Paper Group ANR 325
An Incremental Path-Following Splitting Method for Linearly Constrained Nonconvex Nonsmooth Programs. Simultaneous Traffic Sign Detection and Boundary Estimation using Convolutional Neural Network. Community Interaction and Conflict on the Web. An Exploratory Study of (#)Exercise in the Twittersphere. Cyber Anomaly Detection Using Graph-node Role-d …
An Incremental Path-Following Splitting Method for Linearly Constrained Nonconvex Nonsmooth Programs
| Title | An Incremental Path-Following Splitting Method for Linearly Constrained Nonconvex Nonsmooth Programs |
| Authors | Linbo Qiao, Wei Liu, Steven Hoi |
| Abstract | The stationary point of Problem 2 is NOT the stationary point of Problem 1. We are sorry and we are working on fixing this error. |
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| Published | 2018-01-30 |
| URL | http://arxiv.org/abs/1801.10119v6 |
| http://arxiv.org/pdf/1801.10119v6.pdf | |
| PWC | https://paperswithcode.com/paper/an-incremental-path-following-splitting |
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Simultaneous Traffic Sign Detection and Boundary Estimation using Convolutional Neural Network
| Title | Simultaneous Traffic Sign Detection and Boundary Estimation using Convolutional Neural Network |
| Authors | Hee Seok Lee, Kang Kim |
| Abstract | We propose a novel traffic sign detection system that simultaneously estimates the location and precise boundary of traffic signs using convolutional neural network (CNN). Estimating the precise boundary of traffic signs is important in navigation systems for intelligent vehicles where traffic signs can be used as 3D landmarks for road environment. Previous traffic sign detection systems, including recent methods based on CNN, only provide bounding boxes of traffic signs as output, and thus requires additional processes such as contour estimation or image segmentation to obtain the precise sign boundary. In this work, the boundary estimation of traffic signs is formulated as a 2D pose and shape class prediction problem, and this is effectively solved by a single CNN. With the predicted 2D pose and the shape class of a target traffic sign in an input image, we estimate the actual boundary of the target sign by projecting the boundary of a corresponding template sign image into the input image plane. By formulating the boundary estimation problem as a CNN-based pose and shape prediction task, our method is end-to-end trainable, and more robust to occlusion and small targets than other boundary estimation methods that rely on contour estimation or image segmentation. The proposed method with architectural optimization provides an accurate traffic sign boundary estimation which is also efficient in compute, showing a detection frame rate higher than 7 frames per second on low-power mobile platforms. |
| Tasks | Semantic Segmentation |
| Published | 2018-02-27 |
| URL | http://arxiv.org/abs/1802.10019v1 |
| http://arxiv.org/pdf/1802.10019v1.pdf | |
| PWC | https://paperswithcode.com/paper/simultaneous-traffic-sign-detection-and |
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Community Interaction and Conflict on the Web
| Title | Community Interaction and Conflict on the Web |
| Authors | Srijan Kumar, William L. Hamilton, Jure Leskovec, Dan Jurafsky |
| Abstract | Users organize themselves into communities on web platforms. These communities can interact with one another, often leading to conflicts and toxic interactions. However, little is known about the mechanisms of interactions between communities and how they impact users. Here we study intercommunity interactions across 36,000 communities on Reddit, examining cases where users of one community are mobilized by negative sentiment to comment in another community. We show that such conflicts tend to be initiated by a handful of communities—less than 1% of communities start 74% of conflicts. While conflicts tend to be initiated by highly active community members, they are carried out by significantly less active members. We find that conflicts are marked by formation of echo chambers, where users primarily talk to other users from their own community. In the long-term, conflicts have adverse effects and reduce the overall activity of users in the targeted communities. Our analysis of user interactions also suggests strategies for mitigating the negative impact of conflicts—such as increasing direct engagement between attackers and defenders. Further, we accurately predict whether a conflict will occur by creating a novel LSTM model that combines graph embeddings, user, community, and text features. This model can be used toreate early-warning systems for community moderators to prevent conflicts. Altogether, this work presents a data-driven view of community interactions and conflict, and paves the way towards healthier online communities. |
