OpenRaiser/Intern-Atlas huggingface dataset:
Personalized Dynamics Models for Adaptive Assistive Navigation Systems
Venue: arXiv cs.LG 2018
Year: 2018

Consider an assistive system that guides visually impaired users through speech and haptic feedback to their destination. Existing robotic and ubiquitous navigation technologies (e.g., portable, ground, or wearable systems) often operate in a generic, user-agnostic manner. However, to minimize confusion and navigation errors, our real-world analysis reveals a crucial need to adapt the instructional guidance across different end-users with diverse mobility skills. To address this practical issue in scalable system design, we propose a novel model-based reinforcement learning framework for personalizing the system-user interaction experience. When incrementally adapting the system to new users, we propose to use a weighted experts model for addressing data-efficiency limitations in transfer learning with deep models. A real-world dataset of navigation by blind users is used to show that the proposed approach allows for (1) more accurate long-term human behavior prediction (up to 20 seconds into the future) through improved reasoning over personal mobility characteristics, interaction with surrounding obstacles, and the current navigation goal, and (2) quick adaptation at the onset of learning, when data is limited.
Detecting Malicious PowerShell Commands using Deep Neural Networks
Venue: arXiv cs.CR 2018
Year: 2018

Microsoft's PowerShell is a command-line shell and scripting language that is installed by default on Windows machines. While PowerShell can be configured by administrators for restricting access and reducing vulnerabilities, these restrictions can be bypassed. Moreover, PowerShell commands can be easily generated dynamically, executed from memory, encoded and obfuscated, thus making the logging and forensic analysis of code executed by PowerShell challenging.For all these reasons, PowerShell is increasingly used by cybercriminals as part of their attacks' tool chain, mainly for downloading malicious contents and for lateral movement. Indeed, a recent comprehensive technical report by Symantec dedicated to PowerShell's abuse by cybercrimials reported on a sharp increase in the number of malicious PowerShell samples they received and in the number of penetration tools and frameworks that use PowerShell. This highlights the urgent need of developing effective methods for detecting malicious PowerShell commands.In this work, we address this challenge by implementing several novel detectors of malicious PowerShell commands and evaluating their performance. We implemented both "traditional" natural language processing (NLP) based detectors and detectors based on character-level convolutional neural networks (CNNs). Detectors' performance was evaluated using a large real-world dataset.Our evaluation results show that, although our detectors individually yield high performance, an ensemble detector that combines an NLP-based classifier with a CNN-based classifier provides the best performance, since the latter classifier is able to detect malicious commands that succeed in evading the former. Our analysis of these evasive commands reveals that some obfuscation patterns automatically detected by the CNN classifier are intrinsically difficult to detect using the NLP techniques we applied.
Deep Differential Recurrent Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Due to the special gating schemes of Long Short-Term Memory (LSTM), LSTMs have shown greater potential to process complex sequential information than the traditional Recurrent Neural Network (RNN). The conventional LSTM, however, fails to take into consideration the impact of salient spatio-temporal dynamics present in the sequential input data. This problem was first addressed by the differential Recurrent Neural Network (dRNN), which uses a differential gating scheme known as Derivative of States (DoS). DoS uses higher orders of internal state derivatives to analyze the change in information gain caused by the salient motions between the successive frames. The weighted combination of several orders of DoS is then used to modulate the gates in dRNN. While each individual order of DoS is good at modeling a certain level of salient spatio-temporal sequences, the sum of all the orders of DoS could distort the detected motion patterns. To address this problem, we propose to control the LSTM gates via individual orders of DoS and stack multiple levels of LSTM cells in an increasing order of state derivatives. The proposed model progressively builds up the ability of the LSTM gates to detect salient dynamical patterns in deeper stacked layers modeling higher orders of DoS, and thus the proposed LSTM model is termed deep differential Recurrent Neural Network (d2RNN). The effectiveness of the proposed model is demonstrated on two publicly available human activity datasets: NUS-HGA and Violent-Flows. The proposed model outperforms both LSTM and non-LSTM based state-of-the-art algorithms.
Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media
Venue: arXiv cs.CL 2018
Year: 2018

