OpenRaiser/Intern-Atlas huggingface dataset:
Neurocontrol methods review
Venue: arXiv cs.AI 2015
Year: 2015

Methods of applying neural networks to control plants are considered. Methods and schemes are described, their advantages and disadvantages are discussed.
Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks
Venue: arXiv cs.SD 2015
Year: 2015

Traditional methods to tackle many music information retrieval tasks typically follow a two-step architecture: feature engineering followed by a simple learning algorithm. In these "shallow" architectures, feature engineering and learning are typically disjoint and unrelated. Additionally, feature engineering is difficult, and typically depends on extensive domain expertise. In this paper, we present an application of convolutional neural networks for the task of automatic musical instrument identification. In this model, feature extraction and learning algorithms are trained together in an end-to-end fashion. We show that a convolutional neural network trained on raw audio can achieve performance surpassing traditional methods that rely on hand-crafted features.
Design of Massive-MIMO-NOMA with Limited Feedback
Venue: arXiv cs.IT 2015
Year: 2015

In this letter, a low-feedback non-orthogonal multiple access (NOMA) scheme using massive multiple-input multiple-output (MIMO) transmission is proposed. In particular, the proposed scheme can decompose a massive-MIMO-NOMA system into multiple separated single-input single-output NOMA channels, and analytical results are developed to evaluate the performance of the proposed scheme for two scenarios, with perfect user ordering and with one-bit feedback, respectively.
Bounded Stability in Networked Systems with Parameter Mismatch and Adaptive Decentralized Estimation
Venue: arXiv cs.SY 2015
Year: 2015

Here, we study the ultimately bounded stability of network of mismatched systems using Lyapunov direct method. The upper bound on the error of oscillators from the center of the neighborhood is derived. Then the performance of an adaptive compensation via decentralized control is analyzed. Finally, the analytical results for a network of globally connected Lorenz oscillators are verified.
Learning Discriminative Representations for Semantic Cross Media Retrieval
Venue: arXiv cs.IR 2015
Year: 2015

Heterogeneous gap among different modalities emerges as one of the critical issues in modern AI problems. Unlike traditional uni-modal cases, where raw features are extracted and directly measured, the heterogeneous nature of cross modal tasks requires the intrinsic semantic representation to be compared in a unified framework. This paper studies the learning of different representations that can be retrieved across different modality contents. A novel approach for mining cross-modal representations is proposed by incorporating explicit linear semantic projecting in Hilbert space. The insight is that the discriminative structures of different modality data can be linearly represented in appropriate high dimension Hilbert spaces, where linear operations can be used to approximate nonlinear decisions in the original spaces. As a result, an efficient linear semantic down mapping is jointly learned for multimodal data, leading to a common space where they can be compared. The mechanism of "feature up-lifting and down-projecting" works seamlessly as a whole, which accomplishes crossmodal retrieval tasks very well. The proposed method, named as shared discriminative semantic representation learning (\textbf{SDSRL}), is tested on two public multimodal dataset for both within- and inter- modal retrieval. The experiments demonstrate that it outperforms several state-of-the-art methods in most scenarios.
Discovering Underlying Plans Based on Distributed Representations of Actions
Venue: arXiv cs.AI 2015
Year: 2015

Plan recognition aims to discover target plans (i.e., sequences of actions) behind observed actions, with history plan libraries or domain models in hand. Previous approaches either discover plans by maximally "matching" observed actions to plan libraries, assuming target plans are from plan libraries, or infer plans by executing domain models to best explain the observed actions, assuming complete domain models are available. In real world applications, however, target plans are often not from plan libraries and complete domain models are often not available, since building complete sets of plans and complete domain models are often difficult or expensive. In this paper we view plan libraries as corpora and learn vector representations of actions using the corpora; we then discover target plans based on the vector representations. Our approach is capable of discovering underlying plans that are not from plan libraries, without requiring domain models provided. We empirically demonstrate the effectiveness of our approach by comparing its performance to traditional plan recognition approaches in three planning domains.
Compositional Memory for Visual Question Answering
Venue: arXiv cs.CV 2015
Year: 2015

