OpenRaiser/Intern-Atlas huggingface dataset:
Fictitious play for cooperative action selection in robot teams
Venue: arXiv cs.MA 2016
Year: 2016

A game theoretic distributed decision making approach is presented for the problem of control effort allocation in a robotic team based on a novel variant of fictitious play. The proposed learning process allows the robots to accomplish their objectives by coordinating their actions in order to efficiently complete their tasks. In particular, each robot of the team predicts the other robots' planned actions while making decisions to maximise their own expected reward that depends on the reward for joint successful completion of the task. Action selection is interpreted as an $n$-player cooperative game. The approach presented can be seen as part of the \emph{Belief Desire Intention} (BDI) framework, also can address the problem of cooperative, legal, safe, considerate and emphatic decisions by robots if their individual and group rewards are suitably defined. After theoretical analysis the performance of the proposed algorithm is tested on four simulation scenarios. The first one is a coordination game between two material handling robots, the second one is a warehouse patrolling task by a team of robots, the third one presents a coordination mechanism between two robots that carry a heavy object on a corridor and the fourth one is an example of coordination on a sensors network.
A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer Survival
Venue: arXiv cs.LG 2016
Year: 2016

Cancer survival prediction is an active area of research that can help prevent unnecessary therapies and improve patient's quality of life. Gene expression profiling is being widely used in cancer studies to discover informative biomarkers that aid predict different clinical endpoint prediction. We use multiple modalities of data derived from RNA deep-sequencing (RNA-seq) to predict survival of cancer patients. Despite the wealth of information available in expression profiles of cancer tumors, fulfilling the aforementioned objective remains a big challenge, for the most part, due to the paucity of data samples compared to the high dimension of the expression profiles. As such, analysis of transcriptomic data modalities calls for state-of-the-art big-data analytics techniques that can maximally use all the available data to discover the relevant information hidden within a significant amount of noise. In this paper, we propose a pipeline that predicts cancer patients' survival by exploiting the structure of the input (manifold learning) and by leveraging the unlabeled samples using Laplacian support vector machines, a graph-based semi supervised learning (GSSL) paradigm. We show that under certain circumstances, no single modality per se will result in the best accuracy and by fusing different models together via a stacked generalization strategy, we may boost the accuracy synergistically. We apply our approach to two cancer datasets and present promising results. We maintain that a similar pipeline can be used for predictive tasks where labeled samples are expensive to acquire.
Cross-Domain Face Verification: Matching ID Document and Self-Portrait Photographs
Venue: arXiv cs.CV 2016
Year: 2016

Cross-domain biometrics has been emerging as a new necessity, which poses several additional challenges, including harsh illumination changes, noise, pose variation, among others. In this paper, we explore approaches to cross-domain face verification, comparing self-portrait photographs ("selfies") to ID documents. We approach the problem with proper image photometric adjustment and data standardization techniques, along with deep learning methods to extract the most prominent features from the data, reducing the effects of domain shift in this problem. We validate the methods using a novel dataset comprising 50 individuals. The obtained results are promising and indicate that the adopted path is worth further investigation.
The Freiburg Groceries Dataset
Venue: arXiv cs.CV 2016
Year: 2016

With the increasing performance of machine learning techniques in the last few years, the computer vision and robotics communities have created a large number of datasets for benchmarking object recognition tasks. These datasets cover a large spectrum of natural images and object categories, making them not only useful as a testbed for comparing machine learning approaches, but also a great resource for bootstrapping different domain-specific perception and robotic systems. One such domain is domestic environments, where an autonomous robot has to recognize a large variety of everyday objects such as groceries. This is a challenging task due to the large variety of objects and products, and where there is great need for real-world training data that goes beyond product images available online. In this paper, we address this issue and present a dataset consisting of 5,000 images covering 25 different classes of groceries, with at least 97 images per class. We collected all images from real-world settings at different stores and apartments. In contrast to existing groceries datasets, our dataset includes a large variety of perspectives, lighting conditions, and degrees of clutter. Overall, our images contain thousands of different object instances. It is our hope that machine learning and robotics researchers find this dataset of use for training, testing, and bootstrapping their approaches. As a baseline classifier to facilitate comparison, we re-trained the CaffeNet architecture (an adaptation of the well-known AlexNet) on our dataset and achieved a mean accuracy of 78.9%. We release this trained model along with the code and data splits we used in our experiments.
On the Query Complexity of Black-Peg AB-Mastermind
Venue: arXiv cs.DS 2016
Year: 2016

