Reviewerly/exHarmony huggingface dataset:
Argumentation schemes: From genetics to international relations to environmental science policy to AI ethics

Argumentation schemes have played a key role in our research projects on computational models of natural argument over the last decade. The catalogue of schemes in Walton, Reed and Macagno’s 2008 book, Argumentation Schemes, served as our starting point for analysis of the naturally occurring arguments in written text, i.e., text in different genres having different types of author, audience, and subject domain (genetics, international relations, environmental science policy, AI ethics), for different argument goals, and for different possible future applications. We would often first attempt to analyze the arguments in our corpora in terms of those schemes, then adapt schemes as needed for the goals of the project, and in some cases implement them for use in computational models. Among computational researchers, the main interest in argumentation schemes has been for use in argument mining by applying machine learning methods to existing argument corpora. In contrast, a primary goal of our research has been to learn more about written arguments themselves in various contemporary fields. Our approach has been to manually analyze semantics, discourse structure, argumentation, and rhetoric in texts. Another goal has been to create sharable digital corpora containing the results of our studies. Our approach has been to define argument schemes for use by human corpus annotators or for use in logic programs for argument mining. The third goal is to design useful computer applications based upon our studies, such as argument diagramming systems that provide argument schemes as building blocks. This paper describes each of the various projects: the methods, the argument schemes that were identified, and how they were used. Then a synthesis of the results is given with a discussion of open issues.
Towards Regulation Change Aware Warning System

Compliance has been a research topic for more than two decades. However, in most cases it has concerned static regulations and possibilities to ensure that organizational business processes and financial matters adhere to specific laws and other regulatory requirements. This paper looks at a different perspective. The research question addressed is a possibility to predict how business entities will be impacted by changes in normative acts (regulations). These changes are detected by the proposed warning system that not only monitors changes in normative acts, but also gives an opportunity to analyse organizational data with the purpose of identifying performance of which legal entities could be negatively impacted by the changes in regulations and, thus, which entities are to be warned regarding the estimated consequences of regulatory changes.
High-Capacity Reversible Data Hiding in Encrypted Images Based on Adaptive Predictor and Compression of Prediction Errors

Reversible data hiding in encrypted images (RDHEI) is a technology which embeds secret data into encrypted images in a reversible way. In this paper, we proposed a novel high-capacity RDHEI method which is based on the compression of prediction errors. Before image encryption, an adaptive linear regression predictor is trained from the original image. Then, the predictor is used to obtain the prediction errors of the pixels in the original image, and the prediction errors are compressed by Huffman coding. The compressed prediction errors are used to vacate additional room with no loss. After image encryption, the vacated room is reserved for data embedding. The receiver can extract the secret data and recover the image with no errors. Compared with existing approaches, the proposed method efficiently improves the embedding capacity.
Using text as a native speckle pattern in digital image correlation

In certain applications, native surface patterns can be used in place of speckle patterns in digital image correlation (DIC). This paper explores the feasibility of using text as a native speckle pattern in DIC. Five text speckle patterns are tested in three different scenarios: a rigid body translation test, a rigid body rotation test, and an out of plane bending test. The patterns are benchmarked against a sixth, random speckle pattern applied using traditional DIC speckling methods. Rigid body translation tests are additionally performed on text patterns with varying font types and line spacings. In general, text patterns have good contrast, but low density as line spacing increases. Measurement uncertainty for the text patterns was comparable to measurement uncertainty in the random speckle pattern. Results from these tests show that while text patterns cannot be expected to perform better than a traditional DIC speckle pattern, text patterns can be effective speckle patterns in situations where already present on a specimen and applying a traditional speckle pattern is difficult.
Multipath-aware TCP for Data Center Traffic Load-balancing

Traffic load-balancing is important to data center performance. However, existing data center load-balancing solutions are either limited to simple topologies or cannot provide satisfactory performance. In this paper, we propose a multipath-aware TCP (MA-TCP) which can sense the path migration of TCP flows. With this new mechanism, the reduction in TCP congestion window due to packet reordering during the path migration can be avoided. This, in turn, makes the path migration more timely as soon as the original path is congested. Furthermore, if the new path is congested (again), the flow can securely continue to migrate without worrying about transmitting rate reduction. Through NS-3 simulations, we show that MA-TCP achieves better flow completion time (FCT) than existing data center load-balancing solutions.
A Dialogue Contextual Flow Model for Utterance Intent Recognition in Multi-turn Online Conversation

