title: Decoding of short non-binary LDPC codes using a non iterative decoding algorithm

abstract: In this paper, we investigate the decoding of short Non-Binary (NB)-LDPC codes of rate 1/2 using a non-iterative approach based on the Maximum-Likelihood (ML) principle. The traditional decoding algorithms used to decode the NB-LDPC codes are by nature iterative where the Variables Nodes (VN) and Check Nodes (CN) exchange data iteratively during, at least, eight iterations which imposes a long decoding time to achieve good performance in terms of Frame Error Rate (FER). In this paper we propose a decoding algorithm based on the Maximum Likelihood (ML) search named Near ML approach where the number of tested words considered as potential codewords is highly reduced. Simulation of codes of lengths 16 and 48 are presented and the results show that the proposed algorithm achieves the performance offered by the EMS algorithm. The NB-LDPC of length 16 is shown to outperform the EMS algorithm.


title: An Improvement of Fuzzy C-Means Clustering Using Adaptive Particle Swarm Optimization

abstract: Fuzzy C-Means (FCM) algorithm is one of the most popular fuzzy clustering techniques. However, it is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global optimization model, which is used in many optimization problems. In this paper, a hybrid clustering algorithm, called HAPF, based on adaptive PSO (APSO) and FCM is proposed, in order to take advantage of the merits of both APSO and FCM. In HAPF the state of swarm aggregation is divided into three situations: strong, loose, and medium, respectively representing swarm's exploitation phrase, exploration phrase, and a balance between the two phrases. In addition, the interval of population diversity measured by the variance of population fitness is partitioned into three sections. After mapping the relationship between the swarm aggregation situations and the value of population diversity, three inertia weight groups are dynamically adjusted accordingly to the real-time state of population diversity. Experimental results show that the proposed HAPF is able to escape local optima and find better optima than other seven well-known clustering algorithms.


title: Exposing Digital Image Forgeries by Illumination Color Classification

abstract: For decades, photographs have been used to document space-time events and they have often served as evidence in courts. Although photographers are able to create composites of analog pictures, this process is very time consuming and requires expert knowledge. Today, however, powerful digital image editing software makes image modifications straightforward. This undermines our trust in photographs and, in particular, questions pictures as evidence for real-world events. In this paper, we analyze one of the most common forms of photographic manipulation, known as image composition or splicing. We propose a forgery detection method that exploits subtle inconsistencies in the color of the illumination of images. Our approach is machine-learning-based and requires minimal user interaction. The technique is applicable to images containing two or more people and requires no expert interaction for the tampering decision. To achieve this, we incorporate information from physics- and statistical-based illuminant estimators on image regions of similar material. From these illuminant estimates, we extract texture- and edge-based features which are then provided to a machine-learning approach for automatic decision-making. The classification performance using an SVM meta-fusion classifier is promising. It yields detection rates of 86% on a new benchmark dataset consisting of 200 images, and 83% on 50 images that were collected from the Internet.


title: Modeling Animating and Rendering Complex Scenes Using Volumetric Textures

abstract: Complex repetitive scenes containing forests, foliage, grass, hair, or fur, are challenging for common modeling and rendering tools. The amount of data, the tediousness of modeling and animation tasks, and the cost of realistic rendering have caused such kind of scene to see only limited use even in high-end productions. The author describes how the use of volumetric textures is well suited to such scenes. These primitives can greatly simplify modeling and animation tasks. More importantly, they can be very efficiently rendered using ray tracing with few aliasing artifacts. The main idea, initially introduced by Kajiya and Kay (1989), is to represent a pattern of 3D geometry in a reference volume, that is tiled over an underlying surface much like a regular 2D texture. In our contribution, the mapping is independent of the mesh subdivision, the pattern can contain any kind of shape, and it is prefiltered at different scales as for MIP-mapping. Although the model encoding is volumetric, the rendering method differs greatly from traditional volume rendering. A volumetric texture only exists in the neighborhood of a surface, and the repeated instances (called texels) of the reference volume are spatially deformed. Furthermore, each voxel of the reference volume contains a key feature which controls the reflectance function that represents aggregate intravoxel geometry. This allows for ray tracing of highly complex scenes with very few aliasing artifacts, using a single ray per pixel (for the part of the scene using the volumetric texture representation). The major technical considerations of our method lie in the ray-path determination and in the specification of the reflectance function.


title: The effects of financial innovation on financial savings in Ghana

abstract: The study principally looked at the relationship between financial innovations and financial savings in Ghana, over 1963 - 2006. The other subsidiary objectives were to establish the extent of financial innovation development and to identify, if any, challenges confronting financial innovations in Ghana. Co-integration and vector error-correction models were applied in order to study the relationship between financial innovations, financial savings, deposit rates, lending rates, inflation, and exchange rates in accordance with the theoretical concepts. The study presents interesting results on the effects of financial innovations on financial savings.


title: Advances in Prospect Theory: Cumulative Representation of Uncertainty

abstract: We develop a new version of prospect theory that employs cumulative rather than separable decision weights and extends the theory in several respects. This version, called cumulative prospect theory, applies to uncertain as well as to risky prospects with any number of outcomes, and it allows different weighting functions for gains and for losses. Two principles, diminishing sensitivity and loss aversion, are invoked to explain the characteristic curvature of the value function and the weighting functions. A review of the experimental evidence and the results of a new experiment confirm a distinctive fourfold pattern of risk attitudes: risk aversion for gains and risk seeking for losses of high probability; risk seeking for gains and risk aversion for losses of low probability.


title: Indoor positioning in Bluetooth networks using fingerprinting and lateration approach

abstract: Global Positioning System (GPS) is a well known navigation system for outdoor applications; however this technology does not work in indoor environments. In order to overcome this limitation, Bluetooth technology can be employed. Furthermore, Bluetooth technology provides an accurate and low cost solution for short range wireless communication. Most of the digital devices provide the Bluetooth functionality which also makes it a good candidate for indoor positioning. In this paper, we use Bluetooth devices for indoor positioning and use signal based parameters such as received power level for position estimation. The accuracy of indoor positioning system is greatly dependant on the parameters selected for estimation and the measurements obtained from the environment. However, the measurements are corrupted by various environmental conditions such as temperature, reflection, presence of obstacles, human body and other communication signals. Therefore, we need to filter the measurements. This paper presents an experimental relationship between the received power level and distance using the standard radio propagation model. The idea behind this study is to provide an accurate distance estimate for Trilateration approach. Based on the experiments performed, the average error is minimized from 5.87 meters to 2.67 meters using gradient filter. The use of gradient filter improves the accuracy by 45 %.


title: Automatic natural language style classification and transformation

abstract: Style is an integral part of natural language in written, spoken or machine generated forms. Humans have been dealing with style in language since the beginnings of language itself, but computers and machine processes have only recently begun to process natural language styles. Automatic processing of styles poses two interrelated challenges: classification and transformation. There have been recent advances in corpus classification, automatic clustering and authorship attribution along many dimensions but little work directly related to writing styles directly and even less in transformation. In this paper we examine relevant literature to define and operationalize a notion of "style" which we employ to designate style markers usable in classification machines. A measurable reading of these markers also helps guide style transformation algorithms. We demonstrate the concept by showing a detectable stylistic shift in a sample piece of text relative to a target corpus. We present ongoing work in building a comprehensive style recognition and transformation system and discuss our results.


title: Cure: Strong Semantics Meets High Availability and Low Latency

abstract: Developers of cloud-scale applications face a difficult decision of which kind of storage to use, summarised by the CAP theorem. Currently the choice is between classical CP databases, which provide strong guarantees but are slow, expensive, and unavailable under partition, and NoSQL-style AP databases, which are fast and available, but too hard to program against. We present an alternative: Cure provides the highest level of guarantees that remains compatible with availability. These guarantees include: causal consistency (no ordering anomalies), atomicity (consistent multi-key updates), and support for high-level data types (developer friendly API) with safe resolution of concurrent updates (guaranteeing convergence). These guarantees minimise the anomalies caused by parallelism and distribution, thus facilitating the development of applications. This paper presents the protocols for highly available transactions, and an experimental evaluation showing that Cure is able to achieve scalability similar to eventually-consistent NoSQL databases, while providing stronger guarantees.


title: Dialog-based Interactive Image Retrieval

abstract: Inspired by the enormous growth of huge online media collections of many types (e.g. images, audio, video, e-books, etc.), and the paucity of intelligent retrieval systems, this paper introduces a novel approach to interactive visual content retrieval. The proposed retrieval framework is guided by free-form natural language feedback from users, allowing for more natural and effective communication. Such a system constitutes a multi-modal dialog protocol where in each dialog turn, a user submits a natural language request to a retrieval agent, which then attempts to retrieve the optimal object. We formulate the retrieval task as a reinforcement learning problem, and reward the dialog system for improving the rank of the target object during each dialog turn. This framework can be applied to a variety of visual media types (images, videos, graphics, etc.), and in this paper, we study in-depth its application on the task of interactive image retrieval. To avoid the cumbersome and costly process of collecting human-machine conversations as the dialog system learns, we train the dialog system with a user simulator, which is itself trained to describe the differences between target and candidate images. The efficacy of our approach is demonstrated in a footwear image retrieval application. Extensive experiments on both simulated and real-world data show that: 1) our proposed learning framework achieves better accuracy than other supervised and reinforcement learning baselines; and 2) user feedback based on natural language rather than pre-specified attributes leads to more effective retrieval results, and a more natural and expressive communication interface.


