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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Ford, John Arnold, David Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

Hands-on learning environments and cyber ranges are popular tools in cybersecurity education. These resources provide students with practical assessments to strengthen their abilities and can assist in transferring material from the classroom to real-world scenarios. Additionally, virtualization environments, such as Proxmox, provide scalability and network flexibility that can be adapted to newly discovered threats. However, due to the increasing demand for cybersecurity skills and experience, learning environments must support an even greater number of students each term. Manual provisioning and management of environments for large student populations can consume valuable time for the instructor. To address this challenge, we developed an Environment Provisioning and Management Tool for cybersecurity education. Our solution interacts with the exposed Proxmox API to automate the process of user creation, server provisioning, and server destruction for a large set of users. Remote access will be managed by a pfSense firewall. Based on our testing, a six-machine user environment could be provisioned in 14.96 seconds and destroyed in 15.06 seconds. © 2023 IEEE.

关键词： Environmental management

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Combining Relative and Absolute Learning Formulations to Predict Emotional Attributes From Speech

Combining Relative and Absolute Learning Formulations to Pre...

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2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Naini, Abinay Reddy Subramanium, Shruthi Leem, Seong-Gyun Busso, Carlos Laboratory Department of Electrical and Computer Engineering RichardsonTX75080 United States

ISBN: (纸本)9798350306897

Predicting absolute scores is the most common speech-emotion recognition (SER) task when predicting emotional attributes (i.e., valence, arousal, and dominance). However, studies have shown that emotion has an ordinal nature where it is more reliable to establish a preference between speech samples (e.g., one sample is more positive than the other). This paper pursues a novel direction to combine absolute and relative learning formulations for SER. The proposed multitask formulation can simultaneously estimate preference between speech samples and predict their absolute score, providing a flexible tool to analyze emotional content in speech. Both tasks mutually complement each other, allowing the model to outperform SER systems that are exclusively trained to either predict absolute scores or estimate preferences. The multitask weights can be set according to the intended applications, prioritizing one task while slightly compromising the performance of the other task. © 2023 IEEE.

关键词： Forecasting

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Learning Multiresolution Features for Unsupervised Anomaly Localization on Industrial Textured Surfaces

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 127-139页

作者： Tao, Xian Yan, Shaohua Gong, Xinyi Adak, Chandranath Institute of Automation Chinese Academy of Sciences Beijing100190 China Binzhou Institute of Technology Binzhou256606 China CAS Engineering Laboratory for Intelligent Industrial Vision Beijing100190 China School of Artificial Intelligence University of ChineseAcademy of Sciences Beijing100049 China Department of Computer Science and Engineering Indian Institute of Technology Bihar Patna801106 India

In industrial quality assessment, monitoring whether the textured product contains defects is a critical step. Compared to a large number of defect-free images that are easy to obtain, anomaly samples are limited and vary randomly in size and type. It is challenging to develop an automatic and accurate texture defect localization system that only uses normal images for training. In this article, a multiresolution feature learning network is proposed to detect various texture defects in an unsupervised manner. A robust pretrained model is first employed to extract the perceptual features from the input image, then the perceptual features of various layers are fed to the corresponding multiscale autoencoder framework. This hierarchical alignment strategy aids in receiving multilevel information for locating anomalies of various sizes. Moreover, a residual attention module is embedded in the architecture to further improve the detection performance. Our proposed method has achieved state-of-the-art performance on the texture dataset of MVTecAD. We also extended the experiment to the real industrial texture datasets, and its detection result is better than the major existing advanced techniques. © 2020 IEEE.

关键词： Textures

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CATPlan: Loss-based Collision Prediction in End-to-End Autonomous Driving

arXiv

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arXiv 2025年

作者： Xiong, Ziliang Liu, Shipeng Helgesen, Nathaniel Johnander, Joakim Forssén, Per-Erik Computer Vision Laboratory Department of Electrical Engineering Linköping University Sweden Zenseact Sweden

In recent years, there has been increased interest in the design, training, and evaluation of end-to-end autonomous driving (AD) systems. One often overlooked aspect is the uncertainty of planned trajectories predicted by these systems, despite awareness of their own uncertainty being key to achieve safety and robustness. We propose to estimate this uncertainty by adapting loss prediction from the uncertainty quantification literature. To this end, we introduce a novel light-weight module, dubbed CATPlan, that is trained to decode motion and planning embeddings into estimates of the collision loss used to partially supervise end-to-end AD systems. During inference, these estimates are interpreted as collision risk. We evaluate CATPlan on the safety-critical, nerf-based, closed-loop benchmark NeuroNCAP and find that it manages to detect collisions with a 54.8% relative improvement to average precision over a GMM-based baseline in which the predicted trajectory is compared to the forecasted trajectories of other road users. Our findings indicate that the addition of CATPlan can lead to safer end-to-end AD systems and hope that our work will spark increased interest in uncertainty quantification for such systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk assessment

