Query: VR-STEP: Walking-in-Place using Inertial Sensing for Hands Free Navigation in Mobile VR Environments

Relevant passages:
Belt: An Unobtrusive Touch Input Device for Head-worn Displays <=SEP=> Belt is a novel unobtrusive input device for wearable displays that incorporates a touch surface encircling the user's hip. The wide input space is leveraged for a horizontal spatial mapping of quickly accessible information and applications. We discuss social implications and interaction capabilities for unobtrusive touch input and present our hardware implementation and a set of applications that benefit from the quick access time. In a qualitative user study with 14 participants we found out that for short interactions (2-4 seconds), most of the surface area is considered as appropriate input space, while for longer interactions (up to 10 seconds), the front areas above the trouser pockets are preferred.

Redirected Walking in Place <=SEP=> This paper describes a method for allowing people to virtually move around a CAVETM without ever having to turn to face the missing back wall. We describe the method, and report a pilot study of 28 participants, half of whom moved through the virtual world using a hand-held controller, and the other half used the new technique called ‘Redirected Walking in Place’ (RWP). The results show that the current instantiation of the RWP technique does not result in a lower frequency of looking towards the missing wall. However, the results also show that the sense of presence in the virtual environment is significantly and negatively correlated with the amount that the back wall is seen. There is evidence that RWP does reduce the chance of seeing the blank wall for some participants. The increased sense of presence through never having to face the blank wall, and the results of this pilot study show the RWP has promise and merits further development.

Walking > Walking-in-Place > Flying, in Virtual Environments <=SEP=> A study by Slater, et al., [1995] indicated that naive subjects in an immersive virtual environment experience a higher subjective sense of presence when they locomote by walking-in-place (virtual walking) than when they push-button-fly (along the floor plane). We replicated their study, adding real walking as a third condition. Our study confirmed their findings. We also found that real walking is significantly better than both virtual walking and flying in ease (simplicity, straightforwardness, naturalness) as a mode of locomotion. The greatest difference in subjective presence was between flyers and both kinds of walkers. In addition, subjective presence was higher for real walkers than virtual walkers, but the difference was statistically significant only in some models. Follow-on studies show virtual walking can be substantially improved by detecting footfalls with a head accelerometer. As in the Slater study, subjective presence significantly correlated with subjects’ degree of association with their virtual bodies (avatars). This, our strongest statistical result, suggests that substantial potential presence gains can be had from tracking all limbs and customizing avatar appearance. An unexpected by-product was that real walking through our enhanced version of Slater’s visual-cliff virtual environment (Figure 1) yielded a strikingly compelling virtual experience—the strongest we and most of our visitors have yet experienced. The most needed system improvement is the substitution of wireless technology for all links to the user. CR

On temporal-spatial realism in the virtual reality environment <=SEP=> The Polhemus Isotrak is often used as an orientation and position tracking device in virtual reality environments. When it is used to dynamically determine the user’s viewpoint and line of sight ( e.g. in the case of a head mounted display) the noise and delay in its measurement data causes temporal-spatial distortion, perceived by the user as jittering of images and lag between head movement and visual feedback. To tackle this problem, we first examined the major cause of the distortion, and found that the lag felt by the user is mainly due to the delay in orientation data, and the jittering of images is caused mostly by the noise in position data. Based on these observations, a predictive Kalman filter was designed to compensate for the delay in orientation data, and an anisotropic low pass filter was devised to reduce the noise in position data. The effectiveness and limitations of both approaches were then studied, and the results shown to be satisfactory.

Virtual Reality on a WIM: Interactive Worlds in Miniature <=SEP=> This paper explores a user interface technique which augments an immersive head tracked display with a hand-held miniature copy of the virtual environment. We call this interface technique the Worlds in Miniature (WIM) metaphor. In addition to the first-person perspective offered by a virtual reality system, a World in Miniature offers a second dynamic viewport onto the virtual environment. Objects may be directly manipulated either through the immersive viewport or through the three-dimensional viewport offered by the WIM. In addition to describing object manipulation, this paper explores ways in which Worlds in Miniature can act as a single unifying metaphor for such application independent interaction techniques as object selection, navigation, path planning, and visualization. The WIM metaphor offers multiple points of view and multiple scales at which the user can operate, without requiring explicit modes or commands. Informal user observation indicates that users adapt to the Worlds in Miniature metaphor quickly and that physical props are helpful in manipulating the WIM and other objects in the environment.

