OpenRaiser/Intern-Atlas huggingface dataset:
Category coding with neural network application
Venue: arXiv cs.IT 2018
Year: 2018

In many applications of neural network, it is common to introduce huge amounts of input categorical features, as well as output labels. However, since the required network size should have rapid growth with respect to the dimensions of input and output space, there exists huge cost in both computation and memory resources. In this paper, we present a novel method called category coding (CC), where the design philosophy follows the principle of minimal collision to reduce the input and output dimension effectively. In addition, we introduce three types of category coding based on different Euclidean domains. Experimental results show that all three proposed methods outperform the existing state-of-the-art coding methods, such as standard cut-off and error-correct output coding (ECOC) methods.
Performance Reproduction and Prediction of Selected Dynamic Loop Scheduling Experiments
Venue: arXiv cs.DC 2018
Year: 2018

Scientific applications are complex, large, and often exhibit irregular and stochastic behavior. The use of efficient loop scheduling techniques in computationally-intensive applications is crucial for improving their performance on high-performance computing (HPC) platforms. A number of dynamic loop scheduling (DLS) techniques have been proposed between the late 1980s and early 2000s, and efficiently used in scientific applications. In most cases, the computing systems on which they have been tested and validated are no longer available. This work is concerned with the minimization of the sources of uncertainty in the implementation of DLS techniques to avoid unnecessary influences on the performance of scientific applications. Therefore, it is important to ensure that the DLS techniques employed in scientific applications today adhere to their original design goals and specifications. The goal of this work is to attain and increase the trust in the implementation of DLS techniques in present studies. To achieve this goal, the performance of a selection of scheduling experiments from the 1992 original work that introduced factoring is reproduced and predicted via both, simulative and native experimentation. The experiments show that the simulation reproduces the performance achieved on the past computing platform and accurately predicts the performance achieved on the present computing platform. The performance reproduction and prediction confirm that the present implementation of the DLS techniques considered both, in simulation and natively, adheres to their original description. The results confirm the hypothesis that reproducing experiments of identical scheduling scenarios on past and modern hardware leads to an entirely different behavior from expected.
Energy balancing for robotic aided clustered wireless sensor networks using mobility diversity algorithms
Venue: arXiv cs.DC 2018
Year: 2018

We consider the problem of energy balancing in a clustered wireless sensor network (WSN) deployed randomly in a large field and aided by a mobile robot (MR). The sensor nodes (SNs) are tasked to monitor a region of interest (ROI) and report their test statistics to the cluster heads (CHs), which subsequently report to the fusion center (FC) over a wireless fading channel. To maximize the lifetime of the WSN, the MR is deployed to act as an adaptive relay between a subset of the CHs and the FC. To achieve this we develop a multiple-link mobility diversity algorithm (MDA) executed by the MR that will allow to compensate simultaneously for the small-scale fading at the established wireless links (i.e., the MR-to-FC as well as various CH-to-MR communication links). Simulation results show that the proposed MR aided technique is able to significantly reduce the transmission power required and thus extend the operational lifetime of the WSN. We also show how the effect of small-scale fading at various wireless links is mitigated by using the proposed multiple-link MDA executed by a MR equipped with a single antenna.
A Correlation Measure Based on Vector-Valued $L_p$-Norms
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we introduce a new measure of correlation for bipartite quantum states. This measure depends on a parameter $α$, and is defined in terms of vector-valued $L_p$-norms. The measure is within a constant of the exponential of $α$-Rényi mutual information, and reduces to the trace norm (total variation distance) for $α=1$. We will prove some decoupling type theorems in terms of this measure of correlation, and present some applications in privacy amplification as well as in bounding the random coding exponents. In particular, we establish a bound on the secrecy exponent of the wiretap channel (under the total variation metric) in terms of the $α$-Rényi mutual information according to \emph{Csiszár's proposal}.
Implementation of Chua's chaotic oscillator with an HP memristor
Venue: arXiv cs.ET 2018
Year: 2018

This paper proposes an innovative chaotic circuit based on Chua's oscillator. It combines traditional realization of a non-linear resistor in Chua's chaotic oscillator with a promising memristive circuitry. This mixed implementation connects old research works that were focused on diodes with relatively new research papers that are, now, concentrated on memristors. As a result, more reliable chaotic circuit with an HP memristor is obtained that could be used as a source of randomness. Dynamic behavior of the circuit is studied by obtaining fft analysis, different chaotic attractors and Lyapunov exponent spectrum. The results show that the addition of a memristor enhances chaotic behavior of the circuit while maintaining the same power dissipation.
Understanding Self-Paced Learning under Concave Conjugacy Theory
Venue: arXiv cs.LG 2018
Year: 2018

