title: Drug-Drug Interaction Extraction via Convolutional Neural Networks

abstract: Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.


title: On some models of customer loyalty

abstract: En Business databases are sources of information that a company can use together with analyses of business statistics for acquiring knowledge of its own customers. Such large availability of information allows companies to devise new models of market segmentation, customer satisfaction and customer loyalty, which require great amount of data. We investigate the possibility of defining new types of customer loyalty by using strategies of market segmentation and statistical indexes of customer satisfaction.


title: The loci and mechanisms of leadership: Exploring a more comprehensive view of leadership theory

abstract: Through a qualitative review of the leadership literature, we derive two fundamental principles for codifying the last century of leadership theory and research: the locus and mechanism of leadership. Our systematic review and categorization of past theories suggests that further development of the follower, collective and context loci, and the affect mechanism is needed. Building on these insights, we propose that by simultaneously considering all five loci and four mechanisms in their theories, leadership scholars can advance a more comprehensive and integrative understanding of the leadership phenomenon. We then demonstrate the potential of using the locus and mechanism framework for examining and expanding current and future leadership theory including work on shared and strategic leadership.


title: Combining Geographic Information Systems for Transportation and Mixed Integer Linear Programming in Facility Location-Allocation Problems

abstract: In this study, we aimed to ::: assess the solution quality for location-allocation problems from facilities ::: generated by the software TransCAD® , a Geographic Information System ::: for Transportation (GIS-T). Such facilities were obtained after using two ::: routines together: Facility Location and Transportation Problem, when compared ::: with optimal solutions from exact mathematical models, based on Mixed Integer ::: Linear Programming (MILP), developed externally for the GIS. The models were ::: applied to three simulations: the first one proposes opening factories and ::: customer allocation in the state of Sao Paulo, Brazil; the second involves a ::: wholesaler and a study of location and allocation of distribution centres for ::: retail customers; and the third one involves the location of day-care centers ::: and allocation of demand (0 - 3 years old children). The results showed that ::: when considering facility capacity, the MILP optimising model presents results ::: up to 37% better than the GIS and proposes different locations to open new ::: facilities.


title: Exploiting social context for review quality prediction

abstract: Online reviews in which users publish detailed commentary about their experiences and opinions with products, services, or events are extremely valuable to users who rely on them to make informed decisions. However, reviews vary greatly in quality and are constantly increasing in number, therefore, automatic assessment of review helpfulness is of growing importance. Previous work has addressed the problem by treating a review as a stand-alone document, extracting features from the review text, and learning a function based on these features for predicting the review quality. In this work, we exploit contextual information about authors' identities and social networks for improving review quality prediction. We propose a generic framework for incorporating social context information by adding regularization constraints to the text-based predictor. Our approach can effectively use the social context information available for large quantities of unlabeled reviews. It also has the advantage that the resulting predictor is usable even when social context is unavailable. We validate our framework within a real commerce portal and experimentally demonstrate that using social context information can help improve the accuracy of review quality prediction especially when the available training data is sparse.


title: Guided HTM: Hierarchical Topic Model with Dirichlet Forest Priors

abstract: Despite the proliferation of topic models, the organization of topics from the probabilistic models needs improvement in two ways: the better structured presentation of topics and the incorporation of domain knowledge on the corpus. The structured presentation, i.e., the hierarchical topic model, helps in categorizing similar topics; the incorporation of domain knowledge enables the concentrated sampling of predefined keywords in the mixture parameter learning. This paper presents a hierarchical topic models with incorporated domain knowledge, called Guided Hierarchical Topic Model (GHTM) . Specifically, we allocated the prior information from the knowledge to the Dirichlet Forest prior. From the prior adjustment, we obtained the topic tree guided by the domain knowledge. This paper also contributes in enumerating four different knowledge extraction methods and applying the extracted knowledge to GHTM. We evaluated the performance of GHTM in terms of the hierarchical clustering accuracy, and we found a significant improvement of hierarchical clustering measured by F-measures. This improvement is also verified by the perplexity analyses. Additionally, we measured topic quality with KL-divergence and visualization, and these confirm the ability to better separate topic distributions. Finally, we tested the hierarchical topic quality through human experiments, and this also revealed significant improvements originating from the guidance.


title: Sancta simplicitas - on the efficiency and achievable results of SLAM using ICP-based incremental registration

abstract: This paper presents an efficient combination of algorithms for SLAM in dynamic environments. The overall approach is based on range image registration using the ICP algorithm. Different extensions to this algorithm are used to incrementally construct point models of the robot's workspace. A simple heuristic allows for determining which points in a newly acquired range image are already contained in the point model and for adding only those points that provide new information. Furthermore, the means for dealing with environment dynamics are presented which allow for continuously conducting SLAM and updating the point model according to changes in a dynamic environment. The achievable results of the overall approach are compared to Rao-Blackwellized Particle Filters as a state-of-the-art solution to the SLAM problem and evaluated using a recently published benchmark by Burgard et al. (2009).


title: CREATE: Multimodal Dataset for Unsupervised Learning, Generative Modeling and Prediction of Sensory Data from a Mobile Robot in Indoor Environments

abstract: The CREATE database is composed of 14 hours of multimodal recordings from a mobile robotic platform based on the iRobot Create. The various sensors cover vision, audition, motors and proprioception. The dataset has been designed in the context of a mobile robot that can learn multimodal representations of its environment, thanks to its ability to navigate the environment. This ability can also be used to learn the dependencies and relationships between the different modalities of the robot (e.g. vision, audition), as they reflect both the external environment and the internal state of the robot. The provided multimodal dataset is expected to have multiple usages, such as multimodal unsupervised object learning, multimodal prediction and egomotion/causality detection.


title: Object-aware bundle adjustment for correcting monocular scale drift

abstract: Without knowledge of the absolute baseline between images, the scale of a map from single-camera simultaneous localization and mapping system is subject to calamitous drift over time. We describe a monocular approach that in addition to point measurements also considers object detections to resolve this scale ambiguity and drift. By placing a prior on the size of the objects, the scale estimation can be seamlessly integrated into a bundle adjustment. When object observations are available, the local scale of the map is then determined jointly with the camera pose in local adjustments. Unlike many previous visual odometry methods, our approach does not impose restrictions such as approximately constant camera height or planar roadways, and is therefore applicable to a much wider range of applications. We evaluate our approach on the KITTI dataset and show that it reduces scale drift over long-range outdoor sequences with a total length of 40 km. Qualitative evaluation is also performed on video footage from a hand-held camera.


title: A cooccurrence-based thesaurus and two applications to information retrieval

abstract: Abstract This paper presents a new method for computing a thesaurus from a text corpus. Each word is represented as a vector in a multi-dimensional space that captures cooccurrence information. Words are defined to be similar if they have similar cooccurrence patterns. Two different methods for using these thesaurus vectors in information retrieval are shown to significantly improve performance over the Tipster reference corpus as compared to a term vector space baseline.


title: Air Traffic Volume and Air Traffic Control Human Errors

abstract: Navigable airspaces are becoming more crowded with increasing air traffic, and the number of accidents caused by human errors is increasing. The main objective of this paper is to evaluate the relationship between air traffic volume and human error in air traffic control (ATC). First, the paper identifies categories and elements of ATC human error through a review of existing literature, and a study through interviews and surveys of ATC safety experts. And then the paper presents the results of an experiment conducted on 52 air traffic controllers sampled from the Korean ATC organization to find out if there is any relationship between traffic volume and air traffic controller human errors. An analysis of the experiment clearly showed that several types of ATC human error are influenced by traffic volume. We hope that the paper will make its contribution to aviation safety by providing a realistic basis for securing proper manpower and facility in accordance with the level of air traffic volume.


Modeling Late Reverberation in Acoustic Echo Suppression


title: The Effects of Amino Acid Supplementation on Muscular Performance During Resistance Training Overreaching

abstract: The purpose of this study was to examine the effects of amino acid supplementation on muscular strength, power, and high-intensity endurance during short-term resistance training overreaching. Seventeen resistance-trained men were randomly assigned to either an amino acid (AA) or placebo (P) group and underwent 4 weeks of total-body resistance training consisting of two 2-week phases of overreaching (phase 1: 3 3 8‐12 repetitions maximum [RM], 8 exercises; phase 2: 5 3 3‐5RM, 5 exercises). Muscle strength, power, and high-intensity endurance were determined before (T1) and at the end of each training week (T2‐T5). One repetition maximum squat and bench press decreased at T2 in P (5.2 and 3.4 kg, respectively) but not in AA, and significant increases in 1RM squat and bench press were observed at T3‐ T5 in both groups. A decrease in the ballistic bench press peak power was observed at T3 in P but not AA. The fatigue index during the 20-repetition jump squat assessment did not change in the P group at T3 and T5 (fatigue index 5 18.6 and 18.3%, respectively) whereas a trend for reduction was observed in the AA group (p 5 0.06) at T3 (12.8%) but not T5 (15.2%; p 5 0.12). These results indicate that the initial impact of high-volume resistance training overreaching reduces muscle strength and power, and it appears that these reductions are attenuated with amino acid supplementation. In addition, an initial high-volume, moderate-intensity phase of overreaching followed by a higher intensity, moderate-volume phase appears to be very effective for enhancing muscle strength in resistance-trained men.


title: A gentle introduction to coded computational photography

abstract: Computational photography tries to expand the concept of traditional photography (a static two dimensional projection of a scene) using state-of-the-art technology. While this can be achieved by combining information from multiple conventional pictures, a more interesting challenge consists in encoding and recovering additional information from one (or more) image(s). Since a photograph results from the convolution of scene radiance with the camera's aperture (integrated over the exposure time), researchers have designed apertures with certain desirable spectral properties to facilitate the deconvolution process and, consequently, the recovery of scene information. Images captured using these so-called coded apertures can be deconvolved to create all-in-focus images, and to estimate scene depth, among other things. Images of moving objects acquired using a coded exposure (obtained by switching between a fully-closed and a fully-opened aperture, according to a predefined pattern) can be deconvolved to reduce motion blur. The notion of encoding information during image acquisition opens up new and exciting possibilities, which researchers have just begun to explore. This article provides a gentle introduction to coded photography, focusing on the fundamental concepts and essential mathematical tools.


