空间优化问题（SOP）的特征是管理决策变量，目标和/或约束功能的空间关系。在本文中，我们关注一种称为空间分区的特定类型的SOP，这是一个组合问题，这是由于存在离散空间单元。精确的优化方法不会随着问题的大小而扩展，尤其是在可行的时间限制内。这促使我们开发基于人群的元启发式学来解决此类SOP。但是，这些基于人群的方法采用的搜索操作员主要是为实参与者连续优化问题而设计的。为了使这些方法适应SOP，我们将域知识应用于设计空间感知的搜索操作员，以在保留空间约束的同时有效地通过离散搜索空间进行有效搜索。为此，我们提出了一种简单而有效的算法，称为基于群的空间模因算法（空间），并在学校（RE）区域问题上进行测试。对现实世界数据集进行了详细的实验研究，以评估空间的性能。此外，进行消融研究以了解空间各个组成部分的作用。此外，我们讨论空间〜如何在现实生活计划过程及其对不同方案的适用性并激发未来的研究方向有帮助。

了解全文学术文章的关键见解至关重要，因为它使我们能够确定有趣的趋势，洞悉研究和发展，并构建知识图。但是，只有在考虑全文时才可用一些有趣的关键见解。尽管研究人员在简短文档中的信息提取方面取得了重大进展，但从全文学术文献中提取科学实体仍然是一个具有挑战性的问题。这项工作提出了一种称为ENEREX的自动端对端研究实体提取器，用于提取技术集，客观任务，全文学术学术研究文章等技术方面。此外，我们提取了三个新颖的方面，例如源代码，计算资源，编程语言/库中的链接。我们演示了Enerex如何从计算机科学领域的大规模数据集中提取关键见解和趋势。我们进一步测试了多个数据集上的管道，发现ENEREX在最新模型的状态下进行了改进。我们强调了现有数据集的能力如何受到限制，以及enerex如何适应现有知识图。我们还向未来研究的指针进行了详细的讨论。我们的代码和数据可在https://github.com/discoveryanalyticscenter/enerex上公开获取。

最近，越来越多的研究人员，尤其是在政治重新划分领域的研究人员，提出了基于抽样的技术，以从各个地区计划的广阔空间中制定一部分计划。这些技术已被美国法院和独立委员会越来越多地采用，作为确定游击队的工具。在这些最近的发展的促进下，我们开发了一系列基于翻转建议的学校边界的类似抽样技术。请注意，此处的翻转提案是指单个任务的区域计划的更改。这些基于抽样的技术具有双重目的。它们可以用作基线，用于比较基于本地搜索的重新划分算法。此外，这些技术可以帮助推断出可以进一步用于开发有效重新分配方法的问题特征。关于学校重新划分问题，我们从经验上谈到了这两个方面。

建模传染病传播的时空性质可以提供有用的直觉，以了解疾病传播的时变方面，并且在人们的行动模式中观察到的潜在的复杂空间依赖性。此外，可以利用县级多相关时间序列信息，以便在单个时间序列进行预测。添加到这一挑战是实时数据常常偏离单向高斯分布假设，并且可以显示一些复杂的混合模式。由此激励，我们开发了一种基于深度学习的时间序列模型，用于自动回归混合密度动态扩散网络（ARM3DNet）的概率预测，其认为人们的移动性和疾病在动态定向图上传播。实现高斯混合模型层以考虑从多个相关时间序列学习的实时数据的多模式性质。我们展示了我们的模型，当由于动态协变量特征和混合成分的最佳组合培训时，可以超越传统的统计和深度学习模式，以预测美国县级的Covid-19死亡和案例的数量。

Online Social Networks have embarked on the importance of connection strength measures which has a broad array of applications such as, analyzing diffusion behaviors, community detection, link predictions, recommender systems. Though there are some existing connection strength measures, the density that a connection shares with it's neighbors and the directionality aspect has not received much attention. In this paper, we have proposed an asymmetric edge similarity measure namely, Neighborhood Density-based Edge Similarity (NDES) which provides a fundamental support to derive the strength of connection. The time complexity of NDES is $O(nk^2)$. An application of NDES for community detection in social network is shown. We have considered a similarity based community detection technique and substituted its similarity measure with NDES. The performance of NDES is evaluated on several small real-world datasets in terms of the effectiveness in detecting communities and compared with three widely used similarity measures. Empirical results show NDES enables detecting comparatively better communities both in terms of accuracy and quality.

