OpenRaiser/Intern-Atlas huggingface dataset:
You want to survive the data deluge: Be careful, Computational Intelligence will not serve you as a rescue boat
Venue: arXiv cs.AI 2016
Year: 2016

We are at the dawn of a new era, where advances in computer power, broadband communication and digital sensor technologies have led to an unprecedented flood of data inundating our surrounding. It is generally believed that means such as Computational Intelligence will help to outlive these tough times. However, these hopes are improperly high. Computational Intelligence is a surprising composition of two mutually exclusive and contradicting constituents that could be coupled only if you disregard and neglect their controversies: "Computational" implies reliance on data processing and "Intelligence" implies reliance on information processing. Only those who are indifferent to data-information discrepancy can believe that such a combination can be viable. We do not believe in miracles, so we will try to share with you our reservations.
Spanning Trees in 2-trees
Venue: arXiv cs.DM 2016
Year: 2016

A spanning tree of a graph $G$ is a connected acyclic spanning subgraph of $G$. We consider enumeration of spanning trees when $G$ is a $2$-tree, meaning that $G$ is obtained from one edge by iteratively adding a vertex whose neighborhood consists of two adjacent vertices. We use this construction order both to inductively list the spanning trees without repetition and to give bounds on the number of them. We determine the $n$-vertex $2$-trees having the most and the fewest spanning trees. The $2$-tree with the fewest is unique; it has $n-2$ vertices of degree $2$ and has $n2^{n-3}$ spanning trees. Those with the most are all those having exactly two vertices of degree $2$, and their number of spanning trees is the Fibonacci number $F_{2n-2}$.
Identifying Candidate Risk Factors for Prescription Drug Side Effects using Causal Contrast Set Mining
Venue: arXiv cs.AI 2016
Year: 2016

Big longitudinal observational databases present the opportunity to extract new knowledge in a cost effective manner. Unfortunately, the ability of these databases to be used for causal inference is limited due to the passive way in which the data are collected resulting in various forms of bias. In this paper we investigate a method that can overcome these limitations and determine causal contrast set rules efficiently from big data. In particular, we present a new methodology for the purpose of identifying risk factors that increase a patients likelihood of experiencing the known rare side effect of renal failure after ingesting aminosalicylates. The results show that the methodology was able to identify previously researched risk factors such as being prescribed diuretics and highlighted that patients with a higher than average risk of renal failure may be even more susceptible to experiencing it as a side effect after ingesting aminosalicylates.
Weierstrass Semigroups from Kummer Extensions
Venue: arXiv cs.IT 2016
Year: 2016

The Weierstrass semigroups and pure gaps can be helpful in constructing codes with better parameters. In this paper, we investigate explicitly the minimal generating set of the Weierstrass semigroups associated with several totally ramified places over arbitrary Kummer extensions. Applying the techniques provided by Matthews in her previous work, we extend the results of specific Kummer extensions studied in the literature. Some examples are included to illustrate our results.
Adaptive Data Communication Interface: A User-Centric Visual Data Interpretation Framework
Venue: arXiv cs.HC 2016
Year: 2016

In this position paper, we present ideas about creating a next generation framework towards an adaptive interface for data communication and visualisation systems. Our objective is to develop a system that accepts large data sets as inputs and provides user-centric, meaningful visual information to assist owners to make sense of their data collection. The proposed framework comprises four stages: (i) the knowledge base compilation, where we search and collect existing state-ofthe-art visualisation techniques per domain and user preferences; (ii) the development of the learning and inference system, where we apply artificial intelligence techniques to learn, predict and recommend new graphic interpretations (iii) results evaluation; and (iv) reinforcement and adaptation, where valid outputs are stored in our knowledge base and the system is iteratively tuned to address new demands. These stages, as well as our overall vision, limitations and possible challenges are introduced in this article. We also discuss further extensions of this framework for other knowledge discovery tasks.
Onsager-corrected deep learning for sparse linear inverse problems
Venue: arXiv cs.IT 2016
Year: 2016

Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse problem encountered in compressive sensing, where one seeks to recover a sparse signal from a small number of noisy linear measurements. In this paper, we propose a novel neural-network architecture that decouples prediction errors across layers in the same way that the approximate message passing (AMP) algorithm decouples them across iterations: through Onsager correction. Numerical experiments suggest that our "learned AMP" network significantly improves upon Gregor and LeCun's "learned ISTA" network in both accuracy and complexity.
Interactive Illumination Invariance
Venue: arXiv cs.CV 2016
Year: 2016

