title: Cross-Border Organizing in the Apparel Industry: Lessons from Central America and the Caribbean

abstract: Successful cross-border organizing in developing nations demands that transnational corporate campaigns operate in tandem with local drives for a union contract. On-site organizing drives are most effective via quiet leadership development and house visits, which prepare sufficient participants to request negotiations. Only at this point are widespread in-plant publicity and international attention beneficial. In the export apparel sector's historical context, specific organizing drives in the Do minican Republic, Guatemala, and Honduras validate such an approach. They also verify the primacy of local participation and close union com munication with outside monitors in the functioning of transnational cam paigns.


title: A review on the combination of binary classifiers in multiclass problems

abstract: Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of problems with only two classes, also named binary classification problems. However, many problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.


Crisis Management 2.0: Towards a Systematization of Social Software Use in Crisis Situations


title: Blind inpainting using the fully convolutional neural network

abstract: Most of existing inpainting techniques require to know beforehandwhere those damaged pixels are, i.e., non-blind inpainting methods. However, in many applications, such information may not be readily available. In this paper, we propose a novel blind inpainting method based on a fully convolutional neural network. We term this method as blind inpainting convolutional neural network (BICNN). It purely cascades three convolutional layers to directly learn an end-to-end mapping between a pre-acquired dataset of corrupted/ground truth subimage pairs. Stochastic gradient descent with standard backpropagation is used to train the BICNN. Once the BICNN is learned, it can automatically identify and remove the corrupting patterns from a corrupted image without knowing the specific regions. The learned BICNN takes a corrupted image of any size as input and directly produces a clean output by only one pass of forward propagation. Experimental results indicate that the proposed method can achieve a better inpainting performance than the existing inpainting methods for various corrupting patterns.


title: A novel word segmentation method based on object detection and deep learning

abstract: The segmentation of individual words is a crucial step in several data mining methods for historical handwritten documents. Examples of applications include visual searching for query words (word spotting) and character-by-character text recognition. In this paper, we present a novel method for word segmentation that is adapted from recent advances in computer vision, deep learning and generic object detection. Our method has unique capabilities and it has found practical use in our current research project. It can easily be trained for different kinds of historical documents, uses full gray scale information, does not require binarization as pre-processing or prior segmentation of individual text lines. We evaluate its performance using established error metrics, previously used in competitions for word segmentation, and demonstrate its usefulness for a 15th century handwritten document.


title: Clustering of Gaze During Dynamic Scene Viewing is Predicted by Motion

abstract: Where does one attend when viewing dynamic scenes? Research into the factors influencing gaze location during static scene viewing have reported that low-level visual features contribute very little to gaze location especially when opposed by high-level factors such as viewing task. However, the inclusion of transient features such as motion in dynamic scenes may result in a greater influence of visual features on gaze allocation and coordination of gaze across viewers. In the present study, we investigated the contribution of low- to mid-level visual features to gaze location during free-viewing of a large dataset of videos ranging in content and length. Signal detection analysis on visual features and Gaussian Mixture Models for clustering gaze was used to identify the contribution of visual features to gaze location. The results show that mid-level visual features including corners and orientations can distinguish between actual gaze locations and a randomly sampled baseline. However, temporal features such as flicker, motion, and their respective contrasts were the most predictive of gaze location. Additionally, moments in which all viewers’ gaze tightly clustered in the same location could be predicted by motion. Motion and mid-level visual features may influence gaze allocation in dynamic scenes, but it is currently unclear whether this influence is involuntary or due to correlations with higher order factors such as scene semantics.


title: Composite Controller for Antagonistic Tendon Driven Joints With Elastic Tendons and Its Experimental Verification

abstract: In this paper, we present the dynamic modeling and controller design of a tendon-driven system that is antagonistically driven by elastic tendons. In the dynamic modeling, the tendons are approximated as linear axial springs, neglecting their masses. An overall equation for motion is established by following the Euler–Lagrange formalism of dynamics, combined with rigid-body rotation and vibration. The controller is designed using the singular perturbation approach, which leads to a composite controller (i.e., consisting of a fast sub-controller and a slow sub-controller). An appropriate internal force is superposed to the control action to ensure the tendons to be in tension for all configurations. Experimental results are provided to demonstrate the validity and effectiveness of the proposed controller for the antagonistic tendon-driven system.


title: 60-GHz Groove Gap Waveguide Based Wideband $H$ -Plane Power Dividers and Transitions: For Use in High-Gain Slot Array Antenna

abstract: Wideband design of power dividers, T-junctions, and transitions based on groove gap waveguide (GGW) technology is presented in this paper with the goal to use these components in high-gain millimeter-wave antenna array design at 60-GHz frequency range. Since this GGW technology does not require electrical contact between the different metal layers of a complex 3-D waveguide structure, the fabrication cost and mechanical complexity are decreased. The designed T-junctions and different power dividers exhibit wide operational bandwidth and low output power and phase imbalance over the 60-GHz frequency band. Also, two transitions from GGW to a standard rectangular waveguide have been designed. To validate the performance of the designed components, a 64-way power divider in combination with 256 radiating slots is designed, prototyped, and measured at 60-GHz band. Measurement results agree well with the simulated performance of the complete array antenna, and the antenna gain is more than 32.5 dBi. The total radiation efficiency is more than 80% over the operating frequency range from 57 to 67 GHz. Also, the measured sidelobe levels are found to be agreeing well with the simulated level.


title: A Conceptual View on Trajectories ⋆

abstract: Analysis of trajectory data is the key to a growing number of applications aiming at global understanding and management of complex phenomena that involve moving objects (e.g. worldwide courier distribution, city traffic management, bird migration monitoring). Current DBMS support for such data is limited to the ability to store and query raw movement (i.e. the spatio-temporal position of an object). This paper explores how conceptual modeling could provide applications with direct support of trajectories (i.e. movement data that is structured into countable semantic units) as a first class concept. A specific concern is to allow enriching trajectories with semantic annotations allowing users to attach semantic data to specific parts of the trajectory. Building on a preliminary requirement analysis and an application example, the paper proposes two modeling approaches, one based on a design pattern, the other based on dedicated data types, and illustrates their differences in terms of implementation in an extended-relational context.


title: Learning with Heterogeneous Side Information Fusion for Recommender Systems

abstract: Recommender System (RS) is a hot area where artificial intelligence (AI) techniques can be effectively applied to improve performance. Since the well-known Netflix Challenge, collaborative filtering (CF) has become the most popular and effective recommendation method. Despite their success in CF, various AI techniques still have to face the data sparsity and cold start problems. Previous works tried to solve these two problems by utilizing auxiliary information, such as social connections among users and meta-data of items. However, they process different types of information separately, leading to information loss. In this work, we propose to utilize Heterogeneous Information Network (HIN), which is a natural and general representation of different types of data, to enhance CF-based recommending methods. HIN-based recommender systems face two problems: how to represent high-level semantics for recommendation and how to fuse the heterogeneous information to recommend. To address these problems, we propose to applying meta-graph to HIN-based RS and solve the information fusion problem with a "matrix factorization (MF) + factorization machine (FM)" framework. For the "MF" part, we obtain user-item similarity matrices from each meta-graph and adopt low-rank matrix approximation to get latent features for both users and items. For the "FM" part, we propose to apply FM with Group lasso (FMG) on the obtained features to simultaneously predict missing ratings and select useful meta-graphs. Experimental results on two large real-world datasets, i.e., Amazon and Yelp, show that our proposed approach is better than that of the state-of-the-art FM and other HIN-based recommending methods.


title: Towards the Narrative Annotation of Personal Information and Gaming Environments

abstract: This paper presents an analogy between SemanticWeb technologies and an existing narrative theory. Narrative generation is presented as an alternative method of human computer interaction, in the form of context based search. The discussions are grounded in domains of Memories for Life, and Massively Multiplayer Online Role Playing Games.


title: Collaborative multi-level embedding learning from reviews for rating prediction

abstract: We investigate the problem of personalized review-based rating prediction which aims at predicting users' ratings for items that they have not evaluated by using their historical reviews and ratings. Most of existing methods solve this problem by integrating topic model and latent factor model to learn interpretable user and items factors. However, these methods cannot utilize word local context information of reviews. Moreover, it simply restricts user and item representations equivalent to their review representations, which may bring some irrelevant information in review text and harm the accuracy of rating prediction. ::: ::: In this paper, we propose a novel Collaborative Multi-Level Embedding (CMLE) model to address these limitations. The main technical contribution of CMLE is to integrate word embedding model with standard matrix factorization model through a projection level. This allows CMLE to inherit the ability of capturing word local context information from word embedding model and relax the strict equivalence requirement by projecting review embedding to user and item embeddings. A joint optimization problem is formulated and solved through an efficient stochastic gradient ascent algorithm. Empirical evaluations on real datasets show CMLE outperforms several competitive methods and can solve the two limitations well.


