OpenRaiser/Intern-Atlas huggingface dataset:
Reconstruction of r-Regular Objects from Trinary Images
Venue: arXiv cs.CG 2019
Year: 2019

We study digital images of r-regular objects where a pixel is black if it is completely inside the object, white if it is completely inside the complement of the object, and grey otherwise. We call such images trinary. We discuss possible configurations of pixels in trinary images of r-regular objects at certain resolutions and propose a method for reconstructing objects from such images. We show that the reconstructed object is close to the original object in Hausdorff norm, and that there is a homeomorphism of the plane taking the reconstructed set to the original.
A Tight Runtime Analysis for the cGA on Jump Functions---EDAs Can Cross Fitness Valleys at No Extra Cost
Venue: arXiv cs.NE 2019
Year: 2019

We prove that the compact genetic algorithm (cGA) with hypothetical population size $μ= Ω(\sqrt n \log n) \cap \text{poly}(n)$ with high probability finds the optimum of any $n$-dimensional jump function with jump size $k < \frac 1 {20} \ln n$ in $O(μ\sqrt n)$ iterations. Since it is known that the cGA with high probability needs at least $Ω(μ\sqrt n + n \log n)$ iterations to optimize the unimodal OneMax function, our result shows that the cGA in contrast to most classic evolutionary algorithms here is able to cross moderate-sized valleys of low fitness at no extra cost. Our runtime guarantee improves over the recent upper bound $O(μn^{1.5} \log n)$ valid for $μ= Ω(n^{3.5+\varepsilon})$ of Hasenöhrl and Sutton (GECCO 2018). For the best choice of the hypothetical population size, this result gives a runtime guarantee of $O(n^{5+\varepsilon})$, whereas ours gives $O(n \log n)$. We also provide a simple general method based on parallel runs that, under mild conditions, (i)~overcomes the need to specify a suitable population size, but gives a performance close to the one stemming from the best-possible population size, and (ii)~transforms EDAs with high-probability performance guarantees into EDAs with similar bounds on the expected runtime.
Constructions of MDS convolutional codes using superregular matrices
Venue: arXiv cs.IT 2019
Year: 2019

Maximum distance separable convolutional codes are the codes that present best performance in error correction among all convolutional codes with certain rate and degree. In this paper, we show that taking the constant matrix coefficients of a polynomial matrix as submatrices of a superregular matrix, we obtain a column reduced generator matrix of an MDS convolutional code with a certain rate and a certain degree. We then present two novel constructions that fulfill these conditions by considering two types of superregular matrices.
Learning Accurate, Comfortable and Human-like Driving
Venue: arXiv cs.CV 2019
Year: 2019

Autonomous vehicles are more likely to be accepted if they drive accurately, comfortably, but also similar to how human drivers would. This is especially true when autonomous and human-driven vehicles need to share the same road. The main research focus thus far, however, is still on improving driving accuracy only. This paper formalizes the three concerns with the aim of accurate, comfortable and human-like driving. Three contributions are made in this paper. First, numerical map data from HERE Technologies are employed for more accurate driving; a set of map features which are believed to be relevant to driving are engineered to navigate better. Second, the learning procedure is improved from a pointwise prediction to a sequence-based prediction and passengers' comfort measures are embedded into the learning algorithm. Finally, we take advantage of the advances in adversary learning to learn human-like driving; specifically, the standard L1 or L2 loss is augmented by an adversary loss which is based on a discriminator trained to distinguish between human driving and machine driving. Our model is trained and evaluated on the Drive360 dataset, which features 60 hours and 3000 km of real-world driving data. Extensive experiments show that our driving model is more accurate, more comfortable and behaves more like a human driver than previous methods. The resources of this work will be released on the project page.
Effect of Personality Traits on UX Evaluation Metrics: A Study on Usability Issues, Valence-Arousal and Skin Conductance
Venue: arXiv cs.HC 2019
Year: 2019

Personality affect the way someone feels or acts. This paper examines the effect of personality traits, as operationalized by the Big-five questionnaire, on the number, type, and severity of the identified usability issues, physiological signals (skin conductance), and subjective emotional ratings (valence-arousal).Twenty-four users interacted with a web service and then participated in a retrospective thinking aloud session. Results revealed that the number of usability issues is significantly affected by the Openness trait. Emotional Stability significantly affects the type of reported usability issues. Problem severity is not affected by any trait. Valence ratings are significantly affected by Conscientiousness, whereas Agreeableness, Emotional Stability and Openness significantly affect arousal ratings. Finally, Openness has a significant effect on the number of detected peaks in user's skin conductance.
Weighted Multisource Tradaboost
Venue: arXiv cs.LG 2019
Year: 2019