By simulating the easy-to-hard learning manners of humans/animals, the learning regimes called curriculum learning~(CL) and self-paced learning~(SPL) have been recently investigated and invoked broad interests. However, the intrinsic mechanism for analyzing why such learning regimes can work has not been comprehensively investigated. To this issue, this paper proposes a concave conjugacy theory for looking into the insight of CL/SPL. Specifically, by using this theory, we prove the equivalence of the SPL regime and a latent concave objective, which is closely related to the known non-convex regularized penalty widely used in statistics and machine learning. Beyond the previous theory for explaining CL/SPL insights, this new theoretical framework on one hand facilitates two direct approaches for designing new SPL models for certain tasks, and on the other hand can help conduct the latent objective of self-paced curriculum learning, which is the advanced version of both CL/SPL and possess advantages of both learning regimes to a certain extent. This further facilitates a theoretical understanding for SPCL, instead of only CL/SPL as conventional. Under this theory, we attempt to attain intrinsic latent objectives of two curriculum forms, the partial order and group curriculums, which easily follow the theoretical understanding of the corresponding SPCL regimes.
Invariant Representations from Adversarially Censored Autoencoders
Venue: arXiv cs.LG 2018
Year: 2018

We combine conditional variational autoencoders (VAE) with adversarial censoring in order to learn invariant representations that are disentangled from nuisance/sensitive variations. In this method, an adversarial network attempts to recover the nuisance variable from the representation, which the VAE is trained to prevent. Conditioning the decoder on the nuisance variable enables clean separation of the representation, since they are recombined for model learning and data reconstruction. We show this natural approach is theoretically well-founded with information-theoretic arguments. Experiments demonstrate that this method achieves invariance while preserving model learning performance, and results in visually improved performance for style transfer and generative sampling tasks.
Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot Learning
Venue: arXiv cs.CV 2018
Year: 2018

Zero-Shot Learning (ZSL) is achieved via aligning the semantic relationships between the global image feature vector and the corresponding class semantic descriptions. However, using the global features to represent fine-grained images may lead to sub-optimal results since they neglect the discriminative differences of local regions. Besides, different regions contain distinct discriminative information. The important regions should contribute more to the prediction. To this end, we propose a novel stacked semantics-guided attention (S2GA) model to obtain semantic relevant features by using individual class semantic features to progressively guide the visual features to generate an attention map for weighting the importance of different local regions. Feeding both the integrated visual features and the class semantic features into a multi-class classification architecture, the proposed framework can be trained end-to-end. Extensive experimental results on CUB and NABird datasets show that the proposed approach has a consistent improvement on both fine-grained zero-shot classification and retrieval tasks.
A New Lower Bound for Agnostic Learning with Sample Compression Schemes
Venue: arXiv cs.LG 2018
Year: 2018

We establish a tight characterization of the worst-case rates for the excess risk of agnostic learning with sample compression schemes and for uniform convergence for agnostic sample compression schemes. In particular, we find that the optimal rates of convergence for size-$k$ agnostic sample compression schemes are of the form $\sqrt{\frac{k \log(n/k)}{n}}$, which contrasts with agnostic learning with classes of VC dimension $k$, where the optimal rates are of the form $\sqrt{\frac{k}{n}}$.
Cancer Research UK Drug Discovery Process Mining
Venue: arXiv cs.DB 2018
Year: 2018

Background. The Drug Discovery Unit (DDU) of Cancer Research UK (CRUK) is using the software Dotmatics for storage and analysis of scientific data during drug discovery process. Whilst the data include event logs, time stamps, activities, and user information are mostly sitting in the database without fully utilising their potential value. Aims. This dissertation aims at extracting knowledge from event logs data which recorded during drug discovery process, to capture the operational business process of the DDU of Cancer Research UK (CRUK) as it was being executed. It provides the evaluations and methodologies of drawing the process mining panoramic models for the drug discovery process. Thus by enabling the DDU to maximise its efficiency in reviewing its resources and works allocations, patients will benefit from more new treatments faster. Conclusion. Management of organisations can be benefit from the process mining methodologies. Disco is excellent for non-experts on management purposes. ProM is great for expert on research purposes. However, the process mining is not once and for all but is a regular operation management process. Indeed, event logs needs to be understand more on the target organisational behaviours and organisational business process. The researchers have to be aware that event logs data are the most important and priority elements in process mining.
Finding forbidden minors in sublinear time: a $n^{1/2+o(1)}$-query one-sided tester for minor closed properties on bounded degree graphs
Venue: arXiv cs.DM 2018
Year: 2018

