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Smartphone-based colorimetric detection of formaldehyde in the air

Building Simulation 2024年第11期17卷 2007-2015页

作者： Meng Yang Jin Ye Tao Yu Ying Song Hua Qian Tianyi Liu Yang Chen Junqi Wang Shi-jie Cao Cong Liu School of Energy and Environment Southeast UniversityNanjing210096China School of Energy and Power Jiangsu University of Science and TechnologyZhenjiangJiangsu212100China Wuhan Second Ship Design and Research Institute Wuhan430205China Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test Hubei Provincial Institute for Food Supervision and TestWuhan430075China Laboratory of Image Science and Technology the School of Computer Science and EngineeringSoutheast UniversityNanjingChina Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast UniversityNanjingChina School of Cyber Science and Engineering Southeast UniversityNanjingChina Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationNanjingChina School of Architecture Southeast UniversityNanjingChina Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control Southeast UniversityNanjingChina Global Centre for Clean Air Research(GCARE) University of SurreyGuildfordUK

Adverse impacts of exposure to formaldehyde on human health significantly increases attention in monitoring formaldehyde concentrations in the *** formaldehyde detection methods typically rely on large and costly instruments and requires high skills of expertise,preventing it from being widely accessible to *** study introduced a novel approach utilizing smartphone-based colorimetric *** of green channel signals of digital images by a smartphone successfully capture variation of purple color of 4-amino-3-hydrazino-5-mercapto-1,2,4-triazol solution,which is proportional to formaldehyde *** is because that green and purple are complimentary color pairs.A calibration curve was established between green channel signals and formaldehyde concentrations,with a correlation coefficient of *** limit of the smartphone-based method is 0.008 mg/m^(3).Measurement errors decrease as formaldehyde concentrations increase,with median relative errors of 34%,17%,and 6%for concentration ranges of 0–0.06 mg/m^(3),0.06–0.12 mg/m^(3),and 0.12–0.35 mg/m^(3),*** method replaced scientific instrumentation with ordinary items,greatly reducing cost and operation *** would provide an opportunity to realize onsite measurements for formaldehyde by occupants themselves and increase awareness of air quality for better health protection.

关键词： indoor air quality VOC RGB color identification machine vision

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MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures

arXiv

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arXiv 2023年

作者： Wang, Zhuoyuan Mi, Jiacong Lu, Shan He, Jieyue School of Computer Science and Engineering Key Lab of Computer Network and Information Integration MOE Southeast University Jiangsu Nanjing210018 China Nanjing Fenghuo Tiandi Communication Technology Co. Ltd. Jiangsu Nanjing211161 China

The quest for accurate prediction of drug molecule properties poses a fundamental challenge in the realm of Artificial Intelligence Drug Discovery (AIDD). An effective representation of drug molecules emerges as a pivotal component in this pursuit. Contemporary leading-edge research predominantly resorts to self-supervised learning (SSL) techniques to extract meaningful structural representations from large-scale, unlabeled molecular data, subsequently fine-tuning these representations for an array of downstream tasks. However, an inherent shortcoming of these studies lies in their singular reliance on one modality of molecular information, such as molecule image or SMILES representations, thus neglecting the potential complementarity of various molecular modalities. In response to this limitation, we propose MolIG, a novel MultiModaL molecular pre-training framework for predicting molecular properties based on Image and Graph structures. MolIG model innovatively leverages the coherence and correlation between molecule graph and molecule image to execute self-supervised tasks, effectively amalgamating the strengths of both molecular representation forms. This holistic approach allows for the capture of pivotal molecular structural characteristics and high-level semantic information. Upon completion of pre-training, Graph Neural network (GNN) Encoder is used for the prediction of downstream tasks. In comparison to advanced baseline models, MolIG exhibits enhanced performance in downstream tasks pertaining to molecular property prediction within benchmark groups such as MoleculeNet Benchmark Group and ADMET Benchmark Group. © 2023, CC BY.

