A YOLOv10-based measurement software to analyze agricultural vacuum seed meters was developed to be widely utilized by growers to plant peanuts. The software can process videos recorded on a test stand or from a performing planter under field conditions. Initially, images under various light conditions and camera angles were captured and manually annotated to create a dataset. Advanced image augmentation techniques were applied to enhance the dataset, which was utilized to train a YOLOv10 object detection model, achieving an impressive mAP50 score of 99.4%. Paired with a counting algorithm, the model accurately identified singulated peanuts, empty cells (missing peanuts/spots), and multiple transported peanuts (doubles). Thus, the software automatically calculated and exported detection percentages for each category. The data enables the assessment of seed meter performance across different variables, offering valuable insights for optimizing seed plate and singulator design, vacuum pressure, and rotational speed. The software demonstrated a low error rate of 1.91% and ensured reliable performance measurement metrics.

Developing deep learning tools for automated quantification of arbuscular mycorrhizal fungi (AMF) necessitates large, high-quality datasets capturing the diversity of AMF-root interactions under varied environmental conditions. We present MycorrhiSEE, a 15-Terabyte (Tb), high-resolution image dataset comprising approximately 137,500 whole-slide images (WSIs) of root segments from 5,500 sorghum plants representing 337 genotypes, grown under diverse agronomic treatments. Images were acquired using the ZEISS AxioScan 7 high-throughput robotic microscope equipped with a 5-megapixel resolution color camera. This dataset provides unprecedented detail and diversity, offering a valuable resource for developing deep learning applications in AMF research.

This paper focuses on thermal-to-visible face matching through image synthesis. Most of the legacy face image datasets are composed of visible band data. Thermal band as well as dual band, i.e. visible and thermal face datasets, are limited. Operating in the thermal band and therefore working on visible thermal face recognition (FR) systems can be beneficial in various scenarios. The challenge is cross-spectral matching, i.e. matching gallery, visible band, face images against thermal ones. To address this problem, we train and test two of the most popular image-to-image translation Generative Adversarial Networks (GANs). These are Pix2pix and StarGAN2. In this work, the two aforementioned GAN trained models are tested, and the visible face images generated are matched against the ground truth visible faces using one of the most powerful visible-to-visible face matching algorithms, namely Facenet. We also perform an ablation study where the original thermal and visible images are photometrically normalized before training the image synthesis-specific models. The main outcomes of our study are that the FR accuracy from the pix2pix model did not vary significantly; when using StarGAN2, the original face images yield much higher accuracy compared to the photometrically normalized ones; finally, we observe that, when using the pix2pix model for image synthesis, bearded and non-Caucasian generated face images suffer the most from different noise factors. Specifically, the FR accuracy when using pix2pix after image synthesis yields a face verification area under curve (AUC) of 58.3%, while the same models when tested on data excluding bearded and non-Caucasian faces yields an accuracy of 68.6%.

Convolutional neural networks in the task of object detection and localization have been evolving in the last few years. Various convolutional network models have been proposed such as Faster Region-Based, Mask Region-Based, Single Shot Detection, and "You Only Look Once" models (with different versions). Although instance segmentation has been explored with many models, the Mask Region-Based Convolutional Neural Network has been one of the most competitive models in terms of overall object detection performance. Its widespread use in many different applications encouraged us to take a closer look at model performance in a unique object detection task, namely the detection of spacecraft images in the wild. The main research question in this paper, is whether this off-the-shelf proposed architecture can effectively detect and localize a spacecraft when using the Spacecraft Pose Estimation Dataset, under a variety of different image degradation factors and at various degradation levels. The inspiration for this investigation is the effect of deep space environments on charge-coupled device image sensors, and other imaging hardware. The capability of detection and localization to continue in the face of pixel loss and Gaussian noise are explored. The effects of training augmentation on object detection performance is another task that has also been studied. Some of our main findings include that supplementing training on degraded images improve significantly the detection results. In low degradation scenarios, the improvement is better than the baseline results. Also, the proposed models is able to detect and localize properly on spacecraft images degraded by pixel loss. The proposed model continues to perform close to baseline in conditions even up to 80% pixel loss for the black background experiments.

