title: A probabilistic data-driven model for planar pushing

abstract: This paper presents a data-driven approach to model planar pushing interaction to predict both the most likely outcome of a push and its expected variability. The learned models rely on a variation of Gaussian processes with input-dependent noise called Variational Heteroscedastic Gaussian processes (VHGP) that capture the mean and variance of a stochastic function. We show that we can learn accurate models that outperform analytical models after less than 100 samples and saturate in performance with less than 1000 samples. We validate the results against a collected dataset of repeated trajectories, and use the learned models to study questions such as the nature of the variability in pushing, and the validity of the quasi-static assumption.


title: Privacy-preserving data publishing for cluster analysis

abstract: Releasing person-specific data could potentially reveal sensitive information about individuals. k-anonymization is a promising privacy protection mechanism in data publishing. Although substantial research has been conducted on k-anonymization and its extensions in recent years, only a few prior works have considered releasing data for some specific purpose of data analysis. This paper presents a practical data publishing framework for generating a masked version of data that preserves both individual privacy and information usefulness for cluster analysis. Experiments on real-life data suggest that by focusing on preserving cluster structure in the masking process, the cluster quality is significantly better than the cluster quality of the masked data without such focus. The major challenge of masking data for cluster analysis is the lack of class labels that could be used to guide the masking process. Our approach converts the problem into the counterpart problem for classification analysis, wherein class labels encode the cluster structure in the data, and presents a framework to evaluate the cluster quality on the masked data.


AI as evaluator: Search driven playtesting of modern board games


title: A new EEG recording system for passive dry electrodes

abstract: Abstract Objective We present a new, low power EEG recording system with an ultra-high input impedance that enables the use of long-lasting, passive dry electrodes. It incorporates Bluetooth wireless connectivity and is designed to be suitable for long-term monitoring during daily activities. Methods The new EEG system is compared to a standard and clinically available reference EEG system using wet electrodes in three separate sets of experiments. In the first two experiments, each dry electrode was surrounded by four standard wet electrodes and the alpha and mu-rhythms were recorded. In the third experiment, serial monopolar (referred to the left ear) recordings of flash visual evoked potential were performed using the new EEG system and a reference system. Results These experiments showed that the signal recorded using the new EEG system is almost identical to that recorded with standard clinical EEG equipment; our measurements showed that the correlation coefficient between the dry electrode recordings and the average of the four standard electrodes surrounding each dry electrode is greater than 0.85. Conclusion We conclude that the new EEG system performs similarly to reference EEG systems, while providing the advantages of portability, ease of application and minimal scalp preparation. Significance The proposed system using passive dry electrodes suitable for single use while performing as good as standard EEG equipment provides ease of application and minimal scalp preparation.


title: An RNA aptamer that specifically binds pancreatic adenocarcinoma up-regulated factor inhibits migration and growth of pancreatic cancer cells.

abstract: Previously, we reported that a novel secretory protein, pancreatic adenocarcinoma up-regulated factor (PAUF), which is highly expressed in pancreatic cancer and mediates the growth and metastasis of pancreatic cancer cells. In this study, we generated and characterized a 2'-fluoropyrimidine modified RNA aptamer (P12FR2) directed against human PAUF. P12FR2 binds specifically to human PAUF with an estimated apparent K(D) of 77nM. P12FR2 aptamer inhibits PAUF-induced migration of PANC-1, human pancreatic cancer cells, in a wound healing assay. Moreover, intraperitoneal injection of P12FR2 decreased tumor growth by about 60% in an in vivo xenograft model with CFPAC-1 pancreatic cancer cells, without causing a loss of weight in the treated mice. Taken together, we propose here that PAUF-specific RNA aptamer, P12FR2, has the potential to be effective in the therapy of human pancreatic cancer.


Personal Daq / 3000 Series USB 1-MHz , 16-Bit Multifunction Modules


title: Context-aware Access Control in Pervasive Healthcare

abstract: Progress in mobile devices, wireless networks and context-aware technologies are bringing pervasive healthcare into reality. With the help of wireless PDAs and portable computers, people may enjoy high quality care from a well-orchestrated team of healthcare professionals in the comfort of their own homes. The main technical challenges include mobility, adaptability, privacy, access authorization, and resource awareness. This paper presents a rule-based approach to context-aware access control in pervasive healthcare. The system is designed to work on resource-limited mobile devices over a peer-to-peer wireless network. Dynamic access authorization is achieved in real time by actively collecting context information, integrating the appropriate access control rules, and performing logical inference on the mobile device. Performance evaluations of the prototype implementation show the efficiency of the proposed mechanism.


Time-frequency kernel-based CNN for speech recognition.


title: A Dual-Fed Aperture-Coupled Microstrip Antenna With Polarization Diversity

abstract: This communication presents a dual-port reconfigurable square patch antenna with polarization diversity for 2.4 GHz. By controlling the states of four p-i-n diodes on the patch, the polarization of the proposed antenna can be switched among linear polarization (LP), left- or right-hand circular polarization (CP) at each port. The air substrate and aperture-coupled feed structure are employed to simplify the bias circuit of p-i-n diodes. With high isolation and low cross-polarization level in LP modes, both ports can work simultaneously as a dual linearly polarized antenna for polarimetric radars. Different CP waves are obtained at each port, which are suitable for addressing challenges ranging from mobility, adverse weather conditions and non-line-of-sight applications. The antenna has advantages of simple biasing network, easy fabrication and adjustment, which can be widely applied in polarization diversity applications.


title: Programming & storytelling: opportunities for learning about coding & composition

abstract: The focus of this paper is to investigate how writing computer programs can help children develop their storytelling and creative writing abilities. The process of writing a program---coding---has long been considered only in terms of computer science, but such coding is also reflective of the imaginative and narrative elements of fiction writing workshops. Writing to program can also serve as programming to write, in which a child learns the importance of sequence, structure, and clarity of expression---three aspects characteristic of effective coding and good storytelling alike. While there have been efforts examining how learning to write code can be facilitated by storytelling, there has been little exploration as to how such creative coding can also be directed to teach students about the narrative and storytelling process. Using the introductory programming language Scratch, this paper explores the potential of having children create their own digital stories with the software and how the narrative structure of these stories offers kids the opportunity to better understand the process of expanding an idea into the arc of a story.


title: Dietary fat and physical performance.

abstract: Although the relationship between dietary carbohydrate and physical performance is well described, there is much controversy about the relationship between dietary fat and physical performance. Recently, several studies have tried to clarify this relationship. Here the effects of acute fat on metabolism and performance will be discussed, as well as the effects of short-term and long-term high-fat diets.


title: A Spatio-Temporal Data-Mining Approach for Identification of Potential Fishing Zones Based on Oceanographic Characteristics in the Eastern Indian Ocean

abstract: The traditional approach for determining potential fishing zones (PFZs) relies on oceanographic factors (biological, physical, and chemical) and fishermen's expertise. This approach has disadvantages particularly when it comes to the analysis of combining these factors to find an exact PFZ spatially and temporally. In this study, we proposed a framework for identifying PFZs based on a data-mining approach in the Eastern Indian Ocean. We utilized a spatio-temporal clustering method to identify clusters of zones with data on the largest number of fish catch, which were then integrated with the sea surface temperature (SST) and the sea surface chlorophyll a (SSC) data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. The results of this data integration method were used as training data in the classification process, which was then used to determine PFZs. During the classification process, we utilized the $k$-nearest neighbor (KNN) classification method. The result gave an average accuracy of 87.11%, which showed that the proposed framework can be used effectively to determine PFZs. To validate the framework, we compared its performance against the heuristic rules taken from the knowledge-based expert system model on the SST and chlorophyll a data. The results showed that the proposed data-mining framework outperformed the heuristic rules from the knowledge-based expert system model.


title: Neural Language Correction with Character-Based Attention

abstract: Natural language correction has the potential to help language learners improve their writing skills. While approaches with separate classifiers for different error types have high precision, they do not flexibly handle errors such as redundancy or non-idiomatic phrasing. On the other hand, word and phrase-based machine translation methods are not designed to cope with orthographic errors, and have recently been outpaced by neural models. Motivated by these issues, we present a neural network-based approach to language correction. The core component of our method is an encoder-decoder recurrent neural network with an attention mechanism. By operating at the character level, the network avoids the problem of out-of-vocabulary words. We illustrate the flexibility of our approach on dataset of noisy, user-generated text collected from an English learner forum. When combined with a language model, our method achieves a state-of-the-art $F_{0.5}$-score on the CoNLL 2014 Shared Task. We further demonstrate that training the network on additional data with synthesized errors can improve performance.


title: The Double-Deficit Hypothesis A Comprehensive Analysis of the Evidence

abstract: The double-deficit hypothesis of developmental dyslexia proposes that deficits in phonological processing and naming speed represent independent sources of dysfunction in dyslexia. The present article is a review of the evidence for the double-deficit hypothesis, including a discussion of recent findings related to the hypothesis. Studies in this area have been characterized by variability in methodology— how dyslexia is defined and identified, and how dyslexia subtypes are classified. Such variability sets limitations on the extent to which conclusions may be drawn with respect to the double-deficit hypothesis. Furthermore, the literature is complicated by the persistent finding that measures of phonological processing and naming speed are significantly correlated, resulting in a statistical artifact that makes it difficult to disentangle the influence of naming speed from that of phonological processing. Longitudinal and intervention studies of the double-deficit hypothesis are needed to accumulate evid...