ReSuMe - New Supervised Learning Method


title: Towards a New Personal Information Technology Acceptance Model: Conceptualization and Empirical Evidence from a Bring Your Own Device Dataset

abstract: By considering recent findings from cognitive dual process theories, I propose a new technology acceptance model for situations in which IT is used everyday. The so$called “Personal Information Technology Acceptance Model” (PITAM) will be applied to the IT$consumerization area and it will be evaluated by a Bring Your Own Device (BYOD) dataset from 171 working professionals aged from 18 to 68 years. As a result I found empirical evidence for the speculation that BYOD user behavior is primarily driven by Perceived Enjoyment as a System 1 IS construct and System 2 IS constructs such as Perceived Usefulness are results of post hoc constructions/justifications of (intended) BYOD usage.


title: OCRdroid : A Framework to Digitize Text Using Mobile Phones

abstract: As demand grows for mobile phone applications, research in optical character recognition, a technology well developed for scanned documents, is shifting focus to the recognition of text embedded in digital photographs. In this paper, we present OCRdroid, a generic framework for developing OCR-based applications on mobile phones. OCRdroid combines a light-weight image preprocessing suite installed inside the mobile phone and an OCR engine connected to a backend server. We demonstrate the power and functionality of this framework by implementing two applications called PocketPal and PocketReader based on OCRdroid on HTC Android G1 mobile phone. Initial evaluations of these pilot experiments demonstrate the potential of using OCRdroid framework for real-world OCR-based mobile applications.


title: Distortion Discriminant Analysis for Audio Fingerprinting

abstract: Mapping audio data to feature vectors for the classification, retrieval or identification tasks presents four principal challenges. The dimensionality of the input must be significantly reduced; the resulting features must be robust to likely distortions of the input; the features must be informative for the task at hand; and the feature extraction operation must be computationally efficient. We propose distortion discriminant analysis (DDA), which fulfills all four of these requirements. DDA constructs a linear, convolutional neural network out of layers, each of which performs an oriented PCA dimensional reduction. We demonstrate the effectiveness of DDA on two audio fingerprinting tasks: searching for 500 audio clips in 36 h of audio test data; and playing over 10 days of audio against a database with approximately 240 000 fingerprints. We show that the system is robust to kinds of noise that are not present in the training procedure. In the large test, the system gives a false positive rate of 1.5 /spl times/ 10/sup -8/ per audio clip, per fingerprint, at a false negative rate of 0.2% per clip.


title: Dynamic curvature path tracking control for autonomous vehicle: Experimental results

abstract: This paper discusses a new path tracking controller strategy for autonomous vehicle. Preliminary works done by researchers shows that most of the controller performs well in a straight path and normal curve. However, the problem arises in tight curve or discontinuous path. Therefore, this work aims to reduce the gap on the tight corner problem. The controller is designed based on the dynamic curvature estimation to predict the path condition and modify the wheel speed and steering wheel angle accordingly. In this technique, the curvature estimation algorithm is presented as a required input to the controller. The curvature estimation allows the vehicle to react based on the curvature profile and threshold region detection. The comparison is made based on three different path types. The experimental results on the autonomous prototype platform are discussed to show the effectiveness of the controller.


title: Automatic parking slot detection based on around view monitor (AVM) systems

abstract: Automatic parking systems have significant effects on intelligent transport systems (ITS) and have been extensively researched. However, most existing vision-based automatic parking slot detection methods cannot obtain the desired results due to variation in light intensity or complex obstacle conditions. Besides, most previous parking slot detection methods only consider the target position occupied by vehicles but ignore the existence of small objects such as traffic cones and parking lock inside the parking marking. In order to overcome these insufficiency, this paper proposes a novel vision-based automatic parking slot detection method to detect various parking slot markings by Around View Monitor (AVM) system which consists of four fisheye cameras around the vehicle. The process utilizes a Line Segment Detector (LSD) based on edge information to detect parking slot markings which have a pair of parallel lines with the fixed distance in AVM image, then use the image segmentation algorithm and the stereo vision algorithm to calculate the height of small obstacles inside the parking marking. Experimental results show the proposed methods perform better than method based on Hough transforms in continuity and completeness. Furthermore, the proposed method can effectively reduce the frequency of false detections and missing detections.


title: CrackTree: Automatic crack detection from pavement images

abstract: Pavement cracks are important information for evaluating the road condition and conducting the necessary road maintenance. In this paper, we develop CrackTree, a fully-automatic method to detect cracks from pavement images. In practice, crack detection is a very challenging problem because of (1) low contrast between cracks and the surrounding pavement, (2) intensity inhomogeneity along the cracks, and (3) possible shadows with similar intensity to the cracks. To address these problems, the proposed method consists of three steps. First, we develop a geodesic shadow-removal algorithm to remove the pavement shadows while preserving the cracks. Second, we build a crack probability map using tensor voting, which enhances the connection of the crack fragments with good proximity and curve continuity. Finally, we sample a set of crack seeds from the crack probability map, represent these seeds by a graph model, derive minimum spanning trees from this graph, and conduct recursive tree-edge pruning to identify desirable cracks. We evaluate the proposed method on a collection of 206 real pavement images and the experimental results show that the proposed method achieves a better performance than several existing methods.


title: Radiological Assessment of the Effect of Congenital C3-4 Synostosis on Adjacent Segments

abstract: Study Design: Retrospective case series. Purpose: To assess the effect of non-kyphotic aligned congenital C3–4 synostosis on the adjacent segment in 10 patients. Overview of Literature: In the cervical spine, fusion disease at the adjacent motion segments may be a risk factor for potential neurological compromise and death. Methods: Radiograms of 10 patients 13 to 69 years of age presenting with neck/shoulder discomfort or pain with or without trauma history were examined. C3–4 synostosis was found incidentally in all patients on routine examination radiographs of cervical spine. Results: Adjacent segment disease (ASD) was not found in the three patients younger than 39 years of age. Five of the 10 (50%) patients, including a 67-year-old man, did not develop spondylosis in any of the cervical mobile segments. Spondylosis was observed only in the caudal 1–2 mobile segments in the remaining five patients. The youngest was a 40-year-old male who had spondylosis in the two caudal mobile segments (C4–5 and C5–6). Spondylosis was limited to the two close caudal mobile segments and was not in the cranial segments. Flaring of the lower part of synostotic vertebra associated with advanced narrowed degenerate disc was evident in five patients. Conclusions: Mobile segment spondylosis in the individuals with congenital monosegment C3–4 synostosis over age of 40 years may be a natural manifestation of aging and is not solely an adjacent segment disease directly and fully related with congenital C3–4 synostosis.


title: Predictors of Reading and Spelling Abilities in First- and Second-Language Learners.

abstract: This study examined the basic literacy skills and related processes of 1st- through 4th-grade children speaking English as a 1st language (L1) and English as a 2nd language (ESL). The performances of the L1 and ESL children on phonological awareness, word and pseudoword reading, and word and pseudoword spelling tasks were highly similar. The ESL children were at an advantage with regard to lexical access but performed more poorly on verbal working memory and syntactic awareness tasks. The results suggest that the main processes underlying L1 children's basic reading ability in Grades 1 and 2, namely phonological awareness and lexical access, are of equal importance for ESL children. Phonological awareness remained the strongest predictor of word reading ability for L1 and ESL children in Grades 3 and 4. However, the processes involved in L1 and ESL word reading and spelling appeared to vary at other points. Verbal working memory and syntactic awareness were found to be of importance for the word reading and spelling abilities of L1 children but not for ESL children. Lexical access was found to be of more importance for ESL children.


title: Generalized model of multi-phase induction motor drive using matlab/simulink

abstract: This paper presents a generalized model of multi-phase induction motor drive using matlab/simulink. Multiphase induction motor (more than three phases) drives possess several advantages over conventional three-phase drives, such as lower torque pulsation, higher torque density, fault tolerance, stability, high efficiency and lower current ripple. When the number of phases increases, it is also possible to increase the power /torque per rms ampere for the same volume machine. In this paper, a generalized dq-axis model is developed in Matlab/Simulink for a multi-phase induction motor. The simulation results are presented for 5, 6, 7, 9 and 12 phase induction motor under varying load conditions. Transient response of the multi-phase induction motors are given for different number of phases.


