OpenRaiser/Intern-Atlas huggingface dataset:
Influence Dynamics and Consensus in an Opinion-Neighborhood based Modified Vicsek-like Social Network
Venue: arXiv cs.SI 2018
Year: 2018

We propose a modified Vicsek-like model to study influence dynamics and opinion formation in social networks. We work on the premise that opinions of members of a group may be considered to be analogous to the direction of motion of a particle in space. The opinions are susceptible to change under the influence of familiar individuals who maintain similar beliefs. This is unlike the bounded-confidence models which solely rely on interactions based on closeness of opinions. The influence network evolves either when similar-minded individuals acquaint or when they fall out over their beliefs. This yields an adaptive network to which are assigned dynamic centrality scores and varying influence strengths. A mix of individuals - rigid and flexible - is assumed to constitute groups - liberal and conservative. We analyse emergent group behaviours subject to different initial conditions, agent types, their densities and tolerances. The model accurately predicts the role of rigid agents in hampering consensus. Also, a few structural properties of the dynamic network, which result as a consequence of the proposed model have been established.
Spoofing PRNU Patterns of Iris Sensors while Preserving Iris Recognition
Venue: arXiv cs.CV 2018
Year: 2018

The principle of Photo Response Non-Uniformity (PRNU) is used to link an image with its source, i.e., the sensor that produced it. In this work, we investigate if it is possible to modify an iris image acquired using one sensor in order to spoof the PRNU noise pattern of a different sensor. In this regard, we develop an image perturbation routine that iteratively modifies blocks of pixels in the original iris image such that its PRNU pattern approaches that of a target sensor. Experiments indicate the efficacy of the proposed perturbation method in spoofing PRNU patterns present in an iris image whilst still retaining its biometric content.
Multimodal Continuous Turn-Taking Prediction Using Multiscale RNNs
Venue: arXiv cs.CL 2018
Year: 2018

In human conversational interactions, turn-taking exchanges can be coordinated using cues from multiple modalities. To design spoken dialog systems that can conduct fluid interactions it is desirable to incorporate cues from separate modalities into turn-taking models. We propose that there is an appropriate temporal granularity at which modalities should be modeled. We design a multiscale RNN architecture to model modalities at separate timescales in a continuous manner. Our results show that modeling linguistic and acoustic features at separate temporal rates can be beneficial for turn-taking modeling. We also show that our approach can be used to incorporate gaze features into turn-taking models.
Cognate-aware morphological segmentation for multilingual neural translation
Venue: arXiv cs.CL 2018
Year: 2018

This article describes the Aalto University entry to the WMT18 News Translation Shared Task. We participate in the multilingual subtrack with a system trained under the constrained condition to translate from English to both Finnish and Estonian. The system is based on the Transformer model. We focus on improving the consistency of morphological segmentation for words that are similar orthographically, semantically, and distributionally; such words include etymological cognates, loan words, and proper names. For this, we introduce Cognate Morfessor, a multilingual variant of the Morfessor method. We show that our approach improves the translation quality particularly for Estonian, which has less resources for training the translation model.
Social Network Structure is Predictive of Health and Wellness
Venue: arXiv cs.SI 2018
Year: 2018

Social networks influence health-related behaviors, such as obesity and smoking. While researchers have studied social networks as a driver for diffusion of influences and behaviors, it is less understood how the structure or topology of the network, in itself, impacts an individual's health behaviors and wellness state. In this paper, we investigate whether the structure or topology of a social network offers additional insight and predictability on an individual's health and wellness. We develop a model called the Network-Driven health predictor (NetCARE) that leverages features representative of social network structure. Using a large longitudinal data set of students enrolled in the NetHealth study at the University of Notre Dame, we show that the NetCARE model improves the overall prediction performance over the baseline models -- that use demographics and physical attributes -- by 38%, 65%, 55%, and 54% for the wellness states -- stress, happiness, positive attitude, and self-assessed health -- considered in this paper.
Estimation for Quadrotors
Venue: arXiv cs.RO 2018
Year: 2018