Recognizing Textual Entailment via Multi-task Knowledge Assisted LSTM <=SEP=> Recognizing Textual Entailment (RTE) plays an important role in NLP applications like question answering, information retrieval, etc. Most previous works either use classifiers to employ elaborately designed features and lexical similarity or bring distant supervision and reasoning technique into RTE task. However, these approaches are hard to generalize due to the complexity of feature engineering and are prone to cascading errors and data sparsity problems. For alleviating the above problems, some work use LSTM-based recurrent neural network with word-by-word attention to recognize textual entailment. Nevertheless, these work did not make full use of knowledge base (KB) to help reasoning. In this paper, we propose a deep neural network architecture called Multi-task Knowledge Assisted LSTM (MKAL), which aims to conduct implicit inference with the assistant of KB and use predicate-topredicate attention to detect the entailment between predicates. In addition, our model applies a multi-task architecture to further improve the performance. The experimental results show that our proposed method achieves a competitive result compared to the previous work.

Leaf segmentation in plant phenotyping: a collation study <=SEP=> Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first-of-its-kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping workshop in 2014. Four methods are presented: three segment leaves by processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be accomplished with satisfactory accuracy ( $$>$$ > 90 % Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Additionally, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (online at http://www.plant-phenotyping.org/datasets ) to support future challenges beyond segmentation within this application domain.

Automatic inspection of pavement cracking distress <=SEP=> 15. Supplementary Notes Project performed in cooperation with the Texas Department of Transportation and the Federal Highway Administration. Project Title: Implementation of Automated Pavement Distress Rating Systems 16. Abstract This paper presents the image-processing algorithm customized for high-speed, real-time inspection of pavement cracking. In the algorithm, a pavement image is divided into grid cells of 8 x 8 pixels, and each cell is classified as a non-crack or crack cell using the grayscale information of the border pixels. Whether a crack cell can be regarded as a basic element (or seed) depends on its contrast to the neighboring cells. A number of crack seeds can be called a crack cluster if they fall on a linear string. A crack cluster corresponds to a dark strip in the original image that may or may not be a section of a real crack. Additional conditions to verify a crack cluster include the requirements in the contrast, width, and length of the strip. If verified crack clusters are oriented in similar directions, they will be joined to become one crack. Because many operations are performed on crack seeds rather than on the original image, crack detection can be executed simultaneously when the frame grabber is forming a new image, which permits a real-time, online pavement survey. The trial test results show good repeatability and accuracy when multiple surveys were conducted in different driving conditions.

MAPS: A Multi Aspect Personalized POI Recommender System <=SEP=> The evolution of the World Wide Web (WWW) and the smart-phone technologies have played a key role in the revolution of our daily life. The location-based social networks (LBSN) have emerged and facilitated the users to share the check-in information and multimedia contents. The Point of Interest (POI) recommendation system uses the check-in information to predict the most potential check-in locations. The different aspects of the check-in information, for instance, the geographical distance, the category, and the temporal popularity of a POI; and the temporal check-in trends, and the social (friendship) information of a user play a crucial role in an efficient recommendation. In this paper, we propose a fused recommendation model termed MAPS (Multi Aspect Personalized POI Recommender System) which will be the first in our knowledge to fuse the categorical, the temporal, the social and the spatial aspects in a single model. The major contribution of this paper are: (i) it realizes the problem as a graph of location nodes with constraints on the category and the distance aspects (i.e. the edge between two locations is constrained by a threshold distance and the category of the locations), (ii) it proposes a multi-aspect fused POI recommendation model, and (iii) it extensively evaluates the model with two real-world data sets.

