OpenRaiser/Intern-Atlas huggingface dataset:
Resource Management and Quality of Service Provisioning in 5G Cellular Networks
Venue: arXiv cs.NI 2020
Year: 2020

With the commercial launch of 5G technologies and fast pace of expansion of cellular network infrastructure, it is expected that cellular and mobile networks traffic will exponentially increase. In addition, new services are expected to spread widely, such as the Internet of Things connected to mobile networks. This will add additional burden in terms of traffic load. As a result, some studies suggest that mobile traffic may increase more than 1000 times compared to the amount of traffic that is generated nowadays. This means that network resources for mobile services must be managed and controlled in a smart way, because resources are always limited, but the demand for services and the need for keeping user equipment always connected to mobile networks can be considered unlimited, leaving gap between huge service demands and available resources. In order to narrow this gap, major consideration should be given to the management of network resources to avoid network congestion and performance degradation during peak hour/s and traffic spikes, and allow access to network services to more customers when demand is high. On the other hand, guaranteeing quality of service requirements for the wide range of new services is another challenge that must be met in 5G networks. In this paper we will review 5G networks characteristics and specifications, then carry out a survey on resource management and QoS provisioning to improve and manage resource utilization in 5G networks.
Privacy Preserving Recalibration under Domain Shift
Venue: arXiv cs.LG 2020
Year: 2020

Classifiers deployed in high-stakes real-world applications must output calibrated confidence scores, i.e. their predicted probabilities should reflect empirical frequencies. Recalibration algorithms can greatly improve a model's probability estimates; however, existing algorithms are not applicable in real-world situations where the test data follows a different distribution from the training data, and privacy preservation is paramount (e.g. protecting patient records). We introduce a framework that abstracts out the properties of recalibration problems under differential privacy constraints. This framework allows us to adapt existing recalibration algorithms to satisfy differential privacy while remaining effective for domain-shift situations. Guided by our framework, we also design a novel recalibration algorithm, accuracy temperature scaling, that outperforms prior work on private datasets. In an extensive empirical study, we find that our algorithm improves calibration on domain-shift benchmarks under the constraints of differential privacy. On the 15 highest severity perturbations of the ImageNet-C dataset, our method achieves a median ECE of 0.029, over 2x better than the next best recalibration method and almost 5x better than without recalibration.
Deletion to Induced Matching
Venue: arXiv cs.DS 2020
Year: 2020

In the DELETION TO INDUCED MATCHING problem, we are given a graph $G$ on $n$ vertices, $m$ edges and a non-negative integer $k$ and asks whether there exists a set of vertices $S \subseteq V(G) $ such that $|S|\le k$ and the size of any connected component in $G-S$ is exactly 2. In this paper, we provide a fixed-parameter tractable (FPT) algorithm of running time $O^*(1.748^{k})$ for the DELETION TO INDUCED MATCHING problem using branch-and-reduce strategy and path decomposition. We also extend our work to the exact-exponential version of the problem.
Team DoNotDistribute at SemEval-2020 Task 11: Features, Finetuning, and Data Augmentation in Neural Models for Propaganda Detection in News Articles
Venue: arXiv cs.CL 2020
Year: 2020

This paper presents our systems for SemEval 2020 Shared Task 11: Detection of Propaganda Techniques in News Articles. We participate in both the span identification and technique classification subtasks and report on experiments using different BERT-based models along with handcrafted features. Our models perform well above the baselines for both tasks, and we contribute ablation studies and discussion of our results to dissect the effectiveness of different features and techniques with the goal of aiding future studies in propaganda detection.
SOTER on ROS: A Run-Time Assurance Framework on the Robot Operating System
Venue: arXiv cs.RO 2020
Year: 2020

We present an implementation of SOTER, a run-time assurance framework for building safe distributed mobile robotic (DMR) systems, on top of the Robot Operating System (ROS). The safety of DMR systems cannot always be guaranteed at design time, especially when complex, off-the-shelf components are used that cannot be verified easily. SOTER addresses this by providing a language-based approach for run-time assurance for DMR systems. SOTER implements the reactive robotic software using the language P, a domain-specific language designed for implementing asynchronous event-driven systems, along with an integrated run-time assurance system that allows programmers to use unfortified components but still provide safety guarantees. We describe an implementation of SOTER for ROS and demonstrate its efficacy using a multi-robot surveillance case study, with multiple run-time assurance modules. Through rigorous simulation, we show that SOTER enabled systems ensure safety, even when using unknown and untrusted components.
COVID-19 Pandemic Outbreak in the Subcontinent: A data-driven analysis
Venue: arXiv cs.CY 2020
Year: 2020

