OpenRaiser/Intern-Atlas huggingface dataset:
On σ-LCD codes
Venue: arXiv cs.IT 2017
Year: 2017

Linear complementary pairs (LCP) of codes play an important role in armoring implementations against side-channel attacks and fault injection attacks. One of the most common ways to construct LCP of codes is to use Euclidean linear complementary dual (LCD) codes. In this paper, we first introduce the concept of linear codes with $σ$ complementary dual ($σ$-LCD), which includes known Euclidean LCD codes, Hermitian LCD codes, and Galois LCD codes. As Euclidean LCD codes, $σ$-LCD codes can also be used to construct LCP of codes. We show that, for $q > 2$, all q-ary linear codes are $σ$-LCD and that, for every binary linear code $\mathcal C$, the code $\{0\}\times \mathcal C$ is $σ$-LCD. Further, we study deeply $σ$-LCD generalized quasi-cyclic (GQC) codes. In particular, we provide characterizations of $σ$-LCD GQC codes, self-orthogonal GQC codes and self-dual GQC codes, respectively. Moreover, we provide constructions of asymptotically good $σ$-LCD GQC codes. Finally, we focus on $σ$-LCD Abelian codes and prove that all Abelian codes in a semi-simple group algebra are $σ$-LCD. The results derived in this paper extend those on the classical LCD codes and show that $σ$-LCD codes allow the construction of LCP of codes more easily and with more flexibility.
A note on minimal dispersion of point sets in the unit cube
Venue: arXiv cs.CG 2017
Year: 2017

We study the dispersion of a point set, a notion closely related to the discrepancy. Given a real $r\in (0,1)$ and an integer $d\geq 2$, let $N(r,d)$ denote the minimum number of points inside the $d$-dimensional unit cube $[0,1]^d$ such that they intersect every axis-aligned box inside $[0,1]^d$ of volume greater than $r$. We prove an upper bound on $N(r,d)$, matching a lower bound of Aistleitner et al. up to a multiplicative constant depending only on $r$. This fully determines the rate of growth of $N(r,d)$ if $r\in(0,1)$ is fixed.
Find, Understand, and Extend Development Screencasts on YouTube
Venue: arXiv cs.SE 2017
Year: 2017

A software development screencast is a video that captures the screen of a developer working on a particular task while explaining its implementation details. Due to the increased popularity of software development screencasts (e.g., available on YouTube), we study how and to what extent they can be used as additional source of knowledge to answer developer's questions about, for example, the use of a specific API. We first differentiate between development and other types of screencasts using video frame analysis. By using the Cosine algorithm, developers can expect ten development screencasts in the top 20 out of 100 different YouTube videos. We then extracted popular development topics on which screencasts are reporting on YouTube: database operations, system set-up, plug-in development, game development, and testing. Besides, we found six recurring tasks performed in development screencasts, such as object usage and UI operations. Finally, we conducted a similarity analysis by considering only the spoken words (i.e., the screencast transcripts but not the text that might appear in a scene) to link API documents, such as the Javadoc, to the appropriate screencasts. By using Cosine similarity, we identified 38 relevant documents in the top 20 out of 9455 API documents.
Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models
Venue: arXiv cs.AI 2017
Year: 2017

