title: Knowledge, attitudes, and breast feeding practices of postnatal mothers: A cross sectional survey.

abstract: BACKGROUND ::: Breast feeding has several benefits for both the infants and mothers. However, despite strong evidences in support of breast feeding its prevalence has remained low worldwide. The objective of the present study was to examine the knowledge and attitude towards breast feeding and infant feeding practices among Indian postnatal mothers. ::: ::: ::: METHODOLOGY ::: A cross sectional descriptive study was carried out among randomly selected postnatal mothers at Pediatric outpatient department at a tertiary care center. Data was collected through face-to-face interview using a structured questionnaire. ::: ::: ::: RESULTS ::: Our findings revealed that a majority (88.5%) of the mothers were breast feeders. However, merely 27% of the mothers were exclusive breast feeders and only 36.9% initiated breast feeding within an hour. While mothers have good knowledge on breast feeding (12.05±1.74, M±SD), the average score of the Iowa Infant Feeding Scale (IIFAS) (58.77±4.74, M ±SD) indicate neutral attitudes toward breast feeding. Mothers those who were currently breast feeding (58.83 ± 4.74) had more positive attitudes than non- breastfeed mothers (45.21±5.22). ::: ::: ::: CONCLUSION ::: Our findings also show that the level of exclusive breast-feeding was low. Thus, it is important to provide prenatal education to mothers and fathers on breast-feeding. We also recommend strengthening the public health education campaigns to promote breast-feeding.


title: When can the maximin share guarantee be guaranteed

abstract: The fairness notion of maximin share (MMS) guarantee underlies a deployed algorithm for allocating indivisible goods under additive valuations. Our goal is to understand when we can expect to be able to give each player his MMS guarantee. Previous work has shown that such an MMS allocation may not exist, but the counterexample requires a number of goods that is exponential in the number of players; we give a new construction that uses only a linear number of goods. On the positive side, we formalize the intuition that these counterexamples are very delicate by designing an algorithm that provably finds an MMS allocation with high probability when valuations are drawn at random.


title: Improved Monte-Carlo Search

abstract: Monte-Carlo search has been successful in many non-deterministic games, and recently in deterministic games with high branching factor. One of the drawbacks of the current approaches is that even if the iterative process would last for a very long time, the selected move does not necessarily converge to a game-theoretic optimal one. In this paper we introduce a new algorithm, UCT, which extends a bandit algorithm for Monte-Carlo search. It is proven that the probability that the algorithm selects the correct move converges to 1. Moreover it is shown empirically that the algorithm converges rather fast even in comparison with alpha-beta search. Experiments in Amazons and Clobber indicate that the UCT algorithm outperforms considerably a plain Monte-Carlo version, and it is competitive against alpha-beta based game programs.


title: Attachment theory and emotions in close relationships: Exploring the attachment-related dynamics of emotional reactions to relational events

abstract: Attachment theory is a powerful framework for understanding affect regulation. In this article, we examine the role played by attachment orientation in shaping emotional reactions to interpersonal transactions within close relationships. Using our recent integrative model of attachment-system activation and dynamics as a guide (M. Mikulincer & P. R. Shaver, 2003), we review relevant evidence, present new findings, and propose hypotheses concerning how people with different attachment styles are likely to react emotionally to relational events. Specifically, we focus on attachment-related variations in the emotional states elicited by a relationship partner’s positive and negative behaviors and by signals of a partner’s (relationship relevant or relationship irrelevant) distress or pleasure. In so doing, we organize existing knowledge and point the way to future research on attachment-related emotions in close relationships.


title: AGORA: attributed goal-oriented requirements analysis method

abstract: This paper presents an extended version of the goal-oriented requirements analysis method called AGORA, where attribute values, e.g. contribution values and preference matrices, are added to goal graphs. An analyst attaches contribution values and preference values to edges and nodes of a goal graph respectively during the process for refining and decomposing the goals. The contribution value of an edge stands for the degree of the contribution of the sub-goal to the achievement of its parent goal, while the preference matrix of a goal represents the preference of the goal for each stakeholder. These values can help an analyst to choose and adopt a goal from the alternatives of the goals, to recognize the conflicts among the goals, and to analyze the impact of requirements changes. Furthermore the values on a goal graph and its structural characteristics allow the analyst to estimate the quality of the resulting requirements specification, such as correctness, unambiguity, completeness etc. The estimated quality values can suggest which goals should be improved and/or refined. In addition, we have applied AGORA to a user account system and assessed it.


title: Multi-scale Single Image Dehazing Using Perceptual Pyramid Deep Network

abstract: Haze adversely degrades quality of an image thereby affecting its aesthetic appeal and visibility in outdoor scenes. Single image dehazing is particularly challenging due to its ill-posed nature. Most existing work, including the recent convolutional neural network (CNN) based methods, rely on the classical mathematical formulation where the hazy image is modeled as the superposition of attenuated scene radiance and the atmospheric light. In this work, we explore CNNs to directly learn a non-linear function between hazy images and the corresponding clear images. We present a multi-scale image dehazing method using Perceptual Pyramid Deep Network based on the recently popular dense blocks and residual blocks. The proposed method involves an encoder-decoder structure with a pyramid pooling module in the decoder to incorporate contextual information of the scene while decoding. The network is learned by minimizing the mean squared error and perceptual losses. Multi-scale patches are used during training and inference process to further improve the performance. Experiments on the recently released NTIRE2018-Dehazing dataset demonstrates the superior performance of the proposed method over recent state-of-the-art approaches. Additionally, the proposed method is ranked among top-3 methods in terms of quantitative performance in the recently conducted NTIRE2018-Dehazing challenge. Code can be found at https://github.com/hezhangsprinter/NTIRE-2018-Dehazing-Challenge


title: HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds

abstract: Workflows have been used to represent a variety of applications involving high processing and storage demands. As a solution to supply this necessity, the cloud computing paradigm has emerged as an on-demand resources provider. While public clouds charge users in a per-use basis, private clouds are owned by users and can be utilized with no charge. When a public cloud and a private cloud are merged, we have what we call a hybrid cloud. In a hybrid cloud, the user has elasticity provided by public cloud resources that can be aggregated to the private resources pool as necessary. One question faced by the users in such systems is: Which are the best resources to request from a public cloud based on the current demand and on resources costs? In this paper we deal with this problem, presenting HCOC: The Hybrid Cloud Optimized Cost scheduling algorithm. HCOC decides which resources should be leased from the public cloud and aggregated to the private cloud to provide sufficient processing power to execute a workflow within a given execution time. We present extensive experimental and simulation results which show that HCOC can reduce costs while achieving the established desired execution time.


title: Effect of polishing plane vibration on large-size optical workpieces in continuous polishing

abstract: Effect of the polishing plane vibration on large-size optical workpieces in continuous polishing is studied. The vibration equation was deduced based on the existence of inclination between the polishing plane and z-axis direction. Influences of different parameters, such as the inclination, rotation speeds of the polishing plane and workpiece, the eccentricity and workpiece radius, on the polishing plane vibration were simulated. The simulations results show that rotation speeds of the polishing plane and workpiece is the most significant factor. The chaotic vibration of the polishing plane increases with increasing rotation speeds differences between the polishing plane and workpiece. When differences are small, periodic ups and downs of the polishing plane occur with the increase of polishing time. Experiments verified the influence of rotation speeds differences on the polishing plane vibration. The vibration affects PV of large-size optical workpieces in continuous polishing.


title: A Cooperative Operation of Novel PV Inverter Control Scheme and Storage Energy Management System Based on ANFIS for Voltage Regulation of Grid-Tied PV System

abstract: In this paper, the voltage regulation problem in low-voltage power distribution networks integrated with increased amount of solar photovoltaics (PV) has been addressed. This paper proposes and evaluates the cooperative performance of a novel proportional-integral-derivative (PID) control scheme for PV interfacing inverter based on intelligent adaptive neuro-fuzzy inference system (ANFIS) and an ANFIS-based supervisory storage energy management system (EMS) for regulating the voltage of three-phase grid-connected solar PV system under any nonlinear and fluctuating operating conditions. The proposed ANFIS-based PID control scheme (ANFISPID) dynamically controls the PV inverter to inject/ absorb appropriate reactive power to regulate the voltage at point of common coupling (PCC) and provides robust response at any system worst case scenarios and grid faults. And the proposed ANFIS-based supervisory EMS controls the charge/discharge of the energy storage system when there is voltage deviation to cooperate with ANFISPID in PCC voltage regulation. The proposed ANFISPID-based PV inverter control scheme and ANFIS-based supervisory EMS are developed and simulated in MATLAB/ Simulink environment and their dynamic cooperative performances are compared with cooperative performances of conventional PID-based PV inverter control scheme and state-based EMS.


title: The design of glass crack detection system based on image preprocessing technology

abstract: This paper Put forward a glass crack detection algorithm based on digital image processing technology, obtain identification information of glass surface crack image by making use of pre-processing, image segmentation, feature extraction on the glass crack image, calculate the target area and perimeter of the roundness index to judge whether this image with a crack, make use of Visual Basico.O programming language to impolder the crack detection system, achieve the function of each part in crack detection process.


