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Machine learning (ML) is effective to establish the complicated trilateral relationship among structures, properties, and photovoltaic performance, which is fundamental issue in developing novel materials for improving power conversion efficiency (PCE) of organic solar cells (OSCs). Herein, we constructed the database of 397 donor–acceptor pairs of OSCs with photovoltaic parameters and descriptor sets, which include donor–acceptor weight ratio within the active layer of the OSCs, root mean square of roughness, and 1024-bit Morgan molecular fingerprint for donor (Fp-D) and acceptor (Fp-A). The ML models random forest (RF), adaptive boosting (AdaBoost), extra trees regression, and gradient boosting regression trees were trained based on the descriptor set. The metrics determination coefficient (R2), Pearson correlation coefficient (r), root mean square error, and mean absolute error were selected to evaluate ML model performances. The results showed that the RF model exhibits the highest accuracy and stability for PCE prediction among these four ML models. Moreover, based on the decomposition of non-fullerene acceptors L8-BO, BTP-ec9, AQx-2, and IEICO, 20 acceptor molecules with symmetric A–D–A and A–π–D–π–A architectures were designed. The photovoltaic parameters of the designed acceptors were predicted using the trained RF model, and the virtual screening of designed acceptors was conducted based on the predicted PCE. The results indicate that six designed acceptors can reach the predicted PCE higher than 12% when P3HT was adopted as a donor. While PM6 was applied as a donor, five designed acceptors can achieve the predicted PCE higher than 16%. [ABSTRACT FROM AUTHOR]

A painting, like human skin, develops cracks on the surface as it dries and ages. The painting cracks, also known as craquelure, are often considered analogous to human fingerprints; these have been regarded as a unique signature reflective of the painting’s characteristics and are important in art authentication. Intriguingly, studies in other fields, such as geology, have observed the presence of distinctive characteristics in soil desiccation cracks. These cracks exhibit self-similarity, forming patterns that suggest broader geological processes at work. In light of this connection, the primary objective of this study is to investigate whether the painting cracks also exhibit a self-similar nature. By delving into this, we seek to shed light on the underlying properties of the painting cracks. This study also aims to investigate whether the characteristic self-similar trait of the cracks can serve as an identifier in relation to the provenances of the paintings. To this end, this study adopts the methodology originally designed to characterize the phenotypic traits of 3D particle geometries in granular materials research. This study develops a 2D equivalent concept, focusing on the phenotypic traits of the individual islands enclosed by cracks within paintings. The results successfully demonstrate that the phenotypic trait of painting cracks exhibits a self-similar nature, which can reveal characteristics associated with the provenances of paintings. The findings will offer valuable insights into the scientific examination of artworks based on painting cracks. [ABSTRACT FROM AUTHOR]

Currently, a significant number of pre-trained models are published online to provide services to users owing to the rapid maturation and popularization of machine learning as a service (MLaaS). Some malicious users have pre-trained models illegally to redeploy them and earn money. However, most of the current methods focus on verifying the copyright of the model rather than tracing responsibility for the suspect model. In this study, TraceGuard is proposed, the first framework based on steganography for tracing a suspect self-supervised learning (SSL) pre-trained model, to ascertain which authorized user illegally released the suspect model or if the suspect model is independent. Concretely, the framework contains an encoder and decoder pair and the SSL pre-trained model. Initially, the base pre-trained model is frozen, and the encoder and decoder are jointly learned to ensure the two modules can embed the secret key into the cover image and extract the secret key from the embedding output by the base pre-trained model. Subsequently, the base pre-trained model is fine-tuned using stego images to implement a fingerprint while the encoder and decoder are frozen. To assure the effectiveness and robustness of the fingerprint and the utility of fingerprinted pre-trained models, three alternate steps of model stealing simulations, fine-tuning for uniqueness, and fine-tuning for utility are designed. Finally, the suspect pre-trained model is traced to its user by querying stego images. Experimental results demonstrate that TraceGuard can reliably trace suspect models and is robust against common fingerprint removal attacks such as fine-tuning, pruning, and model stealing. In the future, we will further improve the robustness against model stealing attack. [ABSTRACT FROM AUTHOR]

Enhancing the endurance, performance, and energy efficiency of encrypted Non-Volatile Main Memory (NVMM) can be achieved by minimizing written data through inline deduplication. However, existing approaches applying inline deduplication to encrypted NVMM suffer from substantial performance degradation due to high computing, memory footprint, and index-lookup overhead to generate, store, and query the cryptographic hash (fingerprint). In the preliminary ESD [14], we proposed the Error Correcting Code (ECC) assisted selective deduplication scheme, utilizing the ECC information as a fingerprint to identify similar data effectively and then leveraging the selective deduplication technique to eliminate a large amount of redundant data with high reference counts. In this article, we proposed FSDedup. Compared with ESD, FSDedup could leverage the prefetch cache to reduce the read overhead during similarity comparison and utilize the cache refresh mechanism to identify further and eliminate more redundant data. Extensive experimental evaluations demonstrate that FSDedup can enhance the performance of the NVMM system further than the ESD. Experimental results show that FSDedup can improve both write and read speed by up to 1.8×, enhance Instructions Per Cycle by up to 1.5×, and reduce energy consumption by up to 2.0×, compared to ESD. [ABSTRACT FROM AUTHOR]

Large fingerprint databases can make the automated search process tedious and time‐consuming. Fingerprint pattern classification is a significant step in the identification system's complexity in terms of time and speed. Although several fingerprint algorithms have been developed for classification tasks, further improvements in performance and efficiency are still required. Most of the fingerprint algorithms use deep learning techniques. However, some deep learning techniques can be resource‐intensive and computationally expensive, while others can disregard the spatial relationships between the features used in classifying fingerprint patterns. This study proposes using lightweight deep learning models (i.e., MobileNet and EfficientNet‐B0) integrated with attention modules to classify fingerprint patterns. The two lightweight models are modified, yielding MobileNet+ and EfficientNet‐B0+ models. The lightweight deep learning models can help achieve optimal performance and reduce computational complexity. The attention modules focus on distinctive features for classification. Our proposed approach integrates four attention modules for fingerprint pattern classification into two lightweight deep learning models, that is, MobileNet+ and EfficientNet‐B0+. To evaluate our approach, we use two publicly available fingerprint datasets, that is, the NIST special database 301 dataset and the LivDet dataset. The evaluation results show that the EfficientNet‐B0+ model achieves the highest classification accuracy of 97% with only 854,086 training parameters. As a conclusion, we consider the training parameters small enough for the EfficientNet‐B0+ model to be deployed on low‐resource devices. [ABSTRACT FROM AUTHOR]

