Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN

Text classification to a hierarchical taxonomy of topics is a common and practical problem. Traditional approaches simply use bag-of-words and have achieved good results. However, when there are a lot of labels with different topical granularities, bagof-words representation may not be enough. Deep learning models have been proven to be effective to automatically learn different levels of representations for image data. It is interesting to study what is the best way to represent texts. In this paper, we propose a graph-CNN based deep learning model to first convert texts to graph-of-words, and then use graph convolution operations to convolve the word graph. Graph-ofwords representation of texts has the advantage of capturing non-consecutive and long-distance semantics. CNN models have the advantage of learning different level of semantics. To further leverage the hierarchy of labels, we regularize the deep architecture with the dependency among labels. Our results on both RCV1 and NYTimes datasets show that we can significantly improve large-scale hierarchical text classification over traditional hierarchical text classification and existing deep models.


Calibrated recommendations

When a user has watched, say, 70 romance movies and 30 action movies, then it is reasonable to expect the personalized list of recommended movies to be comprised of about 70% romance and 30% action movies as well. This important property is known as calibration, and recently received renewed attention in the context of fairness in machine learning. In the recommended list of items, calibration ensures that the various (past) areas of interest of a user are reflected with their corresponding proportions. Calibration is especially important in light of the fact that recommender systems optimized toward accuracy (e.g., ranking metrics) in the usual offline-setting can easily lead to recommendations where the lesser interests of a user get crowded out by the user's main interests-which we show empirically as well as in thought-experiments. This can be prevented by calibrated recommendations. To this end, we outline metrics for quantifying the degree of calibration, as well as a simple yet effective re-ranking algorithm for post-processing the output of recommender systems.


Improved Bat Algorithm (IBA) on Continuous Optimization Problems

—Heuristic optimization techniques have became very popular techniques and have widespread usage areas. Since they do not include mathematical terms, heuristic methods have been carried out on many fields by researchers. Main purpose of these techniques is to achieve good performance on problem of interest. One of these techniques is Bat Algorithm (BA). BA is an optimization algorithm based on echolocation characteristic of bats and developed by mimics of bats' foraging behavior. In this study, exploration and exploitation mechanisms of BA are improved by three modifications. Performance of proposed and standard version of algorithm is compared on ten basic benchmark test problems. Results indicate that proposed version is better than standard version in terms of solution quality.


Classifying sanctions and designing a conceptual sanctioning process model for socio-technical systems

We understand a socio-technical system (STS) as a cyber-physical system in which two or more autonomous parties interact via or about technical elements, including the parties’ resources and actions. As information technology begins to pervade every corner of human life, STSs are becoming ever more common, and the challenge of governing STSs is becoming increasingly important. We advocate a normative basis for governance, wherein norms represent the standards of correct behaviour that each party in an STS expects from others. A major benefit of focussing on norms is that they provide a socially realistic view of interaction among autonomous parties that abstracts low-level implementation details. Overlaid on norms is the notion of a sanction as a negative or positive reaction to potentially any violation of or compliance with an expectation. Although norms have been well studied as regards governance for STSs, sanctions have not. Our understanding and usage of norms is inadequate for the purposes of governance unless we incorporate a comprehensive representation of sanctions. We address the aforementioned gap by proposing (i) a sanction typology that reflects the relevant features of sanctions, and (ii) a conceptual sanctioning process model providing a functional structure for sanctioning in STS. We demonstrate our contributions via a motivating scenario from the domain of renewable energy trading.


Classification of learning outcomes : evidence from the computer games literature

Following up on an earlier issue of The Curriculum Journal (Vol. 16, No. 1), this article focuses on learning outcomes in the context of video games. Learning outcomes are viewed from two theoretical frameworks: Kirkpatrick’s levels of evaluation and the CRESST model of learning. These are used to analyse the outcomes claimed in journal articles that report empirical work, indicating the usefulness of the frameworks, and the necessity to consider the role of affective learning. The article ends with some comments on the relationship of instructional design to effective games and learning outcomes.


Keyword extraction based on tf/idf for Chinese news document

Keyword extraction is an important research topic of information retrieval. This paper gave the specification of keywords in Chinese news documents based on analyzing linguistic characteristics of news documents and then proposed a new keyword extraction method based on tf/idf with multi-strategies. The approach selected candidate keywords of uni-, bi-and tri- grams, and then defines the features according to their morphological characters and context information. Moreover, the paper proposed several strategies to amend the incomplete words gotten from the word segmentation and found unknown potential keywords in news documents. Experimental results show that our proposed method can significantly outperform the baseline method. We also applied it to retrospective event detection. Experimental results show that the accuracy and efficiency of news retrospective event detection can be significantly improved.


Food-Bridging: A New Network Construction to Unveil the Principles of Cooking

In this manuscript, we propose, analyze, and discuss a possible new principle behind traditional cuisine: the Food-bridging hypothesis and its comparison with the food-pairing hypothesis using the same dataset and graphical models employed in the food-pairing study by Ahn et al. (2011). The Food-bridging hypothesis assumes that if two ingredients do not share a strong molecular or empirical affinity, they may become affine through a chain of pairwise affinities. That is, in a graphical model as employed by Ahn et al., a chain represents a path that joints the two ingredients, the shortest path represents the strongest pairwise chain of affinities between the two ingredients. Food-pairing and Food-bridging are different hypotheses that may describe possible mechanisms behind the recipes of traditional cuisines. Food-pairing intensifies flavor by mixing ingredients in a recipe with similar chemical compounds, and food-bridging smoothes contrast between ingredients. Both food-pairing and food-bridging are observed in traditional cuisines, as shown in this work. We observed four classes of cuisines according to food-pairing and food-bridging: (1) East Asian cuisines, at one extreme, tend to avoid food-pairing as well as food-bridging; and (4) Latin American cuisines, at the other extreme, follow both principles. For the two middle classes: (2) Southeastern Asian cuisines, avoid foodpairing and follow food-bridging; and (3) Western cuisines, follow food-pairing and avoid food-bridging.


Drug Treatment of Sleep Disorders

Sleep is a complex behavior that cyclically alternates with wakefulness and is fundamental for both mental and physical wellness. During normal sleep the body acquires a specific posture; any active behavior is absent and mental activity typically oscillates between a state of “loss of consciousness” and the experience of dreaming. Sleep is, by itself, a cyclical process characterized by the alternation of two phases, non-REM (NREM) sleep and “rapid eye movement” (REM) sleep, during which the typical synchronized electroencephalographic (EEG) pattern of NREM sleep is substituted by a “paradoxical” EEG desynchronization. NREM sleep is a state of minimal energy expenditure and motor activity, during which cardiorespiratory and thermoregulatory variables are driven by the autonomic nervous system at a lower and more stable level compared to wakefulness. During REM sleep, beyond the occurrence of REMs, posture control is lost and dreams become more vivid. Furthermore, autonomic activity is highly unstable leading to centrally driven surges in heart rate and blood pressure, breathing becomes irregular, and thermoregulation is suspended or depressed, suggesting a derangement of the integrative regulation of physiological functions. Overall sleep quality is one of the most important parameters considered for the subjective assessment of quality of life and general health status. However, although sleep function(s) is intuitively associated with some kind of rest that seems to be mostly required by the central nervous system, and many theories on sleep function have been proposed, a full comprehension of sleep function has not yet been achieved and is probably not imminent. C. Berteotti • M. Cerri • M. Luppi • A. Silvani • R. Amici (*) Department of Biomedical and Neuromotor Sciences—Physiology, Alma Mater Studiorum-University of Bologna, Piazza P.ta S. Donato, 2, 40126 Bologna, Italy e-mail: chiara.berteotti@unibo.it; matteo.cerri@unibo.it; marco.luppi@unibo.it; alessandro.silvani3@unibo.it; roberto.amici@unibo.it; http://www.unibo.it/Faculty/default. aspx?UPN1⁄4roberto.amici%40unibo.it © Springer International Publishing Switzerland 2015 A. Guglietta (ed.), Drug Treatment of Sleep Disorders, Milestones in Drug Therapy, DOI 10.1007/978-3-319-11514-6_1 3 1 Neurophysiology and Neurobiology of Sleep


Inductance and Resistance Calculations in Three-Dimensional Packaging Using Cylindrical Conduction-Mode Basis Functions

For the successful electrical design of system-in-package, this paper proposes an efficient method for extracting wideband resistance and inductance from a large number of 3-D interconnections. The proposed method uses the modal equivalent network from the electric field integral equation with cylindrical conduction-mode basis function, which reduces the matrix size for large 3-D interconnection problems. Additional enhancement schemes proposed further reduce the cost for computing the partial inductances. Therefore, the method discussed in this paper can be used to construct accurate models of a large number of 3-D interconnection structures such as more than 100 bonding wires used for stacking chips.


Aligning English Strings with Abstract Meaning Representation Graphs

We align pairs of English sentences and corresponding Abstract Meaning Representations (AMR), at the token level. Such alignments will be useful for downstream extraction of semantic interpretation and generation rules. Our method involves linearizing AMR structures and performing symmetrized EM training. We obtain 86.5% and 83.1% alignment F score on development and test sets.


