OpenRaiser/Intern-Atlas huggingface dataset:
EagleEye: Attack-Agnostic Defense against Adversarial Inputs (Technical Report)
Venue: arXiv cs.CR 2018
Year: 2018

Deep neural networks (DNNs) are inherently vulnerable to adversarial inputs: such maliciously crafted samples trigger DNNs to misbehave, leading to detrimental consequences for DNN-powered systems. The fundamental challenges of mitigating adversarial inputs stem from their adaptive and variable nature. Existing solutions attempt to improve DNN resilience against specific attacks; yet, such static defenses can often be circumvented by adaptively engineered inputs or by new attack variants. Here, we present EagleEye, an attack-agnostic adversarial tampering analysis engine for DNN-powered systems. Our design exploits the {\em minimality principle} underlying many attacks: to maximize the attack's evasiveness, the adversary often seeks the minimum possible distortion to convert genuine inputs to adversarial ones. We show that this practice entails the distinct distributional properties of adversarial inputs in the input space. By leveraging such properties in a principled manner, EagleEye effectively discriminates adversarial inputs and even uncovers their correct classification outputs. Through extensive empirical evaluation using a range of benchmark datasets and DNN models, we validate EagleEye's efficacy. We further investigate the adversary's possible countermeasures, which implies a difficult dilemma for her: to evade EagleEye's detection, excessive distortion is necessary, thereby significantly reducing the attack's evasiveness regarding other detection mechanisms.
Identifying Financial Institutions by Transaction Signatures
Venue: arXiv cs.CY 2018
Year: 2018

Financial data aggregators and Personal Financial Management (PFM) services are software products that help individuals manage personal finances by collecting information from multiple accounts at various Financial Institutes (FIs), presenting data in a coherent and concentrated way, and highlighting insights and suggestions. Money transfers consist of two sides and a direction. From the perspective of a financial data aggregator, an incoming transaction consists of a date, an amount, and a description string, but not the explicit identity of the sending FI. In this paper we investigate supervised learning based methods to infer the identity of the sending FI from the description string of a money transfer transaction, using a blend of traditional and RNN based NLP methods. Our approach is based on the observation that the textual description field associated with a transactions is subjected to various types of normalizations and standardizations, resulting in unique patterns that identify the issuer. We compare multiple methods using a large real-word dataset of over $10$ million transactions.
Real-time image-based instrument classification for laparoscopic surgery
Venue: arXiv cs.CV 2018
Year: 2018

During laparoscopic surgery, context-aware assistance systems aim to alleviate some of the difficulties the surgeon faces. To ensure that the right information is provided at the right time, the current phase of the intervention has to be known. Real-time locating and classification the surgical tools currently in use are key components of both an activity-based phase recognition and assistance generation. In this paper, we present an image-based approach that detects and classifies tools during laparoscopic interventions in real-time. First, potential instrument bounding boxes are detected using a pixel-wise random forest segmentation. Each of these bounding boxes is then classified using a cascade of random forest. For this, multiple features, such as histograms over hue and saturation, gradients and SURF feature, are extracted from each detected bounding box. We evaluated our approach on five different videos from two different types of procedures. We distinguished between the four most common classes of instruments (LigaSure, atraumatic grasper, aspirator, clip applier) and background. Our method succesfully located up to 86% of all instruments respectively. On manually provided bounding boxes, we achieve a instrument type recognition rate of up to 58% and on automatically detected bounding boxes up to 49%. To our knowledge, this is the first approach that allows an image-based classification of surgical tools in a laparoscopic setting in real-time.
Monolingual and Cross-lingual Zero-shot Style Transfer
Venue: arXiv cs.CL 2018
Year: 2018

We introduce the task of zero-shot style transfer between different languages. Our training data includes multilingual parallel corpora, but does not contain any parallel sentences between styles, similarly to the recent previous work. We propose a unified multilingual multi-style machine translation system design, that allows to perform zero-shot style conversions during inference; moreover, it does so both monolingually and cross-lingually. Our model allows to increase the presence of dissimilar styles in corpus by up to 3 times, easily learns to operate with various contractions, and provides reasonable lexicon swaps as we see from manual evaluation.
What Happens - After the First Race? Enhancing the Predictive Power of Happens - Before Based Dynamic Race Detection
Venue: arXiv cs.PL 2018
Year: 2018

