OpenRaiser/Intern-Atlas huggingface dataset:
Mitigating the Impact of Adversarial Attacks in Very Deep Networks
Venue: arXiv cs.CV 2020
Year: 2020

Deep Neural Network (DNN) models have vulnerabilities related to security concerns, with attackers usually employing complex hacking techniques to expose their structures. Data poisoning-enabled perturbation attacks are complex adversarial ones that inject false data into models. They negatively impact the learning process, with no benefit to deeper networks, as they degrade a model's accuracy and convergence rates. In this paper, we propose an attack-agnostic-based defense method for mitigating their influence. In it, a Defensive Feature Layer (DFL) is integrated with a well-known DNN architecture which assists in neutralizing the effects of illegitimate perturbation samples in the feature space. To boost the robustness and trustworthiness of this method for correctly classifying attacked input samples, we regularize the hidden space of a trained model with a discriminative loss function called Polarized Contrastive Loss (PCL). It improves discrimination among samples in different classes and maintains the resemblance of those in the same class. Also, we integrate a DFL and PCL in a compact model for defending against data poisoning attacks. This method is trained and tested using the CIFAR-10 and MNIST datasets with data poisoning-enabled perturbation attacks, with the experimental results revealing its excellent performance compared with those of recent peer techniques.
Fine-Grained Network Analysis for Modern Software Ecosystems
Venue: arXiv cs.SE 2020
Year: 2020

Modern software development is increasingly dependent on components, libraries and frameworks coming from third-party vendors or open-source suppliers and made available through a number of platforms (or forges). This way of writing software puts an emphasis on reuse and on composition, commoditizing the services which modern applications require. On the other hand, bugs and vulnerabilities in a single library living in one such ecosystem can affect, directly or by transitivity, a huge number of other libraries and applications. Currently, only product-level information on library dependencies is used to contain this kind of danger, but this knowledge often reveals itself too imprecise to lead to effective (and possibly automated) handling policies. We will discuss how fine-grained function-level dependencies can greatly improve reliability and reduce the impact of vulnerabilities on the whole software ecosystem.
A Data-Driven Analytical Framework of Estimating Multimodal Travel Demand Patterns using Mobile Device Location Data
Venue: arXiv cs.LG 2020
Year: 2020

While benefiting people's daily life in so many ways, smartphones and their location-based services are generating massive mobile device location data that has great potential to help us understand travel demand patterns and make transportation planning for the future. While recent studies have analyzed human travel behavior using such new data sources, limited research has been done to extract multimodal travel demand patterns out of them. This paper presents a data-driven analytical framework to bridge the gap. To be able to successfully detect travel modes using the passively collected location information, we conduct a smartphone-based GPS survey to collect ground truth observations. Then a jointly trained single-layer model and deep neural network for travel mode imputation is developed. Being "wide" and "deep" at the same time, this model combines the advantages of both types of models. The framework also incorporates the multimodal transportation network in order to evaluate the closeness of trip routes to the nearby rail, metro, highway and bus lines and therefore enhance the imputation accuracy. To showcase the applications of the introduced framework in answering real-world planning needs, a separate mobile device location data is processed through trip end identification and attribute generation, in a way that the travel mode imputation can be directly applied. The estimated multimodal travel demand patterns are then validated against typical household travel surveys in the same Washington D.C. and Baltimore Metropolitan Regions.
A Statistical Test for Probabilistic Fairness
Venue: arXiv cs.LG 2020
Year: 2020

Algorithms are now routinely used to make consequential decisions that affect human lives. Examples include college admissions, medical interventions or law enforcement. While algorithms empower us to harness all information hidden in vast amounts of data, they may inadvertently amplify existing biases in the available datasets. This concern has sparked increasing interest in fair machine learning, which aims to quantify and mitigate algorithmic discrimination. Indeed, machine learning models should undergo intensive tests to detect algorithmic biases before being deployed at scale. In this paper, we use ideas from the theory of optimal transport to propose a statistical hypothesis test for detecting unfair classifiers. Leveraging the geometry of the feature space, the test statistic quantifies the distance of the empirical distribution supported on the test samples to the manifold of distributions that render a pre-trained classifier fair. We develop a rigorous hypothesis testing mechanism for assessing the probabilistic fairness of any pre-trained logistic classifier, and we show both theoretically as well as empirically that the proposed test is asymptotically correct. In addition, the proposed framework offers interpretability by identifying the most favorable perturbation of the data so that the given classifier becomes fair.
Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework
Venue: arXiv cs.CV 2020
Year: 2020

