OpenRaiser/Intern-Atlas huggingface dataset:
Chair Segments: A Compact Benchmark for the Study of Object Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Over the years, datasets and benchmarks have had an outsized influence on the design of novel algorithms. In this paper, we introduce ChairSegments, a novel and compact semi-synthetic dataset for object segmentation. We also show empirical findings in transfer learning that mirror recent findings for image classification. We particularly show that models that are fine-tuned from a pretrained set of weights lie in the same basin of the optimization landscape. ChairSegments consists of a diverse set of prototypical images of chairs with transparent backgrounds composited into a diverse array of backgrounds. We aim for ChairSegments to be the equivalent of the CIFAR-10 dataset but for quickly designing and iterating over novel model architectures for segmentation. On Chair Segments, a U-Net model can be trained to full convergence in only thirty minutes using a single GPU. Finally, while this dataset is semi-synthetic, it can be a useful proxy for real data, leading to state-of-the-art accuracy on the Object Discovery dataset when used as a source of pretraining.
Pareto Deterministic Policy Gradients and Its Application in 5G Massive MIMO Networks
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we consider jointly optimizing cell load balance and network throughput via a reinforcement learning (RL) approach, where inter-cell handover (i.e., user association assignment) and massive MIMO antenna tilting are configured as the RL policy to learn. Our rationale behind using RL is to circumvent the challenges of analytically modeling user mobility and network dynamics. To accomplish this joint optimization, we integrate vector rewards into the RL value network and conduct RL action via a separate policy network. We name this method as Pareto deterministic policy gradients (PDPG). It is an actor-critic, model-free and deterministic policy algorithm which can handle the coupling objectives with the following two merits: 1) It solves the optimization via leveraging the degree of freedom of vector reward as opposed to choosing handcrafted scalar-reward; 2) Cross-validation over multiple policies can be significantly reduced. Accordingly, the RL enabled network behaves in a self-organized way: It learns out the underlying user mobility through measurement history to proactively operate handover and antenna tilt without environment assumptions. Our numerical evaluation demonstrates that the introduced RL method outperforms scalar-reward based approaches. Meanwhile, to be self-contained, an ideal static optimization based brute-force search solver is included as a benchmark. The comparison shows that the RL approach performs as well as this ideal strategy, though the former one is constrained with limited environment observations and lower action frequency, whereas the latter ones have full access to the user mobility. The convergence of our introduced approach is also tested under different user mobility environment based on our measurement data from a real scenario.
CDP: a Content Discovery Protocol for Mobile P2P Systems
Venue: arXiv cs.NI 2020
Year: 2020

The emergence of affordable wireless and mobile devices was a key step towards deploying mobile peer-to-peer (P2P) systems. The latter allow users to share and search diverse multimedia resources over Mobile Ad-hoc Networks (MANETs). Due to the nature of MANETs, P2P mobile systems brought up many new thriving challenges, in particular with regard to the content discovery issue. Thus, the design of an efficient content discovery protocol has been of paramount importance. The thriving challenge is to (i) locate relevant peers sharing pertinent resources for users queries and then (ii) to ensure that those peers would be reached by considering different MANET constraints (e.g., peer mobility, battery energy, peer load, to cite a few). Even though the literature witnesses a wealthy number of content discovery protocols, only few of them have considered the above-mentioned requirements. To overcome this shortage, we introduce in this paper an efficient Content Discovery Protocol (CDP) for P2P mobile systems. The idea underlying our proposal is to route users queries to relevant peers that are (1) more suitable to answer the user query according to its content, (2) more stable to relay query hits, (3) less loaded to avoid the network congestion issue and (4) having more battery lifetime to avoid the network partitioning issue. The performed experiments show that CDP outperforms its competitor in terms of effectiveness and efficiency.
Do Reservoir Computers Work Best at the Edge of Chaos?
Venue: arXiv cs.NE 2020
Year: 2020

It has been demonstrated that cellular automata had the highest computational capacity at the edge of chaos, the parameter at which their behavior transitioned from ordered to chaotic. This same concept has been applied to reservoir computers; a number of researchers have stated that the highest computational capacity for a reservoir computer is at the edge of chaos, although others have suggested that this rule is not universally true. Because many reservoir computers do not show chaotic behavior but merely become unstable, it is felt that a more accurate term for this instability transition is the "edge of stability"Here I find two examples where the computational capacity of a reservoir computer decreases as the edge of stability is approached; in one case, because generalized synchronization breaks down, and in the other case because the reservoir computer is a poor match to the problem being solved. The edge of stability as an optimal operating point for a reservoir computer is not in general true, although it may be true in some cases.
PatchmatchNet: Learned Multi-View Patchmatch Stereo
Venue: arXiv cs.CV 2020
Year: 2020