In this paper we propose an improved method for transfer learning that takes into account the balance between target and source data. This method builds on the state-of-the-art Multisource Tradaboost, but weighs the importance of each datapoint taking into account the amount of target and source data available. A comparative study is then presented exposing the performance of four transfer learning methods as well as the proposed Weighted Multisource Tradaboost. The experimental results show that the proposed method is able to outperform the base method as the number of target samples increase. These results are promising in the sense that source-target ratio weighing may be a path to improve current methods of transfer learning. However, against the asymptotic conjecture, all transfer learning methods tested in this work get outperformed by a no-transfer SVM for large number on target samples.
Nuts and Bolts of a Realistic Stochastic Geometric Analysis of mmWave HetNets: Hardware Impairments and Channel Aging
Venue: arXiv cs.IT 2019
Year: 2019

Motivated by heterogeneous networks (HetNets) design in improving coverage and by millimeter-wave (mmWave) transmission offering an abundance of extra spectrum, we present a general analytical framework shedding light to the downlink of realistic mmWave HetNets consisting of $K$ tiers of randomly located base stations (BSs). Specifically, we model, by virtue of stochastic geometry tools, the \textit{multi-tier} \textit{multi-user multiple-input multiple-output (MU-MIMO) mmWave} network degraded by the inevitable \textit{residual additive transceiver hardware impairments (RATHIs)} and \textit{channel aging}. Given this setting, we derive the coverage probability and the area spectral efficiency (ASE), and we subsequently evaluate the impact of residual transceiver hardware impairments (RTHIs) and channel aging on these metrics. Different path-loss laws for line-of-sight (LOS) and non-line-of-sight (NLOS) are accounted for the analysis, which are among the distinguishing features of mmWave systems. Among the findings, we show that the RATHIs have a meaningful impact at the high signal-to-noise ratio (SNR) regime, while the transmit additive distortion degrades further than the receive distortion the system performance. Moreover, serving fewer users proves to be preferable, and the more directive the mmWaves are, the higher the ASE becomes.
Designing Succinct Secondary Indexing Mechanism by Exploiting Column Correlations (Extended Version)
Venue: arXiv cs.DB 2019
Year: 2019

Database administrators construct secondary indexes on data tables to accelerate query processing in relational database management systems (RDBMSs). These indexes are built on top of the most frequently queried columns according to the data statistics. Unfortunately, maintaining multiple secondary indexes in the same database can be extremely space consuming, causing significant performance degradation due to the potential exhaustion of memory space. In this paper, we demonstrate that there exist many opportunities to exploit column correlations for accelerating data access. We propose HERMIT, a succinct secondary indexing mechanism for modern RDBMSs. HERMIT judiciously leverages the rich soft functional dependencies hidden among columns to prune out redundant structures for indexed key access. Instead of building a complete index that stores every single entry in the key columns, HERMIT navigates any incoming key access queries to an existing index built on the correlated columns. This is achieved through the Tiered Regression Search Tree (TRS-Tree), a succinct, ML-enhanced data structure that performs fast curve fitting to adaptively and dynamically capture both column correlations and outliers. Our extensive experimental study in two different RDBMSs have confirmed that HERMIT can significantly reduce space consumption with limited performance overhead in terms of query response time and index maintenance time, especially when supporting complex range queries.
Infringement of Tweets Geo-Location Privacy: an approach based on Graph Convolutional Neural Networks
Venue: arXiv cs.SI 2019
Year: 2019

The tremendous popularity gained by Online Social Networks (OSNs) raises natural concerns about user privacy in social media platforms. Though users in OSNs can tune their privacy by deliberately deciding what to share, the interaction with other individuals within the social network can expose, and eventually disclose, sensitive information. Among all the sharable personal data, geo-location is particularly interesting. On one hand, users tend to consider their current location as a very sensitive information, avoiding to share it most of the time. On the other hand, service providers are interested to extract and utilize geo-tagged data to offer tailored services. In this work, we consider the problem of inferring the current location of a user utilizing only the available information of other social contacts in the OSN. For this purpose, we employ a graph-based deep learning architecture to learn a model between the users' known and unknown geo-location during a considered period of time. As a study case, we consider Twitter, where the user generated content (i.e., tweet) can embed user's current location. Our experiments validate our approach and further confirm the concern related to data privacy in OSNs. Results show the presence of a critical-mass phenomenon, i.e., if at least 10% of the users provide their tweets with geo-tags, then the privacy of all the remaining users is seriously put at risk. In fact, our approach is able to localize almost 50% of the tweets with an accuracy below 1km relying only on a small percentage of available information.
Mimicking the In-Camera Color Pipeline for Camera-Aware Object Compositing
Venue: arXiv cs.CV 2019
Year: 2019

