Mobile internet: content, security and terminal
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".