Human civilization is experiencing a critical situation that presents itself for a new coronavirus disease 2019 (COVID-19). This virus emerged in late December 2019 in Wuhan city, Hubei, China. The grim fact of COVID-19 is, it is highly contagious in nature, therefore, spreads rapidly all over the world and causes severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Responding to the severity of COVID-19 research community directs the attention to the analysis of COVID-19, to diminish its antagonistic impact towards society. Numerous studies claim that the subcontinent, i.e., Bangladesh, India, and Pakistan, could remain in the worst affected region by the COVID-19. In order to prevent the spread of COVID-19, it is important to predict the trend of COVID-19 beforehand the planning of effective control strategies. Fundamentally, the idea is to dependably estimate the reproduction number to judge the spread rate of COVID-19 in a particular region. Consequently, this paper uses publicly available epidemiological data of Bangladesh, India, and Pakistan to estimate the reproduction numbers. More specifically, we use various models (for example, susceptible infection recovery (SIR), exponential growth (EG), sequential Bayesian (SB), maximum likelihood (ML) and time dependent (TD)) to estimate the reproduction numbers and observe the model fitness in the corresponding data set. Experimental results show that the reproduction numbers produced by these models are greater than 1.2 (approximately) indicates that COVID-19 is gradually spreading in the subcontinent.
Fungal sensing skin
Venue: arXiv cs.ET 2020
Year: 2020

A fungal skin is a thin flexible sheet of a living homogeneous mycelium made by a filamentous fungus. The skin could be used in future living architectures of adaptive buildings and as a sensing living skin for soft self-growing/adaptive robots. In experimental laboratory studies we demonstrate that the fungal skin is capable for recognising mechanical and optical stimulation. The skin reacts differently to loading of a weight, removal of the weight, and switching illumination on and off. These are the first experimental evidences that fungal materials can be used not only as mechanical `skeletons' in architecture and robotics but also as intelligent skins capable for recognition of external stimuli and sensorial fusion.
Self-Competitive Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Deep Neural Networks (DNNs) have improved the accuracy of classification problems in lots of applications. One of the challenges in training a DNN is its need to be fed by an enriched dataset to increase its accuracy and avoid it suffering from overfitting. One way to improve the generalization of DNNs is to augment the training data with new synthesized adversarial samples. Recently, researchers have worked extensively to propose methods for data augmentation. In this paper, we generate adversarial samples to refine the Domains of Attraction (DoAs) of each class. In this approach, at each stage, we use the model learned by the primary and generated adversarial data (up to that stage) to manipulate the primary data in a way that look complicated to the DNN. The DNN is then retrained using the augmented data and then it again generates adversarial data that are hard to predict for itself. As the DNN tries to improve its accuracy by competing with itself (generating hard samples and then learning them), the technique is called Self-Competitive Neural Network (SCNN). To generate such samples, we pose the problem as an optimization task, where the network weights are fixed and use a gradient descent based method to synthesize adversarial samples that are on the boundary of their true labels and the nearest wrong labels. Our experimental results show that data augmentation using SCNNs can significantly increase the accuracy of the original network. As an example, we can mention improving the accuracy of a CNN trained with 1000 limited training data of MNIST dataset from 94.26% to 98.25%.
Proverum: A Hybrid Public Verifiability and Decentralized Identity Management
Venue: arXiv cs.CR 2020
Year: 2020

Trust in electoral processes is fundamental for democracies. Further, the identity management of citizen data is crucial, because final tallies cannot be guaranteed without the assurance that every final vote was cast by an eligible voter. In order to establish a basis for a hybrid public verifiability of voting, this work (1) introduces Proverum, an approach combining a private environment based on private permissioned Distributed Ledgers with a public environment based on public Blockchains, (2) describes the application of the Proverum architecture to the Swiss Remote Postal Voting system, mitigating threats present in the current system, and (3) addresses successfully the decentralized identity management in a federalistic state.
Few-Shot Learning with Intra-Class Knowledge Transfer
Venue: arXiv cs.CV 2020
Year: 2020

We consider the few-shot classification task with an unbalanced dataset, in which some classes have sufficient training samples while other classes only have limited training samples. Recent works have proposed to solve this task by augmenting the training data of the few-shot classes using generative models with the few-shot training samples as the seeds. However, due to the limited number of the few-shot seeds, the generated samples usually have small diversity, making it difficult to train a discriminative classifier for the few-shot classes. To enrich the diversity of the generated samples, we propose to leverage the intra-class knowledge from the neighbor many-shot classes with the intuition that neighbor classes share similar statistical information. Such intra-class information is obtained with a two-step mechanism. First, a regressor trained only on the many-shot classes is used to evaluate the few-shot class means from only a few samples. Second, superclasses are clustered, and the statistical mean and feature variance of each superclass are used as transferable knowledge inherited by the children few-shot classes. Such knowledge is then used by a generator to augment the sparse training data to help the downstream classification tasks. Extensive experiments show that our method achieves state-of-the-art across different datasets and $n$-shot settings.
The linear codes of t-designs held in the Reed-Muller and Simplex codes
Venue: arXiv cs.IT 2020
Year: 2020

