title: Focused clustering and outlier detection in large attributed graphs

abstract: Graph clustering and graph outlier detection have been studied extensively on plain graphs, with various applications. Recently, algorithms have been extended to graphs with attributes as often observed in the real-world. However, all of these techniques fail to incorporate the user preference into graph mining, and thus, lack the ability to steer algorithms to more interesting parts of the attributed graph. In this work, we overcome this limitation and introduce a novel user-oriented approach for mining attributed graphs. The key aspect of our approach is to infer user preference by the so-called focus attributes through a set of user-provided exemplar nodes. In this new problem setting, clusters and outliers are then simultaneously mined according to this user preference. Specifically, our FocusCO algorithm identifies the focus, extracts focused clusters and detects outliers. Moreover, FocusCO scales well with graph size, since we perform a local clustering of interest to the user rather than global partitioning of the entire graph. We show the effectiveness and scalability of our method on synthetic and real-world graphs, as compared to both existing graph clustering and outlier detection approaches.


title: Classification of small UAVs and birds by micro-Doppler signatures

abstract: The problem of unmanned aerial vehicles classification using continuous wave radar is considered in this paper. Classification features are extracted from micro-Doppler signature. Before the classification, the micro-Doppler signature is filtered and aligned to compensate the Doppler shift caused by the target's body motion. Eigenpairs extracted from the correlation matrix of the signature are used as informative features for classification. The proposed approach is verified on real radar measurements collected with 9.5 GHz radar. Planes, quadrocopter, helicopters and stationary rotors as well as birds are considered for classification. Moreover, a possibility of distinguishing different number of rotors is considered. The obtained results show the effectiveness of the proposed approach. It provides capability of correct classification with a probability of around 95%.


title: Detecting Epileptic Seizures from EEG Data using Neural Networks

abstract: We explore the use of neural networks trained with dropout in predicting epileptic seizures from electroencephalographic data (scalp EEG). The input to the neural network is a 126 feature vector containing 9 features for each of the 14 EEG channels obtained over 1-second, non-overlapping windows. The models in our experiments achieved high sensitivity and specificity on patient records not used in the training process. This is demonstrated using leave-one-out-cross-validation across patient records, where we hold out one patient's record as the test set and use all other patients' records for training; repeating this procedure for all patients in the database.


title: The underlying structure of visuospatial working memory in children with mathematical learning disability

abstract: This study examined visual, spatial-sequential, and spatial-simultaneous working memory (WM) performance in children with mathematical learning disability (MLD) and low mathematics achievement (LMA) compared with typically developing (TD) children. Groups were matched on reading decoding performance and verbal intelligence. Besides statistical significance testing, we used bootstrap confidence interval estimation and computed effect sizes. Children were individually tested with six computerized tasks, two for each visuospatial WM subcomponent. We found that both MLD and LMA children had low visuospatial WM function in both spatial-simultaneous and spatial-sequential WM tasks. The WM deficit was most expressed in MLD children and less in LMA children. This suggests that WM scores are distributed along a continuum with TD children achieving top scores and MLD children achieving low scores. The theoretical and practical significance of findings is discussed. Statement of Contribution What is already known on this subject? Working memory plays an important role in mathematical achievement. Children with mathematical learning disability (MLD) usually have low working memory resources. Conflicting results have been reported concerning the role of VSWM in individuals with MLD. What the present study adds? Children with different degree of impairment in math achievement and typically developing children were tested. Visual, spatial-sequential, and spatial-simultaneous working memory tasks were examined. Only spatial-sequential and spatial-simultaneous working memory tasks discriminated the two impairments groups.


title: Using Presence Questionnaires in Reality

abstract: A between-group experiment was carried out to assess whether two different presence questionnaires can distinguish between real and virtual experiences. One group of ten subjects searched for a box in a real office environment. A second group of ten subjects carried out the same task in a virtual environment that simulated the same office. Immediately after their experience, subjects were given two different presence questionnaires in randomized order: the Witmer and Singer Presence (WS), and the questionnaire developed by Slater, Usoh, and Steed (SUS). The paper argues that questionnaires should be able to pass a “reality test” whereby under current conditions the presence scores should be higher for real experiences than for virtual ones. Nevertheless, only the SUS had a marginally higher mean score for the real compared to the virtual, and there was no significant difference at all between the WS mean scores. It is concluded that, although such questionnaires may be useful when all subjects experience the same type of environment, their utility is doubtful for the comparison of experiences across environments, such as immersive virtual compared to real, or desktop compared to immersive virtual.


title: Understanding script-based stories using commonsense reasoning

abstract: This paper investigates the use of commonsense reasoning to understand texts involving stereotypical activities or scripts. We present a system that understands news stories involving four terrorism scripts. The system (1) builds a commonsense reasoning problem given an information extraction template representing a terrorist incident, and (2) uses commonsense reasoning and a commonsense knowledge base to build a model of the terrorist incident. The reasoning problem, commonsense knowledge base, and model are expressed in the classical logic event calculus. The system was developed using the MUC3 and MUC4 development data set. We present the results of running the system on the MUC3 and MUC4 test data sets, using manually generated answer key templates and templates generated automatically by two MUC4 information extraction systems. We present a detailed analysis of the models produced by the system given automatically generated templates. We present methods for answering questions based on the models produced by our system. We assess the portability of the system by extending it to handle 10 scripts frequent in Project Gutenberg American literature texts.


title: Field Weakening Operation of High Torque Density Five-Phase Permanent Magnet Motor Drives

abstract: This paper investigates the field weakening operation of a five-phase permanent magnet motor. The proposed motor has concentrated windings such that the produced back-EMF is almost trapezoidal. The motor is supplied with the combined sinusoidal plus third harmonic of currents. This motor, while generating the same average torque as an equivalent permanent magnet brushless DC motor, overcomes its disadvantages. It is shown that torque producing and flux producing components of current for this motor can be decoupled by using multiple reference frame transformation. Therefore, vector control is easily applicable to the motor. Field weakening algorithm is derived and implemented. This motor benefits from high torque density of a BLDC motor below the rated speed and controllability of PMSM above the rated speed and during the field weakening region. Simulation and experimental results are provided to prove the validity of the superior performance of this drive


title: A temperature compensated relaxation oscillator for SoC implementations

abstract: A temperature and supply independent on-chip reference relaxation oscillator for low voltage design is described. The frequency of oscillation is mainly a function of a PVT robust biasing current. The comparator for the relaxation oscillator is replaced with a high speed common-source stage to eliminate the temperature dependency of the comparator delay. The current sources and voltages are biased by a PVT robust references derived from a bandgap circuitry. This oscillator is designed in TSMC 65nm CMOS process to operate with a minimum supply voltage of 1.4V and consumes 100μW at 157MHz frequency of oscillation. The oscillator exhibits frequency variation of 1.6% for supply changes from 1.4V to 1.9V, and ±1.2% for temperature changes from 20°C to 120°C.


title: A Wireless Greenhouse Monitoring System Based on Solar Energy

abstract: To resolve the problems of complicated cabling and costly wired network in the current system, we designed a wireless greenhouse monitoring system based on ZigBee and GSM technology. The system consists of two parts: a wireless sensor network and remote control terminal. According to parameters distribution in the monitoring regional, a wireless transmission network was formed, all of the node in the network using solar power. In the remote control terminal, the study developed a simplified expert decision system, in which the part of greenhouse control decision adopts the fuzzy decoupling control algorithm to realize the temperature and humidity decoupling control and increase the accuracy of decision-making.According to the experimental test, the monitoring system can run well under the conditions in northern China greenhouses. It can realize real-time, accurate monitoring and collecting of parameters data in the greenhouse environment; the remote control terminal can give effective decision management solutions. Our future work will mainly be solar photovoltaic panel servo system and image transmission. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3305 Full Text: PDF


Simulating Charge Stability Diagrams for Double and Triple Quantum Dot Systems


title: Open theoretical questions in reinforcement learning

abstract: Reinforcement learning (RL) concerns the problem of a learning agent interacting with its environment to achieve a goal. Instead of being given examples of desired behavior, the learning agent must discover by trial and error how to behave in order to get the most reward. The environment is a Markov decision process (MDP) with state set, \( \mathcal{S} \), and action set, \( \mathcal{A} \). The agent and the environment interact in a sequence of discrete steps, t = 0, 1, 2,... The state and action at one time step, \( s_t \in \mathcal{S} \) and \( a_t \in \mathcal{A} \), determine the probability distribution for the state at the next time step, \( s_{t + 1} \in \mathcal{S} \) and, jointly, the distribution for the next reward, r t+1 ∈ ℜ. The agent’s objective is to chose each aint to maximize the subsequent return: ::: ::: $$ R_t = \sum\limits_{k = 0}^\infty {\gamma ^k r_{t + 1 + k} ,} $$ ::: ::: where the discount rate, 0 ≤ γ ≤ 1, determines the relative weighting of immediate and delayed rewards. In some environments, the interaction consists of a sequence of episodes, each starting in a given state and ending upon arrival in a terminal state, terminating the series above. In other cases the interaction is continual, without interruption, and the sum may have an infinite number of terms (in which case we usually assume γ < 1). Infinite horizon cases with γ = 1 are also possible though less common (e.g., see Mahadevan, 1996).


