OpenRaiser/Intern-Atlas huggingface dataset:
Learning Linguistic Biomarkers for Predicting Mild Cognitive Impairment using Compound Skip-grams
Venue: arXiv cs.CL 2015
Year: 2015

Predicting Mild Cognitive Impairment (MCI) is currently a challenge as existing diagnostic criteria rely on neuropsychological examinations. Automated Machine Learning (ML) models that are trained on verbal utterances of MCI patients can aid diagnosis. Using a combination of skip-gram features, our model learned several linguistic biomarkers to distinguish between 19 patients with MCI and 19 healthy control individuals from the DementiaBank language transcript clinical dataset. Results show that a model with compound of skip-grams has better AUC and could help ML prediction on small MCI data sample.
SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception
Venue: arXiv cs.CV 2015
Year: 2015

In this paper, a novel face dataset with attractiveness ratings, namely, the SCUT-FBP dataset, is developed for automatic facial beauty perception. This dataset provides a benchmark to evaluate the performance of different methods for facial attractiveness prediction, including the state-of-the-art deep learning method. The SCUT-FBP dataset contains face portraits of 500 Asian female subjects with attractiveness ratings, all of which have been verified in terms of rating distribution, standard deviation, consistency, and self-consistency. Benchmark evaluations for facial attractiveness prediction were performed with different combinations of facial geometrical features and texture features using classical statistical learning methods and the deep learning method. The best Pearson correlation (0.8187) was achieved by the CNN model. Thus, the results of our experiments indicate that the SCUT-FBP dataset provides a reliable benchmark for facial beauty perception.
VideoStory Embeddings Recognize Events when Examples are Scarce
Venue: arXiv cs.CV 2015
Year: 2015

This paper aims for event recognition when video examples are scarce or even completely absent. The key in such a challenging setting is a semantic video representation. Rather than building the representation from individual attribute detectors and their annotations, we propose to learn the entire representation from freely available web videos and their descriptions using an embedding between video features and term vectors. In our proposed embedding, which we call VideoStory, the correlations between the terms are utilized to learn a more effective representation by optimizing a joint objective balancing descriptiveness and predictability.We show how learning the VideoStory using a multimodal predictability loss, including appearance, motion and audio features, results in a better predictable representation. We also propose a variant of VideoStory to recognize an event in video from just the important terms in a text query by introducing a term sensitive descriptiveness loss. Our experiments on three challenging collections of web videos from the NIST TRECVID Multimedia Event Detection and Columbia Consumer Videos datasets demonstrate: i) the advantages of VideoStory over representations using attributes or alternative embeddings, ii) the benefit of fusing video modalities by an embedding over common strategies, iii) the complementarity of term sensitive descriptiveness and multimodal predictability for event recognition without examples. By it abilities to improve predictability upon any underlying video feature while at the same time maximizing semantic descriptiveness, VideoStory leads to state-of-the-art accuracy for both few- and zero-example recognition of events in video.
Sentiment Expression via Emoticons on Social Media
Venue: arXiv cs.CL 2015
Year: 2015

Emoticons (e.g., :) and :( ) have been widely used in sentiment analysis and other NLP tasks as features to ma- chine learning algorithms or as entries of sentiment lexicons. In this paper, we argue that while emoticons are strong and common signals of sentiment expression on social media the relationship between emoticons and sentiment polarity are not always clear. Thus, any algorithm that deals with sentiment polarity should take emoticons into account but extreme cau- tion should be exercised in which emoticons to depend on. First, to demonstrate the prevalence of emoticons on social media, we analyzed the frequency of emoticons in a large re- cent Twitter data set. Then we carried out four analyses to examine the relationship between emoticons and sentiment polarity as well as the contexts in which emoticons are used. The first analysis surveyed a group of participants for their perceived sentiment polarity of the most frequent emoticons. The second analysis examined clustering of words and emoti- cons to better understand the meaning conveyed by the emoti- cons. The third analysis compared the sentiment polarity of microblog posts before and after emoticons were removed from the text. The last analysis tested the hypothesis that removing emoticons from text hurts sentiment classification by training two machine learning models with and without emoticons in the text respectively. The results confirms the arguments that: 1) a few emoticons are strong and reliable signals of sentiment polarity and one should take advantage of them in any senti- ment analysis; 2) a large group of the emoticons conveys com- plicated sentiment hence they should be treated with extreme caution.
Explicit Knowledge-based Reasoning for Visual Question Answering
Venue: arXiv cs.CV 2015
Year: 2015