Illumination effects cause problems for many computer vision algorithms. We present a user-friendly interactive system for robust illumination-invariant image generation. Compared with the previous automated illumination-invariant image derivation approaches, our system enables users to specify a particular kind of illumination variation for removal. The derivation of illumination-invariant image is guided by the user input. The input is a stroke that defines an area covering a set of pixels whose intensities are influenced predominately by the illumination variation. This additional flexibility enhances the robustness for processing non-linearly rendered images and the images of the scenes where their illumination variations are difficult to estimate automatically. Finally, we present some evaluation results of our method.
Person Re-identification for Real-world Surveillance Systems
Venue: arXiv cs.CV 2016
Year: 2016

Appearance based person re-identification in a real-world video surveillance system with non-overlapping camera views is a challenging problem for many reasons. Current state-of-the-art methods often address the problem by relying on supervised learning of similarity metrics or ranking functions to implicitly model appearance transformation between cameras for each camera pair, or group, in the system. This requires considerable human effort to annotate data. Furthermore, the learned models are camera specific and not transferable from one set of cameras to another. Therefore, the annotation process is required after every network expansion or camera replacement, which strongly limits their applicability. Alternatively, we propose a novel modeling approach to harness complementary appearance information without supervised learning that significantly outperforms current state-of-the-art unsupervised methods on multiple benchmark datasets.
Fragment Allocation Configuration in Distributed Database Systems
Venue: arXiv cs.DB 2016
Year: 2016

In distributed database (DDB) management systems, fragment allocation is one of the most important components that can directly affect the performance of DDB. In this research work, we will show that declarative programming languages, e.g. logic programming languages, can be used to represent different data fragment allocation techniques. Results indicate that, using declarative programming language significantly simplifies the representation of fragment allocation algorithm, thus opens door for any further developments and optimizations. The under consideration case study also show that our approach can be extended to be used in different areas of distributed systems.
Guided macro-mutation in a graded energy based genetic algorithm for protein structure prediction
Venue: arXiv cs.NE 2016
Year: 2016

Protein structure prediction is considered as one of the most challenging and computationally intractable combinatorial problem. Thus, the efficient modeling of convoluted search space, the clever use of energy functions, and more importantly, the use of effective sampling algorithms become crucial to address this problem. For protein structure modeling, an off-lattice model provides limited scopes to exercise and evaluate the algorithmic developments due to its astronomically large set of data-points. In contrast, an on-lattice model widens the scopes and permits studying the relatively larger proteins because of its finite set of data-points. In this work, we took the full advantage of an on-lattice model by using a face-centered-cube lattice that has the highest packing density with the maximum degree of freedom. We proposed a graded energy-strategically mixes the Miyazawa-Jernigan (MJ) energy with the hydrophobic-polar (HP) energy-based genetic algorithm (GA) for conformational search. In our application, we introduced a 2x2 HP energy guided macro-mutation operator within the GA to explore the best possible local changes exhaustively. Conversely, the 20x20 MJ energy model-the ultimate objective function of our GA that needs to be minimized-considers the impacts amongst the 20 different amino acids and allow searching the globally acceptable conformations. On a set of benchmark proteins, our proposed approach outperformed state-of-the-art approaches in terms of the free energy levels and the root-mean-square deviations.
Strong Hardness of Privacy from Weak Traitor Tracing
Venue: arXiv cs.CR 2016
Year: 2016

Despite much study, the computational complexity of differential privacy remains poorly understood. In this paper we consider the computational complexity of accurately answering a family $Q$ of statistical queries over a data universe $X$ under differential privacy. A statistical query on a dataset $D \in X^n$ asks "what fraction of the elements of $D$ satisfy a given predicate $p$ on $X$?" Dwork et al. (STOC'09) and Boneh and Zhandry (CRYPTO'14) showed that if both $Q$ and $X$ are of polynomial size, then there is an efficient differentially private algorithm that accurately answers all the queries, and if both $Q$ and $X$ are exponential size, then under a plausible assumption, no efficient algorithm exists. We show that, under the same assumption, if either the number of queries or the data universe is of exponential size, and the other has size at least $\tilde{O}(n^7)$, then there is no differentially private algorithm that answers all the queries. In both cases, the result is nearly quantitatively tight, since there is an efficient differentially private algorithm that answers $\tildeΩ(n^2)$ queries on an exponential size data universe, and one that answers exponentially many queries on a data universe of size $\tildeΩ(n^2)$. Our proofs build on the connection between hardness results in differential privacy and traitor-tracing schemes (Dwork et al., STOC'09; Ullman, STOC'13). We prove our hardness result for a polynomial size query set (resp., data universe) by showing that they follow from the existence of a special type of traitor-tracing scheme with very short ciphertexts (resp., secret keys), but very weak security guarantees, and then constructing such a scheme.
Supervised Adverse Drug Reaction Signalling Framework Imitating Bradford Hill's Causality Considerations
Venue: arXiv cs.AI 2016
Year: 2016

Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunately there are many complexities and underlying issues when analysing longitudinal observational data. Due to these complexities, existing methods for large-scale detection of negative side effects using observational data all tend to have issues distinguishing between association and causality. New methods that can better discriminate causal and non-causal relationships need to be developed to fully utilise the data. In this paper we propose using a set of causality considerations developed by the epidemiologist Bradford Hill as a basis for engineering features that enable the application of supervised learning for the problem of detecting negative side effects. The Bradford Hill considerations look at various perspectives of a drug and outcome relationship to determine whether it shows causal traits. We taught a classifier to find patterns within these perspectives and it learned to discriminate between association and causality. The novelty of this research is the combination of supervised learning and Bradford Hill's causality considerations to automate the Bradford Hill's causality assessment. We evaluated the framework on a drug safety gold standard know as the observational medical outcomes partnership's nonspecified association reference set. The methodology obtained excellent discriminate ability with area under the curves ranging between 0.792-0.940 (existing method optimal: 0.73) and a mean average precision of 0.640 (existing method optimal: 0.141). The proposed features can be calculated efficiently and be readily updated, making the framework suitable for big observational data.
Greedy bi-criteria approximations for $k$-medians and $k$-means
Venue: arXiv cs.DS 2016
Year: 2016

This paper investigates the following natural greedy procedure for clustering in the bi-criterion setting: iteratively grow a set of centers, in each round adding the center from a candidate set that maximally decreases clustering cost. In the case of $k$-medians and $k$-means, the key results are as follows. $\bullet$ When the method considers all data points as candidate centers, then selecting $\mathcal{O}(k\log(1/\varepsilon))$ centers achieves cost at most $2+\varepsilon$ times the optimal cost with $k$ centers. $\bullet$ Alternatively, the same guarantees hold if each round samples $\mathcal{O}(k/\varepsilon^5)$ candidate centers proportionally to their cluster cost (as with $\texttt{kmeans++}$, but holding centers fixed). $\bullet$ In the case of $k$-means, considering an augmented set of $n^{\lceil1/\varepsilon\rceil}$ candidate centers gives $1+\varepsilon$ approximation with $\mathcal{O}(k\log(1/\varepsilon))$ centers, the entire algorithm taking $\mathcal{O}(dk\log(1/\varepsilon)n^{1+\lceil1/\varepsilon\rceil})$ time, where $n$ is the number of data points in $\mathbb{R}^d$. $\bullet$ In the case of Euclidean $k$-medians, generating a candidate set via $n^{\mathcal{O}(1/\varepsilon^2)}$ executions of stochastic gradient descent with adaptively determined constraint sets will once again give approximation $1+\varepsilon$ with $\mathcal{O}(k\log(1/\varepsilon))$ centers in $dk\log(1/\varepsilon)n^{\mathcal{O}(1/\varepsilon^2)}$ time. Ancillary results include: guarantees for cluster costs based on powers of metrics; a brief, favorable empirical evaluation against $\texttt{kmeans++}$; data-dependent bounds allowing $1+\varepsilon$ in the first two bullets above, for example with $k$-medians over finite metric spaces.
2016 Google Scholar Metrics released: a matter of languages... and something else
Venue: arXiv cs.DL 2016
Year: 2016

The 2016 edition of Google Scholar Metrics was released on July 15th 2016. There haven't been any structural changes respect to previous versions, which means that most of its limitations still persist. The biggest changes are the addition of five new language rankings (Russian, Korean, Polish, Ukrainian, and Indonesian) and elimination of two other language rankings (Italian and Dutch). In addition, for reasons still unknown, this new edition doesn't include as many working paper and discussion paper series as previous editions.
On Optimal Heterogeneous Regenerating Codes
Venue: arXiv cs.IT 2016
Year: 2016

Heterogeneous Distributed Storage Systems (DSSs) are close to the real world applications for data storage. Each node of the considered DSS, may store different number of packets and each having different repair bandwidth with uniform repair traffic. For such heterogeneous DSS, a failed node can be repaired with the help of some specific nodes. In this work, a family of codes based on graph theory, is constructed which achieves the fundamental bound on file size for the particular heterogeneous DSS.
La representación de la variación contextual mediante definiciones terminológicas flexibles
Venue: arXiv cs.CL 2016
Year: 2016