A fascinating topic of combinatorics is $t$-designs, which have a very long history. The incidence matrix of a $t$-design generates a linear code over GF$(q)$ for any prime power $q$, which is called the linear code of the $t$-design over GF$(q)$. On the other hand, some linear codes hold $t$-designs for some $t \geq 1$. The purpose of this paper is to study the linear codes of some $t$-designs held in the Reed-Muller and Simplex codes. Some general theory for the linear codes of $t$-designs held in linear codes is presented. Open problems are also presented.
Translating Paintings Into Music Using Neural Networks
Venue: arXiv cs.SD 2020
Year: 2020

We propose a system that learns from artistic pairings of music and corresponding album cover art. The goal is to 'translate' paintings into music and, in further stages of development, the converse. We aim to deploy this system as an artistic tool for real time 'translations' between musicians and painters. The system's outputs serve as elements to be employed in a joint live performance of music and painting, or as generative material to be used by the artists as inspiration for their improvisation.
An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the web
Venue: arXiv cs.CL 2020
Year: 2020

Although a great deal of attention has been paid to how conspiracy theories circulate on social media and their factual counterpart conspiracies, there has been little computational work done on describing their narrative structures. We present an automated pipeline for the discovery and description of the generative narrative frameworks of conspiracy theories on social media, and actual conspiracies reported in the news media. We base this work on two separate repositories of posts and news articles describing the well-known conspiracy theory Pizzagate from 2016, and the New Jersey conspiracy Bridgegate from 2013. We formulate a graphical generative machine learning model where nodes represent actors/actants, and multi-edges and self-loops among nodes capture context-specific relationships. Posts and news items are viewed as samples of subgraphs of the hidden narrative network. The problem of reconstructing the underlying structure is posed as a latent model estimation problem. We automatically extract and aggregate the actants and their relationships from the posts and articles. We capture context specific actants and interactant relationships by developing a system of supernodes and subnodes. We use these to construct a network, which constitutes the underlying narrative framework. We show how the Pizzagate framework relies on the conspiracy theorists' interpretation of "hidden knowledge" to link otherwise unlinked domains of human interaction, and hypothesize that this multi-domain focus is an important feature of conspiracy theories. While Pizzagate relies on the alignment of multiple domains, Bridgegate remains firmly rooted in the single domain of New Jersey politics. We hypothesize that the narrative framework of a conspiracy theory might stabilize quickly in contrast to the narrative framework of an actual one, which may develop more slowly as revelations come to light.
Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing
Venue: arXiv cs.SE 2020
Year: 2020

Millions of mobile apps are available in app stores, such as Apple's App Store and Google Play. For a mobile app, it would be increasingly challenging to stand out from the enormous competitors and become prevalent among users. Good user experience and well-designed functionalities are the keys to a successful app. To achieve this, popular apps usually schedule their updates frequently. If we can capture the critical app issues faced by users in a timely and accurate manner, developers can make timely updates, and good user experience can be ensured. There exist prior studies on analyzing reviews for detecting emerging app issues. These studies are usually based on topic modeling or clustering techniques. However, the short-length characteristics and sentiment of user reviews have not been considered. In this paper, we propose a novel emerging issue detection approach named MERIT to take into consideration the two aforementioned characteristics. Specifically, we propose an Adaptive Online Biterm Sentiment-Topic (AOBST) model for jointly modeling topics and corresponding sentiments that takes into consideration app versions. Based on the AOBST model, we infer the topics negatively reflected in user reviews for one app version, and automatically interpret the meaning of the topics with most relevant phrases and sentences. Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT in identifying emerging app issues, improving the state-of-the-art method by 22.3% in terms of F1-score. In terms of efficiency, MERIT can return results within acceptable time.
Detecting signal from science:The structure of research communities and prior knowledge improves prediction of genetic regulatory experiments
Venue: arXiv cs.SI 2020
Year: 2020

