title: Explanation in Artificial Intelligence: Insights from the Social Sciences

abstract: Abstract There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to provide more transparency to their algorithms. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a ‘good’ explanation. There exist vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations to the explanation process. This paper argues that the field of explainable artificial intelligence can build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.


title: A methodology for creating reusable ontologies

abstract: Applications within the Internet of Things (IoT) increasingly build upon Semantic Web technologies to ensure interoperability of devices and applications. Hereby, a central aspect is knowledge representation, and in particular how devices can store and exchange semantic data. This paper defines and discusses a methodology for creating application-specific ontologies for the IoT. The two main objectives are to reuse existing knowledge on one hand and to derive reusable ontologies on the other hand. Thereby, a multi-agent system for switching optimization in the smart energy domain serves as a motivating example.


title: Asynchronous successive approximation ADC for wireless applications in 180 nm CMOS technology

abstract: This paper describes an asynchronous successive approximation ADC. Six comparators are used for each conversion bit, clocked by a ripple clock which was generated after each comparison. Due to these six comparators feedback delay is eliminated. The SAR ADC is used for compact implementation, low power and high speed applications.


title: Linear dynamical neural population models through nonlinear embeddings

abstract: A body of recent work in modeling neural activity focuses on recovering low-dimensional latent features that capture the statistical structure of large-scale neural populations. Most such approaches have focused on linear generative models, where inference is computationally tractable. Here, we propose fLDS, a general class of nonlinear generative models that permits the firing rate of each neuron to vary as an arbitrary smooth function of a latent, linear dynamical state. This extra flexibility allows the model to capture a richer set of neural variability than a purely linear model, but retains an easily visualizable low-dimensional latent space. To fit this class of non-conjugate models we propose a variational inference scheme, along with a novel approximate posterior capable of capturing rich temporal correlations across time. We show that our techniques permit inference in a wide class of generative models.We also show in application to two neural datasets that, compared to state-of-the-art neural population models, fLDS captures a much larger proportion of neural variability with a small number of latent dimensions, providing superior predictive performance and interpretability.


title: Characterization of apolipoprotein A-I as a potential biomarker for cholangiocarcinoma.

abstract: Serum samples from 60 cholangiocarcinoma (CC), 60 benign diseases of hepatobiliary and 53 normal individuals were analysed by SELDI-TOF-MS (Surface Enhanced Laser Desorption/Ionization Time of Flight Mass Spectrometry). It was found that a 28 k m/z peak was significantly decreased in CC and retained discriminatory value between CC and normal group, also between CC and benign groups. Then 1-D, 2-D gel electrophoresis and tandem mass spectroscopy were employed to isolate and identify the protein that correlates with observed SELDI-TOF-MS (m/z) value. The results demonstrated that 28 k m/z peak was apolipoprotein A-I (ApoA-I) and its identity was further validated by immunodepletion and Western blotting analysis. Subsequently, it was inspiring found that the decreased level of ApoA-I analysed by enzyme linked immunosorbent assay was consistent with SELDI-TOF-MS analysis. Therefore, it suggested that ApoA-I could be a potential useful biomarker for CC.


title: A Large Self-Annotated Corpus for Sarcasm

abstract: We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for sarcasm research and for training and evaluating systems for sarcasm detection. The corpus has 1.3 million sarcastic statements -- 10 times more than any previous dataset -- and many times more instances of non-sarcastic statements, allowing for learning in both balanced and unbalanced label regimes. Each statement is furthermore self-annotated -- sarcasm is labeled by the author, not an independent annotator -- and provided with user, topic, and conversation context. We evaluate the corpus for accuracy, construct benchmarks for sarcasm detection, and evaluate baseline methods.


title: Question answer system for online feedable new born Chatbot

abstract: Designing a new born Chatbot and feeding it from the web with specific areas of interest is a new research field. Few researchers have investigated empty database Chatbots and populating it from web pages or plain text corpora. Extracting data from web pages needs considerable processing before the response sentences are ready for the Chatbot. Feature extraction is also needed in order to filter and quantify the extracted plain text. In addition, ranking and classification are also required. This paper presents a new method that employs multiple feature extraction methods to quantify text responses for a new born (uneducated) Chatbot. Multiple measurement metrics are examined simultaneously in order to find the nearest match to a query. The nearest matches with the highest score have been obtained by re-ranking the scores of extracted features for text responses. The results show that the highest scored sentences have subjectively a good match to the query. The evaluation results indicate that the performance of the system increases significantly by using cosine similarity to find lexical match between the query and the response sentence rather than Jaccard's coefficient.


title: First I "like" it, then I hide it: Folk Theories of Social Feeds

abstract: Many online platforms use curation algorithms that are opaque to the user. Recent work suggests that discovering a filtering algorithm's existence in a curated feed influences user experience, but it remains unclear how users reason about the operation of these algorithms. In this qualitative laboratory study, researchers interviewed a diverse, non-probability sample of 40 Facebook users before, during, and after being presented alternative displays of Facebook's News Feed curation algorithm's output. Interviews revealed 10 "folk theories' of automated curation, some quite unexpected. Users who were given a probe into the algorithm's operation via an interface that incorporated "seams,' visible hints disclosing aspects of automation operations, could quickly develop theories. Users made plans that depended on their theories. We conclude that foregrounding these automated processes may increase interface design complexity, but it may also add usability benefits.


Database Cracking


title: The Benefits of Interactions with Physically Present Robots over Video-Displayed Agents

abstract: This paper explores how a robot’s physical presence affects human judgments of the robot as a social partner. For this experiment, participants collaborated on simple book-moving tasks with a humanoid robot that was either physically present or displayed via a live video feed. Multiple tasks individually examined the following aspects of social interaction: greetings, cooperation, trust, and personal space. Participants readily greeted and cooperated with the robot whether present physically or in live video display. However, participants were more likely both to fulfill an unusual request and to afford greater personal space to the robot when it was physically present, than when it was shown on live video. The same was true when the live video displayed robot’s gestures were augmented with disambiguating 3-D information. Questionnaire data support these behavioral findings and also show that participants had an overall more positive interaction with the physically present robot.


title: Comparison between Various Software Development Methodologies

abstract: From the beginning of software development, it always tried to formulate some process or process sequences or steps to develop or making software. Development of software should be in systematic manner. Today there are variety of software projects some require only good GUI, some require security, some software are made for mission critical tasks, for all these different types of projects require different type of model for development of software. Few well know software development models are waterfall, v-shaped, agile, and object-oriented. All these have their own advantages and disadvantages. This paper try to solve the problem of choosing right methodology for particular software by comparing all software development methodologies. It also figure out the advantages and disadvantages of different methodologies in useful manner not to criticize, so that a particular model will be chosen by an organization or company. This paper divided into two parts first is dedicated to advantage and disadvantages and second part id for comparison between all most popular software development life cycle models. This paper also used for choosing best model for particular project for developing particular software. This paper broadly categorize all software development models into four categories flow based model, iteration based model, object oriented model, structured based model.


title: Relief texture mapping

abstract: We present an extension to texture mapping that supports the representation of 3-D surface details and view motion parallax. The results are correct for viewpoints that are static or moving, far away or nearby. Our approach is very simple: a relief texture (texture extended with an orthogonal displacement per texel) is mapped onto a polygon using a two-step process: First, it is converted into an ordinary texture using a surprisingly simple 1-D forward transform. The resulting texture is then mapped onto the polygon using standard texture mapping. The 1-D warping functions work in texture coordinates to handle the parallax and visibility changes that result from the 3-D shape of the displacement surface. The subsequent texture-mapping operation handles the transformation from texture to screen coordinates.


CAR ACCIDENT DETECTION SYSTEM USING GPS AND GSM


title: Age, geochemical composition, and distribution of Oligocene ignimbrites in the northern Sierra Nevada, California: implications for landscape morphology, elevation, and drainage divide geography of the Nevadaplano

abstract: To gain a better understanding of the topographic and landscape evolution of the Cenozoic Sierra Nevada and Basin and Range, we combine geochemical and isotopic age correlations with palaeoaltimetry data from widely distributed ignimbrites in the northern Sierra Nevada, California. A sequence of Oligocene rhyolitic ignimbrites is preserved across the modern crest of the range and into the western foothills. Using trace and rare earth element geochemical analyses of volcanic glass, these deposits have been correlated to ignimbrites described and isotopically dated in the Walker Lane fault zone and in central Nevada (Henry et al., 2004, Geologic map of the Dogskin mountain quadrangle; Washoe County, Nevada; Faulds et al., 2005, Geology, v. 33, p. 505–508). Ignimbrite deposits were sampled within the northern Sierra Nevada and western Nevada, and four distinct geochemical compositions were identified. The majority of samples from within the northern Sierra Nevada have compositions similar to the tuffs of Axe...


title: Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

abstract: Deep convolutional networks have achieved great success for visual recognition in still images. However, for action recognition in videos, the advantage over traditional methods is not so evident. This paper aims to discover the principles to design effective ConvNet architectures for action recognition in videos and learn these models given limited training samples. Our first contribution is temporal segment network (TSN), a novel framework for video-based action recognition. which is based on the idea of long-range temporal structure modeling. It combines a sparse temporal sampling strategy and video-level supervision to enable efficient and effective learning using the whole action video. The other contribution is our study on a series of good practices in learning ConvNets on video data with the help of temporal segment network. Our approach obtains the state-the-of-art performance on the datasets of HMDB51 (\( 69.4\,\% \)) and UCF101 (\( 94.2\,\% \)). We also visualize the learned ConvNet models, which qualitatively demonstrates the effectiveness of temporal segment network and the proposed good practices (Models and code at https://github.com/yjxiong/temporal-segment-networks).