We describe a method for visual question answering which is capable of reasoning about contents of an image on the basis of information extracted from a large-scale knowledge base. The method not only answers natural language questions using concepts not contained in the image, but can provide an explanation of the reasoning by which it developed its answer. The method is capable of answering far more complex questions than the predominant long short-term memory-based approach, and outperforms it significantly in the testing. We also provide a dataset and a protocol by which to evaluate such methods, thus addressing one of the key issues in general visual ques- tion answering.
Enacting textual entailment and ontologies for automated essay grading in chemical domain
Venue: arXiv cs.AI 2015
Year: 2015

We propose a system for automated essay grading using ontologies and textual entailment. The process of textual entailment is guided by hypotheses, which are extracted from a domain ontology. Textual entailment checks if the truth of the hypothesis follows from a given text. We enact textual entailment to compare students answer to a model answer obtained from ontology. We validated the solution against various essays written by students in the chemistry domain.
Massive Online Crowdsourced Study of Subjective and Objective Picture Quality
Venue: arXiv cs.CV 2015
Year: 2015

Most publicly available image quality databases have been created under highly controlled conditions by introducing graded simulated distortions onto high-quality photographs. However, images captured using typical real-world mobile camera devices are usually afflicted by complex mixtures of multiple distortions, which are not necessarily well-modeled by the synthetic distortions found in existing databases. The originators of existing legacy databases usually conducted human psychometric studies to obtain statistically meaningful sets of human opinion scores on images in a stringently controlled visual environment, resulting in small data collections relative to other kinds of image analysis databases. Towards overcoming these limitations, we designed and created a new database that we call the LIVE In the Wild Image Quality Challenge Database, which contains widely diverse authentic image distortions on a large number of images captured using a representative variety of modern mobile devices. We also designed and implemented a new online crowdsourcing system, which we have used to conduct a very large-scale, multi-month image quality assessment subjective study. Our database consists of over 350000 opinion scores on 1162 images evaluated by over 7000 unique human observers. Despite the lack of control over the experimental environments of the numerous study participants, we demonstrate excellent internal consistency of the subjective dataset. We also evaluate several top-performing blind Image Quality Assessment algorithms on it and present insights on how mixtures of distortions challenge both end users as well as automatic perceptual quality prediction models.
Analyzing Stability of Convolutional Neural Networks in the Frequency Domain
Venue: arXiv cs.CV 2015
Year: 2015

Understanding the internal process of ConvNets is commonly done using visualization techniques. However, these techniques do not usually provide a tool for estimating the stability of a ConvNet against noise. In this paper, we show how to analyze a ConvNet in the frequency domain using a 4-dimensional visualization technique. Using the frequency domain analysis, we show the reason that a ConvNet might be sensitive to a very low magnitude additive noise. Our experiments on a few ConvNets trained on different datasets revealed that convolution kernels of a trained ConvNet usually pass most of the frequencies and they are not able to effectively eliminate the effect of high frequencies. Our next experiments shows that a convolution kernel which has a more concentrated frequency response could be more stable. Finally, we show that fine-tuning a ConvNet using a training set augmented with noisy images can produce more stable ConvNets.
Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing
Venue: arXiv cs.IR 2015
Year: 2015

A typical image retrieval pipeline starts with the comparison of global descriptors from a large database to find a short list of candidate matches. A good image descriptor is key to the retrieval pipeline and should reconcile two contradictory requirements: providing recall rates as high as possible and being as compact as possible for fast matching. Following the recent successes of Deep Convolutional Neural Networks (DCNN) for large scale image classification, descriptors extracted from DCNNs are increasingly used in place of the traditional hand crafted descriptors such as Fisher Vectors (FV) with better retrieval performances. Nevertheless, the dimensionality of a typical DCNN descriptor --extracted either from the visual feature pyramid or the fully-connected layers-- remains quite high at several thousands of scalar values. In this paper, we propose Unsupervised Triplet Hashing (UTH), a fully unsupervised method to compute extremely compact binary hashes --in the 32-256 bits range-- from high-dimensional global descriptors. UTH consists of two successive deep learning steps. First, Stacked Restricted Boltzmann Machines (SRBM), a type of unsupervised deep neural nets, are used to learn binary embedding functions able to bring the descriptor size down to the desired bitrate. SRBMs are typically able to ensure a very high compression rate at the expense of loosing some desirable metric properties of the original DCNN descriptor space. Then, triplet networks, a rank learning scheme based on weight sharing nets is used to fine-tune the binary embedding functions to retain as much as possible of the useful metric properties of the original space. A thorough empirical evaluation conducted on multiple publicly available dataset using DCNN descriptors shows that our method is able to significantly outperform state-of-the-art unsupervised schemes in the target bit range.
Big Data and Business Intelligence: Debunking the Myths
Venue: arXiv cs.CY 2015
Year: 2015

