Graph Tools Research Articles

A Study on Visualization through Graph Conversion of Source Code and Utilization of Graph Tools

A Study on Visualization through Graph Conversion of Source Code and Utilization of Graph Tools

Deep Reinforcement Learning for Intelligent Penetration Testing Path Design

Penetration testing is an important method to evaluate the security degree of a network system. The importance of penetration testing attack path planning lies in its ability to simulate attacker behavior, identify vulnerabilities, reduce potential losses, and continuously improve security strategies. By systematically simulating various attack scenarios, it enables proactive risk assessment and the development of robust security measures. To address the problems of inaccurate path prediction and difficult convergence in the training process of attack path planning, an algorithm which combines attack graph tools (i.e., MulVAL, multi-stage vulnerability analysis language) and the double deep Q network is proposed. This algorithm first constructs an attack tree, searches paths in the attack graph, and then builds a transfer matrix based on depth-first search to obtain all reachable paths in the target system. Finally, the optimal path for target system attack path planning is obtained by using the deep double Q network (DDQN) algorithm. The MulVAL double deep Q network(MDDQN) algorithm is tested in different scale penetration testing environments. The experimental results show that, compared with the traditional deep Q network (DQN) algorithm, the MDDQN algorithm is able to reach convergence faster and more stably and improve the efficiency of attack path planning.

Integrasi Jalur Evakuasi pada Bangunan Publik Bertingkat Menggunakan Pendekatan Space Syntax

Title: Integration of Multi-stories Public Buildings Evacuation Routes with Space Syntax Approach Case Study: The Design of Kanisius Junior High School, Kalasan Fires and earthquakes are the two major disasters that threaten buildings. Public buildings require a design that can mitigate the adverse effects of the disaster, which requires excellent attention to the evacuation aspect when a disaster occurs. This study will evaluate the design of one of the public buildings that became the study material, Kanisius Junior High School (SMP), located in Kalasan, D.I. Yogyakarta. The building consists of 3 floors with an area per floor of 755 m2. It has a vertical emergency evacuation route through stairs at both ends. Using the Space Syntax method, the path design integration evaluation uses the Integrity tools in the Axial Map series. In contrast, the Visibility Graph tools are used to determine the level of visibility of the path and the ideal need for placing emergency exit signage. The result is that the level of integration of the evacuation route in the Kanisius Kalasan Junior High School building will be known, and the ideal signage placement will be recommended. This research is expected to contribute knowledge in the field of architecture related to the optimization method of emergency evacuation route design in multi-story buildings, as well as provide input for relevant stakeholders.

University management perspectives: A systematic literature review through bibliometrix

The university is a key actor in the creation of knowledge that must respond to the demands of diferent interest groups that asked pertinent responses and in line with global trends; so, its management has become, over time, a complex activity. Based on the above, this work is presented, which aims to publicize trends in university management through the review of the Web of Science and Scopus databases. The records obtained were analyzed using Graph theory and tools such as Bibliometrix. The results allowed identifying four perspectives: a. technology transfer and university entrepreneurship; b. educational model; c. Change management in academic institutions and d. Interest groups. Through a network analysis, it was determined that the most relevant authors are Henry Etzkowitz, Mario Raposo and G.E. Zborovsky. For its part, the region with the highest production in the subject is the United Kingdom and it is pertinent to note that the research carried out allows us to appreciate that the subject is in the boom phase.

SIGNOR 3.0, the SIGnaling network open resource 3.0: 2022 update.

The SIGnaling Network Open Resource (SIGNOR 3.0, https://signor.uniroma2.it) is a public repository that captures causal information and represents it according to an 'activity-flow' model. SIGNOR provides freely-accessible static maps of causal interactions that can be tailored, pruned and refined to build dynamic and predictive models. Each signaling relationship is annotated with an effect (up/down-regulation) and with the mechanism (e.g. binding, phosphorylation, transcriptional activation, etc.) causing the regulation of the target entity. Since its latest release, SIGNOR has undergone a significant upgrade including: (i) a new website that offers an improved user experience and novel advanced search and graph tools; (ii) a significant content growth adding up to a total of approx. 33,000 manually-annotated causal relationships between more than 8900 biological entities; (iii) an increase in the number of manually annotated pathways, currently including pathways deregulated by SARS-CoV-2 infection or involved in neurodevelopment synaptic transmission and metabolism, among others; (iv) additional features such as new model to represent metabolic reactions and a new confidence score assigned to each interaction.

