title: Implementation of IEEE single precision floating point addition and multiplication on FPGAs

abstract: Floating point operations are hard to implement on FPGAs because of the complexity of their algorithms. On the other hand, many scientific problems require floating point arithmetic with high levels of accuracy in their calculations. Therefore, we have explored FPGA implementations of addition and multiplication for IEEE single precision floating-point numbers. Customizations were performed where this was possible in order to save chip area, or get the most out of our prototype board. The implementations tradeoff area and speed for accuracy. The adder is a bit-parallel adder, and the multiplier is a digit-serial multiplier. Prototypes have been implemented on Altera FLEX8000s, and peak rates of 7 MFlops for 32-bit addition and 2.3 MFlops for 32-bit multiplication have been obtained.


title: Asparagine Synthetase Deficiency: New Inborn Errors of Metabolism

abstract: Background: Asparagine synthetase deficiency (ASD) is a newly identified neurometabolic disorder characterized by severe congenital microcephaly, severe global developmental delay, intractable seizure disorder, and spastic quadriplegia. Brain MRI showed brain atrophy, delayed myelination, and simplified gyriform pattern.


title: Pedestrian detection with a Large-Field-Of-View deep network

abstract: Pedestrian detection is of crucial importance to autonomous driving applications. Methods based on deep learning have shown significant improvements in accuracy, which makes them particularly suitable for applications, such as pedestrian detection, where reducing the miss rate is very important. Although they are accurate, their runtime has been at best in seconds per image, which makes them not practical for onboard applications. We present a Large-Field-Of-View (LFOV) deep network for pedestrian detection, that can achieve high accuracy and is designed to make deep networks work faster for detection problems. The idea of the proposed Large-Field-of-View deep network is to learn to make classification decisions simultaneously and accurately at multiple locations. The LFOV network processes larger image areas at much faster speeds than typical deep networks have been able to, and can intrinsically reuse computations. Our pedestrian detection solution, which is a combination of a LFOV network and a standard deep network, works at 280 ms per image on GPU and achieves 35.85 average miss rate on the Caltech Pedestrian Detection Benchmark.


title: A distributed, collective intelligence framework for collision-free navigation through busy intersections

abstract: Connected autonomous vehicles have the potential to cooperate at a higher level than human drivers, for instance to navigate busy intersections with relaxed traffic rules. This paper presents a novel system for autonomous traffic management through intersections based on distributed cooperative intelligence. In this framework each vehicle decides its own motion, rather than entrusting control of all vehicles to a central agent which then becomes a single point of failure. The motion plans are developed by reinforcement learning on simulations of intersection traffic. The developed system is shown to route vehicles to their destination in a safe and timely manner. In the near future, this system can be deployed to as a collision warning/collision avoidance aid to human drivers by selectively acting on the discrepancy between a human's behavior and the AI-determined safe behavior.


title: Generalized Stability Approach for Regularized Graphical Models

abstract: Selecting regularization parameters in penalized high-dimensional graphical models in a principled, data-driven, and computationally efficient manner continues to be one of the key challenges in high-dimensional statistics. We present substantial computational gains and conceptual generalizations of the Stability Approach to Regularization Selection (StARS), a state-of-the-art graphical model selection scheme. Using properties of the Poisson-Binomial distribution and convex non-asymptotic distributional modeling we propose lower and upper bounds on the StARS graph regularization path which results in greatly reduced computational cost without compromising regularization selection. We also generalize the StARS criterion from single edge to induced subgraph (graphlet) stability. We show that simultaneously requiring edge and graphlet stability leads to superior graph recovery performance independent of graph topology. These novel insights render Gaussian graphical model selection a routine task on standard multi-core computers.


title: Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases

abstract: Recent years have seen a surge of interest in Statistical Relational Learning (SRL) models that combine logic with probabilities. One prominent and highly expressive SRL model is Markov Logic Networks (MLNs), but the expressivity comes at the cost of learning complexity. Most of the current methods for learning MLN structure follow a two-step approach where first they search through the space of possible clauses (i.e. structures) and then learn weights via gradient descent for these clauses. We present a functional-gradient boosting algorithm to learn both the weights (in closed form) and the structure of the MLN simultaneously. Moreover most of the learning approaches for SRL apply the closed-world assumption, i.e., whatever is not observed is assumed to be false in the world. We attempt to open this assumption. We extend our algorithm for MLN structure learning to handle missing data by using an EM-based approach and show this algorithm can also be used to learn Relational Dependency Networks and relational policies. Our results in many domains demonstrate that our approach can effectively learn MLNs even in the presence of missing data.


title: Learn to Design the Heuristics for Vehicle Routing Problem

abstract: This paper presents an approach to learn the local-search heuristics that iteratively improves the solution of Vehicle Routing Problem (VRP). A local-search heuristics is composed of a destroy operator that destructs a candidate solution, and a following repair operator that rebuilds the destructed one into a new one. The proposed neural network, as trained through actor-critic framework, consists of an encoder in form of a modified version of Graph Attention Network where node embeddings and edge embeddings are integrated, and a GRU-based decoder rendering a pair of destroy and repair operators. Experiment results show that it outperforms both the traditional heuristics algorithms and the existing neural combinatorial optimization for VRP on medium-scale data set, and is able to tackle the large-scale data set (e.g., over 400 nodes) which is a considerable challenge in this area. Moreover, the need for expertise and handcrafted heuristics design is eliminated due to the fact that the proposed network learns to design the heuristics with a better performance. Our implementation is available online.


title: Teaching OO concepts-a case study using CRC-cards and BlueJ

abstract: The transition to object-oriented programming is more than just a matter of programming language. Traditional syllabi fail to teach students the "big picture" and students have difficulties taking advantage of object-oriented concepts. In this paper we present a holistic approach to a CSI course in Java favouring general object-oriented concepts over the syntactical details of the language. We present goals for designing such a course and a case study showing interesting results.


title: A Comprehensive Study of Implemented International Standards, Technical Challenges, Impacts and Prospects for Electric Vehicles

abstract: The impending environmental issues and growing concerns for global energy crises are driving the need for new opportunities and technologies that can meet significantly higher demand for cleaner and sustainable energy systems. This necessitates the development of transportation and power generation systems. The electrification of the transportation system is a promising approach to green the transportation systems and to reduce the issues of climate change. This paper inspects the present status, latest deployment, and challenging issues in the implementation of Electric vehicles (EVs) infrastructural and charging systems in conjunction with several international standards and charging codes. It further analyzes EVs impacts and prospects in society. A complete assessment of charging systems for EVs with battery charging techniques is explained. Moreover, the beneficial and harmful impacts of EVs are categorized and thoroughly reviewed. Remedial measures for harmful impacts are presented and benefits obtained therefrom are highlighted. Bidirectional charging offers the fundamental feature of vehicle to grid technology. In this paper, the current challenging issues due to the massive deployment of EVs, and upcoming research trends are also presented. It is envisioned that the researchers interested in such areas can find this paper valuable and an informative one-stop source.


title: Neural Kinematic Networks for Unsupervised Motion Retargetting

abstract: We propose a recurrent neural network architecture with a Forward Kinematics layer and cycle consistency based adversarial training objective for unsupervised motion retargetting. Our network captures the high-level properties of an input motion by the forward kinematics layer, and adapts them to a target character with different skeleton bone lengths (e.g., shorter, longer arms etc.). Collecting paired motion training sequences from different characters is expensive. Instead, our network utilizes cycle consistency to learn to solve the Inverse Kinematics problem in an unsupervised manner. Our method works online, i.e., it adapts the motion sequence on-the-fly as new frames are received. In our experiments, we use the Mixamo animation data to test our method for a variety of motions and characters and achieve state-of-the-art results. We also demonstrate motion retargetting from monocular human videos to 3D characters using an off-the-shelf 3D pose estimator.


EVALUATION OF EVENT-RELATED DESYNCHRONIZATION ( ERD ) PRECEDING AND FOLLOWING VOLUNTARY SELF-PACED MOVEMENT


Design Thinking Methods and Tools for Innovation in Multidisciplinary Teams


title: Segmental tibial shaft fractures.

abstract: A series of 54 cases with multiple fractures of the tibia is reported. Sixty-eight per cent of the group were pedestrians hit by a car. Fifty-two per cent were open fractures. Osteosynthesis was performed in 33 cases, whereas 21 were treated conservatively. Plate osteosynthesis was accomplished in 23 cases, seven had intramedullary nailing, and two were treated by Hoffman's external fixation device. Only one patient sustained a severe complication, viz., deep wound infection and osteomyelitis; however, even this infection was transient. All fractures healed except those sustained by two patients who died within 3 months; these deaths were, however, without any causal relationship to the osteosynthesis. One patient had a traumatic vascular lesion concomitant with the fracture, and his leg was amputated 3 days after the injury. Another patient had his leg amputated 1 year after his accident, in this case also because of injury to the vessels and nerves. It was concluded from the present series that segmental or multiple tibial shaft fractures do not entail more complications nor do they exhibit a slower rate of union than simple tibial shaft fractures if the treatssue injury.


