OpenRaiser/Intern-Atlas huggingface dataset:
Towards Formally Verified Compilation of Tag-Based Policy Enforcement
Venue: arXiv cs.CR 2020
Year: 2020

Hardware-assisted reference monitoring is receiving increasing attention as a way to improve the security of existing software. One example is the PIPE architecture extension, which attaches metadata tags to register and memory values and executes tag-based rules at each machine instruction to enforce a software-defined security policy. To use PIPE effectively, engineers should be able to write security policies in terms of source-level concepts like functions, local variables, and structured control operators, which are not visible at machine level. It is the job of the compiler to generate PIPE-aware machine code that enforces these source-level policies. The compiler thus becomes part of the monitored system's trusted computing base -- and hence a prime candidate for verification. To formalize compiler correctness in this setting, we extend the source language semantics with its own form of user-specified tag-based monitoring, and show that the compiler preserves that monitoring behavior. The challenges of compilation include mapping source-level monitoring policies to instruction-level tag rules, preserving fail-stop behaviors, and satisfying the surprisingly complex preconditions for conventional optimizations. In this paper, we describe the design and verification of Tagine, a small prototype compiler that translates a simple tagged WHILE language to a tagged register transfer language and performs simple optimizations. Tagine is based on the RTLgen and Deadcode phases of the CompCert compiler, and hence is written and verified in Coq. This work is a first step toward verification of a full-scale compiler for a realistic tagged source language.
Small Business Classification By Name: Addressing Gender and Geographic Origin Biases
Venue: arXiv cs.LG 2020
Year: 2020

Small business classification is a difficult and important task within many applications, including customer segmentation. Training on small business names introduces gender and geographic origin biases. A model for predicting one of 66 business types based only upon the business name was developed in this work (top-1 f1-score = 60.2%). Two approaches to removing the bias from this model are explored: replacing given names with a placeholder token, and augmenting the training data with gender-swapped examples. The results for these approaches is reported, and the bias in the model was reduced by hiding given names from the model. However, bias reduction was accomplished at the expense of classification performance (top-1 f1-score = 56.6%). Augmentation of the training data with gender-swapping samples proved less effective at bias reduction than the name hiding approach on the evaluated dataset.
Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty
Venue: arXiv cs.MA 2020
Year: 2020

Green Security Games (GSGs) have been successfully used in the protection of valuable resources such as fisheries, forests and wildlife. While real-world deployment involves both resource allocation and subsequent coordinated patrolling with communication and real-time, uncertain information, previous game models do not fully address both of these stages simultaneously. Furthermore, adopting existing solution strategies is difficult since they do not scale well for larger, more complex variants of the game models. We therefore first propose a novel GSG model that combines defender allocation, patrolling, real-time drone notification to human patrollers, and drones sending warning signals to attackers. The model further incorporates uncertainty for real-time decision-making within a team of drones and human patrollers. Second, we present CombSGPO, a novel and scalable algorithm based on reinforcement learning, to compute a defender strategy for this game model. CombSGPO performs policy search over a multi-dimensional, discrete action space to compute an allocation strategy that is best suited to a best-response patrolling strategy for the defender, learnt by training a multi-agent Deep Q-Network. We show via experiments that CombSGPO converges to better strategies and is more scalable than comparable approaches. Third, we provide a detailed analysis of the coordination and signaling behavior learnt by CombSGPO, showing group formation between defender resources and patrolling formations based on signaling and notifications between resources. Importantly, we find that strategic signaling emerges in the final learnt strategy. Finally, we perform experiments to evaluate these strategies under different levels of uncertainty.
Smart Refrigerator using Internet of Things and Android
Venue: arXiv cs.HC 2020
Year: 2020

