Paper Group ANR 486
Deep Kinship Verification via Appearance-shape Joint Prediction and Adaptation-based Approach. Lost in Interpretation: Predicting Untranslated Terminology in Simultaneous Interpretation. A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification. A Concert-planning Tool for Independent Musicians by Machine Learning Models. ICSTra …
Deep Kinship Verification via Appearance-shape Joint Prediction and Adaptation-based Approach
| Title | Deep Kinship Verification via Appearance-shape Joint Prediction and Adaptation-based Approach |
| Authors | Heming Zhang, Xiaolong Wang, C. -C. Jay Kuo |
| Abstract | Kinship verification aims to identify the kin relation between two given face images. It is a very challenging problem due to the lack of training data and facial similarity variations between kinship pairs. In this work, we build a novel appearance and shape based deep learning pipeline. First we adopt the knowledge learned from general face recognition network to learn general facial features. Afterwards, we learn kinship oriented appearance and shape features from kinship pairs and combine them for the final prediction. We have evaluated the model performance on a widely used popular benchmark and demonstrated the superiority over the state-of-the-art. |
| Tasks | Face Recognition |
| Published | 2019-05-15 |
| URL | https://arxiv.org/abs/1905.05964v1 |
| https://arxiv.org/pdf/1905.05964v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-kinship-verification-via-appearance |
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Lost in Interpretation: Predicting Untranslated Terminology in Simultaneous Interpretation
| Title | Lost in Interpretation: Predicting Untranslated Terminology in Simultaneous Interpretation |
| Authors | Nikolai Vogler, Craig Stewart, Graham Neubig |
| Abstract | Simultaneous interpretation, the translation of speech from one language to another in real-time, is an inherently difficult and strenuous task. One of the greatest challenges faced by interpreters is the accurate translation of difficult terminology like proper names, numbers, or other entities. Intelligent computer-assisted interpreting (CAI) tools that could analyze the spoken word and detect terms likely to be untranslated by an interpreter could reduce translation error and improve interpreter performance. In this paper, we propose a task of predicting which terminology simultaneous interpreters will leave untranslated, and examine methods that perform this task using supervised sequence taggers. We describe a number of task-specific features explicitly designed to indicate when an interpreter may struggle with translating a word. Experimental results on a newly-annotated version of the NAIST Simultaneous Translation Corpus (Shimizu et al., 2014) indicate the promise of our proposed method. |
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| Published | 2019-04-01 |
| URL | http://arxiv.org/abs/1904.00930v1 |
| http://arxiv.org/pdf/1904.00930v1.pdf | |
| PWC | https://paperswithcode.com/paper/lost-in-interpretation-predicting |
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A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification
| Title | A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification |
| Authors | Sara Hosseinzadeh Kassani, Peyman Hosseinzadeh kassani, Michal J. Wesolowski, Kevin A. Schneider, Ralph Deters |
| Abstract | Automatic detection of leukemic B-lymphoblast cancer in microscopic images is very challenging due to the complicated nature of histopathological structures. To tackle this issue, an automatic and robust diagnostic system is required for early detection and treatment. In this paper, an automated deep learning-based method is proposed to distinguish between immature leukemic blasts and normal cells. The proposed deep learning based hybrid method, which is enriched by different data augmentation techniques, is able to extract high-level features from input images. Results demonstrate that the proposed model yields better prediction than individual models for Leukemic B-lymphoblast classification with 96.17% overall accuracy, 95.17% sensitivity and 98.58% specificity. Fusing the features extracted from intermediate layers, our approach has the potential to improve the overall classification performance. |
| Tasks | Data Augmentation |
| Published | 2019-09-26 |
| URL | https://arxiv.org/abs/1909.11866v1 |
| https://arxiv.org/pdf/1909.11866v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-hybrid-deep-learning-architecture-for-2 |
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A Concert-planning Tool for Independent Musicians by Machine Learning Models
| Title | A Concert-planning Tool for Independent Musicians by Machine Learning Models |