An Impedance and Multi-Wavelength Near-Infrared Spectroscopy IC for Non-Invasive Blood Glucose Estimation

A multi-modal spectroscopy IC combining impedance spectroscopy (IMPS) and multi-wavelength near-infrared spectroscopy (mNIRS) is proposed for high precision non-invasive glucose level estimation. A combination of IMPS and mNIRS can compensate for the glucose estimation error to improve its accuracy. The IMPS circuit measures dielectric characteristics of the tissue using the RLC resonant frequency and the resonant impedance to estimate the glucose level. To accurately find resonant frequency, a 2-step frequency sweep sinusoidal oscillator (FSSO) is proposed: 1) 8-level coarse frequency switching (fSTEP = 9.4 kHz) in 10-76 kHz, and 2) fine analog frequency sweep in the range of 18.9 kHz. During the frequency sweep, the adaptive gain control loop stabilizes the output voltage swing (400 mVp-p). To improve accuracy of mNIRS, three wavelengths, 850 nm, 950 nm, and 1,300 nm, are used. For highly accurate glucose estimation, the measurement data of the IMPS and mNIRS are combined by an artificial neural network (ANN) in external DSP. The proposed ANN method reduces the mean absolute relative difference to 8.3% from 15% of IMPS, and 15-20% of mNIRS in 80-180 mg/dL blood glucose level. The proposed multi-modal spectroscopy IC occupies 12.5 mm 2 in a 0.18 μm 1P6M CMOS technology and dissipates a peak power of 38 mW with the maximum radiant emitting power of 12.1 mW.


Goalie Actions Attackers and Defenders Goalie Role Allocation Layer Action Selection Layer Vector Field Action Shoot Action Action Selection Layer Vector Field Action Shoot Action Action Selection Layer Vector Field Action Shoot Action Action Selection Layer Vector Field Action

The robot soccer system is being used as a test bed to develop the next generation of field robots. In the multiagent system, action selection is important for the cooperation and coordination among agents. There are many techniques in choosing a proper action of the agent. As the environment is dynamic, reinforcement learning is more suitable than supervised learning. Reinforcement learning is based on trial and error method through experience. Because of this, reinforcement learning is applied to many practical problems. However, straightforward application of reinforcement learning algorithm may not successfully scale up to more complex multi-agent learning problems. To solve this problem, modular Q-learning is employed for multi-agent learning in reinforcement methods. A modified uni-vector field is used for robot navigation. This paper discusses the Y2K2 NaroSot(Nano-Robot World Cup Soccer Tournament) robot soccer system.


Bernhard Ø. Palsson Cambridge U. Press, New York, 2006. $75.00 (322 pp.). ISBN 978-0-521-85903-5 Reviewed by Nigel Goldenfeld For some of us physicists, the recent explosion of interest in biology by our colleagues is something of a mystery— perhaps even a betrayal. Biology seems to offer none of the aesthetic elegance and beauty that is the hallmark of such cherished creations as the general theory of relativity and the standard model of strong, weak, and electromagnetic interactions. Neither does biology provide the breathtaking precision with which physical experiment and theory can agree in such phenomena as quantum electrodynamics or Josephson effects. And don’t even think about summarizing all of biology on a T-shirt; biology textbooks, unlike those for physics, generally describe reactions and structures in monstrous detail, without an equation in sight. So why the allure of the subject among physicists? And what meaningful contributions can physical scientists make to the field? One answer is the emergence of a new science of biological circuits, or systems biology, which has become advanced enough, both scientifically and promotionally, that textbooks are warranted. Uri Alon’s An Introduction to Systems Biology: Design Principles of Biological Circuits and Bernhard Ø. Palsson’s Systems Biology: Properties of Reconstructed Networks are two recent offerings by leaders in this new field. These texts deserve serious attention from any quantitative scientist or physicist who hopes to learn about modern biology. Both books are well written; each has a different focus. Thus they could be used together to create an accessible course for beginners, with the instructor’s choice of topics from each book. The books are a welcome departure from the typical biology textbook. First, they emphasize quantitative modeling rather than nomenclature; second, they concentrate on essentials of the modeling rather than on exhaustive lists of components that offer no meaningful integration; and third, the approaches described in the texts make nontrivial quantitative predictions that can be and have been verified. Alon’s book is the better place for physicists to start. It assumes no prior knowledge of or even interest in biology. Yet right from chapter 1 the author succeeds in explaining in an intellectually exciting way what the cell does and what degrees of freedom enable it to function. The book proceeds with detailed discussions of some of the key network motifs, circuitelement designs that are believed to be repeated over and over again in biological systems. Those motifs include autoregulation, feed-forward loops, positive-feedback loops, and kinetic proofreading. The discussions in all cases introduce the particular motif, use simple differential equations in most cases as a way to model it, and offer plenty of comparisons with experimental data. Alon does not forget to ask a question physicists obsess over: Are any general physical principles at work in biology? Alon does not overplay that point but has included a nice chapter on robustness. He focuses on concrete examples such as chemotaxis and developmental pattern formation. He also devotes a chapter to explaining the precision with which biological processes can so accurately identify the right molecules amidst a sea of potentially confusing “wrong” targets. The basis of this process is kinetic proofreading, a concept first presented by physicist John Hopfield, and one that is based on simple nonequilibrium kinetics. Alon ends his book with an epilogue on simplicity in biology. He draws the detailed strands together into an appealing and inspiring overview of biology. Without the preceding attention to biological detail, this exercise would be vacuous. One final aspect of An Introduction to Systems Biology that must be mentioned is the wonderful set of exercises that accompany each chapter. The exercises range from elementary to advanced research areas. For example, chapter 9 contains problems on the errorminimization capabilities of the genetic code, which I and several other physicists, including Alon, are still elucidating. I would have liked to see evolution play a more central role in the text—for example, in a chapter of its own that amplifies the important comments in the epilogue about the evolution of modularity. Perhaps when it’s time for the second edition, there will be enough concrete material to further justify including the topic. Palsson’s Systems Biology presents a completely different perspective. The book focuses less on principles and more on techniques for reconstructing detailed, elaborate, and predictive circuit models of biological systems. During the past 10 years these techniques have become feasible for entire organisms, thanks to the availability of fully sequenced genomes. It is now possible to map out the metabolic networks of bacteria such as Escherichia coli and simpler eukaryotes. However, other networks in organisms control other


Ultra Low Power Bioelectronics Fundamentals , Biomedical Applications , and Bio-inspired Systems

This book provides, for the first time, a broad and deep treatment of the fields of both ultra low power electronics and bioelectronics. It discusses fundamental principles and circuits for ultra low power electronic design and their applications in biomedical systems. It also discusses how ultra energy-efficient cellular and neural systems in biology can inspire revolutionary low power architectures in mixed-signal and RF electronics. The book presents a unique, unifying view of ultra low power analog and digital electronics and emphasizes the use of the ultra energy-efficient subthreshold regime of transistor operation in both. Chapters on batteries, energy harvesting, and the future of energy provide an understanding of fundamental relationships between energy use and energy generation at small scales and at large scales. A wealth of insights and examples from brain implants, cochlear implants, biomolecular sensing, cardiac devices, and bio-inspired systems make the book useful and engaging for students and practicing engineers.


An analog bionic ear processor with zero-crossing detection

A 75 dB 251 /spl mu/W analog speech processor is described that preserves the performance, robustness, and programmability needed for deaf patients at a reduced power consumption compared to that of implementations with A/D and DSP. It also provides zero-crossing outputs for stimulation strategies that use phase information to improve performance.


Genomics of cellulosic biofuels

The development of alternatives to fossil fuels as an energy source is an urgent global priority. Cellulosic biomass has the potential to contribute to meeting the demand for liquid fuel, but land-use requirements and process inefficiencies represent hurdles for large-scale deployment of biomass-to-biofuel technologies. Genomic information gathered from across the biosphere, including potential energy crops and microorganisms able to break down biomass, will be vital for improving the prospects of significant cellulosic biofuel production.


A 10-nW 12-bit accurate analog storage cell with 10-aA leakage

Medium-term analog storage offers a compact, accurate, and low-power method of implementing temporary local memory that can be useful in adaptive circuit applications. The performance of these cells is characterized by the sampling accuracy and voltage droop that can be achieved with a given level of die area and power. Hand calculations suggest past implementations have not achieved minimum voltage droop due to uncompensated MOS leakage mechanisms. In this paper, the dominant sources of MOS leakage are experimentally characterized in a standard 1.5-/spl mu/m CMOS process using an on-chip current integration technique, focusing specifically on the 1 fA to 1 aA current range. These measurements reveal an accumulation-mode source-drain coupling mechanism that can easily dominate diode leakage under certain bias conditions and may have limited previous designs. A simple rule-of-thumb is offered for avoiding this leakage effect, leading to a novel ultra-low leakage switch topology. A differential storage cell incorporating this new switch achieves an average leakage of 10 aA at room temperature, an 8/spl times/ reduction over past designs. The cell loses one bit of voltage accuracy, 700 /spl mu/V on a 12-bit scale and 11.3 mV on an 8-bit scale, in 3.3 and 54 min, respectively. This represents a 15/spl times/ increase in hold time at these voltage accuracies over the lowest leakage cell to date, in only 92% of the area. Since the leakage is independent of amplifier bias, the cell can operate on as little as 10 nW of power. Initial measurements also indicate the switch's leakage decreases with the square of process feature size.


