OpenRaiser/Intern-Atlas huggingface dataset:
Learning Error-Driven Curriculum for Crowd Counting
Venue: arXiv cs.CV 2020
Year: 2020

Density regression has been widely employed in crowd counting. However, the frequency imbalance of pixel values in the density map is still an obstacle to improve the performance. In this paper, we propose a novel learning strategy for learning error-driven curriculum, which uses an additional network to supervise the training of the main network. A tutoring network called TutorNet is proposed to repetitively indicate the critical errors of the main network. TutorNet generates pixel-level weights to formulate the curriculum for the main network during training, so that the main network will assign a higher weight to those hard examples than easy examples. Furthermore, we scale the density map by a factor to enlarge the distance among inter-examples, which is well known to improve the performance. Extensive experiments on two challenging benchmark datasets show that our method has achieved state-of-the-art performance.
Improving the HardNet Descriptor
Venue: arXiv cs.CV 2020
Year: 2020

In the thesis we consider the problem of local feature descriptor learning for wide baseline stereo focusing on the HardNet descriptor, which is close to state-of-the-art. AMOS Patches dataset is introduced, which improves robustness to illumination and appearance changes. It is based on registered images from selected cameras from the AMOS dataset. We provide recommendations on the patch dataset creation process and evaluate HardNet trained on data of different modalities. We also introduce a dataset combination and reduction methods, that allow comparable performance on a significantly smaller dataset. HardNet8, consistently outperforming the original HardNet, benefits from the architectural choices made: connectivity pattern, final pooling, receptive field, CNN building blocks found by manual or automatic search algorithms -- DARTS. We show impact of overlooked hyperparameters such as batch size and length of training on the descriptor quality. PCA dimensionality reduction further boosts performance and also reduces memory footprint. Finally, the insights gained lead to two HardNet8 descriptors: one performing well on a variety of benchmarks -- HPatches, AMOS Patches and IMW Phototourism, the other is optimized for IMW Phototourism.
Analyzing Optical TDMA to Mitigate Interference in Downlink LiFi Optical Attocell Networks
Venue: arXiv cs.IT 2020
Year: 2020

Co-channel interference in the downlink of LiFi attocell networks significantly decreases the network performance in terms of rate. Analysis of multiple access schemes is essential to mitigate interference and improve rate. The light emitting diodes (LEDs) being centrally monitored, the time division multiple access (TDMA) scheme over the LEDs will be suitable to analyze. This work considers the interference characterization in [1] over M-PAM modulated signals to derive an exact expression for the goodput G of the time scheduled attocell network, which is arranged as a deterministic square lattice in two dimensions. Given this TDMA over the LEDs, numerical simulations show that the LEDs can be optimally time scheduled to maximize the goodput, which implies that the TDMA mitigates interference in an attocell network compared to the case when the LEDs are unscheduled.
Coverage Analysis of a Thinned LiFi Optical Attocell Network
Venue: arXiv cs.IT 2020
Year: 2020

This work analyzes coverage in the downlink of a thinned LiFi attocell network of deterministically arranged LEDs. The network is thinned by a Bernoulli probability p over all the LEDs to decide whether each one of them acts as a LiFi source or not. Then we use the series approximation approach used in [1] to obtain closed form expressions for the probability of coverage in such thinned LiFi attocell networks and validate them using numerical simulations.
Octahedral Frames for Feature-Aligned Cross-Fields
Venue: arXiv cs.GR 2020
Year: 2020

We present a method for designing smooth cross fields on surfaces that automatically align to sharp features of an underlying geometry. Our approach introduces a novel class of energies based on a representation of cross fields in the spherical harmonic basis. We provide theoretical analysis of these energies in the smooth setting, showing that they penalize deviations from surface creases while otherwise promoting intrinsically smooth fields. We demonstrate the applicability of our method to quad-meshing and include an extensive benchmark comparing our fields to other automatic approaches for generating feature-aligned cross fields on triangle meshes.
Beyond Single Stage Encoder-Decoder Networks: Deep Decoders for Semantic Image Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Single encoder-decoder methodologies for semantic segmentation are reaching their peak in terms of segmentation quality and efficiency per number of layers. To address these limitations, we propose a new architecture based on a decoder which uses a set of shallow networks for capturing more information content. The new decoder has a new topology of skip connections, namely backward and stacked residual connections. In order to further improve the architecture we introduce a weight function which aims to re-balance classes to increase the attention of the networks to under-represented objects. We carried out an extensive set of experiments that yielded state-of-the-art results for the CamVid, Gatech and Freiburg Forest datasets. Moreover, to further prove the effectiveness of our decoder, we conducted a set of experiments studying the impact of our decoder to state-of-the-art segmentation techniques. Additionally, we present a set of experiments augmenting semantic segmentation with optical flow information, showing that motion clues can boost pure image based semantic segmentation approaches.
Exploiting vulnerabilities of deep neural networks for privacy protection
Venue: arXiv cs.CV 2020
Year: 2020