When Do Libraries of Articulated Images Obey


title: Dual-band planar quadrature hybrid with enhanced bandwidth response

abstract: This paper presents the theory, design procedure, and implementation of a dual-band planar quadrature hybrid with enhanced bandwidth. The topology of the circuit is a three-branch-line (3-BL) quadrature hybrid, which provides much larger flexibility to allocate the desired operating frequencies and necessary bandwidths than other previously published configurations. A performance comparison with other dual-band planar topologies is presented. Finally, a 3-BL quadrature hybrid for dual band (2.4 and 5 GHz) wireless local area network systems was fabricated, aimed to cover the bands corresponding to the standards IEEE802.11a/b. The measurements show a 16% and 18% bandwidth for the lower and upper frequency, respectively, satisfying and exceeding the bandwidth requirements for the above standards


title: Effect of the adapted virtual reality cognitive training program among Chinese older adults with chronic schizophrenia: a pilot study

abstract: Objective ::: ::: To examine the effect of the adapted virtual reality cognitive training program in older adults with chronic schizophrenia. ::: ::: ::: ::: Methods ::: ::: Older adults with chronic schizophrenia were recruited from a long-stay care setting and were randomly assigned into intervention (n = 12) and control group (n = 15). The intervention group received 10-session of VR program that consisted of 2 VR activities using IREX. The control group attended the usual programs in the setting. ::: ::: ::: ::: Results ::: ::: After the 10-session intervention, older adults with chronic schizophrenia preformed significantly better than control in overall cognitive function (p .000), and in two cognitive subscales: repetition (p .001) and memory (p .040). These participants engaged in the VR activities volitionally. No problem of cybersickness was observed. ::: ::: ::: ::: Conclusions ::: ::: The results of the current study indicate that engaging in the adapted virtual reality cognitive training program offers the potential for significant gains in cognitive function of the older adults with chronic schizophrenia. Copyright © 2009 John Wiley & Sons, Ltd.


title: A longitudinal study of the effects of a high school robotics and computational thinking class on academic achievement (WIP)

abstract: While there has been a rising interest in computational thinking (CT) and a push to include it into the K-12 curriculum, there is little empirical evidence that a class that teaches CT will have any measurable long-term effects on student performance. Using robotics as an example of CT instruction, I propose to examine a high school that has had a large number of robotics students over the past six years to find evidence for the long-term benefits of CT. I will analyze school records (e.g. STEM class enrollment, STEM test scores, SAT scores) for differences between robotics and non-robotics students and compare interviews with selected students.


title: Compact Wideband SIW Horn Antenna Fed by Elevated-CPW Structure

abstract: This communication presents a compact substrate-integrated waveguide (SIW) H-plane horn antenna fed by a novel elevated coplanar waveguide (ECPW) structure. First, the wideband characteristic of the SIW horn antenna is achieved through loading a dielectric slab with gradually decreasing dielectric constants, which can be realized through simply perforating different air vias on the extended slab. Second, in order to sustain an efficient feeding for the relatively thick substrate ( $0.27 {\lambda}_{0}$ ), an additional metal ground is inserted in the middle of the grounded coplanar waveguide (GCPW). Moreover, a triangular-shaped transition structure is placed at the end of the ECPW to smoothly transmit the energy from the thin ECPW to the thick SIW horn antenna. Finally, a prototype is fabricated to validate the proposed concept. Measured results indicate that the proposed horn antenna operates from 17.4 to 24 GHz. Stable radiation patterns can be observed in the whole operating band. The measured results show good accordance with the simulated ones. Above all, the proposed antenna occupies an area of $22 \times 56.5 \times 4\;{\text{mm}}^{3}$ ( $1.47{\lambda}_{0} \times 3.77 {\lambda}_{0} \times 0.27{\lambda}_{0}$ ), which is much more compact than the previous rectangular waveguide-fed horn antenna ( $2.33{\lambda}_{0} \times 9.21{\lambda}_{0} \times 0.31{\lambda}_{0}$ ) (where ${\lambda}_{0}$ is the wavelength at 20 GHz in the free space).


Classification of EEG with Recurrent Neural Networks


title: An unlumped model for linear series elastic actuators with ball screw drives

abstract: Series elastic actuators are frequently modeled using a conventional lumped mass model which has remained mostly unchanged since their introduction almost two decades ago. The lumped model has served well for early development but more descriptive models are now needed for new actuator designs and control approaches. In this paper we propose a new unlumped model specifically for linear series elastic actuators which uses a rack & pinion conceptualization to intuitively depict the mechanics of a linear ball screw drive. Results from hardware experiments are presented and compared to the predicted simulation results for both the conventional model and the new unlumped model. The results demonstrate that the new unlumped model is significantly more representative of the true actuator dynamics.


title: Multi-view matching for unordered image sets , or “ How do I organize my holiday snaps ? ”

abstract: There has been considerable success in automated reconstruction for image sequences where small baseline algorithms can be used to establish matches across a number of images. In contrast in the case of widely separated views, methods have generally been restricted to two or three views.In this paper we investigate the problem of establishing relative viewpoints given a large number of images where no ordering information is provided. A typical application would be where images are obtained from different sources or at different times: both the viewpoint (position, orientation, scale) and lighting conditions may vary significantly over the data set.Such a problem is not fundamentally amenable to exhaustive pair wise and triplet wide baseline matching because this would be prohibitively expensive as the number of views increases. Instead, we investiate how a combination of image invariants, covariants, and multiple view relations can be used in concord to enable efficient multiple view matching. The result is a matching algorithm which is linear in the number of views.The methods are illustrated on several real image data sets. The output enables an image based technique for navigating in a 3D scene, moving from one image to whichever image is the next most appropriate.


title: Classification of brain disease in magnetic resonance images using two-stage local feature fusion

abstract: BACKGROUND ::: Many classification methods have been proposed based on magnetic resonance images. Most methods rely on measures such as volume, the cerebral cortical thickness and grey matter density. These measures are susceptible to the performance of registration and limited in representation of anatomical structure. This paper proposes a two-stage local feature fusion method, in which deformable registration is not desired and anatomical information is represented from moderate scale. ::: ::: ::: METHODS ::: Keypoints are firstly extracted from scale-space to represent anatomical structure. Then, two kinds of local features are calculated around the keypoints, one for correspondence and the other for representation. Scores are assigned for keypoints to quantify their effect in classification. The sum of scores for all effective keypoints is used to determine which group the test subject belongs to. ::: ::: ::: RESULTS ::: We apply this method to magnetic resonance images of Alzheimer's disease and Parkinson's disease. The advantage of local feature in correspondence and representation contributes to the final classification. With the help of local feature (Scale Invariant Feature Transform, SIFT) in correspondence, the performance becomes better. Local feature (Histogram of Oriented Gradient, HOG) extracted from 16×16 cell block obtains better results compared with 4×4 and 8×8 cell block. ::: ::: ::: DISCUSSION ::: This paper presents a method which combines the effect of SIFT descriptor in correspondence and the representation ability of HOG descriptor in anatomical structure. This method has the potential in distinguishing patients with brain disease from controls.


title: Climate change and health: impacts, vulnerability, adaptation and mitigation

abstract: Global climate change is progressing and health impacts have been observed in a number of countries, including Australia. The main health impacts will be due to direct heat exposure, extreme weather, air pollution, reduced local food production, food- and vectorborne infectious diseases and mental stress. The issue is one of major public health importance. Adaptation to reduce the effects of climate change involves many different sectors to minimise negative health outcomes. Wide-scale mitigation is also required, in order to reduce the effects of climate change. In addition, future urban design must be modified to mitigate and adapt to the effects of climate change. Strategies for mitigation and adaptation can create co-benefits for both individual and community health, by reducing non-climate-related health hazard exposures and by encouraging health promoting behaviours and lifestyles.


title: Compact Multi-signatures for Smaller Blockchains

abstract: We construct new multi-signature schemes that provide new functionality. Our schemes are designed to reduce the size of the Bitcoin blockchain, but are useful in many other settings where multi-signatures are needed. All our constructions support both signature compression and public-key aggregation. Hence, to verify that a number of parties signed a common message m, the verifier only needs a short multi-signature, a short aggregation of their public keys, and the message m. We give new constructions that are derived from Schnorr signatures and from BLS signatures. Our constructions are in the plain public key model, meaning that users do not need to prove knowledge or possession of their secret key.


