pwc-archive/papers-with-abstracts huggingface dataset:
Cyber-All-Intel: An AI for Security related Threat Intelligence
Authors: Sudip Mittal, Anupam Joshi, Tim Finin
Date: 2019-05-07 00:00:00
Tasks: All, Knowledge Graphs

Keeping up with threat intelligence is a must for a security analyst today. There is a volume of information present in `the wild' that affects an organization. We need to develop an artificial intelligence system that scours the intelligence sources, to keep the analyst updated about various threats that pose a risk to her organization. A security analyst who is better `tapped in' can be more effective. In this paper we present, Cyber-All-Intel an artificial intelligence system to aid a security analyst. It is a system for knowledge extraction, representation and analytics in an end-to-end pipeline grounded in the cybersecurity informatics domain. It uses multiple knowledge representations like, vector spaces and knowledge graphs in a 'VKG structure' to store incoming intelligence. The system also uses neural network models to pro-actively improve its knowledge. We have also created a query engine and an alert system that can be used by an analyst to find actionable cybersecurity insights.
Convolutional Neural Networks Considering Local and Global features for Image Enhancement
Authors: Yuma Kinoshita, Hitoshi Kiya
Date: 2019-05-07 00:00:00
Tasks: Decoder, Image Enhancement

In this paper, we propose a novel convolutional neural network (CNN) architecture considering both local and global features for image enhancement. Most conventional image enhancement methods, including Retinex-based methods, cannot restore lost pixel values caused by clipping and quantizing. CNN-based methods have recently been proposed to solve the problem, but they still have a limited performance due to network architectures not handling global features. To handle both local and global features, the proposed architecture consists of three networks: a local encoder, a global encoder, and a decoder. In addition, high dynamic range (HDR) images are used for generating training data for our networks. The use of HDR images makes it possible to train CNNs with better-quality images than images directly captured with cameras. Experimental results show that the proposed method can produce higher-quality images than conventional image enhancement methods including CNN-based methods, in terms of various objective quality metrics: TMQI, entropy, NIQE, and BRISQUE.
Intentional Attention Mask Transformation for Robust CNN Classification
Authors: Masanari Kimura, Masayuki Tanaka
Date: 2019-05-07 00:00:00
Tasks: Attribute, Classification, General Classification

Convolutional Neural Networks have achieved impressive results in various tasks, but interpreting the internal mechanism is a challenging problem. To tackle this problem, we exploit a multi-channel attention mechanism in feature space. Our network architecture allows us to obtain an attention mask for each feature while existing CNN visualization methods provide only a common attention mask for all features. We apply the proposed multi-channel attention mechanism to multi-attribute recognition task. We can obtain different attention mask for each feature and for each attribute. Those analyses give us deeper insight into the feature space of CNNs. Furthermore, our proposed attention mechanism naturally derives a method for improving the robustness of CNNs. From the observation of feature space based on the proposed attention mask, we demonstrate that we can obtain robust CNNs by intentionally emphasizing features that are important for attributes. The experimental results for the benchmark dataset show that the proposed method gives high human interpretability while accurately grasping the attributes of the data, and improves network robustness.
Forest Representation Learning Guided by Margin Distribution
Authors: Shen-Huan Lv, Liang Yang, Zhi-Hua Zhou
Date: 2019-05-07 00:00:00
Tasks: Representation Learning

In this paper, we reformulate the forest representation learning approach as an additive model which boosts the augmented feature instead of the prediction. We substantially improve the upper bound of generalization gap from $\mathcal{O}(\sqrt\frac{\ln m}{m})$ to $\mathcal{O}(\frac{\ln m}{m})$, while $\lambda$ - the margin ratio between the margin standard deviation and the margin mean is small enough. This tighter upper bound inspires us to optimize the margin distribution ratio $\lambda$. Therefore, we design the margin distribution reweighting approach (mdDF) to achieve small ratio $\lambda$ by boosting the augmented feature. Experiments and visualizations confirm the effectiveness of the approach in terms of performance and representation learning ability. This study offers a novel understanding of the cascaded deep forest from the margin-theory perspective and further uses the mdDF approach to guide the layer-by-layer forest representation learning.
A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks
Authors: Lior Deutsch, Erik Nijkamp, Yu Yang
Date: 2019-05-07 00:00:00
Tasks: Computational Efficiency, Diversity