| Authors | Xiaohan Yang, Qingyin Ge |
| Abstract | Our project aims at helping independent musicians to plan their concerts based on the economies of agglomeration in the music industry. Initially, we planned to design an advisory tool for both concert pricing and location selection. Nonetheless, after implementing SGD linear regression and support vector regression models, we realized that concert price does not vary significantly according to different music types, concert time, concert location and ticket venues. Therefore, to offer more useful suggestions, we focus on the location choice problem by turning it to a classification task. The overall performance of our classification model is pretty good. After tuning hyperparameters, we discovered the Random Forest gives the best performance, improving the classification result by 316%. This result reveals that we could help independent musicians better locate their concerts to where similar musicians would go, namely a place with higher network effects. |
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| Published | 2019-08-29 |
| URL | https://arxiv.org/abs/1908.11200v1 |
| https://arxiv.org/pdf/1908.11200v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-concert-planning-tool-for-independent |
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ICSTrace: A Malicious IP Traceback Model for Attacking Data of Industrial Control System
| Title | ICSTrace: A Malicious IP Traceback Model for Attacking Data of Industrial Control System |
| Authors | Feng Xiao, Qiang Xu |
| Abstract | Considering the attacks against industrial control system are mostly organized and premeditated actions, IP traceback is significant for the security of industrial control system. Based on the infrastructure of the Internet, we have developed a novel malicious IP traceback model-ICSTrace, without deploying any new services. The model extracts the function codes and their parameters from the attack data according to the format of industrial control protocol, and employs a short sequence probability method to transform the function codes and their parameter into a vector, which characterizes the attack pattern of malicious IP addresses. Furthermore, a Partial Seeded K-Means algorithm is proposed for the pattern’s clustering, which helps in tracing the attacks back to an organization. ICSTrace is evaluated basing on the attack data captured by the large-scale deployed honeypots for industrial control system, and the results demonstrate that ICSTrace is effective on malicious IP traceback in industrial control system. |
| Tasks | |
| Published | 2019-12-30 |
| URL | https://arxiv.org/abs/1912.12828v1 |
| https://arxiv.org/pdf/1912.12828v1.pdf | |
| PWC | https://paperswithcode.com/paper/icstrace-a-malicious-ip-traceback-model-for |
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Conformity bias in the cultural transmission of music sampling traditions
| Title | Conformity bias in the cultural transmission of music sampling traditions |
| Authors | Mason Youngblood |
| Abstract | One of the fundamental questions of cultural evolutionary research is how individual-level processes scale up to generate population-level patterns. Previous studies in music have revealed that frequency-based bias (e.g. conformity and novelty) drives large-scale cultural diversity in different ways across domains and levels of analysis. Music sampling is an ideal research model for this process because samples are known to be culturally transmitted between collaborating artists, and sampling events are reliably documented in online databases. The aim of the current study was to determine whether frequency-based bias has played a role in the cultural transmission of music sampling traditions, using a longitudinal dataset of sampling events across three decades. Firstly, we assessed whether turn-over rates of popular samples differ from those expected under neutral evolution. Next, we used agent-based simulations in an approximate Bayesian computation framework to infer what level of frequency-based bias likely generated the observed data. Despite anecdotal evidence of novelty bias, we found that sampling patterns at the population-level are most consistent with conformity bias. |
| Tasks | |
| Published | 2019-06-27 |
| URL | https://arxiv.org/abs/1906.11928v1 |
| https://arxiv.org/pdf/1906.11928v1.pdf | |
| PWC | https://paperswithcode.com/paper/conformity-bias-in-the-cultural-transmission |
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Analysis of Probabilistic multi-scale fractional order fusion-based de-hazing algorithm
| Title | Analysis of Probabilistic multi-scale fractional order fusion-based de-hazing algorithm |
| Authors | U. A. Nnolim |