title: Study of Aerosol Influence on Nighttime Land Surface Temperature Retrieval Based on Two Methods

abstract: The aim of this study is to evaluate the aerosol influence on LST retrieval with two algorithms (split-window (SW) method and a four-channel based method) using simulated data under typical conditions. The results show that the root mean square error (RMSE) decreases to approximately 2.3 K for SW method and 1.5 K for four channel based method when VZA = 60° and visibility = 3 km; an RMSE would be increased by approximately 1.0 K when visibility varies from 3 km to 23 km. Moreover, a detailed sensitivity analysis under a visibility of 3 km and 23 km is performed in terms of uncertainties of land surface emissivity (LSE), water vapor content (WVC), and instrument noise, respectively. It is noted that the four-channel based method is more sensitive to LSE than SW method, especially for dry atmosphere; LST error caused by a WVC uncertainty of 20% is within 1.5 K for SW method and within 0.8 K for four-channel based method; the instrument noise would introduce LST error with a maximum standard deviation of 0.5 K and 0.04 K for the four-channel based method and SW method, respectively.


title: Jumping the PBL Implementation Hurdle: Supporting the Efforts of K-12 Teachers.

abstract: While problem-based learning (PBL) has a relatively long history of successful use in medical and pre-professional schools, it has yet to be widely adopted by K–12 teachers. This may be due, in part, to the numerous challenges teachers experience when implementing PBL. In this paper, we describe specific hurdles that teachers are likely to encounter during the implementation process and provide specific suggestions for supporting teachers’ classroom efforts. Implementation challenges relate to 1) creating a culture of collaboration and interdependence, 2) adjusting to changing roles, and 3) scaffolding student learning and performance. By supporting teachers’ initial and ongoing efforts, we anticipate that more teachers will recognize the potential of PBL as an effective instructional approach for developing learners who are flexible thinkers and successful problem solvers.


title: Setting the stage for embodied activity: scientific discussion around a multitouch tabletop display

abstract: Multitouch tabletop displays are a promising technology for supporting small group discussion and learning activities. Based on a view of human activity as multimodal, distributed, and embodied, we examine the pedagogical affordances of interactive tabletop technology, how it supports small group scientific discussion, and compare it with use of traditional paper-based study materials. We analyse video data from a five-week study involving 20 students from a university neuroscience course. Our analysis illustrates subtle but important differences in how students exploit their hands, bodies, and environmental resources depending on the medium. Finally, we discuss several characteristics of multitouch tabletop displays that make them particularly well-suited for supporting embodied conceptualisations during science learning.


title: FF-SKPCCA: Kernel probabilistic canonical correlation analysis

abstract: Several information fusion methods are developed for increasing the recognition accuracy in multimodal systems. Canonical correlation analysis (CCA), cross-modal factor analysis (CFA) and their kernel versions are known as successful fusion techniques but they cannot digest the data variability. Probabilistic CCA (PCCA) is suggested as a linear fusion method to capture input variability. A new kernel PCCA (KPCCA) is proposed here to capture both the nonlinear correlations of sources and input variability. The functionality of KPCCA decreases when the number of samples, which determines the size of kernel matrix increases. In the conventional fusion methods the latent variables of different modalities are concatenated; consequently, a large-scale covariance matrix with just limited number of samples must be estimated To overcome this drawback, a sparse KPCCA (SKPCCA) is introduced which scarifies the covariance matrix elements at the cost of decreasing its rank. In the final stage of the gradual evolution of KPCCA, a new feature fusion manner is proposed for SKPCCA (FF-SKPCCA) as a second stage fusion. This proposed method unifies the latent variables of two modalities into a feature vector with an acceptable size. Audio-visual databases like M2VTS (for speech recognition) eNTERFACE and RML (for emotion recognition) are applied to assess FF-SKPCCA compared to state-of-the-art fusion methods. The comparative results indicate the superiority of the proposed method in most cases.


title: A Privacy Preserving System for Cloud Computing

abstract: Cloud computing is changing the way that organizations manage their data, due to its robustness, low cost and ubiquitous nature. Privacy concerns arise whenever sensitive data is outsourced to the cloud. This paper introduces a cloud database storage architecture that prevents the local administrator as well as the cloud administrator to learn about the outsourced database content. Moreover, machine readable rights expressions are used in order to limit users of the database to a need-to-know basis. These limitations are not changeable by administrators after the database related application is launched, since a new role of rights editors is defined once an application is launced. Furthermore, trusted computing is applied to bind cryptographic key information to trusted states. By limiting the necessary trust in both corporate as well as external administrators and service providers, we counteract the often criticized privacy and confidentiality risks of corporate cloud computing.


title: ASR — A real-time speech recognition on portable devices

abstract: This paper presents the implementation of real-time automatic speech recognition (ASR) for portable devices. The speech recognition is performed offline using PocketSphinx which is the implementation of Carnegie Mellon University's Sphinx speech recognition engine for portable devices. In this work, machine Learning approach is used which converts graphemes into phonemes using the TensorFlow's Sequence-to-Sequence model to produce the pronunciations of words. This paper also explains the implementation of statistical language model for ASR. The novelty of ASR is its offline speech recognition and thus requires no Internet connection compared to other related works. A speech recognition service currently provides the cloud based processing of speech and therefore has access to the speech data of users. However, the speech is processed on the handheld device in offline ASR and therefore enhances the privacy of users.


title: The Epi.q-1 Hybrid Mobile Robot

abstract: In this paper we propose an innovative solution for a small size hybrid mobile robot called Epi.q-1. Overall dimensions are about 160 mm x 360 mm x 280 mm (height x length x width). The Epi.q-1 robot moves on flat, steep or uneven ground. It can climb over obstacles or steps, even if they are non-uniform in size. Its operating mode adapts to ground conditions and changes accordingly: from rolling (on wheels) to stepping (on legs); thanks to its great mobility it can follow complex routes. It is easy to control with just a few actuators. Robot locomotion drive-generating units employ an original driving mechanism, where degrees of freedom (DOFs) can be limited to some extent according to the operating conditions or thanks to a switching device. Locomotion units consist of a motor linked to a gear (double epicyclical chain) and an axial device (mini-motor and lead screw system) able to lock or unlock some DOFs of the kinematic chain. Moreover, the robot locomotion unit can change its size, from small to large and vice versa, in order to be able to reach restricted spaces and to overcome even quite tall obstacles. It was experimentally tested on flat ground and slopes. It can overcome 90 mm obstacles, that are 72% of the height of the locomotion unit, and climb stairs.


title: Modelling and Simulation for Optimal Control of Nonlinear Inverted Pendulum Dynamical System Using PID Controller and LQR

abstract: This paper presents the modelling and simulation for optimal control design of nonlinear inverted pendulum-cart dynamic system using Proportional-Integral-Derivative (PID) controller and Linear Quadratic Regulator (LQR). LQR, an optimal control technique, and PID control method, both of which are generally used for control of the linear dynamical systems have been used in this paper to control the nonlinear dynamical system. The nonlinear system states are fed to LQR which is designed using linear state-space model. Inverted pendulum, a highly nonlinear unstable system is used as a benchmark for implementing the control methods. Here the control objective is to control the system such that the cart reaches at a desired position and the inverted pendulum stabilizes in upright position. The MATLAB-SIMULINK models have been developed for simulation of control schemes. The simulation results justify the comparative advantages of LQR control methods.


title: An Effective Scalable SQL Engine for NoSQL Databases

abstract: NoSQL databases were initially devised to support a few concrete extreme scale applications. Since the specificity and scale of the target systems justified the investment of manually crafting application code their limited query and indexing capabilities were not a major impediment. However, with a considerable number of mature alternatives now available there is an increasing willingness to use NoSQL databases in a wider and more diverse spectrum of applications and, to most of them, hand-crafted query code is not an enticing trade-off.


title: Crossing Nets: Dual Generative Models with a Shared Latent Space for Hand Pose Estimation.

abstract: State-of-the-art methods for 3D hand pose estimation from depth images require large amounts of annotated training data. We propose to model the statistical relationships of 3D hand poses and corresponding depth images using two deep generative models with a shared latent space. By design, our architecture allows for learning from unlabeled image data in a semi-supervised manner. Assuming a one-to-one mapping between a pose and a depth map, any given point in the shared latent space can be projected into both a hand pose and a corresponding depth map. Regressing the hand pose can then be done by learning a discriminator to estimate the posterior of the latent pose given some depth map. To improve generalization and to better exploit unlabeled depth maps, we jointly train a generator and a discriminator. At each iteration, the generator is updated with the back-propagated gradient from the discriminator to synthesize realistic depth maps of the articulated hand, while the discriminator benefits from an augmented training set of synthesized and unlabeled samples. The proposed discriminator network architecture is highly efficient and runs at 90 FPS on the CPU with accuracies comparable or better than state-of-art on 3 publicly available benchmarks.


title: Query Segmentation Revisited

abstract: We address the problem of query segmentation: given a keyword query, the task is to group the keywords into phrases, if possible. Previous approaches to the problem achieve reasonable segmentation performance but are tested only against a small corpus of manually segmented queries. In addition, many of the previous approaches are fairly intricate as they use expensive features and are difficult to be reimplemented. The main contribution of this paper is a new method for query segmentation that is easy to implement, fast, and that comes with a segmentation accuracy comparable to current state-of-the-art techniques. Our method uses only raw web n-gram frequencies and Wikipedia titles that are stored in a hash table. At the same time, we introduce a new evaluation corpus for query segmentation. With about 50,000 human-annotated queries, it is two orders of magnitude larger than the corpus being used up to now.


