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Journal articles on the topic 'Adversarial Testing'
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1
Lindley, Dennis V., and Nozer D. Singpurwalla. "Adversarial Life Testing." Journal of the Royal Statistical Society: Series B (Methodological) 55, no. 4 (September 1993): 837–47. http://dx.doi.org/10.1111/j.2517-6161.1993.tb01944.x.
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Rufo, M. J., J. Martín, and C. J. Pérez. "Adversarial life testing: A Bayesian negotiation model." Reliability Engineering & System Safety 131 (November 2014): 118–25. http://dx.doi.org/10.1016/j.ress.2014.06.007.
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Dürr, Christoph, Thomas Erlebach, Nicole Megow, and Julie Meißner. "An Adversarial Model for Scheduling with Testing." Algorithmica 82, no. 12 (July 10, 2020): 3630–75. http://dx.doi.org/10.1007/s00453-020-00742-2.
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Edmond, Gary. "Forensic science and the myth of adversarial testing." Current Issues in Criminal Justice 32, no. 2 (December 1, 2019): 146–79. http://dx.doi.org/10.1080/10345329.2019.1689786.
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Hoque, Endadul, Hyojeong Lee, Rahul Potharaju, Charles Killian, and Cristina Nita-Rotaru. "Automated Adversarial Testing of Unmodified Wireless Routing Implementations." IEEE/ACM Transactions on Networking 24, no. 6 (December 2016): 3369–82. http://dx.doi.org/10.1109/tnet.2016.2520474.
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Liu, Xiaolei, Xiaosong Zhang, Nadra Guizani, Jiazhong Lu, Qingxin Zhu, and Xiaojiang Du. "TLTD: A Testing Framework for Learning-Based IoT Traffic Detection Systems." Sensors 18, no. 8 (August 10, 2018): 2630. http://dx.doi.org/10.3390/s18082630.
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With the popularization of IoT (Internet of Things) devices and the continuous development of machine learning algorithms, learning-based IoT malicious traffic detection technologies have gradually matured. However, learning-based IoT traffic detection models are usually very vulnerable to adversarial samples. There is a great need for an automated testing framework to help security analysts to detect errors in learning-based IoT traffic detection systems. At present, most methods for generating adversarial samples require training parameters of known models and are only applicable to image data. To address the challenge, we propose a testing framework for learning-based IoT traffic detection systems, TLTD. By introducing genetic algorithms and some technical improvements, TLTD can generate adversarial samples for IoT traffic detection systems and can perform a black-box test on the systems.
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CHAN-HON-TONG, Adrien. "An Algorithm for Generating Invisible Data Poisoning Using Adversarial Noise That Breaks Image Classification Deep Learning." Machine Learning and Knowledge Extraction 1, no. 1 (November 9, 2018): 192–204. http://dx.doi.org/10.3390/make1010011.
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Today, the main two security issues for deep learning are data poisoning and adversarial examples. Data poisoning consists of perverting a learning system by manipulating a small subset of the training data, while adversarial examples entail bypassing the system at testing time with low-amplitude manipulation of the testing sample. Unfortunately, data poisoning that is invisible to human eyes can be generated by adding adversarial noise to the training data. The main contribution of this paper includes a successful implementation of such invisible data poisoning using image classification datasets for a deep learning pipeline. This implementation leads to significant classification accuracy gaps.
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Liu, Xiaolei, Xiaojiang Du, Xiaosong Zhang, Qingxin Zhu, Hao Wang, and Mohsen Guizani. "Adversarial Samples on Android Malware Detection Systems for IoT Systems." Sensors 19, no. 4 (February 25, 2019): 974. http://dx.doi.org/10.3390/s19040974.
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Many IoT (Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aimed at image data. To solve this problem, we propose a testing framework for learning-based Android malware detection systems (TLAMD) for IoT Devices. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample without affecting the features of the application. By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android application with a success rate of nearly 100% and can perform black-box testing on the system.
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Zhang, Pengfei, and Xiaoming Ju. "Adversarial Sample Detection with Gaussian Mixture Conditional Generative Adversarial Networks." Mathematical Problems in Engineering 2021 (September 13, 2021): 1–18. http://dx.doi.org/10.1155/2021/8268249.
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It is important to detect adversarial samples in the physical world that are far away from the training data distribution. Some adversarial samples can make a machine learning model generate a highly overconfident distribution in the testing stage. Thus, we proposed a mechanism for detecting adversarial samples based on semisupervised generative adversarial networks (GANs) with an encoder-decoder structure; this mechanism can be applied to any pretrained neural network without changing the network’s structure. The semisupervised GANs also give us insight into the behavior of adversarial samples and their flow through the layers of a deep neural network. In the supervised scenario, the latent feature of the semisupervised GAN and the target network’s logit information are used as the input of the external classifier support vector machine to detect the adversarial samples. In the unsupervised scenario, first, we proposed a one-class classier based on the semisupervised Gaussian mixture conditional generative adversarial network (GM-CGAN) to fit the joint feature information of the normal data, and then, we used a discriminator network to detect normal data and adversarial samples. In both supervised scenarios and unsupervised scenarios, experimental results show that our method outperforms latest methods.
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Bateman, Ian, Daniel Kahneman, Alistair Munro, Chris Starmer, and Robert Sugden. "Testing competing models of loss aversion: an adversarial collaboration." Journal of Public Economics 89, no. 8 (August 2005): 1561–80. http://dx.doi.org/10.1016/j.jpubeco.2004.06.013.
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Qiu, Shilin, Qihe Liu, Shijie Zhou, and Chunjiang Wu. "Review of Artificial Intelligence Adversarial Attack and Defense Technologies." Applied Sciences 9, no. 5 (March 4, 2019): 909. http://dx.doi.org/10.3390/app9050909.
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In recent years, artificial intelligence technologies have been widely used in computer vision, natural language processing, automatic driving, and other fields. However, artificial intelligence systems are vulnerable to adversarial attacks, which limit the applications of artificial intelligence (AI) technologies in key security fields. Therefore, improving the robustness of AI systems against adversarial attacks has played an increasingly important role in the further development of AI. This paper aims to comprehensively summarize the latest research progress on adversarial attack and defense technologies in deep learning. According to the target model’s different stages where the adversarial attack occurred, this paper expounds the adversarial attack methods in the training stage and testing stage respectively. Then, we sort out the applications of adversarial attack technologies in computer vision, natural language processing, cyberspace security, and the physical world. Finally, we describe the existing adversarial defense methods respectively in three main categories, i.e., modifying data, modifying models and using auxiliary tools.
