OpenRaiser/Intern-Atlas huggingface dataset:
Knowledge Questions from Knowledge Graphs
Venue: arXiv cs.CL 2016
Year: 2016

We address the novel problem of automatically generating quiz-style knowledge questions from a knowledge graph such as DBpedia. Questions of this kind have ample applications, for instance, to educate users about or to evaluate their knowledge in a specific domain. To solve the problem, we propose an end-to-end approach. The approach first selects a named entity from the knowledge graph as an answer. It then generates a structured triple-pattern query, which yields the answer as its sole result. If a multiple-choice question is desired, the approach selects alternative answer options. Finally, our approach uses a template-based method to verbalize the structured query and yield a natural language question. A key challenge is estimating how difficult the generated question is to human users. To do this, we make use of historical data from the Jeopardy! quiz show and a semantically annotated Web-scale document collection, engineer suitable features, and train a logistic regression classifier to predict question difficulty. Experiments demonstrate the viability of our overall approach.
Capturing the "Whole Tale" of Computational Research: Reproducibility in Computing Environments
Venue: arXiv cs.DL 2016
Year: 2016

We present an overview of the recently funded "Merging Science and Cyberinfrastructure Pathways: The Whole Tale" project (NSF award #1541450). Our approach has two nested goals: 1) deliver an environment that enables researchers to create a complete narrative of the research process including exposure of the data-to-publication lifecycle, and 2) systematically and persistently link research publications to their associated digital scholarly objects such as the data, code, and workflows. To enable this, Whole Tale will create an environment where researchers can collaborate on data, workspaces, and workflows and then publish them for future adoption or modification. Published data and applications will be consumed either directly by users using the Whole Tale environment or can be integrated into existing or future domain Science Gateways.
Generating Sentiment Lexicons for German Twitter
Venue: arXiv cs.CL 2016
Year: 2016

Despite a substantial progress made in developing new sentiment lexicon generation (SLG) methods for English, the task of transferring these approaches to other languages and domains in a sound way still remains open. In this paper, we contribute to the solution of this problem by systematically comparing semi-automatic translations of common English polarity lists with the results of the original automatic SLG algorithms, which were applied directly to German data. We evaluate these lexicons on a corpus of 7,992 manually annotated tweets. In addition to that, we also collate the results of dictionary- and corpus-based SLG methods in order to find out which of these paradigms is better suited for the inherently noisy domain of social media. Our experiments show that semi-automatic translations notably outperform automatic systems (reaching a macro-averaged F1-score of 0.589), and that dictionary-based techniques produce much better polarity lists as compared to corpus-based approaches (whose best F1-scores run up to 0.479 and 0.419 respectively) even for the non-standard Twitter genre.
Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search
Venue: arXiv cs.IR 2016
Year: 2016

Retrieval pipelines commonly rely on a term-based search to obtain candidate records, which are subsequently re-ranked. Some candidates are missed by this approach, e.g., due to a vocabulary mismatch. We address this issue by replacing the term-based search with a generic k-NN retrieval algorithm, where a similarity function can take into account subtle term associations. While an exact brute-force k-NN search using this similarity function is slow, we demonstrate that an approximate algorithm can be nearly two orders of magnitude faster at the expense of only a small loss in accuracy. A retrieval pipeline using an approximate k-NN search can be more effective and efficient than the term-based pipeline. This opens up new possibilities for designing effective retrieval pipelines. Our software (including data-generating code) and derivative data based on the Stack Overflow collection is available online.
Hydropower optimization: an industrial approach
Venue: arXiv cs.CE 2016
Year: 2016

Nowadays hydroelectric energy is one of the best energy sources: it is cleaner, safer and more programmable than other sources. For this reason, its manage could not be done in an approssimative way, but advance mathematical models must be use. In this article we consider an overview of the problem: we introduce the problem, then we show its simplest but quite exaustive mathematical formulation and in the end we produce numerical results under the ipothesis that all input are deterministic.
Tensor Switching Networks
Venue: arXiv cs.NE 2016
Year: 2016

We present a novel neural network algorithm, the Tensor Switching (TS) network, which generalizes the Rectified Linear Unit (ReLU) nonlinearity to tensor-valued hidden units. The TS network copies its entire input vector to different locations in an expanded representation, with the location determined by its hidden unit activity. In this way, even a simple linear readout from the TS representation can implement a highly expressive deep-network-like function. The TS network hence avoids the vanishing gradient problem by construction, at the cost of larger representation size. We develop several methods to train the TS network, including equivalent kernels for infinitely wide and deep TS networks, a one-pass linear learning algorithm, and two backpropagation-inspired representation learning algorithms. Our experimental results demonstrate that the TS network is indeed more expressive and consistently learns faster than standard ReLU networks.
Submodular Maximization over Sliding Windows
Venue: arXiv cs.DS 2016
Year: 2016

