title: Localization for mobile sensor networks

abstract: Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions.


title: Mielog: A highly interactive visual log browser using information visualization and statistical analysis

abstract: System administration has become an increasingly important function, with the fundamental task being the inspection of computer log-files. It is not, however, easy to perform such tasks for two reasons. One is the high recognition load of log contents due to the massive amount of textual data. It is a tedious, time-consuming and often error-prone task to read through them. The other problem is the difficulty in extracting unusual messages from the log. If an administrator does not have the knowledge or experience, he or she cannot readily recognize unusual log messages. To help address these issues, we have developed a highly interactive visual log browser called "MieLog." MieLog uses two techniques for manual log inspection tasks: information visualization and statistical analysis. Information visualization is helpful in reducing the recognition load because it provides an alternative method of interpreting textual information without reading. Statistical analysis enables the extraction of unusual log messages without domain specific knowledge. We will give three examples that illustrate the ability of the MieLog system to isolate unusual messages more easily than before.


title: Design and Demonstration of a 2.5-D Glass Interposer BGA Package for High Bandwidth and Low Cost

abstract: Consumer demand for mobile services is expected to grow with the continued proliferation of connected devices including smartphones, wearables, and Internet of things. As a result, high-performance computing systems that support the core network and cloud infrastructures for these connected devices require unprecedented die-to-die bandwidth at low latency. To achieve next-generation performance requirements and to apply to commercial products, fundamental parameters for 2.5-D interposers are considered including: 1) high interconnect density at short interconnect length; 2) low power consumption; and 3) low packaging cost. The 2.5-D glass interposer described in this paper is superior to silicon interposer in cost and electrical performance, and to organic interposer in interconnect density. This paper describes a 2.5-D glass interposer as a ball grid array (BGA) package to achieve high bandwidth at low cost to improve bandwidth per unit watt signal power per unit dollar cost (BWF) compared to both silicon and organic interposers. Due to its high modulus and excellent surface finish, glass affords ultrafine line lithography to form high-density interconnects comparable to silicon, and the process described in this paper goes beyond silicon back-end-of-line processes by implementing a double-side semi-additive process (SAP) at increased copper layer thickness. This thicker metallization results in reduced conductor losses and improved bandwidth per channel compared to silicon. In addition, the low loss tangent of glass reduces dielectric losses in nets requiring through vias including clock distribution and high-speed off-package signals. Availability of glass in thin panel as well as in roll-to-roll formats beyond 500 mm in size reduces packaging cost compared to 300-mm wafer silicon interposer. The focus of this paper is on the integration of three enabling technologies: 1) advanced SAP for high-density redistribution layers (RDLs); 2) excimer laser ablation of RDL vias; and 3) fine-pitch thermocompression bonding with copper pillar die assembly—for a 2.5-D glass interposer at interconnect densities comparable to that of silicon to achieve terabit per second interdie bandwidth at highest BWF.


title: Advances on plant–pathogen interactions from molecular toward systems biology perspectives

abstract: Summary ::: In the past two decades, progress in molecular analyses of the plant immune system has revealed key elements of a complex response network. Current paradigms depict the interaction of pathogen secreted molecules with host target molecules leading to the activation of multiple plant response pathways. Further research will be required to fully understand how these responses are integrated in space and time, and exploit this knowledge in agriculture. In this review, we highlight systems biology as a promising approach to reveal properties of molecular plant pathogen interactions and predict the outcome of such interactions. We first illustrate a few key concepts in plant immunity with a network and systems biology perspective. Next, we present some basic principles of systems biology and show how they allow integrating multi-omics data and predict cell phenotypes. We identify challenges for systems biology of plant-pathogen interactions, including the reconstruction of multiscale mechanistic models and the connection of host and pathogen models. Finally, we outline studies on resistance durability through the robustness of immune system networks, the identification of trade-offs between immunity and growth and in silico plant-pathogen co-evolution as exciting perspectives in the field. We conclude that the development of sophisticated models of plant diseases incorporating plant, pathogen and climate properties represent a major challenge for agriculture in the future. ::: ::: This article is protected by copyright. All rights reserved.


title: Variational Depth from Focus Reconstruction

abstract: This paper deals with the problem of reconstructing a depth map from a sequence of differently focused images, also known as depth from focus or shape from focus. We propose to state the depth from focus problem as a variational problem including a smooth but nonconvex data fidelity term, and a convex nonsmooth regularization, which makes the method robust to noise and leads to more realistic depth maps. Additionally, we propose to solve the nonconvex minimization problem with a linearized alternating directions method of multipliers (ADMM), allowing to minimize the energy very efficiently. A numerical comparison to classical methods on simulated as well as on real data is presented.


title: Network Analysis with the Enron Email Corpus

abstract: We use the Enron email corpus to study relationships in a network by applying six different measures of centrality. Our results came out of an in-semester undergraduate research seminar. The Enron corpus is well suited to statistical analyses at all levels of undergraduate education. Through this note's focus on centrality, students can explore the dependence of statistical models on initial assumptions and the interplay between centrality measures and hierarchical ranking, and they can use completed studies as springboards for future research. The Enron corpus also presents opportunities for research into many other areas of analysis, including social networks, clustering, and natural language processing.


title: Dynamic pricing for hotel revenue management using price multipliers

abstract: In this article we propose a new dynamic pricing approach for the hotel revenue management problem. The proposed approach is based on having ‘price multipliers’ that vary around ‘1’ and provide a varying discount/premium over some seasonal reference price. The price multipliers are a function of certain influencing variables (for example, hotel occupancy, time until arrival). We apply an optimization algorithm for determining the parameters of these multipliers, the goal being to maximize the revenue, taking into account current demand, and the demand-price sensitivity of the hotel's guest. The optimization algorithm makes use of a Monte Carlo simulator that simulates all the hotel's processes, such as reservations arrivals, cancellations, duration of stay, no shows, group reservations, seasonality and trend, as faithfully as possible. We have tested the proposed approach by successfully applying it to the revenue management problem of Plaza Hotel, Alexandria, Egypt, as a case study.


title: Real-Time Control System for a Two-Wheeled Inverted Pendulum Mobile Robot

abstract: The research on two-wheeled inverted pendulum (T-WIP) mobile robots or commonly known as balancing robots have gained momentum over the last decade in a number of robotic laboratories around the world (Solerno & Angeles, 2003;Grasser et al., 2002; Solerno & Angeles, 2007;Koyanagi, Lida & Yuta, 1992;Ha & Yuta, 1996; Kim, Kim & Kwak, 2003). This chapter describes the hardware design of such a robot. The objective of the design is to develop a T-WIP mobile robot as well as MATLABTM interfacing configuration to be used as flexible platform which comprises of embedded unstable linear plant intended for research and teaching purposes. Issues such as selection of actuators and sensors, signal processing units, MATLABTM Real Time Workshop coding, modeling and control scheme is addressed and discussed. The system is then tested using a well-known state feedback controller to verify its functionality.


title: Grounded Video Description

abstract: Video description is one of the most challenging problems in vision and language understanding due to the large variability both on the video and language side. Models, hence, typically shortcut the difficulty in recognition and generate plausible sentences that are based on priors but are not necessarily grounded in the video. In this work, we explicitly link the sentence to the evidence in the video by annotating each noun phrase in a sentence with the corresponding bounding box in one of the frames of a video. Our dataset, ActivityNet-Entities, augments the challenging ActivityNet Captions dataset with 158k bounding box annotations, each grounding a noun phrase. This allows training video description models with this data, and importantly, evaluate how grounded or "true" such model are to the video they describe. To generate grounded captions, we propose a novel video description model which is able to exploit these bounding box annotations. We demonstrate the effectiveness of our model on our dataset, but also show how it can be applied to image description on the Flickr30k Entities dataset. We achieve state-of-the-art performance on video description, video paragraph description, and image description and demonstrate our generated sentences are better grounded in the video.


title: Dominance of the Right Hemisphere and Role of Area 2 in Human Kinesthesia

abstract: We have previously shown that motor areas are engaged when subjects experience illusory limb movements elicited by tendon vibration. However, traditionally cytoarchitectonic area 2 is held responsible for kinesthesia. Here we use functional magnetic resonance imaging and cytoarchitectural mapping to examine whether area 2 is engaged in kinesthesia, whether it is engaged bilaterally because area 2 in non-human primates has strong callosal connections, which other areas are active members of the network for kinesthesia, and if there is a dominance for the right hemisphere in kinesthesia as has been suggested. Ten right-handed blindfolded healthy subjects participated. The tendon of the extensor carpi ulnaris muscles of the right or left hand was vibrated at 80 Hz, which elicited illusory palmar flexion in an immobile hand (illusion). As control we applied identical stimuli to the skin over the processus styloideus ulnae, which did not elicit any illusions (vibration). We found robust activations in cortical...


title: Analysis, Design, and Control of an Omnidirectional Mobile Robot in Rough Terrain

abstract: An omnidirectional mobile robot is able, kinematically, to move in any direction regardless of current pose. To date, nearly all designs and analyses of omnidirectional mobile robots have considered the case of motion on flat, smooth terrain. In this paper, an investigation of the design and control of an omnidirectional mobile robot for use in rough terrain is presented. Kinematic and geometric properties of the active split offset caster drive mechanism are investigated along with system and subsystem design guidelines. An optimization method is implemented to explore the design space. The use of this method results in a robot that has higher mobility than a robot designed using engineering judgment. A simple kinematic controller that considers the effects of terrain unevenness via an estimate of the wheel-terrain contact angles is also presented. It is shown in simulation that under the proposed control method, near-omnidirectional tracking performance is possible even in rough, uneven terrain.


