OpenRaiser/Intern-Atlas huggingface dataset:
Evaluation of Digital Forensic Process Models with Respect to Digital Forensics as a Service
Venue: arXiv cs.CR 2017
Year: 2017

Digital forensic science is very much still in its infancy, but is becoming increasingly invaluable to investigators. A popular area for research is seeking a standard methodology to make the digital forensic process accurate, robust, and efficient. The first digital forensic process model proposed contains four steps: Acquisition, Identification, Evaluation and Admission. Since then, numerous process models have been proposed to explain the steps of identifying, acquiring, analysing, storage, and reporting on the evidence obtained from various digital devices. In recent years, an increasing number of more sophisticated process models have been proposed. These models attempt to speed up the entire investigative process or solve various of problems commonly encountered in the forensic investigation. In the last decade, cloud computing has emerged as a disruptive technological concept, and most leading enterprises such as IBM, Amazon, Google, and Microsoft have set up their own cloud-based services. In the field of digital forensic investigation, moving to a cloud-based evidence processing model would be extremely beneficial and preliminary attempts have been made in its implementation. Moving towards a Digital Forensics as a Service model would not only expedite the investigative process, but can also result in significant cost savings - freeing up digital forensic experts and law enforcement personnel to progress their caseload. This paper aims to evaluate the applicability of existing digital forensic process models and analyse how each of these might apply to a cloud-based evidence processing paradigm.
Learning Discriminative Alpha-Beta-divergence for Positive Definite Matrices (Extended Version)
Venue: arXiv cs.CV 2017
Year: 2017

Symmetric positive definite (SPD) matrices are useful for capturing second-order statistics of visual data. To compare two SPD matrices, several measures are available, such as the affine-invariant Riemannian metric, Jeffreys divergence, Jensen-Bregman logdet divergence, etc.; however, their behaviors may be application dependent, raising the need of manual selection to achieve the best possible performance. Further and as a result of their overwhelming complexity for large-scale problems, computing pairwise similarities by clever embedding of SPD matrices is often preferred to direct use of the aforementioned measures. In this paper, we propose a discriminative metric learning framework, Information Divergence and Dictionary Learning (IDDL), that not only learns application specific measures on SPD matrices automatically, but also embeds them as vectors using a learned dictionary. To learn the similarity measures (which could potentially be distinct for every dictionary atom), we use the recently introduced alpha-beta-logdet divergence, which is known to unify the measures listed above. We propose a novel IDDL objective, that learns the parameters of the divergence and the dictionary atoms jointly in a discriminative setup and is solved efficiently using Riemannian optimization. We showcase extensive experiments on eight computer vision datasets, demonstrating state-of-the-art performances.
On the Quest for an Acyclic Graph
Venue: arXiv cs.LO 2017
Year: 2017

The paper aims at finding acyclic graphs under a given set of constraints. More specifically, given a propositional formula ϕ over edges of a fixed-size graph, the objective is to find a model of ϕ that corresponds to a graph that is acyclic. The paper proposes several encodings of the problem and compares them in an experimental evaluation using stateof-the-art SAT solvers.
Referenceless Quality Estimation for Natural Language Generation
Venue: arXiv cs.CL 2017
Year: 2017

Traditional automatic evaluation measures for natural language generation (NLG) use costly human-authored references to estimate the quality of a system output. In this paper, we propose a referenceless quality estimation (QE) approach based on recurrent neural networks, which predicts a quality score for a NLG system output by comparing it to the source meaning representation only. Our method outperforms traditional metrics and a constant baseline in most respects; we also show that synthetic data helps to increase correlation results by 21% compared to the base system. Our results are comparable to results obtained in similar QE tasks despite the more challenging setting.
A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms
Venue: arXiv cs.HC 2017
Year: 2017

