OpenRaiser/Intern-Atlas huggingface dataset:
Towards Predicting the Likeability of Fashion Images
Venue: arXiv cs.CV 2015
Year: 2015

In this paper, we propose a method for ranking fashion images to find the ones which might be liked by more people. We collect two new datasets from image sharing websites (Pinterest and Polyvore). We represent fashion images based on attributes: semantic attributes and data-driven attributes. To learn semantic attributes from limited training data, we use an algorithm on multi-task convolutional neural networks to share visual knowledge among different semantic attribute categories. To discover data-driven attributes unsupervisedly, we propose an algorithm to simultaneously discover visual clusters and learn fashion-specific feature representations. Given attributes as representations, we propose to learn a ranking SPN (sum product networks) to rank pairs of fashion images. The proposed ranking SPN can capture the high-order correlations of the attributes. We show the effectiveness of our method on our two newly collected datasets.
Counting and Generating Terms in the Binary Lambda Calculus (Extended version)
Venue: arXiv cs.LO 2015
Year: 2015

In a paper entitled Binary lambda calculus and combinatory logic, John Tromp presents a simple way of encoding lambda calculus terms as binary sequences. In what follows, we study the numbers of binary strings of a given size that represent lambda terms and derive results from their generating functions, especially that the number of terms of size n grows roughly like 1.963447954. .. n. In a second part we use this approach to generate random lambda terms using Boltzmann samplers.
Cyclic codes over $\mathbb{F}_{2^m}[u]/\langle u^k\rangle$ of oddly even length
Venue: arXiv cs.IT 2015
Year: 2015

Let $\mathbb{F}_{2^m}$ be a finite field of characteristic $2$ and $R=\mathbb{F}_{2^m}[u]/\langle u^k\rangle=\mathbb{F}_{2^m} +u\mathbb{F}_{2^m}+\ldots+u^{k-1}\mathbb{F}_{2^m}$ ($u^k=0$) where $k\in \mathbb{Z}^{+}$ satisfies $k\geq 2$. For any odd positive integer $n$, it is known that cyclic codes over $R$ of length $2n$ are identified with ideals of the ring $R[x]/\langle x^{2n}-1\rangle$. In this paper, an explicit representation for each cyclic code over $R$ of length $2n$ is provided and a formula to count the number of codewords in each code is given. Then a formula to calculate the number of cyclic codes over $R$ of length $2n$ is obtained. Moreover, the dual code of each cyclic code and self-dual cyclic codes over $R$ of length $2n$ are investigated. (AAECC-1522)
Edge-b-coloring Trees
Venue: arXiv cs.DM 2015
Year: 2015

A b-coloring of the vertices of a graph is a proper coloring where each color class contains a vertex which is adjacent to at least one vertex in each other color class. The b-chromatic number of $G$ is the maximum integer $b(G)$ for which $G$ has a b-coloring with $b(G)$ colors. This problem was introduced by Irving and Manlove in 1999, where they showed that computing $b(G)$ is $\mathcal{NP}$-hard in general and polynomial-time solvable for trees. Since then, a number of complexity results were shown, including NP-hardness results for chordal graphs (Havet et. al., 2011) and line graphs (Campos et. al., 2015). In this article, we present a polynomial time algorithm that solves the problem restricted to claw-free block graphs, an important subclass of chordal graphs and line graphs. This is equivalent to solving the edge coloring version of the problem restricted to trees.
MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving combinatorial optimization problems
Venue: arXiv cs.NE 2015
Year: 2015

