OpenRaiser/Intern-Atlas huggingface dataset:
Employing Second-Order Circular Suprasegmental Hidden Markov Models to Enhance Speaker Identification Performance in Shouted Talking Environments
Venue: arXiv cs.SD 2017
Year: 2017

Speaker identification performance is almost perfect in neutral talking environments; however, the performance is deteriorated significantly in shouted talking environments. This work is devoted to proposing, implementing and evaluating new models called Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s) to alleviate the deteriorated performance in the shouted talking environments. These proposed models possess the characteristics of both Circular Suprasegmental Hidden Markov Models (CSPHMMs) and Second-Order Suprasegmental Hidden Markov Models (SPHMM2s). The results of this work show that CSPHMM2s outperform each of: First-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM1s), Second-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM2s) and First-Order Circular Suprasegmental Hidden Markov Models (CSPHMM1s) in the shouted talking environments. In such talking environments and using our collected speech database, average speaker identification performance based on LTRSPHMM1s, LTRSPHMM2s, CSPHMM1s and CSPHMM2s is 74.6%, 78.4%, 78.7% and 83.4%, respectively. Speaker identification performance obtained based on CSPHMM2s is close to that obtained based on subjective assessment by human listeners.
A Deep Multimodal Approach for Cold-start Music Recommendation
Venue: arXiv cs.IR 2017
Year: 2017

An increasing amount of digital music is being published daily. Music streaming services often ingest all available music, but this poses a challenge: how to recommend new artists for which prior knowledge is scarce? In this work we aim to address this so-called cold-start problem by combining text and audio information with user feedback data using deep network architectures. Our method is divided into three steps. First, artist embeddings are learned from biographies by combining semantics, text features, and aggregated usage data. Second, track embeddings are learned from the audio signal and available feedback data. Finally, artist and track embeddings are combined in a multimodal network. Results suggest that both splitting the recommendation problem between feature levels (i.e., artist metadata and audio track), and merging feature embeddings in a multimodal approach improve the accuracy of the recommendations.
Pattern of Internet Usage in Cyber Cafes in Manila: An Exploratory Study
Venue: arXiv cs.CY 2017
Year: 2017

This study determined the profile and pattern of Internet usage of respondents in cyber cafes in Manila. The study employed an exploratory-descriptive design in which a validated descriptive-survey form was used as the research instrument. Forty-seven cyber cafes in 14 districts in the City of Manila were randomly selected. There were 545 respondents. It was found that most of the respondents were Manila settlers (f = 368, 70%), students (f = 382, 73%), pursuing or had attained a college degree (f = 374, 72%), male (f = 356, 68%), young (19 and below) (f = 314, 60%), Roman Catholic (f = 423, 81%), single (f = 470, 90%), had a computer at home (f = 269, 51%), belonged to the middle-income class (f = 334, 64%), and used the Internet in the afternoon (f = 274, 50.3%,chi^2 = 113.98, DF = 2, p < 0.01) once to twice a week (f = 193, 36.9%, chi^2 = 90.04, DF = 3, p < 0.01). Frequency of visit of Internet users was not equally distributed during the week and Internet users showed the tendency to visit cyber cafes at a particular time of the day when grouped according to profile. The first hypothesis stated that frequency of visiting a cyber cafe would not be equally distributed during the week when grouped according to profile was accepted. The second hypothesis, which stated that respondents would not show a tendency to use the internet in a cyber cafe at a particular time of the day was rejected. The study also discusses the limitations and implications of the findings.
Dynamical selection of Nash equilibria using Experience Weighted Attraction Learning: emergence of heterogeneous mixed equilibria
Venue: arXiv cs.GT 2017
Year: 2017

We study the distribution of strategies in a large game that models how agents choose among different double auction markets. We classify the possible mean field Nash equilibria, which include potentially segregated states where an agent population can split into subpopulations adopting different strategies. As the game is aggregative, the actual equilibrium strategy distributions remain undetermined, however. We therefore compare with the results of Experience-Weighted Attraction (EWA) learning, which at long times leads to Nash equilibria in the appropriate limits of large intensity of choice, low noise (long agent memory) and perfect imputation of missing scores (fictitious play). The learning dynamics breaks the indeterminacy of the Nash equilibria. Non-trivially, depending on how the relevant limits are taken, more than one type of equilibrium can be selected. These include the standard homogeneous mixed and heterogeneous pure states, but also \emph{heterogeneous mixed} states where different agents play different strategies that are not all pure. The analysis of the EWA learning involves Fokker-Planck modeling combined with large deviation methods. The theoretical results are confirmed by multi-agent simulations.
Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning
Venue: arXiv cs.RO 2017
Year: 2017