Electric Motor Drive Selection Issues for HEV Propulsion Systems: A Comparative Study

This paper describes a comparative study allowing the selection of the most appropriate electric-propulsion system for a parallel hybrid electric vehicle (HEV). This paper is based on an exhaustive review of the state of the art and on an effective comparison of the performances of the four main electric-propulsion systems, namely the dc motor, the induction motor (IM), the permanent magnet synchronous motor, and the switched reluctance motor. The main conclusion drawn by the proposed comparative study is that it is the cage IM that better fulfills the major requirements of the HEV electric propulsion


Blockchain and its uses

Blockchain, the technology behind most cryptocurrencies that exist today, offers a paradigm shifting technology that has the potential to transform the way that we record and verify events on the Internet. By offering a decentralized, immutable, community verified record of transactions, regardless of what these transactions represent, blockchain technology promises to transform many industries. In this paper, we provide a survey of this very important technology and discuss its possible use-cases and its effect on society. The purpose of this paper is to familiarize the reader with the current state-of-the-art in the blockchain world both in terms of technology and social impact.


ShapeNet: An Information-Rich 3D Model Repository

We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations. Annotations are made available through a public web-based interface to enable data visualization of object attributes, promote data-driven geometric analysis, and provide a large-scale quantitative benchmark for research in computer graphics and vision. At the time of this technical report, ShapeNet has indexed more than 3,000,000 models, 220,000 models out of which are classified into 3,135 categories (WordNet synsets). In this report we describe the ShapeNet effort as a whole, provide details for all currently available datasets, and summarize future plans.


A New Aggregate Local Mobility (ALM) Clustering Algorithm for VANETs

We present a beacon-based clustering algorithm aimed at prolonging the cluster lifetime in VANETs. We use a new aggregate local mobility criterion to decide upon cluster re-organisation. The scheme incorporates a contention method to avoid triggering frequent re-organisations when two clusterheads encounter each other for a short period of time. Simulation results show a significant improvement of cluster lifetime and reduced node state/role changes compared to previous popular clustering algorithms.


Kinematic analysis of electroactive polymer actuators as soft and smart structures with more DoF than inputs

Electroactive polymer (EAP) actuators have been attracting the attention of researchers due to their muscle-like behaviour and unusual properties. Several modelling methods have been proposed to understand their mechanical, chemical, electrical behaviours or `electro-chemo-mechanical' behaviour. However, estimating the whole shape or configuration of the EAP actuators has always been challenging due to their highly non-linear bending behaviour. This paper reports on an effective method to estimate the whole shape deflection of the EAP actuators by employing a so-called backbone approach. Tri-layer configured polypyrrole (PPy) based EAP actuators were used as a soft and smart structure with more degrees of freedom than its input. After deriving the inverse kinematic model of the actuator, its complete shape is estimated by solving the inverse kinematic model with an angle optimization (AngleOPT) method. The experimental results and numerical results have demonstrated the effectiveness of the method in estimating the highly non-linear bending behaviour of the PPy actuators and applicability of this modelling approach to other EAP actuators.


A Bidirectional Bridge Modular Switched-Capacitor-Based Power Electronics Transformer

This paper proposes a new bridge modular switched-capacitor-based ac–ac converter, which is intended to replace conventional transformers in commercial and residential applications, especially for microgrids. The main advantages of the proposed converter lie in high efficiency and power factor, bidirectional operation, and capacitor voltage self-balancing ability. Another distinct merit is its symmetric construction, with which the voltage ripple of charge/pump capacitors can be cancelled out, and the output voltage will be smooth with significant ripple reduction. It is also noteworthy that the symmetric construction ensures that the two symmetric parts can work independently under a simplified control strategy. In addition, the modular configuration contributes to convenient voltage extension. Compared with the reported flying-capacitor switched-capacitor ac–ac converter, the proposed converter requires fewer switches and capacitors per unit of voltage conversion, thus suffering lower voltage stress. To demonstrate the performance of the converter, a prototype with 600-W 220-Vrms high-side voltage, 55-Vrms low-side voltage, and a switching frequency of 100 kHz was designed and fabricated. The relevant experimental results are reported herein. The maximum and rated power efficiency obtained were 95.9% and 94.4%, respectively.


Symbol Interdependency in Symbolic and Embodied Cognition

Whether computational algorithms such as latent semantic analysis (LSA) can both extract meaning from language and advance theories of human cognition has become a topic of debate in cognitive science, whereby accounts of symbolic cognition and embodied cognition are often contrasted. Albeit for different reasons, in both accounts the importance of statistical regularities in linguistic surface structure tends to be underestimated. The current article gives an overview of the symbolic and embodied cognition accounts and shows how meaning induction attributed to a specific statistical process or to activation of embodied representations should be attributed to language itself. Specifically, the performance of LSA can be attributed to the linguistic surface structure, more than special characteristics of the algorithm, and embodiment findings attributed to perceptual simulations can be explained by distributional linguistic information.


Challenges and business models for mobile location-based services and advertising

Mobile advertising will become more pervasive and profitable, but not before addressing key technical and business challenges.


Recognition and volume estimation of food intake using a mobile device

We present a system that improves accuracy of food intake assessment using computer vision techniques. Traditional dietetic method suffers from the drawback of either inaccurate assessment or complex lab measurement. Our solution is to use a mobile phone to capture images of foods, recognize food types, estimate their respective volumes and finally return quantitative nutrition information. Automated and accurate food recognition presents the following challenges. First, there exist a large variety of food types that people consume in everyday life. Second, a single category of food may contain large variations due to different ways of preparation. Also, diverse lighting conditions may lead to varying visual appearance of foods. All of these pose a challenge to the state of the art recognition approaches. Moreover, the low quality images captured using cellphones make the task of 3D reconstruction difficult. In this paper, we combine several vision techniques (visual recognition and 3D reconstruction) to achieve quantitative food intake estimation. Evaluation of both recognition and reconstruction is provided in the experimental results.


Automatic Array Alignment in Data-Parallel Programs

Data-parallel languages like Fortran 90 express parallelism in the form of operations on data aggregates such as arrays. Misalignment of the operands of an array operation can reduce program performance on a distributed-memory parallel machine by requiring nonlocal data accesses. Determining array alignments that reduce communication is therefore a key issue in compiling such languages.
We present a framework for the automatic determination of array alignments in data-parallel languages such as Fortran 90. Our language model handles array sectioning, reductions, spreads, transpositions, and masked operations. We decompose alignment functions into three constituents: axis, stride, and offset. For each of these subproblems, we show how to solve the alignment problem for a basic block of code, possibly containing common subexpressions. Alignments are generated for all array objects in the code, both named program variables and intermediate results. The alignments obtained by our algorithms are more general than those provided by the “owner-computes” rule. Finally, we present some ideas for dealing with control flow, replication, and dynamic alignments that depend on loop induction variables.


Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome.

Methods for assessment, e.g., anthropometric indicators and imaging techniques, of several phenotypes of human obesity, with special reference to abdominal fat content, have been evaluated. The correlation of fat distribution with age, gender, total body fat, energy balance, adipose tissue lipoprotein lipase and lipolytic activity, adipose tissue receptors, and genetic characteristics are discussed. Several secreted or expressed factors in the adipocyte are evaluated in the context of fat tissue localization. The body fat distribution and the metabolic profile in nonobese and obese individuals is discussed relative to lipolysis, antilypolysis and lipogenesis, insulin sensitivity, and glucose, lipid, and protein metabolism. Finally, the endocrine regulation of abdominal visceral fat in comparison with the adipose tissue localized in other areas is presented.


The language of mental health problems in social media

Online social media, such as Reddit, has become an important resource to share personal experiences and communicate with others. Among other personal information, some social media users communicate about mental health problems they are experiencing, with the intention of getting advice, support or empathy from other users. Here, we investigate the language of Reddit posts specific to mental health, to define linguistic characteristics that could be helpful for further applications. The latter include attempting to identify posts that need urgent attention due to their nature, e.g. when someone announces their intentions of ending their life by suicide or harming others. Our results show that there are a variety of linguistic features that are discriminative across mental health user communities and that can be further exploited in subsequent classification tasks. Furthermore, while negative sentiment is almost uniformly expressed across the entire data set, we demonstrate that there are also condition-specific vocabularies used in social media to communicate about particular disorders. Source code and related materials are available from: https: //github.com/gkotsis/ reddit-mental-health.


CipherXRay: Exposing Cryptographic Operations and Transient Secrets from Monitored Binary Execution

Malwares are becoming increasingly stealthy, more and more malwares are using cryptographic algorithms (e.g., packing, encrypting C&C communication) to protect themselves from being analyzed. The use of cryptographic algorithms and truly transient cryptographic secrets inside the malware binary imposes a key obstacle to effective malware analysis and defense. To enable more effective malware analysis, forensics, and reverse engineering, we have developed CipherXRay - a novel binary analysis framework that can automatically identify and recover the cryptographic operations and transient secrets from the execution of potentially obfuscated binary executables. Based on the avalanche effect of cryptographic functions, CipherXRay is able to accurately pinpoint the boundary of cryptographic operation and recover truly transient cryptographic secrets that only exist in memory for one instant in between multiple nested cryptographic operations. CipherXRay can further identify certain operation modes (e.g., ECB, CBC, CFB) of the identified block cipher and tell whether the identified block cipher operation is encryption or decryption in certain cases. We have empirically validated CipherXRay with OpenSSL, popular password safe KeePassX, the ciphers used by malware Stuxnet, Kraken and Agobot, and a number of third party softwares with built-in compression and checksum. CipherXRay is able to identify various cryptographic operations and recover cryptographic secrets that exist in memory for only a few microseconds. Our results demonstrate that current software implementations of cryptographic algorithms hardly achieve any secrecy if their execution can be monitored.


