title: Review: 3D geo-database research: Retrospective and future directions

abstract: 3D geo-database research is a promising field to support challenging applications such as 3D urban planning, environmental monitoring, infrastructure management, and early warning or disaster management and response. In these fields, interdisciplinary research in GIScience and related fields is needed to support the modelling, analysis, management, and integration of large geo-referenced data sets, which describe human activities and geophysical phenomena. Geo-databases may serve as platforms to integrate 2D maps, 3D geo-scientific models, and other geo-referenced data. However, current geo-databases do not provide sufficient 3D data modelling and data handling techniques. New 3D geo-databases are needed to handle surface and volume models. This article first presents a 25-year retrospective of geo-database research. Data modelling, standards, and indexing of geo-data are discussed in detail. New directions for the development of 3D geo-databases to open new fields for interdisciplinary research are addressed. Two scenarios in the fields of early warning and emergency response demonstrate the combined management of human and geophysical phenomena. The article concludes with a critical outlook on open research problems.


title: Simulating device-to-device communications in OMNeT++ with SimuLTE: scenarios and configurations

abstract: SimuLTE is a tool that enables system-level simulations of LTE/LTE-Advanced networks within OMNeT++. It is designed such that it can be plugged within network elements as an additional Network Interface Card (NIC) to those already provided by the INET framework (e.g. Wi-Fi). Recently, device-to-device (D2D) technology has been widely studied by the research community, as a mechanism to allow direct communications between devices of a LTE cellular network. In this work, we present how SimuLTE can be employed to simulate both one-to-one and one-to-many D2D communications, so that the latter can be exploited as a new communication opportunity in several research fields, like vehicular networks, IoT and machine-to-machine (M2M) applications.


title: Neural Correlates of Forward and Inverse Models for Eye Movements: Evidence from Three-Dimensional Kinematics

abstract: Inverse and forward dynamic models have been conceptually important in computational motor control. In particular, inverse models are thought to convert desired action into appropriate motor commands. In parallel, forward models predict the consequences of the motor command on behavior by constructing an efference copy of the actual movement. Despite theoretical appeal, their neural representation has remained elusive. Here, we provide evidence supporting the notion that a group of premotor neurons called burst-tonic (BT) cells represent the output of the inverse model for eye movements. We show that BT neurons, like extraocular motoneurons but different from the evoked eye movement, do not carry signals appropriate for the half-angle rule of ocular kinematics during smooth-pursuit eye movements from eccentric positions. Along with findings of identical response dynamics as motoneurons, these results strongly suggest that BT cells carry a replica of the motor command. In contrast, eye-head (EH) neurons, a premotor cell type that is the target of Purkinje cell inhibition from the cerebellar flocculus/ventral paraflocculus, exhibit properties that could be consistent with the half-angle rule. Therefore, EH cells may be functionally related to the output of a forward internal model thought to construct an efference copy of the actual eye movement.


Risks of Antibiotic Exposures Early in Life on the Developing Microbiome


title: An improved fractional slot concentrated winding for low-poles induction machines

abstract: Recent investigations on IMs with FSCWs show that, due to the higher MMF harmonic content of concentrated windings, this machine type is characterized by low torque capability and quality, as well as, low efficiency and thermal problems. This paper presents a new fundamental wave FSCW with sinusoidal MMF waveform and a non-overlapping winding arrangement. The proposed winding solution consists of two different FSCW types and a dual slot-layer stator structure. The first winding is located on the outer slot-layer region, while the second one is located on the inner slot-layer. Additionally, both windings are shifted in space to each other for a specific angle. This winding configuration has several advantages like high quality MMF wave with low harmonic contents, shorter end-winding length, and applicability to low poles IMs. Obtained results from the analysis of different IMs show that the proposed FSCW provides high performances in different application areas.


title: Model Predictive Control of Parallel-Connected Inverters for Uninterruptible Power Supplies

abstract: Uninterruptible power supplies (UPSs) have been used in many installations for critical loads that cannot afford power failure or surge during operation. It is often difficult to upgrade the UPS system as the load grows over time. Due to lower cost and maintenance, as well as ease of increasing system capacity, the parallel operation of modularized small-power UPS has attracted much attention in recent years. In this paper, a new scheme for parallel operation of inverters is introduced. A multiple-input-multiple-output state-space model is developed to describe the parallel-connected inverters system, and a model-predictive-control scheme suitable for paralleled inverters control is proposed. In this algorithm, the control objectives of voltage tracking and current sharing are formulated using a weighted cost function. The effectiveness and the hot-swap capability of the proposed parallel-connected inverters system have been verified with experimental results.


title: An architecture pattern for safety critical automated driving applications: Design and analysis

abstract: Introduction of automated driving increases complexity of automotive systems. As a result, architecture design becomes a major concern for ensuring non-functional requirements such as safety, and modifiability. In the ISO 26262 standard, architecture patterns are recommended for system development. However, the existing architecture patterns may not be able to answer requirements of automated driving completely. When applying these patterns in the automated driving context, modification and analysis of these patterns are needed. In this paper, we present a novel architecture pattern for safety critical automated driving functions. In addition, we propose a generic approach to compare our pattern with a number of existing ones. The comparison results can be used as a basis for project specific architectural decisions. Our Safety Channel pattern is validated by its implementation for a real-life truck platooning application.


title: Credit Card Fraud Detection

abstract: The usage of credit cards for online and regular purchases is exponentially increasing and so is the fraud related with it. A large number of fraud transactions are made every day. Various modern techniques like Data Mining, Genetic Programming, etc. are used in detecting fraudulent transactions. This paper uses genetic algorithm which comprises of techniques for finding optimal solution for the problem and implicitly generating the result of the fraudulent transaction. The main aim is to detect the fraudulent transaction and to develop a method of generating test data. This algorithm is a heuristic approach used to solve high complexity computational problems. It is an optimization technique and evolutionary search based on the genetic and natural selection. The implementation of an efficient fraud detection system is imperative for all credit card issuing companies and their clients to minimize their losses.


title: Determinants of Information Security Awareness: An Empirical Investigation in Higher Education.

abstract: This paper aims to identify the determinants that affect information security awareness (ISA) of college students. With the prevalence and continuous growing of information technology (IT) in our daily life, we are experiencing immense benefits of the computerintensive business environment. However, information security is a critical issue at the same time. It is necessary to examine the ISA and intention to take action to reduce security risks. ISA refers to what degree that users understand the risks of using information systems (IS) and the corresponding methods to protect them from being compromised. It is demanded to understand what factors affecting users’ ISA that will directly affect their behaviors. We will collect the data by sending out survey through college students. They are a special group of users of IS due to their young age and their academic environment. The expected contributions and implications are discussed.


title: Weather prediction based on fuzzy logic algorithm for supporting general farming automation system

abstract: Many automation and monitoring systems in agriculture do not have a calculation system for watering based on weather. Of these issues, will be discussed weather prediction system using fuzzy logic algorithm for supporting General Farming Automation. The weather calculation system works by taking a weather prediction data from the Weather Service Provider (WSP). Furthermore, it also retrieves soil moisture sensor value and rainfall sensor value. After that, the system will calculate using fuzzy logic algorithm whether the plant should be watered or not. The weather calculation system will help the performance of the General Farming Automation Control System in order to work automatically. So, the plants still obtain water and nutrients intake are not excessive.


title: A Rough Set Approach for Customer Segmentation

abstract: Customer segmentation is a process that divides a business's total customers into groups according to their diversity of purchasing behavior and characteristics. The data mining clustering technique can be used to accomplish this customer segmentation. This technique clusters the customers in such a way that the customers in one group behave similarly when compared to the customers in other groups. The customer related data are categorical in nature. However, the clustering algorithms for categorical data are few and are unable to handle uncertainty. Rough set theory (RST) is a mathematical approach that handles uncertainty and is capable of discovering knowledge from a database. This paper proposes a new clustering technique called MADO (Minimum Average Dissimilarity between Objects) for categorical data based on elements of RST. The proposed algorithm is compared with other RST based clustering algorithms, such as MMR (Min-Min Roughness), MMeR (Min Mean Roughness), SDR (Standard Deviation Roughness), SSDR (Standard deviation of Standard Deviation Roughness), and MADE (Maximal Attributes DEpendency). The results show that for the real customer data considered, the MADO algorithm achieves clusters with higher cohesion, lower coupling, and less computational complexity when compared to the above mentioned algorithms. The proposed algorithm has also been tested on a synthetic data set to prove that it is also suitable for high dimensional data.


title: OpenCL framework for ARM processors with NEON support

abstract: The state-of-the-art ARM processors provide multiple cores and SIMD instructions. OpenCL is a promising programming model for utilizing such parallel processing capability because of its SPMD programming model and built-in vector support. Moreover, it provides portability between multicore ARM processors and accelerators in embedded systems. In this paper, we introduce the design and implementation of an efficient OpenCL framework for multicore ARM processors. Computational tasks in a program are implemented as OpenCL kernels and run on all CPU cores in parallel by our OpenCL framework. Vector operations and built-in functions in OpenCL kernels are optimized using the NEON SIMD instruction set. We evaluate our OpenCL framework using 37 benchmark applications. The result shows that our approach is effective and promising.


