OpenRaiser/Intern-Atlas huggingface dataset:
Dialectica Fuzzy Petri Nets
Venue: arXiv cs.LO 2020
Year: 2020

Brown and Gurr have introduced a model of Petri Nets that is based on de~Paiva's Dialectica categories. This model was refined in an unpublished technical report, where Petri nets with multiplicities, instead of {\em elementary} nets (i.e., nets with multiplicities zero and one only) were considered. In this note we expand this modelling to deal with {\em fuzzy} petri nets. The basic idea is to use as the dualizing object in the Dialectica categories construction, the unit interval that has all the properties of a {\em lineale} structure.
Restore from Restored: Single Image Denoising with Pseudo Clean Image
Venue: arXiv cs.CV 2020
Year: 2020

In this study, we propose a simple and effective fine-tuning algorithm called "restore-from-restored", which can greatly enhance the performance of fully pre-trained image denoising networks. Many supervised denoising approaches can produce satisfactory results using large external training datasets. However, these methods have limitations in using internal information available in a given test image. By contrast, recent self-supervised approaches can remove noise in the input image by utilizing information from the specific test input. However, such methods show relatively lower performance on known noise types such as Gaussian noise compared to supervised methods. Thus, to combine external and internal information, we fine-tune the fully pre-trained denoiser using pseudo training set at test time. By exploiting internal self-similar patches (i.e., patch-recurrence), the baseline network can be adapted to the given specific input image. We demonstrate that our method can be easily employed on top of the state-of-the-art denoising networks and further improve the performance on numerous denoising benchmark datasets including real noisy images.
New advances in enumerative biclustering algorithms with online partitioning
Venue: arXiv cs.LG 2020
Year: 2020

This paper further extends RIn-Close_CVC, a biclustering algorithm capable of performing an efficient, complete, correct and non-redundant enumeration of maximal biclusters with constant values on columns in numerical datasets. By avoiding a priori partitioning and itemization of the dataset, RIn-Close_CVC implements an online partitioning, which is demonstrated here to guide to more informative biclustering results. The improved algorithm is called RIn-Close_CVC3, keeps those attractive properties of RIn-Close_CVC, as formally proved here, and is characterized by: a drastic reduction in memory usage; a consistent gain in runtime; additional ability to handle datasets with missing values; and additional ability to operate with attributes characterized by distinct distributions or even mixed data types. The experimental results include synthetic and real-world datasets used to perform scalability and sensitivity analyses. As a practical case study, a parsimonious set of relevant and interpretable mixed-attribute-type rules is obtained in the context of supervised descriptive pattern mining.
Performance Analysis of NOMA Uplink Networks under Statistical QoS Delay Constraints
Venue: arXiv cs.IT 2020
Year: 2020

In the fifth generation and beyond (B5G), delayconstraints emerge as a topic of particular interest, e.g. forultra-reliable low latency communications (URLLC) such asautonomous vehicles and enhanced reality. In this paper, westudythe performance of a two-user uplink NOMA network understatistical quality of service (QoS) delay constraints, capturedthrough each user s effective capacity (EC). We propose novelclosed-form expressions for the EC of the NOMA users andshow that in the high signal to noise ratio (SNR) region, the 'strong' NOMA user has a limited EC, assuming the same delayconstraint as the 'weak' user. We demonstrate that for the weakuser, OMA achieves higher EC than NOMA at small values ofthe transmit SNR, while NOMA outperforms OMA in terms ofEC at high SNRs. On the other hand, for the strong user theopposite is true, i.e., NOMA achieves higher EC than OMA atsmall SNRs, while OMA becomes more beneficial at high SNRs.This result raises the question of introducing 'adaptive' OMA /NOMA policies, based jointly on the users delay constraints aswell as on the available transmit power.
A Fuzzy-Based Optimization Method for Integrating Value Dependencies into Software Requirement Selection
Venue: arXiv cs.SE 2020
Year: 2020

Software requirement selection aims to find an optimal subset of the requirements with the highest value while respecting the budget. But the value of a requirement may depend on the presence or absence of other requirements in the optimal subset. Existing requirement selection methods, however, do not consider Value Dependencies, thus increasing the risk of value loss. To address this, we have proposed Dependency-Aware Requirement Selection (DARS) method with two main components: (i) a fuzzy-based technique for identifying and modeling value dependencies, and (ii) an Integer Programming model that takes into account value dependencies in software requirement selection. We have further, proposed an alternative optimization model for situations where quantifying value dependencies is hard. The scalability of DARS and its effectiveness in reducing the risk of value loss are demonstrated through exhaustive simulations.
Interval Temporal Logic Decision Tree Learning
Venue: arXiv cs.LO 2020
Year: 2020