In this paper a review is presented of the research on eye gaze estimation techniques and applications, that has progressed in diverse ways over the past two decades. Several generic eye gaze use-cases are identified: desktop, TV, head-mounted, automotive and handheld devices. Analysis of the literature leads to the identification of several platform specific factors that influence gaze tracking accuracy. A key outcome from this review is the realization of a need to develop standardized methodologies for performance evaluation of gaze tracking systems and achieve consistency in their specification and comparative evaluation. To address this need, the concept of a methodological framework for practical evaluation of different gaze tracking systems is proposed.
Universally consistent predictive distributions
Venue: arXiv cs.LG 2017
Year: 2017

This paper describes simple universally consistent procedures of probability forecasting that satisfy a natural property of small-sample validity, under the assumption that the observations are produced independently in the IID fashion.
Empathy in Bimatrix Games
Venue: arXiv cs.GT 2017
Year: 2017

Although the definition of what empathetic preferences exactly are is still evolving, there is a general consensus in the psychology, science and engineering communities that the evolution toward players' behaviors in interactive decision-making problems will be accompanied by the exploitation of their empathy, sympathy, compassion, antipathy, spitefulness, selfishness, altruism, and self-abnegating states in the payoffs. In this article, we study one-shot bimatrix games from a psychological game theory viewpoint. A new empathetic payoff model is calculated to fit empirical observations and both pure and mixed equilibria are investigated. For a realized empathy structure, the bimatrix game is categorized among four generic class of games. Number of interesting results are derived. A notable level of involvement can be observed in the empathetic one-shot game compared the non-empathetic one and this holds even for games with dominated strategies. Partial altruism can help in breaking symmetry, in reducing payoff-inequality and in selecting social welfare and more efficient outcomes. By contrast, partial spite and self-abnegating may worsen payoff equity. Empathetic evolutionary game dynamics are introduced to capture the resulting empathetic evolutionarily stable strategies under wide range of revision protocols including Brown-von Neumann-Nash, Smith, imitation, replicator, and hybrid dynamics. Finally, mutual support and Berge solution are investigated and their connection with empathetic preferences are established. We show that pure altruism is logically inconsistent, only by balancing it with some partial selfishness does it create a consistent psychology.
Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Diabetic Foot Ulcer (DFU) is a major complication of Diabetes, which if not managed properly can lead to amputation. DFU can appear anywhere on the foot and can vary in size, colour, and contrast depending on various pathologies. Current clinical approaches to DFU treatment rely on patients and clinician vigilance, which has significant limitations such as the high cost involved in the diagnosis, treatment and lengthy care of the DFU. We introduce a dataset of 705 foot images. We provide the ground truth of ulcer region and the surrounding skin that is an important indicator for clinicians to assess the progress of ulcer. Then, we propose a two-tier transfer learning from bigger datasets to train the Fully Convolutional Networks (FCNs) to automatically segment the ulcer and surrounding skin. Using 5-fold cross-validation, the proposed two-tier transfer learning FCN Models achieve a Dice Similarity Coefficient of 0.794 ($\pm$0.104) for ulcer region, 0.851 ($\pm$0.148) for surrounding skin region, and 0.899 ($\pm$0.072) for the combination of both regions. This demonstrates the potential of FCNs in DFU segmentation, which can be further improved with a larger dataset.
Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model
Venue: arXiv cs.AI 2017
Year: 2017

