OpenRaiser/Intern-Atlas huggingface dataset:
Piano Transcription in the Studio Using an Extensible Alternating Directions Framework
Venue: arXiv cs.SD 2016
Year: 2016

Given a musical audio recording, the goal of automatic music transcription is to determine a score-like representation of the piece underlying the recording. Despite significant interest within the research community, several studies have reported on a 'glass ceiling' effect, an apparent limit on the transcription accuracy that current methods seem incapable of overcoming. In this paper, we explore how much this effect can be mitigated by focusing on a specific instrument class and making use of additional information on the recording conditions available in studio or home recording scenarios. In particular, exploiting the availability of single note recordings for the instrument in use we develop a novel signal model employing variable-length spectro-temporal patterns as its central building blocks - tailored for pitched percussive instruments such as the piano. Temporal dependencies between spectral templates are modeled, resembling characteristics of factorial scaled hidden Markov models (FS-HMM) and other methods combining Non-Negative Matrix Factorization with Markov processes. In contrast to FS-HMMs, our parameter estimation is developed in a global, relaxed form within the extensible alternating direction method of multipliers (ADMM) framework, which enables the systematic combination of basic regularizers propagating sparsity and local stationarity in note activity with more complex regularizers imposing temporal semantics. The proposed method achieves an f-measure of 93-95% for note onsets on pieces recorded on a Yamaha Disklavier (MAPS DB).
Unified Framework for Quantification
Venue: arXiv cs.LG 2016
Year: 2016

Quantification is the machine learning task of estimating test-data class proportions that are not necessarily similar to those in training. Apart from its intrinsic value as an aggregate statistic, quantification output can also be used to optimize classifier probabilities, thereby increasing classification accuracy. We unify major quantification approaches under a constrained multi-variate regression framework, and use mathematical programming to estimate class proportions for different loss functions. With this modeling approach, we extend existing binary-only quantification approaches to multi-class settings as well. We empirically verify our unified framework by experimenting with several multi-class datasets including the Stanford Sentiment Treebank and CIFAR-10.
ICTs Effect on Parents Feelings of Presence, Awareness, and Connectedness during a Childs Hospitalization
Venue: arXiv cs.CY 2016
Year: 2016

This study evaluates how off-the-shelf commercial ICTs can contribute to creating a feeling of Presence, Connectedness, and Awareness between parents and their hospitalized child. Thematic analysis and descriptive statistics are used to analyse qualitative and quantitative data collected through a survey of thirty eight parents whose children were admitted to the Royal Childrens Hospital. Through analysis of data, Presence is found to be less facilitated through ICT than are Awareness and Connectedness. Although participants reported that voice call on mobile phones was the most common way of communication, their ideal was a video-chat application such as Skype, or a combination of Skype and TV to facilitate feeling of Presence. A strong desire to use rich media such as video-audio to help them have a greater feeling of Presence was identified in parents responses to the questionnaire.
Factors of people-centric security climate: conceptual model and exploratory study in Vietnam
Venue: arXiv cs.CY 2016
Year: 2016

There is an increasing focus on the persuasive approach to develop a people-centric security climate where employees are aware of the priority of security and perform conscious security behaviour proactively. Employees can evaluate the priority of security as they observe and interact with the security features that constitute the security climate of the workplace. We examined the fundamental challenge that not every employee could recognise those features. In this multi-stage research, we adopted the theoretical lens of symbolic interactionism to advance a conceptual model which explains the relationship between organisation's social networks and the formation of information security climate. A descriptive case study in Vietnam was then conducted to refine the proposed model. The findings validated and extended the dimensions of information security climate, as well as identified the relevant organisation's social networks (i.e. information, affect, and power) that lead to its formation.
Supporting 'Good Habits' through User-Led Design of Food Safety Applications - Findings from a Survey of Red Meat Consumers
Venue: arXiv cs.CY 2016
Year: 2016

Mitigating consumer health risks and reducing food wastage has stimulated research into mechanisms for improving consumers' food safety knowledge and food management practice. Many studies report success, but differences in methodology and in the type and range of foods and consumers involved has made comparison and transferability of results challenging. While most studies advocate for the importance of information in consumer education, few provide detailed insight into what 'good' information means. Determining appropriate content, formats, and methods of delivery for different types of consumers as well as evaluating how different choices impact on consumers' food safety knowledge and behaviour remains unclear. Within a larger research project on enhancing provenance, stability and traceability of red meat value chains, this paper presents findings from a survey of Australian red meat consumers (n=217). It identifies consumers' food safety issues and reveals information and communication preferences that may support good safety habits with food.
Multipair Massive MIMO Relaying with Pilot-Data Transmission Overlay
Venue: arXiv cs.IT 2016
Year: 2016

