OpenRaiser/Intern-Atlas huggingface dataset:
Attitude Control of the Asteroid Origins Satellite 1 (AOSAT 1)
Venue: arXiv cs.SY 2017
Year: 2017

Exploration of asteroids and small-bodies can provide valuable insight into the origins of the solar system, into the origins of Earth and the origins of the building blocks of life. However, the low-gravity and unknown surface conditions of asteroids presents a daunting challenge for surface exploration, manipulation and for resource processing. This has resulted in the loss of several landers or shortened missions. Fundamental studies are required to obtain better readings of the material surface properties and physical models of these small bodies. The Asteroid Origins Satellite 1 (AOSAT 1) is a CubeSat centrifuge laboratory that spins at up to 4 rpm to simulate the milligravity conditions of sub 1 km asteroids. Such a laboratory will help to de-risk development and testing of landing and resource processing technology for asteroids. Inside the laboratory are crushed meteorites, the remains of asteroids. The laboratory is equipped with cameras and actuators to perform a series of science experiments to better understand material properties and asteroid surface physics. These results will help to improve our physics models of asteroids. The CubeSat has been designed to be low-cost and contains 3-axis magnetorquers and a single reaction-wheel to induce spin. In our work, we first analyze how the attitude control system will de-tumble the spacecraft after deployment. Further analysis has been conducted to analyze the impact and stability of the attitude control system to shifting mass (crushed meteorites) inside the spacecraft as its spinning in its centrifuge mode. AOSAT 1 will be the first in a series of low-cost CubeSat centrifuges that will be launched setting the stage for a larger, permanent, on-orbit centrifuge laboratory for experiments in planetary science, life sciences and manufacturing.
Rational Parametrization of Linear Pentapod's Singularity Variety and the Distance to it
Venue: arXiv cs.RO 2017
Year: 2017

A linear pentapod is a parallel manipulator with five collinear anchor points on the motion platform (end-effector), which are connected via extendible legs to the base. This manipulator has five controllable degrees-of-freedom and the remaining one is a free rotation around the motion platform axis (which in fact is an axial spindle). In this paper we present a rational parametrization of the singularity variety of the linear pentapod. Moreover we compute the shortest distance to this rational variety with respect to a suitable metric. Kinematically this distance can be interpreted as the radius of the maximal singularity free-sphere.
Robust Multilingual Named Entity Recognition with Shallow Semi-Supervised Features
Venue: arXiv cs.CL 2017
Year: 2017

We present a multilingual Named Entity Recognition approach based on a robust and general set of features across languages and datasets. Our system combines shallow local information with clustering semi-supervised features induced on large amounts of unlabeled text. Understanding via empirical experimentation how to effectively combine various types of clustering features allows us to seamlessly export our system to other datasets and languages. The result is a simple but highly competitive system which obtains state of the art results across five languages and twelve datasets. The results are reported on standard shared task evaluation data such as CoNLL for English, Spanish and Dutch. Furthermore, and despite the lack of linguistically motivated features, we also report best results for languages such as Basque and German. In addition, we demonstrate that our method also obtains very competitive results even when the amount of supervised data is cut by half, alleviating the dependency on manually annotated data. Finally, the results show that our emphasis on clustering features is crucial to develop robust out-of-domain models. The system and models are freely available to facilitate its use and guarantee the reproducibility of results.
Towards A Time Based Video Search Engine for Al Quran Interpretation
Venue: arXiv cs.IR 2017
Year: 2017

The number of Internet Muslim-users is remarkably increasing from all over the world countries. There are a lot of structured, and well-documented text resources for the Quran interpretation, Tafsir, over the Internet with several languages. Nevertheless, when searching for the meaning of specific words, many users prefer watching short videos rather than reading a script or a book. This paper introduces the solution for the challenge of partitioning the common Tafsir videos into short videos according to the search query and sharing these result videos on the social networks. Furthermore, we provide the ability of user commenting on a specific time-based frame on the video or a specific verse in a particular subject. It would be very valuable to apply the current technologies on Holy Quran and Tafsir to easy the query for verses, understanding of its meaning, and sharing it on the different social media.
Deep Stochastic Radar Models
Venue: arXiv cs.RO 2017
Year: 2017