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Shu, Michelle, Chenxi Liu, Weichao Qiu, and Alan Yuille. "Identifying Model Weakness with Adversarial Examiner." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11998–2006. http://dx.doi.org/10.1609/aaai.v34i07.6876.
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Machine learning models are usually evaluated according to the average case performance on the test set. However, this is not always ideal, because in some sensitive domains (e.g. autonomous driving), it is the worst case performance that matters more. In this paper, we are interested in systematic exploration of the input data space to identify the weakness of the model to be evaluated. We propose to use an adversarial examiner in the testing stage. Different from the existing strategy to always give the same (distribution of) test data, the adversarial examiner will dynamically select the next test data to hand out based on the testing history so far, with the goal being to undermine the model's performance. This sequence of test data not only helps us understand the current model, but also serves as constructive feedback to help improve the model in the next iteration. We conduct experiments on ShapeNet object classification. We show that our adversarial examiner can successfully put more emphasis on the weakness of the model, preventing performance estimates from being overly optimistic.
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Feng, Yan, Bin Chen, Tao Dai, and Shu-Tao Xia. "Adversarial Attack on Deep Product Quantization Network for Image Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 10786–93. http://dx.doi.org/10.1609/aaai.v34i07.6708.
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Deep product quantization network (DPQN) has recently received much attention in fast image retrieval tasks due to its efficiency of encoding high-dimensional visual features especially when dealing with large-scale datasets. Recent studies show that deep neural networks (DNNs) are vulnerable to input with small and maliciously designed perturbations (a.k.a., adversarial examples). This phenomenon raises the concern of security issues for DPQN in the testing/deploying stage as well. However, little effort has been devoted to investigating how adversarial examples affect DPQN. To this end, we propose product quantization adversarial generation (PQ-AG), a simple yet effective method to generate adversarial examples for product quantization based retrieval systems. PQ-AG aims to generate imperceptible adversarial perturbations for query images to form adversarial queries, whose nearest neighbors from a targeted product quantizaiton model are not semantically related to those from the original queries. Extensive experiments show that our PQ-AQ successfully creates adversarial examples to mislead targeted product quantization retrieval models. Besides, we found that our PQ-AG significantly degrades retrieval performance in both white-box and black-box settings.
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Swamidurai, Rajendran, and David A. Umphress. "Collaborative-Adversarial Pair Programming." ISRN Software Engineering 2012 (August 21, 2012): 1–11. http://dx.doi.org/10.5402/2012/516184.
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This paper presents a study called collaborative-adversarial pair (CAP) programming which is an alternative to pair programming (PP). Its objective is to exploit the advantages of pair programming while at the same time downplaying its disadvantages. Unlike traditional pairs, where two people work together in all the phases of software development, CAPs start by designing together; splitting into independent test construction and code implementation roles; then joining again for testing. An empirical study was conducted in fall 2008 and in spring 2009 with twenty-six computer science and software engineering senior and graduate students at Auburn University. The subjects were randomly divided into two groups (CAP/experimental group and PP/control group). The subjects used Eclipse and JUnit to perform three programming tasks with different degrees of complexity. The results of this experiment point in favor of CAP development methodology and do not support the claim that pair programming in general reduces the software development duration, overall software development cost or increases the program quality or correctness.
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Chamberlain, R. T. "Legal issues related to drug testing in the clinical laboratory." Clinical Chemistry 34, no. 3 (March 1, 1988): 633–36. http://dx.doi.org/10.1093/clinchem/34.3.633.
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Abstract As has been reported many times by the lay press, urine drug testing may pose some unique challenges. The clinical laboratory interested in industrial drug testing (typically known as employee drug testing) should be aware of the many challenges that may be brought on by the fact that the result may be contested in an adversarial proceeding. This is what makes the urine drug test a forensic test. It may be one piece of evidence or the only piece of evidence used in an adversarial proceeding that may decide on punitive or rehabilitative action against an employee. As a result, unique standards for governmental contract laboratories have been proposed from the National Institute on Drug Abuse, and special proficiency testing and accreditation procedures have been promoted by professional societies. These standards illustrate the sensitive nature of the results. Because the results are subject to adversarial proceedings, all parties concerned in the testing process should be aware of the legal issues surrounding urine drug testing. There are constitutional and statutory issues as well as tort issues such as negligence, defamation, invasion of privacy, battery, infliction of emotional distress, and others. Laboratories should be especially aware of these issues, since they may be brought in as a third-party defendant to a suit or brought in as a participant in gathering the evidence. The laboratory should also be aware of other legal ramifications such as chain of custody, expert testimony, and the acceptability of scientific evidence.
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Nazário Coelho, Vitor, Rodolfo Pereira Araújo, Haroldo Gambini Santos, Wang Yong Qiang, and Igor Machado Coelho. "A MILP Model for a Byzantine Fault Tolerant Blockchain Consensus." Future Internet 12, no. 11 (October 29, 2020): 185. http://dx.doi.org/10.3390/fi12110185.
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Mixed-integer mathematical programming has been widely used to model and solve challenging optimization problems. One interesting feature of this technique is the ability to prove the optimality of the achieved solution, for many practical scenarios where a linear programming model can be devised. This paper explores its use to model very strong Byzantine adversaries, in the context of distributed consensus systems. In particular, we apply the proposed technique to find challenging adversarial conditions on a state-of-the-art blockchain consensus: the Neo dBFT. Neo Blockchain has been using the dBFT algorithm since its foundation, but, due to the complexity of the algorithm, it is challenging to devise definitive algebraic proofs that guarantee safety/liveness of the system (and adjust for every change proposed by the community). Core developers have to manually devise and explore possible adversarial attacks scenarios as an exhaustive task. The proposed multi-objective model is intended to assist the search of possible faulty scenario, which includes three objective functions that can be combined as a maximization problem for testing one-block finality or a minimization problem for ensuring liveness. Automated graphics help developers to visually observe attack conditions and to quickly find a solution. This paper proposes an exact adversarial model that explores current limits for practical blockchain consensus applications such as dBFT, with ideas that can also be extended to other decentralized ledger technologies.
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Ma, Qianli, Wanqing Zhuang, Sen Li, Desen Huang, and Garrison Cottrell. "Adversarial Dynamic Shapelet Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5069–76. http://dx.doi.org/10.1609/aaai.v34i04.5948.
