OpenRaiser/Intern-Atlas huggingface dataset:
3D Multi-Drone-Cell Trajectory Design for Efficient IoT Data Collection
Venue: arXiv cs.NI 2019
Year: 2019

Drone cell (DC) is an emerging technique to offer flexible and cost-effective wireless connections to collect Internet-of-things (IoT) data in uncovered areas of terrestrial networks. The flying trajectory of DC significantly impacts the data collection performance. However, designing the trajectory is a challenging issue due to the complicated 3D mobility of DC, unique DC-to-ground (D2G) channel features, limited DC-to-BS (D2B) backhaul link quality, etc. In this paper, we propose a 3D DC trajectory design for the DC-assisted IoT data collection where multiple DCs periodically fly over IoT devices and relay the IoT data to the base stations (BSs). The trajectory design is formulated as a mixed integer non-linear programming (MINLP) problem to minimize the average user-to-DC (U2D) pathloss, considering the state-of-the-art practical D2G channel model. We decouple the MINLP problem into multiple quasi-convex or integer linear programming (ILP) sub-problems, which optimizes the user association, user scheduling, horizontal trajectories and DC flying altitudes of DCs, respectively. Then, a 3D multi-DC trajectory design algorithm is developed to solve the MINLP problem, in which the sub-problems are optimized iteratively through the block coordinate descent (BCD) method. Compared with the static DC deployment, the proposed trajectory design can lower the average U2D pathloss by 10-15 dB, and reduce the standard deviation of U2D pathloss by 56%, which indicates the improvements in both link quality and user fairness.
Bembel: The Fast Isogeometric Boundary Element C++ Library for Laplace, Helmholtz, and Electric Wave Equation
Venue: arXiv cs.MS 2019
Year: 2019

In this article, we present Bembel, the C++ library featuring higher order isogeometric Galerkin boundary element methods for Laplace, Helmholtz, and Maxwell problems. Bembel is compatible with geometries from the Octave NURBS package and provides an interface to the Eigen template library for linear algebra operations. For computational efficiency, it applies an embedded fast multipole method tailored to the isogeometric analysis framework and a parallel matrix assembly based on OpenMP.
RVHyper: A Runtime Verification Tool for Temporal Hyperproperties
Venue: arXiv cs.LO 2019
Year: 2019

We present RVHyper, a runtime verification tool for hyperproperties. Hyperproperties, such as non-interference and observational determinism, relate multiple computation traces with each other. Specifications are given as formulas in the temporal logic HyperLTL, which extends linear-time temporal logic (LTL) with trace quantifiers and trace variables. RVHyper processes execution traces sequentially until a violation of the specification is detected. In this case, a counter example, in the form of a set of traces, is returned. As an example application, we show how RVHyper can be used to detect spurious dependencies in hardware designs.
Self-supervised Body Image Acquisition Using a Deep Neural Network for Sensorimotor Prediction
Venue: arXiv cs.LG 2019
Year: 2019

This work investigates how a naive agent can acquire its own body image in a self-supervised way, based on the predictability of its sensorimotor experience. Our working hypothesis is that, due to its temporal stability, an agent's body produces more consistent sensory experiences than the environment, which exhibits a greater variability. Given its motor experience, an agent can thus reliably predict what appearance its body should have. This intrinsic predictability can be used to automatically isolate the body image from the rest of the environment. We propose a two-branches deconvolutional neural network to predict the visual sensory state associated with an input motor state, as well as the prediction error associated with this input. We train the network on a dataset of first-person images collected with a simulated Pepper robot, and show how the network outputs can be used to automatically isolate its visible arm from the rest of the environment. Finally, the quality of the body image produced by the network is evaluated.
Pangloss: a novel Markov chain prefetcher
Venue: arXiv cs.AR 2019
Year: 2019

We present Pangloss, an efficient high-performance data prefetcher that approximates a Markov chain on delta transitions. With a limited information scope and space/logic complexity, it is able to reconstruct a variety of both simple and complex access patterns. This is achieved by a highly-efficient representation of the Markov chain to provide accurate values for transition probabilities. In addition, we have added a mechanism to reconstruct delta transitions originally obfuscated by the out-of-order execution or page transitions, such as when streaming data from multiple sources. Our single-level (L2) prefetcher achieves a geometric speedup of 1.7% and 3.2% over selected state-of-the-art baselines (KPCP and BOP). When combined with an equivalent for the L1 cache (L1 & L2), the speedups rise to 6.8% and 8.4%, and 40.4% over non-prefetch. In the multi-core evaluation, there seems to be a considerable performance improvement as well.
Secure Distributed On-Device Learning Networks With Byzantine Adversaries
Venue: arXiv cs.LG 2019
Year: 2019