title: The effects of MATAS Hopscotch technique in the learning of fractions among Year 5 pupils

abstract: This study is to investigate the use of MATAS Hopscotch technique to solve the subtraction of three types of fractions among secondary school students. The study involved 56 pupils from two Year 5 classes. A quasi-experimental, nonrandomized control group, pre-test-post-test delayed post-test design was conducted on two intact groups, randomly assigned into control and experimental groups. Paired samples t- test was conducted to evaluate the impact of the teaching method on the pupils’ scores on the test among the control and treatment groups. The findings showed there was a statistically significant increase in the pupils’ scores from pre-test (M=0.43, SD = .690) to post-test (M= 9.89, SD=2.424), t(27) =-23.467, p < .05 among the control group. This finding indicated the control group showed improvement in the pupils’ scores after the implementation of the traditional method. The findings also showed there was a statistically significant increase in the pupils’ scores from pre-test (M=0.50, SD = .745) to post-test (M= 16.07, SD=1.676), t(27) =-53.163, p < .05 (two-tailed) among the treatment group. These findings indicated the treatment group showed improvement in the pupils’ scores after the implementation of the MATAS Hopscotch technique. However, the score gain of the treatment group was almost twice as much as that for the control group.


title: BanditSum: Extractive Summarization as a Contextual Bandit

abstract: In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels. We call our approach BanditSum as it treats extractive summarization as a contextual bandit (CB) problem, where the model receives a document to summarize (the context), and chooses a sequence of sentences to include in the summary (the action). A policy gradient reinforcement learning algorithm is used to train the model to select sequences of sentences that maximize ROUGE score. We perform a series of experiments demonstrating that BanditSum is able to achieve ROUGE scores that are better than or comparable to the state-of-the-art for extractive summarization, and converges using significantly fewer update steps than competing approaches. In addition, we show empirically that BanditSum performs significantly better than competing approaches when good summary sentences appear late in the source document.


title: A Blockchain Technology Evolution Between Business Process Management (BPM) and Internet-of-Things (IoT)

abstract: A Blockchain is considered the main mechanism for Bitcoin concurrency. A Blockchain is known by a public ledger and public transactions stored in a chain. The properties of blockchain demonstrate in decentralization as distribution blocks, stability, anonymity, and auditing. Blockchain can enhance the results of network efficiency and improve the security of network. It also can be applied in several fields like financial and banking services, healthcare systems, and public services. However, the research is still opening at this point. It includes a big number of technical challenges which prevents the wide application of blockchain, for example, scalability problem, privacy leakage, etc. This paper shows a proposed comprehensive study of blockchain technology. It also examines the research efforts in blockchain. It presents a proposed blockchain lifecycle which refers to an evolution and a linked ring between business process management improvement and Internet-of-Things concepts. Then, this paper presents a practical proof of this relationship for smart city. It presents a new algorithm and a proposed blockchain framework for 38 blocks (which recognized as smart-houses). Finally, the future directions are well presented in blockchain field.


title: Design of a Water Environment Monitoring System Based on Wireless Sensor Networks

abstract: A water environmental monitoring system based on a wireless sensor network is proposed. It consists of three parts: data monitoring nodes, data base station and remote monitoring center. This system is suitable for the complex and large-scale water environment monitoring, such as for reservoirs, lakes, rivers, swamps, and shallow or deep groundwaters. This paper is devoted to the explanation and illustration for our new water environment monitoring system design. The system had successfully accomplished the online auto-monitoring of the water temperature and pH value environment of an artificial lake. The system's measurement capacity ranges from 0 to 80 °C for water temperature, with an accuracy of ±0.5 °C; from 0 to 14 on pH value, with an accuracy of ±0.05 pH units. Sensors applicable to different water quality scenarios should be installed at the nodes to meet the monitoring demands for a variety of water environments and to obtain different parameters. The monitoring system thus promises broad applicability prospects.


title: Adaptive Electric Vehicle Charging Coordination on Distribution Network

abstract: Electric vehicles (EVs) with large battery charging demands may cause detrimental impact on distribution grid stability without EV charging coordination. This paper proposes an on-line adaptive EV charing scheduling (OACS) framework to optimize EV charging schedules and reduce flow limit, voltage magnitude limit, 3-phase voltage imbalance limit, and transformer capacity violations. EV user convenience is considered and EV charging cost is optimized. DC power flow based optimizations is proposed for EV charging scheduling approximation and parallel ac power flow verification is used to verify the scheduling results. Incremental feasibility improvement procedure is further proposed to correct the scheduling discrepancy between dc linear model and the ac model. Experiments are performed on a modified IEEE 34 14.7 kV distribution system with different EV penetration levels to demonstrate performance comparisons between different scheduling schemes. The result shows that our proposed OACS framework optimizes the EV charging coordination problem efficiently.


title: Supporting Interactive Multi-Attribute Electronic Negotiations with ebXML

abstract: The inter-organizational character of electronic negotiations raises social, legal, organizational and technical research questions. One of the main weaknesses of electronic negotiation applications today is that they do not sufficiently take into consideration the integration requirements that stem from the latter two of these aspects. Our objective is to design and build an electronic negotiation application that complies with these requirements. A major task within this work is the modeling and specification of the negotiation process and negotiation objects. To meet the integration requirements, the use of standards is essential. In our paper, we apply the ebXML framework to the task of modeling interactive bilateral multi-attribute electronic negotiations. For this purpose, we introduce the negotiation process flow underlying our application scenario. We explain the significance of ebXML for standardized business transaction modeling. We develop ebXML process and object definitions and suggest modifications of and additions to the current ebXML standard. Finally, we discuss the scope and the limitations of our concept and prototype.


Filter and Wrapper-based Feature Selection Using Mutual Information for Rolling Elements Bearing Diagnosis


title: A single source k-shortest paths algorithm to infer regulatory pathways in a gene network

abstract: Motivation: Inferring the underlying regulatory pathways within a gene interaction network is a fundamental problem in Systems Biology to help understand the complex interactions and the regulation and flow of information within a system-of-interest. Given a weighted gene network and a gene in this network, the goal of an inference algorithm is to identify the potential regulatory pathways passing through this gene. ::: ::: Results: In a departure from previous approaches that largely rely on the random walk model, we propose a novel single-source k-shortest paths based algorithm to address this inference problem. An important element of our approach is to explicitly account for and enhance the diversity of paths discovered by our algorithm. The intuition here is that diversity in paths can help enrich different functions and thereby better position one to understand the underlying system-of-interest. Results on the yeast gene network demonstrate the utility of the proposed approach over extant state-of-the-art inference algorithms. Beyond utility, our algorithm achieves a significant speedup over these baselines. ::: ::: Availability: All data and codes are freely available upon request. ::: ::: Contact: srini@cse.ohio-state.edu ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.


title: Ergodicity of Approximate MCMC Chains with Applications to Large Data Sets

abstract: In many modern applications, difficulty in evaluating the posterior density makes performing even a single MCMC step slow. This difficulty can be caused by intractable likelihood functions, but also appears for routine problems with large data sets. Many researchers have responded by running approximate versions of MCMC algorithms. In this note, we develop quantitative bounds for showing the ergodicity of these approximate samplers. We then use these bounds to study the bias-variance trade-off of approximate MCMC algorithms. We apply our results to simple versions of recently proposed algorithms, including a variant of the"austerity"framework of Korratikara et al.


title: The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty

abstract: The Hearsay-II system, developed during the DARPA-sponsored five-year speech-understanding research program, represents both a specific solution to the speech-understanding problem and a general framework for coordinating independent processes to achieve cooperative problem-solving behavior. As a computational problem, speech understanding reflects a large number of intrinsically interesting issues. Spoken sounds are achieved by a long chain of successive transformations, from intentions, through semantic and syntactic structuring, to the eventually resulting audible acoustic waves. As a consequence, interpreting speech means effectively inverting these transformations to recover the speaker's intention from the sound. At each step in the interpretive process, ambiguity and uncertainty arise. ::: ::: The Hearsay-II problem-solving framework reconstructs an intention from hypothetical interpretations formulated at various levels of abstraction. In addition, it allocates limited processing resources first to the most promising incremental actions. The final configuration of the Hearsay-II system comprises problem-solving components to generate and evaluate speech hypotheses, and a focus-of-control mechanism to identify potential actions of greatest value. Many of these specific procedures reveal novel approaches to speech problems. Most important, the system successfully integrates and coordinates all of these independent activities to resolve uncertainty and control combinatorics. Several adaptations of the Hearsay-II framework have already been undertaken in other problem domains, and it is anticipated that this trend will continue; many future systems necessarily will integrate diverse sources of knowledge to solve complex problems cooperatively. ::: ::: Discussed in this paper are the characteristics of the speech problem in particular, the special kinds of problem-solving uncertainty in that domain, the structure of the Hearsay-II system developed to cope with that uncertainty, and the relationship between Hearsay-II's structure and those of other speech-understanding systems. The paper is intended for the general computer science audience and presupposes no speech or artificial intelligence background.


title: Selectively compliant underactuated hand for mobile manipulation

abstract: The demands of mobile manipulation are leading to a new class of multi-fingered hands with a premium on being lightweight and robust as well as being able to grasp and perform basic manipulations with a wide range of objects. A promising approach to addressing these goals is to use compliant, underactuated hands with selectively lockable degrees of freedom. This paper presents the design of one such hand that combines series-elastic actuation and electrostatic braking at the joints. A numerical analysis shows how the maximum pullout force varies as a function of kinematic parameters, spring forces at the joints and brake torques.


