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23 results for “group signatures”
Artifacts for ASE 2022 Paper -- FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures
<p><strong>Artifacts for FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures</strong></p> <p>Fuzzing has been an important approach for finding bugs and vulnerabilities in programs. Many fuzzers deployed in industry run daily and can generate an overwhelming number of crashes. Diagnosing such crashes can be very challenging and time consuming. Existing fuzzers typically employ heuristics such as code coverage or call stack hashes to weed out duplicate reporting of bugs. While these heuristics are cheap, they are often imprecise and end up still reporting many "unique" crashes corresponding to the same bug. In this paper, we present <em>FuzzerAid</em> that uses <em>fault signatures</em> to group crashes reported by the fuzzers. Fault signature is a small executable program and consists of a selection of necessary statements from the original program that can reproduce a bug. In our approach, we first generate a fault signature using a given crash. We then execute the fault signature with other crash inducing inputs. If the failure is reproduced, we classify the crashes into the group labeled with the fault signature; if not, we generate a new fault signature. After all the crash inducing inputs are classified, we further merge the fault signatures of the same root cause into a group. We implemented our approach in a tool called <em>FuzzerAid</em> and evaluated it on 3020 crashes generated from 15 real-world bugs and 4 large open source projects. Our evaluation shows that we are able to correctly group 99.1% of the crashes and reported only 17 (+2) "unique" bugs, outperforming the state-of-the-art fuzzers.</p> <p> </p> <p><strong>Change log for v1.0.1:</strong></p> <p>Fix wrong Bug ID for <em>sqlite</em> and add README clarification.</p> <p><strong>Change log for v1.0.2:</strong></p> <p>Added an example linking data in the repository to the table.</p>
Fig. 8 Urine miRNA profile analysis among different groups. a in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 8 Urine miRNA profile analysis among different groups. a The volcano plot shows the individual statistically significant miRNA between acutely infected rabbits and control rabbits. In this plot, the x-axis is log2 fold-change, which shows the direction of the change (negative scale is decrease and positive scale is increase) in the levels of miRNA expression, while the y-axis is the –log10 FDR, which shows the significance of the change. b The volcano plot shows the individual statistically significant miRNA between chronically infected rabbits and control rabbits. c The volcano plot shows the individual statistically significant miRNA between acutely infected rabbits and chronically infected rabbits. d Venn diagram shows number of differentially expressed miRNA among different comparison pairs
Fig. 6 Serum miRNA profile analysis among different groups. a in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 6 Serum miRNA profile analysis among different groups. a The volcano plot shows the individual statistically significant miRNA between acutely infected group and control group. In this plot, the x-axis is log2 fold-change, which shows the direction of the change (negative scale is decrease and positive scale is increase) in the levels of miRNA expression, while the y-axis is the –log10 FDR, which shows the significance of the change. b The volcano plot shows the individual statistically significant miRNA between chronically infected rabbits and control rabbits. c The volcano plot shows the individual statistically significant miRNA between acutely infected rabbits and chronically infected rabbits. d Venn diagram shows number of differentially expressed miRNA among different comparison pairs. FDR represents false discovery rate