Surrogate task based methods have recently shown great promise for unsupervised image anomaly detection. However, there is no guarantee that the surrogate tasks share the consistent optimization direction with anomaly detection. In this paper, we return to a direct objective function for anomaly detection with information theory, which maximizes the distance between normal and anomalous data in terms of the joint distribution of images and their representation. Unfortunately, this objective function is not directly optimizable under the unsupervised setting where no anomalous data is provided during training. Through mathematical analysis of the above objective function, we manage to decompose it into four components. In order to optimize in an unsupervised fashion, we show that, under the assumption that distribution of the normal and anomalous data are separable in the latent space, its lower bound can be considered as a function which weights the trade-off between mutual information and entropy. This objective function is able to explain why the surrogate task based methods are effective for anomaly detection and further point out the potential direction of improvement. Based on this object function we introduce a novel information theoretic framework for unsupervised image anomaly detection. Extensive experiments have demonstrated that the proposed framework significantly outperforms several state-of-the-arts on multiple benchmark data sets.
AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we present an Assertion-based Multi-View Fusion network (AMVNet) for LiDAR semantic segmentation which aggregates the semantic features of individual projection-based networks using late fusion. Given class scores from different projection-based networks, we perform assertion-guided point sampling on score disagreements and pass a set of point-level features for each sampled point to a simple point head which refines the predictions. This modular-and-hierarchical late fusion approach provides the flexibility of having two independent networks with a minor overhead from a light-weight network. Such approaches are desirable for robotic systems, e.g. autonomous vehicles, for which the computational and memory resources are often limited. Extensive experiments show that AMVNet achieves state-of-the-art results in both the SemanticKITTI and nuScenes benchmark datasets and that our approach outperforms the baseline method of combining the class scores of the projection-based networks.
Representation Extraction and Deep Neural Recommendation for Collaborative Filtering
Venue: arXiv cs.IR 2020
Year: 2020

Many Deep Learning approaches solve complicated classification and regression problems by hierarchically constructing complex features from the raw input data. Although a few works have investigated the application of deep neural networks in recommendation domain, they mostly extract entity features by exploiting unstructured auxiliary data such as visual and textual information, and when it comes to using user-item rating matrix, feature extraction is done by using matrix factorization. As matrix factorization has some limitations, some works have been done to replace it with deep neural network. but these works either need to exploit unstructured data such item's reviews or images, or are specially designed to use implicit data and don't take user-item rating matrix into account. In this paper, we investigate the usage of novel representation learning algorithms to extract users and items representations from rating matrix, and offer a deep neural network for Collaborative Filtering. Our proposed approach is a modular algorithm consisted of two main phases: REpresentation eXtraction and a deep neural NETwork (RexNet). Using two joint and parallel neural networks in RexNet enables it to extract a hierarchy of features for each entity in order to predict the degree of interest of users to items. The resulted predictions are then used for the final recommendation. Unlike other deep learning recommendation approaches, RexNet is not dependent to unstructured auxiliary data such as visual and textual information, instead, it uses only the user-item rate matrix as its input. We evaluated RexNet in an extensive set of experiments against state of the art recommendation methods. The results show that RexNet significantly outperforms the baseline algorithms in a variety of data sets with different degrees of density.
Performance and Complexity of Sequential Decoding of PAC Codes
Venue: arXiv cs.IT 2020
Year: 2020

Performance and complexity of sequential decoding of polarization-adjusted convolutional (PAC) codes is studied. In particular, a performance and computational complexity comparison of PAC codes with 5G polar codes and convolutional codes is given. A method for bounding the complexity of sequential decoding of PAC codes is proposed.
Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Acquiring sufficient ground-truth supervision to train deep visual models has been a bottleneck over the years due to the data-hungry nature of deep learning. This is exacerbated in some structured prediction tasks, such as semantic segmentation, which requires pixel-level annotations. This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation. We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths, which can be used for training more accurate segmentation models. In particular, we devise a graph neural network (GNN) for group-wise semantic mining, wherein input images are represented as graph nodes, and the underlying relations between a pair of images are characterized by an efficient co-attention mechanism. Moreover, in order to prevent the model from paying excessive attention to common semantics only, we further propose a graph dropout layer, encouraging the model to learn more accurate and complete object responses. The whole network is end-to-end trainable by iterative message passing, which propagates interaction cues over the images to progressively improve the performance. We conduct experiments on the popular PASCAL VOC 2012 and COCO benchmarks, and our model yields state-of-the-art performance. Our code is available at: https://github.com/Lixy1997/Group-WSSS.
On the theory and applications of mechanism design and coalitional games in electricity markets
Venue: arXiv cs.GT 2020
Year: 2020

