title: SiC versus Si—Evaluation of Potentials for Performance Improvement of Inverter and DC–DC Converter Systems by SiC Power Semiconductors

abstract: Switching devices based on wide bandgap materials such as silicon carbide (SiC) offer a significant performance improvement on the switch level (specific on resistance, etc.) compared with Si devices. Well-known examples are SiC diodes employed, for example, in inverter drives with high switching frequencies. In this paper, the impact on the system-level performance, i.e., efficiency, power density, etc., of industrial inverter drives and of dc-dc converter resulting from the new SiC devices is evaluated based on analytical optimization procedures and prototype systems. There, normally on JFETs by SiCED and normally off JFETs by SemiSouth are considered.


title: Hidden Markov Models for Forex Trends Prediction

abstract: Foreign Exchange (Forex) market is a complex and challenging task for prediction due to uncertainty movement of exchange rate. However, these movements over timeframe also known as historical Forex data that offered a generic repeated trend patterns. This paper uses the features extracted from trend patterns to model and predict the next day trend. Hidden Markov Models (HMMs) is applied to learn the historical trend patterns, and use to predict the next day movement trends. We use the 2011 Forex historical data of Australian Dollar (AUS) and European Union Dollar (EUD) against the United State Dollar (USD) for modeling, and the 2012 and 2013 Forex historical data for validating the proposed model. The experimental results show outperforms prediction result for both years.


title: Social Capital and Health Care Access

abstract: There is a growing interest in community-level characteristics such as social capital and its relationship to health care access. To assess the rigor with which this construct has been empirically applied in research on health care access, a systematic review was conducted. A total of 2,396 abstracts were reviewed, and 21 met the criteria of examining some measure of social capital and its effects on health care access. The review found a lack of congruence in how social capital was measured and interpreted and a general inconsistency in findings, which made it difficult to draw firm conclusions about the effects of social capital on health care access. Insights from the social network literature can help improve the conceptual and measurement problems. Future work should distinguish among bonding, bridging, and linking social capital and their sources and benefits, and examine whether three dimensions of social capital actually exist: cognitive, behavioral, and structural.


title: Deep-Learning-Based Drug–Target Interaction Prediction

abstract: Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug–target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug–drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-a...


title: Resisting Rape The Effects of Victim Self-Protection on Rape Completion and Injury

abstract: The impact of victim resistance on rape completion and injury was examined utilizing a large probability sample of sexual assault incidents, derived from the National Crime Victimization Survey (1992-2002), and taking into account whether harm to the victim followed or preceded self-protection (SP) actions. Additional injuries besides rape, particularly serious injuries, following victim resistance are rare. Results indicate that most SP actions, both forceful and nonforceful, reduce the risk of rape completion, and do not significantly affect the risk of additional injury.


title: 3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes

abstract: While deep convolutional neural networks (CNN) have been successfully applied for 2D image analysis, it is still challenging to apply them to 3D anisotropic volumes, especially when the within-slice resolution is much higher than the between-slice resolution and when the amount of 3D volumes is relatively small. On one hand, direct learning of CNN with 3D convolution kernels suffers from the lack of data and likely ends up with poor generalization; insufficient GPU memory limits the model size or representational power. On the other hand, applying 2D CNN with generalizable features to 2D slices ignores between-slice information. Coupling 2D network with LSTM to further handle the between-slice information is not optimal due to the difficulty in LSTM learning. To overcome the above challenges, we propose a 3D Anisotropic Hybrid Network (AH-Net) that transfers convolutional features learned from 2D images to 3D anisotropic volumes. Such a transfer inherits the desired strong generalization capability for within-slice information while naturally exploiting between-slice information for more effective modelling. The focal loss is further utilized for more effective end-to-end learning. We experiment with the proposed 3D AH-Net on two different medical image analysis tasks, namely lesion detection from a Digital Breast Tomosynthesis volume, and liver and liver tumor segmentation from a Computed Tomography volume and obtain the state-of-the-art results.


