OpenRaiser/Intern-Atlas huggingface dataset:
When silver glitters more than gold: Bootstrapping an Italian part-of-speech tagger for Twitter
Venue: arXiv cs.CL 2016
Year: 2016

We bootstrap a state-of-the-art part-of-speech tagger to tag Italian Twitter data, in the context of the Evalita 2016 PoSTWITA shared task. We show that training the tagger on native Twitter data enriched with little amounts of specifically selected gold data and additional silver-labelled data scraped from Facebook, yields better results than using large amounts of manually annotated data from a mix of genres.
Evaluating the Effect of Centralization on Routing Convergence on a Hybrid BGP-SDN Emulation Framework
Venue: arXiv cs.NI 2016
Year: 2016

A lot of applications depend on reliable and stable Internet connectivity. These characteristics are crucial for mission-critical services such as telemedical applications. An important factor that can affect connection availability is the convergence time of BGP, the de-facto inter-domain routing (IDR) protocol in the Internet. After a routing change, it may take several minutes until the network converges and BGP routing becomes stable again. Kotronis et al propose a novel Internet routing approach based on SDN principles that combines several Autonomous Systems (AS) into groups, called clusters, and introduces a logically centralized routing decision process for the cluster participants. One of the goals of this concept is to stabilize the IDR system and bring down its convergence time. However, testing whether such approaches can improve on BGP problems requires hybrid SDN and BGP experimentation tools that can emulate multiple ASes. Presently, there is a lack of an easy to use public tool for this purpose. This work fills this gap by building a suitable emulation framework and evaluating the effect that a proof-of-concept IDR controller has on IDR convergence time.
Computationally Efficient Target Classification in Multispectral Image Data with Deep Neural Networks
Venue: arXiv cs.CV 2016
Year: 2016

Detecting and classifying targets in video streams from surveillance cameras is a cumbersome, error-prone and expensive task. Often, the incurred costs are prohibitive for real-time monitoring. This leads to data being stored locally or transmitted to a central storage site for post-incident examination. The required communication links and archiving of the video data are still expensive and this setup excludes preemptive actions to respond to imminent threats. An effective way to overcome these limitations is to build a smart camera that transmits alerts when relevant video sequences are detected. Deep neural networks (DNNs) have come to outperform humans in visual classifications tasks. The concept of DNNs and Convolutional Networks (ConvNets) can easily be extended to make use of higher-dimensional input data such as multispectral data. We explore this opportunity in terms of achievable accuracy and required computational effort. To analyze the precision of DNNs for scene labeling in an urban surveillance scenario we have created a dataset with 8 classes obtained in a field experiment. We combine an RGB camera with a 25-channel VIS-NIR snapshot sensor to assess the potential of multispectral image data for target classification. We evaluate several new DNNs, showing that the spectral information fused together with the RGB frames can be used to improve the accuracy of the system or to achieve similar accuracy with a 3x smaller computation effort. We achieve a very high per-pixel accuracy of 99.1%. Even for scarcely occurring, but particularly interesting classes, such as cars, 75% of the pixels are labeled correctly with errors occurring only around the border of the objects. This high accuracy was obtained with a training set of only 30 labeled images, paving the way for fast adaptation to various application scenarios.
Scalable Compression of a Weighted Graph
Venue: arXiv cs.DS 2016
Year: 2016

Graph is a useful data structure to model various real life aspects like email communications, co-authorship among researchers, interactions among chemical compounds, and so on. Supporting such real life interactions produce a knowledge rich massive repository of data. However, efficiently understanding underlying trends and patterns is hard due to large size of the graph. Therefore, this paper presents a scalable compression solution to compute summary of a weighted graph. All the aforementioned interactions from various domains are represented as edge weights in a graph. Therefore, creating a summary graph while considering this vital aspect is necessary to learn insights of different communication patterns. By experimenting the proposed method on two real world and publically available datasets against a state of the art technique, we obtain order of magnitude performance gain and better summarization accuracy.
SoK: Applying Machine Learning in Security - A Survey
Venue: arXiv cs.CR 2016
Year: 2016

