OpenRaiser/Intern-Atlas huggingface dataset:
Weakly-Supervised Video Object Grounding from Text by Loss Weighting and Object Interaction
Venue: arXiv cs.CV 2018
Year: 2018

We study weakly-supervised video object grounding: given a video segment and a corresponding descriptive sentence, the goal is to localize objects that are mentioned from the sentence in the video. During training, no object bounding boxes are available, but the set of possible objects to be grounded is known beforehand. Existing approaches in the image domain use Multiple Instance Learning (MIL) to ground objects by enforcing matches between visual and semantic features. A naive extension of this approach to the video domain is to treat the entire segment as a bag of spatial object proposals. However, an object existing sparsely across multiple frames might not be detected completely since successfully spotting it from one single frame would trigger a satisfactory match. To this end, we propagate the weak supervisory signal from the segment level to frames that likely contain the target object. For frames that are unlikely to contain the target objects, we use an alternative penalty loss. We also leverage the interactions among objects as a textual guide for the grounding. We evaluate our model on the newly-collected benchmark YouCook2-BoundingBox and show improvements over competitive baselines.
Statistical Dimension Identification and Implementation for Student Progression System
Venue: arXiv cs.SE 2018
Year: 2018

Descriptive Analytics is the summarization of the past data and generates some useful patterns from that data. This work focuses on analyzing and querying large academic dataset for generating Student Progression using visualization and dashboards. Presently projects on Progression Systems exist but no descriptive or predictive analytics has been performed on these datasets. The proposed system collects data from different departments of University store data into the large data warehouse of the University and generate validated set of KPIs, based on the past dataset of University department. These KPIs are obtained after applying Statistical techniques on various sets of dimension in the academic datasets. After completion of this step, analysis of the data has been achieved with Online Analytical Processing (OLAP) operations, which have been show cased with the help of visualization and dashboards.
Movie rating prediction using content-based and link stream features
Venue: arXiv cs.SI 2018
Year: 2018

While graph-based collaborative filtering recommender systems have been introduced several years ago, there are still several shortcomings to deal with, the temporal information being one of the most important. The new link stream paradigm is aiming at extending graphs for correctly modelling the graph dynamics, without losing crucial information. We investigate the impact of such link stream features for recommender systems. by designing link stream features, that capture the intrinsic structure and dynamics of the data. We show that such features encode a fine-grained and subtle description of the underlying recommender system. Focusing on a traditional recommender system context, the rating prediction on the MovieLens20M dataset, we input these features along with some content-based ones into a gradient boosting machine (XGBoost) and show that it outperforms significantly a sole content-based solution. These encouraging results call for further exploration of this original modelling and its integration to complete state-of-the-art recommender systems algorithms. Link streams and graphs, as natural visualizations of recommender systems, can offer more interpretability in a time when algorithm transparency is an increasingly important topic of discussion. We also hope to sparkle interesting discussions in the community about the links between link streams and tensor factorization methods: indeed, they are two sides of the same object.
Cooperative Control of Multiple Agents with Unknown High-frequency Gain Signs under Unbalanced and Switching Topologies
Venue: arXiv cs.SY 2018
Year: 2018

Existing results on cooperative control of multi-agent systems with unknown control directions require that the underlying topology is either fixed with a strongly connected graph or switching between different strongly connected graphs. Furthermore, in most cases the graph is assumed to be balanced. This paper proposes a new class of nonlinear PI based algorithms to relax these requirements and allow for unbalanced and switching topologies having a jointly strongly connected basis. This is made possible for single-integrator (SI) and double-integrator (DI) agents with non-identical unknown control directions by a suitable selection of the distributed nonlinear PI functions. Moreover, as a special case, the proposed algorithms are applied to strongly connected and fixed graphs. Finally, simulation examples are given to show the validity of our theoretical results.
Ideal Relative Flow Distribution on Directed Network
Venue: arXiv cs.SI 2018
Year: 2018

In this paper we propose a new concept to prioritize the importance of a link in a directed network graph based on an ideal flow distribution. An ideal flow is the infinite limit of relative aggregated count of random walk agents' trajectories on a network graph distributed over space and time. The standard ideal flow, which is uniformly distributed flow over space and time, maximize the entropy for the utilization of a network. We show that the simulated trajectories of random walk agents would form an ideal relative flow distribution is converged to stationary values. This implies that ideal flow matrix depends only on the network structure. Ideal flow matrix is invariant to scalar multiplication and remarkably it is always premagic. Demonstration of ideal flow to the real world network was fitted into Sioux Falls transportation network.
Adaptive-Latency DRAM: Reducing DRAM Latency by Exploiting Timing Margins
Venue: arXiv cs.AR 2018
Year: 2018