title: The Role of Puberty in the Developing Adolescent Brain

abstract: Adolescence refers to the period of physical and psychological development between childhood and adulthood. The beginning of adolescence is loosely anchored to the onset of puberty, which brings dramatic alterations in hormone levels and a number of consequent physical changes. Puberty onset is also associated with profound changes in drives, motivations, psychology, and social life; these changes continue throughout adolescence. There is an increasing number of neuroimaging studies looking at the development of the brain, both structurally and functionally, during adolescence. Almost all of these studies have defined development by chronological age, which shows a strong—but not unitary—correlation with pubertal stage. Very few neuroimaging studies have associated brain development with pubertal stage, and yet there is tentative evidence to suggest that puberty might play an important role in some aspects of brain and cognitive development. In this paper we describe this research, and we suggest that, in the future, developmental neuroimaging studies of adolescence should consider the role of puberty. Hum Brain Mapp, 2010. © 2010 Wiley-Liss, Inc.


title: Stereo odometry based on careful feature selection and tracking

abstract: In this paper we present a novel algorithm for fast and robust stereo visual odometry based on feature selection and tracking (SOFT). The reduction of drift is based on careful selection of a subset of stable features and their tracking through the frames. Rotation and translation between two consecutive poses are estimated separately. The five point method is used for rotation estimation, whereas the three point method is used for estimating translation. Experimental results show that the proposed algorithm has an average pose error of 1.03% with processing speed above 10 Hz. According to publicly available KITTI leaderboard, SOFT outperforms all other validated methods. We also present a modified IMU-aided version of the algorithm, fast and suitable for embedded systems. This algorithm employs an IMU for outlier rejection and Kalman filter for rotation refinement. Experiments show that the IMU based system runs at 20 Hz on an ODROID U3 ARM-based embedded computer without any hardware acceleration. Integration of all components is described and experimental results are presented.


title: Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration

abstract: Any learning algorithm for recommendation faces a fundamental trade-off between exploiting partial knowledge of a user»s interests to maximize satisfaction in the short term and discovering additional user interests to maximize satisfaction in the long term. To enable discovery, a machine learning algorithm typically elicits feedback on items it is uncertain about, which is termed algorithmic exploration in machine learning. This exploration comes with a cost to the user, since the items an algorithm chooses for exploration frequently turn out to not match the user»s interests. In this paper, we study how users tolerate such exploration and how presentation strategies can mitigate the exploration cost. To this end, we conduct a behavioral study with over 600 people, where we vary how algorithmic exploration is mixed into the set of recommendations. We find that users respond non-linearly to the amount of exploration, where some exploration mixed into the set of recommendations has little effect on short-term satisfaction and behavior. For long-term satisfaction, the overall goal is to learn via exploration about the items presented. We therefore also analyze the quantity and quality of implicit feedback signals such as clicks and hovers, and how they vary with different amounts of mix-in exploration. Our findings provide insights into how to design presentation strategies for algorithmic exploration in interactive recommender systems, mitigating the short-term costs of algorithmic exploration while aiming to elicit informative feedback data for learning.


title: Sums of independent lognormally distributed random variables

abstract: The probability-density function of the sum of lognormally distributed random variables is studied by a method that involves the calculation of the Fourier transform of the characteristic function; this method is exact. When the number of terms in the sum is large, we employ an asymptotic series in N−1, where N is the number of terms, developed by Cramer. This method is employed in order to show that the permanence of the lognormal probability-density function is a consequence of the fact that the skewness coefficient of the lognormal variables is nonzero. Finally, a simplified proof, by use of the Carleman criterion, is presented to show that the lognormal is not uniquely determined by its moments.


title: Clustering large data sets with mixed numeric and categorical values

abstract: Efficient partitioning of large data sets into homogenous clusters is a fundamental problem in data mining. The standard hierarchical clustering methods provide no solution for this problem due to their computational inefficiency. The k-means based methods are promising for their efficiency in processing large data sets. However, their use is often limited to numeric data. In this paper we present a k-prototypes algorithm which is based on the k-means paradigm but removes the numeric data limitation whilst preserving its efficiency. In the algorithm, objects are clustered against k prototypes. A method is developed to dynamically update the k prototypes in order to maximise the intra cluster similarity of objects. When applied to numeric data the algorithm is identical to the kmeans. To assist interpretation of clusters we use decision tree induction algorithms to create rules for clusters. These rules, together with other statistics about clusters, can assist data miners to understand and identify interesting clusters.


title: Three-Dimensional (3D) Nanometrology Based on Scanning Electron Microscope (SEM) Stereophotogrammetry †

abstract: Three-dimensional (3D) reconstruction of a sample surface from scanning electron microscope (SEM) images taken at two perspectives has been known for decades. Nowadays, there exist several commercially available stereophotogrammetry software packages. For testing these software packages, in this study we used Monte Carlo simulated SEM images of virtual samples. A virtual sample is a model in a computer, and its true dimensions are known exactly, which is impossible for real SEM samples due to measurement uncertainty. The simulated SEM images can be used for algorithm testing, development, and validation. We tested two stereophotogrammetry software packages and compared their reconstructed 3D models with the known geometry of the virtual samples used to create the simulated SEM images. Both packages performed relatively well with simulated SEM images of a sample with a rough surface. However, in a sample containing nearly uniform and therefore low-contrast zones, the height reconstruction error was ≈46%. The present stereophotogrammetry software packages need further improvement before they can be used reliably with SEM images with uniform zones.


title: Next-generation transcriptome assembly

abstract: Transcriptomics studies often rely on partial reference transcriptomes that fail to capture the full catalog of transcripts and their variations. Recent advances in sequencing technologies and assembly algorithms have facilitated the reconstruction of the entire transcriptome by deep RNA sequencing (RNA-seq), even without a reference genome. However, transcriptome assembly from billions of RNA-seq reads, which are often very short, poses a significant informatics challenge. This Review summarizes the recent developments in transcriptome assembly approaches - reference-based, de novo and combined strategies-along with some perspectives on transcriptome assembly in the near future.


Sensation seeking in England and America: Cross-cultural, age, and sex comparisons.


title: Noise radar using random phase and frequency modulation

abstract: Pulse compression radar is used in a great number of applications. Excellent range resolution and high electronic counter-countermeasures performance is achieved by wideband long pulses, which spread out the transmitted energy in frequency and time. By using a random noise waveform, the range ambiguity is suppressed as well. In most applications, the random signal is transmitted directly from a noise-generating microwave source. A sine wave, which is phase or frequency modulated by random noise, is an alternative, and in this paper, the ambiguity function and the statistical characteristics of the correlation output for the latter configuration are further analyzed. Range resolution is then improved because the noise bandwidth of the modulated carrier is wider than that of the modulating signal, and the range sidelobes are also further suppressed. Random biphase modulation gives a 4-dB (/spl pi//sup 2//4) improvement, but much higher sidelobe suppression could be achieved using continuous phase/frequency modulation. Due to the randomness of the waveform, the output correlation integral is accompanied by a noise floor, which limits the possible sidelobe suppression as determined by the time-bandwidth product. In synthetic aperture radar (SAR) applications with distributed targets, this product should be large compared with the number of resolution elements inside the antenna main beam. The advantages of low range sidelobes and enhanced range resolution make frequency/phase-modulated noise radar attractive for many applications, including SAR mapping, surveillance, altimetry, and scatterometry. Computer algorithms for reference signal delay and compression are discussed as replacements for the classical delay line implementation.


title: Bayesian sparse representation for hyperspectral image super resolution

abstract: Despite the proven efficacy of hyperspectral imaging in many computer vision tasks, its widespread use is hindered by its low spatial resolution, resulting from hardware limitations. We propose a hyperspectral image super resolution approach that fuses a high resolution image with the low resolution hyperspectral image using non-parametric Bayesian sparse representation. The proposed approach first infers probability distributions for the material spectra in the scene and their proportions. The distributions are then used to compute sparse codes of the high resolution image. To that end, we propose a generic Bayesian sparse coding strategy to be used with Bayesian dictionaries learned with the Beta process. We theoretically analyze the proposed strategy for its accurate performance. The computed codes are used with the estimated scene spectra to construct the super resolution hyperspectral image. Exhaustive experiments on two public databases of ground based hyperspectral images and a remotely sensed image show that the proposed approach outperforms the existing state of the art.


title: Content based approach to find the credibility of user in social networks: an application of cyberbullying

abstract: Cyberbullying is derogatory act carried out intentionally by sending or posting harmful material on social networks to cheat or tarnish anybody’s image in real world. Today it has become a significant problem among teenagers and kids as they spend much time on social networking. Two types of cyberbullying have been observed in the messages posted on social network: direct cyberbullying and indirect cyberbullying. Direct cyberbullying is to send disrespectful/abusing material in the form of text, images, videos and audios to harass/torture individual directly. Indirect cyberbullying is to attack or torture individuals indirectly by doing activities like sending objectionable contents such as false rumors, lies etc. concerning them, tagging their embarrassing images, refuse to socialize with the victim. These type of activities can be viewed by large number of audience on social media. Ground breaking research is being carried out only on the identification of cyberbullying and not on its categories such as direct and indirect cyberbullying. As indirect cyberbullying is much harmful than direct cyberbullying due to the messages posted online are visible to large number of users, which may adversely impact the victim’s reputation/position. So, it becomes necessary to find the solution for this problem. In this paper, we first categorize the messages into direct and indirect bullying messages and then proceed to find the solution for controlling the bullying through checking the credibility of user .


title: Driving automation: learning from aviation about design philosophies

abstract: Full vehicle automation is predicted to be on British roads by 2030 (Walker et al., 2001). However, experience in aviation gives us some cause for concern for the 'drive-by-wire' car (Stanton and Marsden, 1996). Two different philosophies have emerged in aviation for dealing with the human factor: hard vs. soft automation, depending on whether the computer or the pilot has ultimate authority (Hughes and Dornheim, 1995). This paper speculates whether hard or soft automation provides the best solution for road vehicles, and considers an alternative design philosophy in vehicles of the future based on coordination and cooperation.


