Debasis Das

Yunuo Cen, Debasis Das, Xuanyao Fong

Simulated annealing (SA) attracts more attention among classical heuristic algorithms because the solution of the combinatorial optimization problem can be naturally mapped to the ground state of the Ising Hamiltonian. However, in practical implementation, the annealing process cannot be arbitrarily slow and hence, it may deviate from the expected stationary Boltzmann distribution and become trapped in a local energy minimum. To overcome this problem, this paper proposes a heuristic search algorithm by expanding search space from a Markov chain to a recursive depth limited tree based on SA, where the parent and child nodes represent the current and future spin states. At each iteration, the algorithm will select the best near-optimal solution within the feasible search space by exploring along the tree in the sense of `look ahead'. Furthermore, motivated by coherent Ising machine (CIM), we relax the discrete representation of spin states to continuous representation with a regularization term and utilize the reduced dynamics of the oscillators to explore the surrounding neighborhood of the selected tree nodes. We tested our algorithm on a representative NP-hard problem (MAX-CUT) to illustrate the effectiveness of this algorithm compared to semi-definite programming (SDP), SA, and simulated CIM. Our results show that above the primal heuristics SA and CIM, our high-level tree search strategy is able to provide solutions within fewer epochs for Ising formulated NP-optimization problems.

Shubham Gupta, Nandini Saini, Suman Kundu et al.

Pervasive use of social media has become the emerging source for real-time information (like images, text, or both) to identify various events. Despite the rapid growth of image and text-based event classification, the state-of-the-art (SOTA) models find it challenging to bridge the semantic gap between features of image and text modalities due to inconsistent encoding. Also, the black-box nature of models fails to explain the model's outcomes for building trust in high-stakes situations such as disasters, pandemic. Additionally, the word limit imposed on social media posts can potentially introduce bias towards specific events. To address these issues, we proposed CrisisKAN, a novel Knowledge-infused and Explainable Multimodal Attention Network that entails images and texts in conjunction with external knowledge from Wikipedia to classify crisis events. To enrich the context-specific understanding of textual information, we integrated Wikipedia knowledge using proposed wiki extraction algorithm. Along with this, a guided cross-attention module is implemented to fill the semantic gap in integrating visual and textual data. In order to ensure reliability, we employ a model-specific approach called Gradient-weighted Class Activation Mapping (Grad-CAM) that provides a robust explanation of the predictions of the proposed model. The comprehensive experiments conducted on the CrisisMMD dataset yield in-depth analysis across various crisis-specific tasks and settings. As a result, CrisisKAN outperforms existing SOTA methodologies and provides a novel view in the domain of explainable multimodal event classification.

Trupil Limbasiya, Debasis Das, Sanjay K. Sahay

The pioneering concept of connected vehicles has transformed the way of thinking for researchers and entrepreneurs by collecting relevant data from nearby objects. However, this data is useful for a specific vehicle only. Moreover, vehicles get a high amount of data (e.g., traffic, safety, and multimedia infotainment) on the road. Thus, vehicles expect adequate storage devices for this data, but it is infeasible to have a large memory in each vehicle. Hence, the vehicular cloud computing (VCC) framework came into the picture to provide a storage facility by connecting a road-side-unit (RSU) with the vehicular cloud (VC). In this, data should be saved in an encrypted form to preserve security, but there is a challenge to search for information over encrypted data. Next, we understand that many of vehicular communication schemes are inefficient for data transmissions due to its poor performance results and vulnerable to different fundamental security attacks. Accordingly, on-device performance is critical, but data damages and secure on-time connectivity are also significant challenges in a public environment. Therefore, we propose reliable data transmission protocols for cutting-edge architecture to search data from the storage, to resist against various security attacks, and provide better performance results. Thus, the proposed data transmission protocol is useful in diverse smart city applications (business, safety, and entertainment) for the benefits of society.