OpenRaiser/Intern-Atlas huggingface dataset:
What is Fair? Exploring Pareto-Efficiency for Fairness Constrained Classifiers
Venue: arXiv cs.LG 2019
Year: 2019

The potential for learned models to amplify existing societal biases has been broadly recognized. Fairness-aware classifier constraints, which apply equality metrics of performance across subgroups defined on sensitive attributes such as race and gender, seek to rectify inequity but can yield non-uniform degradation in performance for skewed datasets. In certain domains, imbalanced degradation of performance can yield another form of unintentional bias. In the spirit of constructing fairness-aware algorithms as societal imperative, we explore an alternative: Pareto-Efficient Fairness (PEF). Theoretically, we prove that PEF identifies the operating point on the Pareto curve of subgroup performances closest to the fairness hyperplane, maximizing multiple subgroup accuracy. Empirically we demonstrate that PEF outperforms by achieving Pareto levels in accuracy for all subgroups compared to strict fairness constraints in several UCI datasets.
Improved Local Computation Algorithm for Set Cover via Sparsification
Venue: arXiv cs.DS 2019
Year: 2019

We design a Local Computation Algorithm (LCA) for the set cover problem. Given a set system where each set has size at most $s$ and each element is contained in at most $t$ sets, the algorithm reports whether a given set is in some fixed set cover whose expected size is $O(\log{s})$ times the minimum fractional set cover value. Our algorithm requires $s^{O(\log{s})} t^{O(\log{s} \cdot (\log \log{s} + \log \log{t}))}$ queries. This result improves upon the application of the reduction of [Parnas and Ron, TCS'07] on the result of [Kuhn et al., SODA'06], which leads to a query complexity of $(st)^{O(\log{s} \cdot \log{t})}$. To obtain this result, we design a parallel set cover algorithm that admits an efficient simulation in the LCA model by using a sparsification technique introduced in [Ghaffari and Uitto, SODA'19] for the maximal independent set problem. The parallel algorithm adds a random subset of the sets to the solution in a style similar to the PRAM algorithm of [Berger et al., FOCS'89]. However, our algorithm differs in the way that it never revokes its decisions, which results in a fewer number of adaptive rounds. This requires a novel approximation analysis which might be of independent interest.
Sobolev Independence Criterion
Venue: arXiv cs.LG 2019
Year: 2019

We propose the Sobolev Independence Criterion (SIC), an interpretable dependency measure between a high dimensional random variable X and a response variable Y . SIC decomposes to the sum of feature importance scores and hence can be used for nonlinear feature selection. SIC can be seen as a gradient regularized Integral Probability Metric (IPM) between the joint distribution of the two random variables and the product of their marginals. We use sparsity inducing gradient penalties to promote input sparsity of the critic of the IPM. In the kernel version we show that SIC can be cast as a convex optimization problem by introducing auxiliary variables that play an important role in feature selection as they are normalized feature importance scores. We then present a neural version of SIC where the critic is parameterized as a homogeneous neural network, improving its representation power as well as its interpretability. We conduct experiments validating SIC for feature selection in synthetic and real-world experiments. We show that SIC enables reliable and interpretable discoveries, when used in conjunction with the holdout randomization test and knockoffs to control the False Discovery Rate. Code is available at http://github.com/ibm/sic.
Rate Distortion Study for Time-Varying Autoregressive Gaussian Process
Venue: arXiv cs.IT 2019
Year: 2019

Rate-distortion formulation is the information-theoretic approach to the study of signal encoding systems. Since a more general approach to model the nonstationarity exhibited by real-world signals is to use appropriately fitted time varying autoregressive (TVAR) models, we have investigated the rate-distortion function for the class of time varying nonstationary signals. In this study, we present formulations of the rate-distortion function for the Gaussian TVAR processes. The rate-distortion function can serve as an information-theoretic bound on the performance achievable by source encoding techniques when the processing signal is represented exclusively by a Gaussian TVAR model.
Cross-Domain Face Synthesis using a Controllable GAN
Venue: arXiv cs.CV 2019
Year: 2019