The idea of applying machine learning(ML) to solve problems in security domains is almost 3 decades old. As information and communications grow more ubiquitous and more data become available, many security risks arise as well as appetite to manage and mitigate such risks. Consequently, research on applying and designing ML algorithms and systems for security has grown fast, ranging from intrusion detection systems(IDS) and malware classification to security policy management(SPM) and information leak checking. In this paper, we systematically study the methods, algorithms, and system designs in academic publications from 2008-2015 that applied ML in security domains. 98 percent of the surveyed papers appeared in the 6 highest-ranked academic security conferences and 1 conference known for pioneering ML applications in security. We examine the generalized system designs, underlying assumptions, measurements, and use cases in active research. Our examinations lead to 1) a taxonomy on ML paradigms and security domains for future exploration and exploitation, and 2) an agenda detailing open and upcoming challenges. Based on our survey, we also suggest a point of view that treats security as a game theory problem instead of a batch-trained ML problem.
Top-k Spatial-keyword Publish/Subscribe Over Sliding Window
Venue: arXiv cs.DB 2016
Year: 2016

With the prevalence of social media and GPS-enabled devices, a massive amount of geo-textual data has been generated in a stream fashion, leading to a variety of applications such as location-based recommendation and information dissemination. In this paper, we investigate a novel real-time top-k monitoring problem over sliding window of streaming data; that is, we continuously maintain the top-k most relevant geo-textual messages (e.g., geo-tagged tweets) for a large number of spatial-keyword subscriptions (e.g., registered users interested in local events) simultaneously. To provide the most recent information under controllable memory cost, sliding window model is employed on the streaming geo-textual data. To the best of our knowledge, this is the first work to study top-k spatial-keyword publish/subscribe over sliding window. A novel centralized system, called Skype (Topk Spatial-keyword Publish/Subscribe), is proposed in this paper. In Skype, to continuously maintain top-k results for massive subscriptions, we devise a novel indexing structure upon subscriptions such that each incoming message can be immediately delivered on its arrival. To reduce the expensive top-k re-evaluation cost triggered by message expiration, we develop a novel cost-based k-skyband technique to reduce the number of re-evaluations in a cost-effective way. Extensive experiments verify the great efficiency and effectiveness of our proposed techniques. Furthermore, to support better scalability and higher throughput, we propose a distributed version of Skype, namely, DSkype, on top of Storm, which is a popular distributed stream processing system. With the help of fine-tuned subscription/message distribution mechanisms, DSkype can achieve orders of magnitude speed-up than its centralized version.
A Memristor Crossbar-Based Computation Scheme with High Precision
Venue: arXiv cs.ET 2016
Year: 2016

The memristor is promising to be the basic cell of next-generation computation systems. Compared to the traditional MOSFET device, the memristor is efficient over energy and area. But one of the biggest challenges faced with researchers is how to program a memristor's resistance precisely. Recently, an algorithm designed to save 8 valid bits in each memristor is proposed, but this is still not sufficient for precise computation. In this paper, we propose a crossbar-based memristor computation scheme supporting precise computations whose operands have 32 valid bits. As a brief introduction, in a multiplication with two operands, one operand is programmed as input signal, and the other operand is saved into a so-called crossbar structure, which contains a group of memristors, and each memristor saves several valid bits, usually one or two bits only. The computation results,i.e. the multiplication of the two operands, are contained in the outputs of the crossbar structure together with noise. Analog-to-Digital Converters (ADCs) are then used to extract the valid bits, which are the most significant bits of outputs. These valid bits can be combined together with Digital-to-Analog Converters(DACs) to get the final results. What's more, the precision of this computation scheme can be adjusted according to the definition of the user, 32 valid bits at most, so it is qualified for different application contexts.
Computing threshold functions using dendrites
Venue: arXiv cs.NE 2016
Year: 2016