Big data is one of the most discussed, and possibly least understood, terms in use in business today. Big data is said to offer not only unprecedented levels of business intelligence concerning the habits of consumers and rivals, but also to herald a revolution in the way in which business are organized and run. However, big data is not as straightforward as it might seem, particularly when it comes to the so-called dark data from social media. It is not simply the quantity of data that has changed, it is also the speed and the variety of formats with which it is delivered. This article sets out to look at big data and debunk some of the myths that surround it. It focuses on the role of data from social media in particular and highlights two common myths about big data. The first is that because a data set contains billions of items, traditional methodological issues no longer matter. The second is the belief that big data is both a complete and unbiased source of data upon which to base decisions.
Attribute-Based Multi-Dimensional Scalable Access Control For Social Media Sharing
Venue: arXiv cs.MM 2015
Year: 2015

Media sharing is an extremely popular paradigm of social interaction in online social networks (OSNs) nowadays. The scalable media access control is essential to perform information sharing among users with various access privileges. In this paper, we present a multi-dimensional scalable media access control (MD-SMAC) system based on the proposed scalable ciphertext policy attribute-based encryption (SCP-ABE) algorithm. In the proposed MD-SMAC system, fine-grained access control can be performed on the media contents encoded in a multi-dimensional scalable manner based on data consumers' diverse attributes. Through security analysis, we show that the proposed MC-SMAC system is able to resist collusion attacks. Additionally, we conduct experiments to evaluate the efficiency performance of the proposed system, especially on mobile devices.
Go-Smart: Web-based Computational Modeling of Minimally Invasive Cancer Treatments
Venue: arXiv cs.CY 2015
Year: 2015

The web-based Go-Smart environment is a scalable system that allows the prediction of minimally invasive cancer treatment. Interventional radiologists create a patient-specific 3D model by semi-automatic segmentation and registration of pre-interventional CT (Computed Tomography) and/or MRI (Magnetic Resonance Imaging) images in a 2D/3D browser environment. This model is used to compare patient-specific treatment plans and device performance via built-in simulation tools. Go-Smart includes evaluation techniques for comparing simulated treatment with real ablation lesions segmented from follow-up scans. The framework is highly extensible, allowing manufacturers and researchers to incorporate new ablation devices, mathematical models and physical parameters.
User-Centred Security Education: A Game Design to Thwart Phishing Attacks
Venue: arXiv cs.CY 2015
Year: 2015

Phishing is an online identity theft that aims to steal sensitive information such as username, password and online banking details from its victims. Phishing education needs to be considered as a means to combat this threat. This paper reports on a design and development of a mobile game prototype as an educational tool helping computer users to protect themselves against phishing attacks. The elements of a game design framework for avoiding phishing attacks were used to address the game design issues. Game design principles served as guidelines for structuring and presenting information. Our mobile game design aimed to enhance the users' avoidance behaviour through motivation to protect themselves against phishing threats. A think-aloud study was conducted, along with a pre- and post-test, to assess the game design framework though the developed mobile game prototype. The study results showed a significant improvement of participants' phishing avoidance behaviour in their post-test assessment. Furthermore, the study findings suggest that participants' threat perception, safeguard effectiveness, self-efficacy, perceived severity and perceived susceptibility elements positively impact threat avoidance behaviour, whereas safeguard cost had a negative impact on it.
MAINT: Localization of Mobile Sensors with Energy Control
Venue: arXiv cs.DC 2015
Year: 2015

Localization is an important issue for Wireless Sensor Networks (WSN). A mobile sensor may change its position rapidly and thus require localization calls frequently. A localization may require network wide information and increase traffic over the network. It dissipates valuable energy for message communication. Thus localization is very costly. The control of the number of localization calls may save energy consumption, as it is rather expensive. To reduce the frequency of localization calls for a mobile sensor, we propose a technique that involves \textit{Mobility Aware Interpolation} (MAINT) for position estimation. It controls the number of localizations which gives much better result than the existing localization control schemes using mobility aware extrapolation. The proposed method involves very low arithmetic computation overheads. We find analytical expressions for the expected error in position estimation. A parameter, the time interval, has been introduced to externally control the energy dissipation. Simulation studies are carried out to compare the performances of the proposed method with some existing localization control schemes as well as the theoretical results. The simulation results shows that the expected error at any point of time may be computed from this expression. We have seen that constant error limit can be maintained increasing the time period of localization proportional to rate of change of direction of its motion. Increasing time period, the energy may be saved with a stable error limit.
Embedded Ensemble Propagation for Improving Performance, Portability and Scalability of Uncertainty Quantification on Emerging Computational Architectures
Venue: arXiv cs.MS 2015
Year: 2015