We present a method for compositing virtual objects into a photograph such that the object colors appear to have been processed by the photo's camera imaging pipeline. Compositing in such a camera-aware manner is essential for high realism, and it requires the color transformation in the photo's pipeline to be inferred, which is challenging due to the inherent one-to-many mapping that exists from a scene to a photo. To address this problem for the case of a single photo taken from an unknown camera, we propose a dual-learning approach in which the reverse color transformation (from the photo to the scene) is jointly estimated. Learning of the reverse transformation is used to facilitate learning of the forward mapping, by enforcing cycle consistency of the two processes. We additionally employ a feature sharing schema to extract evidence from the target photo in the reverse mapping to guide the forward color transformation. Our dual-learning approach achieves object compositing results that surpass those of alternative techniques.
Improving Route Choice Models by Incorporating Contextual Factors via Knowledge Distillation
Venue: arXiv cs.LG 2019
Year: 2019

Route Choice Models predict the route choices of travelers traversing an urban area. Most of the route choice models link route characteristics of alternative routes to those chosen by the drivers. The models play an important role in prediction of traffic levels on different routes and thus assist in development of efficient traffic management strategies that result in minimizing traffic delay and maximizing effective utilization of transport system. High fidelity route choice models are required to predict traffic levels with higher accuracy. Existing route choice models do not take into account dynamic contextual conditions such as the occurrence of an accident, the socio-cultural and economic background of drivers, other human behaviors, the dynamic personal risk level, etc. As a result, they can only make predictions at an aggregate level and for a fixed set of contextual factors. For higher fidelity, it is highly desirable to use a model that captures significance of subjective or contextual factors in route choice. This paper presents a novel approach for developing high-fidelity route choice models with increased predictive power by augmenting existing aggregate level baseline models with information on drivers' responses to contextual factors obtained from Stated Choice Experiments carried out in an Immersive Virtual Environment through the use of knowledge distillation.
Convolution Attack on Frequency-Hopping by Full-Duplex Radios
Venue: arXiv cs.IT 2019
Year: 2019

We propose a new adversarial attack on frequency-hopping based wireless communication between two users, namely Alice and Bob. In this attack, the adversary, referred to as Eve, instantaneously modifies the transmitted signal by Alice before forwarding it to Bob within the symbol-period. We show that this attack forces Bob to incorporate Eve's signal in the decoding process; otherwise, treating it as noise would further degrade the performance akin to jamming. Through this attack, we show that Eve can convert a slow-fading channel between Alice and Bob to a rapid-fading one by modifying every transmitted symbol independently. As a result, neither pilot-assisted coherent detection techniques nor blind-detection methods are directly applicable as countermeasures. As potential mitigation strategies, we explore the applicability of frequency-hopping along with (i) On-Off keying (OOK) and (ii) Binary Frequency-Shift-Keying (FSK) as modulation schemes. In the case of OOK, the attacker attempts to introduce deep-fades on the tone carrying the information bit, whereas in the case of BFSK, the attacker pours comparable energy levels on the tones carrying bit-$0$ and bit-$1$, thereby degrading the performance. Based on extensive analyses and experimental results, we show that (i) when using OOK, Bob must be equipped with a large number of receive antennas to reliably detect Alice's signal, and (ii) when using BFSK, Alice and Bob must agree upon a secret-key to randomize the location of the tones carrying the bits, in addition to randomizing the carrier-frequency of communication.
Towards Stable Symbol Grounding with Zero-Suppressed State AutoEncoder
Venue: arXiv cs.LG 2019
Year: 2019

While classical planning has been an active branch of AI, its applicability is limited to the tasks precisely modeled by humans. Fully automated high-level agents should be instead able to find a symbolic representation of an unknown environment without supervision, otherwise it exhibits the knowledge acquisition bottleneck. Meanwhile, Latplan (Asai and Fukunaga 2018) partially resolves the bottleneck with a neural network called State AutoEncoder (SAE). SAE obtains the propositional representation of the image-based puzzle domains with unsupervised learning, generates a state space and performs classical planning. In this paper, we identify the problematic, stochastic behavior of the SAE-produced propositions as a new sub-problem of symbol grounding problem, the symbol stability problem. Informally, symbols are stable when their referents (e.g. propositional values) do not change against small perturbation of the observation, and unstable symbols are harmful for symbolic reasoning. We analyze the problem in Latplan both formally and empirically, and propose "Zero-Suppressed SAE", an enhancement that stabilizes the propositions using the idea of closed-world assumption as a prior for NN optimization. We show that it finds the more stable propositions and the more compact representations, resulting in an improved success rate of Latplan. It is robust against various hyperparameters and eases the tuning effort, and also provides a weight pruning capability as a side effect.
3D Face Mask Presentation Attack Detection Based on Intrinsic Image Analysis
Venue: arXiv cs.CV 2019
Year: 2019