Neurons, modeled as linear threshold unit (LTU), can in theory compute all thresh- old functions. In practice, however, some of these functions require synaptic weights of arbitrary large precision. We show here that dendrites can alleviate this requirement. We introduce here the non-Linear Threshold Unit (nLTU) that integrates synaptic input sub-linearly within distinct subunits to take into account local saturation in dendrites. We systematically search parameter space of the nTLU and TLU to compare them. Firstly, this shows that the nLTU can compute all threshold functions with smaller precision weights than the LTU. Secondly, we show that a nLTU can compute significantly more functions than a LTU when an input can only make a single synapse. This work paves the way for a new generation of network made of nLTU with binary synapses.
Fuzzy Logic-based Implicit Authentication for Mobile Access Control
Venue: arXiv cs.CR 2016
Year: 2016

In order to address the increasing compromise of user privacy on mobile devices, a Fuzzy Logic based implicit authentication scheme is proposed in this paper. The proposed scheme computes an aggregate score based on selected features and a threshold in real-time based on current and historic data depicting user routine. The tuned fuzzy system is then applied to the aggregated score and the threshold to determine the trust level of the current user. The proposed fuzzy-integrated implicit authentication scheme is designed to: operate adaptively and completely in the background, require minimal training period, enable high system accuracy while provide timely detection of abnormal activity. In this paper, we explore Fuzzy Logic based authentication in depth. Gaussian and triangle-based membership functions are investigated and compared using real data over several weeks from different Android phone users. The presented results show that our proposed Fuzzy Logic approach is a highly effective, and viable scheme for lightweight real-time implicit authentication on mobile devices.
Temperature-Insensitive Analog Vector-by-Matrix Multiplier Based on 55 nm NOR Flash Memory Cells
Venue: arXiv cs.ET 2016
Year: 2016

We have fabricated and successfully tested an analog vector-by-matrix multiplier, based on redesigned 10x12 arrays of 55 nm commercial NOR flash memory cells. The modified arrays enable high-precision individual analog tuning of each cell, with sub-1% accuracy, while keeping the highly optimized cells, with their long-term state retention, intact. The array has an area of 0.33 um^2 per cell, and is at least one order of magnitude more dense than the reported prior implementations of nonvolatile analog memories. The demonstrated vector-by-vector multiplier, using gate coupling to additional periphery cells, has ~2% precision, limited by the aggregate effect of cell noise, retention, mismatch, process variations, tuning precision, and capacitive crosstalk. A differential version of the multiplier has allowed us to demonstrate sub-3% temperature drift of the output signal in the range between 25C and 85C.
Reconfigurable Dual Mode IEEE 802.15.4 Digital Baseband Receiver for Diverse IoT Applications
Venue: arXiv cs.IT 2016
Year: 2016

IEEE 802.15.4 takes a center stage in IoT as Low- Rate Wireless Personal Area Networks(LR-WPANs). The standard specifies Offset Quadrature Phase Shift Keying Physical Layer (O-QPSK PHY) with half-sine pulse shaping which can be either analyzed under the class of M-ary PSK signals (QPSK signal with offset) or as Minimum Shift Keying (MSK) signal. M-ary PSK demodulation is requires perfect carrier and has minimal error. MSK signals which falls under Continuous Phase Frequency Shift Keying can be demodulated non-coherently but error performance is not as good. In our paper, this dual nature of IEEE 802.15.4 PHY is exploited to propose a dual mode receiver comprising of QPSK demodulator chain and MSK demodulator chain as a single system on chip. The mode can be configured manually depending on the type of application or based on the feedback from a Signal to Noise (SNR) indicator employed in the proposed receiver. M-ary PSK chain is selected for lower SNRs and MSK for higher SNRs. Each of these properties are analyzed in detail for both demodulator chains and we go on to prove that MSK detection can be used for low power, low complex and low latency while QPSK detection is employed for minimal error.
Landmark-based consonant voicing detection on multilingual corpora
Venue: arXiv cs.CL 2016
Year: 2016

