OpenRaiser/Intern-Atlas huggingface dataset:
A New Benchmark Dataset for Texture Image Analysis and Surface Defect Detection
Venue: arXiv cs.CV 2019
Year: 2019

Texture analysis plays an important role in many image processing applications to describe the image content or objects. On the other hand, visual surface defect detection is a highly research field in the computer vision. Surface defect refers to abnormalities in the texture of the surface. So, in this paper a dual purpose benchmark dataset is proposed for texture image analysis and surface defect detection titled stone texture image (STI dataset). The proposed benchmark dataset consist of 4 different class of stone texture images. The proposed benchmark dataset have some unique properties to make it very near to real applications. Local rotation, different zoom rates, unbalanced classes, variation of textures in size are some properties of the proposed dataset. In the result part, some descriptors are applied on this dataset to evaluate the proposed STI dataset in comparison with other state-of-the-art datasets.
Curriculum Learning for Deep Generative Models with Clustering
Venue: arXiv cs.LG 2019
Year: 2019

Training generative models like Generative Adversarial Network (GAN) is challenging for noisy data. A novel curriculum learning algorithm pertaining to clustering is proposed to address this issue in this paper. The curriculum construction is based on the centrality of underlying clusters in data points. The data points of high centrality takes priority of being fed into generative models during training. To make our algorithm scalable to large-scale data, the active set is devised, in the sense that every round of training proceeds only on an active subset containing a small fraction of already trained data and the incremental data of lower centrality. Moreover, the geometric analysis is presented to interpret the necessity of cluster curriculum for generative models. The experiments on cat and human-face data validate that our algorithm is able to learn the optimal generative models (e.g. ProGAN) with respect to specified quality metrics for noisy data. An interesting finding is that the optimal cluster curriculum is closely related to the critical point of the geometric percolation process formulated in the paper.
Evaluating an Automated Mediator for Joint Narratives in a Conflict Situation
Venue: arXiv cs.HC 2019
Year: 2019

Joint narratives are often used in the context of reconciliation interventions for people in social conflict situations, which arise, for example, due to ethnic or religious differences. The interventions aim to encourage a change in attitudes of the participants towards each other. Typically, a human mediator is fundamental for achieving a successful intervention. In this work, we present an automated approach to support remote interactions between pairs of participants as they contribute to a shared story in their own language. A key component is an automated cognitive tutor that guides the participants through a controlled escalation/de-escalation process during the development of a joint narrative. We performed a controlled study comparing a trained human mediator to the automated mediator. The results demonstrate that an automated mediator, although simple at this stage, effectively supports interactions and helps to achieve positive outcomes comparable to those attained by the trained human mediator.
Structural Contracts -- Contracts for Type Construction & Dependent Types to Ensure Consistency of Extra-Functional Reasoning
Venue: arXiv cs.PL 2019
Year: 2019

Targeting to use contract-based design for the specification and refinement of extra-functional properties, this research abstract suggests to use type constraints and dependent types to ensure correct and consistent top-down decomposition of contracts with respect to a specifiable type constructor. For this, we summarize the composition problem and give a short draft of our approach, called Structural Contracts.
On the Energy Efficiency of Limited-Backhaul Cell-Free Massive MIMO
Venue: arXiv cs.IT 2019
Year: 2019

We investigate the energy efficiency performance of cell-free Massive multiple-input multiple-output (MIMO), where the access points (APs) are connected to a central processing unit (CPU) via limited-capacity links. Thanks to the distributed maximum ratio combining (MRC) weighting at the APs, we propose that only the quantized version of the weighted signals are sent back to the CPU. Considering the effects of channel estimation errors and using the Bussgang theorem to model the quantization errors, an energy efficiency maximization problem is formulated with per-user power and backhaul capacity constraints as well as with throughput requirement constraints. To handle this non-convex optimization problem, we decompose the original problem into two sub-problems and exploit a successive convex approximation (SCA) to solve original energy efficiency maximization problem. Numerical results confirm the superiority of the proposed optimization scheme.
Evaluating data-flow coverage in spectrum-based fault localization
Venue: arXiv cs.SE 2019
Year: 2019