Dynamic race detection is the problem of determining if an observed program execution reveals the presence of a data race in a program. The classical approach to solving this problem is to detect if there is a pair of conflicting memory accesses that are unordered by Lamport's happens-before (HB) relation. HB based race detection is known to not report false positives, i.e., it is sound. However, the soundness guarantee of HB only promises that the first pair of unordered, conflicting events is a schedulable data race. That is, there can be pairs of HB-unordered conflicting data accesses that are not schedulable races because there is no reordering of the events of the execution, where the events in race can be executed immediately after each other. We introduce a new partial order, called schedulable happens-before (SHB) that exactly characterizes the pairs of schedulable data races --- every pair of conflicting data accesses that are identified by SHB can be scheduled, and every HB-race that can be scheduled is identified by SHB. Thus, the SHB partial order is truly sound. We present a linear time, vector clock algorithm to detect schedulable races using SHB. Our experiments demonstrate the value of our algorithm for dynamic race detection --- SHB incurs only little performance overhead and can scale to executions from real-world software applications without compromising soundness.
An AI Based Super Nodes Selection Algorithm in BlockChain Networks
Venue: arXiv cs.CR 2018
Year: 2018

In blockchain systems, especially cryptographic currencies such as Bitcoin, the double-spending and Byzantine-general-like problem are solved by reaching consensus protocols among all nodes. The state-of-the-art protocols include Proof-of-Work, Proof-of-Stake and Delegated-Proof-of-Stake. Proof-of-Work urges nodes to prove their computing power measured in hash rate in a crypto-puzzle solving competition. The other two take into account the amount of stake of each nodes and even design a vote in Delegated-Proof-of-Stake. However, these frameworks have several drawbacks, such as consuming a large number of electricity, leading the whole blockchain to a centralized system and so on. In this paper, we propose the conceptual framework, fundamental theory and research methodology, based on artificial intelligence technology that exploits nearly complementary information of each nodes. And we designed a particular convolutional neural network and a dynamic threshold, which obtained the super nodes and the random nodes, to reach the consensus. Experimental results demonstrate that our framework combines the advantages of Proof-of-Work, Proof-of-Stake and Delegated-Proof-of-Stake by avoiding complicated hash operation and monopoly. Furthermore, it compares favorably to the three state-of-the-art consensus frameworks, in terms of security and the speed of transaction confirmation.
Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs
Venue: arXiv cs.CV 2018
Year: 2018

Deep Learning (DL) applications are gaining momentum in the realm of Artificial Intelligence, particularly after GPUs have demonstrated remarkable skills for accelerating their challenging computational requirements. Within this context, Convolutional Neural Network (CNN) models constitute a representative example of success on a wide set of complex applications, particularly on datasets where the target can be represented through a hierarchy of local features of increasing semantic complexity. In most of the real scenarios, the roadmap to improve results relies on CNN settings involving brute force computation, and researchers have lately proven Nvidia GPUs to be one of the best hardware counterparts for acceleration. Our work complements those findings with an energy study on critical parameters for the deployment of CNNs on flagship image and video applications: object recognition and people identification by gait, respectively. We evaluate energy consumption on four different networks based on the two most popular ones (ResNet/AlexNet): ResNet (167 layers), a 2D CNN (15 layers), a CaffeNet (25 layers) and a ResNetIm (94 layers) using batch sizes of 64, 128 and 256, and then correlate those with speed-up and accuracy to determine optimal settings. Experimental results on a multi-GPU server endowed with twin Maxwell and twin Pascal Titan X GPUs demonstrate that energy correlates with performance and that Pascal may have up to 40% gains versus Maxwell. Larger batch sizes extend performance gains and energy savings, but we have to keep an eye on accuracy, which sometimes shows a preference for small batches. We expect this work to provide a preliminary guidance for a wide set of CNN and DL applications in modern HPC times, where the GFLOPS/w ratio constitutes the primary goal.
On a hierarchical control strategy for multi-agent formation without reflection
Venue: arXiv cs.SY 2018
Year: 2018

This paper considers a formation shape control problem for point agents in a two-dimensional ambient space, where the control is distributed, is based on achieving desired distances between nominated agent pairs, and avoids the possibility of reflection ambiguities. This has potential applications for large-scale multi-agent systems having simple information exchange structure. One solution to this type of problem, applicable to formations with just three or four agents, was recently given by considering a potential function which consists of both distance error and signed triangle area terms. However, it seems to be challenging to apply it to formations with more than four agents. This paper shows a hierarchical control strategy which can be applicable to any number of agents based on the above type of potential function and a formation shaping incorporating a grouping of equilateral triangles, so that all controlled distances are in fact the same. A key analytical result and some numerical results are shown to demonstrate the effectiveness of the proposed method.
Robots Racialized in the Likeness of Marginalized Social Identities are Subject to Greater Dehumanization than those racialized as White
Venue: arXiv cs.CY 2018
Year: 2018