title: Is REThink therapeutic game effective in preventing emotional disorders in children and adolescents? Outcomes of a randomized clinical trial

abstract: Abstract ::: Therapeutic games represent a promising solution for addressing emotional difficulties in youths. The aim of the present study was to investigate the effectiveness of the REThink game, in helping children and adolescents, to develop psychological resilience. Therefore, 165 children aged between 10 and 16 years were randomly assigned in one of the three groups: 54 participants in the REThink condition, 55 participants in the Rational Emotive Behavior Education condition, and 56 participants in the waitlist condition. Results indicated that the REThink intervention had a significant impact on emotional symptoms (a moderate-effect size, d = 0.46) and on depressive mood (a large-effect size, d = 0.84). Furthermore, REThink had a significant impact on children’s ability to regulate their emotions, with a significant effect on emotional awareness (d = 0.64), and on the ability for emotional control (d = 0.69). In conclusion, the implications of the REThink game are discussed in relationship with resiliency building programs designed for youths.


title: Systematically Understanding the Cyber Attack Business: A Survey

abstract: Cyber attacks are increasingly menacing businesses. Based on the literature review and publicly available reports, this article conducts an extensive and consistent survey of the services used by the cybercrime business, organized using the value chain perspective, to understand cyber attack in a systematic way. Understanding the specialization, commercialization, and cooperation for cyber attacks helps us to identify 24 key value-added activities and their relations. These can be offered “as a service” for use in a cyber attack. This framework helps to understand the cybercriminal service ecosystem and hacking innovations. Finally, a few examples are provided showing how this framework can help to build a more cyber immune system, like targeting cybercrime control-points and assigning defense responsibilities to encourage collaboration.


title: Elizabeth Hooton and the Lived Politics of Toleration in Massachusetts Bay

abstract: Quaker prophetess Elizabeth Hooton traveled throughout England, the West Indies, Virginia, New Netherland, and New England in the mid-seventeenth century. Though best known as one of George Fox’s first converts and a leading female preacher, she engaged in savvy political activity as well as zealous prophecy. The lived politics of her encounters included not only whippings and banishments but also small-scale negotiations and unexpected alliances. Obtaining a license from Charles II to purchase land anywhere in his colonies, she leveraged royal authority to influence colonial politics. Most effectively, during the 1664–65 visit of the royal commissioners to Massachusetts Bay, she and other Friends provoked local magistrates to seditious speech. Hooton’s journeys highlight the profound tension between religious fervor and political stability in the midst of competing visions of the common good. They point to a history of toleration in early America that attends to fine-grained local religious, economic, legal, and social contexts, and they reveal strategic and significant Quaker interventions.


title: THE APPLICATION OF DESIGN THINKING METHODOLOGY ON RESEARCH PRACTICES: A MIND-MAP OF TOOLS AND METHOD

abstract: The difficult task of innovation is a key facet of Research & Development (R&D) institutions. Innovation is also closely related with processes oriented to achieve solutions in design. By addressing this topic, we propose to research new emerging design methods and provide an overview of design thinking tools that can be applied in an early stage of the R&D research process in order to produce meaningful results. This research presents a set of experimental guidelines and an analysis method for the application of these tools. In particular, it is taken into account that design, through design thinking, extends to the experience that costumers/users have with products, services, spaces or even multidimensional experiences, which is a relevant input for R&D innovation development. It is well known that design has changed its scope beyond the conception of artifacts: Design thinking methodologies have resulted from the advancement of current design paradigms. However, design thinking is not a new design discipline. It is a fresh multi-disciplinary platform that utilizes conceptual tools in order to provide steps towards innovation. In fact, the establishment of coherent guidelines for the design thinking process is a very complex task, due to its interdisciplinary requirements, that convey many diverse mindsets. The main focus of this study is creating an analysis toolkit that enables non-specialist and specialist users to perform high-quality design production. This methodological tool is being applied in the framework of two research centers, namely, the Research center for Science and Technology of the Arts and the Telecommunication Institute of Oporto University. These trials provide valuable pilot studies in order to determine the efficiency of this ‘mind map’ in the R&D innovation process.


title: SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling

abstract: We propose a novel deep architecture, SegNet, for semantic pixel wise image labelling. SegNet has several attractive properties; (i) it only requires forward evaluation of a fully learnt function to obtain smooth label predictions, (ii) with increasing depth, a larger context is considered for pixel labelling which improves accuracy, and (iii) it is easy to visualise the effect of feature activation(s) in the pixel label space at any depth. SegNet is composed of a stack of encoders followed by a corresponding decoder stack which feeds into a soft-max classification layer. The decoders help map low resolution feature maps at the output of the encoder stack to full input image size feature maps. This addresses an important drawback of recent deep learning approaches which have adopted networks designed for object categorization for pixel wise labelling. These methods lack a mechanism to map deep layer feature maps to input dimensions. They resort to ad hoc methods to upsample features, e.g. by replication. This results in noisy predictions and also restricts the number of pooling layers in order to avoid too much upsampling and thus reduces spatial context. SegNet overcomes these problems by learning to map encoder outputs to image pixel labels. We test the performance of SegNet on outdoor RGB scenes from CamVid, KITTI and indoor scenes from the NYU dataset. Our results show that SegNet achieves state-of-the-art performance even without use of additional cues such as depth, video frames or post-processing with CRF models.


title: Neural networks applied to wireless communications

abstract: This paper presents a time-delayed neural network (TDNN) model that has the capability of learning and predicting the dynamic behavior of nonlinear elements that compose a wireless communication system. This model could help speeding up system deployment by reducing modeling time. This paper presents results of effective application of the TDNN model to an amplifier, part of a wireless transmitter.


title: Real-time quantification of TEL-AML1 fusion transcripts for MRD detection in relapsed childhood acute lymphoblastic leukaemia. Comparison with antigen receptor-based MRD quantification methods.

abstract: Abstract Response to therapy of ALL assessed by molecular methods has been proved to be a predictor of outcome. Alternatively to established but very labour-intensive DNA-based PCR-techniques the TEL – AML1 fusion transcript can serve as a marker for MRD monitoring. MRD quantification using TEL – AML1 is of particular interest if the results are directly comparable to data obtained by established DNA-based assays. Investigation of the potential of MRD monitoring using LightCycler technology for TEL – AML1 real-time quantification and comparison to results from established DNA-based MRD assays revealed corresponding results. Accordingly, TEL – AML1 MRD quantification is a sensitive, specific and rapid method that can supplement clone-specific MRD detection.


title: A proposal of unified reference model for smart manufacturing

abstract: This paper examines “Unified Reference Model-Map & Methodology (URM-MM)” as a development guideline for open-ecosystem, which support users to integrate individual components developed by diversified vendors in order to address complicated challenges, which is not easy to solve by a single vendor. The first contribution of this paper is finding insufficiency of not only functionality but also a process for building open-ecosystems through a survey on existing reference models/architecture. The second contribution is to shape URM-MM as a unified model, a development guideline for boosting discussions towards the realization of smart manufacturing.


title: A Brain Mechanism for Facilitation of Insight by Positive Affect

abstract: Previous research has shown that people solve insight or creative problems better when in a positive mood (assessed or induced), although the precise mechanisms and neural substrates of this facilitation remain unclear. We assessed mood and personality variables in 79 participants before they attempted to solve problems that can be solved by either an insight or an analytic strategy. Participants higher in positive mood solved more problems, and specifically more with insight, compared with participants lower in positive mood. fMRI was performed on 27 of the participants while they solved problems. Positive mood (and to a lesser extent and in the opposite direction, anxiety) was associated with changes in brain activity during a preparatory interval preceding each solved problem; modulation of preparatory activity in several areas biased people to solve either with insight or analytically. Analyses examined whether (a) positive mood modulated activity in brain areas showing responsivity during preparation; (b) positive mood modulated activity in areas showing stronger activity for insight than noninsight trials either during preparation or solution; and (c) insight effects occurred in areas that showed mood-related effects during preparation. Across three analyses, the ACC showed sensitivity to both mood and insight, demonstrating that positive mood alters preparatory activity in ACC, biasing participants to engage in processing conducive to insight solving. This result suggests that positive mood enhances insight, at least in part, by modulating attention and cognitive control mechanisms via ACC, perhaps enhancing sensitivity to detect non-prepotent solution candidates.


title: Memory and Computation Efficient PCA via Very Sparse Random Projections

abstract: Algorithms that can efficiently recover principal components in very high-dimensional, streaming, and/or distributed data settings have become an important topic in the literature. In this paper, we propose an approach to principal component estimation that utilizes projections onto very sparse random vectors with Bernoulli-generated nonzero entries. Indeed, our approach is simultaneously efficient in memory/storage space, efficient in computation, and produces accurate PC estimates, while also allowing for rigorous theoretical performance analysis. Moreover, one can tune the sparsity of the random vectors deliberately to achieve a desired point on the tradeoffs between memory, computation, and accuracy. We rigorously characterize these tradeoffs and provide statistical performance guarantees. In addition to these very sparse random vectors, our analysis also applies to more general random projections. We present experimental results demonstrating that this approach allows for simultaneously achieving a substantial reduction of the computational complexity and memory/storage space, with little loss in accuracy, particularly for very high-dimensional data.


