OpenRaiser/Intern-Atlas huggingface dataset:
Software Implementation And Evaluation Of Lightweight Symmetric Block Ciphers Of The Energy Perspectives And Memory
Venue: arXiv cs.CR 2017
Year: 2017

Lightweight ciphers are the form of encryption that strictly limited to devices such as tags, RFID, wireless sensor networks applications. Low-resource devices has many limitations in power, energy and memory. In this work, the lightweight block ciphers is implemented on the Atmega128 microprocessor and the results of the energy perspectives and memory were assessed. The results of the evaluation show that the SPECK(64,96) cipher has been the best value of the perspective of energy and is appropriate for wireless sensor networks with the main requirement of energy. If the measure is memory usage, the TWINE-80 is the most appropriate implemented lightweight ciphers on the Atmega128 microprocessor. The SPECK(64,96) cipher is in third place of the perspective of consumer memory. In other side, TWINE-80 cipher is In the second place of the perspective of energy. SPECK(64,96) and TWINE-80 ciphers are the most appropriate for wireless sensor networks.
Exploring Code Clones in Programmable Logic Controller Software
Venue: arXiv cs.SE 2017
Year: 2017

The reuse of code fragments by copying and pasting is widely practiced in software development and results in code clones. Cloning is considered an anti-pattern as it negatively affects program correctness and increases maintenance efforts. Programmable Logic Controller (PLC) software is no exception in the code clone discussion as reuse in development and maintenance is frequently achieved through copy, paste, and modification. Even though the presence of code clones may not necessary be a problem per se, it is important to detect, track and manage clones as the software system evolves. Unfortunately, tool support for clone detection and management is not commonly available for PLC software systems or limited to generic tools with a reduced set of features. In this paper, we investigate code clones in a real-world PLC software system based on IEC 61131-3 Structured Text and C/C++. We extended a widely used tool for clone detection with normalization support. Furthermore, we evaluated the different types and natures of code clones in the studied system and their relevance for refactoring. Results shed light on the applicability and usefulness of clone detection in the context of industrial automation systems and it demonstrates the benefit of adapting detection and management tools for IEC 611313-3 languages.
Recommendations for Marketing Campaigns in Telecommunication Business based on the footprint analysis
Venue: arXiv cs.AI 2017
Year: 2017

A major investment made by a telecom operator goes into the infrastructure and its maintenance, while business revenues are proportional to how big and good the customer base is. We present a data-driven analytic strategy based on combinatorial optimization and analysis of historical data. The data cover historical mobility of the users in one region of Sweden during a week. Applying the proposed method to the case study, we have identified the optimal proportion of geo-demographic segments in the customer base, developed a functionality to assess the potential of a planned marketing campaign, and explored the problem of an optimal number and types of the geo-demographic segments to target through marketing campaigns. With the help of fuzzy logic, the conclusions of data analysis are automatically translated into comprehensible recommendations in a natural language.
A Supervised Approach to Extractive Summarisation of Scientific Papers
Venue: arXiv cs.CL 2017
Year: 2017

Automatic summarisation is a popular approach to reduce a document to its main arguments. Recent research in the area has focused on neural approaches to summarisation, which can be very data-hungry. However, few large datasets exist and none for the traditionally popular domain of scientific publications, which opens up challenging research avenues centered on encoding large, complex documents. In this paper, we introduce a new dataset for summarisation of computer science publications by exploiting a large resource of author provided summaries and show straightforward ways of extending it further. We develop models on the dataset making use of both neural sentence encoding and traditionally used summarisation features and show that models which encode sentences as well as their local and global context perform best, significantly outperforming well-established baseline methods.
Long-Term Video Interpolation with Bidirectional Predictive Network
Venue: arXiv cs.CV 2017
Year: 2017

This paper considers the challenging task of long-term video interpolation. Unlike most existing methods that only generate few intermediate frames between existing adjacent ones, we attempt to speculate or imagine the procedure of an episode and further generate multiple frames between two non-consecutive frames in videos. In this paper, we present a novel deep architecture called bidirectional predictive network (BiPN) that predicts intermediate frames from two opposite directions. The bidirectional architecture allows the model to learn scene transformation with time as well as generate longer video sequences. Besides, our model can be extended to predict multiple possible procedures by sampling different noise vectors. A joint loss composed of clues in image and feature spaces and adversarial loss is designed to train our model. We demonstrate the advantages of BiPN on two benchmarks Moving 2D Shapes and UCF101 and report competitive results to recent approaches.
Procedural Wang Tile Algorithm for Stochastic Wall Patterns
Venue: arXiv cs.GR 2017
Year: 2017

