OpenRaiser/Intern-Atlas huggingface dataset:
Securing Infrastructure Facilities: When does proactive defense help?
Venue: arXiv cs.GT 2018
Year: 2018

Infrastructure systems are increasingly facing new security threats due to the vulnerabilities of cyber-physical components that support their operation. In this article, we investigate how the infrastructure operator (defender) should prioritize the investment in securing a set of facilities in order to reduce the impact of a strategic adversary (attacker) who can target a facility to increase the overall usage cost of the system. We adopt a game-theoretic approach to model the defender-attacker interaction and study two models: normal-form game -- where both players move simultaneously; and sequential game -- where attacker moves after observing the defender's strategy. For each model, we provide a complete characterization of how the set of facilities that are secured by the defender in equilibrium vary with the costs of attack and defense. Importantly, our analysis provides a sharp condition relating the cost parameters for which the defender has the first mover advantage. Specifically, we show that to fully deter the attacker from targeting any facility, the defender needs to proactively secure all "vulnerable facilities" at an appropriate level of effort. We illustrate the outcome of the attacker-defender interaction on a simple transportation network. We also suggest a dynamic learning setup to understand how this outcome can affect the ability of imperfectly informed users to make their decisions about using the system in the post-attack stage.
Maximum Realizability for Linear Temporal Logic Specifications
Venue: arXiv cs.LO 2018
Year: 2018

Automatic synthesis from linear temporal logic (LTL) specifications is widely used in robotic motion planning, control of autonomous systems, and load distribution in power networks. A common specification pattern in such applications consists of an LTL formula describing the requirements on the behaviour of the system, together with a set of additional desirable properties. We study the synthesis problem in settings where the overall specification is unrealizable, more precisely, when some of the desirable properties have to be (temporarily) violated in order to satisfy the system's objective. We provide a quantitative semantics of sets of safety specifications, and use it to formalize the "best-effort" satisfaction of such soft specifications while satisfying the hard LTL specification. We propose an algorithm for synthesizing implementations that are optimal with respect to this quantitative semantics. Our method builds upon the idea of the bounded synthesis approach, and we develop a MaxSAT encoding which allows for maximizing the quantitative satisfaction of the safety specifications. We evaluate our algorithm on scenarios from robotics and power distribution networks.
A Vehicle Detection Approach using Deep Learning Methodologies
Venue: arXiv cs.CV 2018
Year: 2018

The purpose of this study is to successfully train our vehicle detector using R-CNN, Faster R-CNN deep learning methods on a sample vehicle data sets and to optimize the success rate of the trained detector by providing efficient results for vehicle detection by testing the trained vehicle detector on the test data. The working method consists of six main stages. These are respectively; loading the data set, the design of the convolutional neural network, configuration of training options, training of the Faster R-CNN object detector and evaluation of trained detector. In addition, in the scope of the study, Faster R-CNN, R-CNN deep learning methods were mentioned and experimental analysis comparisons were made with the results obtained from vehicle detection.
Fixed-sized representation learning from Offline Handwritten Signatures of different sizes
Venue: arXiv cs.CV 2018
Year: 2018

Methods for learning feature representations for Offline Handwritten Signature Verification have been successfully proposed in recent literature, using Deep Convolutional Neural Networks to learn representations from signature pixels. Such methods reported large performance improvements compared to handcrafted feature extractors. However, they also introduced an important constraint: the inputs to the neural networks must have a fixed size, while signatures vary significantly in size between different users. In this paper we propose addressing this issue by learning a fixed-sized representation from variable-sized signatures by modifying the network architecture, using Spatial Pyramid Pooling. We also investigate the impact of the resolution of the images used for training, and the impact of adapting (fine-tuning) the representations to new operating conditions (different acquisition protocols, such as writing instruments and scan resolution). On the GPDS dataset, we achieve results comparable with the state-of-the-art, while removing the constraint of having a maximum size for the signatures to be processed. We also show that using higher resolutions (300 or 600dpi) can improve performance when skilled forgeries from a subset of users are available for feature learning, but lower resolutions (around 100dpi) can be used if only genuine signatures are used. Lastly, we show that fine-tuning can improve performance when the operating conditions change.
Database as a Service - Current Issues and Its Future
Venue: arXiv cs.DB 2018
Year: 2018

With the prevalence of applications in cloud, Database as a Service (DBaaS) becomes a promising method to provide cloud applications with reliable and flexible data storage services. It provides a number of interesting features to cloud developers, however, it suffers a few drawbacks: long learning curve and development cycle, lacking of in-depth support for NoSQL, lacking of flexible configuration for security and privacy, and high cost models. In this paper, we investigate these issues among current DBaaS providers and propose a novel Trinity Model that can significantly reduce the learning curves, improve the security and privacy, and accelerate database design and development. We further elaborate our ongoing and future work on developing large real-world SaaS projects using this new DBaaS model.
NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter
Venue: arXiv cs.CL 2018
Year: 2018

