OpenRaiser/Intern-Atlas huggingface dataset:
SafeSpec: Banishing the Spectre of a Meltdown with Leakage-Free Speculation
Venue: arXiv cs.CR 2018
Year: 2018

Speculative execution which is used pervasively in modern CPUs can leave side effects in the processor caches and other structures even when the speculated instructions do not commit and their direct effect is not visible. The recent Meltdown and Spectre attacks have shown that this behavior can be exploited to expose privileged information to an unprivileged attacker. In particular, the attack forces the speculative execution of a code gadget that will carry out the illegal read, which eventually gets squashed, but which leaves a side-channel trail that can be used by the attacker to infer the value. Several attack variations are possible, allowing arbitrary exposure of the full kernel memory to an unprivileged attacker. In this paper, we introduce a new model (SafeSpec) for supporting speculation in a way that is immune to side-channel leakage necessary for attacks such as Meltdown and Spectre. In particular, SafeSpec stores side effects of speculation in a way that is not visible to the attacker while the instructions are speculative. The speculative state is then either committed to the main CPU structures if the branch commits, or squashed if it does not, making all direct side effects of speculative code invisible. The solution must also address the possibility of a covert channel from speculative instructions to committed instructions before these instructions are committed. We show that SafeSpec prevents all three variants of Spectre and Meltdown, as well as new variants that we introduce. We also develop a cycle accurate model of modified design of an x86-64 processor and show that the performance impact is negligible. We build prototypes of the hardware support in a hardware description language to show that the additional overhead is small. We believe that SafeSpec completely closes this class of attacks, and that it is practical to implement.
Weighted Tanimoto Coefficient for 3D Molecule Structure Similarity Measurement
Venue: arXiv cs.CV 2018
Year: 2018

Similarity searching of molecular structure has been an important application in the Chemoinformatics, especially in drug discovery. Similarity searching is a common method used for identification of molecular structure. It involve three main principal component of similarity searching: structure representation; weighting scheme; and similarity coefficient. In this paper, we introduces Weighted Tanimoto Coefficient based on weighted Euclidean distance in order to investigate the effect of weight function on the result for similarity searching. The Tanimoto coefficient is one of the popular similarity coefficients used to measure the similarity between pairs of the molecule. The most of research area found that the similarity searching is based on binary or fingerprint data. Meanwhile, we used non-binary data and was set amphetamine structure as a reference or targeted structure and the rest of the dataset becomes a database structure. Throughout this study, it showed that there is definitely gives a different result between a similarity searching with and without weight.
Quasi-tight Framelets with Directionality or High Vanishing Moments Derived from Arbitrary Refinable Functions
Venue: arXiv cs.IT 2018
Year: 2018

Construction of multivariate tight framelets is known to be a challenging problem. Multivariate dual framelets with vanishing moments generalize tight framelets and are not easy to be constructed either. Compactly supported multivariate framelets with directionality or high vanishing moments are of interest and importance in both theory and applications. In this paper we introduce the notion of a quasi-tight framelet, which is a dual framelet, but behaves almost like a tight framelet. Let $ϕ\in L_2(R^d)$ be an arbitrary compactly supported $M$-refinable function such that its underlying low-pass filter satisfies the basic sum rule. We first constructively prove by a step-by-step algorithm that we can always easily derive from the arbitrary $M$-refinable function $ϕ$ a directional compactly supported quasi-tight $M$-framelet in $L_2(R^d)$ associated with a directional quasi-tight $M$-framelet filter bank, each of whose high-pass filters has only two nonzero coefficients with opposite signs. If in addition all the coefficients of its low-pass filter are nonnegative, such a quasi-tight $M$-framelet becomes a directional tight $M$-framelet in $L_2(R^d)$. Furthermore, we show by a constructive algorithm that we can always derive from the arbitrary $M$-refinable function $ϕ$ a compactly supported quasi-tight $M$-framelet in $L_2(R^d)$ with the highest possible order of vanishing moments. We shall also present a result on quasi-tight framelets whose associated high-pass filters are purely differencing filters with the highest order of vanishing moments. Several examples will be provided to illustrate our main theoretical results and algorithms in this paper.
Analysis of Search Stratagem Utilisation
Venue: arXiv cs.IR 2018
Year: 2018

