OpenRaiser/Intern-Atlas huggingface dataset:
A Benchmark for the Performance of Time-varying Closed-loop Flow Control with Application to TCP
Venue: arXiv cs.NI 2016
Year: 2016

Closed-loop flow control protocols, such as the prominent implementation TCP, are prevalent in the Internet, today. TCP has continuously been improved for greedy traffic sources to achieve high throughput over networks with large bandwidth delay products. Recently, the increasing use for streaming and interactive applications, such as voice and video, has shifted the focus towards its delay performance. Given the need for real-time communication of non-greedy sources via TCP, we present an estimation method for performance evaluation of closed-loop flow control protocols. We characterize an end-to-end connection by a transfer function that provides statistical service guarantees for arbitrary traffic. The estimation is based on end-to-end measurements at the application level that include all effects induced by the network and by the protocol stacks of the end systems. From our measurements, we identify different causes for delays. We show that significant delays are due to queueing in protocol stacks. Notably, this occurs even if the utilization is moderate. Using our estimation method, we compare the impact of fundamental mechanisms of TCP on delays at the application level: In detail, we analyze parameters relevant for network dimensioning, including buffer provisioning and active queue management, and parameters for server configuration, such as the congestion control algorithm. By applying our method as a benchmark, we find that a good selection can largely improve the delay performance of TCP.
Single photon in hierarchical architecture for physical reinforcement learning: Photon intelligence
Venue: arXiv cs.LG 2016
Year: 2016

Understanding and using natural processes for intelligent functionalities, referred to as natural intelligence, has recently attracted interest from a variety of fields, including post-silicon computing for artificial intelligence and decision making in the behavioural sciences. In a past study, we successfully used the wave-particle duality of single photons to solve the two-armed bandit problem, which constitutes the foundation of reinforcement learning and decision making. In this study, we propose and confirm a hierarchical architecture for single-photon-based reinforcement learning and decision making that verifies the scalability of the principle. Specifically, the four-armed bandit problem is solved given zero prior knowledge in a two-layer hierarchical architecture, where polarization is autonomously adapted in order to effect adequate decision making using single-photon measurements. In the hierarchical structure, the notion of layer-dependent decisions emerges. The optimal solutions in the coarse layer and in the fine layer, however, conflict with each other in some contradictive problems. We show that while what we call a tournament strategy resolves such contradictions, the probabilistic nature of single photons allows for the direct location of the optimal solution even for contradictive problems, hence manifesting the exploration ability of single photons. This study provides insights into photon intelligence in hierarchical architectures for future artificial intelligence as well as the potential of natural processes for intelligent functionalities.
Randomized Prediction Games for Adversarial Machine Learning
Venue: arXiv cs.LG 2016
Year: 2016

In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time; e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different evasion attacks and modifying the classification function accordingly. However, both the classification function and the simulated data manipulations have been modeled in a deterministic manner, without accounting for any form of randomization. In this work, we overcome this limitation by proposing a randomized prediction game, namely, a non-cooperative game-theoretic formulation in which the classifier and the attacker make randomized strategy selections according to some probability distribution defined over the respective strategy set. We show that our approach allows one to improve the trade-off between attack detection and false alarms with respect to state-of-the-art secure classifiers, even against attacks that are different from those hypothesized during design, on application examples including handwritten digit recognition, spam and malware detection.
Spectral Efficiency Bounds for Interference-Limited SVD-MIMO Cellular Communication Systems
Venue: arXiv cs.IT 2016
Year: 2016

The ergodic spectral efficiency (SE) in interference-limited multiple-input multiple-output (MIMO) downlink cellular systems is characterized based on stochastic geometry. A single user is served by using singular value decomposition precoding and combining. By approximating the expectations of the channel eigenvalues, we derive upper and lower bounds on the ergodic SE. The obtained upper bound is the best possible system-level performance of any MIMO strategy in non-cooperative cellular networks. We validate our analytical results through simulation. We also conjecture that there exists the optimal number of streams being proportional to the pathloss exponent.
High Dimensional Human Guided Machine Learning
Venue: arXiv cs.AI 2016
Year: 2016

Have you ever looked at a machine learning classification model and thought, I could have made that? Well, that is what we test in this project, comparing XGBoost trained on human engineered features to training directly on data. The human engineered features do not outperform XGBoost trained di- rectly on the data, but they are comparable. This project con- tributes a novel method for utilizing human created classifi- cation models on high dimensional datasets.
Dispersion Compensation using High-Positive Dispersive Optical Fibers
Venue: arXiv cs.IT 2016
Year: 2016

