OpenRaiser/Intern-Atlas huggingface dataset:
$ρ$-hot Lexicon Embedding-based Two-level LSTM for Sentiment Analysis
Venue: arXiv cs.CL 2018
Year: 2018

Sentiment analysis is a key component in various text mining applications. Numerous sentiment classification techniques, including conventional and deep learning-based methods, have been proposed in the literature. In most existing methods, a high-quality training set is assumed to be given. Nevertheless, constructing a high-quality training set that consists of highly accurate labels is challenging in real applications. This difficulty stems from the fact that text samples usually contain complex sentiment representations, and their annotation is subjective. We address this challenge in this study by leveraging a new labeling strategy and utilizing a two-level long short-term memory network to construct a sentiment classifier. Lexical cues are useful for sentiment analysis, and they have been utilized in conventional studies. For example, polar and privative words play important roles in sentiment analysis. A new encoding strategy, that is, $ρ$-hot encoding, is proposed to alleviate the drawbacks of one-hot encoding and thus effectively incorporate useful lexical cues. We compile three Chinese data sets on the basis of our label strategy and proposed methodology. Experiments on the three data sets demonstrate that the proposed method outperforms state-of-the-art algorithms.
Modeling the distribution of the arrival angle based on transmitter antenna pattern
Venue: arXiv cs.IT 2018
Year: 2018

An angular distribution of received signals has a significant impact on their spectral and correlational properties. Most of angular dispersion models do not consider antenna patterns. The developed procedure for determining the propagation path parameters enables a wide range of assessment of the impact of the propagation environment on the received signal properties. In contrast to the other models, this procedure is based on a geometrical structure, which parameters are defined on the basis of power delay profile or spectrum This modeling method allows also the power radiation pattern (PRP) of the transmitting antenna. The aim of the paper is to present the influence of the transmitter antenna PRP on the scattering propagation paths that arrive at the receiver. This analysis is realized on the basis of simulations studies using the developed procedure. Presented in this paper procedure maps the effects of propagation phenomena that predominate in an azimuth plane.
Expeditious Generation of Knowledge Graph Embeddings
Venue: arXiv cs.CL 2018
Year: 2018

Knowledge Graph Embedding methods aim at representing entities and relations in a knowledge base as points or vectors in a continuous vector space. Several approaches using embeddings have shown promising results on tasks such as link prediction, entity recommendation, question answering, and triplet classification. However, only a few methods can compute low-dimensional embeddings of very large knowledge bases without needing state-of-the-art computational resources. In this paper, we propose KG2Vec, a simple and fast approach to Knowledge Graph Embedding based on the skip-gram model. Instead of using a predefined scoring function, we learn it relying on Long Short-Term Memories. We show that our embeddings achieve results comparable with the most scalable approaches on knowledge graph completion as well as on a new metric. Yet, KG2Vec can embed large graphs in lesser time by processing more than 250 million triples in less than 7 hours on common hardware.
Investigating Collaboration Within Online Communities: Software Development Vs. Artistic Creation
Venue: arXiv cs.SI 2018
Year: 2018

Online creative communities have been able to develop large, open source software (OSS) projects like Linux and Firefox throughout the successful collaborations carried out over the Internet. These communities have also expanded to creative arts domains such as animation, video games, and music. Despite their growing popularity, the factors that lead to successful collaborations in these communities are not entirely understood. In the following, I describe my PhD research project aimed at improving communication, collaboration, and retention in creative arts communities, starting from the experience gained from the literature about OSS communities.
A Distributed Control Framework of Multiple Unmanned Aerial Vehicles for Dynamic Wildfire Tracking
Venue: arXiv cs.RO 2018
Year: 2018

Wild-land fire fighting is a hazardous job. A key task for firefighters is to observe the "fire front" to chart the progress of the fire and areas that will likely spread next. Lack of information of the fire front causes many accidents. Using Unmanned Aerial Vehicles (UAVs) to cover wildfire is promising because it can replace humans in hazardous fire tracking and significantly reduce operation costs. In this paper we propose a distributed control framework designed for a team of UAVs that can closely monitor a wildfire in open space, and precisely track its development. The UAV team, designed for flexible deployment, can effectively avoid in-flight collisions and cooperate well with neighbors. They can maintain a certain height level to the ground for safe flight above fire. Experimental results are conducted to demonstrate the capabilities of the UAV team in covering a spreading wildfire.
Similar Elements and Metric Labeling on Complete Graphs
Venue: arXiv cs.DS 2018
Year: 2018

