OpenRaiser/Intern-Atlas huggingface dataset:
Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization
Venue: arXiv cs.CV 2017
Year: 2017

When approaching a novel visual recognition problem in a specialized image domain, a common strategy is to start with a pre-trained deep neural network and fine-tune it to the specialized domain. If the target domain covers a smaller visual space than the source domain used for pre-training (e.g. ImageNet), the fine-tuned network is likely to be over-parameterized. However, applying network pruning as a post-processing step to reduce the memory requirements has drawbacks: fine-tuning and pruning are performed independently; pruning parameters are set once and cannot adapt over time; and the highly parameterized nature of state-of-the-art pruning methods make it prohibitive to manually search the pruning parameter space for deep networks, leading to coarse approximations. We propose a principled method for jointly fine-tuning and compressing a pre-trained convolutional network that overcomes these limitations. Experiments on two specialized image domains (remote sensing images and describable textures) demonstrate the validity of the proposed approach.
Network Formation in the Sky: Unmanned Aerial Vehicles for Multi-hop Wireless Backhauling
Venue: arXiv cs.IT 2017
Year: 2017

To reap the benefits of dense small base station (SBS) deployment, innovative backhaul solutions are needed in order to manage scenarios in which high-speed ground backhaul links are either unavailable or limited in capacity. In this paper, a novel backhaul scheme that utilizes unmanned aerial vehicles (UAVs) as an on-demand flying network linking ground SBSs and the core network is proposed. The design of the aerial backhaul scheme is formulated as a network formation game among UAVs that seek to form a multi-hop backhaul network in the air. To solve this game, a myopic network formation algorithm which reaches a pairwise stable network upon convergence, is introduced. The proposed network formation algorithm enables the UAVs to form the necessary multi-hop backhaul network in a decentralized manner thus adapting the backhaul architecture to the dynamics of the network. Simulation results show that the proposed network formation algorithm achieves substantial performance gains in terms of both rate and delay reaching, respectively, up to 40% and 41% compared to the formation of direct communication links with the gateway node (for a network with 15 UAVs).
Sparse Deep Nonnegative Matrix Factorization
Venue: arXiv cs.CV 2017
Year: 2017

Nonnegative matrix factorization is a powerful technique to realize dimension reduction and pattern recognition through single-layer data representation learning. Deep learning, however, with its carefully designed hierarchical structure, is able to combine hidden features to form more representative features for pattern recognition. In this paper, we proposed sparse deep nonnegative matrix factorization models to analyze complex data for more accurate classification and better feature interpretation. Such models are designed to learn localized features or generate more discriminative representations for samples in distinct classes by imposing $L_1$-norm penalty on the columns of certain factors. By extending one-layer model into multi-layer one with sparsity, we provided a hierarchical way to analyze big data and extract hidden features intuitively due to nonnegativity. We adopted the Nesterov's accelerated gradient algorithm to accelerate the computing process with the convergence rate of $O(1/k^2)$ after $k$ steps iteration. We also analyzed the computing complexity of our framework to demonstrate their efficiency. To improve the performance of dealing with linearly inseparable data, we also considered to incorporate popular nonlinear functions into this framework and explored their performance. We applied our models onto two benchmarking image datasets, demonstrating our models can achieve competitive or better classification performance and produce intuitive interpretations compared with the typical NMF and competing multi-layer models.
Empirical Evaluation of Abstract Argumentation: Supporting the Need for Bipolar and Probabilistic Approaches
Venue: arXiv cs.AI 2017
Year: 2017

In dialogical argumentation it is often assumed that the involved parties always correctly identify the intended statements posited by each other, realize all of the associated relations, conform to the three acceptability states (accepted, rejected, undecided), adjust their views when new and correct information comes in, and that a framework handling only attack relations is sufficient to represent their opinions. Although it is natural to make these assumptions as a starting point for further research, removing them or even acknowledging that such removal should happen is more challenging for some of these concepts than for others. Probabilistic argumentation is one of the approaches that can be harnessed for more accurate user modelling. The epistemic approach allows us to represent how much a given argument is believed by a given person, offering us the possibility to express more than just three agreement states. It is equipped with a wide range of postulates, including those that do not make any restrictions concerning how initial arguments should be viewed, thus potentially being more adequate for handling beliefs of the people that have not fully disclosed their opinions in comparison to Dung's semantics. The constellation approach can be used to represent the views of different people concerning the structure of the framework we are dealing with, including cases in which not all relations are acknowledged or when they are seen differently than intended. Finally, bipolar argumentation frameworks can be used to express both positive and negative relations between arguments. In this paper we describe the results of an experiment in which participants judged dialogues in terms of agreement and structure. We compare our findings with the aforementioned assumptions as well as with the constellation and epistemic approaches to probabilistic argumentation and bipolar argumentation.
A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously
Venue: arXiv cs.CL 2017
Year: 2017