The kitchen is regarded as the central unit of the traditional as well as modern homes. It is where people cook meals and where our families sit together to eat food. The refrigerator is the pivotal of all that, and hence it plays an important part in our regular lives. The idea of this project is to improvise the normal refrigerator into a smart one by making it to place order for food items and to create an virtual interactive environment between it and the user.
Base Station Coordination Scheme for Multi-tier Ultra-dense Networks
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we consider a relative received link power (RRLP)-based coordinated multi-point (CoMP) joint transmission (JT) in the multi-tier ultra-dense networks (UDN). In this CoMP scheme, we identify the cooperating base stations (BSs) by comparing the average received link power (ARLP) of the neighbouring BSs with respect to the BS having the strongest ARLP (i.e., the main link BS) to a user. To analyze the performance of this CoMP scheme in the downlink multi-tier UDN, we first approximate the received signal power distribution, and derive the coverage probability using stochastic geometry. After revisiting the area spectral efficiency (ASE) to make it more suitable for CoMP transmission in UDN, we also analyze the ASE and the network energy efficiency (NEE). Using simulations, we validate the derived coverage probability, and investigate the CoMP performance in multi-tier UDN. Our simulations show that the RRLP-based CoMP scheme can outperform the fixed number of strongest BS-based CoMP scheme in the high BS density regime. Our study of the NEE performance reveals that not only the RRLP-based CoMP scheme is more efficient than conventional non-CoMP transmission scenario, but also its NEE performance improves with the average number of cooperating BSs.
Human 3D keypoints via spatial uncertainty modeling
Venue: arXiv cs.CV 2020
Year: 2020

We introduce a technique for 3D human keypoint estimation that directly models the notion of spatial uncertainty of a keypoint. Our technique employs a principled approach to modelling spatial uncertainty inspired from techniques in robust statistics. Furthermore, our pipeline requires no 3D ground truth labels, relying instead on (possibly noisy) 2D image-level keypoints. Our method achieves near state-of-the-art performance on Human3.6m while being efficient to evaluate and straightforward to
Towards Coarse and Fine-grained Multi-Graph Multi-Label Learning
Venue: arXiv cs.LG 2020
Year: 2020

Multi-graph multi-label learning (\textsc{Mgml}) is a supervised learning framework, which aims to learn a multi-label classifier from a set of labeled bags each containing a number of graphs. Prior techniques on the \textsc{Mgml} are developed based on transfering graphs into instances and focus on learning the unseen labels only at the bag level. In this paper, we propose a \textit{coarse} and \textit{fine-grained} Multi-graph Multi-label (cfMGML) learning framework which directly builds the learning model over the graphs and empowers the label prediction at both the \textit{coarse} (aka. bag) level and \textit{fine-grained} (aka. graph in each bag) level. In particular, given a set of labeled multi-graph bags, we design the scoring functions at both graph and bag levels to model the relevance between the label and data using specific graph kernels. Meanwhile, we propose a thresholding rank-loss objective function to rank the labels for the graphs and bags and minimize the hamming-loss simultaneously at one-step, which aims to addresses the error accumulation issue in traditional rank-loss algorithms. To tackle the non-convex optimization problem, we further develop an effective sub-gradient descent algorithm to handle high-dimensional space computation required in cfMGML. Experiments over various real-world datasets demonstrate cfMGML achieves superior performance than the state-of-arts algorithms.
Network Reconnaissance in IPv6-based Residential Broadband Networks
Venue: arXiv cs.NI 2020
Year: 2020