This paper summarizes the idea of Adaptive-Latency DRAM (AL-DRAM), which was published in HPCA 2015, and examines the work's significance and future potential. AL-DRAM is a mechanism that optimizes DRAM latency based on the DRAM module and the operating temperature, by exploiting the extra margin that is built into the DRAM timing parameters. DRAM manufacturers provide a large margin for the timing parameters as a provision against two worst-case scenarios. First, due to process variation, some outlier DRAM chips are much slower than others. Second, chips become slower at higher temperatures. The timing parameter margin ensures that the slow outlier chips operate reliably at the worst-case temperature, and hence leads to a high access latency. Using an FPGA-based DRAM testing platform, our work first characterizes the extra margin for 115 DRAM modules from three major manufacturers. The experimental results demonstrate that it is possible to reduce four of the most critical timing parameters by a minimum/maximum of 17.3%/54.8% at 55C while maintaining reliable operation. AL-DRAM uses these observations to adaptively select reliable DRAM timing parameters for each DRAM module based on the module's current operating conditions. AL-DRAM does not require any changes to the DRAM chip or its interface; it only requires multiple different timing parameters to be specified and supported by the memory controller. Our real system evaluations show that AL-DRAM improves the performance of memory-intensive workloads by an average of 14% without introducing any errors. Our characterization and proposed techniques have inspired several other works on analyzing and/or exploiting different sources of latency and performance variation within DRAM chips.
Fast Feature Extraction with CNNs with Pooling Layers
Venue: arXiv cs.CV 2018
Year: 2018

In recent years, many publications showed that convolutional neural network based features can have a superior performance to engineered features. However, not much effort was taken so far to extract local features efficiently for a whole image. In this paper, we present an approach to compute patch-based local feature descriptors efficiently in presence of pooling and striding layers for whole images at once. Our approach is generic and can be applied to nearly all existing network architectures. This includes networks for all local feature extraction tasks like camera calibration, Patchmatching, optical flow estimation and stereo matching. In addition, our approach can be applied to other patch-based approaches like sliding window object detection and recognition. We complete our paper with a speed benchmark of popular CNN based feature extraction approaches applied on a whole image, with and without our speedup, and example code (for Torch) that shows how an arbitrary CNN architecture can be easily converted by our approach.
Learning on the Edge: Explicit Boundary Handling in CNNs
Venue: arXiv cs.CV 2018
Year: 2018

Convolutional neural networks (CNNs) handle the case where filters extend beyond the image boundary using several heuristics, such as zero, repeat or mean padding. These schemes are applied in an ad-hoc fashion and, being weakly related to the image content and oblivious of the target task, result in low output quality at the boundary. In this paper, we propose a simple and effective improvement that learns the boundary handling itself. At training-time, the network is provided with a separate set of explicit boundary filters. At testing-time, we use these filters which have learned to extrapolate features at the boundary in an optimal way for the specific task. Our extensive evaluation, over a wide range of architectural changes (variations of layers, feature channels, or both), shows how the explicit filters result in improved boundary handling. Consequently, we demonstrate an improvement of 5% to 20% across the board of typical CNN applications (colorization, de-Bayering, optical flow, and disparity estimation).
Flexible-Latency DRAM: Understanding and Exploiting Latency Variation in Modern DRAM Chips
Venue: arXiv cs.AR 2018
Year: 2018

This article summarizes key results of our work on experimental characterization and analysis of latency variation and latency-reliability trade-offs in modern DRAM chips, which was published in SIGMETRICS 2016, and examines the work's significance and future potential. The goal of this work is to (i) experimentally characterize and understand the latency variation across cells within a DRAM chip for these three fundamental DRAM operations, and (ii) develop new mechanisms that exploit our understanding of the latency variation to reliably improve performance. To this end, we comprehensively characterize 240 DRAM chips from three major vendors, and make six major new observations about latency variation within DRAM. Notably, we find that (i) there is large latency variation across the cells for each of the three operations; (ii) variation characteristics exhibit significant spatial locality: slower cells are clustered in certain regions of a DRAM chip; and (iii) the three fundamental operations exhibit different reliability characteristics when the latency of each operation is reduced. Based on our observations, we propose Flexible-LatencY DRAM (FLY-DRAM), a mechanism that exploits latency variation across DRAM cells within a DRAM chip to improve system performance. The key idea of FLY-DRAM is to exploit the spatial locality of slower cells within DRAM, and access the faster DRAM regions with reduced latencies for the fundamental operations. Our evaluations show that FLY-DRAM improves the performance of a wide range of applications by 13.3%, 17.6%, and 19.5%, on average, for each of the three different vendors' real DRAM chips, in a simulated 8-core system.
Crowdtesting : When is The Party Over?
Venue: arXiv cs.SE 2018
Year: 2018