Lifted inference algorithms commonly exploit symmetries in a probabilistic graphical model (PGM) for efficient inference. However, existing algorithms for Boolean-valued domains can identify only those pairs of states as symmetric, in which the number of ones and zeros match exactly (count symmetries). Moreover, algorithms for lifted inference in multi-valued domains also compute a multi-valued extension of count symmetries only. These algorithms miss many symmetries in a domain. In this paper, we present first algorithms to compute non-count symmetries in both Boolean-valued and multi-valued domains. Our methods can also find symmetries between multi-valued variables that have different domain cardinalities. The key insight in the algorithms is that they change the unit of symmetry computation from a variable to a variable-value (VV) pair. Our experiments find that exploiting these symmetries in MCMC can obtain substantial computational gains over existing algorithms.
Understanding Aesthetics in Photography using Deep Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Evaluating aesthetic value of digital photographs is a challenging task, mainly due to numerous factors that need to be taken into account and subjective manner of this process. In this paper, we propose to approach this problem using deep convolutional neural networks. Using a dataset of over 1.7 million photos collected from Flickr, we train and evaluate a deep learning model whose goal is to classify input images by analysing their aesthetic value. The result of this work is a publicly available Web-based application that can be used in several real-life applications, e.g. to improve the workflow of professional photographers by pre-selecting the best photos.
A Locally Adapting Technique for Boundary Detection using Image Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Rapid growth in the field of quantitative digital image analysis is paving the way for researchers to make precise measurements about objects in an image. To compute quantities from the image such as the density of compressed materials or the velocity of a shockwave, we must determine object boundaries. Images containing regions that each have a spatial trend in intensity are of particular interest. We present a supervised image segmentation method that incorporates spatial information to locate boundaries between regions with overlapping intensity histograms. The segmentation of a pixel is determined by comparing its intensity to distributions from local, nearby pixel intensities. Because of the statistical nature of the algorithm, we use maximum likelihood estimation theory to quantify uncertainty about each boundary. We demonstrate the success of this algorithm on a radiograph of a multicomponent cylinder and on an optical image of a laser-induced shockwave, and we provide final boundary locations with associated bands of uncertainty.
Beamspace Channel Estimation in mmWave Systems via Cosparse Image Reconstruction Technique
Venue: arXiv cs.IT 2017
Year: 2017

This paper considers the beamspace channel estimation problem in 3D lens antenna array under a millimeter-wave communication system. We analyze the focusing capability of the 3D lens antenna array and the sparsity of the beamspace channel response matrix. Considering the analysis, we observe that the channel matrix can be treated as a 2D natural image; that is, the channel is sparse, and the changes between adjacent elements are subtle. Thus, for the channel estimation, we incorporate an image reconstruction technique called sparse non-informative parameter estimator-based cosparse analysis AMP for imaging (SCAMPI) algorithm. The SCAMPI algorithm is faster and more accurate than earlier algorithms such as orthogonal matching pursuit and support detection algorithms. To further improve the SCAMPI algorithm, we model the channel distribution as a generic Gaussian mixture (GM) probability and embed the expectation maximization learning algorithm into the SCAMPI algorithm to learn the parameters in the GM probability. We show that the GM probability outperforms the common uniform distribution used in image reconstruction. We also propose a phase-shifter-reduced selection network structure to decrease the power consumption of the system and prove that the SCAMPI algorithm is robust even if the number of phase shifters is reduced by 10%.
MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension
Venue: arXiv cs.AI 2017
Year: 2017

Machine comprehension(MC) style question answering is a representative problem in natural language processing. Previous methods rarely spend time on the improvement of encoding layer, especially the embedding of syntactic information and name entity of the words, which are very crucial to the quality of encoding. Moreover, existing attention methods represent each query word as a vector or use a single vector to represent the whole query sentence, neither of them can handle the proper weight of the key words in query sentence. In this paper, we introduce a novel neural network architecture called Multi-layer Embedding with Memory Network(MEMEN) for machine reading task. In the encoding layer, we employ classic skip-gram model to the syntactic and semantic information of the words to train a new kind of embedding layer. We also propose a memory network of full-orientation matching of the query and passage to catch more pivotal information. Experiments show that our model has competitive results both from the perspectives of precision and efficiency in Stanford Question Answering Dataset(SQuAD) among all published results and achieves the state-of-the-art results on TriviaQA dataset.
Human Pose Forecasting via Deep Markov Models
Venue: arXiv cs.CV 2017
Year: 2017