ASCII Art Synthesis with Convolutional Networks


title: Indoor air quality in U.K. school classrooms ventilated by natural ventilation windcatchers

abstract: AbstractThe provision of good IAQ in schools is important both for the health of students and in maximising educational achievement. It is, however, common for school classrooms to be significantly under-ventilated and this can lead to high levels of CO2 and other pollutants. Natural ventilation offers the potential to improve IAQ within schools whilst, at the same time reducing running and maintenance costs. Accordingly, this article examines a natural ventilation strategy based on the use of a roof mounted split-duct Windcatcher ventilator. Here, 16 U.K. classrooms are studied and CO2, temperature, relative humidity and ventilation rates are measured for the summer and winter seasons. Results show that, during the summer months, the ventilator is capable of significantly improving ventilation rates as well as reducing CO2 levels, especially when used in combination with open windows. However, in the winter months, the ventilator is seen not to open for a sufficient length of time and so CO2 levels rise ...


title: Improving Rete algorithm to enhance performance of rule engine systems

abstract: Rete algorithm is the most efficient forward inference algorithm, widely used in rule engine systems. This paper describes in detail that the realization of improvement for Rete algorithm applied in rule engine systems such as Telecom Billing System in two ways—Alpha-Node-Hashing which specifies how to find Alpha Nodes in the Alpha Memory by hashing, and Beta-Node-Indexing which mainly describes in creating index for Beta Nodes in the Beta Memory to speed searching. Then a series of experiments were made to verify the results. The results show that these two methods significantly improve system performance, and effectively make up for deficiencies in performance bottleneck of processing efficiency in telecom billing system.


title: Hand Gesture Recognition With Multiscale Weighted Histogram of Contour Direction Normalization for Wearable Applications

abstract: This paper proposes a static hand gesture recognition method with low computation and memory consumption for wearable applications. The hand contour is chosen as the hand gesture feature and support vector machine is used to classify the feature. A multiscale weighted histogram of contour direction-based direction normalization is proposed to ensure good recognition performance. In order to improve efficiency, the proposed histogram only counts the direction of the contour point to focus on the most significant hand feature in the first-person view of wearable devices. Based on the hand’s anatomy, the proposed histogram is weighted by considering each contour point’s position and direction jointly using the direction-angle map, to ensure robustness. Experimental results show that the proposed method can give a recognition accuracy of 97.1% with a frame rate of 30 fps on a PC.


title: “Facebook depression?” Social networking site use and depression in older adolescents

abstract: The objective of this study was to evaluate the association between social networking site (SNS) use and depression in older adolescents using an experience sample method (ESM) approach.


title: High-Frequency High-Efficiency GaN-Based Interleaved CRM Bidirectional Buck/Boost Converter with Inverse Coupled Inductor

abstract: This paper presents a high-frequency high-efficiency GaN device-based interleaved critical current mode (CRM) bidirectional buck/boost converter with an inverse coupled inductor. The switching frequency is continually driven to megahertz range with GaN devices due to their small switching loss and driving loss, which greatly reduces the size of the passive components. The coupled inductor further reduces the core volume due to certain dc flux reductions. The equivalent inductance and the impact of the inverse coupled inductor on the CRM buck–boost converter are analyzed in detail. The resonant period in CRM is less with an inverse coupled inductor than with a noncoupled inductor, which is beneficial for the high-frequency operation. The soft-switching range and the circulating energy are both improved using an inverse coupled inductor in CRM. Experimental results validate the theoretical analysis, and the coupled inductor prototype efficiency is 98.5% at 1 MHz, which is 0.3% higher than a prototype with a noncoupled inductor.


title: A level set method based on local direction gradient for image segmentation with intensity inhomogeneity

abstract: Many medical and real images are suffered from intensity inhomogeneity and weak edges. For higher image segmentation quality, lots of level set-based methods have been proposed. Some of them however cannot take advantage of image gradient information. And severe intensity inhomogeneity and weak edges are not disposed properly. To address these problems, a new level set method integrated with local direction gradient information is presented in this paper. Firstly, according to the two assumptions on image intensity inhomogeneity adopted by many existing methods, a new pixel classification model based on image gradient is introduced. Secondly, we employ variational level set method combined with image spatial information, which improves the anti-noise capability of the proposed method. Finally, considering the gray gradients in homogeneous regions are close to constants, an improved diffusion process is incorporated into the level set function to make the evolving curves stay around true image edges. To verify our method, different testing images including synthetic images, magnetic resonance imaging (MRI) and real-world images are introduced. The image segmentation results demonstrate that our method can deal with the relatively severe intensity inhomogeneity and obtain the comparatively ideal segmentation results efficiently.


title: From Macro to Micro Expression Recognition: Deep Learning on Small Datasets Using Transfer Learning

abstract: This paper presents the methods used in our submission to 2018 Facial Micro-Expression Grand Challenge (MEGC). The object of the challenge is to recognize micro-expression in two provided databases, including holdout-database recognition and composite database recognition. Considering the small size of the databases, we follow a rout of transfer learning to implement convolutional neural network to recognize the micro-expression. ResNet10 pre-trained on ImageNet dataset was fine-tuned on macro-expression datasets with large size and then on the provided micro-expression datasets. Experimental results show that the method can achieve weighted average recall (WAR) of 0.561 and unweighted average recall (UAR) of 0.389 in Holdout-database Evaluation Task, and F1 Score of 0.64 in Composite Database Evaluation Task, which are much higher than what baseline methods (LBP-TOP, HOOF, HOG3D) can achieve.


title: BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth

abstract: We introduce a novel method for 3D object detection and pose estimation from color images only. We first use segmentation to detect the objects of interest in 2D even in presence of partial occlusions and cluttered background. By contrast with recent patch-based methods, we rely on a "holistic" approach: We apply to the detected objects a Convolutional Neural Network (CNN) trained to predict their 3D poses in the form of 2D projections of the corners of their 3D bounding boxes. This, however, is not sufficient for handling objects from the recent T-LESS dataset: These objects exhibit an axis of rotational symmetry, and the similarity of two images of such an object under two different poses makes training the CNN challenging. We solve this problem by restricting the range of poses used for training, and by introducing a classifier to identify the range of a pose at run-time before estimating it. We also use an optional additional step that refines the predicted poses. We improve the state-of-the-art on the LINEMOD dataset from 73.7% to 89.3% of correctly registered RGB frames. We are also the first to report results on the Occlusion dataset using color images only. We obtain 54% of frames passing the Pose 6D criterion on average on several sequences of the T-LESS dataset, compared to the 67% of the state-of-the-art on the same sequences which uses both color and depth. The full approach is also scalable, as a single network can be trained for multiple objects simultaneously.


title: Comparative Analysis of Distributed Ledger Technologies

abstract: Distributed Ledger Technology (DLT) refers to a digital method for registering virtual transactions and other similar data in multiple locations simultaneously. The main characteristic of distributed ledgers is that they do not have a central administration component, due to advanced algorithms and methods used for record-keeping. Thus, transactions are faster and reasonable, as they do not require a central authority to validate them. The network is formed by multiple nodes through which the participant can access recordings, making the network become less vulnerable when referring to cyberattacks. After several years of technological development in this area, different distributed ledger technologies are available. This article presents Blockchain and Tangle technologies along with their main characteristics, in order to make an extended comparison of their approaches. In addition to this, the paper contains experimentation details and results for each technology presented.


title: Population Variation in Migraine Prevalence - The Unsolved Problem

abstract: Migraine is one of the most prevalent types of headache, which imposes a substantial burden on society as measured by direct and indirect costs. In the present work we review the obstacles to carrying out migraine epidemiological studies. Knowledge of the epidemiology of migraine has expanded because of the recent proliferation of large scale population-based studies using standardized case definitions. Previous surveys of the prevalence of migraine have given widely differing results. Even in population-based studies, there have been wide variations in estimates of migraine prevalence. The development of International Headache Society (IHS) criteria for migraine has facilitated international comparisons in epidemiologic studies by providing explicit and consistent diagnostic criteria, Recent studies using IHS diagnostic criteria have given relatively consistent estimates of migraine prevalence (about 15–18% of women and 6% of men). Studies of racial diversity in the prevalence of migraine and other headaches are still not conclusive. Further work is needed on epidemiology of migraine and collaboration between epidemiologists and clinicians seems the most likely way to advance understanding in this field.


title: Color image interpolation in the DCT domain using a wavelet-based differential value

abstract: We propose a new image-interpolation technique using a combination of an adaptive directional wavelet transform (ADWT) and discrete cosine transform (DCT). In the proposed method, we use ADWT to decompose the low-resolution image into different frequency subband images. The high-frequency subband images are further transformed to DCT coefficients, and the zero-padding method is used for interpolation. Simultaneously, the low-frequency subband image is replaced by the original low-resolution image. Finally, we generate the interpolated image by combining the original low-resoultion image and the interpolated subband images by using inverse DWT. Experimental results demonstrate that the proposed algorithm yields better quality images in terms of subjective and objective quality metrics compared to the other methods considered in this paper.