关键词： Molecules

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Brainnetformer: Decoding Brain Cognitive States with Spatial-Temporal Cross Attention

Brainnetformer: Decoding Brain Cognitive States with Spatial...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Leheng Sheng Wenhan Wang Zhiyi Shi Jichao Zhan Youyong Kong Chien-Shiung Wu College Southeast University China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

Learning about the cognitive state of the brain has always been a popular topic. Based on the fact that fluctuations of brain signals and functional connectome (FC) relate to specific human behaviors, deep learning based methods have shown promising results on the prediction of such behaviors by analyzing biological signals. Existing methods either model from static perspectives or apply spatial-temporal graph convolution to extract dynamic properties. However, the static information and dynamic information can reflect global brain activities and local brain activities respectively. Thus, we propose BrainNetFormer to incorporate both static and dynamic properties for human behavior prediction. To be specific, a spatial cross attention module and a temporal cross attention module are introduced for information fusion. In addition, since a specific behavior of subjects can be decomposed into a series of subtasks, we introduce a sub-task regularization loss to assist in training and empower the model to recognize subtasks at each moment. Experiments on the HCP-Task dataset demonstrate the superior performance of the proposed model.

关键词： Training Convolution Fuses Magnetic resonance imaging Predictive models Brain modeling Behavioral sciences

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Modeling and application of service relationships 3

Modeling and application of service relationships

引用

3rd IEEE International Conference on Civil Aviation Safety and information Technology, ICCASIT 2021

作者： Shaoxi, Liu Jun, Shen Jie, Ren Ying, Huang Southeast University Key Laboratory of Computer Network & Information Integration of Ministry of Education Nanjing China AVIC Chengdu Aircraft Design and Research Institute Chengdu China Nanjing Origin Information Technology Co. Ltd Nanjing China

ISBN: (纸本)9781665425186

In some new network architectures, in order to improve the scalability of services, scholars in recent years have been trying their best to explore the strategy of combining several small-grained services into large-grained services that can respond to user needs. In response to this problem, this paper studies and analyzes the relationship between services, and proposes a service relationship model based on fragmented logic. The model defines the service relationship structure and its related relationship operations. Then the model is applied in the SDEN network to complete the system requirements of service dynamic combination, thereby verifying that the model can improve the flexibility and scalability of the actual application system. © 2021 IEEE

关键词： Scalability

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Accelerating Mega-Scale Satellite network Simulation in NS-3 via MPI-based Parallelization

Accelerating Mega-Scale Satellite Network Simulation in NS-3...

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IEEE International Conference on Communications (ICC)

作者： Haibin Song Tian Pan Guohao Ruan Yan Zheng Ying Wan Jiao Zhang Tao Huang Yunjie Liu Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China China Mobile (Suzhou) Software Technology Co. Ltd Suzhou China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Due to the high costs of low Earth orbit (LEO) satellite manufacturing and launch, as well as the complexity of in-orbit network protocol debugging, simulating and verifying satellite network protocols on the ground before satellite launch holds significant importance. Compared to the expensive emulation with one-to-one replication, simulation (e.g., using ns-3) can achieve discrete event processing at a relatively lower cost by extending the wall clock time. However, very few studies have used ns-3 for LEO satellite network simulation, facing challenges such as faithfully simulating the on/off state switching of inter-satellite links (ISLs) and achieving simulation performance scalability for high-density satellite constellations. In this work, we propose a system to accelerate mega-scale LEO satellite network simulation in ns-3 via MPI-based parallelization. Specifically, we simulate ISLs based on ns-3's P2P channels/P2P remote channels and achieve runtime link connection/disconnection by implementing stateful traffic dropping inside the network interface. Then, we conduct concurrent simulation with ns-3's parallel and distributed simulation capability and partition the satellite constellation into multiple simulation processes through a hierarchical clustering algorithm and automated scripts, considering satellite locality and inter-process workload balance. Our evaluation shows significant speed improvements via parallelization, e.g., a 373% speedup with 12 processes for LEO-192, and a 156% speedup with 3 processes for LEO-3072.

