title: Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization

abstract: The nuclear norm is widely used to induce low-rank solutions for many optimization problems with matrix variables. Recently, it has been shown that the augmented Lagrangian method (ALM) and the alternating direction method (ADM) are very efficient for many convex programming problems arising from various applications, provided that the resulting subproblems are sufficiently simple to have closed-form solutions. In this paper, we are interested in the application of the ALM and the ADM for some nuclear norm involved minimization problems. When the resulting subproblems do not have closed-form solutions, we propose to linearize these subproblems such that closed-form solutions of these linearized subproblems can be easily derived. Global convergence of these linearized ALM and ADM are established under standard assumptions. Finally, we verify the effectiveness and efficiency of these new methods by some numerical experiments.


title: A scalable routing mechanism for stateful microservices

abstract: Scalability is an important requirement in the development and the operation of applications in a cloud environment. To handling heavy concurrency in the input load, many design-related and operational factors should be considered. The microservice architecture patterns provide better means to increase the scalability than traditional software architecture patterns. However, certain aspects of applications such as the need to persist/maintain the application state require additional measures in the design and the supporting mechanism. We propose a scalable routing mechanism for applications designed according to the microservice architecture. In particular, a cloud infrastructure resource reservation application has been designed with some stateful services. The proposed approach maintains a good scalability, which provides a mean to achieve the efficient usage of the infrastructure resources.


title: Deep neural network based spectral feature mapping for robust speech recognition.

abstract: Automatic speech recognition (ASR) systems suffer from performance degradation under noisy and reverberant conditions. In this work, we explore a deep neural network (DNN) based approach for spectral feature mapping from corrupted speech to clean speech. The DNN based mapping substantially reduces interference and produces estimated clean spectral features for ASR training and decoding. We experiment with several different feature mapping approaches and demonstrate that a DNN trained to predict clean log filterbank coefficients from noisy spectrogram directly can be extremely effective. The experiments show that the ASR systems with these cleaned features perform well under joint noisy and reverberant conditions, and achieve the state-of-the-art results on the CHiME-2 corpus with stereo (corrupted and clean) data.


title: Perspectives on the economic history of the Antarctic region

abstract: This paper starts out by indicating how the economic history of the Antarctic could be conceptualized, given the peculiarities of the continent and the region (no permanent population, no sovereignty in a traditional sense, extreme remoteness, rigorous climate etc.). Second, it describes the main industries throughout Antarctic history. Third, it examines the quantitative data available on economic activity in the region, suggests how we should proceed to analyse the economic activity throughout history, and finally ask the question; Is it possible to (re)construct a historical ‘national product’ of the Antarctica?


title: Prevention and management of vision loss relating to facial filler injections.

abstract: INTRODUCTION ::: With the increased use of filler and fat injections for aesthetic purposes, there has been a corresponding increase in the incidence of complications. Vision loss as an uncommon but devastating vascular side effect of filler injections was the focus of this paper.


title: Improving bug localization using structured information retrieval

abstract: Locating bugs is important, difficult, and expensive, particularly for large-scale systems. To address this, natural language information retrieval techniques are increasingly being used to suggest potential faulty source files given bug reports. While these techniques are very scalable, in practice their effectiveness remains low in accurately localizing bugs to a small number of files. Our key insight is that structured information retrieval based on code constructs, such as class and method names, enables more accurate bug localization. We present BLUiR, which embodies this insight, requires only the source code and bug reports, and takes advantage of bug similarity data if available. We build BLUiR on a proven, open source IR toolkit that anyone can use. Our work provides a thorough grounding of IR-based bug localization research in fundamental IR theoretical and empirical knowledge and practice. We evaluate BLUiR on four open source projects with approximately 3,400 bugs. Results show that BLUiR matches or outperforms a current state-of-the-art tool across applications considered, even when BLUiR does not use bug similarity data used by the other tool.


title: MonetDB/XQuery: a fast XQuery processor powered by a relational engine

abstract: Relational XQuery systems try to re-use mature relational data management infrastructures to create fast and scalable XML database technology. This paper describes the main features, key contributions, and lessons learned while implementing such a system. Its architecture consists of (i) a range-based encoding of XML documents into relational tables, (ii) a compilation technique that translates XQuery into a basic relational algebra, (iii) a restricted (order) property-aware peephole relational query optimization strategy, and (iv) a mapping from XML update statements into relational updates. Thus, this system implements all essential XML database functionalities (rather than a single feature) such that we can learn from the full consequences of our architectural decisions. While implementing this system, we had to extend the state-of-the-art with a number of new technical contributions, such as loop-lifted staircase join and efficient relational query evaluation strategies for XQuery theta-joins with existential semantics. These contributions as well as the architectural lessons learned are also deemed valuable for other relational back-end engines. The performance and scalability of the resulting system is evaluated on the XMark benchmark up to data sizes of 11GB. The performance section also provides an extensive benchmark comparison of all major XMark results published previously, which confirm that the goal of purely relational XQuery processing, namely speed and scalability, was met.


title: Agents of Things (AoT): An intelligent operational concept of the Internet of Things (IoT)

abstract: In this conceptual paper, we review the definitions, characteristics, and architecture of the Internet of Things (IoT) concept. We then identify the deficiencies of the IoT concept, analyze them, and discover the issue of lack of reasoning and intelligence in the IoT concept. We propose a solution to augment the IoT with intelligent software agents resulting in a new concept called the Agents of Things (AoT). The paper presents the AoT architecture and a scenario for one application of the proposed concept. Finally, it discusses the benefits of implementing AoT concept to solve real world issues and the future work.


title: Fairness testing: testing software for discrimination

abstract: This paper defines software fairness and discrimination and develops a testing-based method for measuring if and how much software discriminates, focusing on causality in discriminatory behavior. Evidence of software discrimination has been found in modern software systems that recommend criminal sentences, grant access to financial products, and determine who is allowed to participate in promotions. Our approach, Themis, generates efficient test suites to measure discrimination. Given a schema describing valid system inputs, Themis generates discrimination tests automatically and does not require an oracle. We evaluate Themis on 20 software systems, 12 of which come from prior work with explicit focus on avoiding discrimination. We find that (1) Themis is effective at discovering software discrimination, (2) state-of-the-art techniques for removing discrimination from algorithms fail in many situations, at times discriminating against as much as 98% of an input subdomain, (3) Themis optimizations are effective at producing efficient test suites for measuring discrimination, and (4) Themis is more efficient on systems that exhibit more discrimination. We thus demonstrate that fairness testing is a critical aspect of the software development cycle in domains with possible discrimination and provide initial tools for measuring software discrimination.


title: A Review of Wearable Sensor Systems for Monitoring Body Movements of Neonates

abstract: Characteristics of physical movements are indicative of infants' neuro-motor development and brain dysfunction. For instance, infant seizure, a clinical signal of brain dysfunction, could be identified and predicted by monitoring its physical movements. With the advance of wearable sensor technology, including the miniaturization of sensors, and the increasing broad application of micro- and nanotechnology, and smart fabrics in wearable sensor systems, it is now possible to collect, store, and process multimodal signal data of infant movements in a more efficient, more comfortable, and non-intrusive way. This review aims to depict the state-of-the-art of wearable sensor systems for infant movement monitoring. We also discuss its clinical significance and the aspect of system design.


GM decaf coffee grown on trees


title: GIS-Based Safety Bus Stops— Serdang and Seri Kembangan Case Study

abstract: Enhancing a bus transit system is a possible solution to the growth of congestion in urban areas. Issues related to the safety of bus passengers, either on board or during their travel to a bus stop, should be considered. This article presents a GIS method to identify risky bus stops on a single bus route in the Serdang and Seri Kempangan area according to 3 attributes: location, characteristics, and surface. The aim is to improve the safety of bus stops in the area. Results show that GIS is a good tool to achieve the purpose of this study.


title: A meta-analysis of cloud forensic frameworks and tools

abstract: Cloud computing enables users to reduce their infrastructure cost and rely on cloud to deploy their applications. The ability to access cloud from any device and user friendly cloud services allows criminals to perform malicious activities. To perform investigation in cloud environment, digital forensics should be applied to cloud and this is termed as cloud forensics. This paper presents various challenges in the stages of digital forensics and their dissimilarity in the cloud environment. Many researchers proposed various flavors of cloud forensic framework but adversaries increase the malicious usage by taking advantage of lack of a standard cloud forensic framework. This made us to explore various existing frameworks for cloud forensics which could help to come up with a standardized framework. In this paper, existing tools for digital forensics is presented with their applicability to cloud environment.


title: A Survey of Physics-Based Attack Detection in Cyber-Physical Systems

abstract: Monitoring the “physics” of cyber-physical systems to detect attacks is a growing area of research. In its basic form, a security monitor creates time-series models of sensor readings for an industrial control system and identifies anomalies in these measurements to identify potentially false control commands or false sensor readings. In this article, we review previous work on physics-based anomaly detection based on a unified taxonomy that allows us to identify limitations and unexplored challenges and to propose new solutions.