Background Road collisions and casualties pose a serious threat to commuters around the globe. Autonomous Vehicles (AVs) aim to make the use of technology to reduce the road accidents. However, the most of research work in the context of collision avoidance has been performed to address, separately, the rear end, front end and lateral collisions in less congested and with high inter-vehicular distances. Purpose The goal of this paper is to introduce the concept of a social agent, which interact with other AVs in social manners like humans are social having the capability of predicting intentions, i.e. mentalizing and copying the actions of each other, i.e. mirroring. The proposed social agent is based on a human-brain inspired mentalizing and mirroring capabilities and has been modelled for collision detection and avoidance under congested urban road traffic. Method We designed our social agent having the capabilities of mentalizing and mirroring and for this purpose we utilized Exploratory Agent Based Modeling (EABM) level of Cognitive Agent Based Computing (CABC) framework proposed by Niazi and Hussain. Results Our simulation and practical experiments reveal that by embedding Richardson's arms race model within AVs, collisions can be avoided while travelling on congested urban roads in a flock like topologies. The performance of the proposed social agent has been compared at two different levels.
Research on Human Dynamics of Information Release of WeChat Users
Venue: arXiv cs.SI 2017
Year: 2017

The information release behavior of WeChat users is influenced by many factors, and studying the rules of the behavior of users in WeChat can provide theoretical help for the dynamic research of mobile social network users. By crawling WeChat moments information of nine users within 5 years, we used the human behavioral dynamics system to analyze users' behavior. The results show that the information distribution behavior of WeChat users is consistent with the power-law distribution for a certain period of time. Meanwhile, there is an anti-memory characteristic in information release behavior of WeChat users, which is significantly different from other user behavior patterns in online social networks. The results of the study provide theoretical support for the further study of information release behavior of Wechat users.
Outage Probability and Rate for $κ$-$μ$ Shadowed Fading in Interference Limited Scenario
Venue: arXiv cs.IT 2017
Year: 2017

The $κ$-$μ$ shadowed fading model is a very general fading model as it includes both $κ$-$μ$ and $η$-$μ$ as special cases. In this work, we derive the expression for outage probability when the signal-of-interest (SoI) and interferers both experience $κ$-$μ$ shadowed fading in an interference limited scenario. The derived expression is valid for arbitrary SoI parameters, arbitrary $κ$ and $μ$ parameters for all interferers and any value of the parameter $m$ for the interferers excepting the limiting value of $m\rightarrow \infty$. The expression can be expressed in terms of Pochhammer integral where the integrands of integral only contains elementary functions. The outage probability expression is then simplified for various special cases, especially when SoI experiences $η$-$μ$ or $κ$-$μ$ fading. Further, the rate expression is derived when the SoI experiences $κ$-$μ$ shadowed fading with integer values of $μ$, and interferers experience $κ$-$μ$ shadowed fading with arbitrary parameters. The rate expression can be expressed in terms of sum of Lauricella's function of the fourth kind. The utility of our results is demonstrated by using the derived expression to study and compare FFR and SFR in the presence of $κ$-$μ$ shadowed fading. Extensive simulation results are provided and these further validate our theoretical results.
From Appearance to Essence: Comparing Truth Discovery Methods without Using Ground Truth
Venue: arXiv cs.DB 2017
Year: 2017

Truth discovery has been widely studied in recent years as a fundamental means for resolving the conflicts in multi-source data. Although many truth discovery methods have been proposed based on different considerations and intuitions, investigations show that no single method consistently outperforms the others. To select the right truth discovery method for a specific application scenario, it becomes essential to evaluate and compare the performance of different methods. A drawback of current research efforts is that they commonly assume the availability of certain ground truth for the evaluation of methods. However, the ground truth may be very limited or even out-of-reach in practice, rendering the evaluation biased by the small ground truth or even unfeasible. In this paper, we present CompTruthHyp, a general approach for comparing the performance of truth discovery methods without using ground truth. In particular, our approach calculates the probability of observations in a dataset based on the output of different methods. The probability is then ranked to reflect the performance of these methods. We review and compare twelve existing truth discovery methods and consider both single-valued and multi-valued objects. Empirical studies on both real-world and synthetic datasets demonstrate the effectiveness of our approach for comparing truth discovery methods.
Unbalancing Sets and an Almost Quadratic Lower Bound for Syntactically Multilinear Arithmetic Circuits
Venue: arXiv cs.CC 2017
Year: 2017