This paper describes our NIHRIO system for SemEval-2018 Task 3 "Irony detection in English tweets". We propose to use a simple neural network architecture of Multilayer Perceptron with various types of input features including: lexical, syntactic, semantic and polarity features. Our system achieves very high performance in both subtasks of binary and multi-class irony detection in tweets. In particular, we rank third using the accuracy metric and fifth using the F1 metric. Our code is available at https://github.com/NIHRIO/IronyDetectionInTwitter
Location data quality in context: directions and challenges
Venue: arXiv cs.NI 2018
Year: 2018

In the last decade, following the emergence of the mobile applications domain, the significance of location information has changed radically. Nowadays, location data not only is a key component of geospatial databases, but also a critical resource for a broad spectrum of applications, including Location-based Services and IOT solutions. This change of perspective has been made possible by the availability of localization technologies providing accurate and reliable location information on moving entities in real time. We refer to real-time location as positioning data. Positioning data has unique characteristics, such as the strong dependency from both the localization technology and the environmental context, which make the specification of standard quality metrics and quality assessment procedures a complex task. In this paper, we elaborate on such an aspect, focusing in particular on indoor positioning data.
Adversarial Spatio-Temporal Learning for Video Deblurring
Venue: arXiv cs.CV 2018
Year: 2018

Camera shake or target movement often leads to undesired blur effects in videos captured by a hand-held camera. Despite significant efforts having been devoted to video-deblur research, two major challenges remain: 1) how to model the spatio-temporal characteristics across both the spatial domain (i.e., image plane) and temporal domain (i.e., neighboring frames), and 2) how to restore sharp image details w.r.t. the conventionally adopted metric of pixel-wise errors. In this paper, to address the first challenge, we propose a DeBLuRring Network (DBLRNet) for spatial-temporal learning by applying a modified 3D convolution to both spatial and temporal domains. Our DBLRNet is able to capture jointly spatial and temporal information encoded in neighboring frames, which directly contributes to improved video deblur performance. To tackle the second challenge, we leverage the developed DBLRNet as a generator in the GAN (generative adversarial network) architecture, and employ a content loss in addition to an adversarial loss for efficient adversarial training. The developed network, which we name as DeBLuRring Generative Adversarial Network (DBLRGAN), is tested on two standard benchmarks and achieves the state-of-the-art performance.
A Systematic Review of Automated Grammar Checking in English Language
Venue: arXiv cs.CL 2018
Year: 2018

Grammar checking is the task of detection and correction of grammatical errors in the text. English is the dominating language in the field of science and technology. Therefore, the non-native English speakers must be able to use correct English grammar while reading, writing or speaking. This generates the need of automatic grammar checking tools. So far many approaches have been proposed and implemented. But less efforts have been made in surveying the literature in the past decade. The objective of this systematic review is to examine the existing literature, highlighting the current issues and suggesting the potential directions of future research. This systematic review is a result of analysis of 12 primary studies obtained after designing a search strategy for selecting papers found on the web. We also present a possible scheme for the classification of grammar errors. Among the main observations, we found that there is a lack of efficient and robust grammar checking tools for real time applications. We present several useful illustrations- most prominent are the schematic diagrams that we provide for each approach and a table that summarizes these approaches along different dimensions such as target error types, linguistic dataset used, strengths and limitations of the approach. This facilitates better understandability, comparison and evaluation of previous research.
Codes with Combined Locality and Regeneration Having Optimal Rate, $d_{\text{min}}$ and Linear Field Size
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we study vector codes with all-symbol locality, where the local code is either a Minimum Bandwidth Regenerating (MBR) code or a Minimum Storage Regenerating (MSR) code. In the first part, we present vector codes with all-symbol MBR locality, for all parameters, that have both optimal minimum-distance and optimal rate. These codes combine ideas from two popular codes in the distributed storage literature, Product-Matrix codes and Tamo-Barg codes. In the second part which deals with codes having all-symbol MSR locality, we follow a Pairwise Coupling Transform-based approach to arrive at optimal minimum-distance and optimal rate, for a range of parameters. All the code constructions presented in this paper have a low field-size that grows linearly with the code-length $n$.
The Effectiveness of Classification on Information Retrieval System (Case Study)
Venue: arXiv cs.IR 2018
Year: 2018

