OpenRaiser/Intern-Atlas huggingface dataset:
Does The Cloud Need Stabilizing?
Venue: arXiv cs.DC 2018
Year: 2018

The last decade has witnessed rapid proliferation of cloud computing. While even the smallest distributed programs (with 3-5 actions) produce many unanticipated error cases due to concurrency involved, it seems short of a miracle these web-services are able to operate at those vast scales. In this paper, we explore the factors that contribute most to the high-availability of cloud computing services and examine where self-stabilization could fit in that picture.
Multidimensional Analysis of Psychological Factors affecting Students Academic Performance
Venue: arXiv cs.CY 2018
Year: 2018

Academic performance of any individual is dependent upon numerous aspects regarding the day to day life of the individual under consideration. Academic performance is measured in terms of the grade point average or GPA as it is called. Grade point average is dependent not only on the faculty but also on various psychological parameters including the study habits, social anxiety and allied. In this study, a detail analysis of numerous psychological factors impacting the grade point was carried and based upon various psychological factors the performance for the student in forth coming examination was forecasted.
ROBOTIKANDO: a Web Tool for Supporting Teacher Practicing Robotics in Kindergarten
Venue: arXiv cs.CY 2018
Year: 2018

This paper describes ROBOTIKANDO, a web application for supporting both kindergarten teachers in planning educational robotics activities and educational robotics experts who would like to share their knowledge and experience. ROBOTIKANDO has been designed and implemented following a co-design process, which devised a conceptual map aiming to connect educational robotics and kindergarten education principles. As future work, we are planning a longitudinal evaluation with Preschools teachers. Moreover, we are thinking of extending the application to teachers of primary and secondary schools.
Investigating Mental Representations about Robots in Preschool Children
Venue: arXiv cs.CY 2018
Year: 2018

This paper refers to an observational research that investigates preschool children's mental representations of robots. Our hypotheses were that: a) three to six years-old children think about robots as human-like entities, concerning to both the physical and the conceptual nature of human beings, and b) they do not understand the concept of the software programming behind the operation of a robot. The study is based on two different data collection systems: an individual-based system and a group-based one. In both cases, the investigation uses a complex and multimodal research structure that combines a drawing-based approach with a conversational-discursive methodology. This paper focuses on the individual based data collection (348 drawings and 118 interviews). Preliminary results show that the human-like representation of robots is not the only one, even though it is present in about 64%of the drawings, and that this percentage decreases after the interview about robotics. Moreover, in several cases children refer to some concepts related to programming (e.g. the presence of sensors or human beings that make robots do things).
Design Challenges for Reconnecting in Later Life: A Qualitative Study
Venue: arXiv cs.CY 2018
Year: 2018

Friendships and social interactions are renown contributors to wellbeing. As such, keeping a healthy amount of relationships becomes very important as people age and the size of their social network tends to decrease. In this paper, we take a step back and explore reconnection -- find out about or re-contact old friends, an emerging topic due to the increased use of computer-mediated technology by older adults to maintain friendships and form new ones. We report on our findings from semi-structured interviews with 28 individuals from Costa Rica and Poland. The interviews aimed to explore whether there is a wish to reconnect, and the challenges encountered by older adults to reconnect. We contribute with design considerations for tools allow- ing older adults to reconnect, discussing opportunities for technology.
Automatically Generating a Large, Culture-Specific Blocklist for China
Venue: arXiv cs.CY 2018
Year: 2018

Internet censorship measurements rely on lists of websites to be tested, or "block lists" that are curated by third parties. Unfortunately, many of these lists are not public, and those that are tend to focus on a small group of topics, leaving other types of sites and services untested. To increase and diversify the set of sites on existing block lists, we use natural language processing and search engines to automatically discover a much wider range of websites that are censored in China. Using these techniques, we create a list of 1125 websites outside the Alexa Top 1,000 that cover Chinese politics, minority human rights organizations, oppressed religions, and more. Importantly, $\textit{none of the sites we discover are present on the current largest block list}$. The list that we develop not only vastly expands the set of sites that current Internet measurement tools can test, but it also deepens our understanding of the nature of content that is censored in China. We have released both this new block list and the code for generating it.
Incorporating Features Learned by an Enhanced Deep Knowledge Tracing Model for STEM/Non-STEM Job Prediction
Venue: arXiv cs.CY 2018
Year: 2018