关键词： Satellite constellations Satellites Runtime Protocols Costs Low earth orbit satellites Clustering algorithms

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Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation

arXiv

引用

arXiv 2022年

作者： Shi, Xinyu Wei, Dong Zhang, Yu Lu, Donghuan Ning, Munan Chen, Jiashun Ma, Kai Zheng, Yefeng School of Computer Science & Engineering Key Lab of Computer Network & Information Integration Ministry of Education Southeast Univ. Nanjing China Tencent Jarvis Lab Shenzhen China

Research into Few-shot Semantic Segmentation (FSS) has attracted great attention, with the goal to segment target objects in a query image given only a few annotated support images of the target class. A key to this challenging task is to fully utilize the information in the support images by exploiting fine-grained correlations between the query and support images. However, most existing approaches either compressed the support information into a few class-wise prototypes, or used partial support information (e.g., only foreground) at the pixel level, causing non-negligible information loss. In this paper, we propose Dense pixel-wise Cross-query-and-support Attention weighted Mask Aggregation (DCAMA), where both foreground and background support information are fully exploited via multi-level pixel-wise correlations between paired query and support features. Implemented with the scaled dot-product attention in the Transformer architecture, DCAMA treats every query pixel as a token, computes its similarities with all support pixels, and predicts its segmentation label as an additive aggregation of all the support pixels’ labels—weighted by the similarities. Based on the unique formulation of DCAMA, we further propose efficient and effective one-pass inference for n-shot segmentation, where pixels of all support images are collected for the mask aggregation at once. Experiments show that our DCAMA significantly advances the state of the art on standard FSS benchmarks of PASCAL-5i, COCO-20i, and FSS-1000, e.g., with 3.1%, 9.7%, and 3.6% absolute improvements in 1-shot mIoU over previous best records. Ablative studies also verify the design DCAMA. © 2022, CC BY-NC-ND.

关键词： Pixels

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Deep Iterative Reconstruction network Based on Residual Constraint for Low-Dose CT Imaging

Deep Iterative Reconstruction Network Based on Residual Cons...

引用

International Conference on Systems and Informatics (ICSAI)

作者： Jin Liu Yanqin Kang Tao Liu TingYu Zhang Yikun Zhang School of Computer and Information Anhui Polytechnic University Wuhu China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781665474498

clinical low X-ray dose computed tomography (LDCT) scanner often induce high intensity strip artifact and spot nosie, compromising diagnoses and intervention plans. Recently, sparsely constrained and network learning-based frameworks have been shown to be efficient in mitigating such issue. In this work, we propose a deep iterative reconstruction network (DIRNet) model with a residual constraint to synergize the advantages of feature learning and image reconstruction to address the LDCT imaging problem. DIR-Net compose by few iteration units, and all iteration units include three different network modules: projection restoration, residual constraint and image update block. DIR-Net is a promising approach for building an end-to-reconstruction mapping strategy and directly obtaining high-quality CT images. Furthermore, LISTA is used to conFigure the network, and the whole network architecture yields improved interpretability. Qualitative and quantitative analysis in test data shown the promising imaging effects of DIR-Net in quantum noise reduction, block artifact removal and tissue detail texture mantian.

关键词： Strips Statistical analysis Sonogram Computed tomography Imaging network architecture Image restoration

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Enhancing Unknown Encrypted Traffic Clustering with Self-Supervised Learning

Enhancing Unknown Encrypted Traffic Clustering with Self-Sup...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Ke Ding Xia Zhou Xiaoyan Hu Guang Cheng Ruidong Li Hua Wu School of Cyber Science & Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Jiangsu Province Engineering Research Center of Security for Ubiquitous Network Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration (Southeast University) China Institute of Science and Engineering Kanazawa University Kanazawa Japan