title: Active compensation of current unbalance in paralleled silicon carbide MOSFETs

abstract: Current unbalance in paralleled power electronic devices can affect the performance and reliability of them. In this paper, the factors causing current unbalance in parallel connected silicon carbide (SiC) MOSFETs are analyzed, and the threshold mismatch is identified as the major factor. Then the distribution and temperature dependence of SiC MOSFETs' threshold voltage are studied experimentally. Based on the analysis and study, an active current balancing (ACB) scheme is presented. The scheme directly measures the unbalance current, and eliminates it in closed loop by varying the gate delay to each device. The turn-on and turn-off current unbalance are sensed and independently compensated to yield an optimal performance at both switching transitions. The proposed scheme provides robust compensation of current unbalance in fast-switching wide-band-gap devices while keeping circuit complexity and cost low. The performance of the proposed ACB scheme is verified by both simulation and experimental results.


title: Searching for the optimal racing line using genetic algorithms

abstract: Finding the racing line to follow on the track is at the root of the development of any controller in racing games. In commercial games this issue is usually addressed by using human-designed racing lines provided by domain experts and represents a rather time consuming process. In this paper we introduce a novel approach to compute the racing line without any human intervention. In the proposed approach, the track is decomposed into several segments where a genetic algorithm is applied to search for the best trade-off between the minimization of two conflicting objectives: the length and the curvature of the racing line. The fitness of the candidate solutions is computed through a simulation performed with The Open Racing Car Simulator (TORCS), an open source simulator used as testbed in this work. Finally, to test our approach we carried out an experimental analysis that involved 11 tracks provided with the TORCS distribution. In addition, we compared the performance of our approach to the one achieved by a related approach, previously introduced in the literature, and to the performance of the fastest controller available for TORCS. Our results are very promising and show that the presented approach is able to reach the best performance in almost all the tracks considered.


title: Voice Quality Evaluation of Recent Open Source Codecs

abstract: This paper introduces Silk, CELT, and BroadVoice that are available in the internet as open source voice codecs. Their voice quality is evaluated with a subjective listening test. AMR, AMR-WB, G.718, G.718B, and G.722.1C were used as standardized references. In addition Skype’s Silk codec’s peculiar bandwidth and bitrate characteristics are examined in more detail.


title: Finger-vein biometric identification using convolutional neural network

abstract: A novel approach using a convolutional neural network (CNN) for finger-vein biometric identification is presented in this paper. Unlike existing biometric techniques such as fingerprint and face, vein patterns are inside the body, making them virtually impossible to replicate. This also makes finger-vein biometrics a more secure alternative without being susceptible to forgery, damage, or change with time. In conventional finger-vein recognition methods, complex image processing is required to remove noise and extract and enhance the features before the image classification can be performed in order to achieve high performance accuracy. In this regard, a significant advantage of the CNN over conventional approaches is its ability to simultaneously extract features, reduce data dimensionality, and classify in one network structure. In addition, the method requires only minimal image preprocessing since the CNN is robust to noise and small misalignments of the acquired images. In this paper, a reduced-complexity four-layer CNN with fused convolutional-subsampling architecture is proposed for finger-vein recognition. For network training, we have modified and applied the stochastic diagonal Levenberg{Marquardt algorithm, which results in a faster convergence time. The proposed CNN is tested on a finger-vein database developed in-house that contains 50 subjects with 10 samples from each finger. An identification rate of 100.00% is achieved, with an 80/20 percent ratio for separation of training and test samples, respectively. An additional number of subjects have also been tested, in which for 81 subjects an accuracy of 99.38% is achieved.


Measuring Colourfulness in Natural Images


title: The IFCC Working Group on laboratory errors and patient safety.

abstract: The increasing attention paid to patient safety, and the awareness that the information provided by clinical laboratories impacts directly on the treatment received by patients, has made it a priority for clinical laboratories to reduce their error rates and promote an excellent level of quality. The capacity for reporting, analysing and learning from experience is still seriously hampered by a lack of methodological uniformity in identification and measurement, inadequate reporting schemes for error events, the fear of professional liability, and weak information systems. In previous years, the use of quality indicators to assess and monitor the quality systems of clinical laboratories considerably benefited quality management. Yet currently there are no guidelines available for identifying and correcting development of quality indicators in laboratory medicine, although the International Standard ISO 15189:2007 for Accreditation of Medical Laboratory requires them to be implemented. The aim of this work was therefore to report on the project, nominated "Model of Quality Indicator", undertaken by the Working Group, "Laboratory Errors and Patient Safety (WG-LEPS)" instituted by the division of Education and Management (EMD) of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). The mission of the WG-LEPS is to promote and encourage investigations into errors in laboratory medicine, collect data available on this issue and recommend strategies and procedures for improving patient safety.


title: Comparison of instruments to assess hand function in children with radius deficiencies.

abstract: PURPOSE ::: For treatment evaluation of children with radius deficiencies (RDs), standardized assessment of hand function in performing activities is required. Instruments to measure hand function have been developed for other diagnoses. The current study aimed to find additional evidence for validity, reliability, and usefulness of these instruments for children with RDs. ::: ::: ::: METHODS ::: In this study, 20 children with RDs (aged 4-12 years) participated; 16 were boys, and 13 children were unilaterally affected. Children were assessed using the Assisting Hand Assessment, the Unilateral Below Elbow Test, the Prosthetic Upper Extremity Functional Index, and ABILHAND-Kids. Construct and convergent validity of the instruments were studied focusing on predefined hypotheses and relationships with other instruments and the therapist's global assessment. Test-retest reliability was assessed in 10 children by means of the intraclass correlation coefficients and the smallest detectable differences. ::: ::: ::: RESULTS ::: For children with RDs, the Assisting Hand Assessment and the Prosthetic Upper Extremity Functional Index appeared to be the most valid function test and questionnaire according to the relationships found with type of RD, functional hand grips, and the therapist's global assessment of hand function. Regarding test-retest reliability, intraclass correlation coefficients ranged from 0.82 to 0.91, and smallest detectable differences were acceptably small. ::: ::: ::: CONCLUSIONS ::: The current results contribute to the evidence that the instruments, especially the Assisting Hand Assessment and the Prosthetic Upper Extremity Functional Index, provide valid and reliable results in children with RDs. ::: ::: ::: TYPE OF STUDY/LEVEL OF EVIDENCE ::: Diagnostic I.


Cyber Terrorism : Menace or Myth ?


title: Limited incision nonendoscopic brow lift.

abstract: BACKGROUND: The authors compared the nonendoscopic brow lift technique to the popular endoscopic procedure to determine whether it offers a less complex and less expensive but equally effective alternative. METHODS: A retrospective comparison of the senior author's experience with the endoscopic brow lift (100 patients; years 1999 to 2004) and the nonendoscopic brow lift (93 patients; years 2002 to 2005) was conducted. Using a three-incision approach for both procedures (one midline and two temporal), endoscopic visualization was used to assist in the last 2 cm of subperiosteal dissection over the superior orbital rim only in the endoscopic technique. In the nonendoscopic technique, this final dissection was performed without the endoscope, and the expected path of the supraorbital and supratrochlear neurovascular bundles through preoperative marking of their meridians was respected. Effective brow elevation, operative times, size of incisions, complications, and overall patient satisfaction were compared between groups. RESULTS: The authors found no significant difference in average brow elevation between the two brow lift groups (4 mm). However, the nonendoscopic brow lift was completed, on average, 20 minutes faster than the endoscopic brow lift (30 minutes versus 50 minutes) and required a smaller incision than the endoscopic brow lift (2 cm versus 2.5 cm). No nonendoscopic patient experienced permanent complications, but one endoscopic patient developed permanent paresthesias of the forehead secondary to supraorbital/supratrochlear nerve injury. Overall patient satisfaction was equivalent in both groups. CONCLUSIONS: The limited incision nonendoscopic brow lift is a safe and effective alternative to the endoscopic technique. With thorough anatomical knowledge of this region, it offers equivalent brow elevation, shorter operative times, smaller incisions, similarly low complications rates, and patient satisfaction and eliminates the need for costly and cumbersome endoscopic equipment.


title: Multigrid Geometric Active Contour Models

abstract: Geometric active contour models are very popular partial differential equation-based tools in image analysis and computer vision. We present a new multigrid algorithm for the fast evolution of level-set-based geometric active contours and compare it with other established numerical schemes. We overcome the main bottleneck associated with most numerical implementations of geometric active contours, namely the need for very small time steps to avoid instability, by employing a very stable fully 2-D implicit-explicit time integration numerical scheme. The proposed scheme is more accurate and has improved rotational invariance properties compared with alternative split schemes, particularly when big time steps are utilized. We then apply properly designed multigrid methods to efficiently solve the occurring sparse linear system. The combined algorithm allows for the rapid evolution of the contour and convergence to its final configuration after very few iterations. Image segmentation experiments demonstrate the efficiency and accuracy of the method.