Let $G$ be an undirected, bounded degree graph with $n$ vertices. Fix a finite graph $H$, and suppose one must remove $\varepsilon n$ edges from $G$ to make it $H$-minor free (for some small constant $\varepsilon > 0$). We give an $n^{1/2+o(1)}$-time randomized procedure that, with high probability, finds an $H$-minor in such a graph. As an application, suppose one must remove $\varepsilon n$ edges from a bounded degree graph $G$ to make it planar. This result implies an algorithm, with the same running time, that produces a $K_{3,3}$ or $K_5$ minor in $G$. No prior sublinear time bound was known for this problem. By the graph minor theorem, we get an analogous result for any minor-closed property. Up to $n^{o(1)}$ factors, this resolves a conjecture of Benjamini-Schramm-Shapira (STOC 2008) on the existence of one-sided property testers for minor-closed properties. Furthermore, our algorithm is nearly optimal, by an $Ω(\sqrt{n})$ lower bound of Czumaj et al (RSA 2014). Prior to this work, the only graphs $H$ for which non-trivial one-sided property testers were known for $H$-minor freeness are the following: $H$ being a forest or a cycle (Czumaj et al, RSA 2014), $K_{2,k}$, $(k\times 2)$-grid, and the $k$-circus (Fichtenberger et al, Arxiv 2017).
Algorithmic and algebraic aspects of unshuffling permutations
Venue: arXiv cs.DS 2018
Year: 2018

A permutation is said to be a square if it can be obtained by shuffling two order-isomorphic patterns. The definition is intended to be the natural counterpart to the ordinary shuffle of words and languages. In this paper, we tackle the problem of recognizing square permutations from both the point of view of algebra and algorithms. On the one hand, we present some algebraic and combinatorial properties of the shuffle product of permutations. We follow an unusual line consisting in defining the shuffle of permutations by means of an unshuffling operator, known as a coproduct. This strategy allows to obtain easy proofs for algebraic and combinatorial properties of our shuffle product. We besides exhibit a bijection between square $(213,231)$-avoiding permutations and square binary words. On the other hand, by using a pattern avoidance criterion on directed perfect matchings, we prove that recognizing square permutations is {\bf NP}-complete.
The Sounds of Cyber Threats
Venue: arXiv cs.CR 2018
Year: 2018

The Internet enables users to access vast resources, but it can also expose users to harmful cyber-attacks. This paper investigates human factors issues concerning the use of sounds in a cyber-security domain. It describes a methodology, referred to as sonification, to effectively design and develop auditory cyber-security threat indicators to warn users about cyber-attacks. A case study is presented, along with the results, of various types of usability testing with a number of Internet users who are visually impaired. The paper concludes with a discussion of future steps to enhance this work.
Human Aspects and Perception of Privacy in Relation to Personalization
Venue: arXiv cs.CY 2018
Year: 2018

The concept of privacy is inherently intertwined with human attitudes and behaviours, as most computer systems are primarily designed for human use. Especially in the case of Recommender Systems, which feed on information provided by individuals, their efficacy critically depends on whether or not information is externalized, and if it is, how much of this information contributes positively to their performance and accuracy. In this paper, we discuss the impact of several factors on users' information disclosure behaviours and privacy-related attitudes, and how users of recommender systems can be nudged into making better privacy decisions for themselves. Apart from that, we also address the problem of privacy adaptation, i.e. effectively tailoring Recommender Systems by gaining a deeper understanding of people's cognitive decision-making process.
Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints
Venue: arXiv cs.CV 2018
Year: 2018

We consider the optimization of deep convolutional neural networks (CNNs) such that they provide good performance while having reduced complexity if deployed on either conventional systems utilizing spatial-domain convolution or lower complexity systems designed for Winograd convolution. Furthermore, we explore the universal quantization and compression of these networks. In particular, the proposed framework produces one compressed model whose convolutional filters can be made sparse either in the spatial domain or in the Winograd domain. Hence, one compressed model can be deployed universally on any platform, without need for re-training on the deployed platform, and the sparsity of its convolutional filters can be exploited for further complexity reduction in either domain. To get a better compression ratio, the sparse model is compressed in the spatial domain which has a less number of parameters. From our experiments, we obtain $24.2\times$, $47.7\times$ and $35.4\times$ compressed models for ResNet-18, AlexNet and CT-SRCNN, while their computational cost is also reduced by $4.5\times$, $5.1\times$ and $23.5\times$, respectively.
The Swarmathon: An Autonomous Swarm Robotics Competition
Venue: arXiv cs.MA 2018
Year: 2018