Background: Debugging is a key task during the software development cycle. Spectrum-based Fault Localization (SFL) is a promising technique to improve and automate debugging. SFL techniques use control-flow spectra to pinpoint the most suspicious program elements. However, data-flow spectra provide more detailed information about the program execution, which may be useful for fault localization. Aims: We evaluate the effectiveness and efficiency of ten SFL ranking metrics using data-flow spectra. Method: We compare the performance of data- and control-flow spectra for SFL using 163 faults from 5 real-world open source programs, which contain from 468 to 4130 test cases. The data- and control-flow spectra types used in our evaluation are definition-use associations (DUAs) and lines, respectively. Results: Using data-flow spectra, up to 50% more faults are ranked in the top-15 positions compared to control-flow spectra. Also, most SFL ranking metrics present better effectiveness using data-flow to inspect up to the top-40 positions. The execution cost of data-flow spectra is higher than control-flow, taking from 22 seconds to less than 9 minutes. Data-flow has an average overhead of 353% for all programs, while the average overhead for control-flow is of 102%. Conclusions: The results suggest that SFL techniques can benefit from using data-flow spectra to classify faults in better positions, which may lead developers to inspect less code to find bugs. The execution cost to gather data-flow is higher compared to control-flow, but it is not prohibitive. Moreover, data-flow spectra also provide information about suspicious variables for fault localization, which may improve the developers' performance using SFL.
Link weights recovery in heterogeneous information networks
Venue: arXiv cs.SI 2019
Year: 2019

Socio-technical systems usually consists of many intertwined networks, each connecting different types of objects (or actors) through a variety of means. As these networks are co-dependent, one can take advantage of this entangled structure to study interaction patterns in a particular network from the information provided by other related networks. A method is hence proposed and tested to recover the weights of missing or unobserved links in heterogeneous information networks (HIN) - abstract representations of systems composed of multiple types of entities and their relations. Given a pair of nodes in a HIN, this work aims at recovering the exact weight of the incident link to these two nodes, knowing some other links present in the HIN. To do so, probability distributions resulting from path-constrained random walks i.e., random walks where the walker is forced to follow only a specific sequence of node types and edge types, capable to capture specific semantics and commonly called a meta-path, are combined in a linearly fashion in order to approximate the desired result. This method is general enough to compute the link weight between any types of nodes. Experiments on Twitter and bibliographic data show the applicability of the method.
The Impact of Preprocessing on Arabic-English Statistical and Neural Machine Translation
Venue: arXiv cs.CL 2019
Year: 2019

Neural networks have become the state-of-the-art approach for machine translation (MT) in many languages. While linguistically-motivated tokenization techniques were shown to have significant effects on the performance of statistical MT, it remains unclear if those techniques are well suited for neural MT. In this paper, we systematically compare neural and statistical MT models for Arabic-English translation on data preprecossed by various prominent tokenization schemes. Furthermore, we consider a range of data and vocabulary sizes and compare their effect on both approaches. Our empirical results show that the best choice of tokenization scheme is largely based on the type of model and the size of data. We also show that we can gain significant improvements using a system selection that combines the output from neural and statistical MT.
A Sweet Recipe for Consolidated Vulnerabilities: Attacking a Live Website by Harnessing a Killer Combination of Vulnerabilities
Venue: arXiv cs.CR 2019
Year: 2019

The recent emergence of new vulnerabilities is an epoch-making problem in the complex world of website security. Most of the websites are failing to keep updating to tackle their websites from these new vulnerabilities leaving without realizing the weakness of the websites. As a result, when cyber-criminals scour such vulnerable old version websites, the scanner will represent a set of vulnerabilities. Once found, these vulnerabilities are then exploited to steal data, distribute malicious content, or inject defacement and spam content into the vulnerable websites. Furthermore, a combination of different vulnerabilities is able to cause more damages than anticipation. Therefore, in this paper, we endeavor to find connections among various vulnerabilities such as cross-site scripting, local file inclusion, remote file inclusion, buffer overflow CSRF, etc. To do so, we develop a Finite State Machine (FSM) attacking model, which analyzes a set of vulnerabilities towards the road to finding connections. We demonstrate the efficacy of our model by applying it to the set of vulnerabilities found on two live websites.
Data Consortia
Venue: arXiv cs.CY 2019
Year: 2019

Today, web-based companies use user data to provide and enhance services to users, both individually and collectively. Some also analyze user data for other purposes, for example to select advertisements or price offers for users. Some even use or allow the data to be used to evaluate investments in financial markets. Users' concerns about how their data is or may be used has prompted legislative action in the European Union and congressional questioning in the United States. But data can also benefit society, for example giving early warnings for disease outbreaks, allowing in-depth study of relationships between genetics and disease, and elucidating local and macroeconomic trends in a timely manner. So, instead of just a focus on privacy, in the future, users may insist that their data be used on their behalf. We explore potential frameworks for groups of consenting, informed users to pool their data for their own benefit and that of society, discussing directions, challenges, and evolution for such efforts.
Faster and Better Nested Dissection Orders for Customizable Contraction Hierarchies
Venue: arXiv cs.DS 2019
Year: 2019