There are many intents of dialogues that cannot be recognized due to the contextual features of conversation, resulting in service failure for online chatting robots. Current methods leverage memory networks or machine reading comprehension (MRC) for multi-turn conversation intent recognition. We proposed a novel model for dialogue intent recognition, which leverages the advantages of MRC and memory networks. The model uses a self-attention and co-attention based contextual flow block to aggregate the dialogue utterances for intent recognition. We built a Chinese multi-turn dialogue dataset and designed a multi-task learning method to train the model. The experiment results are exciting, where the proposed model gets 82.75% accuracy and 78.13% F1 score. Those results show promising feasibility to apply our method in online chatting robot.
QBT: Efficient and Flexible Resource Allocation Method for Data Center of State Grid Scenario

With the rapid development of computer technology, service platforms have increasingly become the industry's first choice for building service center. In order to use resources more efficiently, flexible resource allocation is a very important part of the service platform construction. In the service of State Grid, the issue of flexible resource allocation is super important. However, the business of the State Grid is very complicated, and the service will include the invocation of various business logics, and resource allocation cannot be simply carried out. In order to solve this problem, this paper designs an efficient and flexible resource allocation scheme-QBT (quota boosting task) based on this project. QBT has designed three functional modules: log collection, log analysis, and flexible resource allocation. Log collection and log analysis will restore the real request path and construct it as a DAG. The system will count the QPS of each DAG, and calculate the load corresponding to the service on the DAG. Based on this information, the elastic resource allocation will perform elastic resource allocation in the two situations when the remaining resources are sufficient and insufficient. The experimental results show that the QBT design scheme can successfully solve the resource scheduling problem of the National Grid Service Platform.
An Integrated Model of Task, Information Needs, Sources and Uncertainty to Design Task-Aware Search Systems

The varieties of information seeking behavior encompass a range of practices and constructs such as the realization of an information need, selecting the nature of information, as well as information sources. Most of the past works have studied various constructs of the information seeking process, i.e., information, information need, and information sources individually. However, a person forms and re-forms his or her information seeking strategy based on continually shifting values of these dimensions associated with information seeking. This preliminary study conducted a survey with 15 search scenarios and multiple-choice characteristics completed by 114 Amazon's Mechanical Turk workers to find out more about how these constructs play a role in people's preferences regarding information seeking strategies. The study took an exploratory and inferential research approach to investigate how different forms of information and information needs might lead to different information sources by building binary classification models. The results show that the choice of sources can be predicted (with 80% accuracy) if the information need, representation, and form of information are apparent.
A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing

The ability of robots to write Chinese strokes, which is recognized as a sophisticated task, involves complicated kinematic control algorithms. The conventional approaches for robotic writing of Chinese strokes often suffer from limited font generation methods, which limits the ability of robots to perform high-quality writing. This article instead proposes a developmental evolutionary learning framework that enables a robot to learn to write fundamental Chinese strokes. The framework first considers the learning process of robotic writing as an evolutionary easy-to-difficult procedure. Then, a developmental learning mechanism called “Lift-constraint, act and saturate” that stems from developmental robotics is used to determine how the robot learns tasks ranging from simple to difficult by building on the learning results from the easy tasks. The developmental constraints, which include altitude adjustments, number of mutation points, and stroke trajectory points, determine the learning complexity of robot writing. The developmental algorithm divides the evolutionary procedure into three developmental learning stages. In each stage, the stroke trajectory points gradually increase, while the number of mutation points and adjustment altitudes gradually decrease, allowing the learning difficulties involved in these three stages to be categorized as easy, medium, and difficult. Our robot starts with an easy learning task and then gradually progresses to the medium and difficult tasks. Under various developmental constraint setups in each stage, the robot applies an evolutionary algorithm to handle the basic shapes of the Chinese strokes and eventually acquires the ability to write with good quality. The experimental results demonstrate that the proposed framework allows a calligraphic robot to gradually learn to write five fundamental Chinese strokes and also reveals a developmental pattern similar to that of humans. Compared to an evolutionary algorithm without the developmental mechanism, the proposed framework achieves good writing quality more rapidly.
Age and Gender (Face) Recognition: A Brief Survey

Age and gender estimation using face images is an exciting task in the field of computer vision. The traits from the face images are used to determine age, gender, ethnic background and emotion of people. By learning representations through the use of Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), a significant increase in performance can be obtained on these tasks, respect to the commonly used hand-crafted methodologies. This paper presents a comparative overview of the state-of-the-art approaches which estimate age and gender from human faces, some of them proposing novel network architectures or the addition of new components to already known models.
A Detailed Survey Study on Various Issues and Techniques for Security and Privacy of Healthcare Records