title: Design, analysis and realization of SVPWM using embedded code generation technique for a three phase, two level inverter

abstract: In a last few decades, development in power electronics systems has created its necessity in industrial and domestic applications like electric drives, UPS, solar and wind power conversion and many more. This paper presents the design, simulation, analysis and fabrication of a three phase, two-level inverter. The Space Vector Pulse Width Modulation (SVPWM) technique is used for the generation of gating signals for the three phase inverter. The proposed work is about real time embedded code generation technique that can be implemented using any microprocessor or microcontroller board of choice. The proposed technique reduces the analogue circuitry and eliminates the need of coding for generation of pulses, thereby making it simple and easy to implement. Control structure of SVPWM is simulated in MATLAB Simulink environment for analysis of different parameters of inverter. Comparative analysis of simulation results and hardware results is presented which shows that embedded code generation technique is very reliable and accurate.


title: Activity sensing in the wild: a field trial of ubifit garden

abstract: Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer people's activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity.


title: An Iterative Convolutional Neural Network Algorithm Improves Electron Microscopy Image Segmentation

abstract: To build the connectomics map of the brain, we developed a new algorithm that can automatically refine the Membrane Detection Probability Maps (MDPM) generated to perform automatic segmentation of electron microscopy (EM) images. To achieve this, we executed supervised training of a convolutional neural network to recover the removed center pixel label of patches sampled from a MDPM. MDPM can be generated from other machine learning based algorithms recognizing whether a pixel in an image corresponds to the cell membrane. By iteratively applying this network over MDPM for multiple rounds, we were able to significantly improve membrane segmentation results.


title: A review of soft biometrics for IoT

abstract: The Internet of Things (IoT) can be defined as everyday physical objects being connected to the internet and being able to identify themselves to other devices. In recent years the Internet of Things (IoT) was identified as one of the emerging technologies. Research in this area has increased considerably and future research will have to include other technologies such as biometrics to complement the development of IoT devices. The idea of this paper is to give an overview of biometric characteristics applicable to IoT with emphasis on soft biometric characteristics and possible application ideas in IoT.


title: Modified High-Efficiency LLC Converters With Two Split Resonant Branches for Wide Input-Voltage Range Applications

abstract: This paper proposes a novel modified LLC converter with two split resonant branches for wide input-range applications. It can operate in low-gain (LG) mode or medium-gain (MG) mode. The voltage gain of MG mode is 1.5 times that of LG mode, while they have same range of voltage gain $M_{{\rm{tank\_range}}}$ , i.e., the ratio of maximum voltage gain to minimum one. Therefore, the input voltage can be regulated in a wide range by operating the converter in different modes. To achieve the transition between modes, the required $M_{{\rm{tank\_range}}}$ of the proposed converter is as low as 1.5, resulting in lower magnetizing current and higher efficiency within the entire operation range. Smooth transition can be achieved by adjusting the duty cycles of switches gradually. The proposed LLC converter with primary dual-bridge is analyzed in detail as an example. To verify the theoretical analysis and performance of proposed solution, a 1-kW 400-V output prototype with input voltage ranging from 80 to 200 V is built, tested, and compared with two existing full-bridge LLC converters. Analysis and experimental results indicate that smaller volume of transformers, reduced rectifying diodes, and higher overall efficiency are achieved with the proposed converter.


title: Longitudinal genetic analysis of brain volumes in normal elderly male twins

abstract: This study investigated the role of genetic and environmental influences on individual differences in brain volumes measured at two time points in normal elderly males from the National Heart, Lung, and Blood Institute Twin Study. The MRI scans were conducted four years apart on 33 monozygotic and 33 dizygotic male twin pairs, aged 68 to 77 years when first scanned. Volumetric measures of total brain and total cerebrospinal fluid were significantly heritable at baseline (over 70%). For both volumes genetic influences at follow-up were entirely accounted for by genetic influences at baseline, suggesting that the same genetic factors influence variability in brain volume at each time of assessment. Variability in 4-year volume change was due to shared and individual-specific environmental influences. There was little evidence for heritable influences on change measures. These results suggest that variation in longitudinal change of some brain volume measures may have different underlying genetic and environmental architecture from variation in repeat cross-sectional measures, which could have implications for intervention strategies for age-related illness associated with brain morphology. The results of this study are discussed in the context of the small sample size and associated limitations of statistical power.


Solving Geometric Construction Problems Supported by Theorem Proving


title: GFDM Frame Design for 5G Application Scenarios

abstract: The services foreseen for 5G networks will demand a vast number of challenging requirements to be fulfilled by the physical layer. These services can be grouped into application scenarios, each one with a key requirement to be addressed by the 5G network. A flexible waveform associated with a appropriate data frame is an essential feature in order to guarantee the support of contrasting requirements from different application scenarios such as Enhanced Mobile Broadband, Internet of Things, Tactile Internet and Internet Access for Remote Areas. In this paper, we propose a flexible data frame based on Generalized Frequency Division Multiplexing (GFDM) that can be tailored to address the specific key requirements of the different 5G scenarios. The paper also presents the physical layer parametrization that can be used for each application.


title: Analytical methods for the extraction of solar-cell single- and double-diode model parameters from I-V characteristics

abstract: Analytical solutions for the rapid extraction of single- and double-diode model parameters from experimental data are described. The resulting parameters' values are shown to have less than 10 percent error for most solar cells. Error contours are also illustrated to define the range of validity of these methods.


title: Learning Compact Hash Codes for Multimodal Representations Using Orthogonal Deep Structure

abstract: As large-scale multimodal data are ubiquitous in many real-world applications, learning multimodal representations for efficient retrieval is a fundamental problem. Most existing methods adopt shallow structures to perform multimodal representation learning. Due to a limitation of learning ability of shallow structures, they fail to capture the correlation of multiple modalities. Recently, multimodal deep learning was proposed and had proven its superiority in representing multimodal data due to its high nonlinearity. However, in order to learn compact and accurate representations, how to reduce the redundant information lying in the multimodal representations and incorporate different complexities of different modalities in the deep models is still an open problem. In order to address the aforementioned problem, in this paper we propose a hashing-based orthogonal deep model to learn accurate and compact multimodal representations. The method can better capture the intra-modality and inter-modality correlations to learn accurate representations. Meanwhile, in order to make the representations compact, the hashing-based model can generate compact hash codes and the proposed orthogonal structure can reduce the redundant information lying in the codes by imposing orthogonal regularizer on the weighting matrices. We also theoretically prove that, in this case, the learned codes are guaranteed to be approximately orthogonal. Moreover, considering the different characteristics of different modalities, effective representations can be attained with different number of layers for different modalities. Comprehensive experiments on three real-world datasets demonstrate a substantial gain of our method on retrieval tasks compared with existing algorithms.


title: Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study

abstract: Robotic ultrasound has the potential to assist and guide physicians during interventions. In this work, we present a set of methods and a workflow to enable autonomous MRI-guided ultrasound acquisitions. Our approach uses a structured-light 3D scanner for patient-to-robot and image-to-patient calibration, which in turn is used to plan 3D ultrasound trajectories. These MRI-based trajectories are followed autonomously by the robot and are further refined online using automatic MRI/US registration. Despite the low spatial resolution of structured light scanners, the initial planned acquisition path can be followed with an accuracy of 2.46 ± 0.96 mm. This leads to a good initialization of the MRI/US registration: the 3D-scan-based alignment for planning and acquisition shows an accuracy (distance between planned ultrasound and MRI) of 4.47 mm, and 0.97 mm after an online-update of the calibration based on a closed loop registration.


Work Motivation : Relationships with Job Satisfaction , Locus of Control and Motivation Orientation


title: Biologically plausible reinforcement learning of continuous actions

abstract: Humans and animals have the ability to perform very precise movements to obtain rewards. For instance, it is no problem at all to pick up a mug of coffee from your desk while you are working. Unfortunately, it is unknown how exactly the non-linear mapping between sensory inputs (e.g. your mug on the retina) and the correct motor actions (e.g. a set of joint angles) are learned by the brain. Here we show how a biologically plausible learning scheme can learn to perform non-linear transformations from sensory inputs to continuous actions based on reinforcement learning. ::: ::: To arrive at our novel scheme, we built on the idea of attention-gated reinforcement learning (AGREL) [1], a biologically plausible learning scheme that explains how networks of neurons can learn to perform non-linear transformations from sensory inputs to discrete actions (e.g. pressing a button) based on reinforcement learning [2]. We recently showed that the AGREL learning scheme can be generalized to perform multiple simultaneous discrete actions [3], and we now show how this scheme can be further generalized to continuous action spaces. The key idea is that motor areas have feedback connections to earlier processing layers which inform the network about the selected action. Synaptic plasticity is constrained to those synapses that were involved in the decision, and it follows a simple Hebbian rule which is gated by a globally available neuromodulatory signal that codes reward prediction errors. In our novel scheme motor units are situated in a population coding layer that encodes the outcome of the decision process as a bump of activations [4]. This contrasts to our earlier work where single motor units code for actions [1,3]. We show that the synaptic updates perform stochastic gradient descent on the prediction error that results from the combined action-value prediction of all the motor units that encoded the decision. Unlike other reinforcement learning based approaches, e.g. [5], our reinforcement learning rule is powerful enough to learn tasks that require non-linear transformations. The distribution of population centers in the motor layer can also be automatically adapted to task demands, yielding more representational power when actions need to be precise. ::: ::: We show that the novel scheme can learn to perform non-linear transformations from sensory inputs to motor outputs in a variety of direct reward tasks. The model can explain how visuomotor coordinate transforms might be learned by reinforcement learning instead of semi-supervised learning as used in [6]. It might also explain how humans learn to weigh the accuracy of their movement against the potential rewards and punishments for making inaccurate movements as in the visually guided movement task described in [7].


