title: Height and gradient from shading

abstract: The method described here for recovering the shape of a surface from a shaded image can deal with complex, wrinkled surfaces. Integrability can be enforced easily because both surface height and gradient are represented. (A gradient field is integrable if it is the gradient of some surface height function.) The robustness of the method stems in part from linearization of the reflectance map about the current estimate of the surface orientation at each picture cell. (The reflectance map gives the dependence of scene radiance on surface orientation.) The new scheme can find an exact solution of a given shape-from-shading problem even though a regularizing term is included. The reason is that the penalty term is needed only to stabilize the iterative scheme when it is far from the correct solution; it can be turned off as the solution is approached. This is a reflection of the fact that shape-from-shading problems are not ill posed when boundary conditions are available, or when the image contains singular points.This article includes a review of previous work on shape from shading and photoclinometry. Novel features of the new scheme are introduced one at a time to make it easier to see what each contributes. Included is a discussion of implementation details that are important if exact algebraic solutions of synthetic shape-from-shading problems are to be obtained. The hope is that better performance on synthetic data will lead to better performance on real data.


title: A Pattern Language: Towns, Buildings, Construction

abstract: You can use this book to design a house for yourself with your family; you can use it to work with your neighbors to improve your town and neighborhood; you can use it to design an office, or a workshop, or a public building. And you can use it to guide you in the actual process of construction. After a ten-year silence, Christopher Alexander and his colleagues at the Center for Environmental Structure are now publishing a major statement in the form of three books which will, in their words, "lay the basis for an entirely new approach to architecture, building and planning, which will we hope replace existing ideas and practices entirely." The three books are The Timeless Way of Building, The Oregon Experiment, and this book, A Pattern Language. At the core of these books is the idea that people should design for themselves their own houses, streets, and communities. This idea may be radical (it implies a radical transformation of the architectural profession) but it comes simply from the observation that most of the wonderful places of the world were not made by architects but by the people. At the core of the books, too, is the point that in designing their environments people always rely on certain "languages," which, like the languages we speak, allow them to articulate and communicate an infinite variety of designs within a forma system which gives them coherence. This book provides a language of this kind. It will enable a person to make a design for almost any kind of building, or any part of the built environment. "Patterns," the units of this language, are answers to design problems (How high should a window sill be? How many stories should a building have? How much space in a neighborhood should be devoted to grass and trees?). More than 250 of the patterns in this pattern language are given: each consists of a problem statement, a discussion of the problem with an illustration, and a solution. As the authors say in their introduction, many of the patterns are archetypal, so deeply rooted in the nature of things that it seemly likely that they will be a part of human nature, and human action, as much in five hundred years as they are today.


title: Circularly polarized high gain antenna using array of parasitic patches on superstrate layer

abstract: This paper proposes a circular polarized microstrip antenna fed high gain antenna using array of square and circular parasitic patches on a superstrate layer. Here parasitic patches are used to enhance the gain of antenna. The antenna structure consists of a microstrip antenna fabricated on FR4 substrate and placed at 1mm from finite ground plane. This MSA feeds array of square parasitic and circular parasitic patches fabricated on a FR4 superstrate. The patches on superstrate are suspended in air at about λ0/2. Circular polarization is obtained by diagonal feeding of microstrip antenna and using two shorting pins. The array size of parasitic patches is varied from 1×1 to 5×5. The printed MSA is fed by a 50 Ω coaxial probe. The VSWR is < 2 over 5.725 – 5.875 GHz, ISM frequency band. The antenna provides increase in gain from 8 dB to 14 dB gain as we increase the array of parasitic patches from MSA0 to MSA 4×4 over 5.725– 5.875 GHz. But gain decreases as we increase array of parasitic patches to 5×5. So maximum gain can be obtained in circular polarized antenna is by using 4×4 arrays of parasitic patches. The antenna structure also provides more than 70 % efficiency.


title: Exposure Modeling of Benzene Exploiting Passive–Active Sampling Data

abstract: The objective of the present study is the exploitation of active sampling personal exposure data in assessing the factors that affect exposure to benzene in combination with the widely accepted scheme of passive sampling—time microenvironment–activity diaries (TMAD). The campaign included personal exposure measurements with both passive and active sampling in several microenvironments, evaluation of TMAD kept by the volunteers, and a variety of environmental data (ambient air benzene determination, traffic and meteorological observations). Due to the relatively elevated benzene traffic emissions, average personal exposure was determined to be equal to 8.9 μg/m3, ranging between 5 and 20 μg/m3, which is a value highly related to the average urban concentration (9.2 μg/m3). The information gained from TMAD was embedded (in terms of spatial and temporal distribution) into three zones respectively, in order to draw statistically significant conclusions about the exposure levels and the activity patterns. The contribution of the activities to the overall amount of exposure was further quantified and refined by active sampling measurements. These data revealed that driving in a traffic-congested road was the main activity leading to elevated exposure levels (up to 70 μg/m3), followed by walking on the roadside of a congested road (up to 35 μg/m3). Indoor exposure to benzene was in general lower than outdoor (indicating that traffic is the dominant source of benzene emissions in the wider area), and it was significantly affected by the presence of environmental tobacco smoke. The higher significance of the regression coefficients obtained by statistical analysis of the active sampling data was fundamental for the development of a regression-based prediction exposure model. The model was evaluated through comparison with the passive sampling data, which were considered as an unknown but realistic data exposure pattern. The model performed very well in terms of expressing the variance of the exposure data with an average score of R 2 equal to 0.935. All of the above indicate that active sampling is a necessary albeit more laborious tool that needs to be used as a complement to passive sampling for precise quantification of the factors determining personal exposure patterns.


Efficient Implementation of the Rijndael S-box


title: An abstractive approach to sentence compression

abstract: In this article we generalize the sentence compression task. Rather than simply shorten a sentence by deleting words or constituents, as in previous work, we rewrite it using additional operations such as substitution, reordering, and insertion. We present an experimental study showing that humans can naturally create abstractive sentences using a variety of rewrite operations, not just deletion. We next create a new corpus that is suited to the abstractive compression task and formulate a discriminative tree-to-tree transduction model that can account for structural and lexical mismatches. The model incorporates a grammar extraction method, uses a language model for coherent output, and can be easily tuned to a wide range of compression-specific loss functions.


title: A Worldwide Map of Plasmodium falciparum K13-Propeller Polymorphisms.

abstract: BACKGROUND: ::: Recent gains in reducing the global burden of malaria are threatened by the emergence of Plasmodium falciparum resistance to artemisinins. The discovery that mutations in portions of a P. falciparum gene encoding kelch (K13)-propeller domains are the major determinant of resistance has provided opportunities for monitoring such resistance on a global scale. ::: ::: METHODS: ::: We analyzed the K13-propeller sequence polymorphism in 14,037 samples collected in 59 countries in which malaria is endemic. Most of the samples (84.5%) were obtained from patients who were treated at sentinel sites used for nationwide surveillance of antimalarial resistance. We evaluated the emergence and dissemination of mutations by haplotyping neighboring loci. ::: ::: RESULTS: ::: We identified 108 nonsynonymous K13 mutations, which showed marked geographic disparity in their frequency and distribution. In Asia, 36.5% of the K13 mutations were distributed within two areas--one in Cambodia, Vietnam, and Laos and the other in western Thailand, Myanmar, and China--with no overlap. In Africa, we observed a broad array of rare nonsynonymous mutations that were not associated with delayed parasite clearance. The gene-edited Dd2 transgenic line with the A578S mutation, which expresses the most frequently observed African allele, was found to be susceptible to artemisinin in vitro on a ring-stage survival assay. ::: ::: CONCLUSIONS: ::: No evidence of artemisinin resistance was found outside Southeast Asia and China, where resistance-associated K13 mutations were confined. The common African A578S allele was not associated with clinical or in vitro resistance to artemisinin, and many African mutations appear to be neutral. (Funded by Institut Pasteur Paris and others.).


title: Analyzing features for activity recognition

abstract: Human activity is one of the most important ingredients of context information. In wearable computing scenarios, activities such as walking, standing and sitting can be inferred from data provided by body-worn acceleration sensors. In such settings, most approaches use a single set of features, regardless of which activity to be recognized. In this paper we show that recognition rates can be improved by careful selection of individual features for each activity. We present a systematic analysis of features computed from a real-world data set and show how the choice of feature and the window length over which the feature is computed affects the recognition rates for different activities. Finally, we give a recommendation of suitable features and window lengths for a set of common activities.