title: Games for Crowds: A Crowdsourcing Game Platform for the Enterprise

abstract: In this paper, we present a crowdsourcing game platform that allows users to play, create, and share simple games that harness the collective intelligence of employees within the enterprise. The platform uses the wizard design pattern to guide users through the process of creating a game. We describe the platform in detail and report our findings from deploying it within a large global organization for a period of three months, in which 34 games were created by 25 employees and played by 339. We combine qualitative and quantitative analysis to understand the characteristics of the different games and their impact on popularity and engagement, to validate our design goals, and to suggest potential enhancements.


title: doi:10.1017/S0022278X10000649 African Shenzhen: China’s special economic zones in Africa

abstract: This article examines recent Chinese efforts to construct a series of official economic cooperation zones in Africa. These zones are a central platform in China's announced strategy of engagement in Africa as ‘mutual benefit’. We analyse the background, motives and implementation of the zones, and argue that they form a unique, experimental model of development cooperation in Africa: market-based decisions and investment by Chinese companies are combined with support and subsidies from an Asian ‘developmental state’. Though this cooperation provides a promising new approach to sustainable industrialisation, we also identify serious political, economic and social challenges. Inadequate local learning and local participation could affect the ability of the zones to catalyse African industrialisation. The synergy between Chinese enterprises, the Chinese government and African governments has been evolving through practice. A case study of Egypt provides insight into this learning process.


title: A framework for evaluating the usability of mobile phones based on multi-level, hierarchical model of usability factors

abstract: As a mobile phone has various advanced functionalities or features, usability issues are increasingly challenging. Due to the particular characteristics of a mobile phone, typical usability evaluation methods and heuristics, most of which are relevant to a software system, might not effectively be applied to a mobile phone. Another point to consider is that usability evaluation activities should help designers find usability problems easily and produce better design solutions. To support usability practitioners of the mobile phone industry, we propose a framework for evaluating the usability of a mobile phone, based on a multi-level, hierarchical model of usability factors, in an analytic way. The model was developed on the basis of a set of collected usability problems and our previous study on a conceptual framework for identifying usability impact factors. It has multi-abstraction levels, each of which considers the usability of a mobile phone from a particular perspective. As there are goal-means relationships between adjacent levels, a range of usability issues can be interpreted in a holistic as well as diagnostic way. Another advantage is that it supports two different types of evaluation approaches: task-based and interface-based. To support both evaluation approaches, we developed four sets of checklists, each of which is concerned, respectively, with task-based evaluation and three different interface types: Logical User Interface (LUI), Physical User Interface (PUI) and Graphical User Interface (GUI). The proposed framework specifies an approach to quantifying usability so that several usability aspects are collectively measured to give a single score with the use of the checklists. A small case study was conducted in order to examine the applicability of the framework and to identify the aspects of the framework to be improved. It showed that it could be a useful tool for evaluating the usability of a mobile phone. Based on the case study, we improved the framework in order that usability practitioners can use it more easily and consistently.


title: A Calculus for Security Bootstrapping in Distributed Systems

abstract: A calculus of channel security properties is presented which allows the analysis and comparison of protocols for establishing secure channels in a distributed open system at a high level of abstraction. A channel is characterized by its direction, its time of availability and its security properties. Cryptographic primitives as well as trust relations are interpreted as transformations for channel security properties, and a cryptographic protocol can be viewed as a sequence of such transformations. A protocol thus allows to transform a set of secure channels established during an initial setup phase, together with a set of insecure channels available during operation of the system, into the set of secure channels specified by the security requirements. The necessary and sufficient requirements for establishing a secure channel between two entities A and B are characterized in terms of secure channels to be made available during the initial setup phase and in terms of the minimal trust A and B must have into other entities or into trusted third parties.


title: CRF learning with CNN features for hyperspectral image segmentation

abstract: This paper proposes a method that uses both spectral and spatial information to segment remote sensing hyperspectral images. After a hyperspectral image is over-segmented into superpixels, a deep Convolutional Neural Network (CNN) is used to perform superpixel-level labelling. To further delineate objects from a hyperspectral scene, this paper attempts to combine the properties of CNN and Conditional Random Field (CRF). A mean-field approximation algorithm for CRF inference is used and formulated with Gaussian pairwise potentials as Recurrent Neural Network. This combined network is then plugged into the CNN which leads to a deep network that has robust characteristics of both CNN and CRF. Preliminary results suggest the usefulness of this framework to a promising extent.


title: Entropy-based Selection of Graph Cuboids

abstract: Emerging applications face the need to store and analyze interconnected data that are naturally depicted as graphs. Recent proposals take the idea of data cubes that have been successfully applied to multidimensional data and extend them to work for interconnected datasets. In our work we revisit the graph cube framework and propose novel mechanisms inspired from information theory in order to help the analyst quickly locate interesting relationships within the rich information contained in the graph cube. The proposed entropy-based filtering of data reveals irregularities and non-uniformity which are often what the decision maker is looking for. We experimentally validate our techniques and demonstrate that the proposed entropy-based filtering can help eliminate large portions of the respective graph cubes.


title: Convergence rate analysis for averaged fixed point iterations in the presence of H\"older regularity

abstract: In this paper, we establish sublinear and linear convergence of fixed point iterations generated by averaged operators in a Hilbert space. Our results are achieved under a bounded Holder regularity...


title: An Analysis of Video Lecture in MOOC

abstract: Video is a content delivery form used for delivering lecture content in Massive Open Online Course (MOOC). While institutions plan to launch MOOC on their own platform or adapt an existing one, there is a need to specify the features required for video lecture in MOOC. In this paper, we present a checklist of features for video lecture incorporated in MOOC from the learner‟s perspective. The use case based approach has been followed for identifying the features of video lecture in MOOC. The checklist helps during requirement specification of video in MOOC as the provider select the desired features from the checklist.


Assessment of Spoken Dialogu-What are We really


title: A Platform Architecture for Multi-Tenant Blockchain-Based Systems

abstract: Blockchain has attracted a broad range of interests from start-ups, enterprises and governments to build next generation applications in a decentralized manner. Similar to cloud platforms, a single blockchain-based system may need to serve multiple tenants simultaneously. However, design of multi-tenant blockchain-based systems is challenging to architects in terms of data and performance isolation, as well as scalability. First, tenants must not be able to read other tenants' data and tenants with potentially higher workload should not affect read/write performance of other tenants. Second, multi-tenant blockchain-based systems usually require both scalability for each individual tenant and scalability with number of tenants. Therefore, in this paper, we propose a scalable platform architecture for multi-tenant blockchain-based systems to ensure data integrity while maintaining data privacy and performance isolation. In the proposed architecture, each tenant has an individual permissioned blockchain to maintain their own data and smart contracts. All tenant chains are anchored into a main chain, in a way that minimizes cost and load overheads. The proposed architecture has been implemented in a proof-of-concept prototype with our industry partner, Laava ID Pty Ltd (Laava). We evaluate our proposal in a three-fold way: fulfilment of the identified requirements, qualitative comparison with design alternatives, and quantitative analysis. The evaluation results show that the proposed architecture can achieve data integrity, performance isolation, data privacy, configuration flexibility, availability, cost efficiency and scalability.


title: SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model

abstract: In this paper, we propose a novel method for discovering characteristic patterns in a time series called SAX-VSM. This method is based on two existing techniques - Symbolic Aggregate approximation and Vector Space Model. SAX-VSM automatically discovers and ranks time series patterns by their "importance" to the class, which not only facilitates well-performing classification procedure, but also provides an interpretable class generalization. The accuracy of the method, as shown through experimental evaluation, is at the level of the current state of the art. While being relatively computationally expensive within a learning phase, our method provides fast, precise, and interpretable classification.


title: Information and Communication Engineers RFID Mutual Authentication Scheme based on Synchronized Secret Information ∗

abstract: Radio Frequency IDentiflcation (RFID) is an automatic identiflcation system, relying on storing and remotely retrieving data about objects we want to manage using devices called \RFID tag". Even though RFID system is widely useful for industrial and individual applications, RFID tag has a serious privacy problem, i.e., traceability. To protect users from tracing, we propose an RFID mutual authentication scheme which utilizes a hash function and synchronized secret information like others. To the best of our knowledge, our scheme ofiers the most enhanced security feature in RFID mutual authentication scheme with respect to user privacy including resistance against tag cloning allowing an additional hash operation.


title: Automatic recognition and understanding of spoken language - a first step toward natural human-machine communication

abstract: The promise of a powerful computing device to help people in productivity as well as in recreation can only be realized with proper human-machine communication. Automatic recognition and understanding of spoken language is the first step toward natural human-machine interaction. Research in this field has produced remarkable results, leading to many exciting expectations and new challenges. We summarize the development of the spoken language technology from both a vertical (chronology) and a horizontal (spectrum of technical approaches) perspective. We highlight the introduction of statistical methods in dealing with language-related problems, as this represents a paradigm shift in the research field of spoken language processing. Statistical methods are designed to allow the machine to learn structural regularities in the speech signal, directly from data, for the purpose of automatic speech recognition and understanding. Research results in spoken language processing have led to a number of successful applications, ranging from dictation software for personal computers and telephone-call processing systems for automatic call routing, to automatic sub-captioning for television broadcasts. We analyze the technical successes that support these applications. Along with an assessment of the state of the art in this broad technical field, we also discuss the limitations of the current technology, and point out the challenges that are ahead. This paper presents an accurate overview of spoken language technology as a basis to inspire future advances.