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Shapelets are discriminative subsequences for time series classification. Recently, learning time-series shapelets (LTS) was proposed to learn shapelets by gradient descent directly. Although learning-based shapelet methods achieve better results than previous methods, they still have two shortcomings. First, the learned shapelets are fixed after training and cannot adapt to time series with deformations at the testing phase. Second, the shapelets learned by back-propagation may not be similar to any real subsequences, which is contrary to the original intention of shapelets and reduces model interpretability. In this paper, we propose a novel shapelet learning model called Adversarial Dynamic Shapelet Networks (ADSNs). An adversarial training strategy is employed to prevent the generated shapelets from diverging from the actual subsequences of a time series. During inference, a shapelet generator produces sample-specific shapelets, and a dynamic shapelet transformation uses the generated shapelets to extract discriminative features. Thus, ADSN can dynamically generate shapelets that are similar to the real subsequences rather than having arbitrary shapes. The proposed model has high modeling flexibility while retaining the interpretability of shapelet-based methods. Experiments conducted on extensive time series data sets show that ADSN is state-of-the-art compared to existing shapelet-based methods. The visualization analysis also shows the effectiveness of dynamic shapelet generation and adversarial training.
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Christodoulou, Klitos, Elias Iosif, Antonios Inglezakis, and Marinos Themistocleous. "Consensus Crash Testing: Exploring Ripple’s Decentralization Degree in Adversarial Environments." Future Internet 12, no. 3 (March 16, 2020): 53. http://dx.doi.org/10.3390/fi12030053.
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The inception of Bitcoin as a peer-to-peer payment system, and its underlying blockchain data-structure and protocol, has led to an increased interest in deploying scalable and reliable distributed-ledger systems that build on robust consensus protocols. A critical requirement of such systems is to provide enough fault tolerance in the presence of adversarial attacks or network faults. This is essential to guarantee liveness when the network does not behave as expected and ensure that the underlying nodes agree on a unique order of transactions over a shared state. In comparison with traditional distributed systems, the deployment of a distributed-ledger system should take into account the hidden game theoretical aspects of such protocols, where actors are competing with each other in an environment which is likely to experience various well-motivated malicious and adversarial attacks. Firstly, this paper discusses the fundamental principles of existing consensus protocols in the context of both permissioned and permissionless distributed-ledger systems. The main contribution of this work deals with observations from experimenting with Ripple’s consensus protocol as it is embodied in the XRP Ledger. The main experimental finding suggests that, when a low percentage of malicious nodes is present, the centralization degree of the network can be significantly relaxed ensuring low convergence times. Those findings are of particular importance when engineering a consensus algorithm that would like to balance security with decentralization.
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Bock, Joel R., and Akhilesh Maewal. "Adversarial Learning for Product Recommendation." AI 1, no. 3 (September 1, 2020): 376–88. http://dx.doi.org/10.3390/ai1030025.
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Product recommendation can be considered as a problem in data fusion—estimation of the joint distribution between individuals, their behaviors, and goods or services of interest. This work proposes a conditional, coupled generative adversarial network (RecommenderGAN) that learns to produce samples from a joint distribution between (view, buy) behaviors found in extremely sparse implicit feedback training data. User interaction is represented by two matrices having binary-valued elements. In each matrix, nonzero values indicate whether a user viewed or bought a specific item in a given product category, respectively. By encoding actions in this manner, the model is able to represent entire, large scale product catalogs. Conversion rate statistics computed on trained GAN output samples ranged from 1.323% to 1.763%. These statistics are found to be significant in comparison to null hypothesis testing results. The results are shown comparable to published conversion rates aggregated across many industries and product types. Our results are preliminary, however they suggest that the recommendations produced by the model may provide utility for consumers and digital retailers.
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Brandao, Fernando G. S. L., Aram W. Harrow, James R. Lee, and Yuval Peres. "Adversarial Hypothesis Testing and a Quantum Stein’s Lemma for Restricted Measurements." IEEE Transactions on Information Theory 66, no. 8 (August 2020): 5037–54. http://dx.doi.org/10.1109/tit.2020.2979704.
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Wang, Jinrui, Shanshan Ji, Baokun Han, Huaiqian Bao, and Xingxing Jiang. "Deep Adaptive Adversarial Network-Based Method for Mechanical Fault Diagnosis under Different Working Conditions." Complexity 2020 (July 23, 2020): 1–11. http://dx.doi.org/10.1155/2020/6946702.
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The demand for transfer learning methods for mechanical fault diagnosis has considerably progressed in recent years. However, the existing methods always depend on the maximum mean discrepancy (MMD) in measuring the domain discrepancy. But MMD can not guarantee the different domain features to be similar enough. Inspired by generative adversarial networks (GAN) and domain adversarial training of neural networks (DANN), this study presents a novel deep adaptive adversarial network (DAAN). The DAAN comprises a condition recognition module and domain adversarial learning module. The condition recognition module is constructed with a generator to extract features and classify the health condition of machinery automatically. The domain adversarial learning module is achieved with a discriminator based on Wasserstein distance to learn domain-invariant features. Then spectral normalization (SN) is employed to accelerate convergence. The effectiveness of DAAN is demonstrated through three transfer fault diagnosis experiments, and the results show that the DAAN can converge to zero after approximately 15 training epochs, and all the average testing accuracies in each case can achieve over 92%. It is expected that the proposed DAAN can effectively learn domain-invariant features to bridge the discrepancy between the data from different working conditions.
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Hanji, Param, Muhammad Z. Alam, Nicola Giuliani, Hu Chen, and Rafał K. Mantiuk. "HDR4CV: High Dynamic Range Dataset with Adversarial Illumination for Testing Computer Vision Methods." Journal of Imaging Science and Technology 65, no. 4 (July 1, 2021): 40404–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2021.65.4.040404.
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Abstract Benchmark datasets used for testing computer vision (CV) methods often contain little variation in illumination. The methods that perform well on these datasets have been observed to fail under challenging illumination conditions encountered in the real world, in particular, when the dynamic range of a scene is high. The authors present a new dataset for evaluating CV methods in challenging illumination conditions such as low light, high dynamic range, and glare. The main feature of the dataset is that each scene has been captured in all the adversarial illuminations. Moreover, each scene includes an additional reference condition with uniform illumination, which can be used to automatically generate labels for the tested CV methods. We demonstrate the usefulness of the dataset in a preliminary study by evaluating the performance of popular face detection, optical flow, and object detection methods under adversarial illumination conditions. We further assess whether the performance of these applications can be improved if a different transfer function is used.
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Courtenay, Lloyd A., and Diego González-Aguilera. "Geometric Morphometric Data Augmentation Using Generative Computational Learning Algorithms." Applied Sciences 10, no. 24 (December 21, 2020): 9133. http://dx.doi.org/10.3390/app10249133.
