Detection Efficiency Research Articles

Pornographic video detection based on semantic and image enhancement

Abstract Pornographic video detection is of significant importance in curbing the proliferation of pornographic information on online video platforms. However, existing works often employ generic frame extraction methods that ignore the low-latency requirements of detection scenarios and the characteristics of pornographic videos. Additionally, existing detection methods have difficulties in detail characterization and semantic understanding, resulting in low accuracy. Therefore, this paper proposes an efficient pornographic video detection framework based on semantic and image enhancement. Firstly, a keyframe extraction method tailored for pornographic video detection is proposed to select representative frames. Secondly, a light enhancement method is introduced to facilitate accurate capture of pornographic visual cues. Moreover, a compression-reconstruction network is employed to eliminate adversarial perturbations, enabling models to obtain reliable features. Subsequently, YOLOv5 is introduced to locate and crop human targets in keyframes, reducing background interference and enhancing the expression of human semantic information. Finally, MobileNetV3 is employed to determine if the human targets contain pornographic content. The proposed framework is validated on the publicly available NPDI dataset, achieving an accuracy of 95.9%, surpassing existing baseline methods.

High-throughput sensitive screening of small molecule modulators of microexon alternative splicing using dual Nano and Firefly luciferase reporters

Disruption of alternative splicing frequently causes or contributes to human diseases and disorders. Consequently, there is a need for efficient and sensitive reporter assays capable of screening chemical libraries for compounds with efficacy in modulating important splicing events. Here, we describe a screening workflow employing dual Nano and Firefly luciferase alternative splicing reporters that affords efficient, sensitive, and linear detection of small molecule responses. Applying this system to a screen of ~95,000 small molecules identified compounds that stimulate or repress the splicing of neuronal microexons, a class of alternative exons often disrupted in autism and activated in neuroendocrine cancers. One of these compounds rescues the splicing of several analyzed microexons in the cerebral cortex of an autism mouse model haploinsufficient for Srrm4, a major activator of brain microexons. We thus describe a broadly applicable high-throughput screening system for identifying candidate splicing therapeutics, and a resource of small molecule modulators of microexons with potential for further development in correcting aberrant splicing patterns linked to human disorders and disease.

Asian Screening Array and Next-Generation Sequencing Based Panels Applied to Preimplantation Genetic Testing for Monogenic Disorders Preclinical Workup in 294 Families: A Retrospective Analysis.

Currently, the most commonly used methods for linkage analysis of pre-implantation genetic testing for monogenic disorders (PGT-M) are next generation sequencing (NGS) and SNP array. We aim to investigate whether the application efficacy of Asian screening array (ASA) in PGT-M preclinical workup for the Chinese population is superior to NGS based single nucleotide polymorphism (SNP) panels. We conducted a retrospective analysis by reviewing 294 couples from a single center over the past 4years and compared the detection results between NGS-based SNP panels and ASA. Using the numbers of informative SNPs upstream and downstream flanking of variants, we assessed the detection efficiency of both methods in monogenic diseases, chromosomal microdeletion syndrome and males with de novo variants, among other scenarios. Results indicate that ASA offers a greater number of informative SNPs compared with NGS-based SNP panels. Additionally, data analysis for ASA is generally more straightforward and may require less computational resources. While ASA can address most PGT-M challenges, we have also identified certain genes in previous tests that are not suitable for PGT-M using ASA. The application of ASA in PGT-M preclinical workup for Chinese populations has good practical value as it can perform linkage analysis for most genetic variants. However, for certain variants, NGS or other testing methods, such as mutated allele revealed by sequencing with aneuploidy and linkage analysis (MARSALA), may still be necessary for completion.

