July 19 ~ 20, 2025, Toronto, Canada
Zhen Zhang, Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
This study proposes a behavior-specific filtering method to improve behavior classification accuracy in Precision Livestock Farming (PLF). While traditional filtering methods, such as wavelet denoising, achieved an accuracy of 91.58%, they apply uniform processing to all behaviors. In contrast, the proposed behavior-specific filtering method combines Wavelet Denoising with a Low Pass Filter, tailored to active and inactive pig behaviors, and achieved a peak accuracy of 94.73%. These results highlight the effectiveness of behavior-specific filtering in enhancing animal behavior monitoring, supporting better health management and farm efficiency
Meri¸c Demir¨ors, Ahmet Murat Ozbayo˘glu, and Toygar Akg¨un ¨, TOBB University of Economics and Technology, Ankara, Turkey
The proliferation of 5G technologies and the vast deployment of Internet of Things (IoT) devices have heightened the demand for optimal spectrum utilization, necessitating robust spectrum management strategies. In this context, an efficient energy detector employing wideband spectrum sensing within a 5G environment is essential for identifying underutilized frequency bands suitable for cognitive radio applications across multiple sub-bands. While cooperative spectrum sensing (CSS) can enhance the detection capabilities of energy detectors amidst noise uncertainty, its performance often deteriorates under low signal-to-noise ratio (SNR) conditions. This study proposes an improved CSS framework that combines Maximal Ratio Combining (MRC) with the K-out-of-N fusion rule to address noise uncertainty in a complex Gaussian environment across multiple sub-bands in cooperative wideband spectrum sensing. Comparative performance analysis confirms that this integrated approach enhances detection probability and maintains a low false alarm rate across various low SNR scenarios, significantly outperforming traditional cooperative and non-cooperative wideband spectrum sensing methods. These results highlight the potential for advancing cognitive radio technologies by optimizing detection algorithms to improve performance under challenging conditions.
Signal-Noise Ratio, Maximal Ratio Combining, Wideband Spectrum Sensing, Energy Detection, K-out-of-N fusion rule
Blessing C. Dike andCajetan M. Akujuobi, Center of Excellence for Communication Systems Technology Research, ECE Dept. Prairie View A&M University Prairie View, Texas, USA
Developments in the field of generative-AI have made it extremely difficult to distinguish artificially generated content from real content. As a result, their reliable detection has become more important. The topic of this research is detecting speeches that are generated by future generative-AI models in unknown languages. It focuses on answering ”With what information does a model distinguish fake audio from real audio, does it learn how spoken languages sound, or does it learn a specific trait of generated speech waves?” Multiple models are trained on various datasets to detect synthetic audio signals generated by generative-AI models. After multiple sessions of trains and tests, the best test accuracy scores for different test sessions are 94.92% for known language from unknown generative-AI model, 98. 44% for an unknown language from known generative-AI model, and 95. 18% for an unknown language from unknown generative-AI model.
CNN, Bispectrum
Adrian E. Conway, Assured Networking Solutions, Weston, MA 02493, U.S.A
A physical unclonable function (PUF)-based method is presented for continuously authenticating the physical hardware provenance of data that a sensor streams over time to a receiving device. In contrast to existing PUF-based authenticated remote sensing techniques, the method does not make use of any PUF challenge-response pair (CRP) databases or PUF models. The new method, that we call a Sensor Ratchet, is based on the hitherto developed PUF-based CRP Ratchet protocol for continuously mutually authenticating a pair of devices over time. As such, the Sensor Ratchet inherits the lightweight computational requirements and secure properties of the CRP Ratchet. Three variations of the Sensor Ratchet are developed: a simplex form of authenticated data transfer in which a receiver is the initiator, a simplex form in which the sensor is the initiator, and a half-duplex form that additionally transmits physically authenticated information - such as control signals - to a sensor.
Sensor data physical provenance, physical authentication, ratchet protocol, physical unclonable function, physical unclonable protocol
Shah Mehmood Wagan, Xinli Zhang and Sidra Sidra, Business School, Sichuan University, Chengdu, China
The study focuses on the innovations by artificial intelligence in the workplace that are affecting the productivity of the staff and the overall performance of businesses. It attempts to uncover the mechanism behind technologys technological impact on business operations and labor productivity. A quantitative research technique was used in this study with SmartPLS. It is found in a study that out of 350 small and medium-sized company samples, the first two had the highest adoption rates. The study specifies that perceived value, as well as the ease of use, has a major effect on the adoption of AI solutions. Technology development is one such method, through which increasing the level of work of people in the business raises the companys productivity. The positive experience increases business performance, therefore accuracy of business management to understand the employees of the business and customer satisfaction are positively related these points have been illustrated in this paper. Besides fact that study is mainly based on self-reporting data it may be biased at that point. As next study could thoroughly investigate long-term impacts of technology adoption on productivity in various disciplines of economy. A company can foster productivity of its workforce and boost performance by making available to them friendly AI tools as well as by providing them with training. This investigation contributes to understanding how AI technology can increase organizational performance in a significant way according to theoretical frameworks such as TAM model and Resource-Based View (RBV).