Quantifying simulation uncertainties is a critical component of rigorous predictive simulation. A key component of this is forward propagation of uncertainties in simulation input data to output quantities of interest. Typical approaches involve repeated sampling of the simulation over the uncertain input data, and can require numerous samples when accurately propagating uncertainties from large numbers of sources. Often simulation processes from sample to sample are similar and much of the data generated from each sample evaluation could be reused. We explore a new method for implementing sampling methods that simultaneously propagates groups of samples together in an embedded fashion, which we call embedded ensemble propagation. We show how this approach takes advantage of properties of modern computer architectures to improve performance by enabling reuse between samples, reducing memory bandwidth requirements, improving memory access patterns, improving opportunities for fine-grained parallelization, and reducing communication costs. We describe a software technique for implementing embedded ensemble propagation based on the use of C++ templates and describe its integration with various scientific computing libraries within Trilinos. We demonstrate improved performance, portability and scalability for the approach applied to the simulation of partial differential equations on a variety of CPU, GPU, and accelerator architectures, including up to 131,072 cores on a Cray XK7 (Titan).
Automatic Content-Aware Color and Tone Stylization
Venue: arXiv cs.CV 2015
Year: 2015

We introduce a new technique that automatically generates diverse, visually compelling stylizations for a photograph in an unsupervised manner. We achieve this by learning style ranking for a given input using a large photo collection and selecting a diverse subset of matching styles for final style transfer. We also propose a novel technique that transfers the global color and tone of the chosen exemplars to the input photograph while avoiding the common visual artifacts produced by the existing style transfer methods. Together, our style selection and transfer techniques produce compelling, artifact-free results on a wide range of input photographs, and a user study shows that our results are preferred over other techniques.
Energy Efficiency Optimization for MIMO Distributed Antenna Systems
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we propose a transmit covariance optimization method to maximize the energy efficiency (EE) for a single-user distributed antenna system, where both the remote access units (RAUs) and the user are equipped with multiple antennas. Unlike previous related works, both the rate requirement and RAU selection are taken into consideration. Here, the total circuit power consumption is related to the number of active RAUs. Given this setup, we first propose an optimal transmit covariance optimization method to solve the EE optimization problem under a fixed set of active RAUs. More specifically, we split this problem into three subproblems, i.e., the rate maximization problem, the EE maximization problem without rate constraint, and the power minimization problem, and each subproblem can be efficiently solved. Then, a novel distance-based RAU selection method is proposed to determine the optimal set of active RAUs. Simulation results show that the performance of the proposed RAU selection is almost identical to the optimal exhaustive search method with significantly reduced computational complexity, and the performance of the proposed algorithm significantly outperforms the existing EE optimization methods.
A Multilingual FrameNet-based Grammar and Lexicon for Controlled Natural Language
Venue: arXiv cs.CL 2015
Year: 2015

Berkeley FrameNet is a lexico-semantic resource for English based on the theory of frame semantics. It has been exploited in a range of natural language processing applications and has inspired the development of framenets for many languages. We present a methodological approach to the extraction and generation of a computational multilingual FrameNet-based grammar and lexicon. The approach leverages FrameNet-annotated corpora to automatically extract a set of cross-lingual semantico-syntactic valence patterns. Based on data from Berkeley FrameNet and Swedish FrameNet, the proposed approach has been implemented in Grammatical Framework (GF), a categorial grammar formalism specialized for multilingual grammars. The implementation of the grammar and lexicon is supported by the design of FrameNet, providing a frame semantic abstraction layer, an interlingual semantic API (application programming interface), over the interlingual syntactic API already provided by GF Resource Grammar Library. The evaluation of the acquired grammar and lexicon shows the feasibility of the approach. Additionally, we illustrate how the FrameNet-based grammar and lexicon are exploited in two distinct multilingual controlled natural language applications. The produced resources are available under an open source license.
Distortion-Memory Tradeoffs in Cache-Aided Wireless Video Delivery
Venue: arXiv cs.IT 2015
Year: 2015

Mobile network operators are considering caching as one of the strategies to keep up with the increasing demand for high-definition wireless video streaming. By prefetching popular content into memory at wireless access points or end user devices, requests can be served locally, relieving strain on expensive backhaul. In addition, using network coding allows the simultaneous serving of distinct cache misses via common coded multicast transmissions, resulting in significantly larger load reductions compared to those achieved with conventional delivery schemes. However, prior work does not exploit the properties of video and simply treats content as fixed-size files that users would like to fully download. Our work is motivated by the fact that video can be coded in a scalable fashion and that the decoded video quality depends on the number of layers a user is able to receive. Using a Gaussian source model, caching and coded delivery methods are designed to minimize the squared error distortion at end user devices. Our work is general enough to consider heterogeneous cache sizes and video popularity distributions.