The performance of face recognition (FR) systems applied in video surveillance has been shown to improve when the design data is augmented through synthetic face generation. This is true, for instance, with pair-wise matchers (e.g., deep Siamese networks) that typically rely on a reference gallery with one still image per individual. However, generating synthetic images in the source domain may not improve the performance during operations due to the domain shift w.r.t. the target domain. Moreover, despite the emergence of Generative Adversarial Networks (GANs) for realistic synthetic generation, it is often difficult to control the conditions under which synthetic faces are generated. In this paper, a cross-domain face synthesis approach is proposed that integrates a new Controllable GAN (C-GAN). It employs an off-the-shelf 3D face model as a simulator to generate face images under various poses. The simulated images and noise are input to the C-GAN for realism refinement which employs an additional adversarial game as a third player to preserve the identity and specific facial attributes of the refined images. This allows generating realistic synthetic face images that reflects capture conditions in the target domain while controlling the GAN output to generate faces under desired pose conditions. Experiments were performed using videos from the Chokepoint and COX-S2V datasets, and a deep Siamese network for FR with a single reference still per person. Results indicate that the proposed approach can provide a higher level of accuracy compared to the current state-of-the-art approaches for synthetic data augmentation.
A Review of methods for Textureless Object Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Textureless object recognition has become a significant task in Computer Vision with the advent of Robotics and its applications in manufacturing sector. It has been very challenging to get good performance because of its lack of discriminative features and reflectance properties. Hence, the approaches used for textured objects cannot be applied for textureless objects. A lot of work has been done in the last 20 years, especially in the recent 5 years after the TLess and other textureless dataset were introduced. In our research, we plan to combine image processing techniques (for feature enhancement) along with deep learning techniques (for object recognition). Here we present an overview of the various existing work in the field of textureless object recognition, which can be broadly classified into View-based, Feature-based and Shape-based. We have also added a review of few of the research papers submitted at the International Conference on Smart Multimedia, 2018. Index terms: Computer Vision, Textureless object detection, Textureless object recognition, Feature-based, Edge detection, Deep Learning
Outsourcing Computation: the Minimal Refereed Mechanism
Venue: arXiv cs.GT 2019
Year: 2019

We consider a setting where a verifier with limited computation power delegates a resource intensive computation task---which requires a $T\times S$ computation tableau---to two provers where the provers are rational in that each prover maximizes their own payoff---taking into account losses incurred by the cost of computation. We design a mechanism called the Minimal Refereed Mechanism (MRM) such that if the verifier has $O(\log S + \log T)$ time and $O(\log S + \log T)$ space computation power, then both provers will provide a honest result without the verifier putting any effort to verify the results. The amount of computation required for the provers (and thus the cost) is a multiplicative $\log S$-factor more than the computation itself, making this schema efficient especially for low-space computations.
RLINK: Deep Reinforcement Learning for User Identity Linkage
Venue: arXiv cs.LG 2019
Year: 2019

User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise similarity between identities from different SN, predicting the label of identity pairs or selecting the most relevant identity pair based on the similarity scores. However, most of these methods ignore the results of previously matched identities, which could contribute to the linkage in following matching steps. To address this problem, we convert user identity linkage into a sequence decision problem and propose a reinforcement learning model to optimize the linkage strategy from the global perspective. Our method makes full use of both the social network structure and the history matched identities, and explores the long-term influence of current matching on subsequent decisions. We conduct experiments on different types of datasets, the results show that our method achieves better performance than other state-of-the-art methods.
Duckiefloat: a Collision-Tolerant Resource-Constrained Blimp for Long-Term Autonomy in Subterranean Environments
Venue: arXiv cs.RO 2019
Year: 2019

There are several challenges for search and rescue robots: mobility, perception, autonomy, and communication. Inspired by the DARPA Subterranean (SubT) Challenge, we propose an autonomous blimp robot, which has the advantages of low power consumption and collision-tolerance compared to other aerial vehicles like drones. This is important for search and rescue tasks that usually last for one or more hours. However, the underground constrained passages limit the size of blimp envelope and its payload, making the proposed system resource-constrained. Therefore, a careful design consideration is needed to build a blimp system with on-board artifact search and SLAM. In order to reach long-term operation, a failure-aware algorithm with minimal communication to human supervisor to have situational awareness and send control signals to the blimp when needed.
Image-Conditioned Graph Generation for Road Network Extraction
Venue: arXiv cs.LG 2019
Year: 2019

Deep generative models for graphs have shown great promise in the area of drug design, but have so far found little application beyond generating graph-structured molecules. In this work, we demonstrate a proof of concept for the challenging task of road network extraction from image data. This task can be framed as image-conditioned graph generation, for which we develop the Generative Graph Transformer (GGT), a deep autoregressive model that makes use of attention mechanisms for image conditioning and the recurrent generation of graphs. We benchmark GGT on the application of road network extraction from semantic segmentation data. For this, we introduce the Toulouse Road Network dataset, based on real-world publicly-available data. We further propose the StreetMover distance: a metric based on the Sinkhorn distance for effectively evaluating the quality of road network generation. The code and dataset are publicly available.
Great New Design: How Do We Talk about Media Architecture in Social Media
Venue: arXiv cs.CL 2019
Year: 2019