In Search for the Core of the Business Ecosystem Concept: A Conceptual Comparison of Business Ecosystem, Industry, Cluster, and Inter Organizational Network


title: What should be minimized in a decision tree

abstract: In this paper, we address the issue of evaluating decision trees generated from training examples by a learning algorithm. We give a set of performance measures and show how some of them relate to others. We derive results suggesting that the number of leaves in a decision tree is the important measure to minimize. Minimizing this measure will, in a probabilistic sense, improve performance along the other measures. Notably it is expected to produce trees whose error rates are less likely to exceed some acceptable limit. The motivation for deriving such results is two-fold: 1. To better understand what constitutes a good measure of performance, and 2. To provide guidance when deciding which aspects of a decision tree generation algorithm should be changed in order to improve the quality of the decision trees it generates. ::: ::: The results presented in this paper can be used as a basis for a methodology for formally proving that one decision tree generation algorithm is better than another. This would provide a more satisfactory alternative to the current empirical evaluation method for comparing algorithms.


title: Move prediction in Gomoku using deep learning

abstract: The Gomoku board game is a longstanding challenge for artificial intelligence research. With the development of deep learning, move prediction can help to promote the intelligence of board game agents as proven in AlphaGo. Following this idea, we train deep convolutional neural networks by supervised learning to predict the moves made by expert Gomoku players from RenjuNet dataset. We put forward a number of deep neural networks with different architectures and different hyperparameters to solve this problem. With only the board state as the input, the proposed deep convolutional neural networks are able to recognize some special features of Gomoku and select the most likely next move. The final neural network achieves the accuracy of move prediction of about 42% on the RenjuNet dataset, which reaches the level of expert Gomoku players. In addition, it is promising to generate strong Gomoku agents of human-level with the move prediction as a guide.


title: A Self-Adaptive Parameter Selection Trajectory Prediction Approach via Hidden Markov Models

abstract: Trajectory prediction of objects in moving objects databases (MODs) has garnered wide support in a variety of applications and is gradually becoming an active research area. The existing trajectory prediction algorithms focus on discovering frequent moving patterns or simulating the mobility of objects via mathematical models. While these models are useful in certain applications, they fall short in describing the position and behavior of moving objects in a network-constraint environment. Aiming to solve this problem, a hidden Markov model (HMM)-based trajectory prediction algorithm is proposed, called Hidden Markov model-based Trajectory Prediction (HMTP). By analyzing the disadvantages of HMTP, a self-adaptive parameter selection algorithm called HMTP $\ast$ is proposed, which captures the parameters necessary for real-world scenarios in terms of objects with dynamically changing speed. In addition, a density-based trajectory partition algorithm is introduced, which helps improve the efficiency of prediction. In order to evaluate the effectiveness and efficiency of the proposed algorithms, extensive experiments were conducted, and the experimental results demonstrate that the effect of critical parameters on the prediction accuracy in the proposed paradigm, with regard to HMTP $\ast$ , can greatly improve the accuracy when compared with HMTP, when subjected to randomly changing speeds. Moreover, it has higher positioning precision than HMTP due to its capability of self-adjustment.


title: Security Evaluation of Pattern Classifiers under Attack

abstract: Pattern classification systems are commonly used in adversarial applications, like biometric authentication, network intrusion detection, and spam filtering, in which data can be purposely manipulated by humans to undermine their operation. As this adversarial scenario is not taken into account by classical design methods, pattern classification systems may exhibit vulnerabilities, whose exploitation may severely affect their performance, and consequently limit their practical utility. Extending pattern classification theory and design methods to adversarial settings is thus a novel and very relevant research direction, which has not yet been pursued in a systematic way. In this paper, we address one of the main open issues: evaluating at design phase the security of pattern classifiers, namely, the performance degradation under potential attacks they may incur during operation. We propose a framework for empirical evaluation of classifier security that formalizes and generalizes the main ideas proposed in the literature, and give examples of its use in three real applications. Reported results show that security evaluation can provide a more complete understanding of the classifier's behavior in adversarial environments, and lead to better design choices.


title: Application of Improved Harmony Search Algorithm in Test Case Selection

abstract: Regressiontest is an effective means to ensure the quality of software. But the testefficiency will become lower and lower as the test case library becomesincreasingly large in the test. Therefore, in order to improve the efficiencyand quality of regression test, it is necessary to implement the test casesafety reduction. The optimization selection of the test case is an effectivemeans of the test case safety reduction. In order to solve the problem of testcases optimization selection, this paper proposes a test case selection methodbased on the improved harmony search algorithm. Specifically, the researcheradds an excellent harmony element library based on the classical harmony searchalgorithm for storing the searched excellent test case; meanwhile, he alsoimproves the way of generating the new harmony toallow these excellent test cases to be retained with a certain probability. Inthe search process, the researcher makes the adaptive adjustment of thealgorithm parameter BW in a non-linear incremental manner, improving the globalsearch capability of the algorithm. In addition, the excellence protectionstrategies are also increased to prevent against the degradation in theoptimization search process. In order to verify the feasibility of the improvedalgorithm, the researcher writes four programs: the genetic algorithm,differential evolution algorithm, classical harmony algorithm and improvedalgorithm, and conducted the comparative experiment. The experimental resultshows that the improved algorithm significantly improves the optimizationsearch performance compared with the classical harmony search algorithm and hasthe better performance compared with the genetic algorithm and differentialevolution algorithm.


Linking Career Development Practices to Turnover Intention: The Mediator of Perceived Organizational Support


title: A big data analytics based approach to anomaly detection

abstract: We present a novel Cyber Security analytics framework. We demonstrate a comprehensive cyber security monitoring system to construct cyber security correlated events with feature selection to anticipate behaviour based on various sensors.


title: Self-adaptive differential evolution with multi-trajectory search for large-scale optimization

abstract: In this paper, self-adaptive differential evolution (DE) is enhanced by incorporating the JADE mutation strategy and hybridized with modified multi-trajectory search (MMTS) algorithm (SaDE-MMTS) to solve large-scale continuous optimization problems. The JADE mutation strategy, the “DE/current-to-pbest” which is a variation of the classic “DE/current-to-best”, is used for generating mutant vectors. After the mutation phase, the binomial (uniform) crossover, the exponential crossover as well as no crossover option are used to generate each pair of target and trial vectors. By utilizing the self-adaptation in SaDE, both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, suitable offspring generation strategy along with associated parameter settings will be determined adaptively to match different phases of the search process. MMTS is applied frequently to refine several diversely distributed solutions at different search stages satisfying both the global and the local search requirement. The initialization of step sizes is also defined by a self-adaption during every MMTS step. The success rates of both SaDE and the MMTS are determined and compared; consequently, future function evaluations for both search algorithms are assigned proportionally to their recent past performance. The proposed SaDE-MMTS is employed to solve the 19 numerical optimization problems in special issue of soft computing on scalability of evolutionary algorithms for large-scale continuous optimization problems and competitive results are presented.


Cognitive-Behavioral Therapy and Other Psychosocial Interventions for Substance Use Disorders


title: Innate and Adaptive Principles for an Artificial Immune System

abstract: This paper summarises the current literature on immune system function and behaviour, including pattern recognition receptors, danger theory, central and peripheral tolerance, and memory cells. An artificial immune system framework is then presented based on the analogies of these natural system components and a rule and feature-based problem representation. A data set for intrusion detection is used to highlight the principles of the framework.


title: Mastering the game of Go with deep neural networks and tree search

abstract: The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of stateof-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.


title: Effect of n-3 supplementation on hyperactivity, oxidative stress and inflammatory mediators in children with attention-deficit-hyperactivity disorder.

abstract: BACKGROUND ::: Attention-deficit/hyperactivity disorder (ADHD) is associated with difficulties in learning, behaviour and psychosocial adjustment that persist into adulthood. Decreased omega-3 fatty acids and increased inflammation or oxidative stress may contribute to neuro-developmental and psychiatric disorders such as ADHD. The aim of this study was to determine the effect of n-3 supplementation on hyperactivity, oxidative stress and inflammatory mediators in children with ADHD. ::: ::: ::: METHODS ::: In this double blind study, 103 children (6-12 years) with ADHD receiving maintenance therapy were assigned randomly into two groups. The n-3 group received n-3 fatty acids (635 mg eicosapentaenoic acid (EPA), 195 mg docosahexaenoic acid (DHA)), and the placebo group received olive oil capsules which were visually similar to the n-3 capsules. The duration of supplementation was 8 weeks. Plasma C-reactive protein (CRP), interleukin-6 (IL-6) and the activity of glutathione reductase (GR), catalase (CAT) and superoxide dismutase (SOD) were determined before and after the intervention. Likewise the Conners' Abbreviated Questionnaires (ASQ-P) was applied. ::: ::: ::: RESULTS ::: After 8-week intervention, a significant reduction was observed in the levels of CRP ( P < 0.05, 95% CI = 0.72-2.02) and IL-6 (P < 0.001, 95% CI = 1.93-24.33) in the n-3 group. There was also a significant increase in activity of SOD and GR (P < 0.001). A significant improvement was seen in the ASQ-P scores in the n-3 group (P < 005). ::: ::: ::: CONCLUSION ::: Eight weeks of EPA and DHA supplementation decreased plasma inflammatory mediators and oxidative stress in the children with ADHD. These results suggest that n-3 fatty acid supplementation may offer a safe and efficacious treatment for children with ADHD.