title: A 52 $\mu$ W Wake-Up Receiver With $-$ 72 dBm Sensitivity Using an Uncertain-IF Architecture

abstract: -A dedicated wake-up receiver may be used in wireless sensor nodes to control duty cycle and reduce network latency. However, its power dissipation must be extremely low to minimize the power consumption of the overall link. This paper describes the design of a 2 GHz receiver using a novel "uncertain-IF" architecture, which combines MEMS-based high-Q filtering and a free-running CMOS ring oscillator as the RF LO. The receiver prototype, implemented in 90 nm CMOS technology, achieves a sensitivity of -72 dBm at 100 kbps (10 -3 bit error rate) while consuming just 52 μW from the 0.5 V supply.


title: Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors

abstract: We present a dual contribution to the task of machine reading-comprehension: a technique for creating large-sized machine-comprehension (MC) datasets using paragraph-vector models; and a novel, hybrid neural-network architecture that combines the representation power of recurrent neural networks with the discriminative power of fully-connected multi-layered networks. We use the MC-dataset generation technique to build a dataset of around 2 million examples, for which we empirically determine the high-ceiling of human performance (around 91% accuracy), as well as the performance of a variety of computer models. Among all the models we have experimented with, our hybrid neural-network architecture achieves the highest performance (83.2% accuracy). The remaining gap to the human-performance ceiling provides enough room for future model improvements.


title: A convolutional neural network for steady state visual evoked potential classification under ambulatory environment

abstract: The robust analysis of neural signals is a challenging problem. Here, we contribute a convolutional neural network (CNN) for the robust classification of a steady-state visual evoked potentials (SSVEPs) paradigm. We measure electroencephalogram (EEG)-based SSVEPs for a brain-controlled exoskeleton under ambulatory conditions in which numerous artifacts may deteriorate decoding. The proposed CNN is shown to achieve reliable performance under these challenging conditions. To validate the proposed method, we have acquired an SSVEP dataset under two conditions: 1) a static environment, in a standing position while fixated into a lower-limb exoskeleton and 2) an ambulatory environment, walking along a test course wearing the exoskeleton (here, artifacts are most challenging). The proposed CNN is compared to a standard neural network and other state-of-the-art methods for SSVEP decoding (i.e., a canonical correlation analysis (CCA)-based classifier, a multivariate synchronization index (MSI), a CCA combined with k-nearest neighbors (CCA-KNN) classifier) in an offline analysis. We found highly encouraging SSVEP decoding results for the CNN architecture, surpassing those of other methods with classification rates of 99.28% and 94.03% in the static and ambulatory conditions, respectively. A subsequent analysis inspects the representation found by the CNN at each layer and can thus contribute to a better understanding of the CNN’s robust, accurate decoding abilities.


title: The Java memory model

abstract: This paper describes the new Java memory model, which has been revised as part of Java 5.0. The model specifies the legal behaviors for a multithreaded program; it defines the semantics of multithreaded Java programs and partially determines legal implementations of Java virtual machines and compilers.The new Java model provides a simple interface for correctly synchronized programs -- it guarantees sequential consistency to data-race-free programs. Its novel contribution is requiring that the behavior of incorrectly synchronized programs be bounded by a well defined notion of causality. The causality requirement is strong enough to respect the safety and security properties of Java and weak enough to allow standard compiler and hardware optimizations. To our knowledge, other models are either too weak because they do not provide for sufficient safety/security, or are too strong because they rely on a strong notion of data and control dependences that precludes some standard compiler transformations.Although the majority of what is currently done in compilers is legal, the new model introduces significant differences, and clearly defines the boundaries of legal transformations. For example, the commonly accepted definition for control dependence is incorrect for Java, and transformations based on it may be invalid.In addition to providing the official memory model for Java, we believe the model described here could prove to be a useful basis for other programming languages that currently lack well-defined models, such as C++ and C#.


title: A 60 kb/s–10 Mb/s Adaptive Frequency Hopping Transceiver for Interference-Resilient Body Channel Communication

abstract: An interference-resilient 60 kb/s-10 Mb/s body channel transceiver using the human body as a signal transmission medium is designed for multimedia and medical data transaction in body-area network. The body antenna effect which interferes with signals in the human body channel is examined. The body-induced interferences degrade the SIR of the signal to -22 dB in the worst case. In order to overcome the body antenna effect, a 4-channel adaptive frequency hopping scheme using the 30-120 MHz band is introduced to the body channel transceiver. A direct-switching modulator using dual frequency synthesizers and a DLL-based demodulator are proposed for 10 Mb/s FSK and the 4.2 mus hopping time. The transceiver fabricated with 0.18 mum CMOS withstands -28 dB SIR and its operating distance is over 1.8 m with - 25 dB SIR. Its energy consumption is 0.37 nJ/b with -65 dBm sensitivity.


title: Optimum time delay detection and tracking

abstract: An optimum solution to the time delay tracking problems has been previously achieved by recursively computing the time-delay posterior probability density function (PDF), or time-delay PDF conditioned on knowing all observed data, by numerical means. Any optimum estimate is determined from the posterior PDF. The same approach is extended to an optimum solution for the time delay detection problem with a randomly appearing and disappearing signal. The detector-tracker is suitable for a stand-alone detection and tracking system. >


title: Chinese Text in the Wild

abstract: We introduce Chinese Text in the Wild, a very large dataset of Chinese text in street view images. While optical character recognition (OCR) in document images is well studied and many commercial tools are available, detection and recognition of text in natural images is still a challenging problem, especially for more complicated character sets such as Chinese text. Lack of training data has always been a problem, especially for deep learning methods which require massive training data. In this paper we provide details of a newly created dataset of Chinese text with about 1 million Chinese characters annotated by experts in over 30 thousand street view images. This is a challenging dataset with good diversity. It contains planar text, raised text, text in cities, text in rural areas, text under poor illumination, distant text, partially occluded text, etc. For each character in the dataset, the annotation includes its underlying character, its bounding box, and 6 attributes. The attributes indicate whether it has complex background, whether it is raised, whether it is handwritten or printed, etc. The large size and diversity of this dataset make it suitable for training robust neural networks for various tasks, particularly detection and recognition. We give baseline results using several state-of-the-art networks, including AlexNet, OverFeat, Google Inception and ResNet for character recognition, and YOLOv2 for character detection in images. Overall Google Inception has the best performance on recognition with 80.5% top-1 accuracy, while YOLOv2 achieves an mAP of 71.0% on detection. Dataset, source code and trained models will all be publicly available on the website.


Conditional random fields: An introduction


title: The effect of experimental presentation of thin media images on body satisfaction: A meta-analytic review

abstract: Objective ::: The effect of experimental manipulations of the thin beauty ideal, as portrayed in the mass media, on female body image was evaluated using meta-analysis. ::: ::: Method ::: Data from 25 studies (43 effect sizes) were used to examine the main effect of mass media images of the slender ideal, as well as the moderating effects of pre-existing body image problems, the age of the participants, the number of stimulus presentations, and the type of research design. ::: ::: Results ::: Body image was significantly more negative after viewing thin media images than after viewing images of either average size models, plus size models, or inanimate objects. This effect was stronger for between-subjects designs, participants less than 19 years of age, and for participants who are vulnerable to activation of a thinness schema. ::: ::: Conclusion ::: Results support the sociocultural perspective that mass media promulgate a slender ideal that elicits body dissatisfaction. Implications for prevention and research on social comparison processes are considered. © 2002 by John Wiley & Sons, Inc. Int J Eat Disord 31: 1–16, 2002.


title: Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions

abstract: Matching users to the right items at the right time is a fundamental task in recommendation systems. As users interact with different items over time, users' and items' feature may evolve and co-evolve over time. Traditional models based on static latent features or discretizing time into epochs can become ineffective for capturing the fine-grained temporal dynamics in the user-item interactions. We propose a coevolutionary latent feature process model that accurately captures the coevolving nature of users' and items' feature. To learn parameters, we design an efficient convex optimization algorithm with a novel low rank space sharing constraints. Extensive experiments on diverse real-world datasets demonstrate significant improvements in user behavior prediction compared to state-of-the-arts.


title: Multi-label classification by mining label and instance correlations from heterogeneous information networks

abstract: Multi-label classification is prevalent in many real-world applications, where each example can be associated with a set of multiple labels simultaneously. The key challenge of multi-label classification comes from the large space of all possible label sets, which is exponential to the number of candidate labels. Most previous work focuses on exploiting correlations among different labels to facilitate the learning process. It is usually assumed that the label correlations are given beforehand or can be derived directly from data samples by counting their label co-occurrences. However, in many real-world multi-label classification tasks, the label correlations are not given and can be hard to learn directly from data samples within a moderate-sized training set. Heterogeneous information networks can provide abundant knowledge about relationships among different types of entities including data samples and class labels. In this paper, we propose to use heterogeneous information networks to facilitate the multi-label classification process. By mining the linkage structure of heterogeneous information networks, multiple types of relationships among different class labels and data samples can be extracted. Then we can use these relationships to effectively infer the correlations among different class labels in general, as well as the dependencies among the label sets of data examples inter-connected in the network. Empirical studies on real-world tasks demonstrate that the performance of multi-label classification can be effectively boosted using heterogeneous information net- works.