An efficient iris sensor identification algorithm can be used in certain forensic applications, i.e. detecting mislabeled iris data at large scale iris datasets, and verifying the validity of the data origin of collected iris datasets that are available to be shared. Such knowledge can potentially increase the overall iris recognition system accuracy by offering the operator the option to match same-sensor or cross-sensor iris images. In either case the knowledge of the origin of the sensor used to collect these data, when not available, or the correction of mislabeled data, is expected to result in higher iris matching accuracy. Another benefit of iris sensor identification is that it can assist in improving the detection of fake iris data, i.e. when knowing the iris sensor, we can apply more appropriate models for fake detection that are tuned for a specific iris sensor. In this paper we propose an efficient deep learning-based iris recognition algorithm that is sensor inter-operable. Our approach utilizes a moderate amount of data and is adaptable to learning rate variations as well as variations of the amount of data used for training per class. Our proposed approach uses a set of iris datasets that include iris images captured at different standoff distances. We are using the original captured, dual eye, or periocular images rather than the iris itself, after detecting, segmenting, and normalizing the iris. Thus, the algorithm is efficient, fast, and less depended on additional algorithmic processes that can add computational complexity. Our proposed process includes transfer learning using iris images of higher quality via the utilization of a set of image quality metrics and achieves close to a hundred percent accuracy after cross-validation.

In this paper, we propose a Faster R-CNN based model for detection of firearms in surveillance and Closed-Circuit Television (CCTV) video. Due to the lack of a publicly available benchmark surveillance video database containing firearms, we created our own firearm database composed of only real surveillance footage from scenes with people holding firearms. Portable firearms such as rifles and pistols, often referred to as small arms, have a high degree of variation in surveillance footage in terms of shape, illumination, scale, pose, and occlusion. To deploy a fast and accurate firearm detection system for real world scenarios, it is important to detect all types of common small arms. To accomplish this, we collected images for our database that contain both handguns (pistols and revolvers) and long guns (rifles and shotguns) to capture as much variation of commonly used small arms as possible. To train our firearm detector we first, assess 18 common geometric and photometric data augmentation techniques in order to identify which ones improve detection performance. Next, we identify deep learning based small arm detection techniques that improve detection performance. We reuse these techniques in another more comprehensive augmentation assessment in order to identify the most efficient combination in terms of detection performance. Finally, we optimize the hyper-parameters of all the tested firearm detection models and apply cross-validation. Our proposed model can accurately detect firearms in video frames taken from real surveillance footage close to real-time, yielding precision and recall scores of 93.9% and 96.4% for handguns, and 95.2% and 94.6% for long guns.

Despite the apparent benefits of modern social coding paradigm such as Stack Overflow, its potential security risks have been largely overlooked (e.g., insecure codes could be easily embedded and distributed). To address this imminent issue, in this paper, we bring a significant insight to leverage both social coding properties and code content for automatic detection of insecure code snippets in Stack Overflow. To determine if the given code snippets are insecure, we not only analyze the code content, but also utilize various kinds of relations among users, badges, questions, answers and code snippets in Stack Overflow. To model the rich semantic relationships, we first introduce a structured heterogeneous information network (HIN) for representation and then use meta-path based approach to incorporate higher-level semantics to build up relatedness over code snippets. Later, we propose a novel hierarchical attention-based sequence learning model named CodeHin2Vec to seamlessly integrate node (i.e., code snippet) content with HIN-based relations for representation learning. After that, a classifier is built for insecure code snippet detection. Integrating our proposed method, an intelligent system named iTrustSO is accordingly developed to address the code security issues in modern software coding platforms. Comprehensive experiments on the data collections from Stack Overflow are conducted to validate the effectiveness of our developed system iTrustSO by comparisons with alternative methods.

In this work, we propose an optimal solution for face detection when operating in the thermal and visible bands. Our aim is to train, fine tune, optimize and validate preexisting object detection models using thermal and visible data separately. Thus, we perform an empirical study to determine the most efficient band specific DeepFace detection model in terms of detection performance. The original object detection models that were selected for our study are the Faster R-CNN (Region based Convolutional Neural Network), SSD (Single-shot Multi-Box Detector) and R-FCN (Region-based Fully Convolutional Network). Also, the dual-band dataset used for this work is composed of two challenging MWIR and visible band face datasets, where the faces were captured under variable conditions, i.e. indoors, outdoors, different standoff distances (5 and 10 meters) and poses. Experimental results show that the proposed detection model yields the highest accuracy independent of the band and scenario used. Specifically, we show that a modified and tuned Faster R-CNN architecture with ResNet 101 is the most promising model when compared to all the other models tested. The proposed model yields accuracy of 99.2% and 98.4% when tested on thermal and visible face data respectively. Finally, while the proposed model is relatively slower than its competitors, our further experiments show that the speed of this network can be increased by reducing the number of proposals in RPN (Region Proposal Network), and thus, the computational complexity challenge is significantly minimized.

