OpenRaiser/Intern-Atlas huggingface dataset:
Beam Search for Learning a Deep Convolutional Neural Network of 3D Shapes
Venue: arXiv cs.CV 2016
Year: 2016

This paper addresses 3D shape recognition. Recent work typically represents a 3D shape as a set of binary variables corresponding to 3D voxels of a uniform 3D grid centered on the shape, and resorts to deep convolutional neural networks(CNNs) for modeling these binary variables. Robust learning of such CNNs is currently limited by the small datasets of 3D shapes available, an order of magnitude smaller than other common datasets in computer vision. Related work typically deals with the small training datasets using a number of ad hoc, hand-tuning strategies. To address this issue, we formulate CNN learning as a beam search aimed at identifying an optimal CNN architecture, namely, the number of layers, nodes, and their connectivity in the network, as well as estimating parameters of such an optimal CNN. Each state of the beam search corresponds to a candidate CNN. Two types of actions are defined to add new convolutional filters or new convolutional layers to a parent CNN, and thus transition to children states. The utility function of each action is efficiently computed by transferring parameter values of the parent CNN to its children, thereby enabling an efficient beam search. Our experimental evaluation on the 3D ModelNet dataset demonstrates that our model pursuit using the beam search yields a CNN with superior performance on 3D shape classification than the state of the art.
Crowdsourced Outcome Determination in Prediction Markets
Venue: arXiv cs.AI 2016
Year: 2016

A prediction market is a useful means of aggregating information about a future event. To function, the market needs a trusted entity who will verify the true outcome in the end. Motivated by the recent introduction of decentralized prediction markets, we introduce a mechanism that allows for the outcome to be determined by the votes of a group of arbiters who may themselves hold stakes in the market. Despite the potential conflict of interest, we derive conditions under which we can incentivize arbiters to vote truthfully by using funds raised from market fees to implement a peer prediction mechanism. Finally, we investigate what parameter values could be used in a real-world implementation of our mechanism.
Prerequisites for International Exchanges of Health Information: Comparison of Australian, Austrian, Finnish, Swiss, and US Privacy Policies
Venue: arXiv cs.CY 2016
Year: 2016

Capabilities to exchange health information are critical to accelerate discovery and its diffusion to healthcare practice. However, the same ethical and legal policies that protect privacy hinder these data exchanges, and the issues accumulate if moving data across geographical or organizational borders. This can be seen as one of the reasons why many health technologies and research findings are limited to very narrow domains. In this paper, we compare how using and disclosing personal data for research purposes is addressed in Australian, Austrian, Finnish, Swiss, and US policies with a focus on text data analytics. Our goal is to identify approaches and issues that enable or hinder international health information exchanges. As expected, the policies within each country are not as diverse as across countries. Most policies apply the principles of accountability and/or adequacy and are thereby fundamentally similar. Their following requirements create complications with re-using and re-disclosing data and even secondary data: 1) informing data subjects about the purposes of data collection and use, before the dataset is collected; 2) assurance that the subjects are no longer identifiable; and 3) destruction of data when the research activities are finished. Using storage and compute cloud services as well as other exchange technologies on the Internet without proper permissions is technically not allowed if the data are stored in another country. Both legislation and technologies are available as vehicles for overcoming these barriers. The resulting richness in information variety will contribute to the development and evaluation of new clinical hypotheses and technologies.
Music Generation with Deep Learning
Venue: arXiv cs.SD 2016
Year: 2016

The use of deep learning to solve problems in literary arts has been a recent trend that has gained a lot of attention and automated generation of music has been an active area. This project deals with the generation of music using raw audio files in the frequency domain relying on various LSTM architectures. Fully connected and convolutional layers are used along with LSTM's to capture rich features in the frequency domain and increase the quality of music generated. The work is focused on unconstrained music generation and uses no information about musical structure(notes or chords) to aid learning.The music generated from various architectures are compared using blind fold tests. Using the raw audio to train models is the direction to tapping the enormous amount of mp3 files that exist over the internet without requiring the manual effort to make structured MIDI files. Moreover, not all audio files can be represented with MIDI files making the study of these models an interesting prospect to the future of such models.
State Estimation with Secrecy against Eavesdroppers
Venue: arXiv cs.SY 2016
Year: 2016

We study the problem of remote state estimation, in the presence of an eavesdropper. An authorized user estimates the state of a linear plant, based on the data received from a sensor, while the data may also be intercepted by the eavesdropper. To maintain confidentiality with respect to state, we introduce a novel control-theoretic definition of perfect secrecy requiring that the user's expected error remains bounded while the eavesdropper's expected error grows unbounded. We propose a secrecy mechanism which guarantees perfect secrecy by randomly withholding sensor information, under the condition that the user's packet reception rate is larger than the eavesdropper's interception rate. Given this mechanism, we also explore the tradeoff between user's utility and confidentiality with respect to the eavesdropper, via an optimization problem. Finally, some examples are studied to provide insights about this tradeoff.
Live Score Following on Sheet Music Images
Venue: arXiv cs.SD 2016
Year: 2016