The 2017 ASSISTments Data Mining competition aims to use data from a longitudinal study for predicting a brand-new outcome of students which had never been studied before by the educational data mining research community. Specifically, it facilitates research in developing predictive models that predict whether the first job of a student out of college belongs to a STEM (the acronym for science, technology, engineering, and mathematics) field. This is based on the student's learning history on the ASSISTments blended learning platform in the form of extensive clickstream data gathered during the middle school years. To tackle this challenge, we first estimate the expected knowledge state of students with respect to different mathematical skills using a deep knowledge tracing (DKT) model and an enhanced DKT (DKT+) model. We then combine the features corresponding to the DKT/DKT+ expected knowledge state with other features extracted directly from the student profile in the dataset to train several machine learning models for the STEM/non-STEM job prediction. Our experiments show that models trained with the combined features generally perform better than the models trained with the student profile alone. Detailed analysis of the student's knowledge state reveals that, when compared with non-STEM students, STEM students generally show a higher mastery level and a higher learning gain in mathematics.
Orbital Petri Nets: A Novel Petri Net Approach
Venue: arXiv cs.AI 2018
Year: 2018

Petri Nets is very interesting tool for studying and simulating different behaviors of information systems. It can be used in different applications based on the appropriate class of Petri Nets whereas it is classical, colored or timed Petri Nets. In this paper we introduce a new approach of Petri Nets called orbital Petri Nets (OPN) for studying the orbital rotating systems within a specific domain. The study investigated and analyzed OPN with highlighting the problem of space debris collision problem as a case study. The mathematical investigation results of two OPN models proved that space debris collision problem can be prevented based on the new method of firing sequence in OPN. By this study, new smart algorithms can be implemented and simulated by orbital Petri Nets for mitigating the space debris collision problem as a next work.
Conversational Recommender System
Venue: arXiv cs.IR 2018
Year: 2018

A personalized conversational sales agent could have much commercial potential. E-commerce companies such as Amazon, eBay, JD, Alibaba etc. are piloting such kind of agents with their users. However, the research on this topic is very limited and existing solutions are either based on single round adhoc search engine or traditional multi round dialog system. They usually only utilize user inputs in the current session, ignoring users' long term preferences. On the other hand, it is well known that sales conversion rate can be greatly improved based on recommender systems, which learn user preferences based on past purchasing behavior and optimize business oriented metrics such as conversion rate or expected revenue. In this work, we propose to integrate research in dialog systems and recommender systems into a novel and unified deep reinforcement learning framework to build a personalized conversational recommendation agent that optimizes a per session based utility function.
An Exploration of H-1B Visa Applications in the United States
Venue: arXiv cs.IR 2018
Year: 2018

The H-1B visa program is a very important tool for US-based businesses and educational institutes to recruit foreign talent. While the ultimate decision to certify an application lies with the United States Department of Labor, there are signals that can be used to determine whether an application is likely to be certified or denied. In this paper we first perform a data-driven exploratory analysis. We then leverage the features to train several classifiers and compare their performance. Finally, we discuss the implications of this work and future work that can be done in this area.
#SarcasmDetection is soooo general! Towards a Domain-Independent Approach for Detecting Sarcasm
Venue: arXiv cs.CL 2018
Year: 2018

Automatic sarcasm detection methods have traditionally been designed for maximum performance on a specific domain. This poses challenges for those wishing to transfer those approaches to other existing or novel domains, which may be typified by very different language characteristics. We develop a general set of features and evaluate it under different training scenarios utilizing in-domain and/or out-of-domain training data. The best-performing scenario, training on both while employing a domain adaptation step, achieves an F1 of 0.780, which is well above baseline F1-measures of 0.515 and 0.345. We also show that the approach outperforms the best results from prior work on the same target domain.
PipeDream: Fast and Efficient Pipeline Parallel DNN Training
Venue: arXiv cs.DC 2018
Year: 2018

PipeDream is a Deep Neural Network(DNN) training system for GPUs that parallelizes computation by pipelining execution across multiple machines. Its pipeline parallel computing model avoids the slowdowns faced by data-parallel training when large models and/or limited network bandwidth induce high communication-to-computation ratios. PipeDream reduces communication by up to 95% for large DNNs relative to data-parallel training, and allows perfect overlap of communication and computation. PipeDream keeps all available GPUs productive by systematically partitioning DNN layers among them to balance work and minimize communication, versions model parameters for backward pass correctness, and schedules the forward and backward passes of different inputs in round-robin fashion to optimize "time to target accuracy". Experiments with five different DNNs on two different clusters show that PipeDream is up to 5x faster in time-to-accuracy compared to data-parallel training.
CS-VQA: Visual Question Answering with Compressively Sensed Images
Venue: arXiv cs.CV 2018
Year: 2018