title: Edge color transform: a new operator for natural scene text localization

abstract: Text extraction is one of the most intuitive and natural ways in understanding of natural scene images. Localization is the first and one of the most important steps in this problem. In this paper, we propose a novel method for localizing text in natural scene images. We introduce a new operator so called Edge Color Transform (ECT) to solve this problem. After extracting the edge map of the input image, this operator follows the gradient direction (and the opposite direction) to assign the nearest color to each of the edge pixels. In the next step, each channel (Red, Green and Blue) of these color pixels are normalized to make them more robust to intensity changes. In the following step, these colored pixels are grouped together using a modified version of region growing algorithm. In this customized version of the algorithm, pixels of a region do not have to be spatially connected and their connectivity is decided based on a predefined oval-shaped neighborhood. When the regions are forged, each of them form a text candidate region. Subsequently, all of these candidates are applied to a classifier to extract text regions. Experimental results show that the proposed method performs well on both Farsi and well-known ICDAR 2013 data sets.


title: Toward Analog Neural Computation

abstract: Computationalism about the brain is the view that the brain literally performs computations. For the view to be interesting, we need an account of computation. The most well-developed account of computation is Turing Machine computation, the account provided by theoretical computer science which provides the basis for contemporary digital computers. Some have thought that, given the seemingly-close analogy between the all-or-nothing nature of neural spikes in brains and the binary nature of digital logic, neural computation could be a species of digital computation. A few recent authors have offered arguments against this idea; here, I review recent findings in neuroscience that further cement the implausibility of this view. However, I argue that we can retain the view that the brain is a computer if we expand what we mean by “computation” to include analog computation. I articulate an account of analog computation as the manipulation of analog representations based on previous work on the difference between analog and non-analog representations, extending a view originally articulated in Shagrir (Stud Hist Philos Sci 41(3):271–279, 2010). Given that analog computation constitutes a significant chapter in the history of computation, this revision of computationalism to include analog computation is not an ad hoc addition. Brains may well be computers, but of the analog kind, rather than the digital kind.


title: Thermal model of spiked electrode in Transcutaneous Electrical Nerve Stimulation (TENS)

abstract: Transcutaneous Electrical Nerve Stimulation (TENS) attracts extensive attention in neuromodulation due to its minimal invasiveness. Conventional TENS uses non-invasive surface planar electrodes attached to the skin for delivering the stimulation current to modulate neural circuitries. Previous studies have shown that using these surface planar electrodes in TENS therapy may cause stimulation-induced skin irritation. In order to validate the cause and solve this problem, a bioheat transfer study in a realistic human spinal cord model was built based on finite element method (FEM) and a spiked electrode is proposed to avoid skin burning by taking into consideration both the electrical and thermal properties of the layered skin. Results show the peak temperature of using the spiked electrode for stimulation is around 37.1 °C, much lower than using the planar electrode (51.6 °C). Moreover, in contrast to the traditional surface electrodes, the peak necrosis fraction of using the spiked electrode is 0.02, indicating that the whole biological model is thermally safe and does not cause skin irritation.


title: Software Architecture Reconstruction Method, a Survey

abstract: Architecture reconstruction belongs to a reverse engineering process, in which we move from code to architecture level for reconstructing architecture. Software architectures are the blue prints of projects which depict the external overview of the software system. Mostly maintenance and testing cause the software to deviate from its original architecture, because sometimes for enhancing the functionality of a system the software deviates from its documented specifications, some new modules are included in the system without modifying the architecture of a system which create issues while reconstructing the system, as much as the software is closed to the architecture the more it is easy to maintain and change the document so the conformance of architecture with the product is checked by applying the reverse engineering method. Another reason for reconstructing the architecture is observed in the case of legacy systems, when they need modification or an enhanced version of the system is needed to be developed. This paper includes the methods and tools involved in reconstructing the architecture and by comparing them the best method for reconstructing architecture will be suggested.


title: Nanonewton force-controlled manipulation of biological cells using a monolithic MEMS microgripper with two-axis force feedback

abstract: As mechanical end-effectors, microgrippers enable the pick–transport–place of micrometer-sized objects, such as manipulation and positioning of biological cells in an aqueous environment. This paper reports on a monolithic MEMS-based microgripper with integrated force feedback along two axes and presents the first demonstration of force- controlled micro-grasping at the nanonewton force level. The system manipulates highly deformable biomaterials (porcine interstitial cells) in an aqueous environment using a microgripper that integrates a V-beam electrothermal microactuator and two capacitive force sensors, one for contact detection (force resolution: 38.5 nN) and the other for gripping force measurements (force resolution: 19.9 nN). The MEMS-based microgripper and the force control system experimentally demonstrate the capability of rapid contact detection and reliable force-controlled micrograsping to accommodate variations in size and mechanical properties of objects with a high reproducibility.


title: Parallax-Tolerant Image Stitching

abstract: Parallax handling is a challenging task for image stitching. This paper presents a local stitching method to handle parallax based on the observation that input images do not need to be perfectly aligned over the whole overlapping region for stitching. Instead, they only need to be aligned in a way that there exists a local region where they can be seamlessly blended together. We adopt a hybrid alignment model that combines homography and content-preserving warping to provide flexibility for handling parallax and avoiding objectionable local distortion. We then develop an efficient randomized algorithm to search for a homography, which, combined with content-preserving warping, allows for optimal stitching. We predict how well a homography enables plausible stitching by finding a plausible seam and using the seam cost as the quality metric. We develop a seam finding method that estimates a plausible seam from only roughly aligned images by considering both geometric alignment and image content. We then pre-align input images using the optimal homography and further use content-preserving warping to locally refine the alignment. We finally compose aligned images together using a standard seam-cutting algorithm and a multi-band blending algorithm. Our experiments show that our method can effectively stitch images with large parallax that are difficult for existing methods.


title: Exploring students’ learning behaviour in MOOCs using process mining techniques

abstract: Massive Open Online Courses (MOOCs) provide increasing opportunities for skills acquisition. Their widespread use can be justified by a number of critical motivating factors such as the possibility of free courses, the flexibility of the learning process as well as the reputation of some of the world most prestigious universities offering these courses. This level of popularity has created the need for a deep understanding of learning in MOOCs. This has been so far achieved through Learning Analytics (LA) using data mining techniques. Nevetheless, it is difficult to perform a sytematic analysis of learning processes based on students’ behaviour using these techniques alone. Therefore, we propose to apply process mining since it provides important techniques for understanding learning processes based on students’activities trails from MOOC platforms logs. In this paper, we analyze a Coursera MOOC dataset using several process mining techniques and provide some indications in terms of useful insights and guidance that could inspire intervention measures to improve both the quality and delivery of MOOCs. Keywords: Learning Analytics, Mooc, Coursera, Educational Data Mining, Process Mining,Online Learning


title: Leveraging social media networks for classification

abstract: Social media has reshaped the way in which people interact with each other. The rapid development of participatory web and social networking sites like YouTube, Twitter, and Facebook, also brings about many data mining opportunities and novel challenges. In particular, we focus on classification tasks with user interaction information in a social network. Networks in social media are heterogeneous, consisting of various relations. Since the relation-type information may not be available in social media, most existing approaches treat these inhomogeneous connections homogeneously, leading to an unsatisfactory classification performance. In order to handle the network heterogeneity, we propose the concept of social dimension to represent actors' latent affiliations, and develop a classification framework based on that. The proposed framework, SocioDim, first extracts social dimensions based on the network structure to accurately capture prominent interaction patterns between actors, then learns a discriminative classifier to select relevant social dimensions. SocioDim, by differentiating different types of network connections, outperforms existing representative methods of classification in social media, and offers a simple yet effective approach to integrating two types of seemingly orthogonal information: the network of actors and their attributes.


title: Mobile PHRs Compliance with Android and iOS Usability Guidelines

abstract: Mobile Personal Health Records (PHRs) have achieved a particularly strong market share since the appearance of more powerful mobile devices and popular worldwide mobile application markets such as Apple's App Store and Android's Google Play. However, Android and Apple have a set of recommendations on design and usability targeted towards developers who wish to publish apps in their stores: Android Design Guidelines and iOS Human Interface Guidelines. This paper aims to evaluate compliance with these guidelines by assessing the usability recommendations of a set of 24 selected mobile PHR applications. An analysis process based on a well-known Systematic Literature Review (SLR) protocol was used. The results show that the 24 mobile PHR applications studied are not suitably structured. 46 % of these applications do not use any of the recommended patterns, using instead lists or springboards, which are deprecated patterns for top-level menus. 70 % of the PHRs require a registration to be able to test the application when these interactions should be delayed. Our study will help both PHR users to select user-friendly mobile PHRs and PHR providers and developers to identify the good usability practices implemented by the applications with the highest scores.


title: CLOS: integrating object-oriented and functional programming

abstract: Lisp has a long history as a functional language, * where action is invoked by calling a procedure, and where procedural abstraction and encapsulation provide convenient modularity boundaries. A number of attempts have been made to graft object-oriented programming into this framework without losing the essential character of Lisp—to include the benefits of data abstraction, extensible type classification, incremental operator definition, and code reuse through an inheritance hierarchy. The Common Lisp Object System (CLOS) [3], a result of the ANSI standardization process for Common Lisp, represents a marriage of these two traditions. This article explores the landscape in which the major object-oriented facilities exist, showing how the CLOS solution is effective within the two contexts.


title: Percutaneous epiphysiodesis using transphyseal screws for limb-length discrepancies: high variability among growth predictor models

abstract: PurposePercutaneous epiphysiodesis using transphyseal screws (PETS) was developed as a minimally invasive outpatient procedure to address limb-length discrepancy (LLD) that allowed immediate postoperative weight bearing and was potentially reversible by removing the screws. The aims of our study were to report our results using PETS for LLD and evaluate the accuracy of three growth predictor models.MethodsSixteen patients with an average age of 14 years were treated for LLD using PETS. Thirteen patients had screws inserted in a parallel fashion and 3 had crossed screws. We compared the predicted LLD at skeletal maturity using the three growth predictor methods with the actual LLD at skeletal maturity and preoperative LLD with the final LLD at skeletal maturity.ResultsThe mean LLD at skeletal maturity between the predicted and final measurements was 0.2 cm using the Green−Anderson method, 1.4 cm using the Moseley method, and −0.1 cm using the Paley method. The mean preoperative LLD of 3.1 cm was corrected to 1.7 cm at skeletal maturity (p < 0.001). Six patients complained of pain over the screw heads; however, no patient developed an infection or angular deformity.ConclusionsThe three growth predictor methods predicted the final LLD within an average of 1.4 cm, but there was high variability. Although PETS improved the LLD by a mean of 1.4 cm, we believe the results would have been better if PETS was performed at an earlier skeletal age.