Visual Question Answering (VQA) emerges as one of the most fascinating topics in computer vision recently. Many state of the art methods naively use holistic visual features with language features into a Long Short-Term Memory (LSTM) module, neglecting the sophisticated interaction between them. This coarse modeling also blocks the possibilities of exploring finer-grained local features that contribute to the question answering dynamically over time. This paper addresses this fundamental problem by directly modeling the temporal dynamics between language and all possible local image patches. When traversing the question words sequentially, our end-to-end approach explicitly fuses the features associated to the words and the ones available at multiple local patches in an attention mechanism, and further combines the fused information to generate dynamic messages, which we call episode. We then feed the episodes to a standard question answering module together with the contextual visual information and linguistic information. Motivated by recent practices in deep learning, we use auxiliary loss functions during training to improve the performance. Our experiments on two latest public datasets suggest that our method has a superior performance. Notably, on the DARQUAR dataset we advanced the state of the art by 6$\%$, and we also evaluated our approach on the most recent MSCOCO-VQA dataset.
Using Abduction in Markov Logic Networks for Root Cause Analysis
Venue: arXiv cs.AI 2015
Year: 2015

IT infrastructure is a crucial part in most of today's business operations. High availability and reliability, and short response times to outages are essential. Thus a high amount of tool support and automation in risk management is desirable to decrease outages. We propose a new approach for calculating the root cause for an observed failure in an IT infrastructure. Our approach is based on Abduction in Markov Logic Networks. Abduction aims to find an explanation for a given observation in the light of some background knowledge. In failure diagnosis, the explanation corresponds to the root cause, the observation to the failure of a component, and the background knowledge to the dependency graph extended by potential risks. We apply a method to extend a Markov Logic Network in order to conduct abductive reasoning, which is not naturally supported in this formalism. Our approach exhibits a high amount of reusability and enables users without specific knowledge of a concrete infrastructure to gain viable insights in the case of an incident. We implemented the method in a tool and illustrate its suitability for root cause analysis by applying it to a sample scenario.
Contextual equivalences in configuration structures and reversibility
Venue: arXiv cs.LO 2015
Year: 2015

Contextual equivalence equate terms that have the same observable behaviour in any context. A standard contextual equivalence for CCS is the strong barbed congruence. Configuration structures are a denotational semantics for processes in which one define equivalences that are more discriminating, i.e. that distinguish the denotation of terms equated by barbed congruence. Hereditary history preserving bisimulation (HHPB) is such a relation. We define a strong back-and-forth barbed congruence on RCCS, a reversible variant of CCS. We show that the relation induced by the back-and-forth congruence on configuration structures is equivalent to HHPB, thus providing a contextual characterization of HHPB.
Maximum Hands-off Control without Normality Assumption
Venue: arXiv cs.SY 2015
Year: 2015

Maximum hands-off control is a control that has the minimum L0 norm among all feasible controls. It is known that the maximum hands-off (or L0-optimal) control problem is equivalent to the L1-optimal control under the assumption of normality. In this article, we analyze the maximum hands-off control for linear time-invariant systems without the normality assumption. For this purpose, we introduce the Lp-optimal control with 0<p<1, which is a natural relaxation of the L0 problem. By using this, we investigate the existence and the bang-off-bang property (i.e. the control takes values of 1, 0 and -1) of the maximum hands-off control. We then describe a general relation between the maximum hands-off control and the L1-optimal control. We also prove the continuity and convexity property of the value function, which plays an important role to prove the stability when the (finite-horizon) control is extended to model predictive control.
The Paillier's Cryptosystem and Some Variants Revisited
Venue: arXiv cs.CR 2015
Year: 2015

At Eurocrypt'99, Paillier presented a public-key cryptosystem based on a novel computational problem. It has interested many researchers because it was additively homomorphic. In this paper, we show that there is a big difference between the original Paillier's encryption and some variants. The Paillier's encryption can be naturally transformed into a signature scheme but these variants miss the feature. In particular, we simplify the alternative decryption procedure of Bresson-Catalano-Pointcheval encryption scheme proposed at Asiacrypt'03. The new version is more applicable to cloud computing because of its double trapdoor decryption mechanism and its flexibility to be integrated into other cryptographic schemes. It captures a new feature that its two groups of secret keys can be distributed to different users so as to enhance the robustness of key management.
Problems with the use of Web search engines to find results in foreign languages
Venue: arXiv cs.IR 2015
Year: 2015