In Interactive IR, researchers consider the user behaviour towards systems and search tasks in order to adapt search results and to improve the search experience of users. Analysing the users' past interactions with the system is one typical approach. In this paper, we analyse the user behaviour in retrieval sessions towards Marcia Bates' search stratagems such as Footnote Chasing, Citation Searching, Keyword Searching, Author Searching and Journal Run in a real-life academic search engine. In fact, search stratagems represent high-level search behaviour as the users go beyond simple execution of queries and investigate more of the system functionalities. We performed analyses of these five search stratagems using two datasets extracted from the social sciences search engine sowiport. A specific focus was the detection of the search phase and frequency of the usage of these stratagems. In addition, we explored the impact of these stratagems on the whole search process performance. We addressed mainly the usage patterns' observation of the stratagems, their impact on the conduct of retrieval sessions and explore whether they are used similarly in both datasets. From the observation and metrics proposed, we can conclude that the utilisation of search stratagems in real retrieval sessions leads to an improvement of the precision in terms of positive interactions. However, the difference is that Footnote Chasing, Citation Searching and Journal Run appear mostly at the end of a session while Keyword and Author Searching appear typically at the beginning. Thus, we can conclude from the log analysis that the improvement of search functionalities including personalisation and/or recommendation could be achieved by considering references, citations, and journals in the ranking process.
A Hybrid RF-VLC System for Energy Efficient Wireless Access
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, we propose a new paradigm in designing and realizing energy efficient wireless indoor access networks, namely, a hybrid system enabled by traditional RF access, such as WiFi, as well as the emerging visible light communication (VLC). VLC facilitates the great advantage of being able to jointly perform illumination and communications, and little extra power beyond illumination is required to empower communications, thus rendering wireless access with almost zero power consumption. On the other hand, when illumination is not required from the light source, the energy consumed by VLC could be more than that consumed by the RF. By capitalizing on the above properties, the proposed hybrid RF-VLC system is more energy efficient and more adaptive to the illumination conditions than the individual VLC or RF systems. To demonstrate the viability of the proposed system, we first formulate the problem of minimizing the power consumption of the hybrid RF-VLC system while satisfying the users requests and maintaining acceptable level of illumination, which is NP-complete. Therefore, we divide the problem into two subproblems. In the first subproblems, we determine the set of VLC access points (AP) that needs to be turned on to satisfy the illumination requirements. Given this set, we turn our attention to satisfying the users' requests for real-time communications, and we propose a randomized online algorithm that, against an oblivious adversary, achieves a competitive ratio of $\log(N)\log(M)$ with probability of success $1 - \frac{1}{N}$, where $N$ is the number of users and $M$ is the number of VLC and RF APs. We also show that the best online algorithm to solve this problem can achieve a competitive ratio of $\log(M)$. Simulation results further demonstrate the advantages of the hybrid system.
Identifying the Fake Base Station: A Location Based Approach
Venue: arXiv cs.IT 2018
Year: 2018

Fake base station (FBS) attack is a great security challenge to wireless user equipment (UE). During the cell selection stage, the UE receives multiple synchronization signals (SSs) from multiple nearby base stations (BSs), and then synchronizes itself with the strongest SS. A FBS also can transmit a SS with sufficient power to confuse the UE, which makes the UE connect to the FBS, and may lead to the leakage of private information. In this letter, countermeasure to the FBS attack by utilizing the location information is investigated. Two location awareness based FBS-resistance schemes are proposed by checking the received signal strength according to the position of the UE and a legitimate BS map. The successful cheating rate (SCR) definded as the probability that the UE will connect to the FBS is investigated. Numeric results show that with the two proposed schemes, the SCR can be greatly reduced especially when the transmit power of the FBS is large. Beyond that, a cooperation aided method is further proposed to improve the performance, and we show that the cooperation aided method can further suppress the SCR when the signal strength from the FBS is similar to that from the legitimate BS.
Stabilization with a Specified External Gain for Linear MIMO Systems and Its Applications to Control of Networked Systems
Venue: arXiv cs.SY 2018
Year: 2018

This paper studies a stabilization problem for linear MIMO systems subject to external perturbation that further requires the closed-loop system render a specified gain from the external perturbation to the output. The problem arises from control of networked systems, in particular, robust output synchronization of heterogeneous linear MIMO multi-agent systems via output feedback/communication. We propose a new approach that converts a class of MIMO systems into a normal form via repeated singular value decomposition and prove that a stabilization controller with a specified external gain can be explicitly constructed for the normal form.Two scenarios with static state feedback and dynamic output feedback are investigated. By integrating the reference model and internal model techniques, the robust output synchronization problem for MIMO multi-agent systems is converted into a stabilization problem with a specified externalgain and solved by the developed approach.
Morphological and Language-Agnostic Word Segmentation for NMT
Venue: arXiv cs.CL 2018
Year: 2018