title: 3D depth camera based human posture detection and recognition Using PCNN circuits and learning-based hierarchical classifier

abstract: A new scheme for human posture recognition is proposed based on analysis of key body parts. Utilizing a time-of-flight depth camera, a pulse coupled neural network (PCNN) is employed to detect a moving human in cluttered background. In the posture recognition phase, a hierarchical decision tree is designed for classification of body parts so that the 3D coordinate of the key points of the detected human body can be determined. The features described in each individual layer of the tree can be chained as hierarchical searching indices for retrieval procedure to drastically improve the efficiency of template matching in contrast to conventional shape-context method. Experimental results show that the proposed scheme gives competitive performance as compared with the state-of-the-art counterparts.


title: Estimation of genetic parameters and genotype-by-environment interactions related to acute ammonia stress in Pacific white shrimp (Litopenaeus vannamei) juveniles at two different salinity levels

abstract: Regarding the practical farming of Litopenaeus vannamei, the deterioration of water quality from intensive culture systems and environmental pollution is a common but troublesome problem in the cultivation of this species. The toxicities that result from deteriorating water quality, such as that from ammonia stress, have lethal effects on juvenile shrimp and can increase their susceptibility to pathogens. The toxicity of ammonia plays an important role in the frequently high mortality during the early stage on shrimp farms. However, little information is available regarding the genetic parameters of the ammonia tolerance of juveniles in the early stage, but this information is necessary to understand the potential for the genetic improvement of this trait. Considering the euryhalinity of L. vannamei and the fact that low salinity can increase the toxicity of ammonia stress, we estimated the heritability of ammonia tolerance in juveniles in 30‰ (normal) and 5‰ (low) salinity in this study using the survival time (ST) at individual level and the survival status at the half-lethal time (SS50) at the family level. In the normal and low salinity conditions and for the merged data, the heritability estimates of the ST (0.784±0.070, 0.575±0.068, and 0.517±0.058, respectively) and SS50 (0.402±0.061, 0.216±0.050, and 0.264±0.050, respectively) were all significantly greater than zero, which indicates that the ammonia-tolerance of shrimp can be greatly improved. So it might provide an alternative method to reduce mortality, help to enhance resistance to pathogens and reduce the occurrence of infectious diseases. The significant positive genetic correlation between ST and body length suggested that ammonia is more toxic to shrimp in the early stage. The medium-strength genetic correlations of the ST and SS50 between the two environments (0.394±0.097 and 0.377±0.098, respectively) indicate a strong genotype-by-environment (G×E) interaction for ammonia tolerance between the different salinity levels. Therefore, salinity-specific breeding programs for ammonia tolerance in shrimp should be purposefully implemented.


title: Design and implementation of a connected farm for smart farming system

abstract: Agriculture has been one of the most important industries in human history since it provides humans with absolutely indispensable resources such as food, fiber, and energy. The agriculture industry could be further developed by employing new technologies, in particular, the Internet of Things (IoT). In this paper, we present a connected farm based on IoT systems, which aims to provide smart farming systems for end users. A detailed design and implementation for connected farms are illustrated, and its advantages are explained with service scenarios compared to previous smart farms. We hope this work will show the power of IoT as a disruptive technology helping across multi industries including agriculture.


Electronic shopping


title: Detection & classification of arrow markings on roads using signed edge signatures

abstract: In this paper, we propose a novel method to robustly identify and classify arrow markings in road images. In the proposed method, simple and unique signatures are first derived for the various arrow types, based on signed edge maps and decomposing the arrows into smaller parts. The signed edge maps are processed using Hough Transform (HT), and the resulting Hough spaces are analyzed systematically, using a set of simple rules. The signatures are rotation-invariant and scale-invariant, thereby making the approach robust to variations in the appearance of the arrow markings. It is shown that the method yields a high detection and classification accuracy, of as high as 97% in the test images considered.


title: Prediction Under Uncertainty with Error-Encoding Networks

abstract: In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty. It is based on a simple idea of disentangling com- ponents of the future state which are predictable from those which are inherently unpredictable, and encoding the unpredictable components into a low-dimensional latent variable which is fed into the forward model. Our method uses a simple su- pervised training objective which is fast and easy to train. We evaluate it in the context of video prediction on multiple datasets and show that it is able to consi- tently generate diverse predictions without the need for alternating minimization over a latent space or adversarial training.


Interactive artistic rendering


title: Skeletal and dental components of Class II correction with the bionator and removable headgear splint appliances

abstract: Introduction The purpose of this study was to differentiate the dentoalveolar and skeletal effects to better understand orthodontic treatment. We evaluated the treatment changes associated with the bionator and the removable headgear splint (RHS). Methods The sample comprised 51 consecutively treated Class II patients from 1 office who had all been successfully treated with either a bionator (n = 17) or an RHS appliance (n = 17). Class II patients waiting to start treatment later served as controls (n = 17). A modified version of the Johnston pitchfork analysis was used to quantify the dentoalveolar and skeletal contributions to the anteroposterior correction at the levels of the molars and the incisors. Results Both appliances significantly improved anteroposterior molar relationships (2.15 mm for the bionator, 2.27 mm for the RHS), primarily by dentoalveolar modifications (1.49 and 2.36 mm for the bionator and the RHS, respectively), with greater maxillary molar distalization in the RHS group. Overjet relationships also improved significantly compared with the controls (3.11 and 2.12 mm for the bionator and the RHS, respectively), due primarily to retroclination of the maxillary incisors (2.2 and 2.38 mm for the bionator and the RHS, respectively). The differences between overall corrections and dentoalveolar modifications for both molar and overjet relationships were explained by skeletal responses, with the bionator group showing significantly greater anterior mandibular displacement than the RHS group. Conclusions The bionator and the RHS effectively corrected the molar relationships and overjets of Class II patients primarily by dentoalveolar changes.


title: Mutual Inductance Calculation Between Circular Filaments Arbitrarily Positioned in Space: Alternative to Grover's Formula

abstract: In this paper, we present the full derivation of a new formula for calculating the mutual inductance between inclined circular filaments arbitrarily positioned with respect to each other. Although such a formula was already proposed by Grover more than 50 years ago, the formula presented here is slightly more general and simpler to use, i.e., it involves only a sequential evaluation of expressions and the numerical resolution of a simple numerical integration. We derived the new formula using the method of vector potential, as opposed to Grover's approach, which was based on the Neumann formula. We validated the new formula through a series of examples, which are presented here. Finally, we present the relationship between the two general formulas (i.e., Grover's and our new one) explicitly (Example 12).


title: Garbage Collection in an Uncooperative Environment

abstract: We describe a technique for storage allocation and garbage collection in the absence of significant co-operation from the code using the allocator. This limits garbage collection overhead to the time actually required for garbage collection. In particular, application programs that rarely or never make use of the collector no longer encounter a substantial performance penalty. This approach greatly simplifies the implementation of languages supporting garbage collection. It further allows conventional compilers to be used with a garbage collector, either as the primary means of storage reclamation, or as a debugging tool. ::: ::: ::: ::: Our approach has two potential disadvantages. First, some garbage may fail to be reclaimed. Secondly, we use a ‘stop and collect’ approach, thus making the strategy unsuitable for applications with severe real-time constraints. We argue that the first problem is, to some extent, inherent in any garbage collection system. Furthermore, based on our experience, it is usually not significant in practice. In spite of the second problem, we have had favourable experiences with interactive applications, including some that use a heap of several megabytes.


title: The refeeding syndrome: a review

abstract: Refeeding syndrome has been used to describe those phenomena, such as severe hypophosphatemia and other metabolic complications, seen in malnourished patients receiving concentrated calories via total parenteral nutrition. The purpose of this review is to clarify and broaden this definition, as well as to make recommendations for its recognition and, more importantly, prevention.


title: Modeling Tweet Arrival Times using Log-Gaussian Cox Processes

abstract: Research on modeling time series text corpora has typically focused on predicting what text will come next, but less well studied is predicting when the next text event will occur. In this paper we address the latter case, framed as modeling continuous inter-arrival times under a logGaussian Cox process, a form of inhomogeneous Poisson process which captures the varying rate at which the tweets arrive over time. In an application to rumour modeling of tweets surrounding the 2014 Ferguson riots, we show how interarrival times between tweets can be accurately predicted, and that incorporating textual features further improves predictions.


title: The Spherical Coil as an Inductor, Shield, or Antenna

abstract: The spherical coil is an idealized form of inductor having, on a spherical surface, a single-layer winding of constant axial pitch. Its magnetic field inside is uniform and outside is that of a small coil or magnetic dipole. Its properties exemplify exactly some of the rules that are approximately applicable to practical inductors. Simple formulas are given for self-inductance, mutual inductance, coupling coefficient, effect of iron core, and radiation power factor in free space or sea water. This coil is the basis for evaluating the shielding effect of a closed conductive (nonmagnetic) metal shell. A special winding is described which enables simple and exact computation of self-resonance (the length of wire being just 1/2 wavelength in some cases).


