Mobile internet: content, security and terminal
Notice bibliographique
Résumé
Mobile internet: content, security and terminalThe mobile internet provides the capability and possibility for mobile users to access the Internet and use the services anywhere and anytime.With the rapid development of wireless transmission technologies and the proliferation of mobile terminals, the mobile internet is incontrovertibly a great success that has changed our lives.However, more challenges still exist including content delivery, security problems, and terminal diversity.Various studies have been conducted to address these challenges envisioned in the future mobile internet.Seven papers that cover a broad range of this feature topic were selected for this special issue.These articles are expected to stimulate new ideas and developments in the research community, providing readers with relevant background information and proposed solutions to various technical design issues of future mobile internet.The main contributions of these articles are shown in the following.In the first article, 'User communities and contents co-ranking for user-generated content quality evaluation in social networks' proposes a new graph-theoretic user communities and contents coranking algorithm based on three different relationship networks for user-generated content quality evaluation.Contents and user communities are ranked using a co-ranking algorithm based on the assumption that there is a mutually reinforcing relationship between them.Experiments using realworld data have shown that this algorithm outperforms competitive algorithms by a good margin in most cases, and a user community is more useful than a single user for user-generated content quality evaluation.In the second article, 'Experimentally driven quality of experience-aware multimedia content delivery in modern wireless networks' introduces a dynamic user experience framework that allows mobile users to express their preference with respect to instantaneous experience of their service performance, through the dynamic adaptation of their service-aware utility functions, so that radio resources could be efficiently managed.Based on real user data obtained through experimentation, they quantify appropriately defined QoE-aware utility functions that can be used throughout the overall radio resource management process.Extensive numerical results verify the analytical claims on the efficiency and applicability of the proposed framework in a heterogeneous wireless environment, while significantly optimizing mobile internet experience.In the third article, 'Trigger word mining for relation extraction based on activation force' characterizes the relation extraction as structured feature learning and employs the activation force to extract and construct structured features.Relation extraction is a difficult task, and current methods are more or less heuristic and cannot achieve high accuracy.To deal with this problem, the paper defines the trigger word as a word that is most likely to form a structure corresponding to a special relation and extract the trigger-word dependency pair by the activation force model.Based on the trigger words and trigger-word dependency pairs, the shortest dependency paths (SDPs) are optimized.The experimental results also verify that the modified SDP patterns are superior to the original SDP patterns.In the fourth article, 'Toward mobile Internet-based layered vehicular networks with efficient access management' proposes an architecture of mobile Internet-based layered vehicular networks to resolve the problems resulting from limited remote subscriber units having insufficient resources and unsatisfied user experience.The vehicles are virtualized as special vehicular small cells within the first layer and then integrated with the layered heterogeneous networks at the second layer.They also discuss the access management problem and design an optimal access strategy.Simulation results demonstrate the effectiveness of the proposed scheme
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,008 | 0,010 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,003 |
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 source (Gemma direct ou Codex distillé), 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 ».