Artificial Intelligence; Employee productivity; Business Performance; Perceived Usefulness; Customer Satisfaction
Mohamed Yacine DJEMA, Hacene FOUCHAL, Olivier FLAUZAC, LAB-I*, University of Reims Champagne-Ardenne, France
Large language models (LLMs) remain vulnerable to adversarial prompting, yet state-of-the-art certified defenses such as Erase-and-Check (EC) are too slow for real-time use because they must re-evaluate hundreds of prompt variants. We investigate whether a single, attribution-guided deletion can approximate EC’s robustness at a fraction of the cost. Two variants are proposed. Method A keeps an external safety filter but replaces EC’s exhaustive search with one SHAP/feature-ablation pass, erasing the k most influential tokens before a single re-check. Method B removes the filter entirely: we compute SHAP scores inside the generator (Vicuna-7B), excise the top-r% tokens once, and re-generate. On the AdvBench suite with Greedy-Coordinate-Gradient suffixes (|α| ≤ 20), Method A detects up to 75% of attacks when 55% of tokens are removed—two forward passes instead of EC’s linear-to-combinatorial explosion—while SHAP consistently outperforms feature ablation. Method B, guided solely by SHAP, cuts harmful completions from 100% to 5% after deleting the top-20% tokens and sustains single-digit harm rates for 15–45% deletion budgets, narrowing EC’s safety gap yet adding negligible latency. An explainer comparison shows SHAP recovers nearly every adversarial token within the top-5% importance ranks, whereas LIME is slightly noisier and feature ablation trails far behind. These findings expose a tunable speed–safety trade-off: attribution-guided, single-pass excision delivers large latency gains with a bounded drop in worst-case guarantees. Careful explainer choice and deletion budgeting are critical, but attribution can transform explainability from a diagnostic tool into the backbone of practical, low-latency LLM defenses.
Large Language Models, LLMs, Adversarial Prompting, Jailbreak Attacks, Explainable AI, Greedy Coordinate Gradient, Safety Certification and Robustness.
Salman Bader Hazza, Ibrahim ALhajouj, Abdullah Nader Aldossary, Moteb Abdullah Aldossary, and Saud Alhajaj Aldossari, Department of Electrical Engineering, Prince Sattam bin Abdulaziz University Wadi Aldawaser-11913, Saudi Arabia
With the upcoming farming revolution, this paper presents the development of an AI-powered agricultural monitoring system that integrates IoT devices with machine learning algorithms for real-time soil data analysis and nutrient prediction. A custom-built sensor-based device was designed to collect environmental data, including temperature, humidity, and essential soil nutrients (Nitrogen, Phosphorus, and Potassium). The collected data was preprocessed and used to train various supervised learning models, including Neural Networks, Random Forests, and CatBoost. These models were evaluated using key regression metrics such as MSE, MAE, and R2 to determine their predictive accuracy. The results demonstrate that AI techniques can significantly enhance nutrient estimation and decision support in precision agriculture. This study contributes to the growing field of smart farming by offering a low-cost, sensor-integrated solution for sustainable agricultural monitoring.
IoT, AI, catboost, random forest, neural network, farming technologies
Maher Rebai1 and Taha Houda2, 1De Vinci Higher Education Av. L´eonard de Vinci, 92400 Courbevoie, France, 2Price Mohammad Bin Fahd University 617, Al Jawharah, Khobar, Dhahran 34754, Arabie saoudite
Efficient path planning for mobile sensors is crucial in Wireless Sensor Networks (WSNs) to ensure optimal monitoring of coverage holes while considering real-world constraints. This work addresses the problem of determining an optimal trajectory for mobile sensor ensuring optimal monitoring of coverage holes while efficiently navigating through the sensing field. We introduce a novel Binary Integer Linear Programming (BILP) model that formulates the trajectory planning problem as a discrete optimization task, allowing for fine-grained control over sensor movement and coverage quality. The performance of the proposed approach is thoroughly evaluated through comparative experiments against both exact and heuristic methods from the literature. The obtained results confirm that the proposed approach outperforms the recent existing methods.
Wireless Sensor Network (WSN), Linear programming.