title: The value of visualization

abstract: The field of visualization is getting mature. Many problems have been solved, and new directions are sought for. In order to make good choices, an understanding of the purpose and meaning of visualization is needed. Especially, it would be nice if we could assess what a good visualization is. In this paper an attempt is made to determine the value of visualization. A technological viewpoint is adopted, where the value of visualization is measured based on effectiveness and efficiency. An economic model of visualization is presented, and benefits and costs are established. Next, consequences (brand limitations of visualization are discussed (including the use of alternative methods, high initial costs, subjective/less, and the role of interaction), as well as examples of the use of the model for the judgement of existing classes of methods and understanding why they are or are not used in practice. Furthermore, two alternative views on visualization are presented and discussed: viewing visualization as an art or as a scientific discipline. Implications and future directions are identified.


title: Editorial: Let's talk about sex - the gender binary revisited

abstract: Sex refers to biological differences and gender to socioculturally delineated masculine and feminine roles. Sex or gender are included as a covariate or effect modifier in the majority of child psychology and psychiatry studies, and differences found between boys and girls have inspired many researchers to postulate underlying mechanisms. Empirical tests of whether including these proposed explanatory variables actually reduces the variance explained by gender are lagging behind somewhat. That is a pity, because a lot can be gained from a greater focus on the active agents of specific gender differences. As opposed to biological sex as such, some of the processes explaining why a specific outcome shows gender differences may be changeable and so possible prevention targets. Moreover, while the sex binary may be reasonable adequate as a classification variable, the gender binary is far from perfect. Gender is a multidimensional, partly context-dependent factor, and the dichotomy generally used in research does not do justice to the diversity existing within boys and girls.


title: A Unified Deep Learning Architecture for Abuse Detection

abstract: Hate speech, offensive language, sexism, racism and other types of abusive behavior have become a common phenomenon in many online social media platforms. In recent years, such diverse abusive behaviors have been manifesting with increased frequency and levels of intensity. This is due to the openness and willingness of popular media platforms, such as Twitter and Facebook, to host content of sensitive or controversial topics. However, these platforms have not adequately addressed the problem of online abusive behavior, and their responsiveness to the effective detection and blocking of such inappropriate behavior remains limited. In the present paper, we study this complex problem by following a more holistic approach, which considers the various aspects of abusive behavior. To make the approach tangible, we focus on Twitter data and analyze user and textual properties from different angles of abusive posting behavior. We propose a deep learning architecture, which utilizes a wide variety of available metadata, and combines it with automatically-extracted hidden patterns within the text of the tweets, to detect multiple abusive behavioral norms which are highly inter-related. We apply this unified architecture in a seamless, transparent fashion to detect different types of abusive behavior (hate speech, sexism vs. racism, bullying, sarcasm, etc.) without the need for any tuning of the model architecture for each task. We test the proposed approach with multiple datasets addressing different and multiple abusive behaviors on Twitter. Our results demonstrate that it largely outperforms the state-of-art methods (between 21 and 45\% improvement in AUC, depending on the dataset).


title: Optimized Hexagon-Based Deployment for Large-Scale Ubiquitous Sensor Networks

abstract: Ubiquitous Sensor Network describes an application platform comprised of intelligently networked sensors deployed over a large area, supporting multiple application scenarios. On one hand, at the user-end, storing and managing the large amount of heterogeneous data generated by the network is a daunting task. On the other hand, at the network-end, ensuring network connectivity and longevity in a dynamically changing network environment, while trying to provide context-aware application data to the end-users are very challenging for the resource constrained sensor network. While cloud computing offers a cost-effective solution for storage of the large volume of data generated by the underlying heterogeneous network, an equally elegant solution does not exist on the network interface to provide application-aware data. In this paper, we propose the use of cognitive nodes (CNs) in the underlying sensor network to provide intelligent information processing and knowledge-based services to the end-users. We identify tools and techniques to implement the cognitive functionality and formulate a strategy for the deployment of CNs in the underlying sensor network to ensure a high probability of successful data reception among communicating nodes. From Matlab simulations, we were able to verify that in a network with randomly deployed sensor nodes, CNs can be strategically deployed at pre-determined positions, to deliver application-aware data that satisfies the end-user’s quality of information requirements, even at high application payloads.


title: Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models

abstract: Graphical abstractDisplay Omitted Highlights? We proposed a novel breast cancer treatment decision model. ? We used non-stationary fuzzy sets to model variability in the decisions obtained. ? Twelve fuzzy rules are generated from clinical protocol to characterize the clinician's knowledge. ? The membership functions are designed to adhere to clinical guidelines as accurately as possible. ? The proposed fuzzy model can significantly increase model performance to around 88.1%, p<0.001. It has been often demonstrated that clinicians exhibit both inter-expert and intra-expert variability when making difficult decisions. In contrast, the vast majority of computerized models that aim to provide automated support for such decisions do not explicitly recognize or replicate this variability. Furthermore, the perfect consistency of computerized models is often presented as a de facto benefit. In this paper, we describe a novel approach to incorporate variability within a fuzzy inference system using non-stationary fuzzy sets in order to replicate human variability. We apply our approach to a decision problem concerning the recommendation of post-operative breast cancer treatment; specifically, whether or not to administer chemotherapy based on assessment of five clinical variables: NPI (the Nottingham Prognostic Index), estrogen receptor status, vascular invasion, age and lymph node status. In doing so, we explore whether such explicit modeling of variability provides any performance advantage over a more conventional fuzzy approach, when tested on a set of 1310 unselected cases collected over a fourteen year period at the Nottingham University Hospitals NHS Trust, UK. The experimental results show that the standard fuzzy inference system (that does not model variability) achieves overall agreement to clinical practice around 84.6% (95% CI: 84.1-84.9%), while the non-stationary fuzzy model can significantly increase performance to around 88.1% (95% CI: 88.0-88.2%), p<0.001. We conclude that non-stationary fuzzy models provide a valuable new approach that may be applied to clinical decision support systems in any application domain.


title: Cyber warfare: Issues and challenges

abstract: The topic of cyber warfare is a vast one, with numerous sub topics receiving attention from the research community. We first examine the most basic question of what cyber warfare is, comparing existing definitions to find common ground or disagreements. We discover that there is no widely adopted definition and that the terms cyber war and cyber warfare are not well enough differentiated. To address these issues, we present a definition model to help define both cyber warfare and cyber war. The paper then identifies nine research challenges in cyber warfare and analyses contemporary work carried out in each. We conclude by making suggestions on how the field may best be progressed by future efforts.


title: Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks

abstract: Accurate classification of human aquatic activities using radar has a variety of potential applications such as rescue operations and border patrols. Nevertheless, the classification of activities on water using radar has not been extensively studied, unlike the case on dry ground, due to its unique challenge. Namely, not only is the radar cross section of a human on water small, but the micro-Doppler signatures are much noisier due to water drops and waves. In this paper, we first investigate whether discriminative signatures could be obtained for activities on water through a simulation study. Then, we show how we can effectively achieve high classification accuracy by applying deep convolutional neural networks (DCNN) directly to the spectrogram of real measurement data. From the five-fold cross-validation on our dataset, which consists of five aquatic activities, we report that the conventional feature-based scheme only achieves an accuracy of 45.1%. In contrast, the DCNN trained using only the collected data attains 66.7%, and the transfer learned DCNN, which takes a DCNN pre-trained on a RGB image dataset and fine-tunes the parameters using the collected data, achieves a much higher 80.3%, which is a significant performance boost.


title: The Ensembl gene annotation system

abstract: The Ensembl gene annotation system has been used to annotate over 70 different vertebrate species across a wide range of genome projects. Furthermore, it generates the automatic alignment-based ann ...


title: Rank Entropy-Based Decision Trees for Monotonic Classification

abstract: In many decision making tasks, values of features and decision are ordinal. Moreover, there is a monotonic constraint that the objects with better feature values should not be assigned to a worse decision class. Such problems are called ordinal classification with monotonicity constraint. Some learning algorithms have been developed to handle this kind of tasks in recent years. However, experiments show that these algorithms are sensitive to noisy samples and do not work well in real-world applications. In this work, we introduce a new measure of feature quality, called rank mutual information (RMI), which combines the advantage of robustness of Shannon's entropy with the ability of dominance rough sets in extracting ordinal structures from monotonic data sets. Then, we design a decision tree algorithm (REMT) based on rank mutual information. The theoretic and experimental analysis shows that the proposed algorithm can get monotonically consistent decision trees, if training samples are monotonically consistent. Its performance is still good when data are contaminated with noise.


title: MRI-PET registration with automated algorithm.

abstract: OBJECTIVE ::: We have previously reported an automated method for within-modality (e.g., PET-to-PET) image alignment. We now describe modifications to this method that allow for cross-modality registration of MRI and PET brain images obtained from a single subject. ::: ::: ::: METHODS ::: This method does not require fiducial markers and the user is not required to identify common structures on the two image sets. To align the images, the algorithm seeks to minimize the standard deviation of the PET pixel values that correspond to each MRI pixel value. The MR images must be edited to exclude nonbrain regions prior to using the algorithm. ::: ::: ::: RESULTS AND CONCLUSION ::: The method has been validated quantitatively using data from patients with stereotaxic fiducial markers rigidly fixed in the skull. Maximal three-dimensional errors of < 3 mm and mean three-dimensional errors of < 2 mm were measured. Computation time on a SPARCstation IPX varies from 3 to 9 min to align MR image sets with [18F]fluorodeoxyglucose PET images. The MR alignment with noisy H2(15)O PET images typically requires 20-30 min.