Evolutionary algorithms based on modeling the statistical dependencies (interactions) between the variables have been proposed to solve a wide range of complex problems. These algorithms learn and sample probabilistic graphical models able to encode and exploit the regularities of the problem. This paper investigates the effect of using probabilistic modeling techniques as a way to enhance the behavior of MOEA/D framework. MOEA/D is a decomposition based evolutionary algorithm that decomposes a multi-objective optimization problem (MOP) in a number of scalar single-objective subproblems and optimizes them in a collaborative manner. MOEA/D framework has been widely used to solve several MOPs. The proposed algorithm, MOEA/D using probabilistic Graphical Models (MOEA/D-GM) is able to instantiate both univariate and multi-variate probabilistic models for each subproblem. To validate the introduced framework algorithm, an experimental study is conducted on a multi-objective version of the deceptive function Trap5. The results show that the variant of the framework (MOEA/D-Tree), where tree models are learned from the matrices of the mutual information between the variables, is able to capture the structure of the problem. MOEA/D-Tree is able to achieve significantly better results than both MOEA/D using genetic operators and MOEA/D using univariate probability models, in terms of the approximation to the true Pareto front.
Competitive Multi-scale Convolution
Venue: arXiv cs.CV 2015
Year: 2015

In this paper, we introduce a new deep convolutional neural network (ConvNet) module that promotes competition among a set of multi-scale convolutional filters. This new module is inspired by the inception module, where we replace the original collaborative pooling stage (consisting of a concatenation of the multi-scale filter outputs) by a competitive pooling represented by a maxout activation unit. This extension has the following two objectives: 1) the selection of the maximum response among the multi-scale filters prevents filter co-adaptation and allows the formation of multiple sub-networks within the same model, which has been shown to facilitate the training of complex learning problems; and 2) the maxout unit reduces the dimensionality of the outputs from the multi-scale filters. We show that the use of our proposed module in typical deep ConvNets produces classification results that are either better than or comparable to the state of the art on the following benchmark datasets: MNIST, CIFAR-10, CIFAR-100 and SVHN.
Perancangan teknologi cloud untuk penjualan online kain songket Palembang
Venue: arXiv cs.CY 2015
Year: 2015

Cloud Computing is a paradigm in which information is permanently stored in servers on the Internet and temporarily stored on the user's computer (client) including the desktop. This study aims to design an online sales application based cloud computing technology to help the artisans Palembang songket and do not rule out the possibility for other small medium enterprises (SMEs) to manage assets and market their products online so it can be accessed anytime and anywhere via personal computer, laptop, tabblet, mobile phone or smartphone. Cloud infrastructure used in this study is a Software-as-a-service (SaaS) that enables cloud users to exploit online sales application without having to install on your local computer, set up a dedicated server, operator labor, maintenance costs and other support resources that can make savings in terms of cost.
Trust-in-the-Middle: Towards Establishing Trustworthiness of Authentication Proxies using Trusted Computing
Venue: arXiv cs.CR 2015
Year: 2015

Authentication proxies, which store users' secret credentials and submit them to servers on their behalf, offer benefits with respect to security of the authentication and usability of credential management. However, as being a service that is not in control of users, one important problem they suffer is the trust problem; how users trust that their secrets are handled securely in the proxy and not revealed to third parties. In this paper, we present a solution called Trust-in-the-Middle, a TPM based proxy system which ensures that user credentials are securely stored and submitted without disclosing them even if the proxy is compromised. We build our architecture on a trust chain bootstrapped by TPM DRTM and prevent access to credentials if any entity in the chain is maliciously modified. We use remote attestation to guarantee that all critical operations on the proxy are performed securely and credentials are cryptographically protected when they are not in DRTM-supported isolation.
Sparse learning of maximum likelihood model for optimization of complex loss function
Venue: arXiv cs.LG 2015
Year: 2015