title: Mid-term electricity price forecasting using SVM

abstract: In the modern electricity market, it is very significant to have a precise electricity price forecasting in medium term time horizon. There are few studies done concentrating on the medium term forecasting of the electricity price. Medium term time horizon of electricity price forecasting is very useful and of great importance. It has many applications such as future maintenance scheduling of the power plants, risk management, plan future contracts, purchasing raw materials and determine the market pricing. To forecast the electricity price, some factors are very important, such as choosing the most useful price features that influence the market price, and choosing the proper prediction model that is able to predict the price variations behavior using the historical data. The proposed SVM method and the other employed methods are evaluated using the data from the New England ISO which are published at their official website.


title: Influence and Passivity in Social Media

abstract: The ever-increasing amount of information flowing through Social Media forces the members of these networks to compete for attention and influence by relying on other people to spread their message. A large study of information propagation within Twitter reveals that the majority of users act as passive information consumers and do not forward the content to the network. Therefore, in order for individuals to become influential they must not only obtain attention and thus be popular, but also overcome user passivity. We propose an algorithm that determines the influence and passivity of users based on their information forwarding activity. An evaluation performed with a 2.5 million user dataset shows that our influence measure is a good predictor of URL clicks, outperforming several other measures that do not explicitly take user passivity into account. We demonstrate that high popularity does not necessarily imply high influence and vice-versa.


title: Deep Convolutional Denoising of Low-Light Images

abstract: Poisson distribution is used for modeling noise in photon-limited imaging. While canonical examples include relatively exotic types of sensing like spectral imaging or astronomy, the problem is relevant to regular photography now more than ever due to the booming market for mobile cameras. Restricted form factor limits the amount of absorbed light, thus computational post-processing is called for. In this paper, we make use of the powerful framework of deep convolutional neural networks for Poisson denoising. We demonstrate how by training the same network with images having a specific peak value, our denoiser outperforms previous state-of-the-art by a large margin both visually and quantitatively. Being flexible and data-driven, our solution resolves the heavy ad hoc engineering used in previous methods and is an order of magnitude faster. We further show that by adding a reasonable prior on the class of the image being processed, another significant boost in performance is achieved.


title: Regularized linear and kernel redundancy analysis

abstract: Redundancy analysis (RA) is a versatile technique used to predict multivariate criterion variables from multivariate predictor variables. The reduced-rank feature of RA captures redundant information in the criterion variables in a most parsimonious way. A ridge type of regularization was introduced in RA to deal with the multicollinearity problem among the predictor variables. The regularized linear RA was extended to nonlinear RA using a kernel method to enhance the predictability. The usefulness of the proposed procedures was demonstrated by a Monte Carlo study and through the analysis of two real data sets.


List-only Entity Linking


title: Haptic emulation of games: haptic Sudoku for the blind

abstract: For blind persons, information is often presented through substitute media. We explore the possibility of emulating visual perception through haptic perception completely using a new haptic display and using games as a paradigm. We decsribe an implementation of Sudoku which utilizes the capabilities of the display: representing the full Sudoku square in its natural form; entering numbers and notes in the boxes of the square; emulating visual scanning for information through scanning by touch; interacting by finger movement (gestures). The only non-haptic component consists of sounds played to signal the outcome of actions. This demonstrates the advantages of the planar touch-sensitive refreshable haptic display when compared with other interaction modes for blind persons.


Reliability and validity of analytical construct in the case of virtual work


title: A “Deeper” Look at Detecting Cyberbullying in Social Networks

abstract: As cyberbullying becomes more and more frequent in social networks, automatically detecting it and pro-actively acting upon it becomes of the utmost importance. In this work, a detailed look at the current state-of-the-art in cyberbullying detection reveals that deep learning techniques have seldom been used to tackle this problem, despite growing reputation in other text-based classification tasks. Motivated by neural networks' documented success, three architectures are implemented from similar works: a simple CNN, a hybrid CNN-LSTM and a mixed CNN-LSTM-DNN. In addition, three text representations are trained from three different sources, via the word2vec model: Google-News, Twitter and Formspring. The experiment shows that these models with one of the above embeddings beat other benchmark classifiers (Support Vector Machines and Logistic Regression) both in an unbalanced and balanced version of the same dataset.


title: Hierarchical Tree Long Short-Term Memory for Sentence Representations

abstract: A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they can’t capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and longterm dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly.


title: 3D Feature Pyramid Attention Module for Robust Visual Speech Recognition

abstract: Visual speech recognition is the task to decode the speech content from a video based on visual information, especially the movements of lips. It is also referenced as lipreading. Motivated by two problems existing in lipreading, words with similar pronunciation and the variation of word duration, we propose a novel 3D Feature Pyramid Attention (3D-FPA) module to jointly improve the representation power of features in both the spatial and temporal domains. Specifically, the input features are downsampled for 3 times in both the spatial and temporal dimensions to construct spatiotemporal feature pyramids. Then high-level features are upsampled and combined with low-level features, finally generating a pixel-level soft attention mask to be multiplied with the input features.It enhances the discriminative power of features and exploits the temporal multi-scale information while decoding the visual speeches. Also, this module provides a new method to construct and utilize temporal pyramid structures in video analysis tasks. The field of temporal featrue pyramids are still under exploring compared to the plentiful works on spatial feature pyramids for image analysis tasks. To validate the effectiveness and adaptability of our proposed module, we embed the module in a sentence-level lipreading model, LipNet, with the result of 3.6% absolute decrease in word error rate, and a word-level model, with the result of 1.4% absolute improvement in accuracy.


A Low Voltage RISC-V Heterogeneous System Boosted SRAMs , Machine Learning , and Fault Injection on VELOUR


title: Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input

abstract: The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks. Here we scale up this research by using contemporary deep learning methods and by training reinforcement-learning neural network agents on referential communication games. We extend previous work, in which agents were trained in symbolic environments, by developing agents which are able to learn from raw pixel data, a more challenging and realistic input representation. We find that the degree of structure found in the input data affects the nature of the emerged protocols, and thereby corroborate the hypothesis that structured compositional language is most likely to emerge when agents perceive the world as being structured.


title: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

abstract: 3D object detection is an essential task in autonomous driving. Recent techniques excel with highly accurate detection rates, provided the 3D input data is obtained from precise but expensive LiDAR technology. Approaches based on cheaper monocular or stereo imagery data have, until now, resulted in drastically lower accuracies --- a gap that is commonly attributed to poor image-based depth estimation. However, in this paper we argue that it is not the quality of the data but its representation that accounts for the majority of the difference. Taking the inner workings of convolutional neural networks into consideration, we propose to convert image-based depth maps to pseudo-LiDAR representations --- essentially mimicking the LiDAR signal. With this representation we can apply different existing LiDAR-based detection algorithms. On the popular KITTI benchmark, our approach achieves impressive improvements over the existing state-of-the-art in image-based performance --- raising the detection accuracy of objects within the 30m range from the previous state-of-the-art of 22% to an unprecedented 74%. At the time of submission our algorithm holds the highest entry on the KITTI 3D object detection leaderboard for stereo-image-based approaches.


title: The JPEG still picture compression standard

abstract: For the past few years, a joint ISO/CCITT committee known as JPEG (Joint Photographic Experts Group) has been working to establish the first international compression standard for continuous-tone still images, both grayscale and color. JPEG’s proposed standard aims to be generic, to support a wide variety of applications for continuous-tone images. To meet the differing needs of many applications, the JPEG standard includes two basic compression methods, each with various modes of operation. A DCT-based method is specified for “lossy’’ compression, and a predictive method for “lossless’’ compression. JPEG features a simple lossy technique known as the Baseline method, a subset of the other DCT-based modes of operation. The Baseline method has been by far the most widely implemented JPEG method to date, and is sufficient in its own right for a large number of applications. This article provides an overview of the JPEG standard, and focuses in detail on the Baseline method.


title: The Aneka platform and QoS-driven resource provisioning for elastic applications on hybrid Clouds

abstract: Cloud computing alters the way traditional software systems are built and run by introducing a utility-based model for delivering IT infrastructure, platforms, applications, and services. The consolidation of this new paradigm in both enterprises and academia demanded reconsideration in the way IT resources are used, so Cloud computing can be used together with available resources. A case for the utilization of Clouds for increasing the capacity of computing infrastructures is Desktop Grids: these infrastructures typically provide best effort execution of high throughput jobs and other workloads that fit the model of the platform. By enhancing Desktop Grid infrastructures with Cloud resources, it is possible to offer QoS to users, motivating the adoption of Desktop Grids as a viable platform for application execution. In this paper, we describe how Aneka, a platform for developing scalable applications on the Cloud, supports such a vision by provisioning resources from different sources and supporting different application models. We highlight the key concepts and features of Aneka that support the integration between Desktop Grids and Clouds and present an experiment showing the performance of this integration.


title: 56-Gbaud 4-PAM (112-Gbit/s) operation of flip-chip interconnection lumped-electrode EADFB laser module for equalizer-free transmission

abstract: We fabricated a lumped-electrode type EADFB laser module using a flip-chip interconnection technique which provides a large modulation bandwidth. We obtained clear 56-Gbaud 4-PAM signal after 10-km SMF transmission with no equalizer using this module.