This document describes standard approaches for filtering and estimation for quadrotors, created for the Udacity Flying Cars course. We assume previous knowledge of probability and some knowledge of linear algebra. We do not assume previous knowledge of Kalman filters or Bayes filters. This document derives an EKF for various models of drones in 1D, 2D, and 3D. We use the EKF and notation as defined in Thrun et al. [13]. We also give pseudocode for the Bayes filter, the EKF, and the Unscented Kalman filter [14]. The motivation behind this document is the lack of a step-by-step EKF tutorial that provides the derivations for a quadrotor helicopter. The goal of estimation is to infer the drone's state (pose, velocity, acceleration, and biases) from its sensor values and control inputs. This problem is challenging because sensors are noisy. Additionally, because of weight and cost issues, many drones have limited on-board computation so we want to estimate these values as quickly as possible. The standard method for performing this method is the Extended Kalman filter, a nonlinear extension of the Kalman filter which linearizes a nonlinear transition and measurement model around the current state. However the Unscented Kalman filter is better in almost every respect: simpler to implement, more accurate to estimate, and comparable runtimes.
What do RNN Language Models Learn about Filler-Gap Dependencies?
Venue: arXiv cs.CL 2018
Year: 2018

RNN language models have achieved state-of-the-art perplexity results and have proven useful in a suite of NLP tasks, but it is as yet unclear what syntactic generalizations they learn. Here we investigate whether state-of-the-art RNN language models represent long-distance filler-gap dependencies and constraints on them. Examining RNN behavior on experimentally controlled sentences designed to expose filler-gap dependencies, we show that RNNs can represent the relationship in multiple syntactic positions and over large spans of text. Furthermore, we show that RNNs learn a subset of the known restrictions on filler-gap dependencies, known as island constraints: RNNs show evidence for wh-islands, adjunct islands, and complex NP islands. These studies demonstrates that state-of-the-art RNN models are able to learn and generalize about empty syntactic positions.
Aesthetic Features for Personalized Photo Recommendation
Venue: arXiv cs.IR 2018
Year: 2018

Many photography websites such as Flickr, 500px, Unsplash, and Adobe Behance are used by amateur and professional photography enthusiasts. Unlike content-based image search, such users of photography websites are not just looking for photos with certain content, but more generally for photos with a certain photographic "aesthetic". In this context, we explore personalized photo recommendation and propose two aesthetic feature extraction methods based on (i) color space and (ii) deep style transfer embeddings. Using a dataset from 500px, we evaluate how these features can be best leveraged by collaborative filtering methods and show that (ii) provides a significant boost in photo recommendation performance.
Indicatements that character language models learn English morpho-syntactic units and regularities
Venue: arXiv cs.CL 2018
Year: 2018

Character language models have access to surface morphological patterns, but it is not clear whether or how they learn abstract morphological regularities. We instrument a character language model with several probes, finding that it can develop a specific unit to identify word boundaries and, by extension, morpheme boundaries, which allows it to capture linguistic properties and regularities of these units. Our language model proves surprisingly good at identifying the selectional restrictions of English derivational morphemes, a task that requires both morphological and syntactic awareness. Thus we conclude that, when morphemes overlap extensively with the words of a language, a character language model can perform morphological abstraction.
Privacy, ethics, and data access: A case study of the Fragile Families Challenge
Venue: arXiv cs.CY 2018
Year: 2018

Stewards of social science data face a fundamental tension. On one hand, they want to make their data accessible to as many researchers as possible to facilitate new discoveries. At the same time, they want to restrict access to their data as much as possible in order to protect the people represented in the data. In this paper, we provide a case study addressing this common tension in an uncommon setting: the Fragile Families Challenge, a scientific mass collaboration designed to yield insights that could improve the lives of disadvantaged children in the United States. We describe our process of threat modeling, threat mitigation, and third-party guidance. We also describe the ethical principles that formed the basis of our process. We are open about our process and the trade-offs that we made in the hopes that others can improve on what we have done.
What's a little leakage between friends?
Venue: arXiv cs.CR 2018
Year: 2018

This paper introduces a new attack on recent messaging systems that protect communication metadata. The main observation is that if an adversary manages to compromise a user's friend, it can use this compromised friend to learn information about the user's other ongoing conversations. Specifically, the adversary learns whether a user is sending other messages or not, which opens the door to existing intersection and disclosure attacks. To formalize this compromised friend attack, we present an abstract scenario called the exclusive call center problem that captures the attack's root cause, and demonstrates that it is independent of the particular design or implementation of existing metadata-private messaging systems. We then introduce a new primitive called a private answering machine that can prevent the attack. Unfortunately, building a secure and efficient instance of this primitive under only computational hardness assumptions does not appear possible. Instead, we give a construction under the assumption that users can place a bound on their maximum number of friends and are okay leaking this information.
Test Prioritization in Continuous Integration Environments
Venue: arXiv cs.SE 2018
Year: 2018

