In this paper, we present "Comprehensify," a Chrome extension designed to enhance learning by providing personalized content summarization using large language models (LLMs). By analyzing user knowledge levels across different topics and reading web page content, Comprehensify generates summaries tailored to the user's comprehension level. We integrate various models, including GPT-4o, GPT-4o-mini, LLaMA 3, and Claude 3, and implement a client-side cache as well as a server-side cache to optimize performance. Additionally, user authentication is employed to ensure personalized experiences. Our evaluation shows promising results in providing effective learning aids, with potential future enhancements to further improve the system's functionality.

In recent years, large language models (LLMs) and generative AI have revolutionized natural language processing (NLP), offering unprecedented capabilities in education. This chapter explores the transformative potential of LLMs in automated question generation and answer assessment. It begins by examining the mechanisms behind LLMs, emphasizing their ability to comprehend and generate human-like text. The chapter then discusses methodologies for creating diverse, contextually relevant questions, enhancing learning through tailored, adaptive strategies. Key prompting techniques, such as zero-shot and chain-ofthought prompting, are evaluated for their effectiveness in generating high-quality questions, including open-ended and multiple-choice formats in various languages. Advanced NLP methods like fine-tuning and prompt-tuning are explored for their role in generating task-specific questions, despite associated costs. The chapter also covers the human evaluation of generated questions, highlighting quality variations across different methods and areas for improvement. Furthermore, it delves into automated answer assessment, demonstrating how LLMs can accurately evaluate responses, provide constructive feedback, and identify nuanced understanding or misconceptions. Examples illustrate both successful assessments and areas needing improvement. The discussion underscores the potential of LLMs to replace costly, time-consuming human assessments when appropriately guided, showcasing their advanced understanding and reasoning capabilities in streamlining educational processes.

This paper explores enhancements in Adaptive Cruise Control (ACC) using Frequency Modulated Continuous Wave (FMCW) radar to optimize performance in varied environmental clutter conditions, including rain, fog, and insect interference. By implementing Constant False Alarm Rate (CFAR) algorithms, the study reduces clutter impact, enhancing radar accuracy for both range and velocity measurements of multiple targets. Results demonstrate that CFAR integration mitigates errors in velocity and distance estimation, particularly under adverse conditions, supporting ACC reliability over a range of up to 200 meters. This study underscores the effectiveness of advanced clutter rejection in improving safety and functionality in ACC systems for real-world applications.

The building and construction industry is a significant contributor to global greenhouse gas emissions, with 37% of total emissions attributed to this sector. While efforts have primarily targeted operational carbon emissions, which stem from building operations like heating and cooling, the urgent need to address embodied carbon—associated with the materials used and their life cycle—has gained attention. This paper explores the critical role of material selection and innovative practices in reducing embedded carbon in the built environment. It highlights collaborative models and international cooperation essential for decarbonizing building materials to achieve net-zero emissions by mid-century. The findings underscore that embodied carbon currently represents a growing proportion of a building’s overall carbon footprint, necessitating proactive measures in the design and construction phases. By integrating life cycle assessments and prioritizing sustainable material choices, stakeholders can significantly diminish carbon emissions and align with global climate goals. Through case studies and best practices, this research advocates for a comprehensive approach to carbon reduction that encompasses both operational and embodied emissions in the built environment.

This paper explores various methods for analyzing, manipulating, and evaluating survey data. Using a Jupyter notebook, we examine trends such as the increase in the number of published survey articles over the years. Additionally, we categorize each article into different taxonomy topics. Through data manipulation, we create a TF-IDF matrix, enabling us to predict an article’s taxonomy category based on key words from its title, summary, and associated categories

Recent years have seen transformative progress in machine comprehension and generation of human language, but maintaining thematic coherence and contextual continuity over extended interactions remains a significant hurdle. This paper introduces a novel framework for response generation, the Contextual Relevance Transfer (CRT) enabled by Continuity-Based Response Generation (CBRG), designed to address these challenges by retaining relevance and coherence across multiturn exchanges. CRT and CBRG work in tandem to embed context-specific relevance patterns into large language model architectures, thereby supporting more fluid and thematically aligned interactions. By implementing CRT within an opensource LLM, this research evaluates the capacity for CBRG to facilitate continuity without sacrificing computational efficiency, employing context-sensitive filters, adaptive protocols, and recursive relevance loops. Empirical results illustrate notable advancements in multi-turn coherence, sentiment alignment, and topic transition smoothness, showing CBRG's effectiveness in minimizing response degeneration and supporting a more cohesive user experience. Experimental benchmarks confirm that CBRG not only achieves high continuity scores but also demonstrates reduced response latency, making it viable for realtime conversational applications. The insights offered here reveal the potential for contextually aware frameworks like CBRG to redefine interaction quality in intelligent language-based systems, presenting new possibilities for applications requiring sustained engagement and thematic accuracy.

