Reviewerly/exHarmony huggingface dataset:
Machine learning: A deep learning approach for seismic structural evaluation

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Machine learning: A deep learning approach for seismic structural evaluationAuthors: Pablo Guillen-RondonCarlos CobosGerman LarrazabalAgustin DizPablo Guillen-RondonUniversity of HoustonSearch for more papers by this author, Carlos CobosRepsol-USASearch for more papers by this author, German LarrazabalRepsol-USASearch for more papers by this author, and Agustin DizRepsol-USASearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3216712.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractThe goal of image recognition applied to seismic interpretation for hydrocarbon exploration is to identify the location of geological features that could be related to oil accumulation. The evaluation of structures on the subsurface is an important aspect since a large number of hydrocarbon reservoirs are contained in some kind of structural trap. Many types of structures are created by earth’s stresses and are called structural traps, among these are the anticlines. The anticline patterns range from simple to exceedingly complex, and its evaluation requires the development of new types of computational approaches to extract the useful and valuable underlying information for interpretation. To addresses these approaches, in this paper, we present a case study where a deep learning algorithm: Convolutional Neural Networks (CNN), is invoked to train a predictive model over 2D synthetic seismic images correspond to anticline structures containing gas and water, respectively. We show that the CNN algorithm has the ability to correctly infer and classify anticlines structures not used during the training process of the algorithm. We conclude that CNN is a promising mechanism to automatically detect and identify a high percentage of anticlines structures on seismic data.Presentation Date: Wednesday, September 18, 2019Session Start Time: 1:50 PMPresentation Start Time: 3:05 PMLocation: 301BPresentation Type: OralKeywords: machine learning, artificial intelligence, neural networks, predictive analyticsPermalink: https://doi.org/10.1190/segam2019-3216712.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Pablo Guillen-Rondon, Carlos Cobos, German Larrazabal, and Agustin Diz, (2019), "Machine learning: A deep learning approach for seismic structural evaluation," SEG Technical Program Expanded Abstracts : 4990-4994. https://doi.org/10.1190/segam2019-3216712.1 Plain-Language Summary Keywordsmachine learningartificial intelligenceneural networkspredictive analyticsPDF DownloadLoading ...
A Bi-level Differential Evolutionary Algorithm for Constrained Optimization

Constrained optimization problems are common in theoretical study and engineering practice. The constraint functions can be equality or inequality, convex or non-convex, which makes it difficult to find a constrained optimal solution. Evolutionary algorithms are powerful tools for constrained optimization problems. The balance between constraints and objective functions is always a difficult task and hotspot in EA-community. Many EA-based constraint handling methods have been proposed, but this problem is far to be completely solved. This paper presents a new view of constraint handling: A bi-level differential evolutionary algorithm (BLDE). BLDE has a bi-level structure, which deals with the constraints and searches optimal solutions separately. In the lower level, the objective is to find a population of candidate feasible solutions, and in the upper level, the objective is to search the global optimal solution. The proposed BLDE gets competitive results in 13 benchmark functions, with a comparison of two state-of-the-art constraint handling algorithms. BLDE shows the following advantages: 1) it is efficient in search constrained optimal solutions; 2) it is easy to implement and requires few hyper-parameters; 3) it can guarantee the obtained solutions are feasible or close to feasible.
An Agent-Based Planning Method for Distributed Task Allocation

In multi-agent systems, agents should socially cooperate with their neighboring agents in order to solve task allocation problem in open and dynamic network environments. This paper proposes an agent-based architecture to handle different tasks; in particular, we focus on planning and distributed task allocation. In the proposed approach, each agent uses the fuzzy logic technique to select the alternative plans. We also propose an efficient task allocation algorithm that takes into consideration agent architectures and allows neighboring agents to help to perform a task as well as the indirectly related agents in the system. We illustrate our line of thought with a Benchmark Production System used as a running example in order to explain better our contribution. A set of experiments was conducted to demonstrate the efficiency of our planning approach and the performance of our distributed task allocation method.
Crossing the Adaptation Boundaries of Distinct Testbeds

