OpenRaiser/Intern-Atlas huggingface dataset:
Strong Bisimulation for Control Operators
Venue: arXiv cs.LO 2019
Year: 2019

The purpose of this paper is to identify programs with control operators whose reduction semantics are in exact correspondence. This is achieved by introducing a relation $\simeq$, defined over a revised presentation of Parigot's $λμ$-calculus we dub $ΛM$. Our result builds on two fundamental ingredients: (1) factorization of $λμ$-reduction into multiplicative and exponential steps by means of explicit term operators of $ΛM$, and (2) translation of $ΛM$-terms into Laurent's polarized proof-nets (PPN) such that cut-elimination in PPN simulates our calculus. Our proposed relation $\simeq$ is shown to characterize structural equivalence in PPN. Most notably, $\simeq$ is shown to be a strong bisimulation with respect to reduction in $ΛM$, i.e. two $\simeq$-equivalent terms have the exact same reduction semantics, a result which fails for Regnier's $σ$-equivalence in $λ$-calculus as well as for Laurent's $σ$-equivalence in $λμ$.
Prefix palindromic length of the Thue-Morse word
Venue: arXiv cs.DM 2019
Year: 2019

The prefix palindromic length $PPL_u(n)$ of an infinite word $u$ is the minimal number of concatenated palindromes needed to express the prefix of length $n$ of $u$. In a 2013 paper with Puzynina and Zamboni we stated the conjecture that $PPL_u(n)$ is unbounded for every infinite word $u$ which is not ultimately periodic. Up to now, the conjecture has been proven for almost all words, including all words avoiding some power $p$. However, even in that simple case the existing upper bound for the minimal number $n$ such that $PPL_u(n)>K$ is greater than any constant to the power $K$. Precise values of $PPL_u(n)$ are not known even for simplest examples like the Fibonacci word. In this paper, we give the first example of such a precise computation and compute the function of the prefix palindromic length of the Thue-Morse word, a famous test object for all functions on infinite words. It happens that this sequence is $2$-regular, which raises the question if this fact can be generalized to all automatic sequences.
An enhanced KNN-based twin support vector machine with stable learning rules
Venue: arXiv cs.LG 2019
Year: 2019

Among the extensions of twin support vector machine (TSVM), some scholars have utilized K-nearest neighbor (KNN) graph to enhance TSVM's classification accuracy. However, these KNN-based TSVM classifiers have two major issues such as high computational cost and overfitting. In order to address these issues, this paper presents an enhanced regularized K-nearest neighbor based twin support vector machine (RKNN-TSVM). It has three additional advantages: (1) Weight is given to each sample by considering the distance from its nearest neighbors. This further reduces the effect of noise and outliers on the output model. (2) An extra stabilizer term was added to each objective function. As a result, the learning rules of the proposed method are stable. (3) To reduce the computational cost of finding KNNs for all the samples, location difference of multiple distances based k-nearest neighbors algorithm (LDMDBA) was embedded into the learning process of the proposed method. The extensive experimental results on several synthetic and benchmark datasets show the effectiveness of our proposed RKNN-TSVM in both classification accuracy and computational time. Moreover, the largest speedup in the proposed method reaches to 14 times.
Iris Verification with Convolutional Neural Network and Unit-Circle Layer
Venue: arXiv cs.CV 2019
Year: 2019