Autonomous vacuum cleaner


title: Word Representation Models for Morphologically Rich Languages in Neural Machine Translation

abstract: Dealing with the complex word forms in morphologically rich languages is an open problem in language processing, and is particularly important in translation. In contrast to most modern neural systems of translation, which discard the identity for rare words, in this paper we propose several architectures for learning word representations from character and morpheme level word decompositions. We incorporate these representations in a novel machine translation model which jointly learns word alignments and translations via a hard attention mechanism. Evaluating on translating from several morphologically rich languages into English, we show consistent improvements over strong baseline methods, of between 1 and 1.5 BLEU points.


title: Center-Fed Patch Antenna Array Excited by an Inset Dielectric Waveguide for 60-GHz Applications

abstract: A patch antenna array proximity-coupled fed by the inset dielectric waveguide (IDW) is proposed in 60-GHz frequency band. The antenna array consists of four series-fed subarrays each having eight pairs of circular patch elements, four substrate integrated IDWs, and a compact four-way power divider transmitting the signal from the grounded coplanar waveguide (GCPW) to the IDWs. The IDW is realized by metallized via-hole arrays in a printed circuit board (PCB). The parallel center-fed configuration is applied to suppress the frequency-dependent beam squinting of the series-fed subarray. Measured results show that the proposed antenna exhibits a gain variation between 15.8 and 19.2 dBi, fixed main beam in the direction of $\theta= 0^\circ$ , and the reflection coefficient $\vert S_{11}\vert \le-10 {\text{ dB}}$ over the frequency band from 58 to 64 GHz.


title: Long-term engraftment of systemically transplanted, gene-modified bone marrow-derived cells in the adult mouse retina

abstract: Aims: To provide evidence for a novel route of gene administration to normal and diseased retinas, we performed systemic transplantation of genetically engineered bone marrow-derived cells (BMDCs) to wild-type mice and to mutant mice with retinal degeneration. Methods: Lethally irradiated recipient mice—C57BL/6 (wild-type), SCA7 (spinocerebellar ataxia type 7) and FVB/N ( rd1 mutant)—were transplanted intravenously with 5×106 BMDCs, which were transduced with a retroviral vector to express the enhanced green fluorescent protein (GFP). Chimeras were killed at 1, 3, 8, 11, 12 and 15 months (wild-type) or at 8 and 12 months (mutants) after transplantation. Eyes were enucleated, and the retinas were analysed using immunohistochemistry. Results: In wild-type retinas, BMDCs preferentially engrafted in the inner and outer plexiform layers, the ganglion cell layer and the optic nerve. No BMDCs were found in the photoreceptor layer. BMDCs were more common in the degenerating retinas of the mutant mice. The majority of BMDCs in the retina were identified as microglia based on morphology and immunophenotype. Approximately 8–16% of all CD11b + cells in the retina expressed GFP. None of the BMDCs expressed neuronal cell markers. GFP-expressing BMDCs were found to persist for more than 1 year after transplantation. Conclusions: We demonstrate that gene-modified BMDCs show long-term engraftment and stable expression of GFP from a retrovirus in both wild-type and mutant mouse retinas. Thus, BMDCs may be used as vehicles for gene delivery to the retina.


title: Tightly-Coupled Monocular Visual-Odometric SLAM Using Wheels and a MEMS Gyroscope

abstract: In this paper, we present a novel tightly-coupled probabilistic monocular visual-odometric Simultaneous Localization and Mapping algorithm using wheels and a MEMS gyroscope, which can provide accurate, robust and long-term localization for the ground robot moving on a plane. Firstly, we present an odometer preintegration theory that integrates the wheel encoder measurements and gyroscope measurements to a local frame. The preintegration theory properly addresses the manifold structure of the rotation group SO(3) and carefully deals with uncertainty propagation and bias correction. Then the novel odometer error term is formulated using the odometer preintegration model and it is tightly integrated into the visual optimization framework. Furthermore, we introduce a complete tracking framework to provide different strategies for motion tracking when (1) both measurements are available, (2) visual measurements are not available, and (3) wheel encoder experiences slippage, which leads the system to be accurate and robust. Finally, the proposed algorithm is evaluated by performing extensive experiments, the experimental results demonstrate the superiority of the proposed system.


title: How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews

abstract: App stores allow users to submit feedback for downloaded apps in form of star ratings and text reviews. Recent studies analyzed this feedback and found that it includes information useful for app developers, such as user requirements, ideas for improvements, user sentiments about specific features, and descriptions of experiences with these features. However, for many apps, the amount of reviews is too large to be processed manually and their quality varies largely. The star ratings are given to the whole app and developers do not have a mean to analyze the feedback for the single features. In this paper we propose an automated approach that helps developers filter, aggregate, and analyze user reviews. We use natural language processing techniques to identify fine-grained app features in the reviews. We then extract the user sentiments about the identified features and give them a general score across all reviews. Finally, we use topic modeling techniques to group fine-grained features into more meaningful high-level features. We evaluated our approach with 7 apps from the Apple App Store and Google Play Store and compared its results with a manually, peer-conducted analysis of the reviews. On average, our approach has a precision of 0.59 and a recall of 0.51. The extracted features were coherent and relevant to requirements evolution tasks. Our approach can help app developers to systematically analyze user opinions about single features and filter irrelevant reviews.


title: GNU Radio based software-defined FMCW radar for weather surveillance application

abstract: In this paper, a GNU Radio based software-defined FMCW (Frequency Modulated — Continuous Wave) radar is studied for weather surveillance application. The FMCW radar that has been gaining popularity due to the use of solid state microwave amplifier to generate a signal source is proposed for the design since the current weather surveillance radar is usually using a pulse-radar type that needs high power in pulse generation and high cost in the deployment. In addition, by using software-defined radar, therefore the designed FMCW radar can be implemented and configured at a reduced cost and complexity. The FMCW radar prototype is implemented using both open sources of software and hardware. The software part of the radar is realized using GNU Radio, whilst the hardware part is implemented using USRP (Universal Software Radio Peripheral) N210. Based on the design specification, an FMCW radar prototype based on GNU Radio is then realized and examined. From the performance-test, the prototype that works at a center frequency of 2.1GHz with a bandwidth of 750kHz is able to perform range detection of targets after utilizing the FFT (Fast Fourier Transform) function using MATLAB. In addition, the discussion of system design of software defined FMCW radar and performance-test of its prototype are presented.


title: Using web service gateways and code generation for sustainable IoT system development

abstract: Wireless Sensing and Radio Identification systems have undergone many innovations during the past years. This has led to short product lifetimes for both software and hardware compared to classical industries. However, especially industries dealing with long-term support of products, e.g. of industrial machinery, and product lifetime of 40+ years may especially profit from an Internet of Things. Motivated by a practical industrial servicing use case this paper shows how we hope to make equally sustainable IoT solutions by employing a model driven software development approach based on code generation for multi-protocol web service gateways.


title: ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization

abstract: Indoor localization of mobile devices and tags has received much attention recently, with encouraging fine-grained localization results available with enough line-of-sight coverage and hardware infrastructure. Some of the most promising techniques analyze the time-of-arrival of incoming signals, but the limited bandwidth available to most wireless transmissions fundamentally constrains their resolution. Frequency-agile wireless networks utilize bandwidths of varying sizes and locations in a wireless band to efficiently share the wireless medium between users. ToneTrack is an indoor location system that achieves sub-meter accuracy with minimal hardware and antennas, by leveraging frequency-agile wireless networks to increase the effective bandwidth. Our novel signal combination algorithm combines time-of-arrival data from different transmissions as a mobile device hops across different channels, approaching time resolutions previously not possible with a single narrowband channel. ToneTrack's novel channel combination and spectrum identification algorithms together with the triangle inequality scheme yield superior results even in non-line-of-sight scenarios with one to two walls separating client and APs and also in the case where the direct path from mobile client to an AP is completely blocked. We implement ToneTrack on the WARP hardware radio platform and use six of them served as APs to localize Wi-Fi clients in an indoor testbed over one floor of an office building. Experimental results show that ToneTrack can achieve a median 90 cm accuracy when 20 MHz bandwidth APs overhear three packets from adjacent channels.


