Graph Technique Research Articles

Penerapan JST Backpropagation untuk Prediksi Siswa Penerima Bantuan

The role of education is to educate students and build quality human resources. School Operational Assistance (BOS) from the government is used to relieve students, not free schools but free students from school fees and extracurriculars. BOS can be used to provide scholarships to underprivileged students. Students are entitled to a scholarship if they meet three conditions, namely not having a Smart Indonesia Card (KIP), not having a Prosperous Family Card (KKS), and parental income of less than one million rupiah per month. The determination of BOS scholarship recipients is based on an agreement and joint decision by the management team, the teacher council, and the school committee. The process of determining students as scholarship recipients often encounters obstacles because it is done manually. This study applies backpropagation ANN to determine students who are eligible for BOS scholarships. The system is built based on the web using bootstrap as a framework, Javascript, and PHP. The training process uses a sample of 78 students with input architecture 4, hidden 6, output 1, learning rate 0.7, momentum 0.9, SSE 0.1, output 1, and a maximum of 10,000 iterations, an error of 0.03 is obtained. Testing with 21 student data obtained an error of 0.414923 and the accuracy of the test was 93.67%. The test results using the cause-effect graph technique can display the training process, graphs, error results, weight values and testing time with 93.67% success.

Leverage knowledge graph and GCN for fine-grained-level clickbait detection.

Clickbait is the use of an enticing title as bait to deceive users to click. However, the corresponding content is often disappointing, infuriating or even deceitful. This practice has brought serious damage to our social trust, especially to online media, which is one of the most important channels for information acquisition in our daily life. Currently, clickbait is spreading on the internet and causing serious damage to society. However, research on clickbait detection has not yet been well performed. Almost all existing research treats clickbait detection as a binary classification task and only uses the title as the input. This shallow usage of information and detection technology not only suffers from low performance in real detection (e.g., it is easy to bypass) but is also difficult to use in further research (e.g., potential empirical studies). In this work, we proposed a novel clickbait detection model that incorporated a knowledge graph, a graph convolutional network and a graph attention network to conduct fine-grained-level clickbait detection. According to experiments using a real dataset, our novel proposed model outperformed classical and state-of-the-art baselines. In addition, certain explainability can also be achieved in our model through the graph attention network. Our fine-grained-level results can provide a measurement foundation for future empirical study. To the best of our knowledge, this is the first attempt to incorporate a knowledge graph and deep learning technique to detect clickbait and achieve explainability.

Design and Application of Intelligent Financial Accounting Model Based on Knowledge Graph

With the continuous progress of science and technology, economic globalization has become an important direction for the development of enterprises. In the process of enterprise development, artificial intelligence and machine learning techniques have greatly improved the efficiency of enterprise accounting and financial management and have gradually shifted traditional financial accounting to modern financial management and accounting. Owing to the existing problems of inefficiency, large consumption of time and resources, and low degree of intelligence in the existing computerized financial data prediction systems, this study proposes an intelligent financial accounting and a financial risk monitoring and early warning model based on knowledge graph and deep learning techniques. To validate its performance, the model is trained using financial data of 120 listed companies, and the model is applied to establish the prediction of whether the listed companies are facing a financial crisis, using another 60 companies as the test sample. Results show that the use of deep learning and knowledge graph techniques can significantly improve the regulatory model, enhance regulatory penetration, and alleviate regulatory time lag, thus improving the ability of regulation to detect enterprise problems and prevent risks.

Virus Infection and Host Plant Suitability Affect Feeding Behaviors of Cannabis Aphid (Hemiptera: Aphididae), a Newly Described Vector of Potato Virus Y.