With the increasing demand for information security, current anti‐counterfeiting methods not only suffer from issues such as low information density and vulnerability to forgery, but also inherently involve a trade‐off between information capacity and readout methods. This paper reports a high‐security solution using hierarchical pattern with perfect combination of micro self‐wrinkle and nano phase‐separation as physical unclonable function (PUF) labels, which is generated through self‐organization of anthracene‐functionalized poly(styrene‐block‐butadiene‐block‐styrene) (SBS‐CAN) under UV exposure. The double‐layer morphologies formed by the wrinkle and phase separation are adjustable, independent, and stable. The obtained hierarchical PUF labels exhibit random and unique features similar to the minutiae of fingerprints at both micro and nano scales, ensuring a well‐balanced bit uniformity (>0.492), high uniqueness (>0.496), and outstanding reliability (>96%). As a consequence of the multi‐layered combination of morphologies, the designed PUF label possesses an information density about 1010 times higher than that of human fingerprints. The PUF labels can be quickly obtained through simple visual scanning and exhibit sufficient security. To cope with various application environments, the advanced authentication pipeline designed LPLA guarantees robust label recognition capability in real‐world scenarios. A practical integrated anti‐counterfeiting authentication system is developed by combining hierarchical PUF labels and authentication pipeline. [ABSTRACT FROM AUTHOR]

Fingerprint liveness detection systems have been affected by spoofing, which is a severe threat for fingerprint-based biometric systems. Therefore, it is crucial to develop some techniques to distinguish the fake fingerprints from the real ones. The software based techniques can detect the fingerprint forgery automatically. Also, the scheme shall be resistant against various distortions such as noise contamination, pixel missing and block missing, so that the forgers cannot deceive the detector by adding some distortions to the faked fingerprint. In this paper, we propose a fingerprint forgery detection algorithm based on a suggested adaptive thresholding pattern. The anisotropic diffusion of the input image is passed through three levels of the wavelet transform. The coefficients of different layers are adaptively thresholded and concatenated to produce the feature vector which is classified using the SVM classifier. Another contribution of the paper is to investigate the effect of various distortions such as pixel missing, block missing, and noise contamination. Our suggested approach includes a novel method that exhibits improved resistance against a range of distortions caused by environmental phenomena or manipulations by malicious users. In quantitative comparisons, our proposed method outperforms its counterparts by approximately 8% and 5% in accuracy for missing pixel scenarios of 90% and block missing scenarios of size 70 × 70 , respectively. This highlights the novelty approach in addressing such challenges. [ABSTRACT FROM AUTHOR]

A central goal of neuroscience is to understand how function-relevant brain activations are generated. Here we test the hypothesis that function-relevant brain activations are generated primarily by distributed network flows. We focused on visual processing in human cortex, given the long-standing literature supporting the functional relevance of brain activations in visual cortex regions exhibiting visual category selectivity. We began by using fMRI data from N = 352 human participants to identify category-specific responses in visual cortex for images of faces, places, body parts, and tools. We then systematically tested the hypothesis that distributed network flows can generate these localized visual category selective responses. This was accomplished using a recently developed approach for simulating – in a highly empirically constrained manner – the generation of task-evoked brain activations by modeling activity flowing over intrinsic brain connections. We next tested refinements to our hypothesis, focusing on how stimulus-driven network interactions initialized in V1 generate downstream visual category selectivity. We found evidence that network flows directly from V1 were sufficient for generating visual category selectivity, but that additional, globally distributed (whole-cortex) network flows increased category selectivity further. Using null network architectures we also found that each region's unique intrinsic "connectivity fingerprint" was key to the generation of category selectivity. These results generalized across regions associated with all four visual categories tested (bodies, faces, places, and tools), and provide evidence that the human brain's intrinsic network organization plays a prominent role in the generation of functionally relevant, localized responses. Author summary: A fundamental question in neuroscience has persisted for over a century: to what extent do distributed processes drive brain function? The existence of category-selective regions within visual cortex provides long-standing evidence supporting localized computations, wherein specialized functions (e.g., selective responsiveness to face images) are thought to be primarily generated by within-region processes. This account was recently updated to include category selectivity dispersed across visual cortex, in the absence of category-selective regions. Here we provide groundwork evidence demonstrating that locally-exhibited visual-category-selective responses can be accurately generated via distributed activity flowing over globally connected systems. These processes were simulated via empirically-based computational models initialized by stimulus-evoked activity patterns and empirical connectivity matching each category-selective region's unique intrinsic functional connectivity fingerprint. Results demonstrate that activity flowing over the human brain's distributed network architecture can account for the generation of category selectivity in visual cortex regions. [ABSTRACT FROM AUTHOR]

Crystalline α‐Gd2(MoO4)3 is synthesized by sintering at 800 °C using a coprecipitation method to prepare the precursor. The resulting α‐Gd2(MoO4)3 displayed a monocrystalline structure with a strong main peak (‐221) in the X‐ray diffraction signal. To develop a light‐emitting material, rare earth ions are added during synthesis. By doping with Tb3+ and Eu3+, the Gd2(MoO4)3 down‐conversion phosphor shows potential for use in UV–LED chips, counterfeit money prevention, and fingerprint identification. Additionally, co‐doping with [Er3+]/[Yb3+] and [Ho3+]/[Yb3+] ions produce green and red emissions when applied to a 980 nm LED chip, useful for anti‐counterfeit ink. The magnetic properties of gadolinium are leveraged to confirm magnetic resonance imaging luminescence. A flexible composite for heat detection and explored various applications for its use is also developed. [ABSTRACT FROM AUTHOR]

The device anomaly detection in an industrial control system (ICS) is essential for identifying devices with abnormal operating states or unauthorized access, aiming to protect the ICS from unauthorized access, malware, operational errors, and hardware failures. This paper addresses the issues of numerous manufacturers, complex models, and incomplete information by proposing a fingerprint extraction method based on ICS protocol communication models, applied to an anomaly detection model fine-tuned using the Llama3 model. By considering both hardware and software characteristics of ICS devices, the paper designs a fingerprint vector that can be extracted in both active and passive network communication environments. Experimental data include real ICS network traffic from an oilfield station and extensive ICS device traffic data obtained through network scanning tools. The results demonstrate that the proposed method outperforms existing methods in terms of accuracy and applicability, especially in differentiating devices from various manufacturers and models, significantly enhancing anomaly detection performance. The innovation lies in using large language models for feature extraction and the anomaly detection of device fingerprints, eliminating dependency on specific ICS scenarios and protocols while substantially improving detection accuracy and applicability. [ABSTRACT FROM AUTHOR]

Considering the characteristics of remote sensing land cover images with a wide variety of types and complex object edges as well as the limited receptive field of local convolutions in existing segmentation networks resulting in inadequate utilization of contextual information, leading to issues such as blurred object edges and low segmentation accuracy, this paper proposes a remote sensing land cover segmentation algorithm based on the UNet3+ network architecture. Firstly, during the decoding process, a similarity-aware point affiliation operator is introduced as an upsampling method. This operator aggregates multiple proposals from the feature pyramid to enhance the segmentation capability for object boundary details. Secondly, in the encoding process, a selective kernel module is introduced to optimize the downsampling approach. This module enables neurons to achieve an adaptive receptive field size, facilitating the acquisition of multi-scale information from target features and precise capture of valuable detailed semantic information. Finally, in the skip-connection phase, a dual multi-scale attention module is added to perform weighted fusion of features from different scales, enabling the model to better focus on both local details and global contextual information. Experimental results on the WHDLD and ISPRS Potsdam datasets demonstrate that the proposed algorithm achieves mean intersection over union (MIoU) improvements of 64.4% and 75.4% respectively, compared to baseline models, the improvement is about 2.6 percentage points and 3.2 percentage points respectively. This also validates the effectiveness of the proposed algorithm in addressing the issue of blurry segmentation edges. [ABSTRACT FROM AUTHOR]

