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Enregistrement W4300889023 · doi:10.1145/3133956

Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

2017· paratext· en· W4300889023 sur OpenAlexaboutno aff

Notice bibliographique

Revuenon disponible
Typeparatext
Langueen
DomaineComputer Science
ThématiqueSoftware Engineering Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPresentation (obstetrics)Computer scienceGovernment (linguistics)Inclusion (mineral)Library scienceProcess (computing)Operations researchSociologyEngineeringMedicine

Résumé

récupéré en direct d'OpenAlex

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.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,018
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,190
Score d'incertitude au seuil0,636

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,018
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0030,002
Études des sciences et des technologies0,0030,002
Communication savante0,0090,008
Science ouverte0,0020,004
Intégrité de la recherche0,0020,005
Charge utile insuffisante (le modèle a refusé de juger)0,1900,156

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.

Tête enseignante Opus0,076
Tête enseignante GPT0,335
Écart entre enseignants0,259 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations68
Publié2017
Routes d'admission1
Résumé présentoui

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