OpenRaiser/Intern-Atlas huggingface dataset:
Correlating Satellite Cloud Cover with Sky Cameras
Venue: arXiv cs.CV 2017
Year: 2017

The role of clouds is manifold in understanding the various events in the atmosphere, and also in studying the radiative balance of the earth. The conventional manner of such cloud analysis is performed mainly via satellite images. However, because of its low temporal- and spatial- resolutions, ground-based sky cameras are now getting popular. In this paper, we study the relation between the cloud cover obtained from MODIS images, with the coverage obtained from ground-based sky cameras. This will help us to better understand cloud formation in the atmosphere - both from satellite images and ground-based observations.
Video Synopsis Generation Using Spatio-Temporal Groups
Venue: arXiv cs.CV 2017
Year: 2017

Millions of surveillance cameras operate at 24x7 generating huge amount of visual data for processing. However, retrieval of important activities from such a large data can be time consuming. Thus, researchers are working on finding solutions to present hours of visual data in a compressed, but meaningful way. Video synopsis is one of the ways to represent activities using relatively shorter duration clips. So far, two main approaches have been used by researchers to address this problem, namely synopsis by tracking moving objects and synopsis by clustering moving objects. Synopses outputs, mainly depend on tracking, segmenting, and shifting of moving objects temporally as well as spatially. In many situations, tracking fails, thus produces multiple trajectories of the same object. Due to this, the object may appear and disappear multiple times within the same synopsis output, which is misleading. This also leads to discontinuity and often can be confusing to the viewer of the synopsis. In this paper, we present a new approach for generating compressed video synopsis by grouping tracklets of moving objects. Grouping helps to generate a synopsis where chronologically related objects appear together with meaningful spatio-temporal relation. Our proposed method produces continuous, but a less confusing synopses when tested on publicly available dataset videos as well as in-house dataset videos.
Dynamic Capacity Estimation in Hopfield Networks
Venue: arXiv cs.NE 2017
Year: 2017

Understanding the memory capacity of neural networks remains a challenging problem in implementing artificial intelligence systems. In this paper, we address the notion of capacity with respect to Hopfield networks and propose a dynamic approach to monitoring a network's capacity. We define our understanding of capacity as the maximum number of stored patterns which can be retrieved when probed by the stored patterns. Prior work in this area has presented static expressions dependent on neuron count $N$, forcing network designers to assume worst-case input characteristics for bias and correlation when setting the capacity of the network. Instead, our model operates simultaneously with the learning Hopfield network and concludes on a capacity estimate based on the patterns which were stored. By continuously updating the crosstalk associated with the stored patterns, our model guards the network from overwriting its memory traces and exceeding its capacity. We simulate our model using artificially generated random patterns, which can be set to a desired bias and correlation, and observe capacity estimates between 93% and 97% accurate. As a result, our model doubles the memory efficiency of Hopfield networks in comparison to the static and worst-case capacity estimate while minimizing the risk of lost patterns.
Stability Analysis of Fractional Differential Equations with Unknown Parameters
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, the stability of fractional differential equations (FDEs) with unknown parameters is studied. FDEs bring many advantages to model the physical systems in the nature or man-made systems in the industry. Because this representation has a property between linear differential equations and nonlinear differential equations. Therefore, the designer may use the FDEs to model complex systems instead of nonlinear differential equations which have hard mathematical background. Using the graphical based D-decomposition method, we investigate the parametric stability analysis of FDEs without complicated mathematical analysis. To achieve this, stability boundaries are obtained firstly, and then the stability region set depending on the unknown parameters is found. The applicability of the presented method is shown considering some benchmark equations which are often used to verify the results of a new method. Simulation examples shown that the method is simple and give reliable stability results.
Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue
Venue: arXiv cs.CL 2017
Year: 2017

The use of irony and sarcasm in social media allows us to study them at scale for the first time. However, their diversity has made it difficult to construct a high-quality corpus of sarcasm in dialogue. Here, we describe the process of creating a large- scale, highly-diverse corpus of online debate forums dialogue, and our novel methods for operationalizing classes of sarcasm in the form of rhetorical questions and hyperbole. We show that we can use lexico-syntactic cues to reliably retrieve sarcastic utterances with high accuracy. To demonstrate the properties and quality of our corpus, we conduct supervised learning experiments with simple features, and show that we achieve both higher precision and F than previous work on sarcasm in debate forums dialogue. We apply a weakly-supervised linguistic pattern learner and qualitatively analyze the linguistic differences in each class.
Grade Prediction with Temporal Course-wise Influence
Venue: arXiv cs.LG 2017
Year: 2017