We propose a novel convolutional neural network to verify a~match between two normalized images of the human iris. The network is trained end-to-end and validated on three publicly available datasets yielding state-of-the-art results against four baseline methods. The network performs better by a 10% margin to the state-of-the-art method on the CASIA.v4 dataset. In the network, we use a novel Unit-Circle Layer layer which replaces the Gabor-filtering step in a common iris-verification pipeline. We show that the layer improves the performance of the model up to 15% on previously-unseen data.
On the Secrecy Rate of Spatial Modulation Based Indoor Visible Light Communications
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we investigate the physical-layer security for a spatial modulation (SM) based indoor visible light communication (VLC) system, which includes multiple transmitters, a legitimate receiver, and a passive eavesdropper (Eve). At the transmitters, the SM scheme is employed, i.e., only one transmitter is active at each time instant. To choose the active transmitter, a uniform selection (US) scheme is utilized. Two scenarios are considered: one is with non-negativity and average optical intensity constraints, the other is with non-negativity, average optical intensity and peak optical intensity constraints. Then, lower and upper bounds on the secrecy rate are derived for these two scenarios. Besides, the asymptotic behaviors for the derived secrecy rate bounds at high signal-to-noise ratio (SNR) are analyzed. To further improve the secrecy performance, a channel adaptive selection (CAS) scheme and a greedy selection (GS) scheme are proposed to select the active transmitter. Numerical results show that the lower and upper bounds of the secrecy rate are tight. At high SNR, small asymptotic performance gaps exist between the derived lower and upper bounds. Moreover, the proposed GS scheme has the best performance, followed by the CAS scheme and the US scheme.
Fractional-order Backpropagation Neural Networks: Modified Fractional-order Steepest Descent Method for Family of Backpropagation Neural Networks
Venue: arXiv cs.NE 2019
Year: 2019

This paper offers a novel mathematical approach, the modified Fractional-order Steepest Descent Method (FSDM) for training BackPropagation Neural Networks (BPNNs); this differs from the majority of the previous approaches and as such. A promising mathematical method, fractional calculus, has the potential to assume a prominent role in the applications of neural networks and cybernetics because of its inherent strengths such as long-term memory, nonlocality, and weak singularity. Therefore, to improve the optimization performance of classic first-order BPNNs, in this paper we study whether it could be possible to modified FSDM and generalize classic first-order BPNNs to modified FSDM based Fractional-order Backpropagation Neural Networks (FBPNNs). Motivated by this inspiration, this paper proposes a state-of-the-art application of fractional calculus to implement a modified FSDM based FBPNN whose reverse incremental search is in the negative directions of the approximate fractional-order partial derivatives of the square error. At first, the theoretical concept of a modified FSDM based FBPNN is described mathematically. Then, the mathematical proof of the fractional-order global optimal convergence, an assumption of the structure, and the fractional-order multi-scale global optimization of a modified FSDM based FBPNN are analysed in detail. Finally, we perform comparative experiments and compare a modified FSDM based FBPNN with a classic first-order BPNN, i.e., an example function approximation, fractional-order multi-scale global optimization, and two comparative performances with real data. The more efficient optimal searching capability of the fractional-order multi-scale global optimization of a modified FSDM based FBPNN to determine the global optimal solution is the major advantage being superior to a classic first-order BPNN.
Variational Sequential Labelers for Semi-Supervised Learning
Venue: arXiv cs.CL 2019
Year: 2019

We introduce a family of multitask variational methods for semi-supervised sequence labeling. Our model family consists of a latent-variable generative model and a discriminative labeler. The generative models use latent variables to define the conditional probability of a word given its context, drawing inspiration from word prediction objectives commonly used in learning word embeddings. The labeler helps inject discriminative information into the latent space. We explore several latent variable configurations, including ones with hierarchical structure, which enables the model to account for both label-specific and word-specific information. Our models consistently outperform standard sequential baselines on 8 sequence labeling datasets, and improve further with unlabeled data.
A Uniform Approach to Random Process Model
Venue: arXiv cs.LO 2019
Year: 2019

There is a lot of research on probabilistic transition systems. There are not many studies in probabilistic process models. The lack of investigation into the interactive aspect of probabilistic processes is mainly due to the difficulty caused by the discrepancy between probabilistic actions and nondeterministic behaviours. The paper proposes a uniform approach to probabilistic process models and a bisimulation congruence for probabilistic concurrency.
Experimental Security Analysis of Controller Software in SDNs: A Review
Venue: arXiv cs.NI 2019
Year: 2019