title: Identifying Intention Posts in Discussion Forums

abstract: This paper proposes to study the problem of identifying intention posts in online discussion forums. For example, in a discussion forum, a user wrote “I plan to buy a camera,” which indicates a buying intention. This intention can be easily exploited by advertisers. To the best of our knowledge, there is still no reported study of this problem. Our research found that this problem is particularly suited to transfer learning because in different domains, people express the same intention in similar ways. We then propose a new transfer learning method which, unlike a general transfer learning algorithm, exploits several special characteristics of the problem. Experimental results show that the proposed method outperforms several strong baselines, including supervised learning in the target domain and a recent transfer learning method.


title: Mu-Zero Resonance Antenna

abstract: We present mu-zero resonance (MZR) antennas that use an artificial mu-negative (MNG) transmission line (TL). The equivalent circuit for verifying the peculiarity of the MNG TL is derived and analyzed. To operate the MZR antenna properly, the antenna is fed by magnetic coupling. The analysis and design of the MZR antenna are performed according to theory and simulation based on a dispersion diagram and field distribution. The surface current distribution shows that the radiation mechanism of the MZR antenna is essentially identical to that of a small-loop antenna. Applying the novel concept of the MZR antenna, a dual-band MZR antenna using two MZR antennas with different MZR frequencies is proposed. The radiation characteristics of the antenna are simulated and measured at two frequencies. The measured characteristics show agreement with the simulated results. It is confirmed that the characteristics of the MZR antenna, including the efficiency, gain, and fractional bandwidth, are suitable for a multiband antenna.


From Complexity to Random Behaviors; Generate Random Numbers by Confusion in Cellular Automata State’s


title: Bayes factor consistency

abstract: Good large sample performance is typically a minimum requirement of any model selection criterion. This article focuses on the consistency property of the Bayes factor, a commonly used model comparison tool, which has experienced a recent surge of attention in the literature. We thoroughly review existing results. As there exists such a wide variety of settings to be considered, e.g. parametric vs. nonparametric, nested vs. non-nested, etc., we adopt the view that a unified framework has didactic value. Using the basic marginal likelihood identity of Chib (1995), we study Bayes factor asymptotics by decomposing the natural logarithm of the ratio of marginal likelihoods into three components. These are, respectively, log ratios of likelihoods, prior densities, and posterior densities. This yields an interpretation of the log ratio of posteriors as a penalty term, and emphasizes that to understand Bayes factor consistency, the prior support conditions driving posterior consistency in each respective model under comparison should be contrasted in terms of the rates of posterior contraction they imply.


title: Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures

abstract: Automatic description generation from natural images is a challenging problem that has recently received a large amount of interest from the computer vision and natural language processing communities. In this survey, we classify the existing approaches based on how they conceptualize this problem, viz., models that cast description as either generation problem or as a retrieval problem over a visual or multimodal representational space. We provide a detailed review of existing models, highlighting their advantages and disadvantages. Moreover, we give an overview of the benchmark image datasets and the evaluation measures that have been developed to assess the quality of machine-generated image descriptions. Finally we extrapolate future directions in the area of automatic image description generation.


title: Data assimilation using sequential monte carlo methods in wildfire spread simulation

abstract: Assimilating real-time sensor data into large-scale spatial-temporal simulations, such as simulations of wildfires, is a promising technique for improving simulation results. This asks for advanced data assimilation methods that can work with the complex structures and nonlinear behaviors associated with the simulation models. This article presents a data assimilation framework using Sequential Monte Carlo (SMC) methods for wildfire spread simulations. The models and algorithms of the framework are described, and experimental results are provided. This work demonstrates the feasibility of applying SMC methods to data assimilation of wildfire spread simulations. The developed framework can potentially be generalized to other application areas where sophisticated simulation models are used.


title: Average prime-pair counting formula

abstract: textabstractTaking $r>0$, let $\pi_{2r}(x)$ denote the number of prime pairs $(p,\,p+2r)$ with $p\le x$. The prime-pair conjecture of Hardy and Littlewood (1923) asserts that $\pi_{2r}(x)\sim 2C_{2r}\,{\rm li}_2(x)$ with an explicit constant $C_{2r}>0$. There seems to be no good conjecture for the remainders $\om_{2r}(x)=\pi_{2r(x)-2C_{2r}\,{\rm li}_2(x)$ that corresponds to Riemann's formula for $\pi(x)-{\rm li}(x)$. However, there is a heuristic approximate formula for averages of the remainders $\om_{2r}(x)$ which is supported by numerical results.


title: Studies on the Concentrations of Arsenic, Selenium, Copper, Zinc and Iron in the Hair of Blackfoot Disease Patients in Different Clinical Stages

abstract: Flame atomic absorption spectrophotometric methods were developed for, arsenic, selenium, copper, zinc and iron in hair samples. Data from blackfoot disease patients at five clinical stages were compared with those from healthy controls. The copper and zinc concentrations showed only slight differences in all clinical stages, which indicated the less relation to blackfoot disease. The decrease of selenium and iron in all stages was attributed to the antagonistic effect of arsenic; arsenic increased in the first and second stages, but decreased in the later stages. The decrease of selenium and iron during the progression of the disease is thought to be due to persistence of the antagonistic effect of arsenic in the initial stages, so that very low concentrations of selenium are found in the advanced stages, despite the later decrease of arsenic. There was also a progressive decrease of iron with advance of the disease, and the later stages also showed a decrease in haemoglobin. It was shown that arsenic is a major cause of blackfoot disease, and that it antagonises selenium and iron, which decreased in the advanced clinical stages of the disease.


title: Emerging topic detection on Twitter based on temporal and social terms evaluation

abstract: Twitter is a user-generated content system that allows its users to share short text messages, called tweets, for a variety of purposes, including daily conversations, URLs sharing and information news. Considering its world-wide distributed network of users of any age and social condition, it represents a low level news flashes portal that, in its impressive short response time, has the principal advantage. In this paper we recognize this primary role of Twitter and we propose a novel topic detection technique that permits to retrieve in real-time the most emergent topics expressed by the community. First, we extract the contents (set of terms) of the tweets and model the term life cycle according to a novel aging theory intended to mine the emerging ones. A term can be defined as emerging if it frequently occurs in the specified time interval and it was relatively rare in the past. Moreover, considering that the importance of a content also depends on its source, we analyze the social relationships in the network with the well-known Page Rank algorithm in order to determine the authority of the users. Finally, we leverage a navigable topic graph which connects the emerging terms with other semantically related keywords, allowing the detection of the emerging topics, under user-specified time constraints. We provide different case studies which show the validity of the proposed approach.


title: Integrating object-oriented image analysis and decision tree algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR imagery

abstract: Traditional pixel-based classification methods yield poor results when applied to SAR imagery because of the presence of speckle and limited information in backscatter coefficients. A novel classification method, integrating polarimetric target decomposition, object-oriented image analysis, and decision tree algorithms, is proposed for the classification of polarimetric SAR data (PolSAR). The polarimetric target decomposition is aimed at extracting physical information related to the scattering mechanism of targets for the classification of scattering data. The main purposes of the object-oriented image analysis are delineating objects and extracting various spatial and textural features. The decision tree algorithm provides an efficient way to select features and create a decision tree for the classification. A comparison between the proposed method and the Wishart supervised classification was made. The overall accuracies of these two methods were 89.34% and 79.36%, respectively. The results show that the proposed method is an effective method for the classification of PolSAR data.


title: Social Simulation of Stock Markets: Taking It to the Next Level

abstract: This paper studies the use of social simulation in linking micro level investor behaviour and macro level stock market dynamics. Empirical data from a survey on individual investors' decision-making and social interaction was used to formalize the trading and interaction rules of the agents of the artificial stock market SimStockExchange. Multiple simulation runs were performed with this artificial stock market, which generated macro level results, like stock market prices and returns over time. These outcomes were subsequently compared to empirical macro level data from real stock markets. Partial qualitative as well as quantitative agreement between the simulated asset returns distributions and the asset returns distributions of the real stock markets was found.


title: 1188 Building Memories: Remembering and Forgetting of Verbal Experiences as Predicted by Brain Activity

abstract: A fundamental question about human memory is why some experiences are remembered whereas others are forgotten. Brain activation during word encoding was measured using blocked and event-related functional magnetic resonance imaging to examine how neural activation differs for subsequently remembered and subsequently forgotten experiences. Results revealed that the ability to later remember a verbal experience is predicted by the magnitude of activation in left prefrontal and temporal cortices during that experience. These findings provide direct evidence that left prefrontal and temporal regions jointly promote memory formation for verbalizable events.


title: Short-Term Compassion Training Increases Prosocial Behavior in a Newly Developed Prosocial Game

abstract: Compassion has been suggested to be a strong motivator for prosocial behavior. While research has demonstrated that compassion training has positive effects on mood and health, we do not know whether it also leads to increases in prosocial behavior. We addressed this question in two experiments. In Experiment 1, we introduce a new prosocial game, the Zurich Prosocial Game (ZPG), which allows for repeated, ecologically valid assessment of prosocial behavior and is sensitive to the influence of reciprocity, helping cost, and distress cues on helping behavior. Experiment 2 shows that helping behavior in the ZPG increased in participants who had received short-term compassion training, but not in participants who had received short-term memory training. Interindividual differences in practice duration were specifically related to changes in the amount of helping under no-reciprocity conditions. Our results provide first evidence for the positive impact of short-term compassion training on prosocial behavior towards strangers in a training-unrelated task.