The game and movie industries always face the challenge of reproducing materials. This problem is tackled by combining illumination models and various textures (painted or procedural patterns). Gnerating stochastic wall patterns is crucial in the creation of a wide range of backgrounds (castles, temples, ruins...). A specific Wang tile set was introduced previously to tackle this problem, in a non-procedural fashion. Long lines may appear as visual artifacts. We use this tile set in a new procedural algorithm to generate stochastic wall patterns. For this purpose, we introduce specific hash functions implementing a constrained Wang tiling. This technique makes possible the generation of boundless textures while giving control over the maximum line length. The algorithm is simple and easy to implement, and the wall structure we get from the tiles allows to achieve visuals that reproduce all the small details of artist painted walls.
Accelerated Dual Learning by Homotopic Initialization
Venue: arXiv cs.LG 2017
Year: 2017

Gradient descent and coordinate descent are well understood in terms of their asymptotic behavior, but less so in a transient regime often used for approximations in machine learning. We investigate how proper initialization can have a profound effect on finding near-optimal solutions quickly. We show that a certain property of a data set, namely the boundedness of the correlations between eigenfeatures and the response variable, can lead to faster initial progress than expected by commonplace analysis. Convex optimization problems can tacitly benefit from that, but this automatism does not apply to their dual formulation. We analyze this phenomenon and devise provably good initialization strategies for dual optimization as well as heuristics for the non-convex case, relevant for deep learning. We find our predictions and methods to be experimentally well-supported.
Distributed Detection of Cycles
Venue: arXiv cs.DC 2017
Year: 2017

Distributed property testing in networks has been introduced by Brakerski and Patt-Shamir (2011), with the objective of detecting the presence of large dense sub-networks in a distributed manner. Recently, Censor-Hillel et al. (2016) have shown how to detect 3-cycles in a constant number of rounds by a distributed algorithm. In a follow up work, Fraigniaud et al. (2016) have shown how to detect 4-cycles in a constant number of rounds as well. However, the techniques in these latter works were shown not to generalize to larger cycles $C_k$ with $k\geq 5$. In this paper, we completely settle the problem of cycle detection, by establishing the following result. For every $k\geq 3$, there exists a distributed property testing algorithm for $C_k$-freeness, performing in a constant number of rounds. All these results hold in the classical CONGEST model for distributed network computing. Our algorithm is 1-sided error. Its round-complexity is $O(1/ε)$ where $ε\in(0,1)$ is the property testing parameter measuring the gap between legal and illegal instances.
Getting deep recommenders fit: Bloom embeddings for sparse binary input/output networks
Venue: arXiv cs.LG 2017
Year: 2017

Recommendation algorithms that incorporate techniques from deep learning are becoming increasingly popular. Due to the structure of the data coming from recommendation domains (i.e., one-hot-encoded vectors of item preferences), these algorithms tend to have large input and output dimensionalities that dominate their overall size. This makes them difficult to train, due to the limited memory of graphical processing units, and difficult to deploy on mobile devices with limited hardware. To address these difficulties, we propose Bloom embeddings, a compression technique that can be applied to the input and output of neural network models dealing with sparse high-dimensional binary-coded instances. Bloom embeddings are computationally efficient, and do not seriously compromise the accuracy of the model up to 1/5 compression ratios. In some cases, they even improve over the original accuracy, with relative increases up to 12%. We evaluate Bloom embeddings on 7 data sets and compare it against 4 alternative methods, obtaining favorable results. We also discuss a number of further advantages of Bloom embeddings, such as 'on-the-fly' constant-time operation, zero or marginal space requirements, training time speedups, or the fact that they do not require any change to the core model architecture or training configuration.
On Natural Language Generation of Formal Argumentation
Venue: arXiv cs.AI 2017
Year: 2017

In this paper we provide a first analysis of the research questions that arise when dealing with the problem of communicating pieces of formal argumentation through natural language interfaces. It is a generally held opinion that formal models of argumentation naturally capture human argument, and some preliminary studies have focused on justifying this view. Unfortunately, the results are not only inconclusive, but seem to suggest that explaining formal argumentation to humans is a rather articulated task. Graphical models for expressing argumentation-based reasoning are appealing, but often humans require significant training to use these tools effectively. We claim that natural language interfaces to formal argumentation systems offer a real alternative, and may be the way forward for systems that capture human argument.
Text Extraction From Texture Images Using Masked Signal Decomposition
Venue: arXiv cs.CV 2017
Year: 2017

