Leaf Nodes Research Articles

Abstract 7352: ScanCT: A tree-based machine learning model to detect single-cell genomic features associated with clinical outcomes

Abstract Single-cell technologies represent a revolutionary approach to resolving cell-type heterogeneity, identifying cells in specialized states, and detecting rare disease-associated cells. With the cost of single-cell technology decreasing substantially, its integration into clinical studies is gaining momentum. A new computational tool is needed to accommodate different single-cell genomics and clinical data formats while accounting for unwanted confounders. The study aims to develop a tree-based machine learning model to leverage the unprecedented resolution of single-cell multi-omics data for delineating the genomic and phenotypic drivers behind diverse immunotherapy responses. The proposed model is called single-cell analysis of Clinical Tree (scanCT), inspired by the Generalized Unbiased Interaction Detection and Estimation method for unbiased gene and protein feature selection and easy interpretation. The scanCT model learns from the data to select the genomic feature that best splits the cells from distinct clinical responses for each tree node. The confounding factors will be regressors in the nodes but not be used for branch splitting, while gene and protein features of interest will split the tree but not enter the regression model in each node. scanCT is built to be free from the biased selection towards variables of a larger number of categories or values. With tree-pruning and cross-validation, scanCT overcomes the over-fitting issue and enhances model generalization, especially for clinical studies with limited patients. Particularly, scanCT naturally fits the hierarchical cell type relationship and handles marker gene and protein interaction effects efficiently. Our approach was tested on single-cell datasets from B-cell malignancy patients undergoing Chimeric Antigen Receptor (CAR)-T cell therapy. The results from the scanCT are highly interpretable. For instance, each branch is a gene-protein combination profile, and cells are naturally partitioned by clinical association. The linear regressions at each leaf node are the clinical predictions for cells following the splitting criteria. The regression intercept is an average estimation of toxicity (e.g., neurotoxicity) or efficacy after controlling for confounder (e.g., tumor burden). scanCT accommodates categorical or continuous clinical response and survival data and is robust to missing values, a frequent challenge in oncological studies. scanCT represents a significant step forward in single-cell data analysis, which merges complex genotypic and phenotypic information with clinical outcomes. The efficacy and toxicity-associated genomic signatures will inform new manufacturing strategies to optimize CAR-T cell therapy products. The model and clinical association detections are expected to go beyond the B-cell malignancy field to benefit the broader cancer research community. Citation Format: Ye Zheng, Long Nguyen, Peigen Zhou, Alexandre V. Hirayama. ScanCT: A tree-based machine learning model to detect single-cell genomic features associated with clinical outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7352.

