous devices that are connected to a network. Such attacks carry adverse consequences that include interference with services or the leakage of confidential information. To fight this growing threat, smart and flexible detection techniques are required to counter the new methods cyber attackers use. The aim of this research is to develop a resilient defense against Mirai botnet attacks. The Long Short Term Memory term (LSTM) and XGBoost combined have the best performance of 97.7% accuracy score. With this combination, the aim is to strengthen our cyber defenses against sophisticated and dynamically operating Mirai botnets to further enhance the security of our digital world. © 2024

关键词： Botnet

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Mitigating Bias in Bayesian Optimized Data While Designing MacPherson Suspension Architecture

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 904-915页

作者： Thomas, Sinnu Susan Lamine, Guillaume Palandri, Jacopo Lakehal-Ayat, Mohsen Chakravarty, Punarjay Wolf-Monheim, Friedrich Blaschko, Matthew B. School of Computer Science and Engineering Digital University Kerala Veiloor695317 India Eura Nova Mont-Saint-Guibert1435 Belgium Ford Research and Innovation Center Aachen52072 Germany Planet GaithersburgMD20878 United States Department of Electrical Engineering KU Leuven Leuven3001 Belgium

With the rise of artificial intelligence, the automotive industry searched for novel ways to improve future product design. We focus on designing automatic MacPherson suspension architecture for the automotive sector. It takes time for an automotive engineer to design vehicle parts and thus slows the pace of innovation in this field. Given the car's particular kinematic characteristics, we propose to predict an architecture by positioning the hardpoints. This work deals with the biased data generated using the discipline models using the dataset shift learning paradigm. The optimized data are created with random and uniform sampling, with more samples with random sampling. We resolve the bias in the data, using a novel criterion for tuning the kernel mean matching and a weight estimation algorithm and designing the required target characteristics. © 2023 IEEE.

关键词： Product design

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Assessing the Effect of Model Poisoning Attacks on Federated Learning in Android Malware Detection

Assessing the Effect of Model Poisoning Attacks on Federated...

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Nawshin, Faria Arnal, Romain Unal, Devrim Suganthan, Ponnuthurai Touseau, Lionel KINDI Computing Research Center College of Engineering Qatar University Doha Qatar Department of Computer Science & Engineering College of Engineering Qatar University Doha Qatar Saint-Cyr Coetquidan Military Academy Guer Bretagne France

ISBN: (纸本)9798400716928

Android devices are central to our daily lives, which leads to an increase in mobile security threats. Attackers try to exploit vulnerabilities and steal personal information from the installed applications on these devices. Because of their widespread usage, these devices are the prime targets of cyber attacks. To get rid of this, Android malware detection has become increasingly significant. Federated learning, which is a decentralized machine learning approach, has been utilized to improve the privacy of sensitive user data. However, the integration of federated learning also introduces a vulnerability to model poisoning attacks, where adversaries deliberately bias the learning process of the model to impair the performance metrics. This paper presents a comprehensive assessment of the effect of model poisoning attacks on federated learning systems deployed for Android malware detection. We also explain an exhaustive feature selection methodology that employs both static and dynamic features of Android applications and created a novel dataset. We focus on incorporating recent malware samples while creating the dataset to make the model robust and adaptable to new malware. Furthermore, we quantify the degradation in model accuracy and reliability following a model poisoning attack scenario through a series of experiments. Additionally, we explore the defense mechanisms to mitigate the model poisoning attacks based on recent studies. © 2024 ACM.

关键词： Mobile security

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Interactive Middleware Services for Heterogeneous Systems

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computer Systems science & engineering 2022年第6期41卷 1241-1253页

作者： Vasanthi Raghupathy Osamah Ibrahim Khalaf Carlos Andrés Tavera Romero Sudhakar Sengan Dilip Kumar Sharma Department of Computer Science and Engineering Tagore Institute of Engineering and TechnologyAttur636112India Al-Nahrain University Al-Nahrain Nanorenewable Energy Research CenterBaghdadIraq Universidad Santiago de Cali in Cali Colombia South America Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152India Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226India

Computing has become more invisible, widespread and ubiquitoussince the inception of the Internet of Things (IoT) and Web of Things. Multipledevices that surround us meet user’s requirements everywhere. Multiple Middleware Framework (MF) designs have come into existence because of the rapiddevelopment of interactive services in Heterogeneous Systems. This resulted inthe delivery of interactive services throughout Heterogeneous Environments(HE). Users are given free navigation between devices in a widespread environment and continuously interact with each other from any chosen device. Numerous interactive devices with recent interactive platforms (for example, SmartPhones, Mobile Phones, Personal computer (PC) and Personal Digital Assistant(PDA)) are available in the market. For easy access to information and servicesirrespective of the device used for working and even at the drastic change ofthe environment, the execution of applications on a broad spectrum of computingdevices is propelled by the availability of the above-mentioned platforms. Different applications that need interoperability to coordinate and correspond with eachother should be facilitated. Using a standard interface and data format, HE mustlink various devices from various platforms together to communicate with eachother. To aid the interactive services performed by a middleware framework thatoperates on Application Programming Interface (API) over HEs, this issue aims toendorse an Adaptable Service Application Programming Interface (ASAPI).

关键词： Middleware services ontology interoperability

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