title: A Human Lactobacillus Strain (Lactobacillus Casei sp strain GG) Promotes Recovery From Acute Diarrhea in Children

abstract: To determine the effect of a human Lactobacillus strain ( Lactobacillus casei sp strain GG , Gefilac) on recovery from acute diarrhea (82% rotavirus), 71 wellnourished children between 4 and 45 months of age were studied. After oral rehydration, the patients randomly received either Lactobacillus GG -fermented milk product, 125 g (10 10-11 colony-forming units) twice daily (group 1); Lactobacillus GG freeze-dried powder, one dose (10 10-11 colony-forming units) twice daily (group 2); or a placebo, a pasteurized yogurt (group 3) 125 g twice daily; each diet was given for 5 days, in addition to normal full diet otherwise free of fermented dairy products. The mean (SD) duration of diarrhea after commencing the therapy was significantly shorter in group 1 (1.4 [0.8] days) and in group 2 (1.4 [0.8] days) than in group 3 (2.4 [1.1] days); F = 8.70, P Lactobacillus GG in the form of fermented milk or freeze-dried powder is effective in shortening the course of acute diarrhea.


title: The Effect of Family Size and Composition on Fertility Desires, Contraceptive Adoption, and Method Choice in South Asia

abstract: We examine the influence of family size and composition on reproductive behavior in three South Asian countries - Nepal India and Bangladesh - that are known for strong son preference. Using data from recent Demographic and Health Surveys we analyze whether the choice of contraceptive method adopted (modern versus traditional; temporary versus permanent) and desire for another child differ by parity and sex composition of surviving children. In addition for India we pool data for four northern states (Bihar Madhya Pradesh Rajasthan and Uttar Pradesh) and four southern states (Andhra Pradesh Karnataka Kerala and Tamil Nadu) in order to capture the north-south variation within the country. We also compare West Bengal and Bangladesh to understand how son preference in these culturally similar regions affects fertility desires and contraceptive adoption. The effects of parity and sex composition on fertility desire and contraceptive use are estimated using binary and multinomial logistic regression after controlling for key socioeconomic factors including education work status media exposure household wealth status womans participation in household decision-making and urban/rural residence. We find that independent of socioeconomic factors women with more sons have a lower desire to have another child and are more likely to use contraception than those with more daughters. These effects are more prominent in Nepal and India than in Bangladesh. Within India the effects are stronger in north India than in south India or West Bengal. However considerable proportions of women also express a desire for at least one daughter especially in Bangladesh after having a son. There is a clear shift towards permanent methods of contraception with an increase in the number of sons but we find no systematic pattern in the use of traditional methods by parity or sex composition of children. We conclude that son preference remains widespread in all three countries and it has a major influence on reproductive behavior. The preference for boys is embedded in the cultural and traditional beliefs of these countries. Reducing such preference would require a change in social norms and attitudes of the people and an improvement of the status of women within the household. (authors)


Chernoff bounds , and some applications


Best ManageMent Practices for Pathogen control in Manure ManageMent systeMs


title: SAX-EFG: an evolutionary feature generation framework for time series classification

abstract: A variety of real world applications fit into the broad definition of time series classification. Using traditional machine learning approaches such as treating the time series sequences as high dimensional vectors have faced the well known "curse of dimensionality" problem. Recently, the field of time series classification has seen success by using preprocessing steps that discretize the time series using a Symbolic Aggregate ApproXimation technique (SAX) and using recurring subsequences ("motifs") as features. In this paper we explore a feature construction algorithm based on genetic programming that uses SAX-generated motifs as the building blocks for the construction of more complex features. The research shows that the constructed complex features improve the classification accuracy in a statistically significant manner for many applications.


title: An experimental comparison of localization methods

abstract: Localization is the process of updating the pose of a robot in an environment, based on sensor readings. In this experimental study, we compare two methods for localization of indoor mobile robots: Markov localization, which uses a probability distribution across a grid of robot poses; and scan matching, which uses Kalman filtering techniques based on matching sensor scans. Both these techniques are dense matching methods, that is, they match dense sets of environment features to an a priori map. To arrive at results for a range of situations, we utilize several different types of environments, and add noise to both the dead-reckoning and the sensors. Analysis shows that, roughly, the scan-matching techniques are more efficient and accurate, but Markov localization is better able to cope with large amounts of noise. These results suggest hybrid methods that are efficient, accurate and robust to noise.


title: Retronasal triangle: a sonographic landmark for the screening of cleft palate in the first trimester.

abstract: OBJECTIVES ::: To describe a new first-trimester sonographic landmark, the retronasal triangle, which may be useful in the early screening for cleft palate. ::: ::: ::: METHODS ::: The retronasal triangle, i.e. the three echogenic lines formed by the two frontal processes of the maxilla and the palate visualized in the coronal view of the fetal face posterior to the nose, was evaluated prospectively in 100 consecutive normal fetuses at the time of routine first-trimester sonographic screening at 11 + 0 to 13 + 6 weeks' gestation. In a separate study of five fetuses confirmed postnatally as having a cleft palate, ultrasound images, including multiplanar three-dimensional views, were analyzed retrospectively to review the retronasal triangle. ::: ::: ::: RESULTS ::: None of the fetuses evaluated prospectively was affected by cleft lip and palate. During their first-trimester scan, the retronasal triangle could not be identified in only two fetuses. Reasons for suboptimal visualization of this area included early gestational age at scanning (11 weeks) and persistent posterior position of the fetal face. Of the five cases with postnatal diagnosis of cleft palate, an abnormal configuration of the retronasal triangle was documented in all cases on analysis of digitally stored three-dimensional volumes. ::: ::: ::: CONCLUSIONS ::: This study demonstrates the feasibility of incorporating evaluation of the retronasal triangle into the routine evaluation of the fetal anatomy at 11 + 0 to 13 + 6 weeks' gestation. Because fetuses with cleft palate have an abnormal configuration of the retronasal triangle, focused examination of the midface, looking for this area at the time of the nuchal translucency scan, may facilitate the early detection of cleft palate in the first trimester.


title: Photovoltaic modeling and its characteristics

abstract: A photovoltaic cell converts the solar energy into the electrical energy by the photovoltaic effect. Solar cells are widely used in terrestrial and space applications. The photovoltaic cells must be operated at their maximum power point. The maximum power point varies with illumination, temperature, radiation dose and other ageing effects. In this paper, mathematical modeling, V-I and P-V characteristics of PV cells are studied. Different modeling techniques like empirical model and ANFIS model are proposed and developed. The result obtained by empirical models will be compared with ANFIS model and it will be proved that ANFIS gives better result. The simulated V-I and P-V characteristics of Photovoltaic cell for various temperature and irradiance are presented. Also ANFIS model outputs are presented. This can be used for sizing of PV system.


title: Keyboard acoustic emanations revisited

abstract: We examine the problem of keyboard acoustic emanations. We present a novel attack taking as input a 10-minute sound recording of a user typing English text using a keyboard, and then recovering up to 96% of typed characters. There is no need for a labeled training recording. Moreover the recognizer bootstrapped this way can even recognize random text such as passwords: In our experiments, 90% of 5-character random passwords using only letters can be generated in fewer than 20 attempts by an adversary; 80% of 10-character passwords can be generated in fewer than 75 attempts. Our attack uses the statistical constraints of the underlying content, English language, to reconstruct text from sound recordings without any labeled training data. The attack uses a combination of standard machine learning and speech recognition techniques, including cepstrum features, Hidden Markov Models, linear classification, and feedback-based incremental learning.


title: Influence Maximization in Dynamic Social Networks

abstract: Social influence and influence diffusion has been widely studied in online social networks. However, most existing works on influence diffusion focus on static networks. In this paper, we study the problem of maximizing influence diffusion in a dynamic social network. Specifically, the network changes over time and the changes can be only observed by periodically probing some nodes for the update of their connections. Our goal then is to probe a subset of nodes in a social network so that the actual influence diffusion process in the network can be best uncovered with the probing nodes. We propose a novel algorithm to approximate the optimal solution. The algorithm, through probing a small portion of the network, minimizes the possible error between the observed network and the real network. We evaluate the proposed algorithm on both synthetic and real large networks. Experimental results show that our proposed algorithm achieves a better performance than several alternative algorithms.


title: PROGNOSTIC PERFORMANCE OF A SINGLE-MOLECULE HIGH-SENSITIVITY CARDIAC TROPONIN ASSAY AFTER NON-ST ELEVATION ACUTE CORONARY SYNDROME: ANALYSIS FROM MERLIN-TIMI 36

abstract: New high-sensitivity cardiac troponin (hs-cTn) assays measure levels of cTn previously undetectable by conventional assays; as such, their prognostic relevance remains debated. We investigated the prognostic performance of a novel single-molecule hs-cTnl assay along with established risk indicators


title: Bond behavior between steel bar and engineered cementitious composite (ECC) considering lateral FRP confinement: Test and modeling

abstract: Abstract Engineered cementitious composite (ECC) exhibits good post-cracking resistance and ductility and its unique properties make it a viable material for increasing structural performance under severe loading conditions. To understand the bond behavior between the ECC and steel bar, direct pullout tests were conducted in this paper. A comprehensive analysis was carried out on effects of ECC strength, steel bar diameter, and fiber reinforced polymer (FRP) confinement on steel bar-ECC bonding behavior. Based on the experimental data, performances of existing bond slip models for steel bar-ECC are evaluated and a new model with improved accuracy is proposed. The proposed model considers the influence of FRP confinement on bond-slip behavior.