There is a critical need to develop new educational technology applications that analyze the data collected by universities to ensure that students graduate in a timely fashion (4 to 6 years); and they are well prepared for jobs in their respective fields of study. In this paper, we present a novel approach for analyzing historical educational records from a large, public university to perform next-term grade prediction; i.e., to estimate the grades that a student will get in a course that he/she will enroll in the next term. Accurate next-term grade prediction holds the promise for better student degree planning, personalized advising and automated interventions to ensure that students stay on track in their chosen degree program and graduate on time. We present a factorization-based approach called Matrix Factorization with Temporal Course-wise Influence that incorporates course-wise influence effects and temporal effects for grade prediction. In this model, students and courses are represented in a latent "knowledge" space. The grade of a student on a course is modeled as the similarity of their latent representation in the "knowledge" space. Course-wise influence is considered as an additional factor in the grade prediction. Our experimental results show that the proposed method outperforms several baseline approaches and infer meaningful patterns between pairs of courses within academic programs.
Acquiring Background Knowledge to Improve Moral Value Prediction
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we address the problem of detecting expressions of moral values in tweets using content analysis. This is a particularly challenging problem because moral values are often only implicitly signaled in language, and tweets contain little contextual information due to length constraints. To address these obstacles, we present a novel approach to automatically acquire background knowledge from an external knowledge base to enrich input texts and thus improve moral value prediction. By combining basic text features with background knowledge, our overall context-aware framework achieves performance comparable to a single human annotator. To the best of our knowledge, this is the first attempt to incorporate background knowledge for the prediction of implicit psychological variables in the area of computational social science.
Long-Term Ensemble Learning of Visual Place Classifiers
Venue: arXiv cs.CV 2017
Year: 2017

This paper addresses the problem of cross-season visual place classification (VPC) from a novel perspective of long-term map learning. Our goal is to enable transfer learning efficiently from one season to the next, at a small constant cost, and without wasting the robot's available long-term-memory by memorizing very large amounts of training data. To realize a good tradeoff between generalization and specialization abilities, we employ an ensemble of convolutional neural network (DCN) classifiers and consider the task of scheduling (when and which classifiers to retrain), given a previous season's DCN classifiers as the sole prior knowledge. We present a unified framework for retraining scheduling and discuss practical implementation strategies. Furthermore, we address the task of partitioning a robot's workspace into places to define place classes in an unsupervised manner, rather than using uniform partitioning, so as to maximize VPC performance. Experiments using the publicly available NCLT dataset revealed that retraining scheduling of a DCN classifier ensemble is crucial and performance is significantly increased by using planned scheduling.
DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification
Venue: arXiv cs.CV 2017
Year: 2017

In this work, we present a fully automated lung CT cancer diagnosis system, DeepLung. DeepLung contains two parts, nodule detection and classification. Considering the 3D nature of lung CT data, two 3D networks are designed for the nodule detection and classification respectively. Specifically, a 3D Faster R-CNN is designed for nodule detection with a U-net-like encoder-decoder structure to effectively learn nodule features. For nodule classification, gradient boosting machine (GBM) with 3D dual path network (DPN) features is proposed. The nodule classification subnetwork is validated on a public dataset from LIDC-IDRI, on which it achieves better performance than state-of-the-art approaches, and surpasses the average performance of four experienced doctors. For the DeepLung system, candidate nodules are detected first by the nodule detection subnetwork, and nodule diagnosis is conducted by the classification subnetwork. Extensive experimental results demonstrate the DeepLung is comparable to the experienced doctors both for the nodule-level and patient-level diagnosis on the LIDC-IDRI dataset.
Nonnegative HMM for Babble Noise Derived from Speech HMM: Application to Speech Enhancement
Venue: arXiv cs.SD 2017
Year: 2017

Deriving a good model for multitalker babble noise can facilitate different speech processing algorithms, e.g. noise reduction, to reduce the so-called cocktail party difficulty. In the available systems, the fact that the babble waveform is generated as a sum of N different speech waveforms is not exploited explicitly. In this paper, first we develop a gamma hidden Markov model for power spectra of the speech signal, and then formulate it as a sparse nonnegative matrix factorization (NMF). Second, the sparse NMF is extended by relaxing the sparsity constraint, and a novel model for babble noise (gamma nonnegative HMM) is proposed in which the babble basis matrix is the same as the speech basis matrix, and only the activation factors (weights) of the basis vectors are different for the two signals over time. Finally, a noise reduction algorithm is proposed using the derived speech and babble models. All of the stationary model parameters are estimated using the expectation-maximization (EM) algorithm, whereas the time-varying parameters, i.e. the gain parameters of speech and babble signals, are estimated using a recursive EM algorithm. The objective and subjective listening evaluations show that the proposed babble model and the final noise reduction algorithm significantly outperform the conventional methods.
Data Innovation for International Development: An overview of natural language processing for qualitative data analysis
Venue: arXiv cs.CL 2017
Year: 2017