title: Transfer representation learning for medical image analysis

abstract: There are two major challenges to overcome when developing a classifier to perform automatic disease diagnosis. First, the amount of labeled medical data is typically very limited, and a classifier cannot be effectively trained to attain high disease-detection accuracy. Second, medical domain knowledge is required to identify representative features in data for detecting a target disease. Most computer scientists and statisticians do not have such domain knowledge. In this work, we show that employing transfer learning can remedy both problems. We use Otitis Media (OM) to conduct our case study. Instead of using domain knowledge to extract features from labeled OM images, we construct features based on a dataset entirely OM-irrelevant. More specifically, we first learn a codebook in an unsupervised way from 15 million images collected from ImageNet. The codebook gives us what the encoders consider being the fundamental elements of those 15 million images. We then encode OM images using the codebook and obtain a weighting vector for each OM image. Using the resulting weighting vectors as the feature vectors of the OM images, we employ a traditional supervised learning algorithm to train an OM classifier. The achieved detection accuracy is 88.5% (89.63% in sensitivity and 86.9% in specificity), markedly higher than all previous attempts, which relied on domain experts to help extract features.


title: The Effect of Music on the Human Stress Response

abstract: BACKGROUND ::: Music listening has been suggested to beneficially impact health via stress-reducing effects. However, the existing literature presents itself with a limited number of investigations and with discrepancies in reported findings that may result from methodological shortcomings (e.g. small sample size, no valid stressor). It was the aim of the current study to address this gap in knowledge and overcome previous shortcomings by thoroughly examining music effects across endocrine, autonomic, cognitive, and emotional domains of the human stress response. ::: ::: ::: METHODS ::: Sixty healthy female volunteers (mean age = 25 years) were exposed to a standardized psychosocial stress test after having been randomly assigned to one of three different conditions prior to the stress test: 1) relaxing music ('Miserere', Allegri) (RM), 2) sound of rippling water (SW), and 3) rest without acoustic stimulation (R). Salivary cortisol and salivary alpha-amylase (sAA), heart rate (HR), respiratory sinus arrhythmia (RSA), subjective stress perception and anxiety were repeatedly assessed in all subjects. We hypothesized that listening to RM prior to the stress test, compared to SW or R would result in a decreased stress response across all measured parameters. ::: ::: ::: RESULTS ::: The three conditions significantly differed regarding cortisol response (p = 0.025) to the stressor, with highest concentrations in the RM and lowest in the SW condition. After the stressor, sAA (p=0.026) baseline values were reached considerably faster in the RM group than in the R group. HR and psychological measures did not significantly differ between groups. ::: ::: ::: CONCLUSION ::: Our findings indicate that music listening impacted the psychobiological stress system. Listening to music prior to a standardized stressor predominantly affected the autonomic nervous system (in terms of a faster recovery), and to a lesser degree the endocrine and psychological stress response. These findings may help better understanding the beneficial effects of music on the human body.


title: A Non-Linear Filter for Gyroscope-Based Video Stabilization

abstract: We present a method for video stabilization and rolling-shutter correction for videos captured on mobile devices. The method uses the data from an on-board gyroscope to track the camera’s angular velocity, and can run in real time within the camera capture pipeline. We remove small motions and rolling-shutter distortions due to hand shake, creating the impression of a video shot on a tripod. For larger motions, we filter the camera’s angular velocity to produce a smooth output. To meet the latency constraints of a real-time camera capture pipeline, our filter operates on a small temporal window of three to five frames. Our algorithm performs better than the previous work that uses a gyroscope to stabilize a video stream, and at a similar level with respect to current feature-based methods.


title: An integrated design flow in user interface and interaction for enhancing mobile AR gaming experiences

abstract: Interaction is a prominent feature that distinguishes computer (video) games from other kinds of entertainment [1, 2]. Given that Augmented Reality (AR) presentations enable the creation of user interactions that expand traditional HCI from 2D to 3D spaces, this offer an ideal opportunity for game developers to design more playful and interesting mobile AR games that embed various interaction forms. This paper introduces a mobile AR cooking game that is based on the integration of adopted AR interaction and user interface principles in its design flow. These embedded interactions are designed with considerations and spatial mappings to real cooking mechanisms in order to provide a unique gaming experience and increased engagement for a better overall AR interaction on the multimodal mobile device.


title: Image Inpainting Methods Evaluation and Improvement

abstract: With the upgrowing of digital processing of images and film archiving, the need for assisted or unsupervised restoration required the development of a series of methods and techniques. Among them, image inpainting is maybe the most impressive and useful. Based on partial derivative equations or texture synthesis, many other hybrid techniques have been proposed recently. The need for an analytical comparison, beside the visual one, urged us to perform the studies shown in the present paper. Starting with an overview of the domain, an evaluation of the five methods was performed using a common benchmark and measuring the PSNR. Conclusions regarding the performance of the investigated algorithms have been presented, categorizing them in function of the restored image structure. Based on these experiments, we have proposed an adaptation of Oliveira's and Hadhoud's algorithms, which are performing well on images with natural defects.


Are 'strong' primes needed for RSA


title: Active 220- and 325-GHz Frequency Multiplier Chains in an SiGe HBT Technology

abstract: A 325-GHz ×18 frequency multiplier chain implemented in a fτ/fmax = 250 GHz/380 GHz evaluation SiGe heterojunction bipolar transistor technology is presented. The chain achieves a peak output power of -3 dBm and consists of a balanced doubler driven by two cascaded tripler stages. It operates from 317 to 328 GHz with a 0-dBm 18-GHz input signal and a 1.5-W power consumption. Additionally, 220- and 325-GHz doubler breakout circuits with integrated driver amplifiers are presented. The doublers reach an output power of -1 dBm at 220 GHz and -3 dBm at 325 GHz with a power dissipation of 630 and 420 mW, respectively.


title: A process for systematically reviewing the literature: providing the research evidence for public health nursing interventions.

abstract: BACKGROUND ::: Several groups have outlined methodologies for systematic literature reviews of the effectiveness of interventions. The Effective Public Health Practice Project (EPHPP) began in 1998. Its mandate is to provide research evidence to guide and support the Ontario Ministry of Health in outlining minimum requirements for public health services in the province. Also, the project is expected to disseminate the results provincially, nationally, and internationally. Most of the reviews are relevant to public health nursing practice. ::: ::: ::: AIMS ::: This article describes four issues related to the systematic literature reviews of the effectiveness of public health nursing interventions: (1) the process of systematically reviewing the literature, (2) the development of a quality assessment instrument, (3) the results of the EPHPP to date, and (4) some results of the dissemination strategies used. ::: ::: ::: METHODS ::: The eight steps of the systematic review process including question formulation, searching and retrieving the literature, establishing relevance criteria, assessing studies for relevance, assessing relevant studies for methodological quality, data extraction and synthesis, writing the report, and dissemination are outlined. Also, the development and assessment of content and construct validity and intrarater reliability of the quality assessment questionnaire used in the process are described. ::: ::: ::: RESULTS ::: More than 20 systematic reviews have been completed. Content validity was ascertained by the use of a number of experts to review the questionnaire during its development. Construct validity was demonstrated through comparisons with another highly rated instrument. Intrarater reliability was established using Cohen's Kappa. Dissemination strategies used appear to be effective in that professionals report being aware of the reviews and using them in program planning/policymaking decisions. ::: ::: ::: CONCLUSIONS ::: The EPHPP has demonstrated the ability to adapt the most current methods of systematic literature reviews of effectiveness to questions related to public health nursing. Other positive outcomes from the process include the development of a critical mass of public health researchers and practitioners who can actively participate in the process, and the work on dissemination has been successful in attracting external funds. A program of research in this area is being developed.


title: SLOM: a new measure for local spatial outliers

abstract: We propose a measure, spatial local outlier measure (SLOM), which captures the local behaviour of datum in their spatial neighbourhood. With the help of SLOM, we are able to discern local spatial outliers that are usually missed by global techniques, like "three standard deviations away from the mean". Furthermore, the measure takes into account the local stability around a data point and suppresses the reporting of outliers in highly unstable areas, where data are too heterogeneous and the notion of outliers is not meaningful. We prove several properties of SLOM and report experiments on synthetic and real data sets that show that our approach is novel and scalable to large datasets.


title: Design of ultra-wideband printed log-periodic dipole antenna

abstract: An ultra-wideband low cross-polarization printed log-periodic dipole antenna (LPDA) is systematically investigated. The properties of the proposed antenna have been Simulated and optimized with electromagnetic simulation software FEKO 6.2. The antenna possess the operating bandwidth from 0.8GHz to 7.2GHz with the return loss less than −10dB, the cross-polarization and side lobe level are less than −30dB. The Gain of the antenna is greater than 8.25 dB during the operation bandwidth, and very good End-fire directivity is obtained.