title: Interaction context (ICON): towards a geometric functionality descriptor

abstract: We introduce a contextual descriptor which aims to provide a geometric description of the functionality of a 3D object in the context of a given scene. Differently from previous works, we do not regard functionality as an abstract label or represent it implicitly through an agent. Our descriptor, called interaction context or ICON for short, explicitly represents the geometry of object-to-object interactions. Our approach to object functionality analysis is based on the key premise that functionality should mainly be derived from interactions between objects and not objects in isolation. Specifically, ICON collects geometric and structural features to encode interactions between a central object in a 3D scene and its surrounding objects. These interactions are then grouped based on feature similarity, leading to a hierarchical structure. By focusing on interactions and their organization, ICON is insensitive to the numbers of objects that appear in a scene, the specific disposition of objects around the central object, or the objects' fine-grained geometry. With a series of experiments, we demonstrate the potential of ICON in functionality-oriented shape processing, including shape retrieval (either directly or by complementing existing shape descriptors), segmentation, and synthesis.


title: Ambient-oriented programming

abstract: A new field in distributed computing, called Ambient In-telligence, has emerged as a consequence of the increasing availability of wireless devices and the mobile networks they induce. Developing software for such mobile networks is extremely hard in conventional programming languages because the network is dynamically defined. This hardware phenomenon leads us to postulate a suite of characteristics of future Ambient-Oriented Programming languages. A simple re ective programming language kernel, called AmbientTalk, that meets these characteristics is subsequently presented. The power of the re ective kernel is illustrated by using it to conceive a collection of high level tentative ambient-oriented programming language features.


title: Data-driven Task Allocation for Multi-task Transfer Learning on the Edge

abstract: Edge computing for machine learning has become a heated research topic. On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, one obstacle is that transfer learning imposes heavy computation burden to the resource-constrained edge devices. Motivated by the fact that only a few tasks of Multi-task Transfer Learning (MTL) have a higher potential for overall decision performance improvement, we design a novel task allocation scheme, which assigns more important tasks to more powerful edge devices to maximize the overall decision performance. In this paper, we focus on task allocation under multi-task scenarios by introducing task importance and make the following contributions. First, we reveal that it is important to measure the impact of tasks on overall decision performance improvement and quantify task importance. We also observe the long-tail property of task importance, i.e., only a few tasks are important, which facilitates more efficient task allocation. Second, we show that task allocation with task importance for MTL (TATIM) is in fact a variant of the NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts. To solve TATIM with high computational efficiency, we innovatively propose a Data-driven Cooperative Task Allocation (DCTA) approach. Third, we evaluate the performance of our DCTA approach by applying it to a real-world industrial operation (e.g., AIOps) scenario. Experiments show that our DCTA approach can reduce 3.24 times of processing time compared with the state-of-the-art when solving TATIM. We offer our DCTA approach as an effective and practical mechanism for reducing the required resource associated with performing MTL on edge devices.


title: A New Formulation for Empirical Mode Decomposition Based on Constrained Optimization

abstract: The empirical mode decomposition (EMD) is an algorithmic construction that aims at decomposing a signal into several modes called intrinsic mode functions. In this letter, we present a new approach for the EMD based on the direct construction of the mean envelope of the signal. The definition of the mean envelope is achieved through the resolution of a quadratic programming problem with equality and inequality constraints. Some numerical experiments conclude this letter, and comparisons are carried out with the classical EMD.


title: Neural Architecture Optimization

abstract: Automatic neural architecture design has shown its potential in discovering powerful neural network architectures. Existing methods, no matter based on reinforcement learning or evolutionary algorithms (EA), conduct architecture search in a discrete space, which is highly inefficient. In this paper, we propose a simple and efficient method to automatic neural architecture design based on continuous optimization. We call this new approach neural architecture optimization (NAO). There are three key components in our proposed approach: (1) An encoder embeds/maps neural network architectures into a continuous space. (2) A predictor takes the continuous representation of a network as input and predicts its accuracy. (3) A decoder maps a continuous representation of a network back to its architecture. The performance predictor and the encoder enable us to perform gradient based optimization in the continuous space to find the embedding of a new architecture with potentially better accuracy. Such a better embedding is then decoded to a network by the decoder. Experiments show that the architecture discovered by our method is very competitive for image classification task on CIFAR-10 and language modeling task on PTB, outperforming or on par with the best results of previous architecture search methods with a significantly reduction of computational resources. Specifically we obtain $2.11\%$ test set error rate for CIFAR-10 image classification task and $56.0$ test set perplexity of PTB language modeling task. Furthermore, combined with the recent proposed weight sharing mechanism, we discover powerful architecture on CIFAR-10 (with error rate $3.53\%$) and on PTB (with test set perplexity $56.6$), with very limited computational resources (less than $10$ GPU hours) for both tasks.


title: SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation

abstract: In this work, we examine methods for data augmentation for text-based tasks such as neural machine translation (NMT). We formulate the design of a data augmentation policy with desirable properties as an optimization problem, and derive a generic analytic solution. This solution not only subsumes some existing augmentation schemes, but also leads to an extremely simple data augmentation strategy for NMT: randomly replacing words in both the source sentence and the target sentence with other random words from their corresponding vocabularies. We name this method SwitchOut. Experiments on three translation datasets of different scales show that SwitchOut yields consistent improvements of about 0.5 BLEU, achieving better or comparable performances to strong alternatives such as word dropout (Sennrich et al., 2016a). Code to implement this method is included in the appendix.


title: A 88-dB DR, 84-dB SNDR Very Low-Power Single Op-Amp Third-Order $\Sigma \Delta$ Modulator

abstract: A low-power switched-capacitor third-order sigma- delta (ΣΔ) modulator suitable for portable sensor systems is described. The architecture uses only one operational amplifier working in a time-interleaved fashion. The architecture employs swing reduction techniques to limit the swing and the slew-rate requirements of all internal nodes. Realized in a 0.18-μm 2P6M CMOS technology, the modulator provides 84-dB SNDR and 88-dB dynamic range in a signal bandwidth of 100 kHz and clock at 3.2 MHz. The 1.5-V supplied prototype dissipates 140 μW with a FoM of 54 fJ/conversion-level.


title: Recurrent Topic-Transition GAN for Visual Paragraph Generation

abstract: A natural image usually conveys rich semantic content and can be viewed from different angles. Existing image description methods are largely restricted by small sets of biased visual paragraph annotations, and fail to cover rich underlying semantics. In this paper, we investigate a semi-supervised paragraph generative framework that is able to synthesize diverse and semantically coherent paragraph descriptions by reasoning over local semantic regions and exploiting linguistic knowledge. The proposed Recurrent Topic-Transition Generative Adversarial Network (RTT-GAN) builds an adversarial framework between a structured paragraph generator and multi-level paragraph discriminators. The paragraph generator generates sentences recurrently by incorporating region-based visual and language attention mechanisms at each step. The quality of generated paragraph sentences is assessed by multi-level adversarial discriminators from two aspects, namely, plausibility at sentence level and topic-transition coherence at paragraph level. The joint adversarial training of RTT-GAN drives the model to generate realistic paragraphs with smooth logical transition between sentence topics. Extensive quantitative experiments on image and video paragraph datasets demonstrate the effectiveness of our RTT-GAN in both supervised and semi-supervised settings. Qualitative results on telling diverse stories for an image verify the interpretability of RTT-GAN.


title: Sparse Unmixing of Hyperspectral Data

abstract: Linear spectral unmixing is a popular tool in remotely sensed hyperspectral data interpretation. It aims at estimating the fractional abundances of pure spectral signatures (also called as endmembers) in each mixed pixel collected by an imaging spectrometer. In many situations, the identification of the end-member signatures in the original data set may be challenging due to insufficient spatial resolution, mixtures happening at different scales, and unavailability of completely pure spectral signatures in the scene. However, the unmixing problem can also be approached in semisupervised fashion, i.e., by assuming that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In practice, this is a combinatorial problem which calls for efficient linear sparse regression (SR) techniques based on sparsity-inducing regularizers, since the number of endmembers participating in a mixed pixel is usually very small compared with the (ever-growing) dimensionality (and availability) of spectral libraries. Linear SR is an area of very active research, with strong links to compressed sensing, basis pursuit (BP), BP denoising, and matching pursuit. In this paper, we study the linear spectral unmixing problem under the light of recent theoretical results published in those referred to areas. Furthermore, we provide a comparison of several available and new linear SR algorithms, with the ultimate goal of analyzing their potential in solving the spectral unmixing problem by resorting to available spectral libraries. Our experimental results, conducted using both simulated and real hyperspectral data sets collected by the NASA Jet Propulsion Laboratory's Airborne Visible Infrared Imaging Spectrometer and spectral libraries publicly available from the U.S. Geological Survey, indicate the potential of SR techniques in the task of accurately characterizing the mixed pixels using the library spectra. This opens new perspectives for spectral unmixing, since the abundance estimation process no longer depends on the availability of pure spectral signatures in the input data nor on the capacity of a certain endmember extraction algorithm to identify such pure signatures.


title: Latent orientation field estimation via convolutional neural network

abstract: The orientation field of a fingerprint is crucial for feature extraction and matching. However, estimation of orientation fields in latents is very challenging because latents are usually of poor quality. Inspired by the superiority of convolutional neural networks (ConvNets) for various classification and recognition tasks, we pose latent orientation field estimation in a latent patch to a classification problem, and propose a ConvNet based approach for latent orientation field estimation. The underlying idea is to identify the orientation field of a latent patch as one of a set of representative orientation patterns. To achieve this, 128 representative orientation patterns are learnt from a large number of orientation fields. For each orientation pattern, 10,000 fingerprint patches are selected to train the ConvNet. To simulate the quality of latents, texture noise is added to the training patches. Given image patches extracted from a latent, their orientation patterns are predicted by the trained ConvNet and quilted together to estimate the orientation field of the whole latent. Experimental results on NIST SD27 latent database demonstrate that the proposed algorithm outperforms the state-of-the-art orientation field estimation algorithms and can boost the identification performance of a state-of-the-art latent matcher by score fusion.