Accurate simulation and validation of advanced driver assistance systems requires accurate sensor models. Modeling automotive radar is complicated by effects such as multipath reflections, interference, reflective surfaces, discrete cells, and attenuation. Detailed radar simulations based on physical principles exist but are computationally intractable for realistic automotive scenes. This paper describes a methodology for the construction of stochastic automotive radar models based on deep learning with adversarial loss connected to real-world data. The resulting model exhibits fundamental radar effects while remaining real-time capable.
When Follow is Just One Click Away: Understanding Twitter Follow Behavior in the 2016 U.S. Presidential Election
Venue: arXiv cs.SI 2017
Year: 2017

Motivated by the two paradoxical facts that the marginal cost of following one extra candidate is close to zero and that the majority of Twitter users choose to follow only one or two candidates, we study the Twitter follow behaviors observed in the 2016 U.S. presidential election. Specifically, we complete the following tasks: (1) analyze Twitter follow patterns of the presidential election on Twitter, (2) use negative binomial regression to study the effects of gender and occupation on the number of candidates that one follows, and (3) use multinomial logistic regression to investigate the effects of gender, occupation and celebrities on the choice of candidates to follow.
On the optimality and sharpness of Laguerre's lower bound on the smallest eigenvalue of a symmetric positive definite matrix
Venue: arXiv cs.NA 2017
Year: 2017

Lower bounds on the smallest eigenvalue of a symmetric positive definite matrices $A\in\mathbb{R}^{m\times m}$ play an important role in condition number estimation and in iterative methods for singular value computation. In particular, the bounds based on ${\rm Tr}(A^{-1})$ and ${\rm Tr}(A^{-2})$ attract attention recently because they can be computed in $O(m)$ work when $A$ is tridiagonal. In this paper, we focus on these bounds and investigate their properties in detail. First, we consider the problem of finding the optimal bound that can be computed solely from ${\rm Tr}(A^{-1})$ and ${\rm Tr}(A^{-2})$ and show that so called Laguerre's lower bound is the optimal one in terms of sharpness. Next, we study the gap between the Laguerre bound and the smallest eigenvalue. We characterize the situation in which the gap becomes largest in terms of the eigenvalue distribution of $A$ and show that the gap becomes smallest when ${\rm Tr}(A^{-2})/\{{\rm Tr}(A^{-1})\}^2$ approaches 1 or $\frac{1}{m}$. These results will be useful, for example, in designing efficient shift strategies for singular value computation algorithms.
A Software Reuse Approach and Its Effect On Software Quality, An Empirical Study for The Software Industry
Venue: arXiv cs.SE 2017
Year: 2017

Software reusability has become much interesting because of increased quality and reduce cost. A good process of software reuse leads to enhance the reliability, productivity, quality and the reduction of time and cost. Current reuse techniques focuses on the reuse of software artifact which grounded on anticipated functionality whereas, the non-functional (quality) aspect are also important. So, Software reusability used here to expand quality and productivity of software. It improves overall quality of software in minimum energy and time. Main objective of this study was to present a reuse approach that discovered that how software reuse improves the quality in Software Industry. The V&V technique used for this purpose which is part of software quality management process, it checks the quality and correctness during the software life cycle. A survey study conducted as QUESTIONAIR to find the impact of reuse approach on quality attributes which are requirement specification and design specification. Other quality enhancement techniques like ad hoc, CBSE, MBSE, Product line, COTS reuse checked on existing software industry. Results analyzed with the help of MATLAB tool as it provides effective data management, wide range of options, better output organization, to check weather quality enhancement technique is affected due to reusability and how quality will improve.
WLAN Performance Analysis Ibrahim Group of industries Faisalabad Pakistan
Venue: arXiv cs.NI 2017
Year: 2017

Now a days several organizations are moving their LAN foundation towards remote LAN frame work. The purpose for this is extremely straight forward multinational organizations needs their clients surprise about their office surroundings and they additionally need to make wire free environment in their workplaces. Much IT equipment moved on Wireless for instance all in one Pc portable workstations Wireless IP telephones. Another thing is that step by step WLAN innovation moving towards extraordinary effectiveness. In this exploration work Wireless LAN innovation running in Ibrahim Group gathering of commercial enterprises Faisalabad has been investigated in term of their equipment, Wireless signal quality, data transmission, auto channel moving, and security in WLAN system. This examination work required physical proving ground, some WLAN system analyzer (TamoSof throughput) software, hardware point of interest, security testing programming. The investigation displayed in this examination has fill two key needs. One determination is to accept this kind of system interconnection could be broke down utilizing the exploratory models of the two system bits (wired and remote pieces. Second key factor is to determine the security issue in WLAN.
Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program
Venue: arXiv cs.AI 2017
Year: 2017