In this paper we study the extraction of representative elements in the data stream model in the form of submodular maximization. Different from the previous work on streaming submodular maximization, we are interested only in the recent data, and study the maximization problem over sliding windows. We provide a general reduction from the sliding window model to the standard streaming model, and thus our approach works for general constraints as long as there is a corresponding streaming algorithm in the standard streaming model. As a consequence, we obtain the first algorithms in the sliding window model for maximizing a monotone/non-monotone submodular function under cardinality and matroid constraints. We also propose several heuristics and show their efficiency in real-world datasets.
Embedding Deep Metric for Person Re-identication A Study Against Large Variations
Venue: arXiv cs.CV 2016
Year: 2016

Person re-identification is challenging due to the large variations of pose, illumination, occlusion and camera view. Owing to these variations, the pedestrian data is distributed as highly-curved manifolds in the feature space, despite the current convolutional neural networks (CNN)'s capability of feature extraction. However, the distribution is unknown, so it is difficult to use the geodesic distance when comparing two samples. In practice, the current deep embedding methods use the Euclidean distance for the training and test. On the other hand, the manifold learning methods suggest to use the Euclidean distance in the local range, combining with the graphical relationship between samples, for approximating the geodesic distance. From this point of view, selecting suitable positive i.e. intra-class) training samples within a local range is critical for training the CNN embedding, especially when the data has large intra-class variations. In this paper, we propose a novel moderate positive sample mining method to train robust CNN for person re-identification, dealing with the problem of large variation. In addition, we improve the learning by a metric weight constraint, so that the learned metric has a better generalization ability. Experiments show that these two strategies are effective in learning robust deep metrics for person re-identification, and accordingly our deep model significantly outperforms the state-of-the-art methods on several benchmarks of person re-identification. Therefore, the study presented in this paper may be useful in inspiring new designs of deep models for person re-identification.
MusicMood: Predicting the mood of music from song lyrics using machine learning
Venue: arXiv cs.LG 2016
Year: 2016

Sentiment prediction of contemporary music can have a wide-range of applications in modern society, for instance, selecting music for public institutions such as hospitals or restaurants to potentially improve the emotional well-being of personnel, patients, and customers, respectively. In this project, music recommendation system built upon on a naive Bayes classifier, trained to predict the sentiment of songs based on song lyrics alone. The experimental results show that music corresponding to a happy mood can be detected with high precision based on text features obtained from song lyrics.
Product-based Neural Networks for User Response Prediction
Venue: arXiv cs.LG 2016
Year: 2016

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising. The data in those applications is mostly categorical and contains multiple fields; a typical representation is to transform it into a high-dimensional sparse binary feature representation via one-hot encoding. Facing with the extreme sparsity, traditional models may limit their capacity of mining shallow patterns from the data, i.e. low-order feature combinations. Deep models like deep neural networks, on the other hand, cannot be directly applied for the high-dimensional input because of the huge feature space. In this paper, we propose a Product-based Neural Networks (PNN) with an embedding layer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between inter-field categories, and further fully connected layers to explore high-order feature interactions. Our experimental results on two large-scale real-world ad click datasets demonstrate that PNNs consistently outperform the state-of-the-art models on various metrics.
Robust Spectral Inference for Joint Stochastic Matrix Factorization
Venue: arXiv cs.LG 2016
Year: 2016

Spectral inference provides fast algorithms and provable optimality for latent topic analysis. But for real data these algorithms require additional ad-hoc heuristics, and even then often produce unusable results. We explain this poor performance by casting the problem of topic inference in the framework of Joint Stochastic Matrix Factorization (JSMF) and showing that previous methods violate the theoretical conditions necessary for a good solution to exist. We then propose a novel rectification method that learns high quality topics and their interactions even on small, noisy data. This method achieves results comparable to probabilistic techniques in several domains while maintaining scalability and provable optimality.
Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics
Venue: arXiv cs.RO 2016
Year: 2016