Pressure pattern for fault diagnosis in gas pipelines through graph tools

This paper deals with the detection and localization of damage in gas-filled pipelines by means of an oriented-signals graph. The purpose is to search for transient patterns in a faulty main conduit, when an external acoustic source excites the viscous fluid. The study assumes a linearized model of distributed parameters for the healthy sections of the pipe and two-port lumped models for leaks and blockages. Based on these models, the exact transcendental transfer function of an acoustic source at the upstream end P(0,s) to the pressure P(z0,s) at point z0 is derived and transformed through graph tools in an oriented-signals graph for one and two faults. Thus, one establishes the transient response patterns associated with the faults from the graph paths without numerical parameters. This shown that graph theory is another suitable tool to visualize pressure wave propagation in a damage conduit. To validate the responses for one and two leaks, one compares the features with the analytical solution and the digital simulated pressure.

Natural language report of the composting process status using linguistic perception

The paper presents an automated system to report the evolution of a process in natural language expressions, analyzing wireless sensor data with process patterns by the linguistic description of complex phenomena approach. An implemented system generates reports of the composting process using the temperature measured in the core of a compost pile regarding the temperature pattern during the composting phases. The linguistic reports include sentences about the current status, trend, and behavior of the composting process. The automated system posts each linguistic report via a messaging app, such as Twitter, allowing instant and continuous communication with authorized users on mobile devices. The resulting expressions show that this real-time linguistic report system is a suitable and practical framework to inform users of the process evolution, in preference and complement to a database or graph tools, supporting the users to make decisions for processes that require proper data-interpretation-based supervision.

Сетевая концепция метадисциплинарной платформы конструирования множественных реальных и виртуальных миров

In recent years, the problem concerning mastering augmented and virtual reality has arisen in a wide range of areas of modern life. The paper analyzes the need, requirements and possibilities of metadisciplinary (i.e. non-disciplinary) construction in a generalized space that combines reality and virtuality in educational sphere. At the same time, complexity of combining the worlds, volume of data, and consideration of dynamics are contemplated as problems available for computational solution and practical implementation on the basis of a network platform. Within the framework of the proposed concept, an entrance to the network sub-space containing network imprints of a generalized real-virtual world, coordination of multiple components in it and return back to a previous image, perceived through the developed application of the mixed world is provided. The network platform includes both the network elements of ontology (general description) and representation, storage and processing of data using graph non-relational databases and tools for analyzing and visualizing networks. An architecture with a network component of data processing (networkalization) makes it possible to move forward in the direction of improving the design platform of multiple real and virtual worlds. Special emphasis on the network paradigm is due to the practical value of the ―networked‖ mixed reality (and especially in the educational field) which lies in the fact that information is presented in a well-structured form. It can be expected that tools capable of performing such structuring not only technologically (which is necessary for recognition, tracking and visualization), but also at user level, will be a good way to support educational space.

Identifying ATT&CK Tactics in Android Malware Control Flow Graph through Graph Representation Learning and Interpretability (Student Abstract)

To mitigate a malware threat it is important to understand the malware’s behavior. The MITRE ATT&ACK ontology specifies an enumeration of tactics, techniques, and procedures (TTP) that characterize malware. However, absent are automated procedures that would characterize, given the malware executable, which part of the execution flow is connected with a specific TTP. This paper provides an automation methodology to locate TTP in a sub-part of the control flow graph that describes the execution flow of a malware executable. This methodology merges graph representation learning and tools for machine learning explanation.

Data and Network Analytics for COVID-19 ICU Patients: A Case Study for a Spanish Hospital.