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| Published | 2018-03-09 |
| URL | http://arxiv.org/abs/1803.03697v1 |
| http://arxiv.org/pdf/1803.03697v1.pdf | |
| PWC | https://paperswithcode.com/paper/community-interaction-and-conflict-on-the-web |
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An Exploratory Study of (#)Exercise in the Twittersphere
| Title | An Exploratory Study of (#)Exercise in the Twittersphere |
| Authors | George Shaw, Amir Karami |
| Abstract | Social media analytics allows us to extract, analyze, and establish semantic from user-generated contents in social media platforms. This study utilized a mixed method including a three-step process of data collection, topic modeling, and data annotation for recognizing exercise related patterns. Based on the findings, 86% of the detected topics were identified as meaningful topics after conducting the data annotation process. The most discussed exercise-related topics were physical activity (18.7%), lifestyle behaviors (6.6%), and dieting (4%). The results from our experiment indicate that the exploratory data analysis is a practical approach to summarizing the various characteristics of text data for different health and medical applications. |
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| Published | 2018-12-08 |
| URL | http://arxiv.org/abs/1812.03260v1 |
| http://arxiv.org/pdf/1812.03260v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-exploratory-study-of-exercise-in-the |
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Cyber Anomaly Detection Using Graph-node Role-dynamics
| Title | Cyber Anomaly Detection Using Graph-node Role-dynamics |
| Authors | Anthony Palladino, Christopher J. Thissen |
| Abstract | Intrusion detection systems (IDSs) generate valuable knowledge about network security, but an abundance of false alarms and a lack of methods to capture the interdependence among alerts hampers their utility for network defense. Here, we explore a graph-based approach for fusing alerts generated by multiple IDSs (e.g., Snort, OSSEC, and Bro). Our approach generates a weighted graph of alert fields (not network topology) that makes explicit the connections between multiple alerts, IDS systems, and other cyber artifacts. We use this multi-modal graph to identify anomalous changes in the alert patterns of a network. To detect the anomalies, we apply the role-dynamics approach, which has successfully identified anomalies in social media, email, and IP communication graphs. In the cyber domain, each node (alert field) in the fused IDS alert graph is assigned a probability distribution across a small set of roles based on that node’s features. A cyber attack should trigger IDS alerts and cause changes in the node features, but rather than track every feature for every alert-field node individually, roles provide a succinct, integrated summary of those feature changes. We measure changes in each node’s probabilistic role assignment over time, and identify anomalies as deviations from expected roles. We test our approach using simulations including three weeks of normal background traffic, as well as cyber attacks that occur near the end of the simulations. This paper presents a novel approach to multi-modal data fusion and a novel application of role dynamics within the cyber-security domain. Our results show a drastic decrease in the false-positive rate when considering our anomaly indicator instead of the IDS alerts themselves, thereby reducing alarm fatigue and providing a promising avenue for threat intelligence in network defense. |
| Tasks | Anomaly Detection, Intrusion Detection |
| Published | 2018-12-06 |
| URL | http://arxiv.org/abs/1812.02848v2 |
| http://arxiv.org/pdf/1812.02848v2.pdf | |
| PWC | https://paperswithcode.com/paper/cyber-anomaly-detection-using-graph-node-role |
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Protection of an information system by artificial intelligence: a three-phase approach based on behaviour analysis to detect a hostile scenario
| Title | Protection of an information system by artificial intelligence: a three-phase approach based on behaviour analysis to detect a hostile scenario |
| Authors | Jean-Philippe Fauvelle, Alexandre Dey, Sylvain Navers |