The emergence and spread of humanlike robots into increasingly public domains has revealed a concerning phenomenon: people's unabashed dehumanization of robots, particularly those gendered as female. Here we examined this phenomenon further towards understanding whether other socially marginalized cues (racialization in the likeness of Asian and Black identities), like female-gendering, are associated with the manifestation of dehumanization (e.g., objectification, stereotyping) in human-robot interactions. To that end, we analyzed free-form comments (N=535) on three videos, each depicting a gynoid - Bina48, Nadine, or Yangyang - racialized as Black, White, and Asian respectively. As a preliminary control, we additionally analyzed commentary (N=674) on three videos depicting women embodying similar identity cues. The analyses indicate that people more frequently dehumanize robots racialized as Asian and Black, than they do of robots racialized as White. Additional, preliminary evaluation of how people's responding towards the gynoids compares to that towards other people suggests that the gynoids' ontology (as robots) further facilitates the dehumanization.
PABED A Tool for Big Education Data Analysis
Venue: arXiv cs.CY 2018
Year: 2018

Cloud computing and big data have risen to become the most popular technologies of the modern world. Apparently, the reason behind their immense popularity is their wide range of applicability as far as the areas of interest are concerned. Education and research remain one of the most obvious and befitting application areas. This research paper introduces a big data analytics tool, PABED Project Analyzing Big Education Data, for the education sector that makes use of cloud-based technologies. This tool is implemented using Google BigQuery and R programming language and allows comparison of undergraduate enrollment data for different academic years. Although, there are many proposed applications of big data in education, there is a lack of tools that can actualize the concept into practice. PABED is an effort in this direction. The implementation and testing details of the project have been described in this paper. This tool validates the use of cloud computing and big data technologies in education and shall head start development of more sophisticated educational intelligence tools.
Network-Coding Approach for Information-Centric Networking
Venue: arXiv cs.NI 2018
Year: 2018

The current internet architecture is inefficient in fulfilling the demands of newly emerging internet applications. To address this issue, several over-the-top (OTT) application-level solutions have been employed, making the overall architecture very complex. Information-centric-networking (ICN) architecture has emerged as a promising alternative solution. The ICN architecture decouples the content from the host at the network level and supports the temporary storage of content in an in-network cache. Fundamentally, the ICN can be considered a multisource, multicast content-delivery solution. Because of the benefits of network coding in multicasting scenarios and proven benefits in distributed storage networks, the network coding is apt for the ICN architecture. In this study, we propose a solvable linear network-coding scheme for the ICN architecture. We also propose a practical implementation of the network-coding scheme for the ICN, particularly for the content-centric network (CCN) architecture, which is termed the coded CCN (CCCN). The performance results show that the network-coding scheme improves the performance of the CCN and significantly reduces the network traffic and average download delay.
Deep Appearance Models for Face Rendering
Venue: arXiv cs.GR 2018
Year: 2018

We introduce a deep appearance model for rendering the human face. Inspired by Active Appearance Models, we develop a data-driven rendering pipeline that learns a joint representation of facial geometry and appearance from a multiview capture setup. Vertex positions and view-specific textures are modeled using a deep variational autoencoder that captures complex nonlinear effects while producing a smooth and compact latent representation. View-specific texture enables the modeling of view-dependent effects such as specularity. In addition, it can also correct for imperfect geometry stemming from biased or low resolution estimates. This is a significant departure from the traditional graphics pipeline, which requires highly accurate geometry as well as all elements of the shading model to achieve realism through physically-inspired light transport. Acquiring such a high level of accuracy is difficult in practice, especially for complex and intricate parts of the face, such as eyelashes and the oral cavity. These are handled naturally by our approach, which does not rely on precise estimates of geometry. Instead, the shading model accommodates deficiencies in geometry though the flexibility afforded by the neural network employed. At inference time, we condition the decoding network on the viewpoint of the camera in order to generate the appropriate texture for rendering. The resulting system can be implemented simply using existing rendering engines through dynamic textures with flat lighting. This representation, together with a novel unsupervised technique for mapping images to facial states, results in a system that is naturally suited to real-time interactive settings such as Virtual Reality (VR).
Standards-Based Worldwide Semantic Interoperability for IoT
Venue: arXiv cs.DC 2018
Year: 2018

Global IoT services (GIoTS) are combining locally available IoT resources with Cloud-based services. They are targeting world-wide services. GIoTS require interoperability between the locally installed heterogeneous IoT systems. Semantic processing is an important technology to enable data mediation as well as knowledge-based processing. This paper explains a system architecture for achieving world-wide semantic interoperability using international standards like oneM2M and the OMA NGSI-9/10 context interfaces (as used in the European Future Internet Platform FIWARE). Semantics also enables the use of Knowledge-based Semantic Processing Agents. Furthermore, we explain how semantic verification enables the testing of such complex systems.
Seq2Seq and Multi-Task Learning for joint intent and content extraction for domain specific interpreters
Venue: arXiv cs.LG 2018
Year: 2018

