OpenRaiser/Intern-Atlas huggingface dataset:
Privacy by typing in the $π$-calculus
Venue: arXiv cs.LO 2017
Year: 2017

In this paper we propose a formal framework for studying privacy in information systems. The proposal follows a two-axes schema where the first axis considers privacy as a taxonomy of rights and the second axis involves the ways an information system stores and manipulates information. We develop a correspondence between the above schema and an associated model of computation. In particular, we propose the \Pcalc, a calculus based on the $π$-calculus with groups extended with constructs for reasoning about private data. The privacy requirements of an information system are captured via a privacy policy language. The correspondence between the privacy model and the \Pcalc semantics is established using a type system for the calculus and a satisfiability definition between types and privacy policies. We deploy a type preservation theorem to show that a system respects a policy and it is safe if the typing of the system satisfies the policy. We illustrate our methodology via analysis of two use cases: a privacy-aware scheme for electronic traffic pricing and a privacy-preserving technique for speed-limit enforcement.
Multi-focus image fusion using VOL and EOL in DCT domain
Venue: arXiv cs.CV 2017
Year: 2017

The purpose of multi-focus image fusion is gathering the essential information and the focused parts from the input multi-focus images into a single image. These multi-focused images are captured with different depths of focus of cameras. Multi-focus image fusion is very time-saving and appropriate in discrete cosine transform (DCT) domain, especially when JPEG images are used in visual sensor networks (VSN). The previous works in DCT domain have some errors in selection of the suitable divided blocks according to their criterion for measurement of the block contrast. In this paper, we used variance of Laplacian (VOL) and energy of Laplacian (EOL) as criterion to measure the contrast of image. Also in this paper, the EOL and VOL calculations directly in DCT domain are prepared using vector processing. We developed four matrices which calculate the Laplacian of block easily in DCT domain. Our works greatly reduce error due to unsuitable block selection. The results of the proposed algorithms are compared with the previous algorithms in order to demonstrate the superiority of the output image quality in the proposed methods. The several JPEG multi-focus images are used in experiments and their fused image by our proposed methods and the other algorithms are compared with different measurement criteria.
LASAGNE: Locality And Structure Aware Graph Node Embedding
Venue: arXiv cs.SI 2017
Year: 2017

In this work we propose Lasagne, a methodology to learn locality and structure aware graph node embeddings in an unsupervised way. In particular, we show that the performance of existing random-walk based approaches depends strongly on the structural properties of the graph, e.g., the size of the graph, whether the graph has a flat or upward-sloping Network Community Profile (NCP), whether the graph is expander-like, whether the classes of interest are more k-core-like or more peripheral, etc. For larger graphs with flat NCPs that are strongly expander-like, existing methods lead to random walks that expand rapidly, touching many dissimilar nodes, thereby leading to lower-quality vector representations that are less useful for downstream tasks. Rather than relying on global random walks or neighbors within fixed hop distances, Lasagne exploits strongly local Approximate Personalized PageRank stationary distributions to more precisely engineer local information into node embeddings. This leads, in particular, to more meaningful and more useful vector representations of nodes in poorly-structured graphs. We show that Lasagne leads to significant improvement in downstream multi-label classification for larger graphs with flat NCPs, that it is comparable for smaller graphs with upward-sloping NCPs, and that is comparable to existing methods for link prediction tasks.
Amending the Characterization of Guidance in Visual Analytics
Venue: arXiv cs.HC 2017
Year: 2017

At VAST 2016, a characterization of guidance has been presented. It includes a definition of guidance and a model of guidance based on van Wijk's model of visualization. This note amends the original characterization of guidance in two aspects. First, we provide a clarification of what guidance actually is (and is not). Second, we insert into the model a conceptually relevant link that was missing in the original version.
Detecting Clickbait in Online Social Media: You Won't Believe How We Did It
Venue: arXiv cs.SI 2017
Year: 2017

In this paper, we propose an approach for the detection of clickbait posts in online social media (OSM). Clickbait posts are short catchy phrases that attract a user's attention to click to an article. The approach is based on a machine learning (ML) classifier capable of distinguishing between clickbait and legitimate posts published in OSM. The suggested classifier is based on a variety of features, including image related features, linguistic analysis, and methods for abuser detection. In order to evaluate our method, we used two datasets provided by Clickbait Challenge 2017. The best performance obtained by the ML classifier was an AUC of 0.8, an accuracy of 0.812, precision of 0.819, and recall of 0.966. In addition, as opposed to previous studies, we found that clickbait post titles are statistically significant shorter than legitimate post titles. Finally, we found that counting the number of formal English words in the given content is useful for clickbait detection.
Build Fast and Accurate Lemmatization for Arabic
Venue: arXiv cs.CL 2017
Year: 2017