title: Central and peripheral routes to advertising effectiveness. The moderating role of involvement

abstract: Undergraduates expressed their attitudes about a product after being exposed to a magazine ad under conditions of either high or low product involvement. The ad contained either strong or weak arguments for the product and featured either prominent sports celebrities or average citizens as endorsers. The manipulation of argument quality had a greater impact on attitudes under high than low involvement, but the manipulation of product endorser had a greater impact under low than high involvement. These results are consistent with the view that there are two relatively distinct routes to persuasion.


title: Convexity in complex networks

abstract: Metric graph properties lie in the heart of the analysis of complex networks, while in this paper we study their convexity through mathematical definition of a convex subgraph. A subgraph is convex if every geodesic path between the nodes of the subgraph lies entirely within the subgraph. According to our perception of convexity, convex network is such in which every connected subset of nodes induces a convex subgraph. We show that convexity is an inherent property of many networks that is not present in a random graph. Most convex are spatial infrastructure networks and social collaboration graphs due to their tree-like or clique-like structure, whereas the food web is the only network studied that is truly non-convex. Core-periphery networks are regionally convex as they can be divided into a non-convex core surrounded by a convex periphery. Random graphs, however, are only locally convex meaning that any connected subgraph of size smaller than the average geodesic distance between the nodes is almost certainly convex. We present different measures of network convexity and discuss its applications in the study of networks.


title: Tobacco smoking and oral clefts: a meta-analysis.

abstract: OBJECTIVE ::: To examine the association between maternal smoking and non-syndromic orofacial clefts in infants. ::: ::: ::: METHODS ::: A meta-analysis of the association between maternal smoking during pregnancy was carried out using data from 24 case-control and cohort studies. ::: ::: ::: FINDINGS ::: Consistent, moderate and statistically significant associations were found between maternal smoking and cleft lip, with or without cleft palate (relative risk 1.34, 95% confidence interval 1.25-1.44) and between maternal smoking and cleft palate (relative risk 1.22, 95% confidence interval 1.10-1.35). There was evidence of a modest dose-response effect for cleft lip with or without cleft palate. ::: ::: ::: CONCLUSION ::: The evidence of an association between maternal tobacco smoking and orofacial clefts is strong enough to justify its use in anti-smoking campaigns.


title: FilmTrust: movie recommendations using trust in web-based social networks

abstract: In this paper, we present FilmTrust, a website that integrates Semantic Web-based social networks, augmented with trust, to create predictive movie recommendations. We show how these recommendations are more accurate than other techniques in certain cases, and discuss this technique as a mechanism of Semantic Web interaction.


title: Multiple DG allocation by crow search algorithm for power loss reduction

abstract: This paper proposes a new meta-heuristic method for optimal sizing of distributed generator (DG). The objective of this paper is to reduce power losses and to improve voltage profile of the radial distribution network by placing multiple distributed generators at optimal locations. Analytical expressions are used in solving this paper for setting up of DG. In this study, Crow Search Algorithm (CSA) is used for optimal sizing of distributed generators (DG). CSA is a meta-heuristic algorithm inspired by the intelligent behavior of the crows. Crows stores their excess food in different locations and memorizes those locations to retrieve it when it is needed. They follow each other to do thievery to obtain better food source. This analysis is tested on IEEE 33 bus and IEEE 69 bus under MATLAB environment and the results are compared with the results of Improved analytical (IA) method and identified that percentage loss reduction in crow search algorithm is more than the percentage loss reduction in improved analytical method.


title: Memory Fusion Network for Multi-view Sequential Learning

abstract: Multi-view sequential learning is a fundamental problem in machine learning dealing with multi-view sequences. In a multi-view sequence, there exists two forms of interactions between different views: view-specific interactions and cross-view interactions. In this paper, we present a new neural architecture for multi-view sequential learning called the Memory Fusion Network (MFN) that explicitly accounts for both interactions in a neural architecture and continuously models them through time. The first component of the MFN is called the System of LSTMs, where view-specific interactions are learned in isolation through assigning an LSTM function to each view. The cross-view interactions are then identified using a special attention mechanism called the Delta-memory Attention Network (DMAN) and summarized through time with a Multi-view Gated Memory. Through extensive experimentation, MFN is compared to various proposed approaches for multi-view sequential learning on multiple publicly available benchmark datasets. MFN outperforms all the existing multi-view approaches. Furthermore, MFN outperforms all current state-of-the-art models, setting new state-of-the-art results for these multi-view datasets.


title: Improving Policy Gradient by Exploring Under-appreciated Rewards

abstract: This paper presents a novel form of policy gradient for model-free reinforcement learning (RL) with improved exploration properties. Current policy-based methods use entropy regularization to encourage undirected exploration of the reward landscape, which is ineffective in high dimensional spaces with sparse rewards. We propose a more directed exploration strategy that promotes exploration of under-appreciated reward regions. An action sequence is considered under-appreciated if its log-probability under the current policy under-estimates its resulting reward. The proposed exploration strategy is easy to implement, requiring small modifications to an implementation of the REINFORCE algorithm. We evaluate the approach on a set of algorithmic tasks that have long challenged RL methods. Our approach reduces hyper-parameter sensitivity and demonstrates significant improvements over baseline methods. Our algorithm successfully solves a benchmark multi-digit addition task and generalizes to long sequences. This is, to our knowledge, the first time that a pure RL method has solved addition using only reward feedback.


title: Natural Language Processing for Industry

abstract: Recently, natural language processing applications have become very popular in the industry. Examples of such applications include “semantic” enterprise search engines, document categorizers, speech recognizers and – last but not least – conversational agents, also known as virtual assistants or “chatbots”. The latter in particular are very sought-after in the customer care domain, where the aim is to complement the live agent experience with an artificial intelligence able to help users fulfil a task. In this paper, we discuss the challenges and limitations of industrial chatbot applications, with a particular focus on the “human-in-the-loop” aspect, whereby a cooperation between human and machine takes place in mutual interest. Furthermore, we analyse how the same aspect intervenes in other industrial natural language processing applications.


title: Brain Activation during Face Perception: Evidence of a Developmental Change

abstract: Behavioral studies suggest that children under age 10 process faces using a piecemeal strategy based on individual distinctive facial features, whereas older children use a configural strategy based on the spatial relations among the face's features. The purpose of this study was to determine whether activation of the fusiform gyrus, which is involved in face processing in adults, is greater during face processing in older children (12–14 years) than in younger children (8–10 years). Functional MRI scans were obtained while children viewed faces and houses. A developmental change was observed: Older children, but not younger children, showed significantly more activation in bilateral fusiform gyri for faces than for houses. Activation in the fusiform gyrus correlated significantly with age and with a behavioral measure of configural face processing. Regions believed to be involved in processing basic facial features were activated in both younger and older children. Some evidence was also observed for greater activation for houses versus faces for the older children than for the younger children, suggesting that processing of these two stimulus types becomes more differentiated as children age. The current results provide biological insight into changes in visual processing of faces that occur with normal development.


title: A systematic review on cloud computing

abstract: Cloud computing is an ascending technology that has introduced a new paradigm by rendering a rational computational model possible. It has changed the dynamics of IT consumption by means of a model that provides on-demand services over the Internet. Unlike the traditional hosting service, cloud computing services are paid for per usage and may expand or shrink based on demand. Such services are, in general, fully managed by cloud providers that require users nothing but a personal computer and an Internet access. In recent years, this model has attracted the attention of researchers, investors and practitioners, many of whom have proposed a number of applications, structures and fundamentals of cloud computing, resulting in various definitions, requirements and models. Despite the interest and advances in the field, issues such as security and privacy, service layer agreement, resource sharing, and billing have opened up new questions about the real gains of the model. Although cloud computing is based on a 50-year-old business model, evidence from this study indicates that cloud computing still needs to expand and overcome present limitations that prevent the full use of its potential. In this study, we critically review the state of the art in cloud computing with the aim of identifying advances, gaps and new challenges.


title: Optical caustics of Kerr spacetime: the full structure

abstract: I present an exhaustive numerical investigation of the optical caustics in gravitational lensing by a spinning black hole for an observer at infinity. Besides the primary caustic, I examine higher order caustics, formed by photons performing one or several loops around the black hole. My investigation covers the whole parameter space, including the black hole spin, its inclination with respect to the line of sight, the source distance, and the caustic order. By comparing my results with the available analytical approximations, I find perfect agreement in their respective domains of validity. I then prove that all caustics maintain their shape (a tube with astroidal cross section) in the entire parameter space without suffering any transitions to different caustic shapes. For nearly extremal spin, however, higher order caustics grow so large that their cross sections at fixed radii wind several times around the black hole. As a consequence, for each caustic order, the number of images ranges from 2 to 2(n + 1), where n is the number of loops spanned by the caustic. As for the critical curves, I note that for high values of the spin they develop a small dip on the side corresponding to prograde orbits.


title: A flexible and reconfigurable controller design for buck converters

abstract: There is a need for more flexible and reconfigurable dc-dc converters. This has pushed designers to seek for alternative ways to control the PWM modulated switch inside the dc-dc converter. This paper presents the results of design and implementation of a fully customizable Arduino based controller for a SMPS (buck converter). The controller presented gives the user a high degree of autonomy and permits the variation of several output parameters such as the magnitude of regulated voltage, the percentage of voltage regulation, the switching frequency and therefore the amount of ripple in the output by simple modifications of the code, eliminating the need for hardware changes. The flexibility of the controller has been illustrated by reconfiguring it to meet the requirements of different design specifications. The results obtained have been analysed, the possible advantages of such a reconfigurable buck converter over other existing solutions and its applications have been discussed.