title: An examination of the celebrity endorsements and online customer reviews influence female consumers' shopping behavior

abstract: The goal of this study is to compare the influence of celebrity endorsements to online customer reviews on female shopping behavior. Based on AIDMA and AISAS models, we design an experiment to investigate consumer responses to search good and experience good respectively. The results revealed that search good (shoes) endorsed by a celebrity in an advertisement evoked significantly more attention, desire, and action from the consumer than did an online customer review. We also found that online customer reviews emerged higher than the celebrity endorsement on the scale of participants' memory, search and share attitudes toward the experience good (toner). Implications for marketers as well as suggestions for future research are discussed.


title: What does your selfie say about you?

abstract: Selfies contain cues indicating personality traits.A coding scheme for selfies was developed.We found cues related to self-report and observer judgment of personality.Observers made consistent judgment of personality traits from selfies.Observers accurately predicted openness from selfies. Selfies refer to self-portraits taken by oneself using a digital camera or a smartphone. They become increasingly popular in social media. However, little is known about how selfies reflect their owners' personality traits and how people judge others' personality from selfies. In this study, we examined the association between selfies and personality by measuring participants' Big Five personality and coding their selfies posted on a social networking site. We found specific cues in selfies related to agreeableness, conscientiousness, neuroticism, and openness. We also examined zero-acquaintance personality judgment and found that observers had moderate to strong agreement in their ratings of Big Five personality based on selfies. However, they could only accurately predict selfie owners' degree of openness. This study is the first to reveal personality-related cues in selfies and provide a picture-coding scheme that can be used to analyze selfies. We discussed the difference between personality expression in selfies and other types of photos, and its possible relationship with impression management of social media users.


title: Microstrip slow-wave open-loop resonator filters

abstract: Microstrip slow-wave open-loop resonators allow various filter configurations including those of elliptic or quasi-elliptic function response to be realised. The filters are not only compact in size due to the slow-wave effect, but also have a wider upper stopband resulting from the dispersion effects. These attractive features mean the microstrip slow-wave open-loop resonator filters hold promise for mobile communications, superconducting and other applications.


title: CONSTRUCTIONS OF POTENTIALLY EVENTUALLY POSITIVE SIGN PATTERNS WITH REDUCIBLE POSITIVE PART

abstract: Potentially eventually positive (PEP) sign patterns were introduced in \Sign patterns that allow eventual positivity," Electronic Journal of Linear Algebra, 19 (2010): 108{120, where it was noted thatA is PEP if its positive part is primitive, and an example was given of a 3 3 PEP sign pattern with reducible positive part. We extend these results by constructing n n PEP sign patterns with reducible positive part, for every n 3.


title: Fractional Order Fuzzy Control of Hybrid Power System with Renewable Generation Using Chaotic PSO

abstract: This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants.


title: Globally Trained Handwritten Word Recognizer Using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models

abstract: We introduce a new approach for on-line recognition of handwritten words written in unconstrained mixed style. The preprocessor performs a word-level normalization by fitting a model of the word structure using the EM algorithm. Words are then coded into low resolution "annotated images" where each pixel contains information about trajectory direction and curvature. The recognizer is a convolution network which can be spatially replicated. From the network output, a hidden Markov model produces word scores. The entire system is globally trained to minimize word-level errors.


A Qualitative Exploration of Self-Kindness and “Treating Oneself” in Contexts of Eating, Weight Regulation and Other Health Behaviors: Implications for Mindfulness-Based Eating Programs


title: Glaucoma detection from fundus images using MATLAB GUI

abstract: A troublesome disease in which damages of the optic nerve of eye's is nothing but the glaucoma, which causes irretrievable loss of vision. Glaucoma is a disease where if treatment is get late, the person can blind. Normally glaucoma detects when there is an increase in the fluid in the front of eye. When that extra fluid is increased, the pressure in your eye is also getting increased. Accordingly, the size of the optic disc and optic cup is increased as a result diameter also increased. The ratio of the cup and disc diameter is called cup-to-disc ratio (CDR). Threshold type segmentation method is used in this system for localizing the optic disc and optic cup. Another edge detection and ellipse fitting algorithm are also used. The proposed system for optic disc and optic cup localization and CDR calculation is MATLAB GUI software.


title: Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning

abstract: A novel neural network model is presented that learns by trial-and-error to reproduce complex sensory-motor sequences. One subnetwork, corresponding to the prefrontal cortex (PFC), is responsible for generating unique patterns of activity that represent the continuous state of sequence execution. A second subnetwork, corresponding to the striatum, associates these state-encoding patterns with the correct response at each point in the sequence execution. From a neuroscience perspective, the model is based on the known cortical and subcortical anatomy of the primate oculomotor system. From a theoretical perspective, the architecture is similar to that of a finite automaton in which outputs and state transitions are generated as a function of inputs and the current state. Simulation results for complex sequence reproduction and sequence discrimination are presented.


title: User interactions with everyday applications as context for just-in-time information access

abstract: Our central claim is that user interactions with everyday productivity applications (e.g., word processors, Web browsers, etc.) provide rich contextual information that can be leveraged to support just-in-time access to task-relevant information. We discuss the requirements for such systems, and develop a general architecture for systems of this type. As evidence for our claim, we present Watson, a system which gathers contextual information in the form of the text of the document the user is manipulating in order to proactively retrieve documents from distributed information repositories. We close by describing the results of several experiments with Watson, which show it consistently provides useful information to its users.


title: Unified particle physics for real-time applications

abstract: We present a unified dynamics framework for real-time visual effects. Using particles connected by constraints as our fundamental building block allows us to treat contact and collisions in a unified manner, and we show how this representation is flexible enough to model gases, liquids, deformable solids, rigid bodies and cloth with two-way interactions. We address some common problems with traditional particle-based methods and describe a parallel constraint solver based on position-based dynamics that is efficient enough for real-time applications.


title: Power Control System Design in Induction Heating with Resonant Voltage Inverter

abstract: This paper is concerned with the design of the power control system for a single-phase voltage source inverter feeding a parallel resonant induction heating load. The control of the inverter output current, meaning the active component of the current through the induction coil when the control frequency is equal or slightly exceeds the resonant frequency, is achieved by a Proportional-Integral- Derivative controller tuned in accordance with the Modulus Optimum criterion in Kessler variant. The response of the current loop for different work pipes and set currents has been tested by simulation under the Matlab-Simulink environment and illustrates a very good behavior of the control system. 


title: An undergraduate course on software bug detection tools and techniques

abstract: The importance of software bug detection tools is high with the constant threat of malicious activity. Companies are increasingly relying on software bug detection tools to catch exploitable bugs before the program is released. This paper describes a course on software bug detection techniques that is aimed at undergraduates. Courses in software verification are often taught at the graduate level and too theoretical and research oriented for undergraduates. A key component of the course is the programming assignments where students gain practical experience in creating their own software bug detection tools using a source to source converter for a subset of C++.


title: Comparison of deep learning architectures for H&E histopathology images

abstract: Deep learning has achieved outstanding performance in many fields such as image classification and target recognition. Recently multiple research efforts are focusing on deep learning to medical image processing. While it is common in image processing to apply transfer learning for problems with small sample sizes, the statistics of histopathological stains are known to be very different from the photographic RGB images in common deep learning imaging tasks such as Imagenet and MIT Places. This paper evaluates the performance of fine-tuned models on Haematoxylin and Eosin(H&E) histopathology stain data. Furthermore, to analyze the performance of different deep learning architectures on these domains, we compare three convolutional neural network(CNN) architectures in various settings. Finally, the impact of the size of the context of training samples is evaluated. We use the BreaKHis dataset consisting of H&E stained microscopical scans of breast cancer tissue [1]. Our results show that fine-tuned architectures perform favorably over neural networks that are trained from scratch in terms of accuracy and patient rate.


title: A high-accuracy, low-cost localization system for wireless sensor networks

abstract: The problem of localization of wireless sensor nodes has long been regarded as very difficult to solve, when considering the realities of real world environments. In this paper, we formally describe, design, implement and evaluate a novel localization system, called Spotlight. Our system uses the spatio-temporal properties of well controlled events in the network (e.g., light), to obtain the locations of sensor nodes. We demonstrate that a high accuracy in localization can be achieved without the aid of expensive hardware on the sensor nodes, as required by other localization systems. We evaluate the performance of our system in deployments of Mica2 and XSM motes. Through performance evaluations of a real system deployed outdoors, we obtain a 20cm localization error. A sensor network, with any number of nodes, deployed in a 2500m2 area, can be localized in under 10 minutes, using a device that costs less than $1000. To the best of our knowledge, this is the first report of a sub-meter localization error, obtained in an outdoor environment, without equipping the wireless sensor nodes with specialized ranging hardware.


