Bio: Shailendra is a keynote speaker, influencer and a thought-leader in the Artificial Intelligence space . With an experience of over 23 years working with Corporates, Software Vendors and Consulting companies, Shailendra has delivered over One Billion Dollars through advanced analytics. He has established and lead several data science businesses and teams to generate revenue and drive incremental growth by creating multiple Artificial Intelligence solutions across a variety of sectors, including Telcos, Financial Services, Retail and Public Sector. Shailendra has published multiple articles about Advanced Analytics, Machine Learning, IoT, Artificial Intelligence and Blockchain; and recently published an Amazon bestseller "Making Money Out of Data" which showcases five business stories from various industries on how successful companies make millions of dollars in incremental value using analytics. Shailendra has held senior executive level positions at SAP, IBM, Accenture, Woolworths and Coles with Asia and ANZ mandates.

Affective computing [31] is "computing that relates to, arises from, or influences emotions." It is very important in human-machine interaction, as humans cannot have longlasting intimate relationships with machines if they cannot understand our affects and respond appropriately. Both affect classification and regression have been extensively studied in the literature [24], [43], [45], [46], [48]. For affect classification, the most commonly used categories are the six basic emotions (anger, disgust, fear, happiness, sadness, and surprise) proposed by Ekman et al. [5]. For regression, affects are usually represented as numbers in the 2D space of arousal and valence [35], or in the 3D space of arousal, valence, and dominance [25]. Recently, Yannakakis et al. [50] also argued that the nature of emotions is ordinal, and hence preference learning [51] should also play an important role in affective computing. Various input signals could be used in affective computing, e.g., speech [21], [47], facial expressions [8], [29], physiological signals [7], [43], and multimodal combination [26], [53]. Numerous features could be extracted from each modality. For example, 6,373 acoustic features were extracted by OpenSMILE [6] in the InterSpeech 2013 Computational Paralinguistics Challenge.

Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization ability. However, many times it is relatively easy to collect a large number of unlabeled samples, but time-consuming or expensive to label them. Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection. This paper proposes two new ALR approaches based on greedy sampling (GS). The first approach (GSy) selects new samples to increase the diversity in the output space, and the second (iGS) selects new samples to increase the diversity in both input and output spaces. Extensive experiments on 12 UCI and CMU StatLib datasets from various domains, and on 15 subjects on EEG-based driver drowsiness estimation, verified their effectiveness and robustness.

Acquisition of labeled training samples for affective computing is usually costly and time-consuming, as affects are intrinsically subjective, subtle and uncertain, and hence multiple human assessors are needed to evaluate each affective sample. Particularly, for affect estimation in the 3D space of valence, arousal and dominance, each assessor has to perform the evaluations in three dimensions, which makes the labeling problem even more challenging. Many sophisticated machine learning approaches have been proposed to reduce the data labeling requirement in various other domains, but so far few have considered affective computing. This paper proposes two multi-task active learning for regression approaches, which select the most beneficial samples to label, by considering the three affect primitives simultaneously. Experimental results on the VAM corpus demonstrated that our optimal sample selection approaches can result in better estimation performance than random selection and several traditional single-task active learning approaches. Thus, they can help alleviate the data labeling problem in affective computing, i.e., better estimation performance can be obtained from fewer labeling queries.

Abstract--Distributed gradient descent (DGD) is an efficient way of implementing gradient descent (GD), especially for large data sets, by dividing the computation tasks into smaller sub-tasks and assigning to different computing servers (CSs) to be executed in parallel. In standard parallel execution, per-iteration waiting time is limited by the execution time of the straggling servers. Coded DGD techniques have been introduced recently, which can tolerate straggling servers via assigning redundant computation tasks to the CSs. In most of the existing DGD schemes, either with coded computation or coded communication, the non-straggling CSs transmit one message per iteration once they complete all their assigned computation tasks. However, although the straggling servers cannot complete all their assigned tasks, they are often able to complete a certain portion of them. In this paper, we allow multiple transmissions from each CS at each iteration in order to make sure a maximum number of completed computations can be reported to the aggregating server (AS), including the straggling servers. We numerically show that the average completion time per iteration can be reduced significantly by slightly increasing the communication load per server . Index Terms --Distributed gradient descent, coded computation, coded gradient, polynomial codes, maximum-distance separable codes.