Mastermind game is a two players zero sum game of imperfect information. The first player, called codemaker, chooses a secret code and the second player, called codebreaker, tries to break the secret code by making as few guesses as possible, exploiting information that is given by the codemaker after each guess. In this paper, we consider the so called Black-Peg variant of Mastermind, where the only information concerning a guess is the number of positions in which the guess coincides with the secret code. More precisely, we deal with a special version of the Black-Peg game with n holes and k<=n colors where no repetition of colors is allowed. We present upper and lower bounds on the number of guesses necessary to break the secret code. We first come back to the upper bound results introduced by El Ouali and Sauerland (2013). For the case k=n the secret code can be algorithmically identified within less than (n-3)*ld(n)+5n/2 queries. That result improves the result of Ker-I Ko and Shia-Chung Teng (1985) by almost a factor of 2. For the case k>n we prove an upper bound for the problem of (n-1)*ld(n)+k+1. Furthermore we prove a new lower bound for (a generalization of) the case k=n that improves the recent result of Berger et al. (2016) from n-log(log(n)) to n. We also give a lower bound of k queries for the case k>n.
Wirelessly Powered Urban Crowd Sensing over Wearables: Trading Energy for Data
Venue: arXiv cs.NI 2016
Year: 2016

In this article, we put forward the mobile crowd sensing paradigm based on ubiquitous wearable devices carried by human users. The key challenge for mass user involvement into prospective urban crowd sending applications, such as monitoring of large-scale phenomena (e.g., traffic congestion and air pollution levels), is the appropriate sources of motivation. We thus advocate for the use of wireless power transfer provided in exchange for sensed data to incentivize the owners of wearables to participate in collaborative data collection. Based on this construction, we develop the novel concept of wirelessly powered crowd sensing and offer the corresponding network architecture considerations together with a systematic review of wireless charging techniques to implement it. Further, we contribute a detailed system-level feasibility study that reports on the achievable performance levels for the envisioned setup. Finally, the underlying energy-data trading mechanisms are discussed, and the work is concluded with outlining open research opportunities.
Fast and reconfigurable packet classification engine in FPGA-based firewall
Venue: arXiv cs.AR 2016
Year: 2016

In data communication via internet, security is becoming one of the most influential aspects. One way to support it is by classifying and filtering ethernet packets within network devices. Packet classification is a fundamental task for network devices such as routers, firewalls, and intrusion detection systems. In this paper we present architecture of fast and reconfigurable Packet Classification Engine (PCE). This engine is used in FPGA-based firewall. Our PCE inspects multi-dimensional field of packet header sequentially based on tree-based algorithm. This algorithm simplifies overall system to a lower scale and leads to a more secure system. The PCE works with an adaptation of single cycle processor architecture in the system. Ethernet packet is examined with PCE based on Source IP Address, Destination IP Address, Source Port, Destination Port, and Protocol fields of the packet header. These are basic fields to know whether it is a dangerous or normal packet before inspecting the content. Using implementation of tree-based algorithm in the architecture, firewall rules are rebuilt into 24-bit sub-rules which are read as processor instruction in the inspection process. The inspection process is comparing one sub-rule with input field of header every clock cycle. The proposed PCE shows 91 MHz clock frequency in Cyclone II EP2C70F896C6 with 13 clocks throughput average from input to output generation. The use of tree-based algorithm simplifies the multidimensional packet inspection and gives us reconfigurable as well as scalable system. The architecture is fast, reliable, and adaptable and also can maximize the advantages of the algorithm very well. Although the PCE has high frequency and little amount of clock, filtering speed of a firewall also depends on the other components, such as packet FIFO buffer. Fast and reliable FIFO buffer is needed to support the PCE. This PCE also is not completed with rule update mechanism yet. This proposed PCE is tested as a component of FPGA-based firewall to filter Ethernet packet with FPGA DE2 Board using NIOS II platform.
A Machine Learning Approach to Model the Received Signal in Molecular Communications
Venue: arXiv cs.ET 2016
Year: 2016

A molecular communication channel is determined by the received signal. Received signal models form the basis for studies focused on modulation, receiver design, capacity, and coding depend on the received signal models. Therefore, it is crucial to model the number of received molecules until time $t$ analytically. Modeling the diffusion-based molecular communication channel with the first-hitting process is an open issue for a spherical transmitter. In this paper, we utilize the artificial neural networks technique to model the received signal for a spherical transmitter and a perfectly absorbing receiver (i.e., first hitting process). The proposed technique may be utilized in other studies that assume a spherical transmitter instead of a point transmitter.
ModelHub: Towards Unified Data and Lifecycle Management for Deep Learning
Venue: arXiv cs.DB 2016
Year: 2016