A Double-Sided Parallel-Strip Line Push–Pull Oscillator <=SEP=> A novel double-sided parallel-strip line (DSPSL) push-pull oscillator using two identical sub-oscillators on the opposite sides of a dielectric substrate is proposed. The two sub-oscillators, sharing a common DSPSL resonator and common ground in the middle of the substrate, generate out-of-phase fundamental signals and in-phase second harmonics. At the common DSPSL output, the second harmonics are cancelled out while the fundamental signals are well combined. By this design, an additional combiner at the output, as required by the conventional push-pull circuits, is not needed, which greatly reduces the circuit size and simplifies the design procedures of the proposed push-pull oscillator.

Use of Non-conductive Film (NCF) with Nano-Sized Filler Particles for Solder Interconnect: Research and Development on NCF Material and Process Characterization <=SEP=> As three-dimensional Through-Silicon Via (3D-TSV) packaging is emerging in the semiconductor market to integrate multiple functions in a system for further miniaturization, thermal compression bonding (TCB), which stacks multiple bare chips on top of each other with Cu pillar bumps with solder cap, has become an indispensable new packaging technology. The novel non-conductive film (NCF) described in this paper is an epoxy-type thermosetting material in film form, with lower density of Nano-sized silica filler particles (average particle size is 100 Nano meters). Advantages of this NCF material with Nano-sized fillers include: transparency of the NCF material so that the TCB bonder's image recognition system can easily identify fiducial marks on the chip, ability of the Nano-filler to flow out with the NCF resin during thermal compression bonding, to mitigate filler entrapment between solder joints, which is critical to ensure reliable solder connections, compatibility with fine pitch applications with extremely narrow chip-to-chip and chip-to-substrate gaps to form void-free underfill. Instead of the previous processes of die attach, fluxing, traditional oven mass re-flow, flux cleaning, and capillary underfill (CUF), the current process involves the use of NCF & TCB. The NCF is applied on a wafer in a film form by lamination, and then a wafer is diced with the pre-laminated NCF in pieces. Then they are joined by TCB, with typical parameters of 250°C for 10 seconds with 80N (solder cap on 15μm diameter Cu pillar, 40μm pitch, 1000 bumps). NCF heated by TCB is quickly liquidized to lower its viscosity after a few seconds. Other advantages of NCF is that it has a fluxing function included in the material, which eliminates the need for separate flux apply and flux clean steps, which simplifies process and costs. Also, NCF can control extrusion along the package's edge line since NCF is a half-cured B-stage material which has some mechanical rigidity during handling and lamination. Thus NCF Thermal Compression Flip Chip Bonding method is the vital solution to ensure good reliability with mass productivity for next generation's packaging. The characterization of NCF material and the importance of controlling viscosity and elastic modulus during thermal-compression will be analyzed and discussed in this paper. Process and reliability data on test vehicles will be shown.

Online continuous stereo extrinsic parameter estimation <=SEP=> Stereo visual odometry and dense scene reconstruction depend critically on accurate calibration of the extrinsic (relative) stereo camera poses. We present an algorithm for continuous, online stereo extrinsic re-calibration operating only on sparse stereo correspondences on a per-frame basis. We obtain the 5 degree of freedom extrinsic pose for each frame, with a fixed baseline, making it possible to model time-dependent variations. The initial extrinsic estimates are found by minimizing epipolar errors, and are refined via a Kalman Filter (KF). Observation covariances are derived from the Cramer-Rao lower bound of the solution uncertainty. The algorithm operates at frame rate with unoptimized Matlab code with over 1000 correspondences per frame. We validate its performance using a variety of real stereo datasets and simulations.