title: Sensor data collection and irrigation control on vegetable crop using smart phone and wireless sensor networks for smart farm

abstract: Feeding of the world in the 21st century is the biggest challenge, especially for smart farm business. The smart farm has used agriculture automation system instead of traditional agriculture. Traditional agricultural methods employed by the local people are highly sustainable, although the all inclusive cost is not cheap. Our research goal is to provide long term sustainable solution for automation of agriculture. Agriculture automation has several methods to getting data from vegetable crop like sensor for environmental measurement. Therefore, we developed a portable measurement technology including soil moisture sensor, air humidity sensor and air temperature sensor. Moreover, irrigation system using wireless sensor network has installed these sensors, with the purpose for collecting the environment data and controlling the irrigation system via smart phone. The purpose of the experiment is to find better ways of controlling an irrigation system with automatic system and manual control by smart phone. In order to control an irrigation system, we have developed the communication methodology of the wireless sensor network for collected environment data and sending control command to turn on/off irrigation system. It is successful for controlling the irrigation system and controlling the water near the vegetable roots. In this paper, we have attempted to demonstrate the automation of the irrigation system that is useful for farm business which make it comfortable than using traditional agriculture by using smart phone for monitoring and controlling the system. Accordingly, in the long-term has reduced cost as well. The experimental result shows that the accuracy of sending and receiving command control for irrigation system is 96 percent and accuracy of environment collection is 98 percent.


title: Brute force attack on UNIX passwords with SIMD computer

abstract: As computer technology improves, the security of specific ciphers and one-way hash functions periodically must be reevaluated in light of new technological advances. In this paper we evaluate the security of the UNIX password scheme. We show that the UNIX password scheme is vulnerable to brute-force attack. Using PixelFlow, a SIMD parallel machine, we are able to "crack" a large fraction of passwords used in practice [12] in 2-3 days of computation. We explain how a SIMD machine built in today's technology could "crack" any UNIX password in two days. We also describe in this paper a simple modification to the UNIX password scheme that makes it harder to break encrypted passwords using dictionary and brute force attack, thus extending the useful life of the UNIX password scheme. The modified password scheme is compatible with the existing password scheme.


title: Remote sensing imagery in vegetation mapping : a review

abstract: Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover. Methods Specifically, this paper focuses on the comparisons of popular remote sensing sensors, commonly adopted image processing methods and prevailing classification accuracy assessments. Important findings The basic concepts, available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced, analyzed and compared. The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover from remote sensed images.


title: Bioinformatic Workflows for Generating Complete Plastid Genome Sequences—An Example from Cabomba (Cabombaceae) in the Context of the Phylogenomic Analysis of the Water-Lily Clade

abstract: The sequencing and comparison of plastid genomes are becoming a standard method in plant genomics, and many researchers are using this approach to infer plant phylogenetic relationships. Due to the widespread availability of next-generation sequencing, plastid genome sequences are being generated at breakneck pace. This trend towards massive sequencing of plastid genomes highlights the need for standardized bioinformatic workflows. In particular, documentation and dissemination of the details of genome assembly, annotation, alignment and phylogenetic tree inference are needed, as these processes are highly sensitive to the choice of software and the precise settings used. Here, we present the procedure and results of sequencing, assembling, annotating and quality-checking of three complete plastid genomes of the aquatic plant genus Cabomba as well as subsequent gene alignment and phylogenetic tree inference. We accompany our findings by a detailed description of the bioinformatic workflow employed. Importantly, we share a total of eleven software scripts for each of these bioinformatic processes, enabling other researchers to evaluate and replicate our analyses step by step. The results of our analyses illustrate that the plastid genomes of Cabomba are highly conserved in both structure and gene content.


title: UP!: Engineering Persuasive Interactive

abstract: Persuasive technology aims at supporting people to change their attitudes and/or behaviors sustainably. Application areas include energy saving, green mobility, medical observance, addiction, etc. Emergency to solve these societal challenges makes the field meet a great success. We propose UP!, a problem space to structure the exploration of the design space, an increment to the SEPIA framework. UP! combines two perspectives, the User and the Phenomenon under study, so that to create the right system that makes the user understand the problematic phenomenon and act appropriately to change sustainably.


title: Multiple Description Video Coding using Hierarchical B Pictures

abstract: A generalized multiple description video coding based on hierarchical B pictures is proposed in this paper. Two descriptions are generated by employing the H.264/MPEG4-AVC codec conforming coding with hierarchical B pictures on the original video sequence. The only difference between the two descriptions lies in the selection of key pictures. Temporal scalability for each description is achieved benefiting from the high-efficiency hierarchical structure. More importantly, good central-side-distortion-rate tradeoff is obtained by our proposed scheme. Experimental results show that it is an effective and efficient design.


title: An efficient real-time emotion detection using camera and facial landmarks

abstract: Emotion recognition has many useful applications in daily lives. In this paper, we present a potential approach to detect human emotion in real time. For any face detected in camera, we extract the corresponding facial landmarks and examine different kinds of features and models for predicting human emotion. The experiments show that our proposed system can naturally detect human emotion in real time and achieve an average accuracy about 70.65%.


title: Flexible Navigation: Finite state machine-based integrated navigation and control for ROS enabled robots

abstract: This paper describes the Flexible Navigation system that extends the ROS Navigation stack and compatible libraries to separate computation from decision making, and integrates the system with FlexBE — the Flexible Behavior Engine, which provides intuitive supervision with adjustable autonomy. Although the ROS Navigation plugin model offers some customization, many decisions are internal to move_base. In contrast, the Flexible Navigation system separates global planning from local planning and control, and uses a hierarchical finite state machine to coordinate behaviors. The Flexible Navigation system includes Python-based state implementations and ROS nodes derived from the move_base plugin model to provide compatibility with existing libraries as well as future extensibility. The paper concludes with complete system demonstrations in both simulation and hardware using the iRobot Create and Kobuki-based Turtlebot running under ROS Kinetic. The system supports multiple independent robots.


title: Pairwise global alignment of protein interaction networks by matching neighborhood topology

abstract: We describe an algorithm, IsoRank, for global alignment of two protein-protein interaction (PPI) networks. IsoRank aims to maximize the overall match between the two networks; in contrast, much of previous work has focused on the local alignment problem-- identifying many possible alignments, each corresponding to a local region of similarity. IsoRank is guided by the intuition that a protein should be matched with a protein in the other network if and only if the neighbors of the two proteins can also be well matched. We encode this intuition as an eigenvalue problem, in a manner analogous to Google's PageRank method. We use IsoRank to compute the first known global alignment between the S. cerevisiae and D. melanogaster PPI networks. The common subgraph has 1420 edges and describes conserved functional components between the two species. Comparisons of our results with those of a well-known algorithm for local network alignment indicate that the globally optimized alignment resolves ambiguity introduced by multiple local alignments. Finally, we interpret the results of global alignment to identify functional orthologs between yeast and fly; our functional ortholog prediction method is much simpler than a recently proposed approach and yet provides results that are more comprehensive.


title: A Survey of Mobile Cloud Computing Applications: Perspectives and Challenges

abstract: As mobile computing has been developed for decades, a new model for mobile computing, namely, mobile cloud computing, emerges resulting from the marriage of powerful yet affordable mobile devices and cloud computing. In this paper we survey existing mobile cloud computing applications, as well as speculate future generation mobile cloud computing applications. We provide insights for the enabling technologies and challenges that lie ahead for us to move forward from mobile computing to mobile cloud computing for building the next generation mobile cloud applications. For each of the challenges, we provide a survey of existing solutions, identify research gaps, and suggest future research areas.


title: Automatic Tablature Transcription of Electric Guitar Recordings by Estimation of Score- and Instrument-Related Parameters

abstract: In this paper we present a novel algorithm for automatic analysis, transcription, and parameter extraction from isolated polyphonic guitar recordings. In addition to general score-related information such as note onset, duration, and pitch, instrumentspecific information such as the plucked string, the applied plucking and expression styles are retrieved automatically. For this purpose, we adapted several state-of-the-art approaches for onset and offset detection, multipitch estimation, string estimation, feature extraction, and multi-class classification. Furthermore we investigated a robust partial tracking algorithm with respect to inharmonicity, an extensive extraction of novel and known audio features as well as the exploitation of instrument-based knowledge in the form of plausability filtering to obtain more reliable prediction. Our system achieved very high accuracy values of 98 % for onset and offset detection as well as multipitch estimation. For the instrument-related parameters, the proposed algorithm also showed very good performance with accuracy values of 82 % for the string number, 93 % for the plucking style, and 83 % for the expression style. Index Terms - playing techniques, plucking style, expression style, multiple fundamental frequency estimation, string classification, fretboard position, fingering, electric guitar, inharmonicity coefficient, tablature


Emersonbot : Information-Focused Conversational


title: Comprehensible Context-driven Text Game Playing

abstract: In order to train a computer agent to play a text-based computer game, we must represent each hidden state of the game. A Long Short-Term Memory (LSTM) model running over observed texts is a common choice for state construction. However, a normal Deep Q-learning Network (DQN) for such an agent requires millions of steps of training or more to converge. As such, an LSTM-based DQN can take tens of days to finish the training process. Though we can use a Convolutional Neural Network (CNN) as a text-encoder to construct states much faster than the LSTM, doing so without an understanding of the syntactic context of the words being analyzed can slow convergence. In this paper, we use a fast CNN to encode position-and syntax-oriented structures extracted from observed texts as states. We additionally augment the reward signal in a universal and practical manner. Together, we show that our improvements can not only speed up the process by one order of magnitude but also learn a superior agent.


title: A Pragmatic AI Approach to Creating Artistic Visual Variations by Neural Style Transfer

abstract: On a constant quest for inspiration, designers can become more effective with tools that facilitate their creative process and let them overcome design fixation. This paper explores the practicality of applying neural style transfer as an emerging design tool for generating creative digital content. To this aim, the present work explores a well-documented neural style transfer algorithm (Johnson 2016) in four experiments on four relevant visual parameters: number of iterations, learning rate, total variation, content vs. style weight. The results allow a pragmatic recommendation of parameter configuration (number of iterations: 200 to 300, learning rate: 2e-1 to 4e-1, total variation: 1e-4 to 1e-8, content weights vs. style weights: 50:100 to 200:100) that saves extensive experimentation time and lowers the technical entry barrier. With this rule-of-thumb insight, visual designers can effectively apply deep learning to create artistic visual variations of digital content. This could enable designers to leverage AI for creating design works as state-of-the-art.