title: Image Denoising Using Quadtree-Based Nonlocal Means With Locally Adaptive Principal Component Analysis

abstract: In this letter, we present an efficient image denoising method combining quadtree-based nonlocal means (NLM) and locally adaptive principal component analysis. It exploits nonlocal multiscale self-similarity better, by creating sub-patches of different sizes using quadtree decomposition on each patch. To achieve spatially uniform denoising, we propose a local noise variance estimator combined with denoiser based on locally adaptive principal component analysis. Experimental results demonstrate that our proposed method achieves very competitive denoising performance compared with state-of-the-art denoising methods, even obtaining better visual perception at high noise levels.


title: Medical Image Analysis using Convolutional Neural Networks: A Review

abstract: The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.


title: A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networks

abstract: With the tremendous growth in the number of scientific papers being published, searching for references while writing a scientific paper is a time-consuming process. A technique that could add a reference citation at the appropriate place in a sentence will be beneficial. In this perspective, context-aware citation recommendation has been researched upon for around two decades. Many researchers have utilized the text data called the context sentence, which surrounds the citation tag, and the metadata of the target paper to find the appropriate cited research. However, the lack of well-organized benchmarking datasets and no model that can attain high performance has made the research difficult. ::: In this paper, we propose a deep learning based model and well-organized dataset for context-aware paper citation recommendation. Our model comprises a document encoder and a context encoder, which uses Graph Convolutional Networks (GCN) layer and Bidirectional Encoder Representations from Transformers (BERT), which is a pre-trained model of textual data. By modifying the related PeerRead dataset, we propose a new dataset called FullTextPeerRead containing context sentences to cited references and paper metadata. To the best of our knowledge, This dataset is the first well-organized dataset for context-aware paper recommendation. The results indicate that the proposed model with the proposed datasets can attain state-of-the-art performance and achieve a more than 28% improvement in mean average precision (MAP) and recall@k.


title: The See-Through System: A VANET-enabled assistant for overtaking maneuvers

abstract: The use of wireless technology based on Vehicular Ad hoc Networks (VANETs) for information exchange can influence the drivers' behavior towards improving driving performance and reducing road accidents. This information can even be more relevant if it is presented as a video stream. In this paper we propose a system that relies on VANET and video-streaming technology: the See-Through System (STS). The system enhances driver's visibility and supports the driver's overtaking decision in challenging situations, such as overtaking a vision-obstructing vehicle. The use of the See-Through System provides the driver with an additional tool for determining if traffic conditions permit starting an overtaking maneuver thus reducing the risk of overtaking. We tested the See-Through System on an experimental vehicle on the road as well as in the context of a driving simulator for real world environment. Results are promising, since the use of the 802.11p standard wireless communication protocol allows a vehicle-to-vehicle connection without significant delay and the totality of the participants regarded the information provided by the STS as useful.


title: Learning to Segment Object Candidates

abstract: Recent object detection systems rely on two critical steps: (1) a set of object proposals is predicted as efficiently as possible, and (2) this set of candidate proposals is then passed to an object classifier. Such approaches have been shown they can be fast, while achieving the state of the art in detection performance. In this paper, we propose a new way to generate object proposals, introducing an approach based on a discriminative convolutional network. Our model is trained jointly with two objectives: given an image patch, the first part of the system outputs a class-agnostic segmentation mask, while the second part of the system outputs the likelihood of the patch being centered on a full object. At test time, the model is efficiently applied on the whole test image and generates a set of segmentation masks, each of them being assigned with a corresponding object likelihood score. We show that our model yields significant improvements over state-of-the-art object proposal algorithms. In particular, compared to previous approaches, our model obtains substantially higher object recall using fewer proposals. We also show that our model is able to generalize to unseen categories it has not seen during training. Unlike all previous approaches for generating object masks, we do not rely on edges, superpixels, or any other form of low-level segmentation.


title: Defending Yarbus: Eye movements reveal observers' task

abstract: In a very influential yet anecdotal illustration, Yarbus suggested that human eye-movement patterns are modulated top down by different task demands. While the hypothesis that it is possible to decode the observer’s task from eye movements has received some support (e.g., Henderson, Shinkareva, Wang, Luke, & Olejarczyk, 2013; Iqbal & Bailey, 2004), Greene, Liu, and Wolfe (2012) argued against it by reporting a failure. In this study, we perform a more systematic investigation of this problem, probing a larger number of experimental factors than previously. Our main goal is to determine the informativeness of eye movements for task and mental state decoding. We perform two experiments. In the first experiment, we reanalyze the data from a previous study by Greene et al. (2012) and contrary to their conclusion, we report that it is possible to decode the observer’s task from aggregate eye-movement features slightly but significantly above chance, using a Boosting classifier (34.12% correct vs. 25% chance level; binomial test, p 1⁄4 1.0722e – 04). In the second experiment, we repeat and extend Yarbus’s original experiment by collecting eye movements of 21 observers viewing 15 natural scenes (including Yarbus’s scene) under Yarbus’s seven questions. We show that task decoding is possible, also moderately but significantly above chance (24.21% vs. 14.29% chancelevel; binomial test, p 1⁄4 2.4535e – 06). We thus conclude that Yarbus’s idea is supported by our data and continues to be an inspiration for future computational and experimental eye-movement research. From a broader perspective, we discuss techniques, features, limitations, societal and technological impacts, and future directions in task decoding from eye movements. Introduction


title: An Efficient Exact-PGA Algorithm for Constant Curvature Manifolds

abstract: Manifold-valued datasets are widely encountered in many computer vision tasks. A non-linear analog of the PCA algorithm, called the Principal Geodesic Analysis (PGA) algorithm suited for data lying on Riemannian manifolds was reported in literature a decade ago. Since the objective function in the PGA algorithm is highly non-linear and hard to solve efficiently in general, researchers have proposed a linear approximation. Though this linear approximation is easy to compute, it lacks accuracy especially when the data exhibits a large variance. Recently, an alternative called the exact PGA was proposed which tries to solve the optimization without any linearization. For general Riemannian manifolds, though it yields a better accuracy than the original (linearized) PGA, for data that exhibit large variance, the optimization is not computationally efficient. In this paper, we propose an efficient exact PGA algorithm for constant curvature Riemannian manifolds (CCM-EPGA). The CCM-EPGA algorithm differs significantly from existing PGA algorithms in two aspects, (i) the distance between a given manifold-valued data point and the principal submanifold is computed analytically and thus no optimization is required as in the existing methods. (ii) Unlike the existing PGA algorithms, the descent into codimension-1 submanifolds does not require any optimization but is accomplished through the use of the Rimeannian inverse Exponential map and the parallel transport operations. We present theoretical and experimental results for constant curvature Riemannian manifolds depicting favorable performance of the CCM-EPGA algorithm compared to existing PGA algorithms. We also present data reconstruction from the principal components which has not been reported in literature in this setting.


title: Avatars, characters, players and users: multiple identities at/in play

abstract: Avatars are ubiquitous in virtual worlds (VWs). As such, they have become central to how we understand the way they are experienced. Common conceptualisations of the user avatar relationship invoke an identity binary which has influenced discussions of presence and game enjoyment. This paper presents findings from EVE Online which suggest there are more than two identities involved in playing games in VWs. As such, we argue for a more nuanced approach to notions of identity in VWs and discuss how this approach understands the impact of VW design on the way identity is constructed by players.


title: Mathematical Formulation and Numerical Modelling of the Extraction of H− ions

abstract: In the past, ion extraction of volume produced H− ions has either been simulated with programs for the extraction of positive ions or by programs, which wrongly claimed to have a “real” and “genuine” option for H− ions. Although “reasonable” results have been obtained in both ways, the mathematical formulation of the physics at the plasma sheath is wrong, and the modelling of electrodes near the sheath must fail. In this paper a self consistent formulation of the extraction problem for H− ions is presented, which takes into account any number of positive ions, like fast or thermal protons, thermal cesium and molecular ions like H2+ and H3+, which all are essential for the generation of H− ions in the plasma volume. Equally important is the porting of this formulation to a simulation program and the verification of experimental results. This has lead to the development of the program nlGUN©.


title: The magic lens: refractive steganography

abstract: We present an automatic approach to design and manufacture passive display devices based on optical hidden image decoding. Motivated by classical steganography techniques we construct Magic Lenses, composed of refractive lenslet arrays, to reveal hidden images when placed over potentially unstructured printed or displayed source images. We determine the refractive geometry of these surfaces by formulating and efficiently solving an inverse light transport problem, taking into account additional constraints imposed by the physical manufacturing processes. We fabricate several variants on the basic magic lens idea including using a single source image to encode several hidden images which are only revealed when the lens is placed at prescribed orientations on the source image or viewed from different angles. We also present an important special case, the universal lens, that forms an injection mapping from the lens surface to the source image grid, allowing it to be used with arbitrary source images. We use this type of lens to generate hidden animation sequences. We validate our simulation results with many real-world manufactured magic lenses, and experiment with two separate manufacturing processes.