Abstract 820: Decolonizing data: Diversifying cancer registries to include SWANA

Abstract Southwest Asian/North African communities (SWANA) make up over 3-4% of immigrants in the U.S. and yet their health status is largely unknown because these ethnic groups are misclassified within the U.S. racial schema as White, deeming them ‘invisible minorities’. Administrative forms specify that White includes Middle-Eastern but SWANA persons may also self-identify as Black, Asian, and Other. With the rise of Islamophobia and increased US intervention in the Middle Eastern region, SWANA Americans face unique challenges that require a deeper understanding of their health status.One methodology to obtain cancer statistics on SWANA is using naming algorithms. Similar to SWANA, the Latine population was invisible in administrative data prior to the 1970’s. Grassroots efforts and advocacy from the Latine community led to the development of validated Latine surname algorithms which have been implemented by the National Cancer Institute. Similarly, SWANA activists have advocated for the creation of a federal identification category for over 50 years arguing that SWANA communities are not perceived as White due, in large part, to a long-standing history of political racism in the United States.The purpose of this study was to develop a SWANA Surname Algorithm (SSA) to inclusively identify SWANA in cancer health data. We used surnames by country of descent to leverage interpretable decision trees to effectively distinguish SWANA from non-SWANA individuals by iteratively selecting the best surname roots at which to split the data to maximize the separation of SWANA individuals from others based on their surname. We integrated these patterns into our SSA so that when presented with a new surname, the algorithm simply follows the decision patterns down to the leaf nodes, otherwise known as the predicted class (SWANA vs non-SWANA).We developed a preliminary SWANA Surname List (SSL) using publicly available naming databases by country of origin (N=71,300). We cross-referenced the SSL against the VCU Massey Cancer Center data repository and found 4.9% of all cancer patients from 2016 to 2020 matched as SWANA. Notably, the prevalence of SWANA patients has been increasing over the last few decades, 3.8% in 1991-1995, to 4.2% in 2001-2005, and then most recently 4.9% in 2016-2020. We will use our SSA to validate these findings. These preliminary findings underscore the valuable insights that naming algorithms can provide in elucidating the true demographic composition of cancer patients. Lack of racial/ethnic disaggregation perpetuates existing inequities in access to essential health resources among SWANA communities. The inclusion of SWANA in cancer disparities research would allow researchers to better examine the cancer health status of this underrepresented but growing community while also aligning with the true racialization of SWANA in the United States. Citation Format: Guleer Shahab, Michael Preston. Decolonizing data: Diversifying cancer registries to include SWANA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 820.

Counting Rules for Computing the Number of Independent Sets of a Grid Graph

The issue of counting independent sets of a graph, G, represented as i(G), is a significant challenge within combinatorial mathematics. This problem finds practical applications across various fields, including mathematics, computer science, physics, and chemistry. In chemistry, i(G) is recognized as the Merrifield–Simmons (M-S) index for molecular graphs, which is one of the most relevant topological indices related to the boiling point in chemical compounds. This article introduces an innovative algorithm designed for tallying independent sets within grid-like structures. The proposed algorithm is based on the ‘branch-and-bound’ technique and is applied to compute i(Gm,n) for a square grid formed by m rows and n columns. The proposed approach incorporates the widely recognized vertex reduction rule as the basis for splitting the current subgraph. The methodology involves breaking down the initial grid iteratively until outerplanar graphs are achieved, serving as the ’basic cases’ linked to the leaf nodes of the computation tree or when no neighborhood is incident to a minimum of five rectangular internal faces. The time complexity of the branch-and-bound algorithm speeds up the computation of i(Gm,n) compared to traditional methods, like the transfer matrix method. Furthermore, the scope of the proposed algorithm is more general than the algorithms focused on grids since it could be applied to process general mesh graphs.

Effects of uncommon non-isochronicities on remote synchronization

Remote synchronization (RS) is referred to as the weak synchronization between indirectly coupled nodes (among leaf nodes) while asynchronous between the directed coupled nodes (i.e., hub and leaf nodes). The underlying mechanisms are related to (i) coupling terms indirectly connecting two nearly identical oscillators while having a much different mediator in between, (ii) non-isochronicity of the oscillators. Here we show the effects of uncommon non-isochronicity values of oscillators on the occurrence of RS. In addition, we show clear difference of using either the first order phase approximations of the Stuart–Landau oscillators or Kuramoto–Sakaguchi (KS) models to study RS. By both theoretical and numerical analysis, we show that the critical coupling for synchronization between the directly connected nodes is inversely proportional to the difference value of non-isochronicity. On the contrary, the critical coupling for RS between indirectly connected leaf nodes is determined by the sum of non-isochronicity. In addition, RS may experience fading out after the first appearance when the coupling is progressively increased, which is indeed time delayed phase synchronization.

