OpenRaiser/Intern-Atlas huggingface dataset:
On the additivity of preference aggregation methods
Venue: arXiv cs.GT 2015
Year: 2015

The paper reviews some axioms of additivity concerning ranking methods used for generalized tournaments with possible missing values and multiple comparisons. It is shown that one of the most natural properties, called consistency, has strong links to independence of irrelevant comparisons, an axiom judged unfavourable when players have different opponents. Therefore some directions of weakening consistency are suggested, and several ranking methods, the score, generalized row sum and least squares as well as fair bets and its two variants (one of them entirely new) are analysed whether they satisfy the properties discussed. It turns out that least squares and generalized row sum with an appropriate parameter choice preserve the relative ranking of two objects if the ranking problems added have the same comparison structure.
Cloudifying the 3GPP IP Multimedia Subsystem for 4G and Beyond: A Survey
Venue: arXiv cs.NI 2015
Year: 2015

4G systems have been continuously evolving to cope with the emerging challenges of human-centric and machine-to- machine (M2M) applications. Research has also now started on 5G systems. Scenarios have been proposed and initial requirements derived. 4G and beyond systems are expected to easily deliver a wide range of human-centric and M2M applications and services in a scalable, elastic, and cost efficient manner. The 3GPP IP multimedia subsystem (IMS) was standardized as the service delivery platform for 3G networks. Unfortunately, it does not meet several requirements for provisioning applications and services in 4G and beyond systems. However, cloudifying it will certainly pave the way for its use as a service delivery platform for 4G and beyond. This article presents a critical overview of the architectures proposed so far for cloudifying the IMS. There are two classes of approaches; the first focuses on the whole IMS system, and the second deals with specific IMS entities. Research directions are also discussed. IMS granularity and a PaaS for the development and management of IMS functional entities are the two key directions we currently foresee.
CacheDiff: Fast Random Sampling
Venue: arXiv cs.DS 2015
Year: 2015

We present a sampling method called, CacheDiff, that has both time and space complexity of O(k) to randomly select k items from a pool of N items, in which N is known.
Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision
Venue: arXiv cs.CV 2015
Year: 2015

Feature selection is essential for effective visual recognition. We propose an efficient joint classifier learning and feature selection method that discovers sparse, compact representations of input features from a vast sea of candidates, with an almost unsupervised formulation. Our method requires only the following knowledge, which we call the \emph{feature sign}---whether or not a particular feature has on average stronger values over positive samples than over negatives. We show how this can be estimated using as few as a single labeled training sample per class. Then, using these feature signs, we extend an initial supervised learning problem into an (almost) unsupervised clustering formulation that can incorporate new data without requiring ground truth labels. Our method works both as a feature selection mechanism and as a fully competitive classifier. It has important properties, low computational cost and excellent accuracy, especially in difficult cases of very limited training data. We experiment on large-scale recognition in video and show superior speed and performance to established feature selection approaches such as AdaBoost, Lasso, greedy forward-backward selection, and powerful classifiers such as SVM.
Probabilistic Latent Semantic Analysis (PLSA) untuk Klasifikasi Dokumen Teks Berbahasa Indonesia
Venue: arXiv cs.CL 2015
Year: 2015

One task that is included in managing documents is how to find substantial information inside. Topic modeling is a technique that has been developed to produce document representation in form of keywords. The keywords will be used in the indexing process and document retrieval as needed by users. In this research, we will discuss specifically about Probabilistic Latent Semantic Analysis (PLSA). It will cover PLSA mechanism which involves Expectation Maximization (EM) as the training algorithm, how to conduct testing, and obtain the accuracy result.
Klasifikasi Komponen Argumen Secara Otomatis pada Dokumen Teks berbentuk Esai Argumentatif
Venue: arXiv cs.CL 2015
Year: 2015

By automatically recognize argument component, essay writers can do some inspections to texts that they have written. It will assist essay scoring process objectively and precisely because essay grader is able to see how well the argument components are constructed. Some reseachers have tried to do argument detection and classification along with its implementation in some domains. The common approach is by doing feature extraction to the text. Generally, the features are structural, lexical, syntactic, indicator, and contextual. In this research, we add new feature to the existing features. It adopts keywords list by Knott and Dale (1993). The experiment result shows the argument classification achieves 72.45% accuracy. Moreover, we still get the same accuracy without the keyword lists. This concludes that the keyword lists do not affect significantly to the features. All features are still weak to classify major claim and claim, so we need other features which are useful to differentiate those two kind of argument components.
The MegaFace Benchmark: 1 Million Faces for Recognition at Scale
Venue: arXiv cs.CV 2015
Year: 2015