title: Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud

abstract: A ubiquitous environment for road travel that uses wireless networks requires the minimization of data exchange between vehicles. An algorithm that can segment the ground in real time is necessary to obtain location data between vehicles simultaneously executing autonomous drive. This paper proposes a framework for segmenting the ground in real time using a sparse three-dimensional (3D) point cloud acquired from undulating terrain. A sparse 3D point cloud can be acquired by scanning the geography using light detection and ranging (LiDAR) sensors. For efficient ground segmentation, 3D point clouds are quantized in units of volume pixels (voxels) and overlapping data is eliminated. We reduce nonoverlapping voxels to two dimensions by implementing a lowermost heightmap. The ground area is determined on the basis of the number of voxels in each voxel group. We execute ground segmentation in real time by proposing an approach to minimize the comparison between neighboring voxels. Furthermore, we experimentally verify that ground segmentation can be executed at about 19.31 ms per frame.


title: Algorithms for Characterization and Trend Detection in Spatial Databases

abstract: The number and the size of spatial databases, e.g. for geo-marketing, traffic control or environmental studies, are rapidly growing which results in an increasing need for spatial data mining. In this paper, we present new algorithms for spatial characterization and spatial trend analysis. For spatial characterization it is important that class membership of a database object is not only determined by its non-spatial attributes but also by the attributes of objects in its neighborhood. In spatial trend analysis, patterns of change of some non-spatial attributes in the neighborhood of a database object are determined. We present several algorithms for these tasks. These algorithms were implemented within a general framework for spatial data mining providing a small set of database primitives on top of a commercial spatial database management system. A performance evaluation using a real geographic database demonstrates the effectiveness of the proposed algorithms. Furthermore, we show how the algorithms can be combined to discover even more interesting spatial knowledge.


title: NaturalJava: a natural language interface for programming in Java

abstract: NaturalJava is a prototype for an intelligent natural-language-based user interface for creating, modifying, and examining Java programs. The interface exploits three subsystems. The Sundance natural language processing system accepts English sentences as input and uses information extraction techniques to generate case frames representing program construction and editing directives. A knowledge-based case frame interpreter, PRISM, uses a decision tree to infer program modification operations from the case frames. A Java abstract syntax tree manager, TreeFace, provides the interface that PRISM uses to build and navigate the tree representation of an evolving Java program. In this paper, we describe the technical details of each component, explain the capabilities of the user interface, and present examples of NaturalJava in use.


title: Understanding knowledge sharing activities in free/open source software projects

abstract: Free/Open Source Software (F/OSS) projects are people-oriented and knowledge intensive software development environments. Many researchers focused on mailing lists to study coding activities of software developers. How expert software developers interact with each other and with non-developers in the use of community products have received little attention. This paper discusses the altruistic sharing of knowledge between knowledge providers and knowledge seekers in the Developer and User mailing lists of the Debian project. We analyze the posting and replying activities of the participants by counting the number of email messages they posted to the lists and the number of replies they made to questions others posted. We found out that participants interact and share their knowledge a lot, their positing activity is fairly highly correlated with their replying activity, the characteristics of posting and replying activities are different for different kinds of lists, and the knowledge sharing activity of self-organizing Free/Open Source communities could best be explained in terms of what we called ''Fractal Cubic Distribution'' rather than the power-law distribution mostly reported in the literature. The paper also proposes what could be researched in knowledge sharing activities in F/OSS projects mailing list and for what purpose. The research findings add to our understanding of knowledge sharing activities in F/OSS projects.


Extraction of Vehicle Trajectories from Online Video Streams


title: Observations on Experience and Flow in Movement-Based Interaction

abstract: Movement-based interfaces assume that their users move. Users have to perform exercises, they have to dance, they have to golf or football, or they want to train particular bodily skills. Many examples of those interfaces exist, sometimes asking for subtle interaction between user and interface and sometimes asking for ‘brute force’ interaction between user and interface. Often these interfaces mediate between players of a game. Obviously, one of the players may be a virtual human. We embed this interface research in ambient intelligence and entertainment computing research, and the interfaces we consider are not only mediating, but they also ‘add’ intelligence to the interaction. Intelligent movement-based interfaces, being able to know and learn about their users, should also be able to provide means to keep their users engaged in the interaction. Issues that will be discussed in this chapter are ‘flow’ and ‘immersion’ for movement-based interfaces and we look at the possible role of interaction synchrony to measure and support engagement.


title: A Scalable Hierarchical Clustering Algorithm Using Spark

abstract: Clustering is often an essential first step in data mining intended to reduce redundancy, or define data categories. Hierarchical clustering, a widely used clustering technique, can offer a richer representation by suggesting the potential group structures. However, parallelization of such an algorithm is challenging as it exhibits inherent data dependency during the hierarchical tree construction. In this paper, we design a parallel implementation of Single-linkage Hierarchical Clustering by formulating it as a Minimum Spanning Tree problem. We further show that Spark is a natural fit for the parallelization of single-linkage clustering algorithm due to its natural expression of iterative process. Our algorithm can be deployed easily in Amazon's cloud environment. And a thorough performance evaluation in Amazon's EC2 verifies that the scalability of our algorithm sustains when the datasets scale up.


title: A Concept Language Model for Ad-hoc Retrieval

abstract: We propose an extension to language models for information retrieval. Typically, language models estimate the probability of a document generating the query, where the query is considered as a set of independent search terms. We extend this approach by considering the concepts implied by both the query and words in the document. The model combines the probability of the document generating the concept embodied by the query, and the traditional language model probability of the document generating the query terms. We use a word embedding space to express concepts. The similarity between two vectors in this space is estimated using a weighted cosine distance. The weighting significantly enhances the discrimination between vectors. We evaluate our model on benchmark datasets (TREC 6--8) and empirically demonstrate it outperforms state-of-the-art baselines.


Concentration, sources and light absorption characteristics of dissolved organic carbon on a typical glacier, the northeastern Tibetan Plateau


title: Respiratory Changes during Reading in Mandarin-Speaking Adolescents with Prelingual Hearing Impairment

abstract: Objective: Most people with severe to profound hearing impairment (SHI) exhibit speech breathing changes, but little is known about the breath group (BG) structure for this population. The purposes of this study were to investigate, compared to speakers with normal hearing, if Mandarin-speaking adolescents with prelingual SHI take inspirations more often at syntactically inappropriate positions and exhibit a difference in the temporal BG characteristics. Patients and Methods: Forty participants, 20 speakers with prelingual SHI and 20 normal-hearing controls matched for age, sex and education level were recruited. While wearing a circumferentially vented mask connected to a pneumotachograph, the subjects read three passages. The airflow signal was used to locate inspiratory loci in the speech samples. Temporal parameters of BG structure were derived from the acoustic signal. Results: The SHI group, compared to the control group, had significantly (1) more inspiratory loci at inappropriate and minor syntactic boundaries; (2) fewer syllables per BG, slower speaking rate, longer inter-BG pauses, and longer noninspiratory pauses, but comparable inspiratory duration, expiration duration, and BG duration. Conclusion: The slower speaking rate within BGs and longer inter-BG pauses mainly account for the respiratory changes in Mandarin-speaking adolescents with prelingual SHI.


title: Is the malaris muscle the anti-aging missing link of the midface?

abstract: Background ::: The malaris muscle is a superficial and fine facial muscle. It is not well known because of its inconsistency in Caucasians. It could play a role in midface aging prevention as that observed in subjects with prominent cheekbones like Asian people. The purpose of this study was to explain how the malaris muscle is able to produce an anti-aging effect on the midface.


title: Intelligent travel chatbot for predictive recommendation in echo platform

abstract: Chatbot is a computer application that interacts with users using natural language in a similar way to imitate a human travel agent. A successful implementation of a chatbot system can analyze user preferences and predict collective intelligence. In most cases, it can provide better user-centric recommendations. Hence, the chatbot is becoming an integral part of the future consumer services. This paper is an implementation of an intelligent chatbot system in travel domain on Echo platform which would gather user preferences and model collective user knowledge base and recommend using the Restricted Boltzmann Machine (RBM) with Collaborative Filtering. With this chatbot based on DNN, we can improve human to machine interaction in the travel domain.


title: Bacterial Colonization In Suspected Sexually Abused Children

abstract: Abstract Objective Previous studies concluded that symptomatic prepubertal children with Gardnerella vaginalis infection should be investigated for child sexual abuse. Gardnerella vaginalis is only one out of a group of organism that cause bacterial vaginosis. The aim of the study was to report the frequency of bacterial colonization as well as sexually transmitted diseases and to correlate these data with patient characteristics. Methods Data were collected from 1996 to 2006. Medical records of 180 girls, median age at first sexual abuse 7.44 years (range 1–16, SD 3.937), have been evaluated retrospectively. SPSS software 12.0 has been used for statistical analysis. Results Only 18.3% of patients complained of vaginal discharge or pruritus. Sexually transmitted diseases were documented: Gonorrhoea 1 (1.8%, 56 tested), Chlamydia trachomatis 1 (1.6%, 62 tested), Syphilis 0 (0%, 5 tested), Trichomonas vaginalis 1 (0.7%, 136 tested) and HIV 0 (0%, 27 tested). Bacterial colonization were documented (121 tested): Gardnerella vaginalis 29 (24%), Enterobacteriaceae 50 (41.3%), Haemophilus influenzae 11 (9.1%), Streptococcus a haemolyticus 35 (28.9%), Streptococcus Group B 8 (6.6%), Staphylococcus Koag neg. 37 (30.6%), Staphylococcus aureus 10 (8.3%), Pseudomonas aeruginosa 4 (4.1%), Bacteroides 19 (15.7%), Prevotella 24 (19.8%), Klebsiella 4 (3.3%), Corynebacteria 20 (16.5%) and Ureaplasma 9 (7.4%). Conclusions Sexually transmitted diseases are infrequent in children suspected for child sexual abuse. Bacterial colonization is common, however, not correlated with clinical symptoms (except for Bacteroides) and hymenal/vaginal injuries.


title: Vestibular papillomatosis as a normal vulvar anatomical condition.

abstract: At the beginning of the nineteen-eighties, vulvar vestibular papillomatosis (VVP) was thought to be a human papilloma virus (HPV) disease. Based upon these findings many clinicians have been treating this condition with laser ablation or by topical application of podophyllin or trichloroacetic acid. Currently, most authors believe that VVP should be considered an anatomical variant of the vestibular mucosa and not HPV related. We present a case of VVP in which there was no histological or molecular evidence of HPV; unnecessary treatment should be avoided.