title: Off The Beaten Lane: AI Challenges In MOBAs Beyond Player Control

abstract: MOBAs represent a huge segment of online gaming and are growing as both an eSport and a casual genre. The natural starting point for AI researchers interested in MOBAs is to develop an AI to play the game better than a human - but MOBAs have many more challenges besides adversarial AI. In this paper we introduce the reader to the wider context of MOBA culture, propose a range of challenges faced by the community today, and posit concrete AI projects that can be undertaken to begin solving them.


title: Hermaphroditism and intersexuality in Portuguese medical photograph

abstract: The interest harboured by the Visual Culture of Medicine in photographic images of hermaphrodites dates back in Portugal to the birth of medical photography, with the photographic recording of a case of male hermaphroditism, that was studied in 1864 by Carlos Miguel Augusto May Figueira (1829-1913) at the Medical Clinic Service of the S. Jose Hospital, only four years after the publication of the seminal work on this topic by Felix Nadar. This 1864 study is one of the founding elements of a Portuguese scientia sexualis in the wider context of modern sexology, as described by Michel Foucault, that evolved in Portugal between the mid-19th century and the 1930s-1940s. The thematic lineage of research into hermaphroditism and intersexuality, initiated by May Figueira was subsequently pursued by Portuguese clinicians and medical scientists from the first decade of the 20th century up until the 1940s. The ultimate goal of the medical photographic recording of hermaphrodites was to pave the way to surgical correction, in strict compliance with binary sexual dimorphism that the original diagnosis never dared to question.


title: Face and Body Association for Video-Based Face Recognition

abstract: In recent years face recognition has made extraordinary leaps, yet unconstrained video-based face identification in the wild remains an open and interesting problem. Videos, unlike still-images, offer a myriad of data for face modeling, sampling, and recognition, but, on the other hand, contain low-quality frames and motion blur. A key component in video-based face recognition is the way in which faces are associated through the video sequence before being used for recognition. In this paper, we present a video-based face recognition method taking advantage of face and body association (FBA). To track and associate subjects that appear across frames in multiple shots, we solve a data association problem using both face and body appearance. The final recovered track is then used to build a face representation for recognition. We evaluate our FBA method for video-based face recognition on a challenging dataset. Our experiments show up to 5% improvement in the identification rate over the state-of-the-art.


title: Computational Creativity Research: Towards Creative Machines

abstract: Computational Creativity, Concept Invention, and General Intelligence in their own right all are flourishing research disciplines producing surprising and captivating results that continuously influence and change our view on where the limits of intelligent machines lie, each day pushing the boundaries a bit further. By 2014, all three fields also have left their marks on everyday life machine-composed music has been performed in concert halls, automated theorem provers are accepted tools in enterprises R&D departments, and cognitive architectures are being integrated in pilot assistance systems for next generation airplanes. Still, although the corresponding aims and goals are clearly similar (as are the common methods and approaches), the developments in each of these areas have happened mostly individually within the respective community and without closer relationships to the goings-on in the other two disciplines. In order to overcome this gap and to provide a common platform for interaction and exchange between the different directions, the International Workshops on Computational Creativity, Concept Invention, and General Intelligence (C3GI) have been started. At ECAI-2012 and IJCAI-2013, the first and second edition of C3GI each gathered researchers from all three fields, presenting recent developments and results from their research and in dialogue and joint debates bridging the disciplinary boundaries. The chapters contained in this book are based on expanded versions of accepted contributions to the workshops and additional selected contributions by renowned researchers in the relevant fields. Individually, they give an account of the state-of-the-art in their respective area, discussing both, theoretical approaches as well as implemented systems. When taken together and looked at from an integrative perspective, the book in its totality offers a starting point for a (re)integration of Computational Creativity, Concept Invention, and General Intelligence, making visible common lines of work and theoretical underpinnings, and pointing at chances and opportunities arising from the interplay of the three fields.


title: Low-Profile, Electrically Small, Huygens Source Antenna With Pattern-Reconfigurability That Covers the Entire Azimuthal Plane

abstract: A pattern-reconfigurable, low-profile, efficient, electrically small, near-field resonant parasitic (NFRP), Huygens source antenna is presented. The design incorporates both electric and magnetic NFRP elements. The electric ones are made reconfigurable by the inclusion of a set of p-i-n diodes. By arranging these electric and magnetic NFRP elements properly, a set of three Huygens sources are attained, each covering a 120° sector. Pattern reconfigurability is obtained by switching the diodes on or off; it encompasses the entire 360° azimuth range. A prototype was fabricated and tested. The numerical and experimental studies are in good agreement. The experimental results indicate that in each of its instantaneous states at $f_{0} = 1.564$ GHz, the antenna provides uniform peak realized gains, front-to-back ratios, and radiation efficiencies, respectively, as high as 3.55 dBi, 17.5 dB, and 84.9%, even though it is electrically small: $ka = 0.92$ , and low profile: $0.05~\lambda _{{\mathbf {0}}}$ .


title: Approximate DCT Image Compression Using Inexact Computing

abstract: This paper proposes a new framework for digital image processing; it relies on inexact computing to address some of the challenges associated with the discrete cosine transform (DCT) compression. The proposed framework has three levels of processing; the first level uses approximate DCT for image compressing to eliminate all computational intensive floating-point multiplications and executing the DCT processing by integer additions and in some cases logical right/left shifts. The second level further reduces the amount of data (from the first level) that need to be processed by filtering those frequencies that cannot be detected by human senses. Finally, to reduce power consumption and delay, the third level introduces circuit level inexact adders to compute the DCT. For assessment, a set of standardized images are compressed using the proposed three-level framework. Different figures of merits (such as energy consumption, delay, power-signal-to-noise-ratio, average-difference, and absolute-maximum-difference) are compared to existing compression methods; an error analysis is also pursued confirming the simulation results. Results show very good improvements in reduction for energy and delay, while maintaining acceptable accuracy levels for image processing applications.


title: Mathematical modeling and simulation of selected components on-board autonomous power supply system (ASE), in accordance with the concept of a more electric aircraft (MEA)

abstract: The paper presents mathematical models of the key components onboard autonomous power supply system (ASE) plane "conventional" and more electric aircraft MEA "More Electric Aircraft". The subject, and also the aim of this work is the analysis and simulation of selected components of the power system of modern aircraft (synchronous generator, integrated starter/generator, voltage regulator, as device control and regulation of the generator and power electronic rectifiers multi-pulse). Simulation tests of key components confirmed the results obtained by the analytical and based on their mathematical models. The final part of the paper presents the main conclusions of the analysis and simulation based on the mathematical models of the selected components of the ASE system in accordance with the concept of a more electric aircraft.


title: Human Tails: Ownership and Control of Extended Humanoid Avatars

abstract: This paper explores body ownership and control of an 'extended' humanoid avatar that features a distinct and flexible tail-like appendage protruding from its coccyx. Thirty-two participants took part in a between-groups study to puppeteer the avatar in an immersive CAVETM -like system. Participantsa' body movement was tracked, and the avatara's humanoid body synchronously reflected this motion. However, sixteen participants experienced the avatara's tail moving around randomly and asynchronous to their own movement, while the other participants experienced a tail that they could, potentially, control accurately and synchronously through hip movement. Participants in the synchronous condition experienced a higher degree of body ownership and agency, suggesting that visuomotor synchrony enhanced the probability of ownership over the avatar body despite of its extra-human form. Participants experiencing body ownership were also more likely to be more anxious and attempt to avoid virtual threats to the tail and body. The higher task performance of participants in the synchronous condition indicates that people are able to quickly learn how to remap normal degrees of bodily freedom in order to control virtual bodies that differ from the humanoid form. We discuss the implications and applications of extended humanoid avatars as a method for exploring the plasticity of the braina's representation of the body and for gestural human-computer interfaces.


title: Adaptive Undulatory Locomotion of a C. elegans Inspired Robot

abstract: Although significant progress has been made in the development of robots with serpentine properties, the issues of motion control and adaptation to environmental constraints still require substantial research. This is particularly true for search and rescue applications, where reliable operation in extremely difficult terrain is essential. This paper presents a novel robot design based on the mechanics and neural control of locomotion in Caenorhabditis elegans, a tiny nematode worm. Equipped with an extremely simple yet powerful neurally-inspired decentralized control system, the robot presented here is capable of effective serpentine locomotion. More importantly, it exhibits sensorless path finding, in which obstacles in the environment are overcome, based purely on proprioceptive feedback encoding body shape. Indeed, the robot lacks any form of external sensory capability. The design and implementation of the prototype robot and its control strategy are discussed. In order to validate the control strategy for path finding, experiments and analyses have been performed. The results show that the robot can find its path successfully in the majority of cases. The current limitations have also been discussed.