It is noticed that the exponential data growth in the healthcare domain is manageable by the application of Big Data architecture and techniques. Various Machine Learning (ML) and Big Data techniques are influencing healthcare. It is essential to propose a secure and smart healthcare information system with the latest security mechanism. Similarly, provisions have to be made to secure classified healthcare records in the cloud. Cryptosystems, service-oriented architecture, secure multi-party computation, and secret share schemes are some of the security mechanism methods. Evaluation of a classification model in a cloud computing environment is considered in this paper for privacy preserving. For the success of healthcare organizations, a detailed survey study about privacy and security aspects is also dealt with in this paper. This has resulted in machine learning-based secured data processing of healthcare records in the cloud environment.
An Effective Algorithm for Classification of Text with Weak Sequential Relationships

Text classification is a fundamental task that is widely used in various sub-domains of natural language processing, such as information extraction, semantic understanding, etc. For the general text classification problems, various deep learning models, such as Bi-LSTM, Transformer, BERT, etc. have been used which achieved good performance. In this paper, however, we consider a new problem on how to deal with a special scenario in text classification which has a weak sequential relationship among different classification entities. A typical example is in the block classification of resumes where there are sequential relationships existing amongst different blocks. By fully utilizing this useful sequential feature, we in this paper propose an effective hybrid model which combines a fully connected neural network model and a block-level recurrent neural network model with feature fusion that makes full use of such a sequential feature. The experimental results show that the average F1-score value of our model on three 1,400 real resume datasets is 5.5–11% higher than the existing mainstream algorithms.
Pitfalls and insights from a machine learning contest on log facies classification

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsPitfalls and insights from a machine learning contest on log facies classificationAuthors: Marcelo GuaridoDavid J. EmeryMarie MacquetDaniel O. TradKristopher A. InnanenMarcelo GuaridoUniversity of CalgarySearch for more papers by this author, David J. EmeryUniversity of CalgarySearch for more papers by this author, Marie MacquetUniversity of CalgarySearch for more papers by this author, Daniel O. TradUniversity of CalgarySearch for more papers by this author, and Kristopher A. InnanenUniversity of CalgarySearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3580872.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractFORCE: Machine Predicted Lithology was a challenging facies classification contest using well logs from the North Sea’s Norwegian coast. As we built different machine learning workflows to predict the lithofacies best, we encountered a few interesting challenges. The first involved merging different lithofacies to one single class and balancing the predictions accordingly with the class frequencies. The second was related to the contest metric. It did not consider the imbalanced classes. In fact, the most sampled classes, like the shale, which corresponded to 62% of the observations, were more critical. Our initial workflow focused on balancing the classes, and our balanced accuracy metric score was 0.56, while the contest metric was −1.35. In the second approach, we threw off the imbalanced classes consideration, and by doing so, we could improve the contest metric to −0.58, but with a trade-off on the balance accuracy score, which decreased to 0.41.Keywords: machine learning, log analysis, faciesPermalink: https://doi.org/10.1190/segam2021-3580872.1FiguresReferencesRelatedDetails First International Meeting for Applied Geoscience & Energy Expanded AbstractsISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2021 Pages: 3561 publication data© 2021 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 01 Sep 2021 CITATION INFORMATION Marcelo Guarido, David J. Emery, Marie Macquet, Daniel O. Trad, and Kristopher A. Innanen, (2021), "Pitfalls and insights from a machine learning contest on log facies classification," SEG Technical Program Expanded Abstracts : 1671-1675. https://doi.org/10.1190/segam2021-3580872.1 Plain-Language Summary Keywordsmachine learninglog analysisfaciesPDF DownloadLoading ...
A novel chaotic teaching learning based optimization algorithm and its application in optimization of extreme learning machine

Abstract Recent ten years, the teaching learning based optimization algorithm (TLBO) has been widely concerned and successfully applied to solve various constraints and non-constraints problems. However, its convergence accuracy and convergence speed should be further improved. Therefore, a novel chaotic teaching learning based optimization algorithm (called CTLBO) is proposed. Firstly, chaotic variables are applied to initialize population individuals for increasing the diversity of population. Secondly, a kind of self-adaptive acceleration coefficient is introduced into teaching phase to enhance the convergence speed and solution quality. Finally, two population updating mechanisms are proposed to balance the exploration and exploitation capabilities in the learning phase. One is neighbor elitist search mechanism, another is chaos optimization mechanism. The performance of CTLBO is compared with five state-of-the-art optimization algorithms by several CEC mathematical problems. The experiment results show that the CTLBO yields better convergence rate than other algorithms on most testing functions. Additionally, the proposed CTLBO is applied to optimize the model parameters of extreme learning machine(ELM) and the tuned ELM is adopted to establish the NOx emissions model. Experiment results reveal that the NOx emissions model has good accuracy and meets the engineering requirement.
Processing outcrop point clouds to 3D rock structure using open source software