This paper tests the hypothesis that distinctive feature classifiers anchored at phonetic landmarks can be transferred cross-lingually without loss of accuracy. Three consonant voicing classifiers were developed: (1) manually selected acoustic features anchored at a phonetic landmark, (2) MFCCs (either averaged across the segment or anchored at the landmark), and(3) acoustic features computed using a convolutional neural network (CNN). All detectors are trained on English data (TIMIT),and tested on English, Turkish, and Spanish (performance measured using F1 and accuracy). Experiments demonstrate that manual features outperform all MFCC classifiers, while CNNfeatures outperform both. MFCC-based classifiers suffer an F1reduction of 16% absolute when generalized from English to other languages. Manual features suffer only a 5% F1 reduction,and CNN features actually perform better in Turkish and Span-ish than in the training language, demonstrating that features capable of representing long-term spectral dynamics (CNN and landmark-based features) are able to generalize cross-lingually with little or no loss of accuracy
Vision-aided Localization and Navigation Based on Trifocal Tensor
Venue: arXiv cs.RO 2016
Year: 2016

In this paper, a novel method for vision-aided navigation based on trifocal tensor is presented. The main goal of the proposed method is to provide position estimation in GPS-denied environments for vehicles equipped with a standard inertial navigation systems(INS) and a single camera only. We treat the trifocal tensor as the measurement model, being only concerned about the vehicle state and do not estimate the the position of the tracked landmarks. The performance of the proposed method is demonstrated using simulation and experimental data.
Greedy Step Averaging: A parameter-free stochastic optimization method
Venue: arXiv cs.LG 2016
Year: 2016

In this paper we present the greedy step averaging(GSA) method, a parameter-free stochastic optimization algorithm for a variety of machine learning problems. As a gradient-based optimization method, GSA makes use of the information from the minimizer of a single sample's loss function, and takes average strategy to calculate reasonable learning rate sequence. While most existing gradient-based algorithms introduce an increasing number of hyper parameters or try to make a trade-off between computational cost and convergence rate, GSA avoids the manual tuning of learning rate and brings in no more hyper parameters or extra cost. We perform exhaustive numerical experiments for logistic and softmax regression to compare our method with the other state of the art ones on 16 datasets. Results show that GSA is robust on various scenarios.
On Proving Confluence Modulo Equivalence for Constraint Handling Rules
Venue: arXiv cs.LO 2016
Year: 2016

Previous results on proving confluence for Constraint Handling Rules are extended in two ways in order to allow a larger and more realistic class of CHR programs to be considered confluent. Firstly, we introduce the relaxed notion of confluence modulo equivalence into the context of CHR: while confluence for a terminating program means that all alternative derivations for a query lead to the exact same final state, confluence modulo equivalence only requires the final states to be equivalent with respect to an equivalence relation tailored for the given program. Secondly, we allow non-logical built-in predicates such as var/1 and incomplete ones such as is/2, that are ignored in previous work on confluence. To this end, a new operational semantics for CHR is developed which includes such predicates. In addition, this semantics differs from earlier approaches by its simplicity without loss of generality, and it may also be recommended for future studies of CHR. For the purely logical subset of CHR, proofs can be expressed in first-order logic, that we show is not sufficient in the present case. We have introduced a formal meta-language that allows reasoning about abstract states and derivations with meta-level restrictions that reflect the non-logical and incomplete predicates. This language represents subproofs as diagrams, which facilitates a systematic enumeration of proof cases, pointing forward to a mechanical support for such proofs.
Oriented bounding boxes using multiresolution contours for fast interference detection of arbitrary geometry objects
Venue: arXiv cs.GR 2016
Year: 2016