title: An Active Vision Architecture based on Iconic Representations

abstract: Abstract Active vision systems have the capability of continuously interacting with the environment. The rapidly changing environment of such systems means that it is attractive to replace static representations with visual routines that compute information on demand. Such routines place a premium on image data structures that are easily computed and used. The purpose of this paper is to propose a general active vision architecture based on efficiently computable iconic representations. This architecture employs two primary visual routines, one for identifying the visual image near the fovea (object identification), and another for locating a stored prototype on the retina (object location). This design allows complex visual behaviors to be obtained by composing these two routines with different parameters. The iconic representations are comprised of high-dimensional feature vectors obtained from the responses of an ensemble of Gaussian derivative spatial filters at a number of orientations and scales. These representations are stored in two separate memories. One memory is indexed by image coordinates while the other is indexed by object coordinates. Object location matches a localized set of model features with image features at all possible retinal locations. Object identification matches a foveal set of image features with all possible model features. We present experimental results for a near real-time implementation of these routines on a pipeline image processor and suggest relatively simple strategies for tackling the problems of occlusions and scale variations. We also discuss two additional visual routines, one for top-down foveal targeting using log-polar sensors and another for looming detection, which are facilitated by the proposed architecture.


title: Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks

abstract: A key component of a mobile robot system is the ability to localize itself accurately and, simultaneously, to build a map of the environment. Most of the existing algorithms are based on laser range finders, sonar sensors or artificial landmarks. In this paper, we describe a vision-based mobile robot localization and mapping algorithm, which uses scale-invariant image features as natural landmarks in unmodified environments. The invariance of these features to image translation, scaling and rotation makes them suitable landmarks for mobile robot localization and map building. With our Triclops stereo vision system, these landmarks are localized and robot ego-motion is estimated by least-squares minimization of the matched landmarks. Feature viewpoint variation and occlusion are taken into account by maintaining a view direction for each landmark. Experiments show that these visual landmarks are robustly matched, robot pose is estimated and a consistent three-dimensional map is built. As image features are...


title: The working principle of resistive tactile sensor cells

abstract: Tactile sensors systems are very important for today's service robotics. Designed as a holohedral cover for a robot, they are suitable for collision detection when working in unstructured environments, for human-machine interaction or, with a high resolution, as object sensors enabling dexterous hands for reactive gripping. In this paper, we explain construction and working principle of resistive tactile sensor cells. The latter is based on the change of the electrical resistance between a conductive polymer and at least two electrodes. For this effect, we formulate a model to describe the dependence between the sensor's electrical resistance and the applied load. The model enables further improvements of resistive tactile sensor cells.


title: Adaptive forwarding in named data networking

abstract: In Named Data Networking (NDN) architecture, packets carry data names rather than source or destination addresses. This change of paradigm leads to a new data plane: data consumers send out Interest packets, routers forward them and maintain the state of pending Interests, which is used to guide Data packets back to the consumers. NDN routers' forwarding process is able to detect network problems by observing the two-way traffic of Interest and Data packets, and explore multiple alternative paths without loops. This is in sharp contrast to today's IP forwarding process which follows a single path chosen by the routing process, with no adaptability of its own. In this paper we outline the design of NDN's adaptive forwarding, articulate its potential benefits, and identify open research issues.


title: A model transformation from NL to SBVR

abstract: In Requirement Engineering, requirements are usually written in sentences of natural language and natural languages are ambiguous and inconsistent, so the requirements written in natural languages also tend to be ambiguous. To avoid this problem of ambiguity we present an approach of model transformation to generate requirements based on SBVR (Semantics of Business Vocabulary and Business Rules). The information provided in source metamodel (NL) is automatically transformed into target metamodel (SBVR). SBVR metamodel can not only be processed by machine but also provides precise and reliable model for software design. The standard SBVR metamodel is already available but for natural language we proposed our own metamodel because there is no standard metamodel available for natural languages.


title: THE BUSINESS PERSPECTIVE OF CLOUD COMPUTING: ACTORS, ROLES, AND VALUE NETWORKS

abstract: With the rise of a ubiquitous provision of computing resources over the past years, cloud computing has been established as a prominent research topic. Many researchers, however, focus exclusively on the technical aspects of cloud computing, thereby neglecting the business opportunities and potentials cloud computing can offer. Enabled through this technology, new market players and business value networks arise and break up the traditional value chain of service provision. The focus of this paper therefore lies on the business aspects of cloud computing. Its main contribution - besides elaborating on a comprehensive definition of cloud computing - is the compilation of cloud actors and their roles within a new cloud computing value network and a conceptual development of a generic value network of different actors in cloud computing. Extending the prevailing technical perspective of cloud computing, this paper shifts the focus from an exclusive technological perspective to a broader understanding of business opportunities and business value.


title: Efficient Coding of Image Subbands using Blockbased Modified SPIHT

abstract: Real time transmission of images through handheld mobile/portable devices require an image coding algorithm that performs best at very low bit rate. A number of very successful wavelet-based image coding algorithms have been proposed in the literature. These algorithms are mainly categorized into zerotree and zeroblock approaches. Zerotree algorithms make use of the inter-subband correlations existing among different subbands of an image. The inefficiency of zeroblock algorithms lie in that thay does not take care of the inter-subband correlations existing among different subbands. The proposed BMSPIHT algorithm combines both the features of zerotree and zeroblock algorithms into a single algorithm. Simulation results show that the proposed BMSPIHT algorithm has a better coding efficiency over SPIHT at very low bit rates. Index terms: Image coding, SPIHT, SPECK , wavelet image coding, Embedded Coding, Block-based Modified SPIHT (BMSPIHT), PSNR, Spatial Orientation trees (SOT).


title: PredictionIO: a distributed machine learning server for practical software development

abstract: One of the biggest challenges for software developers to build real-world predictive applications with machine learning is the steep learning curve of data processing frameworks, learning algorithms and scalable system infrastructure. We present PredictionIO, an open source machine learning server that comes with a step-by-step graphical user interface for developers to (i) evaluate, compare and deploy scalable learning algorithms, (ii) tune hyperparameters of algorithms manually or automatically and (iii) evaluate model training status. The system also comes with an Application Programming Interface (API) to communicate with software applications for data collection and prediction retrieval. The whole infrastructure of PredictionIO is horizontally scalable with a distributed computing component based on Hadoop. The demonstration shows a live example and workflows of building real-world predictive applications with the graphical user interface of PredictionIO, from data collection, algorithm tuning and selection, model training and re-training to real-time prediction querying.


title: Personalizing PageRank for Word Sense Disambiguation

abstract: In this paper we propose a new graph-based method that uses the knowledge in a LKB (based on WordNet) in order to perform unsupervised Word Sense Disambiguation. Our algorithm uses the full graph of the LKB efficiently, performing better than previous approaches in English all-words datasets. We also show that the algorithm can be easily ported to other languages with good results, with the only requirement of having a wordnet. In addition, we make an analysis of the performance of the algorithm, showing that it is efficient and that it could be tuned to be faster.


title: Protecting DNS services from IP spoofing: SDN collaborative authentication approach

abstract: As DNS packet are mostly UDP-based, make it as a perfect tool for hackers to launch a well-known type of distributed denial of service (DDoS). The purpose of this attack is to saturate the DNS server availability and resources. This type of attack usually utilizes a large number of botnet and perform spoofing on the IP address of the targeted victim. We take a different approach for IP spoofing detection and mitigation strategies to protect the DNS server by utilizing Software Defined Networking (SDN). In this paper, we present CAuth, a novel mechanism that autonomously block the spoofing query packet while authenticate the legitimate query. By manipulating Openflow control message, we design a collaborative approach between client and server network. Whenever a server controller receives query packet, it will send an authentication packet back to the client network and later the client controller also replies via authentication packet back to the server controller. The server controller will only forward the query to the DNS server if it receives the replied authentication packet from the client. From the evaluation, CAuth instantly manage to block spoofing query packet while authenticate the legitimate query as soon as the mechanism started. Most notably, our mechanism designed with no changes in existing DNS application and Openflow protocol.


title: Comparative efficacy, speed, and adverse effects of three PTSD treatments: Exposure therapy, EMDR, and relaxation training.

abstract: The authors examined the efficacy, speed, and incidence of symptom worsening for 3 treatments of posttraumatic stress disorder (PTSD): prolonged exposure, relaxation training, or eye movement desensitization and reprocessing (EMDR;N = 60). Treatments did not differ in attrition, in the incidence of symptom worsening, or in their effects on numbing and hyperarousal symptoms. Compared with EMDR and relaxation training, exposure therapy (a) produced significantly larger reductions in avoidance and reexperiencing symptoms, (b) tended to be faster at reducing avoidance, and (c) tended to yield a greater proportion of participants who no longer met criteria for PTSD after treatment. EMDR and relaxation did not differ from one another in speed or efficacy.