3 . 12 Interferometric Synthetic Aperture Radar Geodesy


title: Leveraging Affective Hashtags for Ranking Music Recommendations

abstract: Mood and emotion play an important role when it comes to choosing musical tracks to listen to. In the field of music information retrieval and recommendation, emotion is considered contextual information that is hard to capture, albeit highly influential. In this study, we analyze the connection between users' emotional states and their musical choices. Particularly, we perform a large-scale study based on two data sets containing 560,000 and 90,000 #nowplaying tweets, respectively. We extract affective contextual information from hashtags contained in these tweets by applying an unsupervised sentiment dictionary approach. Subsequently, we utilize a state-of-the-art network embedding method to learn latent feature representations of users, tracks and hashtags. Based on both the affective information and the latent features, a set of eight ranking methods is proposed. We find that relying on a ranking approach that incorporates the latent representations of users and tracks allows for capturing a user's general musical preferences well (regardless of used hashtags or affective information). However, for capturing context-specific preferences (a more complex and personal ranking task), we find that ranking strategies that rely on affective information and that leverage hashtags as context information outperform the other ranking strategies.


title: Face Detection in the Near-IR Spectrum

abstract: Abstract Face detection is an important prerequisite step for successful face recognition. The performance of previous face detection methods reported in the literature is far from perfect and deteriorates ungracefully where lighting conditions cannot be controlled. We propose a method that outperforms state-of-the-art face detection methods in environments with stable lighting. In addition, our method can potentially perform well in environments with variable lighting conditions. The approach capitalizes upon our near-IR skin detection method reported elsewhere [Proceedings IEEE Workshop on Computer Vision beyond the Visible Spectrum: Methods and Applications; 2000, IEEE Trans. Int. Trans. Sys.; vol. 1; 72–85]. It ascertains the existence of a face within the skin region by finding the eyes and eyebrows. The eye–eyebrow pairs are determined by extracting appropriate features from multiple near-IR bands. Very successful feature extraction is achieved by simple algorithmic means like integral projections and template matching. This is because processing is constrained in the skin region and aided by the near-IR phenomenology. The effectiveness of our method is substantiated by comparative experimental results with the Identix face detector [ http://www.faceit.com ].


title: In vitro flower formation from thin epidermal cell layers of a partial somatic hybrid between Petunia hybrida (Hort.) and Nicotiana plumbaginifolia (Viv.)

abstract: In vitro flowers have been obtained by culturing thin epidermal cell layers of a partial somatic intergeneric hybrid. The phenotype of these flowers differs from that of flowers formed on seed-grown plants (in situ flowers) and from that of flowers of either parental line. In addition, modifications in the phenotype were observed when cultures were sustained for more than four months. Dimorphic leaves present in juvenile and adult stages of mother plants of Nicotiana plumbaginifolia and the somatic hybrid were formed on different ends of the thin epidermal cell layers. No anomalies were observed during microsporogenesis and in the meiotic and mitotic figures of the somatic hybrid, which resembled those of Nicotiana plumbaginifolia.


title: Splitless injection and the solvent effect

abstract: Splitless injection is based on the solvent effect as a mechanism condensing large vapour clouds down to infinitely shortened bands. The effect is controlled by the parameters column temperature, volatility and amount of solvent, and rate of injection. By properly selecting the variables the effect can easily be optimized for any combination of sample and column. It is the purpose of this paper to provide the mechanistic understanding as required for this optimization, as well as some rules for the experimental realization. Potentialities and limitations of splitless injection are discussed, and the role of the solvent effect in on-column injection is emphasized.


High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models


title: What is wrong with this word? dyseggxia: a game for children with dyslexia

abstract: We present Dyseggxia, a game application with word exercises for children with dyslexia. We design the content of the game combining linguistic and pedagogical criteria as well as corpus analysis. The main contributions are (i) designing exercises by using the analysis of errors written by people with dyslexia and (i) presenting Spanish reinforcement exercises in the form of a computer game. The game is available for free on iOS and Android.


title: Human Shape from Silhouettes Using Generative HKS Descriptors and Cross-Modal Neural Networks

abstract: In this work, we present a novel method for capturing human body shape from a single scaled silhouette. We combine deep correlated features capturing different 2D views, and embedding spaces based on 3D cues in a novel convolutional neural network (CNN) based architecture. We first train a CNN to find a richer body shape representation space from pose invariant 3D human shape descriptors. Then, we learn a mapping from silhouettes to this representation space, with the help of a novel architecture that exploits correlation of multi-view data during training time, to improve prediction at test time. We extensively validate our results on synthetic and real data, demonstrating significant improvements in accuracy as compared to the state-of-the-art, and providing a practical system for detailed human body measurements from a single image.


abstract: This paper presents a high efficiency LCCL series resonant inverter for ultrasonic dispersion system with resonant frequency tracking and asymmetrical voltage cancellation control. With the proposed control, the operating frequency of the inverter is automatically adjusted to track the resonant frequency under load parameter variation due to surrounding condition. The zero-voltage switching (ZVS) is maintained throughout the operation. The output power is controlled using the asymmetrical voltage cancellation (AVC) technique. A full-bridge hardware prototype is constructed and the control algorithm is implemented using an STM32F4 microcontroller. Computer simulation and hardware experiment are provided at an operating frequency range of 37–42 kHz and the output power of 30–125 W.


Notes on Scalable Blockchain Protocols (version 0.3.2)


title: Differential association of two PTPN22 coding variants with Crohnʼs disease and ulcerative colitis

abstract: Background: The PTPN22 gene is an important risk factor for human autoimmunity. The aim of this study was to evaluate for the first time the role of the R263Q PTPN22 polymorphism in ulcerative colitis (UC) and Crohn's disease (CD), and to reevaluate the association of the R620W PTPN22 polymorphism with both diseases. Methods: A total of 1677 UC patients, 1903 CD patients, and 3111 healthy controls from an initial case-control set of Spanish Caucasian ancestry and two independent sample sets of European ancestry (Dutch and New Zealand) were included in the study. Genotyping was performed using TaqMan SNP assays for the R263Q (rs33996649) and R620W (rs2476601) PTPN22 polymorphisms. Meta-analysis was performed on 6977 CD patients, 5695 UC patients, and 9254 controls to test the overall effect of the minor allele of R620W and R263Q polymorphisms. Results: The PTPN22 263Q loss-of-function variant showed initial evidence of association with UC in the Spanish cohort (P =0.026, odds ratio [OR] = 0.61, 95% confidence interval [CI]: 0.39-0.95), which was confirmed in the meta-analysis (P = 0.013 pooled, OR 0.69, 95% CI: 0.51-0.93). In contrast, the 263Q allele showed no association with CD (P = 0.22 pooled, OR = 1.16, 95% CI: 0.91-1.47). We found in the pooled analysis that the PTPN22 620W gain-of-function variant was associated with reduced risk of CD (P = 7.4E-06 pooled OR 0.81, 95% CI: 0.75-0.89) but not of UC (P = 0.88 pooled, OR = 0.98, 95% CI: 0.85-1.15). Conclusions: Our data suggest that two autoimmunity-associated polymorphisms of the PTPN22 gene are differentially associated with CD and UC. The R263Q polymorphism only associated with UC, whereas the R620W was significantly associated with only CD. (Inflamm Bowel Dis 2011;17:2287-2294)


title: Resource Allocation for Underlay Cognitive Radio Networks: A Survey

abstract: For conventional wireless networks, the main target of resource allocation (RA) is to efficiently utilize the available resources. Generally, there are no changes in the available spectrum, thus static spectrum allocation policies were adopted. However, these allocation policies lead to spectrum under-utilization. In this regard, cognitive radio networks (CRNs) have received great attention due to their potential to improve the spectrum utilization. In general, efficient spectrum management and resource allocation are essential and very crucial for CRNs. This is due to the fact that unlicensed users should attain the most benefit from accessing the licensed spectrum without causing adverse interference to the licensed ones. The cognitive users or called secondary users have to effectively capture the arising spectrum opportunities in time, frequency, and space to transmit their data. Mainly, two aspects characterize the resource allocation for CRNs: 1) primary (licensed) network protection and 2) secondary (unlicensed) network performance enhancement in terms of quality-of-service, throughput, fairness, energy efficiency, etc. CRNs can operate in one of three known operation modes: 1) interweave; 2) overlay; and 3) underlay. Among which the underlay cognitive radio mode is known to be highly efficient in terms of spectrum utilization. This is because the unlicensed users are allowed to share the same channels with the active licensed users under some conditions. In this paper, we provide a survey for resource allocation in underlay CRNs. In particular, we first define the RA process and its components for underlay CRNs. Second, we provide a taxonomy that categorizes the RA algorithms proposed in literature based on the approaches, criteria, common techniques, and network architecture. Then, the state-of-the-art resource allocation algorithms are reviewed according to the provided taxonomy. Additionally, comparisons among different proposals are provided. Finally, directions for future research are outlined.


