Summary Fog computing offers cloud‐like facilities at the network edge, delivering reduced response times to latency sensitive applications. It comprises of fog devices/micro data centers/cloudlets located between users and the cloud data center. Fog devices are generally susceptible to privacy, security, and trust issues. We propose RT‐TADS (Real Time‐Trust Aware Dynamic Scheduling), a scheduling algorithm that accounts for privacy, trust and real‐time performance. To compute the trustworthiness of fog devices, we propose a trust computation model. This model factors in direct and recommended trust techniques for each fog device, and updates their aggregated trust values at regular intervals. User tasks are tagged as: private, semi‐private, and public. Fog devices are classified as: extremely highly trusted, highly trusted, normal trusted, low trusted, and untrusted. RT‐TADS maps the input jobs according to their privacy constraints on trustworthy fog devices, which increases the overall Success Ratio, hence improving real‐time performance. Using the Bitbrain dataset, the real‐time performance of RT‐TADS has been demonstrated, versus comparable algorithms. The results indicate that the proposed RT‐TADS offers an average improvement of 13%, 45%, and 71% in task success ratio compared to RLTCM , no‐trust , and cdc‐only respectively.

Cloud computing is widely used to support offloaded data processing for various applications. However, latency constrained data processing has requirements that may not always be suitable for cloud-based processing. Fog computing brings processing closer to data generation sources, by reducing propagation and data transfer delays. It is a viable alternative for processing tasks with real-time requirements. We propose a scheduling algorithm$RTH^{2}S$(RealTimeHeterogeneousHierarchicalScheduling) for a set of real-time tasks on a heterogeneous integrated fog-cloud architecture. We consider a hierarchical model for fog nodes, with nodes at higher tiers having greater computational capacity than nodes at lower tiers, though with greater latency from data generation sources. Tasks with various profiles have been considered. For the regular profile jobs, we use least laxity first (LLF) to find the preferred fog node for scheduling. In case of “tagged” profiles, based on their tag values, the jobs are split in order to finish execution before the deadline, or the LLF heuristic is used. Using HPC2N workload traces across 3.5 years of activity, the real-time performance of$RTH^{2}S$versus comparable algorithms is demonstrated. We also consider Microsoft Azure-based costs for the proposed algorithm. Our proposed approach is validated using both simulation (to demonstrate scale up) as well as a lab-based testbed.

Abstract Fog computing has emerged as a viable framework for processing delay sensitive applications. Modern applications consist of latency‐sensitive and latency‐tolerant jobs, leading to fog architectures that are often multi‐tiered/hierarchical. FiFSA (hierarchical first fog scheduling algorithm) and EFSA (hierarchical elected fog scheduling algorithm) are capable of scheduling both online and batch jobs on hierarchical fog‐cloud architectures. We consider heterogeneity in computing capacity—both among fog devices in separate layers, and among fog devices in the same layers. In general, online jobs with modest cpu requirements are scheduled on lower tier fog devices, and batch jobs with significant cpu requirements are scheduled on higher tier‐fog nodes, or the cloud data center (cdc). FiFSA assigns jobs to the first fog device with sufficient spare capacity. EFSA employs a MinMin heuristic that assigns jobs to the fog device that results in minimum completion time, while considering fog load. The performance of the proposed algorithms has been evaluated on a real‐life workload trace, using both simulation scenarios and a prototype testbed. FiFSA and EFSA offer an improvement of 19% to 70% in completion times and an improvement of 42% to 72% in system cost over other comparable algorithms.

In recent years, student engagement estimation has gained focus in the affective computing community. The absence of student monitoring during online MOOC courses makes it challenging to estimate behavioural student engagement during online classes. The non availability of consistent engagement datasets makes it difficult to build cross data automatic behavioural engagement estimation technique. In this paper, we propose an unsupervised topic modeling technique for engagement detection as it captures multiple behavioral cues which are indicators of engagement level such as eye gaze, head movement, facial expression and body posture. We have addressed the various challenges such as less volume of our datasets, large decision unit (annotated for 5 minutes duration) and uneven distribution of different engagement categories with domain adaptation based solution for cross data implementation. We present results on engagement prediction using different clustering techniques such as K-Means and Latent Dirichlet Allocation (LDA) along with different regressors and neural network based attention mechanisms.

This paper details the sixth Emotion Recognition in the Wild (EmotiW) challenge. EmotiW 2018 is a grand challenge in the ACM International Conference on Multimodal Interaction 2018, Colarado, USA. The challenge aims at providing a common platform to researchers working in the affective computing community to benchmark their algorithms on 'in the wild' data. This year EmotiW contains three sub-challenges: a) Audio-video based emotion recognition; b) Student engagement prediction; and c) Group-level emotion recognition. The databases, protocols and baselines are discussed in detail.

Analysis of the student engagement in an e-learning environment would facilitate effective task accomplishment and learning. Generally, engagement/disengagement can be estimated from facial expressions, body movements and gaze pattern. The focus of this Ph.D. work is to explore automatic student engagement assessment while watching Massive Open Online Courses (MOOCs) video material in the real-world environment. Most of the work till now in this area has been focusing on engagement assessment in lab-controlled environments. There are several challenges involved in moving from lab-controlled environments to real-world scenarios such as face tracking, illumination, occlusion, and context. The early work in this Ph.D. project explores the student engagement while watching MOOCs. The unavailability of any publicly available dataset in the domain of user engagement motivates to collect dataset in this direction. The dataset contains 195 videos captured from 78 subjects which are about 16.5 hours of recording. This dataset is independently annotated by different labelers and final label is derived from the statistical analysis of the individual labels given by the different annotators. Various traditional machine learning algorithm and deep learning based networks are used to derive baseline of the dataset. Engagement prediction and localization are modeled as Multi-Instance Learning (MIL) problem. In this work, the importance of Hierarchical Attention Network (HAN) is studied. This architecture is motivated from the hierarchical nature of the problem where a video is made up of segments and segments are made up of frames.

Analyzing personality of a subject is an important aspect of automatic human behavior understanding. Generally, estimation of the OCEAN (Openness, Conscientiousness, Extroversion, Agreeableness, Neuroticism) traits are used to represent the personality of an individual. Personality assessment based only on individual actions is not sufficient and considering social context is also important. The focus of this Ph.D. work is to explore automatic personality assessment (APA) in the real-world environment. Most of the work till now in this area has been focusing on BF and related traits prediction of the subject in lab-controlled environments. There are several challenges involved in moving from lab-controlled environments to real-world APA scenarios such as face tracking, illumination, occlusion and social context. The early work in this Ph.D. project explores the evolution of graphs, which capture the interaction patterns and structural changes of a group of people. We call them Personality Interaction Graphs (PIG). PIGs are constructed based on the nonverbal cues to study the behavior of a subject both at a group level and an individual level. This work brings in the power of PIGs to improve the prediction accuracy and visualization of the summary of personality traits with valid cause-effect analysis at both the individual and group level. Furthermore, various machine learning techniques to analyze the personality and emotion of subjects will be explored.