title: Trusted design in FPGAs

abstract: Using FPGAs, a designer can separate the design process from the manufacturing flow. Therefore, the owner of a sensitive design need not expose the design to possible theft and tampering during its manufacture, dramatically simplifying the process of assuring trust in that design. Modern FPGAs include bitstream security features that turn the fielded design trust problem into an information security problem, with well-known cryptographic information security solutions. The generic nature of the FPGA base array allows the validation expense to be amortized over all designs targeted to that base array. Even the task of checking design tools is simplified by using non-destructive checks of the FPGA design.


title: Bangla handwritten digit recognition using autoencoder and deep convolutional neural network

abstract: Handwritten digit recognition is a typical image classification problem. Convolutional neural networks, also known as ConvNets, are powerful classification models for such tasks. As different languages have different styles and shapes of their numeral digits, accuracy rates of the models vary from each other and from language to language. However, unsupervised pre-training in such situation has shown improved accuracy for classification tasks, though no such work has been found for Bangla digit recognition. This paper presents the use of unsupervised pre-training using autoencoder with deep ConvNet in order to recognize handwritten Bangla digits, i.e., 0–9. The datasets that are used in this paper are CMATERDB 3.1.1 and a dataset published by the Indian Statistical Institute (ISI). This paper studies four different combinations of these two datasets-two experiments are done against their own training and testing images, other two experiments are done cross validating the datasets. In one of these four experiments, the proposed approach achieves 99.50% accuracy, which is so far the best for recognizing handwritten Bangla digits. The ConvNet model is trained with 19,313 images of ISI handwritten character dataset and tested with images of CMATERDB dataset.


title: A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks

abstract: Location-based social networks (LBSNs), such as Gowalla, Facebook, Foursquare, Brightkite, and so on, have attracted millions of users to share their social friendship and their locations via check-ins in the past few years. Plenty of valuable information is accumulated based on the check-in behaviors, which makes it possible to learn users’ moving patterns as well as their preferences. In LBSNs, point-of-interest (POI) recommendation is one of the most significant tasks because it can help targeted users explore their surroundings as well as help third-party developers provide personalized services. Matrix factorization is a promising method for this task because it can capture users’ preferences to locations and is widely adopted in traditional recommender systems such as movie recommendation. However, the sparsity of the check-in data makes it difficult to capture users’ preferences accurately. Geographical influence can help alleviate this problem and have a large impact on the final recommendation result. By studying users’ moving patterns, we find that users tend to check in around several centers and different users have different numbers of centers. Based on this, we propose a Multi-center Gaussian Model (MGM) to capture this pattern via modeling the probability of a user’s check-in on a location. Moreover, users are usually more interested in the top 20 or even top 10 recommended POIs, which makes personalized ranking important in this task. From previous work, directly optimizing for pairwise ranking like Bayesian Personalized Ranking (BPR) achieves better performance in the top-k recommendation than directly using matrix matrix factorization that aims to minimize the point-wise rating error. To consider users’ preferences, geographical influence and personalized ranking, we propose a unified POI recommendation framework, which unifies all of them together. Specifically, we first fuse MGM with matrix factorization methods and further with BPR using two different approaches. We conduct experiments on Gowalla and Foursquare datasets, which are two large-scale real-world LBSN datasets publicly available online. The results on both datasets show that our unified POI recommendation framework can produce better performance.


title: Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

abstract: We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits. The dataset is freely available at this https URL


title: Short-term solar irradiance forecasting model based on artificial neural network using statistical feature parameters

abstract: Short-term solar irradiance forecasting (STSIF) is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN) is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. After discussing the relation between weather variations and irradiance, the characteristics of the statistical feature parameters of irradiance under different weather conditions are figured out. A novel ANN model using statistical feature parameters (ANN-SFP) for STSIF is proposed in this paper. The input vector is reconstructed with several statistical feature parameters of irradiance and ambient temperature. Thus sufficient information can be effectively extracted from relatively few inputs and the model complexity is reduced. The model structure is determined by cross-validation (CV), and the Levenberg-Marquardt algorithm (LMA) is used for the network training. Simulations are carried out to validate and compare the proposed model with the conventional ANN model using historical data series (ANN-HDS), and the results indicated that the forecast accuracy is obviously improved under variable weather conditions.


title: Entity ranking in Wikipedia: utilising categories, links and topic difficulty prediction

abstract: Entity ranking has recently emerged as a research field that aims at retrieving entities as answers to a query. Unlike entity extraction where the goal is to tag names of entities in documents, entity ranking is primarily focused on returning a ranked list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated on the INEX Wikipedia test collection. In this paper, we describe a system we developed for ranking Wikipedia entities in answer to a query. The entity ranking approach implemented in our system utilises the known categories, the link structure of Wikipedia, as well as the link co-occurrences with the entity examples (when provided) to retrieve relevant entities as answers to the query. We also extend our entity ranking approach by utilising the knowledge of predicted classes of topic difficulty. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the optimal values for the retrieval parameters of our entity ranking system. Our experiments demonstrate that the use of categories and the link structure of Wikipedia can significantly improve entity ranking effectiveness, and that topic difficulty prediction is a promising approach that could also be exploited to further improve the entity ranking performance.


title: CD4+ T Cells Targeting Dominant and Cryptic Epitopes from Bacillus anthracis Lethal Factor

abstract: Anthrax is an endemic infection in many countries, particularly in the developing world. The causative agent, Bacillus anthracis, mediates disease through the secretion of binary exotoxins. Until recently, research into adaptive immunity targeting this bacterial pathogen has largely focused on the humoral response to these toxins. There is, however, growing recognition that cellular immune responses involving IFNγ producing CD4+ T cells also contribute significantly to a protective memory response. An established concept in adaptive immunity to infection is that during infection of host cells, new microbial epitopes may be revealed, leading to immune recognition of so called ‘cryptic’ or ‘subdominant’ epitopes. We analyzed the response to both cryptic and immunodominant T cell epitopes derived from the toxin component lethal factor and presented by a range of HLA-DR alleles. Using IFNγ-ELISpot assays we characterized epitopes that elicited a response following immunization with synthetic peptide and the whole protein and tested their capacities to bind purified HLA-DR molecules in vitro. We found that DR1 transgenics demonstrated T cell responses to a greater number of domain III cryptic epitopes than other HLA-DR transgenics, and that this pattern was repeated with the immunodominant epitopes, as a greater proportion of these epitopes induced a T cell response when presented within the context of the whole protein. Immunodominant epitopes LF457-476 and LF467-487 were found to induce a T cell response to the peptide, as well as to the whole native LF protein in DR1 and DR15, but not in DR4 transgenics. The analysis of Domain I revealed the presence of several unique cryptic epitopes all of which showed a strong to moderate relative binding affinity to HLA-DR4 molecules. However, none of the cryptic epitopes from either domain III or I displayed notably high binding affinities across all HLA-DR alleles assayed. These responses were influenced by the specific HLA alleles presenting the peptide, and imply that construction of future epitope string vaccines which are immunogenic across a wide range of HLA alleles could benefit from a combination of both cryptic and immunodominant anthrax epitopes.


title: Point-Set Registration: Coherent Point Drift

abstract: Point set registration is a key component in many computer vision tasks. The goal of point set registration is to assign correspondences between two sets of points and to recover the transformation that maps one point set to the other. Multiple factors, including an unknown nonrigid spatial transformation, large dimensionality of point set, noise, and outliers, make the point set registration a challenging problem. We introduce a probabilistic method, called the Coherent Point Drift (CPD) algorithm, for both rigid and nonrigid point set registration. We consider the alignment of two point sets as a probability density estimation problem. We fit the Gaussian mixture model (GMM) centroids (representing the first point set) to the data (the second point set) by maximizing the likelihood. We force the GMM centroids to move coherently as a group to preserve the topological structure of the point sets. In the rigid case, we impose the coherence constraint by reparameterization of GMM centroid locations with rigid parameters and derive a closed form solution of the maximization step of the EM algorithm in arbitrary dimensions. In the nonrigid case, we impose the coherence constraint by regularizing the displacement field and using the variational calculus to derive the optimal transformation. We also introduce a fast algorithm that reduces the method computation complexity to linear. We test the CPD algorithm for both rigid and nonrigid transformations in the presence of noise, outliers, and missing points, where CPD shows accurate results and outperforms current state-of-the-art methods.


title: European fusion target work

abstract: Abstract Target concepts developed in the context of the HIDIF project (Heavy Ion Driven Inertial Fusion) are presented. Target design has evolved, from the beginning of the study in 1993, in parallel with the evolution of accelerator parameters. Two different phases can be distinguished: (a) up to 1998 the goal was just demonstration of ignition with heavy ions, (b) from 1998 target and driver are being upgraded to high-gain designs able to be used for energy production. Analytical models and numerical tools developed in this context are summarized, and future research directions are discussed.


title: Acceptability of multiple micronutrient supplements by pregnant and lactating women in Mali.