Nowadays, huge devices are connected to the internet to deal with many cloud environments for storing data efficiently. Also, it is used to store big data determined in business activities. However, the data storage in the cloud avoids duplicate copies of files and reduces complexity issues. However, this platform is utilized by many users for storing the file in cloud and this creates a data redundancy problem. Also, fingerprint indexing is a major issue in data storage in the cloud. So in order to overcome this issue a novel Enhanced Arithmetic Optimization Algorithm with Mixed-Kernel-based Extreme Learning Machine-based Random Forest (EAOA-MKELM-RF) method is proposed that overcome the deduplication issues. In spite, the scalability issues are solved, and the limitations determined in fingerprint indexing. Further, the pipelining and parallelizing hash function values are utilized to address the scalability issue in the deduplication process. The data storage container organizes the programs and files into data blocks, metadata files and nonduplicate blocks. However, the proposed methods determine the similarity issues and accelerate the fingerprint queries for better scalability. The datasets employed to evaluate the proposed approach are CorrAL-100 dataset, Madelon dataset, XOR-100 dataset, Infocom'05 dataset and Linux kernel dataset. The experimentation results demonstrated that the proposed method attained a superior accuracy by 96.8% in terms of restoration accuracy, robustness and delivery ratio. Additionally, the proposed approach reduces buffer time, improving overall system performance. This validation optimized the storage resources, diminished storage costs and improved overall data management. [ABSTRACT FROM AUTHOR]

As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making emitter identification in remote sensing practices more essential. Effective recognition of radio frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a progressive maximum similarity-based unsupervised domain adaptation (PMS-UDA) method for RF signal attribute recognition. First, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, a domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenarios. The experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as across both ground-to-ground and air-to-ground scenarios, compared to baseline methods. [ABSTRACT FROM AUTHOR]

This article explores the use of omics data, such as genomic, transcriptomic, proteomic, and metabolomic data, in chemical grouping and read-across for chemical risk assessment. It explains that traditional approaches to chemical safety rely on in vivo toxicity data, but there is a growing recognition of the need for more mechanistically based approaches that do not involve animal testing. The article introduces non-experts to the concept of grouping and read-across, discusses the value of omics technologies in this context, presents case studies, and identifies challenges and next steps for incorporating omics data into chemical grouping. It also discusses the regulatory landscape and the need for standardized reporting templates. However, it acknowledges that there are limitations to chemical grouping, as structural similarity does not always predict similar biological responses. Overall, the article highlights the potential of omics data in chemical grouping and the need for further research and implementation in this area. [Extracted from the article]

As training a high-performance deep neural network (DNN) model requires a large amount of data, powerful computing resources and expert knowledge, protecting well-trained DNN models from intellectual property (IP) infringement has raised serious concerns in recent years. Most existing methods using DNN watermarks to verify the ownership of the models after IP infringement occurs, which is reactive in the sense that they cannot prevent unauthorized users from using the model in the first place. Different from these methods, in this article, we propose an active authorization control and user's fingerprint tracking method for the IP protection of DNN models by utilizing sample-specific backdoor attack. The proposed method inversely and multiplely exploits sample-specific trigger as the key to implement authorization control for DNN model, in which the generated triggers are imperceptible and sample-specific for clean images. Specifically, a U-Net model is used to generate backdoor instances. Then, the target model is trained on the clean images and backdoor instances, which are inversely labeled as wrong classes and correct classes, respectively. Only authorized users can use the target model normally by pre-processing the clean images through the U-Net model. Moreover, the images processed by the U-Net model will contain unique fingerprint that can be extracted to verify and track the corresponding user's identity. This article is the first work that utilizes the sample-specific backdoor attack to implement active authorization control and user's fingerprint management for DNN model under black-box scenarios. Extensive experimental results on ImageNet dataset and YouTube Aligned Face dataset demonstrate that the proposed method is effective in protecting the DNN model from unauthorized usage. Specifically, the protected model has a low inference accuracy (1.00%) for unauthorized users, while maintaining a normal inference accuracy (97.67%) for authorized users. Besides, the proposed method can achieve 100% fingerprint tracking success rates on both the ImageNet and YouTube Aligned Face datasets. Moreover, it is demonstrated that the proposed method is robust against fine-tuning attack, pruning attack, pruning attack with retraining, reverse-engineering attack, adaptive attack, and JPEG compression attack. The code is available at https://github.com/nuaaaisec/SSAT. [ABSTRACT FROM AUTHOR]

As cloud native technology advances, the scale and complexity of applications built on microservice architecture continue to expand, leading to increasingly intricate differences between software within the same application. Microservice applications, offering high flexibility, are deployed in data centers as black boxes from the users' perspective, leaving them with no insight into the orchestration of cloud service providers. Consequently, users face challenges in promptly recognizing performance imbalances within their deployed applications. Meanwhile, cloud service providers may cut costs by offering a mix of qualified and unqualified services, potentially deceiving users. To enhance the understanding of microservice application organization, we propose a non‐intrusive measurement framework, termed NMPI. NMPI facilitates rapid identification of microservice application defects, offering insights into cloud services and detecting fraudulent behavior in microservice‐based applications. We model microservice applications using a queue analysis‐based approach and filter the dominant frequency components of average response time signals by employing k‐means on the fast fourier transform (FFT). Our model constructs a library of performance portraits for various software, with these portraits resembling human fingerprints that carry and mark the software's internal information. Utilizing a two‐tier microservices‐based application incorporating a database as a case study allows us to demonstrate the effectiveness of NMPI. Our experimental results show that NMPI can produce differentiable profiles of data service performance portraits across a diverse and extensive range of workloads, enabling the identification of software types and the analysis of performance conditions. [ABSTRACT FROM AUTHOR]

The multifaceted, multivendor-based global design supply chain induces hardware threats of intellectual property (IP) piracy for modern computing and electronic systems. Current hardware watermarking techniques fall short either in terms of watermark strength (size of covert constraints generated) or number of security layers/variables involved in the security constraints generation process. This paper presents a novel approach for high level synthesis (HLS) watermarking by bio-mimicking DNA fingerprint profiling to counter hardware IP piracy. The proposed approach effectively captures the vital DNA fingerprint profiling phases such as DNA sequencing, DNA fragmentation, fragment replication, DNA ligase, etc. and bio-mimics them to generate a digital watermarking framework. The presented approach has been demonstrated on convolutional layer and JPEG compression-decompression (CODEC) algorithms that are widely used in several medical and machine learning applications. The proposed approach has been thoroughly compared with several state-of-the-art approaches. The proposed approach depicts superior security in the probability of coincidence of up to ~ 104 and tamper tolerance of up to ~ 10368 at 0% overhead as compared to the prior approaches. [ABSTRACT FROM AUTHOR]