title: Anon-Pass: Practical Anonymous Subscriptions

abstract: We present the design, security proof, and implementation of an anonymous subscription service. Users register for the service by providing some form of identity, which might or might not be linked to a real-world identity such as a credit card, a web login, or a public key. A user logs on to the system by presenting a credential derived from information received at registration. Each credential allows only a single login in any authentication window, or epoch. Logins are anonymous in the sense that the service cannot distinguish which user is logging in any better than random guessing. This implies unlinkability of a user across different logins. We find that a central tension in an anonymous subscription service is the service provider's desire for a long epoch (to reduce server-side computation) versus users' desire for a short epoch (so they can repeatedly "re-anonymize" their sessions). We balance this tension by having short epochs, but adding an efficient operation for clients who do not need unlinkability to cheaply re-authenticate themselves for the next time period. We measure performance of a research prototype of our protocol that allows an independent service to offer anonymous access to existing services. We implement a music service, an Android-based subway-pass application, and a web proxy, and show that adding anonymity adds minimal client latency and only requires 33 KB of server memory per active user.


title: Optimal Step Nonrigid ICP Algorithms for Surface Registration

abstract: We show how to extend the ICP framework to nonrigid registration, while retaining the convergence properties of the original algorithm. The resulting optimal step nonrigid ICP framework allows the use of different regularisations, as long as they have an adjustable stiffness parameter. The registration loops over a series of decreasing stiffness weights, and incrementally deforms the template towards the target, recovering the whole range of global and local deformations. To find the optimal deformation for a given stiffness, optimal iterative closest point steps are used. Preliminary correspondences are estimated by a nearest-point search. Then the optimal deformation of the template for these fixed correspondences and the active stiffness is calculated. Afterwards the process continues with new correspondences found by searching from the displaced template vertices. We present an algorithm using a locally affine regularisation which assigns an affine transformation to each vertex and minimises the difference in the transformation of neighbouring vertices. It is shown that for this regularisation the optimal deformation for fixed correspondences and fixed stiffness can be determined exactly and efficiently. The method succeeds for a wide range of initial conditions, and handles missing data robustly. It is compared qualitatively and quantitatively to other algorithms using synthetic examples and real world data.


title: Managing the interstitials, a System of Systems framework suited for the Ballistic Missile Defense System

abstract: Recent engineering experiences with the Missile Defense Agency (MDA) Ballistic Missile Defense System (BMDS) highlight the need to analyze the BMDS System of Systems (SoS) including the numerous potential interactions between independently developed elements of the system. The term “interstitials” is used to define the domain of interfaces, interoperability, and integration between constituent systems in an SoS. The authors feel that this domain, at an SoS level, has received insufficient attention within systems engineering literature. The BMDS represents a challenging SoS case study as many of its initial elements were assembled from existing programs of record. The elements tend to perform as designed but their performance measures may not be consistent with the higher level SoS requirements. One of the BMDS challenges is interoperability, to focus the independent elements to interact in a number of ways, either subtle or overt, for a predictable and sustainable national capability. New capabilities desired by national leadership may involve modifications to kill chains, Command and Control (C2) constructs, improved coordination, and performance. These capabilities must be realized through modifications to programs of record and integration across elements of the system that have their own independent programmatic momentum. A challenge of SoS Engineering is to objectively evaluate competing solutions and assess the technical viability of tradeoff options. This paper will present a multifaceted technical approach for integrating a complex, adaptive SoS to achieve a functional capability. Architectural frameworks will be explored, a mathematical technique utilizing graph theory will be introduced, adjuncts to more traditional modeling and simulation techniques such as agent based modeling will be explored, and, finally, newly developed technical and managerial metrics to describe design maturity will be introduced. A theater BMDS construct will be used as a representative set of elements together with the interstitials representing the integration domain. Increased attention to the interstitial space of the overarching BMDS SoS construct and applying appropriate technical rigor and engineering due diligence with these added tools should greatly assist the BMDS in realizing its potential. © 2010 Wiley Periodicals, Inc. Syst Eng 14: 87–109, 2011


title: An empirical study of cognition and theatrical improvisation

abstract: This paper presents preliminary findings from our empirical study of the cognition employed by performers in improvisational theatre. Our study has been conducted in a laboratory setting with local improvisers. Participants performed predesigned improv "games", which were videotaped and shown to each individual participant for a retrospective protocol collection. The participants were then shown the video again as a group to elicit data on group dynamics, misunderstandings, etc. This paper presents our initial findings that we have built based on our initial analysis of the data and highlights details of interest.


title: Trust Management for SOA-Based IoT and Its Application to Service Composition

abstract: A future Internet of Things (IoT) system will connect the physical world into cyberspace everywhere and everything via billions of smart objects. On the one hand, IoT devices are physically connected via communication networks. The service oriented architecture (SOA) can provide interoperability among heterogeneous IoT devices in physical networks. On the other hand, IoT devices are virtually connected via social networks. In this paper we propose adaptive and scalable trust management to support service composition applications in SOA-based IoT systems. We develop a technique based on distributed collaborative filtering to select feedback using similarity rating of friendship, social contact, and community of interest relationships as the filter. Further we develop a novel adaptive filtering technique to determine the best way to combine direct trust and indirect trust dynamically to minimize convergence time and trust estimation bias in the presence of malicious nodes performing opportunistic service and collusion attacks. For scalability, we consider a design by which a capacity-limited node only keeps trust information of a subset of nodes of interest and performs minimum computation to update trust. We demonstrate the effectiveness of our proposed trust management through service composition application scenarios with a comparative performance analysis against EigenTrust and PeerTrust.


title: LTCC differential-fed patch antenna sub-array with low cross-polarization

abstract: A two-element patch antenna sub-array with low cross-polarization is proposed. Unlike conventional antenna array, the proposed array employs a novel differential via-wall feeding structure, by which it can have low cross-polarization in H-plane and symmetric radiation pattern in E-plane. The via-wall feeding structure can also enhance the bandwidth. Low-temperature co-fired ceramic (LTCC) fabrication process is adopted to realize this antenna at 13 GHz. The proposed differential antenna integrates easily with ready-made differential devices. The proposed design achieved the 10-dB impedance bandwidth of 10% and peak gain of 6.8 dBi. The simulated results also show that an excellent H-plane cross-polarization of less than -27 dB is obtained.


title: Multigrid Neural Architectures

abstract: We propose a multigrid extension of convolutional neural networks (CNNs). Rather than manipulating representations living on a single spatial grid, our network layers operate across scale space, on a pyramid of grids. They consume multigrid inputs and produce multigrid outputs, convolutional filters themselves have both within-scale and cross-scale extent. This aspect is distinct from simple multiscale designs, which only process the input at different scales. Viewed in terms of information flow, a multigrid network passes messages across a spatial pyramid. As a consequence, receptive field size grows exponentially with depth, facilitating rapid integration of context. Most critically, multigrid structure enables networks to learn internal attention and dynamic routing mechanisms, and use them to accomplish tasks on which modern CNNs fail. Experiments demonstrate wide-ranging performance advantages of multigrid. On CIFAR and ImageNet classification tasks, flipping from a single grid to multigrid within the standard CNN paradigm improves accuracy, while being compute and parameter efficient. Multigrid is independent of other architectural choices, we show synergy in combination with residual connections. Multigrid yields dramatic improvement on a synthetic semantic segmentation dataset. Most strikingly, relatively shallow multigrid networks can learn to directly perform spatial transformation tasks, where, in contrast, current CNNs fail. Together, our results suggest that continuous evolution of features on a multigrid pyramid is a more powerful alternative to existing CNN designs on a flat grid.


title: A Survey on Object Detection and Tracking Methods

abstract: The goal of object tracking is segmenting a region of interest from a video scene and keeping track of its motion, positioning and occlusion.The object detection and object classification are preceding steps for tracking an object in sequence of images. Object detection is performed to check existence of objects in video and to precisely locate that object. Then detected object can be classified in various categories such as humans, vehicles, birds, floating clouds, swaying tree and other moving objects. Object tracking is performed using monitoring objects’ spatial and temporal changes during a video sequence, including its presence, position, size, shape, etc.Object tracking is used in several applications such as video surveillance, robot vision, traffic monitoring, Video inpainting and Animation. This paper presents a brief survey of different object detection, object classification and object tracking algorithms available in the literature including analysis and comparative study of different techniques used for various stages of tracking.


title: The Effectiveness of Depth Data in Liveness Face Authentication Using 3D Sensor Cameras †

abstract: Even though biometric technology increases the security of systems that use it, they are prone to spoof attacks where attempts of fraudulent biometrics are used. To overcome these risks, techniques on detecting liveness of the biometric measure are employed. For example, in systems that utilise face authentication as biometrics, a liveness is assured using an estimation of blood flow, or analysis of quality of the face image. Liveness assurance of the face using real depth technique is rarely used in biometric devices and in the literature, even with the availability of depth datasets. Therefore, this technique of employing 3D cameras for liveness of face authentication is underexplored for its vulnerabilities to spoofing attacks. This research reviews the literature on this aspect and then evaluates the liveness detection to suggest solutions that account for the weaknesses found in detecting spoofing attacks. We conduct a proof-of-concept study to assess the liveness detection of 3D cameras in three devices, where the results show that having more flexibility resulted in achieving a higher rate in detecting spoofing attacks. Nonetheless, it was found that selecting a wide depth range of the 3D camera is important for anti-spoofing security recognition systems such as surveillance cameras used in airports. Therefore, to utilise the depth information and implement techniques that detect faces regardless of the distance, a 3D camera with long maximum depth range (e.g., 20 m) and high resolution stereo cameras could be selected, which can have a positive impact on accuracy.