Purpose - To test the ability of major search engines, Google, Yahoo, MSN, and Ask, to distinguish between German and English-language documents Design/methodology/approach - 50 queries, using words common in German and in English, were posed to the engines. The advanced search option of language restriction was used, once in German and once in English. The first 20 results per engine in each language were investigated. Findings - While none of the search engines faces problems in providing results in the language of the interface that is used, both Google and MSN face problems when the results are restricted to a foreign language. Research limitations/implications - Search engines were only tested in German and in English. We have only anecdotal evidence that the problems are the same with other languages. Practical implications - Searchers should not use the language restriction in Google and MSN when searching for foreign-language documents. Instead, searchers should use Yahoo or Ask. If searching for foreign language documents in Google or MSN, the interface in the target language/country should be used. Value of paper - Demonstrates a problem with search engines that has not been previously investigated.
The relationship between internet user type and user performance when carrying out simple vs. complex search tasks
Venue: arXiv cs.IR 2015
Year: 2015

It is widely known that people become better at an activity if they perform this activity long and often. Yet, the question is whether being active in related areas like communicating online, writing blog articles or commenting on community forums have an impact on a persons ability to perform Web searches, is still unanswered. Web searching has become a key task conducted online; in this paper we present our findings on whether the user type, which categorises a persons online activities, has an impact on her or his search capabilities. We show (1) the characteristics of different user types when carrying out simple search tasks; (2) their characteristics when carrying out complex search tasks; and, (3) the significantly different user type characteristics between simple and complex search tasks. The results are based on an experiment with 56 ordinary Web users in a laboratory environment. The Search-Logger study framework was used to analyze and measure user behavior when carrying out a set of 12 predefined search tasks. Our findings include the fact that depending on task type (simple or complex) significant differences can be observed between users of different types.
Measuring Responsiveness in the Online Public Sphere for the 2016 U.S. Election: Concepts
Venue: arXiv cs.CY 2015
Year: 2015

The election narrative is formed under the competitions of ideas among critical players involving politicians, news media, public influentials, and the general public. Untangling the complex process of narrative formation, however, is no easy task due to implicit influences among the key players. This paper outlines a conceptual framework to untangle this complex process. We propose the problem of measuring "responsiveness" that quantifies a player's influence on another given a specific election topic over time. In particular, we make use of multivariate Hawkes Process to infer the influence network between pairs of election players. We demonstrate an early version system of analytic pipeline of online public sphere discussions from data ingestion, influence inference, to visualization. The paper concludes by showcasing some preliminary results based on Twitter and news media election-related contents from July to October 2015 and discussing plans for future research.
Studying the control of non invasive prosthetic hands over large time spans
Venue: arXiv cs.LG 2015
Year: 2015

The electromyography (EMG) signal is the electrical manifestation of a neuromuscular activation that provides access to physiological processes which cause the muscle to generate force and produce movement. Non invasive prostheses use such signals detected by the electrodes placed on the user's stump, as input to generate hand posture movements according to the intentions of the prosthesis wearer. The aim of this pilot study is to explore the repeatability issue, i.e. the ability to classify 17 different hand postures, represented by EMG signal, across a time span of days by a control algorithm. Data collection experiments lasted four days and signals were collected from the forearm of a single subject. We find that Support Vector Machine (SVM) classification results are high enough to guarantee a correct classification of more than 10 postures in each moment of the considered time span.
Designing for Collaborative Sensemaking: Using Expert & Non-Expert Crowd
Venue: arXiv cs.HC 2015
Year: 2015

Crime solving is a domain where solution discovery is often serendipitous. Unstructured mechanisms, like Reddit, for crime solving through crowds have failed so far. Mechanisms, collaborations, workflows, and micro-tasks necessary for successful crime solving might also vary across different crimes. Cognitively, while experts might have deeper domain knowledge, they might also fall prey to biased analysis. Non-experts, while lacking formal training, might instead offer non-conventional perspectives requiring direction. The analytical process is itself an iterative process of foraging and sensemaking. Users would explore to broaden solution space and narrow down to a solution iteratively until identifying the global maxima instead of local maxima. In this proposal, my research aims to design systems for enabling complex sensemaking tasks that require collaboration between remotely located non-expert crowds with expert crowds to compensate for their cognitive challenges and lack of training. This would require better understanding of the structure, workflow, and micro-tasks necessary for successful collaborations. This proposal builds upon previous work on collaborative sensemaking between remote partners in lab experiments and endeavors to scale it across multiple team members, with varying expertise levels.
Transfer Learning for Speech and Language Processing
Venue: arXiv cs.CL 2015
Year: 2015

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.
Interference Management with Partial Uplink/Downlink Spectrum Overlap
Venue: arXiv cs.IT 2015
Year: 2015