Face presentation attacks have become a major threat to face recognition systems and many countermeasures have been proposed in the past decade. However, most of them are devoted to 2D face presentation attacks, rather than 3D face masks. Unlike the real face, the 3D face mask is usually made of resin materials and has a smooth surface, resulting in reflectance differences. So, we propose a novel detection method for 3D face mask presentation attack by modeling reflectance differences based on intrinsic image analysis. In the proposed method, the face image is first processed with intrinsic image decomposition to compute its reflectance image. Then, the intensity distribution histograms are extracted from three orthogonal planes to represent the intensity differences of reflectance images between the real face and 3D face mask. After that, the 1D convolutional network is further used to capture the information for describing different materials or surfaces react differently to changes in illumination. Extensive experiments on the 3DMAD database demonstrate the effectiveness of our proposed method in distinguishing a face mask from the real one and show that the detection performance outperforms other state-of-the-art methods.
Admission Control based Traffic-Agnostic Delay-Constrained Random Access (AC/DC-RA) for M2M Communication
Venue: arXiv cs.NI 2019
Year: 2019

The problem of wireless M2M communication is twofold: the reliability aspect and the scalability aspect. The solution of this problem demands a delay constrained random access protocol. To this end we propose Admission Control based Traffic Agnostic Delay Constrained Random Access (AC/DC-RA) protocol. Our main contribution is enabling the stochastic delay constraints agnostic to the traffic, such that the stochastic delay constraint is valid with respect to varying number of arrivals. We achieve this with an admission control decision that uses a novel collision estimation algorithm for active number of arrivals per contention resource. We use an adaptive contention resolution algorithm to react to the varying number of arrivals. Using these tools, the admission control solves the stability problem. We show with simulations that AC/DC-RA provide stochastic delay constrained random access in a traffic agnostic way to sustain a stable performance against Poisson and Beta arrivals without any modification to the protocol.
Performance Assessment of MIMO Precoding on Realistic mmWave Channels
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, the performance of multi-user Multiple-Input Multiple-Output (MIMO) systems is evaluated in terms of SINR and capacity. We focus on the case of a downlink single-cell scenario where different precoders have been studied. Among the considered precoders, we range from different Grid of Beams (GoB) optimization approaches to linear precoders (e.g., matched filtering and zero forcing). This performance evaluation includes imperfect channel estimation, and is carried out over two realistic mmWave 5G propagation channels, which are simulated following either the measurement campaign done by New York University (NYU) or the 3GPP channel model. Our evaluation allows grasping knowledge on the precoding performance in mmWave realistic scenarios. The results highlight the good performance of GoB optimization approaches when a realistic channel model with directionality is adopted.
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks
Venue: arXiv cs.LG 2019
Year: 2019

Modern neural networks are highly non-robust against adversarial manipulation. A significant amount of work has been invested in techniques to compute lower bounds on robustness through formal guarantees and to build provably robust models. However, it is still difficult to get guarantees for larger networks or robustness against larger perturbations. Thus attack strategies are needed to provide tight upper bounds on the actual robustness. We significantly improve the randomized gradient-free attack for ReLU networks [9], in particular by scaling it up to large networks. We show that our attack achieves similar or significantly smaller robust accuracy than state-of-the-art attacks like PGD or the one of Carlini and Wagner, thus revealing an overestimation of the robustness by these state-of-the-art methods. Our attack is not based on a gradient descent scheme and in this sense gradient-free, which makes it less sensitive to the choice of hyperparameters as no careful selection of the stepsize is required.
Local Search for Fast Matrix Multiplication
Venue: arXiv cs.LO 2019
Year: 2019

Laderman discovered a scheme for computing the product of two 3x3 matrices using only 23 multiplications in 1976. Since then, some more such schemes were proposed, but it remains open how many there are and whether there exist schemes with fewer than 23 multiplications. In this paper we present two independent SAT-based methods for finding new schemes. Both methods allow computing a few hundred new schemes individually, and many thousands when combined. Local search SAT solvers outperform CDCL solvers consistently in this application.
The XENON1T Data Distribution and Processing Scheme
Venue: arXiv cs.DC 2019
Year: 2019

The XENON experiment is looking for non-baryonic particle dark matter in the universe. The setup is a dual phase time projection chamber (TPC) filled with 3200 kg of ultra-pure liquid xenon. The setup is operated at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy. We present a full overview of the computing scheme for data distribution and job management in XENON1T. The software package Rucio, which is developed by the ATLAS collaboration, facilitates data handling on Open Science Grid (OSG) and European Grid Infrastructure (EGI) storage systems. A tape copy at the Center for High Performance Computing (PDC) is managed by the Tivoli Storage Manager (TSM). Data reduction and Monte Carlo production are handled by CI Connect which is integrated into the OSG network. The job submission system connects resources at the EGI, OSG, SDSC's Comet, and the campus HPC resources for distributed computing. The previous success in the XENON1T computing scheme is also the starting point for its successor experiment XENONnT, which starts to take data in autumn 2019.