Deep learning has improved state-of-the-art results in many important fields, and has been the subject of much research in recent years, leading to the development of several systems for facilitating deep learning. Current systems, however, mainly focus on model building and training phases, while the issues of data management, model sharing, and lifecycle management are largely ignored. Deep learning modeling lifecycle generates a rich set of data artifacts, such as learned parameters and training logs, and comprises of several frequently conducted tasks, e.g., to understand the model behaviors and to try out new models. Dealing with such artifacts and tasks is cumbersome and largely left to the users. This paper describes our vision and implementation of a data and lifecycle management system for deep learning. First, we generalize model exploration and model enumeration queries from commonly conducted tasks by deep learning modelers, and propose a high-level domain specific language (DSL), inspired by SQL, to raise the abstraction level and accelerate the modeling process. To manage the data artifacts, especially the large amount of checkpointed float parameters, we design a novel model versioning system (dlv), and a read-optimized parameter archival storage system (PAS) that minimizes storage footprint and accelerates query workloads without losing accuracy. PAS archives versioned models using deltas in a multi-resolution fashion by separately storing the less significant bits, and features a novel progressive query (inference) evaluation algorithm. Third, we show that archiving versioned models using deltas poses a new dataset versioning problem and we develop efficient algorithms for solving it. We conduct extensive experiments over several real datasets from computer vision domain to show the efficiency of the proposed techniques.
Geometric Controllability of The Purcell's Swimmer and its Symmetrized Cousin
Venue: arXiv cs.SY 2016
Year: 2016

We analyse weak and strong controllability notions for the locomotion of the 3-link Purcell's swimmer, the simplest possible swimmer at low Reynolds number from a geometric framework. After revisiting a purely kinematic form of the equations, we apply an extension of Chow's theorem to analyze controllability in the strong and weak sense. Further, the connection form for the symmetric version of the Purcell's swimmer is derived, based on which, the controllability analysis utilizing the Abelian nature of the structure group is presented. The novelty in our approach is the usage of geometry and the principal fiber bundle structure of the configuration manifold of the system to arrive at strong and weak controllability notions.
Spotting Rumors via Novelty Detection
Venue: arXiv cs.SI 2016
Year: 2016

Rumour detection is hard because the most accurate systems operate retrospectively, only recognizing rumours once they have collected repeated signals. By then the rumours might have already spread and caused harm. We introduce a new category of features based on novelty, tailored to detect rumours early on. To compensate for the absence of repeated signals, we make use of news wire as an additional data source. Unconfirmed (novel) information with respect to the news articles is considered as an indication of rumours. Additionally we introduce pseudo feedback, which assumes that documents that are similar to previous rumours, are more likely to also be a rumour. Comparison with other real-time approaches shows that novelty based features in conjunction with pseudo feedback perform significantly better, when detecting rumours instantly after their publication.
Gendered Conversation in a Social Game-Streaming Platform
Venue: arXiv cs.SI 2016
Year: 2016

Online social media and games are increasingly replacing offline social activities. Social media is now an indispensable mode of communication; online gaming is not only a genuine social activity but also a popular spectator sport. With support for anonymity and larger audiences, online interaction shrinks social and geographical barriers. Despite such benefits, social disparities such as gender inequality persist in online social media. In particular, online gaming communities have been criticized for persistent gender disparities and objectification. As gaming evolves into a social platform, persistence of gender disparity is a pressing question. Yet, there are few large-scale, systematic studies of gender inequality and objectification in social gaming platforms. Here we analyze more than one billion chat messages from Twitch, a social game-streaming platform, to study how the gender of streamers is associated with the nature of conversation. Using a combination of computational text analysis methods, we show that gendered conversation and objectification is prevalent in chats. Female streamers receive significantly more objectifying comments while male streamers receive more game-related comments. This difference is more pronounced for popular streamers. There also exists a large number of users who post only on female or male streams. Employing a neural vector-space embedding (paragraph vector) method, we analyze gendered chat messages and create prediction models that (i) identify the gender of streamers based on messages posted in the channel and (ii) identify the gender a viewer prefers to watch based on their chat messages. Our findings suggest that disparities in social game-streaming platforms is a nuanced phenomenon that involves the gender of streamers as well as those who produce gendered and game-related conversation.
Learning Fully Convolutional Networks for Iterative Non-blind Deconvolution
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we propose a fully convolutional networks for iterative non-blind deconvolution We decompose the non-blind deconvolution problem into image denoising and image deconvolution. We train a FCNN to remove noises in the gradient domain and use the learned gradients to guide the image deconvolution step. In contrast to the existing deep neural network based methods, we iteratively deconvolve the blurred images in a multi-stage framework. The proposed method is able to learn an adaptive image prior, which keeps both local (details) and global (structures) information. Both quantitative and qualitative evaluations on benchmark datasets demonstrate that the proposed method performs favorably against state-of-the-art algorithms in terms of quality and speed.
Incremental Exact Min-Cut in Poly-logarithmic Amortized Update Time
Venue: arXiv cs.DS 2016
Year: 2016