Visual Question Answering (VQA) is a complex semantic task requiring both natural language processing and visual recognition. In this paper, we explore whether VQA is solvable when images are captured in a sub-Nyquist compressive paradigm. We develop a series of deep-network architectures that exploit available compressive data to increasing degrees of accuracy, and show that VQA is indeed solvable in the compressed domain. Our results show that there is nominal degradation in VQA performance when using compressive measurements, but that accuracy can be recovered when VQA pipelines are used in conjunction with state-of-the-art deep neural networks for CS reconstruction. The results presented yield important implications for resource-constrained VQA applications.
A Graph Model with Indirect Co-location Links
Venue: arXiv cs.SI 2018
Year: 2018

Graph models are widely used to analyse diffusion processes embedded in social contacts and to develop applications. A range of graph models are available to replicate the underlying social structures and dynamics realistically. However, most of the current graph models can only consider concurrent interactions among individuals in the co-located interaction networks. However, they do not account for indirect interactions that can transmit spreading items to individuals who visit the same locations at different times but within a certain time limit. The diffusion phenomena occurring through direct and indirect interactions is called same place different time (SPDT) diffusion. This paper introduces a model to synthesize co-located interaction graphs capturing both direct interactions, where individuals meet at a location, and indirect interactions, where individuals visit the same location at different times within a set timeframe. We analyze 60 million location updates made by 2 million users from a social networking application to characterize the graph properties, including the space-time correlations and its time evolving characteristics, such as bursty or ongoing behaviors. The generated synthetic graph reproduces diffusion dynamics of a realistic contact graph, and reduces the prediction error by up to 82% when compare to other contact graph models demonstrating its potential for forecasting epidemic spread.
Word Familiarity and Frequency
Venue: arXiv cs.CL 2018
Year: 2018

Word frequency is assumed to correlate with word familiarity, but the strength of this correlation has not been thoroughly investigated. In this paper, we report on our analysis of the correlation between a word familiarity rating list obtained through a psycholinguistic experiment and the log-frequency obtained from various corpora of different kinds and sizes (up to the terabyte scale) for English and Japanese. Major findings are threefold: First, for a given corpus, familiarity is necessary for a word to achieve high frequency, but familiar words are not necessarily frequent. Second, correlation increases with the corpus data size. Third, a corpus of spoken language correlates better than one of written language. These findings suggest that cognitive familiarity ratings are correlated to frequency, but more highly to that of spoken rather than written language.
Abstaining Classification When Error Costs are Unequal and Unknown
Venue: arXiv cs.CV 2018
Year: 2018

Abstaining classificaiton aims to reject to classify the easily misclassified examples, so it is an effective approach to increase the clasificaiton reliability and reduce the misclassification risk in the cost-sensitive applications. In such applications, different types of errors (false positive or false negative) usaully have unequal costs. And the error costs, which depend on specific applications, are usually unknown. However, current abstaining classification methods either do not distinguish the error types, or they need the cost information of misclassification and rejection, which are realized in the framework of cost-sensitive learning. In this paper, we propose a bounded-abstention method with two constraints of reject rates (BA2), which performs abstaining classification when error costs are unequal and unknown. BA2 aims to obtain the optimal area under the ROC curve (AUC) by constraining the reject rates of the positive and negative classes respectively. Specifically, we construct the receiver operating characteristic (ROC) curve, and stepwise search the optimal reject thresholds from both ends of the curve, untill the two constraints are satisfied. Experimental results show that BA2 obtains higher AUC and lower total cost than the state-of-the-art abstaining classification methods. Meanwhile, BA2 achieves controllable reject rates of the positive and negative classes.
Utility-Energy Efficiency Oriented User Association with Power Control in Heterogeneous Networks
Venue: arXiv cs.IT 2018
Year: 2018

This letter investigates optimizing utility-energy efficiency (UEE), defined as the achieved network utility when consuming a unit of power, rather than a typical energy efficiency metric, in a heterogeneous network (HetNet). To tackle the nonconvexity of the problem due to integer constraints and coupled variables, we devise an alternating optimization algorithm. It applies Lagrangian dual analysis with auxiliary variables, which successfully transforms each subproblem to a convex one with efficient solutions. The proposed algorithm is consequently guaranteed to converge to a local optimum with noticeable performance gain via simulation verifications.
A Logical Framework for Verifying Privacy Breaches of Social Networks
Venue: arXiv cs.SE 2018
Year: 2018