CHECK: a document plagiarism detection system


The Evolution of Customer Relationship Management System


title: Predictive Analytics for Supply Chains: a Systematic Literature Review

abstract: Predictive analytics is a group of methods that uses statistical and other empirical techniques to predict future events, based on past occurrences. Predictive analytics can generate valuable information for the management of a supply chain company to improve decision-making. Even though the importance of the topic is clear, there is no clear overview of the use of predictive analytics in the supply chain currently. This research provides a state-ofthe-art by performing a systematic literature review. In the literature review we have found the models, methods, techniques and applications of predictive analytics in the supply chain and have determined the trends and literature gaps. The most important nding is that even though there is only a limited amount of literature available, the interest in this topic is growing gradually. We also provide future research directions for further research on this subject.


Decolonisation Revisited and the Obligation not to Divide a Non-Self-Governing Territory


title: A hierarchical adaptive probabilistic approach for zero hour phish detection

abstract: Phishing attacks are a significant threat to users of the Internet, causing tremendous economic loss every year. In combating phish, industry relies heavily on manual verification to achieve a low false positive rate, which, however, tends to be slow in responding to the huge volume of unique phishing URLs created by toolkits. Our goal here is to combine the best aspects of human verified blacklists and heuristic-based methods, i.e., the low false positive rate of the former and the broad and fast coverage of the latter. To this end, we present the design and evaluation of a hierarchical blacklist-enhanced phish detection framework. The key insight behind our detection algorithm is to leverage existing human-verified blacklists and apply the shingling technique, a popular near-duplicate detection algorithm used by search engines, to detect phish in a probabilistic fashion with very high accuracy. To achieve an extremely low false positive rate, we use a filtering module in our layered system, harnessing the power of search engines via information retrieval techniques to correct false positives. Comprehensive experiments over a diverse spectrum of data sources show that our method achieves 0% false positive rate (FP) with a true positive rate (TP) of 67.15% using search-oriented filtering, and 0.03% FP and 73.53% TP without the filtering module. With incremental model building capability via a sliding window mechanism, our approach is able to adapt quickly to new phishing variants, and is thus more responsive to the evolving attacks.


title: Dynamic Fusion With Intra- and Inter-Modality Attention Flow for Visual Question Answering

abstract: Learning effective fusion of multi-modality features is at the heart of visual question answering. We propose a novel method of dynamically fuse multi-modal features with intra- and inter-modality information flow, which alternatively pass dynamic information between and across the visual and language modalities. It can robustly capture the high-level interactions between language and vision domains, thus significantly improves the performance of visual question answering. We also show that, the proposed dynamic intra modality attention flow conditioned on the other modality can dynamically modulate the intra-modality attention of the current modality, which is vital for multimodality feature fusion. Experimental evaluations on the VQA 2.0 dataset show that the proposed method achieves the state-of-the-art VQA performance. Extensive ablation studies are carried out for the comprehensive analysis of the proposed method.


title: A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

abstract: Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches provide stronger guarantees in this setting, but remain somewhat unsatisfactory as they train either non-stationary or stochastic policies and require a large number of iterations. In this paper, we propose a new iterative algorithm, which trains a stationary deterministic policy, that can be seen as a no regret algorithm in an online learning setting. We show that any such no regret algorithm, combined with additional reduction assumptions, must find a policy with good performance under the distribution of observations it induces in such sequential settings. We demonstrate that this new approach outperforms previous approaches on two challenging imitation learning problems and a benchmark sequence labeling problem.


title: Dependency-based long short term memory network for drug-drug interaction extraction

abstract: BackgroundDrug-drug interaction extraction (DDI) needs assistance from automated methods to address the explosively increasing biomedical texts. In recent years, deep neural network based models have been developed to address such needs and they have made significant progress in relation identification.MethodsWe propose a dependency-based deep neural network model for DDI extraction. By introducing the dependency-based technique to a bi-directional long short term memory network (Bi-LSTM), we build three channels, namely, Linear channel, DFS channel and BFS channel. All of these channels are constructed with three network layers, including embedding layer, LSTM layer and max pooling layer from bottom up. In the embedding layer, we extract two types of features, one is distance-based feature and another is dependency-based feature. In the LSTM layer, a Bi-LSTM is instituted in each channel to better capture relation information. Then max pooling is used to get optimal features from the entire encoding sequential data. At last, we concatenate the outputs of all channels and then link it to the softmax layer for relation identification.ResultsTo the best of our knowledge, our model achieves new state-of-the-art performance with the F-score of 72.0% on the DDIExtraction 2013 corpus. Moreover, our approach obtains much higher Recall value compared to the existing methods.ConclusionsThe dependency-based Bi-LSTM model can learn effective relation information with less feature engineering in the task of DDI extraction. Besides, the experimental results show that our model excels at balancing the Precision and Recall values.


title: An Unsupervised Model with Attention Autoencoders for Question Retrieval

abstract: Question retrieval is a crucial subtask for community question answering. Previous research focus on supervised models which depend heavily on training data and manual feature engineering. In this paper, we propose a novel unsupervised framework, namely reduced attentive matching network (RAMN), to compute semantic matching between two questions. Our RAMN integrates together the deep semantic representations, the shallow lexical mismatching information and the initial rank produced by an external search engine. For the first time, we propose attention autoencoders to generate semantic representations of questions. In addition, we employ lexical mismatching to capture surface matching between two questions, which is derived from the importance of each word in a question. We conduct experiments on the open CQA datasets of SemEval-2016 and SemEval-2017. The experimental results show that our unsupervised model obtains comparable performance with the state-of-the-art supervised methods in SemEval-2016 Task 3, and outperforms the best system in SemEval-2017 Task 3 by a wide margin.


title: Bipartite graph reinforcement model for web image annotation

abstract: Automatic image annotation is an effective way for managing and retrieving abundant images on the internet. In this paper, a bipartite graph reinforcement model (BGRM) is proposed for web image annotation. Given a web image, a set of candidate annotations is extracted from its surrounding text and other textual information in the hosting web page. As this set is often incomplete, it is extended to include more potentially relevant annotations by searching and mining a large-scale image database. All candidates are modeled as a bipartite graph. Then a reinforcement algorithm is performed on the bipartite graph to re-rank the candidates. Only those with the highest ranking scores are reserved as the final annotations. Experimental results on real web images demonstrate the effectiveness of the proposed model.


title: An experimental study of skin friction around piles in clay

abstract: Load tests on a model pile installed in Speswhite kaolin are described. The soil surrounding the pile was consolidated under a range of stress ratios K, and the pile was loaded under drained conditions in such a way that only shaft friction was generated. Pile installation techniques minimized soil disturbance so that the failure criterion for pile shaft friction could be investigated. The results, based on tests on nine normally consolidated and one overconsolidated sample, show that the angle of shaft friction is independent of the stress ratio in the soil before loading (0·7 <K <1·5), and is only justless than φ′triaxial for normally and overconsolidated kaolin. When loaded axially the lateral stresses on the pile shaft decrease with increasing shaft load for normally consolidated soil, and increase on loading in overconsolidated soil. Differences between adhesion factors α; back-calculated from the model tests and those normally encountered in practice highlight the effects of disturbance caused by mo...


