84 research outputs found
The cybersecurity awareness Inventory (CAIN). Early phases of development of a tool for assessing cybersecurity knowledge based on the ISO/IEC 27032
No full textKnowledge of possible cyber threats as well as awareness of appropriate security measures plays a crucial role in the ability of individuals to not only discriminate between an innocuous versus a dangerous cyber event, but more importantly to initiate appropriate cybersecurity behaviors. The purpose of this study was to construct a Cybersecurity Awareness INventory (CAIN) to be used as an instrument to assess users’ cybersecurity knowledge by providing a proficiency score that could be correlated with cyber security behaviors. A scale consisting of 46 items was derived from ISO/IEC 27032. The questionnaire was administered to a sample of college students (N = 277). Based on cybersecurity behaviors reported to the research team by the college’s IT department, each participant was divided into three groups according to the risk reports they received in the past nine months (no risk, low risk, and medium risk). The ANOVA results showed a statistically significant difference in CAIN scores between those in the no risk and medium-risk groups; as expected, CAIN scores were lower in the medium-risk group. The CAIN has the potential to be a useful assessment tool for cyber training programs as well as future studies investigating individuals’ vulnerability to cyberthreats
Extrapolation of wideband speech from the telephone band
No full textgrantor:
University of TorontoTelephone speech is bandlimited to the frequency range between 300 and 3300 Hz, which compromises its quality. Wideband speech, accommodating frequencies up to 7000 Hz, provides higher quality but at a cost of increased transmission bandwidth. The proposed pseudo-wideband (PWB) speech algorithm regenerates approximations of the bands missing from telephone speech. This is possible because of the strong inter-band correlations which stem from the acoustics of the production apparatus. For this receiver-based algorithm, the improvement in effective bandwidth requires no extra transmission bandwidth, and involves no codec standardization issues. The spectral envelope and spectral detail are deconvolved via linear predictive analysis, and each is mapped independently to its PWB counterpart. The algorithm is based on parametric analysis using a uniform tube tract model, and has good potential for speaker independence. Performance was encouraging for a preliminary investigation, but a more sophisticated acoustic model is desirable for additional quality improvement.M.A.Sc
Extrapolation of wideband speech from the telephone band
No full textgrantor:
University of TorontoTelephone speech is bandlimited to the frequency range between 300 and 3300 Hz, which compromises its quality. Wideband speech, accommodating frequencies up to 7000 Hz, provides higher quality but at a cost of increased transmission bandwidth. The proposed pseudo-wideband (PWB) speech algorithm regenerates approximations of the bands missing from telephone speech. This is possible because of the strong inter-band correlations which stem from the acoustics of the production apparatus. For this receiver-based algorithm, the improvement in effective bandwidth requires no extra transmission bandwidth, and involves no codec standardization issues. The spectral envelope and spectral detail are deconvolved via linear predictive analysis, and each is mapped independently to its PWB counterpart. The algorithm is based on parametric analysis using a uniform tube tract model, and has good potential for speaker independence. Performance was encouraging for a preliminary investigation, but a more sophisticated acoustic model is desirable for additional quality improvement.M.A.Sc
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Effects of Problem Format in Arithmetic: ‘3+9’ versus ‘three + nine’ versus ‘thrie + nyne’
No full textFew-Shot Information Operation Detection Using Active Learning Approach
No full textPrevious research suggested that supervised machine learning can be utilized to detect information operations (IO) on social media. Most of the related research assumes that the new data will always be available in the exact timing that models set to be updated. In practice, however, the detection and attribution of IO accounts is time-consuming. There is thus a mismatch between the performance assessment procedures in existing work and the real-world problem they seek to solve. We bridge this gap by demonstrating how active learning approaches can extend the application of classifiers by reducing their dependence on new data. We evaluate the performance of an existing classifier when it gets updated according to five active learning strategies. Using state-sponsored information operation Twitter data, the results show that if querying from Twitter is possible, the best active learning strategy requires 5–10 times less tweets than the original model while only showing 1–3% reduction in the average monthly F1 scores across countries and prediction tasks. If querying from Twitter is not possible, the corresponding active learning strategy requires 5–10 times less tweets while showing 1–9% reduction in the average monthly F1 scores. Depending on the country, a hand-full to few hundred new ground-truth examples would suffice to achieve a reasonable performance
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Why Do The Math? The Impact of Calculator Use on Participants' Actual and Perceived Retention of Arithmetic Facts
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The Conscious-Subconscious Interface: An Emerging Metaphor in HCI
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The Influence of Outcome Severity on Ascriptions of Intention & Punishment
No full textPhysiological versus Self-Report Measures of Arousal During Tactical Training Involving a Synthetic Topographic Environment
No full textThis research examines the relationship between the electrodermal activity (EDA) of 43 West Point Cadets while viewing military tactics displays and compares that to results from the Self-Assessment Manikin (SAM; Bradley & Lang, 1994). First there is a need to understand how EDA varies between two different types of presentation formats. Second it was expected that the EDA data would negatively correlate to self-report data based on previous research (Boyce, Reyes, et al., 2016), and third was that EDA and self-report data would be able to predict performance. Results did not indicate significant differences based on display type, however the results did support the negative correlation between EDA and SAM. Finally there was a trend toward predicting performance but it did not reach statistically significant levels, warranting the need for further investigation
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