OpenRaiser/Intern-Atlas huggingface dataset:
Parametric model order reduction for large-scale and complex thermal systems
Venue: arXiv cs.SY 2018
Year: 2018

In this paper, a parametric model order reduction (pMOR) technique is proposed to find a simplified system representation of a large-scale and complex thermal system. The main principle behind this technique is that any change of the physical parameters in the high-fidelity model can be updated directly in the simplified model. For deriving the parametric reduced model, a Krylov subspace method is employed which yields the relevant subspaces of the projected state. With the help of the projection operator, first moments of the low-rank model are set identical to the correspondent moments of the original model. Additionally, a prior upper bound of the error induced by the approximation is derived.
Face-MagNet: Magnifying Feature Maps to Detect Small Faces
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we introduce the Face Magnifier Network (Face-MageNet), a face detector based on the Faster-RCNN framework which enables the flow of discriminative information of small scale faces to the classifier without any skip or residual connections. To achieve this, Face-MagNet deploys a set of ConvTranspose, also known as deconvolution, layers in the Region Proposal Network (RPN) and another set before the Region of Interest (RoI) pooling layer to facilitate detection of finer faces. In addition, we also design, train, and evaluate three other well-tuned architectures that represent the conventional solutions to the scale problem: context pooling, skip connections, and scale partitioning. Each of these three networks achieves comparable results to the state-of-the-art face detectors. With extensive experiments, we show that Face-MagNet based on a VGG16 architecture achieves better results than the recently proposed ResNet101-based HR method on the task of face detection on WIDER dataset and also achieves similar results on the hard set as our other method SSH.
A Static Program Slicing Approach for Output Stream Objects in JEE Applications
Venue: arXiv cs.SE 2018
Year: 2018

In this paper, we propose a program slicing approach for the output stream object in JEE applications. Our approach is based on extracting a dependency call graph from KDM models of JEE applications. Then, it applies breath-first search algorithm to identify the program slice as a graph reachability problem. The proposed approach is implemented as an extension of our DeJEE tool.
Optimization of Scheduling in Wireless Ad-Hoc Networks Using Matrix Games
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, we present a novel application of matrix game theory for optimization of link scheduling in wireless ad-hoc networks. Optimum scheduling is achieved by soft coloring of network graphs. Conventional coloring schemes are based on assignment of one color to each region or equivalently each link is member of just one partial topology. These algorithms based on coloring are not optimal when links are not activated with the same rate. Soft coloring, introduced in this paper, solves this problem and provide optimal solution for any requested link usage rate. To define the game model for optimum scheduling, first all possible components of the graph are identified. Components are defined as sets of the wireless links can be activated simultaneously without suffering from mutual interference. Then by switching between components with appropriate frequencies (usage rate) optimum scheduling is achieved. We call this kind of scheduling as soft coloring because any links can be member of more than one partial topology, in different time segments. To simplify this problem, we model relationship between link rates and components selection frequencies by a matrix game which provides a simple and helpful tool to simplify and solve the problem. This proposed game theoretic model is solved by fictitious playing method. Simulation results prove the efficiency of the proposed technique compared to conventional scheduling based on coloring
Geo-Network Coding Function Virtualization for Reliable Communication over Satellite
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, we propose a design solution for the implementation of Virtualized Network Coding Functionality (VNCF) over a service coverage area. Network Function Virtualization (NFV) and Network Coding (NC) architectural designs are integrated as a toolbox of NC design domains so that NC can be implemented over different underlying physical networks including satellite or hybrid networks. The design includes identifying theoretical limits of NC over wireless networks in terms of achievable rate region and optimizing coding rates for nodes that implement VNCF. The overall design target is to achieve a given multicast transmission target reliability at receiver sides. In addition, the optimization problem uses databases with geo-tagged link statistics and geo-location information of network nodes in the deployment area for some computational complexity/energy constraints. Numerical results provide validation of our design solution on how network conditions and system constraints impact the design and implementation of NC and how VNCF allows reliable communication over wireless networks with reliability and connectivity up to theoretical limits.
One Net Fits All: A unifying semantics of Dynamic Fault Trees using GSPNs
Venue: arXiv cs.SE 2018
Year: 2018

Dynamic Fault Trees (DFTs) are a prominent model in reliability engineering. They are strictly more expressive than static fault trees, but this comes at a price: their interpretation is non-trivial and leaves quite some freedom. This paper presents a GSPN semantics for DFTs. This semantics is rather simple and compositional. The key feature is that this GSPN semantics unifies all existing DFT semantics from the literature. All semantic variants can be obtained by choosing appropriate priorities and treatment of non-determinism.
Use of recurrent infomax to improve the memory capability of input-driven recurrent neural networks
Venue: arXiv cs.NE 2018
Year: 2018