Traditional machine learning methods usually minimize a simple loss function to learn a predictive model, and then use a complex performance measure to measure the prediction performance. However, minimizing a simple loss function cannot guarantee that an optimal performance. In this paper, we study the problem of optimizing the complex performance measure directly to obtain a predictive model. We proposed to construct a maximum likelihood model for this problem, and to learn the model parameter, we minimize a com- plex loss function corresponding to the desired complex performance measure. To optimize the loss function, we approximate the upper bound of the complex loss. We also propose impose the sparsity to the model parameter to obtain a sparse model. An objective is constructed by combining the upper bound of the loss function and the sparsity of the model parameter, and we develop an iterative algorithm to minimize it by using the fast iterative shrinkage- thresholding algorithm framework. The experiments on optimization on three different complex performance measures, including F-score, receiver operating characteristic curve, and recall precision curve break even point, over three real-world applications, aircraft event recognition of civil aviation safety, in- trusion detection in wireless mesh networks, and image classification, show the advantages of the proposed method over state-of-the-art methods.
Toward Transparent Heterogeneous Systems
Venue: arXiv cs.PF 2015
Year: 2015

Heterogeneous parallel systems are widely spread nowadays. Despite their availability, their usage and adoption are still limited, and even more rarely they are used to full power. Indeed, compelling new technologies are constantly developed and keep changing the technological landscape, but each of them targets a limited sub-set of supported devices, and nearly all of them require new programming paradigms and specific toolsets. Software, however, can hardly keep the pace with the growing number of computational capabilities, and developers are less and less motivated in learning skills that could quickly become obsolete. In this paper we present our effort in the direction of a transparent system optimization based on automatic code profiling and Just-In-Time compilation, that resulted in a fully-working embedded prototype capable of dynamically detect computing-intensive code blocks and automatically dispatch them to different computation units. Experimental results show that our system allows gains up to 32x in performance --- after an initial warm-up phase --- without requiring any human intervention.
Towards Lateral Inhibition and Collective Perception in Unorganised Non-Neural Systems
Venue: arXiv cs.ET 2015
Year: 2015

Could simple organisms such as slime mould approximate LI without recourse to neural tissue? We describe a model whereby LI can emerge without explicit inhibitory wiring, using only bulk transport effects. We use a multi-agent model of slime mould to reproduce the char- acteristic edge contrast amplification effects of LI using excitation via attractant based stimuli. We also explore a counterpart behaviour, Lateral Activation (where stimulated regions are inhibited and lateral regions are excited), using simulated exposure to light irradiation. In both cases restoration of baseline activity occurs when the stimuli are removed. In addition to the enhancement of local edge contrast the long-term change in population density distribution corresponds to a collective response to the global brightness of 2D image stimuli, including the scalloped inten- sity profile of the Chevreul staircase and the perceived difference of two identically bright patches in the Simultaneous Brightness Contrast (SBC) effect. This simple modelapproximatesLIcontrastenhancementphenomenaandglobalbrightnessper- ception in collective unorganised systems without fixed neural architectures. This may encourage further research into unorganised analogues of neural processes in simple organisms and suggests novel mechanisms to generate collective perception of contrast and brightness in distributed computing and robotic devices.
The Retrieval Effectiveness of Web Search Engines: Considering Results Descriptions
Venue: arXiv cs.IR 2015
Year: 2015

Purpose: To compare five major Web search engines (Google, Yahoo, MSN, Ask.com, and Seekport) for their retrieval effectiveness, taking into account not only the results but also the results descriptions. Design/Methodology/Approach: The study uses real-life queries. Results are made anonymous and are randomised. Results are judged by the persons posing the original queries. Findings: The two major search engines, Google and Yahoo, perform best, and there are no significant differences between them. Google delivers significantly more relevant result descriptions than any other search engine. This could be one reason for users perceiving this engine as superior. Research Limitations: The study is based on a user model where the user takes into account a certain amount of results rather systematically. This may not be the case in real life. Practical Implications: Implies that search engines should focus on relevant descriptions. Searchers are advised to use other search engines in addition to Google. Originality/Value: This is the first major study comparing results and descriptions systematically and proposes new retrieval measures to take into account results descriptions
Ranking library materials
Venue: arXiv cs.DL 2015
Year: 2015