Two heuristics namely diversity-based (DBTP) and history-based test prioritization (HBTP) have been separately proposed in the literature. Yet, their combination has not been widely studied in continuous integration (CI) environments. The objective of this study is to catch regression faults earlier, allowing developers to integrate and verify their changes more frequently and continuously. To achieve this, we investigated six open-source projects, each of which included several builds over a large time period. Findings indicate that previous failure knowledge seems to have strong predictive power in CI environments and can be used to effectively prioritize tests. HBTP does not necessarily need to have large data, and its effectiveness improves to a certain degree with larger history interval. DBTP can be used effectively during the early stages, when no historical data is available, and also combined with HBTP to improve its effectiveness. Among the investigated techniques, we found that history-based diversity using NCD Multiset is superior in terms of effectiveness but comes with relatively higher overhead in terms of method execution time. Test prioritization in CI environments can be effectively performed with negligible investment using previous failure knowledge, and its effectiveness can be further improved by considering dissimilarities among the tests.
Open Data Analytical Model for Human Development Index Optimization to Support Government Policy
Venue: arXiv cs.CY 2018
Year: 2018

The transparency nature of Open Data is beneficial for citizens to evaluate government work performance. In Indonesia, each government bodies or ministry have their own standard operating procedure on data treatment resulting in incoherent information between agent and likely to miss valuable insight. Therefore, our motivation is to show the advantage of Open Data movement to support unified government decision making. We use the dataset from data.go.id which publish official data from each government bodies. The idea is by using those official but limited data, we can find important pattern. The case study is on Human Development Index value prediction and its clustered nature. We explore the data pattern using two important data analytics methods classification and clustering procedure. Data analytics is the collection of activities to reveal unknown data pattern. Specifically, we use Artificial Neural Network classification and K-means clustering. The classification objective is to categorize different level of Human Development Index of cities or region in Indonesia based on Gross Domestic Product, Number of Population in Poverty, Number of Internet User, Number of Labors and Number of Population indicators data. We determined which city belongs to four categories of Human Development stated by UNDP standard. The clustering objective is to find the group characteristics between Human Development Index and Gross Domestic Product.
Improving Visual Relationship Detection using Semantic Modeling of Scene Descriptions
Venue: arXiv cs.CL 2018
Year: 2018

Structured scene descriptions of images are useful for the automatic processing and querying of large image databases. We show how the combination of a semantic and a visual statistical model can improve on the task of mapping images to their associated scene description. In this paper we consider scene descriptions which are represented as a set of triples (subject, predicate, object), where each triple consists of a pair of visual objects, which appear in the image, and the relationship between them (e.g. man-riding-elephant, man-wearing-hat). We combine a standard visual model for object detection, based on convolutional neural networks, with a latent variable model for link prediction. We apply multiple state-of-the-art link prediction methods and compare their capability for visual relationship detection. One of the main advantages of link prediction methods is that they can also generalize to triples, which have never been observed in the training data. Our experimental results on the recently published Stanford Visual Relationship dataset, a challenging real world dataset, show that the integration of a semantic model using link prediction methods can significantly improve the results for visual relationship detection. Our combined approach achieves superior performance compared to the state-of-the-art method from the Stanford computer vision group.
Pillar Universities in Russia: The Rise of "the Second Wave"
Venue: arXiv cs.DL 2018
Year: 2018

The problem of identifying the leading universities in a country is rather easy to solve, one may focus, for example, on highly cited papers or other indicators of excellence. Sometimes it is more challenging to find the universities of "the second wave" which deserve to receive additional support because it may help them to become the most prominent ones, "the first". We study progress of the initial phase of such universities, named "pillar" in Russian education system. The first results, measured by 'short-distance bibliometrics', seem to be encouraging.
Parameter Sharing Methods for Multilingual Self-Attentional Translation Models
Venue: arXiv cs.CL 2018
Year: 2018

In multilingual neural machine translation, it has been shown that sharing a single translation model between multiple languages can achieve competitive performance, sometimes even leading to performance gains over bilingually trained models. However, these improvements are not uniform; often multilingual parameter sharing results in a decrease in accuracy due to translation models not being able to accommodate different languages in their limited parameter space. In this work, we examine parameter sharing techniques that strike a happy medium between full sharing and individual training, specifically focusing on the self-attentional Transformer model. We find that the full parameter sharing approach leads to increases in BLEU scores mainly when the target languages are from a similar language family. However, even in the case where target languages are from different families where full parameter sharing leads to a noticeable drop in BLEU scores, our proposed methods for partial sharing of parameters can lead to substantial improvements in translation accuracy.
Opinion Conflicts: An Effective Route to Detect Incivility in Twitter
Venue: arXiv cs.SI 2018
Year: 2018