Markov chain sampling methods for Dirichlet process mixture models


title: The evolution of brain activation during temporal processing

abstract: Timing is crucial to many aspects of human performance. To better understand its neural underpinnings, we used event-related fMRI to examine the time course of activation associated with different components of a time perception task. We distinguished systems associated with encoding time intervals from those related to comparing intervals and implementing a response. Activation in the basal ganglia occurred early, and was uniquely associated with encoding time intervals, whereas cerebellar activation unfolded late, suggesting an involvement in processes other than explicit timing. Early cortical activation associated with encoding of time intervals was observed in the right inferior parietal cortex and bilateral premotor cortex, implicating these systems in attention and temporary maintenance of intervals. Late activation in the right dorsolateral prefrontal cortex emerged during comparison of time intervals. Our results illustrate a dynamic network of cortical-subcortical activation associated with different components of temporal information processing.


title: Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images

abstract: Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error. In this study, we utilize several computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer. In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry (IHC) staining scores of bladder and breast cancers. Our classification pipeline includes a basic CNN architecture, Google's Inceptions with three training strategies, and an ensemble of two state-of-the-art algorithms, Inception and ResNet. Training strategies include training the last layer of Google's Inceptions, training the network from scratch, and fine-tunning the parameters for our data using two pre-trained version of Google's Inception architectures, Inception-V1 and Inception-V3. We demonstrate the power of deep learning approaches for identifying cancer subtypes, and the robustness of Google's Inceptions even in presence of extensive tumor heterogeneity. On average, our pipeline achieved accuracies of 100%, 92%, 95%, and 69% for discrimination of various cancer tissues, subtypes, biomarkers, and scores, respectively. Our pipeline and related documentation is freely available at https://github.com/ih-_lab/CNN_Smoothie.


title: Innovating Transport with QUIC: Design Approaches and Research Challenges

abstract: In today's Internet, there are many challenges such as low-latency support for interactive communication, security and privacy of user data, as well as development and deployment of new transport mechanisms. Quick UDP Internet Connections (QUIC) is a new transport protocol that addresses these challenges, focusing on HTTP/2 transmission as a first use case. The first QUIC working group meeting took place at IETF-97 in November 2016, and it has begun the standardization process. This article introduces the key features of QUIC and discusses the potential challenges that require further consideration.


title: A beam-steering array using liquid crystal phase shifter

abstract: A beam-steering array operating in the 28-GHz band is described, including a 1-by-4 patch antenna array, liquid crystal (LC) based microstrip meander line phase shifters and feeding networks. The RF signal is coupled to the patch antenna array from the slots in the ground plane. The LC phase shifters are implemented in a kind of inverted microstrip line (IMSL) topology by filling liquid crystal between two RT/duroid 5880 substrates(e r = 2.2, tanδ = 0.0009) with a figure-of-merit of 45.2°/dB at 28 GHz. Simulation results of the scattering parameters and the radiation patterns of the antenna are presented and discussed here.


title: Speech/music discrimination using discrete hidden Markov models

abstract: A speech/music discrimination system using discrete Hidden Markov Models has been designed. The system has been evaluated using separate training, development and test databases. The discrimination ability was examined using different features and feature combinations and results are presented as error rate on the development and test databases. Features were chosen from knowledge about the speech signal. Adding the zero crossing rate, autocorrelation function and spectral gravity features to cepstrum coefficients helped to improve the discrimination result, while the cepstrum features were found to be more robust. The impact of the model size on the Speech/Music discrimination result was especially evaluated. Different compositions of the database were also explored, with and without a good match. The best result on a mismatched situation, 2.3% error rate on test data, was achieved with 2x13 LFCC (13 Cepstrum coefficients and first time differentials), using 24 states and 96 symbols. In the matched situation, the best result on test data was achieved using a combination of all features extracted (47-dimensional), yielding 1% error rate or just below, at the optimal model size, 20-24 states and 48-54 symbols. These tests used a 1-second decision window size. By using transcribed speech files it was also possible to compare the state assignment with the uttered phoneme for each segment. Investigating these results showed that the assigned states from the Viterbi search in a 3-state HMM, could be considered as phoneme classes Introduction and background Speech/Music discrimination (SMD) systems are used for different purposes. Before transcription in mixed audio database systems, a segmentation and classification preprocessor is often used to detect the speech segments. A sound classifier that could be an (SMD) system, is a natural component in such a pre-processor. Other applications could be music detection for listening purposes or to adapt appropriate signal processing on different kinds of sound, such as speech or music, in broadcasting. The design of an SMD system, including feature extraction, can be influenced by investigating the speech and/or the music signal. In this work, knowledge about the acoustic speech signal has influenced the design of the system. Human speech can, in one aspect, be described as concatenated syllables. As a result, the acoustic signal varies between high energetic nuclei of the syllables surrounded by weaker segments. Even the spectral shape varies with the syllable rhythm. This behaviour is clearly recognized in a spectrogram, which is composed by quasi-stationary spectral states. Speech production could thus be considered as a state machine, where the states are phoneme classes. Ajmera et al. (2003) have shown that the probability dynamism, which captures the dynamic behaviour of the probability values, is larger for a speech signal than for a music signal, while the entropy of a speech signal is smaller than for a music signal. Similar observations in the speech signal have been reported, for example by Greenberg (1995). He describes a number of properties to Karneback S: Speech/music discrimination using discrete Hidden Markov Models 42 be found in the speech signal from the perception perspective, for example the micromodulation, in the interval of 3 to 12 ms, also considered as the pitch and the macromodulation, 50-250 ms, associated with segments and syllables in the speech. Especially the macro-modulation supports this behaviour and it has been used in SMD tasks by the author in earlier reports (Karneback, 2001 and 2002). These reports showed that a feature based on the low frequency modulation, its amplitude and deviation (LFMAD), and especially a combination of LFMAD and MFCC's constitute robust features for SMD tasks. Saunders (1996) used some of the characteristic features pointed out by Greenberg to successfully discriminate speech/music in FM broadcasting. Since the task was to develop a real-time system, he used only time domain features, mostly from ZCR (Zero Crossing Rate). Samouelian et al. (1998) also used some time domain features combined with two frequency features. Static spectral domain features which are used in speaker and speech recognition tasks can also be used for discriminating between speech, music and other sound sources. Hain & Woodland (1998) used MFCC (including normalised log energy and the first and second order time differentials) and Gaussian Mixture Models (GMM) separating four different sound sources (Speech, Music, Noise and Telephony speech). Gauvain et al. (1999) used a GMM system with MFCC as input features. They performed a segmentation and clustering step followed by a transcription session. Since MFCCs are often used in speech recognition systems for transcription, they were natural to use in the segmentation part too. Scheirer & Slaney (1997) pointed out and used some features for speech/music discrimination, which are closely related to the nature of human speech. Some of the features they used were the 4 Hz energy, percent of low energy frames and spectral centroid and spectral “flux” (Delta Spectrum Magnitude). Nordqvist & Leijon (2002) used discrete HMMs to classify the acoustic environment in order to set gain and filter parameters in hearing aid equipment. By manually adjusting the constants for transitions they created a twolevel system. They used 12 delta-cepstrum coefficients and modelled three sound sources (wide-band speech, traffic and telephony speech). The transitions between these sound sources were used by a second level HMM with four states. The objective was to decide whether the conversation was held on a telephone line or face-to-face. Even though some features are already known to reflect this state switching in the speech signal, typically the energy feature, there might be other features with a strong impact from this behaviour. In order to get a tool to investigate this effect from individual features and feature combinations, a signal classification system is developed and presented in this report. The system is designed to discriminate between two signals and primarily evaluated on the SMD task. It could easily be further developed by arranging several discriminators in parallel or cascade for more complex classification tasks. The assumption in this work is that a music signal, albeit switching, does not show the same pattern as the speech signal and that it would be a good method to construct an SMD system using HMMs, since the dynamic behaviour is taken care of in the HMM. Other sound sources are also presumed to show different behaviour than speech on this matter. HMMs are commonly used in speech recognition, speaker verification and speaker recognition systems. In SMD and classification tasks, HMMs are also used in different ways. HMM/ANN hybrid systems are used by Ajmera et al. (2003) and Williams & Ellis (1999). Continuous HMMs, containing one state per sound timbre and one model per sound source are used by Zhang & Kuo (1999). Allegro et al. (2001) also use per sound source individually trained HMMs. The most commonly used features are cepstrum coefficients, or secondary features derived from these (Ajmera et al., 2003; Williams & Ellis, 1999). This report describes a sound classification system, evaluated on the SMD task, based on discrete ergodic HMMs, using both cepstrum coefficients and other features modelling speech and music sound sources separately. This is a first step in a feature evaluation work and the objective is to evaluate if the design is useful for its purpose by performing evaluations on some features and feature combinations. As a side-effect, preliminary evaluations of a phoneme classification test, investigating the quasi-stationary segments, could also be performed. The system is TMH-QPSR, KTH, Vol. 46, 2004 Speech, Music and Hearing, KTH, Stockholm, Sweden TMH-QPSR, KTH, Vol. 46: 41-59, 2004 43 described in section 3 and the databases in section 4. In section 5, the technical data is presented together with a discussion on feature selection. The results are presented in section 6. In section 7, the result is discussed and conclusions are drawn. Objectives and method The objective of this work has been to design a sound classification system to be used mainly as an SMD system. The design is based on the assumption that the switching behaviour in the acoustic speech signal between phones or phone classes, is to a large degree speech specific and less found in other sound sources. The phone classes can be considered as quasi-stationary states and thus this assumption leads to the use of Hidden Markov Models (HMMs). Since HMMs are known as an effective method in automatic speech recognition tasks, it is a natural choice also for SMD tasks. The sound classification system should be used to evaluate the effect of the individual features on the discrimination or classification ability. The relation between the uttered phoneme and the states in the optimal path sequence should also be possible to investigate. A system for training and testing was developed. The issues reported on in this paper are mainly • feature selection • number of observation symbols in the HMM • number of states in the HMM • feature evaluation • decision window size • matched and mismatched databases The assumption that the states could be considered as phoneme classes was evaluated in the investigation of the assignment of each label. An argument for the use of discrete HMMs is that both the symbols and the states have some phonetic correspondence that can be evaluated. Transcriptions from the database used are available and comparison could be performed. By looking at each frame inside a label, a comparison between the assigned state, or symbol, and the uttered phoneme is performed. Only some of the feature combinations were examined on this issue.