The privacy concern exists when the central server has the copies of datasets. Hence, there is a paradigm shift for the learning networks to change from centralized in-cloud learning to distributed \mbox{on-device} learning. Benefit from the parallel computing, the on-device learning networks have a lower bandwidth requirement than the in-cloud learning networks. Moreover, the on-device learning networks also have several desirable characteristics such as privacy preserving and flexibility. However, the \mbox{on-device} learning networks are vulnerable to the malfunctioning terminals across the networks. The worst-case malfunctioning terminals are the Byzantine adversaries, that can perform arbitrary harmful operations to compromise the learned model based on the full knowledge of the networks. Hence, the design of secure learning algorithms becomes an emerging topic in the on-device learning networks with Byzantine adversaries. In this article, we present a comprehensive overview of the prevalent secure learning algorithms for the two promising on-device learning networks: Federated-Learning networks and decentralized-learning networks. We also review several future research directions in the \mbox{Federated-Learning} and decentralized-learning networks.
Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization
Venue: arXiv cs.LG 2019
Year: 2019

Times series classification can be successfully tackled by jointly learning a shapelet-based representation of the series in the dataset and classifying the series according to this representation. However, although the learned shapelets are discriminative, they are not always similar to pieces of a real series in the dataset. This makes it difficult to interpret the decision, i.e. difficult to analyze if there are particular behaviors in a series that triggered the decision. In this paper, we make use of a simple convolutional network to tackle the time series classification task and we introduce an adversarial regularization to constrain the model to learn more interpretable shapelets. Our classification results on all the usual time series benchmarks are comparable with the results obtained by similar state-of-the-art algorithms but our adversarially regularized method learns shapelets that are, by design, interpretable.
Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks Under Hardware Fault Attacks
Venue: arXiv cs.CR 2019
Year: 2019

Deep neural networks (DNNs) have been shown to tolerate "brain damage": cumulative changes to the network's parameters (e.g., pruning, numerical perturbations) typically result in a graceful degradation of classification accuracy. However, the limits of this natural resilience are not well understood in the presence of small adversarial changes to the DNN parameters' underlying memory representation, such as bit-flips that may be induced by hardware fault attacks. We study the effects of bitwise corruptions on 19 DNN models---six architectures on three image classification tasks---and we show that most models have at least one parameter that, after a specific bit-flip in their bitwise representation, causes an accuracy loss of over 90%. We employ simple heuristics to efficiently identify the parameters likely to be vulnerable. We estimate that 40-50% of the parameters in a model might lead to an accuracy drop greater than 10% when individually subjected to such single-bit perturbations. To demonstrate how an adversary could take advantage of this vulnerability, we study the impact of an exemplary hardware fault attack, Rowhammer, on DNNs. Specifically, we show that a Rowhammer enabled attacker co-located in the same physical machine can inflict significant accuracy drops (up to 99%) even with single bit-flip corruptions and no knowledge of the model. Our results expose the limits of DNNs' resilience against parameter perturbations induced by real-world fault attacks. We conclude by discussing possible mitigations and future research directions towards fault attack-resilient DNNs.
NodeDrop: A Condition for Reducing Network Size without Effect on Output
Venue: arXiv cs.LG 2019
Year: 2019

Determining an appropriate number of features for each layer in a neural network is an important and difficult task. This task is especially important in applications on systems with limited memory or processing power. Many current approaches to reduce network size either utilize iterative procedures, which can extend training time significantly, or require very careful tuning of algorithm parameters to achieve reasonable results. In this paper we propose NodeDrop, a new method for eliminating features in a network. With NodeDrop, we define a condition to identify and guarantee which nodes carry no information, and then use regularization to encourage nodes to meet this condition. We find that NodeDrop drastically reduces the number of features in a network while maintaining high performance, reducing the number of parameters by a factor of 114x for a VGG like network on CIFAR10 without a drop in accuracy.
Probabilistic Existence Results for Parent-Identifying Schemes
Venue: arXiv cs.IT 2019
Year: 2019

Parent-identifying schemes provide a way to identify causes from effects for some information systems such as digital fingerprinting and group testing. In this paper, we consider combinatorial structures for parent-identifying schemes. First, we establish an equivalent relationship between parent-identifying schemes and forbidden configurations. Based on this relationship, we derive probabilistic existence lower bounds for two related combinatorial structures, that is, $t$-parent-identifying set systems ($t$-IPPS) and $t$-multimedia parent-identifying codes ($t$-MIPPC), which are used in broadcast encryption and multimedia fingerprinting respectively. The probabilistic lower bound for the maximum size of a $t$-IPPS has the asymptotically optimal order of magnitude in many cases, and that for $t$-MIPPC provides the asymptotically optimal code rate when $t=2$ and the best known asymptotic code rate when $t\geq 3$. Furthermore, we analyze the structure of $2$-IPPS and prove some bounds for certain cases.
Do place cells dream of conditional probabilities? Learning Neural Nyström representations
Venue: arXiv cs.LG 2019
Year: 2019

