Recent advances in fish transportation technologies and deep machine learning-based fish classification have created an opportunity for real-time, autonomous fish sorting through a selective passage mechanism. This research presents a case study of a novel application that utilizes deep machine learning to detect partially dewatered fish exiting an Archimedes Screw Fish Lift (ASFL). A MobileNet SSD model was trained on images of partially dewatered fish volitionally passing through an ASFL. Then, this model was integrated with a network video recorder to monitor video from the ASFL. Additional models were also trained using images from a similar fish scanning device to test the feasibility of this approach for fish classification. Open source software and edge computing design principles were employed to ensure that the system is capable of fast data processing. The findings from this research demonstrate that such a system integrated with an ASFL can support real-time fish detection. This research contributes to the goal of automated data collection in a selective fish passage system and presents a viable path towards realizing optical fish sorting.

The Bergman kernels of monomial polyhedra are explicitly computed. Monomial polyhedra are a class of bounded pseudoconvex Reinhardt domains defined as sublevel sets of Laurent monomials. Their kernels are rational functions and are obtained by an application of Bell's transformation formula.

Ethical hacking and penetration testing is a vital task by cybersecurity professionals to find and exploit possible vulnerabilities in a system before malicious actors do. However, system hacking has a high barrier to entry that necessitates years of experiential learning and formal education. The rapid development of generative artificial intelligence (AI) may potentially lower the barrier to entry. This research experiments with automatic penetration testing via large language models (LLMs) augmented with security information. This research uses a locally hosted Mistral 7B model with Low-Rank Adaptation (LoRA) fine-tuning and Retrieval-Augmented Generation (RAG) to improve penetration testing. When the LLMs are fine tuned with limited and unstructured security data such as privilege escalation articles from a few public web sites, the system succeeds in achieving privilege escalation on Linux hosts. The results of this research suggest that no-cost LLM-assisted penetration testing is possible even on ordinary PCs using locally hosted models. Future research is needed to achieve more diversified attacks and discover zero-day vulnerabilities, perhaps with better prompt engineering, models, and security data.

OBJECTIVES:These data enable the development of machine learned models to detect unintended passage of salmonids over in-stream barriers. Such models are key to fully characterizing the effectiveness of selective passage systems, as they detect and quantify fish passage which occurs outside of the intended transit passage and selection mechanism. These data were used to construct custom surveillance tools for FishPass ( https://www.glfc.org/fishpass.php ), a 20-year restoration project to provide selective up- and down-stream passage of desirable fishes while simultaneously blocking or removing undesirable fishes. DATA DESCRIPTION:The datasets contain over 2300 annotated images of emerged salmonids collected in a natural riverine environment. The images stem from surveillance video collected during 2022 and 2023 fall runs of several pacific salmonid species introduced to the Laurentian Great Lakes on the Boardman (Ottaway) River in Traverse City, MI, USA. In addition to images of fully emerged salmonids, datasets are provided containing images of partially emerged salmonids, fully submerged fish, and other wildlife present in a riverine environment. The environmental conditions represented by most of the images were clear or partly cloudy. These datasets could be used to develop custom object detection models for emerged fish in riverine environments.

This research paper presents a novel approach to fish surveillance, specifically detecting fish jumping out of water, by leveraging deep learning-based object detection techniques. The study focuses on the use of an EfficientDet-Lite model, a lightweight and efficient model suitable for edge computing devices. The model was trained using a machine learning pipeline that significantly reduces the amount of data requiring manual review. The performance of the model was evaluated using standard average precision (AP) metrics and manual image- and clip-based evaluation. For the TFLite model running on the GPU, the precision derived from the manual evaluation was 0.841 and the recall was 0.279. For the same model compiled for and run on the Coral Edge TPU, the precision was slightly lower at 0.838 with a recall of 0.274. In the clip-based evaluation, the Edge TPU in the Frigate environment achieved a precision of 0.030 and a recall of 0.11. Although the AP metrics appear relatively low, the model demonstrated a high capacity to accurately detect and approximate the location of fish, which is the primary objective of this research. The model was further evaluated in a simulated production environment, demonstrating its potential for real-time fish detection during jumping events. Overall, this study contributes to the field of fish surveillance by introducing an efficient and effective method for fish detection. This research serves as a proof of concept for fish emergence observation using object detection.

This study investigated the leap characteristics of rainbow trout (also known as steelhead) (Oncorhynchus mykiss) present in the Laurentian Great Lakes. To aid in the collection and annotation of leaps, a custom web application was developed and through the labeling of key markers, the launch speed, launch angle, and length of the fish were calculated. Data collection took place during migratory runs in the spring of 2022 and 2023 that resulted in 173 total leaps annotated with mean launch angles of 58.73 and 68.2 degrees, in 2022 and 2023, respectively. The mean launch speed normalized by body length was consistent across years at 8.6 body lengths per second. The integration of leaping data with computational fluid dynamics simulations revealed steelhead launch angle aligns closely with the water velocity direction as the velocity magnitude increases. Applications of this study include hazard analyses for unintended escapement and informed design of intelligent migratory barriers such as those to be developed at FishPass, an instream research facility under design for the Boardman (Ottaway) River in Traverse City, MI, USA.

In recent years, artificial intelligence (AI) and machine learning (ML) have become extremely promising in almost every aspect of our lives, including in cybersecurity. For instance, intrusion detection systems (IDS) and spam filters use machine learning algorithms to constantly monitor networks for abnormal behavior. However, the security of AI/ML-based solutions remains largely unknown and a cause for concerns. This study examines the possibility of cheating AI/ML-based cybersecurity solutions such as spam filters. In particular, we developed an Adaptive Gradient-based Word Embedding Perturbations (AG-WEP) framework for automatically generating adversarial spam examples. AGWEP smartly chooses the optimal perturbations across all features of the word vectors to minimize the degree of modifications to real spam messages. The experimental study suggests the adversarial model is effective to generate meaningful adversarial examples to fool a CNN-based spam classifier.