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A passive,reusable,and resonating wearable sensing system for ondemand,non-invasive,and wireless molecular stress biomarker detection

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Nano Research 2024年第8期17卷 7542-7556页

作者： Shingirirai Chakoma Xiaochang Pei Huiting Qin Anita Ghandehari Sahar NajafiKhoshnoo Jerome Rajendran Rahim Esfandyarpour Department of Electrical Engineering and Computer Science University of CaliforniaIrvineCA 92697USA Laboratory for Integrated Nano BioElectronics Innovation The Henry Samueli School of EngineeringUniversity of CaliforniaIrvineCA 92697USA Henry Samueli School of Engineering University of CaliforniaIrvineCA 92697USA

The significant impact of stress on health necessitates accurate assessment methods,where traditional questionnaires lack reliability and *** advancements like wearables with electrocardiogram(ECG)and galvanic skin response(GSR)sensors face accuracy and artifact *** biosensors detecting cortisol,a critical stress hormone,present a promising ***,existing cortisol assays,requiring saliva,urine,or blood,are complex,expensive,and unsuitable for continuous *** study introduces a passive,molecularly imprinted polymer-radio-frequency(MIP-RF)wearable sensing system for real-time,non-invasive sweat cortisol *** system is wireless,flexible,battery-free,reusable,environmentally stable,and designed for long-term monitoring,using an inductance-capacitance *** transducer translates cortisol concentrations into resonant frequency shifts with high sensitivity(~160 kHz/(log(μM)))across a physiological range of 0.025–1μ*** with near-field communication(NFC)for wireless and battery-free operation,and threedimensional(3D)-printed microfluidic channel for in-situ sweat collection,it enables daily activity cortisol level *** of cortisol circadian rhythm through morning and evening measurements demonstrates its effectiveness in tracking and monitoring sweat cortisol levels.A 28-day stability test and the use of cost-effective 3D nanomaterials printing enhance its economic viability and *** innovation paves the way for a new era in realistic,on-demand health monitoring outside the laboratory,leveraging wearable technology for molecular stress biomarker detection.

关键词： cortisol wearable flexible sensors three-dimensional(3D)nanomaterials printing reusable environmentally stable

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Ultrasonic Imaging and Flaw Detection with Optimized Convolutional Transformer Neural Networks

Ultrasonic Imaging and Flaw Detection with Optimized Convolu...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Zhang, Xin Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9798350346459

Flaw detection using the ultrasonic imaging technique has been widely used for Structural Health Monitoring (SHM). Ultrasonic testing has the advantages of one-sided measurement, high penetration depth, and inspection accuracy. Artificial intelligence (AI) such as Deep Learning (DL) methods can automate the inspection process with high reliability for imaging and SHM. Neural Networks (NNs) and DL methods can detect flaws using ultrasonic images with high accuracy but suffer extensive computational costs for training and deployment. In this study, we propose a lightweight transformer NN, UFDCTNet: Ultrasonic Flaw Detection Convolutional Transformer Neural Network (TNN), optimized with data-efficient Convolutional NN (CNN) for flaw detection using ultrasonic imaging. TNN utilizes the self-attention network architecture that learns global representations and allows high parallelism in computation resulting in reduced training time. CNNs learn local representations with fewer parameters because of inherent spatial inductive bias. This UFDCTNet utilizes the advantages of CNNs to learn spatially local representations resulting in fewer model parameters for fast training. For performance analysis, we trained data-efficient TNN and CNN using ultrasonic images to detect flaws. To examine training results, NNs are trained with the USimgAIST dataset consisting of 7000 experimental B-scan images representing without-flaw and with-flaw cases. A pulsed laser ultrasonic scanning system was used to collect these B-scan images from 17 stainless steel specimen plates with various types of flaws and some plates without any damage. © 2023 IEEE.