title: Case amazon: ratings and reviews as part of recommendations

abstract: We studied user behavior in a recommender-rich environment, Amazon online store, to see what role the algorithm-based and user-generated recommendations play in finding items of interest. We used applied ethnography, on-location interviewing and observation, to get an accurate picture of user activity. We were especially interested in the role of customer ratings and reviews and what kind of strategies users had developed for such an environment. Our results underline the need to develop recommender systems as a whole. The way the recommendations are shown affects which items get picked, and for improving the interface, it is necessary to study the whole in addition to studying the parts in isolation.


title: Phylogenetic convolutional neural networks in metagenomics

abstract: Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user.


title: The Optimal Control of Partially Observable Markov Processes over a Finite Horizon

abstract: This paper formulates the optimal control problem for a class of mathematical models in which the system to be controlled is characterized by a finite-state discrete-time Markov process. The states of this internal process are not directly observable by the controller; rather, he has available a set of observable outputs that are only probabilistically related to the internal state of the system. The formulation is illustrated by a simple machine-maintenance example, and other specific application areas are also discussed. The paper demonstrates that, if there are only a finite number of control intervals remaining, then the optimal payoff function is a piecewise-linear, convex function of the current state probabilities of the internal Markov process. In addition, an algorithm for utilizing this property to calculate the optimal control policy and payoff function for any finite horizon is outlined. These results are illustrated by a numerical example for the machine-maintenance problem.


title: A Reconfigurable Polarization Converter Using Active Metasurface and Its Application in Horn Antenna

abstract: This paper proposes a reconfigurable polarization converter based on a p-i-n diode controlled active metasurface (AMS). The AMS consists of a thin dielectric substrate and is tuned by two identical layers of elliptic split rings loaded with p-i-n diodes. The p-i-n diodes are positioned between the split gaps and biased through the interconnected elliptic split rings without extra bias network. The active design achieves conversion from linear to circular polarization when the p-i-n diodes are OFF, whereas no conversion takes place when the diodes are ON. A prototype of the proposed design is fabricated, and the operational characteristics are measured. Both the simulated and the experimental results verify the viability of the design. Subsequently, the converter is applied to a linearly polarized horn antenna as a superstrate, making the polarization of the antenna reconfigurable.


title: First Dutch guidelines on lipedema using the international classification of functioning, disability and health

abstract: IntroductionLipedema is a chronic, progressive condition that can result in considerable disability. In 2011, the Dutch Society of Dermatology and Venereology organized a task force to create guidelines on lipedema, using the International Classification of Functioning, Disability and Health of the World Health Organization.Guideline developmentClinical questions on significant issues in lipedema care were proposed, involving (1) making the diagnosis of lipedema; (2) clinimetric measurements for early detection and adequate follow-up; and (3) treatment. A systematic review of literature published up to June 2013 was conducted. Based on available evidence and experience of the task force, answers were formed and recommendations were stated. The guidelines define criteria to make a medical diagnosis of lipedema, a minimum data set of (repeated) clinical measurements that should be used to ensure early detection and an individually outlined follow-up plan, pillars on which conservative treatment should be ba...


title: Males ’ and Females ’ Script Debugging Strategies

abstract: Little research has addressed IT professionals' script debugging strategies, or considered whether there may be gender differences in these strategies. What strategies do male and female scripters use and what kinds of mechanisms do they employ to successfully fix bugs? Also, are scripters' debugging strategies similar to or different from those of spreadsheet debuggers? Without the answers to these questions, tool designers do not have a target to aim at for supporting how male and female scripters want to go about debugging. We conducted a think-aloud study to bridge this gap. Our results include (1) a generalized understanding of debugging strategies used by spreadsheet users and scripters, (2) identification of the multiple mechanisms scripters employed to carry out the strategies, and (3) detailed examples of how these debugging strategies were employed by males and females to successfully fix bugs.


title: High-Performance Pattern-Matching for Intrusion Detection

abstract: New generations of network intrusion detection systems create the need for advanced pattern-matching engines. This paper presents a novel scheme for pattern-matching, called BFPM, that exploits a hardware-based programmable statemachine technology to achieve deterministic processing rates that are independent of input and pattern characteristics on the order of 10 Gb/s for FPGA and at least 20 Gb/s for ASIC implementations. BFPM supports dynamic updates and is one of the most storage-efficient schemes in the industry, supporting two thousand patterns extracted from Snort with a total of 32 K characters in only 128 KB of memory.


title: Measuring tidal volume and functional residual capacity change in sleeping infants using a volume displacement plethysmograph

abstract: The noninvasive measurement of infant lung function during unsedated sleep in infants has been a long-standing objective in paediatric respiratory medicine. This note reports on the design and performance of a head-out volume-displacement plethysmograph (VDP) that overcomes some of the limitations of traditional lung function apparatus. The VDP comprises a rigid acrylic box with an integral water-sealed spirometer and a novel neck seal. The bilayer neck seal is of variable compliance and is comfortable and simple to use. The spirometer permits volume resolution of 1.5 mL and a dynamic range in excess of 100 mL. The frequency response extends from 0-7 Hz. Spirometer inertance was measured as 0.0015 kPa.L(-1).s(-2), resistance 0.021 kPa.L(-1).s(-1) and box capacitance 0.18L.kPa(-1). Tidal volume, respiratory rate and changes in functional residual capacity can be recorded during unsedated rapid eye movement and nonrapid eye movement whilst monitoring with conventional polysomnographic methods. The head-out configuration allows additional instrumentation to be implemented with ease, avoids facial stimulation and allows unimpeded access to the upper airway. A polysomnograph illustrating the limitations of respiratory inductance plethysmography signals and typical changes in functional residual capacity are shown.