title: Cognition and Motivation as Treatment Targets in Schizophrenia

abstract: In this issue of JAMA Psychiatry, Fervaha and colleagues 1 use the publicly available database derived from 1331 patients with cognitive data in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial to explore the relationship between cognitive impairment and behaviors referred to as “amotivational.” The authors report correlations of about r = 0.30 between cognitive test performance and a cluster of items from the Heinrichs-Carpenter Quality of Life Scale that measure sense of purpose, motivation, and curiosity, which they refer to as “motivation.” The authors conclude that cognitive impairment in schizophrenia “may be partly secondary to amotivation.” This apparently controversial and simple statement is neither. First, we have known for decades that the constellation of behaviors we identify as belonging to the constructs of cognitive performance and motivation are related to one another in patients with schizophrenia, just as they are in healthy people, nonhuman primates, and rodents. The relationship between cognitive impairment and negative symptoms was even reported using the same database in previous articles from the CATIE investigators. In this article, however, Fervaha and colleagues 1 confine their exploration to correlations between cognitive performance and the 3 items purported to measure sense of purpose, motivation, and curiosity. These items are assessed using questions that have cognitive elements to them, such as the following: Do you think about the future, set goals, and have plans? What do you anticipate your situation will be? What have you accomplished? Do you read the newspaper? Are there things you want to know more about? Given that scores on these items are driven in part by cognitive ability, a sizable correlation with cognitive performance is not surprising. Patients with worse cognitive performance are likely to be less interested in planning events, less able to accomplish tasks, and will have less interest in pursuing cognitively demanding activities. The correlations reported by Fervaha et al 1 are not evidence of amotivation causing poor cognitive test performance any more than they are evidence that poor cognitive performance causes low scores on items rating sense of purpose, motivation, and curiosity. What is particularly relevant about the relationship between these constructs is that each of them has a role in causing the other over time. We know now that cognitive impairment leads to functional disability prior to the onset of psychosis. 2 The self-defeat that is generated from


title: Ear Recognition: More Than a Survey

abstract: Automatic identity recognition from ear images represents an active field of research within the biometric community. The ability to capture ear images from a distance and in a covert manner makes the technology an appealing choice for surveillance and security applications as well as other application domains. Significant contributions have been made in the field over recent years, but open research problems still remain and hinder a wider (commercial) deployment of the technology. This paper presents an overview of the field of automatic ear recognition (from 2D images) and focuses specifically on the most recent, descriptor-based methods proposed in this area. Open challenges are discussed and potential research directions are outlined with the goal of providing the reader with a point of reference for issues worth examining in the future. In addition to a comprehensive review on ear recognition technology, the paper also introduces a new, fully unconstrained dataset of ear images gathered from the web and a toolbox implementing several state-of-the-art techniques for ear recognition. The dataset and toolbox are meant to address some of the open issues in the field and are made publicly available to the research community.


title: Techniques for Solving Sudoku Puzzles

abstract: Solving Sudoku puzzles is one of the most popular pastimes in the world. Puzzles range in difficulty from easy to very challenging; the hardest puzzles tend to have the most empty cells. The current paper explains and compares three algorithms for solving Sudoku puzzles. Backtracking, simulated annealing, and alternating projections are generic methods for attacking combinatorial optimization problems. Our results favor backtracking. It infallibly solves a Sudoku puzzle or deduces that a unique solution does not exist. However, backtracking does not scale well in high-dimensional combinatorial optimization. Hence, it is useful to expose students in the mathematical sciences to the other two solution techniques in a concrete setting. Simulated annealing shares a common structure with MCMC (Markov chain Monte Carlo) and enjoys wide applicability. The method of alternating projections solves the feasibility problem in convex programming. Converting a discrete optimization problem into a continuous optimization problem opens up the possibility of handling combinatorial problems of much higher dimensionality.


title: SemEval-2014 Task 10: Multilingual Semantic Textual Similarity

abstract: In Semantic Textual Similarity, systems rate the degree of semantic equivalence between two text snippets. This year, the participants were challenged with new data sets for English, as well as the introduction of Spanish, as a new language in which to assess semantic similarity. For the English subtask, we exposed the systems to a diversity of testing scenarios, by preparing additional OntoNotesWordNet sense mappings and news headlines, as well as introducing new genres, including image descriptions, DEFT discussion forums, DEFT newswire, and tweet-newswire headline mappings. For Spanish, since, to our knowledge, this is the first time that official evaluations are conducted, we used well-formed text, by featuring sentences extracted from encyclopedic content and newswire. The annotations for both tasks leveraged crowdsourcing. The Spanish subtask engaged 9 teams participating with 22 system runs, and the English subtask attracted 15 teams with 38 system runs.


title: CUSTOMER LIFETIME VALUE: MARKETING MODELS AND APPLICATIONS

abstract: Abstract Customer lifetime value has been a mainstay concept in direct response marketing for many years, and has been increasingly considered in the field of general marketing. However, the vast majority of literature on the topic (a) has been dedicated to extolling its use as a decision-making criterion; (b) has presented isolated numerical examples of its calculation/determination; and (c) has considered it as part of the general discussions of profitability and discussed its role in customer acquisition decisions and customer acquisition/retention trade-offs. There has been a dearth of general modeling of the topic. This paper presents a series of mathematical models for determination of customer lifetime value. The choice of the models is based on a systematic theoretical taxonomy and on assumptions grounded in customer behavior. In addition, selected managerial applications of these general models of customer lifetime value are offered.


title: Structured nursing communication on interdisciplinary acute care teams improves perceptions of safety, efficiency, understanding of care plan and teamwork as well as job satisfaction

abstract: Efficient, accurate, and timely communication is required for quality health care and is strongly linked to health care staff job satisfaction. Developing ways to improve communication is key to increasing quality of care, and interdisciplinary care teams allow for improved communication among health care professionals. This study examines the patient- and family-centered use of structured interdisciplinary bedside rounds (SIBR) on an acute care for the elderly (ACE) unit in a 555-bed metropolitan community hospital. This mixed methods study surveyed 24 nurses, therapists, patient care assistants, and social workers to measure perceptions of teamwork, communication, understanding of the plan for the day, safety, efficiency, and job satisfaction. A similar survey was administered to a control group of 38 of the same staff categories on different units in the same hospital. The control group units utilized traditional physician-centric rounding. Significant differences were found in each category between the SIBR staff on the ACE unit and the control staff. Nurse job satisfaction is an important marker of retention and recruitment, and improved communication may be an important aspect of increasing this satisfaction. Furthermore, improved communication is key to maintaining a safe hospital environment with quality patient care. Interdisciplinary team rounds that take place at the bedside improve both nursing satisfaction and related communication markers of quality and safety, and may help to achieve higher nurse retention and safer patient care. These results point to the interconnectedness and dual benefit to both job satisfaction and patient quality of care that can come from enhancements to team communication.


title: Autonomia: an autonomic computing environment

abstract: The proliferation of Internet technologies, services and devices, have made the current networked system designs, and management tools incapable of designing reliable, secure networked systems and services. In fact, we have reached a level of complexity, heterogeneity, and a rapid change rate that our information infrastructure is becoming unmanageable and insecure. This had led researchers to consider alternative designs and management techniques that are based on strategies used by biological systems to deal with complexity, heterogeneity and uncertainty. The approach is referred to as autonomic computing. An autonomic computing system is the system that has the capabilities of being self-defining, self-healing, self-configuring, self-optimizing, etc. We present our approach to implement an autonomic computing infrastructure, Autonomia that provides dynamically programmable control and management services to support the development and deployment of smart (intelligent) applications. The Autonomia environment provides the application developers with all the tools required to specify the appropriate control and management schemes to maintain any quality of service requirement or application attribute/functionality (e.g., performance, fault, security, etc.) and the core autonomic middleware services to maintain the autonomic requirements of a wide range of network applications and services. We have successfully implemented a proof-of-concept prototype system that can support the self-configuring, self-deploying and self-healing of any networked application.


title: Challenges in Representation Learning: A report on three machine learning contests

abstract: The ICML 2013 Workshop on Challenges in Representation Learning11http://deeplearning.net/icml2013-workshop-competition. focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provide suggestions for organizers of future challenges and some comments on what kind of knowledge can be gained from machine learning competitions.


title: Enhancing Binary Classification by Modeling Uncertain Boundary in Three-Way Decisions

abstract: Text classification is a process of classifying documents into predefined categories through different classifiers learned from labelled or unlabelled training samples. Many researchers who work on binary text classification attempt to find a more effective way to separate relevant texts from a large data set. However, current text classifiers cannot unambiguously describe the decision boundary between positive and negative objects because of uncertainties caused by text feature selection and the knowledge learning process. This paper proposes a three-way decision model for dealing with the uncertain boundary to improve the binary text classification performance based on the rough set techniques and centroid solution. It aims to understand the uncertain boundary through partitioning the training samples into three regions (the positive, boundary, and negative regions) by two main boundary vectors $\vec{C_{P}}$ and $\vec{C_{N}}$ , created from the labeled positive and negative training subsets, respectively, and further resolve the objects in the boundary region by two derived boundary vectors $\vec{B_{P}}$ and $\vec{B_{N}}$ , produced according to the structure of the boundary region. It involves an indirect strategy which is composed of two successive steps in the whole classification process: ‘two-way to three-way’ and ‘three-way to two-way’. Four decision rules are proposed from the training process and applied to the incoming documents for more precise classification. A large number of experiments have been conducted based on the standard data sets RCV1 and Reuters-21578. The experimental results show that the usage of boundary vectors is very effective and efficient for dealing with uncertainties of the decision boundary, and the proposed model has significantly improved the performance of binary text classification in terms of $F_{1}$ measure and $AUC$ area compared with six other popular baseline models.


title: Knowledge discovery and data mining in toxicology

abstract: Knowledge discovery and data mining tools are gaining increasing importance for the analysis of toxicological databases. This paper gives a survey of algorithms, capable to derive interpretable models from toxicological data, and presents the most important application areas.The majority of techniques in this area were derived from symbolic machine learning, one commercial product was developed especially for toxicological applications. The main application area is presently the detection of structure-activity relationships, very few authors have used these techniques to solve problems in epidemiological and clinical toxicology.Although the discussed algorithms are very flexible and powerful, further research is required to adopt the algorithms to the specific learning problems in this area, to develop improved representations of chemical and biological data and to enhance the interpretability of the derived models for toxicological experts.