CBRAP: Contextual Bandits with RAndom Projection


title: Reversal of neuromuscular effects of adenosine by specific adenosine A1-receptor antagonist in live rats.

abstract: Intravenous adenosine in-vivo was shown to potentiate the effects of non-depolarizing neuromuscular blocking agents. This study aimed to determine whether adenosine A1-receptors mediated this potentiation. The authors investigated the effects of intravenous adenosine, N6-cyclopentyladenosine, specific A1-receptor agonist, and 8-cyclopentyl-1,3-dipropylxanthine, specific A1-receptor antagonist, on neuromuscular block by vecuronium, in in-vivo rat sciatic nervetibialis anterior preparations. In the presence of 50% steady state block by vecuronium, adenosine, and N6-cyclopentyladenosine caused similar degree of depressions of twitch tension. Twitch tension returned to its pre-injection value more rapidly when 8-cyclopentyl-1,3-dipropylxanthine was given at the maximal block than when it was allowed to recover spontaneously. It was concluded that in in-vivo adenosine potentiated the neuromuscular effects of vecuronium through adenosine A1-receptors in rats.


title: Census Signal Temporal Logic Inference for Multi-Agent Group Behavior Analysis

abstract: In this paper, we define a novel census signal temporal logic (CensusSTL) that focuses on the number of agents in different subsets of a group that complete a certain task specified by the STL. CensusSTL consists of an “ inner logic ” STL formula and an “ outer logic ” STL formula. We present a new inference algorithm to infer CensusSTL formulas from the trajectory data of a group of agents. We first identify the “ inner logic ” STL formula and then infer the subgroups based on whether the agents’ behaviors satisfy the “ inner logic ” formula at each time point. We use two different approaches to infer the subgroups based on similarity and complementarity, respectively. The “ outer logic ” CensusSTL formula is inferred from the census trajectories of different subgroups. We apply the algorithm in analyzing data from a soccer match by inferring the CensusSTL formula for different subgroups of a soccer team. Note to Practitioners —The method described in this paper can be used to discover subgroups of agents and their behavior patterns based on spatial–temporal data of all agents in the group. The subgroups are formed by agents that behave similarly or complementarily for a certain task. The behavior patterns of the subgroups are expressed in the form of signal temporal logic statements. These are logical statements about the number of agents in the subgroups that are in certain states during a certain time interval. For example, if there are more than two blue agents in the region, then within 2 h, there will be more than three red agents in the same region. The behavior patterns inferred using this method can be used in: 1) analysis of group behavior while performing a task, e.g., in sports tactical analysis and 2) prediction of future group behavior under similar situations.


title: AdSplit: Separating smartphone advertising from applications

abstract: A wide variety of smartphone applications today rely on third-party advertising ::: services, which provide libraries that are linked into the hosting application. ::: Advertising libraries often need additional permissions, requiring applications ::: to issue requests for additional permissions to their users at install time. This ::: article describes our AdSplit model, where we extended Android to allow an application ::: and its advertising to run as separate processes, under separate user IDs, ::: eliminating the need for applications to request permissions on behalf of their ::: advertising libraries.


title: All-Electric Ship Design: From Electrical Propulsion to Integrated Electrical and Electronic Power Systems

abstract: Electrical propulsion is not a novel concept in marine systems. However, the availability of power electronic converters has proved to be the Key Enabling Technology for electrification of large ships. This paper starts with a summary of EP drives, which led to the birth of all-electric ships. Electric power generation and control systems are then presented, which make it possible to exploit the integrated electrical power system. To ease comprehension of the issues in designing such a system, its conventional design process is given. Then, the reasons that are pushing ahead the research in the shipboard power systems sector are discussed. The need for research in the design methods area is demonstrated through an overview of the latest results of technological research. Finally, a summary of the most significant results on the design tools research is given, including early stage design, dependable-oriented design, and the improvements achievable through software simulators and hardware-in-the-loop are discussed. The goal of this paper is to demonstrate why research on design methods is as important as a technological one, on the basis of the needs concerning the design, integration, and management of future “integrated electrical and electronic power systems” (power systems with power conversion quota approaching 100%).


title: Organizational Learning and Communities-of-Practice: Toward a Unified View of Working, Learning, and Innovation

abstract: Recent ethnographic studies of workplace practices indicate that the ways people actually work usually differ fundamentally from the ways organizations describe that work in manuals, training programs, organizational charts, and job descriptions. Nevertheless, organizations tend to rely on the latter in their attempts to understand and improve work practice. We examine one such study. We then relate its conclusions to compatible investigations of learning and of innovation to argue that conventional descriptions of jobs mask not only the ways people work, but also significant learning and innovation generated in the informal communities-of-practice in which they work. By reassessing work, learning, and innovation in the context of actual communities and actual practices, we suggest that the connections between these three become apparent. With a unified view of working, learning, and innovating, it should be possible to reconceive of and redesign organizations to improve all three.


title: Using artificial neural network models in stock market index prediction

abstract: Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models which are known to be dynamic and effective in stock-market predictions. The models analysed are multi-layer perceptron (MLP), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) to extract new input variables. The comparison for each model is done in two view points: Mean Square Error (MSE) and Mean Absolute Deviate (MAD) using real exchange daily rate values of NASDAQ Stock Exchange index.


title: The Effects of Magnesium Supplementation on Subjective Anxiety and Stress—A Systematic Review

abstract: Background: Anxiety related conditions are the most common affective disorders present in the general population with a lifetime prevalence of over 15%. Magnesium (Mg) status is associated with subjective anxiety, leading to the proposition that Mg supplementation may attenuate anxiety symptoms. This systematic review examines the available evidence for the efficacy of Mg supplementation in the alleviation of subjective measures of anxiety and stress. Methods: A systematic search of interventions with Mg alone or in combination (up to 5 additional ingredients) was performed in May 2016. Ovid Medline, PsychInfo, Embase, CINAHL and Cochrane databases were searched using equivalent search terms. A grey literature review of relevant sources was also undertaken. Results: 18 studies were included in the review. All reviewed studies recruited samples based upon an existing vulnerability to anxiety: mildly anxious, premenstrual syndrome (PMS), postpartum status, and hypertension. Four/eight studies in anxious samples, four/seven studies in PMS samples, and one/two studies in hypertensive samples reported positive effects of Mg on subjective anxiety outcomes. Mg had no effect on postpartum anxiety. No study administered a validated measure of subjective stress as an outcome. Conclusions: Existing evidence is suggestive of a beneficial effect of Mg on subjective anxiety in anxiety vulnerable samples. However, the quality of the existing evidence is poor. Well-designed randomised controlled trials are required to further confirm the efficacy of Mg supplementation.


title: The battle against phishing: Dynamic Security Skins

abstract: Phishing is a model problem for illustrating usability concerns of privacy and security because both system designers and attackers battle using user interfaces to guide (or misguide) users.We propose a new scheme, Dynamic Security Skins, that allows a remote web server to prove its identity in a way that is easy for a human user to verify and hard for an attacker to spoof. We describe the design of an extension to the Mozilla Firefox browser that implements this scheme.We present two novel interaction techniques to prevent spoofing. First, our browser extension provides a trusted window in the browser dedicated to username and password entry. We use a photographic image to create a trusted path between the user and this window to prevent spoofing of the window and of the text entry fields.Second, our scheme allows the remote server to generate a unique abstract image for each user and each transaction. This image creates a "skin" that automatically customizes the browser window or the user interface elements in the content of a remote web page. Our extension allows the user's browser to independently compute the image that it expects to receive from the server. To authenticate content from the server, the user can visually verify that the images match.We contrast our work with existing anti-phishing proposals. In contrast to other proposals, our scheme places a very low burden on the user in terms of effort, memory and time. To authenticate himself, the user has to recognize only one image and remember one low entropy password, no matter how many servers he wishes to interact with. To authenticate content from an authenticated server, the user only needs to perform one visual matching operation to compare two images. Furthermore, it places a high burden of effort on an attacker to spoof customized security indicators.


title: Knowledge Transfer Pre-training

abstract: Pre-training is crucial for learning deep neural networks. Most of existing pre-training methods train simple models (e.g., restricted Boltzmann machines) and then stack them layer by layer to form the deep structure. This layer-wise pre-training has found strong theoretical foundation and broad empirical support. However, it is not easy to employ such method to pre-train models without a clear multi-layer structure,e.g., recurrent neural networks (RNNs). This paper presents a new pre-training approach based on knowledge transfer learning. In contrast to the layer-wise approach which trains model components incrementally, the new approach trains the entire model as a whole but with an easier objective function. This is achieved by utilizing soft targets produced by a prior trained model (teacher model). Compared to the conventional layer-wise methods, this new method does not care about the model structure, so can be used to pre-train very complex models. Experiments on a speech recognition task demonstrated that with this approach, complex RNNs can be well trained with a weaker deep neural network (DNN) model. Furthermore, the new method can be combined with conventional layer-wise pre-training to deliver additional gains.


title: Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election

abstract: Recent accounts from researchers, journalists, as well as federal investigators, reached a unanimous conclusion: social media are systematically exploited to manipulate and alter public opinion. Some disinformation campaigns have been coordinated by means of bots, social media accounts controlled by computer scripts that try to disguise themselves as legitimate human users. In this study, we describe one such operation that occurred in the run up to the 2017 French presidential election. We collected a massive Twitter dataset of nearly 17 million posts, posted between 27 April and 7 May 2017 (Election Day). We then set to study the MacronLeaks disinformation campaign: By leveraging a mix of machine learning and cognitive behavioral modeling techniques, we separated humans from bots, and then studied the activities of the two groups independently, as well as their interplay. We provide a characterization of both the bots and the users who engaged with them, and oppose it to those users who didn’t. Prior interests of disinformation adopters pinpoint to the reasons of scarce success of this campaign: the users who engaged with MacronLeaks are mostly foreigners with pre-existing interest in alt-right topics and alternative news media, rather than French users with diverse political views. Concluding, anomalous account usage patterns suggest the possible existence of a black market for reusable political disinformation bots.


