OpenRaiser/Intern-Atlas huggingface dataset:
Detecting LTE-U Duty Cycling Misbehavior for Fair Sharing with Wi-Fi in Shared Bands
Venue: arXiv cs.NI 2017
Year: 2017

Coexistence of Wi-Fi and LTE Unlicensed (LTE-U) in shared or unlicensed bands has drawn growing attention from both academia and industry. An important consideration is fairness between Wi-Fi and duty cycled LTE-U, which is often defined in terms of channel access time, as adopted by the LTE-U Forum. Despite many studies on duty cycle adaptation design for fair sharing, one crucial fact has often been neglected: LTE-U systems unilaterally control LTE-U duty cycles; hence, as self- interested users, they have incentives to misbehave, e.g., transmitting with a larger duty cycle that exceeds a given limit, so as to gain a greater share in channel access time and throughput. In this paper, we propose a scheme that allows the spectrum manager managing the shared bands to estimate the duty cycle of a target LTE-U cell based on PHY layer observations from a nearby Wi-Fi AP, without interrupting normal Wi-Fi operations. We further propose a thresholding scheme to detect duty cycling misbehavior (i.e., determining if the duty cycle exceeds the assigned limit), and analyze its performance in terms of detection and false alarm probabilities. The proposed schemes are implemented in ns3 and evaluated with extensive simulations. Our results show that the proposed scheme provides an estimate within +/- 1% of the true duty cycle, and detects misbehavior with a duty cycle 2.8% higher than the limit with a detection probability of at least 95%, while keeping the false alarm probability less than or equal to 1%.
Determining Impact of Social Media Badges through Joint Clustering of Temporal Traces and User Features
Venue: arXiv cs.SI 2017
Year: 2017

Badges are a common, and sometimes the only, method of incentivizing users to perform certain actions on online sites. However, due to many competing factors influencing user temporal dynamics, it is difficult to determine whether the badge had (or will have) the intended effect or not. In this paper, we introduce two complementary approaches for determining badge influence on users. In the first one, we cluster users' temporal traces (represented with point processes) and apply covariates (user features) to regularize results. In the second approach, we first classify users' temporal traces with a novel statistical framework, and then we refine the classification results with a semi-supervised clustering of covariates. Outcomes obtained from an evaluation on synthetic datasets and experiments on two badges from a popular Q&A platform confirm that it is possible to validate, characterize and to some extent predict users affected by the badge.
An efficient alternative to Ollivier-Ricci curvature based on the Jaccard metric
Venue: arXiv cs.SI 2017
Year: 2017

We study Ollivier-Ricci curvature, a discrete version of Ricci curvature, which has gained popularity over the past several years and has found applications in diverse fields. However, the Ollivier-Ricci curvature requires an optimal mass transport problem to be solved, which can be computationally expensive for large networks. In view of this, we propose two alternative measures of curvature to Ollivier-Ricci which are motivated by the Jaccard coefficient and are demonstrably less computationally intensive, a cheaper Jaccard (JC) and a more expensive generalized Jaccard (gJC) curvature metric. We show theoretically that the gJC closely matches the Ollivier-Ricci curvature for Erdos-Renyi graphs in the asymptotic regime of large networks. Furthermore, we study the goodness of approximation between the proposed curvature metrics and Ollivier-Ricci curvature for several network models and real networks. Our results suggest that in comparison to an alternative curvature metric for graphs, the Forman-Ricci curvature, the gJC exhibits a reasonably good fit to the Ollivier-Ricci curvature for a wide range of networks, while the JC is shown to be a good proxy only for certain scenarios.
Semantic 3D Reconstruction with Finite Element Bases
Venue: arXiv cs.CV 2017
Year: 2017

We propose a novel framework for the discretisation of multi-label problems on arbitrary, continuous domains. Our work bridges the gap between general FEM discretisations, and labeling problems that arise in a variety of computer vision tasks, including for instance those derived from the generalised Potts model. Starting from the popular formulation of labeling as a convex relaxation by functional lifting, we show that FEM discretisation is valid for the most general case, where the regulariser is anisotropic and non-metric. While our findings are generic and applicable to different vision problems, we demonstrate their practical implementation in the context of semantic 3D reconstruction, where such regularisers have proved particularly beneficial. The proposed FEM approach leads to a smaller memory footprint as well as faster computation, and it constitutes a very simple way to enable variable, adaptive resolution within the same model.
Syntactic and Semantic Features For Code-Switching Factored Language Models
Venue: arXiv cs.CL 2017
Year: 2017