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The fossil record is notorious for being incomplete and distorted, frequently conditioning the type of knowledge that can be extracted from it. In many cases, this often leads to issues when performing complex statistical analyses, such as classification tasks, predictive modelling, and variance analyses, such as those used in Geometric Morphometrics. Here different Generative Adversarial Network architectures are experimented with, testing the effects of sample size and domain dimensionality on model performance. For model evaluation, robust statistical methods were used. Each of the algorithms were observed to produce realistic data. Generative Adversarial Networks using different loss functions produced multidimensional synthetic data significantly equivalent to the original training data. Conditional Generative Adversarial Networks were not as successful. The methods proposed are likely to reduce the impact of sample size and bias on a number of statistical learning applications. While Generative Adversarial Networks are not the solution to all sample-size related issues, combined with other pre-processing steps these limitations may be overcome. This presents a valuable means of augmenting geometric morphometric datasets for greater predictive visualization.
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Cohen, P. L., M. A. Olson, and C. B. Fogarty. "Multivariate one-sided testing in matched observational studies as an adversarial game." Biometrika 107, no. 4 (June 3, 2020): 809–25. http://dx.doi.org/10.1093/biomet/asaa024.
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Summary We present a multivariate one-sided sensitivity analysis for matched observational studies, appropriate when the researcher has specified that a given causal mechanism should manifest itself in effects on multiple outcome variables in a known direction. The test statistic can be thought of as the solution to an adversarial game, where the researcher determines the best linear combination of test statistics to combat nature’s presentation of the worst-case pattern of hidden bias. The corresponding optimization problem is convex, and can be solved efficiently even for reasonably sized observational studies. Asymptotically, the test statistic converges to a chi-bar-squared distribution under the null, a common distribution in order-restricted statistical inference. The test attains the largest possible design sensitivity over a class of coherent test statistics, and facilitates one-sided sensitivity analyses for individual outcome variables while maintaining familywise error control through its incorporation into closed testing procedures.
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Zhang, Guokai, Haoping Xiao, Jingwen Jiang, Qinyuan Liu, Yimo Liu, and Liying Wang. "A Multi-Index Generative Adversarial Network for Tool Wear Detection with Imbalanced Data." Complexity 2020 (December 5, 2020): 1–10. http://dx.doi.org/10.1155/2020/5831632.
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The scarcity of abnormal data leads to imbalanced data in the field of monitoring tool wear conditions. In this paper, a novel multi-index generative adversarial network (MI-GAN) is proposed to detect the tool wear conditions subject to imbalanced signal data. First, the generator in the MI-GAN is trained to produce fake normal signals, and the discriminator computes scores of testing signals and generated signals. Next, the generator detects abnormal signals based on the performance of imitating testing signals, and the discriminator will compute the scores of testing signals and generated signals. Subsequently, two indexes, i.e., L 2 -norm and temporal correlation coefficient (CORT), are put forward to measure the similarity between generated signals and testing signals. Finally, our decision-making function further combines L 2 -norm and CORT with two discriminator scores to determine the tool conditions. Experimental results show that our method obtains 97% accuracy in tool wear detection based on imbalanced data without manual feature extraction, which outperforms traditional machine learning methods.
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Spooner, James, Vasile Palade, Madeline Cheah, Stratis Kanarachos, and Alireza Daneshkhah. "Generation of Pedestrian Crossing Scenarios Using Ped-Cross Generative Adversarial Network." Applied Sciences 11, no. 2 (January 6, 2021): 471. http://dx.doi.org/10.3390/app11020471.
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The safety of vulnerable road users is of paramount importance as transport moves towards fully automated driving. The richness of real-world data required for testing autonomous vehicles is limited and furthermore, available data do not present a fair representation of different scenarios and rare events. Before deploying autonomous vehicles publicly, their abilities must reach a safety threshold, not least with regards to vulnerable road users, such as pedestrians. In this paper, we present a novel Generative Adversarial Networks named the Ped-Cross GAN. Ped-Cross GAN is able to generate crossing sequences of pedestrians in the form of human pose sequences. The Ped-Cross GAN is trained with the Pedestrian Scenario dataset. The novel Pedestrian Scenario dataset, derived from existing datasets, enables training on richer pedestrian scenarios. We demonstrate an example of its use through training and testing the Ped-Cross GAN. The results show that the Ped-Cross GAN is able to generate new crossing scenarios that are of the same distribution from those contained in the Pedestrian Scenario dataset. Having a method with these capabilities is important for the future of transport, as it will allow for the adequate testing of Connected and Autonomous Vehicles on how they correctly perceive the intention of pedestrians crossing the street, ultimately leading to fewer pedestrian casualties on our roads.
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Pan, Boxiao, Zhangjie Cao, Ehsan Adeli, and Juan Carlos Niebles. "Adversarial Cross-Domain Action Recognition with Co-Attention." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11815–22. http://dx.doi.org/10.1609/aaai.v34i07.6854.
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Action recognition has been a widely studied topic with a heavy focus on supervised learning involving sufficient labeled videos. However, the problem of cross-domain action recognition, where training and testing videos are drawn from different underlying distributions, remains largely under-explored. Previous methods directly employ techniques for cross-domain image recognition, which tend to suffer from the severe temporal misalignment problem. This paper proposes a Temporal Co-attention Network (TCoN), which matches the distributions of temporally aligned action features between source and target domains using a novel cross-domain co-attention mechanism. Experimental results on three cross-domain action recognition datasets demonstrate that TCoN improves both previous single-domain and cross-domain methods significantly under the cross-domain setting.
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Pievatolo, Antonio, Fabrizio Ruggeri, Refik Soyer, and Simon Wilson. "Decisions in Risk and Reliability: An Explanatory Perspective." Stats 4, no. 2 (March 26, 2021): 228–50. http://dx.doi.org/10.3390/stats4020017.
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The paper discusses issues that surround decisions in risk and reliability, with a major emphasis on quantitative methods. We start with a brief history of quantitative methods in risk and reliability from the 17th century onwards. Then, we look at the principal concepts and methods in decision theory. Finally, we give several examples of their application to a wide variety of risk and reliability problems: software testing, preventive maintenance, portfolio selection, adversarial testing, and the defend-attack problem. These illustrate how the general framework of game and decision theory plays a relevant part in risk and reliability.
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Zunino, Andrea, Jacopo Cavazza, Riccardo Volpi, Pietro Morerio, Andrea Cavallo, Cristina Becchio, and Vittorio Murino. "Predicting Intentions from Motion: The Subject-Adversarial Adaptation Approach." International Journal of Computer Vision 128, no. 1 (September 18, 2019): 220–39. http://dx.doi.org/10.1007/s11263-019-01234-9.