Interference detection of arbitrary geometric objects is not a trivial task due to the heavy computational load imposed by implementation issues. The hierarchically structured bounding boxes help us to quickly isolate the contour of segments in interference. In this paper, a new approach is introduced to treat the interference detection problem involving the representation of arbitrary shaped objects. Our proposed method relies upon searching for the best possible way to represent contours by means of hierarchically structured rectangular oriented bounding boxes. This technique handles 2D objects boundaries defined by closed B-spline curves with roughness details. Each oriented box is adapted and fitted to the segments of the contour using second order statistical indicators from some elements of the segments of the object contour in a multiresolution framework. Our method is efficient and robust when it comes to 2D animations in real time. It can deal with smooth curves and polygonal approximations as well results are present to illustrate the performance of the new method.
DB-Nets: on The Marriage of Colored Petri Nets and Relational Databases
Venue: arXiv cs.DB 2016
Year: 2016

The integrated management of business processes and mas- ter data is being increasingly considered as a fundamental problem, by both the academia and the industry. In this position paper, we focus on the foundations of the problem, arguing that contemporary approaches struggle to find a suitable equilibrium between data- and process-related aspects. We then propose db-nets, a new formal model that balances such two pillars through the marriage of colored Petri nets and relational databases. We invite the research community to build on this model, discussing its potential in modeling, formal verification, and simulation.
Top-k String Auto-Completion with Synonyms
Venue: arXiv cs.IR 2016
Year: 2016

Auto-completion is one of the most prominent features of modern information systems. The existing solutions of auto-completion provide the suggestions based on the beginning of the currently input character sequence (i.e. prefix). However, in many real applications, one entity often has synonyms or abbreviations. For example, "DBMS" is an abbreviation of "Database Management Systems". In this paper, we study a novel type of auto-completion by using synonyms and abbreviations. We propose three trie-based algorithms to solve the top-k auto-completion with synonyms; each one with different space and time complexity trade-offs. Experiments on large-scale datasets show that it is possible to support effective and efficient synonym-based retrieval of completions of a million strings with thousands of synonyms rules at about a microsecond per-completion, while taking small space overhead (i.e. 160-200 bytes per string). The source code of our experiments can be download at: http://udbms.cs.helsinki.fi/?projects/autocompletion/download .
Towards the Science of Security and Privacy in Machine Learning
Venue: arXiv cs.CR 2016
Year: 2016

Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as data analytics, autonomous systems, and security diagnostics. ML is now pervasive---new systems and models are being deployed in every domain imaginable, leading to rapid and widespread deployment of software based inference and decision making. There is growing recognition that ML exposes new vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited. We systematize recent findings on ML security and privacy, focusing on attacks identified on these systems and defenses crafted to date. We articulate a comprehensive threat model for ML, and categorize attacks and defenses within an adversarial framework. Key insights resulting from works both in the ML and security communities are identified and the effectiveness of approaches are related to structural elements of ML algorithms and the data used to train them. We conclude by formally exploring the opposing relationship between model accuracy and resilience to adversarial manipulation. Through these explorations, we show that there are (possibly unavoidable) tensions between model complexity, accuracy, and resilience that must be calibrated for the environments in which they will be used.
Effective sparse representation of X-Ray medical images
Venue: arXiv cs.CV 2016
Year: 2016

Effective sparse representation of X-Ray medical images within the context of data reduction is considered. The proposed framework is shown to render an enormous reduction in the cardinality of the data set required to represent this class of images at very good quality. The particularity of the approach is that it can be implemented at very competitive processing time and low memory requirements
The Complexity of Shelflisting
Venue: arXiv cs.GT 2016
Year: 2016

Optimal shelflisting invites profit maximization to become sensitive to the ways in which purchasing decisions are order-dependent. We study the computational complexity of the corresponding product arrangement problem when consumers are either rational maximizers, use a satisficing procedure, or apply successive choice. The complexity results we report are shown to crucially depend on the size of the top cycle in consumers' preferences over products and on the direction in which alternatives on the shelf are encountered.