Network scanning has been a widely used technique to gather information on the Internet as a whole. The transition from IPv4 to IPv6 causes traditional network scanning to become less useful. An increasing number of hosts is either IPv6-only or not publicly addressable via IPv4 due to the use of NAT, prompting a need for network scanning techniques for the IPv6-based Internet. All current approaches to IPv6 network scanning make use of hitlists (lists of IPv6 addresses to be scanned). A variety of methods for compiling hitlists have been presented, but they have a strong bias towards server hosts, and do not find addresses of client hosts -- smartphones, tablets, PCs, 'smart home' devices, etc. -- in a significant amount. Client hosts are the majority of devices connected to the Internet. Furthermore, when connected to a residential broadband connection, they can exchange data at substantial speeds, making them attractive targets for botnets. Scanning residential broadband networks is challenging because the active addresses are changing much more frequently than addresses of server hosts. This master's thesis aims to adapt prior IPv6 network scanning techniques to residential broadband networks. To this end, the following contributions are made: Description and evaluation of an IPv6 address space visualization method, Introduction of the NTP Pool Project as a public and passive IPv6 hitlist source detecting mostly client hosts, 'Smart Home' devices and CPEs, Description of a scanning technique for Internet access provider networks, Case study on the three major German residential broadband networks.
The importance of silhouette optimization in 3D shape reconstruction system from multiple object scenes
Venue: arXiv cs.CV 2020
Year: 2020

This paper presents a multi stage 3D shape reconstruction system of multiple object scenes by considering the silhouette inconsistencies in shape-from silhouette SFS method. These inconsistencies are common in multiple view images due to object occlusions in different views, segmentation and shadows or reflection due to objects or light directions. These factors raise huge challenges when attempting to construct the 3D shape by using existing approaches which reconstruct only that part of the volume which projects consistently in all the silhouettes, leaving the rest unreconstructed. As a result, final shape are not robust due to multi view objects occlusion and shadows. In this regard, we consider the primary factors affecting reconstruction by analyzing the multiple images and perform pre-processing steps to optimize the silhouettes. Finally, the 3D shape is reconstructed by using the volumetric approach SFS. Theory and experimental results show that, the performance of the modified algorithm was efficiently improved, which can improve the accuracy of the reconstructed shape and being robust to errors in the silhouettes, volume and computational inexpensive.
An Information-Theoretic Framework for Unifying Active Learning Problems
Venue: arXiv cs.LG 2020
Year: 2020

This paper presents an information-theoretic framework for unifying active learning problems: level set estimation (LSE), Bayesian optimization (BO), and their generalized variant. We first introduce a novel active learning criterion that subsumes an existing LSE algorithm and achieves state-of-the-art performance in LSE problems with a continuous input domain. Then, by exploiting the relationship between LSE and BO, we design a competitive information-theoretic acquisition function for BO that has interesting connections to upper confidence bound and max-value entropy search (MES). The latter connection reveals a drawback of MES which has important implications on not only MES but also on other MES-based acquisition functions. Finally, our unifying information-theoretic framework can be applied to solve a generalized problem of LSE and BO involving multiple level sets in a data-efficient manner. We empirically evaluate the performance of our proposed algorithms using synthetic benchmark functions, a real-world dataset, and in hyperparameter tuning of machine learning models.
Probabilistic Dependency Graphs
Venue: arXiv cs.AI 2020
Year: 2020

We introduce Probabilistic Dependency Graphs (PDGs), a new class of directed graphical models. PDGs can capture inconsistent beliefs in a natural way and are more modular than Bayesian Networks (BNs), in that they make it easier to incorporate new information and restructure the representation. We show by example how PDGs are an especially natural modeling tool. We provide three semantics for PDGs, each of which can be derived from a scoring function (on joint distributions over the variables in the network) that can be viewed as representing a distribution's incompatibility with the PDG. For the PDG corresponding to a BN, this function is uniquely minimized by the distribution the BN represents, showing that PDG semantics extend BN semantics. We show further that factor graphs and their exponential families can also be faithfully represented as PDGs, while there are significant barriers to modeling a PDG with a factor graph.
Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Massive account registration has raised concerns on risk management in e-commerce companies, especially when registration increases rapidly within a short time frame. To monitor these registrations constantly and minimize the potential loss they might incur, detecting massive registration and predicting their riskiness are necessary. In this paper, we propose a Dynamic Heterogeneous Graph Neural Network framework to capture suspicious massive registrations (DHGReg). We first construct a dynamic heterogeneous graph from the registration data, which is composed of a structural subgraph and a temporal subgraph. Then, we design an efficient architecture to predict suspicious/benign accounts. Our proposed model outperforms the baseline models and is computationally efficient in processing a dynamic heterogeneous graph constructed from a real-world dataset. In practice, the DHGReg framework would benefit the detection of suspicious registration behaviors at an early stage.
Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks
Venue: arXiv cs.CV 2020
Year: 2020