The Mass Production of BSI CMOS Image Sensors


title: Document Image Binarization Based on Topographic Analysis Using a Water Flow Model

abstract: Abstract This paper proposes a local adaptive thresholding method based on a water flow model, in which an image surface is considered as a three-dimensional (3-D) terrain. To extract characters from backgrounds, we pour water onto the terrain surface. Water flows down to the lower regions of the terrain and fills valleys. Then, the thresholding process is applied to the amount of filled water for character extraction, in which the proposed thresholding method is applied to gray level document images consisting of characters and backgrounds. The proposed method based on a water flow model shows the property of locally adaptive thresholding. Computer simulation with synthetic and real document images shows that the proposed method yields effective adaptive thresholding results for binarization of document images.


Predicting the Rise and Fall of Scientific Topics from Trends in their Rhetorical Framing


title: Nonlinear tracking and landing controller for quadrotor aerial robots

abstract: Quadrotor UAVs are one of the most preferred type of small unmanned aerial vehicles because of the very simple mechanical construction and propulsion principle. However, the nonlinear dynamic behavior requires a more advanced stabilizing control and guidance of these vehicles. In addition, the small payload reduces the amount of batteries that can be carried and thus also limits the operating range of the UAV. One possible solution for a range extension is the application of a base station for recharging purpose even during operation. In order to increase the efficiency of the overall system further, a mobile base station will be applied here. However, landing on a moving base station requires autonomous tracking and landing control of the UAV. In this paper, a novel nonlinear autopilot for quadrotor UAVs is extended with a tracking and landing controller to fulfill the required task. First simulation and experimental results underline the performance of this new control approach for the current realization.


title: 3-D ultrasound imaging using a forward-looking CMUT ring array for intravascular/intracardiac applications

abstract: Forward-viewing ring arrays can enable new applications in intravascular and intracardiac ultrasound. This work presents compelling, full-synthetic, phased-array volumetric images from a forward-viewing capacitive micromachined ultrasonic transducer (CMUT) ring array wire bonded to a custom integrated circuit front end. The CMUT ring array has a diameter of 2 mm and 64 elements each 100 /spl mu/m /spl times/ 100 /spl mu/m in size. In conventional mode, echo signals received from a plane reflector at 5 mm had 70% fractional bandwidth around a center frequency of 8.3 MHz. In collapse mode, 69% fractional bandwidth is measured around 19 MHz. Measured signal-to-noise ratio (SNR) of the echo averaged 16 times was 29 dB for conventional operation and 35 dB for collapse mode. B-scans were generated of a target consisting of steel wires 0.3 mm in diameter to determine resolution performance. The 6 dB axial and lateral resolutions for the B-scan of the wire target are 189 /spl mu/m and 0.112 radians for 8 MHz, and 78 /spl mu/m and 0.051 radians for 19 MHz. A reduced firing set suitable for real-time, intravascular applications was generated and shown to produce acceptable images. Rendered three-dimensional (3-D) images of a Palmaz-Schatz stent also are shown, demonstrating that the imaging quality is sufficient for practical applications.


title: QoS-Aware Virtual Infrastructures Allocation on SDN-Based Clouds

abstract: Virtualization of computing and communication infrastructures were disseminated as possible solutions for networks evolution and deployment of new services on cloud data centers. Although promising, their effective application faces obstacles, mainly caused by rigidity on the management of communication resources. Currently, the Software-Defined Networks (SDN) paradigm has been popularizing customization and flexibility in network management due to separation of control and data planes. However, benefits introduced by SDN are not trivially applied to Virtual Infrastructures (VIs) provisioning on SDN-based cloud providers. An allocation mechanism needs joint information of control and data planes in order to deliver Quality-of-Service (QoS)-aware mappings while achieving provider objectives. In this work, we formulate the online VI allocation on SDN-based cloud data centers as a Mixed Integer Program (MIP). Following, integer constraints are relaxed to obtain a linear program, and rounding techniques are applied. The mechanism performs VI allocation considering latency, bandwidth, and virtual machine requirements. The results indicate that the VIs mean internal latency can be reduced while simultaneously enforcing other QoS constraints.


title: A Fast Implementation of the ISODATA Clustering Algorithm

abstract: Clustering is central to many image processing and remote sensing applications. ISODATA is one of the most popular and widely used clustering methods in geoscience applications, but it can run slowly, particularly with large data sets. We present a more efficient approach to ISODATA clustering, which achieves better running times by storing the points in a kd-tree and through a modification of the way in which the algorithm estimates the dispersion of each cluster. We also present an approximate version of the algorithm which allows the user to further improve the running time, at the expense of lower fidelity in computing the nearest cluster center to each point. We provide both theoretical and empirical justification that our modified approach produces clusterings that are very similar to those produced by the standard ISODATA approach. We also provide empirical studies on both synthetic data and remotely sensed Landsat and MODIS images that show that our approach has significantly lower running times.


title: Finite-Time Analysis of Kernelised Contextual Bandits

abstract: We tackle the problem of online reward maximisation over a large finite set of actions described by their contexts. We focus on the case when the number of actions is too big to sample all of them even once. However we assume that we have access to the similarities between actions' contexts and that the expected reward is an arbitrary linear function of the contexts' images in the related reproducing kernel Hilbert space (RKHS). We propose KernelUCB, a kernelised UCB algorithm, and give a cumulative regret bound through a frequentist analysis. For contextual bandits, the related algorithm GP-UCB turns out to be a special case of our algorithm, and our finite-time analysis improves the regret bound of GP-UCB for the agnostic case, both in the terms of the kernel-dependent quantity and the RKHS norm of the reward function. Moreover, for the linear kernel, our regret bound matches the lower bound for contextual linear bandits.


title: A 26-Gb/s 1.80-pJ/b CMOS-driven transmitter for 850-nm common-cathode VCSELs

abstract: A 26-Gb/s, 1.80-pJ/b transmitter composed of a 65-nm bulk-CMOS driver and an 850nm VCSEL array in a QFN package is demonstrated. The VCSEL's anode is directly modulated without a negative supply voltage at 0.82-dBm OMA.


title: A Micropower Chopper—CDS Operational Amplifier

abstract: A low-power spur-free precision amplifier, which uses input chopping and correlated double sampling for demodulation, is presented. This circuit employs an AC coupling between the first and the second stage that removes the first stage offset without causing ripple. The input rail-to-rail circuit, fabricated in a mixed 0.18-0.5 μm CMOS technology, operates with supply ranging from 1.8 V to 5 V. The circuit achieves a simulated 168-dB DC gain with an overall current consumption of 14.4 μA. The measured offset voltage over the available samples results in a distribution with 2-μV standard deviation. The obtained input noise density at low-frequency equal to 37 nV/√Hz gives a 5.5 noise efficiency factor.


title: The occurrence of Contracaecum sp. larvae (Nematoda: Anisakidae) in four teleostean species from Lake Nasser, Egypt: morphological and molecular studies

abstract: Nematodes of the family Anisakidae are parasites of many fishes and aquatic invertebrates which act as intermediate or paratenic hosts, while mammals and fish-eating birds are definitive hosts. Infective L3 larvae may be incidentally taken by human through eating raw or undercooked fish meat, causing anisakidosis. The main purpose of this study is to provide a basis for the future investigations to discover the genetic diversity of this widely distributed parasite nematodes of fishes and fish eating animals and their effect on fisheries and public health in Egypt and worldwide. One thousand, one hundred and fifteen specimens belong to nine fish species were collected from Lake Nasser, Egypt, and examined for infection with Anisakid larvae. Four fish species (Oreochromis niloticus, Tilapia galilaea, Lates niloticus, and Hydrocynus forskahlii) were found infected with third stage larvae of Contracaecum spp. No other Anisakid nematodes were detected. Larvae were found in the body cavity adhering to mesenteries by a thin membrane, except in Oreochromis niloticus and Tilapia galilaea were found free in branchial chambers. The highest prevalence was recorded in L. niloticus (100%) and H. forskahlii (82%). The mean intensity of infections were 0.17–4.12 and 5.1–10.3 in L. niloticus and H. forskahlii respectively. For further identification, the internal transcribed spacers (ITS-1 and ITS-2) of nuclear ribosomal DNA from isolated larvae (n = 54) were amplified by PCR, followed by single-strand conformation polymorphism (SSCP) analysis which revealed five possible profiles. Light and scanning electron microscope studies revealed that all anisakid larvae in the present study showed the most typical features of the genus Contracaecum. The sequencing (n = 28) and sequence and phylogenetic analyses showed that the present nematode larvae are likely belonging to C. multipapillatum.