This paper presents our latest investigations on different features for factored language models for Code-Switching speech and their effect on automatic speech recognition (ASR) performance. We focus on syntactic and semantic features which can be extracted from Code-Switching text data and integrate them into factored language models. Different possible factors, such as words, part-of-speech tags, Brown word clusters, open class words and clusters of open class word embeddings are explored. The experimental results reveal that Brown word clusters, part-of-speech tags and open-class words are the most effective at reducing the perplexity of factored language models on the Mandarin-English Code-Switching corpus SEAME. In ASR experiments, the model containing Brown word clusters and part-of-speech tags and the model also including clusters of open class word embeddings yield the best mixed error rate results. In summary, the best language model can significantly reduce the perplexity on the SEAME evaluation set by up to 10.8% relative and the mixed error rate by up to 3.4% relative.
InfiniViz: Interactive Visual Exploration using Progressive Bin Refinement
Venue: arXiv cs.DB 2017
Year: 2017

Interactive visualizations can accelerate the data analysis loop through near-instantaneous feedback. To achieve interactivity, techniques such as data cubes and sampling are typically employed. While data cubes can speedup querying for moderate-sized datasets, they are ineffective at doing so at a larger scales due to the size of the materialized data cubes. On the other hand, while sampling can help scale to large datasets, it adds sampling error and the associated issues into the process. While increasing accuracy by looking at more data may sometimes be valuable, providing result minutiae might not be necessary if they do not impart additional significant information. Indeed, such details not only incur a higher \emph{computational} cost, but also tax the \emph{cognitive} load of the analyst with worthless trivia. To reduce both the computational and cognitive expenses, we introduce \emph{InfiniViz}. Through a novel result refinement-based querying paradigm, \emph{InfiniViz} provides error-free results for large datasets by increasing bin resolutions progressively over time. Through real and simulated workloads over real and benchmark datasets, we evaluate and demonstrate \emph{InfiniViz}'s utility at reducing both cognitive and computational costs, while minimizing information loss.
Crisis Communication Patterns in Social Media during Hurricane Sandy
Venue: arXiv cs.SI 2017
Year: 2017

Hurricane Sandy was one of the deadliest and costliest of hurricanes over the past few decades. Many states experienced significant power outage, however many people used social media to communicate while having limited or no access to traditional information sources. In this study, we explored the evolution of various communication patterns using machine learning techniques and determined user concerns that emerged over the course of Hurricane Sandy. The original data included ~52M tweets coming from ~13M users between October 14, 2012 and November 12, 2012. We run topic model on ~763K tweets from top 4,029 most frequent users who tweeted about Sandy at least 100 times. We identified 250 well-defined communication patterns based on perplexity. Conversations of most frequent and relevant users indicate the evolution of numerous storm-phase (warning, response, and recovery) specific topics. People were also concerned about storm location and time, media coverage, and activities of political leaders and celebrities. We also present each relevant keyword that contributed to one particular pattern of user concerns. Such keywords would be particularly meaningful in targeted information spreading and effective crisis communication in similar major disasters. Each of these words can also be helpful for efficient hash-tagging to reach target audience as needed via social media. The pattern recognition approach of this study can be used in identifying real time user needs in future crises.
Can Early Joining Participants Contribute More? - Timeliness Sensitive Incentivization for Crowdsensing
Venue: arXiv cs.GT 2017
Year: 2017

This paper investigates the incentive mechanism design from a novel and practically important perspective in which mobile users as contributors do not join simultaneously and a requester desires large efforts from early contributors. A two-stage Tullock contest framework is constructed:at the second stage the potential contributors compete for splittable reward by exerting efforts, and at the first stage the requester can orchestrate the incentive mechanism to maximize his crowdsensing efficiency given the rewarding budget. A general reward discrimination mechanism is developed for timeliness sensitive crowdsensing where an earlier contributor usually has a larger maximum achievable reward and thus allocates more efforts. Owning to the lack of joining time information, two practical implementations, namely earliest-n and termination time, are announced to the contributors. For each of them, we formulate a Stackelberg Bayesian game in which the joining time of a contributor is his type and not available to his opponents. The uniqueness of Bayesian Nash equilibrium (BNE) is proved in each strategy. To maximize the requester's efficiency, we compute the optimal number of rewarded contributors in the earliest-n scheme and the optimal deadline in the termination time scheme. Our contest framework is applicable not only to the closed crowdsensing with fixed number of contributors, but also to the open crowdsensing that the arrival of contributors is governed by a stochastic process. Extensive simulations manifest that with appropriate reward discriminations, the requester is able to achieve a much higher efficiency with the optimal selection of the number of rewarded contributiors and the termination time.
Machine Learning Based Detection of Clickbait Posts in Social Media
Venue: arXiv cs.CL 2017
Year: 2017