| Abstract | In this report, a de-hazing algorithm based on probability and multi-scale fractional order-based fusion is proposed. The proposed scheme improves on a previously implemented multiscale fraction order-based fusion by augmenting its local contrast and edge sharpening features. It also brightens de-hazed images, while avoiding sky region over-enhancement. The results of the proposed algorithm are analyzed and compared with existing methods from the literature and indicate better performance in most cases. |
| Tasks | |
| Published | 2019-05-10 |
| URL | https://arxiv.org/abs/1905.04302v1 |
| https://arxiv.org/pdf/1905.04302v1.pdf | |
| PWC | https://paperswithcode.com/paper/190504302 |
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Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation
| Title | Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation |
| Authors | Xinlei Pan, Tingnan Zhang, Brian Ichter, Aleksandra Faust, Jie Tan, Sehoon Ha |
| Abstract | Imitation learning is a popular approach for training visual navigation policies. However, collecting expert demonstrations for legged robots is challenging as these robots can be hard to control, move slowly, and cannot operate continuously for a long time. Here, we propose a zero-shot imitation learning approach for training a visual navigation policy on legged robots from human (third-person perspective) demonstrations, enabling high-quality navigation and cost-effective data collection. However, imitation learning from third-person demonstrations raises unique challenges. First, these demonstrations are captured from different camera perspectives, which we address via a feature disentanglement network (FDN) that extracts perspective-invariant state features. Second, as transition dynamics vary across systems, we label missing actions by either building an inverse model of the robot’s dynamics in the feature space and applying it to the human demonstrations or developing a Graphic User Interface(GUI) to label human demonstrations. To train a navigation policy we use a model-based imitation learning approach with FDN and labeled human demonstrations. We show that our framework can learn an effective policy for a legged robot, Laikago, from human demonstrations in both simulated and real-world environments. Our approach is zero-shot as the robot never navigates the same paths during training as those at testing time. We justify our framework by performing a comparative study. |
| Tasks | Imitation Learning, Legged Robots, Visual Navigation |
| Published | 2019-09-27 |
| URL | https://arxiv.org/abs/1909.12971v2 |
| https://arxiv.org/pdf/1909.12971v2.pdf | |
| PWC | https://paperswithcode.com/paper/zero-shot-imitation-learning-from |
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Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network
| Title | Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network |
| Authors | Sangjae Bae, Dhruv Saxena, Alireza Nakhaei, Chiho Choi, Kikuo Fujimura, Scott Moura |
| Abstract | This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of other drivers. This paper focuses on heavy traffic where vehicles cannot change lanes without cooperating with other drivers. In this case, classical robust controls may not apply since there is no safe area to merge to without interacting with the other drivers. That said, modeling complex and interactive human behaviors is highly non-trivial from the perspective of control engineers. We propose a mathematical control framework based on Model Predictive Control (MPC) encompassing a state-of-the-art Recurrent Neural network (RNN) architecture. In particular, RNN predicts interactive motions of other drivers in response to potential actions of the autonomous vehicle, which are then systematically evaluated in safety constraints. We also propose a real-time heuristic algorithm to find locally optimal control inputs. Finally, quantitative and qualitative analysis on simulation studies are presented to illustrate the benefits of the proposed framework. |
| Tasks | Autonomous Driving |
| Published | 2019-09-09 |
| URL | https://arxiv.org/abs/1909.05665v2 |
| https://arxiv.org/pdf/1909.05665v2.pdf | |
| PWC | https://paperswithcode.com/paper/cooperation-aware-lane-change-control-in |
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Deep neural network for pier scour prediction
| Title | Deep neural network for pier scour prediction |
| Authors | Mahesh Pal |