Facial attribute analysis is an important step in many biometric algorithms, including face recognition based human authentication. Detecting the state of face attributes is becoming even more popular in mobile based applications, where a biometric authentication application is used to quickly and accurately verify the claimed identity of the owners device, and also, keep the device secure if an intruder attempts to gain unauthorized access to it. While there is a large number of facial attributes that can be automatically detected to support a face recognition system, in this paper we focus on detecting three specific ones that are useful during both an enrollment and authentication process: (1) determining whether the eyes of a subject are open or closed, (2) determining whether a subject is wearing glasses or not, and, finally, (3) detecting whether a subjects facial pose is either frontal or non-frontal. These attributes are associated with face image quality control, which is a very useful component of modern FR systems under the following context: during live authentication, by limiting low quality face image data, we can enhance the face-based authentication accuracy, while at the same time improve user satisfaction via improved system efficiency (i.e. less false match attempts during authentication). Thus, to automatically and efficiently detect all of the aforementioned facial attributes, we developed both conventional and deep learning based models. These models are trained and tested on diverse and challenging face image datasets, using data captured from traditional cameras and mobile devices, when operating at multiple standoff distances, in either indoor or outdoor conditions. Our proposed attribute-specific detection models are robust, yielding up to 100% accuracy (in terms of F1 score) depending on the attribute tested, as well as the model and dataset(s) used for training and testing.

Ear recognition has its advantages in identifying non-cooperative individuals in unconstrained environments. Ear detection is a major step within the ear recognition algorithmic process. While conventional approaches for ear detection have been used in the past, Faster Region-based Convolutional Neural Network (Faster R-CNN) based detection methods have recently achieved superior detection performance in various benchmark studies, including those on face detection. In this work, we propose an ear detection system that uses Faster R-CNN. The training of the system is performed on two stages: First, an AlexNet model is trained for classifying ear vs. non-ear segments. Second, the unified Region Proposal Network (RPN) with the AlexNet, that shares the convolutional features, are trained for ear detection. The proposed system operates in real-time and accomplishes 98 % detection rate on a test set, composed of data coming from different ear datasets. In addition, the system's ear detection performance is high even when the test images are coming from un-controlled settings with a wide variety of images in terms of image quality, illumination and ear occlusion.

Online underground forums have been widely used by cybercriminals to trade the illicit products, resources and services, which have played a central role in the cybercrim-inal ecosystem. Unfortunately, due to the number of forums, their size, and the expertise required, it's infeasible to perform manual exploration to understand their behavioral processes. In this paper, we propose a novel framework named iDetector to automate the analysis of underground forums for the detection of cybercrime-suspected threads. In iDetector, to detect whether the given threads are cybercrime-suspected threads, we not only analyze the content in the threads, but also utilize the relations among threads, users, replies, and topics. To model this kind of rich semantic relationships (i.e., thread-user, thread-reply, thread-topic, reply-user and reply-topic relations), we introduce a structured heterogeneous information network (HIN) for representation, which is capable to be composed of different types of entities and relations. To capture the complex relationships (e.g., two threads are relevant if they were posted by the same user and discussed the same topic), we use a meta-structure based approach to characterize the semantic relatedness over threads. As different meta-structures depict the relatedness over threads at different views, we then build a classifier using Laplacian scores to aggregate different similarities formulated by different meta-structures to make predictions. To the best of our knowledge, this is the first work to use structural HIN to automate underground forum analysis. Comprehensive experiments on real data collections from underground forums (e.g., Hack Forums) are conducted to validate the effectiveness of our developed system iDetector in cybercrime-suspected thread detection by comparisons with other alternative methods.