On the security and usability of dynamic cognitive game CAPTCHAs


title: A link clustering based overlapping community detection algorithm

abstract: There is a surge of community detection study on complex network analysis in recent years, since communities often play important roles in network systems. However, many real networks have more complex overlapping community structures. This paper proposes a novel algorithm to discover overlapping communities. Different from conventional algorithms based on node clustering, the proposed algorithm is based on link clustering. Since links usually represent unique relations among nodes, the link clustering will discover groups of links that have the same characteristics. Thus nodes naturally belong to multiple communities. The algorithm applies genetic operation to cluster on links. An effective encoding schema is designed and the number of communities can be automatically determined. Experiments on both artificial networks and real networks validate the effectiveness and efficiency of the proposed algorithm.


title: Single shot object detection with top-down refinement

abstract: General object detection is one of the most challenging tasks in computer vision for it requires both high running speed and detection accuracy. In this paper, we propose a single shot object detector with top-down refinement, denoted as SSD-TDR. It not only runs at high speed and also detects multi-scale objects accurately. Concretely, original SSD directly adopts the built-in multi-scale hierarchy of convolutional neural networks for detection. However, object detection needs high semantic knowledge to recognise objects while low-level convolutional features do not have. We thus build a sequence of top-down refinement modules to transmit semantic knowledge backward such that all layers have rich semantics. Experiments on PASCAL VOC 2007 and 2012 demonstrate that our network achieves competitive results both in speed and accuracy compared to other VGG16 based networks.


title: A Load Balancing Strategy of SDN Controller Based on Distributed Decision

abstract: Software-Defined Networking (SDN), enabled by Open Flow, represents a paradigm shift from traditional network to the future Internet. Replicate or distributed controllers have been proposed to address the issues of availability and scalability that a centralized controller suffers from. However, it lacks a flexible mechanism to balance load among distributed controllers. To address this problem, this paper presents DALB, a dynamic and adaptive algorithm for controller load balancing totally based on distributed architecture, without any centralized component. This algorithm is running as a module of SDN controller. On one hand, it adopts an adjustable load collection threshold so as to reduce the overhead of exchanging messages for load collection, and on the other hand it can make policy and election locality in order to reduce the decision delay caused by network transmission. In this paper, we build the prototype system on floodlight to demonstrate our design and test the performance of our algorithm.


title: Enabling Security-Enhanced Attestation With Intel SGX for Remote Terminal and IoT

abstract: Along with the advent and popularity of cloud computing, Internet of Things, and bring your own device, the trust requirement for terminal devices has increased significantly. An untrusted terminal, a terminal that runs in an untrustworthy execution environment, may cause serious security issues for enterprise networks. With the release of Software Guard Extension, Intel has provided a promising way to construct trusted terminals and services. Utilizing this technology, we propose a security-enhanced attestation for remote terminals, which can achieve shielded execution for measurements and attestation programs. Furthermore, we present a policy-based measurement mechanism where sensitive data, including secret keys and policy details are concealed using the enclave-specific keys. We implement our attestation prototype on real platform with Intel Skylake processor. Evaluation results show that our attestation system can provide much stronger security guarantees, yet incurs small performance overhead.


title: Depth and Geometry from a Single 2D Image Using Triangulation

abstract: We present a novel method for computing depth of field and geometry from a single 2D image. This technique, unlike the existing ones measures the absolute depth of field and distances in the scene from single image only using the concept of triangulation. This algorithm requires minimum inputs such as camera height, camera pitch angle and camera field of view for computing the depth of field and 3D coordinates of any given point in the image. In addition, this method can be used to compute the actual size of an object in the scene (width and height) as well as the distance between different objects in the image. The proposed methodology has the potential to be implemented in high impact applications such as distance measurement from mobile phones, robot navigation and aerial surveillances.


title: Detection of xanthine oxidase and immunologically related proteins in fractions from bovine mammary tissue and milk after electrophoresis in polyacrylamide gels containing sodium dodecyl sulphate.