Fingerprint-based indoor localization has been a hot research topic. However, the current fingerprint-based indoor localization approaches still rely on a single fingerprint database, where the average level of data at reference points is used as the fingerprint representation. In variable environmental conditions, the variations in signals caused by changes in the environmental states introduce significant deviations between the average level and the actual fingerprint characteristics. This deviation leads to a mismatch between the constructed fingerprint database and the real-world conditions, thereby affecting the effectiveness of fingerprint matching. Meanwhile, the sharp noise interference caused by uncertainties such as personnel movement has a significant interference on the creation of the fingerprint database and fingerprint matching in online stage. Examination of the sampling data after denoising with Robust Principal Component Analysis (RPCA) revealed distinct multi-fingerprint characteristics with clear boundaries at certain access points. Based on these observations, the concept of constructing a fingerprint database using multiple fingerprints is introduced and its feasibility is explored. Additionally, a multi-fingerprint solution based on naive Bayes classification is proposed to accurately represent fingerprint characteristics under different environmental conditions. This method is based on the online stage fingerprints. The corresponding state space is selected using the naive Bayes classifier, enabling the selection of an appropriate fingerprint database for matching. Through simulations and empirical evaluations, the proposed multi-fingerprints construction scheme consistently outperforms the traditional single-fingerprint database in terms of positioning accuracy across all tested localization algorithms. [ABSTRACT FROM AUTHOR]

A biometric system is essential in improving security and authentication processes across a variety of fields. Due to multiple criteria and alternatives, selecting the most suitable biometric system is a complex decision. We employ a hybrid approach in this study, combining the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the Analytic Hierarchical Process (AHP). Biometric technologies are ranked using the TOPSIS method according to the relative weights that AHP determines. By applying the neutrosophic set theory, this approach effectively handles the ambiguity and vagueness inherent in decision-making. Fingerprint, face, Iris, Voice, Hand Veins, Hand geometry and signature are the seven biometric technologies that are incorporated in the framework. Seven essential characteristics are accuracy, security, acceptability, speed and efficiency, ease of collection, universality, distinctiveness used to evaluate these technologies. The model seeks to determine which biometric technology is best suited for a particular application or situation by taking these factors into account. This technique may be applied in other domains in the future. [ABSTRACT FROM AUTHOR]

More and more attention has been paid to computer security, and its vulnerabilities urgently need more sensitive solutions. Due to the incomplete data of most vulnerability libraries, it is difficult to obtain pre-permission and post-permission of vulnerabilities, and construct vulnerability exploitation chains, so it cannot to respond to vulnerabilities in time. Therefore, a vulnerability extraction and prediction method based on improved information gain algorithm is proposed. Considering the accuracy and response speed of deep neural network, deep neural network is adopted as the basic framework. The Dropout method effectively reduces overfitting in the case of incomplete data, thus improving the ability to extract and predict vulnerabilities. These experiments confirmed that the excellent F1 and Recall of the improved method reached 0.972 and 0.968, respectively. Compared to the function fingerprints vulnerability detection method and K-nearest neighbor algorithm, the convergence is better. Its response time is 0.12 seconds, which is excellent. To ensure the reliability and validity of the proposed method in the face of missing data, the reliability and validity of Mask test are verified. The false negative rate was 0.3% and the false positive rate was 0.6%. The prediction accuracy of this method for existing permissions reached 97.9%, and it can adapt to the development of permissions more actively, so as to deal with practical challenges. In this way, companies can detect and discover vulnerabilities earlier. In security repair, this method can effectively improve the repair speed and reduce the response time. The prediction accuracy of post-existence permission reaches 96.8%, indicating that this method can significantly improve the speed and efficiency of vulnerability response, and strengthen the understanding and construction of vulnerability exploitation chain. The prediction of the posterior permission can reduce the attack surface of the vulnerability, thus reducing the risk of breach, speeding up the detection of the vulnerability, and ensuring the timely implementation of security measures. This model can be applied to public network security and application security scenarios in the field of computer security, as well as personal computer security and enterprise cloud server security. In addition, the model can also be used to analyze attack paths and security gaps after security accidents. However, the prediction of post-permissions is susceptible to dynamic environments and relies heavily on the updated guidance of security policy rules. This method can improve the accuracy of vulnerability extraction and prediction, quickly identify and respond to security vulnerabilities, shorten the window period of vulnerability exploitation, effectively reduce security risks, and improve the overall network security defense capability. Through the application of this model, the occurrence frequency of security vulnerability time is reduced effectively, and the repair time of vulnerability is shortened. [ABSTRACT FROM AUTHOR]

The thriving of high‐performance artificial synaptic neuromorphic platforms poses a significant challenge for creating bionic sensors that can integrate multi‐sensory information. For the purpose of realizing this objective, promising bionic visual‐olfactory co‐sensory artificial synaptic devices are developed based on a hydrogen‐bonded organic framework (Eu@HOF‐12). This probe is capable of dual‐emissive‐responsive to biogenic amines (BAs), which are essential indicators for the evaluation of food freshness. Importantly, it demonstrates outstanding advantages, including high sensitivity, ultrafast responses (<6 s), and practicability of recyclability, which can be combined with smartphones for on‐site BAs detection via the colorimetric and ratiometric hydrogel sensor. Besides, a bionic visual‐olfactory co‐sensory based on the human‐computer interaction platform is constructed, which can realize the identification of five types of BAs with high precision through the specific odor fingerprint. Meanwhile, a logic device is utilized for the rapid identification of food freshness considering the hydrogel sensor's dual‐mode responses to BAs. Overall, this research develops an odor visualization strategy that enables on‐site, real‐time, and sensitive monitoring of food quality assessment. In addition, a quantitative assessment system is designed for fast, damage‐free, and accurate assessment of food freshness, ultimately reducing food waste and generating significant economic value. [ABSTRACT FROM AUTHOR]

