Recently, violence detection systems developed using unified multimodal models have achieved significant success and attracted widespread attention. However, most of these systems face two critical challenges: the lack of interpretability as black-box models and limited functionality, offering only classification or retrieval capabilities. To address these challenges, this paper proposes a novel interpretable violence detection system, termed the Three-in-One (TIO) System. The TIO system integrates knowledge graphs (KG) and graph attention networks (GAT) to provide three core functionalities: detection, retrieval, and explanation. Specifically, the system processes each video frame along with text descriptions generated by a large language model (LLM) for videos containing potential violent behavior. It employs ImageBind to generate high-dimensional embeddings for constructing a knowledge graph, uses GAT for reasoning, and applies lightweight time series modules to extract video embedding features. The final step connects a classifier and retriever for multi-functional outputs. The interpretability of KG enables the system to verify the reasoning process behind each output. Additionally, the paper introduces several lightweight methods to reduce the resource consumption of the TIO system and enhance its efficiency. Extensive experiments conducted on the XD-Violence and UCF-Crime datasets validate the effectiveness of the proposed system. A case study further reveals an intriguing phenomenon: as the number of bystanders increases, the occurrence of violent behavior tends to decrease.

Weakly Supervised Monitoring Anomaly Detection (WSMAD) utilizes weak supervision learning to identify anomalies, a critical task for smart city monitoring. However, existing multimodal approaches often fail to meet the real-time and interpretability requirements of edge devices due to their complexity. This paper presents TCVADS (Two-stage Cross-modal Video Anomaly Detection System), which leverages knowledge distillation and cross-modal contrastive learning to enable efficient, accurate, and interpretable anomaly detection on edge devices.TCVADS operates in two stages: coarse-grained rapid classification and fine-grained detailed analysis. In the first stage, TCVADS extracts features from video frames and inputs them into a time series analysis module, which acts as the teacher model. Insights are then transferred via knowledge distillation to a simplified convolutional network (student model) for binary classification. Upon detecting an anomaly, the second stage is triggered, employing a fine-grained multi-class classification model. This stage uses CLIP for cross-modal contrastive learning with text and images, enhancing interpretability and achieving refined classification through specially designed triplet textual relationships. Experimental results demonstrate that TCVADS significantly outperforms existing methods in model performance, detection efficiency, and interpretability, offering valuable contributions to smart city monitoring applications.

Artificial Intelligence is machine intelligence or ability to think and process information like natural human intelligence in order to create expert systems with human intelligence (reasoning, learning, and problem solving) with help from science and technology disciplines such as Mathematics, Engineering, Biology, Computer Science, Linguistics and Psychology. The term intelligence, literally, means the ability to acquire and apply knowledge and skills. The term Artificial Intelligence ( Artificial Intelligence) is pretty self-explanatory. It is the ability to acquire and apply knowledge and skills artificially. In 1956, a group of researchers from different disciplines of technology gathered for the summit called Dartmouth Summer Research Project.

Emerging technology, like blockchain technology, Artificial Intelligence, virtual reality, drones, cloud computing -- are all on the table of the Narendra Modi government as it pushes ahead to realise the goal of better e-governance for citizens. As part of its vision of providing a One Government experience to citizens, the Modi government is moving forward rapidly to implement the India Enterprise Architecture (IndEA) -- a single window digitisation solution for cashless, paperless and faceless services. At an e-Governance conference held in Shillong, Meghalaya, earlier this month, representatives of all the state governments and Union Territories, as well as business houses, brainstormed on the way ahead to provide better e-governance, and exchanged success stories of various states that could be adopted at the national level. A Shillong Declaration was adopted as a roadmap of the way forward, at the conclusion of the two-day 22nd National Conference on e-Governance (NCeG) 2019, with special focus on the Northeast, held on August 8-9, in Shillong. The conference, that saw over 500 delegates attending, with representation from states at the level of Additional Secretary and Principal Secretary, and top representatives of business houses like Wipro, HP and KPMG, was organised by the Department of Administrative Reforms & Public Grievances (DARPG), in association with the Ministry of Electronics & Information Technology (MeitY), and the Meghalaya government.