Recent face recognition experiments on a major benchmark LFW show stunning performance--a number of algorithms achieve near to perfect score, surpassing human recognition rates. In this paper, we advocate evaluations at the million scale (LFW includes only 13K photos of 5K people). To this end, we have assembled the MegaFace dataset and created the first MegaFace challenge. Our dataset includes One Million photos that capture more than 690K different individuals. The challenge evaluates performance of algorithms with increasing numbers of distractors (going from 10 to 1M) in the gallery set. We present both identification and verification performance, evaluate performance with respect to pose and a person's age, and compare as a function of training data size (number of photos and people). We report results of state of the art and baseline algorithms. Our key observations are that testing at the million scale reveals big performance differences (of algorithms that perform similarly well on smaller scale) and that age invariant recognition as well as pose are still challenging for most. The MegaFace dataset, baseline code, and evaluation scripts, are all publicly released for further experimentations at: megaface.cs.washington.edu.
Extracting Traceability Information from C# Projects
Venue: arXiv cs.SE 2015
Year: 2015

The maintenance portion of the software lifecycle represents a major drain on most software companys resources. The transition from programmers to maintainers is high risk, since usually the maintainers have to learn the system from scratch before they can begin modifying it appropriately. This paper introduces a method for automatically extracting important traceability information from a C# software projects source code. Using this traceability information, maintainers (and programmers) are better able to evaluate the impacts their actions will have on the entire project.
Learning Semantic Similarity for Very Short Texts
Venue: arXiv cs.IR 2015
Year: 2015

Levering data on social media, such as Twitter and Facebook, requires information retrieval algorithms to become able to relate very short text fragments to each other. Traditional text similarity methods such as tf-idf cosine-similarity, based on word overlap, mostly fail to produce good results in this case, since word overlap is little or non-existent. Recently, distributed word representations, or word embeddings, have been shown to successfully allow words to match on the semantic level. In order to pair short text fragments - as a concatenation of separate words - an adequate distributed sentence representation is needed, in existing literature often obtained by naively combining the individual word representations. We therefore investigated several text representations as a combination of word embeddings in the context of semantic pair matching. This paper investigates the effectiveness of several such naive techniques, as well as traditional tf-idf similarity, for fragments of different lengths. Our main contribution is a first step towards a hybrid method that combines the strength of dense distributed representations - as opposed to sparse term matching - with the strength of tf-idf based methods to automatically reduce the impact of less informative terms. Our new approach outperforms the existing techniques in a toy experimental set-up, leading to the conclusion that the combination of word embeddings and tf-idf information might lead to a better model for semantic content within very short text fragments.
Zero-Shot Event Detection by Multimodal Distributional Semantic Embedding of Videos
Venue: arXiv cs.CV 2015
Year: 2015

We propose a new zero-shot Event Detection method by Multi-modal Distributional Semantic embedding of videos. Our model embeds object and action concepts as well as other available modalities from videos into a distributional semantic space. To our knowledge, this is the first Zero-Shot event detection model that is built on top of distributional semantics and extends it in the following directions: (a) semantic embedding of multimodal information in videos (with focus on the visual modalities), (b) automatically determining relevance of concepts/attributes to a free text query, which could be useful for other applications, and (c) retrieving videos by free text event query (e.g., "changing a vehicle tire") based on their content. We embed videos into a distributional semantic space and then measure the similarity between videos and the event query in a free text form. We validated our method on the large TRECVID MED (Multimedia Event Detection) challenge. Using only the event title as a query, our method outperformed the state-of-the-art that uses big descriptions from 12.6% to 13.5% with MAP metric and 0.73 to 0.83 with ROC-AUC metric. It is also an order of magnitude faster.
Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model
Venue: arXiv cs.AI 2015
Year: 2015

Most human behaviors consist of multiple parts, steps, or subtasks. These structures guide our action planning and execution, but when we observe others, the latent structure of their actions is typically unobservable, and must be inferred in order to learn new skills by demonstration, or to assist others in completing their tasks. For example, an assistant who has learned the subgoal structure of a colleague's task can more rapidly recognize and support their actions as they unfold. Here we model how humans infer subgoals from observations of complex action sequences using a nonparametric Bayesian model, which assumes that observed actions are generated by approximately rational planning over unknown subgoal sequences. We test this model with a behavioral experiment in which humans observed different series of goal-directed actions, and inferred both the number and composition of the subgoal sequences associated with each goal. The Bayesian model predicts human subgoal inferences with high accuracy, and significantly better than several alternative models and straightforward heuristics. Motivated by this result, we simulate how learning and inference of subgoals can improve performance in an artificial user assistance task. The Bayesian model learns the correct subgoals from fewer observations, and better assists users by more rapidly and accurately inferring the goal of their actions than alternative approaches.
Contraction based stabilization of nonlinear singularly perturbed systems and application to high gain feedback
Venue: arXiv cs.SY 2015
Year: 2015

