Muhammad Asad Khan

Graph summarization has become integral for managing and analyzing large-scale graphs in diverse real-world applications, including social networks, biological networks, and communication networks. Existing methods for graph summarization often face challenges, being either computationally expensive, limiting their applicability to large graphs, or lacking the incorporation of node attributes. In response, we introduce SsAG , an efficient and scalable lossy graph summarization method designed to preserve the essential structure of the original graph. SsAG computes a sparse representation (summary) of the input graph, accommodating graphs with node attributes. The summary is structured as a graph on supernodes (subsets of vertices of G ), where weighted superedges connect pairs of supernodes. The methodology focuses on constructing a summary graph with k supernodes, aiming to minimize the reconstruction error (the difference between the original graph and the graph reconstructed from the summary) while maximizing homogeneity with respect to the node attributes. The construction process involves iteratively merging pairs of nodes. To enhance computational efficiency, we derive a closed-form expression for efficiently computing the reconstruction error (RE) after merging a pair, enabling constant-time approximation of this score. We assign a weight to each supernode, quantifying their contribution to the score of pairs, and utilize a weighted sampling strategy to select the best pair for merging. Notably, a logarithmic-sized sample achieves a summary comparable in quality based on various measures. Additionally, we propose a sparsification step for the constructed summary, aiming to reduce storage costs to a specified target size with a marginal increase in RE. Empirical evaluations across diverse real-world graphs demonstrate that SsAG exhibits superior speed, being up to 17 × faster, while generating summaries of comparable quality. This work represents a significant advancement in the field, addressing computational challenges and showcasing the effectiveness of SsAG in graph summarization.

Node localization is one of the most essential features of wireless sensor networks (WSNs). Vavarious localization algorithms exist for densely deployed 3-D wireless sensor networks. However, for a sparse 3-D network, range-based localization is still a challenging task because it is difficult to find sufficient anchor nodes and distance information among nodes in a sparse 3-D network. To mitigate the sparseness issues in 3-D sensor networks, we present a component-based localization method in this paper in which we split the entire network into small overlapping sub-networks called components and assign local coordinates to each component. Then, we merge these small components to make a globally coordinated system. With a meager anchor ratio, we localize the whole network. We define merging conditions according to the number of common nodes, actual measured distances among nodes, and the calculated distance based on the local coordinates of the nodes. We assess how well our proposed algorithm performs by conducting extensive simulations. The outcomes confirm that the proposed algorithm works comparatively better in a sparse 3-D sensor network than in a densely deployed 3-D sensor network. Our algorithm localizes more than 83% of nodes at a node degree of 10 having 5% anchor ratio; however, other algorithms localize only 18%-79% in the same scenario.

Cricket is the second most popular sport after soccer in terms of viewership. However, the assessment of individual player performance, a fundamental task in team sports, is currently primarily based on aggregate performance statistics, including average runs and wickets taken. We propose Context-Aware Metric of player Performance, CAMP, to quantify individual players' contributions toward a cricket match outcome. CAMP employs data mining methods and enables effective data-driven decision-making for selection and drafting, coaching and training, team line-ups, and strategy development. CAMP incorporates the exact context of performance, such as opponents' strengths and specific circumstances of games, such as pressure situations. We empirically evaluate CAMP on data of limited-over cricket matches between 2001 and 2019. In every match, a committee of experts declares one player as the best player, called Man of the M}atch (MoM). The top two rated players by CAMP match with MoM in 83\% of the 961 games. Thus, the CAMP rating of the best player closely matches that of the domain experts. By this measure, CAMP significantly outperforms the current best-known players' contribution measure based on the Duckworth-Lewis-Stern (DLS) method.

Optical character recognition (OCR) is the process of extracting handwritten or printed text from a scanned or printed image and converting it to a machine-readable form for further data processing, such as searching or editing. Automatic text extraction using OCR helps to digitize documents for improved productivity and accessibility and for preservation of historical documents. This paper provides a survey of the current state-of-the-art applications, techniques, and challenges in Arabic OCR. We present the existing methods for each step of the complete OCR process to identify the best-performing approach for improved results. This paper follows the keyword-search method for reviewing the articles related to Arabic OCR, including the backward and forward citations of the article. In addition to state-of-art techniques, this paper identifies research gaps and presents future directions for Arabic OCR.

Data analysis require a pairwise proximity measure over objects. Recent work has extended this to situations where the distance information between objects is given as comparison results of distances between three objects (triplets). Humans find the comparison tasks much easier than the exact distance computation and such data can be easily obtained in big quantity via crowd-sourcing. In this work, we propose triplets augmentation, an efficient method to extend the triplets data by inferring the hidden implicit information form the existing data. Triplets augmentation improves the quality of kernel-based and kernel-free data analytics. We also propose a novel set of algorithms for common data analysis tasks based on triplets. These methods work directly with triplets and avoid kernel evaluations, thus are scalable to big data. We demonstrate that our methods outperform the current best-known techniques and are robust to noisy data.

Accurate short-term load forecasting is essential for the efficient operation of the power sector. Forecasting load at a fine granularity such as hourly loads of individual households is challenging due to higher volatility and inherent stochasticity. At the aggregate levels, such as monthly load at a grid, the uncertainties and fluctuations are averaged out; hence predicting load is more straightforward. This paper proposes a method called Forecasting using Matrix Factorization (\textsc{fmf}) for short-term load forecasting (\textsc{stlf}). \textsc{fmf} only utilizes historical data from consumers' smart meters to forecast future loads (does not use any non-calendar attributes, consumers' demographics or activity patterns information, etc.) and can be applied to any locality. A prominent feature of \textsc{fmf} is that it works at any level of user-specified granularity, both in the temporal (from a single hour to days) and spatial dimensions (a single household to groups of consumers). We empirically evaluate \textsc{fmf} on three benchmark datasets and demonstrate that it significantly outperforms the state-of-the-art methods in terms of load forecasting. The computational complexity of \textsc{fmf} is also substantially less than known methods for \textsc{stlf} such as long short-term memory neural networks, random forest, support vector machines, and regression trees.