title: Jointly optimized QoS-aware virtualization and routing in software defined networks

abstract: Software Defined Networks (SDNs) have been recognized as the next-generation networking paradigm that decouples the network control plane from the data forwarding plane. A logically centralized controller is responsible for all the control decisions and communication among the forwarding switches. However, current traffic engineering techniques and state-of-the-art routing algorithms do not effectively use the merits of SDNs, such as global centralized visibility, control and data plane decoupling, network management simplification and great computation capability. In this paper, a multi-tenancy management framework is proposed to enable the jointly optimized design of quality-of-services (QoSs)-aware virtualization and routing by tenant isolation and prioritization as well as flow allocation, fulfilling QoS requirements of tenants' applications. Specifically, a fine-grained network virtualization is first proposed to isolate and prioritize tenants through the design of network and switch hypervisors. Furthermore, a QoS-aware dynamic flow allocation is proposed to enable optimal flow routes selection upon the given network slicing with QoS provisioning. Finally, an adaptive feedback management tool, called QoS-aware Virtualization-enabled Routing (QVR), is proposed to combine virtualization with flow allocation and supports reliable and efficient transmissions with regards of time-varying QoS requirements, network topologies, and traffic statistics. Simulations confirm that QVR achieves much less shared edges, congestion latency, and traffic delay for multiple tenants, thus facilitating virtualization-enabled traffic engineering for multi-tenancy SDNs.


title: Beyond classical meta-analysis: can inadequately reported studies be included?

abstract: Classical meta-analysis requires the same data from each clinical trial, thus data-reporting must be of a high-quality. Imputation methods are used to include studies that provide incomplete information on variability and the fixed and random effects of a drug. Regression models can be used to include studies other than randomized placebo-controlled studies. In the example outlined here, the use of non-randomized single-arm studies and studies against comparator treatments has little influence on the estimation of the treatment effect in comparison with placebo, an effect that is based on the randomized placebo-controlled studies. The inclusion of other studies serves to increase the precision of the effect of the treatment compared with baseline. Although multiple imputation techniques enable a larger number of studies to be included, which will typically increase the precision of the estimated effect, a careful sensitivity analysis is also required.


title: A Neural Network Approach to Intention Modeling for User-Adapted Conversational Agents

abstract: Spoken dialogue systems have been proposed to enable a more natural and intuitive interaction with the environment and human-computer interfaces. In this contribution, we present a framework based on neural networks that allows modeling of the user's intention during the dialogue and uses this prediction to dynamically adapt the dialogue model of the system taking into consideration the user's needs and preferences. We have evaluated our proposal to develop a user-adapted spoken dialogue system that facilitates tourist information and services and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, and the quality perceived by the users.


title: Material classification by tactile sensing using surface textures

abstract: In this paper we describe an application of machine learning to distinguish between seven different materials, based on their surface texture. Applications of such a system includes quality assurance and estimating surface friction during manipulation tasks. A naive Bayes classifier is used to distinguish textures sensed by a bio-inspired artificial finger. The finger has randomly distributed strain gauges and Polyvinylidene Fluoride (PVDF) films embedded in silicone. Different textures induce different intensity of vibrations in the silicone. Textures can be distinguished by the presence of different frequencies in the signal. The data from the finger is pre-processed and the Fourier coefficients of the sensor outputs are used to learn a classifier for different textures. The performance of the classifier is evaluated against a naive time domain based learner. Preliminary results show that our classifier performs better.


title: Analysis of intercrossed open-source software repositories data in GitHub

abstract: The use of public repositories of software is becoming increasingly common as a strategy for reducing costs and streamlining software development processes. However, given the large amount of software available on such deposits, the task of selecting the components and applications of higher quality or that best meet the quality requirements of a product, has become a demanding task in time and effort. In this paper we present a tool to support the analysis and selection of components and software applications in public repositories, particularly GitHub.


title: A novel design of ultra-wideband strip-line power divider for 2–18 GHz

abstract: A new modified ultra-wideband in-phase power divider for frequency band 2-18 GHz has been designed, successfully fabricated and tested. The power divider is based on the coupled strip-lines. Only two balanced resistors are used in the proposed structure. So the power divider has very low insertion loss. The capacitive strips placed over the resistors have been introduced in the suggested design as the novel elements. Due to the introduced capacitive strips the isolation and impedance matching of the divider outputs were improved at the high frequencies. The manufactured power divider shows very high measured performances (amplitude imbalance is ±0.2 dB, phase imbalance is 5°, insertion loss is 0.4 dB, isolation is -18 dB, VSWR = 1.5.


title: Wideband Differential Bandpass Filters on Multimode Slotline Resonator With Intrinsic Common-Mode Rejection

abstract: In this paper, two kinds of wideband differential filters with intrinsic common-mode (CM) rejection are proposed. Both these filters are realized based on the multimode slotline resonators under the dual- and triple-mode resonance, respectively. For Filter 1, the microstrip lines with a pair of differential ports at the two sides are directly tapped on a slotline resonator. For Filter 2, the slotline resonator is connected with two pairs of microstrip open-ended stubs both at top and bottom layers at the beginning. In these two filters, the CM signals are effectively rejected during electromagnetic field conversion. Only the differential-mode performances are left to be focused on. A systematic design method is then presented with simplified circuit models in differential mode, and frequency responses of these models are synthesized and further mapped into physical dimensions. Transformers are used in the mapping procedure and their turn ratios are calculated to find the exact final dimensions or are properly used to realize desired performance with some unattainable impedance values in synthesized simplified models. Finally, two filters are designed, fabricated, and measured to support the design method.


title: How hard can it be? Estimating the difficulty of visual search in an image

abstract: We address the problem of estimating image difficulty defined as the human response time for solving a visual search task. We collect human annotations of image difficulty for the PASCAL VOC 2012 data set through a crowd-sourcing platform. We then analyze what human interpretable image properties can have an impact on visual search difficulty, and how accurate are those properties for predicting difficulty. Next, we build a regression model based on deep features learned with state of the art convolutional neural networks and show better results for predicting the groundtruth visual search difficulty scores produced by human annotators. Our model is able to correctly rank about 75% image pairs according to their difficulty score. We also show that our difficulty predictor generalizes well to new classes not seen during training. Finally, we demonstrate that our predicted difficulty scores are useful for weakly supervised object localization (8% improvement) and semi-supervised object classification (1% improvement).


title: A tail-resonance calibration technique for wide tuning range LC VCOs

abstract: Tail resonance at double the oscillation frequency is widely used in LC oscillators for reducing phase noise. The phase noise improvement is quite sensitive to shifts in the tail resonance frequency. A 6% error in the tail inductance can cause up to 4 dB increase in the close-in phase noise from the optimum value. Phase noise degradation is quite severe for widely tuned high frequency VCOs where modeling errors and changing output frequency contribute to substantial shift from the desired resonance frequency. We propose a technique to automatically calibrate the tail resonance to obtain the best phase noise. The amplitude of the oscillation across the tail resonant tank is sensed using a peak detector and the capacitor array used to tune this tank is adjusted to maximize this amplitude. This setting maximizes the phase noise improvement due to the tail resonance. A 4 stage left handed LC ring VCO with a tuning range of 8.5 GHz to 13.2 GHz maintains a fairly constant phase noise figure of merit of 190 dBc/Hz for offsets greater than 100 kHz, over the entire tuning range when the proposed technique is incorporated.


title: Relational duality: unsupervised extraction of semantic relations between entities on the web

abstract: Extracting semantic relations among entities is an important first step in various tasks in Web mining and natural language processing such as information extraction, relation detection, and social network mining. A relation can be expressed extensionally by stating all the instances of that relation or intensionally by defining all the paraphrases of that relation. For example, consider the ACQUISITION relation between two companies. An extensional definition of ACQUISITION contains all pairs of companies in which one company is acquired by another (e.g. (YouTube, Google) or (Powerset, Microsoft)). On the other hand we can intensionally define ACQUISITION as the relation described by lexical patterns such as X is acquired by Y, or Y purchased X, where X and Y denote two companies. We use this dual representation of semantic relations to propose a novel sequential co-clustering algorithm that can extract numerous relations efficiently from unlabeled data. We provide an efficient heuristic to find the parameters of the proposed coclustering algorithm. Using the clusters produced by the algorithm, we train an L1 regularized logistic regression model to identify the representative patterns that describe the relation expressed by each cluster. We evaluate the proposed method in three different tasks: measuring relational similarity between entity pairs, open information extraction (Open IE), and classifying relations in a social network system. Experiments conducted using a benchmark dataset show that the proposed method improves existing relational similarity measures. Moreover, the proposed method significantly outperforms the current state-of-the-art Open IE systems in terms of both precision and recall. The proposed method correctly classifies 53 relation types in an online social network containing 470; 671 nodes and 35; 652; 475 edges, thereby demonstrating its efficacy in real-world relation detection tasks.


