OpenRaiser/Intern-Atlas huggingface dataset:
Quasi-Orthogonal Z-Complementary Pairs and Their Applications in Fully Polarimetric Radar Systems
Venue: arXiv cs.IT 2020
Year: 2020

One objective of this paper is to propose a novel class of sequence pairs, called "Quasi-orthogonal Z-complementary pairs (QOZCPs)", each depicting Z-complementary property for their aperiodic auto-correlation sums and also have a zero correlation zone when their aperiodic cross-correlation is considered. Construction of QOZCPs based on Successively Distributed Algorithms under Majorization Minimization (SDAMM) is presented. Another objective of this paper is to apply the proposed QOZCPs in fully polarimetric radar systems and analyse the corresponding ambiguity functions. It turns out that QOZCP waveforms are much more Doppler resilient than the known Golay complementary waveforms.
Steps and Traces
Venue: arXiv cs.LO 2020
Year: 2020

In the theory of coalgebras, trace semantics can be defined in various distinct ways, including through algebraic logics, the Kleisli category of a monad or its Eilenberg-Moore category. This paper elaborates two new unifying ideas: 1) coalgebraic trace semantics is naturally presented in terms of corecursive algebras, and 2) all three approaches arise as instances of the same abstract setting. Our perspective puts the different approaches under a common roof, and allows to derive conditions under which some of them coincide.
A Role-Based Encryption Scheme for Securing Outsourced Cloud Data in a Multi-Organization Context
Venue: arXiv cs.CR 2020
Year: 2020

Role-Based Access Control (RBAC) is a popular model which maps roles to access permissions for resources and then roles to the users to provide access control. Role-Based Encryption (RBE) is a cryptographic form of RBAC model that integrates traditional RBAC with the cryptographic encryption method, where RBAC access policies are embedded in encrypted data itself so that any user holding a qualified role can access the data by decrypting it. However, the existing RBE schemes have been focusing on the single-organization cloud storage system, where the stored data can be accessed by users of the same organization. This paper presents a novel RBE scheme with efficient user revocation for the multi-organization cloud storage system, where the data from multiple independent organizations are stored and can be accessed by the authorized users from any other organization. Additionally, an outsourced decryption mechanism is introduced which enables the users to delegate expensive cryptographic operations to the cloud, thereby reducing the overhead on the end-users. Security and performance analyses of the proposed scheme demonstrate that it is provably secure against Chosen Plaintext Attack and can be useful for practical applications due to its low computation overhead.
Adaptive HTAP through Elastic Resource Scheduling
Venue: arXiv cs.DB 2020
Year: 2020

Modern Hybrid Transactional/Analytical Processing (HTAP) systems use an integrated data processing engine that performs analytics on fresh data, which are ingested from a transactional engine. HTAP systems typically consider data freshness at design time, and are optimized for a fixed range of freshness requirements, addressed at a performance cost for either OLTP or OLAP. The data freshness and the performance requirements of both engines, however, may vary with the workload. We approach HTAP as a scheduling problem, addressed at runtime through elastic resource management. We model an HTAP system as a set of three individual engines: an OLTP, an OLAP and a Resource and Data Exchange (RDE) engine. We devise a scheduling algorithm which traverses the HTAP design spectrum through elastic resource management, to meet the data freshness requirements of the workload. We propose an in-memory system design which is non-intrusive to the current state-of-art OLTP and OLAP engines, and we use it to evaluate the performance of our approach. Our evaluation shows that the performance benefit of our system for OLAP queries increases over time, reaching up to 50% compared to static schedules for 100 query sequences, while maintaining a small, and controlled, drop in the OLTP throughput.
Automated and Accurate Geometry Extraction and Shape Optimization of 3D Topology Optimization Results
Venue: arXiv cs.CE 2020
Year: 2020

Designs generated by density-based topology optimization (TO) exhibit jagged and/or smeared boundaries, which forms an obstacle to their integration with existing CAD tools. Addressing this problem by smoothing or manual design adjustments is time-consuming and affects the optimality of TO designs. This paper proposes a fully automated procedure to obtain unambiguous, accurate and optimized geometries from arbitrary 3D TO results. It consists of a geometry extraction stage using a level-set-based design description involving radial basis functions, followed by a shape optimization stage involving local analysis refinements near the structural boundary using the Finite Cell Method. Well-defined bounds on basis function weights ensure that sufficient sensitivity information is available throughout the shape optimization process. Our approach results in highly smooth and accurate optimized geometries, and its effectiveness is illustrated by 2D and 3D examples.
Attribute-based Regularization of Latent Spaces for Variational Auto-Encoders
Venue: arXiv cs.LG 2020
Year: 2020