Recent development of contraction theory based analysis of singularly perturbed system has opened the door for inspecting differential behavior of multi time-scale systems. In this paper a contraction theory based framework is proposed for stabilization of singularly perturbed systems. The primary objective is to design a feedback controller to achieve bounded tracking error for both standard and non-standard singularly perturbed systems. This framework provides relaxation over traditional quadratic Lyapunov based method as there is no need to satisfy interconnection conditions during controller design algorithm. Moreover, the stability bound does not depend on smallness of singularly perturbed parameter. Combined with high gain scaling, the proposed technique is shown to assure contraction of approximate feedback linearizable systems. These findings extend the class of nonlinear systems which can be made contracting.
Occlusion-Aware Human Pose Estimation with Mixtures of Sub-Trees
Venue: arXiv cs.CV 2015
Year: 2015

In this paper, we study the problem of learning a model for human pose estimation as mixtures of compositional sub-trees in two layers of prediction. This involves estimating the pose of a sub-tree followed by identifying the relationships between sub-trees that are used to handle occlusions between different parts. The mixtures of the sub-trees are learnt utilising both geometric and appearance distances. The Chow-Liu (CL) algorithm is recursively applied to determine the inter-relations between the nodes and to build the structure of the sub-trees. These structures are used to learn the latent parameters of the sub-trees and the inference is done using a standard belief propagation technique. The proposed method handles occlusions during the inference process by identifying overlapping regions between different sub-trees and introducing a penalty term for overlapping parts. Experiments are performed on three different datasets: the Leeds Sports, Image Parse and UIUC People datasets. The results show the robustness of the proposed method to occlusions over the state-of-the-art approaches.
Fast Average-Case Pattern Matching on Weighted Sequences
Venue: arXiv cs.DS 2015
Year: 2015

A weighted string over an alphabet of size $σ$ is a string in which a set of letters may occur at each position with respective occurrence probabilities. Weighted strings, also known as position weight matrices or uncertain sequences, naturally arise in many contexts. In this article, we study the problem of weighted string matching with a special focus on average-case analysis. Given a weighted pattern string $x$ of length $m$, a text string $y$ of length $n>m$, and a cumulative weight threshold $1/z$, defined as the minimal probability of occurrence of factors in a weighted string, we present an algorithm requiring average-case search time $o(n)$ for pattern matching for weight ratio $\frac{z}{m} < \min\{\frac{1}{\log z},\frac{\log σ}{\log z (\log m + \log \log σ)}\}$. For a pattern string $x$ of length $m$, a weighted text string $y$ of length $n>m$, and a cumulative weight threshold $1/z$, we present an algorithm requiring average-case search time $o(σn)$ for the same weight ratio. The importance of these results lies on the fact that these algorithms work in average-case sublinear search time in the size of the text, and in linear preprocessing time and space in the size of the pattern, for these ratios.
Bandwidth in the Cloud
Venue: arXiv cs.NI 2015
Year: 2015

The seek for the best quality of service has led Cloud infrastructure clients to disseminate their services, contents and data over multiple cloud data-centers often involving several Cloud Service Providers (CSPs). The consequence of this is that a large amount of bytes must be transmitted across the public Cloud. However, very little is known about its bandwidth dynamics. To address this, we have conducted a measurement campaign for bandwidth between eighteen data-centers of four major CSPs. Such extensive campaign allowed us to characterize the resulting time series of bandwidth as the addition of a stationary component and some infrequent excursions (typically, downtimes). While the former provides a description of the bandwidth users can expect in the Cloud, the latter is closely related to the robustness of the Cloud (i.e., the occurrence of downtimes is correlated). Both components have been studied further by applying a factor analysis, specifically ANOVA, as a mechanism to formally compare data-centers' behaviors and extract generalities. The results show that the stationary process is closely related to data-center locations and CSPs involved in transfers, which fortunately makes both the Cloud more predictable and the set of reported measurements extrapolate. On the other hand, although the correlation in the Cloud is low, i.e., only 10% of the measured pair of paths showed some correlation, we have found evidence that such correlation depends on the particular relationships between pairs of data-centers with little link to more general factors. Positively, this implies that data-centers either at the same area or CSP do not show qualitatively more correlation than others data-centers, which eases the deployment of robust infrastructures. On the downside, this metric is barely generalizable and, consequently, calls for exhaustive monitoring.
Robustly Solvable Constraint Satisfaction Problems
Venue: arXiv cs.CC 2015
Year: 2015