title: A Learning Style Perspective to Investigate the Necessity of Developing Adaptive Learning Systems

abstract: Learning styles are considered to be one of the factors that need to be taken into account in developing adaptive learning systems. However, few studies have been conducted to investigate if students have the ability to choose the best-fit e-learning systems or content presentation styles for themselves in terms of learning style perspective. In this paper, we aim to investigate these issues by using two versions of an educational game developed based on the sequential/global dimension of the learning style proposed by Felder and Silverman. The experimental results showed that the choices made by the students were not related to their cognitive process or learning style; instead, most students made their choices by intuition based on personal preferences. Moreover, the students who learned with learning style-fit versions showed significantly better learning achievement than those who learned with non-fit versions. Consequently, it is concluded that students preferring one game over another does not necessarily mean that they will learn better with that version, revealing the importance and necessity of developing adaptive learning systems based on learning styles.


title: Endurance exercise performance: the physiology of champions

abstract: Efforts to understand human physiology through the study of champion athletes and record performances have been ongoing for about a century. For endurance sports three main factors--maximal oxygen consumption (.VO(2,max)), the so-called 'lactate threshold' and efficiency (i.e. the oxygen cost to generate a given running speed or cycling power output)--appear to play key roles in endurance performance. and lactate threshold interact to determine the 'performance .VO(2)' which is the oxygen consumption that can be sustained for a given period of time. Efficiency interacts with the performance .VO(2) to establish the speed or power that can be generated at this oxygen consumption. This review focuses on what is currently known about how these factors interact, their utility as predictors of elite performance, and areas where there is relatively less information to guide current thinking. In this context, definitive ideas about the physiological determinants of running and cycling efficiency is relatively lacking in comparison with .VO(2,max) and the lactate threshold, and there is surprisingly limited and clear information about the genetic factors that might pre-dispose for elite performance. It should also be cautioned that complex motivational and sociological factors also play important roles in who does or does not become a champion and these factors go far beyond simple physiological explanations. Therefore, the performance of elite athletes is likely to defy the types of easy explanations sought by scientific reductionism and remain an important puzzle for those interested in physiological integration well into the future.


title: Fourier-Series-Based Phase Delay Compensation of Brushless DC Motor Systems

abstract: Brushless direct-current (BLDC) motors are being regarded as a standard of actuation methods for precise and heavy-duty applications, such as robots and vehicles. To improve the efficiency and torque performance of the BLDC motors, various design parameters (e.g., the magnetic flux density, core material, etc.) have been investigated. Phase delay compensation (PDC), also often called phase advance angle control, is also an attractive method that improves the actuation efficiency of BLDC motors without any mechanical modification of the motors. Due to the inductance of the BLDC motor windings, the magnetic flux of a rotor and that of a stator are not generated in phase, which causes a phase delay in magnetization of the coil. Due to this phenomenon, the efficiency of the BLDC motor is remarkably decreased, in particular at a high speed, and the PDC can be applied to compensate for this phase delay. In this paper, Fourier-series-based PDC is proposed to improve the efficiency of the motor; the phase delay angle is calculated based on the dynamic model of the motor windings and its Fourier series. The proposed Fourier-series-based PDC is realized by designing an inverter circuit that switches the phase voltages according to the proposed algorithm. The performance of PDC is verified at experiments in terms of not only T–N curve but also control performance. According to the experimental results, PDC improves output torque, efficiency, and control performance.


title: NomLoc: Calibration-Free Indoor Localization with Nomadic Access Points

abstract: Newly popular indoor location-based services (ILBS), when integrated with commerce and public safety, offer a promising land for wireless indoor localization technologies. WLAN is suggested to be one of the most potential candidates owing to its prevalent infrastructure (i.e., access points (APs)) and low cost. However, the overall performance can be greatly degraded by the spatial localizability variance problem, i.e., the localization accuracy across various locations may have significant differences given any fixed AP deployment. As a result, it brings in user experience inconsistency which is unfavorable for ILBS. In this paper, we propose NomLoc - an indoor localization system using nomadic APs to address the performance variance problem. The key insight of NomLoc is to leverage the mobility of nomadic APs to dynamically adjust the WLAN network topology. A space partition (SP)-based localization algorithm is tailored for NomLoc to perform calibration-free positioning. Moreover, fine-grained channel state information (CSI) is employed to mitigate the performance degradation of the SP-based method due to multipath and none-line-of-sight (NLOS) effects. We have implemented the NomLoc system with off-the-shelf devices and evaluated the performance in two typical indoor environments. The results show that NomLoc can greatly mitigate spatial localizability variance and improve localization accuracy with the assistance of nomadic APs as compared with the corresponding static AP deployment. Moreover, it is robust to the position error of nomadic APs.


abstract: Lappeenranta University of Technology Acta Universitatis Lappeenrantaensis 570 Samuli Kallio Modeling and Parameter Estimation of Double-Star Permanent Magnet Synchronous Machines Lappeenranta 2014 68 p. ISBN 978-952-265-563-9 ISBN 978-952-265-564-6 (PDF) ISSN-L 1456-4491, ISSN 1456-4491 The power rating of wind turbines is constantly increasing; however, keeping the voltage rating at the low-voltage level results in high kilo-ampere currents. An alternative for increasing the power levels without raising the voltage level is provided by multiphase machines. Multiphase machines are used for instance in ship propulsion systems, aerospace applications, electric vehicles, and in other high-power applications including wind energy conversion systems. A machine model in an appropriate reference frame is required in order to design an efficient control for the electric drive. Modeling of multiphase machines poses a challenge because of the mutual couplings between the phases. Mutual couplings degrade the drive performance unless they are properly considered. In certain multiphase machines there is also a problem of high current harmonics, which are easily generated because of the small current path impedance of the harmonic components. However, multiphase machines provide special characteristics compared with the three-phase counterparts: Multiphase machines have a better fault tolerance, and are thus more robust. In addition, the controlled power can be divided among more inverter legs by increasing the number of phases. Moreover, the torque pulsation can be decreased and the harmonic frequency of the torque ripple increased by an appropriate multiphase configuration. By increasing the number of phases it is also possible to obtain more torque per RMS ampere for the same volume, and thus, increase the power density. In this doctoral thesis, a decoupled d–q model of double-star permanent-magnet (PM) synchronous machines is derived based on the inductance matrix diagonalization. The double-star machine is a special type of multiphase machines. Its armature consists of two three-phase winding sets, which are commonly displaced by 30 electrical degrees. In this study, the displacement angle between the sets is considered a parameter. The diagonalization of the inductance matrix results in a simplified model structure, in which the mutual couplings between the reference frames are eliminated. Moreover, the current harmonics are mapped into a reference frame, in which they can be easily controlled. The work also presents methods to determine the machine inductances by a finite-element analysis and by voltage-source inverters on-site. The derived model is validated by experimental results obtained with an example double-star interior PM (IPM) synchronous machine having the sets displaced by 30 electrical degrees. The derived transformation, and consequently, the decoupled d–q machine model, are shown to model the behavior of an actual machine with an acceptable accuracy. Thus, the proposed model is suitable to be used for the model-based control design of electric drives consisting of double-star IPM synchronous machines.


title: Popularity Dynamics and Intrinsic Quality in Reddit and Hacker News

abstract: In this paper we seek to understand the relationship between the online popularity of an article and its intrinsic quality. Prior experimental work suggests that the relationship between quality and popularity can be very distorted due to factors like social influence bias and inequality in visibility. We conduct a study of popularity on two different social news aggregators, Reddit and Hacker News. We define quality as the number of votes an article would have received if each article was shown, in a bias-free way, to an equal number of users. We propose a simple Poisson regression method to estimate this quality metric from time-series voting data. We validate our methods on data from Reddit and Hacker News, as well the experimental data from prior work. Using these estimates, we find that popularity on Reddit and Hacker News is a relatively strong reflection of intrinsic quality.


title: A deep learning approach for generalized speech animation

abstract: We introduce a simple and effective deep learning approach to automatically generate natural looking speech animation that synchronizes to input speech. Our approach uses a sliding window predictor that learns arbitrary nonlinear mappings from phoneme label input sequences to mouth movements in a way that accurately captures natural motion and visual coarticulation effects. Our deep learning approach enjoys several attractive properties: it runs in real-time, requires minimal parameter tuning, generalizes well to novel input speech sequences, is easily edited to create stylized and emotional speech, and is compatible with existing animation retargeting approaches. One important focus of our work is to develop an effective approach for speech animation that can be easily integrated into existing production pipelines. We provide a detailed description of our end-to-end approach, including machine learning design decisions. Generalized speech animation results are demonstrated over a wide range of animation clips on a variety of characters and voices, including singing and foreign language input. Our approach can also generate on-demand speech animation in real-time from user speech input.


title: Dual Swap Disentangling

abstract: Learning interpretable disentangled representations is a crucial yet challenging task. In this paper, we propose a weakly semi-supervised method, termed as Dual Swap Disentangling (DSD), for disentangling using both labeled and unlabeled data. Unlike conventional weakly supervised methods that rely on full annotations on the group of samples, we require only limited annotations on paired samples that indicate their shared attribute like the color. Our model takes the form of a dual autoencoder structure. To achieve disentangling using the labeled pairs, we follow a"encoding-swap-decoding"process, where we first swap the parts of their encodings corresponding to the shared attribute and then decode the obtained hybrid codes to reconstruct the original input pairs. For unlabeled pairs, we follow the"encoding-swap-decoding"process twice on designated encoding parts and enforce the final outputs to approximate the input pairs. By isolating parts of the encoding and swapping them back and forth, we impose the dimension-wise modularity and portability of the encodings of the unlabeled samples, which implicitly encourages disentangling under the guidance of labeled pairs. This dual swap mechanism, tailored for semi-supervised setting, turns out to be very effective. Experiments on image datasets from a wide domain show that our model yields state-of-the-art disentangling performances.