Recent work on mode connectivity in the loss landscape of deep neural networks has demonstrated that the locus of (sub-)optimal weight vectors lies on continuous paths. In this work, we train a neural network that serves as a hypernetwork, mapping a latent vector into high-performance (low-loss) weight vectors, generalizing recent findings of mode connectivity to higher dimensional manifolds. We formulate the training objective as a compromise between accuracy and diversity, where the diversity takes into account trivial symmetry transformations of the target network. We demonstrate how to reduce the number of parameters in the hypernetwork by parameter sharing. Once learned, the hypernetwork allows for a computationally efficient, ancestral sampling of neural network weights, which we recruit to form large ensembles. The improvement in classification accuracy obtained by this ensembling indicates that the generated manifold extends in dimensions other than directions implied by trivial symmetries. For computational efficiency, we distill an ensemble into a single classifier while retaining generalization.
DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks
Authors: Xiaoliang Chen, Baojia Li, Roberto Proietti, Hongbo Lu, Zuqing Zhu, S. J. Ben Yoo
Date: 2019-05-06 00:00:00
Tasks: Blocking, Deep Reinforcement Learning, Reinforcement Learning

This paper proposes DeepRMSA, a deep reinforcement learning framework for routing, modulation and spectrum assignment (RMSA) in elastic optical networks (EONs). DeepRMSA learns the correct online RMSA policies by parameterizing the policies with deep neural networks (DNNs) that can sense complex EON states. The DNNs are trained with experiences of dynamic lightpath provisioning. We first modify the asynchronous advantage actor-critic algorithm and present an episode-based training mechanism for DeepRMSA, namely, DeepRMSA-EP. DeepRMSA-EP divides the dynamic provisioning process into multiple episodes (each containing the servicing of a fixed number of lightpath requests) and performs training by the end of each episode. The optimization target of DeepRMSA-EP at each step of servicing a request is to maximize the cumulative reward within the rest of the episode. Thus, we obviate the need for estimating the rewards related to unknown future states. To overcome the instability issue in the training of DeepRMSA-EP due to the oscillations of cumulative rewards, we further propose a window-based flexible training mechanism, i.e., DeepRMSA-FLX. DeepRMSA-FLX attempts to smooth out the oscillations by defining the optimization scope at each step as a sliding window, and ensuring that the cumulative rewards always include rewards from a fixed number of requests. Evaluations with the two sample topologies show that DeepRMSA-FLX can effectively stabilize the training while achieving blocking probability reductions of more than 20.3% and 14.3%, when compared with the baselines.
Decision Making with Machine Learning and ROC Curves
Authors: Kai Feng, Han Hong, Ke Tang, Jingyuan Wang
Date: 2019-05-05 00:00:00
Tasks: BIG-bench Machine Learning, Binary Classification, Decision Making, General Classification, Model Selection

The Receiver Operating Characteristic (ROC) curve is a representation of the statistical information discovered in binary classification problems and is a key concept in machine learning and data science. This paper studies the statistical properties of ROC curves and its implication on model selection. We analyze the implications of different models of incentive heterogeneity and information asymmetry on the relation between human decisions and the ROC curves. Our theoretical discussion is illustrated in the context of a large data set of pregnancy outcomes and doctor diagnosis from the Pre-Pregnancy Checkups of reproductive age couples in Henan Province provided by the Chinese Ministry of Health.
Human Gait Database for Normal Walk Collected by Smartphone Accelerometer
Authors: Amir Vajdi, Mohammad Reza Zaghian, Nazli Rafei Dehkordi, Elham Rastegari, Kian Maroofi, Saman Farahmand, Shaohua Jia, Marc Pomplun, Nurit Haspel, Akram Bayat
Date: 2019-05-04 00:00:00
Tasks: Gait Recognition