Summary: Indoor location based on Wi‐Fi fingerprint has attracted extensive attention recently, since it only requires a mobile device and existing network; it does not require additional infrastructure and hardware. However, localization using only received signal strength fingerprint is susceptible to dynamic environments and device heterogeneity. Localization using signal strength differences fingerprint and hyperbolic location fingerprint which extracted from received signal strength fingerprint is a powerful measure to overcome the device heterogeneity. Existing fusion‐based methods do not fully use of the mutual fusion function of multiple fingerprints, which leads to low positioning accuracy. In order to further improve the positioning accuracy, we propose a multifingerprint multiclassifier and two‐layer fusion weight positioning model. The proposed approach first creates a multiple fingerprints group by gleaning signal strength differences fingerprint and hyperbolic location fingerprint from received signal strength fingerprint. In order to deal with the accuracy decline caused by a single classifier in complex scenes, three typical classifiers, random forest (RF), K‐nearest neighbor (KNN), and support vector machine (SVM), are adopted, and a fusion weight selection algorithm is proposed to improve the localization accuracy by choosing fusion weights intelligently from the two‐layer fusion weights. Experiment results show that the proposed scheme is effective to resolve device heterogeneity, and the position accuracy is improved about 16.9% and 5.8%, respectively, compared with single fingerprint and classifier schemes. The results show that, compared with the single fingerprint scheme, the position accuracy is improved at least 16.9% and 28.4% for the homogeneous and heterogeneous equipments, respectively; as for the proposed multiclassifier fusion method, it achieves at least 5.8% and 8.7% accuracy improvement for the cases with homogeneous and heterogeneous equipments, respectively, compared with other traditional classifiers such as RF, KNN, and SVM. [ABSTRACT FROM AUTHOR]

The quality score of a biometric sample is intended to predict the sample's degree of utility for biometric recognition. Different authors proposed different definitions for utility. A harmonized definition of utility would be useful to facilitate the comparison of biometric sample quality assessment algorithms. In this article, we compare different definitions of utility and apply them to both face image and fingerprint image data sets containing multiple samples per biometric instance and covering a wide range of potential quality issues. The results differ only slightly. We show that discarding samples with low utility scores results in rapidly declining false non-match rates. The obtained utility scores can be used as target labels for training biometric sample quality assessment algorithms and as baseline when summarizing utility-prediction performance in a single plot or even in a single figure of merit. [ABSTRACT FROM AUTHOR]

Contactless hand biometrics has emerged as an alternative to traditional biometric characteristics, e.g., fingerprint or face, as it possesses distinctive properties that are of interest in forensic investigations. As a result, several hand-based recognition techniques have been proposed with the aim of identifying both wanted criminals and missing victims. The great success of deep neural networks and their application in a variety of computer vision and pattern recognition tasks has led to hand-based algorithms achieving high identification performance on controlled images with few variations in, e.g., background context and hand gestures. This article provides a comprehensive review of the scientific literature focused on contactless hand biometrics together with an in-depth analysis of the identification performance of freely available deep learning-based hand recognition systems under various scenarios. Based on the performance benchmark, the relevant technical considerations and trade-offs of state-of-the-art methods are discussed, as well as further topics related to this research field. [ABSTRACT FROM AUTHOR]

Bound states in the continuum (BICs) are an excellent platform enabling highly efficient light–matter interaction in applications for lasing, nonlinear generation, and sensing. However, the current focus in implementing BICs has primarily been on single sharp resonances, limiting the extent of electric field enhancement for multiple resonances. In this study, experimental demonstrations are conducted to showcase how metasurfaces can enable the control of symmetry‐broken and Friedrich–Wintgen BICs by leveraging the asymmetry of split resonant rings. This approach allows for the existence of multiple free‐control BIC resonances and tailored enhancement of controlling light–matter interactions. Further experiments are conducted to validate the effectiveness and performance of meta‐sensors for the identification of the distinct fingerprint of α‐lactose with high sensitivity using only one single metasurface. These findings present a novel and efficient platform for the development of miniaturized and chip‐scale photonics devices with intense light–matter interaction. [ABSTRACT FROM AUTHOR]

We previously described NMR based fingerprint matching with peptide backbone resonances as a fast and reliable structural dereplication approach for Pseudomonas cyclic lipodepsipeptides (CLiPs). In combination with total synthesis of a small library of configurational CLiP congeners this also allows unambiguous determination of stereochemistry, facilitating structure‐activity relationship studies and enabling three‐dimensional structure determination. However, the on‐resin macrocycle formation in the synthetic workflow brings considerable burden and limits universal applicability. This drawback is here removed altogether by also transforming the native CLiP into a linearized analogue by controlled saponification of the ester bond. This eliminates the need for macrocycle formation, limiting the synthesis effort to linear peptide analogues. NMR fingerprints of such linear peptide analogues display a sufficiently distinctive chemical shift fingerprint to act as effective discriminators. The approach is developed using viscosin group CLiPs and subsequently demonstrated on putisolvin, leading to a structural revision, and tanniamide from Pseudomonas ekonensis COR58, a newly isolated lipododecapeptide that defines a new group characterized by a ten‐residue large macrocycle, the largest to date in the Pseudomonas CLiP portfolio. These examples demonstrate the effectiveness of the saponification‐ enhanced approach that broadens applicability of NMR fingerprint matching for the determination of the stereochemistry of CLiPs. [ABSTRACT FROM AUTHOR]

Machine learning has gained attention due to its ongoing advancements and diverse applications. Within the field of homogeneous catalysis, a prominent area of research in machine learning revolves around predicting reaction yield in Pd‐catalyzed Buchwald–Hartwig amination reactions. This study sought to determine the optimal descriptors for representing the both structural and physical information associated with the reaction, particularly focusing on product details. To achieve this, we assessed the utilization of product extended molecular fingerprints (PEMF) and selected physical parameters (SPP). The utilization of a random forest model incorporating these descriptors yielded promising results in the prediction of reaction yields in Pd‐catalyzed Buchwald–Hartwig amination reactions. The model achieved an impressive R2 value of 0.943. Data preprocessing on PEMF and sorting preprocessing on physical parameters resulted in a significant reduction in data size to 259 bits PEMF+2 SPPs per prediction, much less than the two previous random forest models which utilized 480 physical parameters and 21,073 bits molecular fingerprints. Although establishing definitive correlations between SPPs and reaction yield presented challenges, our findings indicate that the presence of heavier atoms in the aryl halides may have a beneficial impact within the examined Pd‐catalyzed Buchwald–Hartwig amination reactions, as compared to their analogues. [ABSTRACT FROM AUTHOR]

Nowadays, an efficient and robust virtual screening procedure is crucial in the drug discovery process, especially when performed on large and chemically diverse databases. Virtual screening methods, like molecular docking and classic QSAR models, are limited in their ability to handle vast numbers of compounds and to learn from scarce data, respectively. In this study, we introduce a universal methodology that uses a machine learning-based approach to predict docking scores without the need for time-consuming molecular docking procedures. The developed protocol yielded 1000 times faster binding energy predictions than classical docking-based screening. The proposed predictive model learns from docking results, allowing users to choose their preferred docking software without relying on insufficient and incoherent experimental activity data. The methodology described employs multiple types of molecular fingerprints and descriptors to construct an ensemble model that further reduces prediction errors and is capable of delivering highly precise docking score values for monoamine oxidase ligands, enabling faster identification of promising compounds. An extensive pharmacophore-constrained screening of the ZINC database resulted in a selection of 24 compounds that were synthesized and evaluated for their biological activity. A preliminary screen discovered weak inhibitors of MAO-A with a percentage efficiency index close to a known drug at the lowest tested concentration. The approach presented here can be successfully applied to other biological targets as target-specific knowledge is not incorporated at the screening phase. [ABSTRACT FROM AUTHOR]