abstract: A solid-phase immunoassay was used to detect xanthine oxidase in fractions from bovine mammary glands after electrophoresis in polyacrylamide gels containing sodium dodecyl sulphate. Under these conditions the major proportion of xanthine oxidase in either mammary tissue or mild could be recovered as a protein of mol.wt. 150 000. In mammary tissue approx. 80% of the enzyme was in a soluble form and the remainder was accounted for in either 'mitochondrial' or microsomal fractions after tissue homogenization and fractionation. Affinity chromatography of either detergent-solubilized microsomal membranes or postmicrosomal supernatants on immobilized antibody to xanthine oxidase yielded a single protein that cross-reacted with antibody to the enzyme. In milk presumptive degradation products of the enzyme were detected in minor quantities with mol.wts. of 43 000 in the whey fraction and 90 000 in fat-globule membrane. Only the undegraded enzyme was present in the skim-milk membrane fraction. Xanthine oxidase is therefore synthesized and secreted as a protein with a monomeric mol.wt. of 150 000 and is not subjected to extensive proteolytic degradation during the storage of milk in mammary alveoli. The significance of the results is discussed in relation to the overall protein composition of the membranes of milk-fat globules and skim milk.


title: Using theory of planned behavior to predict healthy eating among Danish adolescents

abstract: Purpose – The purpose of the study was to apply the theory of planned behavior to predict Danish adolescents’ behavioral intention for healthy eating.Design/methodology/approach – A cluster sample survey of 410 students aged 11 to 16 years studying in Grade 6 to Grade 10 was conducted in Denmark.Findings – Perceived behavioral control followed by attitudes were the most important factors in predicting behavioral intention. Females and adolescents with a higher Body Mass Index were also found to have a stronger behavioral intention. Healthy eating was perceived to be beneficial and useful, and, to a lesser extent, interesting and desirable. Family, TV programs, and teachers were influential socialization agents.Research limitations/implications – The survey responses may be affected by a social desirability bias. The survey includes a non‐probability sample and results may not be generalized to all adolescents, even in Denmark.Practical implications – The results may inform educators and policy makers in d...


title: Beyond Sacrificial Harm: A Two-Dimensional Model of Utilitarian Psychology

abstract: Recent research has relied on ‘trolley’ type sacrificial moral dilemmas to study ‘utilitarian’ vs. ‘non-utilitarian’ modes of moral decision-making. This approach has generated important insights into people’s willingness to endorse instrumental harm in certain circumstances—typically, sacrificing one individual in order to save a greater number—but also has serious limitations. Most notably, it ignores the positive, altruistic core of utilitarianism, which is characterized by impartial concern for the well-being of everyone, whether near or far. Here, we develop, refine, and validate a new scale – the Oxford Utilitarianism Scale – to dissociate individual differences in the ‘negative’ (willingness to cause instrumental harm) and ‘positive’ (impartial concern about the greater good) dimensions of utilitarian thinking as manifested in the general population. We show that these are two independent dimensions of proto-utilitarian tendencies in the lay population, each exhibiting a distinct psychological profile. Empathic concern, identification with the whole of humanity, and concern for future generations were positively associated with Impartial Beneficence but negatively associated with Instrumental Harm, and while Instrumental Harm was associated with sub-clinical psychopathy, Impartial Beneficence was associated with higher religiosity. Importantly, while these two dimensions were independent in the lay population they were closely associated in a sample of moral philosophers. Acknowledging this dissociation between the Instrumental Harm and Impartial Beneficence components of utilitarian thinking in ordinary people can clarify existing debates about the nature of moral psychology and its relation to moral philosophy as well as generate fruitful avenues for further research.


title: Rationalist explanations for war

abstract: Realist and other scholars commonly hold that rationally led states can and sometimes do fight when no peaceful bargains exist that both would prefer to war. Against this view, I show that under very broad conditions there will exist negotiated settlements that genuinely rational states would mutually prefer to a risky and costly fight. Popular rationalist and realist explanations for war fail either to address or to explain adequately what would prevent leaders from locating a less costly bargain. Essentially just two mechanisms can resolve this puzzle on strictly rationalist terms. The first turns on the fact that states have both private information about capabilities and resolve and the incentive to misrepresent it. The second turns on the fact that in specific strategic contexts states may be unable credibly to commit to uphold a mutually preferable bargain. Historical examples suggest that both mechanisms are empirically plausible.


title: A microstrip-fed modified printed bow-tie antenna for simultaneous operation in the C and X-bands

abstract: This paper presents a modified printed bow-tie antenna designed to simultaneously operate in the C and X-bands from 5.5 to 12.5 GHz. This new antenna design provides an end fire radiation pattern suitable for integration in single and dual polarized phased array systems. The antenna exhibits small size and wide bandwidth approaching 91%. The radiation characteristics are presented for a single element and for a linear array configuration.