| Abstract | The analysis of the behaviour of individuals and entities (UEBA) is an area of artificial intelligence that detects hostile actions (e.g. attacks, fraud, influence, poisoning) due to the unusual nature of observed events, by affixing to a signature-based operation. A UEBA process usually involves two phases, learning and inference. Intrusion detection systems (IDS) available still suffer from bias, including over-simplification of problems, underexploitation of the AI potential, insufficient consideration of the temporality of events, and perfectible management of the memory cycle of behaviours. In addition, while an alert generated by a signature-based IDS can refer to the signature on which the detection is based, the IDS in the UEBA domain produce results, often associated with a score, whose explainable character is less obvious. Our unsupervised approach is to enrich this process by adding a third phase to correlate events (incongruities, weak signals) that are presumed to be linked together, with the benefit of a reduction of false positives and negatives. We also seek to avoid a so-called “boiled frog” bias inherent in continuous learning. Our first results are interesting and have an explainable character, both on synthetic and real data. |
| Tasks | Intrusion Detection |
| Published | 2018-12-03 |
| URL | http://arxiv.org/abs/1812.00622v1 |
| http://arxiv.org/pdf/1812.00622v1.pdf | |
| PWC | https://paperswithcode.com/paper/protection-of-an-information-system-by |
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Generate, Segment and Refine: Towards Generic Manipulation Segmentation
| Title | Generate, Segment and Refine: Towards Generic Manipulation Segmentation |
| Authors | Peng Zhou, Bor-Chun Chen, Xintong Han, Mahyar Najibi, Abhinav Shrivastava, Ser Nam Lim, Larry S. Davis |
| Abstract | Detecting manipulated images has become a significant emerging challenge. The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet. While the intent behind such manipulations varies widely, concerns on the spread of fake news and misinformation is growing. Current state of the art methods for detecting these manipulated images suffers from the lack of training data due to the laborious labeling process. We address this problem in this paper, for which we introduce a manipulated image generation process that creates true positives using currently available datasets. Drawing from traditional work on image blending, we propose a novel generator for creating such examples. In addition, we also propose to further create examples that force the algorithm to focus on boundary artifacts during training. Strong experimental results validate our proposal. |
| Tasks | Detecting Image Manipulation, Image Generation, Image Manipulation Detection, Semantic Segmentation |
| Published | 2018-11-24 |
| URL | https://arxiv.org/abs/1811.09729v3 |
| https://arxiv.org/pdf/1811.09729v3.pdf | |
| PWC | https://paperswithcode.com/paper/generate-segment-and-replace-towards-generic |
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OCLEP+: One-class Anomaly and Intrusion Detection Using Minimal Length of Emerging Patterns
| Title | OCLEP+: One-class Anomaly and Intrusion Detection Using Minimal Length of Emerging Patterns |
| Authors | Guozhu Dong, Sai Kiran Pentukar |
| Abstract | This paper presents a method called One-class Classification using Length statistics of Emerging Patterns Plus (OCLEP+). |
| Tasks | Intrusion Detection |
| Published | 2018-11-24 |
| URL | http://arxiv.org/abs/1811.09842v1 |
| http://arxiv.org/pdf/1811.09842v1.pdf | |
| PWC | https://paperswithcode.com/paper/oclep-one-class-anomaly-and-intrusion |
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Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives
| Title | Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives |
| Authors | Abhishek Divekar, Meet Parekh, Vaibhav Savla, Rudra Mishra, Mahesh Shirole |
| Abstract | Machine Learning has been steadily gaining traction for its use in Anomaly-based Network Intrusion Detection Systems (A-NIDS). Research into this domain is frequently performed using the KDD~CUP~99 dataset as a benchmark. Several studies question its usability while constructing a contemporary NIDS, due to the skewed response distribution, non-stationarity, and failure to incorporate modern attacks. In this paper, we compare the performance for KDD-99 alternatives when trained using classification models commonly found in literature: Neural Network, Support Vector Machine, Decision Tree, Random Forest, Naive Bayes and K-Means. Applying the SMOTE oversampling technique and random undersampling, we create a balanced version of NSL-KDD and prove that skewed target classes in KDD-99 and NSL-KDD hamper the efficacy of classifiers on minority classes (U2R and R2L), leading to possible security risks. We explore UNSW-NB15, a modern substitute to KDD-99 with greater uniformity of pattern distribution. We benchmark this dataset before and after SMOTE oversampling to observe the effect on minority performance. Our results indicate that classifiers trained on UNSW-NB15 match or better the Weighted F1-Score of those trained on NSL-KDD and KDD-99 in the binary case, thus advocating UNSW-NB15 as a modern substitute to these datasets. |
