Sameep Mehta

Bio: Sameep Mehta is an academic researcher from IBM. The author has contributed to research in topics: Service (business) & Resource (project management). The author has an hindex of 22, co-authored 160 publications receiving 2093 citations. Previous affiliations of Sameep Mehta include Lady Hardinge Medical College & All India Institute of Medical Sciences.

Being Aware of the World: Toward Using Social Media to Support the Blind With Navigation

Abstract: This paper lays the ground work for assistive navigation using wearable sensors and social sensors to foster situational awareness for the blind. Our system acquires social media messages to gauge the relevant aspects of an event and to create alerts. We propose social semantics that captures the parameters required for querying and reasoning an event-of-interest, such as what, where, who, when, severity, and action from the Internet of things, using an event summarization algorithm. Our approach integrates wearable sensors in the physical world to estimate user location based on metric and landmark localization. Streaming data from the cyber world are employed to provide awareness by summarizing the events around the user based on the situation awareness factor. It is illustrated using disaster and socialization event scenarios. Discovered local events are fed back using sound localization so that the user can actively participate in a social event or get early warning of any hazardous events. A feasibility evaluation of our proposed algorithm included comparing the output of the algorithm to ground truth, a survey with sighted participants about the algorithm output, and a sound localization user interface study with blind-folded sighted participants. Thus, our framework supports the navigation problem for the blind by combining the advantages of our real-time localization technologies so that the user is being made aware of the world, a necessity for independent travel.

DANIEL: A Deep Architecture for Automatic Analysis and Retrieval of Building Floor Plans

Abstract: Automatically finding out existing building layouts from a repository is always helpful for an architect to ensure reuse of design and timely completion of projects. In this paper, we propose Deep Architecture for fiNdIng alikE Layouts (DANIEL). Using DANIEL, an architect can search from the existing projects repository of layouts (floor plan), and give accurate recommendation to the buyers. DANIEL is also capable of recommending the property buyers, having a floor plan image, the corresponding rank ordered list of alike layouts. DANIEL is based on the deep learning paradigm to extract both low and high level semantic features from a layout image. The key contributions in the proposed approach are: (i) novel deep learning framework to retrieve similar floor plan layouts from repository; (ii) analysing the effect of individual deep convolutional neural network layers for floor plan retrieval task; and (iii) creation of a new complex dataset ROBIN (Repository Of BuildIng plaNs), having three broad dataset categories with 510 real world floor plans.We have evaluated DANIEL by performing extensive experiments on ROBIN and compared our results with eight different state-of-the-art methods to demonstrate DANIEL’s effectiveness on challenging scenarios.

Open Reduction for Late-Presenting Posterior Dislocation of the Elbow:

Abstract: Purpose.To evaluate results of open reduction for late-presenting (more than 3 weeks) posterior dislocation of the elbow in 10 patients.Method.Elbow stiffness was the main indication for surgery. The mean age of the patients was 34 (range, 13–65) years; the mean time since injury was 4 (range, 2–6) months. All patients had non-functional elbow movement for any activity of daily living. Three patients had associated fractures around the elbow joint.Results.At a mean follow-up of 19 (range, 11–28) months, 8 patients regained a functional range of movement for activities of daily living and maintained a median arc of flexion of 100 degrees and a supination-pronation arc of 140 degrees. According to the Mayo Elbow Performance Index, the results of 5 patients were excellent, 3 were good, and 2 were poor. Complications included pin site infection (n=2), ulnar neuritis (n=1), and delayed wound healing (n=1).Conclusion.In patients with late-presenting, unreduced elbow dislocation occurring up to 6 months earlier,...

Generating Adversarial Text Samples

Abstract: Adversarial samples are strategically modified samples, which are crafted with the purpose of fooling a trained classifier. In this paper, we propose a new method of crafting adversarial text samples by modification of the original samples. Modifications of the original text samples are done by deleting or replacing the important or salient words in the text or by introducing new words in the text sample. While crafting adversarial samples, one of the key constraint is to generate meaningful sentences which can at pass off as legitimate from the language (English) viewpoint. Experimental results on IMDB movie review dataset for sentiment analysis and Twitter dataset for gender detection show the efficacy of our proposed method.

The spread of true and false news online

Abstract: We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it.

Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification

Abstract: The paper “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification” by Joy Buolamwini and Timnit Gebru, that will be presented at the Conference on Fairness, Accountability, and Transparency (FAT*) in February 2018, evaluates three commercial API-based classifiers of gender from facial images, including IBM Watson Visual Recognition. The study finds these services to have recognition capabilities that are not balanced over genders and skin tones [1]. In particular, the authors show that the highest error involves images of dark-skinned women, while the most accurate result is for light-skinned men.

A Survey on Bias and Fairness in Machine Learning

Abstract: With the widespread use of AI systems and applications in our everyday lives, it is important to take fairness issues into consideration while designing and engineering these types of systems. Such systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that the decisions do not reflect discriminatory behavior toward certain groups or populations. We have recently seen work in machine learning, natural language processing, and deep learning that addresses such challenges in different subdomains. With the commercialization of these systems, researchers are becoming aware of the biases that these applications can contain and have attempted to address them. In this survey we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined in order to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and how they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.