abstract: Imaging of somatostatin receptor subtype 2 in advanced hepatocellular carcinoma by 68Ga-DOTATOC PET -


title: Three-Dimensional Wax Patterning of Paper Fluidic Devices

abstract: In this paper we describe a method for three-dimensional wax patterning of microfluidic paper-based analytical devices (μPADs). The method is rooted in the fundamental details of wax transport in paper and provides a simple way to fabricate complex channel architectures such as hemichannels and fully enclosed channels. We show that three-dimensional μPADs can be fabricated with half as much paper by using hemichannels rather than ordinary open channels. We also provide evidence that fully enclosed channels are efficiently isolated from the exterior environment, decreasing contamination risks, simplifying the handling of the device, and slowing evaporation of solvents.


title: Fast semantic segmentation of 3D point clouds using a dense CRF with learned parameters

abstract: In this paper, we present an efficient semantic segmentation framework for indoor scenes operating on 3D point clouds. We use the results of a Random Forest Classifier to initialize the unary potentials of a densely interconnected Conditional Random Field, for which we learn the parameters for the pairwise potentials from training data. These potentials capture and model common spatial relations between class labels, which can often be observed in indoor scenes. We evaluate our approach on the popular NYU Depth datasets, for which it achieves superior results compared to the current state of the art. Exploiting parallelization and applying an efficient CRF inference method based on mean field approximation, our framework is able to process full resolution Kinect point clouds in half a second on a regular laptop, more than twice as fast as comparable methods.


title: Discrete-Time Hazard Regression Models with Hidden Heterogeneity The Semiparametric Mixed Poisson Regression Approach

abstract: Previous methodological research has shown that hidden heterogeneity in hazard rate regression modelsâ€”in the form of systematic differences between sample members in the risk or hazard of making a transition due to unobserved variables not accounted for by the measured covariatesâ€”can produce biased parameter estimates and erroneous inferences. However, few empirical applications of hazard regression do more than pay lip service to the complications of hidden heterogeneity. In part, this is due to the relative inaccessibility of the mathematical apparatus of continuous-time hazard regression methodology with flexible nonparametric specifications on the hidden heterogeneity. This article presents new methods for incorporating nonparametric specifications of hidden heterogeneity into hazard regressions by developing discrete-time Poisson rate/complementary log-log hazard regression models with nonparametric hidden heterogeneity that are analogous to the continuous-time models of Heckman and Singer. Maximum-likelihood estimators and associated hypothesis tests are described. An empirical application to data on criminal careers, which illustrates the utility of models that explicitly incorporate hidden heterogeneity, is presented.


title: Electrolytic lesion of the nucleus raphe magnus reduced the antinociceptive effects of bilateral morphine microinjected into the nucleus cuneiformis in rats

abstract: Several lines of investigation show that the rostral ventromedial medulla is a critical relay for midbrain regions, including the nucleus cuneiformis (CnF), which control nociception at the spinal cord. There is some evidence that local stimulation or morphine administration into the CnF produces the effective analgesia through the nucleus raphe magnus (NRM). The present study tries to determine the effect of morphine-induced analgesia following microinjection into the CnF in the absence of NRM. Seven days after the cannulae implantation, morphine was microinjected bilaterally into the CnF at the doses of 0.25, 1, 2.5, 5, 7.5 and 10 microg/0.3 microl saline per side. The morphine-induced antinociceptive effect measured by tail-flick test at 30, 60, 90 and 120 min after microinjection. The results showed that bilateral microinjection of morphine into the CnF dose-dependently causes increase in tail-flick latency (TFL). The 50% effective dose of morphine was determined and microinjected into the CnF (2.5 microg/0.3 microl saline per side) in rats after NRM electrolytic lesion (1 mA, 30 s). Lesion of the NRM significantly decreased TFLs, 30 (P<0.01) and 60 (P<0.05) but not 90-120 min after morphine microinjection into the CnF, compared with sham-lesion group. We concluded that morphine induces the analgesic effects through the opioid receptors in the CnF. It is also appeared that morphine-induced antinociception decreases following the NRM lesion but it seems that there are some other descending pain modulatory pathways that activate in the absence of NRM.


title: High-Efficient Patch Antenna Array for E-Band Gigabyte Point-to-Point Wireless Services

abstract: This letter presents a low-cost, high-gain, and high-efficiency 4 × 4 circular patch array antenna for gigabyte point-to-point wireless services at E-band (81-86 GHz). The antenna structure consists of two layers. The feed network is placed at the bottom layer, while the circular patches are on the top layer. To increase the efficiency of the antenna array, substrate integrated waveguide (SIW) is used to feed the circular patches through longitudinal slots etched on the top metallic surface. Low-cost printed circuit board (PCB) process is used to fabricate the antenna prototype. Simulated and measured bandwidths of the antenna array are 7.2%, which covers the desired frequency range of 81-86 GHz. Measured gain of the 4 × 4 antenna array is 18.5 dBi, which is almost constant within the antenna bandwidth. Measured radiation efficiency of 90.3% is obtained.


title: INTERSPEECH 2010 Search by Voice in Mandarin Chinese

abstract: In this paper we describe our efforts to build a Mandarin Chinese voice search system. We describe our strategies for data collection, language, lexicon and acoustic modeling, as well as issues related to text normalization that are an integral part of building voice search systems. We show excellent performance on typical spoken search queries under a variety of accents and acoustic conditions. The system has been in operation since October 2009 and has received very positive user reviews.


title: Deep unfolding inference for supervised topic model

abstract: Conventional supervised topic model for multi-class classification is inferred via the variational inference algorithm where the model parameters are estimated by maximizing the lower bound of the logarithm of marginal likelihood function over input documents and labels. The classification accuracy is constrained by the variational lower bound. In this study, we aim to improve the classification accuracy by relaxing this constraint through directly maximizing the negative cross entropy error function via a deep unfolding inference (DUI). The inference procedure for class posterior is treated as the layer-wise learning in a deep neural network. The classification accuracy in DUI is accordingly increased by using the estimated topic parameters according to the exponentiated updates. Deep learning of supervised topic model is achieved through an error back-propagation algorithm. Experimental results show the superiority of DUI to variational Bayes inference in supervised topic model.


title: A combined post-mortem magnetic resonance imaging and quantitative histological study of multiple sclerosis pathology

abstract: Multiple sclerosis is a chronic inflammatory neurological condition characterized by focal and diffuse neurodegeneration and demyelination throughout the central nervous system. Factors influencing the progression of pathology are poorly understood. One hypothesis is that anatomical connectivity influences the spread of neurodegeneration. This predicts that measures of neurodegeneration will correlate most strongly between interconnected structures. However, such patterns have been difficult to quantify through post-mortem neuropathology or in vivo scanning alone. In this study, we used the complementary approaches of whole brain post-mortem magnetic resonance imaging and quantitative histology to assess patterns of multiple sclerosis pathology. Two thalamo-cortical projection systems were considered based on their distinct neuroanatomy and their documented involvement in multiple sclerosis: lateral geniculate nucleus to primary visual cortex and mediodorsal nucleus of the thalamus to prefrontal cortex. Within the anatomically distinct thalamo-cortical projection systems, magnetic resonance imaging derived cortical thickness was correlated significantly with both a measure of myelination in the connected tract and a measure of connected thalamic nucleus cell density. Such correlations did not exist between these markers of neurodegeneration across different thalamo-cortical systems. Magnetic resonance imaging lesion analysis depicted clearly demarcated subcortical lesions impinging on the white matter tracts of interest; however, quantitation of the extent of lesion-tract overlap failed to demonstrate any appreciable association with the severity of markers of diffuse pathology within each thalamo-cortical projection system. Diffusion-weighted magnetic resonance imaging metrics in both white matter tracts were correlated significantly with a histologically derived measure of tract myelination. These data demonstrate for the first time the relevance of functional anatomical connectivity to the spread of multiple sclerosis pathology in a ‘tract-specific’ pattern. Furthermore, the persisting relationship between metrics from post-mortem diffusion-weighted magnetic resonance imaging and histological measures from fixed tissue further validates the potential of imaging for future neuropathological studies.


title: Decentralized formation control with variable shapes for aerial robots

abstract: We address formation control for a team of quadrotors in which the robots follow a specified group trajectory while safely changing the shape of the formation according to specifications. The formation is prescribed by shape vectors which dictate the relative separations and bearings between the robots, while the group trajectory is specified as the desired trajectory of a leader or a virtual robot in the group. Each robot plans its trajectory independently based on its local information of neighboring robots which includes both the neighbor's planned trajectory and an estimate of its state. We show that the decentralized trajectory planners (a) result in consensus on the planned trajectory for predefined shapes and (b) achieve safe reconfiguration when changing shapes.


title: Design and Experimental Investigation of a Compact Circularly Polarized Integrated Filtering Antenna for Wearable Biotelemetric Devices

abstract: A compact circularly polarized (CP) integrated filtering antenna is reported for wearable biotelemetric devices in the 2.4 GHz ISM band. The design is based on a mutual synthesis of a CP patch antenna connected to a bandpass filter composed of coupled stripline open-loop resonators, which provides an integrated low-profile radiating and filtering module with a compact form factor of $0.44\lambda_{0}\times 0.44\lambda_{0}\times 0.04\lambda_{0}$ . The optimized filtering antenna is fabricated and measured, achieving an $S_{11} , an axial ratio of less than 3 dB and gain higher than 3.5 dBi in the targeted ISM band. With the integrated filtering functionality, the antenna exhibits good out-of-band rejection over an ultra-wide frequency range of 1–6 GHz . Further full-wave simulations and experiments were carried out, verifying that the proposed filtering antenna maintains these desirable properties even when mounted in close proximity to the human body at different positions. The stable impedance performance and the simultaneous wide axial ratio and radiated power beam widths make it an ideal candidate as a wearable antenna for off-body communications. The additional integrated filtering functionality further improves utility by greatly reducing interference and crosstalk with other existing wireless systems.