Asymmetry-Aware Link Quality Services in Wireless Sensor Networks <=SEP=> Recent study in wireless sensor networks (WSN) has found that the irregular link quality is a common phenomenon. The irregular link quality, especially link asymmetry, has significant impacts on the design of WSN protocols, such as MAC protocols, neighborhood and topology discovery protocols, and routing protocols. In this paper, we propose asymmetryaware link quality services, including the neighborhood link quality service (NLQS) and the link relay service (LRS), to provide the timeliness link quality information of neighbors, and build a relay framework to alleviate effects of the link asymmetry. To demonstrate proposed link quality services, we design and implement two example applications, the shortest hops routing tree (SHRT) and the best path reliability routing tree (BRRT), in the TinyOS platform. To evaluate proposed link quality services, we have conducted both static analysis and simulation through the TOSSIM simulator, in terms of four performance metrics. We found that the performance of two example applications was improved substantially. More than 40% of nodes identify more outbound neighbors and the percentage of increased outbound neighbors is between 14% and 100%. In SHRT, more than 15% of nodes reduce hops of the routing tree and the percentage of reduced hops is between 14% and 100%. In BRRT, more than 16% of nodes improve the path reliability of the routing tree and the percentage of the improved path reliability is between 2% to 50%.

Human wayfinding in information networks <=SEP=> Navigating information spaces is an essential part of our everyday lives, and in order to design efficient and user-friendly information systems, it is important to understand how humans navigate and find the information they are looking for. We perform a large-scale study of human wayfinding, in which, given a network of links between the concepts of Wikipedia, people play a game of finding a short path from a given start to a given target concept by following hyperlinks. What distinguishes our setup from other studies of human Web-browsing behavior is that in our case people navigate a graph of connections between concepts, and that the exact goal of the navigation is known ahead of time. We study more than 30,000 goal-directed human search paths and identify strategies people use when navigating information spaces. We find that human wayfinding, while mostly very efficient, differs from shortest paths in characteristic ways. Most subjects navigate through high-degree hubs in the early phase, while their search is guided by content features thereafter. We also observe a trade-off between simplicity and efficiency: conceptually simple solutions are more common but tend to be less efficient than more complex ones. Finally, we consider the task of predicting the target a user is trying to reach. We design a model and an efficient learning algorithm. Such predictive models of human wayfinding can be applied in intelligent browsing interfaces.

Algorithms for multi-armed bandit problems <=SEP=> The stochastic multi-armed bandit problem is an important model for studying the explorationexploitation tradeoff in reinforcement learning. Although many algorithms for the problem are well-understood theoretically, empirical confirmation of their effectiveness is generally scarce. This paper presents a thorough empirical study of the most popular multi-armed bandit algorithms. Three important observations can be made from our results. Firstly, simple heuristics such as -greedy and Boltzmann exploration outperform theoretically sound algorithms on most settings by a significant margin. Secondly, the performance of most algorithms varies dramatically with the parameters of the bandit problem. Our study identifies for each algorithm the settings where it performs well, and the settings where it performs poorly. These properties are not described by current theory, even though they can be exploited in practice in the design of heuristics. Thirdly, the algorithms’ performance relative each to other is affected only by the number of bandit arms and the variance of the rewards. This finding may guide the design of subsequent empirical evaluations. In the second part of the paper, we turn our attention to an important area of application of bandit algorithms: clinical trials. Although the design of clinical trials has been one of the principal practical problems motivating research on multi-armed bandits, bandit algorithms have never been evaluated as potential treatment allocation strategies. Using data from a real study, we simulate the outcome that a 2001-2002 clinical trial would have had if bandit algorithms had been used to allocate patients to treatments. We find that an adaptive trial would have successfully treated at least 50% more patients, while significantly reducing the number of adverse effects and increasing patient retention. At the end of the trial, the best treatment could have still been identified with a high level of statistical confidence. Our findings demonstrate that bandit algorithms are attractive alternatives to current adaptive treatment allocation strategies.

Mortality in British vegetarians: review and preliminary results from EPIC-Oxford. <=SEP=> BACKGROUND
Three prospective studies have examined the mortality of vegetarians in Britain.


OBJECTIVE
We describe these 3 studies and present preliminary results on mortality from the European Prospective Investigation into Cancer and Nutrition-Oxford (EPIC-Oxford).