Deep Learning Techniques in Tandem with Signal Processing Cues for Phonetic Segmentation for Text to Speech Synthesis in Indian Languages.


title: Self-Esteem, Academic Self-Concept, and Achievement: How the Learning Environment Moderates the Dynamics of Self-Concept

abstract: The authors examine the directionality of effects between global self-esteem, domain-specific academic self-concepts, and academic achievement. Special emphasis is placed on learning environments as potential moderators of the direction of these effects. According to the meritocracy principle presented here, so-called bottom-up effects (i.e., self-esteem is influenced by academic self-concept) are more pronounced in meritocratic learning environments than in ego-protective learning environments. This hypothesis was examined using a three-wave cross-lagged panel design with a large sample of 7th graders from East and West Germany, a total of 5,648 students who were tested shortly after German reunification. Reciprocal effects were found between self-esteem, academic self-concept, and academic achievement. In conformance with the meritocracy principle, support for bottom-up effects was stronger in the meritocratic learning environment.


title: Classification with partial labels

abstract: In this paper, we address the problem of learning when some cases are fully labeled while other cases are only partially labeled, in the form of partial labels. Partial labels are represented as a set of possible labels for each training example, one of which is the correct label. We introduce a discriminative learning approach that incorporates partial label information into the conventional margin-based learning framework. The partial label learning problem is formulated as a convex quadratic optimization minimizing the L2-norm regularized empirical risk using hinge loss. We also present an efficient algorithm for classification in the presence of partial labels. Experiments with different data sets show that partial label information improves the performance of classification when there is traditional fully-labeled data, and also yields reasonable performance in the absence of any fully labeled data.


title: Throughput Maximization in Multi-UAV Enabled Communication Systems With Difference Consideration

abstract: This paper investigates a UAV-enabled wireless communication system with multiple UAVs (multi-UAVs), where the UAVs are dispatched to collect information from a group of ground terminals (GTs) that are energy-constrained. In particular, we consider that UAVs may differ so that each UAV can be individually designed. Besides, for the sake of collision avoidance of these multi-UAVs, an effective security flight mechanism is designed. To achieve a fair performance between GTs, this paper aims to maximize the minimum GT throughput by jointly optimizing the communication scheduling, power allocation, and UAVs’ trajectories. However, the formulated problem is shown to be a mixed integer non-convex optimization problem that is hard to solve. To tackle this problem, we first decompose it into two subproblems, and then, an efficient iterative algorithm is proposed by applying the block coordinate descent, relaxation, as well as successive convex optimization techniques. The proposed algorithm can be effectively utilized in wireless communication and networks. Moreover, a benchmark is set for the purpose of illustrating the superiority of the proposed design. Finally, numerical results show that the proposed design achieves a significant performance gain as compared with the benchmark.


title: Applying convolutional neural network for network intrusion detection

abstract: Recently, Convolutional neural network (CNN) architectures in deep learning have achieved significant results in the field of computer vision. To transform this performance toward the task of intrusion detection (ID) in cyber security, this paper models network traffic as time-series, particularly transmission control protocol / internet protocol (TCP/IP) packets in a predefined time range with supervised learning methods such as multi-layer perceptron (MLP), CNN, CNN-recurrent neural network (CNN-RNN), CNN-long short-term memory (CNN-LSTM) and CNN-gated recurrent unit (GRU), using millions of known good and bad network connections. To measure the efficacy of these approaches we evaluate on the most important synthetic ID data set such as KDDCup 99. To select the optimal network architecture, comprehensive analysis of various MLP, CNN, CNN-RNN, CNN-LSTM and CNN-GRU with its topologies, network parameters and network structures is used. The models in each experiment are run up to 1000 epochs with learning rate in the range [0.01-05]. CNN and its variant architectures have significantly performed well in comparison to the classical machine learning classifiers. This is mainly due to the reason that CNN have capability to extract high level feature representations that represents the abstract form of low level feature sets of network traffic connections.


title: Use of telephone follow-up for post-cardiac surgery patients.

abstract: Telephone follow-up has been used in a variety of settings as a means of supporting patients post-discharge. This paper describes the implementation of a telephone follow-up service for cardiac surgical patients, both to monitor their progress and to bridge the gap between home and hospital. Surgical unit nurses called 1594 patients between May 1995 and October 1997. These calls were made a month after each patient had been discharged and a specially designed form was used as a guideline for the calls. This covered three main aspects of their recovery: discharge monitoring; medical problems; and convalescence problems. As well as providing a framework for the calls, the forms were then used for data collection. The forms were audited on a monthly basis with the results being communicated to the ward staff. Data gathered from the calls have highlighted areas where patient education needs to be improved, e.g. pain control, and this has been addressed. In October 1996, an evaluation of the service was performed. One hundred patients were sent a postal questionnaire asking for their views on the telephone service. Eighty-two of these were returned and the results of this survey are also contained in this report. Over time, the number of patients called each month has increased; information regarding support post-discharge has improved and the form used for calls has been revised to make the questions more appropriate.


title: A material point method for snow simulation

abstract: Snow is a challenging natural phenomenon to visually simulate. While the graphics community has previously considered accumulation and rendering of snow, animation of snow dynamics has not been fully addressed. Additionally, existing techniques for solids and fluids have difficulty producing convincing snow results. Specifically, wet or dense snow that has both solid- and fluid-like properties is difficult to handle. Consequently, this paper presents a novel snow simulation method utilizing a user-controllable elasto-plastic constitutive model integrated with a hybrid Eulerian/Lagrangian Material Point Method. The method is continuum based and its hybrid nature allows us to use a regular Cartesian grid to automate treatment of self-collision and fracture. It also naturally allows us to derive a grid-based semi-implicit integration scheme that has conditioning independent of the number of Lagrangian particles. We demonstrate the power of our method with a variety of snow phenomena including complex character interactions.


title: Flow Regime Identification in Gas-Solid Two-Phase Fluidization via Acoustic Emission Technique

abstract: Abstract To explore flow regime transition processes in gas-solid two-phase fluidization, a non-intrusive, real-time and environment-friendly acoustic emission technique and an auxiliary pressure fluctuation method were applied in this work. Particle activity and collision intensity in the bed are obtained through standard deviation analysis. On that basis, it is found that the acoustic signal measurements can more effectively reflect the transition velocities among major flow regimes, including uc (from bubbling to turbulent fluidization), uk (from turbulent to fast fluidization), and uFD (from fast fluidization to dense phase pneumatic conveying). Meanwhile, the multi-scale resolution of acoustic signals during regime transitions can be obtained through Hurst and wavelet analyses. To be specific, micro-scale signals reflect the inter-particle collision and the particle-wall collision, meso-scale signals indicate the interactions and behaviors of particle clusters and gas phase, and macro-scale signals represent the average flow behavior. According to the comparison of experimental results, the experimental values of transition velocities obtained from acoustic signal measurement are closer to corresponding empirical values. In other words, the integration of standard deviation and multi-scale analyses with acoustic signal measurement can effectively identify flow regime transitions in real-time.


title: 0.9–10GHz low noise amplifier with capacitive cross coupling

abstract: This paper presents a 0.9GHz-10GHz Ultra Wideband Low Noise Amplifier (LNA) designed for software-defined-radios (SDR). Capacitive cross coupling (CCC) is used at both input stage and cascade stage for wideband input impedance matching and small noise figure (NF). A combination of inductor peaking load and series inductor between the cascade stages of LNA is employed for a flat gain and enhanced input matching. This LNA is implemented in a standard 0.18µm CMOS technology. For 0.9GHz-10GHz ultra wideband applications, it achieves a maximum gain of 14dB, 3.5dB∼4.1dB NF and +1.8dBm IIP3. It consumes only7.6mW from a 1.8V power supply.