The state of the art of handling rich morphology in neural machine translation (NMT) is to break word forms into subword units, so that the overall vocabulary size of these units fits the practical limits given by the NMT model and GPU memory capacity. In this paper, we compare two common but linguistically uninformed methods of subword construction (BPE and STE, the method implemented in Tensor2Tensor toolkit) and two linguistically-motivated methods: Morfessor and one novel method, based on a derivational dictionary. Our experiments with German-to-Czech translation, both morphologically rich, document that so far, the non-motivated methods perform better. Furthermore, we iden- tify a critical difference between BPE and STE and show a simple pre- processing step for BPE that considerably increases translation quality as evaluated by automatic measures.
Humor Detection in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System
Venue: arXiv cs.CL 2018
Year: 2018

The tremendous amount of user generated data through social networking sites led to the gaining popularity of automatic text classification in the field of computational linguistics over the past decade. Within this domain, one problem that has drawn the attention of many researchers is automatic humor detection in texts. In depth semantic understanding of the text is required to detect humor which makes the problem difficult to automate. With increase in the number of social media users, many multilingual speakers often interchange between languages while posting on social media which is called code-mixing. It introduces some challenges in the field of linguistic analysis of social media content (Barman et al., 2014), like spelling variations and non-grammatical structures in a sentence. Past researches include detecting puns in texts (Kao et al., 2016) and humor in one-lines (Mihalcea et al., 2010) in a single language, but with the tremendous amount of code-mixed data available online, there is a need to develop techniques which detects humor in code-mixed tweets. In this paper, we analyze the task of humor detection in texts and describe a freely available corpus containing English-Hindi code-mixed tweets annotated with humorous(H) or non-humorous(N) tags. We also tagged the words in the tweets with Language tags (English/Hindi/Others). Moreover, we describe the experiments carried out on the corpus and provide a baseline classification system which distinguishes between humorous and non-humorous texts.
Corner-Sharing Tetrahedra for Modeling Micro-Structure
Venue: arXiv cs.CG 2018
Year: 2018

State-of-the-art representations of volumetric multi-scale shape and structure can be classified into three broad categories: continuous, continuous-from-discrete, and discrete representations. We propose modeling micro-structure with a class of discrete Corner-Sharing Tetrahedra (CoSTs). CoSTs can represent bar-joint, tensegrity, line-incidence, and similar constraint systems that capture local physical constraints and global multi-scale properties for design and analysis. The paper develops a palette of simple geometry processing operations on CoSTs including graph manipulation, hierarchical refinement, randomization, and generating associated continuous representations.
Cardiac Motion Scoring with Segment- and Subject-level Non-Local Modeling
Venue: arXiv cs.CV 2018
Year: 2018

Motion scoring of cardiac myocardium is of paramount importance for early detection and diagnosis of various cardiac disease. It aims at identifying regional wall motions into one of the four types including normal, hypokinetic, akinetic, and dyskinetic, and is extremely challenging due to the complex myocardium deformation and subtle inter-class difference of motion patterns. All existing work on automated motion analysis are focused on binary abnormality detection to avoid the much more demanding motion scoring, which is urgently required in real clinical practice yet has never been investigated before. In this work, we propose Cardiac-MOS, the first powerful method for cardiac motion scoring from cardiac MR sequences based on deep convolution neural network. Due to the locality of convolution, the relationship between distant non-local responses of the feature map cannot be explored, which is closely related to motion difference between segments. In Cardiac-MOS, such non-local relationship is modeled with non-local neural network within each segment and across all segments of one subject, i.e., segment- and subject-level non-local modeling, and lead to obvious performance improvement. Besides, Cardiac-MOS can effectively extract motion information from MR sequences of various lengths by interpolating the convolution kernel along the temporal dimension, therefore can be applied to MR sequences of multiple sources. Experiments on 1440 myocardium segments of 90 subjects from short axis MR sequences of multiple lengths prove that Cardiac-MOS achieves reliable performance, with correlation of 0.926 for motion score index estimation and accuracy of 77.4\% for motion scoring. Cardiac-MOS also outperforms all existing work for binary abnormality detection. As the first automatic motion scoring solution, Cardiac-MOS demonstrates great potential in future clinical application.
Inducing information stability and applications thereof to obtaining information theoretic necessary conditions directly from operational requirements
Venue: arXiv cs.IT 2018
Year: 2018