Adversarial perturbations can be added to images to protect their content from unwanted inferences. These perturbations may, however, be ineffective against classifiers that were not {seen} during the generation of the perturbation, or against defenses {based on re-quantization, median filtering or JPEG compression. To address these limitations, we present an adversarial attack {that is} specifically designed to protect visual content against { unseen} classifiers and known defenses. We craft perturbations using an iterative process that is based on the Fast Gradient Signed Method and {that} randomly selects a classifier and a defense, at each iteration}. This randomization prevents an undesirable overfitting to a specific classifier or defense. We validate the proposed attack in both targeted and untargeted settings on the private classes of the Places365-Standard dataset. Using ResNet18, ResNet50, AlexNet and DenseNet161 {as classifiers}, the performance of the proposed attack exceeds that of eleven state-of-the-art attacks. The implementation is available at https://github.com/smartcameras/RP-FGSM/.
Adaptive Control Plane Load Balancing in vSDN Enabled 5G Network
Venue: arXiv cs.NI 2020
Year: 2020

In this work, we have formulated a controllerhypervisor (C-H) pair deployment approach to provide various network functions (such as differentiation between control and data signals, different translation functions etc.) with low latency. The system model deals with a real network topology and four well defined network latency matrices with an MILP model has been well constructed to optimize latency objectives. Reverse Path-Flow Mechanism (RPFM) has been proposed in order to provide feasible solutions by keeping the network load and link capacity constrained under tolerance limit. We further minimize the H-plane load by distributing the network resources based on the arrival time of SERVICE IN requests from users. Through extensive experiment, the efficacy of the proposed system has been demonstrated in terms of significant latency reduction and an e-ULL (evolved-Ultra Low Latency) experience with efficient handling of all real-time user demands. The proposed approach can be used for similar localization problems like service chain mapping in 5G-NR, baseband unit deployment in 5G C-RAN, firewall deployment in distributed CPS.
Reinforcement Communication Learning in Different Social Network Structures
Venue: arXiv cs.AI 2020
Year: 2020

Social network structure is one of the key determinants of human language evolution. Previous work has shown that the network of social interactions shapes decentralized learning in human groups, leading to the emergence of different kinds of communicative conventions. We examined the effects of social network organization on the properties of communication systems emerging in decentralized, multi-agent reinforcement learning communities. We found that the global connectivity of a social network drives the convergence of populations on shared and symmetric communication systems, preventing the agents from forming many local "dialects". Moreover, the agent's degree is inversely related to the consistency of its use of communicative conventions. These results show the importance of the basic properties of social network structure on reinforcement communication learning and suggest a new interpretation of findings on human convergence on word conventions.
A Gated and Bifurcated Stacked U-Net Module for Document Image Dewarping
Venue: arXiv cs.CV 2020
Year: 2020

Capturing images of documents is one of the easiest and most used methods of recording them. These images however, being captured with the help of handheld devices, often lead to undesirable distortions that are hard to remove. We propose a supervised Gated and Bifurcated Stacked U-Net module to predict a dewarping grid and create a distortion free image from the input. While the network is trained on synthetically warped document images, results are calculated on the basis of real world images. The novelty in our methods exists not only in a bifurcation of the U-Net to help eliminate the intermingling of the grid coordinates, but also in the use of a gated network which adds boundary and other minute line level details to the model. The end-to-end pipeline proposed by us achieves state-of-the-art performance on the DocUNet dataset after being trained on just 8 percent of the data used in previous methods.
Estimation for High-Dimensional Multi-Layer Generalized Linear Model -- Part II: The ML-GAMP Estimator
Venue: arXiv cs.IT 2020
Year: 2020

This is Part II of a two-part work on the estimation for a multi-layer generalized linear model (ML-GLM) in large system limits. In Part I, we had analyzed the asymptotic performance of an exact MMSE estimator, and obtained a set of coupled equations that could characterize its MSE performance. To work around the implementation difficulty of the exact estimator, this paper continues to propose an approximate solution, ML-GAMP, which could be derived by blending a moment-matching projection into the Gaussian approximated loopy belief propagation. The ML-GAMP estimator is then shown to enjoy a great simplicity in its implementation, where its per-iteration complexity is as low as GAMP. Further analysis on its asymptotic performance also reveals that, in large system limits, its dynamical MSE behavior is fully characterized by a set of simple one-dimensional iterating equations, termed state evolution (SE). Interestingly, this SE of ML-GAMP share exactly the same fixed points with an exact MMSE estimator whose fixed points were obtained in Part I via a replica analysis. Given the Bayes-optimality of the exact implementation, this proposed estimator (if converged) is optimal in the MSE sense.
Coinduction Plain and Simple
Venue: arXiv cs.PL 2020
Year: 2020