abstract: BACKGROUND ::: In Mali, an estimated 73% of pregnant women are anaemic largely due to iron deficiency. National policy recommends women to take iron and folic acid supplements daily from first prenatal contact until 3 months postpartum. However, many pregnant women in Mali could benefit from multiple micronutrient supplements. ::: ::: ::: OBJECTIVE ::: To assess pregnant women's acceptability of and adherence to a daily multiple micronutrient supplementation scheme compared with the current daily iron and folic acid supplementation scheme. ::: ::: ::: DESIGN ::: Seventy pregnant women were allocated to either the daily multiple micronutrient or daily iron and folic acid supplementation scheme. Women started receiving supplements at the end of the first trimester of pregnancy until delivery and throughout the first 3 months postpartum. ::: ::: ::: RESULTS ::: No significant differences were observed between comparison groups with respect to women's perceptions about supplement size, colour, taste or flavour. Adherence to the multiple micronutrient supplementation scheme was better (257.5+/-20.9 tablets; average adherence 95.4%) than that to the iron and folic acid supplementation scheme (238.5+/-32.7 tablets; average adherence 92.2%; P=0.008) although both were very good, as were women's perceptions about the benefits of micronutrient supplements to their health and that of their newborns. ::: ::: ::: CONCLUSION ::: Malian women adhere to prenatal/postpartum micronutrient supplementation - no matter what supplement is chosen - when access to supplements is guaranteed and when they are provided with minimum, consistent and easily understandable information and counselling, indicating that these are key elements to ensure effective programmes. These findings, together with those of the global research agenda on the efficacy of multiple micronutrient supplements for pregnant women, will inform policy development in Mali for the effective control of iron deficiency and iron-deficiency anaemia in pregnant women.


title: Recognizing unseen actions in a domain-adapted embedding space

abstract: With the sustaining bloom of multimedia data, Zero-shot Learning (ZSL) techniques have attracted much attention in recent years for its ability to train learning models that can handle “unseen” categories. Existing ZSL algorithms mainly take advantages of attribute-based semantic space and only focus on static image data. Besides, most ZSL studies merely consider the semantic embedded labels and fail to address domain shift problem. In this paper, we purpose a deep two-output model for video ZSL and action recognition tasks by computing both spatial and temporal features from video contents through distinct Convolutional Neural Networks (CNNs) and training a Multi-layer Perceptron (MLP) upon extracted features to map videos to semantic embedding word vectors. Moreover, we introduce a domain adaptation strategy named “ConSSEV” — by combining outputs from two distinct output layers of our MLP to improve the results of zero-shot learning. Our experiments on UCF101 dataset demonstrate the purposed model has more advantages associated with more complex video embedding schemes, and outperforms the state-of-the-art zero-shot learning techniques.


title: Stakeholders in Global Requirements Engineering: Lessons Learned from Practice

abstract: Due to its communication and collaboration-intensive nature, as well as inherent interaction with most other development processes, the practice of requirements engineering is becoming a key challenge in global software engineering (GSE). In distributed projects, cross-functional stakeholder groups must specify and manage requirements across cultural, time-zone, and organizational boundaries. This creates a unique set of problems, not only when an organization opens new development subsidiaries across the world but also when software development is a multiorganizational business affair. We need innovative processes and technologies to manage stakeholders' expectations and interaction in global projects. This article reports on the state of the practice, drawn from industrial empirical studies, of stakeholders' interaction in global RE. The article revisits stakeholders' needs in global RE, discusses the challenges they face in distributed interaction, and offers practical advice to alleviate these challenges, as distilled from empirical studies of GSE practice


title: An Internet of Things based prototype for smart appliance control

abstract: The Internet of Things (IoT) is increasingly being recognized by researchers, companies and analysts all over the world as one of the most advanced technologies that has the capacity to revolutionize the behaviour of everything around us. It possesses the potential to not only affect the health, safety and productivity of billions of people but also has a major economic impact. Inspired by the idea of “smart technologies” and “home automation”, we propose an IoT framework with smart-sensing based automatic appliance control. This paper presents a method and an implementation technique for the management of smart-appliances. A prototype has been developed and tested to verify the efficacy of the implemented technique. The results show that such prototypes can be influential in making home appliances smart enough to operate only when required. The broad application of the proposed work will lead to greater economic benefits, in terms of increasing infrastructure intelligence, and will also have social impacts in terms of global sustainability.


title: Development of a drone-type wall-sticking and climbing robot

abstract: Urban structures need constant maintenance and inspection of the structural health condition and safety of the users, however, to access the structure is getting harder and harder due to their enormous height and size. In order to deal with this problem, though many researchers have developed several robots for wall-climbing there is no guaranteed solution yet. One of the critical reasons why existing wall-climbing robots haven't been available in the field is the risk of accidental fall due to operational failure from the harsh environment like strong wind and surface's unpredictable condition. Therefore, we tried to develop a drone-type wall-climbing aerial robot platform that can approach to any place of the structure by flying and sticking to the target place with pose change and perching mechanism. The robot is equipped with a locomotion mechanism like other wall-climbing robots to move on the vertical surface of the structure. This paper deals with installing the wall-climbing mechanism on the aerial robot, its pose change and wall sticking mechanism, and locomotion on the vertical surface of an urban structure.


title: Promotility Medications – Now and in the Future

abstract: Gastrointestinal promotility drugs stimulate smooth muscle contractions to enhance gastric emptying and small and large bowel transit. Currently available drug classes with prokinetic properties include antidopaminergic agents, serotonergic agents, and motilin-receptor agonists. Due to moderate prokinetic effects, poor symptomatic responses and the presence of adverse effects, there is a clear need for new classes of prokinetics. Several newer prokinetic drugs and drug classes are currently under evaluation. Selecting candidate agents and designing the appropriate therapeutic trials is hampered by the lack of insight in the pathophysiology of motility-related symptoms. As gastrointestinal motor disorders are chronic, relapsing, and remitting disorders, it seems desirable that studies with candidate prokinetic drugs establish a long-term efficacy and not only short-term effects on gastrointestinal functions.


title: Two Is Bigger (and Better) Than One: the Wikipedia Bitaxonomy Project

abstract: We present WiBi, an approach to the automatic creation of a bitaxonomy for Wikipedia, that is, an integrated taxonomy of Wikipage pages and categories. We leverage the information available in either one of the taxonomies to reinforce the creation of the other taxonomy. Our experiments show higher quality and coverage than state-of-the-art resources like DBpedia, YAGO, MENTA, WikiNet and WikiTaxonomy. WiBi is available at http://wibitaxonomy.org.


title: Image-based air quality analysis using deep convolutional neural network

abstract: Air pollution may cause many severe diseases. An efficient air quality monitoring system is of great benefit for human health and air pollution control. In this paper, we study image-based air quality analysis, in particular, the concentration estimation of particulate matter with diameters less than 2.5 micrometers (PM 2.5 ). The proposed method uses a deep Convolutional Neural Network (CNN) to classify natural images into different categories based on their PM2.5 concentrations. In order to evaluate the proposed method, we created a dataset that contains total 591 images taken in Beijing with corresponding PM2.5 concentrations. The experimental results demonstrate that our method are valid for image-based PM2.5 concentration estimation.


title: An Efficient DDoS TCP Flood Attack Detection and Prevention System in a Cloud Environment

abstract: Although the number of cloud projects has dramatically increased over the last few years, ensuring the availability and security of project data, services, and resources is still a crucial and challenging research issue. Distributed denial of service (DDoS) attacks are the second most prevalent cybercrime attacks after information theft. DDoS TCP flood attacks can exhaust the cloud’s resources, consume most of its bandwidth, and damage an entire cloud project within a short period of time. The timely detection and prevention of such attacks in cloud projects are therefore vital, especially for eHealth clouds. In this paper, we present a new classifier system for detecting and preventing DDoS TCP flood attacks (CS_DDoS) in public clouds. The proposed CS_DDoS system offers a solution to securing stored records by classifying the incoming packets and making a decision based on the classification results. During the detection phase, the CS_DDOS identifies and determines whether a packet is normal or originates from an attacker. During the prevention phase, packets, which are classified as malicious, will be denied to access the cloud service and the source IP will be blacklisted. The performance of the CS_DDoS system is compared using the different classifiers of the least squares support vector machine (LS-SVM), naive Bayes, K-nearest, and multilayer perceptron. The results show that CS_DDoS yields the best performance when the LS-SVM classifier is adopted. It can detect DDoS TCP flood attacks with about 97% accuracy and with a Kappa coefficient of 0.89 when under attack from a single source, and 94% accuracy with a Kappa coefficient of 0.9 when under attack from multiple attackers. Finally, the results are discussed in terms of accuracy and time complexity, and validated using a K-fold cross-validation model.


