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IEEE Annual Ubiquitous computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Minxiao Wang Ning Yang Ning Weng Computer Engineering Program School of ECBE Southern Illinois University Carbondale IL USA Information Technology Program School of Computing Southern Illinois University Carbondale IL USA

Deep learning (DL) has been proposed as a promising solution for network intrusion detection systems (NIDSs). While most DL-based NIDSs focus on high accuracy, they often overlook the critical issue of NIDSs response time for intrusion behavior. Recently some existing works presented time-aware evaluation metrics or design DL-based solutions for early intrusion detection, yet they primarily focus on the post-performance of NIDSs, without thoroughly investigating the impact of early intrusion detection on DL-based NIDSs due to the black-box nature of DL models. To address this research gap, we explore the impact of early detection on DL-based models’ detection accuracy and input features. In our empirical study, we implement two different existing deep learning-based NIDSs methods and evaluate them on three published benchmark NIDSs datasets. Based on the experiment results on DL-based NIDSs detection accuracy, we observe that the early detection scenario will lead to the NIDSs model’s detection accuracy decreasing up to 50%. To understand the reasons behind it, we adopt the SHapley Additive exPlanations (SHAP) to analyze the importance of features and we observe the varying feature distributions. The feature importance and feature distribution results show that early intrusion detection may lead to a degradation of feature quality.

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IoT-based vital sign monitoring: A literature review

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Smart Health 2024年 32卷

作者： Andrade, Alexandre Cabral, Arthur Tassinari Bellini, Bárbara Facco Rodrigues, Vinicius da Rosa Righi, Rodrigo André da Costa, Cristiano Barbosa, Jorge Luis Victória Applied Computing Program Universidade do Vale do Rio dos Sinos São Leopoldo RS Brazil

The Internet of Things (IoT) applied to the health area is in significant growth, with companies putting effort into developing specialized devices. Remote patient healthcare monitoring, in particular, benefits society as it unburdens hospitals and helps patients with chronic diseases. Analyzing the health status with IoT and Artificial Intelligence (AI) is new in the digital community. The literature yet presents a limited number of references explicitly concerning the topic of qualified data acquisition. In this sense, the present literature review aims to update the joint subject of IoT and vital signs, seeking to understand the state-of-the-art and future directions. We have analyzed 78 articles and IoT manufacturer websites that address vital signs collection to answer a group of primary and specific questions. In particular, we revisited architectures, communication protocols, data acquisition mechanisms, evaluation metrics, and how to efficiently transfer data through the lens of sensors, actuators, and healthcare. Currently, two themes are considered as promising directions for studies in the joint area of IoT and vital sign-based healthcare monitoring. The first is the connection promotion between third-party applications and IoT devices to collect and process time-critical data with the support of edge, fog, and cloud infrastructures. The second theme again brings the focus to data compression methodologies since monitoring vital signs in a smart city geographical area naturally requires strategies to optimize network bandwidth consumption and data storage on computational resources. Moreover, both themes are directly linked to energy-saving approaches and quality of service (QoS) for efficient patient healthcare checking. © 2024 Elsevier Inc.

关键词： Healthcare Internet of things Vital Sign

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Exploring Security Threats by Hardware-Faults in Approximate Arithmetic computing

Exploring Security Threats by Hardware-Faults in Approximate...

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Morgana M. A. Da Rosa Eduardo A. C. Da Costa Rafael I. Soares Sergio Bampi Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil PPGC & PGMICRO - Informatics Institute Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

Approximate computing (AxC) emerged as a design alternative to boost design efficiency by leveraging the inherent error resiliency of many applications. Recent literature shows attackers could exploit some AxC mechanisms to create new attack surfaces. This work proposes integrity checking and exclusive logic-based methods detect attacks by hardware faults on AxC systems. During ASIC-based synthesis, we investigate the security vulnerabilities of the Approximate Parallel Prefix Adder (AxPPA). We compare the expected output of the designer with the output netlists of ASIC-based synthesis. We propose a verification methodology consisting of a golden model (GM) described in a Simulink model and the DUT represented at the netlist level in a standard cell library after logic synthesis. Experimental results show that the AxPPA has an error range of 1.263 × 10 −5 to 0.0098 concerning the designer’s expected output under ASIC-based synthesis attacks.

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Propranolol hydrochloride degradation using La@TiO_(2) functionalized with CMCD

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Journal of Rare Earths 2022年第4期40卷 579-585,I0003页

作者： Gustavo Lopes Colpani Vitor Follmann Santos Rubieli Carla Frezza Zeferino Micheli Zanetti Josiane Maria Muneron de Mello Luciano Luiz Silva Natan Padoin Regina de Fatima Peralta Muniz Moreira Márcio Antônio Fiori Cíntia Soaresc Post-Graduate Program in Technology and Innovation Management Community University of ChapecóRegionChapecó89809-900Brazil Department of Chemical Engineering Community University of ChapecóRegionChapecó89809-900Brazil Laboratory of Materials and Scientific Computing(LabMAC) Department of Chemical and Food EngineeringFederal University of Santa CatarinaFlorianópolis88040-900Brazil