Designing primer-probe sets for Escherichia coli and E. coli O157 detection: Comparative genomic approach and food interference assessment

This study aimed to develop novel primer-probe sets specifically optimized for detecting E. coli and E. coli O157 through comparative genomic analysis. Genomic data from 2396 E. coli strains, including 228 strains of E. coli O157, were retrieved from the National Center for Biotechnology Information (NCBI). From the analysis, yegR and GDP-mannose mannosyl hydrolase (GDPMH) genes were selected as primer targets for E. coli and E. coli O157:H7, respectively. The sensitivity and specificity of the designed primer-probe sets were evaluated in 48 E. coli strains (including 30 E. coli O157) and 17 non-E. coli strains, totaling 65 strains, and the results indicated high sensitivity and specificity. Tryptic soy broth (TSB), carrots, chicken, milk, potatoes, apple juice, and orange juice did not significantly interfere with detecting E. coli and E. coli O157; however, 50% potato samples did. The efficacy of the designed primers was compared among ground pork, lettuce, and potato samples. Reduced detection efficiency was noted in the lettuce and potato samples. Wavelength interference with components of the lettuce homogenate may have reduced detection efficiency. For potato samples, the high affinity of chlorogenic acid, a compound found in potatoes, for Taq DNA polymerase, a component of the mastermix, contributed to the reduced detection efficiency. The primer and probe sets described in this study, coupled with an understanding of possible interference from food components, will aid in detecting E. coli O157 in the food industry.

Iron-based metal-organic framework/graphene oxide composite electrodes for efficient flow-injection amperometric detection of dexamethasone.

A highly stable flow-injection amperometric sensor for dexamethasone (DEX) was developed using a pencil graphite electrode (PGE) modified with Fe-based metal organic frameworks, MIL-100(Fe) and graphene oxide composite materials (MIL-100(Fe)/GO). Scanning electron microscopy and energy-dispersive X-ray spectroscopy, transmission electron microscopy, powder X-ray diffraction, and Fourier-transform infrared spectroscopy were used to characterize the MIL-100(Fe) composites. The MIL-100(Fe)/GO-modified PGE (denoted MIL-100(Fe)/GO/PGE) was further electrochemically characterized using cyclic voltammetry. As an electrode material, MIL-100(Fe) is a sensing element that undergoes oxidation from Fe(ii)-MOF to Fe(iii)-MOF, and GO possesses high conductivity and a large surface area, which exhibits high absorbability. In the presence of DEX, Fe(iii) is reduced, which accelerates electron transfer at the electrode interface. Therefore, DEX can be quantitatively detected by analyzing the anodic current of MIL-100(Fe). When coupled with amperometric flow injection analysis, excellent performance can be obtained even when a low detection potential is applied (+0.10 V vs. Ag/AgCl). The concentration was linear in the range 0.10-5.0 μM and 0.010-5.0 mM with LOD of 0.030 μM based on 3(sd/slope). The modified electrode also exhibited a remarkably stable response under optimized conditions, and up to 55 injections can be used per electrode. The sensor exhibits high repeatability, reproducibility, and anti-interference properties when used for DEX detection. The effective determination of dexamethasone in real pharmaceutical and cosmetic samples demonstrated the feasibility of the electrochemical sensor, and the results were in good agreement with those obtained from the HPLC-DAD analysis. Acceptable percentage recoveries from the spiked pharmaceutical and cosmetic samples were obtained, ranging from 93-111% for this new method compared with 84-107% for the HPLC-DAD standard method.

Target-induced multiregion MNAzyme nanowires for ultrasensitive homogeneous detection of microRNAs

A target-induced multiregion MNAzyme nanowire system is designed for the ultrasensitive and homogeneous detection of microRNAs (miRNAs). miRNA-21 and miRNA-375 are chosen as analytes, and a miRNA-induced primer exchange reaction (PER) is utilized to construct a long DNA strand with repetitive sequences. This innovative design enables the efficient anchoring of numerous MNAzymes. This unique architecture significantly boosts the effective local concentration of MNAzymes, thereby enhancing the sensitivity and efficiency of miRNA detection. Notably, the limit of detection (LOD) achieved with our target-induced multiregion MNAzyme nanowire approach is over an order of magnitude lower than most other MNAzyme-based methods, while the MNAzyme reaction time is reduced from several hours to 50 min. The method has demonstrated successful applications in quantitatively determining the expression levels of two miRNAs in cell lysates of MCF-7, HeLa and MCF-10 A cells, highlighting its potential for assaying miRNA biomarkers in clinical samples.