Fall Detection and Prevention System for Elderly People using MEMS Sensors and IOT


title: Teaching anatomy without cadavers

abstract: Background Anatomy learning is generally seen as essential to medicine, and exposure to cadavers is generally seen as essential to anatomy learning around the world. Few voices dissenting from these propositions can be identified. ::: ::: Aims This paper aims to consider arguments relating to the use of cadavers in anatomy teaching, and to describe the rationale behind the decision of a new UK medical school not to use cadaveric material. ::: ::: Discussion First, the background to use of cadavers in anatomy learning is explored, and some general educational principles are explored. Next, arguments for the use of human cadaveric material are summarised. Then, possible arguments against use of cadavers, including educational principles as well as costs, hazards and practicality, are considered. These are much less well explored in the existing literature. Next, the rationale behind the decision of a new UK medical school not to use cadaveric material is indicated, and the programme of anatomy teaching to be employed in the absence of the use of human remains is described. Curriculum design and development, and evaluation procedures, are briefly described. Issues surrounding pathology training by autopsy, and postgraduate training in surgical anatomy, are not addressed in this paper. ::: ::: Future directions Evidence relating to the effect on medical learning by students not exposed to cadavers is scant, and plainly opportunities will now arise through our programme to gather such evidence. We anticipate that this discussion paper will contribute to an ongoing debate, in which virtually all previous papers on this topic have concluded that use of cadavers is essential to medical learning.


title: Integration of RFID and Wireless Sensor Networks

abstract: Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN) are two important wireless technologies that have wide variety of applications and provide limitless future potentials. However, RFID and sensor networks almost are under development in parallel way. Integration of RFID and wireless sensor networ ks attracts little attention from research community. This paper first presents a brief introduction on RFID, and then investigates recent research works, new products/pa tents and applications that integrate RFID with sensor networks. Four types of integration are discussed. They are integrating tags with sensors, integrating tags with wireless sensor nodes, integrating readers with wir eless sensor nodes and wireless devices, and mix of RFID and sensors. New challenges and future works are discus sed in the end.


title: Towards API-specific automatic program repair

abstract: The domain of Automatic Program Repair (APR) had many research contributions in recent years. So far, most approaches target fixing generic bugs in programs (e.g., off-by-one errors). Nevertheless, recent studies reveal that about 50% of real bugs require API-specific fixes (e.g., adding missing API method calls or correcting method ordering), for which existing APR approaches are not designed. In this paper, we address this problem and introduce the notion of an API-specific program repair mechanism. This mechanism detects erroneous code in a similar way to existing APR approaches. However, to fix such bugs, it uses API-specific information from the erroneous code to search for API usage patterns in other software, with which we could fix the bug. We provide first insights on the applicability of this mechanism and discuss upcoming research challenges.


title: Evaluation of SiC MOSFETs for a high efficiency three-phase buck rectifier

abstract: This paper presents the characteristics of a 1200 V, 33 A SiC MOSFET and a 1200 V, 60 A SiC schottky barrier diode (SBD). The switching characteristics of the devices are tested by a double pulse test (DPT) based on a current-source structure at voltage levels up to 680 V and current up to 20 A. In addition, based on these devices, a 7.5 kW, three-phase buck rectifier for a 400 V dc architecture data center power supply is designed. The total loss of this rectifier is calculated full load. The results show that the SiC based buck rectifier can obtain low power loss and smaller weight and volume than a Si based rectifier.


title: Polymorphic malware detection using sequence classification methods and ensembles

abstract: Identifying malicious software executables is made difficult by the constant adaptations introduced by miscreants in order to evade detection by antivirus software. Such changes are akin to mutations in biological sequences. Recently, high-throughput methods for gene sequence classification have been developed by the bioinformatics and computational biology communities. In this paper, we apply methods designed for gene sequencing to detect malware in a manner robust to attacker adaptations. Whereas most gene classification tools are optimized for and restricted to an alphabet of four letters (nucleic acids), we have selected the Strand gene sequence classifier for malware classification. Strand's design can easily accommodate unstructured data with any alphabet, including source code or compiled machine code. To demonstrate that gene sequence classification tools are suitable for classifying malware, we apply Strand to approximately 500 GB of malware data provided by the Kaggle Microsoft Malware Classification Challenge (BIG 2015) used for predicting nine classes of polymorphic malware. Experiments show that, with minimal adaptation, the method achieves accuracy levels well above 95% requiring only a fraction of the training times used by the winning team's method.


title: Premise Selection for Mathematics by Corpus Analysis and Kernel Methods

abstract: Smart premise selection is essential when using automated reasoning as a tool for large-theory formal proof development. This work develops learning-based premise selection in two ways. First, a fine-grained dependency analysis of existing high-level formal mathematical proofs is used to build a large knowledge base of proof dependencies, providing precise data for ATP-based re-verification and for training premise selection algorithms. Second, a new machine learning algorithm for premise selection based on kernel methods is proposed and implemented. To evaluate the impact of both techniques, a benchmark consisting of 2078 large-theory mathematical problems is constructed, extending the older MPTP Challenge benchmark. The combined effect of the techniques results in a 50 % improvement on the benchmark over the state-of-the-art Vampire/SInE system for automated reasoning in large theories.


title: A Novel Calibration Method of Gain and Time-skew Mismatches for Time-interleaved ADCs Based on Neural Network

abstract: This paper proposes a novel calibration method for time-interleaved analog-to-digital converter (TI-ADC). Based on the polyphase derived operation, the spurious signals due to the gain and time-skew mismatches could be compensated with a simple BP neural network (NN). In the proposed calibration structure, a pilot signal is sent to train the neural network and then the sampled signals could be directly calibrated with the trained network. The main advantage is that the proposed method could achieve higher performance without requiring a feedback loop in comparison with the prior works. Numerical simulations have been presented to demonstrate the effectiveness of the proposed structure, which shows the proposed method could significantly improve the SNR performance of the TI-ADCs.


title: Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer

abstract: We introduce a new approach to tackle the problem of offensive language in online social media. Our approach uses unsupervised text style transfer to translate offensive sentences into non-offensive ones. We propose a new method for training encoder-decoders using non-parallel data that combines a collaborative classifier, attention and the cycle consistency loss. Experimental results on data from Twitter and Reddit show that our method outperforms a state-of-the-art text style transfer system in two out of three quantitative metrics and produces reliable non-offensive transferred sentences.


title: Business artifacts: An approach to operational specification

abstract: Any business, no matter what physical goods or services it produces, relies on business records. It needs to record details of what it produces in terms of concrete information. Business artifacts are a mechanism to record this information in units that are concrete, identifiable, self-describing, and indivisible. We developed the concept of artifacts, or semantic objects, in the context of a technique for constructing formal yet intuitive operational descriptions of a business. This technique, called OpS (Operational Specification), was developed over the course of many business-transformation and business-process-integration engagements for use in IBM's internal processes as well as for use with customers. Business artifacts (or business records) are the basis for the factorization of knowledge that enables the OpS technique. In this paper we present a comprehensive discussion of business artifacts--what they are, how they are represented, and the role they play in operational business modeling. Unlike the more familiar and popular concept of business objects, business artifacts are pure instances rather than instances of a taxonomy of types. Consequently, the key operation on business artifacts is recognition rather than classification.


title: Teachers’ Attitudes and Levels of Technology Use in Classrooms: The Case of Jordan Schools

abstract: Throughout the world there is awareness of the fundamental role of new Information and Communication Technologies (ICTs) in the field of education. Theoretical and empirical studies have considered the importance of ICTs in the process of teaching and learning. This current paper investigates the level of ICT use for educational purposes by teachers in Jordanian rural secondary schools. The paper will contribute to the body of knowledge regarding the level of ICT use and also, concerning the importance of teachers' attitudes towards the use of ICT for educational purposes. The data for the study were collected through the use of quantitative data. In October 2008, a questionnaire was distributed to 650 teachers in Jordan, randomly selected. Four hundred sixty teachers responded to the questionnaire. The survey included questions concerning the level of ICT use as well as questions related to the attitudes of teachers towards the use of ICT. The findings of the study, which were obtained by analyzing the data collected from the teachers revealed that, teachers had a low level of ICT use for educational purpose, teachers hold positive attitudes towards the use of ICT, and a significant positive correlation between teachers’ level of ICT use and their attitudes towards ICT was found. The findings suggest that ICTs use for educational purposes should be given greater consideration than it currently receives. In general, the results were consistent with those previously reported in studies related to the use of ICT in the educational settings.


title: Imaging Performance Analysis of an EO/IR Dual Band Airborne Camera

abstract: An airborne sensor is developed for remote sensing on an aerial vehicle (UV). The sensor is an optical ::: payload for an eletro-optical/infrared (EO/IR) dual band camera that combines visible and IR imaging ::: capabilities in a compact and lightweight package. It adopts a Ritchey-Chretien telescope for the common ::: front end optics with several relay optics that divide and deliver EO and IR bands to a charge-coupleddevice ::: (CCD) and an IR detector, respectively. The EO/IR camera for dual bands is mounted on a two-axis ::: gimbal that provides stabilized imaging and precision pointing in both the along and cross-track directions. ::: We first investigate the mechanical deformations, displacements and stress of the EO/IR camera through ::: finite element analysis (FEA) for five cases: three gravitational effects and two thermal conditions. For ::: investigating gravitational effects, one gravitational acceleration (1 g) is given along each of the +x, +y ::: and +z directions. The two thermal conditions are the overall temperature change to 30℃ from 20℃ and ::: the temperature gradient across the primary mirror pupil from -5℃ to +5℃. Optical performance, ::: represented by the modulation transfer function (MTF), is then predicted by integrating the FEA results ::: into optics design/analysis software. This analysis shows the IR channel can sustain imaging performance ::: as good as designed, i.e., MTF 38% at 13 line-pairs-per-mm (lpm), with refocus capability. Similarly, the ::: EO channel can keep the designed performance (MTF 73% at 27.3 lpm) except in the case of the overall ::: temperature change, in which the EO channel experiences slight performance degradation (MTF 16% drop) ::: for 20℃ overall temperate change.