Purpose: This paper discusses ranking factors suitable for library materials and shows that ranking in general is a complex process and that ranking for library materials requires a variety of techniques. Design/methodology/approach: The relevant literature is reviewed to provide a systematic overview of suitable ranking factors. The discussion is based on an overview of ranking factors used in Web search engines. Findings: While there are a wide variety of ranking factors applicable to library materials, todays library systems use only some of them. When designing a ranking component for the library catalogue, an individual weighting of applicable factors is necessary. Research limitations/applications: While this article discusses different factors, no particular ranking formula is given. However, this article presents the argument that such a formula must always be individual to a certain use case. Practical implications: The factors presented can be considered when designing a ranking component for a librarys search system or when discussing such a project with an ILS vendor. Originality/value: This paper is original in that it is the first to systematically discuss ranking of library materials based on the main factors used by Web search engines.
The Influence of Commercial Intent of Search Results on Their Perceived Relevance
Venue: arXiv cs.IR 2015
Year: 2015

We carried out a retrieval effectiveness test on the three major web search engines (i.e., Google, Microsoft and Yahoo). In addition to relevance judgments, we classified the results according to their commercial intent and whether or not they carried any advertising. We found that all search engines provide a large number of results with a commercial intent. Google provides significantly more commercial results than the other search engines do. However, the commercial intent of a result did not influence jurors in their relevance judgments.
Quantitative Transformation for Implementation of Adder Circuits in Physical Systems
Venue: arXiv cs.ET 2015
Year: 2015

Computing devices are composed of spatial arrangements of simple funda- mental logic gates. These gates may be combined to form more complex adding circuits and, ultimately, complete computer systems. Implementing classical adding circuits using unconventional, or even living substrates such as slime mould Physarum polycephalum, is made difficult and often impracti- cal by the challenges of branching fan-out of inputs and regions where circuit lines must cross without interference. In this report we explore whether it is possible to avoid spatial propagation, branching and crossing completely in the design of adding circuits. We analyse the input and output patterns of a single-bit full adder circuit. A simple quantitative transformation of the input patterns which considers the total number of bits in the input string allows us to map the respective input combinations to the correct outputs patterns of the full adder circuit, reducing the circuit combinations from a 2:1 mapping to a 1:1 mapping. The mapping of inputs to outputs also shows an incremental linear progression, suggesting its implementation in a range of physical systems. We demonstrate an example implementation, first in simulation, inspired by self-oscillatory dynamics of the acellular slime mould Physarum polycephalum. We then assess the potential implementation using plasmodium of slime mould itself. This simple transformation may enrich the potential for using unconventional computing substrates to implement digital circuits.
Collecting and Annotating the Large Continuous Action Dataset
Venue: arXiv cs.CV 2015
Year: 2015

We make available to the community a new dataset to support action-recognition research. This dataset is different from prior datasets in several key ways. It is significantly larger. It contains streaming video with long segments containing multiple action occurrences that often overlap in space and/or time. All actions were filmed in the same collection of backgrounds so that background gives little clue as to action class. We had five humans replicate the annotation of temporal extent of action occurrences labeled with their class and measured a surprisingly low level of intercoder agreement. A baseline experiment shows that recent state-of-the-art methods perform poorly on this dataset. This suggests that this will be a challenging dataset to foster advances in action-recognition research. This manuscript serves to describe the novel content and characteristics of the LCA dataset, present the design decisions made when filming the dataset, and document the novel methods employed to annotate the dataset.
Combining Neural Networks and Log-linear Models to Improve Relation Extraction
Venue: arXiv cs.CL 2015
Year: 2015

The last decade has witnessed the success of the traditional feature-based method on exploiting the discrete structures such as words or lexical patterns to extract relations from text. Recently, convolutional and recurrent neural networks has provided very effective mechanisms to capture the hidden structures within sentences via continuous representations, thereby significantly advancing the performance of relation extraction. The advantage of convolutional neural networks is their capacity to generalize the consecutive k-grams in the sentences while recurrent neural networks are effective to encode long ranges of sentence context. This paper proposes to combine the traditional feature-based method, the convolutional and recurrent neural networks to simultaneously benefit from their advantages. Our systematic evaluation of different network architectures and combination methods demonstrates the effectiveness of this approach and results in the state-of-the-art performance on the ACE 2005 and SemEval dataset.
A pilot study on the daily control capability of s-EMG prosthetic hands by amputees
Venue: arXiv cs.LG 2015
Year: 2015