We present PatchmatchNet, a novel and learnable cascade formulation of Patchmatch for high-resolution multi-view stereo. With high computation speed and low memory requirement, PatchmatchNet can process higher resolution imagery and is more suited to run on resource limited devices than competitors that employ 3D cost volume regularization. For the first time we introduce an iterative multi-scale Patchmatch in an end-to-end trainable architecture and improve the Patchmatch core algorithm with a novel and learned adaptive propagation and evaluation scheme for each iteration. Extensive experiments show a very competitive performance and generalization for our method on DTU, Tanks & Temples and ETH3D, but at a significantly higher efficiency than all existing top-performing models: at least two and a half times faster than state-of-the-art methods with twice less memory usage.
Video Anomaly Detection by Estimating Likelihood of Representations
Venue: arXiv cs.CV 2020
Year: 2020

Video anomaly detection is a challenging task not only because it involves solving many sub-tasks such as motion representation, object localization and action recognition, but also because it is commonly considered as an unsupervised learning problem that involves detecting outliers. Traditionally, solutions to this task have focused on the mapping between video frames and their low-dimensional features, while ignoring the spatial connections of those features. Recent solutions focus on analyzing these spatial connections by using hard clustering techniques, such as K-Means, or applying neural networks to map latent features to a general understanding, such as action attributes. In order to solve video anomaly in the latent feature space, we propose a deep probabilistic model to transfer this task into a density estimation problem where latent manifolds are generated by a deep denoising autoencoder and clustered by expectation maximization. Evaluations on several benchmarks datasets show the strengths of our model, achieving outstanding performance on challenging datasets.
Deep Data Flow Analysis
Venue: arXiv cs.PL 2020
Year: 2020

Compiler architects increasingly look to machine learning when building heuristics for compiler optimization. The promise of automatic heuristic design, freeing the compiler engineer from the complex interactions of program, architecture, and other optimizations, is alluring. However, most machine learning methods cannot replicate even the simplest of the abstract interpretations of data flow analysis that are critical to making good optimization decisions. This must change for machine learning to become the dominant technology in compiler heuristics. To this end, we propose ProGraML - Program Graphs for Machine Learning - a language-independent, portable representation of whole-program semantics for deep learning. To benchmark current and future learning techniques for compiler analyses we introduce an open dataset of 461k Intermediate Representation (IR) files for LLVM, covering five source programming languages, and 15.4M corresponding data flow results. We formulate data flow analysis as an MPNN and show that, using ProGraML, standard analyses can be learned, yielding improved performance on downstream compiler optimization tasks.
Power control with partial observation in wireless ad hoc networks
Venue: arXiv cs.NI 2020
Year: 2020

In this paper, the well-known forwarder's dilemma is generalized by accounting for the presence of link quality fluctuations; the forwarder's dilemma is a four-node interaction model with two source nodes and two destination nodes. It is known to be very useful to study ad hoc networks. To characterize the long-term utility region when the source nodes have to control their power with partial channel state information (CSI), we resort to a recent result in Shannon theory. It is shown how to exploit this theoretical result to find the long-term utility region and determine good power control policies. This region is of prime importance since it provides the best performance possible for a given knowledge at the nodes. Numerical results provide several new insights into the repeated forwarder's dilemma power control problem; for instance, the knowledge of global CSI only brings a marginal performance improvement with respect to the local CSI case.
Mutual Information Maximization on Disentangled Representations for Differential Morph Detection
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we present a novel differential morph detection framework, utilizing landmark and appearance disentanglement. In our framework, the face image is represented in the embedding domain using two disentangled but complementary representations. The network is trained by triplets of face images, in which the intermediate image inherits the landmarks from one image and the appearance from the other image. This initially trained network is further trained for each dataset using contrastive representations. We demonstrate that, by employing appearance and landmark disentanglement, the proposed framework can provide state-of-the-art differential morph detection performance. This functionality is achieved by the using distances in landmark, appearance, and ID domains. The performance of the proposed framework is evaluated using three morph datasets generated with different methodologies.
Regularization via Adaptive Pairwise Label Smoothing
Venue: arXiv cs.LG 2020
Year: 2020