Although the specific structures of electricity markets are diverse around the world, they were all conceived on the premise of predictable, controllable generation with nonnegligible marginal costs. Recent changes, specifically, the increasing renewable integration, have challenged such assumptions. In light of this shift, this thesis intends to devise new frameworks and advance our understanding of the future markets. The first part focuses on mechanism design when the model fully reflects the physics of the grid and the participants. We consider a market that involves continuous goods, general nonconvex constraints, and second stage costs. We then design the payments and conditions under which coalitions cannot influence the outcome. Under the incentive-compatible VCG mechanism, we prove that coalition-proof outcomes are achieved if bids are convex and constraints are polymatroids. By relaxing incentive-compatibility, we investigate core-selecting mechanisms that are coalition-proof without conditions. We show that they generalize the economic rationale of the LMP mechanism, and can approximate truthfulness without the price-taking assumption. Finally, they are budget-balanced. The second part coordinates regional markets to exploit the geographic diversification of renewables. In Europe, reserves remain an exclusive responsibility of regional operators. This limited coordination and the sequential structure hinder the utilization of generation and transmission. To promote reserve exchange, a preemptive model can optimally withdraw inter-area transmission capacity from day-ahead energy for reserves. This bilevel program however does not suggest costs that guarantee coordination. We formulate a new preemptive model that allows us to obtain stable benefits immune to deviations. Our proposal, least-core benefits, achieves minimal stability violation with a tractable computation.
Efficient Complex Event Processing in Information-centric Networking at the Edge
Venue: arXiv cs.DC 2020
Year: 2020

Information-centric Networking (ICN) is an emerging Internet architecture that offers promising features, such as in-network caching and named data addressing, to support the edge computing paradigm, in particular Internet-of-Things (IoT) applications. ICN can benefit from Complex Event Processing (CEP), which is an in-network processing paradigm to specify and perform efficient query operations on data streams. However, integrating CEP into ICN is a challenging task due to the following reasons: (1) typical ICN architectures do not provide support for forwarding and processing continuous data streams; (2) IoT applications often need short response times and require robust event detection, which both are hard to accomplish using existing CEP systems. In this article, we present a novel network architecture, called INetCEP, for efficient CEP-based in-network processing as part of ICN. INetCEP enables efficient data processing in ICN by means of (1) a unified communication model that supports continuous data streams, (2) a meta query language for CEP to specify data processing operations in the data plane, and (3) query processing algorithms to resolve the specified operations. Our experimental results for two IoT use cases and datasets show that INetCEP offers very short response times of up to 73 μs under high workload and is more than 15X faster in terms of forwarding events than the state-of-the-art CEP system Flink. Furthermore, the delivery and processing of complex queries is around 32X faster than Flink and more than 100X faster than a naive pull-based reference approach, while maintaining 100% accuracy.
TaskTracker-tool: a Toolkit for Tracking of Code Snapshots and Activity Data During Solution of Programming Tasks
Venue: arXiv cs.SE 2020
Year: 2020

The process of writing code and use of features in an integrated development environment (IDE) is a fruitful source of data in computing education research. Existing studies use records of students' actions in the IDE, consecutive code snapshots, compilation events, and others, to gain deep insight into the process of student programming. In this paper, we present a set of tools for collecting and processing data of student activity during problem-solving. The first tool is a plugin for IntelliJ-based IDEs (PyCharm, IntelliJ IDEA, CLion). By capturing snapshots of code and IDE interaction data, it allows to analyze the process of writing code in different languages -- Python, Java, Kotlin, and C++. The second tool is designed for the post-processing of data collected by the plugin and is capable of basic analysis and visualization. To validate and showcase the toolkit, we present a dataset collected by our tools. It consists of records of activity and IDE interaction events during solution of programming tasks by 148 participants of different ages and levels of programming experience. We propose several directions for further exploration of the dataset.
Binding and Perspective Taking as Inference in a Generative Neural Network Model
Venue: arXiv cs.LG 2020
Year: 2020