Our aim is to identify new small molecules with antimicrobial potential, especially against colistin-resistant (Col-R) Acinetobacter baumannii and Escherichia coli. After initial hits identification by fingerprint similarity, MIC of 24 heterocyclic derivatives for A. baumannii and E. coli reference strains, and bactericidal activity of selected thiophenes against Col-R strains were determined. We analyzed changes in bacterial membrane permeability and the OMPs profile. Additionally, we determined bacterial adherence to host cells and performed molecular docking studies to assess their binding to bacterial targets. The compounds’ MICs ranged from 4 to >64 mg/L. Thiophene derivatives 4, 5 and 8 exhibited MIC50 values between 16 and 32 mg/L for Col-R A. baumannii and 8 and 32 mg/L for Col-R E. coli. The time-kill curve assay demonstrated that thiophenes 4 and 8 had bactericidal effects against Col-R A. baumannii and E. coli. Furthermore, treatment with them resulted in increased membrane permeabilization and reduced adherence of these isolates to host cells. Finally, the docking studies showed a stronger binding affinity to CarO1 and Omp33 of A. baumannii and OmpW and OmpC of E. coli. These findings indicate that thiophene derivatives possess antibacterial activity against Col-R A. baumannii and E. coli, suggesting that they may enhance the repertoire of drug treatments against bacteria. [ABSTRACT FROM AUTHOR]

Shexiang Tongxin Dropping Pill (STP) is a composite formula of traditional Chinese medicine that is widely used for the treatment of cardiovascular diseases. It consists of seven medicinal extracts thereof or materials, including Bufonis venenum , synthetic Moschus, Panax ginseng , Bovis calculus artifactus , Bear bile powder, Salvia miltiorrhiza Bge and synthetic borneol. However, it is considerably difficult to evaluate the quality of STP due to its complex chemical compositions. This paper was designed to explore a comprehensive and systematic method combining fingerprints and chemical identification for quality assessment of STP samples. Twenty batches of STP samples were analyzed by high-performance liquid chromatography (HPLC) and high-performance liquid chromatography–quadrupole time-of-flight tandem mass spectrometry. Ten common peaks were detected by HPLC fingerprint similarity evaluation system. Meanwhile, 100 compounds belonging to 4 structural characteristics, including 23 bufadienolides, 36 organic acids, 34 saponins and 7 other types, were systematically identified as the basic components in STP. This study could be used for clarifying the multiple bioactive substances and developing a comprehensive quality evaluation method of STP. [ABSTRACT FROM AUTHOR]

Most of the current optical image watermarking schemes are designed for robust watermarking, primarily aimed at protecting image copyrights. However, these robust optical watermarking schemes cannot determine if an image has been tampered with. To address this issue, we propose an optical fragile watermarking scheme based on single-pixel ptychography and variational image decomposition. In our proposed fragile watermarking scheme, the complex watermark image is encoded by the single-pixel ptychography system and embedded into the high-frequency detail component of the color host image obtained through variational image decomposition. Our proposed fragile watermarking scheme is highly sensitive to common image tampering, image processing and geometric attacks. Additionally, by incorporating human fingerprint keys during the encoding and encryption process of the watermark image, the scheme associates the watermarking process with user identity, thereby enhancing security. Extensive numerical simulations validate the feasibility, security and fragility of our proposed optical fragile watermarking scheme. [ABSTRACT FROM AUTHOR]

Background: Recent advances have increased the importance of the human microbiome, including the skin microbiome. Despite the hand microbiome research, the factors affecting the composition of the hand microbiome and their personal characteristics are incompletely known. Objectives: Despite changing environmental factors and personal variation, we aimed to indicate the interpersonal distinction between skin microbiota using simple and rapid molecular methods. Methods: Over a non‐consecutive 10‐day period, samples were taken from 10 adult individuals, and ribotyping analysis of the 16S and 23S genes of S. epidermidis was performed on each skin sample. Additionally, EcoRI and HindIII enzyme reactions and variable number tandem repeat (VNTR) reactions of S. epidermidis obtained from DNA samples were performed. The skin microbiomes of individuals were evaluated along with the microbiome profiles left on the surfaces they touched. Results: In the environmental samples taken, it has been observed that people preserve their core skin microbiota characters and carry them to their environment. It was determined that the highest similarity rate was 77.14%, and the lowest similarity rate was 31.74%. Conclusion: Our study showed that the core skin microbiota retains its characteristics and leaves traces in environments. The fact that the personal microbiome remains unchanged despite environmental differences and has characteristic features has shown that it can be used in forensic sciences to distinguish individuals from each other. These results with simple and rapid methods further increased the importance and significance of the study. The findings indicate that personal skin microbiota can provide a significant contribution to criminal investigations by increasing accuracy and reliability, especially in forensic analyses. [ABSTRACT FROM AUTHOR]

The molecular property prediction (MPP) plays a crucial role in the drug discovery process, providing valuable insights for molecule evaluation and screening. Although deep learning has achieved numerous advances in this area, its success often depends on the availability of substantial labeled data. The few-shot MPP is a more challenging scenario, which aims to identify unseen property with only few available molecules. In this paper, we propose an attribute-guided prototype network (APN) to address the challenge. APN first introduces an molecular attribute extractor, which can not only extract three different types of fingerprint attributes (single fingerprint attributes, dual fingerprint attributes, triplet fingerprint attributes) by considering seven circular-based, five path-based, and two substructure-based fingerprints, but also automatically extract deep attributes from self-supervised learning methods. Furthermore, APN designs the Attribute-Guided Dual-channel Attention module to learn the relationship between the molecular graphs and attributes and refine the local and global representation of the molecules. Compared with existing works, APN leverages high-level human-defined attributes and helps the model to explicitly generalize knowledge in molecular graphs. Experiments on benchmark datasets show that APN can achieve state-of-the-art performance in most cases and demonstrate that the attributes are effective for improving few-shot MPP performance. In addition, the strong generalization ability of APN is verified by conducting experiments on data from different domains. [ABSTRACT FROM AUTHOR]

Wi-Fi fingerprint indoor localization uses Wi-Fi signal strength measurements obtained from a number of access points. This method needs manual data collection across a positioning area and an annotation process to label locations to the measurement sets. To reduce the cost and effort, this paper proposes a Wi-Fi Semi-Supervised Generative Adversarial Network (SSGAN), which produces artificial but realistic trainable fingerprint data. The Wi-Fi SSGAN is based on a deep learning, which is extended from GAN in a semi-supervised learning manner. It is designed to create location-labeled Wi-Fi fingerprint data, which is different to unlabeled data generation by a normal GAN. Also, the proposed Wi-Fi SSGAN network includes a positioning model, so it does not need a external positioning method. When the Wi-Fi SSGAN is applied to a multi-story landmark localization, the experimental results demonstrate a 35% more accurate performance in comparison to a standard supervised deep neural network. [ABSTRACT FROM AUTHOR]