While social media empowers freedom of expression and individual voices, it also enables anti-social behavior, online harassment, cyberbullying, and hate speech. In this paper, we deepen our understanding of online hate speech by focusing on a largely neglected but crucial aspect of hate speech -- its target: either "directed" towards a specific person or entity, or "generalized" towards a group of people sharing a common protected characteristic. We perform the first linguistic and psycholinguistic analysis of these two forms of hate speech and reveal the presence of interesting markers that distinguish these types of hate speech. Our analysis reveals that Directed hate speech, in addition to being more personal and directed, is more informal, angrier, and often explicitly attacks the target (via name calling) with fewer analytic words and more words suggesting authority and influence. Generalized hate speech, on the other hand, is dominated by religious hate, is characterized by the use of lethal words such as murder, exterminate, and kill; and quantity words such as million and many. Altogether, our work provides a data-driven analysis of the nuances of online-hate speech that enables not only a deepened understanding of hate speech and its social implications but also its detection.
Dualities in Tree Representations
Venue: arXiv cs.DS 2018
Year: 2018

A characterization of the tree $T^*$ such that $\mathrm{BP}(T^*)=\overleftrightarrow{\mathrm{DFUDS}(T)}$, the reversal of $\mathrm{DFUDS}(T)$ is given. An immediate consequence is a rigorous characterization of the tree $\hat{T}$ such that $\mathrm{BP}(\hat{T})=\mathrm{DFUDS}(T)$. In summary, $\mathrm{BP}$ and $\mathrm{DFUDS}$ are unified within an encompassing framework, which might have the potential to imply future simplifications with regard to queries in $\mathrm{BP}$ and/or $\mathrm{DFUDS}$. Immediate benefits displayed here are to identify so far unnoted commonalities in most recent work on the Range Minimum Query problem, and to provide improvements for the Minimum Length Interval Query problem.
Cooperative Localisation of a GPS-Denied UAV using Direction of Arrival Measurements
Venue: arXiv cs.RO 2018
Year: 2018

A GPS-denied UAV (Agent B) is localised through INS alignment with the aid of a nearby GPS-equipped UAV (Agent A), which broadcasts its position at several time instants. Agent B measures the signals' direction of arrival with respect to Agent B's inertial navigation frame. Semidefinite programming and the Orthogonal Procrustes algorithm are employed, and accuracy is improved through maximum likelihood estimation. The method is validated using flight data and simulations. A three-agent extension is explored.
MelanoGANs: High Resolution Skin Lesion Synthesis with GANs
Venue: arXiv cs.CV 2018
Year: 2018

Generative Adversarial Networks (GANs) have been successfully used to synthesize realistically looking images of faces, scenery and even medical images. Unfortunately, they usually require large training datasets, which are often scarce in the medical field, and to the best of our knowledge GANs have been only applied for medical image synthesis at fairly low resolution. However, many state-of-the-art machine learning models operate on high resolution data as such data carries indispensable, valuable information. In this work, we try to generate realistically looking high resolution images of skin lesions with GANs, using only a small training dataset of 2000 samples. The nature of the data allows us to do a direct comparison between the image statistics of the generated samples and the real dataset. We both quantitatively and qualitatively compare state-of-the-art GAN architectures such as DCGAN and LAPGAN against a modification of the latter for the task of image generation at a resolution of 256x256px. Our investigation shows that we can approximate the real data distribution with all of the models, but we notice major differences when visually rating sample realism, diversity and artifacts. In a set of use-case experiments on skin lesion classification, we further show that we can successfully tackle the problem of heavy class imbalance with the help of synthesized high resolution melanoma samples.
Creating an extrovert robotic assistant via IoT networking devices
Venue: arXiv cs.CY 2018
Year: 2018

The communication and collaboration of Cyber-Physical Systems, including machines and robots, among themselves and with humans, is expected to attract researchers' interest for the years to come. A key element of the new revolution is the Internet of Things (IoT). IoT infrastructures enable communication between different connected devices using internet protocols. The integration of robots in an IoT platform can improve robot capabilities by providing access to other devices and resources. In this paper we present an IoT-enabled application including a NAO robot which can communicate through an IoT platform with a reflex measurement system and a hardware node that provides robotics-oriented services in the form of RESTful web services. An activity reminder application is also included, illustrating the extension capabilities of the system.
A Survey of Fog Computing and Communication: Current Researches and Future Directions
Venue: arXiv cs.NI 2018
Year: 2018

In this survey, we discuss the evolution of distributed computing from the utility computing to the fog computing, various research challenges for the development of fog computing environments, the current status on fog computing research along with a taxonomy of various existing works in this direction. Then, we focus on the architectures of fog computing systems, technologies for enabling fog, fog computing features, security and privacy of fog, the QoS parameters, applications of fog, and give critical insights of various works done on this domain. Lastly, we briefly discuss about different fog computing associations that closely work on the development of fog based platforms and services, and give a summary of various types of overheads associated with fog computing platforms. Finally, we provide a thorough discussion on the future scopes and open research areas in fog computing as an enabler for the next generation computing paradigm.
MGGAN: Solving Mode Collapse using Manifold Guided Training
Venue: arXiv cs.CV 2018
Year: 2018

