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

Abstract

Research in hardware and software support for Virtual Reality (VR) has significantly increased over the last decade. Given the software platform fragmentation and hardware volatility, there is an apparent disconnect among practitioners while building applications in the VR domain. This paper proposes a role-based model template as a meta-model to specify the bare minimum VR software system. We conducted a grounded-theory-based qualitative study on prevailing and phased-out VR SDKs and standards to propose this meta-model. This model template can help VR practitioners build open-source tools to develop, design, and test VR software systems.

Abstract

Material science enables us to develop a wide array of materials capable enough to address worldly problems.Velostat is a carbon-impregnated polythene material used to address many problems including gait-related problems. This paper presents a novel approach to analyze a gait-cycle parameter called ‘Stance-phase.’ We ideated, designed, and implemented a low-cost customizable Velostat based sensing mat to calculate the stance phase. We validated our VelGmat using sensor density, sensitivity, process time, and hardware performance metrics. Our validation results helped us improve the effectiveness of gait analysis. We conducted a comparative study between our VelGmat and commercially available mat to understand the contrast in stance phase values. We observed 95% accuracy in stance phase values compared with commercially available mat. Index Terms—Piezoresistive; Pressure sensing; Scan time; Sensor density; Gait cycle; Velostat

Abstract

A method for generating a limitless path in a virtual reality environment ( VR ) for a continuous locomotion within a real physical space using Head - Mounted - Display ( HMD ) device associated with a user is provided . The method includes determining a line segment between two points that corre sponds to an initial path travelled by the user . The method includes detecting a boundary of the VR environment to generate a next line segment . The method includes gener ating and adding a new line segment to end of the initial path . The method includes generating and adding the new line segment to the end of the next line segment . The method includes generating an updated path by adding the new line segment in a direction at the angle of shift angle to the direction of the next line segment .The method includes , configuring to output updated path as two - dimensional points to render updated path into VR environment .

Abstract

Image processing algorithms with intrinsic robust- ness to errors can be approximated for significant resource and energy savings while still meeting the end-user requirements. FPGA-based implementations can increase their suitability for real-time high-speed multimedia applications by leveraging Ap- proximate Computing, an independent field that explores meth- ods to reduce computation costs by allowing minor degradation in intermediate computations. With high volume of pixel level computations, corner detection algorithms such as the Harris Corner Detector (HCD), offer wide range of targets for such strategies. In this paper, we propose an hardware implementation of HCD that relies on approximating the intermediate multi- plication operations using Dynamic Range Unbiased Multiplier (DRUM). With run-time configurable bit-width control of DRUM instances, the visual quality of outputs is shown to depend on the varying accuracy of the corner response. We explore how the errors due to approximate operations propagate to the corner response and attempt to find a threshold that adapts to this inaccuracy across images. The experimental results for implementations on Virtex-7 and Zynq-7000 FPGA devices show that our approximate architecture can match the performance of other HCD implementations while hardly utilizing any on-board memory and signal processing resources. Synthesis results show that the proposed implementation achieves over 60% increase in maximum frequency compared to the base implementation. Finally, the quality metric analysis facilitates the selection of approximate configuration suited to the needs of an application. Index Terms—FPGA, Approximate Computing, DRUM, Image Processing, Harris Corner Detector, Bluespec, Zynq, Virtex

Abstract

The impact of climate change on the oxygen saturation content of the world’s surface waters is a signifcant topic for future water quality in a warming environment. While increasing river water temperatures (RWTs) with climate change signals have been the subject of several recent research, how climate change afects Dissolved Oxygen (DO) saturation levels have not been intensively studied. This study examined the direct efect of rising RWTs on saturated DO concentrations. For this, a hybrid deep learning model using Long Short-Term Memory integrated with k-nearest neighbor bootstrap resampling algorithm is developed for RWT prediction addressing sparse spatiotemporal RWT data for seven major polluted river catchments of India at a monthly scale. The summer RWT increase for Tunga-Bhadra, Sabarmati, Musi, Ganga, and Narmada basins are predicted as 3.1, 3.8, 5.8, 7.3, 7.8 °C, respectively, for 2071–2100 with ensemble of NASA Earth Exchange Global Daily Downscaled Projections of air temperature with Representative Concentration Pathway 8.5 scenario. The RWT increases up to7 °C for summer, reaching close to 35 °C, and decreases DO saturation capacity by 2–12% for 2071–2100. Overall, for every 1 °C RWT increase, there will be about 2.3% decrease in DO saturation level concentrations over Indian catchments under climate signals.