Design and Development of a Portable Neutron Detection System Based on a 6Li-Doped ZnS(Ag) Scintillator

In this study, the design and development of a portable neutron detection system based on a 6LiF + ZnS(Ag) scintillator was carried out. The detector system was first modeled using the Monte Carlo simulation code MCNP. As a result of the detector simulation, the materials and photomultiplier tubes (PMTs) were supplied for the system, and the material properties and thicknesses were optimized for the best scintillator, with the EJ-420 (Eljen Technology), consisting of 95% 6Li dispersed in ZnS(Ag), chosen because it has a distinctive feature with regard to high thermal neutron detection efficiency. Since this material has been shown to offer a significant advantage in favor of neutron detection in mixed radiation fields with gammas and neutrons, the pulse shape discrimination method was employed. To achieve this, a proper electronic circuit was developed to discriminate the pulses from neutrons and gammas. In the experimental step, the EJ-420 scintillator with a 50-mm diameter was optically coupled with a special fast PMT (Hamamatsu H1949-51 model). In conclusion, this study, which underlines the performed simulations for a neutron detector configuration, gives the obtained experimental results showing discrimination capability in neutron/gamma detection using a 241Am-Be neutron source. The results show that the EJ-420 is a good scintillator due to its highly enriched 6Li transmator, which results in more effective neutron measurements when a portable neutron detector design is chosen.

A self-assembled nanoprobe for rapid detection of hypochlorite in pure water and its application in living cells, food and environmental systems

As an important ROS species participating in various physiological and pathological processes, high level of hypochlorite (ClO−) poses significant health and safety concerns, necessitating efficient detection methods. Herein, this study introduces a water-soluble fluorescent nanoprobe Nano-SJD, effectively detect ClO− in both food samples and living cells. The small molecular probe SJD with N, N-dimethylthiocarbamyl (DMTC) as recognition moiety was constructed based on a naphthalene derivative. To further improve the water solubility, SJD was assembled with an amphiphilic copolymer (mPEG-DSPE) to prepare a water soluble fluorescent nanoprobe Nano-SJD. Fortunately, the nanoprobe preserves the excellent properties of small molecules and performs very well optical response to ClO− in aqueous solution, possessing the advantages including ultra-rapid response (within 1 s), minimal interference, low detection limits (0.39 μM) and good pH stability. What's more important, we have also developed smartphone-compatible test paper strips for convenient on-site detection of ClO− in real-water samples. Additionally, the robust fluorescent imaging behavior of Nano-SJD for visualization of ClO− in living cells highlights its broad potential in biosystem applicability.

A fully-printed plasmonic nanoparticle-incorporated ZnO-based UV photodetector with very high responsivity and fast response

We report the development of a fully printed plasmonic Ag nanoparticle-enhanced ZnO-nanoparticle-based photodetector (PD) for the efficient detection of ultraviolet (UV) light. The contact electrodes with a gap of 200 µm are printed on a SiO2/Si substrate, and a micropattern of Ag nanoparticles (Ag NPs) is printed within the electrode gap to generate the plasmonic effect. The ZnO nanoparticle thin film is printed onto the array of Ag NPs to fabricate the plasmon-enhanced UV PD. The printed devices exhibit impressive performance with a peak responsivity of 48.8 A W−1, external quantum efficiency of 1.7 × 104%, and detectivity of 1.3 × 1013 Jones at 5 V bias. Moreover, the device shows an ultrafast photoresponse with a rise time of 24.3 µs and a fall time of 33.1 µs. Finite element method-based simulations confirm a significant field enhancement within the ZnO matrix upon incorporation of plasmonic Ag nanoparticles, explaining the increased photoresponse. The performance of the printed plasmon-enhanced UV-PD here offers a promising, simple, and inexpensive approach for the fabrication of future optoelectronic devices.