Human pose forecasting is an important problem in computer vision with applications to human-robot interaction, visual surveillance, and autonomous driving. Usually, forecasting algorithms use 3D skeleton sequences and are trained to forecast for a few milliseconds into the future. Long-range forecasting is challenging due to the difficulty of estimating how long a person continues an activity. To this end, our contributions are threefold: (i) we propose a generative framework for poses using variational autoencoders based on Deep Markov Models (DMMs); (ii) we evaluate our pose forecasts using a pose-based action classifier, which we argue better reflects the subjective quality of pose forecasts than distance in coordinate space; (iii) last, for evaluation of the new model, we introduce a 480,000-frame video dataset called Ikea Furniture Assembly (Ikea FA), which depicts humans repeatedly assembling and disassembling furniture. We demonstrate promising results for our approach on both Ikea FA and the existing NTU RGB+D dataset.
Modular AWG-based Optical Shuffle Network
Venue: arXiv cs.OH 2017
Year: 2017

This paper proposes an arrayed-waveguide grating (AWG) based wavelength-division-multiplexing (WDM) shuffle network. Compared with previous optical shuffle networks, our proposal is compact, easy to implement, highly scalable, and cost effective.
A syntactic tool for proving hardness in the Second Level of the Polynomial-Time Hierarchy
Venue: arXiv cs.LO 2017
Year: 2017

In the nineties Immerman and Medina initiated the search for syn- tactic tools to prove NP-completeness. In their work, amongst several results, they conjecture that the NP-completeness of a problem defined by the conjunction of a sentence in Existential Second Order Logic with a First Order sentence, necessarily imply the NP-completeness of the problem defined by the Existential Second Order sentence alone. This is interesting because if true it would justify the restriction heuristic pro- posed in Garey and Johnson in his classical book on NP completeness, which roughly says that in some cases one can prove NP- complete a problem A by proving NP-complete a problem B contained in A. Borges and Bonet extend some results from Immerman and Medina and they also prove for a host of complexity classes that the Immerman- Medina conjecture is true when the First Order sentence in the conjunc- tion is universal. Our work extends that result to the Second Level of the Polynomial-Time Hierarchy.
Bipartite spanning sub(di)graphs induced by 2-partitions
Venue: arXiv cs.DM 2017
Year: 2017

For a given $2$-partition $(V_1,V_2)$ of the vertices of a (di)graph $G$, we study properties of the spanning bipartite subdigraph $B_G(V_1,V_2)$ of $G$ induced by those arcs/edges that have one end in each $V_i$. We determine, for all pairs of non-negative integers $k_1,k_2$, the complexity of deciding whether $G$ has a 2-partition $(V_1,V_2)$ such that each vertex in $V_i$ has at least $k_i$ (out-)neighbours in $V_{3-i}$. We prove that it is ${\cal NP}$-complete to decide whether a digraph $D$ has a 2-partition $(V_1,V_2)$ such that each vertex in $V_1$ has an out-neighbour in $V_2$ and each vertex in $V_2$ has an in-neighbour in $V_1$. The problem becomes polynomially solvable if we require $D$ to be strongly connected. We give a characterisation, based on the so-called strong component digraph of a non-strong digraph of the structure of ${\cal NP}$-complete instances in terms of their strong component digraph. When we want higher in-degree or out-degree to/from the other set the problem becomes ${\cal NP}$-complete even for strong digraphs. A further result is that it is ${\cal NP}$-complete to decide whether a given digraph $D$ has a $2$-partition $(V_1,V_2)$ such that $B_D(V_1,V_2)$ is strongly connected. This holds even if we require the input to be a highly connected eulerian digraph.
Online Deception Detection Refueled by Real World Data Collection
Venue: arXiv cs.CL 2017
Year: 2017

The lack of large realistic datasets presents a bottleneck in online deception detection studies. In this paper, we apply a data collection method based on social network analysis to quickly identify high-quality deceptive and truthful online reviews from Amazon. The dataset contains more than 10,000 deceptive reviews and is diverse in product domains and reviewers. Using this dataset, we explore effective general features for online deception detection that perform well across domains. We demonstrate that with generalized features - advertising speak and writing complexity scores - deception detection performance can be further improved by adding additional deceptive reviews from assorted domains in training. Finally, reviewer level evaluation gives an interesting insight into different deceptive reviewers' writing styles.
Refuting Feder, Kinne and Rafiey
Venue: arXiv cs.CC 2017
Year: 2017