Mode collapse is a critical problem in training generative adversarial networks. To alleviate mode collapse, several recent studies introduce new objective functions, network architectures or alternative training schemes. However, their achievement is often the result of sacrificing the image quality. In this paper, we propose a new algorithm, namely a manifold guided generative adversarial network (MGGAN), which leverages a guidance network on existing GAN architecture to induce generator learning all modes of data distribution. Based on extensive evaluations, we show that our algorithm resolves mode collapse without losing image quality. In particular, we demonstrate that our algorithm is easily extendable to various existing GANs. Experimental analysis justifies that the proposed algorithm is an effective and efficient tool for training GANs.
Debugging Program Verification Proof Scripts (Tool Paper)
Venue: arXiv cs.LO 2018
Year: 2018

Interactive program verification is characterized by iterations of unfinished proof attempts. To support the process of constructing a complete proof, many interactive program verification systems offer a proof scripting language as a text-based way to describe the non-automatic steps in a proof. Such scripting languages are beneficial, but users spent a lot of effort on inspecting proof scripts and the proofs they construct to detect the cause when a proof attempt is unsuccessful and leads to unintended proof states. We present an offline and replay debugger to support the user in analyzing proof attempts performed with proof scripts. This debugger adapts successful concepts from software debugging to the area of proof script debugging. The tool is built on top of KeY, a system for deductive verification of Java programs. The debugger and its graphical user interface are designed to support program verification in particular, the underlying concepts and the implementation, however, are adaptable to other provers and proof tasks.
Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter
Venue: arXiv cs.SI 2018
Year: 2018

Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in financial markets. However, it has been demonstrated that much of the content shared in microblogging platforms is created and publicized by bots and spammers. Yet, the presence (or lack thereof) and the impact of fake stock microblogs has never systematically been investigated before. Here, we study 9M tweets related to stocks of the 5 main financial markets in the US. By comparing tweets with financial data from Google Finance, we highlight important characteristics of Twitter stock microblogs. More importantly, we uncover a malicious practice - referred to as cashtag piggybacking - perpetrated by coordinated groups of bots and likely aimed at promoting low-value stocks by exploiting the popularity of high-value ones. Among the findings of our study is that as much as 71% of the authors of suspicious financial tweets are classified as bots by a state-of-the-art spambot detection algorithm. Furthermore, 37% of them were suspended by Twitter a few months after our investigation. Our results call for the adoption of spam and bot detection techniques in all studies and applications that exploit user-generated content for predicting the stock market.
Joint Localization and Mapping through Millimeter Wave MIMO in 5G Systems - Extended Version
Venue: arXiv cs.IT 2018
Year: 2018

Millimeter wave signals with multiple transmit and receive antennas are considered as enabling technology for enhanced mobile broadband services in 5G systems. While this combination is mainly associated with achieving high data rates, it also offers huge potential for radio-based positioning. Recent studies showed that millimeter wave systems with multiple transmit and receive antennas are capable of jointly estimating the position and orientation of a mobile terminal while mapping the radio environment simultaneously. To this end, we present a message passing-based estimator which jointly estimates the position and orientation of the mobile terminal, as well as the location of reflectors or scatterers. We provide numerical examples showing that this estimator can provide considerably higher estimation accuracy compared to a state-of-the-art estimator. Our examples demonstrate that our message passing-based estimator neither requires the presence of a line-of-sight path nor prior knowledge regarding any of the parameters to be estimated.
Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification
Venue: arXiv cs.CV 2018
Year: 2018

Person re-identification has received special attention by the human analysis community in the last few years. To address the challenges in this field, many researchers have proposed different strategies, which basically exploit either cross-view invariant features or cross-view robust metrics. In this work, we propose to exploit a post-ranking approach and combine different feature representations through ranking aggregation. Spatial information, which potentially benefits the person matching, is represented using a 2D body model, from which color and texture information are extracted and combined. We also consider background/foreground information, automatically extracted via Deep Decompositional Network, and the usage of Convolutional Neural Network (CNN) features. To describe the matching between images we use the polynomial feature map, also taking into account local and global information. The Discriminant Context Information Analysis based post-ranking approach is used to improve initial ranking lists. Finally, the Stuart ranking aggregation method is employed to combine complementary ranking lists obtained from different feature representations. Experimental results demonstrated that we improve the state-of-the-art on VIPeR and PRID450s datasets, achieving 67.21% and 75.64% on top-1 rank recognition rate, respectively, as well as obtaining competitive results on CUHK01 dataset.
Generative models for local network community detection
Venue: arXiv cs.SI 2018
Year: 2018

