OpenRaiser/Intern-Atlas huggingface dataset:
Removing Algorithmic Discrimination (With Minimal Individual Error)
Venue: arXiv cs.AI 2018
Year: 2018

We address the problem of correcting group discriminations within a score function, while minimizing the individual error. Each group is described by a probability density function on the set of profiles. We first solve the problem analytically in the case of two populations, with a uniform bonus-malus on the zones where each population is a majority. We then address the general case of n populations, where the entanglement of populations does not allow a similar analytical solution. We show that an approximate solution with an arbitrarily high level of precision can be computed with linear programming. Finally, we address the inverse problem where the error should not go beyond a certain value and we seek to minimize the discrimination.
Transfer Learning for Illustration Classification
Venue: arXiv cs.CV 2018
Year: 2018

The field of image classification has shown an outstanding success thanks to the development of deep learning techniques. Despite the great performance obtained, most of the work has focused on natural images ignoring other domains like artistic depictions. In this paper, we use transfer learning techniques to propose a new classification network with better performance in illustration images. Starting from the deep convolutional network VGG19, pre-trained with natural images, we propose two novel models which learn object representations in the new domain. Our optimized network will learn new low-level features of the images (colours, edges, textures) while keeping the knowledge of the objects and shapes that it already learned from the ImageNet dataset. Thus, requiring much less data for the training. We propose a novel dataset of illustration images labelled by content where our optimized architecture achieves $\textbf{86.61\%}$ of top-1 and $\textbf{97.21\%}$ of top-5 precision. We additionally demonstrate that our model is still able to recognize objects in photographs.
There goes Wally: Anonymously sharing your location gives you away
Venue: arXiv cs.CR 2018
Year: 2018

With current technology, a number of entities have access to user mobility traces at different levels of spatio-temporal granularity. At the same time, users frequently reveal their location through different means, including geo-tagged social media posts and mobile app usage. Such leaks are often bound to a pseudonym or a fake identity in an attempt to preserve one's privacy. In this work, we investigate how large-scale mobility traces can de-anonymize anonymous location leaks. By mining the country-wide mobility traces of tens of millions of users, we aim to understand how many location leaks are required to uniquely match a trace, how spatio-temporal obfuscation decreases the matching quality, and how the location popularity and time of the leak influence de-anonymization. We also study the mobility characteristics of those individuals whose anonymous leaks are more prone to identification. Finally, by extending our matching methodology to full traces, we show how large-scale human mobility is highly unique. Our quantitative results have implications for the privacy of users' traces, and may serve as a guideline for future policies regarding the management and publication of mobility data.
Performance of Hierarchical Sparse Detectors for Massive MTC
Venue: arXiv cs.IT 2018
Year: 2018

Recently, a new class of so-called \emph{hierarchical thresholding algorithms} was introduced to optimally exploit the sparsity structure in joint user activity and channel detection problems. In this paper, we take a closer look at the user detection performance of such algorithms under noise and relate its performance to the classical block correlation detector with orthogonal signatures. More specifically, we derive a lower bound for the diversity order which, under suitable choice of the signatures, equals that of the block correlation detector. Surprisingly, in specific parameter settings non-orthogonal pilots, i.e. pilots where (cyclically) shifted versions interfere with each other, outperform the block correlation detector. Altogether, we show that, in wide parameter regimes, the hierarchical thresholding detectors behave like the classical correlator with improved detection performance but operate with much less required pilot subcarriers. We provide mathematically rigorous and easy to handle formulas for numerical evaluations and system design. Finally, we evaluate our findings with numerical examples and show that, in a practical parameter setting, a classical pilot channel can accommodate up to three advanced pilot channels with the same performance.
Stabbing Rectangles by Line Segments - How Decomposition Reduces the Shallow-Cell Complexity
Venue: arXiv cs.CG 2018
Year: 2018

We initiate the study of the following natural geometric optimization problem. The input is a set of axis-aligned rectangles in the plane. The objective is to find a set of horizontal line segments of minimum total length so that every rectangle is stabbed by some line segment. A line segment stabs a rectangle if it intersects its left and its right boundary. The problem, which we call Stabbing, can be motivated by a resource allocation problem and has applications in geometric network design. To the best of our knowledge, only special cases of this problem have been considered so far. Stabbing is a weighted geometric set cover problem, which we show to be NP-hard. A constrained variant of Stabbing turns out to be even APX-hard. While for general set cover the best possible approximation ratio is $Θ(\log n)$, it is an important field in geometric approximation algorithms to obtain better ratios for geometric set cover problems. Chan et al. [SODA'12] generalize earlier results by Varadarajan [STOC'10] to obtain sub-logarithmic performances for a broad class of weighted geometric set cover instances that are characterized by having low shallow-cell complexity. The shallow-cell complexity of Stabbing instances, however, can be high so that a direct application of the framework of Chan et al. gives only logarithmic bounds. We still achieve a constant-factor approximation by decomposing general instances into what we call laminar instances that have low enough complexity. Our decomposition technique yields constant-factor approximations also for the variant where rectangles can be stabbed by horizontal and vertical segments and for two further geometric set cover problems.
Revisiting the Importance of Individual Units in CNNs via Ablation
Venue: arXiv cs.CV 2018
Year: 2018

