OpenRaiser/Intern-Atlas huggingface dataset:
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation
Venue: arXiv cs.NI 2021
Year: 2021

The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated by a server. FedL ignores two important characteristics of contemporary wireless networks, however: (i) the network may contain heterogeneous communication/computation resources, while (ii) there may be significant overlaps in devices' local data distributions. In this work, we develop a novel optimization methodology that jointly accounts for these factors via intelligent device sampling complemented by device-to-device (D2D) offloading. Our optimization aims to select the best combination of sampled nodes and data offloading configuration to maximize FedL training accuracy subject to realistic constraints on the network topology and device capabilities. Theoretical analysis of the D2D offloading subproblem leads to new FedL convergence bounds and an efficient sequential convex optimizer. Using this result, we develop a sampling methodology based on graph convolutional networks (GCNs) which learns the relationship between network attributes, sampled nodes, and resulting offloading that maximizes FedL accuracy. Through evaluation on real-world datasets and network measurements from our IoT testbed, we find that our methodology while sampling less than 5% of all devices outperforms conventional FedL substantially both in terms of trained model accuracy and required resource utilization.
Quadratic Signaling with Prior Mismatch at an Encoder and Decoder: Equilibria, Continuity and Robustness Properties
Venue: arXiv cs.IT 2021
Year: 2021

We consider communications through a Gaussian noise channel between an encoder and a decoder which have subjective probabilistic models on the source distribution. Although they consider the same cost function, the induced expected costs are misaligned due to their prior mismatch, which requires a game theoretic approach. We consider two approaches: a Nash setup, with no prior commitment, and a Stackelberg solution concept, where the encoder is committed to a given announced policy apriori. We show that the Stackelberg equilibrium cost of the encoder is upper semi continuous, under the Wasserstein metric, as encoder's prior approaches the decoder's prior, and it is also lower semi continuous with Gaussian priors. For the Stackelberg setup, the optimality of affine policies for Gaussian signaling no longer holds under prior mismatch, and thus team-theoretic optimality of linear/affine policies are not robust to perturbations. We provide conditions under which there exist informative Nash and Stackelberg equilibria with affine policies. Finally, we show existence of fully informative Nash and Stackelberg equilibria for the cheap talk problem under an absolute continuity condition.
Searching Personalized $k$-wing in Large and Dynamic Bipartite Graphs
Venue: arXiv cs.DB 2021
Year: 2021

There are extensive studies focusing on the application scenario that all the bipartite cohesive subgraphs need to be discovered in a bipartite graph. However, we observe that, for some applications, one is interested in finding bipartite cohesive subgraphs containing a specific vertex. In this paper, we study a new query dependent bipartite cohesive subgraph search problem based on $k$-wing model, named as the personalized $k$-wing search problem. We introduce a $k$-wing equivalence relationship to summarize the edges of a bipartite graph $G$ into groups. Therefore, all the edges of $G$ are segregated into different groups, i.e. $k$-wing equivalence class, forming an efficient and wing number conserving index called EquiWing. Further, we propose a more compact version of EquiWing, EquiWing-Comp, which is achieved by integrating our proposed $k$-butterfly loose approach and discovered hierarchy properties. These indices are used to expedite the personalized $k$-wing search with a non-repetitive access to $G$, which leads to linear algorithms for searching the personalized $k$-wing. Moreover, we conduct a thorough study on the maintenance of the proposed indices for evolving bipartite graphs. We discover novel properties that help us localize the scope of the maintenance at a low cost. By exploiting the discoveries, we propose novel algorithms for maintaining the two indices, which substantially reduces the cost of maintenance. We perform extensive experimental studies in real, large-scale graphs to validate the efficiency and effectiveness of EquiWing and EquiWing-Comp compared to the baseline.
Temporal Contrastive Graph Learning for Video Action Recognition and Retrieval
Venue: arXiv cs.CV 2021
Year: 2021