The ability to flexibly bind features into coherent wholes from different perspectives is a hallmark of cognition and intelligence. Importantly, the binding problem is not only relevant for vision but also for general intelligence, sensorimotor integration, event processing, and language. Various artificial neural network models have tackled this problem with dynamic neural fields and related approaches. Here we focus on a generative encoder-decoder architecture that adapts its perspective and binds features by means of retrospective inference. We first train a model to learn sufficiently accurate generative models of dynamic biological motion or other harmonic motion patterns, such as a pendulum. We then scramble the input to a certain extent, possibly vary the perspective onto it, and propagate the prediction error back onto a binding matrix, that is, hidden neural states that determine feature binding. Moreover, we propagate the error further back onto perspective taking neurons, which rotate and translate the input features onto a known frame of reference. Evaluations show that the resulting gradient-based inference process solves the perspective taking and binding problem for known biological motion patterns, essentially yielding a Gestalt perception mechanism. In addition, redundant feature properties and population encodings are shown to be highly useful. While we evaluate the algorithm on biological motion patterns, the principled approach should be applicable to binding and Gestalt perception problems in other domains.
E3D: Event-Based 3D Shape Reconstruction
Venue: arXiv cs.CV 2020
Year: 2020

3D shape reconstruction is a primary component of augmented/virtual reality. Despite being highly advanced, existing solutions based on RGB, RGB-D and Lidar sensors are power and data intensive, which introduces challenges for deployment in edge devices. We approach 3D reconstruction with an event camera, a sensor with significantly lower power, latency and data expense while enabling high dynamic range. While previous event-based 3D reconstruction methods are primarily based on stereo vision, we cast the problem as multi-view shape from silhouette using a monocular event camera. The output from a moving event camera is a sparse point set of space-time gradients, largely sketching scene/object edges and contours. We first introduce an event-to-silhouette (E2S) neural network module to transform a stack of event frames to the corresponding silhouettes, with additional neural branches for camera pose regression. Second, we introduce E3D, which employs a 3D differentiable renderer (PyTorch3D) to enforce cross-view 3D mesh consistency and fine-tune the E2S and pose network. Lastly, we introduce a 3D-to-events simulation pipeline and apply it to publicly available object datasets and generate synthetic event/silhouette training pairs for supervised learning.
PrivFramework: A System for Configurable and Automated Privacy Policy Compliance
Venue: arXiv cs.CR 2020
Year: 2020

Today's massive scale of data collection coupled with recent surges of consumer data leaks has led to increased attention towards data privacy and related risks. Conventional data privacy protection systems focus on reducing custodial risk and lack features empowering data owners. As an end user there are limited options available to specify and enforce one's own privacy preferences over their data. To address these concerns we present PrivFramework, a user-configurable frame-work for automated privacy policy compliance. PrivFramework allows data owners to write powerful privacy policies to protect their data and automatically enforces these policies against analysis programs written in Python. Using static-analysis PrivFramework automatically checks authorized analysis programs for compliance to user-defined policies.
Optimal oracle inequalities for solving projected fixed-point equations
Venue: arXiv cs.LG 2020
Year: 2020

Linear fixed point equations in Hilbert spaces arise in a variety of settings, including reinforcement learning, and computational methods for solving differential and integral equations. We study methods that use a collection of random observations to compute approximate solutions by searching over a known low-dimensional subspace of the Hilbert space. First, we prove an instance-dependent upper bound on the mean-squared error for a linear stochastic approximation scheme that exploits Polyak--Ruppert averaging. This bound consists of two terms: an approximation error term with an instance-dependent approximation factor, and a statistical error term that captures the instance-specific complexity of the noise when projected onto the low-dimensional subspace. Using information theoretic methods, we also establish lower bounds showing that both of these terms cannot be improved, again in an instance-dependent sense. A concrete consequence of our characterization is that the optimal approximation factor in this problem can be much larger than a universal constant. We show how our results precisely characterize the error of a class of temporal difference learning methods for the policy evaluation problem with linear function approximation, establishing their optimality.
Cross-lingual Word Sense Disambiguation using mBERT Embeddings with Syntactic Dependencies
Venue: arXiv cs.CL 2020
Year: 2020

Cross-lingual word sense disambiguation (WSD) tackles the challenge of disambiguating ambiguous words across languages given context. The pre-trained BERT embedding model has been proven to be effective in extracting contextual information of words, and have been incorporated as features into many state-of-the-art WSD systems. In order to investigate how syntactic information can be added into the BERT embeddings to result in both semantics- and syntax-incorporated word embeddings, this project proposes the concatenated embeddings by producing dependency parse tress and encoding the relative relationships of words into the input embeddings. Two methods are also proposed to reduce the size of the concatenated embeddings. The experimental results show that the high dimensionality of the syntax-incorporated embeddings constitute an obstacle for the classification task, which needs to be further addressed in future studies.