We revisit the importance of the individual units in Convolutional Neural Networks (CNNs) for visual recognition. By conducting unit ablation experiments on CNNs trained on large scale image datasets, we demonstrate that, though ablating any individual unit does not hurt overall classification accuracy, it does lead to significant damage on the accuracy of specific classes. This result shows that an individual unit is specialized to encode information relevant to a subset of classes. We compute the correlation between the accuracy drop under unit ablation and various attributes of an individual unit such as class selectivity and weight L1 norm. We confirm that unit attributes such as class selectivity are a poor predictor for impact on overall accuracy as found previously in recent work \cite{morcos2018importance}. However, our results show that class selectivity along with other attributes are good predictors of the importance of one unit to individual classes. We evaluate the impact of random rotation, batch normalization, and dropout to the importance of units to specific classes. Our results show that units with high selectivity play an important role in network classification power at the individual class level. Understanding and interpreting the behavior of these units is necessary and meaningful.
Multimodal Relational Tensor Network for Sentiment and Emotion Classification
Venue: arXiv cs.CL 2018
Year: 2018

Understanding Affect from video segments has brought researchers from the language, audio and video domains together. Most of the current multimodal research in this area deals with various techniques to fuse the modalities, and mostly treat the segments of a video independently. Motivated by the work of (Zadeh et al., 2017) and (Poria et al., 2017), we present our architecture, Relational Tensor Network, where we use the inter-modal interactions within a segment (intra-segment) and also consider the sequence of segments in a video to model the inter-segment inter-modal interactions. We also generate rich representations of text and audio modalities by leveraging richer audio and linguistic context alongwith fusing fine-grained knowledge based polarity scores from text. We present the results of our model on CMU-MOSEI dataset and show that our model outperforms many baselines and state of the art methods for sentiment classification and emotion recognition.
Findings of the Second Workshop on Neural Machine Translation and Generation
Venue: arXiv cs.CL 2018
Year: 2018

This document describes the findings of the Second Workshop on Neural Machine Translation and Generation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2018). First, we summarize the research trends of papers presented in the proceedings, and note that there is particular interest in linguistic structure, domain adaptation, data augmentation, handling inadequate resources, and analysis of models. Second, we describe the results of the workshop's shared task on efficient neural machine translation, where participants were tasked with creating MT systems that are both accurate and efficient.
Big Data and Privacy Issues for Connected Vehicles in Intelligent Transportation Systems
Venue: arXiv cs.CY 2018
Year: 2018

The evolution of Big Data in large-scale Internet-of-Vehicles has brought forward unprecedented opportunities for a unified management of the transportation sector, and for devising smart Intelligent Transportation Systems. Nevertheless, such form of frequent heterogeneous data collection between the vehicles and numerous applications platforms via diverse radio access technologies has led to a number of security and privacy attacks, and accordingly demands for a secure data collection in such architectures. In this respect, this chapter is primarily an effort to highlight the said challenge to the readers, and to subsequently propose some security requirements and a basic system model for secure Big Data collection in Internet-of-Vehicles. Open research challenges and future directions have also been deliberated.
List-decoding homomorphism codes with arbitrary codomains
Venue: arXiv cs.IT 2018
Year: 2018

The codewords of the homomorphism code $\operatorname{aHom}(G,H)$ are the affine homomorphisms between two finite groups, $G$ and $H$, generalizing Hadamard codes. Following the work of Goldreich--Levin (1989), Grigorescu et al. (2006), Dinur et al. (2008), and Guo and Sudan (2014), we further expand the range of groups for which local list-decoding is possible up to $\textsf{mindist}$, the minimum distance of the code. In particular, for the first time, we do not require either $G$ or $H$ to be solvable. Specifically, we demonstrate a $\operatorname{poly}(1/\varepsilon)$ bound on the list size, i.e., on the number of codewords within distance $(\textsf{mindist}-\varepsilon)$ from any received word, when $G$ is either abelian or an alternating group, and $H$ is an arbitrary (finite or infinite) group. We conjecture that a similar bound holds for all finite simple groups as domains; the alternating groups serve as the first test case. The abelian vs. arbitrary result then permits us to adapt previous techniques to obtain efficient local list-decoding for this case. We also obtain efficient local list-decoding for the permutation representations of alternating groups (i.e., when the codomain is a symmetric group $S_m$) under the restriction that the domain $G=A_n$ is paired with codomain $H=S_m$ satisfying $m < 2^{n-1}/\sqrt{n}$. The limitations on the codomain in the latter case arise from severe technical difficulties stemming from the need to solve the homomorphism extension (HomExt) problem in certain cases; these are addressed in a separate paper (Wuu 2018). However, we also introduce an intermediate "semi-algorithmic" model we call Certificate List-Decoding that bypasses the HomExt bottleneck and works in the alternating vs. arbitrary setting. A certificate list-decoder produces partial homomorphisms that uniquely extend to the homomorphisms in the list.
Novel Sparse-Coded Ambient Backscatter Communication for Massive IoT Connectivity
Venue: arXiv cs.IT 2018
Year: 2018