Attempt to fully discover the temporal diversity and chronological characteristics for self-supervised video representation learning, this work takes advantage of the temporal dependencies within videos and further proposes a novel self-supervised method named Temporal Contrastive Graph Learning (TCGL). In contrast to the existing methods that ignore modeling elaborate temporal dependencies, our TCGL roots in a hybrid graph contrastive learning strategy to jointly regard the inter-snippet and intra-snippet temporal dependencies as self-supervision signals for temporal representation learning. To model multi-scale temporal dependencies, our TCGL integrates the prior knowledge about the frame and snippet orders into graph structures, i.e., the intra-/inter- snippet temporal contrastive graphs. By randomly removing edges and masking nodes of the intra-snippet graphs or inter-snippet graphs, our TCGL can generate different correlated graph views. Then, specific contrastive learning modules are designed to maximize the agreement between nodes in different views. To adaptively learn the global context representation and recalibrate the channel-wise features, we introduce an adaptive video snippet order prediction module, which leverages the relational knowledge among video snippets to predict the actual snippet orders. Experimental results demonstrate the superiority of our TCGL over the state-of-the-art methods on large-scale action recognition and video retrieval benchmarks.
Exploring the Role of Creativity in Software Engineering
Venue: arXiv cs.SE 2021
Year: 2021

In order to solve today's complex problems in the world of software development, technical knowledge is no longer enough. Previous studies investigating and identifying non-technical skills of software engineers show that creative skills also play an important role in tackling difficult problems. However, creativity is typically a very vague concept to which everyone gives their own interpretation. Also, there is little research that focuses specifically on creativity in the field of software engineering. To better understand the role of creativity in this field, we conducted four focus groups, inviting 33 experts from four nationally and internationally renowned companies in total. This resulted in 399 minutes of transcripts, further coded into 39 sub-themes grouped into seven categories: technical knowledge, communication, constraints, critical thinking, curiosity, creative state of mind, and creative techniques. This study identifies the added value of creativity, which creative techniques are used, how creativity can be recognized, the reasons for being creative, and what environment is needed to facilitate creative work. Our ultimate goal is to use these findings to instill and further encourage the creative urge among undergraduate students in higher education.
Identifying centres of interest in paintings using alignment and edge detection: Case studies on works by Luc Tuymans
Venue: arXiv cs.CV 2021
Year: 2021

What is the creative process through which an artist goes from an original image to a painting? Can we examine this process using techniques from computer vision and pattern recognition? Here we set the first preliminary steps to algorithmically deconstruct some of the transformations that an artist applies to an original image in order to establish centres of interest, which are focal areas of a painting that carry meaning. We introduce a comparative methodology that first cuts out the minimal segment from the original image on which the painting is based, then aligns the painting with this source, investigates micro-differences to identify centres of interest and attempts to understand their role. In this paper we focus exclusively on micro-differences with respect to edges. We believe that research into where and how artists create centres of interest in paintings is valuable for curators, art historians, viewers, and art educators, and might even help artists to understand and refine their own artistic method.
Lightweight representation learning for efficient and scalable recommendation
Venue: arXiv cs.IR 2021
Year: 2021

Over the past decades, recommendation has become a critical component of many online services such as media streaming and e-commerce. Recent advances in algorithms, evaluation methods and datasets have led to continuous improvements of the state-of-the-art. However, much work remains to be done to make these methods scale to the size of the internet. Online advertising offers a unique testbed for recommendation at scale. Every day, billions of users interact with millions of products in real-time. Systems addressing this scenario must work reliably at scale. We propose an efficient model (LED, for Lightweight Encoder-Decoder) reaching a new trade-off between complexity, scale and performance. Specifically, we show that combining large-scale matrix factorization with lightweight embedding fine-tuning unlocks state-of-the-art performance at scale. We further provide the detailed description of a system architecture and demonstrate its operation over two months at the scale of the internet. Our design allows serving billions of users across hundreds of millions of items in a few milliseconds using standard hardware.
CLeaR: An Adaptive Continual Learning Framework for Regression Tasks
Venue: arXiv cs.LG 2021
Year: 2021

Catastrophic forgetting means that a trained neural network model gradually forgets the previously learned tasks when being retrained on new tasks. Overcoming the forgetting problem is a major problem in machine learning. Numerous continual learning algorithms are very successful in incremental learning of classification tasks, where new samples with their labels appear frequently. However, there is currently no research that addresses the catastrophic forgetting problem in regression tasks as far as we know. This problem has emerged as one of the primary constraints in some applications, such as renewable energy forecasts. This article clarifies problem-related definitions and proposes a new methodological framework that can forecast targets and update itself by means of continual learning. The framework consists of forecasting neural networks and buffers, which store newly collected data from a non-stationary data stream in an application. The changed probability distribution of the data stream, which the framework has identified, will be learned sequentially. The framework is called CLeaR (Continual Learning for Regression Tasks), where components can be flexibly customized for a specific application scenario. We design two sets of experiments to evaluate the CLeaR framework concerning fitting error (training), prediction error (test), and forgetting ratio. The first one is based on an artificial time series to explore how hyperparameters affect the CLeaR framework. The second one is designed with data collected from European wind farms to evaluate the CLeaR framework's performance in a real-world application. The experimental results demonstrate that the CLeaR framework can continually acquire knowledge in the data stream and improve the prediction accuracy. The article concludes with further research issues arising from requirements to extend the framework.
A Predictive Model for Geographic Distributions of Mangroves
Venue: arXiv cs.LG 2021
Year: 2021

