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This paper presents an effective approach to detect AI-generated text, developed for the Defactify 4.0 shared task at the fourth workshop on multimodal fact checking and hate speech detection. The task consists of two subtasks: Task-A, classifying whether a text is AI generated or human written, and Task-B, classifying the specific large language model that generated the text. Our team (Sarang) achieved the 1st place in both tasks with F1 scores of 1.0 and 0.9531, respectively. The methodology involves adding noise to the dataset to improve model robustness and generalization. We used an ensemble of DeBERTa models to effectively capture complex patterns in the text. The result indicates the effectiveness of our noise-driven and ensemble-based approach, setting a new standard in AI-generated text detection and providing guidance for future developments., Comment: AAAI-25 DEFACTIFY 4.0 Workshop AI generated text detection (1st Rank)

Universities serve as a hub for academic collaboration, promoting the exchange of diverse ideas and perspectives among students and faculty through interdisciplinary dialogue. However, as universities expand in size, conventional networking approaches via student chapters, class groups, and faculty committees become cumbersome. To address this challenge, an academia-specific profile recommendation system is proposed to connect like-minded stakeholders within any university community. This study evaluates three techniques: Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), and a hybrid approach to generate effective recommendations. Due to the unlabelled nature of the dataset, Affinity Propagation cluster-based relabelling is performed to understand the grouping of similar profiles. The hybrid model demonstrated superior performance, evidenced by its similarity score, Silhouette score, Davies-Bouldin index, and Normalized Discounted Cumulative Gain (NDCG), achieving an optimal balance between diversity and relevance in recommendations. Furthermore, the optimal model has been implemented as a mobile application, which dynamically suggests relevant profiles based on users' skills and collaboration interests, incorporating contextual understanding. The potential impact of this application is significant, as it promises to enhance networking opportunities within large academic institutions through the deployment of intelligent recommendation systems., Comment: International Conference on Intelligent Systems and Security - 2024

Traceroutes and geolocation are two essential network measurement tools that aid applications such as network mapping, topology generation, censorship, and Internet path analysis. However, these tools, individually and when combined, have significant limitations that can lead to inaccurate results. Prior research addressed specific issues with traceroutes and geolocation individually, often requiring additional measurements. In this paper, we introduce GeoTrace, a lightweight tool designed to identify, classify, and resolve geolocation anomalies in traceroutes using existing data. GeoTrace leverages the abundant information in traceroutes and geolocation databases to identify anomalous IP addresses with incorrect geolocation. It systematically classifies these anomalies based on underlying causes - such as MPLS effects or interface discrepancies - and refines their geolocation estimates where possible. By correcting these inaccuracies, GeoTrace enhances the reliability of traceroute-based analyses without the need for additional probing. Our work offers a streamlined solution that enhances the accuracy of geolocation in traceroute analysis, paving the way for more reliable measurement studies.

Pediatric brain tumors, particularly gliomas, represent a significant cause of cancer related mortality in children with complex infiltrative growth patterns that complicate treatment. Early, accurate segmentation of these tumors in neuroimaging data is crucial for effective diagnosis and intervention planning. This study presents a novel 3D UNet architecture with a spatial attention mechanism tailored for automated segmentation of pediatric gliomas. Using the BraTS pediatric glioma dataset with multiparametric MRI data, the proposed model captures multi-scale features and selectively attends to tumor relevant regions, enhancing segmentation precision and reducing interference from surrounding tissue. The model's performance is quantitatively evaluated using the Dice similarity coefficient and HD95, demonstrating improved delineation of complex glioma structured. This approach offers a promising advancement in automating pediatric glioma segmentation, with the potential to improve clinical decision making and outcomes., Comment: 8 pages, 9 figures

SrCr$_2$As$_2$ is an itinerant antiferromagnet in the same structural family as the SrFe2As2 high-temperature superconductors. We report our calculations of exchange coupling parameters $J_{ij}$ for SrCr$_2$As$_2$ using a static linear-response method based on first-principles electronic structure calculations. We find that the dominant nearest neighbor exchange coupling $J_{\rm{1}} > 0$ is antiferromagnetic whereas the next-nearest neighbor interaction $J_{\rm{2}} < 0$ is ferromagnetic with $J_{\rm{2}}$/$J_{\rm{1}}$~=~$-0.68$, reinforcing the checkerboard in-plane structure. Thus, unlike other transition-metal arsenides based on Mn, Fe, or Co, we find no competing magnetic interactions in SrCr$_2$As$_2$, which aligns with experimental findings. Moreover, the orbital resolution of exchange interactions shows that $J_1$ and $J_2$ are dominated by direct exchange mediated by the Cr $d$ orbitals. To validate the calculations we conduct inelastic neutron-scattering measurements on powder samples that show steeply dispersive magnetic excitations arising from the magnetic $\Gamma$ points and persisting up to energies of at least 175 meV. The spin-wave spectra are then modeled using the Heisenberg Hamiltonian with the theoretically calculated exchange couplings. The calculated neutron scattering spectra are in good agreement with the experimental data., Comment: 9 pages, 6 figures

In this paper, we discuss the Hyers-Ulam stability of mixed-type additive-cubic Jensen functional equation \begin{align*} 2\mathcal{F}\left(\frac{2u+v}{2}\right)+2\mathcal{F}\left(\frac{2u-v}{2}\right)=\frac{1}{4}[\mathcal{F}(u+v)+\mathcal{F}(u-v)]+3\mathcal{F}(u) \end{align*} in non-Archimedean $(n, \beta)$ normed spaces., Comment: 8 pages

Hand gestures have evolved into a natural and intuitive means of engaging with technology. The objective of this research is to develop a robust system that can accurately recognize and classify hand gestures representing numbers. The proposed approach involves collecting a dataset of hand gesture images, preprocessing and enhancing the images, extracting relevant features, and training a machine learning model. The advancement of computer vision technology and object detection techniques, in conjunction with OpenCV's capability to analyze and comprehend hand gestures, presents a chance to transform the identification of numerical digits and its potential applications. The advancement of computer vision technology and object identification technologies, along with OpenCV's capacity to analyze and interpret hand gestures, has the potential to revolutionize human interaction, boosting people's access to information, education, and employment opportunities. Keywords: Computer Vision, Machine learning, Deep Learning, Neural Networks

This study introduces an approach to optimize Parameter Efficient Fine Tuning (PEFT) for Pretrained Language Models (PLMs) by implementing a Shared Low Rank Adaptation (ShareLoRA). By strategically deploying ShareLoRA across different layers and adapting it for the Query, Key, and Value components of self-attention layers, we achieve a substantial reduction in the number of training parameters and memory usage. Importantly, ShareLoRA not only maintains model performance but also exhibits robustness in both classification and generation tasks across a variety of models, including RoBERTa, GPT-2, LLaMA and LLaMA2. It demonstrates superior transfer learning capabilities compared to standard LoRA applications and mitigates overfitting by sharing weights across layers. Our findings affirm that ShareLoRA effectively boosts parameter efficiency while ensuring scalable and high-quality performance across different language model architectures., Comment: 16 pages, 5 figures