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Abstract This paper aims at investigating the action prediction problem from a pure kinematic perspective. Specifically, we address the problem of recognizing future actions, indeed human intentions, underlying a same initial (and apparently unrelated) motor act. This study is inspired by neuroscientific findings asserting that motor acts at the very onset are embedding information about the intention with which are performed, even when different intentions originate from a same class of movements. To demonstrate this claim in computational and empirical terms, we designed an ad hoc experiment and built a new 3D and 2D dataset where, in both training and testing, we analyze a same class of grasping movements underlying different intentions. We investigate how much the intention discriminants generalize across subjects, discovering that each subject tends to affect the prediction by his/her own bias. Inspired by the domain adaptation problem, we propose to interpret each subject as a domain, leading to a novel subject adversarial paradigm. The proposed approach favorably copes with our new problem, boosting the considered baseline features encoding 2D and 3D information and which do not exploit the subject information.
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Xu, Xing, Jialin Tian, Kaiyi Lin, Huimin Lu, Jie Shao, and Heng Tao Shen. "Zero-shot Cross-modal Retrieval by Assembling AutoEncoder and Generative Adversarial Network." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1s (March 31, 2021): 1–17. http://dx.doi.org/10.1145/3424341.
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Conventional cross-modal retrieval models mainly assume the same scope of the classes for both the training set and the testing set. This assumption limits their extensibility on zero-shot cross-modal retrieval (ZS-CMR), where the testing set consists of unseen classes that are disjoint with seen classes in the training set. The ZS-CMR task is more challenging due to the heterogeneous distributions of different modalities and the semantic inconsistency between seen and unseen classes. A few of recently proposed approaches are inspired by zero-shot learning to estimate the distribution underlying multimodal data by generative models and make the knowledge transfer from seen classes to unseen classes by leveraging class embeddings. However, directly borrowing the idea from zero-shot learning (ZSL) is not fully adaptive to the retrieval task, since the core of the retrieval task is learning the common space. To address the above issues, we propose a novel approach named Assembling AutoEncoder and Generative Adversarial Network (AAEGAN), which combines the strength of AutoEncoder (AE) and Generative Adversarial Network (GAN), to jointly incorporate common latent space learning, knowledge transfer, and feature synthesis for ZS-CMR. Besides, instead of utilizing class embeddings as common space, the AAEGAN approach maps all multimodal data into a learned latent space with the distribution alignment via three coupled AEs. We empirically show the remarkable improvement for ZS-CMR task and establish the state-of-the-art or competitive performance on four image-text retrieval datasets.
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Homoliak, Ivan, Kamil Malinka, and Petr Hanacek. "ASNM Datasets: A Collection of Network Attacks for Testing of Adversarial Classifiers and Intrusion Detectors." IEEE Access 8 (2020): 112427–53. http://dx.doi.org/10.1109/access.2020.3001768.
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Rahnemoonfar, Maryam, Jimmy Johnson, and John Paden. "AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network." Sensors 19, no. 24 (December 12, 2019): 5479. http://dx.doi.org/10.3390/s19245479.
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Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network.
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Caramihale, Traian, Dan Popescu, and Loretta Ichim. "Emotion Classification Using a Tensorflow Generative Adversarial Network Implementation." Symmetry 10, no. 9 (September 19, 2018): 414. http://dx.doi.org/10.3390/sym10090414.
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The detection of human emotions has applicability in various domains such as assisted living, health monitoring, domestic appliance control, crowd behavior tracking real time, and emotional security. The paper proposes a new system for emotion classification based on a generative adversarial network (GAN) classifier. The generative adversarial networks have been widely used for generating realistic images, but the classification capabilities have been vaguely exploited. One of the main advantages is that by using the generator, we can extend our testing dataset and add more variety to each of the seven emotion classes we try to identify. Thus, the novelty of our study consists in increasing the number of classes from N to 2N (in the learning phase) by considering real and fake emotions. Facial key points are obtained from real and generated facial images, and vectors connecting them with the facial center of gravity are used by the discriminator to classify the image as one of the 14 classes of interest (real and fake for seven emotions). As another contribution, real images from different emotional classes are used in the generation process unlike the classical GAN approach which generates images from simple noise arrays. By using the proposed method, our system can classify emotions in facial images regardless of gender, race, ethnicity, age and face rotation. An accuracy of 75.2% was obtained on 7000 real images (14,000, also considering the generated images) from multiple combined facial datasets.
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Zhou, Xingyu, Zhisong Pan, Guyu Hu, Siqi Tang, and Cheng Zhao. "Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets." Mathematical Problems in Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/4907423.
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Stock price prediction is an important issue in the financial world, as it contributes to the development of effective strategies for stock exchange transactions. In this paper, we propose a generic framework employing Long Short-Term Memory (LSTM) and convolutional neural network (CNN) for adversarial training to forecast high-frequency stock market. This model takes the publicly available index provided by trading software as input to avoid complex financial theory research and difficult technical analysis, which provides the convenience for the ordinary trader of nonfinancial specialty. Our study simulates the trading mode of the actual trader and uses the method of rolling partition training set and testing set to analyze the effect of the model update cycle on the prediction performance. Extensive experiments show that our proposed approach can effectively improve stock price direction prediction accuracy and reduce forecast error.
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Ma, Rui-Qiang, Xing-Run Shen, and Shan-Jun Zhang. "Single Image Defogging Algorithm Based on Conditional Generative Adversarial Network." Mathematical Problems in Engineering 2020 (November 24, 2020): 1–8. http://dx.doi.org/10.1155/2020/7938060.
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Outside the house, images taken using a phone in foggy weather are not suitable for automation due to low contrast. Usually, it is revised in the dark channel prior (DCP) method (K. He et al. 2009), but the non-sky bright area exists due to mistakes in the removal. In this paper, we propose an algorithm, defog-based generative adversarial network (DbGAN). We use generative adversarial network (GAN) for training and embed target map (TM) in the anti-network generator, only the part of bright area layer of image, in local attention model image training and testing in deep learning, and the effective processing of the wrong removal part is achieved, thus better restoring the defog image. Then, the DCP method obtains a good defog visual effect, and the evaluation index peak signal-to-noise ratio (PSNR) is used to make a judgment; the simulation result is consistent with the visual effect. We proved the DbGAN is a practical import of target map in the GAN. The algorithm is used defogging in the highlighted area is well realized, which makes up for the shortcomings of the DCP algorithm.
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Putin, E. O., and A. A. Shalyto. "Adversarial Threshold Neural Computer for Small Organic Molecular Structures." Information and Control Systems, no. 4 (September 23, 2018): 52–60. http://dx.doi.org/10.31799/1684-8853-2018-4-52-60.