title: WCDMA direct-conversion receiver front-end comparison in RF-CMOS and SiGe BiCMOS

abstract: Wide-band code-division multiple-access direct-conversion receiver front-ends have been implemented in both 0.25-/spl mu/m RF-CMOS and SiGe BiCMOS technologies. These circuits have been designed for the same application, radio architecture, and system specifications, allowing relevant comparisons to be made. The front-ends include a bypassable low-noise amplifier, a quadrature downconverter, baseband variable-gain amplifiers, and a local-oscillator frequency divider with output buffers. At 24.5 mA of total current consumption from a 2.7-3.3-V supply, the CMOS front-end has a noise figure of 5.3 dB, in-band third-order intercept point (IIP3) and second-order intercept point (IIP2) of -14 and +20.7 dBm, respectively, and out-of-band IIP3 and IIP2 of >+1.2 and +69 dBm, respectively. Compared to an SiGe front-end consuming 22 mA, the CMOS circuit has a 2-dB higher noise figure, comparable out-of-band linearity, 3-dB higher in-band IIP3, 12-dB lower in-band IIP2, and 7-dB higher LO-to-RF leakage.


title: Computed Tomography for Diagnosis of Acute Appendicitis in Adults

abstract: Background: Diagnosing acute appendicitis (appendicitis) based on clinical evaluation, blood testing, and urinalysis can be difficult. Therefore, in persons with suspected appendicitis, abdominopelvic computed tomography (CT) is often used as an add-on test following the initial evaluation to reduce remaining diagnostic uncertainty. The aim of using CT is to assist the clinician in discriminating between persons who need surgery with appendicectomy and persons who do not. Objectives: Primary objective. Our primary objective was to evaluate the accuracy of CT for diagnosing appendicitis in adults with suspected appendicitis. Secondary objectives. Our secondary objectives were to compare the accuracy of contrast-enhanced versus non-contrast-enhanced CT, to compare the accuracy of low-dose versus standard-dose CT, and to explore the influence of CT-scanner generation, radiologist experience, degree of clinical suspicion of appendicitis, and aspects of methodological quality on diagnostic accuracy. Search methods: We searched MEDLINE, Embase, and Science Citation Index until 16 June 2017. We also searched references lists. We did not exclude studies on the basis of language or publication status. Selection criteria: We included prospective studies that compared results of CT versus outcomes of a reference standard in adults (> 14 years of age) with suspected appendicitis. We excluded studies recruiting only pregnant women; studies in persons with abdominal pain at any location and with no particular suspicion of appendicitis; studies in which all participants had undergone ultrasonography (US) before CT and the decision to perform CT depended on the US outcome; studies using a case-control design; studies with fewer than 10 participants; and studies that did not report the numbers of true-positives, false-positives, false-negatives, and true-negatives. Two review authors independently screened and selected studies for inclusion. Data collection and analysis: Two review authors independently collected the data from each study and evaluated methodological quality according to the Quality Assessment of Studies of Diagnostic Accuracy - Revised (QUADAS-2) tool. We used the bivariate random-effects model to obtain summary estimates of sensitivity and specificity. Main results: We identified 64 studies including 71 separate study populations with a total of 10,280 participants (4583 with and 5697 without acute appendicitis). Estimates of sensitivity ranged from 0.72 to 1.0 and estimates of specificity ranged from 0.5 to 1.0 across the 71 study populations. Summary sensitivity was 0.95 (95% confidence interval (CI) 0.93 to 0.96), and summary specificity was 0.94 (95% CI 0.92 to 0.95). At the median prevalence of appendicitis (0.43), the probability of having appendicitis following a positive CT result was 0.92 (95% CI 0.90 to 0.94), and the probability of having appendicitis following a negative CT result was 0.04 (95% CI 0.03 to 0.05). In subgroup analyses according to contrast enhancement, summary sensitivity was higher for CT with intravenous contrast (0.96, 95% CI 0.92 to 0.98), CT with rectal contrast (0.97, 95% CI 0.93 to 0.99), and CT with intravenous and oral contrast enhancement (0.96, 95% CI 0.93 to 0.98) than for unenhanced CT (0.91, 95% CI 0.87 to 0.93). Summary sensitivity of CT with oral contrast enhancement (0.89, 95% CI 0.81 to 0.94) and unenhanced CT was similar. Results show practically no differences in summary specificity, which varied from 0.93 (95% CI 0.90 to 0.95) to 0.95 (95% CI 0.90 to 0.98) between subgroups. Summary sensitivity for low-dose CT (0.94, 95% 0.90 to 0.97) was similar to summary sensitivity for standard-dose or unspecified-dose CT (0.95, 95% 0.93 to 0.96); summary specificity did not differ between low-dose and standard-dose or unspecified-dose CT. No studies had high methodological quality as evaluated by the QUADAS-2 tool. Major methodological problems were poor reference standards and partial verification primarily due to inadequate and incomplete follow-up in persons who did not have surgery. Authors' conclusions: The sensitivity and specificity of CT for diagnosing appendicitis in adults are high. Unenhanced standard-dose CT appears to have lower sensitivity than standard-dose CT with intravenous, rectal, or oral and intravenous contrast enhancement. Use of different types of contrast enhancement or no enhancement does not appear to affect specificity. Differences in sensitivity and specificity between low-dose and standard-dose CT appear to be negligible. The results of this review should be interpreted with caution for two reasons. First, these results are based on studies of low methodological quality. Second, the comparisons between types of contrast enhancement and radiation dose may be unreliable because they are based on indirect comparisons that may be confounded by other factors.


title: A Circularly Polarized Octagon-Star-Shaped Microstrip Patch Antenna With Conical Radiation Pattern

abstract: A new circularly polarized (CP) microstrip patch antenna with conical radiation pattern is proposed and investigated. The proposed CP antenna has a single feed, a low profile, and a very simple structure. The patch has an octagon-star shape, and can be considered as a superimposition of two square patches. By generating two orthogonal degenerated TM11 modes from the two superimposed square patches, omnidirectional CP radiation is achieved for the proposed antenna. The operation mechanism of the antenna is theoretically explained. A prototype of the proposed antenna is fabricated and measured. Measured results are consistent with the simulated and theoretical results. The antenna can produce a conical CP radiation pattern with a measured average axial ratio (AR) of 1.80 dB in the azimuth plane ( $\theta = 45$ °) and a measured 3-dB AR bandwidth covering the global positioning system L1 band.


Religious Myth Utopia Resembled in “the Sound and the Fury”: Dilsey’s Episode


title: Regularized Alternating Least Squares Algorithms for Non-negative Matrix / Tensor Factorization

abstract: Nonnegative Matrix and Tensor Factorization (NMF/NTF) and Sparse Component Analysis (SCA) have already found many potential applications, especially in multi-way Blind Source Separation (BSS), multi-dimensional data analysis, model reduction and sparse signal/image representations. In this paper we propose a family of the modified Regularized Alternating Least Squares (RALS) algorithms for NMF/NTF. By incorporating regularization and penalty terms into the weighted Frobenius norm we are able to achieve sparse and/or smooth representations of the desired solution, and to alleviate the problem of getting stuck in local minima. We implemented the RALS algorithms in our NMFLAB/NTFLAB Matlab Toolboxes, and compared them with standard NMF algorithms. The proposed algorithms are characterized by improved efficiency and convergence properties, especially for large-scale problems.


title: Structure of the variant histone H3.3–H4 heterodimer in complex with its chaperone DAXX

abstract: Mammalian histone H3.3 is a variant of the canonical H3.1 essential for genome reprogramming in fertilized eggs and maintenance of chromatin structure in neuronal cells. An H3.3-specific histone chaperone, DAXX, directs the deposition of H3.3 onto pericentric and telomeric heterochromatin. H3.3 differs from H3.1 by only five amino acids, yet DAXX can distinguish the two with high precision. By a combination of structural, biochemical and cell-based targeting analyses, we show that Ala87 and Gly90 are the principal determinants of human H3.3 specificity. DAXX uses a shallow hydrophobic pocket to accommodate the small hydrophobic Ala87 of H3.3, whereas a polar binding environment in DAXX prefers Gly90 in H3.3 over the hydrophobic Met90 in H3.1. An H3.3-H4 heterodimer is bound by the histone-binding domain of DAXX, which makes extensive contacts with both H3.3 and H4.


title: Autonomous navigation and sign detector learning

abstract: This paper presents an autonomous robotic system that incorporates novel Computer Vision, Machine Learning and Data Mining algorithms in order to learn to navigate and discover important visual entities. This is achieved within a Learning from Demonstration (LfD) framework, where policies are derived from example state-to-action mappings. For autonomous navigation, a mapping is learnt from holistic image features (GIST) onto control parameters using Random Forest regression. Additionally, visual entities (road signs e.g. STOP sign) that are strongly associated to autonomously discovered modes of action (e.g. stopping behaviour) are discovered through a novel Percept-Action Mining methodology. The resulting sign detector is learnt without any supervision (no image labeling or bounding box annotations are used). The complete system is demonstrated on a fully autonomous robotic platform, featuring a single camera mounted on a standard remote control car. The robot carries a PC laptop, that performs all the processing on board and in real-time.


