Abstract

n the rapidly growing computing world, the data is being generated every minute, and it has been a big challenge to manage and maintain privacy of the data when it is required to share among different locations. In this regard the privacy preserving data mining is playing a major role to design and develop the methods and models to preserve privacy of data in data owners perspectives, when ever the necessity of exchanging the information between multiple parties to be done. There has been lot of research going on in the field of computational intelligence, machine learning and soft computing domains and some good number of algorithms and secured models are being developed by the researchers. We have worked on the random forest classification as the best example model to preserve privacy of two parties when a combined classification to be done in order to build a privacy preserving global random …

Abstract

Preserving privacy of data in data mining domain has been a challenging objective to achieve. There has been lot of progress in applying secure algorithms in order to achieve privacy preserving of sensitive information. In distributed data mining perspective, there is necessity to share the information among the different parties aiming for combined outcomes. Privacy preserving methods and algorithms helps in preserving privacy of data when it is compulsory to share the information between the collaborated parties to perform various data mining operations like Classification and Clustering. It is always important to address the privacy issues of an individual when the data is shared among different parties to achieve a common goal. It is observed that there has been great development in addressing the privacy issues and designing privacy preserving algorithms applied to achieve a secured model. Adopting neural …

Abstract

Classical Conditioning is a fundamental learning mechanism where the Ventral 14 Striatum is generally thought to be the source of inhibition to Ventral Tegmental Area (VTA) 15 Dopamine neurons when a reward is expected. However, recent evidences point to a new 16 candidate in VTA GABA encoding expectation for computing the reward prediction error in the VTA. 17 In this system-level computational model, the VTA GABA signal is hypothesised to be a combination 18 of magnitude and timing computed in the Peduncolopontine and Ventral Striatum respectively. 19 This dissociation enables the model to explain recent results wherein Ventral Striatum lesions 20 affected the temporal expectation of the reward but the magnitude of the reward was intact. This 21 model also exhibits other features in classical conditioning namely, progressively decreasing firing 22 for early rewards closer to the actual reward, twin peaks of VTA dopamine during training and 23 cancellation of US dopamine after training

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.