Decision trees are simple, yet powerful, classification models used to classify categorical and numerical data, and, despite their simplicity, they are commonly used in operations research and management, as well as in knowledge mining. From a logical point of view, a decision tree can be seen as a structured set of logical rules written in propositional logic. Since knowledge mining is rapidly evolving towards temporal knowledge mining, and since in many cases temporal information is best described by interval temporal logics, propositional logic decision trees may evolve towards interval temporal logic decision trees. In this paper, we define the problem of interval temporal logic decision tree learning, and propose a solution that generalizes classical decision tree learning.
Mask & Focus: Conversation Modelling by Learning Concepts
Venue: arXiv cs.CL 2020
Year: 2020

Sequence to sequence models attempt to capture the correlation between all the words in the input and output sequences. While this is quite useful for machine translation where the correlation among the words is indeed quite strong, it becomes problematic for conversation modelling where the correlation is often at a much abstract level. In contrast, humans tend to focus on the essential concepts discussed in the conversation context and generate responses accordingly. In this paper, we attempt to mimic this response generating mechanism by learning the essential concepts in the context and response in an unsupervised manner. The proposed model, referred to as Mask \& Focus maps the input context to a sequence of concepts which are then used to generate the response concepts. Together, the context and the response concepts generate the final response. In order to learn context concepts from the training data automatically, we \emph{mask} words in the input and observe the effect of masking on response generation. We train our model to learn those response concepts that have high mutual information with respect to the context concepts, thereby guiding the model to \emph{focus} on the context concepts. Mask \& Focus achieves significant improvement over the existing baselines in several established metrics for dialogues.
Investigating an approach for low resource language dataset creation, curation and classification: Setswana and Sepedi
Venue: arXiv cs.CL 2020
Year: 2020

The recent advances in Natural Language Processing have been a boon for well-represented languages in terms of available curated data and research resources. One of the challenges for low-resourced languages is clear guidelines on the collection, curation and preparation of datasets for different use-cases. In this work, we take on the task of creation of two datasets that are focused on news headlines (i.e short text) for Setswana and Sepedi and creation of a news topic classification task. We document our work and also present baselines for classification. We investigate an approach on data augmentation, better suited to low resource languages, to improve the performance of the classifiers
The COVID-19 Social Media Infodemic
Venue: arXiv cs.SI 2020
Year: 2020

We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction numbers $R_0$ for each social media platform. Moreover, we characterize information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.
Devil is Virtual: Reversing Virtual Inheritance in C++ Binaries
Venue: arXiv cs.CR 2020
Year: 2020

Complexities that arise from implementation of object-oriented concepts in C++ such as virtual dispatch and dynamic type casting have attracted the attention of attackers and defenders alike. Binary-level defenses are dependent on full and precise recovery of class inheritance tree of a given program. While current solutions focus on recovering single and multiple inheritances from the binary, they are oblivious to virtual inheritance. Conventional wisdom among binary-level defenses is that virtual inheritance is uncommon and/or support for single and multiple inheritances provides implicit support for virtual inheritance. In this paper, we show neither to be true. Specifically, (1) we present an efficient technique to detect virtual inheritance in C++ binaries and show through a study that virtual inheritance can be found in non-negligible number (more than 10\% on Linux and 12.5\% on Windows) of real-world C++ programs including Mysql and libstdc++. (2) we show that failure to handle virtual inheritance introduces both false positives and false negatives in the hierarchy tree. These false positves and negatives either introduce attack surface when the hierarchy recovered is used to enforce CFI policies, or make the hierarchy difficult to understand when it is needed for program understanding (e.g., during decompilation). (3) We present a solution to recover virtual inheritance from COTS binaries. We recover a maximum of 95\% and 95.5\% (GCC -O0) and a minimum of 77.5\% and 73.8\% (Clang -O2) of virtual and intermediate bases respectively in the virtual inheritance tree.
New Self-Dual Codes of length 68 from a 2 by 2 block matrix Construction and Group Rings
Venue: arXiv cs.IT 2020
Year: 2020

Many generator matrices for constructing extremal binary self-dual codes of different lengths have the form G=(I|A), where I is the n by n identity matrix and A is the n by n matrix fully determined by the first row. In this work, we define a generator matrix in which A is a block matrix, where the blocks come from group rings and also, A is not fully determined by the elements appearing in the first row. By applying our construction over F_2+uF_2 and by employing the extension method for codes, we were able to construct new extremal binary self-dual codes of length 68. Additionally, by employing a generalised neighbour method to the codes obtained, we were able to construct many new binary self-dual [68,34,12]-codes with the rare parameters gamma=7,8 and 9 in W_{68,2}. In particular, we find 92 new binary self-dual [68,34,12]-codes.
Multi-Objective Variational Autoencoder: an Application for Smart Infrastructure Maintenance
Venue: arXiv cs.LG 2020
Year: 2020