title: A vision based system for traffic lights recognition

abstract: In this article a traffic light recognition with status detection system is introduced. The system is evaluated from a generic point of view but the applications range from Intelligent Transportation System (ITS) to visual impaired and color vision deficiencies aid to safely cross streets. The algorithm is based on color segmentation in HSV color space. After that candidates reduction is performed using a pipeline approach to speed up the algorithm. Resulting candidates are input to feature extraction and support vector machine is then applied. For the training of the Support Vector Machine a database with images collected in Chicago is used. Unlike other works the purpose is to evaluate different performance according to the feature extraction. In particular HOG, HAAR and LBP features are compared. The purpose is also to create a database to be used from other researchers. The result is accurate and reliable provided that good quality images are input to the system.


title: Low Power Wide Area Networks: A Survey

abstract: Low Power Wide Area (LPWA) networks are attracting a lot of attention primarily because of their ability to offer affordable connectivity to the low-power devices distributed over very large geographical areas. In realizing the vision of the Internet of Things (IoT), LPWA technologies complement and sometimes supersede the conventional cellular and short range wireless technologies in performance for various emerging smart city and machine-to-machine (M2M) applications. This survey paper presents the design goals and the techniques, which different LPWA technologies exploit to offer wide-area coverage to low-power devices at the expense of low data rates. We survey several emerging LPWA technologies and the standardization activities carried out by different standards development organizations (e.g., IEEE, IETF, 3GPP, ETSI) as well as the industrial consortia built around individual LPWA technologies (e.g., LORa Alliance,WEIGHTLESS-SIG, and DASH7 Alliance). We further note that LPWA technologies adopt similar approaches, thus sharing the same limitations and challenges. This paper expands on these research challenges and identifies potential directions to address them. While the proprietary LPWA technologies are already hitting the market with large nationwide roll-outs, this paper encourages an active engagement of the research community in solving problems that will shape the connectivity of tens of billions of devices in the next decade.


title: The era of Big Spatial Data

abstract: In this tutorial, we present the recent work in the database community for handling Big Spatial Data. This topic became very hot due to the recent explosion in the amount of spatial data generated by smart phones, satellites and medical devices, among others. This tutorial goes beyond the use of existing systems as-is (e.g., Hadoop, Spark or Impala), and digs deep into the core components of big systems (e.g., indexing and query processing) to describe how they are designed to handle big spatial data. During this 90-minute tutorial, we review the state-of-the-art work in the area of Big Spatial Data while classifying the existing research efforts according to the implementation approach, underlying architecture, and system components. In addition, we provide case studies of full-fledged systems and applications that handle Big Spatial Data which allows the audience to better comprehend the whole tutorial.


title: A Deep Generative Deconvolutional Image Model

abstract: A deep generative model is developed for representation and analysis of images, based on a hierarchical convolutional dictionary-learning framework. Stochastic unpooling is employed to link consecutive layers in the model, yielding top-down image generation. A Bayesian support vector machine is linked to the top-layer features, yielding max-margin discrimination. Deep deconvolutional inference is employed when testing, to infer the latent features, and the top-layer features are connected with the max-margin classifier for discrimination tasks. The model is efficiently trained using a Monte Carlo expectation-maximization (MCEM) algorithm; the algorithm is implemented on graphical processor units (GPU) to enable large-scale learning, and fast testing. Excellent results are obtained on several benchmark datasets, including ImageNet, demonstrating that the proposed model achieves results that are highly competitive with similarly sized convolutional neural networks.


title: Privacy-Preserving Indexing and Query Processing for Secure Dynamic Cloud Storage

abstract: With the increasing popularity of cloud-based data services, data owners are highly motivated to store their huge amount of potentially sensitive personal data files on remote servers in encrypted form. Clients later can query over the encrypted database to retrieve files while protecting privacy of both the queries and the database, by allowing some reasonable leakage information. To this end, the notion of searchable symmetric encryption (SSE) was proposed. Meanwhile, recent literature has shown that most dynamic SSE solutions leaking information on updated keywords are vulnerable to devastating file-injection attacks. The only way to thwart these attacks is to design forward-private schemes. In this paper, we investigate new privacy-preserving indexing and query processing protocols which meet a number of desirable properties, including the multi-keyword query processing with conjunction and disjunction logic queries, practically high privacy guarantees with adaptive chosen keyword attack (CKA2) security and forward privacy, the support of dynamic data operations, and so on. Compared with previous schemes, our solutions are highly compact, practical, and flexible. Their performance and security are carefully characterized by rigorous analysis. Experimental evaluations conducted over a large representative data set demonstrate that our solutions can achieve modest search time efficiency, and they are practical for use in large-scale encrypted database systems.


title: DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers

abstract: With an abundance of research papers in deep learning, reproducibility or adoption of the existing works becomes a challenge. This is due to the lack of open source implementations provided by the authors. Further, re-implementing research papers in a different library is a daunting task. To address these challenges, we propose a novel extensible approach, DLPaper2Code, to extract and understand deep learning design flow diagrams and tables available in a research paper and convert them to an abstract computational graph. The extracted computational graph is then converted into execution ready source code in both Keras and Caffe, in real-time. An arXiv-like website is created where the automatically generated designs is made publicly available for 5,000 research papers. The generated designs could be rated and edited using an intuitive drag-and-drop UI framework in a crowdsourced manner. To evaluate our approach, we create a simulated dataset with over 216,000 valid design visualizations using a manually defined grammar. Experiments on the simulated dataset show that the proposed framework provide more than $93\%$ accuracy in flow diagram content extraction.


title: Development of an alcohol withdrawal protocol: CNS collaborative exemplar.

abstract: PURPOSE ::: The purpose of this process improvement project was to develop an Alcohol Withdrawal Syndrome (AWS) management protocol for acute care. ::: ::: ::: SIGNIFICANCE ::: The prevalence of alcohol abuse in our society presents challenges for health professionals, and few nurses have received formal education on the identification and treatment of AWS, which has frequently resulted in ineffective, nonstandardized care. However, nurses practicing in medical-surgical, emergency, trauma, and critical care settings must be astute in the assessment and management of AWS. DESIGN/BACKGROUND/RATIONALE: Following an analysis of existing management protocols, a behavioral health clinical nurse specialist was asked to lead a work team composed of physicians, pharmacists, and nurses to develop a new evidence-based alcohol withdrawal protocol for acute care. ::: ::: ::: METHODS/DESCRIPTION ::: By implementing a standardized assessment tool and treatment protocol, clinical nurse specialists empowered nursing staff with strategies to prevent the serious medical complications associated with AWS. FINDINGS/OUTCOMES: The development and integration of a safe and effective treatment protocol to manage AWS was facilitated by collaborative, evidence-based decision making. ::: ::: ::: CONCLUSION ::: Clinical experience and specialty expertise were integrated by clinical nurse specialists skilled in group dynamics, problem-solving, and the implementation of change. Improving care of patients in AWS is an exemplar for clinical nurse specialist roles as change agent and patient advocate.


title: Human mobility modeling at metropolitan scales

abstract: Models of human mobility have broad applicability in fields such as mobile computing, urban planning, and ecology. This paper proposes and evaluates WHERE, a novel approach to modeling how large populations move within different metropolitan areas. WHERE takes as input spatial and temporal probability distributions drawn from empirical data, such as Call Detail Records (CDRs) from a cellular telephone network, and produces synthetic CDRs for a synthetic population. We have validated WHERE against billions of anonymous location samples for hundreds of thousands of phones in the New York and Los Angeles metropolitan areas. We found that WHERE offers significantly higher fidelity than other modeling approaches. For example, daily range of travel statistics fall within one mile of their true values, an improvement of more than 14 times over a Weighted Random Waypoint model. Our modeling techniques and synthetic CDRs can be applied to a wide range of problems while avoiding many of the privacy concerns surrounding real CDRs.


title: Linking Virtual Machine Mobility to User Mobility

abstract: Cloud applications heavily rely on the network communication infrastructure, whose stability and latency directly affect the quality of experience. As mobile devices need to rapidly retrieve data from the cloud, it becomes an extremely important goal to deliver the lowest possible access latency at the best reliability. In this paper, we specify a cloud access overlay protocol architecture to improve the cloud access performance in distributed data-center (DC) cloud fabrics. We explore how linking virtual machine (VM) mobility and routing to user mobility can compensate performance decrease due to increased user-cloud network distance, by building an online cloud scheduling solution to optimally switch VM routing locators and to relocate VMs across DC sites, as a function of user-DC overlay network states. We evaluate our solution: 1) on a real distributed DC testbed spanning all of France, showing that we can grant a very high transfer time gain and 2) by emulating the situation of Internet service providers (ISPs) and over-the-top (OTT) cloud providers, exploiting thousands of real France-wide user displacement traces, finding a median throughput gain from 30% for OTT scenarii to 40% for ISP scenarii, the large majority of this gain being granted by adaptive VM mobility.