Gait recognition is the characterization of unique biometric patterns associated with each individual which can be utilized to identify a person without direct contact. A public gait database with a relatively large number of subjects can provide a great opportunity for future studies to build and validate gait authentication models. The goal of this study is to introduce a comprehensive gait database of 93 human subjects who walked between two endpoints (320 meters) during two different sessions and record their gait data using two smartphones, one attached to the right thigh and another one on the left side of the waist. This data is collected to be utilized by a deep learning-based method that requires enough time points. The metadata including age, gender, smoking, daily exercise time, height, and weight of an individual is recorded. this data set is publicly available.
Tag2Vec: Learning Tag Representations in Tag Networks
Authors: Junshan Wang, Zhicong Lu, Guojie Song, Yue Fan, Lun Du, Wei. Lin
Date: 2019-04-19 00:00:00
Tasks: Network Embedding, Representation Learning, TAG

Network embedding is a method to learn low-dimensional representation vectors for nodes in complex networks. In real networks, nodes may have multiple tags but existing methods ignore the abundant semantic and hierarchical information of tags. This information is useful to many network applications and usually very stable. In this paper, we propose a tag representation learning model, Tag2Vec, which mixes nodes and tags into a hybrid network. Firstly, for tag networks, we define semantic distance as the proximity between tags and design a novel strategy, parameterized random walk, to generate context with semantic and hierarchical information of tags adaptively. Then, we propose hyperbolic Skip-gram model to express the complex hierarchical structure better with lower output dimensions. We evaluate our model on the NBER U.S. patent dataset and WordNet dataset. The results show that our model can learn tag representations with rich semantic information and it outperforms other baselines.
Rumour Detection via News Propagation Dynamics and User Representation Learning
Authors: Tien Huu Do, Xiao Luo, Duc Minh Nguyen, Nikos Deligiannis
Date: 2019-04-18 00:00:00
Tasks: Representation Learning, Rumour Detection

Rumours have existed for a long time and have been known for serious
consequences. The rapid growth of social media platforms has multiplied the
negative impact of rumours; it thus becomes important to early detect them.
Many methods have been introduced to detect rumours using the content or the
social context of news. However, most existing methods ignore or do not explore
effectively the propagation pattern of news in social media, including the
sequence of interactions of social media users with news across time. In this
work, we propose a novel method for rumour detection based on deep learning.
Our method leverages the propagation process of the news by learning the users'
representation and the temporal interrelation of users' responses. Experiments
conducted on Twitter and Weibo datasets demonstrate the state-of-the-art
performance of the proposed method.
Working women and caste in India: A study of social disadvantage using feature attribution
Authors: Kuhu Joshi, Chaitanya K. Joshi
Date: 2019-04-27 00:00:00
Tasks: BIG-bench Machine Learning

Women belonging to the socially disadvantaged caste-groups in India have historically been engaged in labour-intensive, blue-collar work. We study whether there has been any change in the ability to predict a woman's work-status and work-type based on her caste by interpreting machine learning models using feature attribution. We find that caste is now a less important determinant of work for the younger generation of women compared to the older generation. Moreover, younger women from disadvantaged castes are now more likely to be working in white-collar jobs.
Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance
Authors: Giles Hooker, Lucas Mentch, Siyu Zhou
Date: 2019-05-01 00:00:00