We consider a problem that involves finding similar elements in a collection of sets. The problem is motivated by applications in machine learning and pattern recognition. We formulate the similar elements problem as an optimization and give an efficient approximation algorithm that finds a solution within a factor of 2 of the optimal. The similar elements problem is a special case of the metric labeling problem and we also give an efficient 2-approximation algorithm for the metric labeling problem on complete graphs.
Planning with a Receding Horizon for Manipulation in Clutter using a Learned Value Function
Venue: arXiv cs.RO 2018
Year: 2018

Manipulation in clutter requires solving complex sequential decision making problems in an environment rich with physical interactions. The transfer of motion planning solutions from simulation to the real world, in open-loop, suffers from the inherent uncertainty in modelling real world physics. We propose interleaving planning and execution in real-time, in a closed-loop setting, using a Receding Horizon Planner (RHP) for pushing manipulation in clutter. In this context, we address the problem of finding a suitable value function based heuristic for efficient planning, and for estimating the cost-to-go from the horizon to the goal. We estimate such a value function first by using plans generated by an existing sampling-based planner. Then, we further optimize the value function through reinforcement learning. We evaluate our approach and compare it to state-of-the-art planning techniques for manipulation in clutter. We conduct experiments in simulation with artificially injected uncertainty on the physics parameters, as well as in real world tasks of manipulation in clutter. We show that this approach enables the robot to react to the uncertain dynamics of the real world effectively.
On Maximizing Sampling Time of RF-Harvesting Sensor Nodes Over Random Channel Gains
Venue: arXiv cs.NI 2018
Year: 2018

In the future, sensor nodes or Internet of Things (IoTs) will be tasked with sampling the environment. These nodes/devices are likely to be powered by a Hybrid Access Point (HAP) wirelessly, and may be programmed by the HAP with a {\em sampling time} to collect sensory data, carry out computation, and transmit sensed data to the HAP. A key challenge, however, is random channel gains, which cause sensor nodes to receive varying amounts of Radio Frequency (RF) energy. To this end, we formulate a stochastic program to determine the charging time of the HAP and sampling time of sensor nodes. Our objective is to minimize the {\em expected} penalty incurred when sensor nodes experience an energy shortfall. We consider two cases: {\em single} and {\em multi} time slots. In the former, we determine a suitable HAP charging time and nodes sampling time on a slot-by-slot basis whilst the latter considers the best charging and sampling time for use in the next $T$ slots. We conduct experiments over channel gains drawn from the Gaussian, Rayleigh or Rician distribution. Numerical results confirm our stochastic program can be used to compute good charging and sampling times that incur the minimum penalty over the said distributions.
Emotional Attachment Framework for People-Oriented Software
Venue: arXiv cs.SE 2018
Year: 2018

In organizational and commercial settings, people often have clear roles and workflows against which functional and non-functional requirements can be extracted. However, in more social settings, such as platforms for enhancing social interaction, successful applications are driven more by using emotional engagement than functionality, and the drivers of user engagement are difficult to identify. A key challenge is to understand people's emotional goals so that they can be incorporated into the design. This paper proposes a novel framework called the Emotional Attachment Framework, which is based on existing models and theories of emotional attachment. Its aim is to facilitate the process of getting a deeper insight into emotional goals in software engineering. To demonstrate the framework in use, emotional goals are elicited for a software application that aims to provide help for homeless people. To measure the effectiveness and efficiency of the proposed technique in this study, a series of evaluations are undertaken: a semi-controlled experiment, a comparison analysis, and domain expert and end-user evaluation. The results indicate that the Emotional Attachment Framework has the potential to give better insight during analysis of emotional goals.
Protocol architectures for IoT domains
Venue: arXiv cs.NI 2018
Year: 2018

In this work we discuss proxy architectures which interconnect IoT domains running CoAP with the rest of Internet including micro datacenters and other domains building scalable hierarchical architectures. We assume that CoAP domain is terminated by an IoT proxy with cache, and we investigate several design issues with respect to successful data transmission, round trip delay and energy consumption. We present performance data for the case when proxy autonomously maintains data freshness which clearly point to efficient design choices.
Life in the "Matrix": Human Mobility Patterns in the Cyber Space
Venue: arXiv cs.SI 2018
Year: 2018

With the wide adoption of the multi-community setting in many popular social media platforms, the increasing user engagements across multiple online communities warrant research attention. In this paper, we introduce a novel analogy between the movements in the cyber space and the physical space. This analogy implies a new way of studying human online activities by modelling the activities across online communities in a similar fashion as the movements among locations. First, we quantitatively validate the analogy by comparing several important properties of human online activities and physical movements. Our experiments reveal striking similarities between the cyber space and the physical space. Next, inspired by the established methodology on human mobility in the physical space, we propose a framework to study human "mobility" across online platforms. We discover three interesting patterns of user engagements in online communities. Furthermore, our experiments indicate that people with different mobility patterns also exhibit divergent preferences to online communities. This work not only attempts to achieve a better understanding of human online activities, but also intends to open a promising research direction with rich implications and applications.
IMHOTEP - Virtual Reality Framework for Surgical Applications
Venue: arXiv cs.HC 2018
Year: 2018