Geometry- and Appearance-Based Reasoning of Construction Progress Monitoring


title: A zero current detector with low negative inductor current using forced freewheel switch operation in synchronous DC-DC converter

abstract: In DC-DC converter at light load, discontinuous conduction mode (DCM) and pulse frequency modulation (PFM) mode are usually used to improve efficiency instead of using low efficient continuous conduction mode (CCM) in a pulse-width modulation (PWM) control system. For these control modes in light load condition, the zero-current detector (ZCD) to prevent reverse current flowing back to the power source is essential. This paper presents a zero current detection method with enhanced time response of the freewheel switch operation. The proposed ZCD was simulated using a 0.18-μm CMOS process in a test system of PWM voltage mode type-III buck converter, with input voltage of 3.3V. The test system works in CCM at heavy load, PFM at light load, and DCM in a short transient of decision to change from CCM to PFM. The PWM switching frequency is 2MHz, and testing output voltage is 1.8V with load current from 20mA up to 300mA. Under the same test condition, the proposed ZCD shows maximum improvement of 6.9% efficiency in PFM compared to the conventional method, making the overall efficiency greater than 89.7%.


title: Electrocardiogram (ECG) Signal Processing

abstract: Signal processing of electrocardiographic signals has a long and rich history and has greatly helped to enhance the diagnostic capability, especially when signals are recorded in noisy environments. This article gives an introductory presentation of the basic set of algorithms used for conditioning the ECG with respect to different types of noise and artifacts, detecting heartbeats, extracting basic ECG measurements, and performing data compression. Signal processing in clinical applications is exemplified by the high-resolution ECG and T wave alternans.


title: Exploring the behaviour of base classifiers in credit scoring ensembles

abstract: Many techniques have been proposed for credit risk assessment, from statistical models to artificial intelligence methods. During the last few years, different approaches to classifier ensembles have successfully been applied to credit scoring problems, demonstrating to be more accurate than single prediction models. However, it is still a question what base classifiers should be employed in each ensemble in order to achieve the highest performance. Accordingly, the present paper evaluates the performance of seven individual prediction techniques when used as members of five different ensemble methods. The ultimate aim of this study is to suggest appropriate classifiers for each ensemble approach in the context of credit scoring. The experimental results and statistical tests show that the C4.5 decision tree constitutes the best solution for most ensemble methods, closely followed by the multilayer perceptron neural network and logistic regression, whereas the nearest neighbour and the naive Bayes classifiers appear to be significantly the worst.


title: Mining Sensor Streams for Discovering Human Activity Patterns over Time

abstract: In recent years, new emerging application domains have introduced new constraints and methods in data mining field. One of such application domains is activity discovery from sensor data. Activity discovery and recognition plays an important role in a wide range of applications from assisted living to security and surveillance. Most of the current approaches for activity discovery assume a static model of the activities and ignore the problem of mining and discovering activities from a data stream over time. Inspired by the unique requirements of activity discovery application domain, in this paper we propose a new stream mining method for finding sequential patterns over time from streaming non-transaction data using multiple time granularities. Our algorithm is able to find sequential patterns, even if the patterns exhibit discontinuities (interruptions) or variations in the sequence order. Our algorithm also addresses the problem of dealing with rare events across space and over time. We validate the results of our algorithms using data collected from two different smart apartments.


title: License plate character segmentation based on stroke width transform

abstract: License plate character segmentation is an important step of license plate recognition (LPR). This paper proposes an approach to segment license plate character based on stroke width transform (SWT). The method detects some character candidates by SWT, reserves real character regions with cluster analysis based on horizontal position and the height of detected candidate regions, and fits two straight lines. According to the tilting degrees and locations of fitted lines, locate precisely position of a plate, and correct the rotation plate. At last, segment characters using connected component analysis and template matching. Experiments show that the proposed method can accurately segment license plate characters, has good adaptability to rotation, slightly fouling. It also saves to be difficult to adjust parameters with a general method to deal with different scale plates, meanwhile not necessary to preciously locate the position of plates before segmentation.


title: Survey Paper Research on gain scheduling �

abstract: Current research on gain scheduling is clarifying customary practices as well as devising new approaches and methods for the design of nonlinear control systems.


title: Large eccentric strength increase using the Copenhagen Adduction exercise in football: A randomized controlled trial

abstract: Hip adductor injuries are frequent in football, and players with low adductor strength appear to be at increased risk of injury. High adductor muscle activity has been shown in the Copenhagen Adduction exercise (CA); however, an associated strength gain has not been investigated. This study aims to examine the eccentric hip adduction strength (EHAD) gain using the CA in-season. Two U-19 sub-elite football teams, including 24 football players, were randomized to either an 8-week supervised progressive training program in addition to the usual training (intervention) or to continue training as usual (control). EHAD, eccentric hip abduction strength (EHAB), and side-bridge endurance were measured using reliable test procedures at baseline and follow-up by a blinded tester. There was a significant interaction between group and time on EHAD, EHAB, and EHAD/EHAB ratio (P 0.335). Compliance was 91.25%, and median muscle soreness ranged from 0 to 2. The CA implemented in-season with an 8-week progressive training program elicited a large significant increase in EHAD, EHAB, and EHAD/EHAB ratio.


title: OPPORTUNITIES OF SOCIO-EDUCATIONAL ASSISTANCE TO INDIVIDUALS SERVING ALTERNATIVE TO IMPRISONMENT SENTENCES: THE PROGRAMME BEHAVIOUR-CONVERSATION-CHANGE APPLICATION ANALYSIS

abstract: The paper focuses on the issue of socio-educational assistance to alcohol addicts serving non-custodial sentences in the Probation Department of Klaipėda Regional Probation Service. It comments on the results of the behavioural correction programme Behaviour-Conversation-Change (BCC), applied to the individuals serving non-custodial sentences. The research was based on the qualitative research strategy. The conducting of the research was underpinned by the works of W.R. Miller, S.Rollnick (1991, 2002) and numerous other authors on a motivational interview, as well as on a motivational interview-based programme Behaviour-Conversation-Change to be used by the penal system institutions (Farbring, Berge, 2004). The participants of the research were individuals with alcohol addicts, serving non-custodial sentences in the Probation Department of Klaipėda Regional Probation Service. In the period 2012 to 2017, 160 respondents took part in the research (accordingly, the sample was 151 respondents). The changes in the respondents ' behaviour were recorded by means of the SOCRATES 8A 19 question- scale (The Stages of Change Readiness and Treatment Eagerness Scale), designed to evaluate three key components: problem recognition, ambivalence, and the steps taken (Miller, Tonigan, 1996). The changes were evaluated at the beginning and at the end of the Behaviour-Conversation-Change programme. As proved by the findings, the impact of the Behaviour-Conversation-Change programme was positive at the recognition and the step taking stages. The results achieved in the application of the Behaviour-Conversation-Change programme meaningfully differed, given the officers approach to the programme.


title: Some reflections on designing construction kits for kids

abstract: In this paper, we present ten guiding principles for designing construction kits for kids, informed by our experiences over the past two decades:* Design for Designers* Low Floor and Wide Walls* Make Powerful Ideas Salient -- Not Forced* Support Many Paths, Many Styles* Make it as Simple as Possible -- and Maybe Even Simpler* Choose Black Boxes Carefully* A Little Bit of Programming Goes a Long Way* Give People What They Want -- Not What They Ask For* Invent Things That You Would Want to Use Yourself* Iterate, Iterate -- then Iterate AgainWhile these principles apply especially to the development of construction kits, we believe that they could be useful for everyone who designs new technologies for kids.


title: S-Paxos: Offloading the Leader for High Throughput State Machine Replication

abstract: Implementations of state machine replication are prevalently using variants of Paxos or other leader-based protocols. Typically these protocols are also leader-centric, in the sense that the leader performs more work than the non-leader replicas. Such protocols scale poorly, because as the number of replicas or the load on the system increases, the leader replica quickly reaches the limits of one of its resources. In this paper we show that much of the work performed by the leader in a leader-centric protocol can in fact be evenly distributed among all the replicas, thereby leaving the leader only with minimal additional workload. This is done (i) by distributing the work of handling client communication among all replicas, (ii) by disseminating client requests among replicas in a distributed fashion, and (iii) by executing the ordering protocol on ids. We derive a variant of Paxos incorporating these ideas. Compared to leader-centric protocols, our protocol not only achieves significantly higher throughput for any given number of replicas, but also increases its throughput with the number of replicas.


title: Obtaining personal data and asking for erasure: Do app vendors and website owners honour your privacy rights?

abstract: EU Directive 95/46/EC and the upcoming EU General Data Protection Regulation grant Europeans the right of access to data pertaining to them. Consumers can approach their service providers to obtain all personal data stored and processed there. Furthermore, they can demand erasure (or correction) of their data. We conducted an undercover field study to determine whether these rights can be exerted in practice. We assessed the behaviour of the vendors of 150 smartphone apps and 120 websites that are popular in Germany. Our deletion requests were fulfilled in 52 to 57% of the cases and less than half of the data provision requests were answered satisfactorily. Further, we observed instances of carelessness: About 20% of website owners would have disclosed our personal data to impostors. The results indicate that exerting privacy rights that have been introduced two decades ago is still a frustrating endeavour most of the time.


title: Time to challenge the spurious hierarchy of systematic over narrative reviews?

abstract: Systematic reviews are generally placed above narrative reviews in an assumed hierarchy of secondary research evidence. We argue that systematic reviews and narrative reviews serve different purposes and should be viewed as complementary. Conventional systematic reviews address narrowly focused questions; their key contribution is summarising data. Narrative reviews provide interpretation and critique; their key contribution is deepening understanding. This article is protected by copyright. All rights reserved.