Clickbait (headlines) make use of misleading titles that hide critical information from or exaggerate the content on the landing target pages to entice clicks. As clickbaits often use eye-catching wording to attract viewers, target contents are often of low quality. Clickbaits are especially widespread on social media such as Twitter, adversely impacting user experience by causing immense dissatisfaction. Hence, it has become increasingly important to put forward a widely applicable approach to identify and detect clickbaits. In this paper, we make use of a dataset from the clickbait challenge 2017 (clickbait-challenge.com) comprising of over 21,000 headlines/titles, each of which is annotated by at least five judgments from crowdsourcing on how clickbait it is. We attempt to build an effective computational clickbait detection model on this dataset. We first considered a total of 331 features, filtered out many features to avoid overfitting and improve the running time of learning, and eventually selected the 60 most important features for our final model. Using these features, Random Forest Regression achieved the following results: MSE=0.035 MSE, Accuracy=0.82, and F1-sore=0.61 on the clickbait class.
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization
Venue: arXiv cs.LG 2017
Year: 2017

Nowadays, the availability of large-scale data in disparate application domains urges the deployment of sophisticated tools for extracting valuable knowledge out of this huge bulk of information. In that vein, low-rank representations (LRRs) which seek low-dimensional embeddings of data have naturally appeared. In an effort to reduce computational complexity and improve estimation performance, LRR has been viewed via a matrix factorization (MF) perspective. Recently, low-rank MF (LRMF) approaches have been proposed for tackling the inherent weakness of MF i.e., the unawareness of the dimension of the low-dimensional space where data reside. Herein, inspired by the merits of iterative reweighted schemes for rank minimization, we come up with a generic low-rank promoting regularization function. Then, focusing on a specific instance of it, we propose a regularizer that imposes column-sparsity jointly on the two matrix factors that result from MF, thus promoting low-rankness on the optimization problem. The problems of denoising, matrix completion and non-negative matrix factorization (NMF) are redefined according to the new LRMF formulation and solved via efficient Newton-type algorithms with proven theoretical guarantees as to their convergence and rates of convergence to stationary points. The effectiveness of the proposed algorithms is verified in diverse simulated and real data experiments.
Portable Tor Router: Easily Enabling Web Privacy for Consumers
Venue: arXiv cs.CY 2017
Year: 2017

On-line privacy is of major public concern. Unfortunately, for the average consumer, there is no simple mechanism to browse the Internet privately on multiple devices. Most available Internet privacy mechanisms are either expensive, not readily available, untrusted, or simply provide trivial information masking. We propose that the simplest, most effective and inexpensive way of gaining privacy, without sacrificing unnecessary amounts of functionality and speed, is to mask the user's IP address while also encrypting all data. We hypothesized that the Tor protocol is aptly suited to address these needs. With this in mind we implemented a Tor router using a single board computer and the open-source Tor protocol code. We found that our proposed solution was able to meet five of our six goals soon after its implementation: cost effectiveness, immediacy of privacy, simplicity of use, ease of execution, and unimpaired functionality. Our final criterion of speed was sacrificed for greater privacy but it did not fall so low as to impair day-to-day functionality. With a total cost of roughly $100.00 USD and a speed cap of around 2 Megabits per second we were able to meet our goal of an affordable, convenient, and usable solution to increased on-line privacy for the average consumer.
HANDY: A Hybrid Association Rules Mining Approach for Network Layer Discovery of Services for Mobile Ad hoc Network
Venue: arXiv cs.NI 2017
Year: 2017

Mobile Ad hoc Network (MANET) is an infrastructure-less network formed between a set of mobile nodes. The discovery of services in MANET is a challenging job due to the unique properties of network. In this paper, a novel service discovery framework called Hybrid Association Rules Based Network Layer Discovery of Services for Ad hoc Networks (HANDY) has been proposed. HANDY provides three major research contributions. At first, it adopts a cross-layer optimized design for discovery of services that is based on simultaneous discovery of services and corresponding routes. Secondly, it provides a multi-level ontology-based approach to describe the services. This resolves the issue of semantic interoperability among the service consumers in a scalable fashion. Finally, to further optimize the performance of the discovery process, HANDY recommends exploiting the inherent associations present among the services. These associations are used in two ways. First, periodic service advertisements are performed based on these associations. In addition, when a response of a service discovery request is generated, correlated services are also attached with the response. The proposed service discovery scheme has been implemented in JIST/SWANS simulator. The results demonstrate that the proposed modifications give rise to improvement in hit ratio of the service consumers and latency of discovery process.
On the Interaction between Personal Comfort Systems and Centralized HVAC Systems in Office Buildings
Venue: arXiv cs.SY 2017
Year: 2017