title: Unlocking the clubhouse: the Carnegie Mellon experience

abstract: In the fall of 1995, just seven of 95 students entering the undergraduate program in computer science at Carnegie Mellon University were women. In 2000, 54 of 130, or 42%, were women. What happened? This article presents a brief history of the transformation at Carnegie Mellon's School of Computer Science, and the research project that lay behind it. A fuller discussion, set in an analysis of gender issues in computing from childhood through college, is found in our book, Unlocking the Clubhouse: Women in Computing [2].The story begins with a research study designed specifically to diagnose and find remedies for the gender gap in Carnegie Mellon's undergraduate computer science program. Female enrollment had hovered below 10% for a number of years, and the fraction of women leaving the program was approximately twice that for men. In 1995, the Alfred P. Sloan Foundation funded our proposal for a two-year program, which was followed up two years later with a two-year extension. The goal was to understand the experiences and choices of both men and women with respect to studying computer science, and to design interventions that would involve more women.


title: Diagnosis of open-circuit switch faults in multilevel active-NPC (ANPC) inverters

abstract: Multilevel converters have been widely used in traction drive systems due to their attractive output performance. However, one major disadvantage of multilevel converters is the large number of switching devices employed, which could increase the failure probability of drive systems. Thus, an effective diagnosis method for switch faults in multilevel converters is of high importance for the fault-tolerant operation of such drive systems. This paper introduces an on-line diagnosis method for open-circuit switch faults that could happen in one of the most promising multilevel converters: Active Neutral-Point-Clamped (ANPC) converter. This diagnostic method is implemented based on the comprehensive information of pole voltages, phase currents, and switching states of ANPC inverters. Compared with the existing diagnostic methods in the literature, the presented method in this paper has lower cost and shorter detection time (less than one fundamental cycle). Simulation results verified the effectiveness and robustness of the introduced fault diagnosis method.


title: Reliable scale estimation and correction for monocular Visual Odometry

abstract: Recovering absolute scale (i.e. metric information) from monocular vision system is a very challenging problem yet is highly desirable for vision-based autonomous driving. This paper proposes a new method for scale recovery, based on the idea of knowing camera height (relative to ground-plane). While this idea of using known camera height is not new in this context, existing implementations of this idea suffer significantly from severe numerical instability arisen in the ground plane homography decomposition stage. Our novel contribution of this work is to alleviate this issue by a divide and conquer approach, i.e. decomposing the motion parameters in the homography from the structure parameters of the ground plane. We also describe a robust procedure to correct scale drift in the monocular visual odometry system. Experimental results on KITTI standard benchmark dataset [1] and our self-collected driving dataset both show significant improvements.


title: Teaching Cybersecurity Analysis Skills in the Cloud

abstract: This paper reports on the experience of using the EDURange framework, a cloud-based resource for hosting on-demand interactive cybersecurity scenarios. Our framework is designed especially for the needs of teaching faculty. The scenarios we have implemented each are designed specifically to nurture the development of analysis skills in students as a complement to both theoretical security concepts and specific software tools. Our infrastructure has two features that make it unique compared to other cybersecurity educational frameworks. First, EDURange is scalable because it is hosted on a commercial, large-scale cloud environment. Second, EDURange supplies instructors with the ability to dynamically change the parameters and characteristics of exercises so they can be replayed and adapted to multiple classes. Our framework has been used successfully in classes and workshops for students and faculty. We present our experiences building the system, testing it, and using feedback from surveys to improve the system and boost user interest.


title: TIMELESS Forms a Complex with PARP1 Distinct from Its Complex with TIPIN and Plays a Role in the DNA Damage Response

abstract: TIMELESS interacts with TIPIN to controltheintra-Sphasecheckpoint.Youngetal.reveal a robust interaction of TIMELESSwith PARP1, which is distinct from itsinteraction with TIPIN. TIMELESS isrecruited to laser-induced DNA damagesites in a PARP1-dependent but PARP1-activity-independent manner andparticipates in the DNA damageresponse.


title: Broadband Dual-Polarized Omnidirectional Antenna for 2G/3G/LTE/WiFi Applications

abstract: A broadband dual-polarized omnidirectional antenna is presented in this letter for the 2G/3G/LTE/WiFi communication systems. It consists of one vertically polarized (VP) element and one horizontally polarized (HP) element. The VP element is composed of a ground plane and a center-fed discone coupled by a circular patch with four shorting legs, while the HP element is composed of a printed dipole array, a feeding network, and four pairs of parasitical branches. Through such deliberate design, a broad bandwidth from 820 MHz to more than 6 GHz (about more than 152%) for VP and from 1530 to 2950 MHz (about 63%) for HP can be achieved while keeping the antenna compact. Good port isolation, omnidirectional patterns in horizontal plane, along with acceptable cross polarization and gain are also obtained. Details of the antenna design as well as experimental results are presented and discussed.


title: ZVS Resonant Converter With Series-Connected Transformers

abstract: This paper presents a new series-resonant converter with series-connected transformers in order to realize zero voltage switching (ZVS) for power switches, zero current switching (ZCS) for rectifier diodes at full load, less transformer secondary winding with a full-wave rectifier, and load current sharing. Two series-resonant converters using the same switches are operated with interleaved half switching cycle. The secondary windings of transformers are connected in series in order to ensure that the primary side currents are balanced to share load current. Thus, the sizes of the transformer core and bobbin are reduced. The full-wave diode rectifier is adopted on the output side such that the voltage stress of the rectifier diodes is equal to the output voltage, rather than being two times the output voltage as in a conventional center-tapped rectifier topology. Based on the resonant behavior, all switches are turned on at ZVS, and rectifier diodes are turned off at ZCS if the switching frequency is less than the series resonant frequency. The proposed converter can be applied for the power supply unit of all-in-one PCs, liquid crystal display (LCD) TVs and servers, and dc-dc converters for fuel-cell power system. Laboratory experiments with a 1000-W prototype, verifying the effectiveness of the proposed converter, are described.


title: Real-time capable methods to determine the magnet temperature of permanent magnet synchronous motors — A review

abstract: The permanent magnet synchronous motor (PMSM) is widely used in highly utilised automotive traction drives and other industrial applications. With regards to the device life-time, safe operation and control performance, the magnet temperature is of great interest. Since a direct magnet temperature measurement is not feasible in most cases, this contribution gives a review on state-of-the-art model-based magnet temperature determination methods in PMSM. In this context, the existing publications can be classified into thermal models, flux observers and voltage signal injection approaches. Firstly, brief introductions of these methods are given, followed by a direct comparison regarding drawbacks and benefits. Finally, this contribution concludes with an outlook of potential further investigations in this research field.


title: Practical considerations in medical cannabis administration and dosing

abstract: Abstract Cannabis has been employed medicinally throughout history, but its recent legal prohibition, biochemical complexity and variability, quality control issues, previous dearth of appropriately powered randomised controlled trials, and lack of pertinent education have conspired to leave clinicians in the dark as to how to advise patients pursuing such treatment. With the advent of pharmaceutical cannabis-based medicines (Sativex/nabiximols and Epidiolex), and liberalisation of access in certain nations, this ignorance of cannabis pharmacology and therapeutics has become untenable. In this article, the authors endeavour to present concise data on cannabis pharmacology related to tetrahydrocannabinol (THC), cannabidiol (CBD) et al., methods of administration (smoking, vaporisation, oral), and dosing recommendations. Adverse events of cannabis medicine pertain primarily to THC, whose total daily dose-equivalent should generally be limited to 30 mg/day or less, preferably in conjunction with CBD, to avoid psychoactive sequelae and development of tolerance. CBD, in contrast to THC, is less potent, and may require much higher doses for its adjunctive benefits on pain, inflammation, and attenuation of THC-associated anxiety and tachycardia. Dose initiation should commence at modest levels, and titration of any cannabis preparation should be undertaken slowly over a period of as much as two weeks. Suggestions are offered on cannabis-drug interactions, patient monitoring, and standards of care, while special cases for cannabis therapeutics are addressed: epilepsy, cancer palliation and primary treatment, chronic pain, use in the elderly, Parkinson disease, paediatrics, with concomitant opioids, and in relation to driving and hazardous activities.


title: Three Birds One Stone: A General Architecture for Salient Object Segmentation, Edge Detection and Skeleton Extraction

abstract: In this paper, we aim at solving pixel-wise binary problems, including salient object segmentation, skeleton extraction, and edge detection, by introducing a unified architecture. Previous works have proposed tailored methods for solving each of the three tasks independently. Here, we show that these tasks share some similarities that can be exploited for developing a unified framework. In particular, we introduce a horizontal cascade, each component of which is densely connected to the outputs of previous component. Stringing these components together allows us to effectively exploit features across different levels hierarchically to effectively address the multiple pixel-wise binary regression tasks. To assess the performance of our proposed network on these tasks, we carry out exhaustive evaluations on multiple representative datasets. Although these tasks are inherently very different, we show that our unified approach performs very well on all of them and works far better than current single-purpose state-of-the-art methods. All the code in this paper will be publicly available.


