Adversarial Black-Box Attacks on Automatic Speech Recognition Systems using Multi-Objective Evolutionary Optimization

Fooling deep neural networks with adversarial input have exposed a significant vulnerability in the current state-of-the-art systems in multiple domains. Both black-box and white-box approaches have been used to either replicate the model itself or to craft examples which cause the model to fail. In this work, we propose a framework which uses multi-objective evolutionary optimization to perform both targeted and un-targeted black-box attacks on Automatic Speech Recognition (ASR) systems. We apply this framework on two ASR systems: Deepspeech and Kaldi-ASR, which increases the Word Error Rates (WER) of these systems by upto 980%, indicating the potency of our approach. During both un-targeted and targeted attacks, the adversarial samples maintain a high acoustic similarity of 0.98 and 0.97 with the original audio.Comment: Published in Interspeech 201

A Visual Programming Paradigm for Abstract Deep Learning Model Development

Deep learning is one of the fastest growing technologies in computer science with a plethora of applications. But this unprecedented growth has so far been limited to the consumption of deep learning experts. The primary challenge being a steep learning curve for learning the programming libraries and the lack of intuitive systems enabling non-experts to consume deep learning. Towards this goal, we study the effectiveness of a no-code paradigm for designing deep learning models. Particularly, a visual drag-and-drop interface is found more efficient when compared with the traditional programming and alternative visual programming paradigms. We conduct user studies of different expertise levels to measure the entry level barrier and the developer load across different programming paradigms. We obtain a System Usability Scale (SUS) of 90 and a NASA Task Load index (TLX) score of 21 for the proposed visual programming compared to 68 and 52, respectively, for the traditional programming methods

Hi, how can I help you?: Automating enterprise IT support help desks

Question answering is one of the primary challenges of natural language understanding. In realizing such a system, providing complex long answers to questions is a challenging task as opposed to factoid answering as the former needs context disambiguation. The different methods explored in the literature can be broadly classified into three categories namely: 1) classification based, 2) knowledge graph based and 3) retrieval based. Individually, none of them address the need of an enterprise wide assistance system for an IT support and maintenance domain. In this domain the variance of answers is large ranging from factoid to structured operating procedures; the knowledge is present across heterogeneous data sources like application specific documentation, ticket management systems and any single technique for a general purpose assistance is unable to scale for such a landscape. To address this, we have built a cognitive platform with capabilities adopted for this domain. Further, we have built a general purpose question answering system leveraging the platform that can be instantiated for multiple products, technologies in the support domain. The system uses a novel hybrid answering model that orchestrates across a deep learning classifier, a knowledge graph based context disambiguation module and a sophisticated bag-of-words search system. This orchestration performs context switching for a provided question and also does a smooth hand-off of the question to a human expert if none of the automated techniques can provide a confident answer. This system has been deployed across 675 internal enterprise IT support and maintenance projects.Comment: To appear in IAAI 201

An Innovative Approach for the Detection of High Boiler Adulterants in Sandalwood and Cedarwood Essential Oils

Owing to the important uses of essential oils, its adulteration is a serious issue of concern. Among the adulterants, the high volatiles can be detected through GC and GC/MS. However, the detection of subtle high boiler adulterants is extremely difficult, and requires development of novel techniques to overcome the challenges faced by the essential oil industry. In current study, the thermogravimetric analysis (TGA) was validated as an innovative approach for quantitative estimation of adulteration in essential oils taking sandalwood and cedarwood oils as case study. The low−cost vegetable oils like castor oil, coconut oil, and synthetic polymer like polyethylene glycol-400 (PEG-400) were used as high boiler adulterants. The physical parameters like specific gravity and refractive index of pure and adulterated oil samples were analyzed followed by their TGA analysis. The physical parameters of adulterated samples did not show significant variation from that of pure essential oils, thus need alternate analytical techniques to overcome this issue. The TGA of pure essential oil was volatized in single−stage around 200–260℃, whereas the high boiler adulterants such as vegetable oils and synthetic PEG-400 majorly volatized in the range 300–500℃ and 260–400℃, respectively. The adulterated samples exhibited mostly two-stage weight loss pattern, which was quantitatively estimated with high accuracy by this technique. Therefore, the TGA analysis can be used as a novel technique for rapid and precise detection of high boiler adulterants in essential oils like sandalwood and cedarwood due to difference in their volatile behaviour

An Innovative Approach for the Detection of High Boiler Adulterants in Sandalwood and Cedarwood Essential Oils

866-874Owing to the important uses of essential oils, its adulteration is a serious issue of concern. Among the adulterants, the high volatiles can be detected through GC and GC/MS. However, the detection of subtle high boiler adulterants is extremely difficult, and requires development of novel techniques to overcome the challenges faced by the essential oil industry. In current study, the thermogravimetric analysis (TGA) was validated as an innovative approach for quantitative estimation of adulteration in essential oils taking sandalwood and cedarwood oils as case study. The low−cost vegetable oils like castor oil, coconut oil, and synthetic polymer like polyethylene glycol-400 (PEG-400) were used as high boiler adulterants. The physical parameters like specific gravity and refractive index of pure and adulterated oil samples were analyzed followed by their TGA analysis. The physical parameters of adulterated samples did not show significant variation from that of pure essential oils, thus need alternate analytical techniques to overcome this issue. The TGA of pure essential oil was volatized in single−stage around 200–260℃, whereas the high boiler adulterants such as vegetable oils and synthetic PEG-400 majorly volatized in the range 300–500℃ and 260–400℃, respectively. The adulterated samples exhibited mostly two-stage weight loss pattern, which was quantitatively estimated with high accuracy by this technique. Therefore, the TGA analysis can be used as a novel technique for rapid and precise detection of high boiler adulterants in essential oils like sandalwood and cedarwood due to difference in their volatile behaviour

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570