OpenRaiser/Intern-Atlas huggingface dataset:
Antara: An Interactive 3D Volume Rendering and Visualization Framework
Venue: arXiv cs.GR 2018
Year: 2018

The goal of 3D visualization is to provide the user with an intuitive interface which enables him to explore the 3D data in an interactive manner. The aim of the exploration is to identify and analyze anomalies or to give proof of the non-anomaly of the visualized organic structures. For 3D Medical Data, Magnetic Resonance Images (MRI) has been used. To create the 3D model, we used the Direct Volume Rendering technique. In the input 3D data, we have $x, y$ and $z$ coordinates and an intensity value for each voxel. The 3D data is used by Volume Ray Casting to compute 2D projections from 3D volumetric data sets. In ray casting, a ray of light is made to pass through the volume data. The interaction of each voxel with this ray is used to assign RGB and alpha values for every voxel in the volume. As a result, we are able to generate the 3D model of the region of interest using the 3D data. The 3D model is interactive, thus enabling us to visualize the different layers of the 3D volume by adjusting the transfer function.
Text data mining and data quality management for research information systems in the context of open data and open science
Venue: arXiv cs.DL 2018
Year: 2018

In the implementation and use of research information systems (RIS) in scientific institutions, text data mining and semantic technologies are a key technology for the meaningful use of large amounts of data. It is not the collection of data that is difficult, but the further processing and integration of the data in RIS. Data is usually not uniformly formatted and structured, such as texts and tables that cannot be linked. These include various source systems with their different data formats such as project and publication databases, CERIF and RCD data model, etc. Internal and external data sources continue to develop. On the one hand, they must be constantly synchronized and the results of the data links checked. On the other hand, the texts must be processed in natural language and certain information extracted. Using text data mining, the quality of the metadata is analyzed and this identifies the entities and general keywords. So that the user is supported in the search for interesting research information. The information age makes it easier to store huge amounts of data and increase the number of documents on the internet, in institutions' intranets, in newswires and blogs is overwhelming. Search engines should help to specifically open up these sources of information and make them usable for administrative and research purposes. Against this backdrop, the aim of this paper is to provide an overview of text data mining techniques and the management of successful data quality for RIS in the context of open data and open science in scientific institutions and libraries, as well as to provide ideas for their application. In particular, solutions for the RIS will be presented.
Towards Automatic Personality Prediction Using Facebook Like Categories
Venue: arXiv cs.SI 2018
Year: 2018

We demonstrate that effortlessly accessible digital records of behavior such as Facebook Likes can be obtained and utilized to automatically distinguish a wide range of highly delicate personal traits including: life satisfaction, cultural ethnicity, political views, age, gender and personality traits. The analysis presented based on a dataset of over 738,000 users who conferred their Facebook Likes, social network activities, egocentric network, demographic characteristics, and the results of various psychometric tests for our extended personality analysis. The proposed model uses unique mapping technique between each Facebook Like object to the corresponding Facebook page category/sub-category object, which is then evaluated as features for a set of machine learning algorithms to predict individual psycho-demographic profiles from Likes. The model , distinguishes between a religious and non-religious individual in 83% of circumstances, Asian and European in 87% of situations, and between emotional stable and emotion unstable in 81% of situations. We provide exemplars of correlations between attributes and Likes and present suggestions for future directions.
Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes
Venue: arXiv cs.LG 2018
Year: 2018

We introduce and analyse two algorithms for exploration-exploitation in discrete and continuous Markov Decision Processes (MDPs) based on exploration bonuses. SCAL$^+$ is a variant of SCAL (Fruit et al., 2018) that performs efficient exploration-exploitation in any unknown weakly-communicating MDP for which an upper bound C on the span of the optimal bias function is known. For an MDP with $S$ states, $A$ actions and $Γ\leq S$ possible next states, we prove that SCAL$^+$ achieves the same theoretical guarantees as SCAL (i.e., a high probability regret bound of $\widetilde{O}(C\sqrt{ΓSAT})$), with a much smaller computational complexity. Similarly, C-SCAL$^+$ exploits an exploration bonus to achieve sublinear regret in any undiscounted MDP with continuous state space. We show that C-SCAL$^+$ achieves the same regret bound as UCCRL (Ortner and Ryabko, 2012) while being the first implementable algorithm with regret guarantees in this setting. While optimistic algorithms such as UCRL, SCAL or UCCRL maintain a high-confidence set of plausible MDPs around the true unknown MDP, SCAL$^+$ and C-SCAL$^+$ leverage on an exploration bonus to directly plan on the empirically estimated MDP, thus being more computationally efficient.
Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction
Venue: arXiv cs.LG 2018
Year: 2018