title: (Cost)effectiveness of life review for Older Adults: Design of a randomized controlled trial

abstract: BackgroundDepression in older adults is a serious health problem with a poor prognosis. There is a need for indicated preventive psychological interventions for older adults, that show to be promising in preventing depressive disorders.Methods/designThis manuscript describes the design of a study evaluating 'Looking for Meaning', a newly developed prevention course for older adults with depressive symptoms, based on life-review. Both clinical and economic effectiveness are evaluated in a pragmatic randomized controlled trial. The control condition of this 12-session preventive intervention is a 20-minute video movie. The primary outcome is symptoms of depression at post-treatment and follow-up (6 months after post-treatment). Secondary outcomes are symptoms of anxiety, satisfaction with life, mastery, reminiscence styles, quality of life, and health care costs. An additional result of this study is the insight into the working elements of the course, provided by the qualitative study. The qualitative data, mainly based on 20 open-ended interviews with participants, are to be analyzed with an emphasis on newly emerging insight.DiscussionThis study will add to the existing scientific knowledge in several ways, especially by also including an economic evaluation and a qualitative study to gain insight into the working mechanisms of the course, both rather new in the field of life review. Positive results of this study will make an evidence-based intervention to improve public health among older people available.Trial registrationCurrent Controlled Trials Ltd, ISRCTN66645855


title: A computational model of affects

abstract: Emotions and feelings (i.e. affects) are a central fea- ture of human behavior. Due to complexity and interdisciplinarity of affective phenomena, attempts to define them have often be en unsatisfactory. This article provides a simple logical str ucture, in which affective concepts can be defined. The set of affects de fined is similar to the set of emotions covered in the OCC model (1), but the model presented in this article is fully computationally defined, whereas the OCC model depends on undefined concepts. Following Matthis (2), affects are seen as unconscious, emo- tions as preconscious and feelings as conscious. Affects are thus a superclass of emotions and feelings with regards to consciousness. A set of affective states and related affect-specific behavi ors and strategies can be defined with unconscious affects only. In addition, affects are defined as processes of change in the body state, that have specific triggers. For example, an affe ct of hope is defined as a specific body state that is triggered when t he agent is becomes informed about a future event, that is positive with regards to the agent's needs. Affects are differentiated from each other by types of causing events. Affects caused by unexpected positive, neutral and neg- ative events are delight, surprise and fright, respectively. Affects caused by expected positive and negative future events are hope and fear. Affects caused by expected past events are as follows: satisfac- tion results from a positive expectation being fulfilled, disappoint- ment results from a positive expectation not being fulfilled, fears- confirmed results from a negative expectation being fulfilled, and relief results from a negative expectation not being fulfilled. Pri de is targeted towards a self-originated positive event, and shame towards a self-originated negative event. Remorse is targeted towards a self-originated action causing a negative event. Pity is targeted towards a liked agent experiencing a negative event, and happy-for towards a liked agent experiencing a positive event. Resentment is targeted towards a disliked agent experiencing a positive event, and gloating towards a disliked agent experiencing a negative event. An agent is liked/loved if it has produced a net utility greater than zero, and disliked/hated if the net uti lity is lower than zero. An agent is desired if it is expected to produce a positive net utility in the future, and disliked if the expe cted net utility is negative. The above model for unconscious affects is easily computa- tionally implementable, and may be used as a starting point in building believable simulation models of human behavior. The models can be used as a starting point in the development of computational psychological, psychiatric, sociological and crimi- nological theories, or in e.g. computer games.


title: A new DGS unequal power divider

abstract: An unequal Wilkinson power divider having rectangular-shaped defected ground structure (DGS) is proposed. DGS microstrip line adopted for the divider is composed of double-layer substrate which removes the ground problem of the previous DGS structures in packaging. The first layer includes DGS on the bottom ground plane. The second substrate of which top metal plane has been removed is attached to the bottom plane of the first layer. At first, a conventional DGS unequal Wilkinson power divider is designed according to the previous method on the first layer, and the second substrate is attached. The proposed DGS with double-layer substrate is described in detail and applied to 1:4 unequal dividers as an application example. The measured unequal Wilkinson power divider has an excellent agreement between prediction and measurement.


title: A High Efficiency Accelerator for Deep Neural Networks

abstract: Deep Neural Networks (DNNs) are the current state of the art for various tasks such as object detection, natural language processing and semantic segmentation. These networks are massively parallel, hierarchical models with each level of hierarchy performing millions of operations on a single input. The enormous amount of parallel computation makes these DNNs suitable for custom acceleration. Custom accelerators can provide real time inference of DNNs at low power thus enabling widespread embedded deployment. In this paper, we present Snowflake, a high efficiency, low power accelerator for DNNs. Snowflake was designed to achieve optimum occupancy at low bandwidths and it is agnostic to the network architecture. Snowflake was implemented on the Xilinx Zynq XC7Z045 APSoC and achieves a peak performance of 128 G-ops/s. Snowflake is able to maintain a throughput of 98 FPS on AlexNet while averaging 1.2 GB/s of memory bandwidth.


Reasoning With Images in Mathematical Activity


title: English to Sanskrit Translator and Synthesizer (ETSTS)

abstract: The development of Machine Translation system for ancient language such as Sanskrit language is much more fascinating and challenging task. In the proposed algorithm we integrate traditional dictionary rule based approach for translation which translate source English sentence into target Sanskrit sentence. It has two models Text to Text Translator module and Text to speech synthesizer module. Due to morphological richness of Sanskrit language, this system uses morphological markings to identify Subject, Object, Verb, Preposition, Adjective, Adverb sentences. This paper presents English to Sanskrit approach for translating well-structured English sentences into well-structured Sanskrit sentences.


title: D (2000) FMRI visualization of brain activity during a monetary incentive delay task. Neuroimage 12

abstract: Abstract Comparative studies have implicated striatal and mesial forebrain circuitry in the generation of autonomic, endocrine, and behavioral responses for incentives. Using blood oxygen level-dependent functional magnetic resonance imaging, we sought to visualize functional activation of these regions in 12 normal volunteers as they anticipated and responded for monetary incentives. Both individual and group analyses of time-series data revealed significant activation of striatal and mesial forebrain structures (including insula, caudate, putamen, and mesial prefrontal cortex) during trials involving both monetary rewards and punishments. In addition to these areas, during trials involving punishment, group analysis revealed activation foci in the anterior cingulate and thalamus. These results corroborate comparative studies which implicate striatal and mesial forebrain circuitry in the elaboration of incentive-driven behavior. This report also introduces a new paradigm for probing the functional integrity of this circuitry in humans.


title: Hybrid video emotional tagging using users’ EEG and video content

abstract: In this paper, we propose novel hybrid approaches to annotate videos in valence and arousal spaces by using users' electroencephalogram (EEG) signals and video content. Firstly, several audio and visual features are extracted from video clips and five frequency features are extracted from each channel of the EEG signals. Secondly, statistical analyses are conducted to explore the relationships among emotional tags, EEG and video features. Thirdly, three Bayesian Networks are constructed to annotate videos by combining the video and EEG features at independent feature-level fusion, decision-level fusion and dependent feature-level fusion. In order to evaluate the effectiveness of our approaches, we designed and conducted the psychophysiological experiment to collect data, including emotion-induced video clips, users' EEG responses while watching the selected video clips, and emotional video tags collected through participants' self-report after watching each clip. The experimental results show that the proposed fusion methods outperform the conventional emotional tagging methods that use either video or EEG features alone in both valence and arousal spaces. Moreover, we can narrow down the semantic gap between the low-level video features and the users' high-level emotional tags with the help of EEG features.


title: MINERVA—a platform for visualization and curation of molecular interaction networks

abstract: Our growing knowledge about various molecular mechanisms is becoming increasingly more structured and accessible. Different repositories of molecular interactions and available literature enable construction of focused and high-quality molecular interaction networks. Novel tools for curation and exploration of such networks are needed, in order to foster the development of a systems biology environment. In particular, solutions for visualization, annotation and data cross-linking will facilitate usage of network-encoded knowledge in biomedical research. To this end we developed the MINERVA (Molecular Interaction NEtwoRks VisuAlization) platform, a standalone webservice supporting curation, annotation and visualization of molecular interaction networks in Systems Biology Graphical Notation (SBGN)-compliant format. MINERVA provides automated content annotation and verification for improved quality control. The end users can explore and interact with hosted networks, and provide direct feedback to content curators. MINERVA enables mapping drug targets or overlaying experimental data on the visualized networks. Extensive export functions enable downloading areas of the visualized networks as SBGN-compliant models for efficient reuse of hosted networks. The software is available under Affero GPL 3.0 as a Virtual Machine snapshot, Debian package and Docker instance at http://r3lab.uni.lu/web/minerva-website/ ::: . We believe that MINERVA is an important contribution to systems biology community, as its architecture enables set-up of locally or globally accessible SBGN-oriented repositories of molecular interaction networks. Its functionalities allow overlay of multiple information layers, facilitating exploration of content and interpretation of data. Moreover, annotation and verification workflows of MINERVA improve the efficiency of curation of networks, allowing life-science researchers to better engage in development and use of biomedical knowledge repositories. A web platform that integrates data on biological networks can help scientists obtain deeper insights into the inner workings of the cell. Life is powered by the complex interplay between myriad 'pathways' composed of interacting proteins, DNA and other biomolecules. Biologists have accumulated vast troves of data on these processes, and now researchers led by Piotr Gawron at the University of Luxembourg have built software that can combine these disparate datasets into a more meaningful 'big picture'. The Molecular Interaction Networks Visualization (MINERVA) platform allows users to interact directly with graphical representations of biological pathways as a means to understand the broader cellular impacts of both diseases and the drugs intended to treat them. The resulting maps can then be manually curated, annotated and exported to other bioinformatics platforms for use by the broader research community.


title: Cryptographic Hash Functions: Recent Design Trends and Security Notions ∗

abstract: Recent years have witnessed an exceptional research interest in cryptographic hash functions, especially after the popular attacks against MD5 and SHA-1 in 2005. In 2007, the U.S. National Institute of Standards and Technology (NIST) has also signicantly boosted this interest by announcing a public competition to select the next hash function standard, to be named SHA-3. Not surprisingly, the hash function literature has since been rapidly growing in an extremely fast pace. In this paper, we provide a comprehensive, up-to-date discussion of the current state of the art of cryptographic hash functions security and design. We rst discuss the various hash functions security properties and notions, then proceed to give an overview of how (and why) hash functions evolved over the years giving raise to the current diverse hash functions design approaches.