Polyoxometalate-based peroxidase-mimicking sensors for colorimetric detection and discrimination of phenolic pollutants

Phenol and its derivatives represent a kind of significant hazards to both human health and the integrity of ecological systems. Exploring an efficient detection and discrimination approach for them is urgent. Herein, a kind of polyoxometalate-based peroxidase-mimicking crystalline colorimetric sensors were developed by integrating dual active sites of Keggin-type polyoxoanions and coordination unsaturated copper centers, for which the formulas are [Cu(dppeo)3]2[Cu(dppeo)2(H2O)](dppeo)0.75[PMo12O40]2·9H2O (1), [Cu(dppeo)2(H2O)]2 [SiW12O40]·6H2O (2), [Cu(dppeo)2]3[PW12O40]2·12H2O (3), (dppeo = 1,2-bis(diphenylphosphino)ethane dioxide). Structural analysis revealed that these compounds exhibit 0-D supramolecular architecture formed through the interplay of electrostatic forces and hydrogen bonding interactions between Keggin-type polyoxoanions and dppeo-chelated copper-organic cationic fragments. Sensors 1–3 exhibit favorable peroxidase-mimicking catalytic activities, which expedite the reaction involving phenol, 4-aminoantipyrine and hydrogen peroxide (H2O2), resulting in a pronounced color change for indicating the target concentration levels. These crystalline sensors responded linearly to phenol concentration within 0.01–1.0 mM, affording low detection limits of 1.19, 1.59 and 3.78 μM for 1–3. These sensors demonstrate exceptional anti-interference capabilities and exhibit excellent applicability in real water samples, ensuring high recovery rates. By incorporating a colorimetric array with principal component analysis, the sensors effectively discriminate between common phenolic compounds. This research presents an innovative methodology for the design of crystalline sensor materials, paving the way for the detection and differentiation of phenolic pollutants within environmental applications.

RTL-YOLOv8n: A Lightweight Model for Efficient and Accurate Underwater Target Detection

Underwater object detection is essential for the advancement of automated aquaculture operations. Addressing the challenges of low detection accuracy and insufficient generalization capabilities for underwater targets, this paper focuses on the development of a novel detection method tailored to such environments. We introduce the RTL-YOLOv8n model, specifically designed to enhance the precision and efficiency of detecting objects underwater. This model incorporates advanced feature-extraction mechanisms—RetBlock and triplet attention—that significantly improve its ability to discern fine details amidst complex underwater scenes. Additionally, the model employs a lightweight coupled detection head (LCD-Head), which reduces its computational requirements by 31.6% compared to the conventional YOLOv8n, without sacrificing performance. Enhanced by the Focaler–MPDIoU loss function, RTL-YOLOv8n demonstrates superior capability in detecting challenging targets, showing a 1.5% increase in mAP@0.5 and a 5.2% improvement in precision over previous models. These results not only confirm the effectiveness of RTL-YOLOv8n in complex underwater environments but also highlight its potential applicability in other settings requiring efficient and precise object detection. This research provides valuable insights into the development of aquatic life detection and contributes to the field of smart aquatic monitoring systems.