The COVID-19 pandemic presents unprecedented challenges to the healthcare systems around the world. In 2020, Spain was among the countries with the highest Intensive Care Unit (ICU) hospitalization and mortality rates. This work analyzes data of COVID-19 patients admitted to a Spanish ICU during the first wave of the pandemic. The patients in our study either died (deceased patients) or were discharged from the ICU (non-deceased patients) and underwent the following landmarks: beginning of symptoms; arrival at the emergency department; beginning of the hospital stay; and ICU admission. Our goal is to create a graph-based data-science methodology to find associations among patients' comorbidities, previous medication, symptoms, and the COVID-19 treatment, and to analyze their evolution across landmarks. Towards that end, we first perform a hypothesis test based on bootstrap to identify discriminative features among deceased and non-deceased patients. Then, we leverage graph-based representations and network analytics to determine pairwise associations and complex relations among clinical features. The descriptive statistical analysis confirms that deceased patients exhibit multiple comorbidities with stronger levels of association and are treated with a wider range of drugs during the ICU stay. We also observe that the most common treatment was the simultaneous administration of lopinavir/ritonavir with hydroxychloroquine, regardless of the patients' outcome. Our results illustrate how graph tools and representations yield insights on the relations among comorbidities, drug treatments, and patients' evolution. All in all, the approach puts forth a new data-analysis tool for clinicians that can be applied to analyze (post-COVID) symptom/patient evolution.

Network analysis and characterization of vulnerability in flood control infrastructure for system-level risk reduction

The number of catastrophic events such as extreme rainfalls and hurricanes has been growing. These events pose a major threat to the life safety and economic prosperity of urban regions. Flood control networks play a pivotal role in mitigating the risk associated with the stormwater generated by extreme rainfalls and hurricanes. The objective of this study is to propose a framework to examine the vulnerability in flood control infrastructure networks. This framework applies graph theory concepts and tools to define a vulnerability index for flood control network components (e.g., channels and rivers). The topological attributes of flood control networks are used to determine the vulnerability index based on structural attributes of flood control networks. First, a flood control network is modeled as a directed graph and storage facilities are incorporated into the network. Second, co-location exposure, upstream channel susceptibility, and discharge redundancy are characterized as important vulnerability attributes of a channel in flood control network. Then, these three characteristics are formulized based on the topological attributes of the network and characteristics of channels. The vulnerability index is then determined based on the three vulnerability characteristics. The proposed vulnerability index can be used to evaluate the impact of different risk reduction policies on flood control network vulnerability and determine the optimal mitigation strategies aiming at flood risk reduction, such as widening vulnerable channels, placement of storage facilities in the network or increasing the redundancy of the network. The framework is implemented on two watersheds in Harris County (Texas, USA) and the results' implications for decision-making in infrastructure management and hazard mitigation planning are discussed. The results highlight the capability of the proposed graph-based framework to inform flood risk reduction through evaluation of the vulnerability of infrastructure networks.

MedFused: A framework to discover the relationships between drug chemical functional group impacts and side effects

It is a well-known fact that there are often side effects to the long-term use of certain medications. These side effects can vary from mild dizziness to, at its most serious, death. The main factors that cause these side effects are the chemical composition, the mode of treatment, and the dose. The dynamics that govern the reaction of a drug heavily depend on its structural composition. The structural composition of a drug is defined by the structural arrangement of the corresponding basic chemical functional groups. Hence, it is essential to investigate the effect of chemical functional groups on the side effects to synthesize drugs with minimal side effects. To support this process, we developed a framework named MedFused (Medical Functional Group Side Effects Database), which is composed of drugs (International Union of Pure and Applied Chemistry: IUPAC nomenclature), functional groups, and the side effects along with other valuable information such as STITCH (search tool for interactions of chemicals) compound ID, and the Unified Medical Language System (UMLS) concept ID. We develop a web framework that functions on the MedFused system database on top of the Django web framework. Our web server supports functionalities such as exploring the database and descriptive graph tools, which provide additional exploration capabilities to the framework. These descriptive tools include histograms, pie charts, and association charts, which further explore the system. Above these basic tools, MedFused includes functionality to discover the drug's “chemical functional group” impact on “side effects”. The method conducts an association rule analysis on the relationships by considering the MedFused database as a collection of transactions. A specific transaction has a list of the functional groups of a drug and one side effect. Hence, a drug that has more than one side effect forms multiple transactions. Next, we generate a binary feature matrix based on the transactions and introduce a pruning mechanism to consider only the potential functional groups and side effects based on their support (frequencies), subjected to a predefined threshold (which can be changed accordingly). As the current version of the MedFused database has a limited number of side effects (hence low support), we restricted the analysis to identify the functional groups which have the most potential of causing a particular side effect, based on a confidence value of 1. Our framework can be further extended with more functions and tools as it supports the model view controller (MVC) architecture, which is inherited from the Django Python web framework.