Capabilities of detecting temporal relations between two events can benefit many applications. Most of existing temporal relation classifiers were trained in a supervised manner. Instead, we explore the observation that regular event pairs show a consistent temporal relation despite of their various contexts, and these rich contexts can be used to train a contextual temporal relation classifier, which can further recognize new temporal relation contexts and identify new regular event pairs. We focus on detecting after and before temporal relations and design a weakly supervised learning approach that extracts thousands of regular event pairs and learns a contextual temporal relation classifier simultaneously. Evaluation shows that the acquired regular event pairs are of high quality and contain rich commonsense knowledge and domain specific knowledge. In addition, the weakly supervised trained temporal relation classifier achieves comparable performance with the state-of-the-art supervised systems.
Hyperprofile-based Computation Offloading for Mobile Edge Networks
Venue: arXiv cs.NI 2017
Year: 2017

In recent studies, researchers have developed various computation offloading frameworks for bringing cloud services closer to the user via edge networks. Specifically, an edge device needs to offload computationally intensive tasks because of energy and processing constraints. These constraints present the challenge of identifying which edge nodes should receive tasks to reduce overall resource consumption. We propose a unique solution to this problem which incorporates elements from Knowledge-Defined Networking (KDN) to make intelligent predictions about offloading costs based on historical data. Each server instance can be represented in a multidimensional feature space where each dimension corresponds to a predicted metric. We compute features for a "hyperprofile" and position nodes based on the predicted costs of offloading a particular task. We then perform a k-Nearest Neighbor (kNN) query within the hyperprofile to select nodes for offloading computation. This paper formalizes our hyperprofile-based solution and explores the viability of using machine learning (ML) techniques to predict metrics useful for computation offloading. We also investigate the effects of using different distance metrics for the queries. Our results show various network metrics can be modeled accurately with regression, and there are circumstances where kNN queries using Euclidean distance as opposed to rectilinear distance is more favorable.
Topology Analysis of International Networks Based on Debates in the United Nations
Venue: arXiv cs.CL 2017
Year: 2017

In complex, high dimensional and unstructured data it is often difficult to extract meaningful patterns. This is especially the case when dealing with textual data. Recent studies in machine learning, information theory and network science have developed several novel instruments to extract the semantics of unstructured data, and harness it to build a network of relations. Such approaches serve as an efficient tool for dimensionality reduction and pattern detection. This paper applies semantic network science to extract ideological proximity in the international arena, by focusing on the data from General Debates in the UN General Assembly on the topics of high salience to international community. UN General Debate corpus (UNGDC) covers all high-level debates in the UN General Assembly from 1970 to 2014, covering all UN member states. The research proceeds in three main steps. First, Latent Dirichlet Allocation (LDA) is used to extract the topics of the UN speeches, and therefore semantic information. Each country is then assigned a vector specifying the exposure to each of the topics identified. This intermediate output is then used in to construct a network of countries based on information theoretical metrics where the links capture similar vectorial patterns in the topic distributions. Topology of the networks is then analyzed through network properties like density, path length and clustering. Finally, we identify specific topological features of our networks using the map equation framework to detect communities in our networks of countries.
Lessons learnt from the NetIDE project: Taking SDN programming to the next level
Venue: arXiv cs.NI 2017
Year: 2017

SDN promises to overcome vendor lock-in by enabling a multi-vendor hardware and software ecosystem in operator networks. However, we observe that this is currently not happening. A framework allowing to compose SDN applications combining different frameworks can help revert the trend. In this paper, we analyze the challenges in the current SDN landscape and then present the multi-controller SDN framework developed by the NetIDE project. Our architecture supports different SDN southbound protocols and we have implemented a proof of concept using the OpenFlow protocol, which has given us a greater insight on its shortcomings.
A Novel Approach for Image Segmentation based on Histograms computed from Hue-data
Venue: arXiv cs.CV 2017
Year: 2017

Computer Vision is growing day by day in terms of user specific applications. The first step of any such application is segmenting an image. In this paper, we propose a novel and grass-root level image segmentation algorithm for cases in which the background has uniform color distribution. This algorithm can be used for images of flowers, birds, insects and many more where such background conditions occur. By image segmentation, the visualization of a computer increases manifolds and it can even attain near-human accuracy during classification.
Intra Prediction Using In-Loop Residual Coding for the post-HEVC Standard
Venue: arXiv cs.MM 2017
Year: 2017