Low-power ambient backscatter communication (AmBC) relying on radio-frequency (RF) energy harvesting is an energy-efficient solution for batteryless Internet of things (IoT). However, ambient backscatter signals are severely faded by dyadic backscatter channel (DBC), limiting connectivity in conventional orthogonal time-division-based AmBC (TD-AmBC). In order to support massive connectivity in AmBC, we propose sparse-coded AmBC (SC-AmBC) based on non-orthogonal signaling. Sparse code utilizes inherent sparsity of AmBC where power supplies of RF tags rely on ambient RF energy harvesting. Consequently, sparse-coded backscatter modulation algorithm (SC-BMA) can enable non-orthogonal multiple access (NOMA) as well as M-ary modulation for concurrent backscatter transmissions, providing additional diversity gain. These sparse codewords from multiple tags can be efficiently detected at access point (AP) using iterative message passing algorithm (MPA). To overcome DBC along with intersymbol interference (ISI), we propose dyadic channel estimation algorithm (D-CEA) and dyadic MPA (D-MPA) exploiting weighted-sum of the ISI for information exchange in factor graph. Simulation results validate the potential of the SC-AmBC in terms of connectivity, detection performance and sum throughput.
DeepFirearm: Learning Discriminative Feature Representation for Fine-grained Firearm Retrieval
Venue: arXiv cs.CV 2018
Year: 2018

There are great demands for automatically regulating inappropriate appearance of shocking firearm images in social media or identifying firearm types in forensics. Image retrieval techniques have great potential to solve these problems. To facilitate research in this area, we introduce Firearm 14k, a large dataset consisting of over 14,000 images in 167 categories. It can be used for both fine-grained recognition and retrieval of firearm images. Recent advances in image retrieval are mainly driven by fine-tuning state-of-the-art convolutional neural networks for retrieval task. The conventional single margin contrastive loss, known for its simplicity and good performance, has been widely used. We find that it performs poorly on the Firearm 14k dataset due to: (1) Loss contributed by positive and negative image pairs is unbalanced during training process. (2) A huge domain gap exists between this dataset and ImageNet. We propose to deal with the unbalanced loss by employing a double margin contrastive loss. We tackle the domain gap issue with a two-stage training strategy, where we first fine-tune the network for classification, and then fine-tune it for retrieval. Experimental results show that our approach outperforms the conventional single margin approach by a large margin (up to 88.5% relative improvement) and even surpasses the strong triplet-loss-based approach.
Machine learning-based colon deformation estimation method for colonoscope tracking
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents a colon deformation estimation method, which can be used to estimate colon deformations during colonoscope insertions. Colonoscope tracking or navigation system that navigates a physician to polyp positions during a colonoscope insertion is required to reduce complications such as colon perforation. A previous colonoscope tracking method obtains a colonoscope position in the colon by registering a colonoscope shape and a colon shape. The colonoscope shape is obtained using an electromagnetic sensor, and the colon shape is obtained from a CT volume. However, large tracking errors were observed due to colon deformations occurred during colonoscope insertions. Such deformations make the registration difficult. Because the colon deformation is caused by a colonoscope, there is a strong relationship between the colon deformation and the colonoscope shape. An estimation method of colon deformations occur during colonoscope insertions is necessary to reduce tracking errors. We propose a colon deformation estimation method. This method is used to estimate a deformed colon shape from a colonoscope shape. We use the regression forests algorithm to estimate a deformed colon shape. The regression forests algorithm is trained using pairs of colon and colonoscope shapes, which contains deformations occur during colonoscope insertions. As a preliminary study, we utilized the method to estimate deformations of a colon phantom. In our experiments, the proposed method correctly estimated deformed colon phantom shapes.
Generating Image Sequence from Description with LSTM Conditional GAN
Venue: arXiv cs.CV 2018
Year: 2018