DESIGN
The Health Food Shoppers Study and the Oxford Vegetarian Study were established in the 1970s and 1980s, respectively; each included about 11 000 subjects and used a short questionnaire on diet and lifestyle. EPIC-Oxford was established in the 1990s and includes about 56 000 subjects who completed detailed food frequency questionnaires. Mortality in all 3 studies was followed though the National Health Service Central Register.


RESULTS
Overall, the death rates of all the subjects in all 3 studies are much lower than average for the United Kingdom. Standardized mortality ratios (95% CIs) for all subjects were 59% (57%, 61%) in the Health Food Shoppers Study, 52% (49%, 56%) in the Oxford Vegetarian Study, and 39% (37%, 42%) in EPIC-Oxford. Comparing vegetarians with nonvegetarians within each cohort, the death rate ratios (DRRs), adjusted for age, sex and smoking, were 1.03 (0.95, 1.13) in the Health Food Shoppers Study, 1.01 (0.89, 1.14) in the Oxford Vegetarian Study, and 1.05 (0.86, 1.27) in EPIC-Oxford. DRRs for ischemic heart disease in vegetarians compared with nonvegetarians were 0.85 (0.71, 1.01) in the Health Food Shoppers Study, 0.86 (0.67, 1.12) in the Oxford Vegetarian Study, and 0.75 (0.41, 1.37) in EPIC-Oxford.


CONCLUSIONS
The mortality of both the vegetarians and the nonvegetarians in these studies is low compared with national rates. Within the studies, mortality for major causes of death was not significantly different between vegetarians and nonvegetarians, but the nonsignificant reduction in mortality from ischemic heart disease among vegetarians was compatible with the significant reduction previously reported in a pooled analysis of mortality in Western vegetarians.

Cloud-assisted industrial cyber-physical systems: An insight <=SEP=> The development of industrialization and information communication technology (ICT) has deeply changed our way of life. In particular, with the emerging theory of ‘‘Industry 4.0”, the integration of cloud technologies and industrial cyber-physical systems (ICPS) becomes increasingly important, as this will greatly improve the manufacturing chain and business services. In this paper, we first describe the development and character of ICPS. ICPS will inevitably play an important role in manufacturing, sales, and logistics. With the support of the cloud, ICPS development will impact value creation, business models, downstream services, and work organization. Then, we present a service-oriented ICPS model. With the support of the cloud, infrastructure platform and service application, ICPS will promote the manufacturing efficiency, increase quality of production, enable a sustainable industrial system and more environmentally friendly businesses. Thirdly, we focus on some key enabling technologies, which are critical in supporting smart factories. These key enabling technologies will also help companies to realize high quality, high output, and low cost. Finally, we talk about some challenges of ICPS implementation and the future work. 2015 Elsevier B.V. All rights reserved.

Applying Genre-Based Ontologies to Enterprise Architecture <=SEP=> This paper elaborates the approach of using ontologies as a conceptual base for enterprise architecture (EA) descriptions. The method focuses on recognising and modelling business critical information concepts, their content, and semantics used to operate the business. Communication genres and open and semi-structured information need interviews are used as a domain analysis method. Ontologies aim to explicate the results of domain analysis and to provide a common reference model for Business Information Architecture (BIA) descriptions. The results are generalised to model further aspects of EA.

Lessons Learned: The Complexity of Accurate Identification of in-Text Citations <=SEP=> The importance of citations is widely recognized by the scientific community. Citations are being used in making a number of vital decisions such as calculating impact factor of journals, calculating impact of a researcher (H-Index), ranking universities and research organizations. Furthermore, citation indexes, along with other criteria, employ citation counts to retrieve and rank relevant research papers. However, citing patterns and in-text citation frequency are not used for such important decisions. The identification of in-text citation from a scientific document is an important problem. However, identification of in-text citation is a tough ask due to the ambiguity between citation tag and content. This research focuses on in-text citation analysis and makes the following specific contributions such as: Provides detailed in-text citation analysis on 16,000 citations of an online journal, reports different pattern of citations-tags and its in-text citations and highlights the problems (mathematical ambiguities, wrong allotments, commonality in content and string variation) in identifying in-text citations from scientific documents. The accurate identification of in-text citations will help information retrieval systems, digital libraries and citation indexes.