Selective manipulation of data attributes using deep generative models is an active area of research. In this paper, we present a novel method to structure the latent space of a Variational Auto-Encoder (VAE) to encode different continuous-valued attributes explicitly. This is accomplished by using an attribute regularization loss which enforces a monotonic relationship between the attribute values and the latent code of the dimension along which the attribute is to be encoded. Consequently, post-training, the model can be used to manipulate the attribute by simply changing the latent code of the corresponding regularized dimension. The results obtained from several quantitative and qualitative experiments show that the proposed method leads to disentangled and interpretable latent spaces that can be used to effectively manipulate a wide range of data attributes spanning image and symbolic music domains.
Brain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning
Venue: arXiv cs.NE 2020
Year: 2020

Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex self-organizes itself through structural and synaptic plasticity mechanisms that are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. In spite of the diversity of the sensory modalities, like sight, sound and touch, the brain arrives at the same concepts (convergence). Moreover, biological observations show that one modality can activate the internal representation of another modality when both are correlated (divergence). In this work, we propose the Reentrant Self-Organizing Map (ReSOM), a brain-inspired neural system based on the reentry theory using Self-Organizing Maps and Hebbian-like learning. We propose and compare different computational methods for unsupervised learning and inference, then quantify the gain of the ReSOM in a multimodal classification task. The divergence mechanism is used to label one modality based on the other, while the convergence mechanism is used to improve the overall accuracy of the system. We perform our experiments on a constructed written/spoken digits database and a DVS/EMG hand gestures database. The proposed model is implemented on a cellular neuromorphic architecture that enables distributed computing with local connectivity. We show the gain of the so-called hardware plasticity induced by the ReSOM, where the system's topology is not fixed by the user but learned along the system's experience through self-organization.
A Pose Proposal and Refinement Network for Better Object Pose Estimation
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we present a novel, end-to-end 6D object pose estimation method that operates on RGB inputs. Our approach is composed of 2 main components: the first component classifies the objects in the input image and proposes an initial 6D pose estimate through a multi-task, CNN-based encoder/multi-decoder module. The second component, a refinement module, includes a renderer and a multi-attentional pose refinement network, which iteratively refines the estimated poses by utilizing both appearance features and flow vectors. Our refiner takes advantage of the hybrid representation of the initial pose estimates to predict the relative errors with respect to the target poses. It is further augmented by a spatial multi-attention block that emphasizes objects' discriminative feature parts. Experiments on three benchmarks for 6D pose estimation show that our proposed pipeline outperforms state-of-the-art RGB-based methods with competitive runtime performance.
Verification of Deep Convolutional Neural Networks Using ImageStars
Venue: arXiv cs.LG 2020
Year: 2020

Convolutional Neural Networks (CNN) have redefined the state-of-the-art in many real-world applications, such as facial recognition, image classification, human pose estimation, and semantic segmentation. Despite their success, CNNs are vulnerable to adversarial attacks, where slight changes to their inputs may lead to sharp changes in their output in even well-trained networks. Set-based analysis methods can detect or prove the absence of bounded adversarial attacks, which can then be used to evaluate the effectiveness of neural network training methodology. Unfortunately, existing verification approaches have limited scalability in terms of the size of networks that can be analyzed. In this paper, we describe a set-based framework that successfully deals with real-world CNNs, such as VGG16 and VGG19, that have high accuracy on ImageNet. Our approach is based on a new set representation called the ImageStar, which enables efficient exact and over-approximative analysis of CNNs. ImageStars perform efficient set-based analysis by combining operations on concrete images with linear programming (LP). Our approach is implemented in a tool called NNV, and can verify the robustness of VGG networks with respect to a small set of input states, derived from adversarial attacks, such as the DeepFool attack. The experimental results show that our approach is less conservative and faster than existing zonotope methods, such as those used in DeepZ, and the polytope method used in DeepPoly.
When Does Unsupervised Machine Translation Work?
Venue: arXiv cs.CL 2020
Year: 2020

Despite the reported success of unsupervised machine translation (MT), the field has yet to examine the conditions under which these methods succeed, and where they fail. We conduct an extensive empirical evaluation of unsupervised MT using dissimilar language pairs, dissimilar domains, diverse datasets, and authentic low-resource languages. We find that performance rapidly deteriorates when source and target corpora are from different domains, and that random word embedding initialization can dramatically affect downstream translation performance. We additionally find that unsupervised MT performance declines when source and target languages use different scripts, and observe very poor performance on authentic low-resource language pairs. We advocate for extensive empirical evaluation of unsupervised MT systems to highlight failure points and encourage continued research on the most promising paradigms.
Hardware Memory Management for Future Mobile Hybrid Memory Systems
Venue: arXiv cs.AR 2020
Year: 2020