关键词： Ultrasonic imaging

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Delegable zk-SNARKs with proxies

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Frontiers of computer Science 2024年第5期18卷 201-217页

作者： Jinrui SHA Shengli LIU School of Cyber Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Cryptology Beijing 100878China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

In this paper,we propose the concept of delegable zero knowledge succinct non-interactive arguments of knowledge(zk-SNARKs).The delegable zk-SNARKKis parameterized by(u,k,k',k").The delegable property of zk-SNARKs allows the prover to delegate its proving ability toμ*** k honest proxies are able to generate the correct proof for a statement,but the collusion of less than k proxies does not obtain information about the witness of the *** also define k'-soundness and k"-zero knowledge by taking into consider of *** propose a construction of(μ,2t+1,t,t)-delegable zk-SNARK for the NPC language of arithmetic circuit *** delegable zk-SNARK stems from Groth's zk-SNARK scheme(Groth16).We take advantage of the additive and multiplicative properties of polynomial-based secret sharing schemes to achieve delegation for *** secret sharing scheme works well with the pairing groups so that the nice succinct properties of Groth's zk-SNARK scheme are preserved,while augmenting the delegable property and keeping soundness and zero-knowledge in the scenario of multi-proxies.

关键词： szk-SNARKs secret sharing delegation bilinear groups

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Evaluation of Homomorphic Encryption for Privacy in Principal Component Analysis

Evaluation of Homomorphic Encryption for Privacy in Principa...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Arnold, David Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

Principal Component Analysis (PCA) is a versatile Unsupervised Learning (UL) technique for reducing the dimensionality of datasets. As a result, PCA is widely used in consumer and research applications as a preprocessing tool for identifying important features prior to further analysis. In instances where on-site personnel or developers do not have the expertise to apply UL techniques, third party processors are frequently retained. However, the release of client or proprietary data poses a substantial security risk. This risk increases the regulatory and contractual burden on analysts when interacting with sensitive or classified information. Homomorphic Encryption (HE) cryptosystems are a novel family of encryption algorithms that permit approximate addition and multiplication on encrypted data. When applied to UL models, such as PCA, experts may apply their expertise while maintaining data privacy. In order to evaluate the potential application of Homomorphic Encryption, we implemented Principal Component Analysis using the Microsoft SEAL HE libraries. The resulting implementation was applied to the MNIST Handwritten dataset for feature reduction and image reconstruction. Based on our results, HE considerably increased the time required to process the dataset. However, the HE algorithm is still viable for non-real-time applications as it had an average pixel error of near-zero for all image reconstructions. © 2023 IEEE.

关键词： Principal component analysis

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Heterogeneity Tolerance in IoT Botnet Attack Classification

Heterogeneity Tolerance in IoT Botnet Attack Classification

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Kalenowski, Samuel Arnold, David Gromov, Mikhail Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

Due to the rapid adoption of Internet of Things (IoT) technologies, many networks are composed of a patchwork of devices designed by different software and hardware developers. In addition to the heterogeneity of IoT networks, the general rush-to-market produced products with poor adherence to core cybersecurity principles. Coupled together, these weaknesses leave organizations vulnerable to attack by botnets, such as Mirai and Gafgyt. Infected devices pose a threat to both internal and external devices as they attempt to add new devices to the collective or to perpetrate targeted attacks within the network or against third parties. Artificial Intelligence (AI) tools for intrusion detection are popular platforms for detecting indicators of botnet infiltration. However, when training AI tools, the heterogeneity of the network hampers detection and classification accuracy due to the differences in device architecture and network layout. To investigate this challenge, we explored the application of a Neural Network (NN) to the N-BaIoT dataset. The NN achieved 94% classification accuracy when trained using data from all devices in the network. Further, we examined the model's transferability by training on a single device and applying it to data from all devices. This resulted in a noticeable decline in classification accuracy. However, when considering cyberattack detection the model retained a very high true positive rate of 99.6%. © 2023 IEEE.

关键词： Internet of things

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Flexible temperature sensor with high sensitivity ranging from liquid nitrogen temperature to 1200℃

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International Journal of Extreme Manufacturing 2023年第1期5卷 355-366页

作者： Zhaojun Liu Bian Tian Zhuangde Jiang Shuimin Li Jiaming Lei Zhongkai Zhang Jiangjiang Liu Peng Shi Qijing Lin State Key Laboratory for Mechanical Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049People’s Republic of China Department of Electrical&Computer Engineering National University of SingaporeSingapore 117576Singapore

Flexible temperature sensors have been extensively investigated due to their prospect of wide application in various flexible electronic ***,most of the current flexible temperature sensors only work well in a narrow temperature range,with their application at high or low temperatures still being a big *** work proposes a flexible thermocouple temperature sensor based on aerogel blanket substrate,the temperature-sensitive layer of which uses the screen-printing technology to prepare indium oxide and indium tin *** has good temperature sensitivity,with the test sensitivity reaching 226.7μV℃^(−1).Most importantly,it can work in a wide temperature range,from extremely low temperatures down to liquid nitrogen temperature to high temperatures up to 1200℃,which is difficult to be achieved by other existing flexible temperature *** temperature sensor has huge application potential in biomedicine,aerospace and other fields.

关键词： flexible sensor temperature thermocouple

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