Multi-way data analysis has become an essential tool for capturing underlying structures in higher-order data sets where standard two-way analysis techniques often fail to discover the hidden correlations between variables in multi-way data. We propose a multi-objective variational autoencoder (MVA) method for smart infrastructure damage detection and diagnosis in multi-way sensing data based on the reconstruction probability of autoencoder deep neural network (ADNN). Our method fuses data from multiple sensors in one ADNN at which informative features are being extracted and utilized for damage identification. It generates probabilistic anomaly scores to detect damage, asses its severity and further localize it via a new localization layer introduced in the ADNN. We evaluated our method on multi-way datasets in the area of structural health monitoring for damage diagnosis purposes. The data was collected from our deployed data acquisition system on a cable-stayed bridge in Western Sydney and from a laboratory based building structure obtained from Los Alamos National Laboratory (LANL). Experimental results show that the proposed method can accurately detect structural damage. It was also able to estimate the different levels of damage severity, and capture damage locations in an unsupervised aspect. Compared to the state-of-the-art approaches, our proposed method shows better performance in terms of damage detection and localization.
Animated Logic: Correct Functional Conversion to Conjunctive Normal Form
Venue: arXiv cs.LO 2020
Year: 2020

We present an approach to obtain formally verified implementations of classical Computational Logic algorithms. We choose the Why3 platform because it allows to implement functions in a style very close to the mathematical definitions, as well as it allows a high degree of automation in the verification process. As proof of concept, we present a mathematical definition of the algorithm to convert propositional formulae to conjunctive normal form, implementations in WhyML (the Why3 language, very similar to OCaml), and proofs of correctness of the implementations. We apply our proposal on two variants of this algorithm: one in direct-style and another with an explicit stack structure. Being both first-order versions, Why3 processes the proofs naturally.
Tensorized Random Projections
Venue: arXiv cs.LG 2020
Year: 2020

We introduce a novel random projection technique for efficiently reducing the dimension of very high-dimensional tensors. Building upon classical results on Gaussian random projections and Johnson-Lindenstrauss transforms~(JLT), we propose two tensorized random projection maps relying on the tensor train~(TT) and CP decomposition format, respectively. The two maps offer very low memory requirements and can be applied efficiently when the inputs are low rank tensors given in the CP or TT format. Our theoretical analysis shows that the dense Gaussian matrix in JLT can be replaced by a low-rank tensor implicitly represented in compressed form with random factors, while still approximately preserving the Euclidean distance of the projected inputs. In addition, our results reveal that the TT format is substantially superior to CP in terms of the size of the random projection needed to achieve the same distortion ratio. Experiments on synthetic data validate our theoretical analysis and demonstrate the superiority of the TT decomposition.
Variable-Based Network Analysis of Datasets on Data Exchange Platforms
Venue: arXiv cs.SI 2020
Year: 2020

Recently, data exchange platforms have emerged in the digital economy to enable better resource allocation in a data-driven society, which requires cross-organizational data collaborations. Understanding the characteristics of the data on these platforms is important for their application; however, the structures of such platforms have not been extensively investigated. In this study, we apply a network approach with a novel variable-based structural analysis to the metadata of datasets on two data platform services. It was noted that the structures of the data networks are locally dense and highly assortative, similar to human-related net-works. Even though the data on these platforms are designed and collected differently, depending on the use objectives, the variables of heterogeneous data exhibit a power distribution, and the data networks exhibit multi-scaling behavior. Furthermore, we found that the data collection strategies of the platforms are related to the variety of variables, density of the networks, and their robustness from the viewpoint of sustainability and social acceptability of the data platforms.
Kernel Quantization for Efficient Network Compression
Venue: arXiv cs.LG 2020
Year: 2020

This paper presents a novel network compression framework Kernel Quantization (KQ), targeting to efficiently convert any pre-trained full-precision convolutional neural network (CNN) model into a low-precision version without significant performance loss. Unlike existing methods struggling with weight bit-length, KQ has the potential in improving the compression ratio by considering the convolution kernel as the quantization unit. Inspired by the evolution from weight pruning to filter pruning, we propose to quantize in both kernel and weight level. Instead of representing each weight parameter with a low-bit index, we learn a kernel codebook and replace all kernels in the convolution layer with corresponding low-bit indexes. Thus, KQ can represent the weight tensor in the convolution layer with low-bit indexes and a kernel codebook with limited size, which enables KQ to achieve significant compression ratio. Then, we conduct a 6-bit parameter quantization on the kernel codebook to further reduce redundancy. Extensive experiments on the ImageNet classification task prove that KQ needs 1.05 and 1.62 bits on average in VGG and ResNet18, respectively, to represent each parameter in the convolution layer and achieves the state-of-the-art compression ratio with little accuracy loss.
Vector symbolic architectures for context-free grammars
Venue: arXiv cs.CL 2020
Year: 2020