Label Smoothing (LS) is an effective regularizer to improve the generalization of state-of-the-art deep models. For each training sample the LS strategy smooths the one-hot encoded training signal by distributing its distribution mass over the non ground-truth classes, aiming to penalize the networks from generating overconfident output distributions. This paper introduces a novel label smoothing technique called Pairwise Label Smoothing (PLS). The PLS takes a pair of samples as input. Smoothing with a pair of ground-truth labels enables the PLS to preserve the relative distance between the two truth labels while further soften that between the truth labels and the other targets, resulting in models producing much less confident predictions than the LS strategy. Also, unlike current LS methods, which typically require to find a global smoothing distribution mass through cross-validation search, PLS automatically learns the distribution mass for each input pair during training. We empirically show that PLS significantly outperforms LS and the baseline models, achieving up to 30% of relative classification error reduction. We also visually show that when achieving such accuracy gains the PLS tends to produce very low winning softmax scores.
Time-Aware Models for Software Effort Estimation
Venue: arXiv cs.SE 2020
Year: 2020

It seems logical to assert that the dynamic nature of software engineering practice would mean that software effort estimation (SEE) modelling should take into account project start and completion dates. That is, we should build models for future projects based only on data from completed projects; and we should prefer data from recent similar projects over data from older similar projects. Research in SEE modelling generally ignores these recommendations. In this study two different model development approaches that take project timing into account are applied to two publicly available datasets and the outcomes are compared to those drawn from three baseline (non-time-aware) models. Our results indicate: that it is feasible to build accurate effort estimation models using project timing information; that the models differ from those built without considering time, in terms of the parameters included and their weightings; and that there is no statistical significance difference as to which of the two model building approaches is superior in terms of accuracy.
Margin-Based Transfer Bounds for Meta Learning with Deep Feature Embedding
Venue: arXiv cs.LG 2020
Year: 2020

By transferring knowledge learned from seen/previous tasks, meta learning aims to generalize well to unseen/future tasks. Existing meta-learning approaches have shown promising empirical performance on various multiclass classification problems, but few provide theoretical analysis on the classifiers' generalization ability on future tasks. In this paper, under the assumption that all classification tasks are sampled from the same meta-distribution, we leverage margin theory and statistical learning theory to establish three margin-based transfer bounds for meta-learning based multiclass classification (MLMC). These bounds reveal that the expected error of a given classification algorithm for a future task can be estimated with the average empirical error on a finite number of previous tasks, uniformly over a class of preprocessing feature maps/deep neural networks (i.e. deep feature embeddings). To validate these bounds, instead of the commonly-used cross-entropy loss, a multi-margin loss is employed to train a number of representative MLMC models. Experiments on three benchmarks show that these margin-based models still achieve competitive performance, validating the practical value of our margin-based theoretical analysis.
Graph Repair and its Application to Meta-Modeling
Venue: arXiv cs.SE 2020
Year: 2020

Model repair is an essential topic in model-driven engineering. We present typed graph-repair programs for specific conditions; application to any typed graph yields a typed graph satisfying the condition. A model graph based on the Eclipse Modeling Framework (EMF), short EMF-model graph, is a typed graph satisfying some structural EMF-constraints. Application of the results to the EMF-world yields model-repair programs for EMFk constraints, a first-order variant of EMF constraints; application to any typed graph yields an EMFk model graph. From these results, we derive results for EMF model repair.
Verifying Graph Programs with First-Order Logic
Venue: arXiv cs.LO 2020
Year: 2020

We consider Hoare-style verification for the graph programming language GP 2. In previous work, graph properties were specified by so-called E-conditions which extend nested graph conditions. However, this type of assertions is not easy to comprehend by programmers that are used to formal specifications in standard first-order logic. In this paper, we present an approach to verify GP 2 programs with a standard first-order logic. We show how to construct a strongest liberal postcondition with respect to a rule schema and a precondition. We then extend this construction to obtain strongest liberal postconditions for arbitrary loop-free programs. Compared with previous work, this allows to reason about a vastly generalised class of graph programs. In particular, many programs with nested loops can be verified with the new calculus.
Relational Learning for Skill Preconditions
Venue: arXiv cs.RO 2020
Year: 2020

To determine if a skill can be executed in any given environment, a robot needs to learn the preconditions for the skill. As robots begin to operate in dynamic and unstructured environments, precondition models will need to generalize to variable number of objects with different shapes and sizes. In this work, we focus on learning precondition models for manipulation skills in unconstrained environments. Our work is motivated by the intuition that many complex manipulation tasks, with multiple objects, can be simplified by focusing on less complex pairwise object relations. We propose an object-relation model that learns continuous representations for these pairwise object relations. Our object-relation model is trained completely in simulation, and once learned, is used by a separate precondition model to predict skill preconditions for real world tasks. We evaluate our precondition model on $3$ different manipulation tasks: sweeping, cutting, and unstacking. We show that our approach leads to significant improvements in predicting preconditions for all 3 tasks, across objects of different shapes and sizes.
Neural Network Approximations of Compositional Functions With Applications to Dynamical Systems
Venue: arXiv cs.LG 2020
Year: 2020