Obstacle avoidance is a fundamental requirement for autonomous robots which operate in, and interact with, the real world. When perception is limited to monocular vision avoiding collision becomes significantly more challenging due to the lack of 3D information. Conventional path planners for obstacle avoidance require tuning a number of parameters and do not have the ability to directly benefit from large datasets and continuous use. In this paper, a dueling architecture based deep double-Q network (D3QN) is proposed for obstacle avoidance, using only monocular RGB vision. Based on the dueling and double-Q mechanisms, D3QN can efficiently learn how to avoid obstacles in a simulator even with very noisy depth information predicted from RGB image. Extensive experiments show that D3QN enables twofold acceleration on learning compared with a normal deep Q network and the models trained solely in virtual environments can be directly transferred to real robots, generalizing well to various new environments with previously unseen dynamic objects.
Runaway Feedback Loops in Predictive Policing
Venue: arXiv cs.CY 2017
Year: 2017

Predictive policing systems are increasingly used to determine how to allocate police across a city in order to best prevent crime. Discovered crime data (e.g., arrest counts) are used to help update the model, and the process is repeated. Such systems have been empirically shown to be susceptible to runaway feedback loops, where police are repeatedly sent back to the same neighborhoods regardless of the true crime rate. In response, we develop a mathematical model of predictive policing that proves why this feedback loop occurs, show empirically that this model exhibits such problems, and demonstrate how to change the inputs to a predictive policing system (in a black-box manner) so the runaway feedback loop does not occur, allowing the true crime rate to be learned. Our results are quantitative: we can establish a link (in our model) between the degree to which runaway feedback causes problems and the disparity in crime rates between areas. Moreover, we can also demonstrate the way in which \emph{reported} incidents of crime (those reported by residents) and \emph{discovered} incidents of crime (i.e. those directly observed by police officers dispatched as a result of the predictive policing algorithm) interact: in brief, while reported incidents can attenuate the degree of runaway feedback, they cannot entirely remove it without the interventions we suggest.
Rational Trust Modeling
Venue: arXiv cs.CR 2017
Year: 2017

Trust models are widely used in various computer science disciplines. The main purpose of a trust model is to continuously measure trustworthiness of a set of entities based on their behaviors. In this article, the novel notion of "rational trust modeling" is introduced by bridging trust management and game theory. Note that trust models/reputation systems have been used in game theory (e.g., repeated games) for a long time, however, game theory has not been utilized in the process of trust model construction; this is where the novelty of our approach comes from. In our proposed setting, the designer of a trust model assumes that the players who intend to utilize the model are rational/selfish, i.e., they decide to become trustworthy or untrustworthy based on the utility that they can gain. In other words, the players are incentivized (or penalized) by the model itself to act properly. The problem of trust management can be then approached by game theoretical analyses and solution concepts such as Nash equilibrium. Although rationality might be built-in in some existing trust models, we intend to formalize the notion of rational trust modeling from the designer's perspective. This approach will result in two fascinating outcomes. First of all, the designer of a trust model can incentivise trustworthiness in the first place by incorporating proper parameters into the trust function, which can be later utilized among selfish players in strategic trust-based interactions (e.g., e-commerce scenarios). Furthermore, using a rational trust model, we can prevent many well-known attacks on trust models. These two prominent properties also help us to predict behavior of the players in subsequent steps by game theoretical analyses.
Approximate Maximin Shares for Groups of Agents
Venue: arXiv cs.GT 2017
Year: 2017

We investigate the problem of fairly allocating indivisible goods among interested agents using the concept of maximin share. Procaccia and Wang showed that while an allocation that gives every agent at least her maximin share does not necessarily exist, one that gives every agent at least $2/3$ of her share always does. In this paper, we consider the more general setting where we allocate the goods to groups of agents. The agents in each group share the same set of goods even though they may have conflicting preferences. For two groups, we characterize the cardinality of the groups for which a constant factor approximation of the maximin share is possible regardless of the number of goods. We also show settings where an approximation is possible or impossible when there are several groups.
Irregular Repetition Slotted ALOHA over the Rayleigh Block Fading Channel with Capture
Venue: arXiv cs.IT 2017
Year: 2017