Aphids are the most prolific vectors of plant viruses resulting in significant yield losses to crops worldwide. Potato virus Y (PVY) is transmitted in a non-persistent manner by 65 species of aphids. With the increasing acreage of hemp (Cannabis sativa L.) (Rosales: Cannabaceae) in the United States, we were interested to know if the cannabis aphid (Phorodon cannabis Passerini) (Hemiptera: Aphididae) is a potential vector of PVY. Here, we conduct transmission assays and utilize the electrical penetration graph (EPG) technique to determine whether cannabis aphids can transmit PVY to hemp (host) and potato (non-host) (Solanum tuberosum L.) (Solanales: Solanaceace). We show for the first time that the cannabis aphid is an efficient vector of PVY to both hemp (96% transmission rate) and potato (91%) using cohorts of aphids. In contrast, individual aphids transmitted the virus more efficiently to hemp (63%) compared to potato (19%). During the initial 15 min of EPG recordings, aphids demonstrated lower number and time spent performing intracellular punctures on potato compared to hemp, which may in part explain low virus transmission to potato using individual aphids. During the entire 8-hour recording, viruliferous aphids spent less time ingesting phloem compared to non-viruliferous aphids on hemp. This reduced host acceptance could potentially cause viruliferous aphids to disperse thereby increasing virus transmission. Overall, our study shows that cannabis aphid is an efficient vector of PVY, and that virus infection and host plant suitability affect feeding behaviors of the cannabis aphid in ways which may increase virus transmission.

Did Myzus persicae (Sulzer) from potato reared on a novel host for 15 years retain its host-related properties?

Myzus persicae (Sulzer) is an important agricultural pest worldwide causing major economic losses due to its ability to transmit over 100 viruses including Potato virus Y (PVY). Myzus persicae shows considerable variation with respect to performance on its host plants. The objective of this study was to use a survival experiment, behavioural observations, including observations of probing and feeding behaviour obtained using the electrical penetration graph (EPG) technique, and a PVY acquisition experiment to determine whether or not potato was still the more suitable host for M. persicae originating on potato and reared on a novel host, table beet, for over 15 years. In a survival experiment, the pre-reproductive period was significantly longer while adult survival and whole longevity were significantly lower for M. persicae reared on beet fed beet leaves compared to M. persicae reared on potato fed potato leaves. The number of progenies produced and fecundity were both significantly reduced (90 and 85%, respectively) for M. persicae reared on beet fed beet leaves. Ethological observations and EPG assessment of M. persicae behaviour reared on beet placed on beet leaves showed significantly impaired behavioural responses compared to M. persicae reared on potato placed on potato leaves. The rate of PVY acquisition was the same for M. persicae reared on beet and on potato. These results indicate that after 15 years on table beet, M. persicae still performs better on its original host, potato, and appears to be a specialized potato-adapted genotype.

Manifold-Preserving Sparse Graph and Deviation Information Based Fisher Discriminant Analysis for Industrial Fault Classification Considering Label-Noise and Unobserved Faults

Fault classification is one of the most important topics in the area of industrial process monitoring. Existing industrial fault classification models based on Fisher Discriminant Analysis (FDA) and its variants still fail to cope with the issues of label-noise and unobserved faults simultaneously. To fill this important gap, a novel Manifold-preserving sparse graph and Deviation information based FDA (MDFDA) industrial fault classification model is originally presented in this paper. Firstly, the manifold-preserving sparse graph technique is utilized to filter training samples with label-noise; by keeping high connectivity between the obtained sub-graph and the original graph, not only the underlying manifold structure of training samples is preserved after being filtered, but also some mislabeled training samples with evident statistical characteristic deviations are filtered; in this way, the information quality of training samples is greatly improved. Secondly, based on the deviation information obtained from the filtered training samples, a deviation threshold is defined for each observed fault; if the deviation information of a testing sample is larger than the deviation threshold of each observed fault, it will be recognized as the testing samples from the unobserved faults. Experiments based on the data from the benchmark Tennessee Eastman process and a real industrial air separation unit demonstrate the effectiveness and superiority of the proposed MDFDA classification model.

Visible Particle Series Search Algorithm and Its Application in Structural Damage Identification.