| Abstract | With the advancement in computing power over last decades, deep neural networks (DNN), consisting of two or more hidden layers with large number of nodes, are being suggested as an alternate to commonly used single-hidden-layer neural networks (ANN). DNN are found to be flexible models with a very large number of parameters, thus making them capable of modelling very complex and highly nonlinear relationships existing between inputs and outputs. This paper investigates the potential of a DNN consisting of 3 hidden layers (100, 80 and 50 nodes) to predict the local scour around bridge piers using field data. To update the weights and bias of DNN, an adaptive learning rate optimization algorithm was used. The dataset consists of 232 pier scour measurements, out of which a total of 154 data were used to train whereas remaining 78 data to test the created model. A correlation coefficient value of 0.957 (root mean square error = 0.306m) was achieved by DNN in comparison to 0.938 (0.388m) by ANN, indicating an improved performance by DNN for scour depth perdition. Encouraging performance on the used dataset in the work suggests the need of more studies on the use of DNN for various civil engineering applications. |
| Tasks | |
| Published | 2019-10-09 |
| URL | https://arxiv.org/abs/1910.03804v1 |
| https://arxiv.org/pdf/1910.03804v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-neural-network-for-pier-scour-prediction |
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Improved explanatory efficacy on human affect and workload through interactive process in artificial intelligence
| Title | Improved explanatory efficacy on human affect and workload through interactive process in artificial intelligence |
| Authors | Byung Hyung Kim, Seunghun Koh, Sejoon Huh, Sungho Jo |
| Abstract | Despite recent advances in the field of explainable artificial intelligence systems, a concrete quantitative measure for evaluating the usability of such systems is nonexistent. Ensuring the success of an explanatory interface in interacting with users requires a cyclic, symbiotic relationship between human and artificial intelligence. We, therefore, propose explanatory efficacy, a novel metric for evaluating the strength of the cyclic relationship the interface exhibits. Furthermore, in a user study, we evaluated the perceived affect and workload and recorded the EEG signals of our participants as they interacted with our custom-built, iterative explanatory interface to build personalized recommendation systems. We found that systems for perceptually driven iterative tasks with greater explanatory efficacy are characterized by statistically significant hemispheric differences in neural signals, indicating the feasibility of neural correlates as a measure of explanatory efficacy. These findings are beneficial for researchers who aim to study the circular ecosystem of the human-artificial intelligence partnership. |
| Tasks | EEG, Recommendation Systems |
| Published | 2019-12-13 |
| URL | https://arxiv.org/abs/1912.07416v1 |
| https://arxiv.org/pdf/1912.07416v1.pdf | |
| PWC | https://paperswithcode.com/paper/improved-explanatory-efficacy-on-human-affect |
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A Machine Learning Analysis of the Features in Deceptive and Credible News
| Title | A Machine Learning Analysis of the Features in Deceptive and Credible News |
| Authors | Qi Jia Sun |
| Abstract | Fake news is a type of pervasive propaganda that spreads misinformation online, taking advantage of social media’s extensive reach to manipulate public perception. Over the past three years, fake news has become a focal discussion point in the media due to its impact on the 2016 U.S. presidential election. Fake news can have severe real-world implications: in 2016, a man walked into a pizzeria carrying a rifle because he read that Hillary Clinton was harboring children as sex slaves. This project presents a high accuracy (87%) machine learning classifier that determines the validity of news based on the word distributions and specific linguistic and stylistic differences in the first few sentences of an article. This can help readers identify the validity of an article by looking for specific features in the opening lines aiding them in making informed decisions. Using a dataset of 2,107 articles from 30 different websites, this project establishes an understanding of the variations between fake and credible news by examining the model, dataset, and features. This classifier appears to use the differences in word distribution, levels of tone authenticity, and frequency of adverbs, adjectives, and nouns. The differentiation in the features of these articles can be used to improve future classifiers. This classifier can also be further applied directly to browsers as a Google Chrome extension or as a filter for social media outlets or news websites to reduce the spread of misinformation. |
| Tasks | |
| Published | 2019-10-05 |
| URL | https://arxiv.org/abs/1910.02223v1 |
| https://arxiv.org/pdf/1910.02223v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-machine-learning-analysis-of-the-features |