Wi-Fi fingerprint-based indoor localization methods are effective in static environments but encounter challenges in dynamic, real-world scenarios due to evolving fingerprint patterns and feature spaces. This study investigates the temporal variations in signal strength over a 25-month period to enhance adaptive long-term Wi-Fi localization. Key aspects explored include the significance of signal features, the effects of sampling fluctuations, and overall accuracy measured by mean absolute error. Techniques such as mean-based feature selection, principal component analysis (PCA), and functional discriminant analysis (FDA) were employed to analyze signal features. The proposed algorithm, Ada-LT IP, which incorporates data reduction and transfer learning, shows improved accuracy compared to state-of-the-art methods evaluated in the study. Additionally, the study addresses multicollinearity through PCA and covariance analysis, revealing a reduction in computational complexity and enhanced accuracy for the proposed method, thereby providing valuable insights for improving adaptive long-term Wi-Fi indoor localization systems. [ABSTRACT FROM AUTHOR]

This article presents the results of the first scientific analysis of the inks of a thirteenth-century Castilian manuscript – British Library Res 20787, a copy of the Fuero de las leyes. The analysis reveals the fingerprint of the inks used in the codex and points the way to future cross-disciplinary research based on a wider corpus of evidence. [ABSTRACT FROM AUTHOR]

Postmortem fingerprinting is the fastest and most reliable way to scientifically identify unknown decedents from mass fatality incidents. In disaster victim identification (DVI), fingerprints, DNA, and dental examinations are the three primary methods of identification. Additional secondary identifiers such as comparative medical radiography, scars, marks, and tattoos can also be used if viable antemortem information can be located. As DNA technology continues to evolve, RAPID DNA may now identify a profile within 90 min if the remains are not degraded or comingled. When there are true unknowns, however, there is usually no DNA, dental, or medical records to retrieve for a comparison without a tentative identity. It is imperative to understand how to properly collect postmortem prints following various postmortem changes, which databases are available to search against, and what additional resources are available prior to any such event occurring. With advances in technology, automated fingerprint searching is faster than ever before. Mobile devices can now search all the major databases within a matter of minutes from a cell phone or tablet utilizing a peripheral fingerprint scanner. Preliminary research into the application of WD‐40® when utilizing optical‐capacitive scanners has shown to greatly increase the efficacy of capturing usable fingerprints. This additional step allows for the digital capture of fingerprints in even complicated settings, both in daily cases and DVI incidents. This article outlines all the available ways to obtain postmortem fingerprints in complex cases, and how they can be applied to efficiently and effectively used in the DVI process. [ABSTRACT FROM AUTHOR]

Fingerprint recognition systems hold a pivotal role across various modern applications, such as criminal investigation, civil identification, and access control. Fingerprint segmentation is commonly the first stage of fingerprint recognition systems, focusing on extracting the foreground from captured fingerprint images. This helps to reduce extracting false minutiae, speed up the extraction process, and hence, improve the overall system performance. In this paper, we introduce a novel fingerprint segmentation method by first proposing new frequency and intensity features with employing fuzzy-c-means and genetic algorithm, accompanied by proposing a new cluster assignment strategy that involves features weighting and cluster assignment probabilities thresholding which helps to improve the accuracy compared to the standard cluster assignment method. Finally, we introduce a new morphological post-processing technique in order to minimize the misclassified pixels and obtain the final mask. Furthermore, we carry out a comprehensive performance evaluation against leading fingerprint segmentation methods on various known databases. As a result, the proposed method achieves an average error rate of 2.52%, which is much lower than the corresponding errors obtained in the literature. By these experimental results, we show that the proposed method outperforms the best existing methods. [ABSTRACT FROM AUTHOR]

Gait recognition has become an important biometric feature for human identification, in addition to data such as face, iris, and fingerprint. The goal of human gait recognition is to identify people based on walking images. Artificial intelligence technologies have revolutionized the field of gait recognition by enabling computers to automatically learn and extract intricate patterns. These techniques examine video recordings to determine key features in an individual's gait, and these features are used to identify the person. This paper examines the existing appearance-based gait recognition methods that have been published in recent years. The primary objective of this paper is to provide an informative survey of the state-of-the-art in appearance-based gait recognition techniques, highlighting their applications, strengths, and limitations. Through our analysis, we aim to highlight the significant advance that has been made in this field, draw attention to the challenges that have been faced, and identify areas of prospective future research and advances in technology. Furthermore, we comprehensively examine common datasets used in gait recognition research. By analyzing the latest developments in appearance-based gait recognition, our study aims to be a helpful resource for researchers, providing an extensive overview of current methods and guiding future attempts in this dynamic field. [ABSTRACT FROM AUTHOR]

Forensic Science is the application of Scientific methods to resolve crime and legal issues. It involves various disciplines, such as Computer Science, Biology, Chemistry and Anthropology. Forensic scientists examine and analyze evidence from crime scenes, such as fingerprints, DNA, blood, or weapons. Digital proof is one of the forms of forensic evidence. It provide real time eye witness of the incident. Video recordings enable investigators to find out what exactly has transpired. Investigators use video evidence as a source for witness statements, and it aids in the search for the missing person or suspect. Video evidence is also used to testify in court and help with investigations and prosecutions. Failure of forensic science results in wrong judgement convicting innocent people and escaping criminals [1]. For most crimes high quality video recordings are often not available. video quality issues such as blurry, speckled, pixelated and low-resolution videos captured at low light are a real challenge in forensic analysis. To address such issues in this research a hybrid model using set of filters including triplemask spatial linear filter, median filter and bilateral filters are used. For denoising images, a novel image filter using sliding window convolution is proposed. For image sharpening a triplemask spatial linear filter is proposed. Triplemask spatial linear filter is created by cascading a series of filters. Identity, shift and fraction-based approach is used in mask processing. For image smoothing and to preserve the edges bilateral filter is used [2]. The performance of convolution operation is compared with distinct convolution, shift rotational convolution and scipy convolution. To handle uncertainty, imprecision, and ambiguity in real-world image data in a precise manner neutrosophic science is used in image analysis. By the generated neutrosophic set of the given input image ambiguous regions in the image are detected. Feature selection is made by calculating the entropy of different image regions. From the generated neutrosophic set entropy the degree of uncertainty, within the input image is quantified. The intensity distributions are measured using entropy values. In feature selection regions with highest and lowest entropy values containing face images are selected, visualized and processed to further aid in forensic analysis in detecting the culprits. Neutrosophic AHP is used for prioritizing criteria based on face detection and indeterminancy. Face detection is performed using single shot detector framework with a resnet base network, trained using caffe deep learning framework. face recognition process is performed using dlibs [9] state-of-the-art face recognition model built with deep learning [10]. Face recognition in this research distance-based similarity measure using neutrosophic sets is performed. These measures are used in conjunction with facenet[59] face recognition algorithm to improve the robustness and accuracy over traditional methods. The model has an accuracy of 99.38% on the labelled faces in the wild benchmark. [ABSTRACT FROM AUTHOR]