title: Single-ISA heterogeneous multi-core architectures for multithreaded workload performance

abstract: A single-ISA heterogeneous multi-core architecture is achip multiprocessor composed of cores of varying size, performance,and complexity. This paper demonstrates that thisarchitecture can provide significantly higher performance inthe same area than a conventional chip multiprocessor. It doesso by matching the various jobs of a diverse workload to thevarious cores. This type of architecture covers a spectrum ofworkloads particularly well, providing high single-thread performancewhen thread parallelism is low, and high throughputwhen thread parallelism is high.This paper examines two such architectures in detail,demonstrating dynamic core assignment policies that providesignificant performance gains over naive assignment, andeven outperform the best static assignment. It examines policiesfor heterogeneous architectures both with and withoutmultithreading cores. One heterogeneous architecture we examineoutperforms the comparable-area homogeneous architectureby up to 63%, and our best core assignment strategyachieves up to 31% speedup over a naive policy.


title: Double MAC: Doubling the performance of convolutional neural networks on modern FPGAs

abstract: This paper presents a novel method to double the computation rate of convolutional neural network (CNN) accelerators by packing two multiply-and-accumulate (MAC) operations into one DSP block of off-the-shelf FPGAs (called Double MAC). While a general SIMD MAC using a single DSP block seems impossible, our solution is tailored for the kind of MAC operations required for a convolution layer. Our preliminary evaluation shows that not only can our Double MAC approach increase the computation throughput of a CNN layer by twice with essentially the same resource utilization, the network level performance can also be improved by 14∼84% over a highly optimized state-of-the-art accelerator solution depending on the CNN hyper-parameters.


title: Image colorization using similar images

abstract: We present a new example-based method to colorize a gray image. As input, the user needs only to supply a reference color image which is semantically similar to the target image. We extract features from these images at the resolution of superpixels, and exploit these features to guide the colorization process. Our use of a superpixel representation speeds up the colorization process. More importantly, it also empowers the colorizations to exhibit a much higher extent of spatial consistency in the colorization as compared to that using independent pixels. We adopt a fast cascade feature matching scheme to automatically find correspondences between superpixels of the reference and target images. Each correspondence is assigned a confidence based on the feature matching costs computed at different steps in the cascade, and high confidence correspondences are used to assign an initial set of chromatic values to the target superpixels. To further enforce the spatial coherence of these initial color assignments, we develop an image space voting framework which draws evidence from neighboring superpixels to identify and to correct invalid color assignments. Experimental results and user study on a broad range of images demonstrate that our method with a fixed set of parameters yields better colorization results as compared to existing methods.


title: Defining the Health Information Technology discipline: results from the 2018 Australian and New Zealand censuses

abstract: The health information technology specialist group is a hidden workforce supporting the information technology and system needs in the healthcare sector. For the first time, this paper explores the demographic, educational, and occupational characteristics of this specialist group in Australia and New Zealand. A total of 227 responses from the 2018 Australian Health Information Workforce Census and the 2018 New Zealand Health Information Workforce Census were analysed. The analysis reports two-third of respondents were born in Australia or New Zealand, with the majority (98.3%) citizens or permanent residents. Most of this specialist group is male (58.1%) aged 45 year or older (53.3%), and nearly half do not possess a formal qualification in this field (47.6%). Most roles are permanent, full-time position in the public healthcare system, such as hospitals or state/federal departments, or in the health technology industry. Roles in this specialist group are still emerging, with respondents working in the field on average 11.9 years, and in their current role 5.6 years. There is an opportunity to build capacity in this specialist group through workforce planning, in particular developing specialist qualification and career pathways. This will be necessary to meet the growing demand for specialist in these roles to support digital transformation and innovation.


title: Linear algebra operators for GPU implementation of numerical algorithms

abstract: In this work, the emphasis is on the development of strategies to realize techniques of numerical computing on the graphics chip. In particular, the focus is on the acceleration of techniques for solving sets of algebraic equations as they occur in numerical simulation. We introduce a framework for the implementation of linear algebra operators on programmable graphics processors (GPUs), thus providing the building blocks for the design of more complex numerical algorithms. In particular, we propose a stream model for arithmetic operations on vectors and matrices that exploits the intrinsic parallelism and efficient communication on modern GPUs. Besides performance gains due to improved numerical computations, graphics algorithms benefit from this model in that the transfer of computation results to the graphics processor for display is avoided. We demonstrate the effectiveness of our approach by implementing direct solvers for sparse matrices, and by applying these solvers to multi-dimensional finite difference equations, i.e. the 2D wave equation and the incompressible Navier-Stokes equations.


title: Microbiota Modulate Behavioral and Physiological Abnormalities Associated with Neurodevelopmental Disorders

abstract: Neurodevelopmental disorders, including autism spectrum disorder (ASD), are defined by core behavioral impairments; however, subsets of individuals display a spectrum of gastrointestinal (GI) abnormalities. We demonstrate GI barrier defects and microbiota alterations in the maternal immune activation (MIA) mouse model that is known to display features of ASD. Oral treatment of MIA offspring with the human commensal Bacteroides fragilis corrects gut permeability, alters microbial composition, and ameliorates defects in communicative, stereotypic, anxiety-like and sensorimotor behaviors. MIA offspring display an altered serum metabolomic profile, and B. fragilis modulates levels of several metabolites. Treating naive mice with a metabolite that is increased by MIA and restored by B. fragilis causes certain behavioral abnormalities, suggesting that gut bacterial effects on the host metabolome impact behavior. Taken together, these findings support a gut-microbiome-brain connection in a mouse model of ASD and identify a potential probiotic therapy for GI and particular behavioral symptoms in human neurodevelopmental disorders.


title: Visual privacy protection methods

abstract: This work is the first comprehensive review about visual privacy protection.Several categories are established for all of the available privacy protection methods.A review of intelligent monitoring systems that takes privacy into account is provided.An overall vision of the state of the art is given to introduce novel researchers.A critical discussion about relevant key topics and open issues is provided. Recent advances in computer vision technologies have made possible the development of intelligent monitoring systems for video surveillance and ambient-assisted living. By using this technology, these systems are able to automatically interpret visual data from the environment and perform tasks that would have been unthinkable years ago. These achievements represent a radical improvement but they also suppose a new threat to individual's privacy. The new capabilities of such systems give them the ability to collect and index a huge amount of private information about each individual. Next-generation systems have to solve this issue in order to obtain the users' acceptance. Therefore, there is a need for mechanisms or tools to protect and preserve people's privacy. This paper seeks to clarify how privacy can be protected in imagery data, so as a main contribution a comprehensive classification of the protection methods for visual privacy as well as an up-to-date review of them are provided. A survey of the existing privacy-aware intelligent monitoring systems and a valuable discussion of important aspects of visual privacy are also provided.


title: Deep reinforcement learning for search, recommendation, and online advertising: a survey.

abstract: Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web. These information seeking techniques, satisfying users' information needs by suggesting users personalized objects (information or services) at the appropriate time and place, play a crucial role in mitigating the information overload problem. With recent great advances in deep reinforcement learning (DRL), there have been increasing interests in developing DRL based information seeking techniques. These DRL based techniques have two key advantages -- (1) they are able to continuously update information seeking strategies according to users' real-time feedback, and (2) they can maximize the expected cumulative long-term reward from users where reward has different definitions according to information seeking applications such as click-through rate, revenue, user satisfaction and engagement. In this paper, we give an overview of deep reinforcement learning for search, recommendation, and online advertising from methodologies to applications, review representative algorithms, and discuss some appealing research directions.


title: Hierarchical emotion classification and emotion component analysis on chinese micro-blog posts

abstract: We classify Chinese micro-blog posts into fine-grained emotion classes.We employ hierarchical classification to improve the performance of classifiers.We propose an ECA algorithm to detect four principal emotions and calculate ratios. Text emotion analysis has long been a hot topic. With the development of social network, text emotion analysis on micro-blog posts becomes a new trend in recent years. However, most researchers classify posts into coarse-grained emotion classes, which cannot depict the emotions accurately. Besides, flat classification is mostly adopted, which brings difficulty for classifiers when given a large dataset. In this paper, by data preprocessing, feature extraction and feature selection, we classify Chinese micro-blog posts into fine-grained emotion classes, employing hierarchical classification to improve the performance of classifiers. Moreover, based on the regression values in classification procedure, we propose an algorithm to detect the principal emotions in posts and calculate their ratios.


title: Void mechanism research and control in solder joint

abstract: Void formation mechanism was studied and so were the key factors for SMT voids. 2 kinds of solders (SAC305 & SnPb) and 2 kinds of pad finishes (ENIG and OSP) were compared. Key factors of reflow profile were also evaluated. Reflow atmosphere were evaluated to observe the effects on void. Stencil thickness on void formation was also studied. Results show that by adjusting all these factors, the maximum void size and void quantity can be controlled. It is also found that using higher melting point solder ball and lower melt point solder paste together will get void-free SMT result which also impresses the understanding of void forming mechanism: Gas of reaction and volatilization is trapped by melted solder particles in solder paste and form voids.