DDoS: threats and mitigation

According to a report from Prolexic, one of the first and largest companies offering DDoS mitigation services,  attack traffic rose 66% over the course of a year (to Q3 2011). 1 Network-layer attacks accounted for 83%, the rest being application-layer attacks. The average duration was 1.4 days and the average bandwidth consumed was 1.5Gbps. The size of attacks is getting bigger, too. In July 2011, Prolexic announced it had mitigated what it believed to be the largest packet-per-second DDoS attack ever seen in Asia. Consisting of SYN and ICMP floods, the attack deployed 176,000 bots (compared to the 5,000-10,000 bots more normally seen by Prolexic) to generate 25 million packets per second. According to the company, the majority of high-end border rout-ers typically forward 70,000 packets per second. It mitigated the attack by distributing traffic among its Tier 1 carrier partners and scrubbing centres. Ben Petro, senior VP network intelligence & availability at Verisign, traces the rise of DDoS attacks back another year. He says that for years there was little awareness of the problem but that, " 2010 was a dramatic shift – not only in the size, scale and trajectory of DDoS, but also in its proliferation and the number of different types of organisation that were hit. " In the years 2006-2008, he adds, the average attack was somewhere around 40Mbps. " And then, all of a sudden, coming in 2010 we started to see 2Gbps, then 5Gbps, then 8Gbps and 15Gbps attacks, culminating in the largest that we've seen coming in at 84Gbps sustained for a week and a half. That is an incredible amount of traffic when you get down to packets or queries per second – you're over the hundreds of thousands of queries per second per location for Verisign, and we have 165 locations. So it's an enormous volume. " The rising popularity of DDoS attacks may be connected directly to their effectiveness. Paul Sop, CTO at Prolexic, says, " people get creative and they use and invent new ways and reasons to use DDoS. When an idea catches on in the industry there's a tipping point – people say, hey here's a type of attack we can launch and it's very likely we won't go to jail unless we're pretty dumb about it. " He adds: " It is like asymmetric warfare – the attackers have so much advantage in terms …


Software-defined optical networks (SDONs): a survey

This paper gives an overview of software-defined optical networks (SDONs). It explains the general concepts on software-defined networks (SDNs), their relationship with network function virtualization, and also about OpenFlow, which is a pioneer protocol for SDNs. It then explains the benefits and challenges of extending SDNs to multilayer optical networks, including flexible grid and elastic optical networks, and how it compares to generalized multi-protocol label switching for implementing a unified control plane. An overview on the industry and research efforts on SDON standardization and implementation is given next, to bring the reader up to speed with the current state of the art in this field. Finally, the paper outlines the benefits achieved by SDONs for network operators, and also some of the important and relevant research problems that need to be addressed.


OCR for bilingual documents using language modeling

Script based features are highly discriminative for text segmentation and recognition. Thus they are widely used in Optical Character Recognition(OCR) problems. But usage of script dependent features restricts the adaptation of such architectures directly for another script. With script independent systems, this problem can be solved to a certain extent for monolingual documents. But the problem aggravates in case of multilingual documents as it is very difficult for a single classifier to learn many scripts. Generally a script identification module identifies text segments and accordingly the script-dependent classifier is selected. This paper presents a unified framework of language model and multiple preprocessing hypotheses for word recognition from bilingual document images. Prior to text recognition, preprocessing steps such as binarization and segmentation are required for ease of recognition. But these steps induce huge combinatorial error propagating to final recognition accuracy. In this paper we use multiple preprocessing routines as alternate hypotheses and use a language model to verify each alternative and choose the best recognized sequence. We test this architecture for word recognition of Kannada-English and Telugu-English bilingual documents and achieved better recognition rates than single methods using same classifier.


A Use Case Identification Framework and Use Case Canvas for identifying and exploring relevant Blockchain opportunities

Blockchain is a new, foundational technology with a vast amount of application possibilities. However, practitioners might not be aware of which use cases in their own business model might benefit from blockchain technology. To aid them in analyzing their business regarding blockchain suitability, this paper introduces a use case identification framework for blockchain and a use case canvas. In the development process they have been evaluated with internal and external reviews in order to offer the best possible guidance. In combination they offer an analysis framework to help practitioners decide which use cases they should take into account for blockchain technology, which characteristics these blockchain implementations would have, and which specific advantages they would offer.


Neural Adaptation Layers for Cross-domain Named Entity Recognition

Recent research efforts have shown that neural architectures can be effective in conventional information extraction tasks such as named entity recognition, yielding state-of-the-art results on standard newswire datasets. However, despite significant resources required for training such models, the performance of a model trained on one domain typically degrades dramatically when applied to a different domain, yet extracting entities from new emerging domains such as social media can be of significant interest. In this paper, we empirically investigate effective methods for conveniently adapting an existing, well-trained neural NER model for a new domain. Unlike existing approaches, we propose lightweight yet effective methods for performing domain adaptation for neural models. Specifically, we introduce adaptation layers on top of existing neural architectures, where no re-training using the source domain data is required. We conduct extensive empirical studies and show that our approach significantly outperforms stateof-the-art methods.


Boosting Optical Character Recognition: A Super-Resolution Approach

Text image super-resolution is a challenging yet open research problem in the computer vision community. In particular, low-resolution images hamper the performance of typical optical character recognition (OCR) systems. In this article, we summarize our entry to the ICDAR2015 Competition on Text Image Super-Resolution. Experiments are based on the provided ICDAR2015 TextSR dataset [3] and the released Tesseract-OCR 3.02 system [1]. We report that our winning entry of text image super-resolution framework has largely improved the OCR performance with low-resolution images used as input, reaching an OCR accuracy score of 77.19%, which is comparable with that of using the original high-resolution images (78.80%).


Preventing Cell Phone Intrusion and Theft using Biometrics

Most cell phones use a password, PIN, or visual pattern to secure the phone. With these types of security methods being used, there is much vulnerability. Another alternative is biometric authentication. Biometric security systems have been researched for many years. Some mobile manufacturers have implemented fingerprint scanners into their phones, such as the old Fujitsu F505i [7] and the current Motorola Atrix. Since theft of cell phones is becoming more common every day, there is a real need for a security system that not only protects the data, but the phone itself. It is proposed through this research that a biometric security system be the alternative to knowledge-based and password-based authentication. Furthermore, a device dongle must be implemented into this infrastructure to establish a reliable security system that deters theft for the majority; biometrics alone is not sufficient. Cell phones need power and must be charged almost daily. A biometric phone charger that acts as a dongle with a solid state relay, will be presented as a viable solution to theft in this research. Additionally, it will be shown through the results of this research that a system dependant only on biometrics is unreliable and unsecure. Essentially, a mobile security system that combines biometrics with dongle technology is believed to be the ideal solution for limiting the black market of stolen cell phones; without the biometric charger/dongle, the stolen cell phone would be rendered useless.


Cuff-less, non-invasive and continuous blood pressure monitoring using indirect methods

Cuff-based methods for measuring blood pressure (BP) have shown some limitations particularly when continuous measurement is required. Moreover, the inflation of the cuff is one of the main disturbing factors as expressed among patients. In this study, a promising approach based on changes in pulse transit time (PTT) for continuous, non-invasive and indirect measurement of BP is proposed to overcome the shortcomings of cuff-based methods. The PTT can be measured using plethysmography (PPG) waveform and the QRS peak of electrocardiogram (ECG) signal and indirectly interpreted as systolic BP. Time synchronization of the two sensors plays an important role in improving the accuracy of measurement. Therefore, it is aimed to employ a tablet-based system for collecting the wireless devices' data at a specific time. It is also our objective to include a triggering mechanism option using ambient sensors in order to start the process of data collection. Bluetooth/IEEE 802.15.4 wireless technologies will be used for real-time sensing and time synchronization. We have found that the proposed indirect measurement of BP can provide continuous information of the BP in a more convenient way from the patients' point of view. Moreover, the result of our indirect measurement of BP indicates that there are some short term fluctuations in the BP.


3D printed RF passive components by liquid metal filling

We present three-dimensional (3D) micro-scale electrical components and systems by means of 3D printing and a liquid-metal-filling technique. The 3D supporting polymer structures with hollow channels and cavities are fabricated from inkjet printing. Liquid metals made of silver particles suspension in this demonstration are then injected into the hollow paths and solidified to form metallic elements and interconnects with high electrical conductivity. In the proof-of-concept demonstrations, various radio-frequency (RF) passive components, including 3D-shaped inductors, capacitors and resistors are fabricated and characterized. High-Q inductors and capacitors up to 1 GHz have been demonstrated. This work establishes an innovative way to construct arbitrary 3D electrical systems with efficient and labor-saving processes.


Strengths and Weaknesses of Quantum Computing

Recently a great deal of attention has focused on quantum computation following a sequence of results [4, 16, 15] suggesting that quantum computers are more powerful than classical probabilistic computers. Following Shor’s result that factoring and the extraction of discrete logarithms are both solvable in quantum polynomial time, it is natural to ask whether all of NP can be efficiently solved in quantum polynomial time. In this paper, we address this question by proving that relative to an oracle chosen uniformly at random, with probability 1, the class NP cannot be solved on a quantum Turing machine in time o(2n/2). We also show that relative to a permutation oracle chosen uniformly at random, with probability 1, the class NP ∩ co–NP cannot be solved on a quantum Turing machine in time o(2n/3). The former bound is tight since recent work of Grover [13] shows how to accept the class NP relative to any oracle on a quantum computer in time O(2n/2). To appear in SIAM Journal on Computing (special issue on quantum computing) ∗ IBM T. J. Watson Research Laboratory, Yorktown Heights, New York, NY 10598, USA. email: bennetc@watson.ibm.com. † 1 Microsoft Way, Redmond, WA 98052 – 6399, USA. email: ethanb@microsoft.com. ‡ Supported in part by Canada’s nserc and Québec’s fcar. §Département IRO, Université de Montréal, C.P. 6128, succursale centre-ville, Montréal (Québec), Canada H3C 3J7. email: brassard@iro.umontreal.ca. ¶ Supported by NSF Grant No. CCR-9310214. Computer Science Division, University of California, Berkeley, CA 94720, USA. email: vazirani@cs.berkeley.edu.