Abstract This study proposes a processing procedure to extract the 3D rock structure directly from point clouds using open source software. The basic processing involves: (1) estimating the Hough’s normal of each point; (2) converting the normal to dip direction and dip of the corresponding plane; (3) coloring each point in HSV color space according to its normal; (4) decoding the sets number using the multivariate kernel density estimators; (5) extracting and visualizing the set-based points; and (6) estimating the set-based geometric parameters and conducting stereographic projection. The result is an actual discrete fracture network aggregated with the set-based point clouds having HSV colors. From the initial point cloud to the completion of processing, we manage all data in one single file. The case studies show that the processing procedure can identify, extract, and quantify the fracture sets that have less exposed areas, which facilitates the evaluation of main risks.
Multi-modal MRI synthesization based on StarGAN

In magnetic resonance image (MRI) analysis, it is often necessary and beneficial to analyze multi-modal MRIs. However, it is typically costly to acquire images of multiple modalities. In this context, cross-modality MRI synthesization has a great potential, for which task the generative adversarial network (GAN) technique has been identified to be useful. The main limitation of GAN is that it can only transfer between two modalities and will not work if more than one modalities need to be generated from another single one. In this work, we propose to use StarGAN for multi-modal MRI synthesization. In other words, StarGAN is used to generate MRIs of multiple modalities from a single modality at one shot. In our experiment, we show that StarGAN is more time-saving than multiple GANs and also more accurate.
Product-line analysis cookbook

Adopting Product Line Engineering (PLE) approaches in the context of software-intensive systems reduces overall development and maintenance costs, reduces time to market and leads to an overall improvement in product quality. The Software and System Product Line (SPL) community has provided a large number of different analysis approaches and tools, which were developed in different contexts, answer different questions, and can contribute to the fulfillment of different analysis goals. Typically, these analysis tools are initially developed as part of a research study, where they serve a specific purpose, e. g. for investigating the use of a new technology, or to demonstrate the transfer of methods from other fields. Generally, such purpose is aligned with a specific, but not explicitly stated, high-level goal. The pursuit of these goals requires holistic approaches, i. e. integrated toolchains and classification of analyses, which are documented as a centralized collection of wisdom. Therefore, we propose a classification system which describes existing analyses and reveals possible combinations, i. e. integrated toolchains, and provide first examples. This method supports the search for toolchains which address complex industrial needs. With the support of the SPL community, we hope to collaboratively document existing analyses and corresponding goals on an open platform.
A Block Based Parallel ZM-SPECK Algorithm

The zero memory set partitioned embedded block (ZM-SPECK) technique is an embedded and memory efficient image compression algorithm. However, it is computationally complex due to the repetitive significance checking of sets and coefficients in each and every bit plane. To overcome this limitation, it is proposed to parallelize the algorithm over smaller blocks to reduce the overall encoding and decoding times of ZM-SPECK algorithm. The proposed approach called block based parallel ZM-SPECK (BPZM-SPECK) decomposes the wavelet transformed image into independent nonoverlapping spatial blocks utilizing the unique child-parent relationships in spatial orientation trees (in wavelet domain) and concurrently encodes every single bits in each bit plane of a block. The experimental results show significant improvement in computation time over the existing ZM-SPECK algorithm.
Feasibility Evaluation of Long-distance Network Timing-based Covert Channels

Network timing characteristics can be used to hide information, and this is known as a covert timing channel (CTC). This paper discusses a number of inherent network effects that may prevent the use of CTC. The paper experimentally demonstrates the feasibility of an Interpacket Delay (IPD)-based CTC over long paths. To our knowledge, long-distance CTC between various locations across the world have not yet been experimentally studied in the literature. For this paper, CTC channels were created between New Zealand and nine AWS instances around the globe. The channels communicated at an hourly interval over a week's duration. Three encoding methods, 1 bit per packet (BPP), 2 BPP, and 3 BPP were designed and studied. The experiment results suggest that one can expect there to be around a 10% error rate, and stress the need for error correcting codes in the CTCs.