{"id":"W4401878764","doi":"10.1109/tmc.2024.3449645","title":"Cross-Modal Generative Semantic Communications for Mobile AIGC: Joint Semantic Encoding and Prompt Engineering","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Robotics and Automated Systems","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Info-communications Media Development Authority; National Natural Science Foundation of China; Singapore University of Technology and Design; Ministry of Education - Singapore; Ministry of Education, India; National Science Foundation","keywords":"Computer science; Joint (building); Encoding (memory); Modal; Semantic integration; Generative grammar; Semantic computing; Artificial intelligence; Semantic Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00110945,0.0007666759,0.0003798898,0.0008627216,0.0005794704,0.001482113,0.001159399,0.001208541,0.004296282],"category_scores_gemma":[0.004559449,0.0003405313,0.0007562905,0.0008939417,0.001250889,0.003373447,0.002411596,0.00139326,0.001030621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001030206,"about_ca_system_score_gemma":0.00136231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005781789,"about_ca_topic_score_gemma":0.005818362,"domain_scores_codex":[0.9992678,0.0002821483,0.00003548928,0.0001701479,0.0001642718,0.00008012557],"domain_scores_gemma":[0.9983314,0.000828455,0.0001013064,0.000415532,0.0002417751,0.0000814926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005222101,0.0002916661,0.00217812,0.0003171752,0.00006165972,0.0009992586,0.003106702,0.0990887,0.04553039,0.3634156,0.009306891,0.4751816],"study_design_scores_gemma":[0.00002563042,0.00007055268,0.0005800625,0.00003275855,0.00004479899,0.0003260594,0.000434557,0.8204355,0.0215378,0.1418193,0.01464216,0.00005087155],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01783421,0.000114908,0.9734277,0.0002876553,0.0000461388,0.00005680617,0.0001417293,0.002715405,0.005375524],"genre_scores_gemma":[0.5844359,0.000272538,0.4079529,0.0002775478,0.00006552153,0.0001264433,0.0006555356,0.0005958562,0.00561778],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005781789,"threshold_uncertainty_score":0.01437247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02176959291219129,"score_gpt":0.2731462034182809,"score_spread":0.2513766105060897,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}