Sumit Kumar

Gayathri S, Akshat Anand, Astha Vijayvargiya et al.

Smart wearables have played an integral part in our day to day life. From recording ECG signals to analysing body fat composition, the smart wearables can do it all. The smart devices encompass various sensors which can be employed to derive meaningful information regarding the user's physical and psychological conditions. Our approach focuses on employing such sensors to identify and obtain the variations in the mood of a user at a given instance through the use of supervised machine learning techniques. The study examines the performance of various supervised learning models such as Decision Trees, Random Forests, XGBoost, LightGBM on the dataset. With our proposed model, we obtained a high recognition rate of 92.5% using XGBoost and LightGBM for 9 different emotion classes. By utilizing this, we aim to improvise and suggest methods to aid emotion recognition for better mental health analysis and mood monitoring.

Dhruv Limbani, Daketi Yatin, Nitish Chaturvedi et al.

Emotion prediction is the field of study to understand human emotions. Existing methods focus on modalities like text, audio, facial expressions, etc., which could be private to the user. Emotion can be derived from the subject's psychological data as well. Various approaches that employ combinations of physiological sensors for emotion recognition have been proposed. Yet, not all sensors are simple to use and handy for individuals in their daily lives. Thus, we propose a system to predict user emotion using smartwatch sensors. We design a framework to collect ground truth in real-time utilizing a mix of English and regional language-based videos to invoke emotions in participants and collect the data. Further, we modeled the problem as binary classification due to the limited dataset size and experimented with multiple machine-learning models. We also did an ablation study to understand the impact of features including Heart Rate, Accelerometer, and Gyroscope sensor data on mood. From the experimental results, Multi-Layer Perceptron has shown a maximum accuracy of 93.75 percent for pleasant-unpleasant (high/low valence classification) moods.

Harichandana B S S, Sumit Kumar

Accurate and automatic detection of mood serves as a building block for use cases like user profiling which in turn power applications such as advertising, recommendation systems, and many more. One primary source indicative of an individual's mood is textual data. While there has been extensive research on emotion recognition, the field of mood prediction has been barely explored. In addition, very little work is done in the area of on-device inferencing, which is highly important from the user privacy point of view. In this paper, we propose for the first time, an on-device deep learning approach for mood prediction from textual data, LEAPMood. We use a novel on-device deployment-focused objective function for hyperparameter tuning based on the Genetic Algorithm (GA) and optimize the parameters concerning both performance and size. LEAPMood consists of Emotion Recognition in Conversion (ERC) as the first building block followed by mood prediction using K-means clustering. We show that using a combination of character embedding, phonetic hashing, and attention along with Conditional Random Fields (CRF), results in a performance closely comparable to that of the current State-Of-the-Art with a significant reduction in model size (> 90%) for the task of ERC. We achieve a Micro F1 score of 62.05% with a memory footprint of a mere 1.67MB on the DailyDialog dataset. Furthermore, we curate a dataset for the task of mood prediction achieving a Macro F1-score of 72.12% with LEAPMood.

Sumit Kumar, Raj Ratn Pranesh

In the past few years, there has been a significant rise in toxic and hateful content on various social media platforms. Recently Black Lives Matter movement came into the picture, causing an avalanche of user generated responses on the internet. In this paper, we have proposed a Black Lives Matter related tweet hate speech dataset TweetBLM. Our dataset comprises 9165 manually annotated tweets that target the Black Lives Matter movement. We annotated the tweets into two classes, i.e., HATE and NONHATE based on their content related to racism erupted from the movement for the black community. In this work, we also generated useful statistical insights on our dataset and performed a systematic analysis of various machine learning models such as Random Forest, CNN, LSTM, BiLSTM, Fasttext, BERTbase, and BERTlarge for the classification task on our dataset. Through our work, we aim at contributing to the substantial efforts of the research community for the identification and mitigation of hate speech on the internet. The dataset is publicly available.

Sunil Kumar Singh, Sumit Kumar

Blockchain was mainly introduced for secure transactions in connection with the mining of cryptocurrency Bitcoin. This article discusses the fundamental concepts of blockchain technology and its components, such as block header, transaction, smart contracts, etc. Blockchain uses the distributed databases, so this article also explains the advantages of distributed Blockchain over a centrally located database. Depending on the application, Blockchain is broadly categorized into two categories; Permissionless and Permissioned. This article elaborates on these two categories as well. Further, it covers the consensus mechanism and its working along with an overview of the Ethereum platform. Blockchain technology has been proved to be one of the remarkable techniques to provide security to IoT devices. An illustration of how Blockchain will be useful for IoT devices has been given. A few applications are also illustrated to explain the working of Blockchain with IoT.