Identifying structural damage is an essential task for ensuring the safety and functionality of civil, mechanical, and aerospace structures. In this study, the structural damage identification scheme is formulated as an optimization problem, and a new meta-heuristic optimization algorithm, called visible particle series search (VPSS), is proposed to tackle that. The proposed VPSS algorithm is inspired by the visibility graph technique, which is a technique used basically to convert a time series into a graph network. In the proposed VPSS algorithm, the population of candidate solutions is regarded as a particle series and is further mapped into a visibility graph network to obtain visible particles. The information captured from the visible particles is then utilized by the algorithm to seek the optimum solution over the search space. The general performance of the proposed VPSS algorithm is first verified on a set of mathematical benchmark functions, and, afterward, its ability to identify structural damage is assessed by conducting various numerical simulations. The results demonstrate the high accuracy, reliability, and computational efficiency of the VPSS algorithm for identifying the location and the extent of damage in structures.

Ontological Ship Behavior Modeling Based on COLREGs for Knowledge Reasoning

Formal expression of ship behavior is the basis for developing autonomous navigation systems, which supports the scene recognition, the intention inference, and the rule-compliant actions of the systems. The Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) offers experience-based expressions of ship behavior for human beings, helping the humans recognize the scene, infer the intention, and choose rule-compliant actions. However, it is still a challenge to teach a machine to interpret the COLREGs. This paper proposed an ontological ship behavior model based on the COLREGs using knowledge graph techniques, which aims at helping the machine interpret the COLREGs rules. In this paper, the ship is seen as a temporal-spatial object and its behavior is described as the change of object elements in time spatial scales by using Resource Description Framework (RDF), function mapping, and set expression methods. To demonstrate the proposed method, the Narrow Channel article (Rule 9) from COLREGs is introduced, and the ship objects and the ship behavior expression based on Rule 9 are shown. In brief, this paper lays a theoretical foundation for further constructing the ship behavior knowledge graph from COLREGs, which is helpful for the complete machine reasoning of ship behavior knowledge in the future.

Leafhopper feeding behaviour on three grapevine cultivars with different susceptibilities to Flavescence dorée

Scaphoideus titanus (Ball) is a grapevine-feeder leafhopper, and the most important vector of Flavescence dorée of grapevine (FD), a disease associated with phytoplasmas belonging to ribosomal subgroups 16Sr-V–C and –D. FD is a major constraint to viticulture in several European countries and, so far, its control has relied on roguing of infected plants and insecticide applications against the vector. Detailed knowledge on different levels of the multifaceted phytoplasma-plant-vector relationship is required to envisage and explore more sustainable ways to control the disease spread. In the present work, S. titanus feeding behaviour was described on three grapevine cultivars: Barbera (susceptible to FD), Brachetto, and Moscato (tolerant to FD) using the Electrical Penetration Graph (EPG) technique. Interestingly, no differences were highlighted in the non-phloem feeding phases, thus suggesting that the tested cultivars have no major differences in the biochemical composition or structure of the leaf cuticle, epidermis or mesophyll, that can affect the first feeding activities. On the contrary, the results showed significant differences in leafhopper feeding behaviour in terms of the duration of the phloem feeding phase, longer on Barbera and shorter on Brachetto and Moscato, and of the frequency of interruption-salivation events inside the phloem, higher on Brachetto and Moscato. These findings indicate a possible preference for the Barbera cultivar, a better host for the leafhopper. Scaphoideus titanus feeding behaviour on Barbera correlates with an enhanced FDp transmission efficiency, thus explaining, at least in part, the higher susceptibility of this cultivar to FD. The mechanisms for the possible non-preference for Brachetto and Moscato are discussed, and an antixenosis is hypothesized. We propose that breeding for resistance against FD should take into account both plant traits associated with the response to the phytoplasmas and to the vector.

Designing rule-based conversational agents with behavioral programming: a study of human subjects