Hierarchical matrix factorization for interpretable collaborative filtering

Matrix factorization (MF) is a simple collaborative filtering technique that achieves superior recommendation accuracy by decomposing the user–item interaction matrix into user and item latent matrices. Because the model typically learns each interaction independently, it may overlook the underlying shared dependencies between users and items, resulting in less stable and interpretable recommendations. Based on these insights, we propose “Hierarchical Matrix Factorization” (HMF), which incorporates clustering concepts to capture the hierarchy, where leaf nodes and other nodes correspond to users/items and clusters, respectively. Central to our approach, called hierarchical embeddings, is the additional decomposition of the latent matrices (embeddings) into probabilistic connection matrices, which link the hierarchy, and a root cluster latent matrix. The embeddings are differentiable, allowing simultaneous learning of interactions and clustering using a single gradient descent method. Furthermore, the obtained cluster-specific interactions naturally summarize user–item interactions and provide interpretability. Experimental results on ratings and ranking predictions show that HMF outperforms existing MF methods, in particular achieving a 1.37 point improvement in RMSE for sparse interactions. Additionally, it was confirmed that the clustering integration of HMF has the potential for faster learning convergence and mitigation of overfitting compared to MF, and also provides interpretability through a cluster-centered case study.

Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method

One of the most important goals of cooperative driving is to control connected automated vehicles (CAVs) passing through conflict areas safely and efficiently without traffic signals. As a typical application scenario, allocating right-of-way reasonably at unsignalized intersections can effectively avoid collisions and reduce traffic delays. Proposed here is a new cooperative driving strategy for CAVs at unsignalized intersections based on distributed Monte Carlo tree search (MCTS). A task-area partition framework is also proposed to decompose the mission of cooperative driving into three main tasks: vehicle information sharing, passing order optimization, and trajectory control. Based on the schedule tree of the vehicle passing order, the root parallelization of MCTS combined with the majority voting rule is used to explore as many feasible passing orders (leaf nodes) as possible in a distributed way and find a nearly global-optimal passing order within the limited planning time. The aim is for CAVs to perform proper trajectory adjustments based on the obtained passing order to minimize traffic delays while making the slightest acceleration adjustments. A coupled simulation platform integrating SUMO and Python is developed to construct the unsignalized intersection scenarios and generate the proposed distributed cooperative driving strategy. Comparative analysis with conventional driving strategies demonstrates that the proposed strategy significantly enhances efficiency, safety, comfort, and emission, aligning well with innovative and environmentally friendly urban mobility aspirations.

Weighted omnidirectional semi-global stereo matching

Traditional semi-global matching (SGM) lacks interaction between scanlines and struggles to deal with the ambiguity of pixels in homogenous areas. In this paper, we propose a novel path-centering graph to perform weighted omnidirectional SGM (WOdSGM), in which the input image is divided into eight sub-trees, corresponding to eight optimization directions. In each pass, the outputs of pixels are recursively computed from leaf nodes to the root node along the tree structure. As the results of SGM from multiple scanlines often show different biases owing to respective message propagation directions, one solution is accurate in certain areas while others are not. By delving into the distribution of aggregated costs for pixels from various structures, we find that the minimum cost of reliable output is heavily biased from others. Therefore, we perform line fitting to approximate the distribution of aggregated costs and use the distance between the fitted line and the point corresponding to the minimum cost to evaluate the reliability of aggregated costs. Furthermore, we present a weighted fusion strategy to incorporate outputs from multiple directions, enabling our method to provide directionally biased constraints. Extensive experiments and analyses on widely used stereo datasets show that our approach outperforms typical traditional SGM-based algorithms.

High-risk sexual behaviors of HIV/AIDS and related factors in young students in Guangzhou