Purpose: The data which is available to surgeons before, during and after surgery is steadily increasing in quantity as well as diversity. When planning a patient's treatment, this large amount of information can be difficult to interpret. To aid in processing the information, new methods need to be found to present multi-modal patient data, ideally combining textual, imagery, temporal and 3D data in a holistic and context-aware system. Methods: We present an open-source framework which allows handling of patient data in a virtual reality (VR) environment. By using VR technology, the workspace available to the surgeon is maximized and 3D patient data is rendered in stereo, which increases depth perception. The framework organizes the data into workspaces and contains tools which allow users to control, manipulate and enhance the data. Due to the framework's modular design, it can easily be adapted and extended for various clinical applications. Results: The framework was evaluated by clinical personnel (77 participants). The majority of the group stated that a complex surgical situation is easier to comprehend by using the framework, and that it is very well suited for education. Furthermore, the application to various clinical scenarios - including the simulation of excitation-propagation in the human atrium - demonstrated the framework's adaptability. As a feasibility study, the framework was used during the planning phase of the surgical removal of a large central carcinoma from a patient's liver. Conclusion: The clinical evaluation showed a large potential and high acceptance for the VR environment in a medical context. The various applications confirmed that the framework is easily extended and can be used in real-time simulation as well as for the manipulation of complex anatomical structures.
Learning-based Model Predictive Control for Safe Exploration
Venue: arXiv cs.SY 2018
Year: 2018

Learning-based methods have been successful in solving complex control tasks without significant prior knowledge about the system. However, these methods typically do not provide any safety guarantees, which prevents their use in safety-critical, real-world applications. In this paper, we present a learning-based model predictive control scheme that can provide provable high-probability safety guarantees. To this end, we exploit regularity assumptions on the dynamics in terms of a Gaussian process prior to construct provably accurate confidence intervals on predicted trajectories. Unlike previous approaches, we do not assume that model uncertainties are independent. Based on these predictions, we guarantee that trajectories satisfy safety constraints. Moreover, we use a terminal set constraint to recursively guarantee the existence of safe control actions at every iteration. In our experiments, we show that the resulting algorithm can be used to safely and efficiently explore and learn about dynamic systems.
Mode Switching Decentralized Multi-Agent Coordination under Local Temporal Logic Tasks
Venue: arXiv cs.SY 2018
Year: 2018

This paper presents a novel control strategy for the coordination of a multi-agent system subject to high-level goals expressed as linear temporal logic formulas. In particular, each agent, which is modeled as a sphere with 2nd order dynamics, has to satisfy a given local temporal logic specification subject to connectivity maintenance and inter-agent collision avoidance. We propose a novel continuous control protocol that guarantees navigation of one agent to a goal point, up to a set of collision-free initial configurations, while maintaining connectivity of the initial neighboring set and avoiding inter-agent collisions. Based on that, we develop a hybrid switching control strategy that ensures that each agent satisfies its temporal logic task. Simulation results depict the validity of the proposed scheme.
A novel real time geolocation tracking tool
Venue: arXiv cs.SY 2018
Year: 2018

Global Positioning System (GPS) is a satellite network that transmits regularly encoded information and makes it possible to pinpoint the exact location on Earth by measuring the distance between satellites and the receiver. While GPS satellites continually emit radio signals, receivers are able to receive these signals. This study proposes a tool in which an electronic circuit that is consisted of integration of SIM908 shield and Arduino card is used as a GPS receiver. The positional data obtained from GPS satellites yields error due to the noise of the signals. Accordingly, in this study Kalman and Average filters are applied respectively in order to reduce these faults and handle the overall positional error. Several experiments were carried out in order to verify the performance of the filters within the GPS data. The results of these enhanced systems are compared with the initial configuration of the system severally. Especially the results obtained using the Kalman filter is quite encouraging.
Signaling Game-based Misbehavior Inspection in V2I-enabled Highway Operations
Venue: arXiv cs.GT 2018
Year: 2018