This study evaluates the performances of an LSTM network for detecting and extracting the intent and content of com- mands for a financial chatbot. It presents two techniques, sequence to sequence learning and Multi-Task Learning, which might improve on the previous task.
Inference of Users Demographic Attributes based on Homophily in Communication Networks
Venue: arXiv cs.SI 2018
Year: 2018

Over the past decade, mobile phones have become prevalent in all parts of the world, across all demographic backgrounds. Mobile phones are used by men and women across a wide age range in both developed and developing countries. Consequently, they have become one of the most important mechanisms for social interaction within a population, making them an increasingly important source of information to understand human demographics and human behaviour. In this work we combine two sources of information: communication logs from a major mobile operator in a Latin American country, and information on the demographics of a subset of the users population. This allows us to perform an observational study of mobile phone usage, differentiated by age groups categories. This study is interesting in its own right, since it provides knowledge on the structure and demographics of the mobile phone market in the studied country. We then tackle the problem of inferring the age group for all users in the network. We present here an exclusively graph-based inference method relying solely on the topological structure of the mobile network, together with a topological analysis of the performance of the algorithm. The equations for our algorithm can be described as a diffusion process with two added properties: (i) memory of its initial state, and (ii) the information is propagated as a probability vector for each node attribute (instead of the value of the attribute itself). Our algorithm can successfully infer different age groups within the network population given known values for a subset of nodes (seed nodes). Most interestingly, we show that by carefully analysing the topological relationships between correctly predicted nodes and the seed nodes, we can characterize particular subsets of nodes for which our inference method has significantly higher accuracy.
Saccadic Predictive Vision Model with a Fovea
Venue: arXiv cs.NE 2018
Year: 2018

We propose a model that emulates saccades, the rapid movements of the eye, called the Error Saccade Model, based on the prediction error of the Predictive Vision Model (PVM). The Error Saccade Model carries out movements of the model's field of view to regions with the highest prediction error. Comparisons of the Error Saccade Model on Predictive Vision Models with and without a fovea show that a fovea-like structure in the input level of the PVM improves the Error Saccade Model's ability to pursue detailed objects in its view. We hypothesize that the improvement is due to poorer resolution in the periphery causing higher prediction error when an object passes, triggering a saccade to the next location.
Toward A Deep Understanding of What Makes a Scientific Visualization Memorable
Venue: arXiv cs.GR 2018
Year: 2018

We report results from a preliminary study exploring the memorability of spatial scientific visualizations, the goal of which is to understand the visual features that contribute to memorability. The evaluation metrics include three objective measures (entropy, feature congestion, the number of edges), four subjective ratings (clutter, the number of distinct colors, familiarity, and realism), and two sentiment ratings (interestingness and happiness). We curate 1142 scientific visualization (SciVis) images from the original 2231 images in published IEEE SciVis papers from 2008 to 2017 and compute memorability scores of 228 SciVis images from data collected on Amazon Mechanical Turk (MTurk). Results showed that the memorability of SciVis images is mostly correlated with clutter and the number of distinct colors. We further investigate the differences between scientific visualization and infographics as a means to understand memorability differences by data attributes.
Improved Quantum Information Set Decoding
Venue: arXiv cs.IT 2018
Year: 2018

In this paper we present quantum information set decoding (ISD) algorithms for binary linear codes. First, we give an alternative view on the quantum walk based algorithms proposed by Kachigar and Tillich (PQCrypto'17). It is more general and allows to consider any ISD algorithm that has certain properties. The algorithms of May-Meuer-Thomae and Becker-Jeux-May-Meuer satisfy these properties. Second, we translate May-Ozerov Near Neighbour technique (Eurocrypt'15) to an `update-and-query' language more suitable for the quantum walk framework. First, this re-interpretation makes possible to analyse a broader class of algorithms and, second, allows us to combine Near Neighbour search with the quantum walk framework and use both techniques to give a quantum version of Dumer's ISD with Near Neighbour.
Debugging Native Extensions of Dynamic Languages
Venue: arXiv cs.SE 2018
Year: 2018

Many dynamic programming languages such as Ruby and Python enable developers to use so called native extensions, code implemented in typically statically compiled languages like C and C++. However, debuggers for these dynamic languages usually lack support for also debugging these native extensions. GraalVM can execute programs implemented in various dynamic programming languages and, by using the LLVM-IR interpreter Sulong, also their native extensions. We added support for source-level debugging to Sulong based on GraalVM's debugging framework by associating run-time debug information from the LLVM-IR level to the original program code. As a result, developers can now use GraalVM to debug source code written in multiple LLVM-based programming languages as well as programs implemented in various dynamic languages that invoke it in a common debugger front-end.