Objective: To explore high-risk sexual behaviors of HIV/AIDS and related factors in young students in Guangzhou. Methods: A cross-sectional survey was conducted in 5 different types of Guangzhou colleges by convenience sampling with minimum number of classes per grade and 600 samples per school from September to November 2021. The R 4.2.2 software was used to consolidate databases. Simultaneously, a logistic regression model and a decision tree algorithm model, stratifying by whether sexual behaviors had occurred before, were constructed. In each layer, the prediction performance of the two models was evaluated through area under receiver operating characteristic and the confusion matrix, and then the model with high prediction performance was retained. Results: A total of 7 346 students were surveyed. The proportion of the respondents reporting sexual experience were 9.08% (667/7 346), in whom 26.24% (175/667) had risky sexual activity in the past year. The decision tree algorithm model performs well in predicting whether high-risk sexual behaviors have occurred in the past year. When the complexity parameter value is 0.018, and nsplit reaches 4, which means there are 5 leaf nodes in the model, the cross error of the tree will be the smallest. The first best grouping variable in the decision tree was whether to use condoms throughout the first sexual behavior. If condoms were used at their sexual debut, but homosexual practices have occurred in the past year, the probability of risky sexual behavior will increase. If homosexual practices have not occurred in the past year, but the age of sexual debut was below 18 years old while the period of HIV education was after high school, the probability of risk sexual behavior will also increase. Conclusions: AIDS-related risky behaviors of young students still deserved attention. The experience of sexual debut and whether AIDS-related health education has been received before the sexual debut were significant predictors for the occurrence of high-risk sexual behavior. The decision tree algorithm model has particular applicability for predicting and screening potential risk populations.

A dynamic approach for visualizing and exploring concept hierarchies from textbooks.

In this study, we propose a visualization technique to explore and visualize concept hierarchies generated from a textbook in the legal domain. Through a human-centered design process, we developed a tool that allows users to effectively navigate through and explore complex hierarchical concepts in three kinds of traversal techniques: top-down, middle-out, and bottom-up. Our concept hierarchies offer an overview over a given domain, with increasing level of detail toward the bottom of the hierarchy which is consisting of entities. In the legal use case we considered, the concepts were adapted from section headings in a legal textbook, whereas references to law or legal cases inside the textbook became entities. The design of this tool is refined following various steps such as gathering user needs, pain points of an existing visualization, prototyping, testing, and refining. The resulting interface offers users several key features such as dynamic search and filter, explorable concept nodes, and a preview of leaf nodes at every stage. A high-fidelity prototype was created to test our theory and design. To test our concept, we used the System Usability Scale as a way to measure the prototype's usability, a task-based survey to asses the tool's ability in assisting users in gathering information and interacting with the prototype, and finally mouse tracking to understand user interaction patterns. Along with this, we gathered audio and video footage of users when participating in the study. This footage also helped us in getting feedback when the survey responses required further information. The data collected provided valuable insights to set the directions for extending this study. As a result, we have accounted for varying hierarchy depths, longer text spans than only one to two words in the elements of the hierarchy, searchability, and exploration of the hierarchies. At the same time, we aimed for minimizing visual clutter and cognitive overload. We show that existing approaches are not suitable to visualize the type of data which we support with our visualization.

DendroX: multi-level multi-cluster selection in dendrograms

BackgroundCluster heatmaps are widely used in biology and other fields to uncover clustering patterns in data matrices. Most cluster heatmap packages provide utility functions to divide the dendrograms at a certain level to obtain clusters, but it is often difficult to locate the appropriate cut in the dendrogram to obtain the clusters seen in the heatmap or computed by a statistical method. Multiple cuts are required if the clusters locate at different levels in the dendrogram.ResultsWe developed DendroX, a web app that provides interactive visualization of a dendrogram where users can divide the dendrogram at any level and in any number of clusters and pass the labels of the identified clusters for functional analysis. Helper functions are provided to extract linkage matrices from cluster heatmap objects in R or Python to serve as input to the app. A graphic user interface was also developed to help prepare input files for DendroX from data matrices stored in delimited text files. The app is scalable and has been tested on dendrograms with tens of thousands of leaf nodes. As a case study, we clustered the gene expression signatures of 297 bioactive chemical compounds in the LINCS L1000 dataset and visualized them in DendroX. Seventeen biologically meaningful clusters were identified based on the structure of the dendrogram and the expression patterns in the heatmap. We found that one of the clusters consisting of mostly naturally occurring compounds is not previously reported and has its members sharing broad anticancer, anti-inflammatory and antioxidant activities.ConclusionsDendroX solves the problem of matching visually and computationally determined clusters in a cluster heatmap and helps users navigate among different parts of a dendrogram. The identification of a cluster of naturally occurring compounds with shared bioactivities implicates a convergence of biological effects through divergent mechanisms.