title: Internet routing instability

abstract: This paper examines the network inter-domain routing information exchanged between backbone service providers at the major U.S. public Internet exchange points. Internet routing instability, or the rapid fluctuation of network reachability information, is an important problem currently facing the Internet engineering community. High levels of network instability can lead to packet loss, increased network latency and time to convergence. At the extreme, high levels of routing instability have lead to the loss of internal connectivity in wide-area, national networks. In this paper, we describe several unexpected trends in routing instability, and examine a number of anomalies and pathologies observed in the exchange of inter-domain routing information. The analysis in this paper is based on data collected from BGP routing messages generated by border routers at five of the Internet core's public exchange points during a nine month period. We show that the volume of these routing updates is several orders of magnitude more than expected and that the majority of this routing information is redundant, or pathological. Furthermore, our analysis reveals several unexpected trends and ill-behaved systematic properties in Internet routing. We finally posit a number of explanations for these anomalies and evaluate their potential impact on the Internet infrastructure.


title: State of charge (SoC) estimation on LiFePO4 battery module using Coulomb counting methods with modified Peukert

abstract: Batteries have an important role in the development of electrical energy utilization, such as in renewable energy and electric vehicles. Batteries with good performance would support the devices which utilized them. Because the amount of energy stored in a battery is limited, so battery getting charging and discharging cycles. Improper charge and discharge process could decrease the battery's performance. Therefore battery management system (BMS) is very necessary so that could maintain performance of the battery at optimum condition. One of the important aspect in BMS is state of charge (SoC), which indicate the amount of energy left in the battery. In this work, SOC estimation calculated using Coulomb Counting method which is calculate and compare the electric charge that came in and came out the battery. Upon Coulomb counting on discharging and charging process, battery's SoC estimated by different ways, namely calculate the Peukert's effect and calculate charging efficiency, respectively. Modified Peukert relationship includes current rate parameters are affecting discharge capacity of a battery. Higher Peukert constant would lead to decreasing of operational battery module voltages. Moreover, it could be studied that Peukert only has an effect in the changes of discharge capacity at zero point of SoC. Furthermore, from experimental result it has been known that energy stored in the battery might be has different deep of discharge depends on Peukert constant.


title: Predicting Daily Activities From Egocentric Images Using Deep Learning

abstract: We present a method to analyze images taken from a passive egocentric wearable camera along with the contextual information, such as time and day of week, to learn and predict everyday activities of an individual. We collected a dataset of 40,103 egocentric images over a 6 month period with 19 activity classes and demonstrate the benefit of state-of-the-art deep learning techniques for learning and predicting daily activities. Classification is conducted using a Convolutional Neural Network (CNN) with a classification method we introduce called a late fusion ensemble. This late fusion ensemble incorporates relevant contextual information and increases our classification accuracy. Our technique achieves an overall accuracy of 83.07% in predicting a person's activity across the 19 activity classes. We also demonstrate some promising results from two additional users by fine-tuning the classifier with one day of training data.


title: The functional brain architecture of human morality

abstract: Human morality provides the foundation for many of the pillars of society, informing political legislation and guiding legal decisions while also governing everyday social interactions. In the past decade, researchers in the field of cognitive neuroscience have made tremendous progress in the effort to understand the neural basis of human morality. The emerging insights from this research point toward a model in which automatic processing in parallel neural circuits, many of which are associated with social emotions, evaluate the actions and intentions of others. Through various mechanisms of competition, only a subset of these circuits ultimately causes a decision or an action. This activity is experienced consciously as a subjective moral sense of right or wrong, and an interpretive process offers post hoc explanations designed to link the social stimulus with the subjective moral response using whatever explicit information is available.


title: Working memory, psychiatric symptoms, and academic performance at school

abstract: Abstract Previous studies of the relationship among working memory function, academic performance, and behavior in children have focused mainly on clinical populations. In the present study, the associations of the performance in audio- and visuospatial working memory tasks to teacher reported academic achievement and psychiatric symptoms were evaluated in a sample of fifty-five 6–13-year-old school children. Working memory function was measured by visual and auditory n -back tasks. Information on incorrect responses, reaction times, and multiple and missed responses were collected during the tasks. The children’s academic performance and behavioral and emotional status were evaluated by the Teacher Report Form. The results showed that good spatial working memory performance was associated with academic success at school. Children with low working memory performance, especially audiospatial memory, were reported to have more academic and attentional/behavioral difficulties at school than children with good working memory performance. An increased number of multiple and missed responses in the auditory and visual tasks was associated with teacher reported attentional/behavioral problems and in visual tasks with teacher reported anxiety/depressive symptoms. The results suggest that working memory deficits may underlie some learning difficulties and behavioral problems related to impulsivity, difficulties in concentration, and hyperactivity. On the other hand, it is possible that anxiety/depressive symptoms affect working memory function, as well as the ability to concentrate, leading to a lower level of academic performance at school.


Privacy-preserving Multi-hop Locks for Blockchain Scalability and Interoperability ∗


title: Strain sensor-embedded soft pneumatic actuators for extension and bending feedback

abstract: For soft robots to leave the lab and enter unstructured environments, proprioception is required to understand how interactions in the field affect the soft structure. In this work, we present sensor-embedded soft pneumatic actuators (sSPA) that can observe both extension and bending. The sensors are strain sensitive capacitors, which are bonded to the interior of fiber-reinforced extension actuators on opposing faces. This construction allows extension and bending to be measured by calculating the mean and difference in sensor responses, respectively. The sSPAs are bonded together to form a flat fascicle to increase the force output and prevent buckling under load, and are robust to component failure by incorporating redundancy. In this paper, we discuss the fabrication of the sensors and their subsequent integration into the actuators. We also report the work capacity and sensor response of the sSPA fascicles under extension, bending, and the combination of both modes of deformation. The sensor-embedded soft pneumatic actuators presented here will advance the field of soft robotics by enabling closed-loop control of soft robots.


title: Dynamic Objects Segmentation for Visual Localization in Urban Environments

abstract: Visual localization and mapping is a crucial capability to address many challenges in mobile robotics. It constitutes a robust, accurate and cost-effective approach for local and global pose estimation within prior maps. Yet, in highly dynamic environments, like crowded city streets, problems arise as major parts of the image can be covered by dynamic objects. Consequently, visual odometry pipelines often diverge and the localization systems malfunction as detected features are not consistent with the precomputed 3D model. In this work, we present an approach to automatically detect dynamic object instances to improve the robustness of vision-based localization and mapping in crowded environments. By training a convolutional neural network model with a combination of synthetic and real-world data, dynamic object instance masks are learned in a semi-supervised way. The real-world data can be collected with a standard camera and requires minimal further post-processing. Our experiments show that a wide range of dynamic objects can be reliably detected using the presented method. Promising performance is demonstrated on our own and also publicly available datasets, which also shows the generalization capabilities of this approach.


title: Neural variational entity set expansion for automatically populated knowledge graphs

abstract: We propose Neural variational set expansion to extract actionable information from a noisy knowledge graph (KG) and propose a general approach for increasing the interpretability of recommendation systems. We demonstrate the usefulness of applying a variational autoencoder to the Entity set expansion task based on a realistic automatically generated KG.


title: The PROBADO Project -- Approach and Lessons Learned in Building a Digital Library System for Heterogeneous Non-textual Documents

abstract: The PROBADO project is a research effort to develop and operate advanced Digital Library support for non-textual documents. The main goal is to contribute to all parts of the Digital Library work flow from content acquisition over indexing to search and presentation. While not limited in terms of supported document types, reference support is developed for classical digital music and 3D architectural models. In this paper, we review the overall goals, approaches taken, and lessons learned so far in a highly integrated effort of university researchers and library experts. We address the problem of technology transfer, aspects of repository compilation, and the problem of inter-domain retrieval. The experiences are relevant for other project efforts in the nontextual Digital Library domain.


title: Tunable Bandpass Filter Design Based on External Quality Factor Tuning and Multiple Mode Resonators for Wideband Applications

abstract: In this paper, a tunable bandpass filter using cross-shaped multiple mode resonators (MMRs) and N:1 transformer based external quality factor tuning structures is proposed. The use of a cross-shaped MMR simplifies inter-resonators control while two Qe tuning structures are investigated and incorporated with the MMR to implement simultaneous center frequency agility and narrow and wide bandwidth tuning. Compared with traditional tunable filters, the proposed architecture requires less tuning elements and is easier to realize wideband and high-order tunable filters. Two examples (Filter I and II) are presented to validate the design. Both filters use a single MMR and six tuning elements to achieve a third-order wideband tunable filter. Filter I reports 58% center frequency tuning with constant bandwidth and 14%-64.4% fractional bandwidth (FBW) tuning when center frequency locates at 1 GHz. Filter II achieves larger frequency agility and wider FBW tuning of 82.9% and 95%, respectively, for the same bandwidth and center frequency of Filter I.


title: Optimization of warehousing and transportation costs, in a multi-product multi-level supply chain system, under a stochastic demand

abstract: The centralized management approach provides a general view to set a better coordination between the ele- ments of the supply chain, and look for the equilibrium between the stock and the shipped quantities. This paper describes a stochastic model predictive control algorithm to optimize the warehousing cost and the shipping cost on the same time. We consider a multi-level and multi-product supply chain dealing with a stochastic demand. We solve the stochastic model predictive control problem by a dynamic programming to determine the optimal policies to minimize the costs of shipping and warehousing products.