Surface electromyography is a valid tool to gather muscular contraction signals from intact and amputated subjects. Electromyographic signals can be used to control prosthetic devices in a noninvasive way distinguishing the movements performed by the particular EMG electrodes activity. According to the literature, several algorithms have been used to control prosthetic hands through s-EMG signals. The main issue is to correctly classify the signals acquired as the movement actually performed. This work presents a study on the Support Vector Machine's performance in a short-time period, gained using two different feature representation (Mean Absolute Value and Waveform Length) of the sEMG signals. In particular, we paid close attention to the repeatability problem, that is the capability to achieve a stable and satisfactory level of accuracy in repeated experiments. Results on a limited setting are encouraging, as they show an average accuracy above 73% even in the worst case scenario.
Simulating Branching Programs with Edit Distance and Friends or: A Polylog Shaved is a Lower Bound Made
Venue: arXiv cs.CC 2015
Year: 2015

A recent and active line of work achieves tight lower bounds for fundamental problems under the Strong Exponential Time Hypothesis (SETH). A celebrated result of Backurs and Indyk (STOC'15) proves that the Edit Distance of two sequences of length n cannot be computed in strongly subquadratic time under SETH. The result was extended by follow-up works to simpler looking problems like finding the Longest Common Subsequence (LCS). SETH is a very strong assumption, asserting that even linear size CNF formulas cannot be analyzed for satisfiability with an exponential speedup over exhaustive search. We consider much safer assumptions, e.g. that such a speedup is impossible for SAT on much more expressive representations, like NC circuits. Intuitively, this seems much more plausible: NC circuits can implement complex cryptographic primitives, while CNFs cannot even approximately compute an XOR of bits. Our main result is a surprising reduction from SAT on Branching Programs to fundamental problems in P like Edit Distance, LCS, and many others. Truly subquadratic algorithms for these problems therefore have consequences that we consider to be far more remarkable than merely faster CNF SAT algorithms. For example, SAT on arbitrary o(n)-depth bounded fan-in circuits (and therefore also NC-Circuit-SAT) can be solved in (2-eps)^n time. A very interesting feature of our work is that we can prove major consequences even from mildly subquadratic algorithms for Edit Distance or LCS. For example, we show that if we can shave an arbitrarily large polylog factor from n^2 for Edit Distance then NEXP does not have non-uniform NC^1 circuits. A more fine-grained examination shows that even shaving a $\log^c{n}$ factor, for a specific constant $c \approx 10^3$, already implies new circuit lower bounds.
Enumeration and Random Generation of Unlabeled Classes of Graphs: A Practical Study of Cycle Pointing and the Dissymmetry Theorem
Venue: arXiv cs.DM 2015
Year: 2015

Our work studies the enumeration and random generation of unlabeled combinatorial classes of unrooted graphs. While the technique of vertex pointing provides a straightforward procedure for analyzing a labeled class of unrooted graphs by first studying its rooted counterpart, the existence of nontrivial symmetries in the unlabeled case causes this technique to break down. Instead, techniques such as the dissymmetry theorem (of Otter) and cycle pointing (of Bodirsky et al.) have emerged in the unlabeled case, with the former providing an enumeration of the class and the latter providing both an enumeration and an unbiased sampler. In this work, we extend the power of the dissymmetry theorem by showing that it in fact provides a Boltzmann sampler for the class in question. We then present an exposition of the cycle pointing technique, with a focus on the enumeration and random generation of the underlying unpointed class. Finally, we apply cycle pointing to enumerate and implement samplers for the classes of distance-hereditary graphs and three-leaf power graphs.