The common and traditional method for dispersion compensation in optical domain is concatenating the transmit optical fiber by a compensating optical fiber having high-negative dispersion coefficient. In this paper, we take an opposite direction and show how an optical fiber with high-positive dispersion coefficient can also be used for dispersion compensation. Our optical dispersion compensating structure is the optical implementation of an iterative algorithm in signal processing. The proposed dispersion compensating system is constructed by cascading a number of compensating sub-systems and its compensation capability is improved by increasing the number of embedded sub-systems. We also show that the compensation capability is a trade-off between transmission length and bandwidth. We use simulation results to validate the performance of the introduced dispersion compensating module. Photonic crystal fibers with high-positive dispersion coefficient can be used for constructing the proposed optical dispersion compensating module.
MmmTurkey: A Crowdsourcing Framework for Deploying Tasks and Recording Worker Behavior on Amazon Mechanical Turk
Venue: arXiv cs.HC 2016
Year: 2016

Internal HITs on Mechanical Turk can be programmatically restrictive, and as a result, many requesters turn to using external HITs as a more flexible alternative. However, creating such HITs can be redundant and time-consuming. We present MmmTurkey, a framework that enables researchers to not only quickly create and manage external HITs, but more significantly also capture and record detailed worker behavioral data characterizing how each worker completes a given task.
Convexified Convolutional Neural Networks
Venue: arXiv cs.LG 2016
Year: 2016

We describe the class of convexified convolutional neural networks (CCNNs), which capture the parameter sharing of convolutional neural networks in a convex manner. By representing the nonlinear convolutional filters as vectors in a reproducing kernel Hilbert space, the CNN parameters can be represented as a low-rank matrix, which can be relaxed to obtain a convex optimization problem. For learning two-layer convolutional neural networks, we prove that the generalization error obtained by a convexified CNN converges to that of the best possible CNN. For learning deeper networks, we train CCNNs in a layer-wise manner. Empirically, CCNNs achieve performance competitive with CNNs trained by backpropagation, SVMs, fully-connected neural networks, stacked denoising auto-encoders, and other baseline methods.
An Analysis of the Cloud Computing Security Problem
Venue: arXiv cs.SE 2016
Year: 2016

Cloud computing is a new computational paradigm that offers an innovative business model for organizations to adopt IT without upfront investment. Despite the potential gains achieved from the cloud computing, the model security is still questionable which impacts the cloud model adoption. The security problem becomes more complicated under the cloud model as new dimensions have entered into the problem scope related to the model architecture, multi-tenancy, elasticity, and layers dependency stack. In this paper we introduce a detailed analysis of the cloud security problem. We investigated the problem from the cloud architecture perspective, the cloud offered characteristics perspective, the cloud stakeholders' perspective, and the cloud service delivery models perspective. Based on this analysis we derive a detailed specification of the cloud security problem and key features that should be covered by any proposed security solution.
Reflections on Shannon Information: In search of a natural information-entropy for images
Venue: arXiv cs.IT 2016
Year: 2016

It is not obvious how to extend Shannon's original information entropy to higher dimensions, and many different approaches have been tried. We replace the English text symbol sequence originally used to illustrate the theory by a discrete, bandlimited signal. Using Shannon's later theory of sampling we derive a new and symmetric version of the second order entropy in 1D. The new theory then naturally extends to 2D and higher dimensions, where by naturally we mean simple, symmetric, isotropic and parsimonious. Simplicity arises from the direct application of Shannon's joint entropy equalities and inequalities to the gradient (del) vector field image embodying the second order relations of the scalar image. Parsimony is guaranteed by halving of the vector data rate using Papoulis' generalized sampling expansion. The new 2D entropy measure, which we dub delentropy, is underpinned by a computable probability density function we call deldensity. The deldensity captures the underlying spatial image structure and pixel co-occurrence. It achieves this because each scalar image pixel value is nonlocally related to the entire gradient vector field. Both deldensity and delentropy are highly tractable and yield many interesting connections and useful inequalities. The new measure explicitly defines a realizable encoding algorithm and a corresponding reconstruction. Initial tests show that delentropy compares favourably with the conventional intensity-based histogram entropy and the compressed data rates of lossless image encoders (GIF, PNG, WEBP, JP2K-LS and JPG-LS) for a selection of images. The symmetric approach may have applications to higher dimensions and problems concerning image complexity measures.
Bi-Text Alignment of Movie Subtitles for Spoken English-Arabic Statistical Machine Translation
Venue: arXiv cs.CL 2016
Year: 2016