Subspace learning machine (SLM): Methodology and performance evaluation

Inspired by the feedforward multilayer perceptron (FF-MLP), decision tree (DT) and extreme learning machine (ELM), a new classification model, called the subspace learning machine (SLM), is proposed in this work. SLM first identifies a discriminant subspace, S0, by examining the discriminant power of each input feature. Then, it uses probabilistic projections of features in S0 to yield 1D subspaces and finds the optimal partition for each of them. This is equivalent to partitioning S0 with hyperplanes. A criterion is developed to choose the best q partitions that yield 2q partitioned subspaces among them. We assign S0 to the root node of a decision tree and the intersections of 2q subspaces to its child nodes of depth one. The partitioning process is recursively applied at each child node to build an SLM tree. When the samples at a child node are sufficiently pure, the partitioning process stops and each leaf node makes a prediction. The idea can be generalized to regression, leading to the subspace learning regressor (SLR). Furthermore, ensembles of SLM/SLR trees can yield a stronger predictor. Extensive experiments are conducted for performance benchmarking among SLM/SLR trees, ensembles and classical classifiers/regressors.

Overexpression of black rice OsC1 confers tissue-specific anthocyanin accumulation in indica rice cv. Kasalath and its potential use as a visible marker in rice transformation

Anthocyanin biosynthesis, a process regulated by distinct MYB and MYC transcription factors, plays a crucial role in determining pigmentation in various tissue of plants. This study aimed to investigate the impact of overexpressing the OsC1 gene from black rice, encoding a MYB transcription factor, on anthocyanin pigmentation in red indica rice cv. Kasalath. Anthocyanin pigmentation was readily observed as purple spots in calli and as purple shoot tips and purple leaf sheath in transgenic seedlings. We confirmed the presence of the transgene using GUS assay and PCR analysis, and the pigmentation segregated following a 3:1 Mendelian ratio. T0 and T1 transgenic plants exhibits anthocyanin accumulation in various tissues including leaf sheaths, auricles, nodes, stigma, apiculus and awns, excluding the pericarp. Notably, the pigmentation in node tissues has not been previously reported for the OsC1 gene, and this gene does not involve in pericarp pigmentation. RT-PCR analysis of transgenic seedlings demonstrated that the overexpression of the OsC1 gene upregulated anthocyanin structural genes, particularly OsDFR, leading to anthocyanin accumulation. Intriguingly, the absence of OsB2 expression, encoding a MYC transcription factor, in transgenic seedlings suggests the involvement of alternative MYC factors in purple leaf sheaths. This study not only expands our understanding of OsC1's role in tissue specific anthocyanin pigmentation but also proposes OsC1 as a potential visible marker in rice transformation. Utilizing OsC1 as a marker provides an alternative approach to address concerns related to antibiotic-resistant genes while providing visually striking pigmentation.

Pulsed sounds caused by internal oxygen transport during photosynthesis in the seagrass Halophila ovalis.