A few years after standardization of the High Efficiency Video Coding (HEVC), now the Joint Video Exploration Team (JVET) group is exploring post-HEVC video compression technologies. In the intra prediction domain, this effort has resulted in an algorithm with 67 internal modes, new filters and tools which significantly improve HEVC. However, the improved algorithm still suffers from the long distance prediction inaccuracy problem. In this paper, we propose an In-Loop Residual coding Intra Prediction (ILR-IP) algorithm which utilizes inner-block reconstructed pixels as references to reduce the distance from predicted pixels. This is done by using the ILR signal for partially reconstructing each pixel, right after its prediction and before its block-level out-loop residual calculation. The ILR signal is decided in the rate-distortion sense, by a brute-force search on a QP-dependent finite codebook that is known to the decoder. Experiments show that the proposed ILR-IP algorithm improves the existing method in the Joint Exploration Model (JEM) up to 0.45% in terms of bit rate saving, without complexity overhead at the decoder side.
Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks
Venue: arXiv cs.CV 2017
Year: 2017

Learning to transfer visual attributes requires supervision dataset. Corresponding images with varying attribute values with the same identity are required for learning the transfer function. This largely limits their applications, because capturing them is often a difficult task. To address the issue, we propose an unsupervised method to learn to transfer visual attribute. The proposed method can learn the transfer function without any corresponding images. Inspecting visualization results from various unsupervised attribute transfer tasks, we verify the effectiveness of the proposed method.
Familia: An Open-Source Toolkit for Industrial Topic Modeling
Venue: arXiv cs.IR 2017
Year: 2017

Familia is an open-source toolkit for pragmatic topic modeling in industry. Familia abstracts the utilities of topic modeling in industry as two paradigms: semantic representation and semantic matching. Efficient implementations of the two paradigms are made publicly available for the first time. Furthermore, we provide off-the-shelf topic models trained on large-scale industrial corpora, including Latent Dirichlet Allocation (LDA), SentenceLDA and Topical Word Embedding (TWE). We further describe typical applications which are successfully powered by topic modeling, in order to ease the confusions and difficulties of software engineers during topic model selection and utilization.
Deep Domain Adaptation by Geodesic Distance Minimization
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose a new approach called Deep LogCORAL for unsupervised visual domain adaptation. Our work builds on the recently proposed Deep CORAL method, which proposed to train a convolutional neural network and simultaneously minimize the Euclidean distance of convariance matrices between the source and target domains. We propose to use the Riemannian distance, approximated by Log-Euclidean distance, to replace the naive Euclidean distance in Deep CORAL. We also consider first-order information, and minimize the distance of mean vectors between two domains. We build an end-to-end model, in which we minimize both the classification loss, and the domain difference based on the first and second order information between two domains. Our experiments on the benchmark Office dataset demonstrate the improvements of our newly proposed Deep LogCORAL approach over the Deep CORAL method, as well as further improvement when optimizing both orders of information.
Convolution with Logarithmic Filter Groups for Efficient Shallow CNN
Venue: arXiv cs.CV 2017
Year: 2017

In convolutional neural networks (CNNs), the filter grouping in convolution layers is known to be useful to reduce the network parameter size. In this paper, we propose a new logarithmic filter grouping which can capture the nonlinearity of filter distribution in CNNs. The proposed logarithmic filter grouping is installed in shallow CNNs applicable in a mobile application. Experiments were performed with the shallow CNNs for classification tasks. Our classification results on Multi-PIE dataset for facial expression recognition and CIFAR-10 dataset for object classification reveal that the compact CNN with the proposed logarithmic filter grouping scheme outperforms the same network with the uniform filter grouping in terms of accuracy and parameter efficiency. Our results indicate that the efficiency of shallow CNNs can be improved by the proposed logarithmic filter grouping.
Representation Learning on Large and Small Data
Venue: arXiv cs.CV 2017
Year: 2017

Deep learning owes its success to three key factors: scale of data, enhanced models to learn representations from data, and scale of computation. This book chapter presented the importance of the data-driven approach to learn good representations from both big data and small data. In terms of big data, it has been widely accepted in the research community that the more data the better for both representation and classification improvement. The question is then how to learn representations from big data, and how to perform representation learning when data is scarce. We addressed the first question by presenting CNN model enhancements in the aspects of representation, optimization, and generalization. To address the small data challenge, we showed transfer representation learning to be effective. Transfer representation learning transfers the learned representation from a source domain where abundant training data is available to a target domain where training data is scarce. Transfer representation learning gave the OM and melanoma diagnosis modules of our XPRIZE Tricorder device (which finished $2^{nd}$ out of $310$ competing teams) a significant boost in diagnosis accuracy.
Deep Discrete Supervised Hashing
Venue: arXiv cs.IR 2017
Year: 2017