title: Cooperative Indoor Localization Using 24-GHz CMOS Radar Transceivers

abstract: This paper presents the first truly wireless 24-GHz round-trip time-of-flight local positioning frontend with an integrated CMOS transceiver. The transceiver in 130-nm CMOS technology features a novel receiver/transceiver switching concept, which reduces RF losses between the receiver/transmitter and antenna and drastically improves the transmit/receive isolation. The low-power RF transceiver chip was integrated with a digital signal-processing unit and mounted on a circuit board to form a system-level demonstrator of a secondary radar node incorporating synchronization and a distributed localization algorithm. The performance of the self-organizing localization network is evaluated in an indoor setup using comparisons with reference trajectories. Experimental results show a distance precision between the active nodes close to the theoretical optimum that can be achieved with the used signal parameters, as well as an absolute localization error in the centimeter range.


title: Feedback-Control & Queueing Theory-Based Resource Management for Streaming Applications

abstract: Recent advances in sensor technologies and instrumentation have led to an extraordinary growth of data sources and streaming applications. A wide variety of devices, from smart phones to dedicated sensors, have the capability of collecting and streaming large amounts of data at unprecedented rates. A number of distinct streaming data models have been proposed. Typical applications for this include smart cites & built environments for instance, where sensor-based infrastructures continue to increase in scale and variety. Understanding how such streaming content can be processed within some time threshold remains a non-trivial and important research topic. We investigate how a cloud-based computational infrastructure can autonomically respond to such streaming content, offering Quality of Service guarantees. We propose an autonomic controller (based on feedback control and queueing theory) to elastically provision virtual machines to meet performance targets associated with a particular data stream. Evaluation is carried out using a federated Cloud-based infrastructure (implemented using CometCloud)-where the allocation of new resources can be based on: (i) differences between sites, i.e., types of resources supported (e.g., GPU versus CPU only), (ii) cost of execution; (iii) failure rate and likely resilience, etc. In particular, we demonstrate how Little's Law-a widely used result in queuing theory-can be adapted to support dynamic control in the context of such resource provisioning.


title: An edge-guided image interpolation algorithm via directional filtering and data fusion

abstract: Preserving edge structures is a challenge to image interpolation algorithms that reconstruct a high-resolution image from a low-resolution counterpart. We propose a new edge-guided nonlinear interpolation technique through directional filtering and data fusion. For a pixel to be interpolated, two observation sets are defined in two orthogonal directions, and each set produces an estimate of the pixel value. These directional estimates, modeled as different noisy measurements of the missing pixel are fused by the linear minimum mean square-error estimation (LMMSE) technique into a more robust estimate, using the statistics of the two observation sets. We also present a simplified version of the LMMSE-based interpolation algorithm to reduce computational cost without sacrificing much the interpolation performance. Experiments show that the new interpolation techniques can preserve edge sharpness and reduce ringing artifacts.


title: Computing With Words for Hierarchical Decision Making Applied to Evaluating a Weapon System

abstract: The perceptual computer (Per-C) is an architecture that makes subjective judgments by computing with words (CWWs). This paper applies the Per-C to hierarchical decision making, which means decision making based on comparing the performance of competing alternatives, where each alternative is first evaluated based on hierarchical criteria and subcriteria, and then, these alternatives are compared to arrive at either a single winner or a subset of winners. What can make this challenging is that the inputs to the subcriteria and criteria can be numbers, intervals, type-1 fuzzy sets, or even words modeled by interval type-2 fuzzy sets. Novel weighted averages are proposed in this paper as a CWW engine in the Per-C to aggregate these diverse inputs. A missile-evaluation problem is used to illustrate it. The main advantages of our approaches are that diverse inputs can be aggregated, and uncertainties associated with these inputs can be preserved and are propagated into the final evaluation.


title: Optimal Betting Under Parameter Uncertainty: Improving the Kelly Criterion

abstract: The Kelly betting criterion ignores uncertainty in the probability of winning the bet and uses an estimated probability. In general, such replacement of population parameters by sample estimates gives poorer out-of-sample than in-sample performance. We show that to improve out-of-sample performance the size of the bet should be shrunk in the presence of this parameter uncertainty, and compare some estimates of the shrinkage factor. From a simulation study and from an analysis of some tennis betting data we show that the shrunken Kelly approaches developed here offer an improvement over the “raw” Kelly criterion. One approximate estimate of the shrinkage factor gives a “back of envelope” correction to the Kelly criterion that could easily be used by bettors. We also study bet shrinkage and swelling for general risk-averse utility functions and discuss the general implications of such results for decision theory.


title: Facial expression recognition using Support Vector Machines

abstract: This paper propose a facial expression recognition approach based on Principal Component Analysis (PCA) and Local Binary Pattern (LBP) algorithms. Experiments were carried out on the Japanese Female Facial Expression (JAFFE) database and our recently introduced Mevlana University Facial Expression (MUFE) database. Support Vector Machine (SVM) was used as classifier. In all conducted experiments on JAFFE and MUFE databases, obtained results reveal that PCA+SVM has an average recognition rate of 87% and 77%, respectively.


title: On systematic modeling of switched capacitor DC-DC converters: Incremental graph approach

abstract: We briefly review and comment in this paper on modeling approaches aimed at systematic formulation and solution of switched capacitor DC-DC converter networks. In our review special attention was paid to computationally efficient and mathematically elegant methods. In so doing we have tried to demonstrate the virtues of incremental graph notion with its direct extensions. Relevant formulas and illustrative examples are given.


title: Spacemint: A Cryptocurrency Based on Proofs of Space

abstract: Bitcoin has become the most successful cryptocurrency ever deployed, and its most distinctive feature is that it is decentralized. Its underlying protocol (Nakamoto consensus) achieves this by using proof of work, which has the drawback that it causes the consumption of vast amounts of energy to maintain the ledger. Moreover, Bitcoin mining dynamics have become less distributed over time.


title: Learning Driving Models with a Surround-View Camera System and a Route Planner

abstract: For people, having a rear-view mirror and side-view mirrors is vital for safe driving. They deliver a better view of what happens around the car. Human drivers also heavily exploit their mental map for navigation. Nonetheless, several methods have been published that learn driving models with only a front-facing camera and without a route planner. This lack of information renders the self-driving task quite intractable. Hence, we investigate the problem with a more realistic setting, which consists of a surround-view camera system with eight cameras, a route planner, and a CAN bus reader. In particular, we develop a sensor setup that provides data for a 360-degree view of the area surrounding the vehicle, the driving route to the destination, and the low-level driving maneuvers (e.g. steering angle and speed) by human drivers. With such sensor setup we collect a new driving dataset, covering diverse driving scenarios and varying weather/illumination conditions. Finally, we learn a novel driving model by integrating information from the surround-view cameras and the route planner. Two route planners are exploited: one based on OpenStreetMap and the other on TomTom Maps. The route planners are exploited in two ways: 1) by representing the planned routes as a stack of GPS coordinates, and 2) by rendering the planned routes on a map and recording the progression into a video. Our experiments show that: 1) 360-degree surround-view cameras help avoid failures made with a single front-view camera for the driving task; and 2) a route planner helps the driving task significantly. We acknowledge that our method is not the best-ever driving model, but that is not our focus. Rather, it provides a strong basis for further academic research, especially on driving relevant tasks by integrating information from street-view images and the planned driving routes. Code and data will be made available.


ON-LINE ANALYTICAL PROCESSING


title: Heteroscedastic Gaussian process regression

abstract: This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametric regression problem. The key point is that we are able to estimate variance locally unlike standard Gaussian Process regression or SVMs. This means that our estimator adapts to the local noise. The problem is cast in the setting of maximum a posteriori estimation in exponential families. Unlike previous work, we obtain a convex optimization problem which can be solved via Newton's method.


title: Traffic sign classification using K-d trees and Random Forests

abstract: In this paper, we evaluate the performance of K-d trees and Random Forests for traffic sign classification using different size Histogram of Oriented Gradients (HOG) descriptors and Distance Transforms. We use the German Traffic Sign Benchmark data set [1] containing 43 classes and more than 50,000 images. The K-d tree is fast to build and search in. We combine the tree classifiers with the HOG descriptors as well as the Distance Transforms and achieve classification rates of up to 97% and 81.8% respectively.


title: Ridge gap waveguide antenna array using integrated coaxial power divider

abstract: A slot array antenna is designed using ridge gap waveguide technology in two sub arrays. Each sub array is fed through a power divider which is designed on a separate ridge- groove gap waveguide. The coupling between two gap waveguides is conducted using coaxial transmission lines. The power divider of ridge-groove gap is fed using coaxial line that can provide a bandwidth of 22% at 14 GHz. The integrated antenna operates within the frequency range of 13.5–15.6 GHz with a gain of 13.9–15.5 dB. Design of the transmission line, power divider and sub-array antenna is presented.