We propose a pilot-data transmission overlay scheme for multipair massive multiple-input multiple-output (MIMO) relaying systems employing either half- or full-duplex (HD or FD) communications at the relay station (RS). In the proposed scheme, pilots are transmitted in partial overlap with data to decrease the channel estimation overhead. The RS can detect the source data with minimal destination pilot interference by exploiting the asymptotic orthogonality of massive MIMO channels. Then pilot-data interference can be effectively suppressed with assistance of the detected source data in the destination channel estimation. Due to the transmission overlay, the effective data period is extended, hence improving system throughput. Both theoretical and simulation results confirm that the proposed pilot-data overlay scheme outperforms the conventional separate pilot-data design in the limited coherence time interval scenario. Moreover, asymptotic analyses at high and low SNR regions demonstrate the superiority of the proposed scheme regardless of the coherence interval length. Because of simultaneous transmission, the proper allocation of source data transmission and relay data forwarding power can further improve the system performance. Hence a power allocation problem is formulated and a successive convex approximation approach is proposed to solve the non-convex optimization problem with the FD pilot-data transmission overlay.
A Graph Theoretical Analysis of Low-Power Coding Schemes for One-Hop Networks
Venue: arXiv cs.DM 2016
Year: 2016

Coding schemes with extremely low computational complexity are required for particular applications, such as wireless body area networks, in which case both very high data accuracy and very low power-consumption are required features. In this paper, coding schemes arising from incidence matrices of graphs are proposed. An analysis of the resilience of such codes to erasures is given using graph theoretical arguments; decodability of a graph is characterized in terms of the rank of its incidence matrix. Bounds are given on the number of decodable subgraphs of a graph and the number of edges that must be deleted in order to arrive at an undecodable subgraph. Algorithms to construct codes that are optimal with respect to these bounds are presented.
On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities
Venue: arXiv cs.CV 2016
Year: 2016

Current object recognition methods fail on object sets that include both diffuse, reflective and transparent materials, although they are very common in domestic scenarios. We show that a combination of cues from multiple sensor modalities, including specular reflectance and unavailable depth information, allows us to capture a larger subset of household objects by extending a state of the art object recognition method. This leads to a significant increase in robustness of recognition over a larger set of commonly used objects.
GRVI Phalanx: A Massively Parallel RISC-V FPGA Accelerator Accelerator
Venue: arXiv cs.AR 2016
Year: 2016

GRVI is an FPGA-efficient RISC-V RV32I soft processor. Phalanx is a parallel processor and accelerator array framework. Groups of processors and accelerators form shared memory clusters. Clusters are interconnected with each other and with extreme bandwidth I/O and memory devices by a 300- bit-wide Hoplite NOC. An example Kintex UltraScale KU040 system has 400 RISC-V cores, peak throughput of 100,000 MIPS, peak shared memory bandwidth of 600 GB/s, NOC bisection bandwidth of 700 Gbps, and uses 13 W.
Soft McEliece: MDPC code-based McEliece cryptosystems with very compact keys through real-valued intentional errors
Venue: arXiv cs.IT 2016
Year: 2016

We propose to use real-valued errors instead of classical bit flipping intentional errors in the McEliece cryptosystem based on moderate-density parity-check (MDPC) codes. This allows to exploit the error correcting capability of these codes to the utmost, by using soft-decision iterative decoding algorithms instead of hard-decision bit flipping decoders. However, soft reliability values resulting from the use of real-valued noise can also be exploited by attackers. We devise new attack procedures aimed at this, and compute the relevant work factors and security levels. We show that, for a fixed security level, these new systems achieve the shortest public key sizes ever reached, with a reduction up to 25% with respect to previous proposals.
Machine Learning for E-mail Spam Filtering: Review,Techniques and Trends
Venue: arXiv cs.LG 2016
Year: 2016

We present a comprehensive review of the most effective content-based e-mail spam filtering techniques. We focus primarily on Machine Learning-based spam filters and their variants, and report on a broad review ranging from surveying the relevant ideas, efforts, effectiveness, and the current progress. The initial exposition of the background examines the basics of e-mail spam filtering, the evolving nature of spam, spammers playing cat-and-mouse with e-mail service providers (ESPs), and the Machine Learning front in fighting spam. We conclude by measuring the impact of Machine Learning-based filters and explore the promising offshoots of latest developments.
Extraction of clinical information from the non-invasive fetal electrocardiogram
Venue: arXiv cs.CV 2016
Year: 2016