title: Delay Tolerant Networking for smart city through drones

abstract: Sensors in future smart cities will continuously monitor the environment in order to prevent critical situations and waste of resources or to offer new services to end users. Likely, the existing networks will not be able to sustain such a traffic without huge investments in the telecommunication infrastructure. One possible solution to overcome these problems is to apply the Delay Tolerant Network (DTN) paradigm, that is to exploit the movement of objects, possibly carrying data toward the final destination. Depending on location of data sources in the area and on paths followed by moving objects some sources could be isolated. In order to fix this issue, we use a drone, flying at proper instant of time over the isolated sources to deliver their data to the final destination. The impact of the presence of a drone as relay of the DTN is investigated accounting for realistic constraints, such as limited drone flight duration.


title: An online multi-object tracking approach by adaptive labeling and kalman filter

abstract: The task of reliable detection and tracking of multiple objects is a challenging problem. In this paper, we introduce an online multi-object tracking approach based on Kalman filter framework. Moving objects are extracted relying on frame difference and labeled by an adaptive labeling method. Each object is assigned an independent Kalman filter to predict the location in consecutive frame. Data association is performed using Hungarian assignment algorithm. Experimental results show that the approach is able to automatically and robustly track a variable number of objects and maintain their identities in complex environment.


title: COARA: Code Offloading on Android with AspectJ

abstract: Smartphones suffer from limited computational capabilities and battery life. A method to mitigate these problems is code offloading: executing application code on a remote server. We introduce COARA, a middleware platform for code offloading on Android that uses aspect-oriented programming (AOP) with AspectJ. AOP allows COARA to intercept code for offloading without a customized compiler or modification of the operating system. COARA requires minimal changes to application source code, and does not require the application developer to be aware of AOP. Since state transfer to the server is often a bottleneck that hinders performance, COARA uses AOP to intercept the transmission of large objects from the client and replaces them with object proxies. The server can begin execution of the offloaded application code, regardless of whether all required objects been transferred to the server. We run COARA with Android applications from the Google Play store on a Nexus 4 running unmodified Android 4.3 to prove that our platform improves performance and reduces energy consumption. Our approach yields speedups of 24x and 6x over WiFi and 3G respectively.


title: Morphological based technique for image segmentation

abstract: In this paper, a morphological image segmentation technique based on the watershed algorithm is proposed. New morphological based pre-and post-processing techniques are proposed to reduce over-segmentation, by means of merging and removing spurious segments. The preprocessing aims at removing trivial regions as well as background noise. The post-processing produces a more concise region representation of the watershed-segmented image, where a region adjacency list (RAL) is built for the region merging process. To control the merging, a similarity function is defined, whence the most similar neighbouring regions are merged. The proposed technique produces effective and significant results in successfully segmenting various objects when tested on a series of well known test images, as shown in this paper


title: The ART of adaptive pattern recognition by a self-organizing neural network

abstract: The adaptive resonance theory (ART) suggests a solution to the stability-plasticity dilemma facing designers of learning systems, namely how to design a learning system that will remain plastic, or adaptive, in response to significant events and yet remain stable in response to irrelevant events. ART architectures are discussed that are neural networks that self-organize stable recognition codes in real time in response to arbitrary sequences of input patterns. Within such an ART architecture, the process of adaptive pattern recognition is a special case of the more general cognitive process of hypothesis discovery, testing, search, classification, and learning. This property opens up the possibility of applying ART systems to more general problems of adaptively processing large abstract information sources and databases. The main computational properties of these ART architectures are outlined and contrasted with those of alternative learning and recognition systems. >


Enhanced Bass Reinforcement Algorithm for Small-sized Transducer


title: A Review of Gaucher Disease Pathophysiology, Clinical Presentation and Treatments

abstract: Gaucher disease (GD, ORPHA355) is a rare, autosomal recessive genetic disorder. It is caused by a deficiency of the lysosomal enzyme, glucocerebrosidase, which leads to an accumulation of its substrate, glucosylceramide, in macrophages. In the general population, its incidence is approximately 1/40,000 to 1/60,000 births, rising to 1/800 in Ashkenazi Jews. The main cause of the cytopenia, splenomegaly, hepatomegaly, and bone lesions associated with the disease is considered to be the infiltration of the bone marrow, spleen, and liver by Gaucher cells. Type-1 Gaucher disease, which affects the majority of patients (90% in Europe and USA, but less in other regions), is characterized by effects on the viscera, whereas types 2 and 3 are also associated with neurological impairment, either severe in type 2 or variable in type 3. A diagnosis of GD can be confirmed by demonstrating the deficiency of acid glucocerebrosidase activity in leukocytes. Mutations in the GBA1 gene should be identified as they may be of prognostic value in some cases. Patients with type-1 GD-but also carriers of GBA1 mutation-have been found to be predisposed to developing Parkinson's disease, and the risk of neoplasia associated with the disease is still subject to discussion. Disease-specific treatment consists of intravenous enzyme replacement therapy (ERT) using one of the currently available molecules (imiglucerase, velaglucerase, or taliglucerase). Orally administered inhibitors of glucosylceramide biosynthesis can also be used (miglustat or eliglustat).


title: Improving robot transparency: Real-time visualisation of robot AI substantially improves understanding in naive observers

abstract: Deciphering the behaviour of intelligent others is a fundamental characteristic of our own intelligence. As we interact with complex intelligent artefacts, humans inevitably construct mental models to understand and predict their behaviour. If these models are incorrect or inadequate, we run the risk of self deception or even harm. Here we demonstrate that providing even a simple, abstracted real-time visualisation of a robot's AI can radically improve the transparency of machine cognition. Findings from both an online experiment using a video recording of a robot, and from direct observation of a robot show substantial improvements in observers' understanding of the robot's behaviour. Unexpectedly, this improved understanding was correlated in one condition with an increased perception that the robot was ‘thinking’, but in no conditions was the robot's assessed intelligence impacted. In addition to our results, we describe our approach, tools used, implications, and potential future research directions.


title: Integrating Automated Fingerprint-Based Attendance into a University Portal System

abstract: Student learning and academic performance hinge largely on frequency of class attendance and participation. The fingerprint recognition system aims at providing an accurate and efficient attendance management service to staff and students within an existing portal system. The integration of a unique and accurate identification system into the existing portal system offers at least, two advantages: accurate and efficient analysis and reporting of student attendance on a continuous basis; and also facilitating the provision of personalised services, enhancing user experience altogether. An integrated portal system was developed to automate attendance management and tested for fifty students in five attempts. The 98% accuracy achieved by the system points to the feasibility of large scale deployment and interoperability of multiple devices using existing technology infrastructure.


title: Update dermatologic laser therapy

abstract: Summary ::: New trends in dermatological laser therapy during the last years are based on new wavelengths, concepts and treatment combinations resulting in a variety of new dermatologic indications. Fractional laser therapy of chronic actinic damage of the skin has been introduced and already represents a standard technique. The concept of fractional non-ablative and ablative laser treatment has been shown to be safe and effective. Also pigmented and vascular skin changes can be treated by this method. New, very promising concepts for laser epilation include linear scanned as well as low fluence laser systems. The first enable very short treatment times for large areas; the latter are the basis for the growing market of laser epilation devices for home use. Nevertheless, the potential of low fluence laser devices for long-term hair reduction has not been tested so far. Furthermore, no data exist on side effects resulting from repetitive application of laser light to melanocytic lesions. Laser lipolysis has been introduced as the latest, minimally invasive way of removing small localised fat deposits. The new procedure may have a great potential for liposculpture; its further development should be thoughtfully observed. The latest innovations for precise ablation are ultra-short pulsed laser systems. Femtosecond lasers avoid thermal damage at the border areas of ablation zones.


title: An overview of patient acceptance of Health Information Technology in developing countries: a review and conceptual model

abstract: The potential to improve the quality, efficiency, outcomes, patient safety and reduce cost of healthcare by Health Information Technology (HIT) has been established by researchers. But unfortunately HIT systems are not properly utilized or are not widely available. This problem is even more glaring in developing countries. This article presents a review of some available HIT systems in order to assess the level of their presence and the technology used in developing them. Works related to acceptance of HIT systems were also reviewed so as to study the gaps in this area and propose a solution in order to fill the gaps identified. The problems discovered from this review include lack of availability of these systems especially in developing countries, low rate of HIT systems acceptance and insufficient works on patient acceptance of HIT systems. Studying the factors that affect the acceptance of HIT systems by patients ::: and considering the factors while developing the systems will play a significant role in getting over the aforementioned limitations. As Technology Acceptance Model (TAM) is one of the most popular models for studying users' perception and acceptance of Information System (IS)/Information Technology (IT), we proposed a conceptual model of HIT acceptance in developing countries based on TAM.


title: Beauty as an Emotion: The Exhilarating Prospect of Mastering a Challenging World

abstract: Beauty has received sparse attention from emotion theorists, some of whom have argued that aesthetic pleasure is cognitive in nature and too “disinterested” to be emotional. This view is supported ...


title: Mathematical Models for Local Nontexture Inpaintings

abstract: Dedicated to Stanley Osher on the occasion of his 60th birthday. Abstract. Inspired by the recent work of Bertalmio et al. on digital inpaintings (SIGGRAPH 2000), we develop general mathematical models for local inpaintings of nontexture images. On smooth regions, inpaintings are connected to the harmonic and biharmonic extensions, and inpainting orders are analyzed. For inpaintings involving the recovery of edges, we study a variational model that is closely connected to the classical total variation (TV) denoising model of Rudin, Osher, and Fatemi (Phys. D, 60 (1992), pp. 259-268). Other models are also discussed based on the Mumford-Shah regularity (Comm. Pure Appl. Math., XLII (1989), pp. 577-685) and curvature driven diffusions (CDD) of Chan and Shen (J. Visual Comm. Image Rep., 12 (2001)). The broad applications of the inpainting models are demonstrated through restoring scratched old photos, disocclusion in vision analysis, text removal, digital zooming, and edge-based image coding.