Oxygen bubbles that leak from seagrass blades during photosynthesis have been hypothesized to cause cavitation sounds in aquatic plants. Here we investigate low-amplitude sounds with regular pulse rates produced during photosynthesis in seagrass beds of Halophila ovalis (Qitou Bay, Penghu islands and Cigu Lagoon, Taiwan). Sound pulses appear in the morning when illumination exceeds 10,000 Lux, peak at midday and decrease in midafternoon on a sunny day. Frequencies peak between 1 to 4 kHz, durations range between ca. 1.8 to 4.8 ms, and sound pressure level 1 cm from the bed is 105.4 ± 0.5 dB re 1 μPa (1100 h on a cloudy day). Sounds attenuate rapidly with distance, disappearing beyond 15 cm. Blocking sunlight or administering herbicide stops ongoing sounds. Gas bubbles are not typically seen during sound production ruling out cavitation, and external force (scissor cutting or plant pressed against the substrate) applied to the patch, leaves, petioles, or rhizomes generally increases pulse rate. We suggest sound emission is caused by internal oxygen transport through pores in diaphragms (a whistle mechanism) at the leaf base and nodes of the rhizome.

Two-level optimization by differential evolution in decision tree learning algorithm

Decision tree learning algorithms have a long history, but they are still popular due to their efficiency. Tree construction starts at the root and continues to each leaf node, creating a “near-optimal” tree. One of the key steps in creating a decision tree is the selection of a feature to split at each root node, which affects the classification accuracy. This process can be quite labor intensive. The article proposes a new approach to constructing decision trees based on the use of differential evolution for two-level tree optimization. Differential evolution works in two stages: at the first level, a feature is selected for separation, and at the second level, the threshold value for this feature is optimized. The results of the work were tested on several examples of classification problems.

Computer Go Research Based on Variable Scale Training and PUB-PMCTS

The mainstream Go AI algorithms represented by AlphaZero and KataGo suffer from low-quality samples in the early training period and low exploration efficiency when performing traditional Monte Carlo Tree Search (MCTS). For the shortcomings mentioned above: The variable scale training is proposed, i.e., introducing a variable scale board with boundary conditions of randomly placed stones at the boundary periphery, to pre-train a small-scale network for recommending local move strategy and ownership. This network is used to improve the backbone network’s moving policy and state value, enhancing the quality of game samples in the early stages of training. To improve the efficiency and convergence speed of the search, we propose the Parallel Monte Carlo Tree Search with Potential-Upper-Bound (PUB-PMCTS), i.e., executing multiple unevaluated searches sequentially and then evaluating multiple leaf nodes in parallel; also, the variance of the node’s action values are used to forecast the potential upper limit of the node. In addition, we add a self-attention mechanism in the network to extract global context information of features and add maximum entropy loss to grow the exploration ability of the model. With the improvements described above, the bot TransGo is designed. Experimental results show that in a 13×13 Go environment, TransGo has more stable performance and higher game level in the early training period compared with other algorithms. After four days of training with TransGo, KataGo, and AlphaZero: TransGo improved by 102 Elo compared to KataGo and over 1000 Elo compared to AlphaZero.

Research on the Optimization of Enterprise Resource Economic Benefits and Management Costs in Cloud Computing Environment

Abstract Based on cloud computing technology, this paper constructs a benefit-optimal enterprise resource scheduling model and an enterprise cost management prediction model to maximize the economic benefits of enterprise resources and optimize the management costs, respectively. Combining the improved greedy algorithm and the benefit function, a cloud resource scheduling algorithm based on benefit optimization is proposed, defining the benefit function about time and cost, and taking the highest user satisfaction as the optimal resource scheduling objective. A mathematical model for management cost prediction is constructed by using the fireworks algorithm to optimize the number of trees and the number of leaf nodes in the random forest, and the Informatica software is used to manage and integrate the enterprise source data. The performance of the company’s operating conditions after optimizing resource management and cost savings is explored as a case study of Company S. In 2023, the total asset turnover ratio of Company S increased to 21.45 from 0.48 in 2022, and the overall asset operating efficiency improved. Working capital rose to 262,852,200 yuan. The current ratio, quick ratio, and cash ratio improved, while the gearing ratio, equity ratio, and equity multiplier showed a downward trend. The net operating margin achieved the maximum profitability in the last three years, net total assets margin, and net equity margin, with an increase of 25.42%, 20.65%, and 31.49%, respectively.