Joint Text Embedding for Personalized Content-based Recommendation <=SEP=> Learning a good representation of text is key to many recommendation applications. Examples include news recommendation where texts to be recommended are constantly published everyday. However, most existing recommendation techniques, such as matrix factorization based methods, mainly rely on interaction histories to learn representations of items. While latent factors of items can be learned eectively from user interaction data, in many cases, such data is not available, especially for newly emerged items. In this work, we aim to address the problem of personalized recommendation for completely new items with text information available. We cast the problem as a personalized text ranking problem and propose a general framework that combines text embedding with personalized recommendation. Users and textual content are embedded into latent feature space. e text embedding function can be learned end-to-end by predicting user interactions with items. To alleviate sparsity in interaction data, and leverage large amount of text data with lile or no user interactions, we further propose a joint text embedding model that incorporates unsupervised text embedding with a combination module. Experimental results show that our model can signicantly improve the eectiveness of recommendation systems on real-world datasets.

Transimpedance Amplifier ( TIA ) Design for 400 Gb / s Optical Fiber Communications <=SEP=> (ABSTRACT) Analogcircuit/IC design for high speed optical fiber communication is a fairly new research area in Dr. Ha's group. In the first project sponsored by ETRI (Electronics and Telecommunication Research Institute) we started to design the building blocks of receiver for next generation 400 Gb/s optical fiber communication. In this thesis research a transceiver architecture based on 4x100 Gb/s parallel communication is proposed. As part of the receiver, a transimpedance amplifier for 100 Gb/s optical communication is designed, analyzed and simulated. Simulation results demonstrate the excellent feasibility of proposed architecture. always dominated the high speed optical transceiver design because of their inherent properties of high mobility and low noise. But they are power hungry and bulky in size which made them less attractive for highly integrated circuit design. On the contrary, CMOS technology always drew attraction because of low cost, low power dissipation and high level of integration facility. But their notorious parasitic characteristic and inferior noise performance makes high speed transceiver design very challenging. The emergence of nano-scale CMOS offer highly scaled feature sized transistors with transition frequencies exceeding 200 GHz and can improve optical receiver performance significantly. Increasing bandwidth to meet the target data rate is the most challenging task of TIA design especially in CMOS technology. Several CMOS TIA architectures have been published recently [6]-[11] for 40 Gb/s data rate having bandwidth no more than 40 GHz. In contrast to existing works, the goal of this research is to step further and design a single channel stand-alone iii TIA compatible in serial 100 Gb/s data rate with enhanced bandwidth and optimized transimpedance gain, input referred noise and group delay variation. A 100 Gb/s transimpedance amplifier (TIA) for optical receiver front end is designed in this work. To achieve wide bandwidth and low group delay variation a differential TIA with active feedback network is proposed. Proposed design also combines regulated cascode front end, peaking inductors and capacitive degeneration to have wide band response. Simulation results show 70 GHz bandwidth, 42 dBΩ transimpedance gain and 2.8 ps of group delay variation for proposed architecture. Input referred noise current density is 26 pA/√ while the total power dissipation from 1.2V supply is 24mW. Performance of the proposed TIA is compared with other existing TIAs, and the proposed TIA shows significant improvement in bandwidth and group delay variation compared to other existing TIA architectures. iv To my parents v Acknowledgements First …

Stronger Baselines for Trustable Results in Neural Machine Translation <=SEP=> Interest in neural machine translation has grown rapidly as its effectiveness has been demonstrated across language and data scenarios. New research regularly introduces architectural and algorithmic improvements that lead to significant gains over “vanilla” NMT implementations. However, these new techniques are rarely evaluated in the context of previously published techniques, specifically those that are widely used in state-of-theart production and shared-task systems. As a result, it is often difficult to determine whether improvements from research will carry over to systems deployed for real-world use. In this work, we recommend three specific methods that are relatively easy to implement and result in much stronger experimental systems. Beyond reporting significantly higher BLEU scores, we conduct an in-depth analysis of where improvements originate and what inherent weaknesses of basic NMT models are being addressed. We then compare the relative gains afforded by several other techniques proposed in the literature when starting with vanilla systems versus our stronger baselines, showing that experimental conclusions may change depending on the baseline chosen. This indicates that choosing a strong baseline is crucial for reporting reliable experimental results.