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Introduction:A special place in the development of new drugs is computer modeling of potential drug candidates. At this stage, the molecular structure of a drug is created and virtually validated. Molecular structures are created mostly by bioinformatics specialists and medical chemists. Therefore, the process of creating and virtual testing of molecules is long and expensive.Purpose:Developing a model of a deep generative adversarial neural network and its reinforcement environment for generating targeted small organic molecular structures with predetermined properties, as well as reward functions for molecular diversity.Results: The developed deep neural network model called ATNC is based on the concepts of adversarial learning and reinforcement learning. The model uses a recurrent neural network with external memory as a generator of molecular structures, and a special neural network block for selecting the generated molecules before their real estimation by the environment. A new objective reward function of internal clustering by diversity is proposed, which allows the model to generate more diverse chemistry. Comparative experiments have shown that the proposed ATNC model is better than its closest competitor in terms of generating unique and more complex valid molecular structures. It has also been demonstrated that the the molecules generated by ATNC match to the a priori distributions of the key molecular descriptors of the training molecules. Experiments were conducted on a large dataset of 15 000 drug-like molecular compounds collected manually from the ChemDiv collection.Practical relevance:The proposed model can be used as an intelligent assistant in developing new drugs by medical chemists.
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Wang, Xiaodong, and Feng Liu. "Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis." Sensors 20, no. 1 (January 6, 2020): 320. http://dx.doi.org/10.3390/s20010320.
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Recently, deep learning methods are becomingincreasingly popular in the field of fault diagnosis and achieve great success. However, since the rotation speeds and load conditions of rotating machines are subject to change during operations, the distribution of labeled training dataset for intelligent fault diagnosis model is different from the distribution of unlabeled testing dataset, where domain shift occurs. The performance of the fault diagnosis may significantly degrade due to this domain shift problem. Unsupervised domain adaptation has been proposed to alleviate this problem by aligning the distribution between labeled source domain and unlabeled target domain. In this paper, we propose triplet loss guided adversarial domain adaptation method (TLADA) for bearing fault diagnosis by jointly aligning the data-level and class-level distribution. Data-level alignment is achieved using Wasserstein distance-based adversarial approach, and the discrepancy of distributions in feature space is further minimized at class level by the triplet loss. Unlike other center loss-based class-level alignment approaches, which hasto compute the class centers for each class and minimize the distance of same class center from different domain, the proposed TLADA method concatenates 2 mini-batches from source and target domain into a single mini-batch and imposes triplet loss to the whole mini-batch ignoring the domains. Therefore, the overhead of updating the class center is eliminated. The effectiveness of the proposed method is validated on CWRU dataset and Paderborn dataset through extensive transfer fault diagnosis experiments.
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Sharifi-Noghabi, Hossein, Shuman Peng, Olga Zolotareva, Colin C. Collins, and Martin Ester. "AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics." Bioinformatics 36, Supplement_1 (July 1, 2020): i380—i388. http://dx.doi.org/10.1093/bioinformatics/btaa442.
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Abstract Motivation The goal of pharmacogenomics is to predict drug response in patients using their single- or multi-omics data. A major challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines), as a source domain, and clinical datasets as a target domain. Two major discrepancies exist between pre-clinical and clinical datasets: (i) in the input space, the gene expression data due to difference in the basic biology, and (ii) in the output space, the different measures of the drug response. Therefore, training a computational model on cell lines and testing it on patients violates the i.i.d assumption that train and test data are from the same distribution. Results We propose Adversarial Inductive Transfer Learning (AITL), a deep neural network method for addressing discrepancies in input and output space between the pre-clinical and clinical datasets. AITL takes gene expression of patients and cell lines as the input, employs adversarial domain adaptation and multi-task learning to address these discrepancies, and predicts the drug response as the output. To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately. Availability and implementation https://github.com/hosseinshn/AITL. Supplementary information Supplementary data are available at Bioinformatics online.
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Suryanto, Naufal, Hyoeun Kang, Yongsu Kim, Youngyeo Yun, Harashta Tatimma Larasati, and Howon Kim. "A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm Optimization." Sensors 20, no. 24 (December 14, 2020): 7158. http://dx.doi.org/10.3390/s20247158.
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Adversarial attack techniques in deep learning have been studied extensively due to its stealthiness to human eyes and potentially dangerous consequences when applied to real-life applications. However, current attack methods in black-box settings mainly employ a large number of queries for crafting their adversarial examples, hence making them very likely to be detected and responded by the target system (e.g., artificial intelligence (AI) service provider) due to its high traffic volume. A recent proposal able to address the large query problem utilizes a gradient-free approach based on Particle Swarm Optimization (PSO) algorithm. Unfortunately, this original approach tends to have a low attack success rate, possibly due to the model’s difficulty of escaping local optima. This obstacle can be overcome by employing a multi-group approach for PSO algorithm, by which the PSO particles can be redistributed, preventing them from being trapped in local optima. In this paper, we present a black-box adversarial attack which can significantly increase the success rate of PSO-based attack while maintaining a low number of query by launching the attack in a distributed manner. Attacks are executed from multiple nodes, disseminating queries among the nodes, hence reducing the possibility of being recognized by the target system while also increasing scalability. Furthermore, we utilize Multi-Group PSO with Random Redistribution (MGRR-PSO) for perturbation generation, performing better than the original approach against local optima, thus achieving a higher success rate. Additionally, we propose to efficiently remove excessive perturbation (i.e, perturbation pruning) by utilizing again the MGRR-PSO rather than a standard iterative method as used in the original approach. We perform five different experiments: comparing our attack’s performance with existing algorithms, testing in high-dimensional space in ImageNet dataset, examining our hyperparameters (i.e., particle size, number of clients, search boundary), and testing on real digital attack to Google Cloud Vision. Our attack proves to obtain a 100% success rate on MNIST and CIFAR-10 datasets and able to successfully fool Google Cloud Vision as a proof of the real digital attack by maintaining a lower query and wide applicability.
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Nie, Yixin, Yicheng Wang, and Mohit Bansal. "Analyzing Compositionality-Sensitivity of NLI Models." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6867–74. http://dx.doi.org/10.1609/aaai.v33i01.33016867.
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Success in natural language inference (NLI) should require a model to understand both lexical and compositional semantics. However, through adversarial evaluation, we find that several state-of-the-art models with diverse architectures are over-relying on the former and fail to use the latter. Further, this compositionality unawareness is not reflected via standard evaluation on current datasets. We show that removing RNNs in existing models or shuffling input words during training does not induce large performance loss despite the explicit removal of compositional information. Therefore, we propose a compositionality-sensitivity testing setup that analyzes models on natural examples from existing datasets that cannot be solved via lexical features alone (i.e., on which a bag-of-words model gives a high probability to one wrong label), hence revealing the models’ actual compositionality awareness. We show that this setup not only highlights the limited compositional ability of current NLI models, but also differentiates model performance based on design, e.g., separating shallow bag-of-words models from deeper, linguistically-grounded tree-based models. Our evaluation setup is an important analysis tool: complementing currently existing adversarial and linguistically driven diagnostic evaluations, and exposing opportunities for future work on evaluating models’ compositional understanding.