The inherent transient dynamics of recurrent neural networks (RNNs) have been exploited as a computational resource in input-driven RNNs. However, the information processing capability varies from RNN to RNN, depending on their properties. Many authors have investigated the dynamics of RNNs and their relevance to the information processing capability. In this study, we present a detailed analysis of the information processing capability of an RNN optimized by recurrent infomax (RI), which is an unsupervised learning scheme that maximizes the mutual information of RNNs by adjusting the connection strengths of the network. Thus, we observe that a delay-line structure emerges from the RI and the network optimized by the RI possesses superior short-term memory, which is the ability to store the temporal information of the input stream in its transient dynamics.
On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Large-scale deep neural networks are both memory intensive and computation-intensive, thereby posing stringent requirements on the computing platforms. Hardware accelerations of deep neural networks have been extensively investigated in both industry and academia. Specific forms of binary neural networks (BNNs) and stochastic computing based neural networks (SCNNs) are particularly appealing to hardware implementations since they can be implemented almost entirely with binary operations. Despite the obvious advantages in hardware implementation, these approximate computing techniques are questioned by researchers in terms of accuracy and universal applicability. Also it is important to understand the relative pros and cons of SCNNs and BNNs in theory and in actual hardware implementations. In order to address these concerns, in this paper we prove that the "ideal" SCNNs and BNNs satisfy the universal approximation property with probability 1 (due to the stochastic behavior). The proof is conducted by first proving the property for SCNNs from the strong law of large numbers, and then using SCNNs as a "bridge" to prove for BNNs. Based on the universal approximation property, we further prove that SCNNs and BNNs exhibit the same energy complexity. In other words, they have the same asymptotic energy consumption with the growing of network size. We also provide a detailed analysis of the pros and cons of SCNNs and BNNs for hardware implementations and conclude that SCNNs are more suitable for hardware.
Targeted change detection in remote sensing images
Venue: arXiv cs.CV 2018
Year: 2018

Recent developments in the remote sensing systems and image processing made it possible to propose a new method of the object classification and detection of the specific changes in the series of satellite Earth images (so called targeted change detection). In this paper we propose a formal problem statement that allows to use effectively the deep learning approach to analyze time-dependent series of remote sensing images. We also introduce a new framework for the development of deep learning models for targeted change detection and demonstrate some cases of business applications it can be used for.
Challenges in Discriminating Profanity from Hate Speech
Venue: arXiv cs.CL 2018
Year: 2018

In this study we approach the problem of distinguishing general profanity from hate speech in social media, something which has not been widely considered. Using a new dataset annotated specifically for this task, we employ supervised classification along with a set of features that includes n-grams, skip-grams and clustering-based word representations. We apply approaches based on single classifiers as well as more advanced ensemble classifiers and stacked generalization, achieving the best result of 80% accuracy for this 3-class classification task. Analysis of the results reveals that discriminating hate speech and profanity is not a simple task, which may require features that capture a deeper understanding of the text not always possible with surface n-grams. The variability of gold labels in the annotated data, due to differences in the subjective adjudications of the annotators, is also an issue. Other directions for future work are discussed.
Max-Min Greedy Matching
Venue: arXiv cs.GT 2018
Year: 2018

A bipartite graph $G(U,V;E)$ that admits a perfect matching is given. One player imposes a permutation $π$ over $V$, the other player imposes a permutation $σ$ over $U$. In the greedy matching algorithm, vertices of $U$ arrive in order $σ$ and each vertex is matched to the lowest (under $π$) yet unmatched neighbor in $V$ (or left unmatched, if all its neighbors are already matched). The obtained matching is maximal, thus matches at least a half of the vertices. The max-min greedy matching problem asks: suppose the first (max) player reveals $π$, and the second (min) player responds with the worst possible $σ$ for $π$, does there exist a permutation $π$ ensuring to match strictly more than a half of the vertices? Can such a permutation be computed in polynomial time? The main result of this paper is an affirmative answer for this question: we show that there exists a polytime algorithm to compute $π$ for which for every $σ$ at least $ρ> 0.51$ fraction of the vertices of $V$ are matched. We provide additional lower and upper bounds for special families of graphs, including regular and Hamiltonian. Interestingly, even for regular graphs with arbitrarily large degree (implying a large number of disjoint perfect matchings), there is no $π$ ensuring to match more than a fraction $8/9$ of the vertices. The max-min greedy matching problem solves an open problem regarding the welfare guarantees attainable by pricing in sequential markets with binary unit-demand valuations. In addition, it has implications for the size of the unique stable matching in markets with global preferences, subject to the graph structure.
Bearing Rigidity Theory and its Applications for Control and Estimation of Network Systems: Life Beyond Distance Rigidity
Venue: arXiv cs.SY 2018
Year: 2018