title: Optimizing Video Object Detection via a Scale-Time Lattice

abstract: High-performance object detection relies on expensive convolutional networks to compute features, often leading to significant challenges in applications, e.g. those that require detecting objects from video streams in real time. The key to this problem is to trade accuracy for efficiency in an effective way, i.e. reducing the computing cost while maintaining competitive performance. To seek a good balance, previous efforts usually focus on optimizing the model architectures. This paper explores an alternative approach, that is, to reallocate the computation over a scale-time space. The basic idea is to perform expensive detection sparsely and propagate the results across both scales and time with substantially cheaper networks, by exploiting the strong correlations among them. Specifically, we present a unified framework that integrates detection, temporal propagation, and across-scale refinement on a Scale-Time Lattice. On this framework, one can explore various strategies to balance performance and cost. Taking advantage of this flexibility, we further develop an adaptive scheme with the detector invoked on demand and thus obtain improved tradeoff. On ImageNet VID dataset, the proposed method can achieve a competitive mAP 79.6% at 20 fps, or 79.0% at 62 fps as a performance/speed tradeoff.


title: A unified approach to local trajectory planning and control for autonomous driving along a reference path

abstract: This paper presents a unified approach to local trajectory planning and control for the autonomous ground vehicle driving along a rough predefined path. In order to cope with the unpredictably changing environment reactively and reason about the global guidance, we develop an efficient sampling-based model predictive local path generation approach to generate a set of kinematically-feasible trajectories aligning with the reference path. A discrete optimization scheme is developed to select the best path based on a specified objective function, then followed by the velocity profile generation. As for the low-level control, to achieve high performance of control, two degree of freedom control architecture is employed by combining the feedforward control with the feedback control. The simulation results demonstrate the capability of the proposed approach to track the curvature-discontinuous reference path robustly, while avoiding collisions with static obstacles.


title: Automotive radar gridmap representations

abstract: In robotic applications gridmaps are a common representation of the environment. For the automotive field, radar as sensing technology is suitable due to its robustness. This paper presents two radar-based grid-mapping algorithms for automotive applications like self-localization. These algorithms involve first an amplitude-based approach, which gains information about the RCS of all targets, and second an occupancy grid-mapping approach with an adapted inverse sensor measurement model. Experiments show that both gridmapping algorithms result in adequate representations of the environment.


title: Synthesis and Design of Wideband Dual-Band Bandpass Filters With Controllable In-Band Ripple Factor and Dual-Band Isolation

abstract: In this paper, an exact synthesis and design procedure has been proposed for a class of wideband dual-band bandpass filters (BPFs) with both controllable in-band ripple factor and isolation between the dual passband. Two multi-mode resonators (MMRs) with short-circuited stubs are parallel connected to form the basic structure of the proposed dual-band BPFs. Up to two pairs of transmission zeros (TZs) could be created with reference to the dual wide passbands. By choosing the impedances of the two paralleled MMRs, one pair of TZs could be moved from imaginary to real frequencies, whereas the second pair of TZs could be adjusted by folding one of the MMRs to form coupled lines. For these two prototypes, short-circuited stubs are utilized to excite the two paralleled MMRs, but not adequate to obtain a determined coupling degree. To provide an additional freedom for controlling the in-band ripple factor, a combination of short- and open-circuited stubs is used. A generic synthesis procedure is developed for the filter design. First, a generalized Chebyshev transfer functions with one TZ at the origin and symmetrical with respect to the origin has been discussed and frequency transferred to form a dual-band filtering function. Next, by equating of the dual-band circuit response and the filtering function, the circuit parameters could be obtained. Three filter prototypes have been designed and measured with good matching between these two sets of results.


title: An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation

abstract: Accurate segmentation of the heart is an important step towards evaluating cardiac function. In this paper, we present a fully automated framework for segmentation of the left (LV) and right (RV) ventricular cavities and the myocardium (Myo) on short-axis cardiac MR images. We investigate various 2D and 3D convolutional neural network architectures for this task. We investigate the suitability of various state-of-the art 2D and 3D convolutional neural network architectures, as well as slight modifications thereof, for this task. Experiments were performed on the ACDC 2017 challenge training dataset comprising cardiac MR images of 100 patients, where manual reference segmentations were made available for end-diastolic (ED) and end-systolic (ES) frames. We find that processing the images in a slice-by-slice fashion using 2D networks is beneficial due to a relatively large slice thickness. However, the exact network architecture only plays a minor role. We report mean Dice coefficients of $0.950$ (LV), $0.893$ (RV), and $0.899$ (Myo), respectively with an average evaluation time of 1.1 seconds per volume on a modern GPU.


title: The impact of waveform on the efficiency of RF to DC conversion using prefabricated energy harvesting device

abstract: This paper is dedicated to the investigation of the influence of different waveforms on the efficiency of radio frequency (RF) to direct current (DC) conversion in wireless power transfer (WPT) systems. Single-tone, multi-tone, random and chaotic signals are used and compared as waveforms for wireless power transfer. The influence of peak-to-average power ratio (PAPR) and average input power of the waveform on the efficiency of RF-to-DC conversion is measured in laboratory conditions and studied. A commercially available RF energy harvesting device — Powercast P2110B is used as RF-to-DC conversion circuitry.


title: Alcohol devaluation has dissociable effects on distinct components of alcohol behaviour

abstract: Rationale ::: Substance-related behaviour is often viewed as an appetitive behaviour, motivated by the reinforcing effects of the drug. However, there are various indices of substance motivation (e.g. attentional bias, behavioural economic demand, craving) and it is unclear how these are related or whether they play an important role in all types of substance-related behaviour.


title: The Effect of Reflexology on the Pain-Insomnia-Fatigue Disturbance Cluster of Breast Cancer Patients During Adjuvant Radiation Therapy

abstract: OBJECTIVE ::: To evaluate the effects of reflexology treatment on quality of life, sleep disturbances, and fatigue in breast cancer patients during radiation therapy. ::: ::: ::: METHODS/SUBJECTS ::: A total of 72 women with breast cancer (stages 1-3) scheduled for radiation therapy were recruited. ::: ::: ::: DESIGN ::: Women were allocated upon their preference either to the group receiving reflexology treatments once a week concurrently with radiotherapy and continued for 10 weeks or to the control group (usual care). ::: ::: ::: OUTCOME MEASURES ::: The Lee Fatigue Scale, General Sleep Disturbance Scale, and Multidimensional Quality of Life Scale Cancer were completed by each patient in both arms at the beginning of the radiation treatment, after 5 weeks, and after 10 weeks of reflexology treatment. ::: ::: ::: RESULTS ::: The final analysis included 58 women. The reflexology treated group demonstrated statistically significant lower levels of fatigue after 5 weeks of radiation therapy (p < 0.001), compared to the control group. It was also detected that although the quality of life in the control group deteriorated after 5 and 10 weeks of radiation therapy (p < 0.01 and p < 0.05, respectively), it was preserved in the reflexology group, which also demonstrated a significant improvement in the quality of sleep after 10 weeks of radiation treatment (p < 0.05). Similar patterns were obtained in the assessment of the pain levels experienced by the patients. ::: ::: ::: CONCLUSIONS ::: The results of the present study indicate that reflexology may have a positive effect on fatigue, quality of sleep, pain, and quality of life in breast cancer patients during radiation therapy. Reflexology prevented the decline in quality of life and significantly ameliorated the fatigue and quality of sleep of these patients. An encouraging trend was also noted in amelioration of pain levels.


title: Image Inpainting for High-Resolution Textures using CNN Texture Synthesis

abstract: Deep neural networks have been successfully applied to problems such as image segmentation, image super-resolution, coloration and image inpainting. In this work we propose the use of convolutional neural networks (CNN) for image inpainting of large regions in high-resolution textures. Due to limited computational resources processing high-resolution images with neural networks is still an open problem. Existing methods separate inpainting of global structure and the transfer of details, which leads to blurry results and loss of global coherence in the detail transfer step. Based on advances in texture synthesis using CNNs we propose patch-based image inpainting by a single network topology that is able to optimize for global as well as detail texture statistics. Our method is capable of filling large inpainting regions, oftentimes exceeding quality of comparable methods for images of high-resolution (2048x2048px). For reference patch look-up we propose to use the same summary statistics that are used in the inpainting process.


title: A Generative Model for Parts-based Object Segmentation

abstract: The Shape Boltzmann Machine (SBM) [1] has recently been introduced as a state-of-the-art model of foreground/background object shape. We extend the SBM to account for the foreground object's parts. Our new model, the Multinomial SBM (MSBM), can capture both local and global statistics of part shapes accurately. We combine the MSBM with an appearance model to form a fully generative model of images of objects. Parts-based object segmentations are obtained simply by performing probabilistic inference in the model. We apply the model to two challenging datasets which exhibit significant shape and appearance variability, and find that it obtains results that are comparable to the state-of-the-art.