Gadge me if you can: secure and efficient ad-hoc instruction-level randomization for x86 and ARM <=SEP=> Code reuse attacks such as return-oriented programming are one of the most powerful threats to contemporary software. ASLR was introduced to impede these attacks by dispersing shared libraries and the executable in memory. However, in practice its entropy is rather low and, more importantly, the leakage of a single address reveals the position of a whole library in memory. The recent mitigation literature followed the route of randomization, applied it at different stages such as source code or the executable binary. However, the code segments still stay in one block. In contrast to previous work, our randomization solution, called Xifer, (1) disperses all code (executable and libraries) across the whole address space, (2) re-randomizes the address space for each run, (3) is compatible to code signing, and (4) does neither require offline static analysis nor source-code. Our prototype implementation supports the Linux ELF file format and covers both mainstream processor architectures x86 and ARM. Our evaluation demonstrates that Xifer performs efficiently at load- and during run-time (1.2% overhead).

Correlated Topic Models <=SEP=> Topic models, such as latent Dirichlet allocation (LDA), can be useful tools for the statistical analysis of document collections and other discrete data. The LDA model assumes that the words of each document arise from a mixture of topics, each of which is a distribution over the vocabulary. A limitation of LDA is the inability to model topic correlation even though, for example, a document about genetics is more likely to also be about disease than x-ray astronomy. This limitation stems from the use of the Dirichlet distribution to model the variability among the topic proportions. In this paper we develop the correlated topic model (CTM), where the topic proportions exhibit correlation via the logistic normal distribution [1]. We derive a mean-field variational inference algorithm for approximate posterior inference in this model, which is complicated by the fact that the logistic normal is not conjugate to the multinomial. The CTM gives a better fit than LDA on a collection of OCRed articles from the journal Science. Furthermore, the CTM provides a natural way of visualizing and exploring this and other unstructured data sets.

Multi-task Gaussian Process Prediction <=SEP=> In this paper we investigate multi-task learning in the context of Gaussian Processes (GP). We propose a model that learns a shared covariance function on input-dependent features and a “free-form” covariance matrix over tasks. This allows for good flexibility when modelling inter-task dependencies while avoiding the need for large amounts of data for training. We show that under the assumption of noise-free observations and a block design, predictions for a given task only depend on its target values and therefore a cancellation of inter-task transfer occurs. We evaluate the benefits of our model on two practical applications: a compiler performance prediction problem and an exam score prediction task. Additionally, we make use of GP approximations and properties of our model in order to provide scalability to large data sets.

Recognizing Human Activities from Raw Accelerometer Data Using Deep Neural Networks <=SEP=> Activity recognition from wearable sensor data has been researched for many years. Previous works usually extracted features manually, which were hand-designed by the researchers, and then were fed into the classifiers as the inputs. Due to the blindness of manually extracted features, it was hard to choose suitable features for the specific classification task. Besides, this heuristic method for feature extraction could not generalize across different application domains, because different application domains needed to extract different features for classification. There was also work that used auto-encoders to learn features automatically and then fed the features into the K-nearest neighbor classifier. However, these features were learned in an unsupervised manner without using the information of the labels, thus might not be related to the specific classification task. In this paper, we recommend deep neural networks (DNNs) for activity recognition, which can automatically learn suitable features. DNNs overcome the blindness of hand-designed features and make use of the precious label information to improve activity recognition performance. We did experiments on three publicly available datasets for activity recognition and compared deep neural networks with traditional methods, including those that extracted features manually and auto-encoders followed by a K-nearest neighbor classifier. The results showed that deep neural networks could generalize across different application domains and got higher accuracy than traditional methods.