title: Robust normal estimation and region growing segmentation of infrastructure 3D point cloud models

abstract: Modern remote sensing technologies such as three-dimensional (3D) laser scanners and image-based 3D scene reconstruction are in increasing demand for applications in civil infrastructure design, maintenance, operation, and as-built construction verification. The complex nature of the 3D point clouds these technologies generate, as well as the often massive scale of the 3D data, make it inefficient and time consuming to manually analyze and manipulate point clouds, and highlights the need for automated analysis techniques. This paper presents one such technique, a new region growing algorithm for the automated segmentation of both planar and non-planar surfaces in point clouds. A core component of the algorithm is a new point normal estimation method, an essential task for many point cloud processing algorithms. The newly developed estimation method utilizes robust multivariate statistical outlier analysis for reliable normal estimation in complex 3D models, considering that these models often contain regions of varying surface roughness, a mixture of high curvature and low curvature regions, and sharp features. An adaptation of Mahalanobis distance, in which the mean vector and covariance matrix are derived from a high-breakdown multivariate location and scale estimator called Deterministic MM-estimator (DetMM) is used to find and discard outlier points prior to estimating the best local tangent plane around any point in a cloud. This approach is capable of more accurately estimating point normals located in highly curved regions or near sharp features. Thereafter, the estimated point normals serve a region growing segmentation algorithm that only requires a single input parameter, an improvement over existing methods which typically require two control parameters. The reliability and robustness of the normal estimation subroutine was compared against well-known normal estimation methods including the Minimum Volume Ellipsoid (MVE) and Minimum Covariance Determinant (MCD) estimators, along with Maximum Likelihood Sample Consensus (MLESAC). The overall region growing segmentation algorithm was then experimentally validated on several challenging 3D point clouds of real-world infrastructure systems. The results indicate that the developed approach performs more accurately and robustly in comparison with conventional region growing methods, particularly in the presence of sharp features, outliers and noise.


title: Face Presentation Attack with Latex Masks in Multispectral Videos

abstract: Face recognition systems are susceptible to presentation attacks such as printed photo attacks, replay attacks, and 3D mask attacks. These attacks, primarily studied in visible spectrum, aim to obfuscate or impersonate a person's identity. This paper presents a unique multispectral video face database for face presentation attack using latex and paper masks. The proposed Multispectral Latex Mask based Video Face Presentation Attack (MLFP) database contains 1350 videos in visible, near infrared, and thermal spectrums. Since the database consists of videos of subjects without any mask as well as wearing ten different masks, the effect of identity concealment is analyzed in each spectrum using face recognition algorithms. We also present the performance of existing presentation attack detection algorithms on the proposed MLFP database. It is observed that the thermal imaging spectrum is most effective in detecting face presentation attacks.


title: Social Media in an Alternative Marketing Communication Model

abstract: While current literature has sufficiently profiled word-of-mouth (WOM) marketing, customer relationship management, brand communities, search engine optimization, viral marketing, guerilla marketing, events-based marketing, and social media each on an isolated, individual basis, there is no comprehensive model that effectively incorporates all of these elements. The first purpose of this paper is to therefore profile the current literature landscape surrounding WOM marketing, alternative marketing communications, and social media as viable components of integrated marketing communications. Additionally, this paper aims to develop an integrated alternative marketing communication conceptual model that can be applied by industrial practitioners to help them achieve their marketing objectives.


title: Protection planning and system design to reduce arc flash incident energy in a multi-voltage level distribution system to 8 CAL/CM2 (HRC 2) or less – part II: Analysis

abstract: The paper demonstrates that in a multi-voltage level (medium and low-voltages) distribution system the incident energy can be reduced to 8 cal/cm2, or even less, (Hazard risk category, HRC 2), so that a PPE outfit of greater than 2 is not required. This is achieved with the current state of the art equipment and protective devices. It is recognized that in the existing distribution systems, not specifically designed with this objective, it may not be possible to reduce arc flash hazard to this low level, unless major changes in the system design and protection are made. A typical industrial distribution system is analyzed, and tables and time coordination plots are provided to support the analysis. Unit protection schemes and practical guidelines for arc flash reduction are provided. The methodology of IEEE 1584 [1] is used for the analyses.


Kinesthetic learning in the classroom


title: Synthetic biology — putting engineering into biology

abstract: Synthetic biology is interpreted as the engineering-driven building of increasingly complex biological entities for novel applications. Encouraged by progress in the design of artificial gene networks, de novo DNA synthesis and protein engineering, we review the case for this emerging discipline. Key aspects of an engineering approach are purpose-orientation, deep insight into the underlying scientific principles, a hierarchy of abstraction including suitable interfaces between and within the levels of the hierarchy, standardization and the separation of design and fabrication. Synthetic biology investigates possibilities to implement these requirements into the process of engineering biological systems. This is illustrated on the DNA level by the implementation of engineering-inspired artificial operations such as toggle switching, oscillating or production of spatial patterns. On the protein level, the functionally self-contained domain structure of a number of proteins suggests possibilities for essentially Lego-like recombination which can be exploited for reprogramming DNA binding domain specificities or signaling pathways. Alternatively, computational design emerges to rationally reprogram enzyme function. Finally, the increasing facility of de novo DNA synthesis---synthetic biology's system fabrication process---supplies the possibility to implement novel designs for ever more complex systems. Some of these elements have merged to realize the first tangible synthetic biology applications in the area of manufacturing of pharmaceutical compounds. ::: ::: Contact: panke@ipe.mavt.ethz.ch


title: An Effective Bail-out Test for RANSAC Consensus Scoring

abstract: The random sample consensus (RANSAC) algorithm, along with its many cousins such as MSAC and MLESAC, has become a standard choice for robust estimation in many computer vision problems. Recently, a raft of modifications to the basic RANSAC algorithm have been proposed aimed at improving its efficiency. Many of these optimizations work by reducing the number of hypotheses that need to be evaluated. This paper proposes a complementary strategy that aims to reduce the average amount of time spent computing the consensus score for each hypothesis. A simple statistical test is proposed that permits the scoring process be terminated early, potentially yielding large computational savings. The proposed test is simple to implement, imposes negligible computational overhead, and is effective for any given size of data set. The approach is evaluated by estimation of the fundamental matrix for a large number of image pairs and is shown to offer a significant reduction in computational cost compared to recently proposed RANSAC modifications.


title: Structural human brain networks : hot topics in diffusion tractography

abstract: PURPOSE OF REVIEW ::: After more than 10 years of methodological developments and clinical applications, diffusion imaging tractography has reached a crossroad. Although the method is still in its infancy, the time has come to address some important questions. Can tractography reproduce reliably known anatomy or describe new anatomical pathways? Are interindividual differences, for example in tract lateralization, important to understand heterogeneity of clinical manifestations? Do novel tractography algorithms provide a real advantage over previous methods? Here we focus on some of the most exciting recent advancements in diffusion tractography and critically highlight their advantages and limitations. ::: ::: ::: RECENT FINDINGS ::: A flourishing of diffusion methods and models are bringing new solutions to the well known limitations of classical tractography based on the tensor model. However, these methods pose also new challenges and require the convergence and integration of different disciplines before they can replace what is currently widely available. ::: ::: ::: SUMMARY ::: Rigorous postmortem validation, clinical optimization and experimental confirmation are obligatory steps before advanced diffusion technologies can translate into clear benefits for neurological patients.


title: System on chip coprocessors for high speed image feature detection and matching

abstract: Successful establishing of point correspondences between consecutive image frames is important in tasks such as visual odometry, structure from motion or simultaneous localization and mapping. In this paper, we describe the architecture of the compact, energy-efficient dedicated hardware processors, enabling fast feature detection and matching.


title: A Fundamental Algorithm for Dependency Parsing

abstract: This paper presents a fundamental algorithm for parsing natural language sentences into dependency trees. Unlike phrase-structure (constituency) parsers, this algorithm operates one word at a time, attaching each word as soon as it can be attached, corresponding to properties claimed for the parser in the human brain. Like phrasestructure parsing, its worst-case complexity is O(n), but in human language, the worst case occurs only for small n.


title: Choosing your informant: weighing familiarity and recent accuracy

abstract: In two experiments, children aged 3, 4 and 5 years ( N = 61) were given conflicting information about the names and functions of novel objects by two informants, one a familiar teacher, the other an unfamiliar teacher. On pre-test trials, all three age groups invested more trust in the familiar teacher. They preferred to ask for information and to endorse the information that she supplied. In a subsequent phase, children watched as the two teachers differed in the accuracy with which they named a set of familiar objects. Half the children saw the familiar teacher name the objects accurately and the unfamiliar teacher name them inaccurately. The remaining half saw the reverse arrangement. In post-test trials, the selective trust initially displayed by 3-year-olds was minimally affected by this intervening experience of differential accuracy. By contrast, the selective trust of 4- and 5-year-olds was affected. If the familiar teacher had been the more accurate, selective trust in her was intensified. If, on the other hand, the familiar teacher had been the less accurate, it was undermined, particularly among 5-year-olds. Thus, by 4 years of age, children trust familiar informants but moderate that trust depending on the informants’ recent history of accuracy or inaccuracy.