Tight bounds on quantum searching

We provide a tight analysis of Grover’s recent algorithm for quantum database searching. We give a simple closed-form formula for the probability of success after any given number of iterations of the algorithm. This allows us to determine the number of iterations necessary to achieve almost certainty of finding the answer. Furthermore, we analyse the behaviour of the algorithm when the element to be found appears more than once in the table and we provide a new algorithm to find such an element even when the number of solutions is not known ahead of time. Using techniques from Shor’s quantum factoring algorithm in addition to Grover’s approach, we introduce a new technique for approximate quantum counting, which allows to estimate the number of solutions. Finally we provide a lower bound on the efficiency of any possible quantum database searching algorithm and we show that Grover’s algorithm nearly comes within a factor 2 of being optimal in terms of the number of probes required in the table.


XMSS - A Practical Forward Secure Signature Scheme Based on Minimal Security Assumptions

We present the hash-based signature scheme XMSS. It is the first provably (forward) secure and practical signature scheme with minimal security requirements: a pseudorandom and a second preimage resistant (hash) function family. Its signature size is reduced to less than 25% compared to the best provably secure hash based signature scheme.


Algorithms for Quantum Computation: Discrete Logarithms and Factoring

A computer is generally considered to be a universal computational device; i.e., it is believed able to simulate any physical computational device with a cost in computation time of at most a polynomial factol: It is not clear whether this is still true when quantum mechanics is taken into consideration. Several researchers, starting with David Deutsch, have developed models for quantum mechanical computers and have investigated their computational properties. This paper gives Las Vegas algorithms for finding discrete logarithms and factoring integers on a quantum computer that take a number of steps which is polynomial in the input size, e.g., the number of digits of the integer to be factored. These two problems are generally considered hard on a classical computer and have been used as the basis of several proposed cryptosystems. (We thus give the first examples of quantum cryptanulysis.)


A Study of Cardiovascular Disease Prediction Models Using Discriminant Analysis

Cardiovascular disease is a condition of interest to clinicians around the world and is one of the chief causes of death. In Korea, cardiovascular disease is the second largest cause of death following cancer, and is classified as one of the four major diseases, making an early diagnosis of cardiovascular disease essential for proper and timely treatment. Although the risk of cardiovascular disease is recognized in Korea, there is a lack of quality studies regarding the disease. In the present study, a prediction model was developed which allows for the calculation of the risk of cardiovascular disease among Korean patients. The prediction model for cardiovascular disease was developed using a discriminant analysis based on the data set of Korean National Health and Nutrition Examinations Survey V (KNHANES V).


High-performance polyline intersection based spatial join on GPU-accelerated clusters

The rapid growing volumes of spatial data have brought significant challenges on developing high-performance spatial data processing techniques in parallel and distributed computing environments. Spatial joins are important data management techniques in gaining insights from large-scale geospatial data. While several distributed spatial join techniques based on spatial partitions have been implemented on top of existing Big Data systems, they are not capable of natively exploiting massively data parallel computing power provided by modern commodity Graphics Processing Units (GPUs). In this study, as an important component of our research initiative in developing high-performance spatial join techniques on GPUs, we have designed and implemented a polyline intersection based spatial join technique that is capable of exploiting massively data parallel computing power on GPUs. The proposed polyline intersection based spatial join technique is integrated into a customized lightweight distributed execution engine that natively supports spatial partitions. We empirically evaluate the performance of the proposed spatial join technique on both a standalone GPU-equipped workstation and Amazon EC2 GPU-accelerated clusters and demonstrate its high performance when comparing with the state-of-the-art.


The Case for Holistic Data Integration

Current data integration approaches are mostly limited to few data sources, partly due to the use of binary match approaches between pairs of sources. We thus advocate for the development of more holistic, clustering-based data integration approaches that scale to many data sources. We outline different use cases and provide an overview of initial approaches for holistic schema/ontology integration and entity clustering. The discussion also considers open data repositories and socalled knowledge graphs.


Comprehensive review of epidemiology, scope, and impact of spinal pain.

Persistent pain interfering with daily activities is common. Chronic pain has been defined in many ways. Chronic pain syndrome is a separate entity from chronic pain. Chronic pain is defined as, "pain that persists 6 months after an injury and beyond the usual course of an acute disease or a reasonable time for a comparable injury to heal, that is associated with chronic pathologic processes that cause continuous or intermittent pain for months or years, that may continue in the presence or absence of demonstrable pathologies; may not be amenable to routine pain control methods; and healing may never occur." In contrast, chronic pain syndrome has been defined as a complex condition with physical, psychological, emotional, and social components. The prevalence of chronic pain in the adult population ranges from 2% to 40%, with a median point prevalence of 15%. Among chronic pain disorders, pain arising from various structures of the spine constitutes the majority of the problems. The lifetime prevalence of spinal pain has been reported as 54% to 80%. Studies of the prevalence of low back pain and neck pain and its impact in general have shown 23% of patients reporting Grade II to IV low back pain (high pain intensity with disability) versus 15% with neck pain. Further, age related prevalence of persistent pain appears to be much more common in the elderly associated with functional limitations and difficulty in performing daily life activities. Chronic persistent low back and neck pain is seen in 25% to 60% of patients, one-year or longer after the initial episode. Spinal pain is associated with significant economic, societal, and health impact. Estimates and patterns of productivity losses and direct health care expenditures among individuals with back and neck pain in the United States continue to escalate. Recent studies have shown significant increases in the prevalence of various pain problems including low back pain. Frequent use of opioids in managing chronic non-cancer pain has been a major issue for health care in the United States placing a significant strain on the economy with the majority of patients receiving opioids for chronic pain necessitating an increased production of opioids, and escalating costs of opioid use, even with normal intake. The additional costs of misuse, abuse, and addiction are enormous. Comorbidities including psychological and physical conditions and numerous other risk factors are common in spinal pain and add significant complexities to the interventionalist's clinical task. This section of the American Society of Interventional Pain Physicians (ASIPP)/Evidence-Based Medicine (EBM) guidelines evaluates the epidemiology, scope, and impact of spinal pain and its relevance to health care interventions.


Design of a Wheelchair with Legs for People with Motor Disabilities

Low cost. For example, golf carts, outdoor chairs and special purpose sand buggies. wheelchair. AbstractA proof-of-concept prototype wheelchair with legs for people with motor disabilities is proposed, with the objective of demonstrating the feasibility of a completely new approach to mobility. Our prototype system consists of a chair equipped with wheels and legs, and is capable of traversing uneven terrain and circumventing obstacles. The important design considerations, the system design and analysis, and an experimental prototype of a chair are discussed. The results from the analysis and experimentation show the feasibility of the proposed concept and its advantages.


Uneasy lies the head that wears the crown: the link between guilt proneness and leadership.

We propose that guilt proneness is a critical characteristic of leaders and find support for this hypothesis across 3 studies. Participants in the first study rated a set of guilt-prone behaviors as more indicative of leadership potential than a set of less guilt-prone behaviors. In a follow-up study, guilt-prone participants in a leaderless group task engaged in more leadership behaviors than did less guilt-prone participants. In a third, and final, study, we move to the field and analyze 360° feedback from a group of young managers working in a range of industries. The results indicate that highly guilt-prone individuals were rated as more capable leaders than less guilt-prone individuals and that a sense of responsibility for others underlies the positive relationship between guilt proneness and leadership evaluations.


Implementing it Service Management: A Case Study Focussing on Critical Success Factors

Queensland Health, a large Australian government agency, implemented a centralised IT service management model based on the ITIL framework. This paper presents an in-depth case study of the implementation. It sheds light on the challenges and breakthroughs, confirms a set of factors that contributed to the project’s success and offers a learning opportunity for other organisations. The study indicates that the commitment of senior management is crucial to the project’s success as is a project champion and the recognition of the need for an appropriate change management strategy to transform the organisational culture to a service-oriented focus. Maintaining close and forthright relationships with multiple vendors facilitates technology transfer to in-house staff while a benefits realisation plan is a valuable tool for tracking and communicating tangible and intangible project benefits to the project stakeholders. An effective project governance and execution process further contributes to the implementation success.