title: A Fog-Based Healthcare Framework for Chikungunya

abstract: Chikungunya virus is an infection which is transmitted to humans by the bite of Aedes agypti and Aedes albopictis mosquitoes. It causes deterioration in health leading to multiorgan failure. Diagnostic tests for this virus are not easily available and affordable in developing countries. The medical technologies are not so efficient in diagnosing and preventing the outbreak of Chikungunya virus. Healthcare services based on a fog-cloud technology are emerging as a proactive and effective solution to provide remote detection and monitoring of users. A fog assisted healthcare system can be used to diagnose users infected by Chikungunya virus in an initial state of their infection so that proper treatment can be given to them on time to enable fast recovery. In this paper, a fog assisted cloud-based healthcare system is designed to diagnose and prevent the outbreak of this virus. Initially, a decision tree is used to classify the category of user’s infection depending on his/her health symptoms and diagnostic alerts are immediately generated on the user’s mobile phone from fog layer. Furthermore, the state of Chikungunya virus outbreak is represented using temporal network analysis (TNA) on cloud layer using proximity data. Various outbreak metrics from TNA graph are calculated which indicate the probability to receive or transmit Chikungunya infection to human beings. Results depict that J48 decision tree classifier has higher accuracy and lower response time in determining the level of Chikungunya virus infection as compared to other classification algorithms. Moreover, alert generation based on real-time healthcare data further enhances the utility of the proposed system.


title: Efficient Methods for Multi-Label Classification

abstract: As a generalized form of multi-class classification, multi-label classification allows each sample to be associated with multiple labels. This task becomes challenging when the number of labels bulks up, which demands a high efficiency. Many approaches have been proposed to address this problem, among which one of the main ideas is to select a subset of labels which can approximately span the original label space, and training is performed only on the selected set of labels. However, these proposed sampling algorithms either require nondeterministic number of sampling trials or are time consuming. In this paper, we propose two label selection methods for multi-label classification (i) clustering based sampling (CBS) that uses deterministic number of sampling trials; and (ii) frequency based sampling (FBS) utilizing only label frequency statistics which makes it more efficient. Moreover, neither of these two algorithms needs to perform singular value decomposition (SVD) on label matrix which is used in previously mentioned approaches. Experiments are performed on several real world multi-label data sets with the number of labels ranging from hundreds to thousands, and it is shown that the proposed approaches achieve the state-of-the-art performance among label space reduction based multi-label classification algorithms.


title: Daytime polysomnogram diagnosis of sleep disorders.

abstract: The daytime polysomnogram was used to evaluate 310 consecutive patients with suspected sleep disorders, referred mainly because of excessive daytime sleepiness. Abnormalities consistent with pathologic sleep apnoea were present in 102 cases, and with narcolepsy-cataplexy in 49 cases. The daytime polysomnogram is a readily accessible, accurate, and cost-effective method for diagnosing many sleep disorders.


title: Advanced Induction Motor Endring Design Features for High Speed Applications

abstract: This paper presents advancements in induction motor endring design to overcome mechanical limitations and extend the operating speed range and joint reliability of induction machines. A novel endring design met the challenging mechanical requirements of this high speed, high temperature, power dense application, without compromising electrical performance. Analysis is presented of the advanced endring design features including a non uniform cross section, hoop stress relief cuts, and an integrated joint boss, which reduced critical stress concentrations, allowing operation under a broad speed and temperature design range. A generalized treatment of this design approach is presented comparing the concept results to conventional design techniques. Additionally, a low temperature joining process of the bar/end ring connection is discussed that provides the required joint strength without compromising the mechanical strength of the age hardened parent metals. A description of a prototype 2 MW, 15,000 rpm flywheel motor generator embodying this technology is presented


title: Secure storage system and key technologies

abstract: With the rapid development of cloud storage, data security in storage receives great attention and becomes the top concern to block the spread development of cloud service. In this paper, we systematically study the security researches in the storage systems. We first present the design criteria that are used to evaluate a secure storage system and summarize the widely adopted key technologies. Then, we further investigate the security research in cloud storage and conclude the new challenges in the cloud environment. Finally, we give a detailed comparison among the selected secure storage systems and draw the relationship between the key technologies and the design criteria.


title: GraphRay: Distributed pathfinder network scaling

abstract: Pathfinder network scaling is a graph sparsification technique that has been popularly used due to its efficacy of extracting the “important” structure of a graph. However, existing algorithms to compute the pathfinder network (PFNET) of a graph have prohibitively expensive time complexity for large graphs: O(n3) for the general case and O(n2 log n) for a specific parameter setting, PFNET(r = ∞, q = n − 1), which is considered in many applications. In this paper, we introduce the first distributed technique to compute the pathfinder network with the specific parameters (r = ∞ and q = n − 1) of a large graph with millions of edges. The results of our experiments show our technique is scalable; it efficiently utilizes a parallel distributed computing environment, reducing the running times as more processing units are added.


title: A mixed method approach to examining Facebook use and its relationship to self-esteem

abstract: The current study examined the relationship between self-esteem and Facebook use in sample of college-age participants (N=201). Online methods were used to collect data. Consistent with the social compensation hypothesis, results indicate that self-esteem level was related to engaging in different on-line behaviors. For example, lower self-esteem was associated with feelings of connectedness to Facebook, more frequently untagging oneself in photos, and accepting friend requests from individuals that one does not know well. Qualitative analyses indicated that individuals with higher self-esteem were more likely to report that a positive aspect of Facebook was the ability to share pictures, thoughts, and ideas, and to report that other posts could become annoying or bothersome.


title: A New Burst-DFA model for SCADA Anomaly Detection

abstract: In Industrial Control Systems (ICS/SCADA), machine to machine data traffic is highly periodic. Past work showed that in many cases, it is possible to model the traffic between each individual Programmable Logic Controller (PLC) and the SCADA server by a cyclic Deterministic Finite Automaton (DFA), and to use the model to detect anomalies in the traffic. However, a recent analysis of network traffic in a water facility in the U.S, showed that cyclic-DFA models have limitations. In our research, we examine the same data corpus; our study shows that the communication on all of the channels in the network is done in bursts of packets, and that the bursts have semantic meaning---the order within a burst depends on the messages. Using these observations, we suggest a new burst-DFA model that fits the data much better than previous work. Our model treats the traffic on each channel as a series of bursts, and matches each burst to the DFA, taking the burst's beginning and end into account. Our burst-DFA model successfully explains between 95% and 99% of the packets in the data-corpus, and goes a long way toward the construction of a practical anomaly detection system.


title: Evaluating Mobile Applications: A Spreadsheet Case Study

abstract: The power of mobile devices has increased dramatically in the last few years. These devices are becoming more sophisticated and allow users to accomplish a wide variety of tasks while on the move. The ease with which mobile apps can be created and distributed has resulted in a number of usability issues becoming more prevalent. This paper describes the range of usability issues encountered at all stages of the mobile app life cycle, from when users begin to search for an app to when they finally remove the app from their device. Using these results the authors developed a number of guidelines for both app developers and app platform developers that will improve the overall usability of mobile apps.


title: Automatic Synthesis of Efficient Intrusion Detection Systems on FPGAs

abstract: This paper presents a tool for automatic synthesis of highly efficient intrusion detection systems using a high-level, graph-based partitioning methodology, and tree-based lookahead architectures. Intrusion detection for network security is a compute-intensive application demanding high system performance. This tool automates the creation of efficient FPGA architectures using system-level optimizations, a relatively unexplored field in this area. The pre-design tool allows for more efficient communication and extensive reuse of hardware components for dramatic increases in area-time performance. The tool is available online for public use.


title: ECA-LP / ECA-RuleML: A Homogeneous Event-Condition-Action Logic Programming Language

abstract: Event-driven reactive functionalities are an urgent need in nowadays distributed service-oriented applications and (Semantic) Web-based environments. An important problem to be addressed is how to correctly and efficiently capture and process the event-based behavioral, reactive logic represented as ECA rules in combination with other conditional decision logic which is represented as derivation rules. In this paper we elaborate on a homogeneous integration approach which combines derivation rules, reaction rules (ECA rules) and other rule types such as integrity constraint into the general framework of logic programming. The developed ECA-LP language provides expressive features such as ID-based updates with support for external and self-updates of the intensional and extensional knowledge, transac-tions including integrity testing and an event algebra to define and process complex events and actions based on a novel interval-based Event Calculus variant.


title: Fast Multiclass Vehicle Detection on Aerial Images

abstract: Detecting vehicles in aerial images provides important information for traffic management and urban planning. Detecting the cars in the images is challenging due to the relatively small size of the target objects and the complex background in man-made areas. It is particularly challenging if the goal is near-real-time detection, i.e., within few seconds, on large images without any additional information, e.g., road database and accurate target size. We present a method that can detect the vehicles on a 21-MPixel original frame image without accurate scale information within seconds on a laptop single threaded. In addition to the bounding box of the vehicles, we extract also orientation and type (car/truck) information. First, we apply a fast binary detector using integral channel features in a soft-cascade structure. In the next step, we apply a multiclass classifier on the output of the binary detector, which gives the orientation and type of the vehicles. We evaluate our method on a challenging data set of original aerial images over Munich and a data set captured from an unmanned aerial vehicle (UAV).