Simultaneous reuse of spectral resources by uplink and downlink, denoted as in-band full duplex (FD) communication, is promoted to double the spectral efficiency when compared to its half-duplex (HD) counterpart. Interference management, however, remains challenging in FD cellular networks, especially when high disparity between uplink and downlink transmission powers exists. The uplink performance can be particularly deteriorated when operating on channels that are simultaneously occupied with downlink transmission. This paper considers a cellular wireless system with partial spectrum overlap between the downlink and uplink. The performance of the system becomes, therefore, a function of the overlap fraction, as well as the power level of both the uplink and downlink transmissions. The paper considers the problem of maximizing an overall network utility to find the uplink/downlink transmission powers and the spectrum overlap fraction between the uplink and downlink spectrum in each cell, and proposes solving the problem using interior point method. Simulations results confirm the vulnerability of the uplink performance to the FD operation, and show the superiority of the proposed scheme over the FD and HD schemes. The results further show that explicit uplink and downlink performance should be considered for efficient design of cellular networks with overlapping uplink/downlink resources.
Towards Open Set Deep Networks
Venue: arXiv cs.CV 2015
Year: 2015

Deep networks have produced significant gains for various visual recognition problems, leading to high impact academic and commercial applications. Recent work in deep networks highlighted that it is easy to generate images that humans would never classify as a particular object class, yet networks classify such images high confidence as that given class - deep network are easily fooled with images humans do not consider meaningful. The closed set nature of deep networks forces them to choose from one of the known classes leading to such artifacts. Recognition in the real world is open set, i.e. the recognition system should reject unknown/unseen classes at test time. We present a methodology to adapt deep networks for open set recognition, by introducing a new model layer, OpenMax, which estimates the probability of an input being from an unknown class. A key element of estimating the unknown probability is adapting Meta-Recognition concepts to the activation patterns in the penultimate layer of the network. OpenMax allows rejection of "fooling" and unrelated open set images presented to the system; OpenMax greatly reduces the number of obvious errors made by a deep network. We prove that the OpenMax concept provides bounded open space risk, thereby formally providing an open set recognition solution. We evaluate the resulting open set deep networks using pre-trained networks from the Caffe Model-zoo on ImageNet 2012 validation data, and thousands of fooling and open set images. The proposed OpenMax model significantly outperforms open set recognition accuracy of basic deep networks as well as deep networks with thresholding of SoftMax probabilities.
Unlicensed LTE/WiFi Coexistence: Is LBT Inherently Fairer Than CSAT?
Venue: arXiv cs.NI 2015
Year: 2015

Ensuring fair co-existence between unlicensed LTE and WiFi networks is currently of major concern to both cellular operators and WiFi providers. Two main unlicensed LTE approaches currently being discussed, namely Carrier Sense Adaptive Transmission (CSAT) and Listen Before Talk (LBT). While these mechanisms differ in their compatibility with existing LTE specifications and regulatory compliance in different countries, they also use fundamentally different approaches to access the channel. Nevertheless, we show in this article that when optimally configured both approaches are capable of providing the same level of fairness to WiFi and that the choice between CSAT and LBT is solely driven by the LTE operator's interests.
Critical Parameters in Particle Swarm Optimisation
Venue: arXiv cs.NE 2015
Year: 2015

Particle swarm optimisation is a metaheuristic algorithm which finds reasonable solutions in a wide range of applied problems if suitable parameters are used. We study the properties of the algorithm in the framework of random dynamical systems which, due to the quasi-linear swarm dynamics, yields analytical results for the stability properties of the particles. Such considerations predict a relationship between the parameters of the algorithm that marks the edge between convergent and divergent behaviours. Comparison with simulations indicates that the algorithm performs best near this margin of instability.
Harvesting comparable corpora and mining them for equivalent bilingual sentences using statistical classification and analogy- based heuristics
Venue: arXiv cs.CL 2015
Year: 2015

Parallel sentences are a relatively scarce but extremely useful resource for many applications including cross-lingual retrieval and statistical machine translation. This research explores our new methodologies for mining such data from previously obtained comparable corpora. The task is highly practical since non-parallel multilingual data exist in far greater quantities than parallel corpora, but parallel sentences are a much more useful resource. Here we propose a web crawling method for building subject-aligned comparable corpora from e.g. Wikipedia dumps and Euronews web page. The improvements in machine translation are shown on Polish-English language pair for various text domains. We also tested another method of building parallel corpora based on comparable corpora data. It lets automatically broad existing corpus of sentences from subject of corpora based on analogies between them.