In this doctoral thesis, we apply premises of cognitive linguistics to terminological definitions and present a proposal called the flexible terminological definition. This consists of a set of definitions of the same concept made up of a general definition (in this case, one encompassing the entire environmental domain) along with additional definitions describing the concept from the perspective of the subdomains in which it is relevant. Since context is a determining factor in the construction of the meaning of lexical units (including terms), we assume that terminological definitions can, and should, reflect the effects of context, even though definitions have traditionally been treated as the expression of meaning void of any contextual effect. The main objective of this thesis is to analyze the effects of contextual variation on specialized environmental concepts with a view to their representation in terminological definitions. Specifically, we focused on contextual variation based on thematic restrictions. To accomplish the objectives of this doctoral thesis, we conducted an empirical study consisting of the analysis of a set of contextually variable concepts and the creation of a flexible definition for two of them. As a result of the first part of our empirical study, we divided our notion of domain-dependent contextual variation into three different phenomena: modulation, perspectivization and subconceptualization. These phenomena are additive in that all concepts experience modulation, some concepts also undergo perspectivization, and finally, a small number of concepts are additionally subjected to subconceptualization. In the second part, we applied these notions to terminological definitions and we presented we presented guidelines on how to build flexible definitions, from the extraction of knowledge to the actual writing of the definition.
Data Scaling in OBDA Benchmarks: The VIG Approach
Venue: arXiv cs.DB 2016
Year: 2016

In this paper we describe VIG, a data scaler for benchmarks in the context of ontology-based data access (OBDA). Data scaling is a relatively recent approach, proposed in the database community, that allows for quickly scaling up an input data instance to s times its size, while preserving certain application-specific characteristics. The advantage of the approach is that the user is not required to manually input the characteristics of the data to be produced, making it particularly suitable for OBDA benchmarks, where the complexity of database schemas might pose a challenge for manual input (e.g., the NPD benchmark contains 70 tables with some containing more than 60 columns). As opposed to a traditional data scaler, VIG includes domain information provided by the OBDA mappings and the ontology in order to produce data. VIG is currently used in the NPD benchmark, but it is not NPD-specific and can be seeded with any data instance. The distinguishing features of VIG are (1) its simple and clear generation strategy; (2) its efficiency, as each value is generated in constant time, without accesses to the disk or to RAM to retrieve previously generated values; (3) and its generality, as the data is exported in CSV files that can be easily imported by any RDBMS system. VIG is a java implementation licensed under Apache 2.0, and its source code is available on GitHub (https://github.com/ontop/vig) in the form of a Maven project. The code is being maintained since two years by the -ontop- team at the Free University of Bozen-Bolzano.
Hierarchical Attention Network for Action Recognition in Videos
Venue: arXiv cs.CV 2016
Year: 2016

Understanding human actions in wild videos is an important task with a broad range of applications. In this paper we propose a novel approach named Hierarchical Attention Network (HAN), which enables to incorporate static spatial information, short-term motion information and long-term video temporal structures for complex human action understanding. Compared to recent convolutional neural network based approaches, HAN has following advantages (1) HAN can efficiently capture video temporal structures in a longer range; (2) HAN is able to reveal temporal transitions between frame chunks with different time steps, i.e. it explicitly models the temporal transitions between frames as well as video segments and (3) with a multiple step spatial temporal attention mechanism, HAN automatically learns important regions in video frames and temporal segments in the video. The proposed model is trained and evaluated on the standard video action benchmarks, i.e., UCF-101 and HMDB-51, and it significantly outperforms the state-of-the arts
On the Exploitation of Admittance Measurements for Wired Network Topology Derivation
Venue: arXiv cs.NI 2016
Year: 2016

The knowledge of the topology of a wired network is often of fundamental importance. For instance, in the context of Power Line Communications (PLC) networks it is helpful to implement data routing strategies, while in power distribution networks and Smart Micro Grids (SMG) it is required for grid monitoring and for power flow management. In this paper, we use the transmission line theory to shed new light and to show how the topological properties of a wired network can be found exploiting admittance measurements at the nodes. An analytic proof is reported to show that the derivation of the topology can be done in complex networks under certain assumptions. We also analyze the effect of the network background noise on admittance measurements. In this respect, we propose a topology derivation algorithm that works in the presence of noise. We finally analyze the performance of the algorithm using values that are typical of power line distribution networks.
3D Character Customization for Non-Professional Users in Handheld Augmented Reality
Venue: arXiv cs.HC 2016
Year: 2016

In gaming, customizing individual characters, can create personal bonds between players and their characters. Hence, character customization is a standard component in many games. While mobile Augmented Reality (AR) games become popular, to date, no 3D character editor for AR games exists. We investigate the feasibility of 3D character customization for smartphone-based AR in an iterative design process. Specifically, we present findings from creating AR prototypes in a handheld AR setting. In a first user study, we found that a tangible AR prototype resulted in higher hedonistic measures than a camera-based approach. In a follow up study, we compared the tangible AR prototype with a non-AR touchscreen version for selection, scaling, translation and rotation tasks in a 3D character customization setting. The tangible AR version resulted in significantly better results for stimulation and novelty measures than the non-AR version. At the same time, it maintained a proficient level in pragmatic measures such as accuracy and efficiency.