[Display omitted] • Effect of high concentration commercial corrosive substances on fingermark recovery studied. • Range of substrates, corrosive substances and age of marks extended beyond previous research. • Fingermarks still developed after exposure to concentrated (18 M) sulfuric acid. • Alkali solutions more detrimental to fingermarks than acids of equivalent concentration. • Powder suspensions found to be the most effective processes for fingermark recovery. Fingermark recovery plays a crucial role in investigating corrosive substance attacks, which are becoming increasingly common. Building upon previous research, this study aimed to identify effective visualization processes for recovering fingermarks from diverse substrates exposed to wide range of commercially available corrosive materials. The study investigated glass, PVC and HDPE substrates with fingermarks deposited 1 day and 2 weeks before exposure to the corrosive substance, and used commercially available substances at concentrations higher than any previous study. It was found that fingermarks could still be recovered from all substrates studied after exposure to most of the corrosive substances, although in general exposure to corrosive substances was detrimental to the quality of marks recovered. The most detrimental corrosive substances were found to be those based on concentrated sulfuric acid. Black and white powder suspensions were the most effective of all processes used in this study, with the highest recovery rates observed from the glass substrate. Age of mark was not found to have a significant effect on recovery rates. Overall the results show that fingermarks may survive exposure to even the most concentrated acids used in this study and provide the initial basis for guidance on processes that could be used on materials used in corrosive substance attacks. [ABSTRACT FROM AUTHOR]

Fingerprint recognition technology is widely employed for identity verification and access control across diverse domains in which the quality of fingerprint images is critical for accurate biometric identification. However, fingerprint images can be damaged or incomplete due to various factors such as sensor limitations, environmental conditions, or physical injuries. To address this, several approaches including attention mechanism based autoencoders, multi-kernel sequential and multi-kernel stacked autoencoders, and multi-kernel ensemble autoencoders are proposed to perform reconstruction of incomplete and damaged fingerprints. Attention mechanisms play a crucial role by selectively emphasizing important regions and details, further enhancing information capture. By utilizing different autoencoder models with varying kernel sizes, local details and global context can be effectively captured, resulting in more precise restoration. The adoption of multi-kernel sequential and multi-kernel stacked autoencoders enables the extraction of increasingly abstract features, enhancing the model's ability to capture complex fingerprint characteristics. Utilizing autoencoder-based methods for the reconstruction of damaged or incomplete fingerprint images can enhance the accuracy and reliability of fingerprint identification systems leading to improved security and efficiency in real-world applications. Evaluation is conducted using image quality assessment metrics and feature-based matching is utilized to compute fingerprint matching accuracy. The multi-kernel ensemble autoencoder model produces the best reconstruction output with an average fingerprint matching accuracy of 93.81 %. The findings highlight the effectiveness of the proposed work in achieving high-quality reconstruction output and accurate fingerprint matching. The proposed work can be applied in various domains that include law enforcement, security, forensic analysis, and biometric authentication. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things (IoT) technology, the number and types of devices accessing the Internet are increasing, leading to increased network security problems such as hacker attacks and botnets. Usually, these attacks are related to the type of device, and the risk can be effectively reduced if the type of network device can be efficiently identified and controlled. The traditional network device identification method uses active detection technology to obtain information about the device and match it with a manually defined fingerprint database to achieve network device identification. This method impacts the smoothness of the network and requires the manual establishment of fingerprint libraries, which imposes a large labor cost but only achieves a low identification efficiency. The traditional machine learning method only considers the information of individual packets; it does not consider the timing relationship between packets, and the recognition effect is poor. Based on the above research, in this paper, we considered the packet temporal relationship, proposed the TCN model of the Inception structure, extracted the packet temporal relationship, and designed a multi-head self-attention mechanism to fuse the features to generate device fingerprints for device identification. Experiments were conducted on the publicly available UNSW dataset, and the results showed that this method achieved notable improvements compared to the traditional machine learning method, with F1 reaching 96.76%. [ABSTRACT FROM AUTHOR]

Copyright of Computer und Recht is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The performance of the existing student classroom behavior detection model is affected by various aspects such as dataset, algorithm and height as well as the differences between different classrooms, and there are problems such as a single dataset, low accuracy and low efficiency. In order to improve the accuracy of student classroom behavior detection algorithm, this paper proposes a student classroom behavior detection method based on improved convolutional neural network algorithm. Firstly, the student behavior detection dataset is constructed, and the student classroom behavior detection technology scheme is designed; secondly, in order to improve the detection accuracy, the features are extracted by using the new jumping bi-directional paths, and the attention mechanism module is added at different positions to improve the path aggregation network; weekly, the embedding positions of the attention mechanism strategy are determined by analyzing multiple sets of experiments, and the proposed student classroom behavior detection algorithm's effectiveness and superiority. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The sound signals of hydraulic pumps contain abundant key information reflecting their internal mechanical states. In environments characterized by high temperatures or high-speed rotation, or where sensor deployment is challenging, acoustic sensors offer non-contact and flexible arrangement features. Therefore, this study aims to develop an intelligent fault diagnosis method for hydraulic pumps based on acoustic signals. Initially, the Adaptive Chirp Mode Decomposition (ACMD) method is employed to remove environmental noise from the acoustic signals, enhancing the feature signals. Subsequently, the Mel spectrum is extracted as the acoustic fingerprint features of various fault states of the hydraulic pump, and these features are used to train the MobileViT network, achieving accurate identification of the different fault modes. The results indicate that the proposed Mel-MobileViT model effectively identifies and classifies various faults in constant pressure variable pumps, outperforming other models. This study not only provides an efficient and reliable intelligent method for the fault diagnosis of critical industrial equipment such as hydraulic pumps, but also offers new perspectives on the application of deep learning in acoustic pattern analysis. [ABSTRACT FROM AUTHOR]

In a large-scale distributed network, a naming service is used to achieve location transparency and provide effective content discovery. However, fast and accurate name retrieval in the massive name set is laborious. Approximate set membership data structures, such as Bloom filter and Cuckoo filter, are very popular in distributed information systems. They obtain high query performance and reduce memory requirements through the abstract representation of information, but at the cost of introducing query error rates, which will ultimately affect content service quality. In this paper, in order to obtain higher space utilization and a lower query false positive rate, we propose a flexible fingerprint cuckoo filter (FFCF) for information storage and retrieval, which can change the length and type of fingerprints adaptively. In our scheme, FFCF uses longer fingerprints under low occupancy and has the ability to correct errors by changing the type of stored fingerprints. Moreover, we give a theoretical proof and evaluate the performance of FFCF by experimental simulations with synthetic data sets and real network packets. The results demonstrate that FFCF can improve memory utilization, significantly reduce false positive errors by nearly 90 % at 50 % occupancy and outperform Cuckoo filter in the full range of occupancy. [ABSTRACT FROM AUTHOR]