We prove a lower bound of $Ω(n^2/\log^2 n)$ on the size of any syntactically multilinear arithmetic circuit computing some explicit multilinear polynomial $f(x_1, \ldots, x_n)$. Our approach expands and improves upon a result of Raz, Shpilka and Yehudayoff ([RSY08]), who proved a lower bound of $Ω(n^{4/3}/\log^2 n)$ for the same polynomial. Our improvement follows from an asymptotically optimal lower bound for a generalized version of Galvin's problem in extremal set theory.
What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?
Venue: arXiv cs.CL 2017
Year: 2017

In neural image captioning systems, a recurrent neural network (RNN) is typically viewed as the primary `generation' component. This view suggests that the image features should be `injected' into the RNN. This is in fact the dominant view in the literature. Alternatively, the RNN can instead be viewed as only encoding the previously generated words. This view suggests that the RNN should only be used to encode linguistic features and that only the final representation should be `merged' with the image features at a later stage. This paper compares these two architectures. We find that, in general, late merging outperforms injection, suggesting that RNNs are better viewed as encoders, rather than generators.
Rank modulation codes for DNA storage
Venue: arXiv cs.IT 2017
Year: 2017

Synthesis of DNA molecules offers unprecedented advances in storage technology. Yet, the microscopic world in which these molecules reside induces error patterns that are fundamentally different from their digital counterparts. Hence, to maintain reliability in reading and writing, new coding schemes must be developed. In a reading technique called shotgun sequencing, a long DNA string is read in a sliding window fashion, and a profile vector is produced. It was recently suggested by Kiah et al. that such a vector can represent the permutation which is induced by its entries, and hence a rank-modulation scheme arises. Although this interpretation suggests high error tolerance, it is unclear which permutations are feasible, and how to produce a DNA string whose profile vector induces a given permutation. In this paper, by observing some necessary conditions, an upper bound for the number of feasible permutations is given. Further, a technique for deciding the feasibility of a permutation is devised. By using insights from this technique, an algorithm for producing a considerable number of feasible permutations is given, which applies to any alphabet size and any window length.
Code Park: A New 3D Code Visualization Tool
Venue: arXiv cs.HC 2017
Year: 2017

We introduce Code Park, a novel tool for visualizing codebases in a 3D game-like environment. Code Park aims to improve a programmer's understanding of an existing codebase in a manner that is both engaging and intuitive, appealing to novice users such as students. It achieves these goals by laying out the codebase in a 3D park-like environment. Each class in the codebase is represented as a 3D room-like structure. Constituent parts of the class (variable, member functions, etc.) are laid out on the walls, resembling a syntax-aware "wallpaper". The users can interact with the codebase using an overview, and a first-person viewer mode. We conducted two user studies to evaluate Code Park's usability and suitability for organizing an existing project. Our results indicate that Code Park is easy to get familiar with and significantly helps in code understanding compared to a traditional IDE. Further, the users unanimously believed that Code Park was a fun tool to work with.
Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowd-sourced Time-Sync Comments
Venue: arXiv cs.CL 2017
Year: 2017

With the prevalence of video sharing, there are increasing demands for automatic video digestion such as highlight detection. Recently, platforms with crowdsourced time-sync video comments have emerged worldwide, providing a good opportunity for highlight detection. However, this task is non-trivial: (1) time-sync comments often lag behind their corresponding shot; (2) time-sync comments are semantically sparse and noisy; (3) to determine which shots are highlights is highly subjective. The present paper aims to tackle these challenges by proposing a framework that (1) uses concept-mapped lexical-chains for lag calibration; (2) models video highlights based on comment intensity and combination of emotion and concept concentration of each shot; (3) summarize each detected highlight using improved SumBasic with emotion and concept mapping. Experiments on large real-world datasets show that our highlight detection method and summarization method both outperform other benchmarks with considerable margins.
LitStoryTeller: An Interactive System for Visual Exploration of Scientific Papers Leveraging Named entities and Comparative Sentences
Venue: arXiv cs.HC 2017
Year: 2017