The current mobile applications have rapidly growing memory footprints, posing a great challenge for memory system design. Insufficient DRAM main memory will incur frequent data swaps between memory and storage, a process that hurts performance, consumes energy and deteriorates the write endurance of typical flash storage devices. Alternately, a larger DRAM has higher leakage power and drains the battery faster. Further, DRAM scaling trends make further growth of DRAMin the mobile space prohibitive due to cost. Emerging non-volatile memory (NVM) has the potential to alleviate these issues due to its higher capacity per cost than DRAM and mini-mal static power. Recently, a wide spectrum of NVM technologies, including phase-change memories (PCM), memristor, and 3D XPoint have emerged. Despite the mentioned advantages, NVM has longer access latency compared to DRAMand NVM writes can incur higher latencies and wear costs. Therefore integration of these new memory technologies in the memory hierarchy requires a fundamental rearchitect-ing of traditional system designs. In this work, we propose a hardware-accelerated memory manager (HMMU) that addresses both types of memory in a flat space address space. We design a set of data placement and data migration policies within this memory manager, such that we may exploit the advantages of each memory technology. By augmenting the system with this HMMU, we reduce the overall memory latency while also reducing writes to the NVM. Experimental results show that our design achieves a 39% reduction in energy consumption with only a 12% performance degradation versus an all-DRAM baseline that is likely untenable in the future.
Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion
Venue: arXiv cs.CV 2020
Year: 2020

Automatic change detection and disaster damage assessment are currently procedures requiring a huge amount of labor and manual work by satellite imagery analysts. In the occurrences of natural disasters, timely change detection can save lives. In this work, we report findings on problem framing, data processing and training procedures which are specifically helpful for the task of building damage assessment using the newly released xBD dataset. Our insights lead to substantial improvement over the xBD baseline models, and we score among top results on the xView2 challenge leaderboard. We release our code used for the competition.
Behavior variations and their implications for popularity promotions: From elites to mass in Weibo
Venue: arXiv cs.SI 2020
Year: 2020

The boom in social media with regard to producing and consuming information simultaneously implies the crucial role of online user influence in determining content popularity. In particular, understanding behavior variations between the influential elites and the mass grassroots is an important issue in communication. However, how their behavior varies across user categories and content domains, and how these differences influence content popularity are rarely addressed. From a novel view of seven content-domains, a detailed picture of behavior variations among five user groups, from both views of elites and mass, is drawn in Weibo, one of the most popular Twitter-like services in China. Interestingly, elites post more diverse contents with video links while the mass possess retweeters of higher loyalty. According to these variations, user-oriented actions of enhancing content popularity are discussed and testified. The most surprising finding is that the diversity of contents do not always bring more retweets, and the mass and elites should promote content popularity by increasing their retweeter counts and loyalty, respectively. Our results for the first time demonstrate the possibility of highly individualized strategies of popularity promotions in social media, instead of a universal principle.
How not to secure wireless sensor networks: A plethora of insecure polynomial-based key pre-distribution schemes
Venue: arXiv cs.CR 2020
Year: 2020

Three closely-related polynomial-based group key pre-distribution schemes have recently been proposed, aimed specifically at wireless sensor networks. The schemes enable any subset of a predefined set of sensor nodes to establish a shared secret key without any communications overhead. It is claimed that these schemes are both secure and lightweight, i.e. making them particularly appropriate for network scenarios where nodes have limited computational and storage capabilities. Further papers have built on these schemes, e.g. to propose secure routing protocols for wireless sensor networks. Unfortunately, as we show in this paper, all three schemes are completely insecure; whilst the details of their operation varies, they share common weaknesses. In every case we show that an attacker equipped with the information built into at most two sensor nodes can compute group keys for all possible groups of which the attacked nodes are not a member, which breaks a fundamental design objective. The attacks can also be achieved by an attacker armed with the information from a single node together with a single group key to which this sensor node is not entitled. Repairing the schemes appears difficult, if not impossible. The existence of major flaws is not surprising given the complete absence of any rigorous proofs of security for the proposed schemes. A further recent paper proposes a group membership authentication and key establishment scheme based on one of the three key pre-distribution schemes analysed here; as we demonstrate, this scheme is also insecure, as the attack we describe on the corresponding pre-distribution scheme enables the authentication process to be compromised.
Community Structures in Information Networks for a Discrete Agent Population
Venue: arXiv cs.SI 2020
Year: 2020