title: Part-based spatio-temporal model for multi-person re-identification

abstract: In this paper we propose an adaptive part-based spatio-temporal model that characterizes person's appearance using color and facial features. Face image selection based on low level cues is used to select usable face images to build a face model. Color features that capture the distribution of colors as well as the representative colors are used to build the color model. The model is built over a sequence of frames of an individual and hence captures the characteristic appearance as well as its variations over time. We also address the problem of multiple person re-identification in the absence of calibration data or prior knowledge about the camera layout. Multiple person re-identification is a open set matching problem with a dynamically evolving and open gallery set and an open probe set. Re-identification is posed as a rectangular assignment problem and is solved to find a bijection that minimizes the overall assignment cost. Open and closed set re-identification is tested on 30 videos collected with nine non-overlapping cameras spanning outdoor and indoor areas, with 40 subjects under observation. A false acceptance reduction scheme based on the developed model is also proposed.


title: Optimal spatial filter selection for illumination-invariant color texture discrimination

abstract: Color textures contain a large amount of spectral and spatial structure that can be exploited for recognition. Recent work has demonstrated that spatial filters offer a convenient means of extracting illumination-invariant spatial information from a color image. In this paper, we address the problem of deriving optimal filters for illumination-invariant color texture discrimination. Color textures are represented by a set of illumination-invariant features that characterize the color distribution of a filtered image region. Similar features have been used in previous studies. Given a pair of color textures, we derive a spatial filter that maximizes the distance between these textures in feature space. We provide a method for using the pairwise result to obtain a filter that maximizes discriminability among multiple classes. A set of experiments on a database of deterministic and random color textures obtained under different illumination conditions demonstrates the improved discriminatory power achieved by using an optimized filter.


title: The nature of individual differences in working memory capacity: Active maintenance in primary memory and controlled search from secondary memory

abstract: Studies examining individual differences in working memory capacity have suggested that individuals with low working memory capacities demonstrate impaired performance on a variety of attention and memory tasks compared with individuals with high working memory capacities. This working memory limitation can be conceived of as arising from 2 components: a dynamic attention component (primary memory) and a probabilistic cue-dependent search component (secondary memory). This framework is used to examine previous individual differences studies of working memory capacity, and new evidence is examined on the basis of predictions of the framework to performance on immediate free recall. It is suggested that individual differences in working memory capacity are partially due to the ability to maintain information accessible in primary memory and the ability to search for information from secondary memory.


title: A Big Data Approach to Computational Creativity

abstract: Computational creativity is an emerging branch of artificial intelligence that places computers in the center of the creative process. Broadly, creativity involves a generative step to produce many ideas and a selective step to determine the ones that are the best. Many previous attempts at computational creativity, however, have not been able to achieve a valid selective step. This work shows how bringing data sources from the creative domain and from hedonic psychophysics together with big data analytics techniques can overcome this shortcoming to yield a system that can produce novel and high-quality creative artifacts. Our data-driven approach is demonstrated through a computational creativity system for culinary recipes and menus we developed and deployed, which can operate either autonomously or semi-autonomously with human interaction. We also comment on the volume, velocity, variety, and veracity of data in computational creativity.


title: Cutaneous malignant melanoma in Eastern Taiwan: Clinicopathologic analysis of 56 cases

abstract: Background: The incidence, histological subtypes, and tumor behaviors of cutaneous malignant melanoma are quite different in Asian and Western countries. Objectives: Our purpose was to define the epidemiology, tumor behaviors, and possible prognostic predictors of cutaneous melanomas based on an Eastern Taiwanese population. Methods: We conducted a retrospective study to collect cases of cutaneous melanoma at Buddhist Tzu-Chi General Hospital, Hualien, Taiwan, from May 1, 2003, to August 31, 2017. We analyzed mean age at onset, gender, primary tumor locations, histological subtypes, Breslow's thickness, Clark's level, ulceration, lymph nodes, and distant metastatic status. Univariate and multivariate analyses for survival were performed by means of Cox proportional hazard model. Survival curves were plotted by Kaplan–Meier method. Results: A total of 56 cases of cutaneous melanoma were recorded. The male-to-female ratio was 3.67:1. The median survival was 2.82 years (0.63–5.00 years). Acral melanoma (AM) accounts for 69.6% of cases, and feet are the most common location (62.5%). Acral lentiginous melanoma (ALM) is the most common histological type (44.6%), followed by nodular melanoma (33.9%). Poor prognostic factors include older age (≧65 years), male gender, Breslow's thickness >4 mm, distant metastasis, and head-and-neck location. Conclusion: AM is the most common location, and ALM is the most common histological type of cutaneous melanoma in Eastern Taiwan. Melanoma in Eastern Taiwan tends to be delay diagnosed with advanced stage and thicker tumors than Western Taiwan. Prudent palm and sole examinations, improved medical accessibility, and more public educational programs may help encourage the early detection of melanoma.


title: An Introduction to Collective Intelligence

abstract: This paper surveys the emerging science of how to design a “COllective INtelligence” (COIN). A COIN is a large multi-agent system where: i) There is little to no centralized communication or control. ii) There is a provided world utility function that rates the possible histories of the full system. In particular, we are interested in COINs in which each agent runs a reinforcement learning (RL) algorithm. The conventional approach to designing large distributed systems to optimize a world utility does not use agents running RL algorithms. Rather, that approach begins with explicit modeling of the dynamics of the overall system, followed by detailed hand-tuning of the interactions between the components to ensure that they “cooperate” as far as the world utility is concerned. This approach is labor-intensive, often results in highly nonrobust systems, and usually results in design techniques that have limited applicability. In contrast, we wish to solve the COIN design problem implicitly, via the “adaptive” character of the RL algorithms of each of the agents. This approach introduces an entirely new, profound design problem: Assuming the RL algorithms are able to achieve high rewards, what reward functions for the individual agents will, when pursued by those agents, result in high world utility? In other words, what reward functions will best ensure that we do not have phenomena like the tragedy of the commons, Braess’s paradox, or the liquidity trap? Although still very young, research specifically concentrating on the COIN design problem has already resulted in successes in artificial domains, in particular in packet-routing, the leader-follower problem, and in variants of Arthur’s El Farol bar problem. It is expected that as it matures and draws upon other disciplines related to COINs, this research will greatly expand the range of tasks addressable by human engineers. Moreover, in addition to drawing on them, such a fully developed science of COIN design may provide much insight into other already established scientific fields, such as economics, game theory, and population biology.


title: Learning to Optimize

abstract: Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm, which we believe to be the first method that can automatically discover a better algorithm. We approach this problem from a reinforcement learning perspective and represent any particular optimization algorithm as a policy. We learn an optimization algorithm using guided policy search and demonstrate that the resulting algorithm outperforms existing hand-engineered algorithms in terms of convergence speed and/or the final objective value.


title: The Need for a Process Mining Evaluation Framework in Research and Practice Position Paper

abstract: Although there has been much progress in developing process mining algorithms in recent years, no effort has been put in developing a common means of assessing the quality of the models discovered by these algorithms. In this paper, we motivate the need for such an evaluation mechanism, and outline elements of an evaluation framework that is intended to enable (a) process mining researchers to compare the performance of their algorithms, and (b) end users to evaluate the validity of their process mining results.


Lexicalised Systematic Polysemy in WordNet


title: Gender and culture differences in emotion

abstract: University of CambridgeIn this article, the authors report a secondary analysis on a cross-cultural dataset ongender differences in 6 emotions, collected in 37 countries all over the world. Theaim was to test the universality of the gender-specific pattern found in studies withWestern respondents, namely that men report more powerful emotions (e.g., anger),whereas women report more powerless emotions (e.g., sadness, fear). The authorsexpected the strength of these gender differences to depend on women’s status androles in their respective countries, as operationalized by the Gender EmpowermentMeasure (GEM; United Nations Development Programme Human DevelopmentReport 2002). Overall, the gender-specific pattern of women reporting to experi-ence and express more powerless emotions and men more powerful emotions wasreplicated, and only some interactions with the GEM were found.


title: Labia minora hypertrophy: causes, impact on women’s health, and treatment options

abstract: INTRODUCTION AND HYPOTHESIS ::: We provide a review of the literature about the onset and development of hypertrophy of the labia minora, together with some expert opinions on the appropriateness of labiaplasty. ::: ::: ::: METHODS ::: We searched PubMed and used popular search engines, with a greater emphasis on the physiology and hormone-mediated metabolism of these structures, and less emphasis on their surgical treatment. ::: ::: ::: RESULTS ::: We describe major embryological, cytological, and biochemical features of this anatomical part and summarize the clinical aspects of its hypertrophy, evaluating types of discomfort reported by women and the medical treatments available. Also, based on what is known about the artificial elongation and spontaneous hypertrophy of the inner labia, we illustrate and discuss the main biological factors that may trigger this medical condition. There are not enough data identifying a clear inheritance of inner labia hypertrophy in the absence of other pathological conditions; instead, we found indirect evidence for an association with transient episodes of local inflammation either before birth or during puberty. We also analyze the role played by estrogen receptors and other factors with regard to the onset of this condition and highlight the importance of their timing in determining the size of women's labia minora. Remarkably, most cases of enlarged labia minora should be considered as outliers that are within the physiological range of size variation described for these structures. ::: ::: ::: CONCLUSIONS ::: We generally advise against surgical treatment of labia minora, especially in young, pre-pubertal girls, unless specific medical conditions are also present and/or the psychological impact on the patient is deemed particularly negative.