PurposeIn this work, the authors propose to harness the advantages of behavioral programming as a new technique for designing rule-based conversational agents.Design/methodology/approachTo examine the study’s hypotheses, the authors perform a first-of-its-kind user study through which the authors examine how potential designers, both expert designers, computationally-oriented designers, and otherwise, leverage behavioral programming (BP) and dialog graphs for designing conversational agents (CAs). The authors also use two standard CA settings common in the literature: designing a CA representative for a user in an online dating service and a non-character player in a role-playing game (RPG).FindingsThe study’s results indicate that BP can be successfully utilized by computationally-oriented designers, with or without prior knowledge in CA design, and can facilitate the design of better CAs (i.e. more accurate and more robust). However, to capitalize on these potential advantages, designers may be required to devote more time to the design process and are likely to encounter higher temporal demand levels. These results suggest that BP, which was initially proposed and evaluated in the general context of software design, can constitute a valuable alternative to the classic rule-based CA design technique commonly practiced today.Research limitations/implicationsAn important limitation of this study is the relatively small participant pool. While the authors do plan to extend this study in the future, the current coronavirus disease 2019 (COVID-19) situation makes it ever more complex to conduct formal user studies of this kind. It is, however, important to note that despite the low number of participants, many of the results are found to be statistically significant.Practical implicationsThe authors plan to continue this line of work and conduct human studies for additional design techniques in other popular agent-based settings. Specifically, the authors seek to explore how people of different backgrounds should design agents for various tasks such as automated negotiation (e.g. how should a person design a representative agent to negotiate on her behalf?) and social choice (e.g. how should a person design a voting bot to represent her in online voting systems?).Originality/valuePeople are increasingly interacting with conversational agents in various settings and for a variety of reasons, as the market size of those agents keeps on growing every year. Through a first-of-its-kind human study (N = 41), consisting of both expert designers, computationally-oriented designers, and otherwise, the authors demonstrate a few key advantages and limitations of BP in the realm of conversational agents and propose its consideration as an alternative to the classic dialog graph technique.

Solving Linear Program with Simplex Method through App Calculator Simplex

Since the simplex approach takes so long to work, many students make mistakes or are just not as cautious as they should be, there is a learning material to help them overcome this. The objectives of this article are to accomplish the work of engineering ideas in Linear Program courses, gain insight into the development of learning media in the Linear Program, and be able to run digital media. This study was conducted in several ways, including using a line of inquiry, the graph technique, the simplex method, and Geogebra. The results reveal that the simplex calculator is uncomplicated to use and that the calculator's solutions are presented gradually and clearly. It is intended that by using this simplex calculator, students will be eligible to function more quickly.

Linear actuator–based knee exoskeleton for stand–sit–stand motions: a bond graph approach

People with knee disorders often find it difficult to perform common mobility tasks, such as stand–sit–stand motions. High knee torque is required to complete such transitions, as the chances of toppling increase during these motions. Most of the existing conventional approaches, such as wheelchairs and crutches, have failed to provide complete independence to the users. Conversely, contemporary systems like lower body exoskeletons which are bulky, complex, and expensive do not specifically target the knee joint instead of assisting other joints. Hence, there is a need to aid the knee joint using a robotic knee exoskeleton capable of accurately providing the desired knee torque. In the present work, to assist the user in performing the stand–sit–stand motions, an electromyography sensor-based four-bar knee exoskeleton actuated by a linear actuator is proposed. The modeling of the complete exoskeleton is developed using bond graph technique, as the components exist in different energy domains and it is possible to frame a dynamic bond graph model using only kinematic equations. The prototype is fabricated, and experiments are carried out on an artificial limb to prove the efficacy of the design of the current knee exoskeleton. The assistive torque developed by the actuator at the knee joint of the exoskeleton is found to be suitable to assist the wearer. As a result, little effort is required by the wearer for performing the stand–sit–stand motions. The rotation of the thigh link of the developed exoskeleton was found to be suitable for performing the stand–sit–stand activity.

LOGO: an efficient local and global data collection mechanism for remote underwater monitoring

The oceans play an important role in our daily life and they form the lungs of our planet. Subsequently, the world ocean provides so many benefits for humans and the planet including oxygen production, climate regulation, transportation, recreation, food, medicine, economic, etc. However, the oceans suffer nowadays from several challenges ranging from pollution to climate change and destruction of underwater habitat. Hence, the use of remote sensing technologies, like sensor networks and IoT, is becoming essential in order to continuously monitor the wide underwater areas and oceans. Unfortunately, the limited battery power constitutes one of the major challenges and limitations of such technologies. In this paper, we propose an efficient LOcal and GlObal data collection mechanism, called LOGO, that aims to conserve the energy in remote sensing applications. LOGO is based on the cluster scheme and works on two network stages: local and global. The local stage is at the sensor node and aims to reduce its data transmission by eliminating on-period and in-period data redundancies. The global stage is at the autonomous underwater vehicle (AUV) level and aims to minimize the data redundancy among neighboring nodes based on a spatial-temporal node correlation and Kempe’s graph techniques. The simulation results on real underwater data confirm that LOGO mechanism is less energy consumption with high data accuracy than the existing techniques.