Theoretical and descriptive issues in machine translation


title: Embodied Artificial Intelligence: Trends and Challenges

abstract: The field of Artificial Intelligence, which started roughly half a century ago, has a turbulent history. In the 1980s there has been a major paradigm shift towards embodiment. While embodied artificial intelligence is still highly diverse, changing, and far from “theoretically stable”, a certain consensus about the important issues and methods has been achieved or is rapidly emerging. In this non-technical paper we briefly characterize the field, summarize its achievements, and identify important issues for future research. One of the fundamental unresolved problems has been and still is how thinking emerges from an embodied system. Provocatively speaking, the central issue could be captured by the question “How does walking relate to thinking?”


title: HandJive: a device for interpersonal haptic entertainment

abstract: The paper describes how we designed and prototyped HandJive, a haptic device for interpersonal entertainment. Handlive is notable because it relies entirely on haptic input and output. The design process included typical steps such as analyzing user needs and performing iterative prototyping 2nd testing. However, developing a haptic interface like HandJive also presented special challenges, such as creating rapid physical prototypes that could withstand abuse, developing a preliminary system of haptic interaction, and testing haptic interfaces through low-tech prototypes.


title: The NIST Model for Role-Based Access Control: Towards A Unified Standard

abstract: This paper describes a unified model for role-based access control (RBAC). RBAC is a proven technology for large-scale authorization. However, lack of a standard model results in uncertainty and confusion about its utility and meaning. The NIST model seeks to resolve this situation by unifying ideas from prior RBAC models, commercial products and research prototypes. It is intended to serve as a foundation for developing future standards. RBAC is a rich and open-ended technology which is evolving as users, researchers and vendors gain experience with it. The NIST model focuses on those aspects of RBAC for which consensus is available. It is organized into four levels of increasing functional capabilities called flat RBAC, hierarchical RBAC, constrained RBAC and symmetric RBAC. These levels are cumulative and each adds exactly one new requirement. An alternate approach comprising flat and hierarchical RBAC in an ordered sequence and two unordered features—constraints and symmetry—is also presented. The paper furthermore identifies important attributes of RBAC not included in the NIST model. Some are not suitable for inclusion in a consensus document. Others require further work and agreement before standardization is feasible.


title: Cyberbullying Detection with a Pronunciation Based Convolutional Neural Network

abstract: Cyberbullying can have a deep and long lasting impact on its victims, who are often adolescents. Accurately detecting cyberbullying helps prevent it. However, the noise and errors in social media posts and messages make detecting cyberbullying very challenging. In this paper, we propose a novel pronunciation based convolutional neural network (PCNN) to address this challenge. Upon observing that the pronunciation of misspelled words in informal online conversations is often unchanged, we used the phoneme codes of the text as the features for a convolutional neural network. This procedure corrects spelling errors that did not alter the pronunciation, thereby alleviating the problem of noise and bullying data sparsity. To overcome class imbalance, a common problem in cyberbullying datasets, we implement three techniques that include threshold-moving, cost function adjusting, and a hybrid solution in our model. We evaluate the performance of our models using two cyberbullying datasets collected from Twitter and Formspring.me. The results of our experiment show that PCNN can achieve improved recall and precision compared to baseline convolutional neural networks.


title: Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs

abstract: Human brain connectivity is complex. Graph theory based analysis has become a powerful and popular approach for analyzing brain imaging data, largely because of its potential to quantitatively illuminate the networks, the static architecture in structure and function, the organization of dynamic behavior over time, and disease related brain changes. The first step in creating brain graphs is to define the nodes and edges connecting them. We review a number of approaches for defining brain nodes including fixed versus data-driven nodes. Expanding the narrow view of most studies which focus on static and/or single modality brain connectivity, we also survey advanced approaches and their performances in building dynamic and multi-modal brain graphs. We show results from both simulated and real data from healthy controls and patients with mental illnesse. We outline the advantages and challenges of these various techniques. By summarizing and inspecting recent studies which analyzed brain imaging data based on graph theory, this article provides a guide for developing new powerful tools to explore complex brain networks.


Cognitive architectures for multimedia learning


SemTagger: A Novel Approach for Semantic Similarity Based Hashtag Recommendation on Twitter.


title: TouchViz: a case study comparing two interfaces for data analytics on tablets

abstract: As more applications move from the desktop to touch devices like tablets, designers must wrestle with the costs of porting a design with as little revision of the UI as possible from one device to the other, or of optimizing the interaction per device. We consider the tradeoffs between two versions of a UI for working with data on a touch tablet. One interface is based on using the conventional desktop metaphor (WIMP) with a control panel, push buttons, and checkboxes -- where the mouse click is effectively replaced by a finger tap. The other interface (which we call FLUID) eliminates the control panel and focuses touch actions on the data visualization itself. We describe our design process and evaluation of each interface. We discuss the significantly better task performance and preference for the FLUID interface, in particular how touch design may challenge certain assumptions about the performance benefits of WIMP interfaces that do not hold on touch devices, such as the superiority of gestural vs. control panel based interaction.


title: Tuning of the structure and parameters of a neural network using an improved genetic algorithm

abstract: This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). It is also shown that the improved GA performs better than the standard GA based on some benchmark test functions. A neural network with switches introduced to its links is proposed. By doing this, the proposed neural network can learn both the input-output relationships of an application and the network structure using the improved GA. The number of hidden nodes is chosen manually by increasing it from a small number until the learning performance in terms of fitness value is good enough. Application examples on sunspot forecasting and associative memory are given to show the merits of the improved GA and the proposed neural network.


title: Cleaning up smart cities — Localization of semi-autonomous floor scrubber

abstract: The paper describes design and features of novel semi-autonomous floor scrubber add-on module, used for cleaning large indoor and outdoor tile/marbles spaces found in modern (smart) cities. Module is designed in such a manner that it can be easily added to and removed from scrubber machine and that additional sensors and capabilities can be introduced (modular design). In the paper localization capabilities of the machine in three sensor setups are presented and analyzed. Analysis is performed in terms of localization accuracy and reliability as well as associated advantages and disadvantages. Obtained results demonstrated that inclusion of UWB subsystem, despite its price and accuracy (±20 cm in ideal, line of sight, conditions), yields more reliable and accurate results in open spaces (up to 25 times in position and 2 times in orientation) where performance of used Lidar might be sub-optimal.


title: The biofilm matrix

abstract: The microorganisms in biofilms live in a self-produced matrix of hydrated extracellular polymeric substances (EPS) that form their immediate environment. EPS are mainly polysaccharides, proteins, nucleic acids and lipids; they provide the mechanical stability of biofilms, mediate their adhesion to surfaces and form a cohesive, three-dimensional polymer network that interconnects and transiently immobilizes biofilm cells. In addition, the biofilm matrix acts as an external digestive system by keeping extracellular enzymes close to the cells, enabling them to metabolize dissolved, colloidal and solid biopolymers. Here we describe the functions, properties and constituents of the EPS matrix that make biofilms the most successful forms of life on earth.


Review of 23 Years of Empirical Research on Information Technology Outsourcing Decisions and Outcomes


title: INJUSTICE: Interactive Live Action Virtual Reality Experience

abstract: Injustice is a three to five minute interactive virtual reality (VR) experience themed around racially motivated police brutality. In Injustice, guests witness an act of racial discrimination happening in front of them, forcing them to make moral and ethical decisions on the spot. The guest comes face to face with the characters of the story, filmed with live action, and interacts in the space with them directly using gaze interaction and voice recognition. Injustice is an experience aimed at exploring the emotional impact of VR space versus traditional film.


title: Video Game-Based Education in Mechanical Engineering: A Look at Student Engagement*

abstract: One of the core courses in the undergraduate mechanical engineering curriculum has been completely redesigned. In the new numerical methods course, all assignments and learning experiences are built around a video/computer game. Students are given the task of writing computer programs to race a simulated car around a track. In doing so, students learn and implement numerical methods content. The paper describes a preliminary study to measure student engagement. Results show that students `playing' the video game in their homework are significantly more engaged than when working on homework in other engineering courses.


title: Affect-LM: A Neural Language Model for Customizable Affective Text Generation

abstract: Human verbal communication includes affective messages which are conveyed through use of emotionally colored words. There has been a lot of research in this direction but the problem of integrating state-of-the-art neural language models with affective information remains an area ripe for exploration. In this paper, we propose an extension to an LSTM (Long Short-Term Memory) language model for generating conversational text, conditioned on affect categories. Our proposed model, Affect-LM enables us to customize the degree of emotional content in generated sentences through an additional design parameter. Perception studies conducted using Amazon Mechanical Turk show that Affect-LM generates naturally looking emotional sentences without sacrificing grammatical correctness. Affect-LM also learns affect-discriminative word representations, and perplexity experiments show that additional affective information in conversational text can improve language model prediction.