title: Sleep loss, learning capacity and academic performance

abstract: Summary At a time when several studies have highlighted the relationship between sleep, learning and memory processes, an in-depth analysis of the effects of sleep deprivation on student learning ability and academic performance would appear to be essential. Most studies have been naturalistic correlative investigations, where sleep schedules were correlated with school and academic achievement. Nonetheless, some authors were able to actively manipulate sleep in order to observe neurocognitive and behavioral consequences, such as learning, memory capacity and school performance. The findings strongly suggest that: (a) students of different education levels (from school to university) are chronically sleep deprived or suffer from poor sleep quality and consequent daytime sleepiness; (b) sleep quality and quantity are closely related to student learning capacity and academic performance; (c) sleep loss is frequently associated with poor declarative and procedural learning in students; (d) studies in which sleep was actively restricted or optimized showed, respectively, a worsening and an improvement in neurocognitive and academic performance. These results may been related to the specific involvement of the prefrontal cortex (PFC) in vulnerability to sleep loss. Most methodological limitations are discussed and some future research goals are suggested.


title: Mathematical Modelling of Diesel-Electric Propulsion Systems for Marine Vessels

abstract: This article presents a mathematical model of a complete diesel-electric propulsion system, including components as diesel generators, distribution network, variable speed thruster-drives, and conventional motor loads. The model is split into two parts: One power generating part where the load is specified with an aggregated active and reactive power load demand. Secondly, a power consumption part where the effects of the different load types as thruster drives, motors and other loads are modelled. The model is written in a state-space form suitable for the purpose of simulation and control design. PID-controllers represent speed governors and automatic voltage regulators.


title: One-Shot Imitation Learning

abstract: Imitation learning has been commonly applied to solve different tasks in isolation. This usually requires either careful feature engineering, or a significant number of samples. This is far from what we desire: ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering. In this paper, we propose a meta-learning framework for achieving such capability, which we call one-shot imitation learning. ::: Specifically, we consider the setting where there is a very large set of tasks, and each task has many instantiations. For example, a task could be to stack all blocks on a table into a single tower, another task could be to place all blocks on a table into two-block towers, etc. In each case, different instances of the task would consist of different sets of blocks with different initial states. At training time, our algorithm is presented with pairs of demonstrations for a subset of all tasks. A neural net is trained that takes as input one demonstration and the current state (which initially is the initial state of the other demonstration of the pair), and outputs an action with the goal that the resulting sequence of states and actions matches as closely as possible with the second demonstration. At test time, a demonstration of a single instance of a new task is presented, and the neural net is expected to perform well on new instances of this new task. The use of soft attention allows the model to generalize to conditions and tasks unseen in the training data. We anticipate that by training this model on a much greater variety of tasks and settings, we will obtain a general system that can turn any demonstrations into robust policies that can accomplish an overwhelming variety of tasks. ::: Videos available at this https URL .


title: Xpander: Towards Optimal-Performance Datacenters

abstract: Despite extensive efforts to meet ever-growing demands, today's datacenters often exhibit far-from-optimal performance in terms of network utilization, resiliency to failures, cost efficiency, incremental expandability, and more. Consequently, many novel architectures for high-performance datacenters have been proposed. We show that the benefits of state-of-the-art proposals are, in fact, derived from the fact that they are (implicitly) utilizing "expander graphs" (aka expanders) as their network topologies, thus unveiling a unifying theme of these proposals. We observe, however, that these proposals are not optimal with respect to performance, do not scale, or suffer from seemingly insurmountable deployment challenges. We leverage these insights to present Xpander, a novel datacenter architecture that achieves near-optimal performance and provides a tangible alternative to existing datacenter designs. Xpander's design turns ideas from the rich graph-theoretic literature on constructing optimal expanders into an operational reality. We evaluate Xpander via theoretical analyses, extensive simulations, experiments with a network emulator, and an implementation on an SDN-capable network testbed. Our results demonstrate that Xpander significantly outperforms both traditional and proposed datacenter designs. We discuss challenges to real-world deployment and explain how these can be resolved.


title: RUNE-Tag: A high accuracy fiducial marker with strong occlusion resilience

abstract: Over the last decades fiducial markers have provided widely adopted tools to add reliable model-based features into an otherwise general scene. Given their central role in many computer vision tasks, countless different solutions have been proposed in the literature. Some designs are focused on the accuracy of the recovered camera pose with respect to the tag; some other concentrate on reaching high detection speed or on recognizing a large number of distinct markers in the scene. In such a crowded area both the researcher and the practitioner are licensed to wonder if there is any need to introduce yet another approach. Nevertheless, with this paper, we would like to present a general purpose fiducial marker system that can be deemed to add some valuable features to the pack. Specifically, by exploiting the projective properties of a circular set of sizeable dots, we propose a detection algorithm that is highly accurate. Further, applying a dot pattern scheme derived from error-correcting codes, allows for robustness with respect to very large occlusions. In addition, the design of the marker itself is flexible enough to accommodate different requirements in terms of pose accuracy and number of patterns. The overall performance of the marker system is evaluated in an extensive experimental section, where a comparison with a well-known baseline technique is presented.


title: BLASTING VIBRATION LIMITS TO PREVENT HUMAN ANNOYANCE REMARKS FROM SOME CASE STUDIES

abstract: Human response to blast vibrations is an important problem for mine and construction managers. On many occasions, people residing in the vicinity of a blasting operation become so annoyed that they resort to drastic means in order to halt the project, especially when it lasts for a long time. Hence, a vibration control study, performed in mines, quarries or other construction activities, should provide an answer to the following basic question: "What is the allowable limit of peak particle velocity (PPV) caused by blasting in the area?". In many projects located in urban areas the vibration thresholds should be based more on human response than on the probability of structural damage or harmful effects. The human response, however, depends on several natural factors and population characteristics unrelated to the vibration intensity and frequency. A set of the most important factors affecting human behaviour towards blasting events, deduced from several case studies, is presented in this paper. These factors should be taken into consideration when determining the specific vibration level for a particular project.


title: 10.1177/0146167202250221 PERSONALITY AND SOCIAL PSYCHOLOGY BULLETIN Roney / EFFECTS OF VISUAL EXPOSURE TO THE OPPOSITE SEX Effects of Visual Exposure to the Opposite Sex: Cognitive Aspects of Mate Attraction in Human Males

abstract: This research is an investigation into the cognitive aspects of mate attraction in human males. Two experiments demonstrate that visual exposure to women (in person or within photographs) can prime large changes in the attitudes, mood states, and personality trait descriptions of male participants. These changes, furthermore, are such that participants show greater conformity to female mate preferences as described in the extant literature: In particular, men exposed to potential mates reported higher valuations of material wealth, greater momentary feelings of ambition, higher valuations of other indicators of social status, and personality trait descriptions indicative of high surgency/extraversion. All such effects occurred without participants’ awareness that their responses had been affected by the experimental manipulations. These findings suggest a model of mate attraction mechanisms in which input cues from potential mates can prime those psychological representations that facilitate the behaviora...


title: Chordease: a midi remapper for intuitive performance of non-modal music

abstract: Improvising to non-modal chord progressions such as those found in jazz necessitates switching between the different scales implied by each chord. This work attempted to simplify improvisation by delegating the process of switching scales to a computer. An open-source software MIDI remapper called ChordEase was developed that dynamically alters the pitch of notes, in order to fit them to the chord scales of a predetermined song. ChordEase modifies the behavior of ordinary MIDI instruments, giving them new interfaces that permit non-modal music to be approached as if it were modal. Multiple instruments can be remapped simultaneously, using a variety of mapping functions, each optimized for a particular musical role. Harmonization and orchestration can also be automated. By facilitating the selection of scale tones, ChordEase enables performers to focus on other aspects of improvisation, and thus creates new possibilities for musical expression.


A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction


title: Archiving cold data in warehouses with clustered network coding

abstract: Modern storage systems now typically combine plain replication and erasure codes to reliably store large amount of data in datacenters. Plain replication allows a fast access to popular data, while erasure codes, e.g., Reed-Solomon codes, provide a storage-efficient alternative for archiving less popular data. Although erasure codes are now increasingly employed in real systems, they experience high overhead during maintenance, i.e., upon failures, typically requiring files to be decoded before being encoded again to repair the encoded blocks stored at the faulty node. In this paper, we propose a novel erasure code system, tailored for networked archival systems. The efficiency of our approach relies on the joint use of random codes and a clustered placement strategy. Our repair protocol leverages network coding techniques to reduce by 50% the amount of data transferred during maintenance, by repairing several cluster files simultaneously. We demonstrate both through an analysis and extensive experimental study conducted on a public testbed that our approach significantly decreases both the bandwidth overhead during the maintenance process and the time to repair lost data. We also show that using a non-systematic code does not impact the throughput, and comes only at the price of a higher CPU usage. Based on these results, we evaluate the impact of this higher CPU consumption on different configurations of data coldness by determining whether the cluster's network bandwidth dedicated to repair or CPU dedicated to decoding saturates first.