title: Software Engineering Methodologies: A Review of the Waterfall Model and Object- Oriented Approach

abstract: This paper discusses two main software engineering methodologies to system development, the waterfall model and the object- oriented approach. A review of literature reveals that waterfall model uses linear approach and is only suitable for sequential or procedural design. In waterfall, errors can only be detected at the end of the whole process and it may be difficult going back to repeat the entire process because the processes are sequential. Also, software based on waterfall approach is difficult to maintain and upgrade due to lack of integration between software components. On the other hand, the Object Oriented approach enables software systems to be developed as integration of software objects that work together to make a holistic and functional system. The software objects are independent of each other, allowing easy upgrading and maintenance of software codes. The paper also highlighted the merits and demerits of each of the approaches. This work concludes with the appropriateness of each approach in relation to the complexity of the problem domain.


title: Eatery: A Multi-Aspect Restaurant Rating System

abstract: This paper presents Eatery, a multi-aspect restaurant rating system that identifies rating values for different aspects of a restaurant by means of aspect-level sentiment analysis. Eatery uses a hierarchical taxonomy that represents relationships between various aspects of the restaurant domain that enables finding the sentiment score of an aspect as a composite sentiment score of its sub-aspects. The system consists of a word co-occurrence based technique to identify multiple implicit aspects appearing in a sentence of a review. An improved version of Analytic Hierarchy Process (AHP) is used to obtain weights specific to a restaurant by utilizing the relationships between aspects, which allows finding the composite sentiment score for each aspect in the taxonomy. The system also has the ability to rate individual food items and food categories. An improved version of Single Pass Partition Method (SPPM) is used to categorise food names to obtain food categories.


Description of a new genus of Bostrychidæ


title: Blind Steganography in Color Images by Double Wavelet Transform and Improved Arnold Transform

abstract: Steganography is a method which can put data into a media without a tangible impact on the cover media. In addition, the hidden data can be extracted with minimal differences. In this paper, two-dimensional discrete wavelet transform is used for steganography in 24-bit color images. This steganography is of blind type that has no need for original images to extract the secret image. In this algorithm, by the help of a structural similarity (SSIM) and a two-dimensional correlation coefficient, it is tried to select part of sub-band cover image instead of embedding location. These sub-bands are obtained by 3-levels of applying the DWT. Also to increase the steganography resistance against cropping or insert visible watermark, two channels of color image is used simultaneously. In order to raise the security, an encryption algorithm based on Arnold transform was also added to the steganography operation.


title: On the institutional archiving of social media

abstract: Social media records the thoughts and activities of countless cultures and subcultures around the globe. Yet institutional efforts to archive social media content remain controversial. We report on 988 responses across six surveys of social media users that included questions to explore this controversy. The quantitative and qualitative results show that the way people think about the issue depends on how personal and ephemeral they view the content to be. They use concepts such as creator privacy, content characteristics, technological capabilities, perceived legal rights, and intrinsic social good to reason about the boundaries of institutional social media archiving efforts.


Recognition of Simple Head Gestures Based on Head Pose Estimation Analysis


title: Pedestrian Detection Based on HOG-LBP Feature

abstract: in this paper, we propose a new framework in pedestrian detection by combining the HOG and uniform LBP feature on blocks. Contrast experiment result shows that detector using combined features is more powerful than one single feature. To further improve the detection performance, we make a contrast experiment that the HOG-LBP features are calculated at variable-size blocks to find the most efficient feature vector. The linear SVM is used to train the pedestrian classifier. Results presented on the INRIA dataset show that our detector is more discriminative and robust than the state-of-the-art algorithms.


title: Challenges and Software Architecture for Fog Computing

abstract: This article presents a detailed description of fog computing (also known as edge computing) and explores its research challenges and problems. Based on the authors' understanding of these challenges and problems, they propose a flexible software architecture, which can incorporate different design choices and user-specified polices. They present their design of WM-FOG, a computing framework for fog environments that embraces this software architecture, and evaluate their prototype system.


title: Intellectual capital – defining key performance indicators for organizational knowledge assets

abstract: Measuring intellectual capital is on the agenda of most 21st century organisations. This paper takes a knowledge‐based view of the firm and discusses the importance of measuring organizational knowledge assets. Knowledge assets underpin capabilities and core competencies of any organisation. Therefore, they play a key strategic role and need to be measured. This paper reviews the existing approaches for measuring knowledge based assets and then introduces the knowledge asset map which integrates existing approaches in order to achieve comprehensiveness. The paper then introduces the knowledge asset dashboard to clarify the important actor/infrastructure relationship, which elucidates the dynamic nature of these assets. Finally, the paper suggests to visualise the value pathways of knowledge assets before designing strategic key performance indicators which can then be used to test the assumed causal relationships. This will enable organisations to manage and report these key value drivers in today's economy.


title: Fan-Beam Millimeter-Wave Antenna Design Based on the Cylindrical Luneberg Lens

abstract: A new design of two-dimensional cylindrical Luneberg lens is introduced based on TE10 mode propagation between parallel plates, with special focus on ease of manufacturing. The parallel plates are partially filled with low cost polymer material (Rexolite epsivr = 2.54) to match Luneberg's law. A planar linear tapered slot antenna (LTSA) is inserted into the air region between the parallel plates at the edge of the Luneberg lens as a feed antenna, with fine positioning to the focal point of the Luneberg lens to optimize the antenna system performance. A combined ray-optics/diffraction method is used to obtain the radiation pattern of the system and results are compared with predictions of a time domain numerical solver. Measurements done on a 10-cm Luneberg lens designed for operation at 30 GHz agree very well with predictions. For this prototype, 3-dB E- and if-plane beamwidths of 6.6deg and 54deg respectively were obtained, and the sidelobe level in the E-plane was -17.7-dB. Although the parallel plate configuration should lead to a narrow band design due to the dispersion characteristics of the TE10 mode, the measurement results demonstrate broadband characteristics with radiation efficiencies varying between 43% and 72% over the tested frequency band of 26.5-37 GHz. The designed cylindrical Luneberg lens can be used to launch multiple beams by implementing an arc array of planar LTSA elements at the periphery of the lens, and can be easily extended to higher mm-wave frequencies.


title: Preventative Effect of an Herbal Preparation (HemoHIM) on Development of Airway Inflammation in Mice via Modulation of Th1/2 Cells Differentiation

abstract: HemoHIM, an herbal preparation of three edible herbs (Angelica gigas Nakai, Cnidium officinale Makino, Paeonia japonica Miyabe) is known to increase the Th1 immune response as well as reduce the allergic response in human mast cells. Here, our goal was to determine whether or not HemoHIM could induce Th1 cell differentiation as well as inhibit the development of airway inflammation. To study Th1/Th2 cell differentiation, naive CD4+ T cells isolated from C57BL/6 mouse spleens were cultured with or without HemoHIM. To examine airway inflammation, C57BL/6 mice were fed HemoHIM for 4 weeks before sensitization and provocation with ovalbumin (OVA). In an in vitro experiment, naive CD4+ T cells displayed increased Th1 (IFN-γ+ cell) as well as decreased Th2 (IL-4+ cell) differentiation in a HemoHIM concentration-dependent manner. Furthermore, in an airway inflammation mice model, eosinophil numbers in BALF, serum levels of OVA-specific IgE and IgG1, and cytokine (IL-4, IL-5, and IL-13) levels in BALF and the supernatant of splenocytes all decreased upon HemoHIM (100 mg/kg body weight) pretreatment (4 weeks). These results show that HemoHIM attenuated allergic airway inflammation in the mouse model through regulation of the Th1/Th2 balance.


Improving internal customer relationship in IT Purchases Case: Helsinki Metropolia University of Applied Sciences


title: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation

abstract: Characterizing the performance of image segmentation approaches has been a persistent challenge. Performance analysis is important since segmentation algorithms often have limited accuracy and precision. Interactive drawing of the desired segmentation by human raters has often been the only acceptable approach, and yet suffers from intra-rater and inter-rater variability. Automated algorithms have been sought in order to remove the variability introduced by raters, but such algorithms must be assessed to ensure they are suitable for the task. The performance of raters (human or algorithmic) generating segmentations of medical images has been difficult to quantify because of the difficulty of obtaining or estimating a known true segmentation for clinical data. Although physical and digital phantoms can be constructed for which ground truth is known or readily estimated, such phantoms do not fully reflect clinical images due to the difficulty of constructing phantoms which reproduce the full range of imaging characteristics and normal and pathological anatomical variability observed in clinical data. Comparison to a collection of segmentations by raters is an attractive alternative since it can be carried out directly on the relevant clinical imaging data. However, the most appropriate measure or set of measures with which to compare such segmentations has not been clarified and several measures are used in practice. We present here an expectation-maximization algorithm for simultaneous truth and performance level estimation (STAPLE). The algorithm considers a collection of segmentations and computes a probabilistic estimate of the true segmentation and a measure of the performance level represented by each segmentation. The source of each segmentation in the collection may be an appropriately trained human rater or raters, or may be an automated segmentation algorithm. The probabilistic estimate of the true segmentation is formed by estimating an optimal combination of the segmentations, weighting each segmentation depending upon the estimated performance level, and incorporating a prior model for the spatial distribution of structures being segmented as well as spatial homogeneity constraints. STAPLE is straightforward to apply to clinical imaging data, it readily enables assessment of the performance of an automated image segmentation algorithm, and enables direct comparison of human rater and algorithm performance.


Blockchain 4 Education