Large amount of unstructured designed information is difficult to deal with. Obtaining specific information is a hard mission and takes a lot of time. Information Retrieval System (IR) is a way to solve this kind of problem. IR is a good mechanism but does not give the perfect solution. Other techniques have been added to IR to develop the result. One of the techniques is text classification. Text classification task is to assign a document to one or more category. It could be done manually or algorithmically. Text classification enhances the output of this process by reducing the results. This study proved that text classification has a positive influence on Information Retrieval Systems.
The Maximum Trajectory Coverage Query in Spatial Databases
Venue: arXiv cs.DB 2018
Year: 2018

With the widespread use of GPS-enabled mobile devices, an unprecedented amount of trajectory data is becoming available from various sources such as Bikely, GPS-wayPoints, and Uber. The rise of innovative transportation services and recent break-throughs in autonomous vehicles will lead to the continued growth of trajectory data and related applications. Supporting these services in emerging platforms will require more efficient query processing in trajectory databases. In this paper, we propose two new coverage queries for trajectory databases: (i) k Maximizing Reverse Range Search on Trajectories (kMaxRRST); and (ii) a Maximum k Coverage Range Search on Trajectories (MaxkCovRST). We propose a novel index structure, the Trajectory Quadtree (TQ-tree) that utilizes a quadtree to hierarchically organize trajectories into different quadtree nodes, and then applies a z-ordering to further organize the trajectories by spatial locality inside each node. This structure is highly effective in pruning the trajectory search space, which is of independent interest. By exploiting the TQ-tree data structure, we develop a divide-and-conquer approach to compute the trajectory "service value", and a best-first strategy to explore the trajectories using the appropriate upper bound on the service value to efficiently process a kMaxRRST query. Moreover, to solve the MaxkCovRST, which is a non-submodular NP-hard problem, we propose a greedy approximation which also exploits the TQ-tree. We evaluate our algorithms through an extensive experimental study on several real datasets, and demonstrate that our TQ-tree based algorithms outperform common baselines by two to three orders of magnitude.
Specification-Driven Multi-Perspective Predictive Business Process Monitoring (Extended Version)
Venue: arXiv cs.AI 2018
Year: 2018

Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Thus, different from previous studies, our approach enables us to deal with various kinds of prediction tasks based on the given specification. A prototype implementing our approach has been developed and experiments using a real-life event log have been conducted.
Confidence from Invariance to Image Transformations
Venue: arXiv cs.CV 2018
Year: 2018

We develop a technique for automatically detecting the classification errors of a pre-trained visual classifier. Our method is agnostic to the form of the classifier, requiring access only to classifier responses to a set of inputs. We train a parametric binary classifier (error/correct) on a representation derived from a set of classifier responses generated from multiple copies of the same input, each subject to a different natural image transformation. Thus, we establish a measure of confidence in classifier's decision by analyzing the invariance of its decision under various transformations. In experiments with multiple data sets (STL-10,CIFAR-100,ImageNet) and classifiers, we demonstrate new state of the art for the error detection task. In addition, we apply our technique to novelty detection scenarios, where we also demonstrate state of the art results.
Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data
Venue: arXiv cs.LG 2018
Year: 2018

We present a generic framework for spatio-temporal (ST) data modeling, analysis, and forecasting, with a special focus on data that is sparse in both space and time. Our multi-scaled framework is a seamless coupling of two major components: a self-exciting point process that models the macroscale statistical behaviors of the ST data and a graph structured recurrent neural network (GSRNN) to discover the microscale patterns of the ST data on the inferred graph. This novel deep neural network (DNN) incorporates the real time interactions of the graph nodes to enable more accurate real time forecasting. The effectiveness of our method is demonstrated on both crime and traffic forecasting.
The simple essence of automatic differentiation
Venue: arXiv cs.PL 2018
Year: 2018

Automatic differentiation (AD) in reverse mode (RAD) is a central component of deep learning and other uses of large-scale optimization. Commonly used RAD algorithms such as backpropagation, however, are complex and stateful, hindering deep understanding, improvement, and parallel execution. This paper develops a simple, generalized AD algorithm calculated from a simple, natural specification. The general algorithm is then specialized by varying the representation of derivatives. In particular, applying well-known constructions to a naive representation yields two RAD algorithms that are far simpler than previously known. In contrast to commonly used RAD implementations, the algorithms defined here involve no graphs, tapes, variables, partial derivatives, or mutation. They are inherently parallel-friendly, correct by construction, and usable directly from an existing programming language with no need for new data types or programming style, thanks to use of an AD-agnostic compiler plugin.
Some new bounds on LCD codes over finite fields
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we show that LCD codes are not equivalent to linear codes over small finite fields. The enumeration of binary optimal LCD codes is obtained. We also get the exact value of LD$(n,2)$ over $\mathbb{F}_3$ and $\mathbb{F}_4$. We study the bound of LCD codes over $\mathbb{F}_q$.
Blockchain-based TLS Notary Service
Venue: arXiv cs.CR 2018
Year: 2018