We describe efforts towards getting better resources for English-Arabic machine translation of spoken text. In particular, we look at movie subtitles as a unique, rich resource, as subtitles in one language often get translated into other languages. Movie subtitles are not new as a resource and have been explored in previous research; however, here we create a much larger bi-text (the biggest to date), and we further generate better quality alignment for it. Given the subtitles for the same movie in different languages, a key problem is how to align them at the fragment level. Typically, this is done using length-based alignment, but for movie subtitles, there is also time information. Here we exploit this information to develop an original algorithm that outperforms the current best subtitle alignment tool, subalign. The evaluation results show that adding our bi-text to the IWSLT training bi-text yields an improvement of over two BLEU points absolute.
An implementation of Deflate in Coq
Venue: arXiv cs.LO 2016
Year: 2016

The widely-used compression format "Deflate" is defined in RFC 1951 and is based on prefix-free codings and backreferences. There are unclear points about the way these codings are specified, and several sources for confusion in the standard. We tried to fix this problem by giving a rigorous mathematical specification, which we formalized in Coq. We produced a verified implementation in Coq which achieves competitive performance on inputs of several megabytes. In this paper we present the several parts of our implementation: a fully verified implementation of canonical prefix-free codings, which can be used in other compression formats as well, and an elegant formalism for specifying sophisticated formats, which we used to implement both a compression and decompression algorithm in Coq which we formally prove inverse to each other -- the first time this has been achieved to our knowledge. The compatibility to other Deflate implementations can be shown empirically. We furthermore discuss some of the difficulties, specifically regarding memory and runtime requirements, and our approaches to overcome them.
PMI Matrix Approximations with Applications to Neural Language Modeling
Venue: arXiv cs.CL 2016
Year: 2016

The negative sampling (NEG) objective function, used in word2vec, is a simplification of the Noise Contrastive Estimation (NCE) method. NEG was found to be highly effective in learning continuous word representations. However, unlike NCE, it was considered inapplicable for the purpose of learning the parameters of a language model. In this study, we refute this assertion by providing a principled derivation for NEG-based language modeling, founded on a novel analysis of a low-dimensional approximation of the matrix of pointwise mutual information between the contexts and the predicted words. The obtained language modeling is closely related to NCE language models but is based on a simplified objective function. We thus provide a unified formulation for two main language processing tasks, namely word embedding and language modeling, based on the NEG objective function. Experimental results on two popular language modeling benchmarks show comparable perplexity results, with a small advantage to NEG over NCE.
OpenSBLI: A framework for the automated derivation and parallel execution of finite difference solvers on a range of computer architectures
Venue: arXiv cs.MS 2016
Year: 2016

Exascale computing will feature novel and potentially disruptive hardware architectures. Exploiting these to their full potential is non-trivial. Numerical modelling frameworks involving finite difference methods are currently limited by the 'static' nature of the hand-coded discretisation schemes and repeatedly may have to be re-written to run efficiently on new hardware. In contrast, OpenSBLI uses code generation to derive the model's code from a high-level specification. Users focus on the equations to solve, whilst not concerning themselves with the detailed implementation. Source-to-source translation is used to tailor the code and enable its execution on a variety of hardware.
Depth Reconstruction and Computer-Aided Polyp Detection in Optical Colonoscopy Video Frames
Venue: arXiv cs.CV 2016
Year: 2016

We present a computer-aided detection algorithm for polyps in optical colonoscopy images. Polyps are the precursors to colon cancer. In the US alone, more than 14 million optical colonoscopies are performed every year, mostly to screen for polyps. Optical colonoscopy has been shown to have an approximately 25% polyp miss rate due to the convoluted folds and bends present in the colon. In this work, we present an automatic detection algorithm to detect these polyps in the optical colonoscopy images. We use a machine learning algorithm to infer a depth map for a given optical colonoscopy image and then use a detailed pre-built polyp profile to detect and delineate the boundaries of polyps in this given image. We have achieved the best recall of 84.0% and the best specificity value of 83.4%.
Creating Interactive Behaviors in Early Sketch by Recording and Remixing Crowd Demonstrations
Venue: arXiv cs.HC 2016
Year: 2016