In Twitter, there is a rising trend in abusive behavior which often leads to incivility. This trend is affecting users mentally and as a result they tend to leave Twitter and other such social networking sites thus depleting the active user base. In this paper, we study factors associated with incivility. We observe that the act of incivility is highly correlated with the opinion differences between the account holder (i.e., the user writing the incivil tweet) and the target (i.e., the user for whom the incivil tweet is meant for or targeted), toward a named entity. We introduce a character level CNN model and incorporate the entity-specific sentiment information for efficient incivility detection which significantly outperforms multiple baseline methods achieving an impressive accuracy of 93.3% (4.9% improvement over the best baseline). In a post-hoc analysis, we also study the behavioral aspects of the targets and account holders and try to understand the reasons behind the incivility incidents. Interestingly, we observe that there are strong signals of repetitions in incivil behavior. In particular, we find that there are a significant fraction of account holders who act as repeat offenders - attacking the targets even more than 10 times. Similarly, there are also targets who get targeted multiple times. In general, the targets are found to have higher reputation scores than the account holders.
A Real-time Control Approach for Unmanned Aerial Vehicles using Brain-computer Interface
Venue: arXiv cs.RO 2018
Year: 2018

Brain-computer interfacing (BCI) is a technology that is almost four decades old and it was developed solely for the purpose of developing and enhancing the impact of neuroprosthetics. However, in the recent times, with the commercialization of non-invasive electroencephalogram (EEG) headsets, the technology has seen a wide variety of applications like home automation, wheelchair control, vehicle steering etc. One of the latest developed applications is the mind-controlled quadrotor unmanned aerial vehicle. These applications, how- ever, do not require a very high-speed response and give satisfactory results when standard classification methods like Support Vector Machine (SVM) and Multi-Layer Perceptron (MLPC). Issues are faced when there is a requirement for high-speed control in the case of fixed-wing unmanned aerial vehicles where such methods are rendered unreliable due to the low speed of classification. Such an application requires the system to classify data at high speeds in order to retain the con- trollability of the vehicle. This paper proposes a novel method of classification which uses a combination of Common Spatial Paradigm and Linear Discriminant Analysis that provides an improved classification accuracy in real time. A non-linear SVM based classification technique has also been discussed. Further, this paper discusses the implementation of the proposed method on a fixed-wing and VTOL unmanned aerial vehicles.
PlayNPort: A Portable Wireless Music Player and Text Reader System
Venue: arXiv cs.OH 2018
Year: 2018

Portable Consumer Electronics has made a mark in the industry. With the ease of use at an accessible price range, they have experienced significant growth in the market. Our idea is to develop a portable wireless music player and text reader using a Cortex-M series microcontroller and bare-metal programming techniques. We chose to use an SD card as the storage device. The resulting electronic device is similar to a consumer grade music player available in a car. The system comprises an MCU, an MP3 encoder/decoder, an LCD, an audio output jack, an SD card and a remote control. We also present various challenges involved in developing the system and solutions we used to overcome the challenges. The intricacy of the work lies in the fact that the system was developed to be consumer-centric by providing a rich User Experience. It can be used as a personal entertainment system in a car.
Hypernyms Through Intra-Article Organization in Wikipedia
Venue: arXiv cs.IR 2018
Year: 2018

We introduce a new measure for unsupervised hypernym detection and directionality. The motivation is to keep the measure computationally light and portatable across languages. We show that the relative physical location of words in explanatory articles captures the directionality property. Further, the phrases in section titles of articles about the word, capture the semantic similarity needed for hypernym detection task. We experimentally show that the combination of features coming from these two simple measures suffices to produce results comparable with the best unsupervised measures in terms of the average precision.
Network-Decomposed Hierarchical Cooperation in Ad Hoc Networks With Social Relationships
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we introduce a network-decomposed hierarchical cooperation (HC) protocol and completely characterize the corresponding throughput--delay trade-off for a large wireless ad hoc network formed in the context of social relationships. Instead of randomly picking source--destination pairings, we first consider a distance-based social formation model characterized by the social group density $γ$ and the number of social contacts per node, $q$, where the probability that any two nodes in distance $d$ away from each other are socially connected is assumed to be proportional to $d^{-γ}$, which is a feasible scenario. Then, using muiltihop and network-decomposed HC protocols under our social formation model, we analyze a generalized throughput--delay trade-off according to the operating regimes with respect to parameters $γ$ and $q$ in both a dense network of unit area and an extended network of unit node density via a non-straightforward network transformation strategy. Our main results reveal that as $γ$ increases, performance on the throughput--delay trade-off can remarkably be improved, compared to the network case with no social relationships. It is also shown that in the dense network, the network-decomposed HC protocol always outperforms the multihop protocol, while the superiority of the network-decomposed HC depends on $γ$ and the path-loss exponent in the extended network.