Centralised visual processing center for remote sensing target detection

Target detection in satellite images is an essential topic in the field of remote sensing and computer vision. Despite extensive research efforts, accurate and efficient target detection in remote sensing images remains unsolved due to the large target scale span, dense distribution, and overhead imaging and complex backgrounds, which result in high target feature similarity and serious occlusion. In order to address the above issues in a comprehensive manner, within this paper, we first propose a Centralised Visual Processing Center (CVPC), this structure is a parallel visual processing center for Transformer encoder and CNN, employing a lightweight encoder to capture broad, long-range interdependencies. Pixel-level Learning Center (PLC) module is used to establish pixel-level correlations and improve the depiction of detailed features. CVPC effectively improves the detection efficiency of remote sensing targets with high feature similarity and severe occlusion. Secondly, we propose a centralised feature cross-layer fusion pyramid structure to fuse the results with the CVPC in a top-down manner to enhance the detailed feature representation capability at each layer. Ultimately, we present a Context Enhanced Adaptive Sparse Convolutional Network (CEASC), which improves the accuracy while ensuring the detection efficiency. Based on the above modules, we designed and conducted a series of experiments. These experiments are conducted on three challenging public datasets, DOTA-v1.0, DIOR, and RSDO, showing that our proposed 3CNet achieves a more advanced detection accuracy while balancing the detection speed (78.62% mAP for DOTA-v1.0, 79.12% mAP for DIOR, and 95.50% mAP for RSOD).

Human Object Interaction Detection in Paintings using Multi-Task Learning

Human Object Interaction (HOI) detection can provide valuable insights into the meaning and interpretation of a painting, as the interactions between humans and objects can reveal information about the scene, characters, and story depicted in the artwork. Automatically detecting HOI in paintings is a challenging task, as the paintings often contain complex scenes with intricate details and variations in artistic style. Additionally, unlike in real-world images, the context and physics of the painting may not follow physical rules, which can further complicate the detection process. This paper proposes a novel system for detecting HOIs in paintings using multi-task learning. The system utilizes an object detection model to detect instances of human figures and objects, and extracts from them visual and spatial features. The appearance features are then combined to produce an optimized model for detecting HOIs. In order to enhance our model’s performance on HOI detection, we train it in a multi-task learning setting with four different tasks. This approach allows us to leverage shared representations across multiple tasks, leading to improved accuracy and efficiency of HOI detection in our system. To train and test our model, we introduce a new benchmark for HOI detection in paintings, by augmenting the existing SemArt dataset with instance detection annotations and interaction classes and call it SemArt-HOI. Through our experiments, we show that our model is able to outperform the state-of-the-art one-stage transformer-based HOI detection model in both single-task and multi-task settings. Furthermore, our system’s superior efficiency, training four times faster than the state-of-the-art model and using fewer resources, makes it ideal for practical and large-scale HOI detection in paintings.

REDef-DETR: real-time and efficient DETR for industrial surface defect detection

Abstract Industrial surface defect detection is an important part of industrial production, which aims to identify and detecting various defects on the surface of product to ensure quality and meet customer requirements. With the development of deep learning and image processing technologies, the surface defect detection methods based on computer vision has become the mainstream method. However, the prevalent convolutional neural network-based defect detection methods also have many problems. For example, these methods rely on post-processing of Non-Maximum Suppression and have poor detection ability for small targets, which affects the speed and accuracy of surface defect detection in industrial scenarios. Therefore, we propose a novel DEtection TRansformer-based surface defect detection method. Firstly, we propose a Multi-scale Contextual Information Dilated module and fuse it into the backbone. The module is mainly composed of large kernel convolutions, which aims to expand the receptive field of the model, thus reducing the leakage rate of the model. Moreover, we design an efficient encoder which mainly contains two important modules, namely feature enhancement based on cascaded group attention module and efficient feature fusion module based on content-aware. The former module effectively enhances the high-level semantic information extracted by the backbone, thus enabling the model to better interpret features, and it can improve the problem of high computational cost of transformer encoder, thus increasing the detection speed. The latter module performs multi-scale feature fusion across the feature information of various scales, thus improving the detection accuracy of the model for small-size defects. Experimental results show that the proposed method achieves 80.6%mAP and 80.3FPS on NEU-DET, and 98.0%mAP and 79.4FPS on PCB-DET. Our proposed method exhibits excellent detection performance and achieves real-time and efficient surface defect detection capability to meet the needs of industrial surface defect detection.