title: Visual analytics methods for movement data

abstract: A reaction between pyromellitic dianhydride (PMDA) and less than two moles of fluoroalcohol produces an intermediate with ester, free acid and anhydride groups. Reaction with an oxirane compound such as epichlorohydrin reacts at the acid sites to produce pendant hydroxyls which react with anhydride on adjacent molecules to produce oligomers. Products with fluoroalcohol:PMDA ratios of, for example, about 1:0.6 to about 1:0.85 produce treated fibers with improved retention of oil repellancy, especially when annealed at 100 DEG -130 DEG C.


title: Biologically inspired robots using Smart Composite Microstructures

abstract: Various biologically inspired small scale robots are built with endoskeleton structures. Generally, endoskeletons consist of rotational joints and rigid links. For millimeter scale robots, endoskeletons should be scalable and easy to manufacture. Layers of composite materials and polymer film can be fabricated into small scale joints and link structures as seen in fig. 1. The structures are called SCM (Smart Composite Microstructures) [1]. These structures have many advantages in small scale robot structures in aspect of friction and manufacturing simplicity. We expand the structure with SMA spring actuator for actuation of small components. In this video, we show three robot prototypes using SCM. The first robot is a fully foldable robot using torsional spring type SMA actuator. As seen in fig. 2, three segments of plate structures can be folded into a compact cube shape. The Second robot is Omegabot: Inchworm inspired robot [3]. It crawls with two anchors gripping the ground alternatively by making its body into an omega shape. This simple locomotion is a suitable moving mechanism for small scale robots. The Omegabot produces motions of crawling, climbing, and steering with tethered power and control. The third robot is a flea inspired robot. The flea can jump more than 130 times of its body length with its body length just below 1mm. The flea uses a torque reversal mechanism which enables storing large amount of energy and releasing it with a small amount of force. This mechanism is simple and efficient, which makes it a good mechanism for small jumping robots. The current prototype of the flea inspired robot jumps 64cm high, which is about 30 times of the robot body length. The SCM enabled these novel small scale robots, and has a potential to enable various small scale robots.


title: Design of Variable-Speed Dish-Stirling Solar–Thermal Power Plant for Maximum Energy Harness

abstract: Analysis on a developed dynamic model of the dish-Stirling (DS) system shows that maximum solar energy harness can be realized through controlling the Stirling engine speed. Toward this end, a control scheme is proposed for the doubly fed induction generator coupled to the DS system, as a means to achieve maximum power point tracking as the solar insolation level varies. Furthermore, the adopted fuzzy supervisory control technique is shown to be effective in controlling the temperature of the receiver in the DS system as the speed changes. Simulation results and experimental measurements validate the maximum energy harness ability of the proposed variable-speed DS solar-thermal system.


title: A complex activity based emotion recognition algorithm for affect aware systems

abstract: The usefulness of affect aware systems can be summarized as optimizing the system's services to improve the user experience. A means to achieve this objective is to make the system aware of the real-world situations and enable intelligent emotion analysis of its users. The ability of smart systems to automatically recognize human affective behavior through their movements and to respond are the key challenges in this area of study. This paper thus proposes an algorithm that can effectively categorize the underlining emotion of an activity as positive or negative and then classify it amongst the six basic emotions — sadness, fear, happiness, anger, disgust and surprise [1]. To portray the efficacy of this proposed model, typical scenes in the context of a smart home have been analyzed and the results have been presented. The results presented show that the proposed model would help developers to understand user experiences in the form of user's emotions in any scenario, which would help them to improve or modify the existing technologies to enhance computerhuman interaction.


title: W-Band Large-Scale High-Gain Planar Integrated Antenna Array

abstract: This communication presents a 32 $\,\times\,$ 32 high-gain patch array antenna fed by the substrate integrated waveguide (SIW) structure at W-band. The array antenna consists of two layers to achieve a compact topology, which allows for mass production using a standard PCB fabrication process. The wideband feeding network is placed in the bottom layer while the radiating patches are on the top layer. This configuration also resolves the trade-off between gain and bandwidth of conventional SIW array antennas. Measured gain of the 32 $\,\times\,$ 32 antenna array is within the range 28.81–29.97 dBi in the working bandwidth of 91–97 GHz. Measured impedance bandwidth covers the same frequency band for $\vert {\rm S} _{11} \vert . The cross-polarization of the antenna array is less than 40 dB at the beam direction. Good agreement between the simulated and measured results validates our design.


Emotion Level Sentiment Analysis: The Affective Opinion Evaluation


title: CONVERSATIONS-- AND NEGOTIATED INTERACTION-- IN TEXT AND VOICE CHAT ROOMS

abstract: Despite the expanded use of the Internet for language learning and practice, little attention if any has been given to the quality of interaction among English L2 speakers in conversational text or voice chat rooms. This study explored the patterns of repair moves in synchronous non-native speaker (NNS) text chat rooms in comparison to voice chat rooms on the Internet. The following questions were posed: (a) Which types of repair moves occur in text and voice chats; and (b) what are the differences, if any, between the repair moves in text chats and voice chats when time is held constant? Repair moves made by anonymous NNSs in 10, 5-minute, synchronous chat room sessions (5 text-chat sessions, 5 voice-chat sessions) were counted and analyzed using chi-square with alpha set at .05. Significant differences were found between the higher number of total repair moves made in voice chats and the smaller number in text chats. Qualitative data analysis showed that repair work in voice chats was often pronunciation-related. The study includes discussion that may affect teachers' and learners' considerations of the value of NNS chat room interaction for second language development.


title: Automatic Playtesting for Game Parameter Tuning via Active Learning

abstract: Game designers use human playtesting to gather feedback about game design elements when iteratively improving a game. Playtesting, however, is expensive: human testers must be recruited, playtest results must be aggregated and interpreted, and changes to game designs must be extrapolated from these results. Can automated methods reduce this expense? We show how active learning techniques can formalize and automate a subset of playtesting goals. Specifically, we focus on the low-level parameter tuning required to balance a game once the mechanics have been chosen. Through a case study on a shoot-‘em-up game we demonstrate the efficacy of active learning to reduce the amount of playtesting needed to choose the optimal set of game parameters for two classes of (formal) design objectives. This work opens the potential for additional methods to reduce the human burden of performing playtesting for a variety of relevant design concerns.


title: Suspended Morphology in Serbian: Clitics vs. Affixes

abstract: This article offers a case study of what appears to be an instance of “suspended affixation” in Serbian. The phenomenon in question is particularly interesting and potentially theoretically significant since it occurs in a language in which suspended affixation is generally impossible. The account I am led to suggests, however, that what is being “suspended” is not an affix but a second position clitic disguised as an affix. This is not a surprising outcome, since Serbian second position clitics, unlike ordinary affixes, can be elided quite easily. The phenomena examined in this paper provide further support to certain aspects of the theoretical model developed in Embick (2007; 2010) and offer new insights into the interaction between linearization, ellipsis and Local Dislocation. In particular, I show that the forms which allow ‘suspended affixation’ are formed in a special way, namely, via Local Dislocation, which affixes a second position enclitic to its host at PF under linear adjacency. Forms which are created by regular head movement, on the other hand disallow suspended affixation, on the assumption that elements that form complex heads (i.e., Subwords) cannot be elided. This article is part of Special Collection: Suspended Affixation


title: Integrating children's contributions in the interaction design process

abstract: In this paper, we describe three studies for the design of a hand-held music device for children. The studies involved researchers from different disciplines and children from different schools. We reflected on what happened during the design activities. And we looked at the outputs produced by the children in order to understand the feasibility of the activities that were included in the design sessions from two perspectives: whether they contributed to the design of the product – termed their capability – and whether they suitably involved children in the process – called their suitability . We then report on how children’s ideas were selected and integrated into the product design through iterative cycles of testing and refinements. This description of the process prompted the discussion on the involvement of children and their ideas throughout the whole process, its benefit and difficulties that could be applicable to a wide variety of design contexts with children.


title: How Good Are My Tests?

abstract: Background: Test quality is a prerequisite for achieving production system quality. While the concept of quality is multidimensional, most of the effort in testing context hasbeen channelled towards measuring test effectiveness. Objective: While effectiveness of tests is certainly important, we aim to identify a core list of testing principles that also address other quality facets of testing, and to discuss how they can be quantified as indicators of test quality. Method: We have conducted a two-day workshop with our industry partners to come up with a list of relevant principles and best practices expected to result in high quality tests. We then utilised our academic and industrial training materials together with recommendations in practitioner oriented testing books to refine the list. We surveyed existing literature for potential metrics to quantify identified principles. Results: We have identified a list of 15 testing principles to capture the essence of testing goals and best practices from quality perspective. Eight principles do not map toexisting test smells and we propose metrics for six of those. Further, we have identified additional potential metrics for the seven principles that partially map to test smells. Conclusion: We provide a core list of testing principles along with a discussion of possible ways to quantify them for assessing goodness of tests. We believe that our work wouldbe useful for practitioners in assessing the quality of their tests from multiple perspectives including but not limited to maintainability, comprehension and simplicity.