Despite outstanding success in vision amongst other domains, many of the recent deep learning approaches have evident drawbacks for robots. This manuscript surveys recent work in the literature that pertain to applying deep learning systems to the robotics domain, either as means of estimation or as a tool to resolve motor commands directly from raw percepts. These recent advances are only a piece to the puzzle. We suggest that deep learning as a tool alone is insufficient in building a unified framework to acquire general intelligence. For this reason, we complement our survey with insights from cognitive development and refer to ideas from classical control theory, producing an integrated direction for a lifelong learning architecture.
Joint Optimization of Data Sponsoring and Edge Caching for Mobile Video Delivery
Venue: arXiv cs.NI 2016
Year: 2016

In this work, we study the joint optimization of edge caching and data sponsoring for a video content provider (CP), aiming at reducing the content delivery cost and increasing the CP's revenue. Specifically, we formulate the joint optimization problem as a two-stage decision problem for the CP. In Stage I, the CP determines the edge caching policy (for a relatively long time period). In Stage II, the CP decides the real-time data sponsoring strategy for each content request within the period. We first propose a Lyapunov-based online sponsoring strategy in Stage II, which reaches 90% of the offline maximum performance (benchmark). We then solve the edge caching problem in Stage I based on the online sponsoring strategy proposed in Stage II, and show that the optimal caching policy depends on the aggregate user request for each content in each location. Simulations show that such a joint optimization can increase the CP's revenue by 30%~100%, comparing with the purely data sponsoring (i.e., without edge caching).
Application of Case-Based Teaching and Learning in Compiler Design Course
Venue: arXiv cs.PL 2016
Year: 2016

Compiler design is a course that discusses ideas used in construction of programming language compilers. Students learn how a program written in high level programming language and designed for humans understanding is systematically converted into low level assembly language understood by machines. We propose and implement a Case-based and Project-based Learning environment for teaching important Compiler design concepts (CPLC) to B.Tech third year students of a Delhi University (India) college. A case is a text that describes a real-life situation providing information but not solution. Previous research shows that case-based teaching helps students to apply the principles discussed in the class for solving complex practical problems. We divide one main project into sub-projects to give to students in order to enhance their practical experience of designing a compiler. To measure the effectiveness of case-based discussions, students complete a survey on their perceptions of benefits of case-based learning. The survey is analyzed using frequency distribution and chi square test of association. The results of the survey show that case-based teaching of compiler concepts does enhance students skills of learning, critical thinking, engagement, communication skills and team work.
Joint Antenna Selection and Spatial Switching for Energy Efficient MIMO SWIPT System
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we investigate joint antenna selection and spatial switching (SS) for quality-of-service (QoS)-constrained energy efficiency (EE) optimization in a multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system. A practical linear power model taking into account the entire transmit-receive chain is accordingly utilized. The corresponding fractional-combinatorial and non-convex EE problem, involving joint optimization of eigen-channel assignment, power allocation, and active receive antenna set selection, subject to satisfying minimum sum-rate and power transfer constraints, is extremely difficult to solve directly. In order to tackle this, we separate the eigen-channel assignment and power allocation procedure with the antenna selection functionality. In particular, we first tackle the EE maximization problem under fixed receive antenna set using Dinkelbach-based convex programming, iterative joint eigen-channel assignment and power allocation, and low-complexity multi-objective optimization (MOO)-based approach. On the other hand, the number of active receive antennas induces a trade-off in the achievable sum-rate and power transfer versus the transmit-independent power consumption. We provide a fundamental study of the achievable EE with antenna selection and accordingly develop dynamic optimal exhaustive search and Frobenius-norm-based schemes. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithms can efficiently approach the optimal EE.
Rapid Prototyping of a Text Mining Application for Cryptocurrency Market Intelligence
Venue: arXiv cs.CY 2016
Year: 2016

Blockchain represents a technology for establishing a shared, immutable version of the truth between a network of participants that do not trust one another, and therefore has the potential to disrupt any financial or other industries that rely on third-parties to establish trust. Recent trends in computing including: prevalence of Free and Open Source Software (FOSS); easy access to High Performance Computing (HPC i.e. 'The Cloud'); and increasingly advanced analytics capabilities such as Natural Language Processing (NLP) and Machine Learning (ML) allow for rapidly prototyping applications for analysis of trends in the emergence of Blockchain technology. A scaleable proof-of-concept pipeline that lays the groundwork for analysis of multiple streams of semi-structured data posted on social media is demonstrated. Preliminary analysis and performance metrics are presented and discussed. Future work is described that will scale the system to cloud-based, real-time, analysis of multiple data streams, with Information Extraction (IE) (ex. sentiment analysis) and Machine Learning capability.
An Experimental Investigation of Hyperbolic Routing with a Smart Forwarding Plane in NDN
Venue: arXiv cs.NI 2016
Year: 2016

