arXiv subjects

Jawwad Ahmed Shamsi

Publications and source records attributed to Jawwad Ahmed Shamsi.

4 recordsLinked to original sources

arXiv2026

Large Language Models for Multilingual Code Intelligence: A Survey

Large language models have transformed AI-assisted software engineering, but current research remains biased toward high-resource languages such as Python, with weaker performance in languages like Rust and OCaml. Since real-world systems are inherently polyglot, robust multilingual code intelligence is crucial. This survey focuses on two key tasks: multilingual code generation from shared natural-language requirements, and multilingual code translation that preserves semantics across languages. It reviews representative methods, benchmarks, and evaluation metrics, and highlights challenges and opportunities for trustworthy cross-language generalization.

cs.SE↗

arXiv2026

From Heuristics to Transformers: A Comprehensive Survey of Type Inference from Stripped Binaries

The recovery of high-level type information from stripped binaries-executables devoid of symbol tables and debugging information-is a cornerstone of software reverse engineering, vulnerability analysis, and decompilation. This survey tracks the evolution of binary type inference from early rule-based heuristics and static analysis to modern deep learning architectures. We analyze the shift from "duck typing" and constraint-solving techniques (e.g., BITY, BinSub) to context-aware neural models (e.g., EKLAVYA, CATI) and finally to state-of-the-art Transformer and Graph Neural Network (GNN) architectures (e.g., SeeType, TYGR). We identify core challenges, including optimization-induced semantics loss and structural type recovery, and propose future research directions in neuro-symbolic inference.

cs.SE↗

arXiv2025

TRACES: Temporal Recall with Contextual Embeddings for Real-Time Video Anomaly Detection

Video anomalies often depend on contextual information available and temporal evolution. Non-anomalous action in one context can be anomalous in some other context. Most anomaly detectors, however, do not notice this type of context, which seriously limits their capability to generalize to new, real-life situations. Our work addresses the context-aware zero-shot anomaly detection challenge, in which systems need to learn adaptively to detect new events by correlating temporal and appearance features with textual traces of memory in real time. Our approach defines a memory-augmented pipeline, correlating temporal signals with visual embeddings using cross-attention, and real-time zero-shot anomaly classification by contextual similarity scoring. We achieve 90.4\% AUC on UCF-Crime and 83.67\% AP on XD-Violence, a new state-of-the-art among zero-shot models. Our model achieves real-time inference with high precision and explainability for deployment. We show that, by fusing cross-attention temporal fusion and contextual memory, we achieve high fidelity anomaly detection, a step towards the applicability of zero-shot models in real-world surveillance and infrastructure monitoring.

cs.CV↗

arXiv2023

Sequential Embedding-based Attentive (SEA) classifier for malware classification

The tremendous growth in smart devices has uplifted several security threats. One of the most prominent threats is malicious software also known as malware. Malware has the capability of corrupting a device and collapsing an entire network. Therefore, its early detection and mitigation are extremely important to avoid catastrophic effects. In this work, we came up with a solution for malware detection using state-of-the-art natural language processing (NLP) techniques. Our main focus is to provide a lightweight yet effective classifier for malware detection which can be used for heterogeneous devices, be it a resource constraint device or a resourceful machine. Our proposed model is tested on the benchmark data set with an accuracy and log loss score of 99.13 percent and 0.04 respectively.

cs.CR↗