Sketch Recognition by Ensemble Matching of Structured Features <=SEP=> Sketch recognition aims to automatically classify human hand sketches of objects into known categories. This has become increasingly a desirable capability due to recent advances in human computer interaction on portable devices. The problem is nontrivial because of the sparse and abstract nature of hand drawings as compared to photographic images of objects, compounded by a highly variable degree of details in human sketches. To this end, we present a method for the representation and matching of sketches by exploiting not only local features but also global structures of sketches, through a star graph based ensemble matching strategy. Different local feature representations were evaluated using the star graph model to demonstrate the effectiveness of the ensemble matching of structured features. We further show that by encapsulating holistic structure matching and learned bag-of-features models into a single framework, notable recognition performance improvement over the state-of-the-art can be observed. Extensive comparative experiments were carried out using the currently largest sketch dataset released by Eitz et al. [15], with over 20,000 sketches of 250 object categories generated by AMT (Amazon Mechanical Turk) crowd-sourcing.

Measuring complexity using FuzzyEn, ApEn, and SampEn. <=SEP=> This paper compares three related measures of complexity, ApEn, SampEn, and FuzzyEn. Since vectors' similarity is defined on the basis of the hard and sensitive boundary of Heaviside function in ApEn and SampEn, the two families of statistics show high sensitivity to the parameter selection and may be invalid in case of small parameter. Importing the concept of fuzzy sets, we developed a new measure FuzzyEn, where vectors' similarity is defined by fuzzy similarity degree based on fuzzy membership functions and vectors' shapes. The soft and continuous boundaries of fuzzy functions ensure the continuity as well as the validity of FuzzyEn at small parameters. The more details obtained by fuzzy functions also make FuzzyEn a more accurate entropy definition than ApEn and SampEn. In addition, similarity definition based on vectors' shapes, together with the exclusion of self-matches, earns FuzzyEn stronger relative consistency and less dependence on data length. Both theoretical analysis and experimental results show that FuzzyEn provides an improved evaluation of signal complexity and can be more conveniently and powerfully applied to short time series contaminated by noise.

Cannabis, a complex plant: different compounds and different effects on individuals. <=SEP=> Cannabis is a complex plant, with major compounds such as delta-9-tetrahydrocannabinol and cannabidiol, which have opposing effects. The discovery of its compounds has led to the further discovery of an important neurotransmitter system called the endocannabinoid system. This system is widely distributed in the brain and in the body, and is considered to be responsible for numerous significant functions. There has been a recent and consistent worldwide increase in cannabis potency, with increasing associated health concerns. A number of epidemiological research projects have shown links between dose-related cannabis use and an increased risk of development of an enduring psychotic illness. However, it is also known that not everyone who uses cannabis is affected adversely in the same way. What makes someone more susceptible to its negative effects is not yet known, however there are some emerging vulnerability factors, ranging from certain genes to personality characteristics. In this article we first provide an overview of the biochemical basis of cannabis research by examining the different effects of the two main compounds of the plant and the endocannabinoid system, and then go on to review available information on the possible factors explaining variation of its effects upon different individuals.

Insertion, Deletion, or Substitution? Normalizing Text Messages without Pre-categorization nor Supervision <=SEP=> Most text message normalization approaches are based on supervised learning and rely on human labeled training data. In addition, the nonstandard words are often categorized into different types and specific models are designed to tackle each type. In this paper, we propose a unified letter transformation approach that requires neither pre-categorization nor human supervision. Our approach models the generation process from the dictionary words to nonstandard tokens under a sequence labeling framework, where each letter in the dictionary word can be retained, removed, or substituted by other letters/digits. To avoid the expensive and time consuming hand labeling process, we automatically collected a large set of noisy training pairs using a novel webbased approach and performed character-level alignment for model training. Experiments on both Twitter and SMS messages show that our system significantly outperformed the stateof-the-art deletion-based abbreviation system and the jazzy spell checker (absolute accuracy gain of 21.69% and 18.16% over jazzy spell checker on the two test sets respectively).