Fig. 7 Serum piRNA profile analysis among different groups. a in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 7 Serum piRNA profile analysis among different groups. a The volcano plot shows the individual statistically significant piRNA between acutely infected rabbits and control rabbits. In this plot, the x-axis is log2 fold-change, which shows the direction of the change (negative scale is decrease and positive scale is increase) in the levels of piRNA expression, while the y-axis is the –log10 FDR, which shows the significance of the change. b The volcano plot shows the individual statistically significant piRNA between chronically infected rabbits and control rabbits. c The volcano plot shows the individual statistically significant piRNA between acutely infected rabbits and chronically infected rabbits. d Venn diagram shows number of differentially expressed piRNA among different comparison pairs. FDR represents false discovery rate
Fig. 9 Urine piRNA profile analysis among different groups. a in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 9 Urine piRNA profile analysis among different groups. a The volcano plot shows the individual statistically significant piRNA between acutely infected group and control group. In this plot, the x-axis is log2 fold-change, which shows the direction of the change (negative scale is decrease and positive scale is increase) in the levels of piRNA expression, while the y-axis is the –log10 FDR, which shows the significance of the change. b The volcano plot shows the individual statistically significant piRNA between chronically infected group and control group. c The volcano plot shows the individual statistically significant piRNA between acutely infected group and chronically infected group. d Venn diagram shows number of differentially expressed piRNA among different comparison pairs
Individual or group signatures in spotted hyena whoops
<p>In animal societies, identity signals mediate interactions within groups, and allow individuals to discriminate group-mates from out-group competitors. However, individual recognition becomes increasingly challenging as group size increases and as signals must be transmitted over greater distances. Group vocal signatures appear to evolve when successful in-group/out-group distinctions are at the crux of fitness-relevant decisions, but individual-based recognition systems may be favored when differentiated within-group relationships are important for decision-making. Spotted hyenas are social carnivores that live in stable clans of <125 individuals composed of multiple unrelated matrilines. Clan members cooperate to defend resources and communal territories from neighboring clans and other mega carnivores; this collective defense is mediated by long-range (up to 5 km range) recruitment vocalizations, called whoops. Here, we use machine learning to determine that spotted hyena whoops contain individual but not group signatures, and that fundamental frequency features that propagate well are critical for individual discrimination. For effective clan-level cooperation, hyenas face the cognitive challenge of remembering and recognizing individual voices at long range. We show that serial redundancy in whoop bouts increases individual classification accuracy and thus extended call bouts used by hyenas likely evolved to overcome the challenges of communicating individual identity at long distance.</p>
Figure 4 in Vocal repertoire and group-specific signature in the Smooth-billed Ani, Crotophaga ani Linnaeus, 1758 (Cuculiformes, Aves)
Figure 4. Boxplots (median and quartiles) of acoustic parameters of the similar vocalizations of Charqueada and Guararema groups of Smooth-billed Ani. Vocalizations:"Ahnee","Whine", "Pre-flight", "Flight" and "Vigil". Acoustic parameters: DUR = duration; MPF = maximum peak frequency; MFF = maximum fundamental frequency; MIF = minimum frequency; MAF = maximum frequency.
Figure 3 in Vocal repertoire and group-specific signature in the Smooth-billed Ani, Crotophaga ani Linnaeus, 1758 (Cuculiformes, Aves)
Figure 3. Spectrograms of the ten types of vocalizations of the Smooth-billed Ani: "Ahnee" (A), "Whine" (B, C, D, E, F and G), "Pre-flight" (H), "Shout" (I), "Flight" (J and K), "Hoot" (L), "Grunt" (M), "Ee-oo-ee" (N), "Vigil" (O),"INR" (P and Q).
Figure 1 in Vocal repertoire and group-specific signature in the Smooth-billed Ani, Crotophaga ani Linnaeus, 1758 (Cuculiformes, Aves)
Figure 1. Location of the studied groups of Smooth-billed Ani in the municipality of Alegre, ES, Brazil.