Coinduction refers to both a technique for the definition of infinite streams, so-called codata, and a technique for proving the equality of coinductively specified codata. This article first reviews coinduction in declarative programming. Second, it reviews and slightly extends the formalism commonly used for specifying codata. Third, it generalizes the coinduction proof principle, which has been originally specified for the equality predicate only, to other predicates. This generalization makes the coinduction proof principle more intuitive and stresses its closeness with structural induction. The article finally suggests in its conclusion extensions of functional and logic programming with limited and decidable forms of the generalized coinduction proof principle.
Acting Selfish for the Good of All: Contextual Bandits for Resource-Efficient Transmission of Vehicular Sensor Data
Venue: arXiv cs.NI 2020
Year: 2020

as a novel client-based method for resource-efficient opportunistic transmission of delay-tolerant vehicular sensor data. BS-CB applies a hybrid approach which brings together all major machine learning disciplines - supervised, unsupervised, and reinforcement learning - in order to autonomously schedule vehicular sensor data transmissions with respect to the expected resource efficiency. Within a comprehensive real world performance evaluation in the public cellular networks of three Mobile Network Operators (MNOs), it is found that 1) The average uplink data rate is improved by 125%-195% 2) The apparently selfish goal of data rate optimization reduces the amount of occupied cell resources by 84%-89% 3) The average transmission-related power consumption can be reduced by 53%-75% 4) The price to pay is an additional buffering delay due to the opportunistic medium access strategy.
Distributed Double Auctions for Large-Scale Device-to-Device Resource Trading
Venue: arXiv cs.GT 2020
Year: 2020

Mobile users in future wireless networks face limited wireless resources such as data plan, computation capacity and energy storage. Given that some of these users may not be utilizing fully their wireless resources, device-to-device (D2D) resource sharing is a promising approach to exploit users' diversity in resource use and for pooling their resources locally. In this paper, we propose a novel two-sided D2D trading market model that enables a large number of locally connected users to trade resources. Traditional resource allocation solutions are mostly centralized without considering users' local D2D connectivity constraints, becoming unscalable for large-scale trading. In addition, there may be market failure since selfish users will not truthfully report their actual valuations and quantities for buying or selling resources. To address these two key challenges, we first investigate the distributed resource allocation problem with D2D assignment constraints. Based on the greedy idea of maximum weighted matching, we propose a fast algorithm to achieve near-optimal average allocative efficiency. Then, we combine it with a new pricing mechanism that adjusts the final trading prices for buying and selling resources in a way that buyers and sellers are incentivized to truthfully report their valuations and available resource quantities. Unlike traditional double auctions with a central controller, this pricing mechanism is fully distributed in the sense that the final trading prices between each matched pair of users only depend on their own declarations and hence can be calculated locally. Finally, we analyze the repeated execution of the proposed D2D trading mechanism in multiple rounds and determine the best trading frequency.
The Decidability of Verification under Promising 2.0
Venue: arXiv cs.PL 2020
Year: 2020

In PLDI'20, Lee et al. introduced the \emph{promising } semantics PS 2.0 of the C++ concurrency that captures most of the common program transformations while satisfying the DRF guarantee. The reachability problem for finite-state programs under PS 2.0 with only release-acquire accesses is already known to be undecidable. Therefore, we address, in this paper, the reachability problem for programs running under PS 2.0 with relaxed accesses together with promises. We show that this problem is undecidable even in the case where the input program has finite state. Given this undecidability result, we consider the fragment of PS 2.0 with only relaxed accesses allowing bounded number of promises. We show that under this restriction, the reachability is decidable, albeit very expensive: it is non-primitive recursive. Given this high complexity with bounded number of promises and the undecidability result for the RA fragment of PS 2.0, we consider a bounded version of the reachability problem. To this end, we bound both the number of promises and the "view-switches", i.e, the number of times the processes may switch their local views of the global memory. We provide a code-to-code translation from an input program under PS 2.0, with relaxed and release-acquire memory accesses along with promises, to a program under SC. This leads to a reduction of the bounded reachability problem under PS 2.0 to the bounded context-switching problem under SC. We have implemented a prototype tool and tested it on a set of benchmarks, demonstrating that many bugs in programs can be found using a small bound.
Filtered Poisson Process Bandit on a Continuum
Venue: arXiv cs.LG 2020
Year: 2020