Propranolol hydrochloride can be considered a persistent and bioaccumulative pharmaceutical in the *** drug and its by-products are potentially toxic and have adverse effects,since these compounds have been associated with endocrine-disrupting effects,reproductive deficiencies,embryo abnormalities and pericardial ***_(2)–La 0.05%–carboxymethyl-β-cyclodextrin(CMCD)nanoparticles were successfully prepared by a simple two-step method,which consists of sonification and *** characterization analyses reveal that lanthanum is dispersed on the semiconductor surface,probably forming Ti–O–La bonds,which can induce oxygen vacancies and surface defects that effectively restrain the recombination of photogenerated electron/holes *** efficiency of TiO_(2)–La 0.05%–CMCD samples in degradation of propranolol under UV-light irradiation is higher than that of pristine TiO_(2) within 20 min reaction,probably due to complex formation between theβ-blocker and the oligosaccharide,which allows us to propose a photocatalytic mechanism based on the formation of intermediates and competition of these compounds to the radicals and CMCD cavities.

关键词： TiO_(2) Lanthanum CMCD Propranolol Photocatalysis Functionalization

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A Machine Learning Approach for the Identification of Learners' Misconceptions in Algebraic Problem-Solving

A Machine Learning Approach for the Identification of Learne...

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International Conference on Advanced Learning Technologies (ICALT)

作者： Joice Cazanoski Gomes Patricia A. Jaques Graduate Program in Informatics (PPGInf) Universidade Federal do Paraná (UFPR) Curitiba Brazil Graduate Program in Computing (PPGC) Universidade Federal de Pelotas (UFPEL) Pelotas Brazil

Misconceptions play a significant role in the learning process as they reflect an inaccurate understanding of a particular concept. Error diagnosis can help teachers and intelligent learning environments determine the most appropriate type of student assistance. Previously, misconceptions were identified using rule-based expert systems (bug libraries) and clustering algorithms. Bug libraries demand extensive work from developers to identify all potential misconceptions and code rules for each one in advance. Additionally, these solutions cannot detect misconceptions for which rules were not explicitly programmed. Clustering-based solutions overcome these drawbacks by automatically identifying misconceptions based on students' most common errors. To effectively and efficiently identify misconceptions, clustering solutions must have a suitable representation of the problem and its steps, and employ machine learning algorithms capable of discerning patterns from them. This paper proposes a solution that utilizes expression trees to represent algebraic problem-solving steps and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to identify misconceptions by clustering similar errors in a database containing 1064 steps from 112 students. This database was collected from an intelligent learning system designed to assist in solving first-degree equations. In our final solution, a Natural Language Processing tokenizer was employed to represent each term numerically, which identified 178 homogeneous clusters with minimal noise and few outliers.

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An Ultra Low-Energy VLSI Approximate Discrete Haar Wavelet Transform for ECG Data Compression

An Ultra Low-Energy VLSI Approximate Discrete Haar Wavelet T...

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Arthur Cardozo Morgana M. A. Rosa Rafael Soares Eduardo Costa Sergio Bampi Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil PPGC & PGMICRO - Informatics Institute Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

This work proposes an ultra-low-energy ECG data compression with VLSI DHWT-based (discrete Haar wavelet transform) architecture to enable storage and transmission in resource-constrained environments. We present original, pruned, and approximate DHWT (ODHWT, PDHWT, and AxDHWT, respectively) hardware architectures for ECG data compression at ultra-high energy efficiency. Our best proposal employing the AxDHWT hardware architecture requires just five additions only. Using a PDHWT technique to improve energy efficiency observes the evolution of the signal-to-noise ratio and the ultimate impact on the ECG data compression application. The DHWT-based configurations architecture proposal achieves a minimum compression ratio of 0.125 (i.e., 1/8) and a PRD (percent root difference) <1.34. We implemented the VLSI architectures in 65nm CMOS technology in maximum and fixed frequencies and two different supply voltages. The AxDHWT occupies a die area of 0.64mm 2 . The measured total power is $0.534\mu {\mathrm {W}}$, the higher energy-savings among the ECG data compression architectures.

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Fast Chaotic Image Encryption with Simultaneous Permutation and Diffusion for IoT Applications

Fast Chaotic Image Encryption with Simultaneous Permutation ...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： João Inácio Moreira Bezerra Gustavo Machado Rafael Soares Alexandre Molter Vinícius V. Camargo Postgraduate Program in Computing - PPGC Postgraduate Program in Mathematical Modeling Federal University of Pelotas - UFPel Pelotas Brazil

ISBN: (纸本)9781665435413

The current prevalence of the internet for secret communication, alongside the development of the Internet of Things (IoT), increases the importance of cryptography for information security. Considering that the most widely shared archives are images and that traditional encryption algorithms, such as the Advanced Encryption Standard (AES), are not optimized for image encryption, researchers have investigated the use of chaos theory in image encryption schemes. However, their elevated encryption time and memory access, due to the number of scannings needed to cipher an image, have been an issue for chaotic ciphers. Considering this, a new cipher is proposed in this work in which the plain-image is divided into blocks and a simultaneous permutation-diffusion is adopted, increasing the cipher's time efficiency. The cipher is executed in a Raspberry Pi 3 device, and it presents a significant increase in throughput compared to other similar works in literature. Additionally, extensive security evaluations showed that the cipher is secure relative to statistical, differential, chosen-plaintext, brute force, noise, and occlusion attacks, which are hence suitable for the application in real-time IoT applications.