Estimation of the fetal heart rate (FHR) has gained interest in the last century, low heart rate variability has been studied to identify intrauterine growth restricted fetuses (prepartum), and abnormal FHR patterns have been associated with fetal distress during delivery (intrapartum). Several monitoring techniques have been proposed for FHR estimation, including auscultation and Doppler ultrasound. This thesis focuses on the extraction of the non-invasive fetal electrocardiogram (NI-FECG) recorded from a limited set of abdominal sensors. The main challenge with NI-FECG extraction techniques is the low signal-to-noise ratio of the FECG signal on the abdominal mixture signal which consists of a dominant maternal ECG component, FECG and noise. However the NI-FECG offers many advantages over the alternative fetal monitoring techniques, the most important one being the opportunity to enable morphological analysis of the FECG which is vital for determining whether an observed FHR event is normal or pathological. In order to advance the field of NI-FECG signal processing, the development of standardised public databases and benchmarking of a number of published and novel algorithms was necessary.
Property-driven State-Space Coarsening for Continuous Time Markov Chains
Venue: arXiv cs.SY 2016
Year: 2016

Dynamical systems with large state-spaces are often expensive to thoroughly explore experimentally. Coarse-graining methods aim to define simpler systems which are more amenable to analysis and exploration; most current methods, however, focus on a priori state aggregation based on similarities in transition rates, which is not necessarily reflected in similar behaviours at the level of trajectories. We propose a way to coarsen the state-space of a system which optimally preserves the satisfaction of a set of logical specifications about the system's trajectories. Our approach is based on Gaussian Process emulation and Multi-Dimensional Scaling, a dimensionality reduction technique which optimally preserves distances in non-Euclidean spaces. We show how to obtain low-dimensional visualisations of the system's state-space from the perspective of properties' satisfaction, and how to define macro-states which behave coherently with respect to the specifications. Our approach is illustrated on a non-trivial running example, showing promising performance and high computational efficiency.
Generic case completeness
Venue: arXiv cs.CC 2016
Year: 2016

In this note we introduce a notion of a generically (strongly generically) NP-complete problem and show that the randomized bounded version of the halting problem is strongly generically NP-complete.
Dependency Parsing as Head Selection
Venue: arXiv cs.CL 2016
Year: 2016

Conventional graph-based dependency parsers guarantee a tree structure both during training and inference. Instead, we formalize dependency parsing as the problem of independently selecting the head of each word in a sentence. Our model which we call \textsc{DeNSe} (as shorthand for {\bf De}pendency {\bf N}eural {\bf Se}lection) produces a distribution over possible heads for each word using features obtained from a bidirectional recurrent neural network. Without enforcing structural constraints during training, \textsc{DeNSe} generates (at inference time) trees for the overwhelming majority of sentences, while non-tree outputs can be adjusted with a maximum spanning tree algorithm. We evaluate \textsc{DeNSe} on four languages (English, Chinese, Czech, and German) with varying degrees of non-projectivity. Despite the simplicity of the approach, our parsers are on par with the state of the art.
A Survey of Research on Control of Teams of Small Robots in Military Operations
Venue: arXiv cs.RO 2016
Year: 2016

While a number of excellent review articles on military robots have appeared in existing literature, this paper focuses on a distinct sub-space of related problems: small military robots organized into moderately sized squads, operating in a ground combat environment. Specifically, we consider the following: - Command of practical small robots, comparable to current generation, small unmanned ground vehicles (e.g., PackBots) with limited computing and sensor payload, as opposed to larger vehicle-sized robots or micro-scale robots; - Utilization of moderately sized practical forces of 3-10 robots applicable to currently envisioned military ground operations; - Complex three-dimensional physical environments, such as urban areas or mountainous terrains and the inherent difficulties they impose, including limited and variable fields of observation, difficult navigation, and intermittent communication; - Adversarial environments where the active, intelligent enemy is the key consideration in determining the behavior of the robotic force; and - Purposeful, partly autonomous, coordinated behaviors that are necessary for such a robotic force to survive and complete missions; these are far more complex than, for example, formation control or field coverage behavior.
An Attentional Neural Conversation Model with Improved Specificity
Venue: arXiv cs.CL 2016
Year: 2016