In our study, we demonstrate the synergy effect between convolutional neural networks and the multiplicity of SMILES. The model we propose, the so-called Convolutional Neural Fingerprint (CNF) model, reaches the accuracy of traditional descriptors such as Dragon (Mauri et al. [22]), RDKit (Landrum [18]), CDK2 (Willighagen et al. [43]) and PyDescriptor (Masand and Rastija [20]). Moreover the CNF model generally performs better than highly fine-tuned traditional descriptors, especially on small data sets, which is of great interest for the chemical field where data sets are generally small due to experimental costs, the availability of molecules or accessibility to private databases. We evaluate the CNF model along with SMILES augmentation during both training and testing. To the best of our knowledge, this is the first time that such a methodology is presented. We show that using the multiplicity of SMILES during training acts as a regulariser and therefore avoids overfitting and can be seen as ensemble learning when considered for testing.
Production-Driven Patch Generation
Venue: arXiv cs.SE 2018
Year: 2018

We present an original concept for patch generation: we propose to do it directly in production. Our idea is to generate patches on-the-fly based on automated analysis of the failure context. By doing this in production, the repair process has complete access to the system state at the point of failure. We propose to perform live regression testing of the generated patches directly on the production traffic, by feeding a sandboxed version of the application with a copy of the production traffic, the 'shadow traffic'. Our concept widens the applicability of program repair, because it removes the requirements of having a failing test case.
Deep Learning with Mixed Supervision for Brain Tumor Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Most of the current state-of-the-art methods for tumor segmentation are based on machine learning models trained on manually segmented images. This type of training data is particularly costly, as manual delineation of tumors is not only time-consuming but also requires medical expertise. On the other hand, images with a provided global label (indicating presence or absence of a tumor) are less informative but can be obtained at a substantially lower cost. In this paper, we propose to use both types of training data (fully-annotated and weakly-annotated) to train a deep learning model for segmentation. The idea of our approach is to extend segmentation networks with an additional branch performing image-level classification. The model is jointly trained for segmentation and classification tasks in order to exploit information contained in weakly-annotated images while preventing the network to learn features which are irrelevant for the segmentation task. We evaluate our method on the challenging task of brain tumor segmentation in Magnetic Resonance images from BRATS 2018 challenge. We show that the proposed approach provides a significant improvement of segmentation performance compared to the standard supervised learning. The observed improvement is proportional to the ratio between weakly-annotated and fully-annotated images available for training.
Evaluating the Impact of Intensity Normalization on MR Image Synthesis
Venue: arXiv cs.CV 2018
Year: 2018

Image synthesis learns a transformation from the intensity features of an input image to yield a different tissue contrast of the output image. This process has been shown to have application in many medical image analysis tasks including imputation, registration, and segmentation. To carry out synthesis, the intensities of the input images are typically scaled--i.e., normalized--both in training to learn the transformation and in testing when applying the transformation, but it is not presently known what type of input scaling is optimal. In this paper, we consider seven different intensity normalization algorithms and three different synthesis methods to evaluate the impact of normalization. Our experiments demonstrate that intensity normalization as a preprocessing step improves the synthesis results across all investigated synthesis algorithms. Furthermore, we show evidence that suggests intensity normalization is vital for successful deep learning-based MR image synthesis.
Information Security Risks Assessment: A Case Study
Venue: arXiv cs.CY 2018
Year: 2018

Owing to recorded incidents of Information technology inclined organisations failing to respond effectively to threat incidents, this project outlines the benefits of conducting a comprehensive risk assessment which would aid proficiency in responding to potential threats. The ultimate goal is primarily to identify, quantify and control the key threats that are detrimental to achieving business objectives. This project carries out a detailed risk assessment for a case study organisation. It includes a comprehensive literature review analysing several professional views on pressing issues in Information security. In the risk register, five prominent assets were identified in respect to their owners. The work is followed by a qualitative analysis methodology to determine the magnitude of the potential threats and vulnerabilities. Collating these parameters enabled the valuation of individual risk per asset, per threat and vulnerability. Evaluating a risk appetite aided in prioritising and determining acceptable risks. From the analysis, it was deduced that human being posed the greatest Information security risk through intentional/ unintentional human error. In conclusion, effective control techniques based on defence in-depth were devised to mitigate the impact of the identified risks from risk register.
Femural Head Autosegmentation for 3D Radiotherapy Planning: Preliminary Results
Venue: arXiv cs.CV 2018
Year: 2018