title: A Novel UWB Sampling Receiver and Its Applications for Impulse GPR Systems

abstract: A novel ultrawideband (UWB) synchronous receiver for a UWB radar system is presented. The developed receiver is the first to employ a sampling phase detector, which integrates a step recovery diode and a pair of Schottky diodes, to sample the UWB signal of a radar, enabling the miniaturization of the design. It achieves a 6-GHz sampling bandwidth, a dynamic range of more than 50 dB, and low harmonic distortion of the output baseband signal. The receiver's down-converted IF signals closely match with their original shape, demonstrating its good performance to reconstruct the microwave signals from a receiving antenna. These IF signals can be converted to digital signals by a low-cost A/D. For verification, the performance of the UWB receiver is tested through the experiments of buried steels at different thicknesses in the sand when it is applied to the ground-penetrating radar (GPR). The results show that the GPR system has good horizontal resolution and large detection depth. Therefore, this GPR system is very attractive and effective for applications of short-range and high-resolution detection.


title: Low-cost medium-range optical underwater modem: short paper

abstract: Enhancing underwater instruments with networking capabilities is opening up exciting new opportunities for remote ocean exploration. Underwater communication is essential in enabling networking, and in this work, we focus specifically on those applications that require a communication range of 5 to 10 meters. We will show that optical wireless technology is an excellent candidate when cost-efficient design is paramount and data rate requirements are modest. Our optical modem uses inexpensive components, and compensates by employing sophisticated detection algorithms. Our design is validated experimentally, establishing it as a viable cost-effective communication technology for low range underwater applications.


title: Advanced algorithm for brain segmentation using fuzzy to localize cancer and epilespy region

abstract: The research which addresses the diseases of the brain in the field of the vision by computer is one of the challenges in recent times in medicine, the engineers and researchers recently launched challenges to carry out innovations of technology pointed in imagery. This paper focuses on a new algorithm for brain segmentation of color images based on fuzzy classification to diagnose accurately the region of cancer and the area of epilepsy. In a first step it proceeds by a fine segmentation using the algorithm of fuzzy c-means (FCM). It then applies a test fusion of fuzzy classes. The result is a coarse segmentation, where each region is the union of elementary regions grown from FCM. The fuzzy C-Means (FCM) clustering is an iterative partitioning method that produces optimal c-partitions. The standard FCM algorithm takes a long time to partition a large data set. The proposed FCM program must read the entire data set into a memory for processing. Our results show that the system performance is robust to different types of images.


title: Weather Forecasting Model using Artificial Neural Network

abstract: Abstract Weather forecasting has become an important field of research in the last few decades. In most of the cases the researcher had attempted to establish a linear relationship between the input weather data and the corresponding target data. But with the discovery of nonlinearity in the nature of weather data, the focus has shifted towards the nonlinear prediction of the weather data. Although, there are many literatures in nonlinear statistics for the weather forecasting, most of them required that the nonlinear model be specified before the estimation is done. But since the weather data is nonlinear and follows a very irregular trend, Artificial Neural Network (ANN) has evolved out to be a better technique to bring out the structural relationship between the various entities. The paper examines the applicability of ANN approach by developing effective and reliable nonlinear predictive models for weather analysis also compare and evaluate the performance of the developed models using different transfer functions, hidden layers and neurons to forecast maximum, temperature for 365 days of the year.


Automated Blood Counter (ABC): an Image Processing Solution


2015 Senior Thesis Project Reports


title: Evaluating ontology mapping techniques: An experiment in public safety information sharing

abstract: The public safety community in the United States consists of thousands of local, state, and federal agencies, each with its own information system. In the past few years, there has been a thrust on the seamless interoperability of systems in these agencies. Ontology-based interoperability approaches in the public safety domain need to rely on mapping between ontologies as each agency has its own representation of information. However, there has been little study of ontology mapping techniques in this domain. We evaluate current mapping techniques with real-world data representations from law-enforcement and public safety data sources. In addition, we implement an information theory based tool called MIMapper that uses WordNet and mutual information between data instances to map ontologies. We find that three tools: PROMPT, Chimaera, and LOM, have average F-measures of 0.46, 0.49, and 0.68 when matching pairs of ontologies with the number of classes ranging from 13-73. MIMapper performs better with an average F-measure of 0.84 in performing the same task. We conclude that the tools that use secondary sources (like WordNet) and data instances to establish mappings between ontologies are likely to perform better in this application domain.


CMU OAQA at TREC 2016 LiveQA: An Attentional Neural Encoder-Decoder Approach for Answer Ranking


title: Basis for the implementation of an EEG-based single-trial binary brain computer interface through the disgust produced by remembering unpleasant odors

abstract: In order to implement an EEG-based brain computer interface (BCI), a very large number of strategies (ranging from sensory-motor, p300, auditory based, visually based) can be used. However, no technique exists which is based on the olfactory stimulation or, better, based on the imagination of olfactory stimuli.The present paper describes an innovative paradigm, that is the voluntary brain activation with the disgust produced by remembering unpleasant odors, and a simple and robust classification method on which a single trial binary BCI can be implemented. In order to classify the signal, mainly the channels P4, C4, T8 and P8 have been used, by spanning the frequency band between 32 and 42Hz, that is a subset of the gamma band external to the bands usually occupied by other tasks (the interval between 1 and 30Hz), and the alpha band between 8 and 12Hz.Right hemisphere of the brain and gamma band of frequencies are particularly sensitive when experiencing negative emotions, such as the disgust produced by smelling or remembering unpleasant odors, while the alpha band is usually modified with concentration. This constitutes an advantage for the proposed classification technique because it is made intrinsically easy by the localization into particular positions and frequencies: different features are mostly based on different frequency bands.The choice of disgust produced by remembering unpleasant odors is twofold: smelling is an ancestral sensation which is so strong that its EEG signal is produced also in persons affected by hyposmia when they imagine an olfactory situation; it can be used without external stimulation, that is the user can decide freely when and if activate it.The proposed method and the experimental setup are described and a series of experimental measurements are presented and discussed. The accuracy of the proposed method is also evaluated and the reached levels are about 90%. The proposed system can be a useful communication alternative for disabled people that cannot use other BCI paradigms.


title: CMU Multiple-choice Question Answering System at NTCIR-11 QA-Lab

abstract: We describe CMU’s UIMA-based modular automatic question answering (QA) system. This system answers multiplechoice English questions for the world history entrance exam. Questions are preceded by short descriptions providing a historical context. Given the context and question-specic instructions, we generate veriable assertions for each answer choice. These assertions are evaluated using several evidencing modules, which assign a plausibility score to each assertion. These scores are then aggregated to produce the most plausible answer choice. In the NTCIR-11 QALab evaluations, our system achieved 51.6% accuracy on the training set, 47.2% on Phase 1 testing set, and 34.1% on Phase 2 testing set.


title: Subcutaneous Fluid Administration: A Potentially Useful Tool in Prehospital Care

abstract: Mass casualty incidents (MCIs) and disaster medical situations are ideal settings in which there is need for a novel approach to infusing fluids and medications into a patient's intravascular space. An attractive new approach would avoid the potentially time-consuming needlestick and venous cannulation requiring a trained practitioner. In multiple-patient situations, trained practitioners are not always available in sufficient numbers to enable timely placement of intravenous catheters. The novel approach for intravascular space infusion, described in this paper involves the preadministration of the enzyme, human recombinant hyaluronidase (HRH), into the subcutaneous (SC) space, via an indwelling catheter. The enzyme "loosens" the SC space effectively enhancing the absorption of fluids and medication.


Reverse-Projection Method for Measuring Camera MTF


title: ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews

abstract: Today, more and more product reviews become available on the Internet, e.g., product review forums, discussion groups, and Blogs. However, it is almost impossible for a customer to read all of the different and possibly even contradictory opinions and make an informed decision. Therefore, mining online reviews (opinion mining) has emerged as an interesting new research direction. Extracting aspects and the corresponding ratings is an important challenge in opinion mining. An aspect is an attribute or component of a product, e.g. 'screen' for a digital camera. It is common that reviewers use different words to describe an aspect (e.g. 'LCD', 'display', 'screen'). A rating is an intended interpretation of the user satisfaction in terms of numerical values. Reviewers usually express the rating of an aspect by a set of sentiments, e.g. 'blurry screen'. In this paper we present three probabilistic graphical models which aim to extract aspects and corresponding ratings of products from online reviews. The first two models extend standard PLSI and LDA to generate a rated aspect summary of product reviews. As our main contribution, we introduce Interdependent Latent Dirichlet Allocation (ILDA) model. This model is more natural for our task since the underlying probabilistic assumptions (interdependency between aspects and ratings) are appropriate for our problem domain. We conduct experiments on a real life dataset, Epinions.com, demonstrating the improved effectiveness of the ILDA model in terms of the likelihood of a held-out test set, and the accuracy of aspects and aspect ratings.


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title: Leveraging Video Descriptions to Learn Video Question Answering

abstract: We propose a scalable approach to learn video-based question answering (QA): answer a "free-form natural language question" about a video content. Our approach automatically harvests a large number of videos and descriptions freely available online. Then, a large number of candidate QA pairs are automatically generated from descriptions rather than manually annotated. Next, we use these candidate QA pairs to train a number of video-based QA methods extended fromMN (Sukhbaatar et al. 2015), VQA (Antol et al. 2015), SA (Yao et al. 2015), SS (Venugopalan et al. 2015). In order to handle non-perfect candidate QA pairs, we propose a self-paced learning procedure to iteratively identify them and mitigate their effects in training. Finally, we evaluate performance on manually generated video-based QA pairs. The results show that our self-paced learning procedure is effective, and the extended SS model outperforms various baselines.