title: A Simple Geometrical Approach for Deinterleaving Radar Pulse Trains

abstract: Some periodic and quasi-periodic pulse trains are emitted by different sources in the environment and a number of sensors receive them through a single channel simultaneously. We are often interested in separating these pulse trains for source identification at sensors. This identification process is termed as deinterleaving pulse trains. Deinterleaving pulse trains has wide applications in communications, radar systems, neural systems, biomedical engineering, and so on. This paper studies the deinterleaving problem with the assumption that both sources and sensors are fixed. In this study, the problem of deinterleaving pulse trains is modeled as a blind source separation (BSS) problem. To solve the BSS problem, we propose a novel geometry-based producer that has not been discussed in the literature yet. The proposed method has superiority over the previous ones in a number of aspects. First, it is a computationally simple method. Second, the proposed algorithm is capable of deinterleaving similar pulse trains. Third, it is able to separate pulse trains with complex pulse repetition interval (PRI) modulations. Finally, the algorithm's performance is not influenced by missing and spurious pulses. Some numerical simulations are provided in order to illustrate the effectiveness of the proposed method.


title: A 0.8V, 560fJ/bit, 14Gb/s injection-locked receiver with input duty-cycle distortion tolerable edge-rotating 5/4X sub-rate CDR in 65nm CMOS

abstract: A quarter-rate forwarded-clock receiver utilizes an edgerotating 5/4X sub-rate CDR for improved jitter tolerance with low power overhead relative to conventional 2X oversampling CDR systems. Low-voltage operation is achieved with efficient quarter-rate clock generation from an injection-locked oscillator (ILO) and through automatic independent phase rotator control that optimizes timing margin of each input quantizer in the presence of receive-side clock static phase errors and transmitter duty-cycle distortion (DCD). Fabricated in GP 65nm CMOS, the receiver operates up to 16Gb/s with a BER<10 -12 , achieves a 1MHz phase tracking bandwidth, tolerates ±50%UIpp DCD on input data, and has 14Gb/s energy efficiency of 560fJ/bit at VDD=0.8V.


title: Biomimetics: process, tools and practice.

abstract: Biomimetics applies principles and strategies abstracted from biological systems to engineering and technological design. With a huge potential for innovation, biomimetics could evolve into a key process in businesses. Yet challenges remain within the process of biomimetics, especially from the perspective of potential users. We work to clarify the understanding of the process of biomimetics. Therefore, we briefly summarize the terminology of biomimetics and bioinspiration. The implementation of biomimetics requires a stated process. Therefore, we present a model of the problem-driven process of biomimetics that can be used for problem-solving activity. The process of biomimetics can be facilitated by existing tools and creative methods. We mapped a set of tools to the biomimetic process model and set up assessment sheets to evaluate the theoretical and practical value of these tools. We analyzed the tools in interdisciplinary research workshops and present the characteristics of the tools. We also present the attempt of a utility tree which, once finalized, could be used to guide users through the process by choosing appropriate tools respective to their own expertize. The aim of this paper is to foster the dialogue and facilitate a closer collaboration within the field of biomimetics.


title: Time-Space Efficient Regression Testing for Configurable Systems

abstract: Abstract Configurable systems are those that can be adapted from a set of input options, reflected in code in form of variations. Testing these systems is challenging because of the vast array of configuration possibilities where bugs can hide. In the context of evolution, testing becomes even more challenging — not only code but also the set of plausible configurations can change across versions. This paper proposes EvoSPLat , a regression testing technique for configurable systems that explores all dynamically reachable configurations from a test. EvoSPLat supports two important application scenarios of regression testing. In the RCS scenario EvoSPLat prunes configurations (not tests) that are not impacted by changes. In the RTS scenario EvoSPLat prunes tests (not configurations) which are not impacted by changes. To evaluate EvoSPLat under the RCS scenario we used a selection of configurable Java programs. Results indicate that EvoSPLat reduced time by ∼22% and reduced the number of configurations tested by ∼45%. To evaluate EvoSPLat under the RTS scenario we used GCC. Results indicate that EvoSPLat reduced time to run tests by ∼35%. Overall, results suggest that EvoSPLat is a promising technique to test configurable systems in the prevalent scenario of evolution.


title: Decoupled Novel Object Captioner

abstract: Image captioning is a challenging task where the machine automatically describes an image by sentences or phrases. It often requires a large number of paired image-sentence annotations for training. However, a pre-trained captioning model can hardly be applied to a new domain in which some novel object categories exist, i.e., the objects and their description words are unseen during model training. To correctly caption the novel object, it requires professional human workers to annotate the images by sentences with the novel words. It is labor expensive and thus limits its usage in real-world applications. In this paper, we introduce the zero-shot novel object captioning task where the machine generates descriptions without extra training sentences about the novel object. To tackle the challenging problem, we propose a Decoupled Novel Object Captioner (DNOC) framework that can fully decouple the language sequence model from the object descriptions. DNOC has two components. 1) A Sequence Model with the Placeholder (SM-P) generates a sentence containing placeholders. The placeholder represents an unseen novel object. Thus, the sequence model can be decoupled from the novel object descriptions. 2) A key-value object memory built upon the freely available detection model, contains the visual information and the corresponding word for each object. A query generated from the SM-P is used to retrieve the words from the object memory. The placeholder will further be filled with the correct word, resulting in a caption with novel object descriptions. The experimental results on the held-out MSCOCO dataset demonstrate the ability of DNOC in describing novel concepts.


title: Electrochemical properties of polyindole and poly(5-cyanoindole) in LiClO4—acetonitrile and in HCl and HClO4 solutions

abstract: Abstract Electropolymerization of indole and 5-cyanoindole leads to the formation of electroactive polymer films. Their analysis by FT-IR spectroscopy, as a function of the oxidation potential in aprotic medium (LiClO 4 —acetonitrile) shows that they exhibit the same type of redox reactions. The coupling between the monomer units occurs in both cases between the C 2 and C 3 positions. On the contrary, when polyindole and poly(5-cyanoindole) are cycled in HCl or HClO 4 solutions, differences appear in their electrochemical behaviour. Two types of redox reversible processes are observed in both polymers. For polyindole, these transformations are pH independent. For poly(5-cyanoindole), the reaction occurring at the most positive potential is pH dependent and is related to both protonic and electronic exchanges between the polymer and the electrolytic solution. The other redox transformation, at less positive potentials, is pH independent and can be described as an electronic exchange only. Mechanisms describing these two redox reversible reactions occurring when poly(5-cyanoindole) is cycled in acid solutions are proposed.


title: A taxonomy and survey of self-protecting software systems

abstract: Self-protecting software systems are a class of autonomic systems capable of detecting and mitigating security threats at runtime. They are growing in importance, as the stovepipe static methods of securing software systems have shown inadequate for the challenges posed by modern software systems. While existing research has made significant progress towards autonomic and adaptive security, gaps and challenges remain. In this paper, we report on an extensive study and analysis of the literature in this area. The crux of our contribution is a comprehensive taxonomy to classify and characterize research efforts in this arena. We also describe our experiences with applying the taxonomy to numerous existing approaches. This has shed light on several challenging issues and resulted in interesting observations that could guide the future research.


title: New Methods for Metadata Extraction from Scientific Literature

abstract: Within the past few decades we have witnessed digital revolution, which moved scholarly communication to electronic media and also resulted in a substantial increase in its volume. Nowadays keeping track with the latest scientific achievements poses a major challenge for the researchers. Scientific information overload is a severe problem that slows down scholarly communication and knowledge propagation across the academia. Modern research infrastructures facilitate studying scientific literature by providing intelligent search tools, proposing similar and related documents, visualizing citation and author networks, assessing the quality and impact of the articles, and so on. In order to provide such high quality services the system requires the access not only to the text content of stored documents, but also to their machine-readable metadata. Since in practice good quality metadata is not always available, there is a strong demand for a reliable automatic method of extracting machine-readable metadata directly from source documents. This research addresses these problems by proposing an automatic, accurate and flexible algorithm for extracting wide range of metadata directly from scientific articles in born-digital form. Extracted information includes basic document metadata, structured full text and bibliography section. Designed as a universal solution, proposed algorithm is able to handle a vast variety of publication layouts with high precision and thus is well-suited for analyzing heterogeneous document collections. This was achieved by employing supervised and unsupervised machine-learning algorithms trained on large, diverse datasets. The evaluation we conducted showed good performance of proposed metadata extraction algorithm. The comparison with other similar solutions also proved our algorithm performs better than competition for most metadata types.


title: Vision-based autonomous mapping and exploration using a quadrotor MAV

abstract: In this paper, we describe our autonomous vision-based quadrotor MAV system which maps and explores unknown environments. All algorithms necessary for autonomous mapping and exploration run on-board the MAV. Using a front-looking stereo camera as the main exteroceptive sensor, our quadrotor achieves these capabilities with both the Vector Field Histogram+ (VFH+) algorithm for local navigation, and the frontier-based exploration algorithm. In addition, we implement the Bug algorithm for autonomous wall-following which could optionally be selected as the substitute exploration algorithm in sparse environments where the frontier-based exploration under-performs. We incrementally build a 3D global occupancy map on-board the MAV. The map is used by the VFH+ and frontier-based exploration in dense environments, and the Bug algorithm for wall-following in sparse environments. During the exploration phase, images from the front-looking camera are transmitted over Wi-Fi to the ground station. These images are input to a large-scale visual SLAM process running off-board on the ground station. SLAM is carried out with pose-graph optimization and loop closure detection using a vocabulary tree. We improve the robustness of the pose estimation by fusing optical flow and visual odometry. Optical flow data is provided by a customized downward-looking camera integrated with a microcontroller while visual odometry measurements are derived from the front-looking stereo camera. We verify our approaches with experimental results.