The software defined networking paradigm relies on the programmability of the network to automatically perform management and reconfiguration tasks. The result of adopting this programmability feature is twofold: first by designing new solutions and, second, by concurrently making room for the exploitation of new security threats. As a malfunction in the controller software may lead to a collapse of the network, assessing the security of solutions before their deployment, is a major concern in SDNs. In light of this, we have conducted a comprehensive review of the literature on the experimental security analysis of the control plane in SDNs, with an emphasis on vulnerabilities of the controller software. Additionally, we have introduced a taxonomy of the techniques found in the literature with regard to the experimental security analysis of SDN controller software. Furthermore, a comparative study has been carried out of existing experimental approaches considering the security requirements defined by the Open Network Foundation (ONF). As a result, we highlighted that there is a need for a standardization of the methodologies employed for automated security analysis, that can meet the appropriate requirements, and support the development of reliable and secure software for SDNs.
A UK Case Study on Cybersecurity Education and Accreditation
Venue: arXiv cs.CR 2019
Year: 2019

This paper presents a national case study-based analysis of the numerous dimensions to cybersecurity education and how they are prioritised, implemented and accredited; from understanding the interaction of hardware and software, moving from theory to practice (and vice versa), to human factors, policy and politics (as well as various other important facets). A multitude of model curricula and recommendations have been presented and discussed in international fora in recent years, with varying levels of impact on education, policy and practice. This paper address three key questions: i) what is taught and what should be taught for cybersecurity to general computer science students; ii) should cybersecurity be taught stand-alone or in an integrated manner to general computer science students; and iii) can accreditation by national professional, statutory and regulatory bodies enhance the provision of cybersecurity within a body's jurisdiction? Evaluating how cybersecurity is taught in all aspects of computer science is clearly a task of considerable size, one that is beyond the scope of this paper. Instead a case study-based research approach -- primarily focusing on the UK -- has been adopted to evaluate the evidence of the teaching of cybersecurity within general computer science to university-level students. Thus, in the context of widespread international computer science/engineering curriculum reform, what does this need to embed cybersecurity knowledge and skills mean more generally for institutions and educators, and how can we teach this subject more effectively? Through this UK case study, and by contrasting with related initiatives in the US, we demonstrate the positive effect that national accreditation requirements can have, and offer some recommendations both for future research and curriculum developments.
Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases
Venue: arXiv cs.CV 2019
Year: 2019

Pathologists find tedious to examine the status of the sentinel lymph node on a large number of pathological scans. The examination process of such lymph node which encompasses metastasized cancer cells is histopathologically organized. However, the task of finding metastatic tissues is gradual which is often challenging. In this work, we present our deep convolutional neural network based model validated on PatchCamelyon (PCam) benchmark dataset for fundamental machine learning research in histopathology diagnosis. We find that our proposed model trained with a semi-supervised learning approach by using pseudo labels on PCam-level significantly leads to better performances to strong CNN baseline on the AUC metric.
Nested Cavity Classifier: performance and remedy
Venue: arXiv cs.LG 2019
Year: 2019

Nested Cavity Classifier (NCC) is a classification rule that pursues partitioning the feature space, in parallel coordinates, into convex hulls to build decision regions. It is claimed in some literatures that this geometric-based classifier is superior to many others, particularly in higher dimensions. First, we give an example on how NCC can be inefficient, then motivate a remedy by combining the NCC with the Linear Discriminant Analysis (LDA) classifier. We coin the term Nested Cavity Discriminant Analysis (NCDA) for the resulting classifier. Second, a simulation study is conducted to compare both, NCC and NCDA to another two basic classifiers, Linear and Quadratic Discriminant Analysis. NCC alone proves to be inferior to others, while NCDA always outperforms NCC and competes with LDA and QDA.
Ranking Policy Gradient
Venue: arXiv cs.LG 2019
Year: 2019