COMPARATIVE EFFICACY OF INSECT REPELLENTS AGAINST MOSQUITO BITES


title: Vibration rejection for inertially stabilized double gimbal platform using acceleration feedforward

abstract: The paper documents a computational design and an experimental verification of a vibration rejection scheme for an inertially stabilized platform mounted on a mobile base. The mechanical configuration of the platform is that of a standard double gimbal, which realizes a mass stabilization of the line of sight of an optoelectronic payload such as a camera an/or laser ranger finder. Nonideal static balancing of the payload gives rise to projection of the carrier vibrations onto a disturbing torque acting on the payload around the joint axes. This disturbance can be left for the existing inertial angular rate feedback to attenuate but a better solution can be devised. The cause of this disturbing torque — the linear acceleration of the carrier — can be measured, filtered and fed forward to the two direct drive motors. Experiments prove that significant vibration rejection is achieved with this feedforward compensation scheme.


title: Bayesian modeling of human sequential decision-making on the multi-armed bandit problem

abstract: In this paper we investigate human exploration/exploitation behavior in sequential-decision making tasks. Previous studies have suggested that people are suboptimal at scheduling exploration, and heuristic decision strategies are better predictors of human choices than the optimal model. By incorporating more realistic assumptions about subject’s knowledge and limitations into models of belief updating, we show that Bayesian models of human behavior for the Multi-Armed Bandit Problem (MAB) on experimental data perform better than previous accounts.


title: Are Iraq and Afghanistan Veterans Using Mental Health Services? New Data From a National Random-Sample Survey

abstract: Many studies of Iraq and Afghanistan veterans with mental disorders document low rates of service use. However, most studies look at use in the first year after returning from a deployment. Do rates of use rise as time since deployment increases? In the National Post-Deployment Adjustment Survey, the median time since deployment was four years, and two-thirds of veterans with probable PTSD or major depression reported past-year use of mental health services. The finding of these notably higher rates is “a valuable message to communicate to veterans,” the authors conclude, and may reduce some veterans’ ambivalence about seeking treatment.


title: Simulating cartoon style animation

abstract: Traditional hand animation is in many cases superior to simulated motion for conveying information about character and events. Much of this superiority comes from an animator's ability to abstract motion and play to human perceptual effects. However, experienced animators are difficult to come by and the resulting motion is typically not interactive. On the other hand, procedural models for generating motion, such as physical simulation, can create motion on the fly but are poor at stylizing movement. We start to bridge this gap with a technique that creates cartoon style deformations automatically while preserving desirable qualities of the object's appearance and motion. Our method is focused on squash-and-stretch deformations based on the velocity and collision parameters of the object, making it suitable for procedural animation systems. The user has direct control of the object's motion through a set of simple parameters that drive specific features of the motion, such as the degree of squash and stretch. We demonstrate our approach with examples from our prototype system.


title: Person Re-identification by Salience Matching

abstract: Human salience is distinctive and reliable information in matching pedestrians across disjoint camera views. In this paper, we exploit the pair wise salience distribution relationship between pedestrian images, and solve the person re-identification problem by proposing a salience matching strategy. To handle the misalignment problem in pedestrian images, patch matching is adopted and patch salience is estimated. Matching patches with inconsistent salience brings penalty. Images of the same person are recognized by minimizing the salience matching cost. Furthermore, our salience matching is tightly integrated with patch matching in a unified structural Rank SVM learning framework. The effectiveness of our approach is validated on the VIPeR dataset and the CUHK Campus dataset. It outperforms the state-of-the-art methods on both datasets.


title: Learning low-precision neural networks without Straight-Through Estimator(STE)

abstract: The Straight-Through Estimator (STE) is widely used for back-propagating gradients through the quantization function, but the STE technique lacks a complete theoretical understanding. We propose an alternative methodology called alpha-blending (AB), which quantizes neural networks to low-precision using stochastic gradient descent (SGD). Our method (AB) avoids STE approximation by replacing the quantized weight in the loss function by an affine combination of the quantized weight w_q and the corresponding full-precision weight w with non-trainable scalar coefficient $\alpha$ and $1-\alpha$. During training, $\alpha$ is gradually increased from 0 to 1; the gradient updates to the weights are through the full-precision term, $(1-\alpha)w$, of the affine combination; the model is converted from full-precision to low-precision progressively. To evaluate the method, a 1-bit BinaryNet on CIFAR10 dataset and 8-bits, 4-bits MobileNet v1, ResNet_50 v1/2 on ImageNet dataset are trained using the alpha-blending approach, and the evaluation indicates that AB improves top-1 accuracy by 0.9%, 0.82% and 2.93% respectively compared to the results of STE based quantization.


title: Non-Autoregressive Machine Translation with Auxiliary Regularization

abstract: As a new neural machine translation approach, Non-Autoregressive machine Translation (NAT) has attracted attention recently due to its high efficiency in inference. However, the high efficiency has come at the cost of not capturing the sequential dependency on the target side of translation, which causes NAT to suffer from two kinds of translation errors: 1) repeated translations (due to indistinguishable adjacent decoder hidden states), and 2) incomplete translations (due to incomplete transfer of source side information via the decoder hidden states). ::: In this paper, we propose to address these two problems by improving the quality of decoder hidden representations via two auxiliary regularization terms in the training process of an NAT model. First, to make the hidden states more distinguishable, we regularize the similarity between consecutive hidden states based on the corresponding target tokens. Second, to force the hidden states to contain all the information in the source sentence, we leverage the dual nature of translation tasks (e.g., English to German and German to English) and minimize a backward reconstruction error to ensure that the hidden states of the NAT decoder are able to recover the source side sentence. Extensive experiments conducted on several benchmark datasets show that both regularization strategies are effective and can alleviate the issues of repeated translations and incomplete translations in NAT models. The accuracy of NAT models is therefore improved significantly over the state-of-the-art NAT models with even better efficiency for inference.


title: Design of air quality meter and pollution detector

abstract: This work describes the design and implementation of a Air Quality Meter and Air Pollution Detector. The technology adopted is a practical implementation of the concept of Internet of Things. This project is an exploration of the possibilities of use of this technology in a rapidly changing world where environmental health is becoming a serious threat. Using the Bluetooth communication technology and microcontroller board of Arduino, the work is implemented. A few sensors are also used such as temperature and humidity sensors and a few gas sensors to monitor changes.


title: 2B-SSD: The Case for Dual, Byte- and Block-Addressable Solid-State Drives

abstract: Performance critical transaction and storage systems require fast persistence of write data. Typically, a non-volatile RAM (NVRAM) is employed on the datapath to the permanent storage, to temporarily and quickly store write data before the system acknowledges the write request. NVRAM is commonly implemented with battery-backed DRAM. Unfortunately, battery-backed DRAM is small and costly, and occupies a precious DIMM slot. In this paper, we make a case for dual, byte- and block-addressable solid-state drive (2B-SSD), a novel NAND flash SSD architecture designed to offer a dual view of byte addressability and traditional block addressability at the same time. Unlike a conventional storage device, 2B-SSD allows accessing the same file with two independent byte- and block-I/O paths. It controls the data transfer between its internal DRAM and NAND flash memory through an intuitive software interface, and manages the mapping of the two address spaces. 2B-SSD realizes a wholly different way and speed of accessing files on a storage device; applications can access them directly using memory-mapped I/O, and moreover write with a DRAM-like latency. To quantify the benefits of 2B-SSD, we modified logging subsystems of major database engines to store log records directly on it without buffering them in the host memory. When running popular workloads, we measured throughput gains in the range of 1.2X and 2.8X with no risk of data loss.


title: Large-Scale Learnable Graph Convolutional Networks

abstract: Convolutional neural networks (CNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs. In CNNs, the trainable local filters enable the automatic extraction of high-level features. The computation with filters requires a fixed number of ordered units in the receptive fields. However, the number of neighboring units is neither fixed nor are they ordered in generic graphs, thereby hindering the applications of convolutional operations. Here, we address these challenges by proposing the learnable graph convolutional layer (LGCL). LGCL automatically selects a fixed number of neighboring nodes for each feature based on value ranking in order to transform graph data into grid-like structures in 1-D format, thereby enabling the use of regular convolutional operations on generic graphs. To enable model training on large-scale graphs, we propose a sub-graph training method to reduce the excessive memory and computational resource requirements suffered by prior methods on graph convolutions. Our experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that our methods can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network datasets. Our results also indicate that the proposed methods using sub-graph training strategy are more efficient as compared to prior approaches.


title: Discriminative features for identifying and interpreting outliers

abstract: We consider the problem of outlier detection and interpretation. While most existing studies focus on the first problem, we simultaneously address the equally important challenge of outlier interpretation. We propose an algorithm that uncovers outliers in subspaces of reduced dimensionality in which they are well discriminated from regular objects while at the same time retaining the natural local structure of the original data to ensure the quality of outlier explanation. Our algorithm takes a mathematically appealing approach from the spectral graph embedding theory and we show that it achieves the globally optimal solution for the objective of subspace learning. By using a number of real-world datasets, we demonstrate its appealing performance not only w.r.t. the outlier detection rate but also w.r.t. the discriminative human-interpretable features. This is the first approach to exploit discriminative features for both outlier detection and interpretation, leading to better understanding of how and why the hidden outliers are exceptional.


title: A 16 b Multi-Step Incremental Analog-to-Digital Converter With Single-Opamp Multi-Slope Extended Counting

abstract: This paper presents a multi-step incremental analog-to-digital converter (IADC) using multi-slope extended counting. Only one active integrator is used in the three-step conversion cycle. The accuracy of the IADC is extended by having it configured asmulti-slope ADCs in two additional steps. The proposed IADC uses the same circuitry as a first-order IADC (IADC1), but it exhibits better performance than a second-order IADC. For the same accuracy, the conversion cycle is shortened by a large factor (by more than 29 for the implemented device) compared with that of a conventional single-step IADC1. Fabricated in 0.18 $\mu \text{m}$ CMOS process, the prototype ADC occupies 0.5 mm2. With a 642 kHz clock, it achieves an SNDR of 52.2 dB in the first step. The SNDR is boosted to 79.8 dB in the second step and to 96.8 dB in the third step, over a 1 kHz signal band. The power consumption is 35 $\mu \text{W}$ from a 1.5 V power supply. This gives an excellent Schreier figure of merit of 174.6 dB.