In this paper we propose a neural conversation model for conducting dialogues. We demonstrate the use of this model to generate help desk responses, where users are asking questions about PC applications. Our model is distinguished by two characteristics. First, it models intention across turns with a recurrent network, and incorporates an attention model that is conditioned on the representation of intention. Secondly, it avoids generating non-specific responses by incorporating an IDF term in the objective function. The model is evaluated both as a pure generation model in which a help-desk response is generated from scratch, and as a retrieval model with performance measured using recall rates of the correct response. Experimental results indicate that the model outperforms previously proposed neural conversation architectures, and that using specificity in the objective function significantly improves performances for both generation and retrieval.
Weighted $\ell_1$-Minimization for Sparse Recovery under Arbitrary Prior Information
Venue: arXiv cs.IT 2016
Year: 2016

Weighted $\ell_1$-minimization has been studied as a technique for the reconstruction of a sparse signal from compressively sampled measurements when prior information about the signal, in the form of a support estimate, is available. In this work, we study the recovery conditions and the associated recovery guarantees of weighted $\ell_1$-minimization when arbitrarily many distinct weights are permitted. For example, such a setup might be used when one has multiple estimates for the support of a signal, and these estimates have varying degrees of accuracy. Our analysis yields an extension to existing works that assume only a single support estimate set upon which a constant weight is applied. We include numerical experiments, with both synthetic signals and real video data, that demonstrate the benefits of allowing non-uniform weights in the reconstruction procedure.
Gamify Employee Collaboration - A Critical Review of Gamification Elements in Social Software
Venue: arXiv cs.CY 2016
Year: 2016

Social software solutions in enterprises such as IBM Connections are said to have the potential to support communication and collaboration among employees. However, companies are faced to manage the adoption of such collaborative tools and therefore need to raise the employees' acceptance and motivation. To solve these problems, developers started to implement Gamification elements in social software tools, which aim to increase users' motivation. In this paper, we critically examine the status quo of the current market of leading social software solutions and seek to find out which Gamification approaches are implementated in these collaborative tools. Our findings show, that most of the major social collaboration solutions do not offer Gamification features by default, but leave the integration to a various number of third party plug-in vendors. Furthermore we identify a trend in which Gamification solutions majorly focus on rewarding quantitative improvement of work activities, neglecting qualitative performance. Subsequently, current solutions do not match recent findings in research and ignore risks that can lower the employees' motivation and work performance in the long run.
Implementation of real-time moving horizon estimation for robust air data sensor fault diagnosis in the RECONFIGURE benchmark
Venue: arXiv cs.SY 2016
Year: 2016

This paper presents robust fault diagnosis and estimation for the calibrated airspeed and angle-of-attack sensor faults in the RECONFIGURE benchmark. We adopt a low-order longitudinal model augmented with wind dynamics. In order to enhance sensitivity to faults in the presence of winds, we propose a constrained residual generator by formulating a constrained moving horizon estimation problem and exploiting the bounds of winds. The moving horizon estimation problem requires solving a nonlinear program in real time, which is challenging for flight control computers. This challenge is addressed by adopting an efficient structure-exploiting algorithm within a real-time iteration scheme. Specific approximations and simplifications are performed to enable the implementation of the algorithm using the Airbus graphical symbol library for industrial validation and verification. The simulation tests on the RECONFIGURE benchmark over different flight points and maneuvers show the efficacy of the proposed approach.
Knowledge Retention through Low-Tech Knowledge Sharing Channels in Loosely-Coupled Networks: A Human-Based Approach
Venue: arXiv cs.CY 2016
Year: 2016

This study examines a human-based approach for knowledge retention that is evolving through various knowledge sharing channels in a low-technology environment with a strong emphasis on social networks in a loosely-coupled inter-organisational government-industry collaboration focused on regional sustainability. Using social network analysis combined with interview and observational analysis, our results show that a combination of close-knit community ties and group interaction promote the development of strong personal networks that provide continued access to group memory to retain the groups knowledge.
Modelling Symbolic Music: Beyond the Piano Roll
Venue: arXiv cs.SD 2016
Year: 2016

In this paper, we consider the problem of probabilistically modelling symbolic music data. We introduce a representation which reduces polyphonic music to a univariate categorical sequence. In this way, we are able to apply state of the art natural language processing techniques, namely the long short-term memory sequence model. The representation we employ permits arbitrary rhythmic structure, which we assume to be given. We show that our model is effective on four out of four piano roll based benchmark datasets. We further improve our model by augmenting our training data set with transpositions of the original pieces through all musical keys, thereby convincingly advancing the state of the art on these benchmark problems. We also fit models to music which is unconstrained in its rhythmic structure, discuss the properties of this model, and provide musical samples which are more sophisticated than previously possible with this class of recurrent neural network sequence models. We also provide our newly preprocessed data set of non piano-roll music data.