In this demo we show a novel approach to score following. Instead of relying on some symbolic representation, we are using a multi-modal convolutional neural network to match the incoming audio stream directly to sheet music images. This approach is in an early stage and should be seen as proof of concept. Nonetheless, the audience will have the opportunity to test our implementation themselves via 3 simple piano pieces.
LIA system description for NIST SRE 2016
Venue: arXiv cs.SD 2016
Year: 2016

This paper describes the LIA speaker recognition system developed for the Speaker Recognition Evaluation (SRE) campaign. Eight sub-systems are developed, all based on a state-of-the-art approach: i-vector/PLDA which represents the mainstream technique in text-independent speaker recognition. These sub-systems differ: on the acoustic feature extraction front-end (MFCC, PLP), at the i-vector extraction stage (UBM, DNN or two-feats posteriors) and finally on the data-shifting (IDVC, mean-shifting). The submitted system is a fusion at the score-level of these eight sub-systems.
Visual Compiler: Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator
Venue: arXiv cs.CV 2016
Year: 2016

We introduce the concept of a Visual Compiler that generates a scene specific pedestrian detector and pose estimator without any pedestrian observations. Given a single image and auxiliary scene information in the form of camera parameters and geometric layout of the scene, the Visual Compiler first infers geometrically and photometrically accurate images of humans in that scene through the use of computer graphics rendering. Using these renders we learn a scene-and-region specific spatially-varying fully convolutional neural network, for simultaneous detection, pose estimation and segmentation of pedestrians. We demonstrate that when real human annotated data is scarce or non-existent, our data generation strategy can provide an excellent solution for bootstrapping human detection and pose estimation. Experimental results show that our approach outperforms off-the-shelf state-of-the-art pedestrian detectors and pose estimators that are trained on real data.
Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
Venue: arXiv cs.CL 2016
Year: 2016

Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually. However, traditional sentence classification approaches have been shown to greatly benefit from jointly classifying subsequent sentences, such as with conditional random fields. In this work, we present an ANN architecture that combines the effectiveness of typical ANN models to classify sentences in isolation, with the strength of structured prediction. Our model achieves state-of-the-art results on two different datasets for sequential sentence classification in medical abstracts.
16 QAM Communication with 1-Bit Transmitters
Venue: arXiv cs.IT 2016
Year: 2016

We present a novel non-linear precoding technique for the transmission of 16 quadrature amplitude modulation (QAM) symbols in a 1-bit massive multi-user (MU) multipleinput- single-output (MISO) downlink system. We deploy low resolution digital-to-analog converters (DACs) at the transmitter for the sake of decreasing the high energy consumption related to the massive MISO system. To mitigate the multi-user interference (MUI) and the distortions due to the low resolution DACs, the minimum bit error ratio (MBER) precoder was introduced in previous work. However, this precoder technique is restricted to quadrature phase shift keying (QPSK) signaling. Our approach consists in upgrading this method to the transmission of 16 QAM symbols. Simulation results show that the performance in terms of uncoded BER is significantly improved for larger massive MISO gain.
Deep Residual Hashing
Venue: arXiv cs.CV 2016
Year: 2016

Hashing aims at generating highly compact similarity preserving code words which are well suited for large-scale image retrieval tasks. Most existing hashing methods first encode the images as a vector of hand-crafted features followed by a separate binarization step to generate hash codes. This two-stage process may produce sub-optimal encoding. In this paper, for the first time, we propose a deep architecture for supervised hashing through residual learning, termed Deep Residual Hashing (DRH), for an end-to-end simultaneous representation learning and hash coding. The DRH model constitutes four key elements: (1) a sub-network with multiple stacked residual blocks; (2) hashing layer for binarization; (3) supervised retrieval loss function based on neighbourhood component analysis for similarity preserving embedding; and (4) hashing related losses and regularisation to control the quantization error and improve the quality of hash coding. We present results of extensive experiments on a large public chest x-ray image database with co-morbidities and discuss the outcome showing substantial improvements over the latest state-of-the art methods.
Basis-Function Modeling of Loudness Variations in Ensemble Performance
Venue: arXiv cs.SD 2016
Year: 2016