The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia). As part of the program, awardees were asked to address one of the following "blue sky" questions: * How could/should Artificial Intelligence (AI) courses incorporate ethics into the curriculum? * How could we teach AI topics at an early undergraduate or a secondary school level? * AI has the potential for broad impact to numerous disciplines. How could we make AI education more interdisciplinary, specifically to benefit non-engineering fields? This paper is a collection of their responses, intended to help motivate discussion around these issues in AI education.
A Kinematic Chain Space for Monocular Motion Capture
Venue: arXiv cs.CV 2017
Year: 2017

This paper deals with motion capture of kinematic chains (e.g. human skeletons) from monocular image sequences taken by uncalibrated cameras. We present a method based on projecting an observation into a kinematic chain space (KCS). An optimization of the nuclear norm is proposed that implicitly enforces structural properties of the kinematic chain. Unlike other approaches our method does not require specific camera or object motion and is not relying on training data or previously determined constraints such as particular body lengths. The proposed algorithm is able to reconstruct scenes with limited camera motion and previously unseen motions. It is not only applicable to human skeletons but also to other kinematic chains for instance animals or industrial robots. We achieve state-of-the-art results on different benchmark data bases and real world scenes.
Foreign-language Reviews: Help or Hindrance?
Venue: arXiv cs.HC 2017
Year: 2017

The number and quality of user reviews greatly affects consumer purchasing decisions. While reviews in all languages are increasing, it is still often the case (especially for non-English speakers) that there are only a few reviews in a person's first language. Using an online experiment, we examine the value that potential purchasers receive from interfaces showing additional reviews in a second language. The results paint a complicated picture with both positive and negative reactions to the inclusion of foreign-language reviews. Roughly 26-28% of subjects clicked to see translations of the foreign-language content when given the opportunity, and those who did so were more likely to select the product with foreign-language reviews than those who did not.
Quantum algorithms for computing short discrete logarithms and factoring RSA integers
Venue: arXiv cs.CR 2017
Year: 2017

In this paper we generalize the quantum algorithm for computing short discrete logarithms previously introduced by Ekerå so as to allow for various tradeoffs between the number of times that the algorithm need be executed on the one hand, and the complexity of the algorithm and the requirements it imposes on the quantum computer on the other hand. Furthermore, we describe applications of algorithms for computing short discrete logarithms. In particular, we show how other important problems such as those of factoring RSA integers and of finding the order of groups under side information may be recast as short discrete logarithm problems. This immediately gives rise to an algorithm for factoring RSA integers that is less complex than Shor's general factoring algorithm in the sense that it imposes smaller requirements on the quantum computer. In both our algorithm and Shor's algorithm, the main hurdle is to compute a modular exponentiation in superposition. When factoring an n bit integer, the exponent is of length 2n bits in Shor's algorithm, compared to slightly more than n/2 bits in our algorithm.
The Average Dimension of the Hermitian Hull of Constayclic Codes over Finite Fields
Venue: arXiv cs.IT 2017
Year: 2017

The hulls of linear and cyclic codes have been extensively studied due to their wide applications. In this paper, the average dimension of the Hermitian hull of constacyclic codes of length $n$ over a finite field $\mathbb{F}_{q^2}$ is determined together with some upper and lower bounds. It turns out that either the average dimension of the Hermitian hull of constacyclic codes of length $n$ over $\mathbb{F}_{q^2}$ is zero or it grows the same rate as $n$. Comparison to the average dimension of the Euclidean hull of cyclic codes is discussed as well.
A Hybrid Evolutionary Algorithm Based on Solution Merging for the Longest Arc-Preserving Common Subsequence Problem
Venue: arXiv cs.AI 2017
Year: 2017

The longest arc-preserving common subsequence problem is an NP-hard combinatorial optimization problem from the field of computational biology. This problem finds applications, in particular, in the comparison of arc-annotated Ribonucleic acid (RNA) sequences. In this work we propose a simple, hybrid evolutionary algorithm to tackle this problem. The most important feature of this algorithm concerns a crossover operator based on solution merging. In solution merging, two or more solutions to the problem are merged, and an exact technique is used to find the best solution within this union. It is experimentally shown that the proposed algorithm outperforms a heuristic from the literature.
Hybrid Fuel Cells Power for Long Duration Robot Missions in Field Environments
Venue: arXiv cs.RO 2017
Year: 2017

