Modeling Dyadic Human Interaction using Sequential Neural Network

Social networks, involving people and their interactions are at core of human society. But many current computational social methods focus more on the individual than their interactions. Deep neural networks have been successfully applied to tasks such as natural language processing, dialog modeling, or analyzing sentiments in a conversation. In these areas, we will often encounter data that originate from multiple sources. These signals can interact with each other synchronously, but detecting such synchrony may prove challenging. In this work we focus on investigating how deep neural network architectures can help us better understand synchrony in social contexts. We investigate different coupled sequential models such as an end-to-end connected gated recurrent unit (GRU), an inherently coupled GRU, message-passing, the role of attention and the use of transformer networks for coupling. We evaluate the effectiveness of our coupling models on multiple datasets. We first test on synthesized sequential coupled data as a sanity-check and then move on to more realistic data. We test our models on three different real-world datasets collected in the context of various social interactions. In two of the datasets, we predict the rapport between two persons based on data extracted from the video of them interacting. In the third dataset, we predict friendship/familiarity between two people based on their interaction. We present the findings from the work and conclude that the coupled transformer network performs the best

Predicting Information Pathways Across Online Communities

The problem of community-level information pathway prediction (CLIPP) aims at predicting the transmission trajectory of content across online communities. A successful solution to CLIPP holds significance as it facilitates the distribution of valuable information to a larger audience and prevents the proliferation of misinformation. Notably, solving CLIPP is non-trivial as inter-community relationships and influence are unknown, information spread is multi-modal, and new content and new communities appear over time. In this work, we address CLIPP by collecting large-scale, multi-modal datasets to examine the diffusion of online YouTube videos on Reddit. We analyze these datasets to construct community influence graphs (CIGs) and develop a novel dynamic graph framework, INPAC (Information Pathway Across Online Communities), which incorporates CIGs to capture the temporal variability and multi-modal nature of video propagation across communities. Experimental results in both warm-start and cold-start scenarios show that INPAC outperforms seven baselines in CLIPP.Comment: In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'23

A Survey of Graph Neural Networks for Social Recommender Systems

Social recommender systems (SocialRS) simultaneously leverage user-to-item interactions as well as user-to-user social relations for the task of generating item recommendations to users. Additionally exploiting social relations is clearly effective in understanding users' tastes due to the effects of homophily and social influence. For this reason, SocialRS has increasingly attracted attention. In particular, with the advance of Graph Neural Networks (GNN), many GNN-based SocialRS methods have been developed recently. Therefore, we conduct a comprehensive and systematic review of the literature on GNN-based SocialRS. In this survey, we first identify 80 papers on GNN-based SocialRS after annotating 2151 papers by following the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analysis). Then, we comprehensively review them in terms of their inputs and architectures to propose a novel taxonomy: (1) input taxonomy includes 5 groups of input type notations and 7 groups of input representation notations; (2) architecture taxonomy includes 8 groups of GNN encoder, 2 groups of decoder, and 12 groups of loss function notations. We classify the GNN-based SocialRS methods into several categories as per the taxonomy and describe their details. Furthermore, we summarize the benchmark datasets and metrics widely used to evaluate the GNN-based SocialRS methods. Finally, we conclude this survey by presenting some future research directions.Comment: GitHub repository with the curated list of papers: https://github.com/claws-lab/awesome-GNN-social-recsy

Effect of Fly Ash on Geotechnical Properties of Soft Soil: A Critical Review

An industrial by-product known as fly ash is produced when coal is burned for electricity production and is considered an environmental pollutant. A comprehensive fly ash utilisation programme must be implemented to reduce environmental pollution, including numerous factors at different levels. Fly ash’s geotechnical qualities, including its specific gravity, permeability, internal angular friction, and consolidation characteristics, make it ideal for structural fill, particularly on clay soils, when building highways and embankments. Much research has been conducted on how fly ash affects soil stability. In order to determine the impact of fly ash addition on soil properties, this inquiry reviewed a few of these papers and conducted a critical assessment. This study also looked at combining fly ash and clay soil. Numerous investigations indicate that fly ash generally improves soil stability, notably when analysing CBR values and soil permeability, and reduces volumetric changes in the soil. The ground becomes compact due to particle size and form and a decrease in volumetric dilatation. Because the additives to the hardened soil do not dissolve, the soil’s behaviour continues to be modified

Planning of Solar Steam Cooking System at SMVDU

This paper presents the planning of the potential and feasibility of a complete solar solution for the mess at the Shri Mata Vaishno Devi University (SMVDU) campus. Since there is ample space near the mess, solar steam generating plants are proposed on the mess to reduce liquified petroleum gas (LPG) consumption substantially. Forty concentrators (sixteen square meters each) are proposed to be installed. The project’s life is proposed to be twenty-five years with a capital cost of USD 19.02 thousand and additional operation and maintenance costs. The financial analysis shows that the total savings from the project are USD 172.82 thousand with a cost-benefit ratio of 6.40. The project’s break-even is approximated to be attained by the fortieth month of operation. Beyond the financial benefit, the project is proposed to have multiple other benefits to the institution. The benefits are that the use of fossil fuels (LPG) for cooking can be avoided by the installation of a thermal cooking system, it shall provide a better sustainability score in various rankings done worldwide for the university, the cost of tender of mess for future can be reduced drastically, the project will be brought up as a project that shall be displayed at every level in the union territory so that we shall promote the development of renewable energy uses

Abstracts of 1st International Conference on Machine Intelligence and System Sciences

This book contains the abstracts of the papers presented at the International Conference on Machine Intelligence and System Sciences (MISS-2021) Organized by the Techno College of Engineering, Agartala, Tripura, India & Tongmyong University, Busan, South Korea, held on 1–2 November 2021. This conference was intended to enable researchers to build connections between different digital technologies based on Machine Intelligence, Image Processing, and the Internet of Things (IoT). Conference Title: 1st International Conference on Machine Intelligence and System SciencesConference Acronym: MISS-2021Conference Date: 1–2 November 2021Conference Location: Techno College of Engineering Agartala, Tripura(w), IndiaConference Organizer: Techno College of Engineering, Agartala, Tripura, India & Tongmyong University, Busan, South Korea