Availability, collection and access to quantitative data, as well as its limitations, often make qualitative data the resource upon which development programs heavily rely. Both traditional interview data and social media analysis can provide rich contextual information and are essential for research, appraisal, monitoring and evaluation. These data may be difficult to process and analyze both systematically and at scale. This, in turn, limits the ability of timely data driven decision-making which is essential in fast evolving complex social systems. In this paper, we discuss the potential of using natural language processing to systematize analysis of qualitative data, and to inform quick decision-making in the development context. We illustrate this with interview data generated in a format of micro-narratives for the UNDP Fragments of Impact project.
Character Distributions of Classical Chinese Literary Texts: Zipf's Law, Genres, and Epochs
Venue: arXiv cs.CL 2017
Year: 2017

We collect 14 representative corpora for major periods in Chinese history in this study. These corpora include poetic works produced in several dynasties, novels of the Ming and Qing dynasties, and essays and news reports written in modern Chinese. The time span of these corpora ranges between 1046 BCE and 2007 CE. We analyze their character and word distributions from the viewpoint of the Zipf's law, and look for factors that affect the deviations and similarities between their Zipfian curves. Genres and epochs demonstrated their influences in our analyses. Specifically, the character distributions for poetic works of between 618 CE and 1644 CE exhibit striking similarity. In addition, although texts of the same dynasty may tend to use the same set of characters, their character distributions still deviate from each other.
Hybrid Fault diagnosis capability analysis of Hypercubes under the PMC model and MM* model
Venue: arXiv cs.DC 2017
Year: 2017

System level diagnosis is an important approach for the fault diagnosis of multiprocessor systems. In system level diagnosis, diagnosability is an important measure of the diagnosis capability of interconnection networks. But as a measure, diagnosability can not reflect the diagnosis capability of multiprocessor systems to link faults which may occur in real circumstances. In this paper, we propose the definition of $h$-edge tolerable diagnosability to better measure the diagnosis capability of interconnection networks under hybrid fault circumstances. The $h$-edge tolerable diagnosability of a multiprocessor system $G$ is the maximum number of faulty nodes that the system can guarantee to locate when the number of faulty edges does not exceed $h$,denoted by $t_h^{e}(G)$. The PMC model and MM model are the two most widely studied diagnosis models for the system level diagnosis of multiprocessor systems. The hypercubes are the most well-known interconnection networks. In this paper, the $h$-edge tolerable diagnosability of $n$-dimensional hypercube under the PMC model and MM$^{*}$ is determined as follows: $t_h^{e}(Q_n)= n-h$, where $1\leq h<n$, $n\geq3$.
Unwritten Languages Demand Attention Too! Word Discovery with Encoder-Decoder Models
Venue: arXiv cs.CL 2017
Year: 2017

Word discovery is the task of extracting words from unsegmented text. In this paper we examine to what extent neural networks can be applied to this task in a realistic unwritten language scenario, where only small corpora and limited annotations are available. We investigate two scenarios: one with no supervision and another with limited supervision with access to the most frequent words. Obtained results show that it is possible to retrieve at least 27% of the gold standard vocabulary by training an encoder-decoder neural machine translation system with only 5,157 sentences. This result is close to those obtained with a task-specific Bayesian nonparametric model. Moreover, our approach has the advantage of generating translation alignments, which could be used to create a bilingual lexicon. As a future perspective, this approach is also well suited to work directly from speech.
A Categorical Approach for Recognizing Emotional Effects of Music
Venue: arXiv cs.AI 2017
Year: 2017

Recently, digital music libraries have been developed and can be plainly accessed. Latest research showed that current organization and retrieval of music tracks based on album information are inefficient. Moreover, they demonstrated that people use emotion tags for music tracks in order to search and retrieve them. In this paper, we discuss separability of a set of emotional labels, proposed in the categorical emotion expression, using Fisher's separation theorem. We determine a set of adjectives to tag music parts: happy, sad, relaxing, exciting, epic and thriller. Temporal, frequency and energy features have been extracted from the music parts. It could be seen that the maximum separability within the extracted features occurs between relaxing and epic music parts. Finally, we have trained a classifier using Support Vector Machines to automatically recognize and generate emotional labels for a music part. Accuracy for recognizing each label has been calculated; where the results show that epic music can be recognized more accurately (77.4%), comparing to the other types of music.
Multipath IP Routing on End Devices: Motivation, Design, and Performance
Venue: arXiv cs.NI 2017
Year: 2017