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Ellis, Desmond. "Marital Separation and Lethal Male Partner Violence." Violence Against Women 23, no. 4 (July 9, 2016): 503–19. http://dx.doi.org/10.1177/1077801216644985.
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Findings reported by many researchers indicate that the association between marital separation and intimate partner femicide has achieved the status of a sociological empirical generalization. The primary objective of this article is to contribute toward the cumulative development of a conflict theoretic explanation of separation- associated femicide by creating and testing a deductive conflict resolution theory that explains the empirical generalization. The causal mechanism identified in the theory is the intensity of conflict that increases with participation in adversarial and separation and divorce proceedings. Interventions logically derived from the theory are presented in the penultimate segment. Limitations are identified in the concluding segment.
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Noh, Kyoung Jun, Jiho Choi, Jin Seong Hong, and Kang Ryoung Park. "Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network." Sensors 21, no. 2 (January 13, 2021): 524. http://dx.doi.org/10.3390/s21020524.
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The conventional finger-vein recognition system is trained using one type of database and entails the serious problem of performance degradation when tested with different types of databases. This degradation is caused by changes in image characteristics due to variable factors such as position of camera, finger, and lighting. Therefore, each database has varying characteristics despite the same finger-vein modality. However, previous researches on improving the recognition accuracy of unobserved or heterogeneous databases is lacking. To overcome this problem, we propose a method to improve the finger-vein recognition accuracy using domain adaptation between heterogeneous databases using cycle-consistent adversarial networks (CycleGAN), which enhances the recognition accuracy of unobserved data. The experiments were performed with two open databases—Shandong University homologous multi-modal traits finger-vein database (SDUMLA-HMT-DB) and Hong Kong Polytech University finger-image database (HKPolyU-DB). They showed that the equal error rate (EER) of finger-vein recognition was 0.85% in case of training with SDUMLA-HMT-DB and testing with HKPolyU-DB, which had an improvement of 33.1% compared to the second best method. The EER was 3.4% in case of training with HKPolyU-DB and testing with SDUMLA-HMT-DB, which also had an improvement of 4.8% compared to the second best method.
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Ibrahim, Yahya, Balázs Nagy, and Csaba Benedek. "Deep Learning-Based Masonry Wall Image Analysis." Remote Sensing 12, no. 23 (November 29, 2020): 3918. http://dx.doi.org/10.3390/rs12233918.
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In this paper we introduce a novel machine learning-based fully automatic approach for the semantic analysis and documentation of masonry wall images, performing in parallel automatic detection and virtual completion of occluded or damaged wall regions, and brick segmentation leading to an accurate model of the wall structure. For this purpose, we propose a four-stage algorithm which comprises three interacting deep neural networks and a watershed transform-based brick outline extraction step. At the beginning, a U-Net-based sub-network performs initial wall segmentation into brick, mortar and occluded regions, which is followed by a two-stage adversarial inpainting model. The first adversarial network predicts the schematic mortar-brick pattern of the occluded areas based on the observed wall structure, providing in itself valuable structural information for archeological and architectural applications. The second adversarial network predicts the pixels’ color values yielding a realistic visual experience for the observer. Finally, using the neural network outputs as markers in a watershed-based segmentation process, we generate the accurate contours of the individual bricks, both in the originally visible and in the artificially inpainted wall regions. Note that while the first three stages implement a sequential pipeline, they interact through dependencies of their loss functions admitting the consideration of hidden feature dependencies between the different network components. For training and testing the network a new dataset has been created, and an extensive qualitative and quantitative evaluation versus the state-of-the-art is given. The experiments confirmed that the proposed method outperforms the reference techniques both in terms of wall structure estimation and regarding the visual quality of the inpainting step, moreover it can be robustly used for various different masonry wall types.
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Kim, Bubryur, N. Yuvaraj, K. R. Sri Preethaa, Gang Hu, and Dong-Eun Lee. "Wind-Induced Pressure Prediction on Tall Buildings Using Generative Adversarial Imputation Network." Sensors 21, no. 7 (April 3, 2021): 2515. http://dx.doi.org/10.3390/s21072515.
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Wind tunnel testing techniques are the main research tools for evaluating the wind loadings of buildings. They are significant in designing structurally safe and comfortable buildings. The wind tunnel pressure measurement technique using pressure sensors is significant for assessing the cladding pressures of buildings. However, some pressure sensors usually fail and cause loss of data, which are difficult to restore. In the literature, numerous techniques are implemented for imputing the single instance data values and data imputation for multiple instantaneous time intervals with accurate predictions needs to be addressed. Thus, the data imputation capacity of machine learning models is used to predict the missing wind pressure data for tall buildings in this study. A generative adversarial imputation network (GAIN) is proposed to predict the pressure coefficients at various instantaneous time intervals on tall buildings. The proposed model is validated by comparing the performance of GAIN with that of the K-nearest neighbor and multiple imputations by chained equation models. The experimental results show that the GAIN model provides the best fit, achieving more accurate predictions with the minimum average variance and minimum average standard deviation. The average mean-squared error for all four sides of the building was the minimum (0.016), and the average R-squared error was the maximum (0.961). The proposed model can ensure the health and prolonged existence of a structure based on wind environment.
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Wang, Xiaodong, Feng Liu, and Dongdong Zhao. "Cross-Machine Fault Diagnosis with Semi-Supervised Discriminative Adversarial Domain Adaptation." Sensors 20, no. 13 (July 4, 2020): 3753. http://dx.doi.org/10.3390/s20133753.