title: A smart parking system based on NB-IoT and third-party payment platform

abstract: In recent years, with the increasing number of vehicles and insufficient parking spaces, the urban traffic congestion has become a great challenge that cannot be neglected. In order to mitigate problems such as high power consumption of sensor node and high deployment costs of wireless network, a smart parking system is proposed in this paper. In the proposed system, the data of the sensor node is transmitted by Narrowband Internet of Things (NB-IoT) module, which is a new cellular technology introduced for Low-Power Wide-Area (LPWA) applications. Basic information management, charge management, sensor node surveillance, task management and business intelligence modules are implemented on the cloud server. With integrated third-party payment platform and parking guide service, the mobile application developed for drivers is easy and convenient to use. Currently, the proposed system has been deployed in two cities to improve the utilization of existing parking facilities effectively.


title: Bitmap Filter: Speeding up Exact Set Similarity Joins with Bitwise Operations

abstract: The Exact Set Similarity Join problem aims to find all similar sets between two collections of sets, with respect to a threshold and a similarity function such as overlap, Jaccard, dice or cosine. The naive approach verifies all pairs of sets and it is often considered impractical due the high number of combinations. So, Exact Set Similarity Join algorithms are usually based on the Filter-Verification Framework, that applies a series of filters to reduce the number of verified pairs. This paper presents a new filtering technique called Bitmap Filter, which is able to accelerate state-of-the-art algorithms for the exact Set Similarity Join problem. The Bitmap Filter uses hash functions to create bitmaps of fixed b bits, representing characteristics of the sets. Then, it applies bitwise operations (such as xor and population count) on the bitmaps in order to infer a similarity upper bound for each pair of sets. If the upper bound is below a given similarity threshold, the pair of sets is pruned. The Bitmap Filter benefits from the fact that bitwise operations are efficiently implemented by many modern general-purpose processors and it was easily applied to four state-of-the-art algorithms implemented in CPU: AllPairs, PPJoin, AdaptJoin and GroupJoin. Furthermore, we propose a Graphic Processor Unit (GPU) algorithm based on the naive approach but using the Bitmap Filter to speedup the computation. The experiments considered 9 collections containing from 100 thousands up to 10 million sets and the joins were made using Jaccard thresholds from 0.50 to 0.95. The Bitmap Filter was able to improve 90% of the experiments in CPU, with speedups of up to 4.50x and 1.43x on average. Using the GPU algorithm, the experiments were able to speedup the original CPU algorithms by up to 577x using an Nvidia Geforce GTX 980 Ti.


title: Image Restoration by the Method of Convex Projections: Part 1ߞTheory

abstract: A projection operator onto a closed convex set in Hilbert space is one of the few examples of a nonlinear map that can be defined in simple abstract terms. Moreover, it minimizes distance and is nonexpansive, and therefore shares two of the more important properties of ordinary linear orthogonal projections onto closed linear manifolds. In this paper, we exploit the properties of these operators to develop several iterative algorithms for image restoration from partial data which permit any number of nonlinear constraints of a certain type to be subsumed automatically. Their common conceptual basis is as follows. Every known property of an original image f is envisaged as restricting it to lie in a well-defined closed convex set. Thus, m such properties place f in the intersection E(0) = E(i) of the corresponding closed convex sets E(1),E(2),...EE(m). Given only the projection operators PE(i) onto the individual E(i)'s, i = 1 --> m, we restore f by recursive means. Clearly, in this approach, the realization of the P(i)'s in a Hilbert space setting is one of the major synthesis problems. Section I describes the geometrical significance of the three main theorems in considerable detail, and most of the underlying ideas are illustrated with the aid of simple diagrams. Section II presents rules for the numerical implementation of 11 specific projection operators which are found to occur frequently in many signal-processing applications, and the Appendix contains proofs of all the major results.


Generalized Transition-based Dependency Parsing via Control Parameters


title: Optimising learning using flashcards: Spacing is more effective than cramming

abstract: SUMMARY The spacing effect—that is, the benefit of spacing learning events apart rather than massing them together—has been demonstrated in hundreds of experiments, but is not well known to educators or learners. I investigated the spacing effect in the realistic context of flashcard use. Learners often divide flashcards into relatively small stacks, but compared to a large stack, small stacks decrease the spacing between study trials. In three experiments, participants used a web-based study programme to learn GRE-type word pairs. Studying one large stack of flashcards (i.e. spacing) was more effective than studying four smaller stacks of flashcards separately (i.e. massing). Spacing was also more effective than cramming—that is, massing study on the last day before the test. Across experiments, spacing was more effective than massing for 90% of the participants, yet after the first study session, 72% of the participants believed that massing had been more effective than spacing. Copyright # 2009 John Wiley & Sons, Ltd.


title: Exploring power behaviors and trade-offs of in-situ data analytics

abstract: As scientific applications target exascale, challenges related to data and energy are becoming dominating concerns. For example, coupled simulation workflows are increasingly adopting in-situ data processing and analysis techniques to address costs and overheads due to data movement and I/O. However it is also critical to understand these overheads and associated trade-offs from an energy perspective. The goal of this paper is exploring data-related energy/performance trade-offs for end-to-end simulation workflows running at scale on current high-end computing systems. Specifically, this paper presents: (1) an analysis of the data-related behaviors of a combustion simulation workflow with an in-situ data analytics pipeline, running on the Titan system at ORNL; (2) a power model based on system power and data exchange patterns, which is empirically validated; and (3) the use of the model to characterize the energy behavior of the workflow and to explore energy/performance trade-offs on current as well as emerging systems.


title: A framework for hypothesis-driven approaches to support data-driven learning analytics in measuring computational thinking in block-based programming

abstract: K-12 classrooms use block-based programming environments (BBPEs) for teaching computer science and computational thinking (CT). To support assessment of student learning in BBPEs, we propose a learning analytics framework that combines hypothesis- and data-driven approaches to discern students' programming strategies from BBPE log data. We use a principled approach to design assessment tasks to elicit evidence of specific CT skills. Piloting these tasks in high school classrooms enabled us to analyze student programs and video recordings of students as they built their programs. We discuss a priori patterns derived from this analysis to support data-driven analysis of log data in order to better assess understanding and use of CT in BBPEs.


Applying Partial Evaluation to VLSI Design Rule Checking


title: RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation

abstract: Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. We carry out comprehensive experiments and set new state-of-the-art results on seven public datasets. In particular, we achieve an intersection-over-union score of 83.4 on the challenging PASCAL VOC 2012 dataset, which is the best reported result to date.


title: AILE – LIA 2, p. 9-41. PERCEPTION AND PRODUCTION OF FRENCH CLOSE AND CLOSE-MID ROUNDED VOWELS BY JAPANESE-SPEAKING LEARNERS

abstract: A series of experiments were conducted to examine the behaviour of Japanese-speaking learners of French (JSL) concerning the perception and production of the French /u/, /y/ and /o/, in order to examine how L2 (second language) vowels which do not have phonemic and/or phonetic equivalents in L1 (native language) are acquired by learners. The results indicate that JSL tend to produce the French /u/ with a high F2 (> 1000 Hz), which is heard as /o/ by native French (NF) listeners. They suggest that the French /u/ is considered by JSL as phonemically similar to Japanese /u/ (as a high non-front vowel) and produced as such, while the phonetic realization of the French /u/ is new and different ; and that /y/ is considered as both phonemically and phonetically new, and /o/ as phonemically new but phonetically (acoustically) similar. Phonemically similar but phonetically new vowels seem to be most difficult to learn to produce accurately for foreign/second language learners.


Hierarchical Attention Flow for Multiple-choice Reading Comprehension


Wireless integrated network sensors


title: CrowdTarget: Target-based Detection of Crowdturfing in Online Social Networks

abstract: Malicious crowdsourcing, also known as crowdturfing, has become an important security problem. However, detecting accounts performing crowdturfing tasks is challenging because human workers manage the crowdturfing accounts such that their characteristics are similar with the characteristics of normal accounts. In this paper, we propose a novel crowdturfing detection method, called CrowdTarget, that aims to detect target objects of crowdturfing tasks (e.g., post, page, and URL) not accounts performing the tasks. We identify that the manipulation patterns of target objects by crowdturfing workers are unique features to distinguish them from normal objects. We apply CrowdTarget to detect collusion-based crowdturfing services to manipulate account popularity on Twitter with artificial retweets. Evaluation results show that CrowdTarget can accurately distinguish tweets receiving crowdturfing retweets from normal tweets. When we fix the false-positive rate at 0.01, the best true-positive rate is up to 0.98.