Pigeon-Inspired Circular Formation Control for Multi-UAV System with Limited Target Information

The problem of cooperative circular formation with limited target information for multiple Unmanned Aerial Vehicle (UAV) system is addressed in this paper. A pigeon-inspired circular formation control method is proposed to form the desired circular distribution in a plane based on the intelligent pigeon behavior during hovering. To reach the goal of prescribed radius and angular distribution, the controller is designed consisting of a circular movement part and a formation distribution part. Therein, the circular movement part is designed to make each UAV rotate around the specified circle at the same angular speed only using the relative position between the UAV and the target. The formation distribution part could adjust the angular distance between each UAV and its neighbors with the jointly connected network to reduce communication cost. To smooth the speed variation, nonlinear PID-type method is delivered throughout the evolution of the system. The convergence analysis of the proposed control protocol is presented using Lyapunov theory and graph tools. The effectiveness of the proposed control strategies is demonstrated through numerical simulations.

Implementation of Artificial Intelligence: The Use of Technology on Diffable Public Service in Banyuwangi

This research focuses on the implementation of Artificial Intelligence in Banyuwangi Innovation on standards diffable public service. Implementation of Artificial Intelligence through technological innovation and special infrastructure for people with disabilities and public service standards for disableds, braille printers, braille scanner, hearing it tools, audio graph tools to measure hearing, deaf speech electrolytes, multi-function wheelchair, regulations on braille printing disabilities, braille computers, jaws applications for pc, talk voice to read mobile applications, embosser printers, braille reference books. The implementation was realized by the government, education service providers, stakeholders, the community, and the private sector. AI formulated important support, needed, applications, technology, machine learning, public facilities, and access to information. Innovation in Banyuwangi manifested the form of support for regional regulations (PERDA) No. 6 of 2017 that concern the protection and fulfillment of the rights of disabilities to realize public service standards for disabilities following technological innovation.

Data Enrichment in the Information Graphs Environment Based on a Specialized Architecture of Information Channels

The paper considers the possibility of constructing a specialized computing system oriented at the transmission of data through information channels, that are determined taking into account the semantics of the selected data. In the process of computations the data is connected with semantic characteristics that describe the channel of computations, which can be considered as a method of semantic data enrichment. The system of information channels as a whole can be considered as an information graph describing the structuring of the processed data. The information graph supports the data model in the form of a network, the framework of which are objects and the relationships between them. The paper proposes language tools for determining the information graph and interpretation tools that provide practical computations.The set of information channels that make up the information graph can be considered as a low-level tool for data enrichment. The paper studies the possibility of determining tools of higher level. An applicative type language is proposed for defining information graphs, the syntax and semantics of the language are specified. The proposed language can be considered as an intermediate level tool for defining semantics. A procedure is proposed for compiling the language into a low-level construct, preserving the semantics of the language.The supporting system for the proposed computing system includes a low-level language interpreter, as well as an intermediate-level language compiler into a low-level language. The supporting system is implemented in an applicative programming environment. Some elements of the supporting system were tested when developing applied information systems in the field of jurisprudence.