Sample inefficiency is a long-lasting problem in reinforcement learning (RL). The state-of-the-art estimates the optimal action values while it usually involves an extensive search over the state-action space and unstable optimization. Towards the sample-efficient RL, we propose ranking policy gradient (RPG), a policy gradient method that learns the optimal rank of a set of discrete actions. To accelerate the learning of policy gradient methods, we establish the equivalence between maximizing the lower bound of return and imitating a near-optimal policy without accessing any oracles. These results lead to a general off-policy learning framework, which preserves the optimality, reduces variance, and improves the sample-efficiency. Furthermore, the sample complexity of RPG does not depend on the dimension of state space, which enables RPG for large-scale problems. We conduct extensive experiments showing that when consolidating with the off-policy learning framework, RPG substantially reduces the sample complexity, comparing to the state-of-the-art.
Constant Amortized Time Enumeration of Independent Sets for Graphs with Bounded Clique Number
Venue: arXiv cs.DS 2019
Year: 2019

In this study, we address the independent set enumeration problem. Although several efficient enumeration algorithms and careful analyses have been proposed for maximal independent sets, no fine-grained analysis has been given for the non-maximal variant. From the main result, we propose an algorithm $\texttt{EIS}$ for the non-maximal variant that runs in $O(q)$ amortized time and linear space, where $q$ is the clique number, i.e., the maximum size of a clique in an input graph. Note that $\texttt{EIS}$ works correctly even if the exact value of $q$ is unknown. Despite its simplicity, $\texttt{EIS}$ is optimal for graphs with a bounded clique number, such as, triangle-free graphs, planar graphs, bounded degenerate graphs, locally bounded expansion graphs, and $F$-free graphs for any fixed graph $F$, where a $F$-free graph is a graph that has no copy of $F$ as a subgraph.
A Dynamic Games Approach to Proactive Defense Strategies against Advanced Persistent Threats in Cyber-Physical Systems
Venue: arXiv cs.GT 2019
Year: 2019

Advanced Persistent Threats (APTs) have recently emerged as a significant security challenge for a cyber-physical system due to their stealthy, dynamic and adaptive nature. Proactive dynamic defenses provide a strategic and holistic security mechanism to increase the costs of attacks and mitigate the risks. This work proposes a dynamic game framework to model a long-term interaction between a stealthy attacker and a proactive defender. The stealthy and deceptive behaviors are captured by the multi-stage game of incomplete information, where each player has his own private information unknown to the other. Both players act strategically according to their beliefs which are formed by the multi-stage observation and learning. The perfect Bayesian Nash equilibrium provides a useful prediction of both players' policies because no players benefit from unilateral deviations from the equilibrium. We propose an iterative algorithm to compute the perfect Bayesian Nash equilibrium and use the Tennessee Eastman process as a benchmark case study. Our numerical experiment corroborates the analytical results and provides further insights into the design of proactive defense-in-depth strategies.
Adversarial Computation of Optimal Transport Maps
Venue: arXiv cs.LG 2019
Year: 2019

Computing optimal transport maps between high-dimensional and continuous distributions is a challenging problem in optimal transport (OT). Generative adversarial networks (GANs) are powerful generative models which have been successfully applied to learn maps across high-dimensional domains. However, little is known about the nature of the map learned with a GAN objective. To address this problem, we propose a generative adversarial model in which the discriminator's objective is the $2$-Wasserstein metric. We show that during training, our generator follows the $W_2$-geodesic between the initial and the target distributions. As a consequence, it reproduces an optimal map at the end of training. We validate our approach empirically in both low-dimensional and high-dimensional continuous settings, and show that it outperforms prior methods on image data.
Heterogeneous Active Messages (HAM) -- Implementing Lightweight Remote Procedure Calls in C++
Venue: arXiv cs.DC 2019
Year: 2019