基于CA-YOLOv8的玉米叶病检测方法

In order to achieve efficient and accurate detection of common corn leaf diseases such as leaf blight, gray spot disease, and rust, a corn leaf disease detection method based on CA-YOLOv8 was proposed. In this method, the Coordinate Attention(CA) attention mechanism was added after the feature map output from the Neck part to enhance the feature extraction capability of the model. The experimental results showed that the precision，recall and mean average precision(mAP) of the CA-YOLOv8 model on the test set were 94.08%, 90.53% and 97.38%, respectively. Compared with the YOLOv8, YOLOv8+SE and YOLOv8+CBAM models, the mAP was improved by 2.15, 0.86 and 2.35 percentage points, respectively. Compared with Faster R-CNN, YOLOv5s, YOLOv7, and YOLOv8 models, the mAP has increased by 63.53, 29.24, 3.21, and 2.15 percentage points, respectively. The study showed that the CA-YOLOv8 model can provide a technical reference for the development of a portable intelligent corn leaf disease detection system.

Compressive strength detection of tunnel lining using hyperspectral images and machine learning

Traditional tunnel lining strength detection techniques are mostly contact-based, with relatively low detection efficiency. This study innovatively proposes hyperspectral imaging method to rapidly detect tunnel concrete lining strength from a machine vision perspective. Hyperspectral cameras were employed in indoor experiments to capture hyperspectral images of concrete specimens with different compressive strength levels. The differences of concrete strength based on hyperspectral reflectance characteristics were analysed using hyperspectral images and machine learning algorithms. Firstly, the K-Nearest Neighbors (KNN) classification algorithm was used to predict the classification of the concrete hyperspectral dataset with accuracy mostly exceeding 90 %. The results indicate distinctive differences in hyperspectral reflectance characteristics among concrete specimens. Furthermore, compressive strength prediction of different concrete specimens was carried out using Principal component regression (PCR), Partial least squares regression (PLSR), and Least Squares Support Vector Machine (LSSVM) machine learning models on both original and Savitzky-Golay(S-G) processed spectral data. LSSVM and PLSR models performed excellently in the visible light spectrums(400–1000 nm), with LSSVM excelling in the near-infrared spectrums(900–1700 nm). Finally, the feasibility of using Hyperspectral imaging(HSI) technology to detect tunnel lining strength was demonstrated at the shield tunnel model site. The predicted results were validated by combining with strength values measured by the rebound hammer, and presented promoising research path for automated non-destructive detection of concrete tunnel lining strength.

Efficient detection of driver fatigue state based on all-weather illumination scenarios

Among the causes of the annually traffic accidents, driving fatigue is the main culprit. In consequence, it is of great practical significance to carry out the research of driving fatigue detection and early warning system. However, there are still two problems in the latest methods of driving fatigue detection: one is that a single information cannot precisely reflect the actual state of the driver in different fatigue phases, another one is the detection effect is not very well or even difficult to detect under abnormal illumination. In this paper, the multi-task cascaded convolutional networks (MTCNN) and infrared-based remote photo-plethysmography (rPPG) theory are used to extract the driver’s facial and physiological information, and the multi-modal specific fatigue information is deeply excavated, and the multi-modal feature fusion model is constructed to comprehensively analyze the driver’s fatigue variation tendency. Aiming at the matter of low detection accuracy under abnormal illumination, the multi-modal features extracted from visible light images and infrared images are fused by multi-loss reconstruction (MLR) module, and the driving fatigue detection module is established which is based on Bi-LSTM model by utilizing fatigue timing. The experiments were validated under all-weather illumination scenarios and were carried out on the datasets NTHU-DDD, UTA-RLDDD and FAHD. The results show that the multi-modal driving fatigue detection model has better performance than the single-modal model, and the accuracy is improved by 8.1%. In the abnormal illumination such as strong and weak light, the accuracy of the method can reach 91.7% at the highest and 83.6% at the lowest. Meanwhile, in the normal illumination, it can reach 93.2%.