The Swarmathon is a swarm robotics programming challenge that engages college students from minority-serving institutions in NASA's Journey to Mars. Teams compete by programming a group of robots to search for, pick up, and drop off resources in a collection zone. The Swarmathon produces prototypes for robot swarms that would collect resources on the surface of Mars. Robots operate completely autonomously with no global map, and each team's algorithm must be sufficiently flexible to effectively find resources from a variety of unknown distributions. The Swarmathon includes Physical and Virtual Competitions. Physical competitors test their algorithms on robots they build at their schools; they then upload their code to run autonomously on identical robots during the three day competition in an outdoor arena at Kennedy Space Center. Virtual competitors complete an identical challenge in simulation. Participants mentor local teams to compete in a separate High School Division. In the first 2 years, over 1,100 students participated. 63% of students were from underrepresented ethnic and racial groups. Participants had significant gains in both interest and core robotic competencies that were equivalent across gender and racial groups, suggesting that the Swarmathon is effectively educating a diverse population of future roboticists.
Success Probability of Grant-Free Random Access with Massive MIMO
Venue: arXiv cs.IT 2018
Year: 2018

Massive MIMO opens up new avenues for enabling highly efficient random access (RA) by offering abundance of spatial degrees of freedom. In this paper, we investigate the grant-free RA with massive MIMO and derive the analytic expressions of success probability of the grant-free RA for conjugate beamforming and zero-forcing beamforming techniques.With the derived analytic expressions, we further shed light on the impact of system parameters on the success probability. Simulation results verify the accuracy of the analyses. It is confirmed that the grant-free RA with massive MIMO is an attractive RA technique with low signaling overhead that could simultaneously accommodate a number of RA users, which is multiple times the number of RA channels, with close-to-one success probability. In addition, when the number of antennas in massive MIMO is sufficiently large, we show that the number of orthogonal preambles would dominate the success probability.
Learning sentence embeddings using Recursive Networks
Venue: arXiv cs.CL 2018
Year: 2018

Learning sentence vectors that generalise well is a challenging task. In this paper we compare three methods of learning phrase embeddings: 1) Using LSTMs, 2) using recursive nets, 3) A variant of the method 2 using the POS information of the phrase. We train our models on dictionary definitions of words to obtain a reverse dictionary application similar to Felix et al. [1]. To see if our embeddings can be transferred to a new task we also train and test on the rotten tomatoes dataset [2]. We train keeping the sentence embeddings fixed as well as with fine tuning.
Modularizing Behavioral and Architectural Crosscutting Concerns in Formal Component-Based Systems - Application to the Behavior Interaction Priority Framework
Venue: arXiv cs.SE 2018
Year: 2018

We define a method to modularize crosscutting concerns in Component-Based Systems (CBSs) expressed using the Behavior Interaction Priority (BIP) framework. Our method is inspired from the Aspect Oriented Programming (AOP) paradigm which was initially conceived to support the separation of concerns during the development of monolithic systems. BIP has a formal operational semantics and makes a clear separation between architecture and behavior to allow for compositional and incremental design and analysis of systems. We distinguish local from global aspects. Local aspects model concerns at the component level and are used to refine the behavior of components. Global aspects model concerns at the architecture level, and hence refine communications (synchronization and data transfer) between components. We formalize local and global aspects as well as their composition and integration into a BIP system through rigorous transformation primitives. We present AOP-BIP, a tool for Aspect-Oriented Programming of BIP systems, demonstrate its use to modularize logging, security, and fault tolerance in a network protocol, and discuss its possible use in runtime verification of CBSs.
Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Facial micro-expression (ME) recognition has posed a huge challenge to researchers for its subtlety in motion and limited databases. Recently, handcrafted techniques have achieved superior performance in micro-expression recognition but at the cost of domain specificity and cumbersome parametric tunings. In this paper, we propose an Enriched Long-term Recurrent Convolutional Network (ELRCN) that first encodes each micro-expression frame into a feature vector through CNN module(s), then predicts the micro-expression by passing the feature vector through a Long Short-term Memory (LSTM) module. The framework contains two different network variants: (1) Channel-wise stacking of input data for spatial enrichment, (2) Feature-wise stacking of features for temporal enrichment. We demonstrate that the proposed approach is able to achieve reasonably good performance, without data augmentation. In addition, we also present ablation studies conducted on the framework and visualizations of what CNN "sees" when predicting the micro-expression classes.
An Integrated First-Order Theory of Points and Intervals over Linear Orders (Part I)
Venue: arXiv cs.LO 2018
Year: 2018

There are two natural and well-studied approaches to temporal ontology and reasoning: point-based and interval-based. Usually, interval-based temporal reasoning deals with points as a particular case of duration-less intervals. A recent result by Balbiani, Goranko, and Sciavicco presented an explicit two-sorted point-interval temporal framework in which time instants (points) and time periods (intervals) are considered on a par, allowing the perspective to shift between these within the formal discourse. We consider here two-sorted first-order languages based on the same principle, and therefore including relations, as first studied by Reich, among others, between points, between intervals, and inter-sort. We give complete classifications of its sub-languages in terms of relative expressive power, thus determining how many, and which, are the intrinsically different extensions of two-sorted first-order logic with one or more such relations. This approach roots out the classical problem of whether or not points should be included in a interval-based semantics.