Most modern HVAC systems suffer from two intrinsic problems. First, inability to meet diverse comfort requirements of the occupants. Second, heat or cool an entire zone even when the zone is only partially occupied. Both issues can be mitigated by using personal comfort systems (PCS) which bridge the comfort gap between what is provided by a central HVAC system and the personal preferences of the occupants. In recent work, we have proposed and deployed such a system, called SPOT. We address the question, "How should an existing HVAC system modify its operation to benefit the availability of PCS like SPOT?" For example, energy consumption could be reduced during sparse occupancy by choosing appropriate thermal set backs, with the PCS providing the additional offset in thermal comfort required for each occupant. Our control strategy based on Model Predictive Control (MPC), employs a bi-linear thermal model, and has two time-scales to accommodate the physical constraints that limit certain components of the central HVAC system from frequently changing their set points. We compare the energy consumption and comfort offered by our SPOT-aware HVAC system with that of a state-of-the-art MPC-based central HVAC system in multiple settings including different room layouts and partial deployment of PCS. Numerical evaluations show that our system obtains, in average, 45% (15%) savings in energy in summer (winter), compared with the benchmark system for the case of homogeneous comfort requirements. For heterogeneous comfort requirements, we observe 51% (29%) improvement in comfort in summer (winter) in addition to significant savings in energy.
Online Photometric Calibration for Auto Exposure Video for Realtime Visual Odometry and SLAM
Venue: arXiv cs.CV 2017
Year: 2017

Recent direct visual odometry and SLAM algorithms have demonstrated impressive levels of precision. However, they require a photometric camera calibration in order to achieve competitive results. Hence, the respective algorithm cannot be directly applied to an off-the-shelf-camera or to a video sequence acquired with an unknown camera. In this work we propose a method for online photometric calibration which enables to process auto exposure videos with visual odometry precisions that are on par with those of photometrically calibrated videos. Our algorithm recovers the exposure times of consecutive frames, the camera response function, and the attenuation factors of the sensor irradiance due to vignetting. Gain robust KLT feature tracks are used to obtain scene point correspondences as input to a nonlinear optimization framework. We show that our approach can reliably calibrate arbitrary video sequences by evaluating it on datasets for which full photometric ground truth is available. We further show that our calibration can improve the performance of a state-of-the-art direct visual odometry method that works solely on pixel intensities, calibrating for photometric parameters in an online fashion in realtime.
Learning Graphical Models from a Distributed Stream
Venue: arXiv cs.AI 2017
Year: 2017

A current challenge for data management systems is to support the construction and maintenance of machine learning models over data that is large, multi-dimensional, and evolving. While systems that could support these tasks are emerging, the need to scale to distributed, streaming data requires new models and algorithms. In this setting, as well as computational scalability and model accuracy, we also need to minimize the amount of communication between distributed processors, which is the chief component of latency. We study Bayesian networks, the workhorse of graphical models, and present a communication-efficient method for continuously learning and maintaining a Bayesian network model over data that is arriving as a distributed stream partitioned across multiple processors. We show a strategy for maintaining model parameters that leads to an exponential reduction in communication when compared with baseline approaches to maintain the exact MLE (maximum likelihood estimation). Meanwhile, our strategy provides similar prediction errors for the target distribution and for classification tasks.
Optimal control of a single leg hopper by Liouvillian system reduction
Venue: arXiv cs.SY 2017
Year: 2017

The benefits of legged locomotion shown in nature overcome challenges such as obstacles or terrain smoothness typically encountered with wheeled vehicles. This paper evaluates the benefits of using optimal control on a single leg hopper during the entire hopping motion. Basic control without considering physical constraints is implemented through hand-tuned PD controllers following the Raibert control framework. The differential flatness of the first-order equations of motion and the Liouvillian property for the second-order equations for the hopper system are proved, enabling flat outputs for control. A two-point boundary value problem (BVP) is then used to minimize jerk in the flat system to gain implicit smoothness in the output controls. This smoothness ensures that the planned trajectories are feasible, allowing for given waypoints to be reached.
BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages
Venue: arXiv cs.CL 2017
Year: 2017

We present BPEmb, a collection of pre-trained subword unit embeddings in 275 languages, based on Byte-Pair Encoding (BPE). In an evaluation using fine-grained entity typing as testbed, BPEmb performs competitively, and for some languages bet- ter than alternative subword approaches, while requiring vastly fewer resources and no tokenization. BPEmb is available at https://github.com/bheinzerling/bpemb