Testbeds to experiment with self-adaptive systems allow for the examination of a range of domain-specific problems without the need for specialized or proprietary equipment. They can focus on a need for adaptive control, such as mission completion and security threats, or study mechanisms to allow self-adaptation, such as embedding component awareness and performing the actual deployment of executable code changes during runtime for dynamic adaptation. Individually, needs often narrow the underlying models and functionality of the testbeds so that the experiments can be controlled and understood. There are multiple options to extend the experiments, such as significantly increasing the testbed components, functional requirements, and potential adaptations. However, these options, while necessary to have a fuller understanding of the scalability of the testbed, could maintain an inherent bias based on how the testbed was intended to perform its original operation, limiting its potential for self-improvement. Another option is to introduce some form of integration with a different testbed to determine how each can influence the other's adaptation mechanisms to improve self-awareness techniques. In this paper, we overview our two existing testbeds created for experimenting with self-adaptation concepts. Each testbed employs different runtime model checking and adaptation risk assessment mechanisms, with distinct functional goals. We discuss the difficulties in crossing the adaptation boundaries to perform self-improvement and to increase the potential for valuable communication and awareness.
An Easy to Use Data Logger for Local User Studies

Tooling user studies tasks is fundamental to reduce the burden of evaluators, practitioners, facilitators, and observers during user interface (UI) evaluations. However, available tools for collecting data beyond clicks are paid, count on limited capabilities for capturing/downloading large datasets, or have complex setup. In this context, we present an open source data logger for local user studies. The tool can be used by specialists as a core tool for empirical evaluations or additional data source in mixed methods assessments, offering greater control on types of events captured and counting on multiple types of report.
Current Unbalance Cluster Analysis Based on Self-organizing Competitive Neural Network

In power system, current unbalance is a kind of common fault that seriously affects the safety and efficiency of power system. There are many reasons that may cause three-phase unbalance, and now rely on manpower to judge according to specific conditions. This paper proposes a new algorithm for clustering analysis of unbalanced three-phase current data. We define a serials of feature parameters, and then use self-organizing competitive neural network for clustering analysis to subdivide current unbalance into five categories. In the experiment, a large amount of historical current data is analyzed by the proposed algorithm. We get five categories with obvious features and differences. The clustering results are reasonable and interpretable. The algorithm makes full use of the large amount of unmarked historical data produced by power system, and is helpful for the early warning of current unbalance and pre-judgment of causes.
Particle Filter with Correction of Initial State for Direction of Arrival Tracking

Generally, particle filter is used in the single snapshot situation and the initial state is assumed to be known. To make the measurement interval be small enough, we construct a multiple measurement vectors model for DOA tracking since it usually outperforms the single measurement vector model. And we take the initial state into consideration. The initial tracking error of the particle filter becomes very large when the initial state is unknown. Thus, we modify the initial state according to the likelihood of the generated random samples. The method is numerically evaluated using a uniform linear array in simulations. The results show that the proposed algorithm has higher tracking accuracy.
Influence of Network Speed on Structured Query Language (SQL) Database Data Transmission Performance in an Internet of Things (IoT) Sensing Device on Single Board Computers (SBC)

Abstract One of the crucial elements in an Internet of Things (IoT) environment is a database. IoT performance will be at stake if the wrong database is adopted. In this research, three Structured Query Language (SQL) databases were tested against multiple network speeds in an IoT device. Single board computers (SBC) were used as a media of testing instead of an ordinary computer. A controlled wireless sensor network (WSN) was developed with several uniform constants and variable network speeds. A new data transmission rate equation also was proposed and tested. From the result, it was found that not all SQL databases can cater IoT devices. Some of them showed that performance decreased along with network speed. The correlation, between 0.90 and 0.96, proved the strong influence between the research subjects. In addition, file-type database is the best option available for SQL-wise IoT storage.
Type-2 fuzzy Continuous Function Between the General type-2 fuzzy Topological spaces

Abstract The main pillar provided by these papers type-2 fuzzy continuous function between the general type-2 fuzzy topological spaces and when will it be realized after the presented of definition the type-2 fuzzy set with some properties and definition the general type-2 Fuzzy topological space.
Research Paper Search Using a Topic-Based Boolean Query Search and a General Query-Based Ranking Model

