arXiv subjects

Hazem Abbas

Publications and source records attributed to Hazem Abbas.

4 recordsLinked to original sources

arXiv2026

How Far Can Lightweight LLMs Go in Intent-to-SLO Translation?

Intent-based networking (IBN) promises QoS provisioning in which the network derives the configuration from operator-stated goals. Its first step, translating a natural-language intent into enforceable service-level objectives (SLOs) grounded in a live snapshot of the network state, increasingly relies on large language models (LLMs). These large models add cost and energy to the management loop and typically require multi-GPU, cloud-hosted infrastructure. Can lightweight open LLMs take over this step dependably? We answer this question with a five-dimensional evaluation of ten LLMs with <10B parameters from five families, under four prompting setups, on 716 placement intents. We evaluate translation performance, intent validation, hallucination, energy impact, and robustness to diverse phrasing. The best configurations translate intents with a strict F1 of 0.943 and reject up to 191 of 195 invalid intents. With few-shot reasoning, the strongest models hallucinate unsupported QoS requirements in only about one response in a hundred, whereas the zero-shot setup struggles. A translated intent costs 0.044-0.148 Wh of GPU energy without reasoning, and added energy cost for reasoning pays off for the weaker models. Furthermore, we show that realistic rephrasing lowers the rejection of invalid intents in 15 of 16 configurations for the best-performing models, leaving robust intent validation as a key open challenge for reliable IBN. Our code and data are available: https://github.com/ggck43/intent2slo.

cs.NI↗

arXiv2026

Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning

Assessing spoken language is challenging, and quantifying pronunciation metrics for machine learning models is even harder. However, for the Holy Quran, this task is enabled by the rigorous recitation rules (Tajweed) established through the efforts of Muslim scholars, making highly effective assessment possible. Despite this advantage, the scarcity of high-quality annotated data remains a significant barrier. In this work, we bridge these gaps by introducing: (1) A 98% automated pipeline to produce high-quality Quranic datasets -- encompassing collection of recitations from expert reciters, segmentation at pause points (waqf) using our fine-tuned wav2vec2-BERT model, transcription of segments, and transcript verification via our novel Tasmeea algorithm; (2) 848 hours of audio (286K annotated utterances); (3) qdat_bench, a benchmark covering phonemes, diacritization, and Tajweed rules (Ghunnah, Qalqalah, Madd) on real recitation errors containing 159 samples; (4) A novel ASR-based approach for pronunciation error detection utilizing our custom Quran Phonetic Script (QPS) to encode Tajweed rules (unlike the IPA standard for Modern Standard Arabic). QPS uses an 11-level script: phoneme level (encoding Arabic letters with short/long vowels) and sifat level (encoding articulation characteristics of every phoneme). We further present comprehensive modeling with our novel multi-level CTC model, which achieved 0.21% and 1.94% average Phoneme Error Rate (PER) on the test set and qdat_bench respectively, with a 75.8% Tajweed F1 score. We release our work as open-source: https://obadx.github.io/quran-muaalem/en/

eess.AS↗

arXiv2021

Improving Depth Estimation using Location Information

The ability to accurately estimate depth information is crucial for many autonomous applications to recognize the surrounded environment and predict the depth of important objects. One of the most recently used techniques is monocular depth estimation where the depth map is inferred from a single image. This paper improves the self-supervised deep learning techniques to perform accurate generalized monocular depth estimation. The main idea is to train the deep model to take into account a sequence of the different frames, each frame is geotagged with its location information. This makes the model able to enhance depth estimation given area semantics. We demonstrate the effectiveness of our model to improve depth estimation results. The model is trained in a realistic environment and the results show improvements in the depth map after adding the location data to the model training phase.

cs.CV↗

arXiv2020

A Transfer Learning End-to-End ArabicText-To-Speech (TTS) Deep Architecture

Speech synthesis is the artificial production of human speech. A typical text-to-speech system converts a language text into a waveform. There exist many English TTS systems that produce mature, natural, and human-like speech synthesizers. In contrast, other languages, including Arabic, have not been considered until recently. Existing Arabic speech synthesis solutions are slow, of low quality, and the naturalness of synthesized speech is inferior to the English synthesizers. They also lack essential speech key factors such as intonation, stress, and rhythm. Different works were proposed to solve those issues, including the use of concatenative methods such as unit selection or parametric methods. However, they required a lot of laborious work and domain expertise. Another reason for such poor performance of Arabic speech synthesizers is the lack of speech corpora, unlike English that has many publicly available corpora and audiobooks. This work describes how to generate high quality, natural, and human-like Arabic speech using an end-to-end neural deep network architecture. This work uses just $\langle$ text, audio $\rangle$ pairs with a relatively small amount of recorded audio samples with a total of 2.41 hours. It illustrates how to use English character embedding despite using diacritic Arabic characters as input and how to preprocess these audio samples to achieve the best results.

eess.AS↗