Local network community detection aims to find a single community in a large network, while inspecting only a small part of that network around a given seed node. This is much cheaper than finding all communities in a network. Most methods for local community detection are formulated as ad-hoc optimization problems. In this work, we instead start from a generative model for networks with community structure. By assuming that the network is uniform, we can approximate the structure of unobserved parts of the network to obtain a method for local community detection. We apply this local approximation technique to two variants of the stochastic block model. To our knowledge, this results in the first local community detection methods based on probabilistic models. Interestingly, in the limit, one of the proposed approximations corresponds to conductance, a popular metric in this field. Experiments on real and synthetic datasets show comparable or improved results compared to state-of-the-art local community detection algorithms.
Towards a Flexible Architecture for Industrial Networking
Venue: arXiv cs.NI 2018
Year: 2018

The digitalization of manufacturing processes is expected to lead to a growing interconnection of production sites, as well as machines, tools and work pieces. In the course of this development, new use-cases arise which have challenging requirements from a communication technology point of view. In this paper we propose a communication network architecture for Industry 4.0 applications, which combines new 5G and non-cellular wireless network technologies with existing (wired) fieldbus technologies on the shop floor. This architecture includes the possibility to use private and public mobile networks together with local networking technologies to achieve a flexible setup that addresses many different industrial use cases. It is embedded into the Industrial Internet Reference Architecture and the RAMI4.0 reference architecture. The paper shows how the advancements introduced around the new 5G mobile technology can fulfill a wide range of industry requirements and thus enable new Industry 4.0 applications. Since 5G standardization is still ongoing, the proposed architecture is in a first step mainly focusing on new advanced features in the core network, but will be developed further later.
On The Efficiency of Widely Linear Precoding and Symbol Extension in Cellular Uplink
Venue: arXiv cs.IT 2018
Year: 2018

We investigate Gaussian widely linear precoding known as improper Gaussian signaling for the cellular uplink with inter-cell interference, known as interference multiple access channel (IMAC). This transmission scheme provides extra degrees of freedom by treating the real and imaginary components of the complex Gaussian signal differently. Since current standards mainly utilize linear beamforming for waveform generation, we highlight the benefits of widely linear beamforming over multiple temporal dimensions (symbol extension in time) in the IMAC. This scheme achieves significantly higher information rates compared to conventional proper Gaussian signaling at the expense of extra complexity at the transmission phase. We study the sum-power minimization problem under rate constraints. This problem is a difference of concave functions (DC) program, hence, a non-convex problem. By numerical simulations, we observe the benefits of improper Gaussian signaling alongside symbol extension in power consumption for both single-antenna and multi-antenna base stations. Interestingly, we observe that at strong interference scenarios, the efficiency of improper Gaussian signaling outperforms conventional proper Gaussian signaling at low rate demands. Moreover, in such scenarios the sum-power required for achieving particular rate demands is significantly reduced.
Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks
Venue: arXiv cs.CV 2018
Year: 2018

Breast cancer is the most frequently diagnosed cancer and leading cause of cancer-related death among females worldwide. In this article, we investigate the applicability of densely connected convolutional neural networks to the problems of histology image classification and whole slide image segmentation in the area of computer-aided diagnoses for breast cancer. To this end, we study various approaches for transfer learning and apply them to the data set from the 2018 grand challenge on breast cancer histology images (BACH).
Personalized Classifier for Food Image Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Currently, food image recognition tasks are evaluated against fixed datasets. However, in real-world conditions, there are cases in which the number of samples in each class continues to increase and samples from novel classes appear. In particular, dynamic datasets in which each individual user creates samples and continues the updating process often have content that varies considerably between different users, and the number of samples per person is very limited. A single classifier common to all users cannot handle such dynamic data. Bridging the gap between the laboratory environment and the real world has not yet been accomplished on a large scale. Personalizing a classifier incrementally for each user is a promising way to do this. In this paper, we address the personalization problem, which involves adapting to the user's domain incrementally using a very limited number of samples. We propose a simple yet effective personalization framework which is a combination of the nearest class mean classifier and the 1-nearest neighbor classifier based on deep features. To conduct realistic experiments, we made use of a new dataset of daily food images collected by a food-logging application. Experimental results show that our proposed method significantly outperforms existing methods.