When conducting a search for research papers, the search should return comprehensive results related to the user's query. In general, a user inputs a Boolean query that reflects the information need, and the search engine ranks the research papers based on the query. However, it is difficult to anticipate all possible terms that authors of relevant papers might have used. Moreover, general query-based ranking methods emphasize how to rank the relevant documents at the top of the results, but require some means of guaranteeing the comprehensiveness of the results. Therefore, two ranking methods that consider the comprehensiveness of relevant papers are proposed. The first uses a topic-based Boolean query search. This search converts every word in the abstract set and query into a topic via topic analysis by Latent Dirichlet Allocation (LDA) and conducts a search at the topic level. The topic assigned to synonyms of a search term is expected to be the same as that assigned to the search term. Each paper is ranked based on the number of times it is matched with each topic-based Boolean query search executed for various LDA parameter settings. The second is a hybrid method that emphasizes better results from our topic-based ranking result and a general query-based ranking result. This method is based on the observation that the paper sets retrieved by our method and by a general ranking method will be different. Through experiments using the NTCIR-1 and -2 datasets, the effectiveness of our topic-based and hybrid methods are demonstrated.
In-depth Evaluation and Experimental Analysis of a Weight Pruning Genetic Algorithm

The topic of optimizing neural networks parameters has garnered considerable attention over the years, more so with the continued succession of ever larger, denser networks. In this work, we present evaluation and analysis of our proposed Weight Pruning Genetic Algorithm, with pruning specific mutators. The results show that evolved weight pruning of MNIST trained multilayer perceptrons and convolutional networks can remove as much as 72.4% and 89.6% of fully connected layer parameters, and yield improvements in test set accuracy without the use of retraining.
On the scalable dynamic taint analysis for distributed systems

To protect the privacy and search sensitive data leaks, we must solve multiple challenges (e.g., applicability, portability, and scalability) for developing an appropriate taint analysis for distributed systems.We hence present DistTaint, a dynamic taint analysis for distributed systems against these challenges. It could infer implicit dependencies from partial-ordering method events in executions to resolve the applicability challenge. DistTaint fully works at application-level without any customization of platforms to overcome the portability challenge. It exploits a multi-phase analysis to achieve scalability. By proposing a pre-analysis, DistTaint narrows down the following fine-grained analysis’ scope to reduce the overall cost significantly. Empirical results showed DistTaint’s practical applicability, portability, and scalability to industry-scale distributed programs, and its capability of discovering security vulnerabilities in real-world distributed systems. The tool package can be downloaded here: https://www.dropbox.com/sh/kfr9ixucyny1jp2/AAC00aI-I8O-d4ywZCqwZ1uaa?dl=0
HAPSOENN: Hybrid Accelerated Particle Swarm Optimized Elman Neural Network

Back propagation (BP) algorithm is a very popular optimization procedure of ANN’s training process. However, traditional BP has some drawbacks such as getting stuck in local minima, and network stagnancy. Recently, some researches proposed the use of Elman Neural Network (ENN) trained with back propagation algorithm to yield faster and more accurate results during learning. Yet, the performance of ENN is still considerably dependent on initial weights in the network. In this paper, a new method known as HAPSOENN which adapts the network weights using Accelerated Particle Swarm Optimization (APSO) was proposed as a mechanism to improve the performance of ENN. The performance of the proposed algorithm is compared with Back-Propagation Neural Network (BPNN) and other similar hybrid variants on benchmarked classification datasets. The simulation results show that the proposed technique performs better and has faster convergence than other algorithms in terms of MSE and accuracy.
An Improved Weighted Average Reprojection Image Denoising Method

Patch-based denoising algorithms have an effective improvement in the image denoising domain. Weighted Average (WAV) reprojection algorithm is a simple and effective patch-based spatial domain denoising algorithm. In this paper, an improved WAV reprojection algorithm is proposed. It improves the method by adaptively deciding the patch sizes to be used based on the image structure. The image structure is identified using a classification method based on the structure tensor matrix. The classification result is also utilized to improve the identification of similar patches in the image. The experimental results show that the denoising performance of the proposed method is better than that of the original WAV reprojection algorithm.
Sequential Image Synthesis for Human Activity Video Generation

In the field of computer graphics and multimedia, automatic synthesis of a new set of image sequences from another different set of image sequences for creating realistic video or animation of some human activity performed is a research challenge. Traditionally, creating such animation or similar visual media contents is done manually, which is a tedious task. Recent advancements in deep learning have made some promising progress for automating this type of media creation process. This work is motivated by the idea to synthesize a temporally coherent sequence of images (e.g., a video) of a person performing some activity by using a video or set of images of a different person performing a similar activity. To achieve that, our approach utilized the cycle-consistent adversarial network (CycleGAN). We present a new approach for learning to transfer a human activity from a source domain to a target domain without using any complicated pose detection or extraction method. Our objective in this work is to learn a mapping between two consecutive sequences of images from two domains representing two different activities and use that mapping to transfer the activity from one domain to another for synthesizing an entirely new consecutive sequence of images, which can be combined to make a video of new human activity. We also present and analyze some qualitative results generated by our method.
Harvesting Visual Objects from Internet Images via Deep-Learning-Based Objectness Assessment