title: Scalable graph-based OLAP analytics over process execution data

abstract: In today's knowledge-, service-, and cloud-based economy, businesses accumulate massive amounts of data from a variety of sources. In order to understand businesses one may need to perform considerable analytics over large hybrid collections of heterogeneous and partially unstructured data that is captured related to the process execution. This data, usually modeled as graphs, increasingly come to show all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics. We use the term big process graph to refer to such large hybrid collections of heterogeneous and partially unstructured process related execution data. Online analytical processing (OLAP) of big process graph is challenging as the extension of existing OLAP techniques to analysis of graphs is not straightforward. Moreover, process data analysis methods should be capable of processing and querying large amount of data effectively and efficiently, and therefore have to be able to scale well with the infrastructure's scale. While traditional analytics solutions (relational DBs, data warehouses and OLAP), do a great job in collecting data and providing answers on known questions, key business insights remain hidden in the interactions among objects: it will be hard to discover concept hierarchies for entities based on both data objects and their interactions in process graphs. In this paper, we introduce a framework and a set of methods to support scalable graph-based OLAP analytics over process execution data. The goal is to facilitate the analytics over big process graph through summarizing the process graph and providing multiple views at different granularity. To achieve this goal, we present a model for process OLAP (P-OLAP) and define OLAP specific abstractions in process context such as process cubes, dimensions, and cells. We present a MapReduce-based graph processing engine, to support big data analytics over process graphs. We have implemented the P-OLAP framework and integrated it into our existing process data analytics platform, ProcessAtlas, which introduces a scalable architecture for querying, exploration and analysis of large process data. We report on experiments performed on both synthetic and real-world datasets that show the viability and efficiency of the approach.


title: On the Network Convergence Process in RPL over IEEE 802.15.4 Multihop Networks: Improvement and Trade-Offs

abstract: The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently developed by the Internet Engineering Task Force (IETF). Given its crucial role in enabling the Internet of Things, a significant amount of research effort has already been devoted to RPL. However, the RPL network convergence process has not yet been investigated in detail. In this paper we study the influence of the main RPL parameters and mechanisms on the network convergence process of this protocol in IEEE 802.15.4 multihop networks. We also propose and evaluate a mechanism that leverages an option available in RPL for accelerating the network convergence process. We carry out extensive simulations for a wide range of conditions, considering different network scenarios in terms of size and density. Results show that network convergence performance depends dramatically on the use and adequate configuration of key RPL parameters and mechanisms. The findings and contributions of this work provide a RPL configuration guideline for network convergence performance tuning, as well as a characterization of the related performance trade-offs.


title: Benefits and limitations of automated software testing: systematic literature review and practitioner survey

abstract: There is a documented gap between academic and practitioner views on software testing. This paper tries to close the gap by investigating both views regarding the benefits and limits of test automation. The academic views are studied with a systematic literature review while the practitioners views are assessed with a survey, where we received responses from 115 software professionals. The results of the systematic literature review show that the source of evidence regarding benefits and limitations is quite shallow as only 25 papers provide the evidence. Furthermore, it was found that benefits often originated from stronger sources of evidence (experiments and case studies), while limitations often originated from experience reports. We believe that this is caused by publication bias of positive results. The survey showed that benefits of test automation were related to test reusability, repeatability, test coverage and effort saved in test executions. The limitations were high initial invests in automation setup, tool selection and training. Additionally, 45% of the respondents agreed that available tools in the market offer a poor fit for their needs. Finally, it was found that 80% of the practitioners disagreed with the vision that automated testing would fully replace manual testing.


title: Pairwise Naive Bayes Classifier

abstract: Class binarizations are effective methods that break multi-class problem down into several 2class or binary problems to improve weak learners. This paper analyzes which effects these methods have if we choose a Naive Bayes learner for thebase classifier. Weconsider the knownunordered and pairwise class binarizations and propose an alternative approach for a pairwise calculation of a modified Naive Bayes classifier.


Recommender Systems for Large-scale E-Commerce: Scalable Neighborhood Formation Using Clustering


title: Path Loss Measurements at 3.5 GHz: A Trial Test WiMAX Based in Rural Environment

abstract: This paper addresses the dimensioning of the emerging wireless broadband networks operating in 3.5 GHz band by focusing on the key problem of propagation loss. The characteristics of the path loss in the 3.5 GHz band measured in a rural macro-cellular environment are presented. The existing empirical prediction models are compared with the measured data and a comparative analysis is carried out. The measurements are performed within the experimental activities developed on a WiMAX based platform located in an Italian rural area.


title: Hierarchical organization of cognitive memory

abstract: This paper addresses the question of the organization of memory processes within the medial temporal lobe. Evidence obtained in patients with late-onset amnesia resulting from medial temporal pathology has given rise to two opposing interpretations of the effects of such damage on long-term cognitive memory. One view is that cognitive memory, including memory for both facts and events, is served in a unitary manner by the hippocampus and its surrounding cortices; the other is that the basic function affected in amnesia is event memory, the memory for factual material often showing substantial preservation. Recent findings in patients with amnesia resulting from relatively selective hippocampal damage sustained early in life suggest a possible reconciliation of the two views. The new findings suggest that the hippocampus may be especially important for event as opposed to fact memory, with the surrounding cortical areas contributing to both. Evidence from neuroanatomical and neurobehavioural studies in monkeys is presented in support of this proposal.


title: A sensing chair using pressure distribution sensors

abstract: The sensing chair project enables an ordinary office chair to become aware of its occupant's actions and needs. Surface-mounted pressure distribution sensors are placed over the seatpan and backrest of the chair for real time capturing of contact information between the chair and its occupant. Given the similarity between a pressure distribution map and a gray-scale image, pattern recognition techniques commonly used in computer and robot vision, such as principal components analysis, were successfully applied to solving the problem of sitting posture classification. The current static posture classification system operates in real time with an overall classification accuracy of 96% and 79% for familiar and unfamiliar users, respectively. The results obtained form important stepping stones toward an intelligent chair that can find applications in many areas including multimodal interfaces, intelligent environment, and safety of automobile operations.


title: Animal-Assisted Therapy with Children Suffering from Insecure Attachment Due to Abuse and Neglect: A Method to Lower the Risk of Intergenerational Transmission of Abuse?

abstract: Children suffering from insecure attachment due to severe abuse and/or neglect are often characterized by internal working models which, although perhaps adaptive within the original family situation, are inappropriate and maladaptive in other relationships and situations. Such children have a higher probability than the general population of becoming abusing or neglecting parents. Besides the usual goals of psychotherapy, an overall goal is to stop the cycle of abuse in which abused children may grow up to be abusing parents. Therapy with these children is complicated by their distrust in adults as well as difficulties in symbolization due to trauma during the preverbal stage. Animal-Assisted Therapy (AAT) provides avenues for circumventing these difficulties, as well as providing additional tools for reaching the inner world of the client. This article gives a brief background of the connection between insecure attachment and intergenerational transmission of abuse and neglect as well as a brief overvie...


title: Data loss and reconstruction in sensor networks

abstract: Reconstructing the environment in cyber space by sensory data is a fundamental operation for understanding the physical world in depth. A lot of basic scientific work (e.g., nature discovery, organic evolution) heavily relies on the accuracy of environment reconstruction. However, data loss in wireless sensor networks is common and has its special patterns due to noise, collision, unreliable link, and unexpected damage, which greatly reduces the accuracy of reconstruction. Existing interpolation methods do not consider these patterns and thus fail to provide a satisfactory accuracy when missing data become large. To address this problem, this paper proposes a novel approach based on compressive sensing to reconstruct the massive missing data. Firstly, we analyze the real sensory data from Intel Indoor, GreenOrbs, and Ocean Sense projects. They all exhibit the features of spatial correlation, temporal stability and low-rank structure. Motivated by these observations, we then develop an environmental space time improved compressive sensing (ESTICS) algorithm to optimize the missing data estimation. Finally, the extensive experiments with real-world sensory data shows that the proposed approach significantly outperforms existing solutions in terms of reconstruction accuracy. Typically, ESTICS can successfully reconstruct the environment with less than 20% error in face of 90% missing data.


title: A Description Logic Primer

abstract: This paper provides a self-contained first introduction to d escription log- ics (DLs). The main concepts and features are explained with examples before syn- tax and semantics of the DLSROIQ are defined in detail. Additional sections review light-weight DL languages, discuss the relationship to the Web Ontology Language OWL and give pointers to further reading.


title: Change detection in streaming data in the era of big data: models and issues

abstract: Big Data is identified by its three Vs, namely velocity, volume, and variety. The area of data stream processing has long dealt with the former two Vs velocity and volume. Over a decade of intensive research, the community has provided many important research discoveries in the area. The third V of Big Data has been the result of social media and the large unstructured data it generates. Streaming techniques have also been proposed recently addressing this emerging need. However, a hidden factor can represent an important fourth V, that is variability or change. Our world is changing rapidly, and accounting to variability is a crucial success factor. This paper provides a survey of change detection techniques as applied to streaming data. The review is timely with the rise of Big Data technologies, and the need to have this important aspect highlighted and its techniques categorized and detailed.


title: Decoding Customer–Firm Relationships: How Attachment Styles Help Explain Customers' Preferences for Closeness, Repurchase Intentions, and Changes in Relationship Breadth

abstract: Abstract Many firms strive to create relationships with customers, but not all customers are motivated to build close commercial relationships. This article introduces a theoretical framework that explains how relationship-specific attachment styles account for customers' distinct preferences for closeness and how both attachment styles and preferences for closeness influence loyalty. The authors test their predictions with survey data from 1199 insurance customers and three years of purchase records for 975 of these customers. They find that attachment styles predict customers' preferences for closeness better than established marketing variables do. Moreover, attachment styles and preferences for closeness influence loyalty intentions and behavior, controlling for established antecedents (e.g., relationship quality). Finally, exploring the underlying process, the authors show that preference for closeness partially mediates the effect of attachment styles on cross-buying behavior. This research provides...