Background / introduction. Vector symbolic architectures (VSA) are a viable approach for the hyperdimensional representation of symbolic data, such as documents, syntactic structures, or semantic frames. Methods. We present a rigorous mathematical framework for the representation of phrase structure trees and parse trees of context-free grammars (CFG) in Fock space, i.e. infinite-dimensional Hilbert space as being used in quantum field theory. We define a novel normal form for CFG by means of term algebras. Using a recently developed software toolbox, called FockBox, we construct Fock space representations for the trees built up by a CFG left-corner (LC) parser. Results. We prove a universal representation theorem for CFG term algebras in Fock space and illustrate our findings through a low-dimensional principal component projection of the LC parser states. Conclusions. Our approach could leverage the development of VSA for explainable artificial intelligence (XAI) by means of hyperdimensional deep neural computation. It could be of significance for the improvement of cognitive user interfaces and other applications of VSA in machine learning.
Induced subgraphs of bounded treewidth and the container method
Venue: arXiv cs.DS 2020
Year: 2020

A hole in a graph is an induced cycle of length at least 4. A hole is long if its length is at least 5. By $P_t$ we denote a path on $t$ vertices. In this paper we give polynomial-time algorithms for the following problems: the Maximum Weight Independent Set problem in long-hole-free graphs, and the Feedback Vertex Set problem in $P_5$-free graphs. Each of the above results resolves a corresponding long-standing open problem. An extended $C_5$ is a five-vertex hole with an additional vertex adjacent to one or two consecutive vertices of the hole. Let $\mathcal{C}$ be the class of graphs excluding an extended $C_5$ and holes of length at least $6$ as induced subgraphs; $\mathcal{C}$ contains all long-hole-free graphs and all $P_5$-free graphs. We show that, given an $n$-vertex graph $G \in \mathcal{C}$ with vertex weights and an integer $k$, one can in time $n^{\Oh(k)}$ find a maximum-weight induced subgraph of $G$ of treewidth less than $k$. This implies both aforementioned results.
A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection
Venue: arXiv cs.CV 2020
Year: 2020

Elder people consequence a variety of problems while living Activities of Daily Living (ADL) for the reason of age, sense, loneliness and cognitive changes. These cause the risk to ADL which leads to several falls. Getting real life fall data is a difficult process and are not available whereas simulated falls become ubiquitous to evaluate the proposed methodologies. From the literature review, it is investigated that most of the researchers used raw and energy features (time domain features) of the signal data as those are most discriminating. However, in real life situations fall signal may be noisy than the current simulated data. Hence the result using raw feature may dramatically changes when using in a real life scenario. This research is using frequency domain Fourier coefficient features to differentiate various human activities of daily life. The feature vector constructed using those Fast Fourier Transform are robust to noise and rotation invariant. Two different supervised classifiers kNN and SVM are used for evaluating the method. Two standard publicly available datasets are used for benchmark analysis. In this research, more discriminating results are obtained applying kNN classifier than the SVM classifier. Various standard measure including Standard Accuracy (SA), Macro Average Accuracy (MAA), Sensitivity (SE) and Specificity (SP) has been accounted. In all cases, the proposed method outperforms energy features whereas competitive results are shown with raw features. It is also noticed that the proposed method performs better than the recently risen deep learning approach in which data augmentation method were not used.
Compacting Frequent Star Patterns in RDF Graphs
Venue: arXiv cs.DB 2020
Year: 2020

Knowledge graphs have become a popular formalism for representing entities and their properties using a graph data model, e.g., the Resource Description Framework (RDF). An RDF graph comprises entities of the same type connected to objects or other entities using labeled edges annotated with properties. RDF graphs usually contain entities that share the same objects in a certain group of properties, i.e., they match star patterns composed of these properties and objects. In case the number of these entities or properties in these star patterns is large, the size of the RDF graph and query processing are negatively impacted; we refer these star patterns as frequent star patterns. We address the problem of identifying frequent star patterns in RDF graphs and devise the concept of factorized RDF graphs, which denote compact representations of RDF graphs where the number of frequent star patterns is minimized. We also develop computational methods to identify frequent star patterns and generate a factorized RDF graph, where compact RDF molecules replace frequent star patterns. A compact RDF molecule of a frequent star pattern denotes an RDF subgraph that instantiates the corresponding star pattern. Instead of having all the entities matching the original frequent star pattern, a surrogate entity is added and related to the properties of the frequent star pattern; it is linked to the entities that originally match the frequent star pattern. We evaluate the performance of our factorization techniques on several RDF graph benchmarks and compare with a baseline built on top of gSpan, a state-of-the-art algorithm to detect frequent patterns. The outcomes evidence the efficiency of proposed approach and show that our techniques are able to reduce execution time of the baseline approach in at least three orders of magnitude reducing the RDF graph size by up to 66.56%.