title: Cortical control of a prosthetic arm for self-feeding

abstract: Arm movement is well represented in populations of neurons recorded from the motor cortex. Cortical activity patterns have been used in the new field of brain-machine interfaces to show how cursors on computer displays can be moved in two- and three-dimensional space. Although the ability to move a cursor can be useful in its own right, this technology could be applied to restore arm and hand function for amputees and paralysed persons. However, the use of cortical signals to control a multi-jointed prosthetic device for direct real-time interaction with the physical environment ('embodiment') has not been demonstrated. Here we describe a system that permits embodied prosthetic control; we show how monkeys (Macaca mulatta) use their motor cortical activity to control a mechanized arm replica in a self-feeding task. In addition to the three dimensions of movement, the subjects' cortical signals also proportionally controlled a gripper on the end of the arm. Owing to the physical interaction between the monkey, the robotic arm and objects in the workspace, this new task presented a higher level of difficulty than previous virtual (cursor-control) experiments. Apart from an example of simple one-dimensional control, previous experiments have lacked physical interaction even in cases where a robotic arm or hand was included in the control loop, because the subjects did not use it to interact with physical objects-an interaction that cannot be fully simulated. This demonstration of multi-degree-of-freedom embodied prosthetic control paves the way towards the development of dexterous prosthetic devices that could ultimately achieve arm and hand function at a near-natural level.


title: Hulk: eliciting malicious behavior in browser extensions

abstract: We present Hulk, a dynamic analysis system that detects malicious behavior in browser extensions by monitoring their execution and corresponding network activity. Hulk elicits malicious behavior in extensions in two ways. First, Hulk leverages HoneyPages, which are dynamic pages that adapt to an extension's expectations in web page structure and content. Second, Hulk employs a fuzzer to drive the numerous event handlers that modern extensions heavily rely upon. We analyzed 48K extensions from the Chrome Web store, driving each with over 1M URLs. We identify a number of malicious extensions, including one with 5.5 million affected users, stressing the risks that extensions pose for today's web security ecosystem, and the need to further strengthen browser security to protect user data and privacy.


title: On query result diversification

abstract: In this paper we describe a general framework for evaluation and optimization of methods for diversifying query results. In these methods, an initial ranking candidate set produced by a query is used to construct a result set, where elements are ranked with respect to relevance and diversity features, i.e., the retrieved elements should be as relevant as possible to the query, and, at the same time, the result set should be as diverse as possible. While addressing relevance is relatively simple and has been heavily studied, diversity is a harder problem to solve. One major contribution of this paper is that, using the above framework, we adapt, implement and evaluate several existing methods for diversifying query results. We also propose two new approaches, namely the Greedy with Marginal Contribution (GMC) and the Greedy Randomized with Neighborhood Expansion (GNE) methods. Another major contribution of this paper is that we present the first thorough experimental evaluation of the various diversification techniques implemented in a common framework. We examine the methods' performance with respect to precision, running time and quality of the result. Our experimental results show that while the proposed methods have higher running times, they achieve precision very close to the optimal, while also providing the best result quality. While GMC is deterministic, the randomized approach (GNE) can achieve better result quality if the user is willing to tradeoff running time.


title: Neural Networks for Real-Time Traffic Signal Control

abstract: Real-time traffic signal control is an integral part of the urban traffic control system, and providing effective real-time traffic signal control for a large complex traffic network is an extremely challenging distributed control problem. This paper adopts the multiagent system approach to develop distributed unsupervised traffic responsive signal control models, where each agent in the system is a local traffic signal controller for one intersection in the traffic network. The first multiagent system is developed using hybrid computational intelligent techniques. Each agent employs a multistage online learning process to update and adapt its knowledge base and decision-making mechanism. The second multiagent system is developed by integrating the simultaneous perturbation stochastic approximation theorem in fuzzy neural networks (NN). The problem of real-time traffic signal control is especially challenging if the agents are used for an infinite horizon problem, where online learning has to take place continuously once the agent-based traffic signal controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District of Singapore has been developed using PARAMICS microscopic simulation program. Simulation results show that the hybrid multiagent system provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as the SPSA-NN-based multiagent system as the complexity of the simulation scenario increases. Using the hybrid NN-based multiagent system, the mean delay of each vehicle was reduced by 78% and the mean stoppage time, by 85% compared to the existing traffic signal control algorithm. The promising results demonstrate the efficacy of the hybrid NN-based multiagent system in solving large-scale traffic signal control problems in a distributed manner


title: Linked Data in Education: A Survey and a Synthesis of Actual Research and Future Challenges

abstract: Linked Data principles and technologies are being investigated in various areas. In the Educational context, many studies are using Linked Data trying to solve problems of interoperability of educational data and resources, enriching educational content, and personalizing and recommending educational content and practices. This article presents a systematic mapping of proposals which have been adopting Linked Data to support education, and, based on analysis of these proposals, we discuss the tools, vocabularies, and datasets being used, providing a research landscape of the area. We also present challenges and trends which can foster future research in this area.


title: IT Alignment in Public Organizations: A Systematic Literature Review

abstract: IT alignment is one of the widely researched topics in the information systems research area and continue to be a top management concern. However, IT alignment in the context of public organizations is still less explored. A systematic literature review is conducted to provide an overview of the current status of the research in this area and the possible future research directions. Major databases and top conference proceedings in information systems and e-government area were searched by using key words to collect articles for analysis. Finally, a total of 58 articles were identified and included in the literature review. The findings of this study indicate case study research as the most preferred research method. But due to the small numbers of public organizations explored in these studies the results are problematic for generalisation. Apart from these findings we have identified and discussed the research concepts addressed in IT alignment in public organizations studies that were categorized using the dimensions of Strategic Alignment Maturity model. The findings of this study have revealed some potential future research directions that have been less explored like organizational culture, organizational politics, social interaction, and informal organizational structure influence on IT alignment in public organizations.


title: Romantic love conceptualized as an attachment process.

abstract: This article explores the possibility that romantic love is an attachment process--a biosocial process by which affectional bonds are formed between adult lovers, just as affectional bonds are formed earlier in life between human infants and their parents. Key components of attachment theory, developed by Bowlby, Ainsworth, and others to explain the development of affectional bonds in infancy, were translated into terms appropriate to adult romantic love. The translation centered on the three major styles of attachment in infancy--secure, avoidant, and anxious/ambivalent--and on the notion that continuity of relationship style is due in part to mental models (Bowlby's "inner working models") of self and social life. These models, and hence a person's attachment style, are seen as determined in part by childhood relationships with parents. Two questionnaire studies indicated that (a) relative prevalence of the three attachment styles is roughly the same in adulthood as in infancy, (b) the three kinds of adults differ predictably in the way they experience romantic love, and (c) attachment style is related in theoretically meaningful ways to mental models of self and social relationships and to relationship experiences with parents. Implications for theories of romantic love are discussed, as are measurement problems and other issues related to future tests of the attachment perspective. One of the landmarks of contemporary psychology is Bowlby's (1969, 1973, 1980) three-volume exploration of attachment, separation, and loss, the processes by which affectional bonds are forged and broken. Bowlby's major purpose was to describe and explain how infants become emotionally attached to their primary caregivers and emotionally distressed when separated from them, although he also contended that "attachment behavior [characterizes] human beings from the cradle to the grave" (1979, p. 129). In recent years, laboratory and naturalistic studies of infants and children (summarized by Bretherton, 1985, and Maccoby, 1980) have provided considerable support for attachment theory, which was proposed by Bowlby and elaborated by several other investigators. The purpose of this article is to explore the possibility that this theory, designed primarily with infants in mind, offers a valuable perspective on adult romantic love. We will suggest that romantic love is an attachment process (a process of becoming attached), We are grateful to Donna Bradshaw for sharing her expertise in the areas of attachment theory and research, to Marry Meitus for allowing us to conduct Study 1 in the


title: The representational geometry of word meanings acquired by neural machine translation models

abstract: This work is the first comprehensive analysis of the properties of word embeddings learned by neural machine translation (NMT) models trained on bilingual texts. We show the word representations of NMT models outperform those learned from monolingual text by established algorithms such as Skipgram and CBOW on tasks that require knowledge of semantic similarity and/or lexical---syntactic role. These effects hold when translating from English to French and English to German, and we argue that the desirable properties of NMT word embeddings should emerge largely independently of the source and target languages. Further, we apply a recently-proposed heuristic method for training NMT models with very large vocabularies, and show that this vocabulary expansion method results in minimal degradation of embedding quality. This allows us to make a large vocabulary of NMT embeddings available for future research and applications. Overall, our analyses indicate that NMT embeddings should be used in applications that require word concepts to be organised according to similarity and/or lexical function, while monolingual embeddings are better suited to modelling (nonspecific) inter-word relatedness.


title: doi:10.1155/2008/197875 Research Article Robust Abandoned Object Detection Using Dual Foregrounds

abstract: As an alternative to the tracking-based approaches that heavily depend on accurate detection of moving objects, which often fail for crowded scenarios, we present a pixelwise method that employs dual foregrounds to extract temporally static image regions. Depending on the application, these regions indicate objects that do not constitute the original background but were brought into the scene at a subsequent time, such as abandoned and removed items, illegally parked vehicles. We construct separate long- and short-term backgrounds that are implemented as pixelwise multivariate Gaussian models. Background parameters are adapted online using a Bayesian update mechanism imposed at different learning rates. By comparing each frame with these models, we estimate two foregrounds. We infer an evidence score at each pixel by applying a set of hypotheses on the foreground responses, and then aggregate the evidence in time to provide temporal consistency. Unlike optical flow-based approaches that smear boundaries, our method can accurately segment out objects even if they are fully occluded. It does not require on-site training to compensate for particular imaging conditions. While having a low-computational load, it readily lends itself to parallelization if further speed improvement is necessary.


Mutual Information-based Intrusion Detection Model for Industrial Internet