A graph-based mathematical morphology reader

This survey paper aims at providing a "literary" anthology of mathematical morphology on graphs. It describes in the English language many ideas stemming from a large number of different papers, hence providing a unified view of an active and diverse field of research

Engineering Massively Parallel MST Algorithms

We develop and extensively evaluate highly scalable distributed-memory algorithms for computing minimum spanning trees (MSTs). At the heart of our solutions is a scalable variant of Boruvka's algorithm. For partitioned graphs with many local edges, we improve this with an effective form of contracting local parts of the graph during a preprocessing step. We also adapt the filtering concept of the best practical sequential algorithm to develop a massively parallel Filter-Boruvka algorithm that is very useful for graphs with poor locality and high average degree. Our experiments indicate that our algorithms scale well up to at least 65 536 cores and are up to 800 times faster than previous distributed MST algorithms.Comment: 12 pages, 6 figure

Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All

Collective entity disambiguation aims to jointly resolve multiple mentions by linking them to their associated entities in a knowledge base. Previous works are primarily based on the underlying assumption that entities within the same document are highly related. However, the extend to which these mentioned entities are actually connected in reality is rarely studied and therefore raises interesting research questions. For the first time, we show that the semantic relationships between the mentioned entities are in fact less dense than expected. This could be attributed to several reasons such as noise, data sparsity and knowledge base incompleteness. As a remedy, we introduce MINTREE, a new tree-based objective for the entity disambiguation problem. The key intuition behind MINTREE is the concept of coherence relaxation which utilizes the weight of a minimum spanning tree to measure the coherence between entities. Based on this new objective, we design a novel entity disambiguation algorithms which we call Pair-Linking. Instead of considering all the given mentions, Pair-Linking iteratively selects a pair with the highest confidence at each step for decision making. Via extensive experiments, we show that our approach is not only more accurate but also surprisingly faster than many state-of-the-art collective linking algorithms

HR Functions Productivity Boost by using AI

In today's fast-paced world, the Human Resources (HR) department plays a pivotal role in the success of any organization. With a plethora of tasks to manage, the HR team often faces the daunting challenge of screening and selecting the best candidates for various positions. To streamline this process, we propose a novel system that integrates the power of Kruskal algorithm for resume screening, conducting a qualifying test called "BTNT," and psychological testing like Emotional Intelligence to analyze the shortlisted candidates. Our proposed system utilizes a Knapsack approach under Dynamic Programming to suggest the most suitable candidates for HR roles. By automating these tedious HR tasks through Artificial Intelligence (AI), we ensure a faster, more accurate, and cost-effective selection process. Our research paper presents a detailed analysis of the proposed system's effectiveness and showcases the benefits of adopting this innovative approach. We believe that this cutting-edge system will revolutionize the HR industry by providing an efficient, objective, and unbiased selection process. The BTNT test's incorporation will help identify candidates' technical skills, while the psychological test will highlight their soft skills. This holistic approach ensures that organizations not only hire the best-fit candidates but also create a positive work environment that fosters growth and development. Our research paper is a must-read for any HR professional looking to optimize their recruitment process and gain a competitive edge in the market. With our proposed system's implementation, companies can attract and retain top talent, improve employee productivity, and ultimately increase their efficiency

Algorithm Engineering for fundamental Sorting and Graph Problems

Fundamental Algorithms build a basis knowledge for every computer science undergraduate or a professional programmer. It is a set of basic techniques one can find in any (good) coursebook on algorithms and data structures. In this thesis we try to close the gap between theoretically worst-case optimal classical algorithms and the real-world circumstances one face under the assumptions imposed by the data size, limited main memory or available parallelism

Sparse optical flow regularisation for real-time visual tracking

Optical flow can greatly improve the robustness of visual tracking algorithms. While dense optical flow algorithms have various applications, they can not be used for real-time solutions without resorting to GPU calculations. Furthermore, most optical flow algorithms fail in challenging lighting environments due to the violation of the brightness constraint. We propose a simple but effective iterative regularisation scheme for real-time, sparse optical flow algorithms, that is shown to be robust to sudden illumination changes and can handle large displacements. The algorithm proves to outperform well known techniques in real life video sequences, while being much faster to calculate. Our solution increases the robustness of a real-time particle filter based tracking application, consuming only a fraction of the available CPU power. Furthermore, a new and realistic optical flow dataset with annotated ground truth is created and made freely available for research purposes