关键词： Ciphers Runtime Chaotic communication Memory management Throughput Real-time systems Encryption

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Joint Cache Placement and Request Routing Optimization in Heterogeneous Cellular Networks

Joint Cache Placement and Request Routing Optimization in He...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Marisangila Alves Guilherme Piĉgas Koslovski Graduate Program in Applied Computing - Santa Catarina State University - UDESC

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

The 5G Quality-of-Service (QoS) objectives con-tributed to the Heterogeneous Cellular Network (HCN) evolution, dictating that applications can rely on low-latency and high-bandwidth networks. However, concurrent requests of large amount of multimedia data generate a burden on the backhaul and fronthaul networks due to redundant retransmissions and pose challenges for achieving the QoS objectives. Although mobile network operators can place content closer to the HCN edge to improve the overall QoS indicators, there are still challenges to design a cache policy aware of limited storage capacity, different content popularity, device mobility, and network congestion. This work innovates by introducing a cooperative policy to join caches placement and routing users' requests atop an HCN. By combining networking and cache QoS requirements, the policy balances the fronthaul network load and dynamically maps the caches to HCN resources. We formulated the cache policy through linear programming and in-depth evaluated its performance using extensive simulation scenarios. The results indicate that the proposed network-aware policy decreases the network latency, even when subject to changes in content popularity distribution and total HCN storage capacity.

关键词： Cellular networks Measurement 5G mobile communication Quality of service Routing Linear programming Low latency communication

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MILPdM: A Predictive Maintenance Architecture for the Military Domain 22

MILPdM: A Predictive Maintenance Architecture for the Milita...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Dalzochio, Jovani Kunst, Rafael Barbosa, Jorge Pignaton De Freitas, Edison Binotto, Alecio University of Vale Do Rio Dos Sinos Graduate Program in Applied Computing São Leopoldo Brazil Institute of Informatics Federal University of Rio Grande Do sui Porto Alegre Brazil Ibm Consulting Asset and Edge Optimization Munich Germany

ISBN: (纸本)9798350345346

Equipment monitoring for failure prediction is receiving attention from different sectors of society, such as industry, healthcare, and defense. In the defense domain, assets like military vehicles generate data that one can use to identify behavior changes and anticipate possible real-time failures, avoiding unnecessary maintenance interventions. Failure anticipation is crucial, as assets operated in the military domain perform critical tasks in which unexpected equipment failures result in high material and human costs. Approaches found in the literature typically deal with failure generation, aiming at analyzing the equipment's behavior. This paper proposes a broader approach called MILPdM. This proposal is a failure prediction architecture covering the whole failure prediction and predictive maintenance procedures. We evaluate MILPdM architecture by analyzing an engine-failure scenario where we train models to predict time series by collecting vibration data that describes the degradation of the vehicle's health. Considering the implementation of an LSTM-based neural network and Random Forest, the acquired results lead to a root mean square error of 0.15015 in the best case, which allows to predict the failure status two minutes in advance with only 3 hours of data history. This result shows that MILPdM is capable of anticipating failures with high assertiveness. © 2023 IEEE.

关键词： IoBT machine learning neural networks predictive maintenance random forest

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Accuracy-, Delay- and Area-Driven Evaluation of Lower-Part Approximate Parallel Prefix Adder

Accuracy-, Delay- and Area-Driven Evaluation of Lower-Part A...

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Morgana M. A. Rosa Eduardo Costa Rafael Soares Sergio Bampi Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil PPGC & PGMICRO - Informatics Institute Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

This work proposes an accuracy-driven evaluation model of an approximate adder (AxA) called an approximate parallel prefix adder (AxPPA). It explores the significance of approximate computing (AxC) in optimizing performance and energy efficiency in computer systems by accepting minor accuracy trade-offs. We examine the AxPPA through a meticulous error analysis model and an automated analysis framework that comprehensively evaluates area and delay based on the fundamental equations of parallel prefix adders (PPAs). Additionally, it emphasizes the importance of lower-part approximation in adders, effectively addressing the elongation of carry chains and enhancing overall efficiency. The choice of approximation method directly impacts computational accuracy, resulting in variations in the number of approximated bits and carry chain length for a given tolerance level. Conducting a thorough comparison of accuracy trade-offs requires a unique design and simulation for each unit and approximation level, which can be time-consuming, particularly in larger circuits. Therefore, the proposed approach facilitates the exploration of larger circuit design space encompassing multiple AxAs, significantly minimizing time investment. In our experiments, the accuracy-driven evaluation of the lower part approximation method in AxA took 71.268 milliseconds for 1 million random inputs, with a K -bits approximation ranging from 1 to 16. The results indicate that AxPPA exhibits a minor error magnitude among the explored literature AxAs, being approximately 50.97% more accurate on average.

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