Electrocardiography (ECG) is the process of recording the electrical activity of the human heart over time using electrodes that are placed over the skin. While the primary usage of electrocardiograms, the recorded signals, has been focused on the check of signs of heart-related diseases, recent studies have moved also toward their usage for human authentication. Thus, an ECG signal can be unique enough to be used independently as a biometric modality. In addition to its inherent liveness detection, it is easy to collect and can be easily captured either via sensors attached to the human body (fingertips, chest, wrist) or even passively using wireless sensors. In this paper, we propose a novel approach that exploits the spectro-temporal dynamic characteristics of the ECG signal to establish personal recognition system using both short-time Fourier transform (STFT) and generalized Morse wavelets (CWT). This process results in enriching the information extracted from the original ECG signal that is inserted in a 2D convolutional neural network (CNN) which extracts higher level and subject-specific ECG-based features for each individual. To validate our proposed CNN model, we performed nested cross-validation using eight different ECG databases. These databases are considered challenging since they include both normal and abnormal heartbeats as well as a dynamic number of subjects. Our proposed algorithms yield superior performance when compared to other state-ofart approaches discussed in the literature, i.e. the STFT-based one achieves an average identification rate, equal error rate (EER), and area under curve (AUC) of 97.86%, 0.0268, and 0.9933 respectively, whereas the CWT achieves comparable to STFT results in 97.5%, 0.0386, and 0.9882 respectively.

Because of the explosive growth of Android malware and due to the severity of its damages, the detection of Android malware has become an increasing important topic in cybersecurity. Currently, the major defense against Android malware is commercial mobile security products which mainly use signature-based method for detection. However, attackers can easily devise methods, such as obfuscation and repackaging, to evade the detection, which calls for new defensive techniques that are harder to evade. In this paper, resting on the analysis of Application Programming Interface (API) calls extracted from the smali files, we further categorize the API calls which belong to the some method in the smali code into a block. Based on the generated API call blocks, we then explore deep neural networks (i.e., Deep Belief Network (DBN) and Stacked AutoEncoders (SAEs)) for newly unknown Android malware detection. Using a real sample collection from Comodo Cloud Security Center, a comprehensive experimental study is performed to compare various malware detection approaches. The experimental results demonstrate that (1) our proposed feature extraction method (i.e., using API call blocks) outperforms using API calls directly in Android malware detection; (2) DBN works better than SAEs in this application; and (3) the detection performance of deep neural networks is better than shallow learning architectures.

In this work, our objective is to study the impact of statistical hypothesis tests for the purpose of improving heterogeneous face recognition (FR). A series of tests are conducted to find the most suitable type of statistical analysis test (parametric vs. non-parametric). To conduct the experiments, we used a multi-spectral face database (visible and Near-IR) collected under challenging conditions, i.e. at night time and at four different standoff distances, namely 30, 60,90 and 120 meters. Next, the selected statistical analysis test is used to find the statistical significance of; (i) image restoration, (ii) fusion of scores. First, Gabor Wavelets, Histogram of gradients (HOG) and Local binary patterns (LBP) feature descriptors are empirically selected. Then the statistical analysis reveals which descriptors result in higher recognition performance. Finally, statistical hypothesis tests are performed to explore the impact of data stratification (grouping of gallery and probe sets) in terms of ethnicity, gender. A set of face identification studies are performed. Experimental results suggest that our proposed image restoration approach, fusion schemes and the usage of stratification result in a significantly better performance results than the baseline, e.g. the rank-one score is improved from 50% to 71% when using image restoration, to 73% when using fusion of scores and to 75% (i.e. in the case of testing FR accuracy only on the female Asian class) when employing database stratification.

Surveillance systems are very important for law enforcement and military applications. Capturing a biometric modality at a distance and under difficult conditions is a very challenging process. While face or gait can be used to identify an individual in such application, tattoos can also help in the identification process whenever available. Tattoos are considered a soft biometric and in some scenarios may be the only clue that can be used to verify the identity of a suspect or to rule out a suspect. One of the major challenges in tattoo recognition systems is image registration, i.e. the alignment of one tattoo image to a reference image. Accurate registration can greatly improve recognition accuracy. In this paper, we propose a two-level automatic tattoo registration and correction system based on SIFT descriptors and the RANSAC algorithm with a homography model. By using image quality index techniques and a postprocessing step (where we refine our original registration results by an automated correction process where outliers are first identified and then re-processed), our system is able to demonstrate accurate registration results. We tested our registration system using two tattoo image databases. The first one is the NIST-Tatt-C database with 109 subjects collected under uncontrolled condition, and the second one is the new WVU tattoo database (WVU-Tatt) with 79 subjects, which is collected under controlled conditions. Experimental results show that, first, we obtained 100% registration accuracy in both databases. Then, the effect of our registration process on tattoo recognition performance was assessed when using both the NIST-Tatt-C database where the accuracy improved from 54.13% (no registration) to 100% (with registration) and the WVU-Tatt database where the accuracy improved from 86.08% (no registration) to 98.73% (with registration).