A Distributed Architecture for Multiplayer Interactive Applications on the Internet


title: Web Page segmentation Using Block Function Tree

abstract: For modern websites currently available web page segmentation algorithms are not yet of practical use. This is what is observed from recent studies. In this paper, a Webpage Segmenter Using Block Function Tree has been described as a new approach for web page segmentation. It uses the fact that every block in the webpage plays a specific function that web designer designs the block for this function. The Segmentation process is divided into two phases: The first one is building the Block Function Tree and the second one is analyzing the resulting Block Function Tree. To support our claim, an evaluation is conducted. We found that our approach works well even with visually complex web pages.


Pothole Detection System using Machine Learning on Android


title: An Efficient Finger-knuckle-print based Recognition System Fusing SIFT and SURF Matching Scores

abstract: This paper presents a novel combination of local-local information for an efficient finger-knuckle-print (FKP) based recognition system which is robust to scale and rotation. The non-uniform brightness of the FKP due to relatively curvature surface is corrected and texture is enhanced. The local features of the enhanced FKP are extracted using the scale invariant feature transform (SIFT) and the speeded up robust features (SURF). Corresponding features of the enrolled and the query FKPs are matched using nearest-neighbour-ratio method and then the derived SIFT and SURF matching scores are fused using weighted sum rule. The proposed system is evaluated using PolyU FKP database of 7920 images for both identification mode and verification mode. It is observed that the system performs with CRR of 100% and EER of 0.215%. Further, it is evaluated against various scales and rotations of the query image and is found to be robust for query images downscaled upto 60% and for any orientation of query image.


title: IT2FS-based ontology with soft-computing mechanism for malware behavior analysis

abstract: Antimalware application is one of the most important research issues in the area of cyber security threat. Nowadays, because hackers continuously develop novel techniques to intrude into computer systems for various reasons, many security researchers should analyze and track new malicious program to protect sensitive and valuable information in the organization. In this paper, we propose a novel soft-computing mechanism based on the ontology model for malware behavioral analysis: Malware Analysis Network in Taiwan (MAN in Taiwan, MiT). The core techniques of MiT contain two parts listed as follows: (1) collect the logs of network connection, registry, and memory from the operation system on the physical-virtual hybrid analysis environment to get and extract more unknown malicious behavior information. The important information is then extracted to construct the ontology model by using the Web Ontology Language and Fuzzy Markup Language. Additionally, MiT is also able to automatically provide and share samples and reports via the cloud storage mechanism; (2) apply the techniques of Interval Type-2 Fuzzy Set to construct the malware analysis domain knowledge, namely the Interval Type-2 Fuzzy Malware Ontology (IT2FMO), for malware behavior analysis. Simulation results show that the proposed approach can effectively execute the malware behavior analysis, and the constructed system has also released under GNU General Public License version 3. In the future, the system is expected to largely collect and analyze malware samples for providing industries or universities to do related applications via the established IT2FMO.


1 Deep convolutional neural networks capabilities for 2 binary classification of polar mesocyclones in 3 satellite mosaics 4


title: Systematic literature reviews in software engineering – A systematic literature review

abstract: Background: In 2004 the concept of evidence-based software engineering (EBSE) was introduced at the ICSE04 conference. Aims: This study assesses the impact of systematic literature reviews (SLRs) which are the recommended EBSE method for aggregating evidence. Method: We used the standard systematic literature review method employing a manual search of 10 journals and 4 conference proceedings. Results: Of 20 relevant studies, eight addressed research trends rather than technique evaluation. Seven SLRs addressed cost estimation. The quality of SLRs was fair with only three scoring less than 2 out of 4. Conclusions: Currently, the topic areas covered by SLRs are limited. European researchers, particularly those at the Simula Laboratory appear to be the leading exponents of systematic literature reviews. The series of cost estimation SLRs demonstrate the potential value of EBSE for synthesising evidence and making it available to practitioners.


title: Student performance prediction model for early-identification of at-risk students in traditional classroom settings

abstract: Student performance prediction is one of the educational data mining tasks that has received great deal of attentions. It enables the educators to improve the quality and effectiveness of classroom management and to help students achieve better academic performance. However, one of the major challenges in developing a prediction model in a traditional classroom setting is the limited amount of information available to be used as student attributes. In this research, we focus on developing a performance prediction models in information-lacking environment for identifying at-risk students who have tendency to receive low grades. To construct the models, five algorithms with different learning paradigms were investigated. The constructed models were evaluated on 5 different subjects. The results have shown that even though the prediction performances for the subjects that less depended on acquirable previous performance information were quite low, the performances for the subjects offered during higher year of the educational program were quite promising.


title: A privacy preserving three-factor authentication protocol for e-Health clouds

abstract: E-Health clouds are gaining increasing popularity by facilitating the storage and sharing of big data in healthcare. However, such an adoption also brings about a series of challenges, especially, how to ensure the security and privacy of highly sensitive health data. Among them, one of the major issues is authentication, which ensures that sensitive medical data in the cloud are not available to illegal users. Three-factor authentication combining password, smart card and biometrics perfectly matches this requirement by providing high security strength. Recently, Wu et al. proposed a three-factor authentication protocol based on elliptic curve cryptosystem which attempts to fulfill three-factor security and resist various existing attacks, providing many advantages over existing schemes. However, we first show that their scheme is susceptible to user impersonation attack in the registration phase. In addition, their scheme is also vulnerable to offline password guessing attack in the login and password change phase, under the condition that the mobile device is lost or stolen. Furthermore, it fails to provide user revocation when the mobile device is lost or stolen. To remedy these flaws, we put forward a robust three-factor authentication protocol, which not only guards various known attacks, but also provides more desired security properties. We demonstrate that our scheme provides mutual authentication using the Burrows---Abadi---Needham logic.


title: A Coordinated Control Method for Leveling PV Output Power Fluctuations of PV–Diesel Hybrid Systems Connected to Isolated Power Utility

abstract: A photovoltaic (PV) system's power output is not constant and fluctuates depending on weather conditions. Fluctuating power causes frequency deviations and reduction in reliability of the isolated power utility or microgrid when large output power from several PV systems is penetrated in the utility. In this paper, to overcome these problems, a simple coordinated control method for leveling the fluctuations of combined power output from multiple PV systems is proposed. The conflicting objective of output power leveling and acquisition power increase is achieved by means of the proposed method. Here, output power command is generated in two steps: central and local. Fuzzy reasoning is used to generate the central leveling output power command considering insolation, variance of insolation, and absolute average of frequency deviation. In local step, a simple coordination is maintained between central power command and local power commands by producing a common tuning factor. Power converters are used to achieve the same output power as local command power employing PI control law for each of the PV generation systems. The proposed method is compared with the method where a modified maximum power point tracking control is used for smoothing the short-term change in each of the PV system's output. Simulation results show that the proposed method is effective for leveling output power fluctuations and feasible to reduce the frequency deviations of the isolated power utility to maintain reliability.


title: Deep Multi-view Learning to Rank

abstract: We study the problem of learning to rank from multiple sources. Though multi-view learning and learning to rank have been studied extensively leading to a wide range of applications, multi-view learning to rank as a synergy of both topics has received little attention. The aim of the paper is to propose a composite ranking method while keeping a close correlation with the individual rankings simultaneously. We propose a multi-objective solution to ranking by capturing the information of the feature mapping from both within each view as well as across views using autoencoder-like networks. Moreover, a novel end-to-end solution is introduced to enhance the joint ranking with minimum view-specific ranking loss, so that we can achieve the maximum global view agreements within a single optimization process. The proposed method is validated on a wide variety of ranking problems, including university ranking, multi-view lingual text ranking and image data ranking, providing superior results.


title: Terrain-adaptive locomotion skills using deep reinforcement learning

abstract: Reinforcement learning offers a promising methodology for developing skills for simulated characters, but typically requires working with sparse hand-crafted features. Building on recent progress in deep reinforcement learning (DeepRL), we introduce a mixture of actor-critic experts (MACE) approach that learns terrain-adaptive dynamic locomotion skills using high-dimensional state and terrain descriptions as input, and parameterized leaps or steps as output actions. MACE learns more quickly than a single actor-critic approach and results in actor-critic experts that exhibit specialization. Additional elements of our solution that contribute towards efficient learning include Boltzmann exploration and the use of initial actor biases to encourage specialization. Results are demonstrated for multiple planar characters and terrain classes.