title: A convolutional neural network based on TensorFlow for face recognition

abstract: Face recognition is a hot research field in computer vision, and it has a high practical value for the detection and recognition of specific sensitive characters. Research found that in traditional hand-crafted features, there are uncontrolled environments such as pose, facial expression, illumination and occlusion influencing the accuracy of recognition and it has poor performance, so the deep learning method is adopted. On the basis of face detection, a Convolutional Neural Network (CNN) based on TensorFlow, an open source deep learning framework, is proposed for face recognition. Experimental results show that the proposed method has better recognition accuracy and higher robustness in complex environment.


title: Effect of Circulation Chamber Dimensions on Aerosol Delivery Efficiency of a Commercial Dry Powder Inhaler Aerolizer®

abstract: Aim of this study was to analyze how modifications in circulation chamber dimensions affect aerosol particle deposition in a Dry Powder Inhaler (DPI) Aerolizer®. Combining computational fluid dynamics (CFD), for simulation of fluid flow (air), with discrete phase model (DPM) for particles simulation, we can better understand particle dispersion within inhalers air flow field. Input in the simulation was 20mg of aerosol particles with initial velocity of 11,79166m/s. Dimension change influences maximum velocities, as well as percentage of deposited particles. Based on these information we were able to calculate the number of particles on the outlet and compare efficiency reduction when circulation chamber height increased. Knowledge obtained in this way can help in device performance optimization.


title: Pileus: protecting user resources from vulnerable cloud services

abstract: Cloud computing platforms are now constructed as distributed, modular systems of cloud services, which enable cloud users to manage their cloud resources. However, in current cloud platforms, cloud services fully trust each other, so a malicious user may exploit a vulnerability in a cloud service to obtain unauthorized access to another user's data. To date, over 150 vulnerabilities have been reported in cloud services in the OpenStack cloud. Research efforts in cloud security have focused primarily on attacks originating from user VMs or compromised operating systems rather than threats caused by the compromise of distributed cloud services, leaving cloud users open to attacks from these vulnerable cloud services. In this paper, we propose the Pileus cloud service architecture, which isolates each user's cloud operations to prevent vulnerabilities in cloud services from enabling malicious users to gain unauthorized access. Pileus deploys stateless cloud services "on demand" to service each user's cloud operations, limiting cloud services to the permissions of individual users. Pileus leverages the decentralized information flow control (DIFC) model for permission management, but the Pileus design addresses special challenges in the cloud environment to: (1) restrict how cloud services may be allowed to make security decisions; (2) select trustworthy nodes for access enforcement in a dynamic, distributed environment; and (3) limit the set of nodes a user must trust to service each operation. We have ported the OpenStack cloud platform to Pileus, finding that we can systematically prevent compromised cloud services from attacking other users' cloud operations with less than 3% additional latency for the operation. Application of the Pileus architecture to Open-Stack shows that confined cloud services can service users' cloud operations effectively for a modest overhead.


DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes


title: Area and power efficient layout design of Schmitt trigger

abstract: Schmitt triggers are widely used in analog as well as digital circuit to enhance immunity to noise and disturbances. This circuit is used in many instrumentation and test measurement systems. Schmitt trigger circuits are also used in open loop configuration for noise immunity and closed loop positive feedback configuration, to implement the multivibrators. In this paper a Schmitt trigger and its layout has been designed using PMOS and NMOS transistors. The result has been compared in terms of power consumption and surface area. The simulated results show that an autogenerated Schmitt trigger consumes 60.32% more power and 60.857% more surface area than the full custom layout of Schmitt trigger.


title: Improving DNN speaker independence with I-vector inputs

abstract: We propose providing additional utterance-level features as inputs to a deep neural network (DNN) to facilitate speaker, channel and background normalization. Modifications of the basic algorithm are developed which result in significant reductions in word error rates (WERs). The algorithms are shown to combine well with speaker adaptation by backpropagation, resulting in a 9% relative WER reduction. We address implementation of the algorithm for a streaming task.


title: Design and implementation of three-stage battery charger for lead-acid battery

abstract: Charging method is crucial for any batteries. Over the years, many charging algorithm are developed to improve the charging method of lead acid battery. Uncontrolled charging of lead acid battery may lead to capacity loss and also reduce the life cycle of battery. To improve the charging method a simple battery charging algorithm is proposed in this paper. The IC (UC3906) is the core of the designed circuit to implement the algorithm. The result shows that the designed circuit based on the algorithm is effective during overcharging and supports the steady charging concept without consuming access charges.


title: Single image super-resolution using Gaussian process regression

abstract: In this paper we address the problem of producing a high-resolution image from a single low-resolution image without any external training set. We propose a framework for both magnification and deblurring using only the original low-resolution image and its blurred version. In our method, each pixel is predicted by its neighbors through the Gaussian process regression. We show that when using a proper covariance function, the Gaussian process regression can perform soft clustering of pixels based on their local structures. We further demonstrate that our algorithm can extract adequate information contained in a single low-resolution image to generate a high-resolution image with sharp edges, which is comparable to or even superior in quality to the performance of other edge-directed and example-based super-resolution algorithms. Experimental results also show that our approach maintains high-quality performance at large magnifications.


title: Robot Manipulator Capability in MATLAB: A Tutorial on Using the Robotics System Toolbox [Tutorial]

abstract: i»?The Robotics System Toolbox for MATLAB provides a wide and growing set of functionalities for creating robotic systems: Robot Operating System (ROS) integration, mobile robotics, and robot manipulator arms. This capability increases with each release and is targeted at industrial developers as well as academic teaching and research. This tutorial is concerned with the robot manipulator kinematic functionality that has been available since MATLAB release 2016b.


title: Grammatical Error Correction with Alternating Structure Optimization

abstract: We present a novel approach to grammatical error correction based on Alternating Structure Optimization. As part of our work, we introduce the NUS Corpus of Learner English (NUCLE), a fully annotated one million words corpus of learner English available for research purposes. We conduct an extensive evaluation for article and preposition errors using various feature sets. Our experiments show that our approach outperforms two baselines trained on non-learner text and learner text, respectively. Our approach also outperforms two commercial grammar checking software packages.


title: Design of a Web-Based LBS Framework Addressing Usability, Cost, and Implementation Constraints

abstract: This research investigates barriers that prevent Location Based Services (LBS) from reaching its full potential. The different constraints, including poor usability, lack of positioning support, costs, and integration difficulties are highlighted. A framework was designed incorporating components based on existing and new technologies that could help address the constraints of LBS and increase end-user acceptance. This research proposes that usability constraints can be addressed by adapting a system to user characteristics which are inferred on the basis of captured user context and interaction data. A prototype LBS system was developed to prove the feasibility and benefit of the framework design, demonstrating that constraints of positioning, cost, and integration can be overcome. Volunteers were asked to use the system, and to answer questions in relation to their proficiency and experience. User-feedback showed that the proposed combination of functionality was well-received, and the prototype was appealing to many users. Ground-truths from the survey were related back to data captured with a user monitoring component in order to investigate whether users can be classified according to their context and how they interact. The results have shown that statistically significant relationships exist, and that by using the C4.5 decision-tree, computer proficiency can be estimated within one class-width in 76.7% of the cases. These results suggest that it may be possible to build a user-model to estimate computer proficiency on the basis of user-interaction data. The user model could then used to improve usability through adaptive user-specific customisations.


title: Understanding Convolutional Neural Networks

abstract: Convoulutional Neural Networks (CNNs) exhibit extraordinary performance on a variety of machine learning tasks. However, their mathematical properties and behavior are quite poorly understood. There is some work, in the form of a framework, for analyzing the operations that they perform. The goal of this project is to present key results from this theory, and provide intuition for why CNNs work.


title: Chipless RFID Sensor Tag for Metal Crack Detection and Characterization

abstract: Chipped radio-frequency identification (RFID) sensor systems have been studied for structural health monitoring (SHM) applications. However, the use of chip in sensor tags and its standardized narrowband operation contribute shortcomings in cost, durability, and detection capability. This paper presents a novel use of the frequency signature-based chipless RFID for metal crack detection and characterization operating in ultra-wideband frequency. The vision is to implement a low-cost and high-temperature-resistant passive wireless sensor able to monitor the crack on a metallic structure with multiparameter detection. We propose a chipless RFID sensor tag integrating four tip-loaded dipole resonators as a 4-bit ID encoder and a circular microstrip patch antenna (CMPA) resonator as a crack sensor. The radar cross section spectrum of the chipless RFID sensor tag generates four resonant frequencies from the dipole resonators and a resonant frequency from the CMPA resonator. Simulation and experimental results show that the resonant frequency shift of the CMPA is a useful feature to indicate the crack orientation and the crack width on a metallic structure. The direction of the resonant frequency shift represents the orientation of the crack, while the magnitude of the resonant frequency shift is proportional to the width of the crack. Furthermore, the experimentation with a natural fatigue crack sample proves that the proposed sensor tag is capable of detecting submillimeter cracks.


