Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security
Bibliographic record
Abstract
Welcome to the 24th ACM Conference on Computer and Communications Security! Since 1993, CCS has been the ACM's flagship conference for research in all aspects of computing and communications security and privacy. This year's conference attracted a record number of 836 reviewed research paper submissions, of which a record number of 151 papers were selected for presentation at the conference and inclusion in the proceedings. The papers were reviewed by a Program Committee of 146 leading researchers from academic, government, and industry from around the world. Reviewing was done in three rounds, with every paper being reviewed by two PC members in the first round, and additional reviews being assigned in later rounds depending on the initial reviews. Authors had an opportunity to respond to reviews received in the first two rounds. We used a subset of PC members, designated as the Discussion Committee, to help ensure that reviewers reconsidered their reviews in light of the author responses and to facilitate substantive discussions among the reviewers. Papers were discussed extensively on-line in the final weeks of the review process, and late reviews were requested from both PC members and external reviewers when additional expertise or perspective was needed to reach a decision. We are extremely grateful to the PC members for all their hard work in the review process, and to the external reviewers that contributed to selecting the papers for CCS. Before starting the review process, of the 842 submissions the PC chairs removed six papers that clearly violated submission requirements or were duplicates, leaving 836 papers to review. In general, we were lenient on the requirements, only excluding papers that appeared to deliberately disregard the submission requirements. Instead of excluding papers which carelessly deanonymized the authors, or which abused appendices in the opinion of the chairs, we redacted (by modifying the submitted PDF) the offending content and allowed the papers to be reviewed, and offered to make redacted content in appendices available to reviewers upon request. Our review process involved three phases. In the first phase, each paper was assigned two reviewers. Following last year's practice, we adopted the Toronto Paper Matching System (TPMS) for making most of the review assignments, which were then adjusted based on technical preferences declared by reviewers. Each reviewer had about 3 weeks to complete reviews for around 12 papers. Based on the results of these reviews, an additional reviewer was assigned to every paper that had at least one positive-leaning review. Papers where both initial reviews were negative, but with low confidence or significant positive aspects, were also assigned additional reviews. At the conclusion of the second reviewing round, authors had an opportunity to see the initial reviews and to submit a short rebuttal. To ensure that all the authors' responses were considered seriously by the reviewers, the Discussion Committee members worked closely with the reviewers to make sure that they considered and responded to the authors' rebuttals. When reviewers could not reach an agreement, or additional expertise was needed, we solicited additional reviews. The on-line discussion period was vibrant and substantive, and at the end of this process the 151 papers you find here were selected for CCS 2017.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.190 | 0.156 |
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".