Abstract

Modeling effective representations using multiple views that positively influence each other is challenging, and the existing methods perform poorly on Electroencephalogram (EEG) signals for sleepstaging tasks. In this paper, we propose a novel multi-view self-supervised method (mulEEG) for unsupervised EEG representation learning. Our method attempts to effectively utilize the complementary information available in multiple views to learn better representations. We introduce diverse loss that further encourages complementary information across multiple views. Our method with no access to labels, beats the supervised training while outperforming multi-view baseline methods on transfer learning experiments carried out on sleep-staging tasks. We posit that our method was able to learn better representations by using complementary multi-views.

Abstract

Can we build multi-view decoders that can decode concepts from brain recordings corresponding to any view (picture, sentence, word cloud) of stimuli? Can we build a system that can use brain recordings to automatically describe what a subject is watching using keywords or sentences? How about a system that can automatically extract important keywords from sentences that a subject is reading? Previous brain decoding efforts have focused only on single view analysis and hence cannot help us build such systems. As a first step toward building such systems, inspired by Natural Language Processing literature on multilingual and cross-lingual modeling, we propose two novel brain decoding setups: (1) multiview decoding (MVD) and (2) cross-view decoding (CVD). In MVD, the goal is to build an MV decoder that can take brain recordings for any view as input and predict the concept. In CVD, the goal is to train a model which takes brain recordings for one view as input and decodes a semantic vector representation of another view. Specifically, we study practically useful CVD tasks like image captioning, image tagging, keyword extraction, and sentence formation.

Abstract

Many studies on animals, children and adults advocate for an inherent mechanism for number perception in humans and animal brains. There are some computational models for visual number perception. The on-centre off-surround Recurrent Neural Network (RNN) From Sengupta et al.(2014) accounts for some major behavioural findings regarding number perception. The RNN entails an abstract form of input and output. In our current work, we have devised a biologically interpretable method to use real images as the network's input and retrieve numerical estimation from the network's output (mean activation), resulting in an end-to-end model for enumeration of images. The model uses information from Saliency maps of images as input current for the neurons for a small duration (presentation time). The activation of the neurons changes based on the internal dynamics of the network, characterised by self …

Abstract

The interference model supports a limited capacity of working memory (WM): if multiple to-be-remembered objects are presented in succession, features of later objects interfere with the recall of features of earlier objects. Feature recall of earlier objects can be strengthened with rehearsal, but this is possible for a limited amount of information. We investigated whether rehearsal can reduce interference in the recall of features of a single object. In the current study (N= 24), a circle was presented with randomly varying color, size, and location over 400 trials. Within a single trial, the stimulus appeared followed by two sequential response screens. We probed the recall of color and location of the circle, and considered two conditions (repeat, non-repeat) to investigate recall accuracy. In the repeat condition, participants recalled one of the features (either color or location) twice in a row. In the non-repeat condition, each …

Abstract

Images are a powerful medium known to induce empathy and emotional response in people. In political protests it has the power for a people-initiated policy change and signifies the deep symbolism of a political system. In this study, we aim to quantify the range of emotional connection a person experiences for photographs of a farmers' protest.The protest was the headlines in all media at the time this experiment was conducted and had polarized public opinion. Each photograph is identified to have a set of physical and semantic features. The three selected features were presence of police, gender and close-up (vs.long-shot) in the frame. The intensity on a range of emotions (fear, disgust, anger, sadness, optimism, pessimism, surprise, shock, happiness, and respect) experienced by the viewer for each feature was collected. By statistical and dimensionality analyses, we isolate and identify influencing factors in an image. We found that the presence of police in aggressive actions and close-up shots of had the highest variation in the emotional responses of participants. Interestingly, the gender of the protesters did not show statistically significant effects. The findings from the exploratory investigation highlights the powerful role photographic features have on emotional responses of people, an understudied but critical factor in a world immersed in social media.

Abstract

In this paper, we have studied the impact of clickbait headlines on the distribution of visual attention on hyperlinked news articles. Visual attention is a driving factor in ad-based revenue models that support online journalism. Importantly, it is also an indicator of cognitive processes involved in reading and comprehension. We hypothesize that articles with clickbait headlines receive lesser visual attention when controlled for articles’ content. This is based on the premise that a significant proportion of clicks on clickbait headlines are driven by readers’ specific epistemic curiosity rather than knowledge acquisition. An eye-tracker setup was used to infer visual attention from the gaze-fixation analysis conducted on data from 60 participants. Our results suggest that clickbait headlines significantly reduce the visual attention on news articles. Though, article content comprehension measured by a recall test was comparable for clickbait and non-clickbait headlines. Our findings add to the discussions on the cognitive attention and the implications of using clickbait headlines for news publishers, newsreaders, and advertising agencies alike.