| Tasks | Intrusion Detection, Network Intrusion Detection |
| Published | 2018-11-13 |
| URL | http://arxiv.org/abs/1811.05372v1 |
| http://arxiv.org/pdf/1811.05372v1.pdf | |
| PWC | https://paperswithcode.com/paper/benchmarking-datasets-for-anomaly-based |
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Question-Answer Selection in User to User Marketplace Conversations
| Title | Question-Answer Selection in User to User Marketplace Conversations |
| Authors | Girish Kumar, Matthew Henderson, Shannon Chan, Hoang Nguyen, Lucas Ngoo |
| Abstract | Sellers in user to user marketplaces can be inundated with questions from potential buyers. Answers are often already available in the product description. We collected a dataset of around 590K such questions and answers from conversations in an online marketplace. We propose a question answering system that selects a sentence from the product description using a neural-network ranking model. We explore multiple encoding strategies, with recurrent neural networks and feed-forward attention layers yielding good results. This paper presents a demo to interactively pose buyer questions and visualize the ranking scores of product description sentences from live online listings. |
| Tasks | Answer Selection, Question Answering |
| Published | 2018-02-06 |
| URL | http://arxiv.org/abs/1802.01766v1 |
| http://arxiv.org/pdf/1802.01766v1.pdf | |
| PWC | https://paperswithcode.com/paper/question-answer-selection-in-user-to-user |
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BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized Weights
| Title | BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized Weights |
| Authors | Penghang Yin, Shuai Zhang, Jiancheng Lyu, Stanley Osher, Yingyong Qi, Jack Xin |
| Abstract | We propose BinaryRelax, a simple two-phase algorithm, for training deep neural networks with quantized weights. The set constraint that characterizes the quantization of weights is not imposed until the late stage of training, and a sequence of \emph{pseudo} quantized weights is maintained. Specifically, we relax the hard constraint into a continuous regularizer via Moreau envelope, which turns out to be the squared Euclidean distance to the set of quantized weights. The pseudo quantized weights are obtained by linearly interpolating between the float weights and their quantizations. A continuation strategy is adopted to push the weights towards the quantized state by gradually increasing the regularization parameter. In the second phase, exact quantization scheme with a small learning rate is invoked to guarantee fully quantized weights. We test BinaryRelax on the benchmark CIFAR and ImageNet color image datasets to demonstrate the superiority of the relaxed quantization approach and the improved accuracy over the state-of-the-art training methods. Finally, we prove the convergence of BinaryRelax under an approximate orthogonality condition. |
| Tasks | Quantization |
| Published | 2018-01-19 |
| URL | http://arxiv.org/abs/1801.06313v3 |
| http://arxiv.org/pdf/1801.06313v3.pdf | |
| PWC | https://paperswithcode.com/paper/binaryrelax-a-relaxation-approach-for |
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A Structured Prediction Approach for Label Ranking
| Title | A Structured Prediction Approach for Label Ranking |
| Authors | Anna Korba, Alexandre Garcia, Florence d’Alché Buc |
| Abstract | We propose to solve a label ranking problem as a structured output regression task. We adopt a least square surrogate loss approach that solves a supervised learning problem in two steps: the regression step in a well-chosen feature space and the pre-image step. We use specific feature maps/embeddings for ranking data, which convert any ranking/permutation into a vector representation. These embeddings are all well-tailored for our approach, either by resulting in consistent estimators, or by solving trivially the pre-image problem which is often the bottleneck in structured prediction. We also propose their natural extension to the case of partial rankings and prove their efficiency on real-world datasets. |
| Tasks | Structured Prediction |
| Published | 2018-07-06 |
| URL | http://arxiv.org/abs/1807.02374v1 |
| http://arxiv.org/pdf/1807.02374v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-structured-prediction-approach-for-label |
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Deep Spatio-Temporal Random Fields for Efficient Video Segmentation
| Title | Deep Spatio-Temporal Random Fields for Efficient Video Segmentation |