In this paper we describe the complexity of building a lemmatizer for Arabic which has a rich and complex derivational morphology, and we discuss the need for a fast and accurate lammatization to enhance Arabic Information Retrieval (IR) results. We also introduce a new data set that can be used to test lemmatization accuracy, and an efficient lemmatization algorithm that outperforms state-of-the-art Arabic lemmatization in terms of accuracy and speed. We share the data set and the code for public.
Toward Common Components for Open Workflow Systems
Venue: arXiv cs.SE 2017
Year: 2017

The role of scalable high-performance workflows and flexible workflow management systems that can support multiple simulations will continue to increase in importance. For example, with the end of Dennard scaling, there is a need to substitute a single long running simulation with multiple repeats of shorter simulations, or concurrent replicas. Further, many scientific problems involve ensembles of simulations in order to solve a higher-level problem or produce statistically meaningful results. However most supercomputing software development and performance enhancements have focused on optimizing single- simulation performance. On the other hand, there is a strong inconsistency in the definition and practice of workflows and workflow management systems. This inconsistency often centers around the difference between several different types of workflows, including modeling and simulation, grid, uncertainty quantification, and purely conceptual workflows. This work explores this phenomenon by examining the different types of workflows and workflow management systems, reviewing the perspective of a large supercomputing facility, examining the common features and problems of workflow management systems, and finally presenting a proposed solution based on the concept of common building blocks. The implications of the continuing proliferation of workflow management systems and the lack of interoperability between these systems are discussed from a practical perspective. In doing so, we have begun an investigation of the design and implementation of open workflow systems for supercomputers based upon common components.
Feature versus Raw Sequence: Deep Learning Comparative Study on Predicting Pre-miRNA
Venue: arXiv cs.LG 2017
Year: 2017

Should we input known genome sequence features or input sequence itself in deep learning framework? As deep learning more popular in various applications, researchers often come to question whether to generate features or use raw sequences for deep learning. To answer this question, we study the prediction accuracy of precursor miRNA prediction of feature-based deep belief network and sequence-based convolution neural network. Tested on a variant of six-layer convolution neural net and three-layer deep belief network, we find the raw sequence input based convolution neural network model performs similar or slightly better than feature based deep belief networks with best accuracy values of 0.995 and 0.990, respectively. Both the models outperform existing benchmarks models. The results shows us that if provided large enough data, well devised raw sequence based deep learning models can replace feature based deep learning models. However, construction of well behaved deep learning model can be very challenging. In cased features can be easily extracted, feature-based deep learning models may be a better alternative.
Visual Progression Analysis of Student Records Data
Venue: arXiv cs.CY 2017
Year: 2017

University curriculum, both on a campus level and on a per-major level, are affected in a complex way by many decisions of many administrators and faculty over time. As universities across the United States share an urgency to significantly improve student success and success retention, there is a pressing need to better understand how the student population is progressing through the curriculum, and how to provide better supporting infrastructure and refine the curriculum for the purpose of improving student outcomes. This work has developed a visual knowledge discovery system called eCamp that pulls together a variety of populationscale data products, including student grades, major descriptions, and graduation records. These datasets were previously disconnected and only available to and maintained by independent campus offices. The framework models and analyzes the multi-level relationships hidden within these data products, and visualizes the student flow patterns through individual majors as well as through a hierarchy of majors. These results support analytical tasks involving student outcomes, student retention, and curriculum design. It is shown how eCamp has revealed student progression information that was previously unavailable.
Can Machine Learning Create an Advocate for Foster Youth?
Venue: arXiv cs.CY 2017
Year: 2017

