Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review

Internet and social media have become a widespread, large scale and easy to use platform for real-time information dissemination. It has become an open stage for discussion, ideology expression, knowledge dissemination, emotions and sentiment sharing. This platform is gaining tremendous attraction and a huge user base from all sections and age groups of society. The matter of concern is that up to what extent the contents that are circulating among all these platforms every second changing the mindset, perceptions and lives of billions of people are verified, authenticated and up to the standards. This paper puts forward a holistic view of how the information is being weaponized to fulfil the malicious motives and forcefully making a biased user perception about a person, event or firm. Further, a taxonomy is provided for the classification of malicious information content at different stages and prevalent technologies to cope up with this issue form origin, propagation, detection and containment stages. We also put forward a research gap and possible future research directions so that the web information content could be more reliable and safer to use for decision making as well as for knowledge sharing.

Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities

Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human–robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.

Human Emotion Recognition: Review of Sensors and Methods.

Emotion Recognition from Physiological Signal Analysis: A Review

All-solid-state Li batteries (ASSBs) employing inorganic solid electrolytes offer improved safety and are exciting candidates for next-generation energy storage. Herein, we report a family of lithium mixed-metal chlorospinels, Li2InxSc0.666−xCl4 (0 ≤ x ≤ 0.666), with high ionic conductivity (up to 2.0 mS cm−1) owing to a highly disordered Li-ion distribution, and low electronic conductivity (4.7 × 10−10 S cm−1), which are implemented for high-performance ASSBs. Owing to the excellent interfacial stability of the SE against uncoated high-voltage cathode materials, ASSBs utilizing LiCoO2 or LiNi0.85Co0.1Mn0.05O2 exhibit superior rate capability and long-term cycling (up to 4.8 V versus Li+/Li) compared to state-of-the-art ASSBs. In particular, the ASSB with LiNi0.85Co0.1Mn0.05O2 exhibits a long life of >3,000 cycles with 80% capacity retention at room temperature. High cathode loadings are also demonstrated in ASSBs with stable capacity retention of >4 mAh cm−2 (~190 mAh g−1). Intensive research is underway to develop solid-state electrolytes for rechargeable batteries. Here the authors report a family of mixed-metal halospinel electrolytes that exhibits promising properties for high-performance solid-state batteries. 

High areal capacity, long cycle life 4 V ceramic all-solid-state Li-ion batteries enabled by chloride solid electrolytes

Respiration-based emotion recognition with deep learning

Capturing the occurrence dynamics is crucial to predicting which type of events will happen next and when. A common method to do this is through Hawkes processes. To enhance their capacity, recurrent neural networks (RNNs) have been incorporated due to RNNs successes in processing sequential data such as languages. Recent evidence suggests that self-Attention is more competent than RNNs in dealing with languages. However, we are unaware of the effectiveness of self-Attention in the context of Hawkes processes. This study aims to fill the gap by designing a self-Attentive Hawkes process (SAHP). SAHP employs self-Attention to summarise the influence of history events and compute the probability of the next event. One deficit of the conventional selfattention, when applied to event sequences, is that its positional encoding only considers the order of a sequence ignoring the time intervals between events. To overcome this deficit, we modify its encoding by translating time intervals into phase shifts of sinusoidal functions. Experiments on goodness-of-fit and prediction tasks show the improved capability of SAHP. Furthermore, SAHP is more interpretable than RNN-based counterparts because the learnt attention weights reveal contributions of one event type to the happening of another type. To the best of our knowledge, this is the first work that studies the effectiveness of self-Attention in Hawkes processes.

/pdf/self-attentive-hawkes-process-3bq77ah45w.pdf

Self-Attentive Hawkes Process

Detecting work-related stress with a wearable device

Social media platforms are a plethora of misinformation and its potential negative influence on the public is a growing concern. This concern has drawn the attention of the research community on developing mechanisms to detect misinformation. The task of misinformation detection consists of classifying whether a claim is True or False. Most research concentrates on developing machine learning models, such as neural networks, that outputs a single value in order to predict the veracity of a claim. One of the major problem faced by these models is the inability of representing the uncertainty of the prediction, which is due incomplete or finite available information about the claim being examined. We address this problem by proposing a Bayesian deep learning model. The Bayesian model outputs a distribution used to represent both the prediction and its uncertainty. In addition to the claim content, we also encode auxiliary information given by people's replies to the claim. First, the model encodes a claim to be verified, and generate a prior belief distribution from which we sample a latent variable. Second, the model encodes all the people's replies to the claim in a temporal order through a Long Short Term Memory network in order to summarize their content. This summary is then used to update the prior belief generating the posterior belief. Moreover, in order to train this model, we develop a Stochastic Gradient Variational Bayes algorithm to approximate the analytically intractable posterior distribution. Experiments conducted on two public datasets demonstrate that our model outperforms the state-of-the-art detection models.

/pdf/reply-aided-detection-of-misinformation-via-bayesian-deep-1s9fy6cvit.pdf

Qiang Zhang

Papers

High areal capacity, long cycle life 4 V ceramic all-solid-state Li-ion batteries enabled by chloride solid electrolytes

Respiration-based emotion recognition with deep learning

Self-Attentive Hawkes Process

Detecting work-related stress with a wearable device

Reply-Aided Detection of Misinformation via Bayesian Deep Learning