Routing in NDN networks must scale in terms of forwarding table size and routing protocol overhead. Hyperbolic routing (HR) presents a potential solution to address the routing scalability problem, because it does not use traditional forwarding tables or exchange routing updates upon changes in network topologies. Although HR has the drawbacks of producing sub-optimal routes or local minima for some destinations, these issues can be mitigated by NDN's intelligent data forwarding plane. However, HR's viability still depends on both the quality of the routes HR provides and the overhead incurred at the forwarding plane due to HR's sub-optimal behavior. We designed a new forwarding strategy called Adaptive Smoothed RTT-based Forwarding (ASF) to mitigate HR's sub-optimal path selection. This paper describes our experimental investigation into the packet delivery delay and overhead under HR as compared with Named-Data Link State Routing (NLSR), which calculates shortest paths. We run emulation experiments using various topologies with different failure scenarios, probing intervals, and maximum number of next hops for a name prefix. Our results show that HR's delay stretch has a median close to 1 and a 95th-percentile around or below 2, which does not grow with the network size. HR's message overhead in dynamic topologies is nearly independent of the network size, while NLSR's overhead grows polynomially at least. These results suggest that HR offers a more scalable routing solution with little impact on the optimality of routing paths.
And the Winner is ...: Bayesian Twitter-based Prediction on 2016 U.S. Presidential Election
Venue: arXiv cs.IR 2016
Year: 2016

This paper describes a Naive-Bayesian predictive model for 2016 U.S. Presidential Election based on Twitter data. We use 33,708 tweets gathered since December 16, 2015 until February 29, 2016. We introduce a simpler data preprocessing method to label the data and train the model. The model achieves 95.8% accuracy on 10-fold cross validation and predicts Ted Cruz and Bernie Sanders as Republican and Democratic nominee respectively. It achieves a comparable result to those in its competitor methods.
Bots as Virtual Confederates: Design and Ethics
Venue: arXiv cs.CY 2016
Year: 2016

The use of bots as virtual confederates in online field experiments holds extreme promise as a new methodological tool in computational social science. However, this potential tool comes with inherent ethical challenges. Informed consent can be difficult to obtain in many cases, and the use of confederates necessarily implies the use of deception. In this work we outline a design space for bots as virtual confederates, and we propose a set of guidelines for meeting the status quo for ethical experimentation. We draw upon examples from prior work in the CSCW community and the broader social science literature for illustration. While a handful of prior researchers have used bots in online experimentation, our work is meant to inspire future work in this area and raise awareness of the associated ethical issues.
Structure vs. Language: Investigating the Multi-factors of Asymmetric Opinions on Online Social Interrelationship with a Case Study
Venue: arXiv cs.SI 2016
Year: 2016

Though current researches often study the properties of online social relationship from an objective view, we also need to understand individuals' subjective opinions on their interrelationships in social computing studies. Inspired by the theories from sociolinguistics, the latest work indicates that interactive language can reveal individuals' asymmetric opinions on their interrelationship. In this work, in order to explain the opinions' asymmetry on interrelationship with more latent factors, we extend the investigation from single relationship to the structural context in online social network. We analyze the correlation between interactive language features and the structural context of interrelationships. The structural context of vertex, edges and triangles in social network are considered. With statistical analysis on Enron email dataset, we find that individuals' opinions (measured by interactive language features) on their interrelationship are related to some of their important structural context in social network. This result can help us to understand and measure the individuals' opinions on their interrelationship with more intrinsic information.
Worst Case Competitive Analysis of Online Algorithms for Conic Optimization
Venue: arXiv cs.DS 2016
Year: 2016

Online optimization covers problems such as online resource allocation, online bipartite matching, adwords (a central problem in e-commerce and advertising), and adwords with separable concave returns. We analyze the worst case competitive ratio of two primal-dual algorithms for a class of online convex (conic) optimization problems that contains the previous examples as special cases defined on the positive orthant. We derive a sufficient condition on the objective function that guarantees a constant worst case competitive ratio (greater than or equal to $\frac{1}{2}$) for monotone objective functions. We provide new examples of online problems on the positive orthant and the positive semidefinite cone that satisfy the sufficient condition. We show how smoothing can improve the competitive ratio of these algorithms, and in particular for separable functions, we show that the optimal smoothing can be derived by solving a convex optimization problem. This result allows us to directly optimize the competitive ratio bound over a class of smoothing functions, and hence design effective smoothing customized for a given cost function.