This paper describes a computational model of loudness variations in expressive ensemble performance. The model predicts and explains the continuous variation of loudness as a function of information extracted automatically from the written score. Although such models have been proposed for expressive performance in solo instruments, this is (to the best of our knowledge) the first attempt to define a model for expressive performance in ensembles. To that end, we extend an existing model that was designed to model expressive piano performances, and describe the additional steps necessary for the model to deal with scores of arbitrary instrumentation, including orchestral scores. We test both linear and non-linear variants of the extended model n a data set of audio recordings of symphonic music, in a leave-one-out setting. The experiments reveal that the most successful model variant is a recurrent, non-linear model. Even if the accuracy of the predicted loudness varies from one recording to another, in several cases the model explains well over 50% of the variance in loudness.
Verification of Data-aware Business Processes in the Presence of Ontologies
Venue: arXiv cs.LO 2016
Year: 2016

The meet up between data, processes and structural knowledge in modeling enterprise systems is a challenging task that has led to the study of combining formalisms from knowledge representation, database theory, and process management. To ensure system correctness, formal verification also comes into play and offers well-established techniques. In line with this, significant results have been obtained within the research on data-aware business processes (DABP), which studies the marriage between static and dynamic aspects of a system within a unified framework. Here, we investigate the verification of DABP in the presence of ontologies, and provide the following contributions: (1) We propose a formal framework called Golog-KABs (GKABs), by leveraging on the state of the art formalisms for DABP equipped with ontologies. GKABs enable us to specify semantically-rich DABP, where the system dynamics are specified using a high-level action language inspired by the Golog programming language. (2) We propose a parametric execution semantics for GKABs that is able to elegantly accommodate a plethora of inconsistency-aware semantics based on the well-known notion of repair, and this leads us to several variants of inconsistency-aware GKABs. (3) We enhance GKABs towards context-sensitive GKABs. (4) We introduce the so-called Alternating-GKABs that allow for a more fine-grained analysis over the systems. (5) We introduce a novel framework called Semantically-Enhanced Data-Aware Processes (SEDAPs) that, by utilizing ontologies, enable us to have a high-level conceptual view over the evolution of the underlying system. We also provide reductions for the verification of sophisticated first-order temporal properties over all of the settings above, and show that verification can be addressed using existing techniques developed for Data-Centric Dynamic Systems (a well-established DABP framework).
On-bird Sound Recordings: Automatic Acoustic Recognition of Activities and Contexts
Venue: arXiv cs.SD 2016
Year: 2016

We introduce a novel approach to studying animal behaviour and the context in which it occurs, through the use of microphone backpacks carried on the backs of individual free-flying birds. These sensors are increasingly used by animal behaviour researchers to study individual vocalisations of freely behaving animals, even in the field. However such devices may record more than an animals vocal behaviour, and have the potential to be used for investigating specific activities (movement) and context (background) within which vocalisations occur. To facilitate this approach, we investigate the automatic annotation of such recordings through two different sound scene analysis paradigms: a scene-classification method using feature learning, and an event-detection method using probabilistic latent component analysis (PLCA). We analyse recordings made with Eurasian jackdaws (Corvus monedula) in both captive and field settings. Results are comparable with the state of the art in sound scene analysis; we find that the current recognition quality level enables scalable automatic annotation of audio logger data, given partial annotation, but also find that individual differences between animals and/or their backpacks limit the generalisation from one individual to another. we consider the interrelation of 'scenes' and 'events' in this particular task, and issues of temporal resolution.
Neural Networks Classifier for Data Selection in Statistical Machine Translation
Venue: arXiv cs.CL 2016
Year: 2016

We address the data selection problem in statistical machine translation (SMT) as a classification task. The new data selection method is based on a neural network classifier. We present a new method description and empirical results proving that our data selection method provides better translation quality, compared to a state-of-the-art method (i.e., Cross entropy). Moreover, the empirical results reported are coherent across different language pairs.
Delta Networks for Optimized Recurrent Network Computation
Venue: arXiv cs.NE 2016
Year: 2016

Many neural networks exhibit stability in their activation patterns over time in response to inputs from sensors operating under real-world conditions. By capitalizing on this property of natural signals, we propose a Recurrent Neural Network (RNN) architecture called a delta network in which each neuron transmits its value only when the change in its activation exceeds a threshold. The execution of RNNs as delta networks is attractive because their states must be stored and fetched at every timestep, unlike in convolutional neural networks (CNNs). We show that a naive run-time delta network implementation offers modest improvements on the number of memory accesses and computes, but optimized training techniques confer higher accuracy at higher speedup. With these optimizations, we demonstrate a 9X reduction in cost with negligible loss of accuracy for the TIDIGITS audio digit recognition benchmark. Similarly, on the large Wall Street Journal speech recognition benchmark even existing networks can be greatly accelerated as delta networks, and a 5.7x improvement with negligible loss of accuracy can be obtained through training. Finally, on an end-to-end CNN trained for steering angle prediction in a driving dataset, the RNN cost can be reduced by a substantial 100X.
A Relative Study of Task Scheduling Algorithms in Cloud Computing Environment
Venue: arXiv cs.DC 2016
Year: 2016