A survey of methods for encrypted network traffic fingerprinting.

Privacy protection in computer communication is gaining attention because plaintext transmission without encryption can be eavesdropped on and intercepted. Accordingly, the use of encrypted communication protocols is on the rise, along with the number of cyberattacks exploiting them. Decryption is essential for preventing attacks, but it risks privacy infringement and incurs additional costs. Network fingerprinting techniques are among the best alternatives, but existing techniques are based on information from the TCP/IP stack. They are expected to be less effective because cloud-based and software-defined networks have ambiguous boundaries, and network configurations not dependent on existing IP address schemes increase. Herein, we investigate and analyze the Transport Layer Security (TLS) fingerprinting technique, a technology that can analyze and classify encrypted traffic without decryption while addressing the problems of existing network fingerprinting techniques. Background knowledge and analysis information for each TLS fingerprinting technique is presented herein. We discuss the pros and cons of two groups of techniques, fingerprint collection and artificial intelligence (AI)-based. Regarding fingerprint collection techniques, separate discussions on handshake messages ClientHello/ServerHello, statistics of handshake state transitions, and client responses are provided. For AI-based techniques, discussions on statistical, time series, and graph techniques according to feature engineering are presented. In addition, we discuss hybrid and miscellaneous techniques that combine fingerprint collection with AI techniques. Based on these discussions, we identify the need for a step-by-step analysis and control study of cryptographic traffic to effectively use each technique and present a blueprint.

Recommender Systems Based on Graph Embedding Techniques: A Review

As a pivotal tool to alleviate the information overload problem, recommender systems aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As for alleviating the sparsity and cold start problems encountered by recommender systems, researchers resort to employing side information or knowledge in recommendation as a strategy for uncovering hidden (indirect) user-item relations, aiming to enrich observed information (or data) for recommendation. However, in the face of the high complexity and large scale of side information and knowledge, this strategy relies for efficient implementation on the scalability of recommendation models. Not until after the prevalence of machine learning did graph embedding techniques be a concentration, which can efficiently utilize complex and large-scale data. In light of that, equipping recommender systems with graph embedding techniques has been widely studied these years, appearing to outperform conventional recommendation implemented directly based on graph topological analysis. As the focus, this article retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs and knowledge graphs, and proposes a general design pipeline of that. In addition, after comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models on simulations, this article manifests that the conventional models can overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the comparative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes suggestions on making a trade-off between graph embedding-based recommendation and conventional recommendation in different tasks, and puts forward open questions.

Dynamic modeling and analysis of a hydro-mechanical power transmission system

This work proposes a hydro-mechanical power transmission technology. It is a combined concept of mechanical and hydraulic power transmission. This scheme overcomes the individual issues of the both power transmission systems, and provides various combined advantages such as it not only attain higher output speed but also provides a suitable provision to store excess energy into it. A dynamic model of hydro-mechanical power transmission technology is developed using bond graph (BG) technique and simulated using SYMBOLS SHAKTI software. Simulation responses of the model indicate that the output speed of the hydro-motor is gained due positive gear ratio and hydraulic system added higher system controllability potential.