NETWORK ANALYSIS OF THE STACK OVERFLOW TAGS

Abstract. In this paper we made analysis for the stack overflow tags by using different criteria's in network science, one of the advantages of network analysis is that complex of connections can be made cleared, we started this work in first step by extracted data from dataset after that applied network concepts node degree distribution, node importance (centrality measures), also we provided a brief demonstration of how we can use graph network and tools to analyze semi-structured text as (Tags).

PROPOSED METHOD FOR IMAGE SEGMENTATION USING GRAPH THEORY

This paper presents an image segmentation technique using graph tools for object detection. Graph theoretical systems have many good features among different segmentation schemes. It organizes the image elements into mathematically and structural form, and makes the problem formulation more flexible, and the computation more efficient. In this paper, the work, consists of two stages, in the first stage we apply the pixel-based labeling algorithm to the binary image, the algorithm works similarly to the eight connectivity labeling, but it is broader than it in terms of the search area, where two (horizontal and vertical) thresholds are first defined so that the search is in a block whose height is the vertical threshold and its width (2 * horizontal threshold), in the second stage the output of the first stage is mapping into an undirected weighted graph, in which each vertex represent region rather than pixel. We evaluate the results by comparing it with other method using (RI) parameter. We use 50 image from online source and image taken from Berkeley database.

Speech Signal Processing on Graphs: Graph Topology, Graph Frequency Analysis and Denoising

The paper investigates the hidden relationships among speech samples by applying graph tools. Specifically, we first estimate an applicable graph topology for unstructured speech signals, which can map speech signals into the vertex domain successfully and construct as Speech graph signals (SGSs). On the basis, we define a new graph Fourier transform for SGSs, which can investigate its related graph Fourier analysis. Moreover, we propose a new Graph structure spectral subtraction (GSSS) method for speech enhancement under different noisy environments. Simulation results show that the performance of the GSSS method can be significantly improved than the classical Basic spectral subtraction (BSS) method in terms of the average Segmental signal-to-noise ratio (SSNR), Perceptual evaluation of speech quality (PESQ) and the computational complexity.

Readability Assessment of Online Rhytidectomy Patient Information.

Patients frequently access online resources for medical information. The National Institutes of Health and the American Medical Association recommend that to be understood by the average American, patient information should be presented at or below the sixth to seventh academic grade level. The popularity of rhytidectomy (facelift) is rising, and providers are frequently using the Internet to attract patients. All rhytidectomy information provided by 100 private practice Web sites in New York City, Los Angeles, Chicago, Houston, and Phoenix was analyzed using Readable.io software. The information was also assessed using the Flesch-Kincaid Grade Level, Gunning-Fog Index, Coleman-Liau Index, Simple Measure of Gobbledygook, Automated Readability Index, Flesch Reading Ease Score, and Fry Reading Graph tools. Analyzed material was written at a higher academic grade level than recommended for the average American. The overall average grade level was 10.99 ± 1.39. Online patient education materials about rhytidectomy provided by private practice clinics in 5 major cities of the United States were written at academic grade levels above the National Institutes of Health and American Medical Association recommended levels. This may lead to rhytidectomy patients having unrealistic or inaccurate expectations related to their surgical procedure.

Detecting long-range cause-effect relationships in game provenance graphs with graph-based representation learning

Game Analytics comprises a set of techniques to analyze both the game quality and player behavior. To succeed in Game Analytics, it is essential to identify what is happening in a game (an effect) and track its causes. Thus, game provenance graph tools have been proposed to capture cause-and-effect relationships occurring in a gameplay session to assist the game design process. However, since game provenance data capture is guided by a set of strict predefined rules established by the game developers, the detection of long-range cause-and-effect relationships may demand huge coding efforts. In this paper, we contribute with a framework named PingUMiL that leverages the recently proposed graph embeddings to represent game provenance graphs in a latent space. The embeddings learned from the data pose as the features of a machine learning task tailored towards detecting long-range cause-and-effect relationships. We evaluate the generalization capacity of PingUMiL when learning from similar games and compare its performance to classical machine learning methods. The experiments conducted on two racing games show that (1) PingUMiL outperforms classical machine learning methods and (2) representation learning can be used to detect long-range cause-and-effect relationships in only partially observed game data provenance graphs.