Hyperspectral image classification (HIC) is an important but challenging task, and a problem that limits the algorithmic development in this field is that the ground truths of hyperspectral images (HSIs) are extremely hard to obtain. Recently a handful of HIC methods are developed based on the graph convolution networks (GCNs), which effectively relieves the scarcity of labeled data for deep learning based HIC methods. To further lift the classification performance, in this work we propose a graph convolution network (GCN) based framework for HSI classification that uses two clustering operations to better exploit multi-hop node correlations and also effectively reduce graph size. In particular, we first cluster the pixels with similar spectral features into a superpixel and build the graph based on the superpixels of the input HSI. Then instead of performing convolution over this superpixel graph, we further partition it into several sub-graphs by pruning the edges with weak weights, so as to strengthen the correlations of nodes with high similarity. This second round of clustering also further reduces the graph size, thus reducing the computation burden of graph convolution. Experimental results on three widely used benchmark datasets well prove the effectiveness of our proposed framework.
Notes on Hazard-Free Circuits
Venue: arXiv cs.CC 2020
Year: 2020

The problem of constructing hazard-free Boolean circuits (those avoiding electronic glitches) dates back to the 1940s and is an important problem in circuit design and even in cybersecurity. We show that a DeMorgan circuit is hazard-free if and only if the circuit produces (purely syntactically) all prime implicants as well as all prime implicates of the Boolean function it computes. This extends to arbitrary DeMorgan circuits a classical result of Eichelberger [IBM J. Res. Develop., 9 (1965)] showing this property for special depth-two circuits. Via an amazingly simple proof, we also strengthen a recent result Ikenmeyer et al. [J. ACM, 66:4 (2019)]: not only the complexities of hazard-free and monotone circuits for monotone Boolean functions do coincide, but every optimal hazard-free circuit for a monotone Boolean function must be monotone. Then we show that hazard-free circuit complexity of a very simple (non-monotone) Boolean function is super-polynomially larger than its unrestricted circuit complexity. This function accepts a Boolean n x n matrix iff every row and every column has exactly one 1-entry. Finally, we show that every Boolean function of n variables can be computed by a hazard-free circuit of size O(2^n/n).
Monte-Carlo Graph Search for AlphaZero
Venue: arXiv cs.AI 2020
Year: 2020

The AlphaZero algorithm has been successfully applied in a range of discrete domains, most notably board games. It utilizes a neural network, that learns a value and policy function to guide the exploration in a Monte-Carlo Tree Search. Although many search improvements have been proposed for Monte-Carlo Tree Search in the past, most of them refer to an older variant of the Upper Confidence bounds for Trees algorithm that does not use a policy for planning. We introduce a new, improved search algorithm for AlphaZero which generalizes the search tree to a directed acyclic graph. This enables information flow across different subtrees and greatly reduces memory consumption. Along with Monte-Carlo Graph Search, we propose a number of further extensions, such as the inclusion of Epsilon-greedy exploration, a revised terminal solver and the integration of domain knowledge as constraints. In our evaluations, we use the CrazyAra engine on chess and crazyhouse as examples to show that these changes bring significant improvements to AlphaZero.
An empirical analysis of success factors in the adaption of the scaled agile framework -- first outcomes from an empirical study
Venue: arXiv cs.SE 2020
Year: 2020