Context-Aware Service Discovery: Graph Techniques for IoT Network Learning and Socially Connected Objects

Adopting Internet-of-things (IoT) in large-scale environments such as smart cities raises compatibility and trustworthiness challenges, hindering conventional service discovery and network navigability processes. The IoT network is known for its highly dynamic topology and frequently changing characteristics (e.g., the devices’ status, such as battery capacity and computational power); traditional methods fail to learn and understand the evolving behavior of the network to enable real-time and context-aware service discovery in such diverse and large-scale topologies of IoT networks. The Social IoT (SIoT) concept, which defines the relationships among the connected objects, can be exploited to extract established relationships between devices and enable trustworthy and context-aware services. In fact, SIoT expresses the possible connections that devices can establish in the network and reflect compatibility, trustworthiness, and so on. In this paper, we investigate the service discovery process in SIoT networks by proposing a low-complexity context-aware Graph Neural Network (GNN) approach to enable rapid and dynamic service discovery. Unlike the conventional graph-based techniques, the proposed approach simultaneously embeds the devices’ features and their SIoT relations. Our simulations on a real-world IoT dataset show that the proposed GNN-based approach can provide more concise clusters compared to traditional techniques, namely the Louvain and Leiden algorithms. This allows a better IoT network learning and understanding and also, speeds up the service lookup search space. Finally, we discuss implementing the GNN-assisted context-service discovery processes in novel smart city IoT-enabled applications.

Path Optimization for Mobile Robots using Genetic Algorithms

This article proposes a path planning strategy for mobile robots based on image processing, the visibility graphs technique, and genetic algorithms as searching/optimization tool. This proposal pretends to improve the overall execution time of the path planning strategy against other ones that use visibility graphs with other searching algorithms. The global algorithm starts from a binary image of the robot environment, where the obstacles are represented in white over a black background. After that four keypoints are calculated for each obstacle by applying some image processing algorithms and geometric measurements. Based on the obtained keypoints, a visibility graph is generated, connecting all of these along with the starting point and the ending point, as well as avoiding collisions with the obstacles taking into account a safety distance calculated by means of using an image dilation operation. Finally, a genetic algorithm is used to optimize a valid path from the start to the end passing through the navigation network created by the visibility graph. This implementation was developed using Python programming language and some modules for working with image processing ang genetic algorithms. After several tests, the proposed strategy shows execution times similar to other tested algorithms, which validates its use on applications with a limited number of ob-stacles presented in the environment and low-medium resolution images.

Effect of pump leakage on slip and pressure loss in a hydraulic cylinder drive

Pump leakage plays a critical role in the safety as well as the functioning of hydraulic drives. Studying the effect of pump leakage on the dynamics of hydraulic drive can improve the drive safety by giving insight into dynamic symptoms of leakage fault and can improve the functioning range of drive by giving insight into the slip and pressure losses of the drive during steady and transient states. This article aims at dynamic characteristic analysis of pump leakage in a hydrostatic drive with linear actuator and lever mechanism. Such hydraulic drives are used in several earth moving machines e.g. crane, dumper truck, front loader etc. To carry out this analysis, the system model is developed using the Bond graph technique and validated using real measurements. Finally, the developed model is utilized to generate and analyse the system dynamics under different intensities of pump leakage. The steady state portion of the results indicate that the pump leakage results in significant slip or flow loss as compared to pressure loss. However, the transient portion of the result indicates that effect of pump leakage is almost same on both slip loss as well as pressure loss of the drive.

Scalable Fuzzy Clustering With Anchor Graph

Fuzzy clustering algorithms have been widely used to reveal the possible hidden structure of data. However, with the increasing of data amount, large scale data has brought genuine challenges for fuzzy clustering. Most fuzzy clustering algorithms suffer from the long time-consumption problem since a large amount of distance calculations are involved to update the solution per iteration. To address this problem, we introduce the popular anchor graph technique into fuzzy clustering and propose a scalable fuzzy clustering algorithm referred to as Scalable Fuzzy Clustering with Anchor Graph (SFCAG). The main characteristic of SFCAG is that it addresses the scalability issue plaguing fuzzy clustering from two perspectives: anchor graph construction and membership matrix learning. Specifically, we select a small number of anchors and construct a sparse anchor graph, which is beneficial to reduce the computational complexity. We then formulate a trace ratio model, which is parameter-free, to learn the membership matrix of anchors to speed up the clustering procedure. In addition, the proposed method enjoys linear time complexity with the data size. Extensive experiments performed on both synthetic and real world datasets demonstrate the superiority (both effectiveness and scalability) of the proposed method over some representative large scale clustering methods.