| Authors | Siddhartha Chandra, Camille Couprie, Iasonas Kokkinos |
| Abstract | In this work we introduce a time- and memory-efficient method for structured prediction that couples neuron decisions across both space at time. We show that we are able to perform exact and efficient inference on a densely connected spatio-temporal graph by capitalizing on recent advances on deep Gaussian Conditional Random Fields (GCRFs). Our method, called VideoGCRF is (a) efficient, (b) has a unique global minimum, and (c) can be trained end-to-end alongside contemporary deep networks for video understanding. We experiment with multiple connectivity patterns in the temporal domain, and present empirical improvements over strong baselines on the tasks of both semantic and instance segmentation of videos. |
| Tasks | Instance Segmentation, Semantic Segmentation, Structured Prediction, Video Semantic Segmentation, Video Understanding |
| Published | 2018-07-03 |
| URL | http://arxiv.org/abs/1807.03148v1 |
| http://arxiv.org/pdf/1807.03148v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-spatio-temporal-random-fields-for |
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The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
| Title | The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities |
| Authors | Joel Lehman, Jeff Clune, Dusan Misevic, Christoph Adami, Lee Altenberg, Julie Beaulieu, Peter J. Bentley, Samuel Bernard, Guillaume Beslon, David M. Bryson, Patryk Chrabaszcz, Nick Cheney, Antoine Cully, Stephane Doncieux, Fred C. Dyer, Kai Olav Ellefsen, Robert Feldt, Stephan Fischer, Stephanie Forrest, Antoine Frénoy, Christian Gagné, Leni Le Goff, Laura M. Grabowski, Babak Hodjat, Frank Hutter, Laurent Keller, Carole Knibbe, Peter Krcah, Richard E. Lenski, Hod Lipson, Robert MacCurdy, Carlos Maestre, Risto Miikkulainen, Sara Mitri, David E. Moriarty, Jean-Baptiste Mouret, Anh Nguyen, Charles Ofria, Marc Parizeau, David Parsons, Robert T. Pennock, William F. Punch, Thomas S. Ray, Marc Schoenauer, Eric Shulte, Karl Sims, Kenneth O. Stanley, François Taddei, Danesh Tarapore, Simon Thibault, Westley Weimer, Richard Watson, Jason Yosinski |
| Abstract | Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic process that transcends the substrate in which it occurs, evolution’s creativity is not limited to nature. Indeed, many researchers in the field of digital evolution have observed their evolving algorithms and organisms subverting their intentions, exposing unrecognized bugs in their code, producing unexpected adaptations, or exhibiting outcomes uncannily convergent with ones in nature. Such stories routinely reveal creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This paper is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems. |
| Tasks | Artificial Life |
| Published | 2018-03-09 |
| URL | https://arxiv.org/abs/1803.03453v4 |
| https://arxiv.org/pdf/1803.03453v4.pdf | |
| PWC | https://paperswithcode.com/paper/the-surprising-creativity-of-digital |
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EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction
| Title | EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction |
| Authors | Youru Li, Zhenfeng Zhu, Deqiang Kong, Hua Han, Yao Zhao |
| Abstract | Time series prediction with deep learning methods, especially long short-term memory neural networks (LSTMs), have scored significant achievements in recent years. Despite the fact that the LSTMs can help to capture long-term dependencies, its ability to pay different degree of attention on sub-window feature within multiple time-steps is insufficient. To address this issue, an evolutionary attention-based LSTM training with competitive random search is proposed for multivariate time series prediction. By transferring shared parameters, an evolutionary attention learning approach is introduced to the LSTMs model. Thus, like that for biological evolution, the pattern for importance-based attention sampling can be confirmed during temporal relationship mining. To refrain from being trapped into partial optimization like traditional gradient-based methods, an evolutionary computation inspired competitive random search method is proposed, which can well configure the parameters in the attention layer. Experimental results have illustrated that the proposed model can achieve competetive prediction performance compared with other baseline methods. |
| Tasks | Time Series, Time Series Prediction |
| Published | 2018-11-09 |
| URL | http://arxiv.org/abs/1811.03760v1 |
| http://arxiv.org/pdf/1811.03760v1.pdf | |
| PWC | https://paperswithcode.com/paper/ea-lstm-evolutionary-attention-based-lstm-for |
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