We consider a version of the continuum armed bandit where an action induces a filtered realisation of a non-homogeneous Poisson process. Point data in the filtered sample are then revealed to the decision-maker, whose reward is the total number of revealed points. Using knowledge of the function governing the filtering, but without knowledge of the Poisson intensity function, the decision-maker seeks to maximise the expected number of revealed points over T rounds. We propose an upper confidence bound algorithm for this problem utilising data-adaptive discretisation of the action space. This approach enjoys O(T^(2/3)) regret under a Lipschitz assumption on the reward function. We provide lower bounds on the regret of any algorithm for the problem, via new lower bounds for related finite-armed bandits, and show that the orders of the upper and lower bounds match up to a logarithmic factor.
CobotGear: Interaction with Collaborative Robots using Wearable Optical Motion Capturing Systems
Venue: arXiv cs.RO 2020
Year: 2020

In industrial applications, complex tasks require human collaboration since the robot doesn't have enough dexterity. However, the robots are still implemented as tools and not as collaborative intelligent systems. To ensure safety in the human-robot collaboration, we introduce a system that presents a new method that integrates low-cost wearable mocap, and an improved collision avoidance algorithm based on the artificial potential fields. Wearable optical motion capturing allows to track the human hand position with high accuracy and low latency on large working areas. To increase the efficiency of the proposed algorithm, two obstacle types are discriminated according to their collision probability. A preliminary experiment was performed to analyze the algorithm behavior and to select the best values for the obstacle's threshold angle $θ_{OBS}$, and for the avoidance threshold distance $d_{AT}$. The second experiment was carried out to evaluate the system performance with $d_{AT}$ = 0.2 m and $θ_{OBS}$ = 45 degrees. The third experiment evaluated the system in a real collaborative task. The results demonstrate the robust performance of the robotic arm generating smooth collision-free trajectories. The proposed technology will allow consumer robots to safely collaborate with humans in cluttered environments, e.g., factories, kitchens, living rooms, and restaurants.
Memory-aware Online Compression of CAN Bus Data for Future Vehicular Systems
Venue: arXiv cs.NI 2020
Year: 2020

Vehicles generate a large amount of data from their internal sensors. This data is not only useful for a vehicle's proper operation, but it provides car manufacturers with the ability to optimize performance of individual vehicles and companies with fleets of vehicles (e.g., trucks, taxis, tractors) to optimize their operations to reduce fuel costs and plan repairs. This paper proposes algorithms to compress CAN bus data, specifically, packaged as MDF4 files. In particular, we propose lightweight, online and configurable compression algorithms that allow limited devices to choose the amount of RAM and Flash allocated to them. We show that our proposals can outperform LZW for the same RAM footprint, and can even deliver comparable or better performance to DEFLATE under the same RAM limitations.
Flood zones detection using a runoff model built on Hexagonal shape based cellular automata
Venue: arXiv cs.CE 2020
Year: 2020

This article presents a 3D geographic information systems (GIS) modeling and simulation of water flow in a landscape defined by a digital terrain model, provided by some available geolocation APIs. The proposed approach uses a cellular automata based algorithm to calculate water flow dynamic. The methodology was tested on a case study area of 27kmx19km located in Tangier, north of Morocco. In fact, we aim to detect flood zones in order to prevent problems related to space occupation in urban and rural regions. Some indices can be deduced from the stream shape using Cellular Automata (CA) based approach that can reduce the complexity related to space structures with multiple changes. A spatiotemporal simulation of the runoff process is provided using 3D visualization that we can pair with geographical information system tools (GIS). The 3D GIS modeling approach that was developed for the analyses of flood zones detection using a runoff model based on cellular automata was comprised of three main steps: Input (collection of data), calculation (CA tool) and visualization (3D simulation).
An Integrated Approach to Goal Selection in Mobile Robot Exploration
Venue: arXiv cs.RO 2020
Year: 2020

This paper deals with the problem of autonomous navigation of a mobile robot in an unknown 2D environment to fully explore the environment as efficiently as possible. We assume a terrestrial mobile robot equipped with a ranging sensor with a limited range and 360 degrees field of view. The key part of the exploration process is formulated as the d-Watchman Route Problem which consists of two coupled tasks - candidate goals generation and finding an optimal path through a subset of goals - which are solved in each exploration step. The latter has been defined as a constrained variant of the Generalized Traveling Salesman Problem and solved using an evolutionary algorithm. An evolutionary algorithm that uses an indirect representation and the nearest neighbor based constructive procedure was proposed to solve this problem. Individuals evolved in this evolutionary algorithm do not directly code the solutions to the problem. Instead, they represent sequences of instructions to construct a feasible solution. The problems with efficiently generating feasible solutions typically arising when applying traditional evolutionary algorithms to constrained optimization problems are eliminated this way. The proposed exploration framework was evaluated in a simulated environment on three maps and the time needed to explore the whole environment was compared to state-of-the-art exploration methods. Experimental results show that our method outperforms the compared ones in environments with a low density of obstacles by up to 12.5%, while it is slightly worse in office-like environments by 4.5% at maximum. The framework has also been deployed on a real robot to demonstrate the applicability of the proposed solution with real hardware.