title: Welfare Effects of Road Pricing and Traffic Information Under Alternative Ownership Regimes

abstract: This paper models strategic interactions between a product supplier, a provider of information about product quality, and end users, in the context of road transportation. Using a game-theoretical analysis of suppliers' pricing strategies, we assess the social welfare effects of traffic information under various road markets with different ownership regimes. The results show that in this context, the distortive welfare effect of monopolistic information pricing appears small to negligible. Collusion of the road operator and information provider yields higher social welfare than independent pricing by two firms. The intuition behind this result resembles that behind the welfare effects of double marginalization, but it is not exactly the same, as traffic information is not strictly complementary to road use.


title: NEURON: An Interactive Natural Language Interface for Understanding Query Execution Plans in RDBMS

abstract: Natural language interfaces for relational databases have been explored for several decades. Majority of the work have focused on translating natural language sentences to SQL queries or narrating SQL queries in natural language. Scant attention has been paid for natural language understanding of query execution plans (QEP) of SQL queries. In this demonstration, we present a novel generic system called NEURON that facilitates natural language interaction with QEPs. NEURON accepts a SQL query (which may include joins, aggregation, nesting, among other things) as input, executes it, and generates a natural language-based description (both in text and voice form) of the execution strategy deployed by the underlying RDBMS. Furthermore, it facilitates understanding of various features related to the QEP through a natural language-based question answering framework. NEURON can be potentially useful to database application developers in comprehending query execution strategies and to database instructors and students for pedagogical support.


Perturbative Neural Networks ( Supplementary Material )


title: Synergy and Self-organization in the Evolution of Complex Systems

abstract: Synergy of various kinds has played a significant creative role in evolution; it has been a prodigious source of evolutionary novelty. Elsewhere it has been proposed that the functional (selective) advantages associated with various forms of synergistic phenomena have been an important cause of the ‘progressive’ evolution of complex systems over time. Underlying the many specific steps in the complexification process, a common functional principle has been operative. Recent mathematical modelling work in biology, utilizing a new generation of non-linear dynamical systems models, has resulted in a radically different hypothesis. It has been asserted that ‘spontaneous’, autocatalytic processes, which are held to be inherent properties of living matter itself, may be responsible for much of the order found in nature and that natural selection is merely a supporting actor. A new ‘physics of biology’ is envisioned in which emerging natural laws of organization will be recognized as being responsible both for driving the evolutionary process and for truncating the role of natural selection. This article describes these two paradigms in some detail and discusses the possible relationship between them. Their relevance to the process of human evolution is also briefly discussed.


title: Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books

abstract: Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story. This paper aims to align books to their movie releases in order to provide rich descriptive explanations for visual content that go semantically far beyond the captions available in the current datasets. To align movies and books we propose a neural sentence embedding that is trained in an unsupervised way from a large corpus of books, as well as a video-text neural embedding for computing similarities between movie clips and sentences in the book. We propose a context-aware CNN to combine information from multiple sources. We demonstrate good quantitative performance for movie/book alignment and show several qualitative examples that showcase the diversity of tasks our model can be used for.


title: IT GOVERNANCE PRACTICES FOR IMPROVING STRATEGIC AND OPERATIONAL BUSINESS-IT ALIGNMENT

abstract: The literature suggests that business-IT alignment is an important antecedent of IS success, business process performance, and competitive advantage. Additionally, IT governance practices are highlighted as being instrumental to fostering business-IT alignment. In this paper, we derive various IT governance practices (in terms of structures, processes, relational mechanisms, and enterprise architecture characteristics) from literature and expert interviews. While prior investigations only considered the effect of such practices on strategic business-IT alignment, we also incorporate alignment at operational level. Using results from a case study in the IT services division of a large multi-national, multi-divisional company acting in diverse industries we highlight the effect of various IT governance practices and offer new insights by showing which mechanisms are effective in facilitating strategic or operational business-IT alignment. Our results indicate the most important practices for both strategic and operational alignment.


Data Mining for Student Outcome Prediction on Moodle: a systematic mapping


title: A survey of practical adversarial example attacks

abstract: Adversarial examples revealed the weakness of machine learning techniques in terms of robustness, which moreover inspired adversaries to make use of the weakness to attack systems employing machine learning. Existing researches covered the methodologies of adversarial example generation, the root reason of the existence of adversarial examples, and some defense schemes. However practical attack against real world systems did not appear until recent, mainly because of the difficulty in injecting a artificially generated example into the model behind the hosting system without breaking the integrity. Recent case study works against face recognition systems and road sign recognition systems finally abridged the gap between theoretical adversarial example generation methodologies and practical attack schemes against real systems. To guide future research in defending adversarial examples in the real world, we formalize the threat model for practical attacks with adversarial examples, and also analyze the restrictions and key procedures for launching real world adversarial example attacks.


title: Comparisons of waist circumferences measured at 4 sites1–3

abstract: Background: Waist circumference (WC) is now accepted as a practical measure of adipose tissue distribution. Four body sites for WC measurements are commonly used, as follows: immediately below the lowest ribs (WC1), the narrowest waist (WC2), the midpoint between the lowest rib and the iliac crest (WC3), and immediately above the iliac crest (WC4). Objective: We sought to compare the magnitude and reliability of WC measured at these 4 sites in males and females. Design: WC was measured at each site 1 time in all subjects [49 males and 62 females, aged 7-83 y, with a body mass index (in kg/ m 2 ) of 9-43] and 3 times in a subgroup (n = 93) by one experienced observer using a heavy-duty inelastic tape. Body fat was measured in a subgroup (n = 74) with the use of dual-energy X-ray absorptiometry. Results: The mean values of WC were WC2 0.99. WC values were significantly correlated with fatness; correlations with trunk fat were higher than correlations with total body fat in both sexes. Conclusions: WC values at the 4 commonly used anatomic sites differ in magnitude depending on sex, are highly reproducible, and are correlated with total body and trunk adiposity in a sex-dependent manner. These observations have implications for the use of WC measurements in clinical practice and patient-oriented research.


title: Big data, Big bang?

abstract: Using a random sample consisting of hundreds of companies worldwide, we are testing the impact on company performance of investing in big data projects targeted on three major business domains (namely, customer interface, company supply chain and competitors). The performance test relies on a so-called trans-logarithmic production function, allowing for a more direct test of the complementarity between big data capital and big data labour investments; further, we have used a Heckman correction to adjust for the fact that companies investing in big data are generally more productive than their peers.


Mobile Malware Evolution , Detection and Defense


title: Human activity recognition using inertial sensors with invariance to sensor orientation

abstract: This work deals with the task of human daily activity recognition using miniature inertial sensors. The proposed method reduces sensitivity to the position and orientation of the sensor on the body, which is inherent in traditional methods, by transforming the observed signals to a “virtual” sensor orientation. By means of this computationally low-cost transform, the inputs to the classification algorithm are made invariant to sensor orientation, despite the signals being recorded from arbitrary sensor placements. Classification results show that improved performance, in terms of both precision and recall, is achieved with the transformed signals, relative to classification using raw sensor signals, and the algorithm performs competitively compared to the state-of-the-art. Activity recognition using data from a sensor with completely unknown orientation is shown to perform very well over a long term recording in a real-life setting.


title: Learning Global Term Weights for Content-based Recommender Systems

abstract: Recommender systems typically leverage two types of signals to effectively recommend items to users: user activities and content matching between user and item profiles, and recommendation models in literature are usually categorized into collaborative filtering models, content-based models and hybrid models. In practice, when rich profiles about users and items are available, and user activities are sparse (cold-start), effective content matching signals become much more important in the relevance of the recommendation. The de-facto method to measure similarity between two pieces of text is computing the cosine similarity of the two bags of words, and each word is weighted by TF (term frequency within the document) x IDF (inverted document frequency of the word within the corpus). In general sense, TF can represent any local weighting scheme of the word within each document, and IDF can represent any global weighting scheme of the word across the corpus. In this paper, we focus on the latter, i.e., optimizing the global term weights, for a particular recommendation domain by leveraging supervised approaches. The intuition is that some frequent words (lower IDF, e.g. ``database'') can be essential and predictive for relevant recommendation, while some rare words (higher IDF, e.g. the name of a small company) could have less predictive power. Given plenty of observed activities between users and items as training data, we should be able to learn better domain-specific global term weights, which can further improve the relevance of recommendation. ::: ::: We propose a unified method that can simultaneously learn the weights of multiple content matching signals, as well as global term weights for specific recommendation tasks. Our method is efficient to handle large-scale training data generated by production recommender systems. And experiments on LinkedIn job recommendation data justify the effectiveness of our approach.