Communities are an important feature of social networks. The goal of this paper is to propose a mathematical model to study the community structure in social networks. For this, we consider a particular case of a social network, namely information networks. We assume that there is a population of agents who are interested in obtaining content. Agents differ in the type of content they are interested in. The goal of agents is to form communities in order to maximize their utility for obtaining and producing content. We use this model to characterize the structure of communities that emerge as a Nash equilibrium in this setting. The work presented in this paper generalizes results in the literature that were obtained for the case of a continuous agent model, to the case of a discrete agent population model. We note that a discrete agent set reflects more accurately real-life information networks, and are needed in order to get additional insights into the community structure, such as for example the connectivity (graph structure) within in a community, as well as information dissemination within a community.
Learning Spatial Relationships between Samples of Patent Image Shapes
Venue: arXiv cs.CV 2020
Year: 2020

Binary image based classification and retrieval of documents of an intellectual nature is a very challenging problem. Variations in the binary image generation mechanisms which are subject to the document artisan designer including drawing style, view-point, inclusion of multiple image components are plausible causes for increasing the complexity of the problem. In this work, we propose a method suitable to binary images which bridges some of the successes of deep learning (DL) to alleviate the problems introduced by the aforementioned variations. The method consists on extracting the shape of interest from the binary image and applying a non-Euclidean geometric neural-net architecture to learn the local and global spatial relationships of the shape. Empirical results show that our method is in some sense invariant to the image generation mechanism variations and achieves results outperforming existing methods in a patent image dataset benchmark.
Vaccination strategies on dynamic networks with indirect transmission links and limited contact information
Venue: arXiv cs.SI 2020
Year: 2020

Infectious diseases are still a major global burden for modern society causing 13 million deaths annually. One way to reduce the morbidity and mortality rates from infectious diseases is through preventative or targeted vaccinations. Current vaccination strategies, however, rely on the highly specific individual contact information that is difficult and costly to obtain, in order to identify influential spreading individuals. Current approaches also focus only on direct contacts between individuals for spreading, and disregard indirect transmission where a pathogen can spread between one infected individual and one susceptible individual that visit the same location within a short time-frame without meeting. This paper presents a novel vaccination strategy that relies on coarse-grained contact information, both direct and indirect, that can be easily and efficiently collected. Rather than tracking exact contact degrees of individuals, our strategy uses the types of places people visit to estimate a range of contact degrees for individuals, considering both direct and indirect contacts. We conduct extensive simulations to evaluate the performance of our strategy in comparison to the state of the art's vaccination strategies. Results show that our strategy achieves comparable performance to the oracle approach and outperforms all existing strategies when considering indirect links.
Deep Siamese Domain Adaptation Convolutional Neural Network for Cross-domain Change Detection in Multispectral Images
Venue: arXiv cs.CV 2020
Year: 2020

Recently, deep learning has achieved promising performance in the change detection task. However, the deep models are task-specific and data set bias often exists, thus it is difficult to transfer a network trained on one multi-temporal data set (source domain) to another multi-temporal data set with very limited (even no) labeled data (target domain). In this paper, we propose a novel deep siamese domain adaptation convolutional neural network (DSDANet) architecture for cross-domain change detection. In DSDANet, a siamese convolutional neural network first extracts spatial-spectral features from multi-temporal images. Then, through multiple kernel maximum mean discrepancy (MK-MMD), the learned feature representation is embedded into a reproducing kernel Hilbert space (RKHS), in which the distribution of two domains can be explicitly matched. By optimizing the network parameters and kernel coefficients with the source labeled data and target unlabeled data, the DSDANet can learn transferrable feature representation that can bridge the discrepancy between two domains. To the best of our knowledge, it is the first time that such a domain adaptation-based deep network is proposed for change detection. The theoretical analysis and experimental results demonstrate the effectiveness and potential of the proposed method.
Approximating percentage of academic traffic in the World Wide Web and rankings of countries based on academic traffic
Venue: arXiv cs.DL 2020
Year: 2020

The paper introduces a novel mechanism for approximating traffic of the academic sites (universities and research institutes) in the World Wide Web based on Alexa rankings. Firstly we introduce and discuss new method for calculating score (weight) of each site based on its Alexa rank. Secondly we calculate percentage of academic traffic in the World Wide Web. Thirdly we introduce and discuss two new rankings of countries based on academic traffic. Finally we discuss about three indicators and effects of them in traffic of the academic sites. Results indicate that the methodology can be useful for approximating traffic of the academic sites and producing rankings of countries in practice