The present study proposes LitStoryTeller, an interactive system for visually exploring the semantic structure of a scientific article. We demonstrate how LitStoryTeller could be used to answer some of the most fundamental research questions, such as how a new method was built on top of existing methods, based on what theoretical proof and experimental evidences. More importantly, LitStoryTeller can assist users to understand the full and interesting story a scientific paper, with a concise outline and important details. The proposed system borrows a metaphor from screen play, and visualizes the storyline of a scientific paper by arranging its characters (scientific concepts or terminologies) and scenes (paragraphs/sentences) into a progressive and interactive storyline. Such storylines help to preserve the semantic structure and logical thinking process of a scientific paper. Semantic structures, such as scientific concepts and comparative sentences, are extracted using existing named entity recognition APIs and supervised classifiers, from a scientific paper automatically. Two supplementary views, ranked entity frequency view and entity co-occurrence network view, are provided to help users identify the "main plot" of such scientific storylines. When collective documents are ready, LitStoryTeller also provides a temporal entity evolution view and entity community view for collection digestion.
Implementation of Torque Controller for Brushless Motors on the Omni-directional Wheeled Mobile Robot
Venue: arXiv cs.RO 2017
Year: 2017

The major issue for the wheeled mobile robot is the low level controller gains tuning up especially in the robot competition. The floor surface can be damaged by the robot wheels during the competition, therefore the surface coefficient can be changed over time. PI gains have to be tuned before every match along the competition. In this research, the torque controller is defined and implemented in order to solve this problem. Torque controller consists of a PI controller for the robot wheel's angular velocity and a dynamic equation of brushless motor. The motor dynamics can be derived from the energy conservation law. Three different carpets, which have the different friction coefficients, are used in the experiments. The robot wheel's angular velocity profiles are generated from the robot kinematics with different initial conditions. The output paths of the robot with the torque controller are compared with the output paths of the robot with regular PI controller when the same wheel angular velocity profiles are applied. The results show that the torque controller can provide a better robot path than the normal PI controller.
FixMyStreet Brussels: Socio-Demographic Inequality in Crowdsourced Civic Participation
Venue: arXiv cs.CY 2017
Year: 2017

FixMyStreet (FMS) is a web-based civic participation platform that allows inhabitants to report environmental defects like potholes and damaged pavements to the government. In this paper, we examine the use of FMS in Brussels, the capital city of Belgium. Analyzing a total of 30,041 reports since its inception in 2013, we demonstrate how civic participation on FMS varies between the ethnically diverse districts in Brussels. We compare FMS use to a range of sociodemographic indicators derived from official city statistics as well as geotagged social media data from Twitter. Our statistical analysis revealed several significant differences between the districts that suggested that crowdsourced civic participation platforms tend to marginalize low-income and ethnically diverse communities. In this respect, our findings provide timely evidence to inform the design of more inclusive crowdsourced, civic participation platforms in the future.
Shortcut-Stacked Sentence Encoders for Multi-Domain Inference
Venue: arXiv cs.CL 2017
Year: 2017

We present a simple sequential sentence encoder for multi-domain natural language inference. Our encoder is based on stacked bidirectional LSTM-RNNs with shortcut connections and fine-tuning of word embeddings. The overall supervised model uses the above encoder to encode two input sentences into two vectors, and then uses a classifier over the vector combination to label the relationship between these two sentences as that of entailment, contradiction, or neural. Our Shortcut-Stacked sentence encoders achieve strong improvements over existing encoders on matched and mismatched multi-domain natural language inference (top non-ensemble single-model result in the EMNLP RepEval 2017 Shared Task (Nangia et al., 2017)). Moreover, they achieve the new state-of-the-art encoding result on the original SNLI dataset (Bowman et al., 2015).