We posit that hippocampal place cells encode information about future locations under a transition distribution observed as an agent explores a given (physical or conceptual) space. The encoding of information about the current location, usually associated with place cells, then emerges as a necessary step to achieve this broader goal. We formally derive a biologically-inspired neural network from Nyström kernel approximations and empirically demonstrate that the network successfully approximates transition distributions. The proposed network yields representations that, just like place cells, soft-tile the input space with highly sparse and localized receptive fields. Additionally, we show that the proposed computational motif can be extended to handle supervised problems, creating class-specific place cells while exhibiting low sample complexity.
Analysis and Improvement of Adversarial Training in DQN Agents With Adversarially-Guided Exploration (AGE)
Venue: arXiv cs.LG 2019
Year: 2019

This paper investigates the effectiveness of adversarial training in enhancing the robustness of Deep Q-Network (DQN) policies to state-space perturbations. We first present a formal analysis of adversarial training in DQN agents and its performance with respect to the proportion of adversarial perturbations to nominal observations used for training. Next, we consider the sample-inefficiency of current adversarial training techniques, and propose a novel Adversarially-Guided Exploration (AGE) mechanism based on a modified hybrid of the $ε$-greedy algorithm and Boltzmann exploration. We verify the feasibility of this exploration mechanism through experimental evaluation of its performance in comparison with the traditional decaying $ε$-greedy and parameter-space noise exploration algorithms.
Sequential Triggers for Watermarking of Deep Reinforcement Learning Policies
Venue: arXiv cs.LG 2019
Year: 2019

This paper proposes a novel scheme for the watermarking of Deep Reinforcement Learning (DRL) policies. This scheme provides a mechanism for the integration of a unique identifier within the policy in the form of its response to a designated sequence of state transitions, while incurring minimal impact on the nominal performance of the policy. The applications of this watermarking scheme include detection of unauthorized replications of proprietary policies, as well as enabling the graceful interruption or termination of DRL activities by authorized entities. We demonstrate the feasibility of our proposal via experimental evaluation of watermarking a DQN policy trained in the Cartpole environment.
Assessing Performance Implications of Deep Copy Operations via Microbenchmarking
Venue: arXiv cs.DC 2019
Year: 2019

As scientific frameworks become sophisticated, so do their data structures. Current data structures are no longer simple in design and they have been progressively complicated. The typical trend in designing data structures in scientific applications are basically nested data structures: pointing to a data structure within another one. Managing nested data structures on a modern heterogeneous system requires tremendous effort due to the separate memory space design. In this paper, we will discuss the implications of deep copy on data transfers on current heterogeneous. Then, we will discuss the two options that are currently available to perform the memory copy operations on complex structures and will introduce pointerchain directive that we proposed. Afterwards, we will introduce a set of extensive benchmarks to compare the available approaches. Our goal is to make our proposed benchmarks a basis to examine the efficiency of upcoming approaches that address the challenge of deep copy operations.
ShEMO -- A Large-Scale Validated Database for Persian Speech Emotion Detection
Venue: arXiv cs.CL 2019
Year: 2019

This paper introduces a large-scale, validated database for Persian called Sharif Emotional Speech Database (ShEMO). The database includes 3000 semi-natural utterances, equivalent to 3 hours and 25 minutes of speech data extracted from online radio plays. The ShEMO covers speech samples of 87 native-Persian speakers for five basic emotions including anger, fear, happiness, sadness and surprise, as well as neutral state. Twelve annotators label the underlying emotional state of utterances and majority voting is used to decide on the final labels. According to the kappa measure, the inter-annotator agreement is 64% which is interpreted as "substantial agreement". We also present benchmark results based on common classification methods in speech emotion detection task. According to the experiments, support vector machine achieves the best results for both gender-independent (58.2%) and gender-dependent models (female=59.4%, male=57.6%). The ShEMO is available for academic purposes free of charge to provide a baseline for further research on Persian emotional speech.
Content Adaptive Optimization for Neural Image Compression
Venue: arXiv cs.CV 2019
Year: 2019

The field of neural image compression has witnessed exciting progress as recently proposed architectures already surpass the established transform coding based approaches. While, so far, research has mainly focused on architecture and model improvements, in this work we explore content adaptive optimization. To this end, we introduce an iterative procedure which adapts the latent representation to the specific content we wish to compress while keeping the parameters of the network and the predictive model fixed. Our experiments show that this allows for an overall increase in rate-distortion performance, independently of the specific architecture used. Furthermore, we also evaluate this strategy in the context of adapting a pretrained network to other content that is different in visual appearance or resolution. Here, our experiments show that our adaptation strategy can largely close the gap as compared to models specifically trained for the given content while having the benefit that no additional data in the form of model parameter updates has to be transmitted.
Transcoding compositionally: using attention to find more generalizable solutions
Venue: arXiv cs.CL 2019
Year: 2019

