Natural human communication is nuanced and inherently multi-modal. Humans possess specialised sensoria for processing vocal, visual, and linguistic, and para-linguistic information, but form an intricately fused percept of the multi-modal data stream to provide a holistic representation. Analysis of emotional content in face-to-face communication is a cognitive task to which humans are particularly attuned, given its sociological importance, and poses a difficult challenge for machine emulation due to the subtlety and expressive variability of cross-modal cues. Inspired by the empirical success of recent so-called End-To-End Memory Networks (Sukhbaatar et al., 2015), we propose an approach based on recursive multi-attention with a shared external memory updated over multiple gated iterations of analysis. We evaluate our model across several large multimodal datasets and show that global contextualised memory with gated memory update can effectively achieve emotion recognition.

Electric power grids, which form an essential part of the critical infrastructure, are evolving into highly distributed, dynamic networks in order to address the climate change. This fundamental transition relies on extensive automation solutions based on communications and information technologies. Thus, it also gives rise to new attack points for malicious actors and consequently, increases the vulnerability of the electric energy system. This study presents a qualitative assessment of power grid cybersecurity through expert interviews across countries in Europe and the U.S. to gain understanding of the latest developments and trends in the cybersecurity of future electric energy systems. The horizon of the assessment is 10 years spanning until the early 2030s. Thereafter, the study identifies how and to which extent the risks identified to be most significant are understood and addressed in the latest research and industry publications aiming at identifying areas deserving specific further attention. The most significant threats based on the assessment are False Data Injection (FDI), Denial of Service (DoS) supply chain, and ransomware and malware attacks.

Sentiment analysis is a process that is a very popular concept nowadays because of the high volume of reviews, micro blogs, comments etc., generated in different sites like e-commerce and social networking sites. The main problem in the current system is, for users to know the polarity result of bulk data, which is very tough because users need to study and understand each review in terms of the polarity. Users are expecting a onetime result of the polarity of bulk reviews, comments, micro blogs etc. In social networking sites, users post their status or opinions to share to the world. In this category, Twitter is the most popular one. In twitter, users post many micro blogs related to a topic or crisis etc, and the topic may be linked with a greater number of micro blogs based on the keywords or hash tags used. In twitter, we can search for any topic with keywords or hash tags, and we get a bulk of responses of the users world-wide. If we want to know the exact opinions of the users, we need to analyze the data with sentiment analysis. Sentiment analysis is a concept of defining a statement as positive, neutral, or negative by analyzing words of the statement. Many concepts have been proposed for this requirement, and many sentidatasets have been prepared for this requirement. But by taking the advantages of Machine Learning we are proposing a concept of sentiment analysis in twitter using ML techniques. In this we use multiple ML techniques such as Random Forest, Naïve Bayes and Support Vector Machine for evaluation and comparison of the results.

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