Statistics are bleak for youth aging out of the United States foster care system. They are often left with few resources, are likely to experience homelessness, and are at increased risk of incarceration and exploitation. The Think of Us platform is a service for foster youth and their advocates to create personalized goals and access curated content specific to aging out of the foster care system. In this paper, we propose the use of a machine learning algorithm within the Think of Us platform to better serve youth transitioning to life outside of foster care. The algorithm collects and collates publicly available figures and data to inform caseworkers and other mentors chosen by the youth on how to best assist foster youth. It can then provide valuable resources for the youth and their advocates targeted directly towards their specific needs. Finally, we examine machine learning as a support system and aid for caseworkers to buttress and protect vulnerable young adults during their transition to adulthood.
Promoting Saving for College Through Data Science
Venue: arXiv cs.CY 2017
Year: 2017

The cost of attending college has been steadily rising and in 10 years is estimated to reach $140,000 for a 4-year public university. Recent surveys estimate just over half of US families are saving for college. State-operated 529 college savings plans are an effective way for families to plan and save for future college costs, but only 3% of families currently use them. The Office of the Illinois State Treasurer (Treasurer) administers two 529 plans to help its residents save for college. In order to increase the number of families saving for college, the Treasurer and Civis Analytics used data science techniques to identify the people most likely to sign up for a college savings plan. In this paper, we will discuss the use of person matching to join accountholder data from the Treasurer to the Civis National File, as well as the use of lookalike modeling to identify new potential signups. In order to avoid reinforcing existing demographic imbalances in who saves for college, the lookalike models used were ensured to be racially and economically balanced. We will also discuss how these new signup targets were then individually served digital ads to encourage opening college savings accounts.
Transforming Speed Sequences into Road Rays on the Map with Elastic Pathing
Venue: arXiv cs.CR 2017
Year: 2017

Advances in technology have provided ways to monitor and measure driving behavior. Recently, this technology has been applied to usage-based automotive insurance policies that offer reduced insurance premiums to policy holders who opt-in to automotive monitoring. Several companies claim to measure only speed data, which they further claim preserves privacy. However, we have developed an algorithm - elastic pathing - that successfully tracks drivers' locations from speed data. The algorithm tracks drivers by assuming a start position, such as the driver's home address (which is typically known to insurance companies), and then estimates the possible routes by fitting the speed data to map data. To demonstrate the algorithm's real-world applicability, we evaluated its performance with driving datasets from central New Jersey and Seattle, Washington, representing suburban and urban areas. We are able to estimate destinations with error within 250 meters for 17% of the traces and within 500 meters for 24% of the traces in the New Jersey dataset, and with error within 250 and 500 meters for 15.5% and 27.5% of the traces, respectively, in the Seattle dataset. Our work shows that these insurance schemes enable a substantial breach of privacy.
First-Person Perceptual Guidance Behavior Decomposition using Active Constraint Classification
Venue: arXiv cs.RO 2017
Year: 2017

Humans exhibit a wide range of adaptive and robust dynamic motion behavior that is yet unmatched by autonomous control systems. These capabilities are essential for real-time behavior generation in cluttered environments. Recent work suggests that human capabilities rely on task structure learning and embedded or ecological cognition in the form of perceptual guidance. This paper describes the experimental investigation of the functional elements of human motion guidance, focusing on the control and perceptual mechanisms. The motion, control, and perceptual data from first-person guidance experiments is decomposed into elemental segments based on invariants. These elements are then analyzed to determine their functional characteristics. The resulting model explains the structure of the agent-environment interaction and provides lawful descriptions of specific perceptual guidance and control mechanisms.
Resilient Randomized Quantized Consensus
Venue: arXiv cs.SY 2017
Year: 2017

We consider the problem of multi-agent consensus where some agents are subject to faults/attacks and might make updates arbitrarily. The network consists of agents taking integer-valued (i.e., quantized) states under directed communication links. The goal of the healthy normal agents is to form consensus in their state values, which may be disturbed by the non-normal, malicious agents. We develop update schemes to be equipped by the normal agents whose interactions are asynchronous and subject to non-uniform and time-varying time delays. In particular, we employ a variant of the so-called mean subsequence reduced (MSR) algorithms, which have been long studied in computer science, where each normal agent ignores extreme values from its neighbors. We solve the resilient quantized consensus problems in the presence of totally/locally bounded adversarial agents and provide necessary and sufficient conditions in terms of the connectivity notion of graph robustness. Furthermore, it will be shown that randomization is essential both in quantization and in the updating times when normal agents interact in an asynchronous manner. The results are examined through a numerical example.
Comparative Analysis of Software Development Methods between Parallel, V-Shaped and Iterative
Venue: arXiv cs.SE 2017
Year: 2017