A Review of Missing Data Treatment Methods


title: Machine learning techniques for intrusion detection on public dataset

abstract: The development of computer based systems expands the usage of computer based application in human life. It can be observed that illegal activities such as unauthorized data access, data theft, data modification and various other intrusion activities are rapidly growing during last decade. Hence, deployment and continuous improvement of Intrusion Detection Systems (IDS) are of paramount importance. Training, testing and evaluation of IDS with real network traffic is significant challenge, so most of IDS evaluation is based on intrusion datasets. Therefore, analysis of intrusion datasets are of paramount importance. In this paper, we evaluated Aegean Wi-Fi Intrusion Dataset (AWID) with different machine learning techniques. Feature reduction techniques such as Information Gain (IG) and Chi-Squared statistics (CH) were applied to evaluate dataset performance with feature reduction. Results of experiments show that feature reduction can lead to better analysis in terms of accuracy, processing time and complexity. It was observed that, the maximum increment of classification accuracy with feature reduction from 110 to 41 is 2.4%.


Peripheral vision location and kinds of complex processing.


title: Challenges for Creating Highly Dependable Service Based Systems

abstract: The paradigm shift from traditional on-premise software to a service based model has gained significant momentum in the past decade. One such concept, Software as a Service (SaaS), delivers the functionality of traditional on-premise software as a service over the web. While a defect or a malfunction in a traditional on-premise application may affect a single user, the affected user base in a SaaS application may span the entire group of customers serviced by the provider. The physical disconnect between end users and the SaaS applications puts onus on service providers to deliver highly dependable systems that are available and reliable at all times. In this paper, we explore the general challenges faced in delivering and analyzing highly dependable service based systems. We quantify the challenges of dependability assessment utilizing a commercial case study. Furthermore, we explore one facet of dependability assessment related to log entries not necessarily related to dependability. We provide a novel approach to log filtering and show that the removal of benign log entries leads to more realistic system dependability analysis. We also show the need to merge multiple types of SaaS logs to support effective analysis.


Comparing scalability of message queue system: ZeroMQ vs RabbitMQ


title: A survey on named data networking

abstract: Named Data Networking (NDN) is an entirely new internet architecture inspired by years of empirical research into network usage. NDN is related to Content Centric Networking. Unique feature of NDN is its adaptive forwarding plane. In NDN, the packets carry the data name instead of the source and destination address. In NDN, communication takes place by the exchange of Interest and Data packets. Data consumers send Interest packets in the form of names. Routers forward the Interest packet based on the data name and also maintain the state information of pending Interests that enable NDN routers to detect loops, measure performance of different path, quickly detect failures and retry alternative path. The producer replies with data packet that takes the reverse path of Interests. In this paper the motivation and the vision of NDN architecture, and basic components and operations of NDN are described. Also the strength and weakness of NDN are reviewed. The final discussion aims to identify challenges and some future directions for NDN deployment.


Accurate Phase Noise Prediction in PLL Synthesizers Here is a method that uses more complete modeling for wireless applications


Differential Privacy in distribution and instance-based noise mechanisms.


title: Comorbidity of attention deficit hyperactivity disorder with conduct, depressive, anxiety, and other disorders

abstract: Objective: Attention deficit hyperactivity disorder is a heterogeneous disoredr of unknown etiology. Little is known about the comorbidity of this disorders other than conduct. Therefore, the authors made a systematic search of the psychiatric and psychological literature for empirical studies dealing with the comorbidity of attention deficit hyperactivity disorder with other disorders. Data Collection: The seach terms included hyperactivity, hyperkinesis, attention deficit disorder, and attention deficit hyperactivity disorder, cross-referenced with antisocial (aggression, conduct disorder, antisocial disorder), depression (depression, mania, depressive disorder, bipolar), anxiety (anxiety disorder, anxiety), learning problems (learning, learning disability, academic achievement), substance abuse (alcoholism, drug abuse), mental retardation, and Tourette's disorder


title: Peer-to-Peer Lending – A (Financial Stability) Risk Perspective

abstract: The aim of this paper is to discuss P2P lending, a subcategory of crowdfunding, from a (financial stability) risk perspective. The discussion focuses on a number of dimensions such as the role of soft information, herding, platform default risk, liquidity risk, and the institutionalization of P2P markets. Overall, we conclude that P2P lending is more risky than traditional banking. However, it is important to recognize that a constant conclusion would be misleading. P2P platforms have evolved and changed their appearance markedly over time, which implies that although our final conclusion of increased riskiness through P2P markets remains valid over time, it is based on different arguments at different points in time. In addition, we discuss that acting on P2P online platforms satisfies most possible definitions of shadow banking and shows significant similarities with many observed aspects of shadow banking. We thus infer that P2P lending can be considered part of the shadow banking sector.


title: TextLuas: Tracking and Visualizing Document and Term Clusters in Dynamic Text Data

abstract: For large volumes of text data collected over time, a key knowledge discovery task is identifying and tracking clusters. These clusters may correspond to emerging themes, popular topics, or breaking news stories in a corpus. Therefore, recently there has been increased interest in the problem of clustering dynamic data. However, there exists little support for the interactive exploration of the output of these analysis techniques, particularly in cases where researchers wish to simultaneously explore both the change in cluster structure over time and the change in the textual content associated with clusters. In this paper, we propose a model for tracking dynamic clusters characterized by the evolutionary events of each cluster. Motivated by this model, the TextLuas system provides an implementation for tracking these dynamic clusters and visualizing their evolution using a metro map metaphor. To provide overviews of cluster content, we adapt the tag cloud representation to the dynamic clustering scenario. We demonstrate the TextLuas system on two different text corpora, where they are shown to elucidate the evolution of key themes. We also describe how TextLuas was applied to a problem in bibliographic network research.


title: Style-Transfer via Texture-Synthesis

abstract: Style transfer is a process of migrating a style from a given image to the content of another, synthesizing a new image, which is an artistic mixture of the two. Recent work on this problem adopting convolutional neural-networks (CNN) ignited a renewed interest in this field, due to the very impressive results obtained. There exists an alternative path toward handling the style transfer task, via the generalization of texture synthesis algorithms. This approach has been proposed over the years, but its results are typically less impressive compared with the CNN ones. In this paper, we propose a novel style transfer algorithm that extends the texture synthesis work of Kwatra et al. (2005), while aiming to get stylized images that are closer in quality to the CNN ones. We modify Kwatra's algorithm in several key ways in order to achieve the desired transfer, with emphasis on a consistent way for keeping the content intact in selected regions, while producing hallucinated and rich style in others. The results obtained are visually pleasing and diverse, shown to be competitive with the recent CNN style transfer algorithms. The proposed algorithm is fast and flexible, being able to process any pair of content + style images.


title: Power-efficient beam sweeping for initial synchronization in mm-Wave wireless networks

abstract: This paper addresses the problem of initial synchronization of users in an indoor mm-Wave scenario. With the use of a massive number of antenna elements at access nodes, the resulting beams have narrow beam width. However, the transmission of individual narrow beams may cause poor coverage in some areas as the energy is concentrated over the direction of their main lobes. To cope with that, a beam sweep procedure using phased arrays is adopted. Access nodes simultaneously transmit individual beams until a certain area of interest is thoroughly scanned. Then, distant points are covered as each beam can radiate energy using an individual transmit power. The goal is to find the minimum power setting by adjusting the individual power levels so that users over the scanned area can observe a minimum received power level. Due to the combinatorial nature and high dimensionality, this paper describes a non-iterative two-stage algorithm to perform the beam sweeping with moderate complexity, which is compared with a baseline iterative selfish algorithm. Preliminary simulation results indicate that the two-stage algorithm may perform at least equally well as the baseline approach in terms of total transmit power, consuming about 7% less power in the used simulation setup, and without suffering from any convergence issue.


title: A discriminative model for semi-supervised learning

abstract: Supervised learning—that is, learning from labeled examples—is an area of Machine Learning that has reached substantial maturity. It has generated general-purpose and practically successful algorithms and the foundations are quite well understood and captured by theoretical frameworks such as the PAC-learning model and the Statistical Learning theory framework. However, for many contemporary practical problems such as classifying web pages or detecting spam, there is often additional information available in the form of unlabeled data, which is often much cheaper and more plentiful than labeled data. As a consequence, there has recently been substantial interest in semi-supervised learning—using unlabeled data together with labeled data—since any useful information that reduces the amount of labeled data needed can be a significant benefit. Several techniques have been developed for doing this, along with experimental results on a variety of different learning problems. Unfortunately, the standard learning frameworks for reasoning about supervised learning do not capture the key aspects and the assumptions underlying these semi-supervised learning methods. In this article, we describe an augmented version of the PAC model designed for semi-supervised learning, that can be used to reason about many of the different approaches taken over the past decade in the Machine Learning community. This model provides a unified framework for analyzing when and why unlabeled data can help, in which one can analyze both sample-complexity and algorithmic issues. The model can be viewed as an extension of the standard PAC model where, in addition to a concept class C, one also proposes a compatibility notion: a type of compatibility that one believes the target concept should have with the underlying distribution of data. Unlabeled data is then potentially helpful in this setting because it allows one to estimate compatibility over the space of hypotheses, and to reduce the size of the search space from the whole set of hypotheses C down to those that, according to one's assumptions, are a-priori reasonable with respect to the distribution. As we show, many of the assumptions underlying existing semi-supervised learning algorithms can be formulated in this framework. After proposing the model, we then analyze sample-complexity issues in this setting: that is, how much of each type of data one should expect to need in order to learn well, and what the key quantities are that these numbers depend on. We also consider the algorithmic question of how to efficiently optimize for natural classes and compatibility notions, and provide several algorithmic results including an improved bound for Co-Training with linear separators when the distribution satisfies independence given the label.


