Proceedings of the 3rd International on Workshop on Physical Analytics
Notice bibliographique
Résumé
Welcome to the 3rd International Workshop on Physical Analytics (WPA) being held in Singapore on June 26, 2016. We are honored to serve as the chairs for this latest WPA edition that continues in the tradition of previous workshops in the series that were also co-located with MobiSys. Broadly speaking, WPA is motivated by the observations that people spend a significant part of their daily lives performing a variety of activities in the physical world---travelling to places (including commuting to/from work using public or private transport), dwelling and engaging in various activities at various locations (e.g., exercising in the gym, eating at restaurants and food courts), interacting with various physical objects and artefacts (e.g., touching or picking up products at a retail store, or browsing through books and magazines at a library), being subject to various audiovisual stimuli (e.g., listening to announcements at transit hubs, watching advertisements on public displays or movies on TV) and interacting with other people (in groups, as part of crowds or one-on-one). These activities and interactions contain a wealth of information about user behavior, preferences, attitudes and interests, that, if harnessed, can benefit both users and consumer-facing businesses. While research has been underway in utilizing various sensing and analytics tools to capture and annotate such behavior (e.g., profile smoking episodes using wearable devices or monitor consumer reactions to advertising content via video analysis), the vast majority of such research focuses on exploring individual sensing techniques targeted at specific activities, and is scattered across various academic forums. The goal of this workshop series is to offer a unified forum to explore both (a) the technologies (current and emerging) that can enable unobtrusive capture of such individual and collective physical world behavior, and (b) the realworld commercial applications and services that leverage upon such understanding of physical world behavior. By bringing together researchers and practitioners from industry to have a continuing conversation on Physical Analytics, our goal is to help coalesce a research agenda for our community. This year we are particularly honored to have Kyle Jamieson (Princeton University) giving the workshop keynote. We look forward to hearing Kyle's perspective on what new innovations in high-precision localization, and breakthroughs in wireless networking more generally, will mean for the physical analytics area.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».