This paper reviews and advocates against the use of permute-and-predict (PaP) methods for interpreting black box functions. Methods such as the variable importance measures proposed for random forests, partial dependence plots, and individual conditional expectation plots remain popular because they are both model-agnostic and depend only on the pre-trained model output, making them computationally efficient and widely available in software. However, numerous studies have found that these tools can produce diagnostics that are highly misleading, particularly when there is strong dependence among features. The purpose of our work here is to (i) review this growing body of literature, (ii) provide further demonstrations of these drawbacks along with a detailed explanation as to why they occur, and (iii) advocate for alternative measures that involve additional modeling. In particular, we describe how breaking dependencies between features in hold-out data places undue emphasis on sparse regions of the feature space by forcing the original model to extrapolate to regions where there is little to no data. We explore these effects across various model setups and find support for previous claims in the literature that PaP metrics can vastly over-emphasize correlated features in both variable importance measures and partial dependence plots. As an alternative, we discuss and recommend more direct approaches that involve measuring the change in model performance after muting the effects of the features under investigation.
Applications of Social Media in Hydroinformatics: A Survey
Authors: Yu-Feng Yu, Yuelong Zhu, Dingsheng Wan, Qun Zhao, Kai Shu, Huan Liu
Date: 2019-05-01 00:00:00
Tasks: Fake News Detection, Management, Survey

Floods of research and practical applications employ social media data for a
wide range of public applications, including environmental monitoring, water
resource managing, disaster and emergency response.Hydroinformatics can benefit
from the social media technologies with newly emerged data, techniques and
analytical tools to handle large datasets, from which creative ideas and new
values could be mined.This paper first proposes a 4W (What, Why, When, hoW)
model and a methodological structure to better understand and represent the
application of social media to hydroinformatics, then provides an overview of
academic research of applying social media to hydroinformatics such as water
environment, water resources, flood, drought and water Scarcity management. At
last,some advanced topics and suggestions of water related social media
applications from data collection, data quality management, fake news
detection, privacy issues, algorithms and platforms was present to
hydroinformatics managers and researchers based on previous discussion.
Fault-Tolerant Routing in Hypercube Networks by Avoiding Faulty Nodes
Authors: Shadrokh Samavi, Pejman Khadivi
Date: 2019-05-01 00:00:00

Next to the high performance, the essential feature of the multiprocessor
systems is their fault-tolerant capability. In this regard, fault-tolerant
interconnection networks and especially fault-tolerant routing methods are
crucial parts of these systems. Hypercube is a popular interconnection network
that is used in many multiprocessors. There are several suggested practices for
fault tolerant routing in these systems. In this paper, a neural routing method
is introduced which is named as Fault Avoidance Routing (FAR). This method
keeps the message as far from the faulty nodes as possible. The proposed method
employs the Hopfield neural network. In comparison with other neural routing
methods, FAR requires a small number of neurons. The simulation results show
that FAR has excellent performance in larger interconnection networks and
networks with a high density of faulty nodes.
Image-based reconstruction for the impact problems by using DPNNs
Authors: Yu Li, Hu Wang, Wenquan Shuai, Honghao Zhang, Yong Peng
Date: 2019-04-08 00:00:00
Tasks: Generative Adversarial Network

With the improvement of the pattern recognition and feature extraction of Deep Neural Networks (DPNNs), image-based design and optimization have been widely used in multidisciplinary researches. Recently, a Reconstructive Neural Network (ReConNN) has been proposed to obtain an image-based model from an analysis-based model [1, 2], and a steady-state heat transfer of a heat sink has been successfully reconstructed. Commonly, this method is suitable to handle stable-state problems. However, it has difficulties handling nonlinear transient impact problems, due to the bottlenecks of the Deep Neural Network (DPNN). For example, nonlinear transient problems make it difficult for the Generative Adversarial Network (GAN) to generate various reasonable images. Therefore, in this study, an improved ReConNN method is proposed to address the mentioned weaknesses. Time-dependent ordered images can be generated. Furthermore, the improved method is successfully applied in impact simulation case and engineering experiment. Through the experiments, comparisons and analyses, the improved method is demonstrated to outperform the former one in terms of its accuracy, efficiency and costs.
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security
Authors: Rahul-Vigneswaran K, Vinayakumar R, Soman Kp, Prabaharan Poornachandran
Date: 2018-10-08 00:00:00
Proceeding: International Conference on Computing, Communication and Networking Technologies (ICCCNT) 2018 10
Tasks: Benchmarking, BIG-bench Machine Learning, Intrusion Detection, Network Intrusion Detection