Climate change is an impending disaster which is of pressing concern more and more every year. Countless efforts have been made to study the long-term effects of climate change on agriculture, land resources, and biodiversity. Studies involving marine life, however, are less prevalent in the literature. Our research studies the available data on the population of mangroves (groups of shrubs or small trees living in saline coastal intertidal zones) and their correlations to climate change variables, specifically, temperature, heat content, various sea levels, and sea salinity. Mangroves are especially relevant to oceanic ecosystems because of their protective nature towards other marine life, as well as their high absorption rate of carbon dioxide, and their ability to withstand varying levels of salinity of our coasts. The change in global distribution was studied based on global distributions of the previous year, as well as ocean heat content, salinity, temperature, halosteric sea level, thermosteric sea level, and total steric sea level. The best performing predictive model was a support vector regressor, which yielded a correlation coefficient of 0.9998.
Guiding GANs: How to control non-conditional pre-trained GANs for conditional image generation
Venue: arXiv cs.CV 2021
Year: 2021

Generative Adversarial Networks (GANs) are an arrange of two neural networks -- the generator and the discriminator -- that are jointly trained to generate artificial data, such as images, from random inputs. The quality of these generated images has recently reached such levels that can often lead both machines and humans into mistaking fake for real examples. However, the process performed by the generator of the GAN has some limitations when we want to condition the network to generate images from subcategories of a specific class. Some recent approaches tackle this \textit{conditional generation} by introducing extra information prior to the training process, such as image semantic segmentation or textual descriptions. While successful, these techniques still require defining beforehand the desired subcategories and collecting large labeled image datasets representing them to train the GAN from scratch. In this paper we present a novel and alternative method for guiding generic non-conditional GANs to behave as conditional GANs. Instead of re-training the GAN, our approach adds into the mix an encoder network to generate the high-dimensional random input vectors that are fed to the generator network of a non-conditional GAN to make it generate images from a specific subcategory. In our experiments, when compared to training a conditional GAN from scratch, our guided GAN is able to generate artificial images of perceived quality comparable to that of non-conditional GANs after training the encoder on just a few hundreds of images, which substantially accelerates the process and enables adding new subcategories seamlessly.
Conditional Local Convolution for Spatio-temporal Meteorological Forecasting
Venue: arXiv cs.LG 2021
Year: 2021

Spatio-temporal forecasting is challenging attributing to the high nonlinearity in temporal dynamics as well as complex location-characterized patterns in spatial domains, especially in fields like weather forecasting. Graph convolutions are usually used for modeling the spatial dependency in meteorology to handle the irregular distribution of sensors' spatial location. In this work, a novel graph-based convolution for imitating the meteorological flows is proposed to capture the local spatial patterns. Based on the assumption of smoothness of location-characterized patterns, we propose conditional local convolution whose shared kernel on nodes' local space is approximated by feedforward networks, with local representations of coordinate obtained by horizon maps into cylindrical-tangent space as its input. The established united standard of local coordinate system preserves the orientation on geography. We further propose the distance and orientation scaling terms to reduce the impacts of irregular spatial distribution. The convolution is embedded in a Recurrent Neural Network architecture to model the temporal dynamics, leading to the Conditional Local Convolution Recurrent Network (CLCRN). Our model is evaluated on real-world weather benchmark datasets, achieving state-of-the-art performance with obvious improvements. We conduct further analysis on local pattern visualization, model's framework choice, advantages of horizon maps and etc.
Optimization and variational principles for the shear strength reduction method
Venue: arXiv cs.CE 2021
Year: 2021