The explosive growth of scientists, scientific journals, articles and findings in recent years exponentially increases the difficulty scientists face in navigating prior knowledge. This challenge is exacerbated by uncertainty about the reproducibility of published findings. The availability of massive digital archives, machine reading and extraction tools on the one hand, and automated high-throughput experiments on the other, allow us to evaluate these challenges at scale and identify novel opportunities for accelerating scientific advance. Here we demonstrate a Bayesian calculus that enables the positive prediction of robust, replicable scientific claims with findings automatically extracted from published literature on gene interactions. We matched these findings, filtered by science, with unfiltered gene interactions measured by the massive LINCS L1000 high-throughput experiment to identify and counteract sources of bias. Our calculus is built on easily extracted publication meta-data regarding the position of a scientific claim within the web of prior knowledge, and its breadth of support across institutions, authors and communities, revealing that scientifically focused but socially and institutionally independent research activity is most likely to replicate. These findings recommend policies that go against the common practice of channeling biomedical research funding into centralized research consortia and institutes rather than dispersing it more broadly. Our results demonstrate that robust scientific findings hinge upon a delicate balance of shared focus and independence, and that this complex pattern can be computationally exploited to decode bias and predict the replicability of published findings. These insights provide guidance for scientists navigating the research literature and for science funders seeking to improve it.
Visible Feature Guidance for Crowd Pedestrian Detection
Venue: arXiv cs.CV 2020
Year: 2020

Heavy occlusion and dense gathering in crowd scene make pedestrian detection become a challenging problem, because it's difficult to guess a precise full bounding box according to the invisible human part. To crack this nut, we propose a mechanism called Visible Feature Guidance (VFG) for both training and inference. During training, we adopt visible feature to regress the simultaneous outputs of visible bounding box and full bounding box. Then we perform NMS only on visible bounding boxes to achieve the best fitting full box in inference. This manner can alleviate the incapable influence brought by NMS in crowd scene and make full bounding box more precisely. Furthermore, in order to ease feature association in the post application process, such as pedestrian tracking, we apply Hungarian algorithm to associate parts for a human instance. Our proposed method can stably bring about 2~3% improvements in mAP and AP50 for both two-stage and one-stage detector. It's also more effective for MR-2 especially with the stricter IoU. Experiments on Crowdhuman, Cityperson, Caltech and KITTI datasets show that visible feature guidance can help detector achieve promisingly better performances. Moreover, parts association produces a strong benchmark on Crowdhuman for the vision community.
Mobile Robot Path Planning in Static Environments using Particle Swarm Optimization
Venue: arXiv cs.RO 2020
Year: 2020

Motion planning is a key element of robotics since it empowers a robot to navigate autonomously. Particle Swarm Optimization is a simple, yet a very powerful optimization technique which has been effectively used in many complex multi-dimensional optimization problems. This paper proposes a path planning algorithm based on particle swarm optimization for computing a shortest collision-free path for a mobile robot in environments populated with static convex obstacles. The proposed algorithm finds the optimal path by performing random sampling on grid lines generated between the robot start and goal positions. Functionality of the proposed algorithm is illustrated via simulation results for different scenarios.
Geometry-guided Dense Perspective Network for Speech-Driven Facial Animation
Venue: arXiv cs.GR 2020
Year: 2020

Realistic speech-driven 3D facial animation is a challenging problem due to the complex relationship between speech and face. In this paper, we propose a deep architecture, called Geometry-guided Dense Perspective Network (GDPnet), to achieve speaker-independent realistic 3D facial animation. The encoder is designed with dense connections to strengthen feature propagation and encourage the re-use of audio features, and the decoder is integrated with an attention mechanism to adaptively recalibrate point-wise feature responses by explicitly modeling interdependencies between different neuron units. We also introduce a non-linear face reconstruction representation as a guidance of latent space to obtain more accurate deformation, which helps solve the geometry-related deformation and is good for generalization across subjects. Huber and HSIC (Hilbert-Schmidt Independence Criterion) constraints are adopted to promote the robustness of our model and to better exploit the non-linear and high-order correlations. Experimental results on the public dataset and real scanned dataset validate the superiority of our proposed GDPnet compared with state-of-the-art model.
Holistic Multi-View Building Analysis in the Wild with Projection Pooling
Venue: arXiv cs.CV 2020
Year: 2020

We address six different classification tasks related to fine-grained building attributes: construction type, number of floors, pitch and geometry of the roof, facade material, and occupancy class. Tackling such a remote building analysis problem became possible only recently due to growing large-scale datasets of urban scenes. To this end, we introduce a new benchmarking dataset, consisting of 49426 images (top-view and street-view) of 9674 buildings. These photos are further assembled, together with the geometric metadata. The dataset showcases various real-world challenges, such as occlusions, blur, partially visible objects, and a broad spectrum of buildings. We propose a new projection pooling layer, creating a unified, top-view representation of the top-view and the side views in a high-dimensional space. It allows us to utilize the building and imagery metadata seamlessly. Introducing this layer improves classification accuracy -- compared to highly tuned baseline models -- indicating its suitability for building analysis.