title: Virtualization of radio access network by Virtual Machine and Docker: Practice and performance analysis

abstract: Software defined networking (SDN) and network function virtualization (NFV) are the embraced technologies for the backhauling of future 5G networks. Virtual Machine (VM) and Docker container based deployments have received much attention. This paper presents the virtualization of a prototyped software defined radio access network (RAN) architecture by using VMs and Docker containers. In addition, it provides an analytical model for the generalized software defined RAN architecture with the practice of VM based and Docker container based implementations. Using measurements obtained from the two testbeds and the introduced queuing model, we compare their performances and analyze the two different architectures. Results verify the superiority of the Docker technology. Some observations from the behavior of the testbeds are concluded for a better understanding of the VM and Docker container based technologies for the future development of 5G SDN controller.


title: Trusted Display on Untrusted Commodity Platforms

abstract: A trusted display service assures the confidentiality and authenticity of content output by a security-sensitive application and thus prevents a compromised commodity operating system or application from surreptitiously reading or modifying the displayed output. Past approaches have failed to provide trusted display on commodity platforms that use modern graphics processing units (GPUs). For example, full GPU virtualization encourages the sharing of GPU address space with multiple virtual machines {\em without} providing adequate hardware protection mechanisms; e.g., address-space separation and instruction execution control. This paper proposes a new trusted display service that has a minimal trusted code base and maintains full compatibility with commodity computing platforms. The service relies on a GPU separation kernel that (1) defines different types of GPU objects, (2) mediates access to security-sensitive objects, and (3) emulates object whenever required by commodity-platform compatibility. The separation kernel employs a new address-space separation mechanism that avoids the challenging problem of GPU instruction verification without adequate hardware support. The implementation of the trusted-display service has a code base that is two orders of magnitude smaller than other similar services, such as those based on full GPU virtualization. Performance measurements show that the trusted-display overhead added over and above that of the underlying trusted system is fairly modest.


title: Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks

abstract: We propose a method for embedding two-dimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms conventional regression-based geolocation and provides a better estimate of uncertainty. We also show the effectiveness of the representation for predicting words from location in lexical dialectology, and evaluate it using the DARE dataset.


title: LambdaMerge: merging the results of query reformulations

abstract: Search engines can automatically reformulate user queries in a variety of ways, often leading to multiple queries that are candidates to replace the original. However, selecting a replacement can be risky: a reformulation may be more effective than the original or significantly worse, depending on the nature of the query, the source of reformulation candidates, and the corpus. In this paper, we explore methods to mitigate this risk by issuing several versions of the query (including the original) and merging their results. We focus on reformulations generated by random walks on the click graph, a method that can produce very good reformulations but is also variable and prone to topic drift. Our primary contribution is λ-Merge, a supervised merging method that is trained to directly optimize a retrieval metric (such as NDCG or MAP) using features that describe both the reformulations and the documents they return. In experiments on Bing data and GOV2, λ-Merge outperforms the original query and several unsupervised merging methods. λ-Merge also outperforms a supervised method to predict and select the best single formulation, and is competitive with an oracle that always selects the best formulation.


title: Quantification of SiO_2 sintering additive in YAG transparent ceramics by laser-induced breakdown spectroscopy (LIBS)

abstract: Transparent ceramics are important optical materials with applications in street lighting, high-strength windows, electro- and magneto-optical isolators, high-power laser gain media and radiation detectors. Their fabrication most often relies on powder densification techniques carried out at high temperatures, sometimes promoted by sintering additives. Here, we describe the application of laser-induced breakdown spectroscopy (LIBS) for following the concentration levels of silica used as a sintering agent in the fabrication of yttrium aluminum garnet (YAG) transparent ceramics. The sensitivity limit of our protocol reaches a few tens of ppm of silica in YAG ceramic samples, showing that LIBS can be implemented reliably for the rapid assessment of sintering additives in advanced ceramic processing.


title: An empirical correction for the MWR brightness temperature smear effect

abstract: The Microwave Radiometer (MWR), on board of the SAC-D/Aquarius satellite, is a Dicke radiometer, operating at 23.8 and 36.5 GHz that was developed by Argentine Space Agency, CoNAE. Since the first earth surface T B images, there has been an anomalous effect when observing strong contrast radiometric scenes, such as land/water crossings. This effect is present in all push-broom beams and results in a “smearing” of T B observations near high contrast areas. This paper presents the initial findings on this MWR brightness temperature anomaly that is best described as a coupling of sequential MWR feed horn measurements. In the present work, an empirical approach is used to mitigate this anomalous effect.


Effect of Training Program Regarding First Aid and Basic Life Support on the Management of Educational Risk injuries among Students in Industrial Secondary Schools


title: Product disassembly sequence planning based on cockroach swarm optimization

abstract: In order to optimize disassembly sequence for waste or malfunctioning products,a product disassembly hybrid graph model is established,which describes the connection and precedence relationships between the product parts.The disassembly condition is deduced based on the hybrid graph model.Then,all the feasible disassembly sequences are obtained through the geometry inference method.The target function is constituted and the cockroach swarm optimization algorithm which suits the disassembly sequence planning is constructed.Further,by inputting some disassembly sequences and other controlling parameters,the optimal valid disassembly sequence can be obtained based on the cockroach swarm optimization.Finally,a case study is given to illustrate the validity of the proposed method and prove that the cockroach swarm optimization is superior to the particle swarm optimization.


title: Active deep learning method for semi-supervised sentiment classification

abstract: Abstract In natural language processing community, sentiment classification based on insufficient labeled data is a well-known challenging problem. In this paper, a novel semi-supervised learning algorithm called active deep network (ADN) is proposed to address this problem. First, we propose the semi-supervised learning framework of ADN. ADN is constructed by restricted Boltzmann machines (RBM) with unsupervised learning based on labeled reviews and abundant of unlabeled reviews. Then the constructed structure is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Second, in the semi-supervised learning framework, we apply active learning to identify reviews that should be labeled as training data, then using the selected labeled reviews and all unlabeled reviews to train ADN architecture. Moreover, we combine the information density with ADN, and propose information ADN (IADN) method, which can apply the information density of all unlabeled reviews in choosing the manual labeled reviews. Experiments on five sentiment classification datasets show that ADN and IADN outperform classical semi-supervised learning algorithms, and deep learning techniques applied for sentiment classification.


title: Location and Navigation Support for Emergency Responders: A Survey

abstract: As this overview of products and projects shows, preinstalled location systems, wireless sensor networks, and inertial sensing all have benefits and drawbacks when considering emergency response requirements.


title: Mathematical models of malaria - a review

abstract: Mathematical models have been used to provide an explicit framework for understanding malaria transmission dynamics in human population for over 100 years. With the disease still thriving and threatening to be a major source of death and disability due to changed environmental and socio-economic conditions, it is necessary to make a critical assessment of the existing models, and study their evolution and efficacy in describing the host-parasite biology. In this article, starting from the basic Ross model, the key mathematical models and their underlying features, based on their specific contributions in the understanding of spread and transmission of malaria have been discussed. The first aim of this article is to develop, starting from the basic models, a hierarchical structure of a range of deterministic models of different levels of complexity. The second objective is to elaborate, using some of the representative mathematical models, the evolution of modelling strategies to describe malaria incidence by including the critical features of host-vector-parasite interactions. Emphasis is more on the evolution of the deterministic differential equation based epidemiological compartment models with a brief discussion on data based statistical models. In this comprehensive survey, the approach has been to summarize the modelling activity in this area so that it helps reach a wider range of researchers working on epidemiology, transmission, and other aspects of malaria. This may facilitate the mathematicians to further develop suitable models in this direction relevant to the present scenario, and help the biologists and public health personnel to adopt better understanding of the modelling strategies to control the disease


title: Trend detection in folksonomies

abstract: As the number of resources on the web exceeds by far the number of documents one can track, it becomes increasingly difficult to remain up to date on ones own areas of interest. The problem becomes more severe with the increasing fraction of multimedia data, from which it is difficult to extract some conceptual description of their contents. One way to overcome this problem are social bookmark tools, which are rapidly emerging on the web. In such systems, users are setting up lightweight conceptual structures called folksonomies, and overcome thus the knowledge acquisition bottleneck. As more and more people participate in the effort, the use of a common vocabulary becomes more and more stable. We present an approach for discovering topic-specific trends within folksonomies. It is based on a differential adaptation of the PageRank algorithm to the triadic hypergraph structure of a folksonomy. The approach allows for any kind of data, as it does not rely on the internal structure of the documents. In particular, this allows to consider different data types in the same analysis step. We run experiments on a large-scale real-world snapshot of a social bookmarking system.