Agile methodologies are used for improving productivity and quality of development originally created for small teams. However , now they are expanding to larger organizations, for which "scaled up" approaches have been proposed. This study presents the preliminary outcomes from a survey on the effects of the Scaled Agile Framework (SAFe), which is considered the most used of such approaches. The apparent advantages and limitations are discussed along with the lines for future research.
Automated segmentation of an intensity calibration phantom in clinical CT images using a convolutional neural network
Venue: arXiv cs.CV 2020
Year: 2020

Purpose: To apply a convolutional neural network (CNN) to develop a system that segments intensity calibration phantom regions in computed tomography (CT) images, and to test the system in a large cohort to evaluate its robustness. Methods: A total of 1040 cases (520 cases each from two institutions), in which an intensity calibration phantom (B-MAS200, Kyoto Kagaku, Kyoto, Japan) was used, were included herein. A training dataset was created by manually segmenting the regions of the phantom for 40 cases (20 cases each). Segmentation accuracy of the CNN model was assessed with the Dice coefficient and the average symmetric surface distance (ASD) through the 4-fold cross validation. Further, absolute differences of radiodensity values (in Hounsfield units: HU) were compared between manually segmented regions and automatically segmented regions. The system was tested on the remaining 1000 cases. For each institution, linear regression was applied to calculate coefficients for the correlation between radiodensity and the densities of the phantom. Results: After training, the median Dice coefficient was 0.977, and the median ASD was 0.116 mm. When segmented regions were compared between manual segmentation and automated segmentation, the median absolute difference was 0.114 HU. For the test cases, the median correlation coefficient was 0.9998 for one institution and was 0.9999 for the other, with a minimum value of 0.9863. Conclusions: The CNN model successfully segmented the calibration phantom's regions in the CT images with excellent accuracy, and the automated method was found to be at least equivalent to the conventional manual method. Future study should integrate the system by automatically segmenting the region of interest in bones such that the bone mineral density can be fully automatically quantified from CT images.
Who will accept my request? Predicting response of link initiation in two-way relation networks
Venue: arXiv cs.AI 2020
Year: 2020

Popularity of social networks has rapidly increased over the past few years, and daily lives interrupt without their proper functioning. Social networking platform provide multiple interaction types between individuals, such as creating and joining groups, sending and receiving messages, sharing interests and creating friendship relationships. This paper addresses an important problem in social networks analysis and mining that is how to predict link initiation feedback in two-way networks. Relationships between two individuals in a two-way network include a link invitation from one of the individuals, which will be an established link if it is accepted by the invitee. We consider a sport gaming social networking platform and construct a multilayer social network between a number of users. The network formed by the link initiation process is on one of the layers, while the other two layers include a messaging relationships and interactions between the users. We propose a methodology to solve the link initiation feedback prediction problem in this multilayer fashion. The proposed method is based on features extracted from meta-paths, i.e. paths defined between different individuals from multiples layers in multilayer networks. We proposed a cluster-based approach to handle the sparsity issue in the dataset. Experimental results show that the proposed method can provide accurate prediction that outperforms state-of-the-art methods.
Infrared image pedestrian target detection based on Yolov3 and migration learning
Venue: arXiv cs.AI 2020
Year: 2020

With the gradual application of infrared night vision vehicle assistance system in automatic driving, the accuracy of the collected infrared images of pedestrians is gradually improved. In this paper, the migration learning method is used to apply YOLOv3 model to realize pedestrian target detection in infrared images. The target detection model YOLOv3 is migrated to the CVC infrared pedestrian data set, and Diou loss is used to replace the loss function of the original YOLO model to test different super parameters to obtain the best migration learning effect. The experimental results show that in the pedestrian detection task of CVC data set, the average accuracy (AP) of Yolov3 model reaches 96.35%, and that of Diou-Yolov3 model is 72.14%, but the latter has a faster convergence rate of loss curve. The effect of migration learning can be obtained by comparing the two models.