Efficient super-resolution via image warping

This paper introduces a new algorithm for enhancing image resolution from an image sequence. The approach we propose herein uses the integrating resampler for warping. The method is a direct computation, which is fundamentally different from the iterative back-projection approaches proposed in previous work. This paper shows that image-warping techniques may have a strong impact on the quality of image resolution enhancement. By coupling the degradation model of the imaging system directly into the integrating resampler, we can better approximate the warping characteristics of real sensors, which also significantly improve the quality of super-resolution images. Examples of super-resolutions are given for gray-scale images. Evaluations are made visuallyby comparing the resulting images and those using bi-linear resampling and back-projection and quantitativelyusing OCR as a fundamental measure. The paper shows that even when the images are qualitatively similar, quantitative differences appear in machine processing. q 2000 Elsevier Science B.V. All rights reserved. Keywords: Super-resolution; Imaging-consistent restoration/reconstruction; Integrating resampling; Integrating resampler; Quantitative evaluatio n; OCR; Bilinear resampling; Image reconstruction; Image restoration; Image warping 1. Background and introduction The basic premise of super-resolution is to combine images (and in some work scene/data models) to produce an image that has higher “resolution” than the original image. It is not sufficient to just resample the image—size is not the same as resolution. Increasing resolution could be viewed as either increasing the signal-to-noise ratio while keeping the size fixed and/or approximating the image at a larger size with reasonable approximations for frequencies higher than those representable at the original size. In either case, it requires additional information and assumptions. There are, of course, some fundamental limits on what can be gained; lens and photo-sites cause blur, and the discrete sampling causes frequencies above the Nyquist rate to alias. While in general the aliasing is theoretically impossible to separate, the problem occurs mostly along sharp “intensity edges”, so multiple images with slightly different imaging positions can be used to reduce its impact. There are two different groups of research that use the term super-resolution—one is based on multiple images, which will be considered within this paper; the other uses “scene/object” models. Early research on image-based super-resolution dates back to the work by Huang and Tsai [8]. They solved the problem in the frequency domain, but they disregarded sensor blurring. Furthermore, they assumed only inter-frame translations. Gross [7] solved the problem by first merging the low resolution images by interpolation and then obtaining the high-resolution image by deblurring the merged image. Again, only inter-frame translations were considered. Peleg and Keren [11,14] solved the problem by first estimating an initial guess of the high-resolution image, then simulating the imaging process and minimizing the difference between the observed and simulated low-resolution images. Irani and Peleg [9,10] used a back-projection method similar to that used in tomography to minimize the same difference. Bascle et al. [1] used the same back-projection method to minimize the difference between the predicted and actual images except that a simple motion blur model was included. All previous work ignores the impact of image warping techniques. In this paper, we show that warping techniques may have a strong impact on the quality of image resolution enhancement. Image warping has been around almost as long as image processing, allowing users to reshape the image geometry. Image warping requires the underlying image to be “resampled” at non-integer locations; hence, it requires image reconstruction. When the goal of warping is to produce output for human viewing, only moderately accurate image intensities are needed. In these cases, techniques Image and Vision Computing 18 (2000) 761–771 0262-8856/00/$ see front matter q 2000 Elsevier Science B.V. All rights reserved. PII: S0262-8856(99)00044-X www.elsevier.com/locate/imavis * Corresponding author. Tel.: 11-610-7584061. E-mail address:tboult@eecs.lehigh.edu (T.E. Boult). using bi-linear interpolation have been found sufficient. However, as a step for applications such as super-resolution, the precision of the warped intensity values is often important. For these problems, bi-linear image reconstruction may not be sufficient. This paper shows how the integrating resampler can be used to enhance image resolution. This builds upon the ideas of imaging-consistent restoration/reconstruction algorithms proposed in Ref. [2] and the integrating resampler proposed in Ref. [5]. A final contribution of this paper is a quantitative comparison and analysis of our techniques. In this paper, we use a character recognition rate based on a commercial OCR program. We chose this because it is easy to make it quantitative, and we consider this to be a good measure for other applications such as license-plate reading and other pattern recognition problems. In what follows in this paper, the image formation process and the relationship between restoration, reconstruction, and super-resolution is briefly reviewed in Section 2. The integrating resampler—an efficient method for warping using imaging-consistent reconstruction/restoration algorithms—is given in Section 3. This algorithm is well suited for today’s pipelined micro-processors. In addition, the integrating resampler can allow for modifications of the intensity values to better approximate the warping characteristics of real sensors. The super-resolution algorithm where we compare the integrating resampler with bi-linear resampling is presented in Section 4. Quantitative measurement of super-resolution imaging using OCR is shown in Section 5. 2. Image formation and image restoration In this section, we briefly review the image formation process as well as the sensor model proposed in Ref. [2]. Image formation is generally described as a sequence of filtering operations. See Fig. 1. Let f x; y be the intensity distribution of a scene at the front aperture of a lens. That distribution is acted upon by h1x; y; the blurring component of the lens, yielding f1x; y: A geometric distortion functionh2x; y is then applied to produce imagef2u; v: At this point, f2 strikes the image sensor where it undergoes additional blurring by a point spread functionh3u; v to generate image f3u; v: This blurring reflects the limitation of the sensor to accurately resolve each point without the influence of neighboring points. We choose to use a simple model wherein this blurring takes place within one pixel because for CCD and CID cameras, the physical boundaries between photo-sites generally allow only insignificant charge transfer between pixels. Imagef3 undergoes spatial sampling as it hits the discrete photo-sites in a CCD or CID camera. The combination of h3 with sampling is known as areas sampling. It reflects the finite size of the sampling area. If h3 is taken to be an impulse, then we have point sampling. This ideal, often assumed for theoretical considerations, is generally not true in practice. In either case, intensities in the sampled image Is are now defined only for integer values of u and v. The digital imageI u; v is obtained via an analog-todigital converter that quantizes the samples of Is. This completes the image formation process, leaving I u; v as the starting point for subsequent processing, including image reconstruction and restoration. Reconstruction and restoration start with I (and models for one or morehis), and seek to solve for one or more of the fjs. Recoveringf is known as image restoration and is of considerable interest in image processing. Note that recovering a functional form off2 is an intra-pixel restoration. Reconstruction limits itself to the problem of deriving the continuous functionf3. Restoration attempts to estimate the original input functionf2 (or f1). The goal of super-resolution is to recover a discrete approximation, with resolution higher than Is, to either f1 (plain super-resolution) orf (super-resolution with deblurring). Because super-resolution increases the signal-to-noise ratio, it significantly aids in the ill-condition problem of deblurring. 3. Imaging-consistency and the integrating resampler An algorithm is called imaging-consistent if it is the exact solution for some input function, which, according to the imaging model, would have generated the measured input. For image reconstruction, we achieve this by first restoring the image to yieldf2, then blurring it again by the pixel’s PSF. Details can be found in Ref. [2]. Only one-dimensional image models are presented herein; higher dimensions are treated separately. M.-C. Chiang, T.E. Boult / Image and Vision Computing 18 (2000) 761–771 762 Fig. 1. The image formation process and the relationship between restoration, reconstruction, and super-resolution. The first imaging-consistent method to consider is based on a piecewise quadratic model for the image. If we assume a Rect PSF filter (1 inside and 0 outside the pixel), the imaging consistent algorithm is easy to derive. To ensure that the function is continuous and that the method is local, we define the value of the reconstruction at the pixel boundarieski andki11 to be equal toEi andEi11: Any method of approximation could be used to compute Ei, though our examples will only include linear interpolation and cubic convolution. See [3, Section 2.3] for more details. Given the valuesEi at the pixel edges, an additional constraint, that the integral across the pixel must equal Vi, results in exactly three constraints: qi21=2  Ei ; qi1=2  Ei11; Z1=2 2 1=2 qix dx Vi : 1 From Eq. (1), one can det


Simplification of Example Sentences for Learners of Japanese Functional Expressions

Learning functional expressions is one of the difficulties for language learners, since functional expressions tend to have multiple meanings and complicated usages in various situations. In this paper, we report an experiment of simplifying example sentences of Japanese functional expressions especially for Chinese-speaking learners. For this purpose, we developed “Japanese Functional Expressions List” and “Simple Japanese Replacement List”. To evaluate the method, we conduct a small-scale experiment with Chinese-speaking learners on the effectiveness of the simplified example sentences. The experimental results indicate that the simplified sentences are helpful in learning Japanese functional expressions.


Algorithmics of Matching Under Preferences

• Page 17, line 13: “constrast” −→ “contrast”. (Due to Mechthild Opperud.) • Page 21, line 17: “attributes” −→ “attributed”. • Page 21, lines -4 to -3: change “pairs in which either (i) ri is unassigned if she is unassigned in both M and M ′, or (ii)” to “pairs obtained as follows: for each resident ri, ri is unassigned if she is unassigned in both M and M ′, otherwise”. • Page 21, line -1: “join” −→ “meet”. (Due to Didac Busquets.) • Page 22, line 1: “meet” −→ “join”. (Due to Didac Busquets.) • Page 23, line 4: add “In an sm instance, any matching is automatically assumed to have size n.”


SNAP, Small-world Network Analysis and Partitioning: An open-source parallel graph framework for the exploration of large-scale networks

We present SNAP (small-world network analysis and partitioning), an open-source graph framework for exploratory study and partitioning of large-scale networks. To illustrate the capability of SNAP, we discuss the design, implementation, and performance of three novel parallel community detection algorithms that optimize modularity, a popular measure for clustering quality in social network analysis. In order to achieve scalable parallel performance, we exploit typical network characteristics of small-world networks, such as the low graph diameter, sparse connectivity, and skewed degree distribution. We conduct an extensive experimental study on real-world graph instances and demonstrate that our parallel schemes, coupled with aggressive algorithm engineering for small-world networks, give significant running time improvements over existing modularity-based clustering heuristics, with little or no loss in clustering quality. For instance, our divisive clustering approach based on approximate edge betweenness centrality is more than two orders of magnitude faster than a competing greedy approach, for a variety of large graph instances on the Sun Fire T2000 multicore system. SNAP also contains parallel implementations of fundamental graph-theoretic kernels and topological analysis metrics (e.g., breadth-first search, connected components, vertex and edge centrality) that are optimized for small- world networks. The SNAP framework is extensible; the graph kernels are modular, portable across shared memory multicore and symmetric multiprocessor systems, and simplify the design of high-level domain-specific applications.