We present a novel approach to deal with transitivity permission-delegation threats that arise in social networks when content is granted permissions by third-party users thereby breaking the privacy policy of the content owner. These types of privacy breaches are often unintentional in social networks like Facebook, and hence, (more) in-place mechanisms are needed to make social network users aware of the consequences of changing their privacy policies. Our approach is unique in its use of formal methods tools and techniques. It builds on a predicate logic definition for social networking that caters for common aspects of existing social networks such as users, social network content, friendship, permissions over content, and content transmission. Our approach is implemented in Yices. For the predicate logic model, we formulate a security policy for the verification of the permission flow of content owned by social network users and demonstrate how this security policy can be verified.
iMAP Beamforming for High Quality High Frame Rate Imaging
Venue: arXiv cs.IT 2018
Year: 2018

We present a statistical interpretation of beamforming to overcome limitations of standard delay-and-sum (DAS) processing. Both the interference and the signal of interest are viewed as random variables and the distribution of the signal of interest is exploited to maximize the a-posteriori distribution of the aperture signals. In this formulation the beamformer output is a maximum-a-posteriori (MAP) estimator of the signal of interest. We provide a closed form expression for the MAP beamformer and estimate the unknown distribution parameters from the available aperture data using an empirical Bayes approach. We propose a simple scheme that iterates between estimation of distribution parameters and computation of the MAP estimator of the signal of interest, leading to an iterative MAP (iMAP) beamformer. This results in a significant improvement of the contrast compared to DAS without severe increase in computational complexity or need for fine-tuning of parameters. By implementing iMAP on both simulated and experimental data, we show that only 13 transmissions are required to obtain contrast comparable to DAS with 75 plane-waves. The proposed method is compared to other interference suppression techniques such as coherence factor and scaled Wiener processing and shows improved contrast and better preserved speckle pattern.
Cell Detection with Star-convex Polygons
Venue: arXiv cs.CV 2018
Year: 2018

Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications. Recent successful learning-based approaches include per-pixel cell segmentation with subsequent pixel grouping, or localization of bounding boxes with subsequent shape refinement. In situations of crowded cells, these can be prone to segmentation errors, such as falsely merging bordering cells or suppressing valid cell instances due to the poor approximation with bounding boxes. To overcome these issues, we propose to localize cell nuclei via star-convex polygons, which are a much better shape representation as compared to bounding boxes and thus do not need shape refinement. To that end, we train a convolutional neural network that predicts for every pixel a polygon for the cell instance at that position. We demonstrate the merits of our approach on two synthetic datasets and one challenging dataset of diverse fluorescence microscopy images.
Sparse Over-complete Patch Matching
Venue: arXiv cs.CV 2018
Year: 2018

Image patch matching, which is the process of identifying corresponding patches across images, has been used as a subroutine for many computer vision and image processing tasks. State -of-the-art patch matching techniques take image patches as input to a convolutional neural network to extract the patch features and evaluate their similarity. Our aim in this paper is to improve on the state of the art patch matching techniques by observing the fact that a sparse-overcomplete representation of an image posses statistical properties of natural visual scenes which can be exploited for patch matching. We propose a new paradigm which encodes image patch details by encoding the patch and subsequently using this sparse representation as input to a neural network to compare the patches. As sparse coding is based on a generative model of natural image patches, it can represent the patch in terms of the fundamental visual components from which it has been composed of, leading to similar sparse codes for patches which are built from similar components. Once the sparse coded features are extracted, we employ a fully-connected neural network, which captures the non-linear relationships between features, for comparison. We have evaluated our approach using the Liberty and Notredame subsets of the popular UBC patch dataset and set a new benchmark outperforming all state-of-the-art patch matching techniques for these datasets.
Semantic Correspondence: A Hierarchical Approach
Venue: arXiv cs.CV 2018
Year: 2018

Establishing semantic correspondence across images when the objects in the images have undergone complex deformations remains a challenging task in the field of computer vision. In this paper, we propose a hierarchical method to tackle this problem by first semantically targeting the foreground objects to localize the search space and then looking deeply into multiple levels of the feature representation to search for point-level correspondence. In contrast to existing approaches, which typically penalize large discrepancies, our approach allows for significant displacements, with the aim to accommodate large deformations of the objects in scene. Localizing the search space by semantically matching object-level correspondence, our method robustly handles large deformations of objects. Representing the target region by concatenated hypercolumn features which take into account the hierarchical levels of the surrounding context, helps to clear the ambiguity to further improve the accuracy. By conducting multiple experiments across scenes with non-rigid objects, we validate the proposed approach, and show that it outperforms the state of the art methods for semantic correspondence establishment.