We present HAM (Heterogeneous Active Messages), a C++-only active messaging solution for heterogeneous distributed systems.Combined with a communication protocol, HAM can be used as a generic Remote Procedure Call (RPC) mechanism. It has been used in HAM-Offload to implement a low-overhead offloading framework for inter- and intra-node offloading between different architectures including accelerators like the Intel Xeon Phi x100 series and the NEC SX-Aurora TSUBASA Vector Engine. HAM uses template meta-programming to implicitly generate active message types and their corresponding handler functions. Heterogeneity is enabled by providing an efficient address translation mechanism between the individual handler code addresses of processes running different binaries on different architectures, as well a hooks to inject serialisation and deserialisation code on a per-type basis. Implementing such a solution in modern C++ sheds some light on the shortcomings and grey areas of the C++ standard when it comes to distributed and heterogeneous environments.
In Hindsight: A Smooth Reward for Steady Exploration
Venue: arXiv cs.LG 2019
Year: 2019

In classical Q-learning, the objective is to maximize the sum of discounted rewards through iteratively using the Bellman equation as an update, in an attempt to estimate the action value function of the optimal policy. Conventionally, the loss function is defined as the temporal difference between the action value and the expected (discounted) reward, however it focuses solely on the future, leading to overestimation errors. We extend the well-established Q-learning techniques by introducing the hindsight factor, an additional loss term that takes into account how the model progresses, by integrating the historic temporal difference as part of the reward. The effect of this modification is examined in a deterministic continuous-state space function estimation problem, where the overestimation phenomenon is significantly reduced and results in improved stability. The underlying effect of the hindsight factor is modeled as an adaptive learning rate, which unlike existing adaptive optimizers, takes into account the previously estimated action value. The proposed method outperforms variations of Q-learning, with an overall higher average reward and lower action values, which supports the deterministic evaluation, and proves that the hindsight factor contributes to lower overestimation errors. The mean average score of 100 episodes obtained after training for 10 million frames shows that the hindsight factor outperforms deep Q-networks, double deep Q-networks and dueling networks for a variety of ATARI games.
Hybrid-Learning approach toward situation recognition and handling
Venue: arXiv cs.HC 2019
Year: 2019

The success of smart environments largely depends on their smartness of understanding the environments' ongoing situations. Accordingly, this task is an essence to smart environment central processors. Obtaining knowledge from the environment is often through sensors, and the response to a particular circumstance is offered by actuators. This can be improved by getting user feedback, and capturing environmental changes. Machine learning techniques and semantic reasoning tools are widely used in this area to accomplish the goal of interpretation. In this paper, we have proposed a hybrid approach utilizing both machine learning and semantic reasoning tools to derive a better understanding from sensors. This method uses situation templates jointly with a decision tree to adapt the system knowledge to the environment. To test this approach we have used a simulation process which has resulted in a better precision for detecting situations in an ongoing environment involving living agents while capturing its dynamic nature.
Characterisation of the $χ$-index and the $rec$-index
Venue: arXiv cs.DL 2019
Year: 2019

Axiomatic characterisation of a bibliometric index provides insight into the properties that the index satisfies and facilitates the comparison of different indices. A geometric generalisation of the $h$-index, called the $χ$-index, has recently been proposed to address some of the problems with the $h$-index, in particular, the fact that it is not scale invariant, i.e., multiplying the number of citations of each publication by a positive constant may change the relative ranking of two researchers. While the square of the $h$-index is the area of the largest square under the citation curve of a researcher, the square of the $χ$-index, which we call the $rec$-index (or {\em rectangle}-index), is the area of the largest rectangle under the citation curve. Our main contribution here is to provide a characterisation of the $rec$-index via three properties: {\em monotonicity}, {\em uniform citation} and {\em uniform equivalence}. Monotonicity is a natural property that we would expect any bibliometric index to satisfy, while the other two properties constrain the value of the $rec$-index to be the area of the largest rectangle under the citation curve. The $rec$-index also allows us to distinguish between {\em influential} researchers who have relatively few, but highly-cited, publications and {\em prolific} researchers who have many, but less-cited, publications.