title: AUTOMATIC CONSTRUCTION OF HIERARCHICAL HIDDEN MARKOV MODEL STRUCTURE FOR DISCOVERING SEMANTIC PATTERNS IN MOTION DATA

abstract: The objective of this paper is to automatically build a Hierarchical Hidden Markov Model (HHMM) (Fine et al., 1998) structure to detect semantic patterns from data with an unknown structure by exploring the natural hierarchical decomposition embedded in the data. The problem is important for effective motion data representation and analysis in a variety of applications: film and game making, military, entertainment, sport and medicine. We propose to represent the patterns of the data as an HHMM built utilising a two-stage learning algorithm. The novelty of our method is that it is the first fully automated approach to build an HHMM structure for motion data. Experimental results on different motion features (3D and angular pose coordinates, silhouettes extracted from the video sequence) demonstrate the approach is effective at automatically constructing efficient HHMM with a structure which naturally represents the underlying motion that allows for accurate modelling of the data for applications such as tracking and motion resynthesis.


title: How is Contrast Encoded in Deep Neural Networks?

abstract: Contrast is a crucial factor in visual information processing. It is desired for a visual system - irrespective of being biological or artificial - to"perceive"the world robustly under large potential changes in illumination. In this work, we studied the responses of deep neural networks (DNN) to identical images at different levels of contrast. We analysed the activation of kernels in the convolutional layers of eight prominent networks with distinct architectures (e.g. VGG and Inception). The results of our experiments indicate that those networks with a higher tolerance to alteration of contrast have more than one convolutional layer prior to the first max-pooling operator. It appears that the last convolutional layer before the first max-pooling acts as a mitigator of contrast variation in input images. In our investigation, interestingly, we observed many similarities between the mechanisms of these DNNs and biological visual systems. These comparisons allow us to understand more profoundly the underlying mechanisms of a visual system that is grounded on the basis of"data-analysis".


title: Chip-First Fan-Out Panel-Level Packaging for Heterogeneous Integration

abstract: The design, materials, process, fabrication, and reliability of a heterogeneous integration of four chips by a fan-out panel-level packaging (FOPLP) method are investigated in this paper. Emphasis is placed on the application of a special assembly process called uni-substrate-integrated package for fabricating the redistribution layers (RDLs) of the FOPLP. The Ajinomoto build-up film is used as the dielectric of the RDLs and is built up by the semiadditive process. The electroless Cu is used to make the seed layer, the laser direct imaging is used for opening the photoresist, and the printed circuit board Cu plating is used for making the conductor wiring of the RDLs. Reliability assessments such as the drop test and thermal cycling test are also performed.


title: Beware of SMOMBIES: Verification of Users Based on Activities While Walking

abstract: Several research evaluated the user's style of walking for the verification of a claimed identity and showed high authentication accuracies in many settings. In this paper we present a system that successfully verifies a user's identity based on many real world smartphone placements and yet not regarded interactions while walking. Our contribution is the distinction of all considered activities into three distinct subsets and a specific one-class Support Vector Machine per subset. Using sensor data of 30 participants collected in a semi-supervised study approach, we prove that unsupervised verification is possible with very low false-acceptance and false-rejection rates. We furthermore show that these subsets can be distinguished with a high accuracy and demonstrate that this system can be deployed on off-the-shelf smartphones.


title: A demystification of the Black–Litterman model: Managing quantitative and traditional portfolio construction

abstract: The purpose of this paper is to present details of Bayesian portfolio construction procedures which have become known in the asset management industry as Black–Litterman models. We explain their construction, present some extensions and argue that these models are valuable tools for financial management.


title: IARPA Janus Benchmark - C: Face Dataset and Protocol

abstract: Although considerable work has been done in recent years to drive the state of the art in facial recognition towards operation on fully unconstrained imagery, research has always been restricted by a lack of datasets in the public domain. In addition, traditional biometrics experiments such as single image verification and closed set recognition do not adequately evaluate the ways in which unconstrained face recognition systems are used in practice. The IARPA Janus Benchmark–C (IJB-C) face dataset advances the goal of robust unconstrained face recognition, improving upon the previous public domain IJB-B dataset, by increasing dataset size and variability, and by introducing end-to-end protocols that more closely model operational face recognition use cases. IJB-C adds 1,661 new subjects to the 1,870 subjects released in IJB-B, with increased emphasis on occlusion and diversity of subject occupation and geographic origin with the goal of improving representation of the global population. Annotations on IJB-C imagery have been expanded to allow for further covariate analysis, including a spatial occlusion grid to standardize analysis of occlusion. Due to these enhancements, the IJB-C dataset is significantly more challenging than other datasets in the public domain and will advance the state of the art in unconstrained face recognition.


title: MQTT protocol for connected OCC small cells

abstract: Over the last several years, the achievement of wireless communication technologies have brought convenience and amazing services to users. The hybrid use of wireless technologies in a system is not a novel idea, however, in this paper, we introduce the feasible platform based on Wi-Fi and OCC that is emerging technology nowadays. Various OCC technologies are connected via an MQTT (Message Queue Telemetry Transportation) broker to bring a promising service of light that allows smartphone users to control and monitoring data with augmented reality user experience. Demonstrations of the system along with implemented software and hardware are given to illustrate the feasibility of our system.


title: Crowd Flow Prediction by Deep Spatio-Temporal Transfer Learning

abstract: Crowd flow prediction is a fundamental urban computing problem. Recently, deep learning has been successfully applied to solve this problem, but it relies on rich historical data. In reality, many cities may suffer from data scarcity issue when their targeted service or infrastructure is new. To overcome this issue, this paper proposes a novel deep spatio-temporal transfer learning framework, called RegionTrans, which can predict future crowd flow in a data-scarce (target) city by transferring knowledge from a data-rich (source) city. Leveraging social network check-ins, RegionTrans first links a region in the target city to certain regions in the source city, expecting that these inter-city region pairs will share similar crowd flow dynamics. Then, we propose a deep spatio-temporal neural network structure, in which a hidden layer is dedicated to keeping the region representation. A source city model is then trained on its rich historical data with this network structure. Finally, we propose a region-based cross-city transfer learning algorithm to learn the target city model from the source city model by minimizing the hidden representation discrepancy between the inter-city region pairs previously linked by check-ins. With experiments on real crowd flow, RegionTrans can outperform state-of-the-arts by reducing up to 10.7% prediction error.


title: An Approach to Automatically Extract Predictive Properties from Nominal Attributes in Relational Databases

abstract: Feature engineering is a fundamental step in data mining and yet it is both difficult and expensive. Hand-crafting features is not only a time-consuming task that requires specific domain knowledge, it also may prevent new information to emerge. The extraction of meaningful features from relational data is particularly difficult due to complex relationships between tables. In the last decade there is an emerging trend towards automating the process of constructing propositional features from relational data and such approaches have been successfully used for solving numerous real-world problems. Despite their success, most of them lack an adequate support of nominal attributes. We present a new approach helping propositionalization methods to extract meaningful features from nominal attributes and improve their predictive performance. In an experimental evaluation on three datasets we demonstrate that the proposed technique is capable of producing novel features that are highly correlated with the target attribute. Furthermore, those features can reveal relationships among the distinct categorical values allowing to compare and order them. Finally, experimental results show that those new features can significantly improve the predictive performance in classification tasks.


title: Automated derivation of behavior vocabularies for autonomous humanoid motion

abstract: In this paper we address the problem of automatically deriving vocabularies of motion modules from human motion data, taking advantage of the underlying spatio-temporal structure in motion. We approach this problem with a data-driven methodology for modularizing a motion stream (or time-series of human motion) into a vocabulary of parameterized primitive motion modules and a set of meta-level behaviors characterizing extended combinations of the primitives. Central to this methodology is the discovery of spatio-temporal structure in a motion stream. We estimate this structure by extending an existing nonlinear dimension reduction technique, Isomap, to handle motion data with spatial and temporal dependencies. The motion vocabularies derived by our methodology provide a substrate of autonomous behavior and can be used in a variety of applications. We demonstrate the utility of derived vocabularies for the application of synthesizing new humanoid motion that is structurally similar to the original demonstrated motion.


title: Alcoholic fermentation of carbon sources in biomass hydrolysates by Saccharomyces cerevisiae: current status

abstract: Fuel ethanol production from plant biomass hydrolysates by Saccharomyces cerevisiae is of great economic and environmental signifi- cance. This paper reviews the current status with respect to alcoholic fermentation of the main plant biomass-derived monosaccharides by this yeast. Wild-type S. cerevisiae strains readily fer- ment glucose, mannose and fructose via the Embden-Meyerhof pathway of glycolysis, while galactose is fermented via the Leloir pathway. Construction of yeast strains that efficiently con- vert other potentially fermentable substrates in plant biomass hydrolysates into ethanol is a major challenge in metabolic engineering. The most abundant of these compounds is xylose. Recent metabolic and evolutionary engineering studies on S. cerevisiae strains that express a fungal xylose isomerase have enabled the rapid and effi- cient anaerobic fermentation of this pentose. L-Arabinose fermentation, based on the expres- sion of a prokaryotic pathway in S. cerevisiae, has also been established, but needs further optimi- zation before it can be considered for industrial implementation. In addition to these already investigated strategies, possible approaches for metabolic engineering of galacturonic acid and rhamnose fermentation by S. cerevisiae are dis- cussed. An emerging and major challenge is to achieve the rapid transition from proof-of-prin- ciple experiments under 'academic' conditions (synthetic media, single substrates or simple substrate mixtures, absence of toxic inhibitors) towards efficient conversion of complex industrial substrate mixtures that contain synergistically acting inhibitors.


title: Spoofing Deep Face Recognition with Custom Silicone Masks

abstract: We investigate the vulnerability of convolutional neural network (CNN) based face-recognition (FR) systems to presentation attacks (PA) performed using custom-made silicone masks. Previous works have studied the vulnerability of CNN-FR systems to 2D PAs such as print-attacks, or digital- video replay attacks, and to rigid 3D masks. This is the first study to consider PAs performed using custom-made flexible silicone masks. Before embarking on research on detecting a new variety of PA, it is important to estimate the seriousness of the threat posed by the type of PA. In this work we demonstrate that PAs using custom silicone masks do pose a serious threat to state-of-the-art FR systems. Using a new dataset based on six custom silicone masks, we show that the vulnerability of each FR system in this study is at least 10 times higher than its false match rate. We also propose a simple but effective presentation attack detection method, based on a low-cost thermal camera.