Trade-offs such as "how much testing is enough" are critical yet challenging project decisions in software engineering. Most existing approaches adopt risk-driven or value-based analysis to prioritize test cases and minimize test runs. However, none of these is applicable to the emerging crowd testing paradigm where task requesters typically have no control over online crowdworkers's dynamic behavior and uncertain performance. In current practice, deciding when to close a crowdtesting task is largely done by guesswork due to lack of decision support. This paper intends to fill this gap by introducing automated decision support for monitoring and determining appropriate time to close the crowdtesting tasks. First, this paper investigates the necessity and feasibility of close prediction of crowdtesting tasks based on industrial dataset. Then,it designs 8 methods for close prediction, based on various models including the bug trend, bug arrival model, capture-recapture model.Finally, the evaluation is conducted on 218 crowdtesting tasks from one of the largest crowdtesting platforms in China, and the results show that a median of 91% bugs can be detected with 49% saved cost.
A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D Images
Venue: arXiv cs.CV 2018
Year: 2018

3D pose estimation from a single 2D image is an important and challenging task in computer vision with applications in autonomous driving, robot manipulation and augmented reality. Since 3D pose is a continuous quantity, a natural formulation for this task is to solve a pose regression problem. However, since pose regression methods return a single estimate of the pose, they have difficulties handling multimodal pose distributions (e.g. in the case of symmetric objects). An alternative formulation, which can capture multimodal pose distributions, is to discretize the pose space into bins and solve a pose classification problem. However, pose classification methods can give large pose estimation errors depending on the coarseness of the discretization. In this paper, we propose a mixed classification-regression framework that uses a classification network to produce a discrete multimodal pose estimate and a regression network to produce a continuous refinement of the discrete estimate. The proposed framework can accommodate different architectures and loss functions, leading to multiple classification-regression models, some of which achieve state-of-the-art performance on the challenging Pascal3D+ dataset.
Robustness Testing of Intermediate Verifiers
Venue: arXiv cs.SE 2018
Year: 2018

Program verifiers are not exempt from the bugs that affect nearly every piece of software. In addition, they often exhibit brittle behavior: their performance changes considerably with details of how the input program is expressed-details that should be irrelevant, such as the order of independent declarations. Such a lack of robustness frustrates users who have to spend considerable time figuring out a tool's idiosyncrasies before they can use it effectively. This paper introduces a technique to detect lack of robustness of program verifiers; the technique is lightweight and fully automated, as it is based on testing methods (such as mutation testing and metamorphic testing). The key idea is to generate many simple variants of a program that initially passes verification. All variants are, by construction, equivalent to the original program; thus, any variant that fails verification indicates lack of robustness in the verifier. We implemented our technique in a tool called "mugie", which operates on programs written in the popular Boogie language for verification-used as intermediate representation in numerous program verifiers. Experiments targeting 135 Boogie programs indicate that brittle behavior occurs fairly frequently (16 programs) and is not hard to trigger. Based on these results, the paper discusses the main sources of brittle behavior and suggests means of improving robustness.
The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing
Venue: arXiv cs.CV 2018
Year: 2018

We propose a novel deep neural network architecture for the challenging problem of single image dehazing, which aims to recover the clear image from a degraded hazy image. Instead of relying on hand-crafted image priors or explicitly estimating the components of the widely used atmospheric scattering model, our end-to-end system directly generates the clear image from an input hazy image. The proposed network has an encoder-decoder architecture with skip connections and instance normalization. We adopt the convolutional layers of the pre-trained VGG network as encoder to exploit the representation power of deep features, and demonstrate the effectiveness of instance normalization for image dehazing. Our simple yet effective network outperforms the state-of-the-art methods by a large margin on the benchmark datasets.
Multi-scale metrics and self-organizing maps: a computational approach to the structure of sensory maps
Venue: arXiv cs.NE 2018
Year: 2018