Intrusion detection system (IDS) has become an essential layer in all the latest ICT system due to an urge towards cyber safety in the day-to-day world. Reasons including uncertainty in finding the types of attacks and increased the complexity of advanced cyber attacks, IDS calls for the need of integration of Deep Neural Networks (DNNs). In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS). A DNN with 0.1 rate of learning is applied and is run for 1000 number of epochs and KDDCup-`99’ dataset has been used for training and benchmarking the network. For comparison purposes, the training is done on the same dataset with several other classical machine learning algorithms and DNN of layers ranging from 1 to 5. The results were compared and concluded that a DNN of 3 layers has superior performance over all the other classical machine learning algorithms.
Introducing LETOR 4.0 Datasets
Authors: Tao Qin, Tie-Yan Liu
Date: 2013-06-09 00:00:00
Tasks: Learning-To-Rank

LETOR is a package of benchmark data sets for research on LEarning TO Rank,
which contains standard features, relevance judgments, data partitioning,
evaluation tools, and several baselines. Version 1.0 was released in April
2007. Version 2.0 was released in Dec. 2007. Version 3.0 was released in Dec.
2008. This version, 4.0, was released in July 2009. Very different from
previous versions (V3.0 is an update based on V2.0 and V2.0 is an update based
on V1.0), LETOR4.0 is a totally new release. It uses the Gov2 web page
collection (~25M pages) and two query sets from Million Query track of TREC
2007 and TREC 2008. We call the two query sets MQ2007 and MQ2008 for short.
There are about 1700 queries in MQ2007 with labeled documents and about 800
queries in MQ2008 with labeled documents. If you have any questions or
suggestions about the datasets, please kindly email us (letor@microsoft.com).
Our goal is to make the dataset reliable and useful for the community.
Monte Carlo Bayesian Reinforcement Learning
Authors: Yi Wang, Kok Sung Won, David Hsu, Wee Sun Lee
Date: 2012-06-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Bayesian reinforcement learning (BRL) encodes prior knowledge of the world in
a model and represents uncertainty in model parameters by maintaining a
probability distribution over them. This paper presents Monte Carlo BRL
(MC-BRL), a simple and general approach to BRL. MC-BRL samples a priori a
finite set of hypotheses for the model parameter values and forms a discrete
partially observable Markov decision process (POMDP) whose state space is a
cross product of the state space for the reinforcement learning task and the
sampled model parameter space. The POMDP does not require conjugate
distributions for belief representation, as earlier works do, and can be solved
relatively easily with point-based approximation algorithms. MC-BRL naturally
handles both fully and partially observable worlds. Theoretical and
experimental results show that the discrete POMDP approximates the underlying
BRL task well with guaranteed performance.
2-bit Model Compression of Deep Convolutional Neural Network on ASIC Engine for Image Retrieval
Authors: Bin Yang, Lin Yang, Xiaochun Li, Wenhan Zhang, Hua Zhou, Yequn Zhang, Yongxiong Ren, Yinbo Shi
Date: 2019-05-08 00:00:00
Tasks: Image Retrieval, Model Compression, Quantization, Retrieval

Image retrieval utilizes image descriptors to retrieve the most similar images to a given query image. Convolutional neural network (CNN) is becoming the dominant approach to extract image descriptors for image retrieval. For low-power hardware implementation of image retrieval, the drawback of CNN-based feature descriptor is that it requires hundreds of megabytes of storage. To address this problem, this paper applies deep model quantization and compression to CNN in ASIC chip for image retrieval. It is demonstrated that the CNN-based features descriptor can be extracted using as few as 2-bit weights quantization to deliver a similar performance as floating-point model for image retrieval. In addition, to implement CNN in ASIC, especially for large scale images, the limited buffer size of chips should be considered. To retrieve large scale images, we propose an improved pooling strategy, region nested invariance pooling (RNIP), which uses cropped sub-images for CNN. Testing results on chip show that integrating RNIP with the proposed 2-bit CNN model compression approach is capable of retrieving large scale images.