The collection of internet images has been growing in an astonishing speed. It is undoubted that these images contain rich visual information that can be useful in many applications, such as visual media creation and data-driven image synthesis. In this article, we focus on the methodologies for building a visual object database from a collection of internet images. Such database is built to contain a large number of high-quality visual objects that can help with various data-driven image applications. Our method is based on dense proposal generation and objectness-based re-ranking. A novel deep convolutional neural network is designed for the inference of proposal objectness , the probability of a proposal containing optimally located foreground object. In our work, the objectness is quantitatively measured in regard of completeness and fullness , reflecting two complementary features of an optimal proposal: a complete foreground and relatively small background. Our experiments indicate that object proposals re-ranked according to the output of our network generally achieve higher performance than those produced by other state-of-the-art methods. As a concrete example, a database of over 1.2 million visual objects has been built using the proposed method, and has been successfully used in various data-driven image applications.
Information Innovation during Manuscript analytics: advances, challenges Plus opportunities

Manuscript mining or Manuscript analytics is the analysis of unstructured data contained in natural language Manuscript using various methods, tools and techniques. It has become an important research process with applications in many different disciplines. I ignite to learn more about advances, challenges and opportunities in Manuscript analytics how is Manuscript mining different from data mining? What is the application of Manuscript mining in different disciplines? How do researchers currently practice Manuscript mining? How is Manuscript mining different from Natural Language Processing? Traditionally industries and businesses have employed data and Manuscript mining, but recently data and Manuscript mining have provided great opportunities for academicians to study different aspects of human/social life by applying various techniques. Can Manuscript mining play an active role in societal improvements? What is your opinion as an academician and sociologist? Researchers use Manuscript mining in different fields and apply different techniques to discover patterns. One sub domain of Manuscript mining is Digital Humanities, which applies tools and techniques of Manuscript mining to cultural resources to find a new pattern. In what other areas is Manuscript mining being applied? What are the latest technology trends in Manuscript mining that every researcher and academician should be aware of? Given that topic modeling emerged out of library science, it makes sense that Manuscript mining tools would be used by librarians to more efficiently evaluate their collections as well as, perhaps, analyze the attitudes and opinions of library patrons.
Human internal organs as a possible and textual world

Ever since Shakespeare had sent a fat king to go a progress through the guts of a lean beggar [31] human internal organs started to serve as a textual locus in fiction and non-fiction, or a subject in a possible world. Their presentation varies depending upon the purpose, the form and the style of writing, semiotic modalities of their exposition, as well as the epistemological development of knowledge. These varieties come under the umbrella property known as “the possibility of the impossible” [12]. In such possible world a cat can walk in the brain as if it were his apartments [3], or together with children travel through the whole system of human internal organs [9], or a concerto could be designed for neurons and synapses [22]. In scientific articles, a textual world takes the form of topographic maps and models, including semantic distribution [11]. With this in the mind, we state the purpose for this paper to classify the types of textual “chronotops” (in a Bakhtinian sense [2]) that characterize fictional and nonfictional loci of human internal organs. We also aim at stating the type of dependences that provide narrative shapes to a possible world inside a human body. For the analyses we attract among others M.&amp;nbsp;Bakhtin’s theories of the “carnival poetics” and “Chronotop” [2], and Yu.&amp;nbsp;Lotman’s theories of “semiotic textualization” [18] and “semantic intersection” [19].&lt;br&gt; We state as our hypotheses that a blend of epistemological knowledge, personal involvement of the authors into any sort of scientific experiment and an educational goal determine the type of the deixis or “chronotop”, the major semiotic modality being “SAVOIR”-TO KNOW (in the Greimasian sense).
Image Super-Resolution Based on Multi-scale Fusion Network

It is important and necessary to obtain high-frequency information and texture details in the image reconstruction applications, such as image super-resolution. Hence, it is proposed the multi-scale fusion network (MCFN) in this paper. In the network, three pathways are designed for different receptive fields and scales, which are expected to obtain more texture details. Meanwhile, the local and global residual learning strategies are employed to prevent overfitting and to improve reconstruction quality. Compared with the classic convolutional neural network-based algorithms, the proposed method achieves better numerical and visual effects.