Random access protocols relying on the transmission of packet replicas in multiple slots and exploiting interference cancellation at the receiver have been shown to achieve per- formance competitive with that of orthogonal schemes. So far the optimization of the repetition degree profile, defining the probability for a user to transmit a given number of replicas, has mainly been performed targeting the collision channel model. In this paper the analysis is extended to a block fading channel model, also assuming capture effect at the receiver. Density evolution equations are developed for the new setting and, based on them, some repetition degree profiles are optimized and analyzed via Monte Carlo simulation in a finite frame length setting. The derived distributions are shown to achieve throughputs largely exceeding 1 [packet/slot].
The Authority of "Fair" in Machine Learning
Venue: arXiv cs.CY 2017
Year: 2017

In this paper, we argue for the adoption of a normative definition of fairness within the machine learning community. After characterizing this definition, we review the current literature of Fair ML in light of its implications. We end by suggesting ways to incorporate a broader community and generate further debate around how to decide what is fair in ML.
Canonical form of linear subspaces and coding invariants: the poset metric point of view
Venue: arXiv cs.IT 2017
Year: 2017

In this work we introduce the concept of a sub-space decomposition, subject to a partition of the coordinates. Considering metrics determined by partial orders in the set of coordinates, the so called poset metrics, we show the existence of maximal decompositions according to the metric. These decompositions turns to be an important tool to obtain the canonical form for codes over any poset metrics and to obtain bounds for important invariants such as the packing radius of a linear subspace. Furthermore, using maximal decompositions, we are able to reduce and optimize the full lookup table algorithm for the syndrome decoding process.
Tight Load Balancing via Randomized Local Search
Venue: arXiv cs.DC 2017
Year: 2017

We consider the following balls-into-bins process with $n$ bins and $m$ balls: each ball is equipped with a mutually independent exponential clock of rate 1. Whenever a ball's clock rings, the ball samples a random bin and moves there if the number of balls in the sampled bin is smaller than in its current bin. This simple process models a typical load balancing problem where users (balls) seek a selfish improvement of their assignment to resources (bins). From a game theoretic perspective, this is a randomized approach to the well-known Koutsoupias-Papadimitriou model, while it is known as randomized local search (RLS) in load balancing literature. Up to now, the best bound on the expected time to reach perfect balance was $O\left({(\ln n)}^2+\ln(n)\cdot n^2/m\right)$ due to Ganesh, Lilienthal, Manjunath, Proutiere, and Simatos (Load balancing via random local search in closed and open systems, Queueing Systems, 2012). We improve this to an asymptotically tight $O\left(\ln(n)+n^2/m\right)$. Our analysis is based on the crucial observation that performing "destructive moves" (reversals of RLS moves) cannot decrease the balancing time. This allows us to simplify problem instances and to ignore "inconvenient moves" in the analysis.
Compaction of Church Numerals for Higher-Order Compression
Venue: arXiv cs.DS 2017
Year: 2017

In this study, we address the problem of compacting Church numerals. Church numerals appear as a representation of the repetitive part of data in higher-order compression. We propose a novel decomposition scheme for a natural number using tetration, which leads to a compact representation of $λ$-terms equivalent to the original Church numerals. For natural number $n$, we prove that the size of the $λ$-term obtained by the proposed method is $O(({\rm slog}_{2}n)^{\log n/ \log \log n})$. Moreover, we quantitatively confirmed experimentally that the proposed method outperforms a binary expression of Church numerals when $n$ is less than approximately 10000.
semantify.it, a Platform for Creation, Publication and Distribution of Semantic Annotations
Venue: arXiv cs.IR 2017
Year: 2017

The application of semantic technologies to content on the web is, in many regards, important and urgent. Search engines, chatbots, intelligent personal assistants and other technologies increasingly rely on content published as semantic structured data. Yet, the process of creating this kind of data is still complicated and widely unknown. The semantify.it platform implements an approach to solve three of the most challenging question regarding the publication of structured semantic data, namely: a) what vocabulary to use, b) how to create annotation files and c) how to publish or integrate annotations within a website without programming. This paper presents the idea and the development of the semantify.it platform. It demonstrates that the creation process of semantically annotated data does not have to be hard, shows use cases and pilot users of the created software and presents where the development of this platform or alike projects lead to in the future.
Superpixel-based Semantic Segmentation Trained by Statistical Process Control
Venue: arXiv cs.CV 2017
Year: 2017

Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks. However, considering that neighboring pixels are heavily dependent on each other, both learning and testing of these methods have a lot of redundant operations. To resolve this problem, the proposed network is trained and tested with only 0.37% of total pixels by superpixel-based sampling and largely reduced the complexity of upsampling calculation. The hypercolumn feature maps are constructed by pyramid module in combination with the convolution layers of the base network. Since the proposed method uses a very small number of sampled pixels, the end-to-end learning of the entire network is difficult with a common learning rate for all the layers. In order to resolve this problem, the learning rate after sampling is controlled by statistical process control (SPC) of gradients in each layer. The proposed method performs better than or equal to the conventional methods that use much more samples on Pascal Context, SUN-RGBD dataset.
Parameterized Complexity of CSP for Infinite Constraint Languages
Venue: arXiv cs.CC 2017
Year: 2017