The problem of distributed control and estimation for multi-agent systems with limited sensing capabilities is a practical challenge motivated by incomplete and imperfect sensing. This article addresses an important case where each agent in a network can only sense the relative bearings to their nearest neighbors. The study of this topic is motivated mainly by the rapid development of bearing-only sensors such as optical cameras or sensor arrays. This article provides a tutorial review on this topic focusing on the problems of formation control and network localization. A key component of this review is a presentation of the recently developed bearing rigidity theory, which defines a necessary architectural feature of multi-agent systems aiming to solve these two problems. This article presents a high-level summary of recently developed algorithms solving these problems, various simulation examples, and discussions pointing to the relevant literature and important remaining challenges in this area.
Context-Aware Mixed Reality: A Framework for Ubiquitous Interaction
Venue: arXiv cs.CV 2018
Year: 2018

Mixed Reality (MR) is a powerful interactive technology that yields new types of user experience. We present a semantic based interactive MR framework that exceeds the current geometry level approaches, a step change in generating high-level context-aware interactions. Our key insight is to build semantic understanding in MR that not only can greatly enhance user experience through object-specific behaviours, but also pave the way for solving complex interaction design challenges. The framework generates semantic properties of the real world environment through dense scene reconstruction and deep image understanding. We demonstrate our approach with a material-aware prototype system for generating context-aware physical interactions between the real and the virtual objects. Quantitative and qualitative evaluations are carried out and the results show that the framework delivers accurate and fast semantic information in interactive MR environment, providing effective semantic level interactions.
A Game-Theoretic Framework for the Virtual Machines Migration Timing Problem
Venue: arXiv cs.CR 2018
Year: 2018

In a multi-tenant cloud, a number of Virtual Machines (VMs) are collocated on the same physical machine to optimize performance, power consumption and maximize profit. This, however, increases the risk of a malicious VM performing side-channel attacks and leaking sensitive information from neighboring VMs. To this end, this paper develops and analyzes a game-theoretic framework for the VM migration timing problem in which the cloud provider decides \emph{when} to migrate a VM to a different physical machine to reduce the risk of being compromised by a collocated malicious VM. The adversary decides the rate at which she launches new VMs to collocate with the victim VMs. Our formulation captures a data leakage model in which the cost incurred by the cloud provider depends on the duration of collocation with malicious VMs. It also captures costs incurred by the adversary in launching new VMs and by the defender in migrating VMs. We establish sufficient conditions for the existence of Nash equilibria for general cost functions, as well as for specific instantiations, and characterize the best response for both players. Furthermore, we extend our model to characterize its impact on the attacker's payoff when the cloud utilizes intrusion detection systems that detect side-channel attacks. Our theoretical findings are corroborated with extensive numerical results in various settings.
Word2Bits - Quantized Word Vectors
Venue: arXiv cs.CL 2018
Year: 2018

Word vectors require significant amounts of memory and storage, posing issues to resource limited devices like mobile phones and GPUs. We show that high quality quantized word vectors using 1-2 bits per parameter can be learned by introducing a quantization function into Word2Vec. We furthermore show that training with the quantization function acts as a regularizer. We train word vectors on English Wikipedia (2017) and evaluate them on standard word similarity and analogy tasks and on question answering (SQuAD). Our quantized word vectors not only take 8-16x less space than full precision (32 bit) word vectors but also outperform them on word similarity tasks and question answering.
Performance and Impairment Modelling for Hardware Components in Millimetre-wave Transceivers
Venue: arXiv cs.IT 2018
Year: 2018

This invited paper details some of the hardware modelling and impairment analysis carried out in the EU mmMAGIC project. The modelling work includes handset and Access Point antenna arrays, where specific millimeter-wave challenges are addressed. In power amplifier related analysis, statistical and behavioural modelling approaches are discussed. Phase Noise, regarded as a main impairment in millimeter-wave, is captured under two models and some analysis into to the impact of phase noise is also provided.
Improved Dynamic Geodesic Nearest Neighbor Searching in a Simple Polygon
Venue: arXiv cs.CG 2018
Year: 2018