Image and video decomposition and editing


title: Huffman Coding-Based Adaptive Spatial Modulation

abstract: Antenna switch enables multiple antennas to share a common RF chain. It also offers an additional spatial dimension, i.e., antenna index, that can be utilized for data transmission via both signal space and spatial dimension. In this paper, we propose a Huffman coding-based adaptive spatial modulation that generalizes both conventional spatial modulation and transmit antenna selection. Through the Huffman coding, i.e., designing variable length prefix codes, the transmit antennas can be activated with different probabilities. When the input signal is Gaussian distributed, the optimal antenna activation probability is derived through optimizing channel capacity. To make the optimization tractable, closed form upper bound and lower bound are derived as the effective approximations of channel capacity. When the input is discrete QAM signal, the optimal antenna activation probability is derived through minimizing symbol error rate. Numerical results show that the proposed adaptive transmission offers considerable performance improvement over the conventional spatial modulation and transmit antenna selection.


Maintaining anchorage with a combination Nance-Goshgarian transpalatal arch.


title: A Survey of Point-of-interest Recommendation in Location-based Social Networks

abstract: Point-of-interest (POI) recommendation that suggests new places for users to visit arises with the popularity of location-based social networks (LBSNs). Due to the importance of POI recommendation in LBSNs, it has attracted much academic and industrial interest. In this paper, we offer a systematic review of this field, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges in POI recommendation, compared with traditional recommendation problems, e.g., movie recommendation. Then, we present a comprehensive review in three aspects: influential factors for POI recommendation, methodologies employed for POI recommendation, and different tasks in POI recommendation. Specifically, we propose three taxonomies to classify POI recommendation systems. First, we categorize the systems by the influential factors check-in characteristics, including the geographical information, social relationship, temporal influence, and content indications. Second, we categorize the systems by the methodology, including systems modeled by fused methods and joint methods. Third, we categorize the systems as general POI recommendation and successive POI recommendation by subtle differences in the recommendation task whether to be bias to the recent check-in. For each category, we summarize the contributions and system features, and highlight the representative work. Moreover, we discuss the available data sets and the popular metrics. Finally, we point out the possible future directions in this area and conclude this survey.


title: A hybrid memristor-CMOS multiplier design based on memristive universal logic gates

abstract: Memristor is considered as one of the promising solutions to the fundamental limitations of the VLSI systems. Logic implementation with memristor device by considering its compatibility with CMOS fabric provides a new vision for digital logic circuits. This work presents a 2 by 2 multiplier cell design using a hybrid CMOS-memristor universal gate. The universal gate based implementation approach is the extension for memristor ratioed logic (MRL) with lower implementation cost. Simulation results confirm functionality of the proposed circuit. This circuit requires 16 memristors, 8 transistors and only one computational time step for multiplication. Compared with previous works, this approach presents considerably lower implementation cost.


Adversarial Adaptation of Synthetic or Stale Data.


title: Propagation through alginate encapsulation of axillary buds of Cannabis sativa L. — an important medicinal plant

abstract: Cannabis sativa L. (Cannabaceae) is an important medicinal plant well known for its pharmacologic and therapeutic potency. Because of allogamous nature of this species, it is difficult to maintain its potency and efficacy if grown from the seeds. Therefore, chemical profile-based screening, selection of high yielding elite clones and their propagation using biotechnological tools is the most suitable way to maintain their genetic lines. In this regard, we report a simple and efficient method for the in vitro propagation of a screened and selected high yielding drug type variety of Cannabis sativa, MX-1 using synthetic seed technology. Axillary buds of Cannabis sativa isolated from aseptic multiple shoot cultures were successfully encapsulated in calcium alginate beads. The best gel complexation was achieved using 5 % sodium alginate with 50 mM CaCl2.2H2O. Regrowth and conversion after encapsulation was evaluated both under in vitro and in vivo conditions on different planting substrates. The addition of antimicrobial substance - Plant Preservative Mixture (PPM) had a positive effect on overall plantlet development. Encapsulated explants exhibited the best regrowth and conversion frequency on Murashige and Skoog medium supplemented with thidiazuron (TDZ 0.5 μM) and PPM (0.075 %) under in vitro conditions. Under in vivo conditions, 100 % conversion of encapsulated explants was obtained on 1:1 potting mix- fertilome with coco natural growth medium, moistened with full strength MS medium without TDZ, supplemented with 3 % sucrose and 0.5 % PPM. Plantlets regenerated from the encapsulated explants were hardened off and successfully transferred to the soil. These plants are selected to be used in mass cultivation for the production of biomass as a starting material for the isolation of THC as a bulk active pharmaceutical.


title: Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages

abstract: We present a transition-based dependency parser that uses a convolutional neural network to compose word representations from characters. The character composition model shows great improvement over the word-lookup model, especially for parsing agglutinative languages. These improvements are even better than using pre-trained word embeddings from extra data. On the SPMRL data sets, our system outperforms the previous best greedy parser (Ballesteros et al., 2015) by a margin of 3% on average.


title: Potential application of renewable energy for rural electrification in Malaysia

abstract: Energy poverty and lack of electricity in rural areas exacerbate the poverty of the developing countries. In Malaysia, 3.8% of the population lives below the poverty line and most of them are settled in rural areas. The electricity coverage in poor states is about 79% in comparison with 99.62% in Peninsular Malaysia. The renewable energy sources can be considered the best alternative to reduce the energy poverty of the rural areas where the grid extension through a difficult terrain and thick jungle is not possible or economic. In this study, the potential for applying renewable sources – solar, wind and hydropower – for rural electrification is investigated, especially in the poorest States. A comparative study on rural electrification policies, in order to have community approval, appropriate siting and financial benefits for the rural community, while considering the three categories of social, institutional and economic issues, is also examined. Finally, the Malaysian policies of rural electrification by applying renewable sources are explained. It is found that in Malaysia, with a maximum solar radiation of about 6.027 kWh/m2 per day in Sabah and 5.303 kWh/m2 per day in Sarawak, the potential for applying solar energy for electrification is too high. However, the potential for micro-hydropower in Sabah and Sarawak is found to be 3182 kW and 6317 kW through 18 and 22 sites, respectively.


title: An Implementation Study of the AODV Routing Protocol

abstract: The Ad hoc On-Demand Distance Vector (AODV) routing protocol is designed for use in ad hoc mobile networks. Because of the difficulty of testing an ad hoc routing protocol in a real-world environment, a simulation was first created so that the protocol design could be tested in a variety of scenarios. Once simulation of the protocol was nearly complete, the simulation was used as the basis for an implementation in the Linux operating system. In the course of converting the simulation into an implementation, certain modifications were needed in AODV and the Linux kernel due to both simplifications made in the simulation of AODV and to incompatibilities of the Linux kernel and the IP-layer to routing in a mobile environment. This paper details many of the changes that were necessary during the development of the implementation.


title: Hand Recognition Using Geometric Classifiers

abstract: We discuss the issues and challenges in the design of a hand outline based recognition system. Our system is easier to use, cheaper to build and more accurate than previous systems. Extensive tests on more than 700 images collected from 70 people are reported. Classification, verification and identification of the input images were done using two simple geometric classifiers. We describe a novel minimum enclosing ball classifier which performs well for hand recognition and could be of interest for other applications.


title: Mutual disambiguation of 3D multimodal interaction in augmented and virtual reality

abstract: We describe an approach to 3D multimodal interaction in immersive augmented and virtual reality environments that accounts for the uncertain nature of the information sources. The resulting multimodal system fuses symbolic and statistical information from a set of 3D gesture, spoken language, and referential agents. The referential agents employ visible or invisible volumes that can be attached to 3D trackers in the environment, and which use a time-stamped history of the objects that intersect them to derive statistics for ranking potential referents. We discuss the means by which the system supports mutual disambiguation of these modalities and information sources, and show through a user study how mutual disambiguation accounts for over 45% of the successful 3D multimodal interpretations. An accompanying video demonstrates the system in action.


title: Deep Neural Network Initialization Methods for Micro-Doppler Classification With Low Training Sample Support

abstract: Deep neural networks (DNNs) require large-scale labeled data sets to prevent overfitting while having good generalization. In radar applications, however, acquiring a measured data set of the order of thousands is challenging due to constraints on manpower, cost, and other resources. In this letter, the efficacy of two neural network initialization techniques—unsupervised pretraining and transfer learning—for dealing with training DNNs on small data sets is compared. Unsupervised pretraining is implemented through the design of a convolutional autoencoder (CAE), while transfer learning from two popular convolutional neural network architectures (VGGNet and GoogleNet) is used to augment measured RF data for training. A 12-class problem for discrimination of micro-Doppler signatures for indoor human activities is utilized to analyze activation maps, bottleneck features, class model, and classification accuracy with respect to training sample size. Results show that on meager data sets, transfer learning outperforms unsupervised pretraining and random initialization by 10% and 25%, respectively, but that when the sample size exceeds 650, unsupervised pretraining surpasses transfer learning and random initialization by 5% and 10%, respectively. Visualization of activation layers and learned models reveals how the CAE succeeds in representing the micro-Doppler signature.