I give an example showing that the recent claimed solution by Feder, Kinne and Rafiey to the CSP Dichotomy Conjecture is not correct.
Bilingual Document Alignment with Latent Semantic Indexing
Venue: arXiv cs.CL 2017
Year: 2017

We apply cross-lingual Latent Semantic Indexing to the Bilingual Document Alignment Task at WMT16. Reduced-rank singular value decomposition of a bilingual term-document matrix derived from known English/French page pairs in the training data allows us to map monolingual documents into a joint semantic space. Two variants of cosine similarity between the vectors that place each document into the joint semantic space are combined with a measure of string similarity between corresponding URLs to produce 1:1 alignments of English/French web pages in a variety of domains. The system achieves a recall of ca. 88% if no in-domain data is used for building the latent semantic model, and 93% if such data is included. Analysing the system's errors on the training data, we argue that evaluating aligner performance based on exact URL matches under-estimates their true performance and propose an alternative that is able to account for duplicates and near-duplicates in the underlying data.
Data Transfer Optimization Based on Offline Knowledge Discovery and Adaptive Real-time Sampling
Venue: arXiv cs.DC 2017
Year: 2017

The amount of data moved over dedicated and non-dedicated network links increases much faster than the increase in the network capacity, but the current solutions fail to guarantee even the promised achievable transfer throughputs. In this paper, we propose a novel dynamic throughput optimization model based on mathematical modeling with offline knowledge discovery/analysis and adaptive online decision making. In offline analysis, we mine historical transfer logs to perform knowledge discovery about the transfer characteristics. Online phase uses the discovered knowledge from the offline analysis along with real-time investigation of the network condition to optimize the protocol parameters. As real-time investigation is expensive and provides partial knowledge about the current network status, our model uses historical knowledge about the network and data to reduce the real-time investigation overhead while ensuring near optimal throughput for each transfer. Our network and data agnostic solution is tested over different networks and achieved up to 93% accuracy compared with the optimal achievable throughput possible on those networks.
Deep Feature Consistent Deep Image Transformations: Downscaling, Decolorization and HDR Tone Mapping
Venue: arXiv cs.CV 2017
Year: 2017

Building on crucial insights into the determining factors of the visual integrity of an image and the property of deep convolutional neural network (CNN), we have developed the Deep Feature Consistent Deep Image Transformation (DFC-DIT) framework which unifies challenging one-to-many mapping image processing problems such as image downscaling, decolorization (colour to grayscale conversion) and high dynamic range (HDR) image tone mapping. We train one CNN as a non-linear mapper to transform an input image to an output image following what we term the deep feature consistency principle which is enforced through another pretrained and fixed deep CNN. This is the first work that uses deep learning to solve and unify these three common image processing tasks. We present experimental results to demonstrate the effectiveness of the DFC-DIT technique and its state of the art performances.
Improved Adversarial Systems for 3D Object Generation and Reconstruction
Venue: arXiv cs.CV 2017
Year: 2017

This paper describes a new approach for training generative adversarial networks (GAN) to understand the detailed 3D shape of objects. While GANs have been used in this domain previously, they are notoriously hard to train, especially for the complex joint data distribution over 3D objects of many categories and orientations. Our method extends previous work by employing the Wasserstein distance normalized with gradient penalization as a training objective. This enables improved generation from the joint object shape distribution. Our system can also reconstruct 3D shape from 2D images and perform shape completion from occluded 2.5D range scans. We achieve notable quantitative improvements in comparison to existing baselines
Mapping the Curricular Structure and Contents of Network Science Courses
Venue: arXiv cs.CY 2017
Year: 2017