This paper is focused on the definition, analysis and numerical solution of a new optimization variant (OPT) of the shear strength reduction (SSR) problem with applications to slope stability problems. This new variant is derived on the basis of recent results by Tschuchnigg et al. 2015, where limit analysis and a modified Davis approach were used for approximation of the standard SSR method. The OPT-SSR method computes the factor of safety without performing an elasto-plastic analysis, similarly as in limit analysis. It is shown that this optimization problem is well-defined. Next, the duality between the static and kinematic principles of OPT-SSR is derived. For the numerical solution, a regularization method is introduced and analyzed. This method is combined with the finite element method, mesh adaptivity and a damped Newton method. In-house codes (Matlab) are used for the implementation of this solution concept. Finally, two slope stability problems are considered, one of which follows from analysis of a real slope. The softwares packages Plaxis and Comsol Multiphysics are used for comparison of the results.
Echelon: Two-Tier Malware Detection for Raw Executables to Reduce False Alarms
Venue: arXiv cs.CR 2021
Year: 2021

Existing malware detection approaches suffer from a simplistic trade-off between false positive rate (FPR) and true positive rate (TPR) due to a single tier classification approach, where the two measures adversely affect one another. The practical implication for malware detection is that FPR must be kept at an acceptably low level while TPR remains high. To this end, we propose a two-tiered learning, called ``Echelon", from raw byte data with no need for hand-crafted features. The first tier locks FPR at a specified target level, whereas the second tier improves TPR while maintaining the locked FPR. The core of Echelon lies at extracting activation information of the hidden layers of first tier model for constructing a stronger second tier model. Echelon is a framework in that it allows any existing CNN based model to be adapted in both tiers. We present experimental results of evaluating Echelon by adapting the state-of-the-art malware detection model ``Malconv" in the first and second tiers.
Caching at the Edge: Outage Probability
Venue: arXiv cs.NI 2021
Year: 2021

Caching at the edge of wireless networks is a keytechnology to reduce traffic in the backhaul link. However, aconcentrated amount of requests during peak-periods may causethe outage of the system, meaning that the network is not ableto serve the whole set of demands. The outage probability is afundamental metric to take into account during the networkdesign. In this paper, we derive the analytical expression ofthe outage probability as a function of the total amount ofusers requests, library size, requests distribution, cache size andcapacity constraints on the backhaul resources. In particular, wefocus on a scenario where end-users have no direct connectionto the master node which holds the complete library of contentthat can be requested. A general formulation of the outage isderived and studied for two relevant caching schemes, i.e. therandom caching scheme and the most popular caching schemes.The exact closed form expressions presented in this paper provideuseful insights on how requests, memory and resources can bebalanced when the parameters of a cache-enabled network haveto designed
Caching in Heterogeneous Satellite Networks with Fountain Codes
Venue: arXiv cs.IT 2021
Year: 2021

In this paper we investigate the performance of caching schemes based on fountain codes in a heterogeneous satellite network. We consider multiple cache-aided hubs which are connected to a geostationary satellite through backhaul links. With the aimof reducing the average number of transmissions over the satellite backhaul link, we propose the use of a caching scheme based on fountain codes. We derive a simple analytical expression of the average backhaul transmission rate and provide a tightupper bound on it. Furthermore, we show how the performance of the fountain code based caching scheme is similar to that of a caching scheme based on maximum distance separable codes.
Deploying Crowdsourcing for Workflow Driven Business Process
Venue: arXiv cs.HC 2021
Year: 2021

The main goal of this paper is to discuss how to integrate the possibilities of crowdsourcing platforms with systems supporting workflow to enable the engagement and interaction with business tasks of a wider group of people. Thus, this work is an attempt to expand the functional capabilities of typical business systems by allowing selected process tasks to be performed by unlimited human resources. Opening business tasks to crowdsourcing, within established Business Process Management Systems (BPMS) will improve the flexibility of company processes and allow for lower work-load and greater specialization among the staff employed on-site. The presented conceptual work is based on the current international standards in this field, promoted by Workflows Management Coalition. To this end, the functioning of business platforms was analysed and their functionality was presented visually, followed by a proposal and a discussion of how to implement crowdsourcing into workflow systems.
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning based object detectors are commonly deployed on mobile devices to solve a variety of tasks. For maximum accuracy, each detector is usually trained to solve one single specific task, and comes with a completely independent set of parameters. While this guarantees high performance, it is also highly inefficient, as each model has to be separately downloaded and stored. In this paper we address the question: can task-specific detectors be trained and represented as a shared set of weights, plus a very small set of additional weights for each task? The main contributions of this paper are the following: 1) we perform the first systematic study of parameter-efficient transfer learning techniques for object detection problems; 2) we propose a technique to learn a model patch with a size that is dependent on the difficulty of the task to be learned, and validate our approach on 10 different object detection tasks. Our approach achieves similar accuracy as previously proposed approaches, while being significantly more compact.