In the early stages of designing graphical user interfaces (GUIs), the look (appearance) can be easily presented by sketching, but the feel (interactive behaviors) cannot, and often requires an accompanying description of how it works (Myers et al. 2008). We propose to use crowdsourcing to augment early sketches with interactive behaviors generated, used, and reused by collective "wizards-of-oz" as opposed to a single wizard as in prior work (Davis et al. 2007). This demo presents an extension of Apparition (Lasecki et al. 2015), a crowd-powered prototyping tool that allows end users to create functional GUIs using speech and sketch. In Apparition, crowd workers collaborate in real-time on a shared canvas to refine the user-requested sketch interactively, and with the assistance of the end users. Our demo extends this functionality to let crowd workers "demonstrate" the canvas changes that are needed for a behavior and refine their demonstrations to improve the fidelity of interactive behaviors. The system then lets workers "remix" these behaviors to make creating future behaviors more efficient.
Features Fusion for Classification of Logos
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, a logo classification system based on the appearance of logo images is proposed. The proposed classification system makes use of global characteristics of logo images for classification. Color, texture, and shape of a logo wholly describe the global characteristics of logo images. The various combinations of these characteristics are used for classification. The combination contains only with single feature or with fusion of two features or fusion of all three features considered at a time respectively. Further, the system categorizes the logo image into: a logo image with fully text or with fully symbols or containing both symbols and texts.. The K-Nearest Neighbour (K-NN) classifier is used for classification. Due to the lack of color logo image dataset in the literature, the same is created consisting 5044 color logo images. Finally, the performance of the classification system is evaluated through accuracy, precision, recall and F-measure computed from the confusion matrix. The experimental results show that the most promising results are obtained for fusion of features.
OpenTripPlanner, OpenStreetMap, General Transit Feed Specification: Tools for Disaster Relief and Recovery
Venue: arXiv cs.CY 2016
Year: 2016

Open Trip Planner was identified as the most promising open source multi-modal trip planning software. Open Street Map, which provides mapping data to Open Trip Planner, is one of the most well-known open source international repository of geographic data. General Transit Feed Specification, which provides transportation data to Open Trip Planner, has been the standard for describing transit systems and platform for numerous applications. Together, when used to implement an instance of Open Trip Planner, these software has been helping in traffic decongestion all over the world by assisting commuters to shift from using private transportation modes to public ones. Their potential however goes beyond providing multi-modal public transportation routes. This paper aims to first discuss the researchers' experience in implementing a public transportation route planner for the purpose of traffic decongestion.The researchers would examine the prospective of using the system for disaster preparedness and recovery and concrete ways on how to realize them.
A Markov Chain based Ensemble Method for Crowdsourced Clustering
Venue: arXiv cs.HC 2016
Year: 2016

In presence of multiple clustering solutions for the same dataset, a clustering ensemble approach aims to yield a single clustering of the dataset by achieving a consensus among the input clustering solutions. The goal of this consensus is to improve the quality of clustering. It has been seen that there are some image clustering tasks that cannot be easily solved by computer. But if these images can be outsourced to the general people (crowd workers) to group them based on some similar features, and opinions are collected from them, then this task can be managed in an efficient manner and time effective way. In this work, the power of crowd has been used to annotate the images so that multiple clustering solutions can be obtained from them and thereafter a Markov chain based ensemble method is introduced to make a consensus of multiple clustering solutions.
Depth Estimation Through a Generative Model of Light Field Synthesis
Venue: arXiv cs.CV 2016
Year: 2016

Light field photography captures rich structural information that may facilitate a number of traditional image processing and computer vision tasks. A crucial ingredient in such endeavors is accurate depth recovery. We present a novel framework that allows the recovery of a high quality continuous depth map from light field data. To this end we propose a generative model of a light field that is fully parametrized by its corresponding depth map. The model allows for the integration of powerful regularization techniques such as a non-local means prior, facilitating accurate depth map estimation.
A Hybrid Citation Retrieval Algorithm for Evidence-based Clinical Knowledge Summarization: Combining Concept Extraction, Vector Similarity and Query Expansion for High Precision
Venue: arXiv cs.CL 2016
Year: 2016

Novel information retrieval methods to identify citations relevant to a clinical topic can overcome the knowledge gap existing between the primary literature (MEDLINE) and online clinical knowledge resources such as UpToDate. Searching the MEDLINE database directly or with query expansion methods returns a large number of citations that are not relevant to the query. The current study presents a citation retrieval system that retrieves citations for evidence-based clinical knowledge summarization. This approach combines query expansion, concept-based screening algorithm, and concept-based vector similarity. We also propose an information extraction framework for automated concept (Population, Intervention, Comparison, and Disease) extraction. We evaluated our proposed system on all topics (as queries) available from UpToDate for two diseases, heart failure (HF) and atrial fibrillation (AFib). The system achieved an overall F-score of 41.2% on HF topics and 42.4% on AFib topics on a gold standard of citations available in UpToDate. This is significantly high when compared to a query-expansion based baseline (F-score of 1.3% on HF and 2.2% on AFib) and a system that uses query expansion with disease hyponyms and journal names, concept-based screening, and term-based vector similarity system (F-score of 37.5% on HF and 39.5% on AFib). Evaluating the system with top K relevant citations, where K is the number of citations in the gold standard achieved a much higher overall F-score of 69.9% on HF topics and 75.1% on AFib topics. In addition, the system retrieved up to 18 new relevant citations per topic when tested on ten HF and six AFib clinical topics.