Hashing has been widely used for large-scale search due to its low storage cost and fast query speed. By using supervised information, supervised hashing can significantly outperform unsupervised hashing. Recently, discrete supervised hashing and deep hashing are two representative progresses in supervised hashing. On one hand, hashing is essentially a discrete optimization problem. Hence, utilizing supervised information to directly guide discrete (binary) coding procedure can avoid sub-optimal solution and improve the accuracy. On the other hand, deep hashing, which integrates deep feature learning and hash-code learning into an end-to-end architecture, can enhance the feedback between feature learning and hash-code learning. The key in discrete supervised hashing is to adopt supervised information to directly guide the discrete coding procedure in hashing. The key in deep hashing is to adopt the supervised information to directly guide the deep feature learning procedure. However, there have not existed works which can use the supervised information to directly guide both discrete coding procedure and deep feature learning procedure in the same framework. In this paper, we propose a novel deep hashing method, called deep discrete supervised hashing (DDSH), to address this problem. DDSH is the first deep hashing method which can utilize supervised information to directly guide both discrete coding procedure and deep feature learning procedure, and thus enhance the feedback between these two important procedures. Experiments on three real datasets show that DDSH can outperform other state-of-the-art baselines, including both discrete hashing and deep hashing baselines, for image retrieval.
Logic and Topology for Knowledge, Knowability, and Belief - Extended Abstract
Venue: arXiv cs.LO 2017
Year: 2017

In recent work, Stalnaker proposes a logical framework in which belief is realized as a weakened form of knowledge. Building on Stalnaker's core insights, and using frameworks developed by Bjorndahl and Baltag et al., we employ topological tools to refine and, we argue, improve on this analysis. The structure of topological subset spaces allows for a natural distinction between what is known and (roughly speaking) what is knowable; we argue that the foundational axioms of Stalnaker's system rely intuitively on both of these notions. More precisely, we argue that the plausibility of the principles Stalnaker proposes relating knowledge and belief relies on a subtle equivocation between an "evidence-in-hand" conception of knowledge and a weaker "evidence-out-there" notion of what could come to be known. Our analysis leads to a trimodal logic of knowledge, knowability, and belief interpreted in topological subset spaces in which belief is definable in terms of knowledge and knowability. We provide a sound and complete axiomatization for this logic as well as its uni-modal belief fragment. We then consider weaker logics that preserve suitable translations of Stalnaker's postulates, yet do not allow for any reduction of belief. We propose novel topological semantics for these irreducible notions of belief, generalizing our previous semantics, and provide sound and complete axiomatizations for the corresponding logics.
Capturing the Connections: Unboxing Internet of Things Devices
Venue: arXiv cs.HC 2017
Year: 2017

Based upon a study of how to capture data from Internet of Things (IoT) devices, this paper explores the challenges for data centric design ethnography. Often purchased to perform specific tasks, IoT devices exist in a complex ecosystem. This paper describes a study that used a variety of methods to capture the interactions an IoT device engaged in when it was first setup. The complexity of the study that is explored through the annotated documentation across video and router activity, presents the ethnographic challenges that designers face in an age of connected things.
Compiling Deep Learning Models for Custom Hardware Accelerators
Venue: arXiv cs.DC 2017
Year: 2017

Convolutional neural networks (CNNs) are the core of most state-of-the-art deep learning algorithms specialized for object detection and classification. CNNs are both computationally complex and embarrassingly parallel. Two properties that leave room for potential software and hardware optimizations for embedded systems. Given a programmable hardware accelerator with a CNN oriented custom instructions set, the compiler's task is to exploit the hardware's full potential, while abiding with the hardware constraints and maintaining generality to run different CNN models with varying workload properties. Snowflake is an efficient and scalable hardware accelerator implemented on programmable logic devices. It implements a control pipeline for a custom instruction set. The goal of this paper is to present Snowflake's compiler that generates machine level instructions from Torch7 model description files. The main software design points explored in this work are: model structure parsing, CNN workload breakdown, loop rearrangement for memory bandwidth optimizations and memory access balancing. The performance achieved by compiler generated instructions matches against hand optimized code for convolution layers. Generated instructions also efficiently execute AlexNet and ResNet18 inference on Snowflake. Snowflake with $256$ processing units was synthesized on Xilinx's Zynq XC7Z045 FPGA. At $250$ MHz, AlexNet achieved in $93.6$ frames/s and $1.2$ GB/s of off-chip memory bandwidth, and $21.4$ frames/s and $2.2$ GB/s for ResNet18. Total on-chip power is $5$ W.