title: Background modelling and background subtraction performance for object detection

abstract: Moving object detection in video applications is usually performed based on techniques such as background subtraction, optical flow and temporal differencing. The most popular literature technique approach to detect moving object from video sequences is background subtraction. This approach utilized mathematical model of static background and comparing it with every new frame of video sequence. In this paper, background subtraction technique using Mixture of Gaussian (MoG) method is conducted for detection of moving object at outdoor environment. Focus is specified at the five parameters of MoG namely background component weight threshold (T S ), standard deviation scaling factor (D), user-define learning rate (α), Total number of Gaussian components (K) and Maximum number of components M in the background model (M) to give significant impact in producing the optimize background subtraction process. Experimental results showed that by varying each of the parameter can produce acceptable results that enable us to propose suitable parameter range of each parameter for detection of moving object in an outdoor environment.


title: A model to support sign language content development for digital television

abstract: This paper proposes a model to support sign language content development and deployment in digital television scenarios. The model objective is to overcome traditional video limitations on the production of signed content and improve accessibility on digital television. Previous work on sign and gesture recognition, sign notation systems and avatar animation are reviewed. Sign communication requirements are investigated and adequate algorithms are identified to enable sign language processing and transmission. An application interface is proposed in order to access the model functionalities. The final discussion lists the benefits and limitations of the proposed model.


title: Applications of corpus-based semantic similarity and word segmentation to database schema matching

abstract: In this paper, we present a method for database schema matching: the problem of identifying elements of two given schemas that correspond to each other. Schema matching is useful in e-commerce exchanges, in data integration/warehousing, and in semantic web applications. We first present two corpus-based methods: one method is for determining the semantic similarity of two target words and the other is for automatic word segmentation. Then we present a name-based element-level database schema matching method that exploits both the semantic similarity and the word segmentation methods. Our word similarity method uses pointwise mutual information (PMI) to sort lists of important neighbor words of two target words; the words which are common in both lists are selected and their PMI values are aggregated to calculate the relative similarity score. Our word segmentation method uses corpus type frequency information to choose the type with maximum length and frequency from "desegmented" text. It also uses a modified forward---backward matching technique using maximum length frequency and entropy rate if any non-matching portions of the text exist. Finally, we exploit both the semantic similarity and the word segmentation methods in our proposed name-based element-level schema matching method. This method uses a single property (i.e., element name) for schema matching and nevertheless achieves a measure score that is comparable to the methods that use multiple properties (e.g., element name, text description, data instance, context description). Our schema matching method also uses normalized and modified versions of the longest common subsequence string matching algorithm with weight factors to allow for a balanced combination. We validate our methods with experimental studies, the results of which suggest that these methods can be a useful addition to the set of existing methods.


title: Cyber-Bullying, Personality and Coping among Pre-Adolescents

abstract: The aim of this study was to investigate the association of personality Five Factor Model, coping and cyber-bullying/victimization experiences among 300 Greek pre-adolescent students attending the upper two primary school grades. Boys reported more frequent involvement in cyber-bullying incidents, while there were no significant gender differences in terms of cyber-victimization. In terms of participant roles, non-involved students scored higher in Conscientiousness, and cyber-bully/victims in Emotional Instability. The latter also tended to use maladaptive coping strategies more frequently, while cyber-bullies reported using more aggression and resignation to cope with interpersonal conflicts. Multiple regression analyses indicated that low conscientious boys who use passive avoidance and aggression were more likely to cyber-bully, while those who use aggression, passive avoidance and situation control to cope with interpersonal stressors were more likely to be cyber-victimized. Implications of the findings are discussed.


title: Extending of the Kinect device functionality and the corresponding database

abstract: This paper deals with a creation of the RGB-D database by using Microsoft Kinect device. One of the main uses of Kinect is measurement and subsequent creation the so-called depth maps of the 3D scenes. The maps obtained by Kinect can be improved. Existence of databases suitable for the experiment is very important for research. One of the possible research directions is use of infrared version of the investigated scene for improvement of the depth map. However, the databases of the Kinect data which would contain the corresponding infrared images do not exist. Therefore, our aim was to create such database. We want to increase the usability of the database by adding stereo images. Moreover, the same scenes were captured by Kinect v2. It was also investigated the impact of simultaneous use Kinect v1 and Kinect v2 to improve depth map investigated the scene. The database contains sequences of objects on turntable and simple scenes containing several objects.


title: Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion

abstract: Community detection is an important task in network analysis. A community (also referred to as a cluster) is a set of cohesive vertices that have more connections inside the set than outside. In many social and information networks, these communities naturally overlap. For instance, in a social network, each vertex in a graph corresponds to an individual who usually participates in multiple communities. In this paper, we propose an efficient overlapping community detection algorithm using a seed expansion approach. The key idea of our algorithm is to find good seeds, and then greedily expand these seeds based on a community metric. Within this seed expansion method, we investigate the problem of how to determine good seed nodes in a graph. In particular, we develop new seeding strategies for a personalized PageRank clustering scheme that optimizes the conductance community score. An important step in our method is the neighborhood inflation step where seeds are modified to represent their entire vertex neighborhood. Experimental results show that our seed expansion algorithm outperforms other state-of-the-art overlapping community detection methods in terms of producing cohesive clusters and identifying ground-truth communities. We also show that our new seeding strategies are better than existing strategies, and are thus effective in finding good overlapping communities in real-world networks.


title: A deep-network solution towards model-less obstacle avoidance

abstract: Obstacle avoidance is the core problem for mobile robots. Its objective is to allow mobile robots to explore an unknown environment without colliding into other objects. It is the basis for various tasks, e.g. surveillance and rescue, etc. Previous approaches mainly focused on geometric models (such as constructing local cost-maps) which could be regarded as low-level intelligence without any cognitive process. Recently, deep learning has made great breakthroughs in computer vision, especially for recognition and cognitive tasks. It takes advantage of the hierarchical models inspired by human brain structures. However, it is a fact that deep learning, up till now, has seldom been used for controlling and decision making. Inspired by the advantages of deep learning, we take indoor obstacle avoidance as example to show the effectiveness of a hierarchical structure that fuses a convolutional neural network (CNN) with a decision process. It is a highly compact network structure that takes raw depth images as input, and generates control commands as network output, by which a model-less obstacle avoidance behavior is achieved. We test our approach in real-world indoor environments. The new findings and results are reported at the end of the paper.


title: A Latent Variable Recurrent Neural Network for Discourse Relation Language Models

abstract: This paper presents a novel latent variable recurrent neural network architecture for jointly modeling sequences of words and (possibly latent) discourse relations between adjacent sentences. A recurrent neural network generates individual words, thus reaping the benefits of discriminatively-trained vector representations. The discourse relations are represented with a latent variable, which can be predicted or marginalized, depending on the task. The resulting model can therefore employ a training objective that includes not only discourse relation classification, but also word prediction. As a result, it outperforms state-of-the-art alternatives for two tasks: implicit discourse relation classification in the Penn Discourse Treebank, and dialog act classification in the Switchboard corpus. Furthermore, by marginalizing over latent discourse relations at test time, we obtain a discourse informed language model, which improves over a strong LSTM baseline.


title: Towards the Success of ERP Systems: Case Study in Two Moroccan Companies

abstract: For over a decade, ERP systems in Morocco have shown a significant increase. Companies make large investments in these scale systems in order to anticipate positive impacts on their organization. Measuring ERP systems success becomes an important criterion for the stakeholders in justifying the associated investment. In this paper, we will examine the success of these systems by using a qualitative research method based on a case study approach. We will provide an insight into how two Moroccan organizations perceive the success of their ERP systems. Findings indicated that Information, system quality and net benefits are the three main dimensions of success.


title: Broadband Quasi-Yagi Antenna for WiFi and WiMax Applications

abstract: In this paper, we present a broadband quasi-Yagi antenna. Good impedance matching is obtained by using parasitic elements. The antenna has been designed and successfully measured. Experimental results show that the 10 dB return loss bandwidth of this antenna is 50% operating from 2.3 GHz to 3.8 GHz. We obtain very flat gain (around 5 dB) over the entire bandwidth. For the design and optimization of antennas, we use HFSS CAD software from ANSOFT.


title: Complex Problem Solving : Identity Matching Based on Social Contextual Information *

abstract: Complex problems like drug crimes often involve a large number of variables interacting with each other. A complex problem may be solved by breaking it into parts (i.e., sub-problems), which can be tackled more easily. The identity matching problem, for example, is a part of the problem of drug and other types of crimes. It is often encountered during crime investigations when a single criminal is represented by multiple identity records in law enforcement databases. Because of the discrepancies among these records, a single criminal may appear to be different people. Following Enid Mumford’s three-stage problem solving framework, we design a new method to address the problem of criminal identity matching for fighting drug-related crimes. Traditionally, the complexity of criminal identity matching was reduced by treating criminals as isolated individuals who maintain certain personal identities. In this research, we recognize the intrinsic complexity of the problem and treat criminals as interrelated rather than isolated individuals. In other words, we take into consideration of the social relationships between criminals during the matching process. We study not only the personal identities but also the social identities of criminals. Evaluation results were quite encouraging and showed that combining social features with personal features could improve the performance of criminal identity matching. In particular, the social features become more useful when data contain many missing values for personal attributes.