Advances in Very Deep Convolutional Neural Networks for LVCSR

Very deep CNNs with small 3 × 3 kernels have recently been shown to achieve very strong performance as acoustic models in hybrid NN-HMM speech recognition systems. In this paper we investigate how to efficiently scale these models to larger datasets. Specifically, we address the design choice of pooling and padding along the time dimension which renders convolutional evaluation of sequences highly inefficient. We propose a new CNN design without timepadding and without timepooling, which is slightly suboptimal for accuracy, but has two significant advantages: it enables sequence training and deployment by allowing efficient convolutional evaluation of full utterances, and, it allows for batch normalization to be straightforwardly adopted to CNNs on sequence data. Through batch normalization, we recover the lost peformance from removing the time-pooling, while keeping the benefit of efficient convolutional evaluation. We demonstrate the performance of our models both on larger scale data than before, and after sequence training. Our very deep CNN model sequence trained on the 2000h switchboard dataset obtains 9.4 word error rate on the Hub5 test-set, matching with a single model the performance of the 2015 IBM system combination, which was the previous best published result.


Non-orthogonal Multiple Access in a Downlink Multiuser Beamforming System

In this paper, we propose a non-orthogonal multiple access-based multiuser beamforming (NOMA-BF) system designed to enhance the sum capacity. In the proposed NOMA-BF system, a single BF vector is shared by two users, so that the number of supportable users can be increased. However, sharing a BF vector leads to interference from other beams as well as from the other user sharing the BF vector. Therefore, to reduce interference and improve the sum capacity, we additionally propose a clustering and power allocation algorithm. This clustering algorithm, which selects two users with high correlation and a large gain-difference between their channels, can reduce the interference from other beams and from the other user as well. Furthermore, power allocation ensures that each user's transmit power is allocated so as to maximize the sum capacity. Numerical results verify that the proposed NOMA-BF system improves the sum capacity, compared to the conventional multiuser BF system.


On Plant Responses to D-Amino Acids Features of Growth , Root Behaviour and Selection for Plant Transformation

Amino acids have been regarded as potential plant nitrogen sources for more than a century. Some amino acids have been shown to support growth while others have growth-retarding effects. The D-isomers with notably adverse effects on plants’ growth and development include D-serine and D-alanine. Recently, D-serine has been recognised as an endogenous ligand of receptor channels mediating calcium fluxes in plants, but otherwise little is known about endogenous roles of D-amino acids in plants. In the studies underlying this thesis, the negative responses to D-serine and D-alanine were converted to positive effects in Arabidopsis thaliana (Arabidopsis) plants by introducing either of two D-amino acid-metabolising enzymes. Transgenic Arabidopsis lines expressing either the D-serine dehydratase (dsdA) gene from Escherichia coli or the D-amino acid oxidase (DAO1) gene from Rhodotorula gracilis grew with otherwise toxic D-amino acids as the sole nitrogen source. I also expressed a transporter specific for D-amino acids, which further increased the transgenic plants’ growth with D-serine as sole nitrogen source. Hence, both assimilation and uptake restrictions can limit plant growth on D-amino acids. The growth of transgenic lines on D-serine or D-alanine provides an unambiguous and highly visible phenotype, which is essential for a selectable marker. Thus, expressing of either the dsdA or DAO1 genes generated transformants that are easy to screen. Furthermore, the DAO1 gene can be readily used for either positive or negative selection, depending on the substrate, thus it provides a unique conditional, substrate-dependent positive/negative selectable marker for plant transformation. In summary, the presented work demonstrates that introducing the ability to catalyse a single metabolic step can allow plants to exploit an otherwise inaccessible or toxic form of organic nitrogen, and provides a versatile marker based on nitrogen nutrition for selecting transgenic plants. A possible role for D-serine in plants’ touch response is also reviewed in the thesis.


LibD: Scalable and Precise Third-Party Library Detection in Android Markets

With the thriving of the mobile app markets, third-party libraries are pervasively integrated in the Android applications. Third-party libraries provide functionality such as advertisements, location services, and social networking services, making multi-functional app development much more productive. However, the spread of vulnerable or harmful third-party libraries may also hurt the entire mobile ecosystem, leading to various security problems. The Android platform suffers severely from such problems due to the way its ecosystem is constructed and maintained. Therefore, third-party Android library identification has emerged as an important problem which is the basis of many security applications such as repackaging detection and malware analysis. According to our investigation, existing work on Android library detection still requires improvement in many aspects, including accuracy and obfuscation resilience. In response to these limitations, we propose a novel approach to identifying third-party Android libraries. Our method utilizes the internal code dependencies of an Android app to detect and classify library candidates. Different from most previous methods which classify detected library candidates based on similarity comparison, our method is based on feature hashing and can better handle code whose package and method names are obfuscated. Based on this approach, we have developed a prototypical tool called LibD and evaluated it with an update-to-date and large-scale dataset. Our experimental results on 1,427,395 apps show that compared to existing tools, LibD can better handle multi-package third-party libraries in the presence of name-based obfuscation, leading to significantly improved precision without the loss of scalability.


Generating Single Subject Activity Videos as a Sequence of Actions Using 3D Convolutional Generative Adversarial Networks

Humans have the remarkable ability of imagination, where within the human mind virtual simulations are done of scenarios whether visual, auditory or any other senses. These imaginations are based on the experiences during interaction with the real world, where human senses help the mind understand their surroundings. Such level of imagination has not yet been achieved using current algorithms, but a current trend in deep learning architectures known as Generative Adversarial Networks (GANs) have proven capable of generating new and interesting images or videos based on the training data. In that way, GANs can be used to mimic human imagination, where the resulting generated visuals of GANs are based on the data used during training. In this paper, we use a combination of Long Short-Term Memory (LSTM) Networks and 3D GANs to generate videos. We use a 3D Convolutional GAN to generate new human action videos based on trained data. The generated human action videos are used to generate longer videos consisting of a sequence of short actions combined creating longer and more complex activities. To generate the sequence of actions needed we use an LSTM network to translate a simple input description text into the required sequence of actions. The generated chunks are then concatenated using a motion interpolation scheme to form a single video consisting of many generated actions. Hence a visualization of the input text description is generated as a video of a subject performing the activity described.


SLAM-based automatic extrinsic calibration of a multi-camera rig

Cameras are often a good choice as the primary outward-looking sensor for mobile robots, and a wide field of view is usually desirable for responsive and accurate navigation, SLAMand relocalisation. While this can potentially be provided by a single omnidirectional camera, it can also be flexibly achieved by multiple cameras with standard optics mounted around the robot. However, such setups are difficult to calibrate. Here we present a general method for fully automatic extrinsic auto-calibration of a fixed multi camera rig, with no requirement for calibration patterns or other infrastructure, which works even in the case where the cameras have completely non-overlapping views. The robot is placed in a natural environment and makes a set of programmed movements including a full horizontal rotation and captures a synchronized image sequence from each camera. These sequences are processed individually with a monocular visual SLAM algorithm. The resulting maps are matched and fused robustly based on corresponding invariant features, and then all estimates are optimised full joint bundle adjustment, where we constrain the relative poses of the cameras to be fixed. We present results showing accurate performance of the method for various two and four camera configurations.


Fusion of laserscannner and video based lanemarking detection for robust lateral vehicle control and lane change maneuvers

The knowledge about lanes and the exact position on the road is fundamental for many advanced driver assistance systems. In this paper, a novel iterative histogram based approach with occupancy grids for the detection of multiple lanes is proposed. In highway scenarios, our approach is highly suitable to determine the correct number of all existing lanes on the road. Additionally, the output of the laserscannner based lane detection is fused with a production-available vision based system. It is shown that both sensor systems perfectly complement each other to increase the robustness of a lane tracking system. The achieved accuracy of the fusion system, the laserscannner and video based system is evaluated with a highly accurate DGPS to investigate the performance with respect to lateral vehicle control applications.


Cultural Differences in Collaborative Authoring of Wikipedia

This article explores the relationship between national culture and computer-mediated communication (CMC) in Wikipedia. The articles on the topic game from the French, German, Japanese, and Dutch Wikipedia websites were studied using content analysis methods. Correlations were investigated between patterns of contributions and the four dimensions of cultural influences proposed by Hofstede (Power Distance, Collectivism versus Individualism, Femininity versus Masculinity, and Uncertainty Avoidance). The analysis revealed cultural differences in the style of contributions across the cultures investigated, some of which are correlated with the dimensions identified by Hofstede. These findings suggest that cultural differences that are observed in the physical world also exist in the virtual world. doi:10.1111/j.1083-6101.2006.00316.x


Study of the General Kalman Filter for Echo Cancellation

The Kalman filter is a very interesting signal processing tool, which is widely used in many practical applications. In this paper, we study the Kalman filter in the context of echo cancellation. The contribution of this work is threefold. First, we derive a different form of the Kalman filter by considering, at each iteration, a block of time samples instead of one time sample as it is the case in the conventional approach. Second, we show how this general Kalman filter (GKF) is connected with some of the most popular adaptive filters for echo cancellation, i.e., the normalized least-mean-square (NLMS) algorithm, the affine projection algorithm (APA) and its proportionate version (PAPA). Third, a simplified Kalman filter is developed in order to reduce the computational load of the GKF; this algorithm behaves like a variable step-size adaptive filter. Simulation results indicate the good performance of the proposed algorithms, which can be attractive choices for echo cancellation.