In social media, we communicate through pictures, videos, short codes, links, partial phrases. It is a rich, and digitally documented communication channel that relies on a multitude of media and forms. These channels are sorted by algorithms as organizers of discourse, mostly with the goal of channeling attention. In this research, we used Twitter to study the way Media Architecture is discussed within the community of architects, designers, researchers and policy makers. We look at the way they spontaneously share opinions on their engagement with digital infrastructures, networked places and hybrid public spaces. What can we do with all those opinions? We propose here the use of text-mining and machine learning techniques to identify important concepts and patterns in this prolific communication stream. We discuss how such techniques could inform the practice and emergence of future trends.
Direct N-body application on low-power and energy-efficient parallel architectures
Venue: arXiv cs.PF 2019
Year: 2019

The aim of this work is to quantitatively evaluate the impact of computation on the energy consumption on ARM MPSoC platforms, exploiting CPUs, embedded GPUs and FPGAs. One of them possibly represents the future of High Performance Computing systems: a prototype of an Exascale supercomputer. Performance and energy measurements are made using a state-of-the-art direct $N$-body code from the astrophysical domain. We provide a comparison of the time-to-solution and energy delay product metrics, for different software configurations. We have shown that FPGA technologies can be used for application kernel acceleration and are emerging as a promising alternative to "traditional" technologies for HPC, which purely focus on peak-performance than on power-efficiency.
ALLSAT compressed with wildcards: Frequent Set Mining
Venue: arXiv cs.DS 2019
Year: 2019

Like any simplicial complex the simplicial complex of all frequent sets can be compressed with wildcards once the maximal frequent sets (=facets) are known. Namely, the task (a particular kind of ALLSAT problem) is achieved by the author's recent algorithm Facets-To-Faces. But how to get the facets in the first place? The novel algorithm Find-All-Facets determines all facets of any (decidable) finite simplicial complex by replacing costly hypergraph dualization (Dualize+Advance and its variants) with the cheaper calculation of the minimal members of certain set families. The latter task is sped up by Vertical Layout. While all of this concerns arbitrary simplicial complexes, the impact to Frequent Set Mining (FSM) seems particularly high.
Run-time Parameter Sensitivity Analysis Optimizations
Venue: arXiv cs.DC 2019
Year: 2019

Efficient execution of parameter sensitivity analysis (SA) is critical to allow for its routinely use. The pathology image processing application investigated in this work processes high-resolution whole-slide cancer tissue images from large datasets to characterize and classify the disease. However, the application is parameterized and changes in parameter values may significantly affect its results. Thus, understanding the impact of parameters to the output using SA is important to draw reliable scientific conclusions. The execution of the application is rather compute intensive, and a SA requires it to process the input data multiple times as parameter values are systematically varied. Optimizing this process is then important to allow for SA to be executed with large datasets. In this work, we employ a distributed computing system with novel computation reuse optimizations to accelerate SA. The new computation reuse strategy can maximize reuse even with limited memory availability where previous approaches would not be able to fully take advantage of reuse. The proposed solution was evaluated on an environment with 256 nodes (7168 CPU-cores) attaining a parallel efficiency of over 92%, and improving the previous reuse strategies in up to 2.8x.
On the Interaction Between Deep Detectors and Siamese Trackers in Video Surveillance
Venue: arXiv cs.CV 2019
Year: 2019

Visual object tracking is an important function in many real-time video surveillance applications, such as localization and spatio-temporal recognition of persons. In real-world applications, an object detector and tracker must interact on a periodic basis to discover new objects, and thereby to initiate tracks. Periodic interactions with the detector can also allow the tracker to validate and/or update its object template with new bounding boxes. However, bounding boxes provided by a state-of-the-art detector are noisy, due to changes in appearance, background and occlusion, which can cause the tracker to drift. Moreover, CNN-based detectors can provide a high level of accuracy at the expense of computational complexity, so interactions should be minimized for real-time applications. In this paper, a new approach is proposed to manage detector-tracker interactions for trackers from the Siamese-FC family. By integrating a change detection mechanism into a deep Siamese-FC tracker, its template can be adapted in response to changes in a target's appearance that lead to drifts during tracking. An abrupt change detection triggers an update of tracker template using the bounding box produced by the detector, while in the case of a gradual change, the detector is used to update an evolving set of templates for robust matching. Experiments were performed using state-of-the-art Siamese-FC trackers and the YOLOv3 detector on a subset of videos from the OTB-100 dataset that mimic video surveillance scenarios. Results highlight the importance for reliable VOT of using accurate detectors. They also indicate that our adaptive Siamese trackers are robust to noisy object detections, and can significantly improve the performance of Siamese-FC tracking.
On Games and Computation
Venue: arXiv cs.LO 2019
Year: 2019