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Hybrid Adaptive Neuro-Fuzzy Inference System for Diagnosing the Liver Disorders
| Title | Hybrid Adaptive Neuro-Fuzzy Inference System for Diagnosing the Liver Disorders |
| Authors | Mina Rajabi, Hajar Sadeghizadeh, Zahra Mola-Amini, Niloofar Ahmadyrad |
| Abstract | In this study, a hybrid method based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) for diagnosing Liver disorders (ANFIS-PSO) is introduced. This smart diagnosis method deals with a combination of making an inference system and optimization process which tries to tune the hyper-parameters of ANFIS based on the data-set. The Liver diseases characteristics are taken from the UCI Repository of Machine Learning Databases. The number of these characteristic attributes are 7, and the sample number is 354. The right diagnosis performance of the ANFIS-PSO intelligent medical system for liver disease is evaluated by using classification accuracy, sensitivity and specificity analysis, respectively. According to the experimental results, the performance of ANFIS-PSO can be more considerable than traditional FIS and ANFIS without optimization phase. |
| Tasks | |
| Published | 2019-10-03 |
| URL | https://arxiv.org/abs/1910.12952v1 |
| https://arxiv.org/pdf/1910.12952v1.pdf | |
| PWC | https://paperswithcode.com/paper/hybrid-adaptive-neuro-fuzzy-inference-system |
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Deployable probabilistic programming
| Title | Deployable probabilistic programming |
| Authors | David Tolpin |
| Abstract | We propose design guidelines for a probabilistic programming facility suitable for deployment as a part of a production software system. As a reference implementation, we introduce Infergo, a probabilistic programming facility for Go, a modern programming language of choice for server-side software development. We argue that a similar probabilistic programming facility can be added to most modern general-purpose programming languages. Probabilistic programming enables automatic tuning of program parameters and algorithmic decision making through probabilistic inference based on the data. To facilitate addition of probabilistic programming capabilities to other programming languages, we share implementation choices and techniques employed in development of Infergo. We illustrate applicability of Infergo to various use cases on case studies, and evaluate Infergo’s performance on several benchmarks, comparing Infergo to dedicated inference-centric probabilistic programming frameworks. |
| Tasks | Decision Making, Probabilistic Programming |
| Published | 2019-06-20 |
| URL | https://arxiv.org/abs/1906.11199v1 |
| https://arxiv.org/pdf/1906.11199v1.pdf | |
| PWC | https://paperswithcode.com/paper/deployable-probabilistic-programming |
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VISIR: Visual and Semantic Image Label Refinement
| Title | VISIR: Visual and Semantic Image Label Refinement |
| Authors | Sreyasi Nag Chowdhury, Niket Tandon, Hakan Ferhatosmanoglu, Gerhard Weikum |
| Abstract | The social media explosion has populated the Internet with a wealth of images. There are two existing paradigms for image retrieval: 1) content-based image retrieval (CBIR), which has traditionally used visual features for similarity search (e.g., SIFT features), and 2) tag-based image retrieval (TBIR), which has relied on user tagging (e.g., Flickr tags). CBIR now gains semantic expressiveness by advances in deep-learning-based detection of visual labels. TBIR benefits from query-and-click logs to automatically infer more informative labels. However, learning-based tagging still yields noisy labels and is restricted to concrete objects, missing out on generalizations and abstractions. Click-based tagging is limited to terms that appear in the textual context of an image or in queries that lead to a click. This paper addresses the above limitations by semantically refining and expanding the labels suggested by learning-based object detection. We consider the semantic coherence between the labels for different objects, leverage lexical and commonsense knowledge, and cast the label assignment into a constrained optimization problem solved by an integer linear program. Experiments show that our method, called VISIR, improves the quality of the state-of-the-art visual labeling tools like LSDA and YOLO. |
| Tasks | Content-Based Image Retrieval, Image Retrieval, Object Detection |
| Published | 2019-09-02 |
| URL | https://arxiv.org/abs/1909.00741v1 |
| https://arxiv.org/pdf/1909.00741v1.pdf | |
| PWC | https://paperswithcode.com/paper/visir-visual-and-semantic-image-label |
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