As demonstrated in many areas of real-life applications, neural networks have the capability of dealing with high dimensional data. In the fields of optimal control and dynamical systems, the same capability was studied and verified in many published results in recent years. Towards the goal of revealing the underlying reason why neural networks are capable of solving some high dimensional problems, we develop an algebraic framework and an approximation theory for compositional functions and their neural network approximations. The theoretical foundation is developed in a way so that it supports the error analysis for not only functions as input-output relations, but also numerical algorithms. This capability is critical because it enables the analysis of approximation errors for problems for which analytic solutions are not available, such as differential equations and optimal control. We identify a set of key features of compositional functions and the relationship between the features and the complexity of neural networks. In addition to function approximations, we prove several formulae of error upper bounds for neural networks that approximate the solutions to differential equations, optimization, and optimal control.
Sparse Semi-Supervised Action Recognition with Active Learning
Venue: arXiv cs.CV 2020
Year: 2020

Current state-of-the-art methods for skeleton-based action recognition are supervised and rely on labels. The reliance is limiting the performance due to the challenges involved in annotation and mislabeled data. Unsupervised methods have been introduced, however, they organize sequences into clusters and still require labels to associate clusters with actions. In this paper, we propose a novel approach for skeleton-based action recognition, called SESAR, that connects these approaches. SESAR leverages the information from both unlabeled data and a handful of sequences actively selected for labeling, combining unsupervised training with sparsely supervised guidance. SESAR is composed of two main components, where the first component learns a latent representation for unlabeled action sequences through an Encoder-Decoder RNN which reconstructs the sequences, and the second component performs active learning to select sequences to be labeled based on cluster and classification uncertainty. When the two components are simultaneously trained on skeleton-based action sequences, they correspond to a robust system for action recognition with only a handful of labeled samples. We evaluate our system on common datasets with multiple sequences and actions, such as NW UCLA, NTU RGB+D 60, and UWA3D. Our results outperform standalone skeleton-based supervised, unsupervised with cluster identification, and active-learning methods for action recognition when applied to sparse labeled samples, as low as 1% of the data.
Folding and Unfolding on Metagraphs
Venue: arXiv cs.AI 2020
Year: 2020

Typed metagraphs are defined as hypergraphs with types assigned to hyperedges and their targets, and the potential to have targets of hyperedges connect to whole links as well as targets. Directed typed metagraphs (DTMGs) are introduced via partitioning the targets of each edge in a typed metagraph into input, output and lateral sets; one can then look at "metapaths" in which edges' output-sets are linked to other edges' input-sets. An initial algebra approach to DTMGs is presented, including introduction of constructors for building up DTMGs and laws regarding relationships among multiple ways of using these constructors. A menagerie of useful morphism types is then defined on DTMGs (catamorphisms, anamorphisms, histomorphisms, futumorphisms, hylomorphisms, chronomorphisms, metamorphisms and metachronomorphisms), providing a general abstract framework for formulating a broad variety of metagraph operations. Deterministic and stochastic processes on typed metagraphs are represented in terms of forests of DTMGs defined over a common TMG, where the various morphisms can be straightforwardly extended to these forests. A variation of the approach to undirected typed metagraphs is presented; and it is indicated how the framework outlined can applied to realistic metagraphs involving complexities like dependent and probabilistic types, multidimensional values and dynamic processing including insertion and deletion of edges.
A Study of Password Security Factors among Bangladeshi Government Websites
Venue: arXiv cs.CR 2020
Year: 2020

The Government of Bangladesh is aggressively transforming its public service landscape by transforming public services into online services via a number of websites. The motivation is that this would be a catalyst for a transformative change in every aspect of citizen life. Some web services must be protected from any unauthorised usages and passwords remain the most widely used credential mechanism for this purpose. However, if passwords are not adopted properly, they can be a cause for security breach. That is why it is important to study different aspects of password security on different websites. In this paper, we present a study of password security among 36 different Bangladeshi government websites against six carefully chosen password security heuristics. This study is the first of its kind in this domain and offers interesting insights. For example, many websites have not adopted proper security measures with respect to security. There is no password construction guideline adopted by many websites, thus creating a barrier for users to select a strong password. Some of them allow supposedly weak passwords and still do not utilise a secure HTTPS channel to transmit information over the Internet.