We study parameterized Constraint Satisfaction Problem for infinite constraint languages. The parameters that we study are weight of the satisfying assignment, number of constraints, maximum number of occurrences of a variable in the instance, and maximum number of occurrences of a variable in each constraint. A dichotomy theorem is already known for finite constraint languages with the weight parameter. We prove some general theorems that show, as new results, that some well-known problems are fixed-parameter tractable and some others are in W[1].
New Integer Linear Programming Models for the Vertex Coloring Problem
Venue: arXiv cs.CG 2017
Year: 2017

The vertex coloring problem asks for the minimum number of colors that can be assigned to the vertices of a given graph such that for all vertices v the color of v is different from the color of any of its neighbors. The problem is NP-hard. Here, we introduce new integer linear programming formulations based on partial orderings. They have the advantage that they are as simple to work with as the classical assignment formulation, since they can be fed directly into a standard integer linear programming solver. We evaluate our new models using Gurobi and show that our new simple approach is a good alternative to the best state-of-the-art approaches for the vertex coloring problem. In our computational experiments, we compare our formulations with the classical assignment formulation and the representatives formulation on a large set of benchmark graphs as well as randomly generated graphs of varying size and density. The evaluation shows that one of the new models dominates both formulations for sparse graphs, while the representatives formulation is the best for dense graphs.
Press F1 for help: participatory design for dealing with on-line and real life security of older adults
Venue: arXiv cs.SI 2017
Year: 2017

In this paper we present the report on the design and development of a platform for the inter-generational exchange of favors. This platform was designed using participatory design approach during a 24-hour hackathon by a team consisting of younger programmers and older adults. The findings of this report show that inter-generational cooperation in which the older adults serve as representatives of the end user, not only improves the design and development of the application, but also provides an effective method for designing and applying solutions aimed at improving the security of older adults while using online and mobile tools.
Contracting a Planar Graph Efficiently
Venue: arXiv cs.DS 2017
Year: 2017

We present a data structure that can maintain a simple planar graph under edge contractions in linear total time. The data structure supports adjacency queries and provides access to neighbor lists in $O(1)$ time. Moreover, it can report all the arising self-loops and parallel edges. By applying the data structure, we can achieve optimal running times for decremental bridge detection, 2-edge connectivity, maximal 3-edge connected components, and the problem of finding a unique perfect matching for a static planar graph. Furthermore, we improve the running times of algorithms for several planar graph problems, including decremental 2-vertex and 3-edge connectivity, and we show that using our data structure in a black-box manner, one obtains conceptually simple optimal algorithms for computing MST and 5-coloring in planar graphs.
Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model
Venue: arXiv cs.AI 2017
Year: 2017

The current paper proposes a novel variational Bayes predictive coding RNN model, which can learn to generate fluctuated temporal patterns from exemplars. The model learns to maximize the lower bound of the weighted sum of the regularization and reconstruction error terms. We examined how this weighting can affect development of different types of information processing while learning fluctuated temporal patterns. Simulation results show that strong weighting of the reconstruction term causes the development of deterministic chaos for imitating the randomness observed in target sequences, while strong weighting of the regularization term causes the development of stochastic dynamics imitating probabilistic processes observed in targets. Moreover, results indicate that the most generalized learning emerges between these two extremes. The paper concludes with implications in terms of the underlying neuronal mechanisms for autism spectrum disorder and for free action.
How biased is your model? Concentration Inequalities, Information and Model Bias
Venue: arXiv cs.IT 2017
Year: 2017

We derive tight and computable bounds on the bias of statistical estimators, or more generally of quantities of interest, when evaluated on a baseline model P rather than on the typically unknown true model Q. Our proposed method combines the scalable information inequality derived by P. Dupuis, K.Chowdhary, the authors and their collaborators together with classical concentration inequalities (such as Bennett's and Hoeffding-Azuma inequalities). Our bounds are expressed in terms of the Kullback-Leibler divergence R(Q||P) of model Q with respect to P and the moment generating function for the statistical estimator under P. Furthermore, concentration inequalities, i.e. bounds on moment generating functions, provide tight and computationally inexpensive model bias bounds for quantities of interest. Finally, they allow us to derive rigorous confidence bands for statistical estimators that account for model bias and are valid for an arbitrary amount of data.