Text extraction is an important problem in image processing with applications from optical character recognition to autonomous driving. Most of the traditional text segmentation algorithms consider separating text from a simple background (which usually has a different color from texts). In this work we consider separating texts from a textured background, that has similar color to texts. We look at this problem from a signal decomposition perspective, and consider a more realistic scenario where signal components are overlaid on top of each other (instead of adding together). When the signals are overlaid, to separate signal components, we need to find a binary mask which shows the support of each component. Because directly solving the binary mask is intractable, we relax this problem to the approximated continuous problem, and solve it by alternating optimization method. We show that the proposed algorithm achieves significantly better results than other recent works on several challenging images.
Serialisable Multi-Level Transaction Control: A Specification and Verification
Venue: arXiv cs.DB 2017
Year: 2017

We define a programming language independent controller TaCtl for multi-level transactions and an operator $TA$, which when applied to concurrent programs with multi-level shared locations containing hierarchically structured complex values, turns their behavior with respect to some abstract termination criterion into a transactional behavior. We prove the correctness property that concurrent runs under the transaction controller are serialisable, assuming an Inverse Operation Postulate to guarantee recoverability. For its applicability to a wide range of programs we specify the transaction controller TaCtl and the operator $TA$ in terms of Abstract State Machines (ASMs). This allows us to model concurrent updates at different levels of nested locations in a precise yet simple manner, namely in terms of partial ASM updates. It also provides the possibility to use the controller TaCtl and the operator $TA$ as a plug-in when specifying concurrent system components in terms of sequential ASMs.
Convergence analysis of belief propagation for pairwise linear Gaussian models
Venue: arXiv cs.LG 2017
Year: 2017

Gaussian belief propagation (BP) has been widely used for distributed inference in large-scale networks such as the smart grid, sensor networks, and social networks, where local measurements/observations are scattered over a wide geographical area. One particular case is when two neighboring agents share a common observation. For example, to estimate voltage in the direct current (DC) power flow model, the current measurement over a power line is proportional to the voltage difference between two neighboring buses. When applying the Gaussian BP algorithm to this type of problem, the convergence condition remains an open issue. In this paper, we analyze the convergence properties of Gaussian BP for this pairwise linear Gaussian model. We show analytically that the updating information matrix converges at a geometric rate to a unique positive definite matrix with arbitrary positive semidefinite initial value and further provide the necessary and sufficient convergence condition for the belief mean vector to the optimal estimate.
Distributed Submodular Maximization with Limited Information
Venue: arXiv cs.DS 2017
Year: 2017

We consider a class of distributed submodular maximization problems in which each agent must choose a single strategy from its strategy set. The global objective is to maximize a submodular function of the strategies chosen by each agent. When choosing a strategy, each agent has access to only a limited number of other agents' choices. For each of its strategies, an agent can evaluate its marginal contribution to the global objective given its information. The main objective is to investigate how this limitation of information about the strategies chosen by other agents affects the performance when agents make choices according to a local greedy algorithm. In particular, we provide lower bounds on the performance of greedy algorithms for submodular maximization, which depend on the clique number of a graph that captures the information structure. We also characterize graph-theoretic upper bounds in terms of the chromatic number of the graph. Finally, we demonstrate how certain graph properties limit the performance of the greedy algorithm. Simulations on several common models for random networks demonstrate our results.
Service Allocation in a Mobile Fog Infrastructure under Availability and QoS Constraints
Venue: arXiv cs.NI 2017
Year: 2017

The next generation of mobile networks, namely 5G, and the Internet of Things (IoT) have brought a large number of delay sensitive services. In this context Cloud services are migrating to the edge of the networks to reduce latency. The notion of Fog computing, where the edge plays an active role in the execution of services, comes to meet the need for the stringent requirements. Thus, it becomes of a high importance to elegantly formulate and optimize this problem of mapping demand to supply. This work does exactly that, taking into account two key aspects of a service allocation problem in the Fog, namely modeling cost of executing a given set of services, and the randomness of resources availability, which may come from pre-existing load or server mobility. We introduce an integer optimization formulation to minimize the total cost under a guarantee of service execution despite the uncertainty of resources availability.
An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing
Venue: arXiv cs.CL 2017
Year: 2017

In this work, we explore multiple neural architectures adapted for the task of automatic post-editing of machine translation output. We focus on neural end-to-end models that combine both inputs $mt$ (raw MT output) and $src$ (source language input) in a single neural architecture, modeling $\{mt, src\} \rightarrow pe$ directly. Apart from that, we investigate the influence of hard-attention models which seem to be well-suited for monolingual tasks, as well as combinations of both ideas. We report results on data sets provided during the WMT-2016 shared task on automatic post-editing and can demonstrate that dual-attention models that incorporate all available data in the APE scenario in a single model improve on the best shared task system and on all other published results after the shared task. Dual-attention models that are combined with hard attention remain competitive despite applying fewer changes to the input.
Transfer entropy-based feedback improves performance in artificial neural networks
Venue: arXiv cs.LG 2017
Year: 2017