Graph partitioning has many applications. We consider the acceleration of shortest path queries in road networks using Customizable Contraction Hierarchies (CCH). It is based on computing a nested dissection order by recursively dividing the road network into parts. Recently, with FlowCutter and Inertial Flow, two flow-based graph bipartitioning algorithms have been proposed for road networks. While FlowCutter achieves high-quality results and thus fast query times, it is rather slow. Inertial Flow is particularly fast due to the use of geographical information while still achieving decent query times. We combine the techniques of both algorithms to achieve more than six times faster preprocessing times than FlowCutter and even faster queries on the Europe road network. We show that using 16 cores of a shared-memory machine, this preprocessing needs four minutes.
Uncertainty Based Detection and Relabeling of Noisy Image Labels
Venue: arXiv cs.CV 2019
Year: 2019

Deep neural networks (DNNs) are powerful tools in computer vision tasks. However, in many realistic scenarios label noise is prevalent in the training images, and overfitting to these noisy labels can significantly harm the generalization performance of DNNs. We propose a novel technique to identify data with noisy labels based on the different distributions of the predictive uncertainties from a DNN over the clean and noisy data. Additionally, the behavior of the uncertainty over the course of training helps to identify the network weights which best can be used to relabel the noisy labels. Data with noisy labels can therefore be cleaned in an iterative process. Our proposed method can be easily implemented, and shows promising performance on the task of noisy label detection on CIFAR-10 and CIFAR-100.
Robust Classification with Sparse Representation Fusion on Diverse Data Subsets
Venue: arXiv cs.CV 2019
Year: 2019

Sparse Representation (SR) techniques encode the test samples into a sparse linear combination of all training samples and then classify the test samples into the class with the minimum residual. The classification of SR techniques depends on the representation capability on the test samples. However, most of these models view the representation problem of the test samples as a deterministic problem, ignoring the uncertainty of the representation. The uncertainty is caused by two factors, random noise in the samples and the intrinsic randomness of the sample set, which means that if we capture a group of samples, the obtained set of samples will be different in different conditions. In this paper, we propose a novel method based upon Collaborative Representation that is a special instance of SR and has closed-form solution. It performs Sparse Representation Fusion based on the Diverse Subset of training samples (SRFDS), which reduces the impact of randomness of the sample set and enhances the robustness of classification results. The proposed method is suitable for multiple types of data and has no requirement on the pattern type of the tasks. In addition, SRFDS not only preserves a closed-form solution but also greatly improves the classification performance. Promising results on various datasets serve as the evidence of better performance of SRFDS than other SR-based methods. The Matlab code of SRFDS will be accessible at http://www.yongxu.org/lunwen.html.
Comparing Machine Learning Approaches for Table Recognition in Historical Register Books
Venue: arXiv cs.CV 2019
Year: 2019

We present in this paper experiments on Table Recognition in hand-written registry books. We first explain how the problem of row and column detection is modeled, and then compare two Machine Learning approaches (Conditional Random Field and Graph Convolutional Network) for detecting these table elements. Evaluation was conducted on death records provided by the Archive of the Diocese of Passau. Both methods show similar results, a 89 F1 score, a quality which allows for Information Extraction. Software and dataset are open source/data.
Tag Clouds for Object-Oriented Source Code Visualization
Venue: arXiv cs.SE 2019
Year: 2019

Software visualization helps software engineers to understand and manage the size and complexity of the object-oriented source code. The tag cloud is a simple and popular visualization technique. The main idea of the tag cloud is to represent tags according to their frequency in an alphabetical order where the most important tags are highlighted via a suitable font size. This paper proposes an original approach to visualize software code using a tag cloud. The approach exploits all software identifier names to visualize software code as a tag cloud. Experiments were conducted on several case studies. To validate the approach, it is applied on NanoXML and ArgoUML. The results of this evaluation validate the relevance and the performance of the proposed approach as all tag names and their frequencies were correctly identified. The proposed tag cloud visualization technique is a helpful addition to the software visualization toolkit. The extracted tag cloud supports software engineers as they filter and browse data.
An Improved CF-MAC Protocol for VANET
Venue: arXiv cs.NI 2019
Year: 2019

Vehicular Ad hoc Network (VANET) is one of the emerging research areas in the mobile computing field which is considered as a future technology and promising topic in the computer science and computer networks. Which provides road safety, updated traffic information, and infotainment. VANET consists of a large number of vehicles moving in high speeds while broadcasting important information like safety and control information which must be sent with high priority. Crowded networks like VANET having many vehicles competing to reserve the channel to send critical information which may lead to high collision scenarios, and therefore, there must be a protocol to send this kind of information with high reliability, low data loss and with no collision. In this research a collision-free protocol will be proposed to manage the channel access among competing vehicles to eliminate the collisions which occur rapidly in VANET especially in dense situations, the proposed protocol hereinafter will be called (I-MAC) protocol expected to enhance the channel performance, achieve load balancing, fairness, and decrease message loss and enhance reliability, The evaluation criteria will examine the channel throughput, message delay, and message loss; the results show that the overall channel performance with regard to collision and packet loss ratio is improved.
Binary optimal linear codes from posets of the disjoint union of two chains
Venue: arXiv cs.IT 2019
Year: 2019