title: Face Recognition Using the Discrete Cosine Transform

abstract: An accurate and robust face recognition system was developed and tested. This system exploits the feature extraction capabilities of the discrete cosine transform (DCT) and invokes certain normalization techniques that increase its robustness to variations in facial geometry and illumination. The method was tested on a variety of available face databases, including one collected at McGill University. The system was shown to perform very well when compared to other approaches.


title: OntoSenticNet: A Commonsense Ontology for Sentiment Analysis

abstract: In this work, we present OntoSenticNet, a commonsense ontology for sentiment analysis based on SenticNet, a semantic network of 100,000 concepts based on conceptual primitives. The key characteristics of OntoSenticNet are: (i) the definition of precise conceptual hierarchy and properties associating concepts and sentiment values; (ii) the support for connecting external information (e.g., word embedding, domain information, and different polarity representations) to each individual defined within the ontology; and (iii) the capability of associating each concept with annotations contained in external resources (e.g., documents and multimodal resources).


title: A hybrid approach for improving predictive accuracy of collaborative filtering algorithms

abstract: Recommender systems represent a class of personalized systems that aim at predicting a user's interest on information items available in the application domain, operating upon user-driven ratings on items and/or item features. One of the most widely used recommendation methods is collaborative filtering that exploits the assumption that users who have agreed in the past in their ratings on observed items will eventually agree in the future. Despite the success of recommendation methods and collaborative filtering in particular, in real-world applications they suffer from the insufficient number of available ratings, which significantly affects the accuracy of prediction. In this paper, we propose recommendation approaches that follow the collaborative filtering reasoning and utilize the notion of lifestyle as an effective user characteristic that can group consumers in terms of their behavior as indicated in consumer behavior and marketing theory. Emanating from a basic lifestyle-based recommendation algorithm we incrementally proceed to the development of hybrid recommendation approaches that address certain dimensions of the sparsity problem and empirically evaluate them providing further evidence of their effectiveness.


title: Suspended plate antennas with shorting strips and slots

abstract: The suspended plate antennas (SPAs) with shorting strips and slots are presented numerically and validated experimentally. Effects of narrow shorting strips on the impedance and radiation performances of the SPAs are studied. The symmetric use of shorting and feeding strips makes the SPAs structurally symmetrical, which is conducive to the radiation performance of the SPAs. Also, effects of narrow rectangular slots cut in the center or the edges of the plates are investigated. Cutting the slots reduces size, realizes dual-band operation, and improves radiation performance of SPAs.


title: Infant Manual Exploration of Objects, Surfaces, and Their Interrelations

abstract: Although a considerable amount is known about the development of object manipulation during the 1st year, less is known about how infants manually explore surfaces and relate objects to surfaces. To address these issues, 60 infants (20 each at 6, 8, and 10 months of age) were presented hard and soft objects on tabletop surfaces, which varied in terms of their material properties. Tabletop surfaces were either liquid, discontinuous, flexible, or rigid. Results indicated that infants explored the objects, surfaces, and their interactions selectively, tailoring their manual actions to the material properties of the objects or surfaces. In some instances, selectivity increased with age. The implications of these findings for understanding the origins of problem solving and tool use are considered. Over the course of the first year, infants acquire a rich and varied repertoire of actions for manipulating objects. Moreover, infants apply these actions selectively, tailoring a particular kind of movement to an object’s unique physical properties. For instance, by the middle of the second half-year, infants finger textured objects more than nontextured ones, shake or bang sounding objects more than nonsounding ones and press pliable objects more than nonpliable ones (Bushnell & Boudreau, 1993; Gibson & Walker, 1984; Lockman & McHale, 1989; Molina & Jouen, 1998; Palmer, 1989; Ruff, 1984). Collectively, these types of achievements belie accounts of sensorimotor development that suggest that object manipulation is undifferentiated during much of the first year. According to such accounts, lack of specificity in infants’ manual behaviors stems from a cognitive gap: Infants are not yet fully able to differentiate


title: A Decision-Theoretic Rough Set Approach for Dynamic Data Mining

abstract: Uncertainty and fuzziness generally exist in real-life data. Approximations are employed to describe the uncertain information approximately in rough set theory. Certain and uncertain rules are induced directly from different regions partitioned by approximations. Approximation can further be applied to data-mining-related task, $\hbox{e.g.}$ , attribute reduction. Nowadays, different types of data collected from different applications evolve with time, especially new attributes may appear while new objects are added. This paper presents an approach for dynamic maintenance of approximations $\hbox{w.r.t.}$ objects and attributes added simultaneously under the framework of decision-theoretic rough set (DTRS). Equivalence feature vector and matrix are defined first to update approximations of DTRS in different levels of granularity. Then, the information system is decomposed into subspaces, and the equivalence feature matrix is updated in different subspaces incrementally. Finally, the approximations of DTRS are renewed during the process of updating the equivalence feature matrix. Extensive experimental results verify the effectiveness of the proposed methods.


title: Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation

abstract: In this paper, we investigate the use of brain activity for person authentication. It has been shown in previous studies that the brainwave pattern of every individual is unique and that the electroencephalogram (EEG) can be used for biometric identification. EEG-based biometry is an emerging research topic and we believe that it may open new research directions and applications in the future. However, very little work has been done in this area and was focusing mainly on person identification but not on person authentication. Person authentication aims to accept or to reject a person claiming an identity, i.e., comparing a biometric data to one template, while the goal of person identification is to match the biometric data against all the records in a database. We propose the use of a statistical framework based on Gaussian mixture models and maximum a posteriori model adaptation, successfully applied to speaker and face authentication, which can deal with only one training session. We perform intensive experimental simulations using several strict train/test protocols to show the potential of our method. We also show that there are some mental tasks that are more appropriate for person authentication than others


title: Predicting degree-completion time with data mining

abstract: Data mining in academic databases nowadays used for analyzing patterns and gaining new useful knowledge. This paper tries to predict the degree-completion time of bachelor's degree students using data mining technique and algorithms especially C4.5 and naive Bayes classifier algorithm, and measure the algorithms accuracy, precision, and recall percentages for both algorithms also exploring some factors that assume in theory have some impact on the model. The result from given dataset to build the models shows that C4.5 algorithm better than naive Bayes classifier algorithm with 78% accuracy, 85% weighted mean class precision, and 65% weighted mean class recall. This research can be expanded with different data mining algorithms or other related attributes that have some effects to the degree-completion time.


title: INSTRUCTIVE SUFFICIENCY: RE-READING THE GOVERNESS THROUGH AGNES GREY

abstract: In nineteenth-century England, a governess was the primary educator of male and female children in a middle-class household; particularly by virtue of her work educating girls through their mid-teens, the governess simultaneously effected and disrupted the transparent transmission of class and gender identity between generations of middle-class women. Recent scholarship has de-emphasized this pedagogical function in its readings of the figure.1 Discussion has centered, instead, on the ways in which the governess repre sented a crisis for early Victorian definitions of bourgeois femininity (which centered on middle-class women's financial dependence and apparent leisure) because she was a middle-class woman who earned her own living. But to read the figure of the Victorian governess through Anne Bronte's Agnes Grey (1847) is to see that she troubled bourgeois hegemony because of her job. I contend that the governess disturbed the early Victorians not only because she blurred the boundary between the separate spheres, but also because she dramatized the potentially illimitable effects of education. When you hire someone to teach your children, how do you ensure that she is teaching what you wish and as you wish? * * * This desire to relinquish responsibility while simultaneously maintaining control re sulted in an image of an ideal governess who could adapt herself to the requirements of any particular family. It is this image which I elaborate here. The ideal governess's most significant qualities were her ability to make her presence unnoticeable and her influence over the children a perfect extension of their parents'. By eliding the individual who had to fill the role of "governess," this doctrine made the governess's influence over the children ? her instruction of them ? sufficient rather than excessive. Such a formulation contained the implication of her supplemental presence ? that the bourgeois family, and particularly the bourgeois mother, were not doing their jobs ? by making her a cipher for maternal pedagogy, and made adaptability and a strange sort of transparency the govern


title: Smart homes can be smarter

abstract: Smart Homes have become firmly established as an active research area with potential for huge social and economic benefits. The concept of a Smart Home refers to the enrichment of a living environment with technology in order to offer improved habitual support to its inhabitants and therefore an improved quality of life for them. ::: ::: In this article we purport how advances from the device and technological side have not necessarily been matched with a similar level of development in processing of the information recorded within the living environment from an algorithmic or ‘intelligent' perspective. We surmise how traditional areas in Artificial Intelligence can bridge this gap and improve the experience for the user within a Smart Home.


title: A High-Power Capacitor Charger Using IGCTs in a Boost Converter Topology

abstract: A compact high-power capacitor charger with a boost converter topology is developed using high-power integrated gate-commutated thyristors as switches and a high-energy density coil as inductive buffer storage. This paper describes the design and the corresponding experimental investigations of the charger in combination with a powerful dc current source, a flywheel energy storage system. A 900-μF capacitor could be charged up to ~ 5 kV within 100 ms. The average charging power of the converter is . The results show that the described capacitor charger is a cost-effective and compact alternative to commercially available products.