We present an efficient dynamic data structure that supports geodesic nearest neighbor queries for a set $S$ of point sites in a static simple polygon $P$. Our data structure allows us to insert a new site in $S$, delete a site from $S$, and ask for the site in $S$ closest to an arbitrary query point $q \in P$. All distances are measured using the geodesic distance, that is, the length of the shortest path that is completely contained in $P$. Our data structure achieves polylogarithmic update and query times, and uses $O(n\log^3n\log m + m)$ space, where $n$ is the number of sites in $S$ and $m$ is the number of vertices in $P$. The crucial ingredient in our data structure is an implicit representation of a vertical shallow cutting of the geodesic distance functions. We show that such an implicit representation exists, and that we can compute it efficiently.
Challenges in Annotation of useR Data for UbiquitOUs Systems: Results from the 1st ARDUOUS Workshop
Venue: arXiv cs.CY 2018
Year: 2018

Labelling user data is a central part of the design and evaluation of pervasive systems that aim to support the user through situation-aware reasoning. It is essential both in designing and training the system to recognise and reason about the situation, either through the definition of a suitable situation model in knowledge-driven applications, or through the preparation of training data for learning tasks in data-driven models. Hence, the quality of annotations can have a significant impact on the performance of the derived systems. Labelling is also vital for validating and quantifying the performance of applications. In particular, comparative evaluations require the production of benchmark datasets based on high-quality and consistent annotations. With pervasive systems relying increasingly on large datasets for designing and testing models of users' activities, the process of data labelling is becoming a major concern for the community. In this work we present a qualitative and quantitative analysis of the challenges associated with annotation of user data and possible strategies towards addressing these challenges. The analysis was based on the data gathered during the 1st International Workshop on Annotation of useR Data for UbiquitOUs Systems (ARDUOUS) and consisted of brainstorming as well as annotation and questionnaire data gathered during the talks, poster session, live annotation session, and discussion session.
Average Cost of QuickXsort with Pivot Sampling
Venue: arXiv cs.DS 2018
Year: 2018

QuickXsort is a strategy to combine Quicksort with another sorting method X, so that the result has essentially the same comparison cost as X in isolation, but sorts in place even when X requires a linear-size buffer. We solve the recurrence for QuickXsort precisely up to the linear term including the optimization to choose pivots from a sample of k elements. This allows to immediately obtain overall average costs using only the average costs of sorting method X (as if run in isolation). We thereby extend and greatly simplify the analysis of QuickHeapsort and QuickMergesort with practically efficient pivot selection, and give the first tight upper bounds including the linear term for such methods.
Studying Invariances of Trained Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Convolutional Neural Networks (CNNs) define an exceptionally powerful class of models for image classification, but the theoretical background and the understanding of how invariances to certain transformations are learned is limited. In a large scale screening with images modified by different affine and nonaffine transformations of varying magnitude, we analyzed the behavior of the CNN architectures AlexNet and ResNet. If the magnitude of different transformations does not exceed a class- and transformation dependent threshold, both architectures show invariant behavior. In this work we furthermore introduce a new learnable module, the Invariant Transformer Net, which enables us to learn differentiable parameters for a set of affine transformations. This allows us to extract the space of transformations to which the CNN is invariant and its class prediction robust.
A Novel Approach in Calculating Stakeholder priority in Requirements Elicitation
Venue: arXiv cs.SE 2018
Year: 2018

The ultimate goal of any software developer seeking a competitive edge is to meet stakeholders needs and expectations. To achieve this, it is necessary to effectively and accurately manage stakeholders system requirements. The paper proposes a systematic way of classifying stakeholders and then describes a novel method for calculating stakeholder priority taking into consideration the fact that different stakeholders will have different importance level and different requirement preference. Finally the requirement preference calculation is done where stakeholders choose the best requirements based on two factors, value and urgency of the requirement. The proposed method actively involves stakeholders in the requirement elicitation process.
Unraveling Go gaming nature by Ising Hamiltonian and common fate graphs: tactics and statistics
Venue: arXiv cs.AI 2018
Year: 2018

Go gaming is a struggle between adversaries, black and white simple stones, and aim to control the most Go board territory for success. Rules are simple but Go game fighting is highly intricate. Stones placement and interaction on board is random-appearance, likewise interaction phenomena among basic elements in physics thermodynamics, chemistry, biology, or social issues. We model the Go game dynamic employing an Ising model energy function, whose interaction coefficients reflect the application of rules and tactics to build long-term strategies. At any step of the game, the energy function of the model assesses the control and strength of a player over the board. A close fit between predictions of the model with actual game's scores is obtained. AlphaGo computer is the current top Go player, but its behavior does not wholly reveal the Go gaming nature. The Ising function allows for precisely model the stochastic evolutions of Go gaming patterns, so, to advance the understanding on Go own-dynamic -beyond the player`s abilities. The analysis of the frequency and combination of tactics shows the formation of patterns in the groups of stones during a game, regarding the turn of each player, or if human or computer adversaries are confronted.