If you suffer with a short temper – you're probably not as smart as you think you are, a new study has found. Angry people are more likely to overestimate their intelligence levels than those with a calmer disposition, scientists have found. That's because being angry is linked with high levels of narcissism, as well as a greater belief in their abilities and competence. Those with a short fuse were also more likely to have problems maintaining a stable relationship, according to the latest findings. This is because individuals with high levels of narcissism struggle to establish bonds with others as they are always trying to dominate them.

In 2017, the Australian military drone-racing team made its competitive debut during the Australian Drone Nationals. Given the technology's long history within the military, you might think they would have an edge. But that's not what happened. Drone racing is a fairly new sport that merges video game racing with real-life drone flying. Racers put on a pair of first-person view (or FPV) goggles, which allow them to see exactly what they would if they were sitting in the teeny-tiny cockpit.

Conventional approaches used supervised learning to estimate off-line writer identifications. In this study, we improved the off-line writer identifica- tions by semi-supervised feature learning pipeline, which trained the extra unla- beled data and the original labeled data simultaneously. In specific, we proposed a weighted label smoothing regularization (WLSR) method, which assigned the weighted uniform label distribution to the extra unlabeled data. We regularized the convolutional neural network (CNN) baseline, which allows learning more discriminative features to represent the properties of different writing styles. Based on experiments on ICDAR2013, CVL and IAM benchmark datasets, our results showed that semi-supervised feature learning improved the baseline meas- urement and achieved better performance compared with existing writer identifications approaches.

The concept of stochastic configuration networks (SCNs) others a solid framework for fast implementation of feedforward neural networks through randomized learning. Unlike conventional randomized approaches, SCNs provide an avenue to select appropriate scope of random parameters to ensure the universal approximation property. In this paper, a deep version of stochastic configuration networks, namely deep stacked stochastic configuration network (DSSCN), is proposed for modeling non-stationary data streams. As an extension of evolving stochastic connfiguration networks (eSCNs), this work contributes a way to grow and shrink the structure of deep stochastic configuration networks autonomously from data streams. The performance of DSSCN is evaluated by six benchmark datasets. Simulation results, compared with prominent data stream algorithms, show that the proposed method is capable of achieving comparable accuracy and evolving compact and parsimonious deep stacked network architecture.

Abstract-- Driving is a social activity: drivers often indicate their intent to change lanes via motion cues. We consider mixed-autonomy traffic where a Human-driven V ehicle (HV) and an Autonomous V ehicle (A V) drive together . We propose a planning framework where the degree to which the A V considers the other agent's reward is controlled by a selfishness factor . We test our approach on a simulated two-lane highway where the A V and HV merge into each other's lanes. In a user study with 21 subjects and 6 different selfishness factors, we found that our planning approach was sound and that both agents had less merging times when a factor that balances the rewards for the two agents was chosen. Our results on double lane merging suggest it to be a nonzero-sum game and encourage further investigation on collaborative decision making algorithms for mixed-autonomy traffic. Driving is a social activity: drivers indicate their willingness to change lanes by subtle cues such as eye contact, or by not-so-subtle cues such as adjusting their speed and position [1]. There has been impressive demonstrations of Autonomous V ehicle (A V) technology [2]-[4], however one of the remaining challenges in this area is reading those cues to estimate the intentions of other agents as well as using cues to communicate the intentions of the A V . As A Vs become commonplace, the situations where A V's and Human-driven V ehicles (HV) interact will increase.