AN IMPROVED INDEXING METHOD FOR QUERYING BIG XML FILES

The exponential growth of bioinformatics in the healthcare domain has revolutionized our understanding of DNA, proteins, and other biomolecular entities. This remarkable progress has generated an overwhelming volume of data, necessitating big data technologies for efficient storage and indexing. While big data technologies like Hadoop offer substantial support for big XML file storage, the challenges of indexing data sizes and XPath query performance persist. To enhance the efficiency of XPath queries and address the data size problem, a novel approach that is derived from the spatial indexing method of the R-tre family. The proposed method is to modify the structure of leaf nodes in the indexing tree to preserve XML-sibling connections. Then, new algorithms for constructing the new tree structure and processing sibling queries better are introduced. Experimental results demonstrate the superior efficiency of sibling XPath queries with reduced data sizes for indexing, while other XPath queries exhibit notable performance improvements. This research contributes to the development of more effective indexing methods for managing and querying large XML datasets in bioinformatics applications, ultimately advancing biomedical research and healthcare initiatives.

CareerMiner: Automatic extraction of professional network from large Chinese resume data

The professional contacts of a person, including their past colleagues and supervisors, can play an important role in job recommendation and intelligent human resources management. However, collecting such information on a large scale can be challenging and costly. In this paper, we propose CareerMiner, a system to automatically construct a dynamic professional network to capture people’s colleague and supervisor relationships based on inter-related work experiences in a large collection of Chinese resumes. Specifically, we extract fine-grained work affiliation and job title information from resume text using a customized Name Entity Recognition model and introduce a trie-like data structure to efficiently index the hierarchical affinities of the implicit organizational structure in work experiences. Then, the topology of leaf nodes is used to identify professional relationships. Experimental results on real-world resume data are presented to demonstrate the effectiveness of the proposed system. Additionally, we develop an open-source demo system that includes the main features of the system. We also conduct a case study to illustrate how CareerMiner can be utilized for the social science research and data mining applications of professional networks.

Partial remote synchronization in star-like networks with partial connections among leaf nodes

Partial remote synchronization in star-like networks with partial connections among leaf nodes

Modified graph-based algorithm to analyze security threats in IoT.

In recent years, the growing and widespread usage of Internet of Things (IoT) systems has led to the emergence of customized structures dependent on these systems. Industrial IoT (IIoT) is a subset of IoT in terms of applications and usage areas. IIoT presents many participants in various domains, such as healthcare, transportation, agriculture, and manufacturing. Besides the daily life benefits, IIoT technology provides major contributions via the Industrial Control System (ICS) and intelligent systems. The convergence of IoT and IIoT systems brings some integration and interoperability problems. In IIoT systems, devices interact with each other using information technologies (IT) and network space. However, these common usages and interoperability led to some security risks. To avoid security risks and vulnerabilities, different systems and protocols have been designed and published. Various public databases and programs identify and provide some of the security threats to make it easier for system administrators' missions. However, effective and long-term security detection mechanisms are needed. In the literature, there are numerous approaches to detecting security threats in IoT-based systems. This article presents two major contributions: First, a graph-based threat detection approach for IoT-based network systems is proposed. Threat path detection is one of the most critical steps in the security of IoT-based systems. To represent vulnerabilities, a directed acyclic graph (DAG) structure is constructed using threat weights. General threats are identified using Common Vulnerabilities and Exposures (CVE). The proposed threat pathfinding algorithm uses the depth first search (DFS) idea and discovers threat paths from the root to all leaf nodes. Therefore, all possible threat paths are detected in the threat graph. Second, threat path-reducing algorithms are proposed considering the total threat weight, hop length, and hot spot thresholds. In terms of available threat pathfinding and hot spot detecting procedures, the proposed reducing algorithms provide better running times. Therefore, all possible threat paths are founded and reduced by the constructed IoT-based DAG structure. Finally, simulation results are compared, and remarkable complexity performances are obtained.