Generating images from word descriptions is a challenging task. Generative adversarial networks(GANs) are shown to be able to generate realistic images of real-life objects. In this paper, we propose a new neural network architecture of LSTM Conditional Generative Adversarial Networks to generate images of real-life objects. Our proposed model is trained on the Oxford-102 Flowers and Caltech-UCSD Birds-200-2011 datasets. We demonstrate that our proposed model produces the better results surpassing other state-of-art approaches.
An Explicit Construction of Systematic MDS Codes with Small Sub-packetization for All-Node Repair
Venue: arXiv cs.IT 2018
Year: 2018

An explicit construction of systematic MDS codes, called HashTag+ codes, with arbitrary sub-packetization level for all-node repair is proposed. It is shown that even for small sub-packetization levels, HashTag+ codes achieve the optimal MSR point for repair of any parity node, while the repair bandwidth for a single systematic node depends on the sub-packetization level. Compared to other codes in the literature, HashTag+ codes provide from 20% to 40% savings in the average amount of data accessed and transferred during repair.
Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Uncertainty estimates of modern neuronal networks provide additional information next to the computed predictions and are thus expected to improve the understanding of the underlying model. Reliable uncertainties are particularly interesting for safety-critical computer-assisted applications in medicine, e.g., neurosurgical interventions and radiotherapy planning. We propose an uncertainty-driven sanity check for the identification of segmentation results that need particular expert review. Our method uses a fully-convolutional neural network and computes uncertainty estimates by the principle of Monte Carlo dropout. We evaluate the performance of the proposed method on a clinical dataset with 30 postoperative brain tumor images. The method can segment the highly inhomogeneous resection cavities accurately (Dice coefficients 0.792 $\pm$ 0.154). Furthermore, the proposed sanity check is able to detect the worst segmentation and three out of the four outliers. The results highlight the potential of using the additional information from the model's parameter uncertainty to validate the segmentation performance of a deep learning model.
VTrails: Inferring Vessels with Geodesic Connectivity Trees
Venue: arXiv cs.CV 2018
Year: 2018

The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative features such as spatial location, direction and scale. In this paper we present an end-to-end approach to extract an acyclic vascular tree from angiographic data by solving a connectivity-enforcing anisotropic fast marching over a voxel-wise tensor field representing the orientation of the underlying vascular tree. The method is validated using synthetic and real vascular images. We compare VTrails against classical and state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field as proof of concept. VTrails performance is evaluated on images with different levels of degradation: we verify that the extracted vascular network is an acyclic graph (i.e. a tree), and we report the extraction accuracy, precision and recall.
A Publish/Subscribe QoS-aware Framework for Massive IoT Traffic Orchestration
Venue: arXiv cs.NI 2018
Year: 2018

Internet of Things (IoT) application deployment requires the allocation of resources such as virtual machines, storage, and network elements that must be deployed over distinct infrastructures such as cloud computing, Cloud of Things (CoT), datacenters and backbone networks. For massive IoT data acquisition, a gateway-based data aggregation approach is commonly used featuring sensor/ actuator seamless access and providing cache/ buffering and preprocessing functionality. In this perspective , gateways acting as producers need to allocate network resources to send IoT data to consumers. In this paper, it is proposed a Publish/-Subscribe (PubSub) quality of service (QoS) aware framework (PSIoT-Orch) that orchestrates IoT traffic and allocates network resources between aggregates and consumers for massive IoT traffic. PSIoT-Orch schedules IoT data flows based on its configured QoS requirements. Additionally , the framework allocates network resources (LSP/ bandwidth) over a controlled backbone network with limited and constrained resources between IoT data users and consumers. Network resources are allocated using a Bandwidth Allocation Model (BAM) to achieve efficient network resource allocation for scheduled IoT data streams. PSIoT-Orch adopts an ICN (Information-Centric Network) PubSub architecture approach to handle IoT data transfers requests among framework components. The proposed framework aims at gathering the inherent advantages of an ICN-centric approach using a PubSub message scheme while allocating resources efficiently keeping QoS awareness and handling restricted network resources (bandwidth) for massive IoT traffic.
Base Station Switch-off with Mutual Repulsion in 5G Massive MIMO Networks
Venue: arXiv cs.NI 2018
Year: 2018

When small cells are densely deployed in the fifth generation (5G) cellular networks, switching off a part of base stations (BSs) is a practical approach for saving energy consumption considering the variation of traffic load. The small cell network with the massive multi-input multi-output (massive MIMO) system is analyzed in this paper due to the dense deployment and low power consumption. Based on the BS switch-off strategy with distance constraints, the energy and coverage efficiency are investigated to illustrate the performance of the BS switch-off strategy. Simulation results indicate that the energy efficiency and coverage efficiency of the proposed strategy are better than the random strategy. The energy efficiency increases with the BS intensity and the minimal distance, and a maximum coverage efficiency can be achieved with the increase of the BS intensity and the minimum distance. In this case, the optimal BS switch-off strategy can be designed under this work in the actual scene.