Community detection in Social Networks is associated with finding and grouping the most similar nodes inherent in the network. These similar nodes are identified by computing tie strength. Stronger ties indicates higher proximity shared by connected node pairs. This work is motivated by Granovetter's argument that suggests that strong ties lies within densely connected nodes and the theory that community cores in real-world networks are densely connected. In this paper, we have introduced a novel method called \emph{Disjoint Community detection using Cascades (DCC)} which demonstrates the effectiveness of a new local density based tie strength measure on detecting communities. Here, tie strength is utilized to decide the paths followed for propagating information. The idea is to crawl through the tuple information of cascades towards the community core guided by increasing tie strength. Considering the cascade generation step, a novel preferential membership method has been developed to assign community labels to unassigned nodes. The efficacy of $DCC$ has been analyzed based on quality and accuracy on several real-world datasets and baseline community detection algorithms.

Information diffusion in Online Social Networks is a new and crucial problem in social network analysis field and requires significant research attention. Efficient diffusion of information are of critical importance in diverse situations such as; pandemic prevention, advertising, marketing etc. Although several mathematical models have been developed till date, but previous works lacked systematic analysis and exploration of the influence of neighborhood for information diffusion. In this paper, we have proposed Common Neighborhood Strategy (CNS) algorithm for information diffusion that demonstrates the role of common neighborhood in information propagation throughout the network. The performance of CNS algorithm is evaluated on several real-world datasets in terms of diffusion speed and diffusion outspread and compared with several widely used information diffusion models. Empirical results show CNS algorithm enables better information diffusion both in terms of diffusion speed and diffusion outspread.

Nature-inspired optimization Algorithms (NIOAs) are nowadays a popular choice for community detection in social networks. Community detection problem in social network is treated as optimization problem, where the objective is to either maximize the connection within the community or minimize connections between the communities. To apply NIOAs, either of the two, or both objectives are explored. Since NIOAs mostly exploit randomness in their strategies, it is necessary to analyze their performance for specific applications. In this paper, NIOAs are analyzed on the community detection problem. A direct comparison approach is followed to perform pairwise comparison of NIOAs. The performance is measured in terms of five scores designed based on prasatul matrix and also with average isolability. Three widely used real-world social networks and four NIOAs are considered for analyzing the quality of communities generated by NIOAs.

The tropical cyclone formation process is one of the most complex natural phenomena which is governed by various atmospheric, oceanographic, and geographic factors that varies with time and space. Despite several years of research, accurately predicting tropical cyclone formation remains a challenging task. While the existing numerical models have inherent limitations, the machine learning models fail to capture the spatial and temporal dimensions of the causal factors behind TC formation. In this study, a deep learning model has been proposed that can forecast the formation of a tropical cyclone with a lead time of up to 60 hours with high accuracy. The model uses the high-resolution reanalysis data ERA5 (ECMWF reanalysis 5th generation), and best track data IBTrACS (International Best Track Archive for Climate Stewardship) to forecast tropical cyclone formation in six ocean basins of the world. For 60 hours lead time the models achieve an accuracy in the range of 86.9% - 92.9% across the six ocean basins. The model takes about 5-15 minutes of training time depending on the ocean basin, and the amount of data used and can predict within seconds, thereby making it suitable for real-life usage.

Fairness of machine learning (ML) software has become a major concern in the recent past. Although recent research on testing and improving fairness have demonstrated impact on real-world software, providing fairness guarantee in practice is still lacking. Certification of ML models is challenging because of the complex decision-making process of the models. In this paper, we proposed Fairify, an SMT-based approach to verify individual fairness property in neural network (NN) models. Individual fairness ensures that any two similar individuals get similar treatment irrespective of their protected attributes e.g., race, sex, age. Verifying this fairness property is hard because of the global checking and non-linear computation nodes in NN. We proposed sound approach to make individual fairness verification tractable for the developers. The key idea is that many neurons in the NN always remain inactive when a smaller part of the input domain is considered. So, Fairify leverages whitebox access to the models in production and then apply formal analysis based pruning. Our approach adopts input partitioning and then prunes the NN for each partition to provide fairness certification or counterexample. We leveraged interval arithmetic and activation heuristic of the neurons to perform the pruning as necessary. We evaluated Fairify on 25 real-world neural networks collected from four different sources, and demonstrated the effectiveness, scalability and performance over baseline and closely related work. Fairify is also configurable based on the domain and size of the NN. Our novel formulation of the problem can answer targeted verification queries with relaxations and counterexamples, which have practical implications.