Mobile robots are often needed for long duration missions. These include search and rescue, sentry, repair, surveillance and entertainment. Current power supply technology limit walking and climbing robots from many such missions. Internal combustion engines have high noise and emit toxic exhaust while rechargeable batteries have low energy densities and high rates of self-discharge. In theory, fuel cells do not have such limitations. In particular Proton Exchange Membrane (PEMs) can provide very high energy densities, are clean and quiet. However, PEM fuel cells are found to be unreliable due to performance degradation. This can be mitigated by protecting the fuel cell in a fuel-cell battery hybrid configuration using filtering electronics that ensure the fuel cell is isolated from electrical noise and a battery to isolate it from power surges. Simulation results are presented for a HOAP 2 humanoid robot that suggests a fuel cell powered hybrid power supply superior to conventional batteries.
Product Graph-based Higher Order Contextual Similarities for Inexact Subgraph Matching
Venue: arXiv cs.CV 2017
Year: 2017

Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched. Pairwise measurements usually consider local attributes but disregard contextual information involved in graph structures. We address this issue by proposing contextual similarities between pairs of nodes. This is done by considering the tensor product graph (TPG) of two graphs to be matched, where each node is an ordered pair of nodes of the operand graphs. Contextual similarities between a pair of nodes are computed by accumulating weighted walks (normalized pairwise similarities) terminating at the corresponding paired node in TPG. Once the contextual similarities are obtained, we formulate subgraph matching as a node and edge selection problem in TPG. We use contextual similarities to construct an objective function and optimize it with a linear programming approach. Since random walk formulation through TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities and better discrimination among the nodes and edges. Experimental results shown on synthetic as well as real benchmarks illustrate that higher order contextual similarities add discriminating power and allow one to find approximate solutions to the subgraph matching problem.
The exponentiated Hencky energy: Anisotropic extension and case studies
Venue: arXiv cs.CE 2017
Year: 2017

In this paper we propose an anisotropic extension of the isotropic exponentiated Hencky energy, based on logarithmic strain invariants. Unlike other elastic formulations, the isotropic exponentiated Hencky elastic energy has been derived solely on differential geometric grounds, involving the geodesic distance of the deformation gradient F to the group of rotations. We formally extend this approach towards anisotropy by defining additional anisotropic logarithmic strain invariants with the help of suitable structural tensors and consider our findings for selected case studies.
Dominance Move: A Measure of Comparing Solution Sets in Multiobjective Optimization
Venue: arXiv cs.NE 2017
Year: 2017

One of the most common approaches for multiobjective optimization is to generate a solution set that well approximates the whole Pareto-optimal frontier to facilitate the later decision-making process. However, how to evaluate and compare the quality of different solution sets remains challenging. Existing measures typically require additional problem knowledge and information, such as a reference point or a substituted set of the Pareto-optimal frontier. In this paper, we propose a quality measure, called dominance move (DoM), to compare solution sets generated by multiobjective optimizers. Given two solution sets, DoM measures the minimum sum of move distances for one set to weakly Pareto dominate the other set. DoM can be seen as a natural reflection of the difference between two solutions, capturing all aspects of solution sets' quality, being compliant with Pareto dominance, and does not need any additional problem knowledge and parameters. We present an exact method to calculate the DoM in the biobjective case. We show the necessary condition of constructing the optimal partition for a solution set's minimum move, and accordingly propose an efficient algorithm to recursively calculate the DoM. Finally, DoM is evaluated on several groups of artificial and real test cases as well as by a comparison with two well-established quality measures.
Deep Learning the Indus Script
Venue: arXiv cs.CV 2017
Year: 2017

Standardized corpora of undeciphered scripts, a necessary starting point for computational epigraphy, requires laborious human effort for their preparation from raw archaeological records. Automating this process through machine learning algorithms can be of significant aid to epigraphical research. Here, we take the first steps in this direction and present a deep learning pipeline that takes as input images of the undeciphered Indus script, as found in archaeological artifacts, and returns as output a string of graphemes, suitable for inclusion in a standard corpus. The image is first decomposed into regions using Selective Search and these regions are classified as containing textual and/or graphical information using a convolutional neural network. Regions classified as potentially containing text are hierarchically merged and trimmed to remove non-textual information. The remaining textual part of the image is segmented using standard image processing techniques to isolate individual graphemes. This set is finally passed to a second convolutional neural network to classify the graphemes, based on a standard corpus. The classifier can identify the presence or absence of the most frequent Indus grapheme, the "jar" sign, with an accuracy of 92%. Our results demonstrate the great potential of deep learning approaches in computational epigraphy and, more generally, in the digital humanities.