Most end devices are now equipped with multiple network interfaces. Applications can exploit all available interfaces and benefit from multipath transmission. Recently Multipath TCP (MPTCP) was proposed to implement multipath transmission at the transport layer and has attracted lots of attention from academia and industry. However, MPTCP only supports TCP-based applications and its multipath routing flexibility is limited. In this paper, we investigate the possibility of orchestrating multipath transmission from the network layer of end devices, and develop a Multipath IP (MPIP) design consisting of signaling, session and path management, multipath routing, and NAT traversal. We implement MPIP in Linux and Android kernels. Through controlled lab experiments and Internet experiments, we demonstrate that MPIP can effectively achieve multipath gains at the network layer. It not only supports the legacy TCP and UDP protocols, but also works seamlessly with MPTCP. By facilitating user-defined customized routing, MPIP can route traffic from competing applications in a coordinated fashion to maximize the aggregate user Quality-of-Experience.
Towards Building a Knowledge Base of Monetary Transactions from a News Collection
Venue: arXiv cs.IR 2017
Year: 2017

We address the problem of extracting structured representations of economic events from a large corpus of news articles, using a combination of natural language processing and machine learning techniques. The developed techniques allow for semi-automatic population of a financial knowledge base, which, in turn, may be used to support a range of data mining and exploration tasks. The key challenge we face in this domain is that the same event is often reported multiple times, with varying correctness of details. We address this challenge by first collecting all information pertinent to a given event from the entire corpus, then considering all possible representations of the event, and finally, using a supervised learning method, to rank these representations by the associated confidence scores. A main innovative element of our approach is that it jointly extracts and stores all attributes of the event as a single representation (quintuple). Using a purpose-built test set we demonstrate that our supervised learning approach can achieve 25% improvement in F1-score over baseline methods that consider the earliest, the latest or the most frequent reporting of the event.
New Algorithms for Minimizing the Weighted Number of Tardy Jobs On a Single Machine
Venue: arXiv cs.DS 2017
Year: 2017

In this paper we study the classical single machine scheduling problem where the objective is to minimize the total weight of tardy jobs. Our analysis focuses on the case where one or more of three natural parameters is either constant or is taken as a parameter in the sense of parameterized complexity. These three parameters are the number of different due dates, processing times, and weights in our set of input jobs. We show that the problem belongs to the class of fixed parameter tractable (FPT)problems when combining any two of these three parameters. We also show that the problem is polynomial-time solvable when the latter two parameters are constant, complementing Karp's result who showed that the problem is NP-hard already for a single due date.
Deploying SDN in GÉANT production network
Venue: arXiv cs.NI 2017
Year: 2017

Since the demand for more bandwidth, agile infrastructures and services grows, it becomes challenging for Service Providers like GÉANT to manage the proprietary underlay, while keeping costs low. In such a scenario, Software Defined Networking (SDN), open hardware and open source software prove to be key components to address those challenges. After one year of development, SDX-L2 and BoD, the SDN-ization of the GÉANT Open and Bandwidth on Demand (BoD) services, have been brought to the pilot status and GÉANT is now testing the outcomes on its operational network. In this demonstration, we show BoD and SDX-L2 "going live" at the GÉANT production infrastructure. The pilots run on the same underlay infrastructure thanks to the virtualization capabilities of the network devices. Provisioning of the services is covered during the demo. In the final steps of the demonstration, we show how the infrastructure is able to automatically manage network events and how it remains operational in the case of fault events.
Decentralized Collision-Free Control of Multiple Robots in 2D and 3D Spaces
Venue: arXiv cs.RO 2017
Year: 2017

Decentralized control of robots has attracted huge research interests. However, some of the research used unrealistic assumptions without collision avoidance. This report focuses on the collision-free control for multiple robots in both complete coverage and search tasks in 2D and 3D areas which are arbitrary unknown. All algorithms are decentralized as robots have limited abilities and they are mathematically proved. The report starts with the grid selection in the two tasks. Grid patterns simplify the representation of the area and robots only need to move straightly between neighbor vertices. For the 100% complete 2D coverage, the equilateral triangular grid is proposed. For the complete coverage ignoring the boundary effect, the grid with the fewest vertices is calculated in every situation for both 2D and 3D areas. The second part is for the complete coverage in 2D and 3D areas. A decentralized collision-free algorithm with the above selected grid is presented driving robots to sections which are furthest from the reference point. The area can be static or expanding, and the algorithm is simulated in MATLAB. Thirdly, three grid-based decentralized random algorithms with collision avoidance are provided to search targets in 2D or 3D areas. The number of targets can be known or unknown. In the first algorithm, robots choose vacant neighbors randomly with priorities on unvisited ones while the second one adds the repulsive force to disperse robots if they are close. In the third algorithm, if surrounded by visited vertices, the robot will use the breadth-first search algorithm to go to one of the nearest unvisited vertices via the grid. The second search algorithm is verified on Pioneer 3-DX robots. The general way to generate the formula to estimate the search time is demonstrated. Algorithms are compared with five other algorithms in MATLAB to show their effectiveness.