Emotion Enhances Learning via Norepinephrine Regulation of AMPA-Receptor Trafficking

Emotion enhances our ability to form vivid memories of even trivial events. Norepinephrine (NE), a neuromodulator released during emotional arousal, plays a central role in the emotional regulation of memory. However, the underlying molecular mechanism remains elusive. Toward this aim, we have examined the role of NE in contextual memory formation and in the synaptic delivery of GluR1-containing alpha-amino-3-hydroxy-5-methyl-4-isoxazoleproprionic acid (AMPA)-type glutamate receptors during long-term potentiation (LTP), a candidate synaptic mechanism for learning. We found that NE, as well as emotional stress, induces phosphorylation of GluR1 at sites critical for its synaptic delivery. Phosphorylation at these sites is necessary and sufficient to lower the threshold for GluR1 synaptic incorporation during LTP. In behavioral experiments, NE can lower the threshold for memory formation in wild-type mice but not in mice carrying mutations in the GluR1 phosphorylation sites. Our results indicate that NE-driven phosphorylation of GluR1 facilitates the synaptic delivery of GluR1-containing AMPARs, lowering the threshold for LTP, thereby providing a molecular mechanism for how emotion enhances learning and memory.


Resource discovery in Internet of Things: Current trends and future standardization aspects

To realize the vision of Internet of Things, there must be mechanisms to discover resources and their capabilities. Thus resource discovery becomes a fundamental requirement of any IoT platform. This paper provides a comprehensive categorization of the current technology landscape for discovery while pointing out their advantages and limitations. Then, a novel search engine based resource discovery framework is proposed. The framework comprises of a proxy layer which includes drivers for various communication technologies and protocols. There is a central registry to store the configurations of the resources and index them based on the configuration parameters. A “lifetime” attribute is introduced which denotes the period through which a resource is discoverable. The search engine ranks the resulting resources of a discovery request and returns an URI to directly access each resource. The functionalities of the proposed framework are exposed using RESTful web services. The framework allows discovery of both smart and legacy devices. We have performed gap analysis of current IoT standards for discovery mechanisms and provide suggestions to improve them to maintain interoperability. The outcomes are communicated to Standard Development Organizations like W3C (in Web of Things Interest Group) and oneM2M.


Study of a bus-based disruption-tolerant network: mobility modeling and impact on routing

We study traces taken from UMass DieselNet, a Disruption-Tolerant Network consisting of WiFi nodes attached to buses. As buses travel their routes, they encounter other buses and in some cases are able to establish pair-wise connections and transfer data between them. We analyze the bus-to-bus contact traces to characterize the contact process between buses and its impact on DTN routing performance. We find that the all-bus-pairs aggregated inter-contact times show no discernible pattern. However, the inter-contact times aggregated at a route level exhibit periodic behavior.Based on analysis of the deterministic inter-meeting times for bus pairs running on route pairs, and consideration of the variability in bus movement and the random failures to establish connections, we construct generative route-level models that capture the above behavior. Through trace-driven simulations of epidemic routing, we find that the epidemic performance predicted by traces generated with this finer-grained route-level model is much closer to the actual performance that would be realized in the operational system than traces generated using the coarse-grained all-bus-pairs aggregated model. This suggests the importance in choosing the rightlevel of model granularity when modelingmobility-related measures such as inter-contact times in DTNs.


Trainable videorealistic speech animation

We describe how to create with machine learning techniques a generative, speech animation module. A human subject is first recorded using a videocamera as he/she utters a predetermined speech corpus. After processing the corpus automatically, a visual speech module is learned from the data that is capable of synthesizing the human subject's mouth uttering entirely novel utterances that were not recorded in the original video. The synthesized utterance is re-composited onto a background sequence which contains natural head and eye movement. The final output is videorealistic in the sense that it looks like a video camera recording of the subject. At run time, the input to the system can be either real audio sequences or synthetic audio produced by a text-to-speech system, as long as they have been phonetically aligned.The two key contributions of this paper are 1) a variant of the multidimensional morphable model (MMM) to synthesize new, previously unseen mouth configurations from a small set of mouth image prototypes; and 2) a trajectory synthesis technique based on regularization, which is automatically trained from the recorded video corpus, and which is capable of synthesizing trajectories in MMM space corresponding to any desired utterance.


Towards Zero Unknown Word in Neural Machine Translation

Neural Machine translation has shown promising results in recent years. In order to control the computational complexity, NMT has to employ a small vocabulary, and massive rare words outside the vocabulary are all replaced with a single unk symbol. Besides the inability to translate rare words, this kind of simple approach leads to much increased ambiguity of the sentences since meaningless unks break the structure of sentences, and thus hurts the translation and reordering of the in-vocabulary words. To tackle this problem, we propose a novel substitution-translation-restoration method. In substitution step, the rare words in a testing sentence are replaced with similar in-vocabulary words based on a similarity model learnt from monolingual data. In translation and restoration steps, the sentence will be translated with a model trained on new bilingual data with rare words replaced, and finally the translations of the replaced words will be substituted by that of original ones. Experiments on Chinese-to-English translation demonstrate that our proposed method can achieve more than 4 BLEU points over the attention-based NMT. When compared to the recently proposed method handling rare words in NMT, our method can also obtain an improvement by nearly 3 BLEU points.


OpenNMT: Open-source Toolkit for Neural Machine Translation

We describe an open-source toolkit for neural machine translation (NMT). The toolkit prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research into model architectures, feature representations, and source modalities, while maintaining competitive performance and reasonable training requirements. The toolkit consists of modeling and translation support, as well as detailed pedagogical documentation about the underlying techniques.


Neural Machine Translation of Rare Words with Subword Units

Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary. In this paper, we introduce a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units. This is based on the intuition that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations). We discuss the suitability of different word segmentation techniques, including simple character ngram models and a segmentation based on the byte pair encoding compression algorithm, and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English→German and English→Russian by up to 1.1 and 1.3 BLEU, respectively.


ASPEC: Asian Scientific Paper Excerpt Corpus

In this paper, we describe the details of the ASPEC (Asian Scientific Paper Excerpt Corpus), which is the first large-size parallel corpus of scientific paper domain. ASPEC was constructed in the Japanese-Chinese machine translation project conducted between 2006 and 2010 using the Special Coordination Funds for Promoting Science and Technology. It consists of a Japanese-English scientific paper abstract corpus of approximately 3 million parallel sentences (ASPEC-JE) and a Chinese-Japanese scientific paper excerpt corpus of approximately 0.68 million parallel sentences (ASPEC-JC). ASPEC is used as the official dataset for the machine translation evaluation workshop WAT (Workshop on Asian Translation).


PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification

We present a new release of the Paraphrase Database. PPDB 2.0 includes a discriminatively re-ranked set of paraphrases that achieve a higher correlation with human judgments than PPDB 1.0’s heuristic rankings. Each paraphrase pair in the database now also includes finegrained entailment relations, word embedding similarities, and style annotations.


Optimizing Paxos with batching and pipelining

Paxos is probably the most popular state machine replication protocol. Two optimizations that can greatly improve its performance are batching and pipelining. However, tuning these two optimizations to achieve high-throughput can be challenging, as their effectiveness depends on many parameters like the network latency and bandwidth, the speed of the nodes, and the properties of the application. We address this question by presenting an analytical model of the performance of Paxos, and showing how it can be used to determine configuration values for the batching and pipelining optimizations that result in high-throughput. We then validate the model, by presenting the results of experiments where we investigate the interaction of these two optimizations both in LAN and WAN environments, and comparing these resultswith the prediction from themodel. The results confirm the accuracy of the model, with the predicted values being usually very close to the ones that provide the highest performance in the experiments. Furthermore, both the model and the experiments give useful insights into the relative effectiveness of batching and pipelining. They show that although batching by itself provides the largest gains in all scenarios, in most cases combining it with pipelining provides a very significant additional increase in throughput, with this being true not only on high-latencyWANbut also inmany LAN scenarios. © 2012 Elsevier B.V. All rights reserved.


OptoCOMM: Introducing a new optical underwater wireless communication modem

OptoCOMM aims at demonstrating the potential, at physical level, of a communication facility for the SUNRISE platform constituted by an Optical Underwater Wireless Communication (OUWC) module with target performance of 10 Mb/s transmission rate at 10 meters range in shallow medium/high turbidity harbour waters. The module, which is based on blue Light Emitting Diode (LED) units and common photodiodes, is an evolution of the proof-of-principle prototype already proven in laboratory (pool). It will constitute an additional node integrated in the Littoral Ocean Observatory Network (LOON) test-bed of the SUNRISE infrastructures, providing a high speed and short-range communication node, which will complete the capability of acoustic modems already present in the test-bed. Three modules (nodes) will be developed and experimentally demonstrated: one for direct integration with the LOON infrastructures, one, battery powered, to be potentially installed on buoys, Remotely Operated Vehicles (ROVs), etc., and one to be installed on the eFolaga Autonomous Underwater Vehicle (AUV) of the proponents. The paper describes in detail the development of the modems as well as the first lab experiments, where the core technology has been successfully tested.