Broadbanding of A-sandwich Radome Using JerusalemCross Frequency Selective Surface


title: A Versatile Power Electronics Test-Bench Architecture Applied to Domestic Induction Heating

abstract: The design of new power-converter solutions optimized for specific applications requires, at a certain step, the design and implementation of several prototypes in order to verify the converter operation. This is a time-consuming task which also involves a significant economical cost. The aim of this paper is to present a versatile power electronics architecture which provides a tool to make the implementation and evaluation of new power converters straightforward. The adopted platform includes a versatile control architecture and a modular power electronics hardware solution. The control architecture is a field-programmable-gate-array-based system-on-programmable-chip solution which combines the advantages of the processor-firmware versatility and the effectiveness of ad hoc paralleled digital hardware. Moreover, the modular power electronics hardware provides a fast method to reconfigure the power-converter topology. The architecture proposed in this paper has been applied to the development of power converters for domestic induction heating, although it can be extended to other applications with similar requirements. A complete development test bench has been carried out, and some experimental results are shown in order to verify the proper system operation.


title: Automated identification of intracranial depth electrodes in computed tomography data

abstract: Intracranial depth electrodes are commonly used to identify the regions of the brain that are responsible for epileptic seizures. Knowledge of the exact location of the electrodes is important as to properly interpret the EEG in relation to the anatomy. In order to provide fast and accurate identification of these electrodes, a procedure has been developed for automatic detection and localization in computed tomography data. Results indicate that in the vast majority of cases the depth electrodes can be automatically found. The localization of the electrodes versus the anatomy showed an acceptably small error when compared to manual positioning. Furthermore, interactive visualization software is developed to show the detected electrodes together with pre-operative MRI images, which enables the physician to confirm that the electrode is placed at the expected anatomical location.


title: Optimization Using Simulation of Traffic Light Signal Timings

abstract: Traffic congestion has become a great challenge and a large burden on both the governments and the citizens in vastly populated cities. The main problem, originally initiated by several factors, continues to threaten the stability of modern cities and the livelihood of its habitants. Hence, improving the control strategies that govern the traffic operations is a powerful solution that can solve the congestion problem. These improvements can be achieved by enhancing the traffic control performance through adjusting the traffic signal timings. This paper focuses on finding various solutions for the addressed problem through the optimization using simulation of traffic signal timings under oversaturated conditions. The system under study is an actual road network in Alexandria, Egypt; where, numerous data have been collected in different time intervals. A series of computer simulation models to represent the actual system as well as proposed solutions have been developed using the ExtendSim simulation environment. Furthermore, an evolutionary optimizer is utilized to attain a set of optimum/near-optimum signal timings to minimize the total time in system of the vehicles, resulting in an improved performance of the road network. Analysis of experimentation results shows that the adopted methodology optimizes the vehicular flow in a multiple-junction urban traffic network.


title: Triple Attention Mixed Link Network for Single Image Super Resolution

abstract: Single image super resolution is of great importance as a low-level computer vision task. Recent approaches with deep convolutional neural networks have achieved im-pressive performance. However, existing architectures have limitations due to the less sophisticated structure along with less strong representational power. In this work, to significantly enhance the feature representation, we proposed Triple Attention mixed link Network (TAN) which consists of 1) three different aspects (i.e., kernel, spatial and channel) of attention mechanisms and 2) fu-sion of both powerful residual and dense connections (i.e., mixed link). Specifically, the network with multi kernel learns multi hierarchical representations under different receptive fields. The output features are recalibrated by the effective kernel and channel attentions and feed into next layer partly residual and partly dense, which filters the information and enable the network to learn more powerful representations. The features finally pass through the spatial attention in the reconstruction network which generates a fusion of local and global information, let the network restore more details and improves the quality of reconstructed images. Thanks to the diverse feature recalibrations and the advanced information flow topology, our proposed model is strong enough to per-form against the state-of-the-art methods on the bench-mark evaluations.


title: WriteAhead2: Mining Lexical Grammar Patterns for Assisted Writing

abstract: This paper describes WriteAhead2, an interactive writing environment that provides lexical and grammatical suggestions for second language learners, and helps them write fluently and avoid common writing errors. The method involves learning phrase templates from dictionary examples, and extracting grammar patterns with example phrases from an academic corpus. At run-time, as the user types word after word, the actions trigger a list after list of suggestions. Each successive list contains grammar patterns and examples, most relevant to the half-baked sentence. WriteAhead2 facilitates steady, timely, and spot-on interactions between learner writers and relevant information for effective assisted writing. Preliminary experiments show that WriteAhead2 has the potential to induce better writing and improve writing skills.


title: Attention-aware Generalized Mean Pooling for Image Retrieval

abstract: It has been shown that image descriptors extracted by convolutional neural networks (CNNs) achieve remarkable results for retrieval problems. In this paper, we apply attention mechanism to CNN, which aims at enhancing more relevant features that correspond to important keypoints in the input image. The generated attention-aware features are then aggregated by the previous state-of-the-art generalized mean (GeM) pooling followed by normalization to produce a compact global descriptor, which can be efficiently compared to other image descriptors by the dot product. An extensive comparison of our proposed approach with state-of-the-art methods is performed on the new challenging ROxford5k and RParis6k retrieval benchmarks. Results indicate significant improvement over previous work. In particular, our attention-aware GeM (AGeM) descriptor outperforms state-of-the-art method on ROxford5k under the `Hard' evaluation protocal.


Telecommunications Fraud: Problem Analysis- an Agent-based KDD Perspective


title: Challenge and solutions of NAT traversal for ubiquitous and pervasive applications on the Internet

abstract: Network Address Translator (NAT) has brought up many changes and opportunities to the Internet. How do the ubiquitous and pervasive applications coexist with NAT and interoperate with each other? In this article, we discuss the essence of NAT sensitive applications as well as the challenge and response for various NAT traversal solutions. All questions pointed to redesign a new NAT framework with a major change to accommodate NAT problems all at once. We introduce a novel next generation NAT (NATng) framework, which consists of a Bi-directional NAT (BNAT) and a Domain Name System Application Level Gateway (DNS_ALG) with a Border Network Address Translator Control Protocol (BNATCP) function to control all BNATs. The above components coordinate and provide bidirectional access capability between intranet and Internet, so all private hosts can be addressed via Fully Qualified Domain Name (FQDN). Logistically, NATng extends the IPv4 address space from 2^3^2to2^4^8 or even more. It features high potential to solve the problems for ubiquitous and pervasive applications which may encounter IPv4 address exhaustion on the current Internet.


title: Wayang fighters: preserving the art of wayang kulit (shadow puppets) through game application

abstract: The art of shadow play puppet evolved as a unique form of theatre in Malaysia. Unfortunately, Wayang Kulit in Malaysia today is threatened with imminent extinction. The existing traditional Wayang Kulit or shadow play entertainment should be preserved through the digital media. This paper describes a new approach for creating a game design application called "Wayang Fighters". It provides an overview of related digitalization and interaction media using Wayang Kulit or shadow play as a motivation for the development of a comprehensive but easily authoring games tool which assists authors in different tasks encountered during the creation process. The methodology model basis of the game design is described, as well as a structured approach including puppet character, User Interface (UI) and game play mode, are outlined in each step of the process. The method and results of a usability test carried out are also presented


title: Interpretable Set Functions

abstract: We propose learning flexible but interpretable functions that aggregate a variable-length set of permutation-invariant feature vectors to predict a label. We use a deep lattice network model so we can architect the model structure to enhance interpretability, and add monotonicity constraints between inputs-and-outputs. We then use the proposed set function to automate the engineering of dense, interpretable features from sparse categorical features, which we call semantic feature engine. Experiments on real-world data show the achieved accuracy is similar to deep sets or deep neural networks, and is easier to debug and understand.