An algorithm for a constraint satisfaction problem is called robust if it outputs an assignment satisfying at least $(1-g(\varepsilon))$-fraction of the constraints given a $(1-\varepsilon)$-satisfiable instance, where $g(\varepsilon) \rightarrow 0$ as $\varepsilon \rightarrow 0$. Guruswami and Zhou conjectured a characterization of constraint languages for which the corresponding constraint satisfaction problem admits an efficient robust algorithm. This paper confirms their conjecture.
Predicting the top and bottom ranks of billboard songs using Machine Learning
Venue: arXiv cs.CL 2015
Year: 2015

The music industry is a $130 billion industry. Predicting whether a song catches the pulse of the audience impacts the industry. In this paper we analyze language inside the lyrics of the songs using several computational linguistic algorithms and predict whether a song would make to the top or bottom of the billboard rankings based on the language features. We trained and tested an SVM classifier with a radial kernel function on the linguistic features. Results indicate that we can classify whether a song belongs to top and bottom of the billboard charts with a precision of 0.76.
An Online Unsupervised Structural Plasticity Algorithm for Spiking Neural Networks
Venue: arXiv cs.NE 2015
Year: 2015

In this article, we propose a novel Winner-Take-All (WTA) architecture employing neurons with nonlinear dendrites and an online unsupervised structural plasticity rule for training it. Further, to aid hardware implementations, our network employs only binary synapses. The proposed learning rule is inspired by spike time dependent plasticity (STDP) but differs for each dendrite based on its activation level. It trains the WTA network through formation and elimination of connections between inputs and synapses. To demonstrate the performance of the proposed network and learning rule, we employ it to solve two, four and six class classification of random Poisson spike time inputs. The results indicate that by proper tuning of the inhibitory time constant of the WTA, a trade-off between specificity and sensitivity of the network can be achieved. We use the inhibitory time constant to set the number of subpatterns per pattern we want to detect. We show that while the percentage of successful trials are 92%, 88% and 82% for two, four and six class classification when no pattern subdivisions are made, it increases to 100% when each pattern is subdivided into 5 or 10 subpatterns. However, the former scenario of no pattern subdivision is more jitter resilient than the later ones.
Max-Pooling Dropout for Regularization of Convolutional Neural Networks
Venue: arXiv cs.LG 2015
Year: 2015

Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage.
Differential Amplify-and-Forward Relaying Using Linear Combining in Time-Varying Channels
Venue: arXiv cs.IT 2015
Year: 2015

Differential encoding and decoding can be employed to circumvent channel estimation in wireless relay networks. This article studies differential amplify-and-forward relaying using linear combining with arbitrary fixed combining weights in time-varying channels. An exact bit error rate (BER) analysis is obtained for this system using DBPSK modulation and over time-varying Rayleigh fading channels. The analysis is verified with simulation results for several sets of combining weights and in various fading scenarios.
Motion trails from time-lapse video
Venue: arXiv cs.CV 2015
Year: 2015

From an image sequence captured by a stationary camera, background subtraction can detect moving foreground objects in the scene. Distinguishing foreground from background is further improved by various heuristics. Then each object's motion can be emphasized by duplicating its positions as a motion trail. These trails clarify the objects' spatial relationships. Also, adding motion trails to a video before previewing it at high speed reduces the risk of overlooking transient events.
Threshold Voltage-Defined Switches for Programmable Gates
Venue: arXiv cs.CR 2015
Year: 2015

Semiconductor supply chain is increasingly getting exposed to variety of security attacks such as Trojan insertion, cloning, counterfeiting, reverse engineering (RE), piracy of Intellectual Property (IP) or Integrated Circuit (IC) and side-channel analysis due to involvement of untrusted parties. In this paper, we propose transistor threshold voltage-defined switches to camouflage the logic gate both logically and physically to resist against RE and IP piracy. The proposed gate can function as NAND, AND, NOR, OR, XOR, XNOR, INV and BUF robustly using threshold-defined switches. The camouflaged design operates at nominal voltage and obeys conventional reliability limits. The proposed gate can also be used to personalize the design during manufacturing.
Sparse Detection of Non-Sparse Signals for Large-Scale Wireless Systems
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we introduce a new detection algorithm for large-scale wireless systems, referred to as post sparse error detection (PSED) algorithm, that employs a sparse error recovery algorithm to refine the estimate of a symbol vector obtained by the conventional linear detector. The PSED algorithm operates in two steps: 1) sparse transformation converting the original non-sparse system into the sparse system whose input is an error vector caused by the symbol slicing and 2) estimation of the error vector using the sparse recovery algorithm. From the asymptotic mean square error (MSE) analysis and empirical simulations performed on large-scale systems, we show that the PSED algorithm brings significant performance gain over classical linear detectors while imposing relatively small computational overhead.