Bibliographic record
Abstract
In this thesis, the problem of decontaminating networks from Black Viruses (BVs) using a team of system mobile agents, i.e., the BVD problem, is investigated.The BV is a dynamic harmful process which, like the extensively studied black hole (BH), destroys any agent arriving at the network site where it resides; when that occurs, unlike a black hole which is static by definition, a BV moves, spreading to all the neighbouring sites, thus increasing its presence in the network.The initial location of BV is unknown a priori.The objective is to permanently remove any presence of the BV from the network with minimum number of site infections (and thus casualties) and prevent any previously decontaminated node from becoming infected again.The BVD problem is first studied in the systems with only one BV.Initial investigations are for some common classes of interconnection networks: (multidimensional) grids, tori, and hypercubes.Optimal solutions are proposed and their complexities are analyzed in terms of node infections, agent team size, and movements.After understanding the basic properties of the decontamination process in these special graphs, the BVD problem is studied in arbitrary networks.Finally research is extended to Multiple BV Decontamination problem (MBVD) both in arbitrary graphs and in special topologies.To help understand the behavior of the protocol developed and support complexity analysis, an experimental study is performed using the simulator for reactive distributed algorithms DisJ.A large number of simulations are carried out on various sizes of graphs with many connectivity densities.The simulation runs show that the propose protocol beats random search; they also disclose many interesting behaviors, and validate the analytical complexity results.The simulation results also provide iii deep understanding on the influence of graph connectivity density and graph size on complexities, i.e., movement, time, and agent size.Finally conclusion remarks are presented and future researches are proposed.ivFirstly, I would like to express my sincere gratitude to my advisors Professors Nicola Santoro and Paola Flocchini for their enthusiastic motivation, judicious and enlightening advices, and patient guidance throughout this research work!Their passion for distributed computing, dedication to knowledge acquiring and science developing, and an unrelenting attitude to detail all inspire me.On the other hand, their modest academic style creates an excellent research environment.They carefully selected my courses and topics, and at the same time, provided me enough flexibility to suit my work and life.Our meetings and discussions always take away their precious lunch-break time... Without their continuous support of my Ph.D. study and related research, it would not be possible for me to complete this work.Last but not the least, I would like to thank my wife, Yan Gong, and my daughter, Jessica Cai.Working full-time and supporting a family, and at the same time, studying and doing research part-time for my Ph.D. degree are definitely challenge to me.Without
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".