As network science has matured as an established field of research, there are already a number of courses on this topic developed and offered at various higher education institutions, often at postgraduate levels. In those courses, instructors adopted different approaches with different focus areas and curricular designs. We collected information about 30 existing network science courses from various online sources, and analyzed the contents of their syllabi or course schedules. The topics and their curricular sequences were extracted from the course syllabi/schedules and represented as a directed weighted graph, which we call the topic network. Community detection in the topic network revealed seven topic clusters, which matched reasonably with the concept list previously generated by students and educators through the Network Literacy initiative. The minimum spanning tree of the topic network revealed typical flows of curricular contents, starting with examples of networks, moving onto random networks and small-world networks, then branching off to various subtopics from there. These results illustrate the current state of consensus formation (including variations and disagreements) among the network science community on what should be taught about networks and how, which may also be informative for K--12 education and informal education.
Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results
Venue: arXiv cs.CV 2017
Year: 2017

In this work we present a novel system for PET estimation using CT scans. We explore the use of fully convolutional networks (FCN) and conditional generative adversarial networks (GAN) to export PET data from CT data. Our dataset includes 25 pairs of PET and CT scans where 17 were used for training and 8 for testing. The system was tested for detection of malignant tumors in the liver region. Initial results look promising showing high detection performance with a TPR of 92.3% and FPR of 0.25 per case. Future work entails expansion of the current system to the entire body using a much larger dataset. Such a system can be used for tumor detection and drug treatment evaluation in a CT-only environment instead of the expansive and radioactive PET-CT scan.
Adaptive Performance Optimization under Power Constraint in Multi-thread Applications with Diverse Scalability
Venue: arXiv cs.PF 2017
Year: 2017

In modern data centers, energy usage represents one of the major factors affecting operational costs. Power capping is a technique that limits the power consumption of individual systems, which allows reducing the overall power demand at both cluster and data center levels. However, literature power capping approaches do not fit well the nature of important applications based on first-class multi-thread technology. For these applications performance may not grow linearly as a function of the thread-level parallelism because of the need for thread synchronization while accessing shared resources, such as shared data. In this paper we consider the problem of maximizing the application performance under a power cap by dynamically tuning the thread-level parallelism and the power state of the CPU-cores. Based on experimental observations, we design an adaptive technique that selects in linear time the optimal combination of thread-level parallelism and CPU-core power state for the specific workload profile of the multi-threaded application. We evaluate our proposal by relying on different benchmarks, configured to use different thread synchronization methods, and compare its effectiveness to different state-of-the-art techniques.
Handling Nested Parallelism and Extreme Load Imbalance in an Orbital Analysis Code
Venue: arXiv cs.DC 2017
Year: 2017

Nested parallelism exists in scientific codes that are searching multi-dimensional spaces. However, implementations of nested parallelism often have overhead and load balance issues. The Orbital Analysis code we present exhibits a sparse search space, significant load imbalances, and stopping when the first solution is reached. All these aspects of the algorithm exacerbate the problem of using nested parallelism effectively. In this paper, we present an inspector/executor strategy for chunking such computations into parallel wavefronts. The presented shared memory parallelization is no longer nested and exhibits significantly less load imbalance. We evaluate this approach on an Orbital analysis code, and we improve the execution time from the original implementation by an order of magnitude. As part of a Graduate Computer Science course in Parallel Programming models, we show how the approach can be implemented in parallel Perl, Python, Chapel, Pthreads, and OpenMP. Future work includes investigating how to automate and generalize the parallelization approach.
Cost and Actual Causation
Venue: arXiv cs.AI 2017
Year: 2017

I propose the purpose our concept of actual causation serves is minimizing various cost in intervention practice. Actual causation has three features: nonredundant sufficiency, continuity and abnormality; these features correspond to the minimization of exploitative cost, exploratory cost and risk cost in intervention practice. Incorporating these three features, a definition of actual causation is given. I test the definition in 66 causal cases from actual causation literature and show that this definition's application fit intuition better than some other causal modelling based definitions.