Contouring of organs at risk is an important but time consuming part of radiotherapy treatment planning. Several authors proposed methods for automatic delineation but the clinical experts eye remains the gold standard method. In this paper, we present a totally visual software for automated delineation of the femural head. The software was successfully characterized in pelvic CT Scan of prostate patients (n=11). The automatic delineation was compared with manual and approved delineation through blind test evaluated by a panel of seniors radiation oncologists (n=9). Clinical experts evaluated that no any contouring correction were need in 77.8% and 67.8% of manual and automatic delineation respectively. Our results show that the software is robust, the automated delineation was reproducible in all patient, and its performance was similar to manually delineation.
Patient-Centric Cellular Networks Optimization using Big Data Analytics
Venue: arXiv cs.NI 2018
Year: 2018

Big data analytics is one of the state-of-the-art tools to optimize networks and transform them from merely being a blind tube that conveys data, into a cognitive, conscious, and self-optimizing entity that can intelligently adapt according to the needs of its users. This, in fact, can be regarded as one of the highest forthcoming priorities of future networks. In this paper, we propose a system for Out-Patient (OP) centric Long Term Evolution-Advanced (LTE-A) network optimization. Big data harvested from the OPs' medical records, along with current readings from their body sensors are processed and analyzed to predict the likelihood of a life-threatening medical condition, for instance, an imminent stroke. This prediction is used to ensure that the OP is assigned an optimal LTE-A Physical Resource Blocks (PRBs) to transmit their critical data to their healthcare provider with minimal delay. To the best of our knowledge, this is the first time big data analytics are utilized to optimize a cellular network in an OP-conscious manner. The PRBs assignment is optimized using Mixed Integer Linear Programming (MILP) and a real-time heuristic. Two approaches are proposed, the Weighted Sum Rate Maximization (WSRMax) approach and the Proportional Fairness (PF) approach. The approaches increased the OPs' average SINR by 26.6% and 40.5%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, however, the PF approach reported higher SINRs for the OPs, better fairness and a lower margin of error.
Designing Artificial Cognitive Architectures: Brain Inspired or Biologically Inspired?
Venue: arXiv cs.AI 2018
Year: 2018

Artificial Neural Networks (ANNs) were devised as a tool for Artificial Intelligence design implementations. However, it was soon became obvious that they are unable to fulfill their duties. The fully autonomous way of ANNs working, precluded from any human intervention or supervision, deprived of any theoretical underpinning, leads to a strange state of affairs, when ANN designers cannot explain why and how they achieve their amazing and remarkable results. Therefore, contemporary Artificial Intelligence R&D looks more like a Modern Alchemy enterprise rather than a respected scientific or technological undertaking. On the other hand, modern biological science posits that intelligence can be distinguished not only in human brains. Intelligence today is considered as a fundamental property of each and every living being. Therefore, lower simplified forms of natural intelligence are more suitable for investigation and further replication in artificial cognitive architectures.
Bridging the Generalization Gap: Training Robust Models on Confounded Biological Data
Venue: arXiv cs.LG 2018
Year: 2018

Statistical learning on biological data can be challenging due to confounding variables in sample collection and processing. Confounders can cause models to generalize poorly and result in inaccurate prediction performance metrics if models are not validated thoroughly. In this paper, we propose methods to control for confounding factors and further improve prediction performance. We introduce OrthoNormal basis construction In cOnfounding factor Normalization (ONION) to remove confounding covariates and use the Domain-Adversarial Neural Network (DANN) to penalize models for encoding confounder information. We apply the proposed methods to simulated and empirical patient data and show significant improvements in generalization.
Optimal Prizes for All-Pay Contests in Heterogeneous Crowdsourcing
Venue: arXiv cs.MA 2018
Year: 2018

Incentives are key to the success of crowdsourcing which heavily depends on the level of user participation. This paper designs an incentive mechanism to motivate a heterogeneous crowd of users to actively participate in crowdsourcing campaigns. We cast the problem in a new, asymmetric all-pay contest model with incomplete information, where an arbitrary n of users exert irrevocable effort to compete for a prize tuple. The prize tuple is an array of prize functions as opposed to a single constant prize typically used by conventional contests. We design an optimal contest that (a) induces the maximum profit---total user effort minus the prize payout---for the crowdsourcer, and (b) ensures users to strictly have the incentive to participate. In stark contrast to intuition and prior related work, our mechanism induces an equilibrium in which heterogeneous users behave independently of one another as if they were in a homogeneous setting. This newly discovered property, which we coin as strategy autonomy (SA), is of practical significance: it (a) reduces computational and storage complexity by n-fold for each user, (b) increases the crowdsourcer's revenue by counteracting an effort reservation effect existing in asymmetric contests, and (c) neutralizes the (almost universal) law of diminishing marginal returns (DMR). Through an extensive numerical case study, we demonstrate and scrutinize the superior profitability of our mechanism, as well as draw insights into the SA property.
Recurrent Neural Networks for Fuzz Testing Web Browsers
Venue: arXiv cs.CR 2018
Year: 2018