Accelerated volume rendering and tomographic reconstruction using texture mapping hardware


title: Collective Intelligence in Humans: A Literature Review

abstract: This literature review focuses on collective intelligence in humans. A keyword search was performed on the Web of Knowledge and selected papers were reviewed in order to reveal themes relevant to collective intelligence. Three levels of abstraction were identified in discussion about the phenomenon: the micro-level, the macro-level and the level of emergence. Recurring themes in the literature were categorized under the above-mentioned framework and directions for future research were identified.


title: Folded-cascode transimpedance amplifiers employing a CMOS inverter as input stage

abstract: This paper presents the design and comparison of two shunt feedback transimpedance amplifiers (TIAs) implemented in standard 350nm CMOS technology from Austria Microsystems. These transimpedance amplifiers are based on a folded cascode topology. The first one is a conventional folded-cascode (CFC-TIA) and the second is a modified version folded-cascod (MFC-TIA) employing a CMOS inverter at the input stage. The proposed MFC-TIA achieves a gain of 80dBΩ, 370MHz bandwidth and minimum input current noise of 1,6pA√Hz with 0.5pF total input capacitance. The achieved results show that MFC-TIAs can match the performance of CFC-TIAs with reduced implementation area, thus being a suitable solution for photo-detector array receivers.


title: The emotion probe: Studies of motivation and attention.

abstract: Emotions are action dispositions--states of vigilant readiness that vary widely in reported affect, physiology, and behavior. They are driven, however, by only 2 opponent motivational systems, appetitive and aversive--subcortical circuits that mediate reactions to primary reinforcers. Using a large emotional picture library, reliable affective psychophysiologies are shown, defined by the judged valence (appetitive/pleasant or aversive/unpleasant) and arousal of picture percepts. Picture-evoked affects also modulate responses to independently presented startle probe stimuli. In other words, they potentiate startle reflexes during unpleasant pictures and inhibit them during pleasant pictures, and both effects are augmented by high picture arousal. Implications are elucidated for research in basic emotions, psychopathology, and theories of orienting and defense. Conclusions highlight both the approach's constraints and promising paths for future study.


title: The arXiv of the future will not look like the arXiv

abstract: The arXiv is the most popular preprint repository in the world. Since its inception in 1991, the arXiv has allowed researchers to freely share publication-ready articles prior to formal peer review. The growth and the popularity of the arXiv emerged as a result of new technologies that made document creation and dissemination easy, and cultural practices where collaboration and data sharing were dominant. The arXiv represents a unique place in the history of research communication and the Web itself, however it has arguably changed very little since its creation. Here we look at the strengths and weaknesses of arXiv in an effort to identify what possible improvements can be made based on new technologies not previously available. Based on this, we argue that a modern arXiv might in fact not look at all like the arXiv of today.


title: Operability study on the multisensory illusion inducible in microsurgical robotic systems

abstract: The Rubber Hand Illusion is a well known multisensory illusion in which the participant feels their own unseen hand become a rubber hand that is placed at the position where their hand is supposedly placed, triggered by synchronized haptic stimuli on their hand and the rubber hand. Our previous study showed that a robotized 1-degree-of-freedom rubber hand in the form of a master-slave system significantly enhanced the illusion compared with the classic rubber hand illusion. The illusion gives the sense that the part of body becomes part of the robot - it is thus assumed that this multisensory illusion is a potentially effective way to increase intuitiveness, and therefore increase the operability of a multi-degrees-of-freedom master-slave system. To further investigate the concept, we built a multisensory illusion inducible microsurgical robotic system based on the findings of the previous study and conducted a series of psychophysical experiments. The experimental results reveal that the multisensory illusion and operability is significantly correlated.


title: Modularized Morphing of Neural Networks

abstract: In this work we study the problem of network morphism, an effective learning scheme to morph a well-trained neural network to a new one with the network function completely preserved. Different from existing work where basic morphing types on the layer level were addressed, we target at the central problem of network morphism at a higher level, i.e., how a convolutional layer can be morphed into an arbitrary module of a neural network. To simplify the representation of a network, we abstract a module as a graph with blobs as vertices and convolutional layers as edges, based on which the morphing process is able to be formulated as a graph transformation problem. Two atomic morphing operations are introduced to compose the graphs, based on which modules are classified into two families, i.e., simple morphable modules and complex modules. We present practical morphing solutions for both of these two families, and prove that any reasonable module can be morphed from a single convolutional layer. Extensive experiments have been conducted based on the state-of-the-art ResNet on benchmark datasets, and the effectiveness of the proposed solution has been verified.


title: Convolutional recurrent neural network for question answering

abstract: Question answering has become a very important task for natural language understanding as most natural language processing problems can be posed as a question answering problem. Recurrent neural network (RNN) is a standard baseline model for various sequence prediction tasks including question answering. Recurrent networks can represent global information for a long period of time but they do not preserve local information very well. To address this problem we propose a model which is a combination of recurrent and convolutional network that can be trained end to end using backpropagation. Our experiments on bAbI dataset demonstrate that this model can achieve significant improvement over RNN model for question answering task.


title: Global Mesh Denoising with Fairness

abstract: We propose a novel global 3D mesh denoising method that is carried out in two steps, i.e. Mollification of normals followed by vertex correction. Both steps involve minimising sparse, quadratic cost functions that yield efficient non-iterative solutions. In the mollification step, we use adaptive weights that allows for appropriate diffusion while preserving features. While many existing methods only correct for the vertex position along the normal direction, we argue that this is inadequate in many scenarios. Instead, we allow for vertex correction in all directions while enforcing a novel face fairness penalty that preserves face shapes in the denoised mesh. We present a number of tests and examples that demonstrate the efficacy of our method in denoising while preserving face fairness. We demonstrate the superiority of our approach over some relevant methods in the literature.


title: An improvement of DBSCAN Algorithm to analyze cluster for large datasets

abstract: Clustering is an important tool which has seen an explosive growth in Machine Learning Algorithms. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is one of the most primary methods for clustering in data mining. DBSCAN has ability to find the clusters of variable sizes and shapes and it will also detect the noise. The two important parameters Epsilon (Eps) and Minimum point (MinPts) are required to be inputted manually in DBSCAN algorithm and on the basis these parameter the algorithm is calculated such as number of cluster, un-clustered instances as well as incorrectly clustered instances and also evaluate the performance on the basic of parameters selection and calculate the time taken by the datasets. Experimental evaluation on the basis of different datasets in ARFF format with help of WEKA tool which shows that quality of clusters of our proposed algorithm is efficient in clustering result and more accurate. This improved work on DBSCAN have used in a large scope.


title: The power of word clusters for text classification

abstract: The recently introduced Information Bottleneck method [21] provides an information theoretic framework, for extracting features of one variable, that are relevant for the values of another variable. Several previous works already suggested applying this method for document clustering, gene expression data analysis, spectral analysis and more. In this work we present a novel implementation of this method for supervised text classification. Specifically, we apply the information bottleneck method to find word-clusters that preserve the information about document categories and use these clusters as features for classification. Previous work [1] used a similar clustering procedure to show that word-clusters can significantly reduce the feature space dimensionality, with only a minor change in classification accuracy. In this work we reproduce these results and go further to show that when the training sample is small word clusters can yield significant improvement in classification accuracy (up to 18%) over the performance using the words directly.


title: Interfacing Power System and ICT Simulators: Challenges, State-of-the-Art, and Case Studies

abstract: With the transition toward a smart grid, the power system has become strongly intertwined with the information and communication technology (ICT) infrastructure. The interdependency of both domains requires a combined analysis of physical and ICT processes, but simulating these together is a major challenge due to the fundamentally different modeling and simulation concepts. After outlining these challenges, such as time synchronization and event handling, this paper presents an overview of state-of-the-art solutions to interface power system and ICT simulators. Due to their prominence in recent research, a special focus is set on co-simulation approaches and their challenges and potentials. Further, two case studies analyzing the impact of ICT on applications in power system operation illustrate the necessity of a holistic approach and show the capabilities of state-of-the-art co-simulation platforms.