We introduce and investigate a range of general notions of a game. Our principal notion is based on a set of agents modifying a relational structure in a discrete evolution sequence. We also introduce and study a variety of ways to model partial and erroneous information in the setting. We discuss the connection of the related general setting to logic and computation formalisms, with emphasis on the recently introduced Turing-complete logic based on game-theoretic semantics.
Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning
Venue: arXiv cs.LG 2019
Year: 2019

Task-oriented dialog presents a difficult challenge encompassing multiple problems including multi-turn language understanding and generation, knowledge retrieval and reasoning, and action prediction. Modern dialog systems typically begin by converting conversation history to a symbolic object referred to as belief state by using supervised learning. The belief state is then used to reason on an external knowledge source whose result along with the conversation history is used in action prediction and response generation tasks independently. Such a pipeline of individually optimized components not only makes the development process cumbersome but also makes it non-trivial to leverage session-level user reinforcement signals. In this paper, we develop Neural Assistant: a single neural network model that takes conversation history and an external knowledge source as input and jointly produces both text response and action to be taken by the system as output. The model learns to reason on the provided knowledge source with weak supervision signal coming from the text generation and the action prediction tasks, hence removing the need for belief state annotations. In the MultiWOZ dataset, we study the effect of distant supervision, and the size of knowledge base on model performance. We find that the Neural Assistant without belief states is able to incorporate external knowledge information achieving higher factual accuracy scores compared to Transformer. In settings comparable to reported baseline systems, Neural Assistant when provided with oracle belief state significantly improves language generation performance.
SignCol: Open-Source Software for Collecting Sign Language Gestures
Venue: arXiv cs.HC 2019
Year: 2019

Sign(ed) languages use gestures, such as hand or head movements, for communication. Sign language recognition is an assistive technology for individuals with hearing disability and its goal is to improve such individuals' life quality by facilitating their social involvement. Since sign languages are vastly varied in alphabets, as known as signs, a sign recognition software should be capable of handling eight different types of sign combinations, e.g. numbers, letters, words and sentences. Due to the intrinsic complexity and diversity of symbolic gestures, recognition algorithms need a comprehensive visual dataset to learn by. In this paper, we describe the design and implementation of a Microsoft Kinect-based open source software, called SignCol, for capturing and saving the gestures used in sign languages. Our work supports a multi-language database and reports the recorded items statistics. SignCol can capture and store colored(RGB) frames, depth frames, infrared frames, body index frames, coordinate mapped color-body frames, skeleton information of each frame and camera parameters simultaneously.
Text-to-image synthesis method evaluation based on visual patterns
Venue: arXiv cs.LG 2019
Year: 2019

A commonly used evaluation metric for text-to-image synthesis is the Inception score (IS) \cite{inceptionscore}, which has been shown to be a quality metric that correlates well with human judgment. However, IS does not reveal properties of the generated images indicating the ability of a text-to-image synthesis method to correctly convey semantics of the input text descriptions. In this paper, we introduce an evaluation metric and a visual evaluation method allowing for the simultaneous estimation of the realism, variety and semantic accuracy of generated images. The proposed method uses a pre-trained Inception network \cite{inceptionnet} to produce high dimensional representations for both real and generated images. These image representations are then visualized in a $2$-dimensional feature space defined by the t-distributed Stochastic Neighbor Embedding (t-SNE) \cite{tsne}. Visual concepts are determined by clustering the real image representations, and are subsequently used to evaluate the similarity of the generated images to the real ones by classifying them to the closest visual concept. The resulting classification accuracy is shown to be a effective gauge for the semantic accuracy of text-to-image synthesis methods.
RankML: a Meta Learning-Based Approach for Pre-Ranking Machine Learning Pipelines
Venue: arXiv cs.LG 2019
Year: 2019

The explosion of digital data has created multiple opportunities for organizations and individuals to leverage machine learning (ML) to transform the way they operate. However, the shortage of experts in the field of machine learning -- data scientists -- is often a setback to the use of ML. In an attempt to alleviate this shortage, multiple approaches for the automation of machine learning have been proposed in recent years. While these approaches are effective, they often require a great deal of time and computing resources. In this study, we propose RankML, a meta-learning based approach for predicting the performance of whole machine learning pipelines. Given a previously-unseen dataset, a performance metric, and a set of candidate pipelines, RankML immediately produces a ranked list of all pipelines based on their predicted performance. Extensive evaluation on 244 datasets, both in regression and classification tasks, shows that our approach either outperforms or is comparable to state-of-the-art, computationally heavy approaches while requiring a fraction of the time and computational cost.