Generation-based fuzzing is a software testing approach which is able to discover different types of bugs and vulnerabilities in software. It is, however, known to be very time consuming to design and fine tune classical fuzzers to achieve acceptable coverage, even for small-scale software systems. To address this issue, we investigate a machine learning-based approach to fuzz testing in which we outline a family of test-case generators based on Recurrent Neural Networks (RNNs) and train those on readily available datasets with a minimum of human fine tuning. The proposed generators do, in contrast to previous work, not rely on heuristic sampling strategies but principled sampling from the predictive distributions. We provide a detailed analysis to demonstrate the characteristics and efficacy of the proposed generators in a challenging web browser testing scenario. The empirical results show that the RNN-based generators are able to provide better coverage than a mutation based method and are able to discover paths not discovered by a classical fuzzer. Our results supplement findings in other domains suggesting that generation based fuzzing with RNNs is a viable route to better software quality conditioned on the use of a suitable model selection/analysis procedure.
A First Look at Emoji Usage on GitHub: An Empirical Study
Venue: arXiv cs.CY 2018
Year: 2018

Emoji is becoming a ubiquitous language and gaining worldwide popularity in recent years including the field of software engineering (SE). As nonverbal cues, emojis are widely used in user understanding tasks such as sentiment analysis, but few work has been done to study emojis in SE scenarios. This paper presents a large scale empirical study on how GitHub users use emojis in development-related communications. We find that emojis are used by a considerable proportion of GitHub users. In comparison to Internet users, developers show interesting usage characteristics and have their own interpretation of the meanings of emojis. In addition, the usage of emojis reflects a positive and supportive culture of this community. Through a manual annotation task, we find that sentimental usage is a main intention of using emojis in issues, pull requests, and comments, while emojis are mainly used to emphasize important contents in README. These findings not only deepen our understanding about the culture of SE communities, but also provide implications on how to facilitate SE tasks with emojis such as sentiment analysis.
Description of algorithms for Ben-Gurion University Submission to the LOCATA challenge
Venue: arXiv cs.SD 2018
Year: 2018

This paper summarizes the methods used to localize the sources recorded for the LOCalization And TrAcking (LOCATA) challenge. The tasks of stationary sources and arrays were considered, i.e., tasks 1 and 2 of the challenge, which were recorded with the Nao robot array, and the Eigenmike array. For both arrays, direction of arrival (DOA) estimation has been performed with measurements in the short time Fourier transform domain, and with direct-path dominance (DPD) based tests, which aim to identify time-frequency (TF) bins dominated by the direct sound. For the recordings with Nao, a DPD test which is applied directly to the microphone signals was used. For the Eigenmike recordings, a DPD based test designed for plane-wave density measurements in the spherical harmonics domain was used. After acquiring DOA estimates with TF bins that passed the DPD tests, a stage of k-means clustering is performed, to assign a final DOA estimate for each speaker.
Processor Hardware Security Vulnerabilities and their Detection by Unique Program Execution Checking
Venue: arXiv cs.CR 2018
Year: 2018

Recent discovery of security attacks in advanced processors, known as Spectre and Meltdown, has resulted in high public alertness about security of hardware. The root cause of these attacks is information leakage across "covert channels" that reveal secret data without any explicit information flow between the secret and the attacker. Many sources believe that such covert channels are intrinsic to highly advanced processor architectures based on speculation and out-of-order execution, suggesting that such security risks can be avoided by staying away from high-end processors. This paper, however, shows that the problem is of wider scope: we present new classes of covert channel attacks which are possible in average-complexity processors with in-order pipelining, as they are mainstream in applications ranging from Internet-of-Things to Autonomous Systems. We present a new approach as a foundation for remedy against covert channels: while all previous attacks were found by clever thinking of human attackers, this paper presents an automated and exhaustive method called "Unique Program Execution Checking" which detects and locates vulnerabilities to covert channels systematically, including those to covert channels unknown so far.
Thwarting Adversarial Examples: An $L_0$-RobustSparse Fourier Transform
Venue: arXiv cs.LG 2018
Year: 2018

We give a new algorithm for approximating the Discrete Fourier transform of an approximately sparse signal that has been corrupted by worst-case $L_0$ noise, namely a bounded number of coordinates of the signal have been corrupted arbitrarily. Our techniques generalize to a wide range of linear transformations that are used in data analysis such as the Discrete Cosine and Sine transforms, the Hadamard transform, and their high-dimensional analogs. We use our algorithm to successfully defend against well known $L_0$ adversaries in the setting of image classification. We give experimental results on the Jacobian-based Saliency Map Attack (JSMA) and the Carlini Wagner (CW) $L_0$ attack on the MNIST and Fashion-MNIST datasets as well as the Adversarial Patch on the ImageNet dataset.