title: Operating Systems for Wireless Sensor Networks: A Survey

abstract: This paper presents a survey on the current state-of-the-art in Wireless Sensor Network (WSN) Operating Systems (OSs). In recent years, WSNs have received tremendous attention in the research community, with applications in battlefields, industrial process monitoring, home automation, and environmental monitoring, to name but a few. A WSN is a highly dynamic network because nodes die due to severe environmental conditions and battery power depletion. Furthermore, a WSN is composed of miniaturized motes equipped with scarce resources e.g., limited memory and computational abilities. WSNs invariably operate in an unattended mode and in many scenarios it is impossible to replace sensor motes after deployment, therefore a fundamental objective is to optimize the sensor motes’ life time. These characteristics of WSNs impose additional challenges on OS design for WSN, and consequently, OS design for WSN deviates from traditional OS design. The purpose of this survey is to highlight major concerns pertaining to OS design in WSNs and to point out strengths and weaknesses of contemporary OSs for WSNs, keeping in mind the requirements of emerging WSN applications. The state-of-the-art in operating systems for WSNs has been examined in terms of the OS Architecture, Programming Model, Scheduling, Memory Management and Protection, Communication Protocols, Resource Sharing, Support for Real-Time Applications, and additional features. These features are surveyed for both real-time and non-real-time WSN operating systems.


title: Simulation of wide area measurement system with optimal phasor measurement unit location

abstract: Traditional supervisory control and data acquisition (SCADA) measurements have been providing information regarding different power system parameters for decades. These measurements are typically taken once every 4 to 10 seconds offering a steady state view of the power system behaviour. However, for monitoring and control of large grid only steady state information may not be sufficient. Phasor measurement units (PMUs) provide dynamic view of the grid. Since PMUs are costly device, optimal placement of PMUs is a thrust areas of research in power system planning. This has been already investigated in previous research findings by different optimization technique. This paper extensively corroborates a simulation model of a wide area network with optimal PMU location. The purpose is to experimentally determine the ability of PMU to observe the entire network from its optimal location. The proposed methodology is implemented and investigated on IEEE six bus system using MATLAB/Simulink.


title: doi: 10.1111/j.1467-9299.2011.01942.x APPRAISING PUBLIC VALUE: PAST, PRESENT AND FUTURES

abstract: Despite the increasing popularity of the concept of ‘public value’ within both academic and practice settings, there has to date been no formal review of the literature on its provenance, empirical basis, and application. This paper seeks to fill this gap. It provides a critical introduction to public value and its conceptual development before presenting the main elements of the published literature. Following this, a series of key areas of disagreement are discussed and implications for future research and practice put forward. The authors argue that if the espoused aspirations for the public value framework are to be realized, a concerted process of research, debate and application is required. Although some criticisms of public value are argued to be unwarranted, the authors acknowledge ongoing concerns over the apparent silence of public value on questions of power and heterogeneity, and the difficulties in empirically testing the framework's propositions.


title: LPV Model and Its Application in Web Server Performance Control

abstract: Performance management is an important issue for Internet servers working in the unpredictable and highly dynamic environment. Feedback control is considered as a potential theoretical tool. However, due to the weakness in capturing the nonlinear natures of Internet servers, the classical linear control designs have been demonstrated ineffectiveness when handling varying workloads to provide sufficient performance guarantees. This paper attempts to apply linear-parameter-varying (LPV) approaches to modeling and controlling the Web server system for absolute connection delay guarantee. To achieve these goals, a LPV model, wherein the workload intensity is considered as the scheduling parameter, is identified experimentally to approximate the Web server system and then a LPV controller is designed for the LPV model. Model validation proves the LPV model is much more accurate than the LTI model with the same order. Closed-loop simulations under fluctuated workloads demonstrate that, the performance of the LPV controller overpasses the proportion-integral controller. By exploring the nature of dependence of server model on the workload intensity, the proposed LPV approach for absolute delay guarantee may find its applications in many other server performance control problems e.g., admission control, quality-of-service (QoS) control and power control.


title: Activity recognition from on-body sensors by classifier fusion: sensor scalability and robustness

abstract: Activity recognition from on-body sensors is affected by sensor degradation, interconnections failures, and jitter in sensor placement and orientation. We investigate how this may be balanced by exploiting redundant sensors distributed on the body. We recognize activities by a meta-classifier that fuses the information of simple classifiers operating on individual sensors. We investigate the robustness to faults and sensor scalability which follows from classifier fusion. We compare a reference majority voting and a naive Bayesian fusion scheme. We validate this approach by recognizing a set of 10 activities carried out by workers in the quality assurance checkpoint of a car assembly line. Results show that classification accuracy greatly increases with additional sensors (50% with 1 sensor, 80% and 98% with 3 and 57 sensors), and that sensor fusion implicitly allows to compensate for typical faults up to high fault rates. These results highlight the benefit of large on- body sensor network rather than a minimum set of sensors for activity recognition and prompts further investigation.


title: Red, Rank, and Romance in Women Viewing Men

abstract: In many nonhuman species of vertebrates, females are attracted to red on male conspecifics. Red is also a signal of male status in many nonhuman vertebrate species, and females show a mating preference for high-status males. These red–attraction and red–status links have been found even when red is displayed on males artificially. In the present research, we document parallels between human and nonhuman females’ response to male red. Specifically, in a series of 7 experiments we demonstrate that women perceive men to be more attractive and sexually desirable when seen on a red background and in red clothing, and we additionally show that status perceptions are responsible for this red effect. The influence of red appears to be specific to women’s romantic attraction to men: Red did not influence men’s perceptions of other men, nor did it influence women’s perceptions of men’s overall likability, agreeableness, or extraversion. Participants showed no awareness that the research focused on the influence of color. These findings indicate that color not only has aesthetic value but can carry meaning and impact psychological functioning in subtle, important, and provocative ways.


title: Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge

abstract: Recently, there has been an increased interest in machine learning methods that transfer knowledge across multiple learning tasks and “learn to learn.” Such methods have repeatedly been found to outperform conventional, single-task learning algorithms when the learning tasks are appropriately related. To increase robustness of such approaches, methods are desirable that can reason about the relatedness of individual learning tasks, in order to avoid the danger arising from tasks that are unrelated and thus potentially misleading.


title: Cross-lingual Projected Expectation Regularization for Weakly Supervised Learning

abstract: We consider a multilingual weakly supervised learning scenario where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide the learning in other languages. Past approaches project labels across bitext and use them as features or gold labels for training. We propose a new method that projects model expectations rather than labels, which facilities transfer of model uncertainty across language boundaries. We encode expectations as constraints and train a discriminative CRF model using Generalized Expectation Criteria (Mann and McCallum, 2010). Evaluated on standard Chinese-English and German-English NER datasets, our method demonstrates F 1 scores of 64% and 60% when no labeled data is used. Attaining the same accuracy with supervised CRFs requires 12k and 1.5k labeled sentences. Furthermore, when combined with labeled examples, our method yields significant improvements over state-of-the-art supervised methods, achieving best reported numbers to date on Chinese OntoNotes and German CoNLL-03 datasets.


title: Extracting Information about Security Vulnerabilities from Web Text

abstract: The Web is an important source of information about computer security threats, vulnerabilities and cyber attacks. We present initial work on developing a framework to detect and extract information about vulnerabilities and attacks from Web text. Our prototype system uses Wikitology, a general purpose knowledge base derived from Wikipedia, to extract concepts that describe specific vulnerabilities and attacks, map them to related concepts from DBpedia and generate machine understandable assertions. Such a framework will be useful in adding structure to already existing vulnerability descriptions as well as detecting new ones. We evaluate our approach against vulnerability descriptions from the National Vulnerability Database. Our results suggest that it can be useful in monitoring streams of text from social media or chat rooms to identify potential new attacks and vulnerabilities or to collect data on the spread and volume of existing ones.


title: Evaluation of existing architectures in IRAM systems

abstract: Computer memory systems are increasingly a bottleneck limiting application performance. IRAM architectures, which integrate a CPU with DRAM main memory on a single chip, promise to remove this limitation by providing tremendous main memory bandwidth and significant reductions in memory latency. To determine whether existing microarchitectures can tap the potential performance advantages of IRAM systems, we examined both execution time analyses of existing microprocessors and system simulation of hypothetical processors. Our results indicate that, for current benchmarks, existing architectures, whether simple, superscalar or out-of-order, are unable to exploit IRAM’s increased memory bandwidth and decreased memory latency to achieve significant performance benefits.


title: Bifurcation of nontrivial periodic solutions for an impulsively controlled pest management model

abstract: This paper investigates the onset of nontrivial periodic solutions for an integrated pest management model which is subject to pulsed biological and chemical controls. The biological control consists in the periodic release of infective individuals, while the chemical control consists in periodic pesticide spraying. It is assumed that both controls are used with the same periodicity, although not simultaneously. To model the spread of the disease which is propagated through the release of infective individuals, an unspecified force of infection is employed. The problem of finding nontrivial periodic solutions is reduced to showing the existence of nontrivial fixed points for the associated stroboscopic mapping of time snapshot equal to the common period of controls. The latter problem is in turn treated via a projection method. It is then shown that once a threshold condition is reached, a stable nontrivial periodic solution emerges via a supercritical bifurcation.