Any organization that will develop software is faced with a difficult choice of choosing the right software development method. Whereas the software development methods used, play a significant role in the overall software development process. Software development methods are needed so that the software development process can be systematic so that it is not only completed within the right time frame but also must have good quality. There are various methods of software development in System Development Lyfe Cycle (SDLC). Each SDLC method provides a general guiding line about different software development and has different characteristics. Each method of software development has its drawbacks and advantages so that the selection of software development methods should be compatible with the capacity of the software developed. This paper will compare three different software development methods: V-Shaped Model, Parallel Development Model, and Iterative Model with the aim of providing an understanding of software developers to choose the right method.
SLING: A framework for frame semantic parsing
Venue: arXiv cs.CL 2017
Year: 2017

We describe SLING, a framework for parsing natural language into semantic frames. SLING supports general transition-based, neural-network parsing with bidirectional LSTM input encoding and a Transition Based Recurrent Unit (TBRU) for output decoding. The parsing model is trained end-to-end using only the text tokens as input. The transition system has been designed to output frame graphs directly without any intervening symbolic representation. The SLING framework includes an efficient and scalable frame store implementation as well as a neural network JIT compiler for fast inference during parsing. SLING is implemented in C++ and it is available for download on GitHub.
Decision Trees for Helpdesk Advisor Graphs
Venue: arXiv cs.AI 2017
Year: 2017

We use decision trees to build a helpdesk agent reference network to facilitate the on-the-job advising of junior or less experienced staff on how to better address telecommunication customer fault reports. Such reports generate field measurements and remote measurements which, when coupled with location data and client attributes, and fused with organization-level statistics, can produce models of how support should be provided. Beyond decision support, these models can help identify staff who can act as advisors, based on the quality, consistency and predictability of dealing with complex troubleshooting reports. Advisor staff models are then used to guide less experienced staff in their decision making; thus, we advocate the deployment of a simple mechanism which exploits the availability of staff with a sound track record at the helpdesk to act as dormant tutors.
Evaluation of Availability of Initial-segments of Video Files in Device-to-Device (D2D) Network
Venue: arXiv cs.NI 2017
Year: 2017

We propose a new caching concept in which wireless nodes in Device-to-Device (D2D) network can cache the initial-segments of the popular video files and may share to users in their proximity upon request. We term this approach as Bootstrapping- D2D (B-D2D) system. Our findings suggest that caching only initial-segments creates a large pool of popular files, thus improving the probability of availability of initial-segments within the vicinity of the requesting users. Our proposed B-D2D system is based on a popular clustering algorithm called density-based spatial clustering of applications with noise (DBSCAN), that groups the users into dense and arbitrary shape clusters based on two predefined thresholds. We then formulate an optimization problem to maximize the probability of availability of initial-segments within the transmission range of requesting wireless devices. The simulation results demonstrate that, caching the initial video segments could substantially improve the availability of segments within the proximity of users without requiring any extra hardware infrastructure cost, and D2D services could be utilized to deliver the video contents with lower startup delays in cellular networks.
Dress like a Star: Retrieving Fashion Products from Videos
Venue: arXiv cs.CV 2017
Year: 2017

This work proposes a system for retrieving clothing and fashion products from video content. Although films and television are the perfect showcase for fashion brands to promote their products, spectators are not always aware of where to buy the latest trends they see on screen. Here, a framework for breaking the gap between fashion products shown on videos and users is presented. By relating clothing items and video frames in an indexed database and performing frame retrieval with temporal aggregation and fast indexing techniques, we can find fashion products from videos in a simple and non-intrusive way. Experiments in a large-scale dataset conducted here show that, by using the proposed framework, memory requirements can be reduced by 42.5X with respect to linear search, whereas accuracy is maintained at around 90%.
On Using Linear Diophantine Equations to Tune the extent of Look Ahead while Hiding Decision Tree Rules
Venue: arXiv cs.AI 2017
Year: 2017

This paper focuses on preserving the privacy of sensitive pat-terns when inducing decision trees. We adopt a record aug-mentation approach for hiding sensitive classification rules in binary datasets. Such a hiding methodology is preferred over other heuristic solutions like output perturbation or crypto-graphic techniques - which restrict the usability of the data - since the raw data itself is readily available for public use. In this paper, we propose a look ahead approach using linear Diophantine equations in order to add the appropriate number of instances while minimally disturbing the initial entropy of the nodes.