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Many malicious attacks are launched through encrypted traffic from unknown proprietary network protocols. Timely identification of such malicious unknown encrypted traffic is essential for the defense. However, it is challenging to acquire labels for unknown protocols in the context of encrypted traffic. Due to the lack of prior knowledge, unsupervised learning is adopted to cluster unknown encrypted traffic. The existing unsupervised encrypted traffic clustering methods do not customize feature extraction and representation of unknown encrypted traffic, resulting in imperfect clustering results. This work innovatively proposes BiFR-SSL to accurately cluster unknown encrypted traffic without prior knowledge. BiFRSSL extracts features of each unknown encrypted bidirectional network flow based on the lengths, arrival time, and directions of packets within the flow to construct a Bidirectional Flowpic Representation (BiFR). Subsequently, it exploits Self-Supervised Learning (SSL) to pre-train a feature extractor that produces feature representations from BiFRs, guaranteeing the closeness of encrypted traffic flows from the same protocol in the representation space. Finally, it clusters unknown encrypted traffic based on their feature representations generated by the pre-trained feature extractor. Our experimental studies demonstrate that BiFR-SSL can effectively cluster encrypted traffic of unknown protocols and outperforms state-of-the-art methods.

关键词： Protocols Clustering methods Clustering algorithms Self-supervised learning Telecommunication traffic Feature extraction Data augmentation Cryptography Global communication Unsupervised learning

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KGAPG: Knowledge-Aware Neural Group Representation Learning for Attentive Prescription Generation of Traditional Chinese Medicine

KGAPG: Knowledge-Aware Neural Group Representation Learning ...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuchen Li Wei Wang Jieyue He Key Lab of Computer Network & Information Integration MOE Southeast University Nanjing China

ISBN: (纸本)9781665429825

Prescriptions play an essential role in the process of Traditional Chinese Medicine (TCM) diagnosis and treatment. Prescription generation is to generate a set of herbs to treat the symptoms of a patient by analyzing the relationship between symptoms and herbs. Although there have been a couple of studies to generate prescriptions, they have ignored the implicit relationship between the different symptoms of the patients. In addition, abundant semantic information and interpretability of the knowledge graph can help to portray the complicated relationships between the various modules of TCM. Therefore, this paper proposes a Knowledge-Aware neural Group representation learning model for Attentive Prescription Generation of Traditional Chinese Medicine (KGAPG), which regards the prescription generation task as a group recommendation problem. More specifically, multiple symptoms of a patient are considered as a symptom group and the complicated semantic information between symptoms and herbs can be captured by the knowledge graph. The syndrome information of multiple symptoms which is summarized by a group aggregation method based on the attention mechanism is applied to simulate the actual process of TCM diagnosis and treatment. The experiment results demonstrate that KGAPG is effective on a TCM prescription benchmark dataset, and its evaluation indicators of Precision, Recall and NDCG exceed other state-of-the-art methods.

关键词： Representation learning Conferences Semantics Benchmark testing Task analysis Bioinformatics

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WMLink: Wearable Smart Devices and Mobile Phones Linking through Passive Sniffing BLE and WiFi Signals

WMLink: Wearable Smart Devices and Mobile Phones Linking thr...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Naixuan Guo Zhaofeng Chen Heyang Xu Zhechun Zhao Yu Liu Sen Xu School of Information Engineering Yancheng Institute of Technology Yancheng China School of Computer Science and Engineering Southeast University Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration (Southeast University)

ISBN: (纸本)9798350309720

In recent years, wearable smart devices have entered people’s lives. These devices can help users to record their daily activities, health data and so on. These devices periodically send out packets to keep connected with mobile phones, which can be observed by any external observer. By analyzing these packets, some information about the device owner will be revealed. Although the observer can sniff the wireless packets, the actual person using the device is unknown. In this paper, we link wearable smart devices with users’ mobile phones which are recognized as users’ electronic identity. Linking these two types of devices opens up new opportunities for applications such as authenticating users by multi-devices or constructing user device graph for advertisements. We deploy sniffing nodes to sniff BLE and WiFi signals, and put forward a multi-stage filtering method to reduce signals’ noises, then link two types of devices by an algorithm based on dynamic time warping. At last, our experiments show that 7 wearable devices can be linked with a correct rate more than 80%. We also find that when one device is observed more than 11 times, its linking accuracy can be close to 100%.

关键词： Meters Filtering Heuristic algorithms Wearable computers Observers Big Data Market research

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