Zobrazeno 1 - 10
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pro vyhledávání: '"Mousavi Seyed"'
We introduced the Scorpion Hunting Strategy (SHS), a novel population-based, nature-inspired optimisation algorithm. This algorithm draws inspiration from the hunting strategy of scorpions, which identify, locate, and capture their prey using the alp
Externí odkaz:
http://arxiv.org/abs/2407.14202
Emotion recognition is the technology-driven process of identifying and categorizing human emotions from various data sources, such as facial expressions, voice patterns, body motion, and physiological signals, such as EEG. These physiological indica
Externí odkaz:
http://arxiv.org/abs/2407.09950
Autor:
Khiarak, Jalil Nourmohammadi, Ahmadi, Ammar, Saeed, Taher Ak-bari, Asgari-Chenaghlu, Meysam, Atabay, Toğrul, Karimi, Mohammad Reza Baghban, Ceferli, Ismail, Hasanvand, Farzad, Mousavi, Seyed Mahboub, Noshad, Morteza
This paper introduces a pioneering English-Azerbaijani (Arabic Script) parallel corpus, designed to bridge the technological gap in language learning and machine translation (MT) for under-resourced languages. Consisting of 548,000 parallel sentences
Externí odkaz:
http://arxiv.org/abs/2407.05189
Autor:
Ravanelli, Mirco, Parcollet, Titouan, Moumen, Adel, de Langen, Sylvain, Subakan, Cem, Plantinga, Peter, Wang, Yingzhi, Mousavi, Pooneh, Della Libera, Luca, Ploujnikov, Artem, Paissan, Francesco, Borra, Davide, Zaiem, Salah, Zhao, Zeyu, Zhang, Shucong, Karakasidis, Georgios, Yeh, Sung-Lin, Champion, Pierre, Rouhe, Aku, Braun, Rudolf, Mai, Florian, Zuluaga-Gomez, Juan, Mousavi, Seyed Mahed, Nautsch, Andreas, Liu, Xuechen, Sagar, Sangeet, Duret, Jarod, Mdhaffar, Salima, Laperriere, Gaelle, Rouvier, Mickael, De Mori, Renato, Esteve, Yannick
SpeechBrain is an open-source Conversational AI toolkit based on PyTorch, focused particularly on speech processing tasks such as speech recognition, speech enhancement, speaker recognition, text-to-speech, and much more. It promotes transparency and
Externí odkaz:
http://arxiv.org/abs/2407.00463
Autor:
Alghisi, Simone, Rizzoli, Massimo, Roccabruna, Gabriel, Mousavi, Seyed Mahed, Riccardi, Giuseppe
We study the limitations of Large Language Models (LLMs) for the task of response generation in human-machine dialogue. Several techniques have been proposed in the literature for different dialogue types (e.g., Open-Domain). However, the evaluations
Externí odkaz:
http://arxiv.org/abs/2406.06399
Publikováno v:
Nonlinear Engineering, Vol 9, Iss 1, Pp 156-168 (2020)
The unsteady convective boundary layer flow of a nanofluid along a permeable shrinking/stretching plate under suction and second-order slip effects has been developed. Buongiorno’s two-component nonhomogeneous equilibrium model is implemented to ta
Externí odkaz:
https://doaj.org/article/ee76353441c2497fb9d70d2a24b011b9
LLMs acquire knowledge from massive data snapshots collected at different timestamps. Their knowledge is then commonly evaluated using static benchmarks. However, factual knowledge is generally subject to time-sensitive changes, and static benchmarks
Externí odkaz:
http://arxiv.org/abs/2404.08700
Autor:
Mousavi, Seyed Mahed, Roccabruna, Gabriel, Alghisi, Simone, Rizzoli, Massimo, Ravanelli, Mirco, Riccardi, Giuseppe
Large Pre-Trained Language Models have demonstrated state-of-the-art performance in different downstream tasks, including dialogue state tracking and end-to-end response generation. Nevertheless, most of the publicly available datasets and benchmarks
Externí odkaz:
http://arxiv.org/abs/2401.02297
AI has been proposed as an important tool to support several efforts related to nature-based climate solutions such as the detection of wildfires that affect forests and vegetation-based offsets. While this and other use-cases provide important demon
Externí odkaz:
http://arxiv.org/abs/2312.11566
Carbon accounting is a fundamental building block in our global path to emissions reduction and decarbonization, yet many challenges exist in achieving reliable and trusted carbon accounting measures. We motivate that carbon accounting not only needs
Externí odkaz:
http://arxiv.org/abs/2312.03722