This paper delves into the inherent graphtopological structure utilized by algorithms such as LandMark in the context of RFID indoor positioning. We uncover the essence of these algorithms, which leverage the implicit topological relationships within signal features for message passing and positioning accuracy. Despite the theoretical advantages, practical applications of the LandMark algorithm have been hindered by issues related to signal propagation, the limitations of topological structures, neighbor tag selection, and simplistic weight distribution methods. To address these limitations, we propose a series of innovative improvements. Our approach includes data preprocessing techniques like B-spline interpolation and normalization to mitigate environmental noise and enhance signal integrity. We introduce the concept of spatiotemporal graphs that map signals into a high-dimensional space, allowing for the construction of dynamic graph structures that more accurately capture the temporal dynamics of signals. Furthermore, we employ the PNAConv algorithm, a graph neural network technique, to refine the message passing and feature aggregation process, optimizing the selection of neighboring tags. Our experiments, conducted across various datasets, demonstrate that our model maintains low error rates, showcasing its high precision and robustness in diverse environments. The results not only validate the effectiveness of our improved algorithm but also highlight the importance of understanding and exploiting the graph-topological structure inherent in signal-based positioning systems.

The escalating frequency and sophistication of malicious encryption attacks have required the development of more effective detection mechanisms. The Dynamic Encryption Pattern Analysis (DEPA) framework introduces a novel approach that focuses on real-time monitoring and analysis of encryption behaviors, enabling the identification of ransomware activities with high accuracy and minimal false positives. Through a modular architecture, DEPA seamlessly integrates into existing cybersecurity infrastructures, offering scalability and adaptability across various organizational environments. Empirical evaluations demonstrate DEPA's efficacy in detecting a wide range of ransomware variants, including those employing advanced obfuscation techniques, while maintaining efficient resource utilization. The findings demonstrate the potential of behavior-based detection methodologies in enhancing defenses against evolving ransomware threats.

Beal conjecture can be proved extending the understanding of Goldbach's discussion.

The long-term drift is well known as one of the most challenging issues regarding gas sensing and applications involving electronic noses, undermining the reliability and accuracy of these systems over time, as the pattern recognition models lose their capability of performing gas discrimination. In this work, we adopt a probabilistic view of the drift problem. This perspective yields a novel univariate scheme for drift compensation, which is grounded on probabilistic modelling. The potential usefulness of the proposed approach is highlighted through experimental results on the gas classification task, which reveal a superior and more stable performance when compared to traditional component correction multivariate methods. Besides its stable performance, the proposed method also presents several desired attributes, such as computational simplicity, continuous adaptability and suitability for online applications. The experiments were conducted using a dataset lasting 7 months, comprising measurements from three different gases at different concentration levels in an array containing 17 polymeric sensors.

This paper presents an in-depth learning process for Activities of Daily Living (ADL) and self-care planning in Adult Day Health Centers. The framework integrates multi-modal sensor data fusion with deep learning architectures to provide continuous monitoring and automated evaluation of older people’s legal status. The system uses a hierarchical system combined with convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, enhanced by monitoring systems for physical-spatial learning. Many data streams from wearable devices, environmental sensors, and medical monitoring devices are becoming pre-processed and de-processed. The framework incorporates a dynamic care plan adaptation strategy utilizing reinforcement learning techniques for intervention optimization. Experimental validation conducted across three Adult Day Health Centers with 150 participants over six months demonstrated superior performance compared to traditional assessment methods. The system achieved 92.8% accuracy in ADL recognition tasks, with a 35% reduction in assessment time and a 62% decrease in false alarm rates. Clinical validation through 25 detailed case studies revealed early detection of health deterioration, averaging 3.2 days ahead of conventional methods. The proposed framework significantly enhances the efficiency and accuracy of elderly care delivery while reducing healthcare provider workload by 40%. This research contributes to advancing intelligent healthcare by creating a solution for ADL measurement and self-correction.

Block-term tensor decomposition (BTD) was recently reconsidered in the context of array signal processing, and shown to be the natural choice in sensor arrays of increased dimensionality and channel multipath. Semi-blind joint channel estimation/data detection (JCD) was addressed in the special but important case of the uniform rectangular array (URA), via efficient algorithms that wed existing JCD schemes with BTD approximation. That work is revisited in this paper from a Bayesian standpoint to address the discrete nature of the input symbol constellation. This offers a new interpretation of the outstanding BTD JCD scheme among those previously developed. Moreover, two probabilistic methodologies are followed, which, when endowed with the BTD structure, result in additional JCD schemes that provide alternatives to our previous work in detection performance and computational complexity. The receivers are evaluated via simulation results at various noise levels, and different array and constellation sizes and favorably compared with the training-only-based solution.