The Transport Layer Security (TLS) protocol is a de facto standard of secure client-server communication on the Internet. Its security can be diminished by a variety of attacks that leverage on weaknesses in its design and implementations. An example of a major weakness is the public-key infrastructure (PKI) that TLS deploys, which is a weakest-link system and introduces hundreds of links (i.e., trusted entities). Consequently, an adversary compromising a single trusted entity can impersonate any website. Notary systems, based on multi-path probing, were early and promising proposals to detect and prevent such attacks. Unfortunately, despite their benefits, they are not widely deployed, mainly due to their long-standing unresolved problems. In this paper, we present Persistent and Accountable Domain Validation (PADVA), which is a next-generation TLS notary service. PADVA combines the advantages of previous proposals, enhancing them, introducing novel mechanisms, and leveraging a blockchain platform which provides new features. PADVA keeps notaries auditable and accountable, introduces service-level agreements and mechanisms to enforce them, relaxes availability requirements for notaries, and works with the legacy TLS ecosystem. We implemented and evaluated PADVA, and our experiments indicate its efficiency and deployability.
Correlated discrete data generation using adversarial training
Venue: arXiv cs.LG 2018
Year: 2018

Generative Adversarial Networks (GAN) have shown great promise in tasks like synthetic image generation, image inpainting, style transfer, and anomaly detection. However, generating discrete data is a challenge. This work presents an adversarial training based correlated discrete data (CDD) generation model. It also details an approach for conditional CDD generation. The results of our approach are presented over two datasets; job-seeking candidates skill set (private dataset) and MNIST (public dataset). From quantitative and qualitative analysis of these results, we show that our model performs better as it leverages inherent correlation in the data, than an existing model that overlooks correlation.
mRUBiS: An Exemplar for Model-Based Architectural Self-Healing and Self-Optimization
Venue: arXiv cs.SE 2018
Year: 2018

Self-adaptive software systems are often structured into an adaptation engine that manages an adaptable software by operating on a runtime model that represents the architecture of the software (model-based architectural self-adaptation). Despite the popularity of such approaches, existing exemplars provide application programming interfaces but no runtime model to develop adaptation engines. Consequently, there does not exist any exemplar that supports developing, evaluating, and comparing model-based self-adaptation off the shelf. Therefore, we present mRUBiS, an extensible exemplar for model-based architectural self-healing and self-optimization. mRUBiS simulates the adaptable software and therefore provides and maintains an architectural runtime model of the software, which can be directly used by adaptation engines to realize and perform self-adaptation. Particularly, mRUBiS supports injecting issues into the model, which should be handled by self-adaptation, and validating the model to assess the self-adaptation. Finally, mRUBiS allows developers to explore variants of adaptation engines (e.g., event-driven self-adaptation) and to evaluate the effectiveness, efficiency, and scalability of the engines.
Is science driven by principal investigators?
Venue: arXiv cs.DL 2018
Year: 2018

In this paper we consider the scientific and career performance of principal investigators (PI's) of publicly funded research projects compared to scientific performance of all researchers. Our study is based on high quality data about (i) research projects awarded in Slovenia in the period 1994-2016 (7508 projects with 2725 PI's in total) and (ii) about scientific productivity of all researchers in Slovenia that were active in the period 1970-2016 - there are 19598 such researchers in total, including the PI's. We compare average productivity, collaboration, internationality and interdisciplinarity of PI's and of all active researchers. Our analysis shows that for all four indicators the average performance of PI's is much higher compared to average performance of all active researchers. Additionally, we analyze careers of both groups of researchers. The results show that the PI's have on average longer and more fruitful career compared to all active researchers, with regards to all career indicators. The PI's that have received a postdoc grant have at the beginning outstanding scientific performance, but later deviate towards average. On long run, the PI's leading the research programs (the most prestigious grants) on average demonstrate the best scientific performance. In the last part of the paper we study 23 co-authorship networks, spanned by all active researchers in the periods 1970-1994, ..., 1970-2016. We find out that they are well connected and that PI's are well distributed across these networks forming their backbones. Even more, PI's generate new PI's, since more than 90% of new PI's are connected (have at least one joint scientific publication) with existing PI's. We believe that our study sheds new light to the relations between the public funding of the science and the scientific output and can be considered as an affirmative answer to the question posed in the title.