While sequence-to-sequence models have shown remarkable generalization power across several natural language tasks, their construct of solutions are argued to be less compositional than human-like generalization. In this paper, we present seq2attn, a new architecture that is specifically designed to exploit attention to find compositional patterns in the input. In seq2attn, the two standard components of an encoder-decoder model are connected via a transcoder, that modulates the information flow between them. We show that seq2attn can successfully generalize, without requiring any additional supervision, on two tasks which are specifically constructed to challenge the compositional skills of neural networks. The solutions found by the model are highly interpretable, allowing easy analysis of both the types of solutions that are found and potential causes for mistakes. We exploit this opportunity to introduce a new paradigm to test compositionality that studies the extent to which a model overgeneralizes when confronted with exceptions. We show that seq2attn exhibits such overgeneralization to a larger degree than a standard sequence-to-sequence model.
Kinetic Market Model: An Evolutionary Algorithm
Venue: arXiv cs.NE 2019
Year: 2019

This research proposes the econophysics kinetic market model as an evolutionary algorithm's instance. The immediate results from this proposal is a new replacement rule for family competition genetic algorithms. It also represents a starting point to adding evolvable entities to kinetic market models.
Anonymity Network Tor and Performance Analysis of ARANEA; an IOT Based Privacy-Preserving Router
Venue: arXiv cs.CR 2019
Year: 2019

There was a time when the word security was only confined to the physical protection of things that were valuable which must be guarded against all the odds. Today, in a world where people can do things virtually have emerged the necessity to protect the virtual world. Every single facet of our life is being controlled by the internet one way or another. There is no privacy in the cyberspace as the data which we are browsing on the internet is being monitored on the other side by someone. Each work we are doing on the internet is getting tracked or the data are getting leaked without consent. To browse the internet securely we developed a router named Aranea which relates to the browser Tor. Tor gives traffic anonymity and security. The Tor browser can be used in both positive and negative purpose. Tor encrypts data, it hides the location and identity of the user, it hides the IP address of the device, it hides the network traffic and many more. By using Tor browser each user can browse the internet safely in the cyber world. Our goal is to create an additional security bridge through the router Aranea for every user so that each user can simply browse the internet anonymously.
Need for Critical Cyber Defence, Security Strategy and Privacy Policy in Bangladesh - Hype or Reality?
Venue: arXiv cs.SI 2019
Year: 2019

Cyber security is one of the burning issues in modern world. Increased IT infrastructure has given rise to enormous chances of security breach. Bangladesh being a relatively new member of cyber security arena has its own demand and appeal. Digitalization is happening in Bangladesh for last few years at an appreciable rate. People are being connected to the worldwide web community with their smart devices. These devices have their own vulnerability issues as well as the data shared over the internet has a very good chances of getting breached. Common vulnerability issues like infecting the device with malware, Trojan, virus are on the rise. Moreover, a lack of proper cyber security policy and strategy might make the existing situation at the vulnerable edge of tipping point. Hence the upcoming new infrastructures will be at a greater risk if the issues are not dealt with at an early age. In this paper common vulnerability issues including their recent attacks on cyber space of Bangladesh, cyber security strategy and need for data privacy policy is discussed and analysed briefly.
Relational Reasoning using Prior Knowledge for Visual Captioning
Venue: arXiv cs.CV 2019
Year: 2019

Exploiting relationships among objects has achieved remarkable progress in interpreting images or videos by natural language. Most existing methods resort to first detecting objects and their relationships, and then generating textual descriptions, which heavily depends on pre-trained detectors and leads to performance drop when facing problems of heavy occlusion, tiny-size objects and long-tail in object detection. In addition, the separate procedure of detecting and captioning results in semantic inconsistency between the pre-defined object/relation categories and the target lexical words. We exploit prior human commonsense knowledge for reasoning relationships between objects without any pre-trained detectors and reaching semantic coherency within one image or video in captioning. The prior knowledge (e.g., in the form of knowledge graph) provides commonsense semantic correlation and constraint between objects that are not explicit in the image and video, serving as useful guidance to build semantic graph for sentence generation. Particularly, we present a joint reasoning method that incorporates 1) commonsense reasoning for embedding image or video regions into semantic space to build semantic graph and 2) relational reasoning for encoding semantic graph to generate sentences. Extensive experiments on the MS-COCO image captioning benchmark and the MSVD video captioning benchmark validate the superiority of our method on leveraging prior commonsense knowledge to enhance relational reasoning for visual captioning.