API fuzzing, a technique widely used to uncover vulnerabilities in web applications, poses significant security risks when exploited maliciously, leading to service disruptions and data breaches. While firewalls can block unauthorized fuzzing attempts, they limit defenders' ability to gather data on attacker methodologies, reducing actionable cyber threat intelligence. Identifying the responsible fuzzers enables defenders to trace the attacker, uncover their motives, and assess the potential impact, which helps security teams prepare more effectively, mitigate attacks, and develop targeted countermeasures to enhance the security of web APIs. However, analyzing the payloads generated by fuzzers remains largely unexplored and presents significant challenges. For instance, fuzzers often generate similar payloads due to shared initial seeds and similar fuzzing strategies, making accurate fuzzer identification more complex. To analyze this, we experimented with four well-known API fuzzers; APIFuzzer, Kiterunner, RESTler, and Schemathesis, and created a comprehensive dataset of their payloads targeting five different web APIs. Our thorough analysis reveals that the overlapping payloads, i.e., the identical generated payloads across these fuzzers, can be substantially large. For instance, ≈17% of payloads generated with Schemathesis overlapped with ≈12% of the payloads generated with RESTler across different web APIs. As a result, defining distinctive payload features that machine learning models can learn to differentiate and identify their fuzzer accurately becomes more difficult. Alternatively, deep learning techniques, known for their ability to automatically extract features, present a compelling alternative. To evaluate this, we experimented with an architecture combining a bidirectional Transformers-based encoder-decoder and a machine learning classifier to classify fuzzers based on their payloads. Rigorous evaluation using k-fold cross-validation demonstrated high precision and recall, averaging 89%, showcasing this combinatorial architecture's robustness and effectiveness. Our findings demonstrate the potential of combining deep learning and machine learning for fuzzer identification and enhancing web API security.

With the growing demand for global connectivity and the proliferation of Internet of Things (IoT) devices, nonterrestrial networks (NTNs) have become critical in providing seamless and ubiquitous coverage and connectivity. However, integrating different NTN platforms and their integration with terrestrial networks presents significant challenges, such as managing interference and optimizing resource allocation. This work proposes a transmissive reconfigurable intelligent surface (T-RIS)-equipped low earth orbit (LEO) satellite communication in cognitive radio-enabled integrated NTNs. In the proposed system, a geostationary (GEO) satellite operates as a primary network, and a T-RIS-equipped LEO satellite operates as a secondary wireless network. The objective is to maximize the sum rate of T-RIS-equipped LEO satellite communication using downlink non-orthogonal multiple access (NOMA) while ensuring the service quality of GEO cellular users. Our framework simultaneously optimizes the total transmit power of LEO, NOMA power allocation for LEO user equipment (LUE), and T-RIS phase shift design, subject to the service quality of LUE and interference temperature to the primary GEO network. To solve the non-convex sum rate maximization problem, we first adopt successive convex approximations to reduce the complexity of the formulated optimization. Then, we divide the problem into two parts, i.e., power allocation of LEO and phase shift design of T-RIS. The power allocation problem is solved using Karushâ Ȃ ŞKuhnâ Ȃ ŞTucker conditions, while the phase shift problem is handled by Taylor approximation and semidefinite programming. Numerical results are provided to validate the proposed optimization framework.

Modern academic research encompasses a broad spectrum of subjects, reflecting the intricate and evolving challenges and opportunities that societies encounter globally. This study delves into the role of literature in shaping cultural identities and the profound impact of technology on traditional structures. Current scholarly contributions offer deep insights into the blend of humanistic concerns and technological advancements, underscoring their combined relevance in tackling contemporary societal issues.

A compact spiral cavity backed substrate integrated waveguide (SIW) Multiple Input multiple output (MIMO) antenna is presented in this paper. The edge-shaped spiral on top of the SIW cavity acts as a dipole antenna. The dual spiral arms are excited from their symmetrical connecting center. The unit cell comprising of dual spiral arms is rotated such that each unit cell is orthogonal to each other forming a compact 4x4 MIMO SIW antenna. The proposed design shows a wide bandwidth of 930 MHz (13.74 GHz to 14.67 GHz) and 67.68% impedance bandwidth. The overall size of proposed MIMO SIW antenna is 0.9 λ o x 0.9 λ o x 0.024 λ o , where λ o is the operating wavelength. A good return loss of-18.4 dB at 14.17 GHz is achieved. The metal pins of plus shape at the center improves the isolation to 19.6 dB at resonant frequency. A pattern diversity is achieved by the top spiral radiating arms and it's complementary structure at the bottom of the cavity. The beam-width of the proposed antenna is 90° varying from-45 deg to +45 deg, useful for reliable signal transmission and reception. Thus proposed antenna design is a symmetrical compact design working at Ku band suitable for radio telescope application.

In recent years, diffusion models have become one of the main methods for generating images. However, detecting images generated by these models remains a challenging task. This paper proposes a novel method for detecting images generated by Latent Diffusion Models (LDM) by identifying artifacts introduced by their autoencoders. By training a detector to distinguish between real images and those reconstructed by the LDM autoencoder, the method enables detection of generated images without directly training on them. The novelty of this research lies in the fact that, unlike similar approaches, this method does not require training on synthesized data, significantly reducing computational costs and enhancing generalization ability. Experimental results show high detection accuracy with minimal false positives, making this approach a promising tool for combating fake images.

We present the training and validation dataset to be utilized in the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. This challenge is being virtually organized by the Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria, and the Medical Imaging and Signal Analysis Hub (MISAHUB) in collaboration with the 9th International Conference on Computer Vision Image Processing (CVIP 2024) organized by the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India.