Recently, Chang and Hyun obtained some classes of binary optimal codes via simplicial complexes. In this letter, we utilize posets of the disjoint union of two chains to construct binary optimal linear codes.
A Neural-based Program Decompiler
Venue: arXiv cs.PL 2019
Year: 2019

Reverse engineering of binary executables is a critical problem in the computer security domain. On the one hand, malicious parties may recover interpretable source codes from the software products to gain commercial advantages. On the other hand, binary decompilation can be leveraged for code vulnerability analysis and malware detection. However, efficient binary decompilation is challenging. Conventional decompilers have the following major limitations: (i) they are only applicable to specific source-target language pair, hence incurs undesired development cost for new language tasks; (ii) their output high-level code cannot effectively preserve the correct functionality of the input binary; (iii) their output program does not capture the semantics of the input and the reversed program is hard to interpret. To address the above problems, we propose Coda, the first end-to-end neural-based framework for code decompilation. Coda decomposes the decompilation task into two key phases: First, Coda employs an instruction type-aware encoder and a tree decoder for generating an abstract syntax tree (AST) with attention feeding during the code sketch generation stage. Second, Coda then updates the code sketch using an iterative error correction machine guided by an ensembled neural error predictor. By finding a good approximate candidate and then fixing it towards perfect, Coda achieves superior performance compared to baseline approaches. We assess Coda's performance with extensive experiments on various benchmarks. Evaluation results show that Coda achieves an average of 82\% program recovery accuracy on unseen binary samples, where the state-of-the-art decompilers yield 0\% accuracy. Furthermore, Coda outperforms the sequence-to-sequence model with attention by a margin of 70\% program accuracy.
Analyzing GDPR Compliance Through the Lens of Privacy Policy
Venue: arXiv cs.CY 2019
Year: 2019

With the arrival of the European Union's General Data Protection Regulation (GDPR), several companies are making significant changes to their systems to achieve compliance. The changes range from modifying privacy policies to redesigning systems which process personal data. This work analyzes the privacy policies of large-scaled cloud services which seek to be GDPR compliant. The privacy policy is the main medium of information dissemination between the data controller and the users. We show that many services that claim compliance today do not have clear and concise privacy policies. We identify several points in the privacy policies which potentially indicate non-compliance; we term these GDPR vulnerabilities. We identify GDPR vulnerabilities in ten cloud services. Based on our analysis, we propose seven best practices for crafting GDPR privacy policies.
Throughput Scaling of Covert Communication over Wireless Adhoc Networks
Venue: arXiv cs.IT 2019
Year: 2019

We consider the problem of covert communication over wireless adhoc networks in which (roughly) $n$ legitimate nodes (LNs) and $n^κ$ for $0<κ<1$ non-communicating warden nodes (WNs) are randomly distributed in a square of unit area. Each legitimate source wants to communicate with its intended destination node while ensuring that every WN is unable to detect the presence of the communication. In this scenario, we study the throughput scaling law. Due the covert communication constraint, the transmit powers are necessarily limited. Under this condition, we introduce a preservation region around each WN. This region serves to prevent transmission from the LNs and to increase the transmit power of the LNs outside the preservation regions. For the achievability results, multi-hop (MH), hierarchical cooperation (HC), and hybrid HC-MH schemes are utilized with some appropriate modifications. In the proposed MH and hybrid schemes, because the preservation regions may impede communication along direct data paths, the data paths are suitably modified by taking a detour around each preservation region. To avoid the concentration of detours resulting extra relaying burdens, we distribute the detours evenly over a wide region. In the proposed HC scheme, we control the symbol power and the scheduling of distributed multiple-input multiple-output transmission. We also present matching upper bounds on the throughput scaling under the assumption that every active LN consumes the same average transmit power over the time period in which the WNs observe the channel outputs.
MGOS: A Library for Molecular Geometry and its Operating System
Venue: arXiv cs.CG 2019
Year: 2019

The geometry of atomic arrangement underpins the structural understanding of molecules in many fields. However, no general framework of mathematical/computational theory for the geometry of atomic arrangement exists. Here we present "Molecular Geometry (MG)" as a theoretical framework accompanied by "MG Operating System (MGOS)" which consists of callable functions implementing the MG theory. MG allows researchers to model complicated molecular structure problems in terms of elementary yet standard notions of volume, area, etc. and MGOS frees them from the hard and tedious task of developing/implementing geometric algorithms so that they can focus more on their primary research issues. MG facilitates simpler modeling of molecular structure problems; MGOS functions can be conveniently embedded in application programs for the efficient and accurate solution of geometric queries involving atomic arrangements. The use of MGOS in problems involving spherical entities is akin to the use of math libraries in general purpose programming languages in science and engineering.