title: Metadata recovery from obfuscated programs using machine learning

abstract: Obfuscation is a mechanism used to hinder reverse engineering of programs. To cope with the large number of obfuscated programs, especially malware, reverse engineers automate the process of deobfuscation i.e. extracting information from obfuscated programs. Deobfuscation techniques target specific obfuscation transformations, which requires reverse engineers to manually identify the transformations used by a program, in what is known as metadata recovery attack. In this paper, we present Oedipus, a Python framework that uses machine learning classifiers viz., decision trees and naive Bayes, to automate metadata recovery attacks against obfuscated programs. We evaluated Oedipus' performance using two datasets totaling 1960 unobfuscated C programs, which were used to generate 11.075 programs obfuscated using 30 configurations of 6 different obfuscation transformations. Our results empirically show the feasibility of using machine learning to implement the metadata recovery attacks with classification accuracies of 100% in some cases.


title: Incidence, diversity and toxin gene characteristics of Bacillus cereus group strains isolated from food products marketed in Belgium

abstract: Abstract The major objectives of this study were to determine the incidence, diversity and characteristics of Bacillus cereus group spp. isolated from food products marketed in Belgium. The food products investigated in this study included cooked pasta, lasagna, bechamel sauce, bolognaise sauce, fresh minced beef, fresh-cut vegetables and raw basmati rice. B . cereus group spp. were detected in 56.3% (324 of 575) of the samples giving rise to 380 strains. The highest incidence (100%) occurred in the raw basmati rice. Although only 10 (2.6%) of the 380 isolates were determined to be psychrotolerant (able to grow at ≤ 7 °C), 25 (6.2%), 189 (49.7%) and 334 (87.9%) isolates were able to grow at mild temperature abuse conditions of 8 °C, 9 °C and 10 °C, respectively. The large diversity of the isolates obtained (overall and between isolates obtained from the same product type) was highlighted by the results of the (GTG) 5 PCR fingerprinting of 80 selected isolates. Sixty-one of these 80 isolates belonged to 15 distinct clusters (≥ 85% Pearson correlation) whereas the remaining 19 were each clustered separately. Further diversity was also found in the distribution of toxin genes as 16 different profiles were observed in the 80 selected isolates. Whilst none of 80 selected strains harboured the ces gene required for the production of the emetic toxin cereulide, 42 strains (52.5%) carried all seven genes required for the production of the diarrhoeal enterotoxins: haemolytic BL, non-haemolytic enterotoxin and cytotoxin K. The results of this study highlight not only the omnipresence but also the highly diverse ecology of B . cereus spp. within and across several food product types available on the retail market in Belgium. They should also provide the impetus for more studies to enable detailed risk assessment studies to be performed.


title: Human symbol manipulation within an integrated cognitive architecture

abstract: This article describes the Adaptive Control of Thought–Rational (ACT–R) cognitive architecture (Anderson et al., 2004; Anderson & Lebiere, 1998) and its detailed application to the learning of algebraic symbol manipulation. The theory is applied to modeling the data from a study by Qin, Anderson, Silk, Stenger, & Carter (2004) in which children learn to solve linear equations and perfect their skills over a 6-day period. Functional MRI data show that: (a) a motor region tracks the output of equation solutions, (b) a prefrontal region tracks the retrieval of declarative information, (c) a parietal region tracks the transformation of mental representations of the equation, (d) an anterior cingulate region tracks the setting of goal information to control the information flow, and (e) a caudate region tracks the firing of productions in the ACT–R model. The article concludes with an architectural comparison of the competence children display in this task and the competence that monkeys have shown in tasks that require manipulations of sequences of elements.


title: INCREMENTAL SENSOR FUSION IN FACTOR GRAPHS WITH UNKNOWN DELAYS

abstract: Sensor fusion by incremental smoothing in factor graphs allows the easy incorporation of asynchronous and delayed measurements, which is one of the main advantages of this approach compared to the ubiquitous filtering techniques. While incorporating delayed measurements into the factor graph representation is in principle easy when the delay is known, handling unknown delays is a non-trivial task that has not been explored before in this context. Our paper addresses the problem of performing incremental sensor fusion in factor graphs when some of the sensor information arrive with a significant unknown delay. We develop and compare two techniques to handle such delayed measurements under mild conditions on the characteristics of that delay: We consider the unknown delay to be bounded and quantizable into multiples of the state transition cycle time. The proposed methods are evaluated using a simulation of a dynamic 3-DoF system that fuses odometry and GPS measurements.


title: Grounded Semantic Composition for Visual Scenes

abstract: We present a visually-grounded language understanding model based on a study of how people verbally describe objects in scenes. The emphasis of the model is on the combination of individual word meanings to produce meanings for complex referring expressions. The model has been implemented, and it is able to understand a broad range of spatial referring expressions. We describe our implementation of word level visually-grounded semantics and their embedding in a compositional parsing framework. The implemented system selects the correct referents in response to natural language expressions for a large percentage of test cases. In an analysis of the system's successes and failures we reveal how visual context influences the semantics of utterances and propose future extensions to the model that take such context into account.


title: Efficient online index construction for text databases

abstract: Inverted index structures are a core element of current text retrieval systems. They can be constructed quickly using offline approaches, in which one or more passes are made over a static set of input data, and, at the completion of the process, an index is available for querying. However, there are search environments in which even a small delay in timeliness cannot be tolerated, and the index must always be queryable and up to date. Here we describe and analyze a geometric partitioning mechanism for online index construction that provides a range of tradeoffs between costs, and can be adapted to different balances of insertion and querying operations. Detailed experimental results are provided that show the extent of these tradeoffs, and that these new methods can yield substantial savings in online indexing costs.


title: QR Code: A New Opportunity for Effective Mobile Marketing

abstract: Quick Response code, commonly known as “QR code” is the latest phenomenon in the mobile marketing world. QR code’s attribution of linking brick and mortar world to digital domain is changing the approach of marketing. It gives marketers an opportunity to interact with consumers and engage them with their brand. Previous research and survey statistics demand the proper implementation of marketing campaign to create awareness among consumers for this new technology. Two cases of QR code implementations are used to illustrate two different frameworks which classify the marketing strategies based on the level of product/services involvement, and the marketing communication system used. Based on the product characteristics, high involvement versus low involvement and intensity of interaction in a hybrid network may result in different QR code implementations taking place.


title: Wages & income mobility in Indian labour market: the post-reform scenario

abstract: Improvement in the living conditions of workers is an important objective of development planners and India is no exception. The crux of this lies in returns from work, or wage level. While non-wage aspects are important, wage level is the most pertinent indicator of condition of workers and increase in real wage level signals improvement in condition of labour market. Though most studies compare wages at different points of time from cross-sectional data, they provide an aggregative view without control for variables that are particular to the household/family. Contrary to this, intergenerational mobility in wage income following life cycle theory observes direction & quantum of movement of workers’ wage relative to their parents, therefore filtering out household characteristics, and providing better measure of workers’ conditions and its trends over time. Another important aspect that can be explored by looking at intergenerational wage mobility is related to the issue of equality. Stickiness of wage income with respect to parental income leads to persistence of income inequality across generations and questions the notional objective of equity in opportunity and openness of any society. Historically some groups are belonging to lower strata of society due to economic and or social discrimination leading to lower income and asset possession as well as capability formation which excluded them from the process of capability formation and income-earning. This exclusion and backwardness surpass the boundary of the current generation and spills over to successive generations as well. As a result Intergenerational Mobility is very low among backward classes. Also of importance is to enquire whether economic liberalization and structural reforms have had any impact on the intergenerational income mobility – has mobility today more than that in the 1990s? In this paper we explore these issues, throwing light on a hitherto neglected area of research in Indian labour market studies – intergenerational income mobility, desegregated across social classes and comparing pre-reform and post-reform results. We observe that wage income mobility between generations have been generally low in India. Though such stickiness over generations is declining over time, especially in the post-reform period, stickiness is still higher for excluded social classes. Improvement over the last decade has occurred mainly for the scheduled castes and not for the tribals who are much more spatially isolated and hence outside the orbit of economic dynamics.


title: Ultra-Fast Shapelets for Time Series Classification

abstract: Time series shapelets are discriminative subsequences and their similarity to a time series can be used for time series classification. Since the discovery of time series shapelets is costly in terms of time, the applicability on long or multivariate time series is difficult. In this work we propose Ultra-Fast Shapelets that uses a number of random shapelets. It is shown that Ultra-Fast Shapelets yield the same prediction quality as current state-of-the-art shapelet-based time series classifiers that carefully select the shapelets by being by up to three orders of magnitudes. Since this method allows a ultra-fast shapelet discovery, using shapelets for long multivariate time series classification becomes feasible. ::: A method for using shapelets for multivariate time series is proposed and Ultra-Fast Shapelets is proven to be successful in comparison to state-of-the-art multivariate time series classifiers on 15 multivariate time series datasets from various domains. Finally, time series derivatives that have proven to be useful for other time series classifiers are investigated for the shapelet-based classifiers. It is shown that they have a positive impact and that they are easy to integrate with a simple preprocessing step, without the need of adapting the shapelet discovery algorithm.