Cloud Computing is a paradigm of both parallel processing and distributed computing. It offers computing facilities as a utility service in pay as par use manner. Virtualization, self service provisioning, elasticity and pay per use are the key features of Cloud Computing. It provides different types of resources over the Internet to perform user submitted tasks. In cloud environment, huge number of tasks are executed simultaneously, an effective Task Scheduling is required to gain better performance of the cloud system. Various Cloud Based Task Scheduling algorithms are available that schedule the task of user to resources for execution. Due to the novelty of Cloud Computing, traditional scheduling algorithms cannot satisfy the needs of cloud , the researchers are trying to modify traditional algorithms that can fulfill the cloud requirements like rapid elasticity, resource pooling and on demand self service. In this paper the current state of Task Scheduling algorithms has been discussed and compared on the basis of various scheduling parameters like execution time, throughput, make span, resource utilization, quality of service, energy consumption, response time and cost.
FPA-Debug: Effective Statistical Fault Localization Considering Fault-proneness Analysis
Venue: arXiv cs.SE 2016
Year: 2016

The aim is to identify faulty predicates which have strong effect on program failure. Statistical debugging techniques are amongst best methods for pinpointing defects within the program source code. However, they have some drawbacks. They require a large number of executions to identify faults, they might be adversely affected by coincidental correctness, and they do not take into consideration fault-proneness associated with different parts of the program code while constructing behavioral models. Additionally, they do not consider the simultaneous impact of predicates on program termination status. To deal with mentioned problems, a new fault-proneness-aware approach based on elastic net regression, namely FPA-Debug has been proposed in this paper. FPA-Debug employs a clustering-based strategy to alleviate coincidental correctness in fault localization and finds the smallest effective subset of program predicates known as bug predictors. Moreover, the approach considers fault-proneness of code during statistical modeling through applying different regularization parameter to each program predicates depending on its location within program source code. The experimental results on well-known test suite, Siemens, reveal the effectiveness and accuracy of the FPA-Debug.
Predicting Completeness in Knowledge Bases
Venue: arXiv cs.DB 2016
Year: 2016

Knowledge bases such as Wikidata, DBpedia, or YAGO contain millions of entities and facts. In some knowledge bases, the correctness of these facts has been evaluated. However, much less is known about their completeness, i.e., the proportion of real facts that the knowledge bases cover. In this work, we investigate different signals to identify the areas where a knowledge base is complete. We show that we can combine these signals in a rule mining approach, which allows us to predict where facts may be missing. We also show that completeness predictions can help other applications such as fact prediction.
Cograph generation with linear delay
Venue: arXiv cs.DM 2016
Year: 2016

Cographs have always been a research target in areas such as coloring, graph decomposition, and spectral theory. In this work, we present an algorithm to generate all unlabeled cographs with $n$ vertices, based on the generation of cotrees. The delay of our algorithm (time spent between two consecutive outputs) is $O(n)$. The time needed to generate the first output is also $O(n)$, which gives an overall $O(n\,M_n)$ time complexity, where $M_n$ is the number of unlabeled cographs with $n$ vertices. The algorithm avoids the generation of duplicates (isomorphic outputs) and produces, as a by-product, a linear ordering of unlabeled cographs wih $n$ vertices.
EgoTransfer: Transferring Motion Across Egocentric and Exocentric Domains using Deep Neural Networks
Venue: arXiv cs.CV 2016
Year: 2016

Mirror neurons have been observed in the primary motor cortex of primate species, in particular in humans and monkeys. A mirror neuron fires when a person performs a certain action, and also when he observes the same action being performed by another person. A crucial step towards building fully autonomous intelligent systems with human-like learning abilities is the capability in modeling the mirror neuron. On one hand, the abundance of egocentric cameras in the past few years has offered the opportunity to study a lot of vision problems from the first-person perspective. A great deal of interesting research has been done during the past few years, trying to explore various computer vision tasks from the perspective of the self. On the other hand, videos recorded by traditional static cameras, capture humans performing different actions from an exocentric third-person perspective. In this work, we take the first step towards relating motion information across these two perspectives. We train models that predict motion in an egocentric view, by observing it from an exocentric view, and vice versa. This allows models to predict how an egocentric motion would look like from outside. To do so, we train linear and nonlinear models and evaluate their performance in terms of retrieving the egocentric (exocentric) motion features, while having access to an exocentric (egocentric) motion feature. Our experimental results demonstrate that motion information can be successfully transferred across the two views.