We present a deterministic incremental algorithm for \textit{exactly} maintaining the size of a minimum cut with $\widetilde{O}(1)$ amortized time per edge insertion and $O(1)$ query time. This result partially answers an open question posed by Thorup [Combinatorica 2007]. It also stays in sharp contrast to a polynomial conditional lower-bound for the fully-dynamic weighted minimum cut problem. Our algorithm is obtained by combining a recent sparsification technique of Kawarabayashi and Thorup [STOC 2015] and an exact incremental algorithm of Henzinger [J. of Algorithm 1997]. We also study space-efficient incremental algorithms for the minimum cut problem. Concretely, we show that there exists an ${O}(n\log n/\varepsilon^2)$ space Monte-Carlo algorithm that can process a stream of edge insertions starting from an empty graph, and with high probability, the algorithm maintains a $(1+\varepsilon)$-approximation to the minimum cut. The algorithm has $\widetilde{O}(1)$ amortized update-time and constant query-time.
Robust Regularized Least-Squares Beamforming Approach to Signal Estimation
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we address the problem of robust adaptive beamforming of signals received by a linear array. The challenge associated with the beamforming problem is twofold. Firstly, the process requires the inversion of the usually ill-conditioned covariance matrix of the received signals. Secondly, the steering vector pertaining to the direction of arrival of the signal of interest is not known precisely. To tackle these two challenges, the standard capon beamformer is manipulated to a form where the beamformer output is obtained as a scaled version of the inner product of two vectors. The two vectors are linearly related to the steering vector and the received signal snapshot, respectively. The linear operator, in both cases, is the square root of the covariance matrix. A regularized least-squares (RLS) approach is proposed to estimate these two vectors and to provide robustness without exploiting prior information. Simulation results show that the RLS beamformer using the proposed regularization algorithm outperforms state-of-the-art beamforming algorithms, as well as another RLS beamformers using a standard regularization approaches.
MAC Protocols for IEEE 802.11ax: Avoiding Collisions on Dense Networks
Venue: arXiv cs.NI 2016
Year: 2016

Wireless networks have become the main form of Internet access. Statistics show that the global mobile Internet penetration should exceed 70\% until 2019. Wi-Fi is an important player in this change. Founded on IEEE 802.11, this technology has a crucial impact in how we share broadband access both in domestic and corporate networks. However, recent works have indicated performance issues in Wi-Fi networks, mainly when they have been deployed without planning and under high user density. Hence, different collision avoidance techniques and Medium Access Control protocols have been designed in order to improve Wi-Fi performance. Analyzing the collision problem, this work strengthens the claims found in the literature about the low Wi-Fi performance under dense scenarios. Then, in particular, this article overviews the MAC protocols used in the IEEE 802.11 standard and discusses solutions to mitigate collisions. Finally, it contributes presenting future trends in MAC protocols. This assists in foreseeing expected improvements for the next generation of Wi-Fi devices.
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. We carry out comprehensive experiments and set new state-of-the-art results on seven public datasets. In particular, we achieve an intersection-over-union score of 83.4 on the challenging PASCAL VOC 2012 dataset, which is the best reported result to date.
A Business Zone Recommender System Based on Facebook and Urban Planning Data
Venue: arXiv cs.SI 2016
Year: 2016

We present ZoneRec---a zone recommendation system for physical businesses in an urban city, which uses both public business data from Facebook and urban planning data. The system consists of machine learning algorithms that take in a business' metadata and outputs a list of recommended zones to establish the business in. We evaluate our system using data of food businesses in Singapore and assess the contribution of different feature groups to the recommendation quality.