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Bearings are ubiquitous in rotating machinery and bearings in good working conditions are essential for the availability and safety of the machine. Various intelligent fault diagnosis models have been widely studied aiming to prevent system failures. These data-driven fault diagnosis models work well when training data and testing data are from the same distribution, which is not easy to sustain in industry since the working environment of rotating machinery is often subject to change. Recently, the domain adaptation methods for fault diagnosis between different working conditions have been extensively researched, which fully utilize the labeled data from the same machine under different working conditions to address this domain shift diploma. However, for a target machine with seldom occurred faulty data under any working conditions, the domain adaptation approaches between working conditions are not applicable. Hence, the cross-machine fault diagnosis tasks are recently proposed to utilize the labeled data from related but not identical machines. The larger domain shift between machines makes the cross-machine fault diagnosis a more challenging task. The large domain shift may cause the well-trained model on source domain deteriorates on target domain, and the ambiguous samples near the decision boundary are prone to be misclassified. In addition, the sparse faulty samples in target domain make a class-imbalanced scenario. To address the two issues, in this paper we propose a semi-supervised adversarial domain adaptation approach for cross-machine fault diagnosis which incorporates the virtual adversarial training and batch nuclear-norm maximization to make the fault diagnosis robust and discriminative. Experiments of transferring between three bearing datasets show that the proposed method is able to effectively learn a discriminative model given only a labeled faulty sample of each class in target domain. The research provides a feasible approach for knowledge transfer in fault diagnosis scenarios.
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Huang, Xiaowei, Daniel Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu, and Xinping Yi. "A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability." Computer Science Review 37 (August 2020): 100270. http://dx.doi.org/10.1016/j.cosrev.2020.100270.
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Hamid, Nor’Adha Bt Abdul, Mohamad Hafifi Hassim, and Tuan Nurhafiza Raja Abdul Aziz. "Non-Adversarial Dispute Resolutions: Studying Of Japanese Non-Litigious Country And Society." Advances in Social Sciences Research Journal 7, no. 8 (August 17, 2020): 188–201. http://dx.doi.org/10.14738/assrj.78.8779.
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In line with the changing of times, the transition from small-scale trade to global trade has invoked changes to the existing trade system to a more challenging one. This has made the impact being felt by the consumers. Users are getting more repressed in many forms of trade that are becoming more and more challenging besides testing the intellectual capabilities of consumers. The latest trading trend needs a change in its dispute resolution system to become more efficient and meet the demands of consumers and traders. The current trade scenario as well as the rise in matters of consumerism have made it crucial to outline a perfect and complete set of laws that is capable to protect the consumers’ rights and solve disputes that are usually arise in consumer-trader contract matters, that is sometimes unexpected. The laws formed can be beneficial if transparent and able to protect consumers involved in trade with traders, both nationally and internationally. This research aimed to discuss on the Japanese non-adversarial dispute resolutions which popularly known as non-litigious country and society. Being a library based-research, reference will be made to relevant authoritative texts, case studies, and applies the method of literature review through content analysis of documents. Overall, this paper finds that, without looking at the different theories introduced, until today, the Japanese still believe that disputes should not occur. If disputes do occur, the resolution is through a mutual resolution agreement contract. In this situation, they will give honest effort to compromise or resolution that they mutually agree upon.
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Husein, Amir Mahmud, Muhammad Arsyal, Sutrisno Sinaga, and Hendra Syahputa. "Generative Adversarial Networks Time Series Models to Forecast Medicine Daily Sales in Hospital." SinkrOn 3, no. 2 (March 13, 2019): 112. http://dx.doi.org/10.33395/sinkron.v3i2.10044.
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The success of the work of Generative Adversarial Networks (GAN) has recently achieved great success in many fields, such as stock market prediction, portfolio optimization, financial information processing and trading execution strategies, because the GAN model generates seemingly realistic data with models generator and discriminator .Planning for drug needs that are not optimal will have an impact on hospital services and economics, so it requires a reliable and accurate prediction model with the aim of minimizing the occurrence of shortages and excess stock, In this paper, we propose the GAN architecture to estimate the amount of drug sales in the next one week by using the drug usage data for the last four years (2015-2018) for training, while testing using data running in 2019 year , the classification results will be evaluated by Actual data uses indicators of Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). From the results of the experiment, seen from the value of MAE, RMSE and MAPE, the proposed model has promising performance, but it still needs to be developed to explore ways to extract factors that are more valuable and influential in the trend disease progression, thus helping in the selection of optimal drugs
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Hu, Guanghua, Junfeng Huang, Qinghui Wang, Jingrong Li, Zhijia Xu, and Xingbiao Huang. "Unsupervised fabric defect detection based on a deep convolutional generative adversarial network." Textile Research Journal 90, no. 3-4 (July 17, 2019): 247–70. http://dx.doi.org/10.1177/0040517519862880.
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Detecting and locating surface defects in textured materials is a crucial but challenging problem due to factors such as texture variations and lack of adequate defective samples prior to testing. In this paper we present a novel unsupervised method for automatically detecting defects in fabrics based on a deep convolutional generative adversarial network (DCGAN). The proposed method extends the standard DCGAN, which consists of a discriminator and a generator, by introducing a new encoder component. With the assistance of this encoder, our model can reconstruct a given query image such that no defects but only normal textures will be preserved in the reconstruction. Therefore, when subtracting the reconstruction from the original image, a residual map can be created to highlight potential defective regions. Besides, our model generates a likelihood map for the image under inspection where each pixel value indicates the probability of occurrence of defects at that location. The residual map and the likelihood map are then synthesized together to form an enhanced fusion map. Typically, the fusion map exhibits uniform gray levels over defect-free regions but distinct deviations over defective areas, which can be further thresholded to produce a binarized segmentation result. Our model can be unsupervisedly trained by feeding with a set of small-sized image patches picked from a few defect-free examples. The training is divided into several successively performed stages, each under an individual training strategy. The performance of the proposed method has been extensively evaluated by a variety of real fabric samples. The experimental results in comparison with other methods demonstrate its effectiveness in fabric defect detection.
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Zhang, Xihui, Jasbir S. Dhaliwal, Mark L. Gillenson, and Thomas F. Stafford. "The Impact of Conflict Judgments between Developers and Testers in Software Development." Journal of Database Management 24, no. 4 (October 2013): 26–50. http://dx.doi.org/10.4018/jdm.2013100102.
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The primary role of testers is to verify and validate the software produced by developers to ensure its quality. Testing is designed to catch problems in the software and report them for correction, so it is a conflict-laden, confrontational, and judgmental process. This “audit” role of testing is inherently adversarial, ensuring the development of components of interpersonal conflict judgments between developers and testers. Prior research indicates that such conflict is likely to be negatively associated with software quality and job satisfaction, producing negative judgments about the artifact production process and about the job itself. This study addresses the question: How do judgments of conflict between developers and testers impact the software development process? The authors develop and empirically test a research model which proposes that the conflict judgment targets of both the tasks and the persons who perform them will have direct impact on both software quality and job satisfaction judgments. Results of testing this model indicate that interpersonal judgments arising from conflict, as well as judgments made by testers and developers about the conflict targets of tasks and persons negatively influence subsequent software quality and job satisfaction judgments. Implications for theory and practice are discussed.