This paper introduces the concept of a bi-scale metric for use in the cooperative phase of the self-organizing map (SOM) algorithm. Use of a bi-scale metric allows segmentation of the map into a number of regions, corresponding to anticipated cluster structure in the data. Such a situation occurs, for example, in the somatotopic maps which inspired the SOM algo- rithm, where clusters of data may correspond to body surface regions whose general structure is known. When a bi-scale metric is appropriately applied, issues with map neurons that are not activated by any point in the training data are reduced or eliminated. The paper also presents results of simulation studies on the plasticity of bi-scale metric maps when they are retrained af- ter loss of groups of map neurons or after changes in training data (such as would occur in a somatotopic map when a body surface region like a finger is lost/removed). The paper further considers situations where tri-scale met- rics may be useful, and an alternative approach suggested by neurobiology, where some map regions adapt more slowly to stimuli because they have a lower learning rate parameter.
Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
Venue: arXiv cs.IR 2018
Year: 2018

Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms - especially collaborative filtering (CF)-based approaches with shallow or deep models - usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely ignored recently due to the research focus on CF approaches. However, structured knowledge exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users' historical behaviors and the knowledge is helpful for providing informed explanations regarding the recommended items. A great challenge for using knowledge bases for recommendation is how to integrate large-scale structured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements in knowledge-base embedding (KBE) sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge for explanation. In this work, we propose to explain knowledge-base embeddings for explainable recommendation. Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items. Experimental results on real-world e-commerce datasets verified the superior recommendation performance and the explainability power of our approach compared with state-of-the-art baselines.
LearningWord Embeddings for Low-resource Languages by PU Learning
Venue: arXiv cs.CL 2018
Year: 2018

Word embedding is a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for learning effective word embedding. However, such a corpus may not be available for some low-resource languages. In this paper, we study how to effectively learn a word embedding model on a corpus with only a few million tokens. In such a situation, the co-occurrence matrix is sparse as the co-occurrences of many word pairs are unobserved. In contrast to existing approaches often only sample a few unobserved word pairs as negative samples, we argue that the zero entries in the co-occurrence matrix also provide valuable information. We then design a Positive-Unlabeled Learning (PU-Learning) approach to factorize the co-occurrence matrix and validate the proposed approaches in four different languages.
Complexity of Proper Suffix-Convex Regular Languages
Venue: arXiv cs.FL 2018
Year: 2018

A language L is suffix-convex if for any words u, v,w, whenever w and uvw are in L, vw is in L as well. Suffix-convex languages include left ideals, suffix-closed languages, and suffix-free languages, which were studied previously. In this paper, we concentrate on suffix-convex languages that do not belong to any one of these classes; we call such languages proper. In order to study this language class, we define a structure called a suffix-convex triple system that characterizes the automata recognizing suffix-convex languages. We find tight upper bounds for reversal, star, product, and boolean operations of proper suffix-convex languages, and we conjecture on the size of the largest syntactic semigroup. We also prove that three witness streams are required to meet all these bounds.
Factors Influencing Cities' Publishing Efficiency
Venue: arXiv cs.DL 2018
Year: 2018

Recently, a vast number of scientific publications have been produced in cities in emerging countries. It has long been observed that the publication output of Beijing has exceeded that of any other city in the world, including such leading centres of science as Boston, New York, London, Paris, and Tokyo. Researchers have suggested that, instead of focusing on cities' total publication output, the quality of the output in terms of the number of highly cited papers should be examined. However, in the period from 2014 to 2016, Beijing produced as many highly cited papers as Boston, London, or New York. In this paper, I propose another method to measure cities' publishing performance; I focus on cities' publishing efficiency (i.e., the ratio of highly cited articles to all articles produced in that city). First, I rank 554 cities based on their publishing efficiency, then I reveal some general factors influencing cities' publishing efficiency. The general factors examined in this paper are as follows: the linguistic environment, cities' economic development level, the location of excellent organisations, cities' international collaboration patterns, and the productivity of scientific disciplines.
Generalized Hermite Reduction, Creative Telescoping and Definite Integration of D-Finite Functions
Venue: arXiv cs.SC 2018
Year: 2018

Hermite reduction is a classical algorithmic tool in symbolic integration. It is used to decompose a given rational function as a sum of a function with simple poles and the derivative of another rational function. We extend Hermite reduction to arbitrary linear differential operators instead of the pure derivative, and develop efficient algorithms for this reduction. We then apply the generalized Hermite reduction to the computation of linear operators satisfied by single definite integrals of D-finite functions of several continuous or discrete parameters. The resulting algorithm is a generalization of reduction-based methods for creative telescoping.