The structure of the majority of modern deep neural networks is characterized by uni- directional feed-forward connectivity across a very large number of layers. By contrast, the architecture of the cortex of vertebrates contains fewer hierarchical levels but many recurrent and feedback connections. Here we show that a small, few-layer artificial neural network that employs feedback will reach top level performance on a standard benchmark task, otherwise only obtained by large feed-forward structures. To achieve this we use feed-forward transfer entropy between neurons to structure feedback connectivity. Transfer entropy can here intuitively be understood as a measure for the relevance of certain pathways in the network, which are then amplified by feedback. Feedback may therefore be key for high network performance in small brain-like architectures.
Anonymization of System Logs for Privacy and Storage Benefits
Venue: arXiv cs.DC 2017
Year: 2017

System logs constitute valuable information for analysis and diagnosis of system behavior. The size of parallel computing systems and the number of their components steadily increase. The volume of generated logs by the system is in proportion to this increase. Hence, long-term collection and storage of system logs is challenging. The analysis of system logs requires advanced text processing techniques. For very large volumes of logs, the analysis is highly time-consuming and requires a high level of expertise. For many parallel computing centers, outsourcing the analysis of system logs to third parties is the only affordable option. The existence of sensitive data within system log entries obstructs, however, the transmission of system logs to third parties. Moreover, the analytical tools for processing system logs and the solutions provided by such tools are highly system specific. Achieving a more general solution is only possible through the access and analysis system of logs of multiple computing systems. The privacy concerns impede, however, the sharing of system logs across institutions as well as in the public domain. This work proposes a new method for the anonymization of the information within system logs that employs de-identification and encoding to provide sharable system logs, with the highest possible data quality and of reduced size. The results presented in this work indicate that apart from eliminating the sensitive data within system logs and converting them into shareable data, the proposed anonymization method provides 25% performance improvement in post-processing of the anonymized system logs, and more than 50% reduction in their required storage space.
Shape-Color Differential Moment Invariants under Affine Transformations
Venue: arXiv cs.CV 2017
Year: 2017

We propose the general construction formula of shape-color primitives by using partial differentials of each color channel in this paper. By using all kinds of shape-color primitives, shape-color differential moment invariants can be constructed very easily, which are invariant to the shape affine and color affine transforms. 50 instances of SCDMIs are obtained finally. In experiments, several commonly used color descriptors and SCDMIs are used in image classification and retrieval of color images, respectively. By comparing the experimental results, we find that SCDMIs get better results.
A Model of Parametric Dependent Type Theory in Bridge/Path Cubical Sets
Venue: arXiv cs.LO 2017
Year: 2017

The purpose of this text is to prove all technical aspects of our model for dependent type theory with parametric quantifiers [Nuyts, Vezzosi and Devriese, 2017]. It is well-known that any presheaf category constitutes a model of dependent type theory [Hofmann, 1997], including a hierarchy of universes if the metatheory has one [Hofmann and Streicher, 1997]. We construct our model by defining the base category BPCube of bridge/path cubes and adapting the general presheaf model over BPCube to suit our needs. Our model is heavily based on the models by Atkey, Ghani and Johann [2014], Huber [2015], Bezem, Coquand and Huber [2014], Cohen, Coquand, Huber and Mörtberg [2016], Moulin [2016] and Bernardy, Coquand and Moulin [2015]. In chapter 1, we review the main concepts of categories with families, and the standard presheaf model of dependent type theory, and we establish the notations we will use. In chapter 2, we capture morphisms of CwFs, and natural transformations and adjunctions between them, in typing rules. We especially study morphisms of CwFs between presheaf categories, that arise from functors between the base categories. In chapter 3, we introduce the category BPCube of bridge/path cubes, and its presheaf category Psh(BPCube) of bridge/path cubical sets. There is a rich interaction with the category of cubical sets Psh(Cube) which we investigate more closely using ideas from axiomatic cohesion [Licata and Shulman, 2016]. In chapter 4, we define discrete types and show that they form a model of dependent type theory. We prove some infrastructural results. In chapter 5, we give an interpretation of the typing rules of ParamDTT [Nuyts, Vezzosi and Devriese, 2017] in Psh(BPCube).
Fine-grained human evaluation of neural versus phrase-based machine translation
Venue: arXiv cs.CL 2017
Year: 2017

We compare three approaches to statistical machine translation (pure phrase-based, factored phrase-based and neural) by performing a fine-grained manual evaluation via error annotation of the systems' outputs. The error types in our annotation are compliant with the multidimensional quality metrics (MQM), and the annotation is performed by two annotators. Inter-annotator agreement is high for such a task, and results show that the best performing system (neural) reduces the errors produced by the worst system (phrase-based) by 54%.