Abstract

Humans report perceptions of unease or eeriness as humanoid/android robots and digital avatars approach human-like physical resemblance, a phenomenon alluded by the Uncanny Valley theory. This study extends the discussions on interactions and acceptance of digital avatars with findings from three experiments. In the first, perceptive evaluation of actors in clips from computer-generated animation and a live-action version of the same movie was examined. In the second experiment, we considered short clips with highly realistic digital avatars to measure recognition ability, the extent of eeriness, and specific physical features identified as unreal. The fixation area and pupil size variation recorded using an eye tracker were analyzed to infer attention to the body, face, and emotional response, respectively. Building on these findings, the third experiment looked at acceptance in roles requiring human skill, empathy, and cognitive ability. The results show that based on perceptions from physical attributes, the eeriness scores diverge from the uncanny valley theory as human-likeness increases. The realistic CGI and mocap technology could have helped cross the valley. Visual attention inferred from gaze behavior was similar for live-action and CGI. At the same time, we observe pupil size changes reflecting emotions like eeriness when the avatars either talked or smiled. Proficiency and acceptance scores were lower for roles requiring complex social cognition processes, such as friends and judicial decision-making. Interestingly, real-life stereotypes of gender roles were transferred to digital avatars too. The findings suggest an ambiguity in accepting human-like avatars in social and professional interactions, emphasizing the need for a multi-dimensional approach when applying the uncanny valley theory. A detailed and contextual examination is imperative as technological advancements have placed humans closer to co-existing with digital or physical android/humanoid robots.

Abstract

Remote Triggered Labs (RTL) are helpful for students to work on laboratory experiments virtually anytime, anywhere. Such setups can facilitate distance learning and are helpful during pandemics. In this paper, the use of Computer Vision (CV) is demonstrated for RTL experiments. For this, a use-case of the Conservation of Mechanical Energy experiment is considered. A CV-based approach is used to estimate an object’s velocity whose setup primarily consists of a microprocessor, a camera and infrared (IR) sensors. The experiment is recorded, and various CV techniques are employed to estimate the object’s velocity. This paper also compares a CV-based and an IR sensor-based approach to estimate the object’s velocity. Linear regression applied to the CV-based implementation resulted in an optimal mean-squared error (MSE), nearly 10 times better than IR-based implementation.

Abstract

An engineer in a product company is expected to design a good solution to a computing problem (Design skill) and articulate the solution well (Expression skill). We expect an industry-ready student (final year student or a fresh campus hire) as well to demonstrate both these skills when working on simple problems assigned to them. This paper reports on the results when we tested a cohort of participants (N=16) for these two skills. We created two participant groups from two different tiers of college, one from a Tier 1 college (who were taking an advanced elective course), and another from Tier 2 colleges (who had been hired for internship in a SaaS product company). We gave them a simple design problem and evaluated the quality of their design and expression. Design quality was evaluated along three design principles of Abstraction, Decomposition, and Precision (adapted from the Software Engineering Book of Knowledge). Expression quality was evaluated using criteria we developed for our study that is based on the diversity and density of the expressions used in the articulation. We found the students lacking in design and expression skills. Specifically, a) they struggled with abstraction as a design principle, b) they did not use enough modes of expressions to articulate their design, and c) they did not use enough formal notations (UML, equations, relations, etc.). We also found significant difference in the performance between the two participant groups.

Abstract

Open source software is increasingly being used by various organi- zations. Open source licenses such as GPL, MIT, Apache, Mozilla, BSD, etc have varying terms and conditions for usage, modification, and distribution. Given the availability of source code for open source software, it is difficult to scrutinize adherence of licenses and more often than not it is left to the good faith of organizations and individuals. Violation of licensing terms knowingly or unknow- ingly can lead to copyright, ethical, compliance and security issues. In this paper, we propose a blockchain based approach in managing open source software licenses by enforcing certain practices to improve awareness and increase transparency, thus minimizing the possibility of violations. We present a solution design that relies on the use of 4 modules: InterPlanetary File System (IPFS), Smart contracts, Transaction manager (Meta-Mask) and a Permissioned Blockchain to enforce the conformance of licenses