EEG signature of grouping strategies in numerosity perception
<p><strong>Behavioral Data</strong></p> <p>The excel file contains for each row data from individual participant. We reported the average response (columns B-E) and precision index (Weber fractions; columns G-J) for 6 and 8 items, both grouped and ungrouped.</p> <p> </p> <p><strong>EEG Data</strong></p> <p>The excel file contains for each row data from individual participant. We reported the N1 latency (columns B-K), N1 amplitude (columns M-V) and P2p amplitude (columns X-AG). Values are reported for the subitizing range (3 and 4 items) and the estimation range (6 and 8 items). In the estimation range values are separately reported for spatial arrangement (grouped and ungrouped) and number of subgroups (3 or 4 subgroups).</p>
Individual or group signatures in spotted hyena whoops
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Data from: Social group signatures in hummingbird displays provide evidence of co-occurrence of vocal and visual learning
Vocal learning, in which animals modify their vocalizations based on social experience, has evolved in several lineages of mammals and birds, including humans. Despite much attention, the question of how this key cognitive trait has evolved remains unanswered. The motor theory for the origin of vocal learning posits that neural centers specialized for vocal learning arose from adjacent areas in the brain devoted to general motor learning. One prediction of this hypothesis is that visual displays that rely on complex motor patterns may also be learned in taxa with vocal learning. While learning of both spoken and gestural languages is well-documented in humans, the occurrence of learned visual displays has rarely been examined in non-human animals. We tested for geographic variation consistent with learning of visual displays in long-billed hermits (Phaethornis longirostris), a lek-mating hummingbird that, like humans, has both learned vocalizations and elaborate visual displays. We found lek-level signatures in both vocal parameters and visual display features, including element proportions, sequence syntax, and fine-scale parameters of elements. This variation was not associated with genetic differentiation between leks. In the absence of genetic differences, geographic variation in vocal signals at small scales is most parsimoniously attributed to learning, suggesting a significant role of social learning in visual display ontogeny. The co-occurrence of learning in vocal and visual displays would be consistent with a parallel evolution of these two signal modalities in this species.
Individual signatures outweigh social group identity in contact calls of a communally nesting parrot
Despite longstanding interest in the evolutionary origins and maintenance of vocal learning, we know relatively little about how social dynamics influence vocal learning processes in natural populations. The "social group membership" hypothesis proposes that socially learned calls evolved and are maintained as signals of group membership. However, in fission-fusion societies, individuals can interact in social groups across various social scales. For learned calls to signal group membership over multiple social scales, they must contain information about group membership over each of these scales, a concept termed "hierarchical mapping". Monk parakeets (Myiopsitta monachus), small parrots native to South America, exhibit vocal mimicry in captivity and fission-fusion social dynamics in the wild. We examined patterns of contact call acoustic similarity in Uruguay to test the hierarchical mapping assumption of the signaling group membership hypothesis. We also asked whether geographic variation patterns matched regional dialects or geographic clines that have been documented in other vocal learning species. We used visual inspection, spectrographic cross-correlation and random forests, a machine learning approach, to evaluate contact call similarity. We compared acoustic similarity across social scales and geographic distance using Mantel tests and spatial autocorrelation. We found high similarity within individuals, and low, albeit significant, similarity within groups at the pair, flock and site social scales. Patterns of acoustic similarity over geographic distance did not match mosaic or graded patterns expected in dialectal or clinal variation. Our findings suggest that monk parakeet social interactions rely more heavily upon individual recognition than group membership at higher social scales.
Figure 2 in Vocal repertoire and group-specific signature in the Smooth-billed Ani, Crotophaga ani Linnaeus, 1758 (Cuculiformes, Aves)
Figure 2. Song structure of the Smooth-billed Ani showing the notes, sillable and harmonic.
Data from: Social group signatures in hummingbird displays provide evidence of co-occurrence of vocal and visual learning
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Individual signatures outweigh social group identity in contact calls of a communally nesting parrot
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Molecular Profiling defines evolutionarily conserved transcription factor signatures of major vestibulospinal neuron groups
GEO Series GSE125197. Gallus gallus; Mus musculus. 29 samples. Type: Expression profiling by high throughput sequencing.
MiR-592 activates the mTOR kinase, ERK1/ERK2 kinase signaling and imparts neuronal differentiation signature characteristic of Group 4 medulloblastoma
GEO Series GSE147145. Homo sapiens. 21 samples. Type: Expression profiling by high throughput sequencing.
Low Input Asay for Transposase-Accessible Chromatin Identifies Epigenetic Signatures of Liver Group 1 Innate Lymphoid Cells
GEO Series GSE296068. Mus musculus. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Signature analysis of high-throughput transcriptomics screening data for mechanistic inference and chemical grouping
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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