Proportional-Resonant Controllers. A New Breed of Controllers Suitable for Grid-Connected Voltage-Source Converters


title: High Efficient Interleaved Multi-channel dc/dc Converter Dedicated to Mobile Applications

abstract: In the transportation field, the empty weight of the vehicle has to be as light as possible in order to have the highest energetic efficiency. For onboard storage electrical systems, dc/dc converters are usually used to control the power flows in the vehicle. This paper will focus on two different dc/dc converter applications: high current / low voltage on the one hand and low current / high voltage on the other hand. For all power mobile applications, a standard dc/dc converter is not realizable due to mass and volume constraints. As the inductor is usually the biggest and heaviest component in the system, it is important to develop a new structure allowing the reduction of its size and weight. The chosen technology is based on interleaved channels operating in discontinuous mode, also using reverse conduction of power mosfet transistors. For both of the cases described above, a prototype has been built composed of 8 and 4 channels respectively. The first converter is used for power assistance (250 A / 18 V) using the MOS reverse conduction property. In this particular case, efficiency is up to 96.3 %. The second is developed for a mobile solar application (3.8 A / 270 V). Efficiencies are up to 98.4 %.


title: Multi-focus image fusion based on the neighbor distance

abstract: The effective measurement of pixel's sharpness is a key factor in multi-focus image fusion. In this paper, a gray image is considered as a two-dimensional surface, and the neighbor distance deduced from the oriented distance in differential geometry is used as a measure of pixel's sharpness, where the smooth image surface is restored by kernel regression. Based on the deduced neighbor distance filter, we construct a multi-scale image analysis framework, and propose a multi-focus image fusion method based on the neighbor distance. The experiments demonstrate that the proposed method is superior to the conventional image fusion methods in terms of some objective evaluation indexes, such as spatial frequency, standard deviation, average gradient, etc.


title: Vehicle Speed Prediction Using Deep Learning

abstract: Global optimization of the energy consumption of dual power source vehicles such as hybrid electric vehicles, plug-in hybrid electric vehicles, and plug in fuel cell electric vehicles requires knowledge of the complete route characteristics at the beginning of the trip. One of the main characteristics is the vehicle speed profile across the route. The profile will translate directly into energy requirements for a given vehicle. However, the vehicle speed that a given driver chooses will vary from driver to driver and from time to time, and may be slower, equal to, or faster than the average traffic flow. If the specific driver speed profile can be predicted, the energy usage can be optimized across the route chosen. The purpose of this paper is to research the application of Deep Learning techniques to this problem to identify at the beginning of a drive cycle the driver specific vehicle speed profile for an individual driver repeated drive cycle, which can be used in an optimization algorithm to minimize the amount of fossil fuel energy used during the trip.


title: Investigating LSTM for punctuation prediction

abstract: We present a neural network based punctuation prediction method using Long Short-Term Memory (LSTM) network. The proposed method uses bidirectional LSTM to encode both the past and future observation as its inputs. It models the dependency between input features and output labels through multiple layers. We also empirically study the impacts of modeling the dependency between output labels. Our results show that using a deep bi-directional LSTM is able to achieve state-of-the-art performance in punctuation prediction.


title: A practical circuit-based model for Li-ion battery cells in electric vehicle applications

abstract: This paper proposes a practical circuit-based model for Li-ion cells, which can be directly connected to a model of a complete electric vehicle (EV) system. The goal of this paper is to provide EV system designers with a tool in simulation programs such as Matlab/Simulink to model the behaviour of Li-ion cells under various operating conditions in EV or other applications. The current direction, state of charge (SoC), temperature and C-rate dependency are represented by empirical equations obtained from measurements on LiFePO 4 cells. Tradeoffs between model complexity and accuracy have been made based on practical considerations in EV applications. Depending on the required accuracy and operating conditions, the EV system designer can choose the influences to be included in the system simulation.


title: Spatially adaptive Total Variation image denoising under salt and pepper noise

abstract: Automated selection of the regularization parameter for Total Variation restoration has shown to give very accurate reconstruction results. Most of the literature is devoted to the l2-TV case (images corrupted with Gaussian noise), whereas for the l1-TV case (images corrupted with salt-and-pepper noise) there are only a couple of published algorithms. In this paper we present a computationally efficient algorithm for l1-TV denoising of grayscale and color images, which spatially adapts its regularization parameter. The proposed algorithm, which is based on the Iteratively Reweighted Norm algorithm, uses an adaptive median filter to initially estimate the outliers of the noisy (observed) image, and then proceeds to solve the l1-TV problem only for the noisy pixels while spatially adapts the regularization parameter based on local statistics. The experimental results show that the proposed method yields impressive results even when 90% of the image pixels are corrupted.


title: Generating Smooth High-Quality Isosurfaces for Interactive Modeling and Visualization of Complex Terrains

abstract: In modeling and rendering of complex procedural terrains the extraction of isosurfaces is an important part. In this paper we introduce an approach to generate high-quality isosurfaces from regular grids at interactive frame rates. The surface extraction is a variation of Dual Marching Cubes and designed as a set of well-balanced parallel computation kernels. In contrast to a straightforward parallelization we generate a quadrilateral mesh with full connectivity information and 1-ring vertex neighborhood. We use this information to smooth the extracted mesh and to approximate the smooth subdivision surface for detail tessellation. Both improve the visual fidelity when modeling procedural terrains interactively. Moreover, our extraction approach is generally applicable, for example in the field of volume visualization.


title: Authorship Attribution Using Relative Compression

abstract: Authorship attribution is a classical classification problem. We use it here to illustrate the performance of a compression-based measure that relies on the notion of relative compression. Besides comparing with recent approaches that use multiple discriminant analysis and support vector machines, we compare it with the Normalized Conditional Compression Distance (a direct approximation of the Normalized Information Distance) and the popular Normalized Compression Distance. The Normalized Relative Compression (NRC) attained 100% correct classification in the data set used, showing consistency between the compression ratio and the classification performance, a characteristic not always present in other compression-based measures.


title: On a new generation of torque controlled light-weight robots

abstract: The paper describes the recent design and development efforts in DLR Robotics Lab towards the second generation of light-weight robots. The design of the light weight mechanics, integrated sensors and electronics is outlined. The fully sensory joint, with motor and link position sensors as well as joint torque sensors enables the implementation of effective vibration damping and advanced control strategies for compliant manipulation. The mechatronic approach incorporates a tight collaboration between mechanics, electronics and controller design. The authors hope that important steps towards a new generation of service and personal robots have been achieved.


title: Sparse Occlusion Detection with Optical Flow

abstract: We tackle the problem of detecting occluded regions in a video stream. Under assumptions of Lambertian reflection and static illumination, the task can be posed as a variational optimization problem, and its solution approximated using convex minimization. We describe efficient numerical schemes that reach the global optimum of the relaxed cost functional, for any number of independently moving objects, and any number of occlusion layers. We test the proposed algorithm on benchmark datasets, expanded to enable evaluation of occlusion detection performance, in addition to optical flow.


title: On Validation Setup for Multiclass Imbalanced Data Sets

abstract: The validation of experiments is commonly evaluated with Cross-Validation methods. In the literature the 10-fold, followed by bootstrap, are the most indicated methods. However there lacks a study of a proper validation procedure for imbalanced data sets, specially for the rare class case. In this work the most used validation methods were tested in ten imbalanced data sets, with a generic and an ad hoc classifiers. Analyses showed that 10-fold, followed by hold-out, are the indicated methods when using a generic classifier. For the ad hoc classifier the 10-fold, followed by bootstrap, are the indicated ones. In the case of rare classes in a data set, the most indicated method is the repeated hold-out.


title: The p53 network in head and neck cancer.

abstract: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide and, despite advances in treatment, still represents a clinical challenge. Inactivation of one or more components of the p53 network is an extremely common event in human neoplasia. In HNSCC, disabling of p53 occurs in a high proportion of cases by mutation in the p53 gene, but other mechanisms of inactivation, such as the presence of human papillomavirus (HPV) and molecular abnormalities in other components of the pathway, are also recognised. The frequent changes occurring in the p53 pathway in HNSCC imply that molecular genetic and immunocytochemical analysis of this critical tumour suppressor network may be of diagnostic and prognostic utility in the clinical management of HNSCC. Further, these changes also provide targets for the development of novel therapeutic approaches to this increasingly common cancer, in which clinical cure for advanced disease remains an elusive goal.


title: Social responses to Facebook status updates: The role of extraversion and social anxiety

abstract: Abstract Posting, reading, and responding to status updates is an integral part of many peoples' daily lives. However, the role of personality in predicting social responses to status updates remains largely unexplored. Based on the social enhancement and the social compensation hypothesis, we assessed the role of extraversion and social anxiety in predicting social responses to status updates. Moreover, we explored the influence of valence in this context. Capitalizing on the assets of a multimethod approach, personality was assessed with self-reports, and valence was evaluated by independent raters. Social responses to status updates were captured (1) by observing direct social feedback (i.e. likes and commenters) and (2) by informant reports on the interpersonal appraisal of participants’ status updates by their friends. In a German and a US sample, for direct social feedback neither extraversion nor social anxiety emerged as significant predictors. However, analyses of the informant reports showed that status updates of more socially anxious individuals were appreciated more by their friends. Furthermore, results pointed to the importance of valence in this context; revealing associations between valence and direct social feedback, valence and extraversion, and a moderation effect of personality on the association between valence and likes in the US sample.


title: Video shot boundary detection using midrange local binary pattern

abstract: The fundamental step in content based video retrieval is shot boundary detection. This step is essential for characterizing videos in any video processing system. Reliable detection of shots in videos is still a challenging issue. In this work, we address automatic detection of abrupt shots in video sequences. We have proposed a method termed as Midrange LBP (MRLBP), which enhances the discriminative capability of basic LBP. Each frame of a video is processed to extract LBP histogram values based on midrange statistics for its description. A dissimilarity measure is applied on the feature vectors of adjacent frames and the distance values obtained are used for shot detection process using adaptive threshold approach. To check the efficacy of the proposed method, we carried out experiments on a subset of standard video data set TRECVID 2001. The results obtained by the proposed approach outperform the existing shot boundary detection algorithms in terms of different performance measures.