A Efficient and Accurate UAV Detection Method Based on YOLOv5s

Due to the limited computational resources of portable devices, target detection models for drone detection face challenges in real-time deployment. To enhance the detection efficiency of low, slow, and small unmanned aerial vehicles (UAVs), this study introduces an efficient drone detection model based on YOLOv5s (EDU-YOLO), incorporating lightweight feature extraction and balanced feature fusion modules. The model employs the ShuffleNetV2 network and coordinate attention mechanisms to construct a lightweight backbone network, significantly reducing the number of model parameters. It also utilizes a bidirectional feature pyramid network and ghost convolutions to build a balanced neck network, enriching the model’s representational capacity. Additionally, a new loss function, EloU, replaces CIoU to improve the model’s positioning accuracy and accelerate network convergence. Experimental results indicate that, compared to the YOLOv5s algorithm, our model only experiences a minimal decrease in mAP by 1.1%, while reducing GFLOPs from 16.0 to 2.2 and increasing FPS from 153 to 188. This provides a substantial foundation for networked optoelectronic detection of UAVs and similar slow-moving aerial targets, expanding the defensive perimeter and enabling earlier warnings.

A Generalized Lightweight Intrusion Detection Model With Unified Feature Selection for Internet of Things Networks

ABSTRACTThe applicability of the Internet of Things (IoT) cutting across different domains has resulted in newer “things” acquiring IP connectivity. These things, technically known as IoT devices, are vulnerable to diverse security threats. Consequently, there has been an exponential increase in IoT malware over the past 5 years, and securing IoT devices from such attacks is a pressing concern in the current era. However, the traditional peripheral security measures do not comply with the lightweight security requirements of the IoT ecosystem. Considering this, we propose a lightweight intrusion detection model for IoT networks (LIDM‐IoT) that demonstrates similar efficiency in exposing malicious activities compared with the existing computationally expensive methods. The crux of the proposed model is that it provides efficient attack detection with lower computational requirements in IoT networks. LIDM‐IoT achieves the feat through a novel unified feature selection strategy that unifies filter‐based and embedded feature selection methods. The proposed feature selection strategy reduces the feature space by 94%. Also, we use only the records of a single attack type to build the model using the XGBoost algorithm. We have tested LIDM‐IoT with unseen attack types to ensure its generalized behavior. The results indicate that the proposed model exhibits efficient attack detection, with a reduced feature set, in IoT networks compared with the state‐of‐the‐art models.

Pyro-Phototronic Effect Induced Circularly Polarized Light Detection with a Broadband Response.

Photopyroelectric-based circularly polarized light (CPL) detection, coupling the pyro-phototronic effect and chiroptical phenomena, has provided a promising platform for high-performance CPL detectors. However, as a novel detection strategy, photopyroelectric-based CPL detection is currently restricted by the short-wave optical response, underscoring the urgent need to extend its response range. Herein, visible-to-near-infrared CPL detection induced by the pyro-phototronic effect is first realized in chiral-polar perovskites. Specifically, chiral-polar multilayered perovskites (S-BPEA)2FAPb2I7 (1-S, S-BPEA = (S)-1-4-Bromophenylethylammonium, FA = formamidinium) with spontaneous polarization shows intrinsic pyroelectric and photopyroelectric performance. Strikingly, combining its merits of the pyro-phototronic effect and intrinsic wide-spectrum spin-selective effect, chiral multilayered 1-S presents efficient photopyroelectric-based broadband CPL detection performance spanning 405-785nm. This research first realizes photopyroelectric-based infrared CPL detection and also sheds light on developing high-performance broadband CPL detectors based on the pyro-phototronic effect in the fields of optics, optoelectronics, and spintronics.