This work constructs a discrete random variable that, when conditioned upon, ensures information stability of quasi-images. Using this construction, a new methodology is derived to obtain information theoretic necessary conditions directly from operational requirements. In particular, this methodology is used to derive new necessary conditions for keyed authentication over discrete memoryless channels and to establish the capacity region of the wiretap channel, subject to finite leakage and finite error, under two different secrecy metrics. These examples establish the usefulness of the proposed methodology.
Learning Human Optical Flow
Venue: arXiv cs.CV 2018
Year: 2018

The optical flow of humans is well known to be useful for the analysis of human action. Given this, we devise an optical flow algorithm specifically for human motion and show that it is superior to generic flow methods. Designing a method by hand is impractical, so we develop a new training database of image sequences with ground truth optical flow. For this we use a 3D model of the human body and motion capture data to synthesize realistic flow fields. We then train a convolutional neural network to estimate human flow fields from pairs of images. Since many applications in human motion analysis depend on speed, and we anticipate mobile applications, we base our method on SpyNet with several modifications. We demonstrate that our trained network is more accurate than a wide range of top methods on held-out test data and that it generalizes well to real image sequences. When combined with a person detector/tracker, the approach provides a full solution to the problem of 2D human flow estimation. Both the code and the dataset are available for research.
Action Learning for 3D Point Cloud Based Organ Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

We propose a novel point cloud based 3D organ segmentation pipeline utilizing deep Q-learning. In order to preserve shape properties, the learning process is guided using a statistical shape model. The trained agent directly predicts piece-wise linear transformations for all vertices in each iteration. This mapping between the ideal transformation for an object outline estimation is learned based on image features. To this end, we introduce aperture features that extract gray values by sampling the 3D volume within the cone centered around the associated vertex and its normal vector. Our approach is also capable of estimating a hierarchical pyramid of non rigid deformations for multi-resolution meshes. In the application phase, we use a marginal approach to gradually estimate affine as well as non-rigid transformations. We performed extensive evaluations to highlight the robust performance of our approach on a variety of challenge data as well as clinical data. Additionally, our method has a run time ranging from 0.3 to 2.7 seconds to segment each organ. In addition, we show that the proposed method can be applied to different organs, X-ray based modalities, and scanning protocols without the need of transfer learning. As we learn actions, even unseen reference meshes can be processed as demonstrated in an example with the Visible Human. From this we conclude that our method is robust, and we believe that our method can be successfully applied to many more applications, in particular, in the interventional imaging space.
Age and Gender Classification From Ear Images
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we present a detailed analysis on extracting soft biometric traits, age and gender, from ear images. Although there have been a few previous work on gender classification using ear images, to the best of our knowledge, this study is the first work on age classification from ear images. In the study, we have utilized both geometric features and appearance-based features for ear representation. The utilized geometric features are based on eight anthropometric landmarks and consist of 14 distance measurements and two area calculations. The appearance-based methods employ deep convolutional neural networks for representation and classification. The well-known convolutional neural network models, namely, AlexNet, VGG-16, GoogLeNet, and SqueezeNet have been adopted for the study. They have been fine-tuned on a large-scale ear dataset that has been built from the profile and close-to-profile face images in the Multi-PIE face dataset. This way, we have performed a domain adaptation. The updated models have been fine-tuned once more time on the small-scale target ear dataset, which contains only around 270 ear images for training. According to the experimental results, appearance-based methods have been found to be superior to the methods based on geometric features. We have achieved 94\% accuracy for gender classification, whereas 52\% accuracy has been obtained for age classification. These results indicate that ear images provide useful cues for age and gender classification, however, further work is required for age estimation.
Adaptive Incentive Design
Venue: arXiv cs.GT 2018
Year: 2018

We apply control theoretic and optimization techniques to adaptively design incentives. In particular, we consider the problem of a planner with an objective that depends on data from strategic decision makers. The planner does not know the process by which the strategic agents make decisions. Under the assumption that the agents are utility maximizers, we model their interactions as a non-cooperative game and utilize the Nash equilibrium concept as well as myopic update rules to model the selection of their decision. By parameterizing the agents' utility functions and the incentives offered, we develop an algorithm that the planner can employ to learn the agents' decision-making processes while simultaneously designing incentives to change their response to a more desirable response from the planner's perspective. We provide convergence results for this algorithm both in the noise-free and noisy cases and present illustrative examples.