title: A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB)

abstract: This work presents a perceptual-based no-reference objective image sharpness/blurriness metric by integrating the concept of just noticeable blur into a probability summation model. Unlike existing objective no-reference image sharpness/blurriness metrics, the proposed metric is able to predict the relative amount of blurriness in images with different content. Results are provided to illustrate the performance of the proposed perceptual-based sharpness metric. These results show that the proposed sharpness metric correlates well with the perceived sharpness being able to predict with high accuracy the relative amount of blurriness in images with different content.


title: The Union of Religion and Health in Ancient Asklepieia

abstract: This study of the cult of Asklepios reveals that happiness, well-being, and health were inseparable from and unified with devotion and religion in the healing arts practiced in the Asklepieia of antiquity. Religion, the tie that binds together a community, was the ultimate means to attain health for the suppliants of the Asklepieia scattered across the ancient Mediterranean world. A brief review of the cult of Asklepios and its health centers and practices, with some insights from the work of Kerenyi, Meier, the Edelsteins, and others, will illuminate the mental-health aspects of the cult and foreshadow some additional insights into this intriguing union of religion and health.


title: Actor-Centric Relation Network

abstract: Current state-of-the-art approaches for spatio-temporal action localization rely on detections at the frame level and model temporal context with 3D ConvNets. Here, we go one step further and model spatio-temporal relations to capture the interactions between human actors, relevant objects and scene elements essential to differentiate similar human actions. Our approach is weakly supervised and mines the relevant elements automatically with an actor-centric relational network (ACRN). ACRN computes and accumulates pair-wise relation information from actor and global scene features, and generates relation features for action classification. It is implemented as neural networks and can be trained jointly with an existing action detection system. We show that ACRN outperforms alternative approaches which capture relation information, and that the proposed framework improves upon the state-of-the-art performance on JHMDB and AVA. A visualization of the learned relation features confirms that our approach is able to attend to the relevant relations for each action.


Nonparametric variable importance using an augmented neural network with multi-task learning


title: A critique of carceral feminist arguments on rape myths and sexual scripts

abstract: Given the seriousness for both women and men of misunderstanding or miscategorising sexual victimization and coercion, scholarly engagement with this topic must be self-critical and careful about its methods and conclusions. This article seeks to test the plausibility and justifiability of some key claims made within feminist scholarship as regards the implications of the traditional sexual script and the prevalence and impact of the “real rape” myth. The criticisms offered below with respect to these claims identify three problems: a) that evidence that would challenge carceral feminists’ framing of the traditional sexual script as essentially a blueprint for rape is either marginalized or excluded from consideration altogether: b) that within that framing the scripted roles of the coercive male and the passive female who is victimized have been allowed to solidify into immovable and immutable stereotypes: c) that studies purporting to show that rape myth acceptance is highly prevalent and influential on popular attitudes are flawed in ways hitherto not fully acknowledged or explored.


title: Application image processing to predict personality based on structure of handwriting and signature

abstract: Handwriting stroke reflects how the author faced his world and the emotional honesty. By examining all elements of handwriting and interpreting them separately or integrated, we could generate a sketch of the writer's character traits, emotional disposition and social style using standard of graphology. As image, the analysis of graphology is divided into two approaches that graphics features and segmentation digit each character. In this research, using graphical approach based on a combination of signature and handwriting to predict the more personality using structure algorithms and multiple artificial neural networks (ANN). The image in A4 paper split into two areas: Signature area which nine features and handwriting based on five features. Each area had pre-processing performed to improve the recognition accuracy. Signature area is classified using ANN based on five features and using multi structure algorithms based on four features. While the handwriting area is classified using multi structure algorithm based on four features (margins, spacing between words and lines, and zone domination) and using ANN after hill valley extraction based on baseline features. Eight features are processed using multi-structure algorithms that provide 87–100% accuracy. In the meantime, six features are classified using an ANN which result an accuracy of 52–100%. It used 100 sets of data testing after training using back propagation with 25–75 data. The system has been implemented with the software so that it can be used for classification of personality from handwriting scanned automatically.


title: Vulnerabilities and Countermeasures - A Survey on the Cyber Security Issues in the Transmission Subsystem of a Smart Grid

abstract: With the increased investment and deployment of embedded computing and communication technologies in the power system – the smart grid vision is shaping up into a reality. The future power grid is a large cyber physical system (CPS) which is vulnerable to cyber security threats. Among the three major subsystems of a power grid – generation, transmission and distribution – this survey focuses on the transmission subsystem because most of the cyberization of the grid has been happening in this subsystem. This is due to the need for distributed measurement, monitoring and control to retain the stability, security, and reliability of power transmission system. Given the geographically dispersed generation facilities, substations, control centers, data concentrators etc., efficient data communication is required, and therefore large scale networking – either proprietary or leased – is happening. The goal of this paper is not to be comprehensive to include all efforts of securing the transmission system from cyber borne threats, but to provide a survey of various vulnerabilities, and countermeasures proposed by various research efforts. One of the focus area in this survey is the Phasor Measurement Units (PMUs) and Wide Area Measurement System (WAMS) technology – mostly due to our familiarity with the issues for this specific technology deployment – rather than any attempt to indicate that this is the most vulnerable technology in the transmission subsystem. Our hope is that this survey will familiarize any uninitiated reader with the issues and provide incentive to un


title: A combined anti-aliasing filter and 2-tap FFE in 65-nm CMOS for 2× blind 2–;10 Gb/s ADC-based receivers

abstract: This paper presents a combined anti-aliasing filter and 2-tap feed-forward equalizer (AAF/FFE) as an analog front-end (AFE) for 2× blind ADC-based receivers. The front-end optimizes the channel/filter characteristics for data-rates of 2–10 Gb/s. The AAF bandwidth scales with the data-rate and the 2-tap FFE is designed without the need for noise-sensitive analog delay cells. The AAF/FFE is implemented in 65-nm CMOS, occupies 0.013 mm2, and consumes 2.4 mW at 10 Gb/s.


title: Robustness beyond shallowness: incremental deep parsing

abstract: Robustness is a key issue for natural language processing in general and parsing in particular, and many approaches have been explored in the last decade for the design of robust parsing systems. Among those approaches is shallow or partial parsing, which produces minimal and incomplete syntactic structures, often in an incremental way. We argue that with a systematic incremental methodology one can go beyond shallow parsing to deeper language analysis, while preserving robustness. We describe a generic system based on such a methodology and designed for building robust analyzers that tackle deeper linguistic phenomena than those traditionally handled by the now widespread shallow parsers. The rule formalism allows the recognition of n-ary linguistic relations between words or constituents on the basis of global or local structural, topological and/or lexical conditions. It offers the advantage of accepting various types of inputs, ranging from raw to chunked or constituent-marked texts, so for instance it can be used to process existing annotated corpora, or to perform a deeper analysis on the output of an existing shallow parser. It has been successfully used to build a deep functional dependency parser, as well as for the task of co-reference resolution, in a modular way.


title: Complex Event Processing in EPC Sensor Network Middleware for Both RFID and WSN

abstract: In an integration system of RFID and wireless sensor network (WSN), RFID is used to identify objects while WSN can provide context environment information of these objects. Thus, it increases system intelligent in pervasive computing. We propose the EPC sensor network (ESN) architecture as an integration system of RFID and WSN. This ESN architecture is based on EPCglobal architecture, the de facto international standard for RFID. The core of ESN is the middleware part which is also implemented in our work. In this paper, complex event processing (CEP) technology is used in our ESN middleware which can handle large volume of events from distributed RFID and sensor readers in real time. Through filtering, grouping, aggregating and constructing complex event, ESN middleware provides a more meaningful report for the clients and increases system automation.


title: High‐Resolution Patterning of Graphene by Screen Printing with a Silicon Stencil for Highly Flexible Printed Electronics

abstract: High-resolution screen printing of pristine graphene is introduced for the rapid fabrication of conductive lines on flexible substrates. Well-defined silicon stencils and viscosity-controlled inks facilitate the preparation of high-quality graphene patterns as narrow as 40 μm. This strategy provides an efficient method to produce highly flexible graphene electrodes for printed electronics.


title: Trainable videorealistic speech animation

abstract: 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.


title: Sound Covert: A Fast and Silent Communication Channel through the Audio Buffer

abstract: We present Sound Covert, a novel technique using the machine's audio buffer to secretly (and silently) communicate between applications. Sound Covert can achieve more than 2.6Mbps, four orders of magnitude faster than previously published covert channels. Sound Covert can be used for bidirectional communication between malicious applications anywhere within a given VM (including across users and sand boxes), as well as for sending information from within a VM to an application running on the host OS of the same physical machine. It can also be used for sending information from a compromised external web page to a local malicious application through an innocent, unaltered web browser running in the target application's VM. Similarly, Sound Covert enables a malicious application in a VM that also runs a web server to send data to a malicious partner application running on any machine (or VM) that has a web browser.


Identifying potential CSCW applications by means of activity theory concepts: a case example