Vehicle-to-Infrastructure (V2I) communications are increasingly supporting highway operations such as electronic toll collection, carpooling, and vehicle platooning. In this paper we study the incentives of strategic misbehavior by individual vehicles who can exploit the security vulnerabilities in V2I communications and negatively impact the highway operations. We consider a V2I-enabled highway segment facing two classes of vehicles (agent populations), each with an authorized access to one server (subset of lanes). Vehicles are strategic in that they can misreport their class (type) to the system operator and get an unauthorized access to the server dedicated to the other class. This misbehavior causes additional congestion externality on the compliant vehicles, and thus, needs to be deterred. We focus on an environment where the operator is able to inspect the vehicles for misbehavior. The inspection is costly and successful detection incurs a fine on the misbehaving vehicle. We formulate a signaling game to study the strategic interaction between the vehicle classes and the operator. Our equilibrium analysis provides conditions on the cost parameters that govern the vehicles' incentive to misbehave or not. We also determine the operator's equilibrium inspection strategy.
Word sense induction using word embeddings and community detection in complex networks
Venue: arXiv cs.CL 2018
Year: 2018

Word Sense Induction (WSI) is the ability to automatically induce word senses from corpora. The WSI task was first proposed to overcome the limitations of manually annotated corpus that are required in word sense disambiguation systems. Even though several works have been proposed to induce word senses, existing systems are still very limited in the sense that they make use of structured, domain-specific knowledge sources. In this paper, we devise a method that leverages recent findings in word embeddings research to generate context embeddings, which are embeddings containing information about the semantical context of a word. In order to induce senses, we modeled the set of ambiguous words as a complex network. In the generated network, two instances (nodes) are connected if the respective context embeddings are similar. Upon using well-established community detection methods to cluster the obtained context embeddings, we found that the proposed method yields excellent performance for the WSI task. Our method outperformed competing algorithms and baselines, in a completely unsupervised manner and without the need of any additional structured knowledge source.
An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterest
Venue: arXiv cs.SI 2018
Year: 2018

While disclosures relating to various forms of Internet advertising are well established and follow specific formats, endorsement marketing disclosures are often open-ended in nature and written by individual publishers. Because such marketing often appears as part of publishers' actual content, ensuring that it is adequately disclosed is critical so that end-users can identify it as such. In this paper, we characterize disclosures relating to affiliate marketing---a type of endorsement based marketing---on two popular social media platforms: YouTube & Pinterest. We find that only roughly one-tenth of affiliate content on both platforms contains disclosures. Based on our findings, we make policy recommendations geared towards various stakeholders in the affiliate marketing industry, highlighting how both social media platforms and affiliate companies can enable better disclosure practices.
Automated Detection of Acute Leukemia using K-mean Clustering Algorithm
Venue: arXiv cs.CV 2018
Year: 2018

Leukemia is a hematologic cancer which develops in blood tissue and triggers rapid production of immature and abnormal shaped white blood cells. Based on statistics it is found that the leukemia is one of the leading causes of death in men and women alike. Microscopic examination of blood sample or bone marrow smear is the most effective technique for diagnosis of leukemia. Pathologists analyze microscopic samples to make diagnostic assessments on the basis of characteristic cell features. Recently, computerized methods for cancer detection have been explored towards minimizing human intervention and providing accurate clinical information. This paper presents an algorithm for automated image based acute leukemia detection systems. The method implemented uses basic enhancement, morphology, filtering and segmenting technique to extract region of interest using k-means clustering algorithm. The proposed algorithm achieved an accuracy of 92.8% and is tested with Nearest Neighbor (KNN) and Naive Bayes Classifier on the data-set of 60 samples.
The Roots of Bias on Uber
Venue: arXiv cs.CY 2018
Year: 2018

In the last decade, there has been a growth in, what we call, digitally mediated workplaces. A digitally mediated workplace is one where interactions between stakeholders are primarily managed by proprietary, algorithmically managed digital platform. The replacement of the relationships between the stakeholders by the platform is a key feature of these workplaces, and is a contributing factor to the decrease in contractual responsibilities each stakeholder has to one another. In this paper, we discuss some of the ways in which this structure and lack of accountability serves as a root of, or at least an enabler to, the realization of biases in the ridesharing application Uber, a digitally mediated workplace.
MultiBooked: A Corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification
Venue: arXiv cs.CL 2018
Year: 2018

While sentiment analysis has become an established field in the NLP community, research into languages other than English has been hindered by the lack of resources. Although much research in multi-lingual and cross-lingual sentiment analysis has focused on unsupervised or semi-supervised approaches, these still require a large number of resources and do not reach the performance of supervised approaches. With this in mind, we introduce two datasets for supervised aspect-level sentiment analysis in Basque and Catalan, both of which are under-resourced languages. We provide high-quality annotations and benchmarks with the hope that they will be useful to the growing community of researchers working on these languages.