Formative Assessment with Technology 2011-12: Second Year of Implementation. Eye on Evaluation. D&A Report No. 13.05.


title: Meteonowcasting using Deep Learning Architecture

abstract: The area of deep learning has enjoyed a resurgence on its peak, in almost every field of interest. Weather forecasting is a complicated and one of the most challenging tasks that includes observing and processing huge amount of data. The present paper proposes an effort to apply deep learning approach for the prediction of weather parameters such as temperature, pressure and humidity of a particular site. The implemented predictive models are based on Deep Belief Network (DBN) and Restricted Boltzmann Machine (RBM). Initially, each model is trained layer by layer in an unsupervised manner to learn the non-linear hierarchical features from the input distribution of dataset. Subsequently, each model is re-trained globally in supervised manner with an output layer to predict the appropriate output. The obtained results are encouraging. It is found that the feature based forecasting model can make predictions with high degree of accuracy. This implies that the model can be suitably adapted for making longer forecasts over larger geographical areas.


title: A Mobile Kalman-Filter Based Solution for the Real-Time Estimation of Spatio-Temporal Gait Parameters

abstract: Gait impairments are among the most disabling symptoms in several musculoskeletal and neurological conditions, severely limiting personal autonomy. Wearable gait sensors have been attracting attention as diagnostic tool for gait and are emerging as promising tool for tutoring and guiding gait execution. If their popularity is continuously growing, still there is room for improvement, especially towards more accurate solutions for spatio-temporal gait parameters estimation. We present an implementation of a zero-velocity-update gait analysis system based on a Kalman filter and off-the-shelf shoe-worn inertial sensors. The algorithms for gait events and step length estimation were specifically designed to comply with pathological gait patterns. More so, an Android app was deployed to support fully wearable and stand-alone real-time gait analysis. Twelve healthy subjects were enrolled to preliminarily tune the algorithms; afterwards sixteen persons with Parkinson's disease were enrolled for a validation study. Over the 1314 strides collected on patients at three different speeds, the total root mean square difference on step length estimation between this system and a gold standard was 2.9%. This shows that the proposed method allows for an accurate gait analysis and paves the way to a new generation of mobile devices usable anywhere for monitoring and intervention.


title: A compact 4-chip package with 64 embedded dual-polarization antennas for W-band phased-array transceivers

abstract: A fully-integrated antenna-in-package (AiP) solution for W-band scalable phased-array systems is demonstrated. We present a fully operational compact W-band transceiver package with 64 dual-polarization antennas embedded in a multilayer organic substrate. This package has 12 metal layers, a size of 16.2 mm × 16.2 mm, and 292 ball-grid-array (BGA) pins with 0.4 mm pitch. Four silicon-germanium (SiGe) transceiver ICs are flip-chip attached to the package. Extensive full-wave electromagnetic simulation and radiation pattern measurements have been performed to optimize the antenna performance in the package environment, with excellent model-to-hardware correlation achieved. Enabled by detailed circuit-package co-design, a half-wavelength spacing, i.e., 1.6 mm at 94 GHz, is maintained between adjacent antenna elements to support array scalability at both the package and board level. Effective isotropic radiated power (EIRP) and radiation patterns are also measured to demonstrate the 64-element spatial power combining.


title: Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification

abstract: Retinal blood vessels have a significant role in the diagnosis and treatment of various retinal diseases such as diabetic retinopathy, glaucoma, arteriosclerosis, and hypertension. For this reason, retinal vasculature extraction is important in order to help specialists for the diagnosis and treatment of systematic diseases. In this paper, a novel approach is developed to extract retinal blood vessel network. Our method comprises four stages: (1) preprocessing stage in order to prepare dataset for segmentation; (2) an enhancement procedure including Gabor, Frangi, and Gauss filters obtained separately before a top-hat transform; (3) a hard and soft clustering stage which includes K-means and Fuzzy C-means (FCM) in order to get binary vessel map; and (4) a postprocessing step which removes falsely segmented isolated regions. The method is tested on color retinal images obtained from STARE and DRIVE databases which are available online. As a result, Gabor filter followed by K-means clustering method achieves 95.94% and 95.71% of accuracy for STARE and DRIVE databases, respectively, which are acceptable for diagnosis systems.


title: Multi-Document Summarization using Sentence-based Topic Models

abstract: Most of the existing multi-document summarization methods decompose the documents into sentences and work directly in the sentence space using a term-sentence matrix. However, the knowledge on the document side, i.e. the topics embedded in the documents, can help the context understanding and guide the sentence selection in the summarization procedure. In this paper, we propose a new Bayesian sentence-based topic model for summarization by making use of both the term-document and term-sentence associations. An efficient variational Bayesian algorithm is derived for model parameter estimation. Experimental results on benchmark data sets show the effectiveness of the proposed model for the multi-document summarization task.


title: Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

abstract: At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. Indeed, the black-box nature of these systems allows powerful predictions, but it cannot be directly explained. This issue has triggered a new debate on explainable AI (XAI). A research field holds substantial promise for improving trust and transparency of AI-based systems. It is recognized as the sine qua non for AI to continue making steady progress without disruption. This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI. Through the lens of the literature, we review the existing approaches regarding the topic, discuss trends surrounding its sphere, and present major research trajectories.


title: Stochastic Models and Wide-Area Network Measurements for Blockchain Design and Analysis

abstract: The Blockchain paradigm provides a popular mechanism for establishing trust and consensus in distributed environments. While Blockchain technology is currently primarily deployed in crypto-currency systems like Bitcoin, the concept is also expected to emerge as a key component of the Internet-of-Things (IoT), enabling novel applications in digital health, smart energy, asset tracking and smart transportation. As Blockchain networks evolve to industrial deployments with large numbers of geographically distributed nodes, the block transfer and processing delays arise as a critical issue which may create greater potential for forks and vulnerability to adversarial attacks. Motivated by these issues, we develop stochastic network models to capture the Blockchain evolution and dynamics and analyze the impact of the block dissemination delay and hashing power of the member nodes on Blockchain performance in terms of the overall block generation rate and required computational power for launching a successful attack. The results provide useful insight in crucial design issues, e.g., how to adjust the ‘difficulty-of-work’ in the presence of delay so as to achieve a target block generation rate or appropriate level of immunity from adversarial attacks. We employ a combination of analytical calculations and simulation experiments to investigate both stationary and transient performance features, and demonstrate close agreement with measurements on a wide-area network testbed running the Ethereum protocol.


title: Collapsar: A VM-based honeyfarm and reverse honeyfarm architecture for network attack capture and detention

abstract: The honeypot has emerged as an effective tool to provide insights into new attacks and exploitation trends. However, a single honeypot or multiple independently operated honeypots only provide limited local views of network attacks. Coordinated deployment of honeypots in different network domains not only provides broader views, but also create opportunities of early network anomaly detection, attack correlation, and global network status inference. Unfortunately, coordinated honeypot operation require close collaboration and uniform security expertise across participating network domains. The conflict between distributed presence and uniform management poses a major challenge in honeypot deployment and operation. ::: ::: To address this challenge, we present Collapsar, a virtual machine-based architecture for network attack capture and detention. A Collapsar center hosts and manages a large number of high-interaction virtual honeypots in a local dedicated network. To attackers, these honeypots appear as real systems in their respective production networks. Decentralized logical presence of honeypots provides a wide diverse view of network attacks, while the centralized operation enables dedicated administration and convenient event correlation, eliminating the need for honeypot expertise in every production network domain. Collapsar realizes the traditional honeyfarm vision as well as our new reverse honeyfarm vision, where honeypots act as vulnerable clients exploited by real-world malicious servers. We present the design, implementation, and evaluation of a Collapsar prototype. Our experiments with a number of real-world attacks demonstrate the effectiveness and practicality of Collapsar.


title: Characterization and Design of Millimeter-Wave Full-Band Waveguide-Based Spatial Power Divider/Combiner

abstract: The design and implementation of millimeter-wave full-band waveguide-based spatial power divider/combiner are presented in this paper. The divider/combiner is based on a compact waveguide- to-microstrip (Wg-Ms) probe-array transition structure, providing full-band frequency coverage and low insertion loss. E-cient design and analysis method for this type of power divider/combiner is developed using spectral domain method combined with the image theory. Ka-band two-way (1 £ 2) and four- way (2 £ 2) power combining structures are analyzed and optimized. The performances of the both optimized power dividers/combiners are evaluated by experiments in back-to-back conflgurations. The measured overall insertion loss for the 1£2 power divider/combiner is better than 1.4dB over the entire Ka-band, which demonstrates the low-loss performance of the divdier/combiner. The optimized 2 £ 2 power divider/combiner shows a same performance as the 1 £ 2 structure without any degradation in operating bandwidth and insertion loss.


PROCEDURAL GENERATION OF SOKOBAN LEVELS


title: Darwin, Deception, and Facial Expression

abstract: Darwin did not focus on deception. Only a few sentences in his book mentioned the issue. One of them raised the very interesting question of whether it is difficult to voluntarily inhibit the emotional expressions that are most difficult to voluntarily fabricate. Another suggestion was that it would be possible to unmask a fabricated expression by the absence of the difficult-to-voluntarily-generate facial actions. Still another was that during emotion body movements could be more easily suppressed than fa- cial expression. Research relevant to each of Darwin's suggestions is re- viewed, as is other research on deception that Darwin did not foresee.