title: Madame bovary on the holodeck: immersive interactive storytelling

abstract: In this paper, we describe a small-scale, yet complete, integration of a real-time immersive interactive storytelling system. While significant progress has been achieved in recent years on the individual component technologies of interactive storytelling, the main objective of this work is to investigate the concept of interactive storytelling in a fully immersive context. We describe each individual component of immersive interactive storytelling from a technical perspective. We have used a commercial game engine as a development environment, supporting real-time visualisation as well as the inclusion of Artificial Intelligence components controlling virtual actors. This visualisation engine has been ported to an immersive setting using dedicated software and hardware supporting real-time stereoscopic visualisation. The hardware platform is built around a 4-sided CAVE-like immersive display operated by a PC-cluster. The interactive storytelling engine is constituted by a planning system based on characters motivations and emotional states. The user can interact with the virtual world using multimodal interaction. We illustrate the system's behaviour on the implementation of excerpts from Madame Bovary, a classic XIXth century novel, and demonstrate the ability for the user to play the role of one of the characters and influence the unfolding of the story by his actions.


title: Vision-Controlled Micro Flying Robots: From System Design to Autonomous Navigation and Mapping in GPS-Denied Environments

abstract: Autonomous microhelicopters will soon play a major role in tasks like search and rescue, environment monitoring, security surveillance, and inspection. If they are further realized in small scale, they can also be used in narrow outdoor and indoor environments and represent only a limited risk for people. However, for such operations, navigating based only on global positioning system (GPS) information is not sufficient. Fully autonomous operation in cities or other dense environments requires microhelicopters to fly at low altitudes, where GPS signals are often shadowed, or indoors and to actively explore unknown environments while avoiding collisions and creating maps. This involves a number of challenges on all levels of helicopter design, perception, actuation, control, and navigation, which still have to be solved. The Swarm of Micro Flying Robots (SFLY) project was a European Union-funded project with the goal of creating a swarm of vision-controlled microaerial vehicles (MAVs) capable of autonomous navigation, three-dimensional (3-D) mapping, and optimal surveillance coverage in GPS-denied environments. The SFLY MAVs do not rely on remote control, radio beacons, or motion-capture systems but can fly all by themselves using only a single onboard camera and an inertial measurement unit (IMU). This article describes the technical challenges that have been faced and the results achieved from hardware design and embedded programming to vision-based navigation and mapping, with an overview of how all the modules work and how they have been integrated into the final system. Code, data sets, and videos are publicly available to the robotics community. Experimental results demonstrating three MAVs navigating autonomously in an unknown GPS-denied environment and performing 3-D mapping and optimal surveillance coverage are presented.


title: A reordering model for Vietnamese-English statistical machine translation using dependency information

abstract: Reordering is a major challenge in machine translation (MT) between two languages with significant differences in word order. In this paper, we present an approach to learn reordering rules as pre-processing step based on a dependency parser in phrase-based statistical machine translation (SMT) from Vietnamese to English. Dependency parser and transformation rules are used to reorder the source sentence and applied for systems translating Vietnamese to English. We evaluated our approach on Vietnamese-English machine translation tasks, and showed that it outperform the baseline phrase-based SMT system.


A Virtual Honeypot Framework


A Space of Presentation Emphasis Techniques for Visualizing Graphs


title: Twelve Issues for Cognitive Science

abstract: I am struck by how little is known about so much of cognition. One goal of this paper is to argue for the need to consider a rich set of interlocking issues in the study of cognition. Mainstream work in cognition—including my own—ignores many critical aspects of animate cognitive systems. Perhaps one reason that existing theories say so little relevant to real world activities is the neglect of social and cultural factors, of emotion, and of the major points that distinguish an animate cognitive system from an artificial one: the need to survive, to regulate its own operation, to maintain itself, to exist in the environment, to change from a small, uneducated, immature system to an adult, developed, knowledgeable one. ::: ::: Human cognition is not the same as artificial cognition, if only because the human organism must also be concerned with the problems of life, of development, of survival. There must be a regulatory system that interacts with the cognitive component. And it may well be that it is the cognitive component that is subservient, evolved primarily for the benefit of the regulatory system, working through the emotions, through affect. ::: ::: I argue that several concepts must become fundamental parts of the study of cognition, including the roles of culture, of social interaction, of emotions, and of motivation. I argue that there are at least 12 issues that should comprise the study of cognition, and thereby, the field of Cognitive Science. We need to study a wide variety of behavior before we can hope to understand a single class. Cognitive scientists as a whole ought to make more use of evidence from the neurosciences, from brain damage and mental illness, from cognitive sociology and anthropology, and from clinical studies of the human. These must be accompanied, of course, with the study of language, of the psychological aspects of human processing structures, and of artificially intelligent mechanisms. The study of Cognitive Science requires a complex interaction among different issues of concern, an interaction that will not be properly understood until all parts are understood, with no part independent of the others, the whole requiring the parts, and the parts the whole.


title: DOES PLANT COLOR AFFECT EMOTIONAL AND PHYSIOLOGICAL RESPONSES TO LANDSCAPES

abstract: Research has shown that people have distinct preferences for and positive emotional responses to trees with a wide canopy. This is the same shape that trees have in regions of Africa’s savannas with appropriate water for human habitation. Tree color is another important cue to habitats that are appropriate for human habitation. For example, nutrient deficient trees would appear yellowish. This paper covers the results of the first phase of a research study designed to test whether people have distinct preferences for and positive emotional and physiological responses to tree canopies of different colors. Results from the first phase showed that people prefer green and red trees to purple and orangish-brown trees. This is consistent with the hypothesis that people respond to specific tree colors because they may be cues to the survival characteristics of a habitat. INTRODUCTION Plants perform many positive functions within the urban environment, such as controlling erosion, enhancing water infiltration into soils, and cooling our cities. Furthermore, research has shown that plants play an essential role in improving air quality (Wolverton et al., 1989). In addition to the physical benefits that plants provide within the built landscape, plants promote positive physiological, psychological, and economical responses in people. Viewing nature is associated with reductions in stress, improvements in mental alertness, greater productivity, and, in areas of commerce, increased spending (Ulrich, 1986; Relf, 1991; Lohr et al., 1996). Additional improved health effects include shorter hospital stays, reduced pain, and fewer headaches (Ulrich, 1984; Lohr and Pearson-Mims, 2000; Tove, 2000;). If we can better understand people’s preferences and responses to individual elements of nature such as tree form or color, then plant producers, landscape designers, and public and private land managers could have a powerful set of tools to utilize when outlining specifications for our environments, both built and natural. Surprisingly, few studies have specifically examined the influence of plant color on people’s emotional and physiological behavior. Sommer and Summit (1995) commented that, in the investigation of generic plant attributes such as tree size and shape, color has been neglected. In fact, early studies on plant preferences intentionally avoided colors other than green to remove distractions (Kaplan and Kaplan, 1989). Some researchers have examined flowering plant color preferences. In their study of preference for geraniums, Behe and others (1999) found that flower color took precedence over leaf color and price. Red and lavender flowers were preferred over white and pink ones. A simulated blue flower color was the least preferred of all. Most customers preferred plant leaves with plain or zonal patterns to ones with white margins. While plant color has been largely unexamined, color has been studied since ancient times in many cultures. Color research has typically involved looking at colored chips or sitting in different colored rooms. These methods showed that blue was preferred to red and that yellow was least preferred (Hemphill, 1996). Hue (the color we see, such Proc. XXVI IHC – Horticulture, Human Well-Being and Life Quality


title: Collaborative Autonomy for Manned/Unmanned Teams

abstract: UAVs offer tantalizing capabilities to the warfighter, such as tireless observation, quick recognition, and rapid reaction to today’s changing battlespace. These trends are important because they aid Warfighter in their duties. Today, unmanned systems exist that extend the vision and the reach of the Warfighter. However, they spend so much time managing these assets that they lose effectiveness as a Warfighter. This is a particular problem if the warfighter’s role is one demanding continuous sensory and mental workload, such as the Co-Pilot/Gunner (CPG) of an Apache Longbow attack helicopter. Autonomy, the ability of vehicles to conduct most of their operation without human supervision, can help relieve the burden of providing continuous oversight of the UAV’s operation. This moves the Warfighter’s role from control to command, enabling them to perform their duties more effectively and successfully. Collaboration, the ability of teams of vehicles to coordinate their activities without human oversight, moves unmanned systems to the level of a true force multiplier, giving a single human warfighter the power of multiple coordinated, intelligent platforms.


title: Rank Aggregation for Filter Feature Selection in Credit Scoring

abstract: The credit industry is a fast growing field, credit institutions collect data about credit customer and use them to build credit model. The collected information may be full of unwanted and redundant features which may speed down the learning process, so, effective feature selection methods are needed for credit dataset. In general, Filter feature selection methods outperform other feature selection techniques because they are effective and computationally fast. Choosing the appropriate filtering method from the wide variety of classical filtering methods proposed in the literature is a crucial issue in machine learning. So, we propose a feature selection fusion model that fuses the results obtained by different filter feature selection methods via aggregation techniques. Evaluations on four credit datasets show that the fusion model achieves good results.


Analysis and Detection of Pathological Voice using Glottal Source Features