title: Fundamental dimensions of social judgment: Understanding the relations between judgments of competence and warmth

abstract: In seems there are two dimensions that underlie most judgments of traits, people, groups, and cultures. Although the definitions vary, the first makes reference to attributes such as competence, agency, and individualism, and the second to warmth, communality, and collectivism. But the relationship between the two dimensions seems unclear. In trait and person judgment, they are often positively related; in group and cultural stereotypes, they are often negatively related. The authors report 4 studies that examine the dynamic relationship between these two dimensions, experimentally manipulating the location of a target of judgment on one and examining the consequences for the other. In general, the authors' data suggest a negative dynamic relationship between the two, moderated by factors the impact of which they explore.


title: Complete genome sequence of bellflower vein chlorosis virus, a novel putative member of the genus Waikavirus

abstract: The complete genome sequence of a new virus isolated from a bellflower (Campanula takesimana) plant was determined. The genome of this virus is composed of monopartite single-stranded RNA of 11,649 nucleotides in length. BLAST searches of protein databases showed that the encoded polyprotein has a maximum amino acid sequence identity of 42% (with 99% coverage) to the polyprotein of the isolate Orissa of rice tungro spherical virus (RTSV; genus Waikavirus). Phylogenetic analysis strongly supports that the identified virus is a member of a new species of the genus Waikavirus. The name bellflower vein chlorosis virus (BVCV) is proposed for this new virus.


title: A conceptual modeling quality framework

abstract: The goal of any modeling activity is a complete and accurate understanding of the real-world domain, within the bounds of the problem at hand and keeping in mind the goals of the stakeholders involved. High-quality representations are critical to that understanding. This paper proposes a comprehensive Conceptual Modeling Quality Framework, bringing together two well-known quality frameworks: the framework of Lindland, Sindre, and Solvberg (LSS) and that of Wand and Weber based on Bunge's ontology (BWW). This framework builds upon the strengths of the LSS and BWW frameworks, bringing together and organizing the various quality cornerstones and then defining the many quality dimensions that connect one to another. It presents a unified view of conceptual modeling quality that can benefit both researchers and practitioners.


title: CHARLIE : An Adaptive Robot Design with Hand and Face Tracking for Use in Autism Therapy

abstract: Basic turn-taking and imitation skills are imperative for effective communication and social interaction (Nehaniv in Imitation and Social Learning in Robots, Springer, New York, 2007). Recently, research has demonstrated that interactive games using turn-taking and imitation have yielded positive results with autistic children who have impaired communication or social skills (Barakova and Brok in Proceedings of the 9th International Conference on Entertainment Computing, pp. 115–126, 2010). This paper describes a robot that plays interactive imitation games using hand and face tracking. The robot is equipped with a head and two arms, each with two degrees of freedom, and a camera. We trained a human hands detector and subsequently, used this detector along with a standard face tracker to create two autonomous interactive games: single-player (“Imitate Me, Imitate You”) and two-player (“Pass the Pose”.) Additionally, we implemented a third setting in which the robot is teleoperated by remote control. In “Imitate Me, Imitate You”, the robot has both passive and active game modes. In the passive mode, the robot waits for the child to initiate an interaction by raising one or both hands. In the second game mode, the robot initiates interactions. The “Pass the Pose” game engages two children in cooperative play by enlisting the robot as a mediator between two children alternately initiating and imitating poses. These games are designed to increase attention, promote turn-taking skills and encourage child-led verbal and non-verbal communication through simple imitative play. This research makes two specific contributions: (1) We present a low-cost robot design which measures and adapts to a child’s actions during interactive games and, (2) we train, test and make freely available, a new hand detector, based on Haar-like features, which is usable in various kinds of human-robot interactions. We present proof-of-concept experiments with a group of typically developing children.


title: Process scheduling under uncertainty: Review and challenges

abstract: Uncertainty is a very important concern in production scheduling since it can cause infeasibilities and production disturbances. Thus scheduling under uncertainty has received a lot of attention in the open literature in recent years from chemical engineering and operations research communities. The purpose of this paper is to review the main methodologies that have been developed to address the problem of uncertainty in production scheduling as well as to identify the main challenges in this area. The uncertainties in process scheduling are first analyzed, and the different mathematical approaches that exist to describe process uncertainties are classified. Based on the different descriptions for the uncertainties, alternative scheduling approaches and relevant optimization models are reviewed and discussed. Further research challenges in the field of process scheduling under uncertainty are identified and some new ideas are discussed.


How to Train good Word Embeddings for Biomedical NLP


title: LRADNN: High-throughput and energy-efficient Deep Neural Network accelerator using Low Rank Approximation

abstract: In this work, we propose an energy-efficient hardware accelerator for Deep Neural Network (DNN) using Low Rank Approximation (LRADNN). Using this scheme, inactive neurons in each layer of the DNN are dynamically identified and the corresponding computations are then bypassed. Accordingly, both the memory accesses and the arithmetic operations associated with these inactive neurons can be saved. Therefore, compared to the architectures using the direct feed-forward algorithm, LRADNN can achieve a higher throughput as well as a lower energy consumption with negligible prediction accuracy loss (within 0.1%). We implement and synthesize the proposed accelerator using TSMC 65nm technology. From the experimental results, a 31% to 53% energy reduction together with a 22% to 43% throughput increase can be achieved.


title: Matching book-spine images for library shelf-reading process automation

abstract: Machine vision has become an important visual inspection technology for many automation applications. Using machine vision for automation can reduce operating costs and increase efficiency and accuracy. This paper presents an image matching technique designed specifically for improving the library inventory (shelf-reading) process. In contrast with more complex color image matching techniques, the proposed method quantizes color images of book spines into a limited number of color indices and performs image matching on the quantized color index images. This approach simplifies and speeds up the processing and improves the overall inventory process. The potential performance of this robust color quantization and image matching technique is demonstrated by the results of preliminary experiments.


title: Automatic Mood-Transferring between Color Images

abstract: This paper presents a new color-conversion method that offers users an intuitive, one-click interface for style conversion. So, rather than having to supply a reference image, users simply select a color mood with a mouse click. To address the color-transfer quality problem, we associate each color mood with a set of up to 10 images from our image database. After selecting their color mood, users choose one associated image. Our histogram matching algorithm then uses the selected image to determine the input image's color distribution. We thereby achieve more accurate and justifiable color conversion results, while also preserving spatial coherence. Here, we further describe our solutions and their results and compare them to existing approaches.


title: DIGBE: Adaptive User Interface Automation

abstract: We have developed an automated reasoning system, called Dynamic Interaction Generation (DIG), which automatically designs and dynamically presents adaptive user interfaces. We have demonstrated DIG in the domain of building management in the form of the DIGBE system. In this report, we focus on the mechanisms we employ to specialize dynamically generated interactions for the situational needs and constraints present in a building operations environment. These constraints are introduced through the control system and domain objects, the users and user roles, the tasks and subtasks that are required, and the need for both anchored and mobile interaction. To adapt the user interface that DIGBE designs for each user and situation, the system uses a combination of mechanisms including role based task composition and object specialization. It responds dynamically by constructing and maintaining realtime models of the system of interest and the user-system interaction, and provides device independence through the separation of interaction and presentation reasoning and the ability for multiple presentation agents to use a single interaction design.


title: Compensation of Load Dynamics for Admittance Controlled Interactive Industrial Robots Using a Quaternion-Based Kalman Filter

abstract: The paper describes a control architecture for industrial robotic applications allowing human/robot interactions, using an admittance control scheme and direct sensing of the human inputs. The aim of the proposed scheme is to support the operator of an industrial robot, equipped with a force/torque (F/T) sensor on the end-effector, during human/robot collaboration tasks involving heavy payloads carried by the robot. In these practical applications, the dynamics of the load may significatively affect the measurements of the F/T sensor. Model-based compensation of such dynamic effects requires to compute linear acceleration and angular acceleration/velocity of the load, that in this paper are estimated by means of a quaternion-based Kalman filter and assuming that the only available measurements come from the forward kinematics of the robot. Experimental results demonstrate the feasibility of the approach and its industrial applicability.


title: Occupancy-driven energy management for smart building automation

abstract: Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.


title: A classification of location privacy attacks and approaches

abstract: In recent years, location-based services have become very popular, mainly driven by the availability of modern mobile devices with integrated position sensors. Prominent examples are points of interest finders or geo-social networks such as Facebook Places, Qype, and Loopt. However, providing such services with private user positions may raise serious privacy concerns if these positions are not protected adequately. Therefore, location privacy concepts become mandatory to ensure the user's acceptance of location-based services. Many different concepts and approaches for the protection of location privacy have been described in the literature. These approaches differ with respect to the protected information and their effectiveness against different attacks. The goal of this paper is to assess the applicability and effectiveness of location privacy approaches systematically. We first identify different protection goals, namely personal information (user identity), spatial information (user position), and temporal information (identity/position + time). Secondly, we give an overview of basic principles and existing approaches to protect these privacy goals. In a third step, we classify possible attacks. Finally, we analyze existing approaches with respect to their protection goals and their ability to resist the introduced attacks.