title: An empirical study on the susceptibility to social engineering in social networking sites: the case of Facebook

abstract: Research suggests that social engineering attacks pose a significant security risk, with social networking sites (SNSs) being the most common source of these attacks. Recent studies showed that social engineers could succeed even among those organizations that identify themselves as being aware of social engineering techniques. Although organizations recognize the serious risks of social engineering, there is little understanding and control of such threats. This may be partly due to the complexity of human behaviors in failing to recognize attackers in SNSs. Due to the vital role that impersonation plays in influencing users to fall victim to social engineering deception, this paper aims to investigate the impact of source characteristics on users’ susceptibility to social engineering victimization on Facebook. In doing so, we identify source credibility dimensions in terms of social engineering on Facebook, Facebook-based source characteristics that influence users to judge an attacker as per these dimensions, and mediation effects that these dimensions play between Facebook-based source characteristics and susceptibility to social engineering victimization.


title: From Isolation to Communication: A Case Study Evaluation of Robot Assisted Play for Children with Autism with a Minimally Expressive Humanoid Robot

abstract: The general context of the work presented in this paper is assistive robotics with our long-term aim to support children with autism. This paper is part of the Aurora project that studies ways in which robotic systems can encourage basic communication and social interaction skills in children with autism. This paper investigates how a small minimally expressive humanoid robot KASPAR can assume the role of a social mediator - encouraging children with low functioning autism to interact with the robot, to break their isolation and importantly, to facilitate interaction with other people. The article provides a case study evaluation of segments of trials where three children with autism, who usually do not interact with other people in their day to day activity, interacted with the robot and with co-present adults. A preliminary observational analysis was undertaken which applied, in abbreviated form, certain principles from conversation analysis - notably attention to the context in which the target behaviour occurred. The analysis was conducted by a social psychologist with expertise in using conversation analysis to understand interactions involving persons with an ASD. The analysis emphasises aspects of embodiment and interaction kinesics and revealed unexpected competencies on the part of the children. It showed how the robot served as a salient object mediating and encouraging interaction between the children and co-present adults.


title: A gentle introduction to mutual recursion

abstract: Recursion is an important topic in computer science curricula. It is related to the acquisition of competences regarding problem decomposition, functional abstraction and the concept of induction. In comparison with direct recursion, mutual recursion is considered to be more complex. Consequently, it is generally addressed superficially in CS1/2 programming courses and textbooks. We show that, when a problem is approached appropriately, not only can mutual recursion be a powerful tool, but it can also be easy to understand and fun. This paper provides several intuitive and attractive algorithms that rely on mutual recursion, and which have been designed to help strengthen students' ability to decompose problems and apply induction. Furthermore, we show that a solution based on mutual recursion may be easier to design, prove and comprehend than other solutions based on direct recursion. We have evaluated the use of these algorithms while teaching recursion concepts. Results suggest that mutual recursion, in comparison with other types of recursion, is not as hard as it seems when: (1) determining the result of a (mathematical) function call, and, most importantly, (2) designing algorithms for solving simple problems.


title: SemMedDB: a PubMed-scale repository of biomedical semantic predications

abstract: Summary: Effective access to the vast biomedical knowledge present in the scientific literature is challenging. Semantic relations are increasingly used in knowledge management applications supporting biomedical research to help address this challenge. We describe SemMedDB, a repository of semantic predications (subject–predicate–object triples) extracted from the entire set of PubMed citations. We propose the repository as a knowledge resource that can assist in hypothesis generation and literature-based discovery in biomedicine as well as in clinical decision-making support. ::: ::: Availability and implementation: The SemMedDB repository is available as a MySQL database for non-commercial use at http://skr3.nlm.nih.gov/SemMedDB. An UMLS Metathesaurus license is required. ::: ::: Contact: vog.hin.liam@hulgocilik


title: Pair programming in software development teams - An empirical study of its benefits

abstract: We present the results of an extensive and substantial case study on pair programming, which was carried out in courses for software development at the University of Dortmund, Germany. Thirteen software development teams with about 100 students took part in the experiments. The groups were divided into two sets with different working conditions. In one set, the group members worked on their projects in pairs. Even though the paired teams could only use half of the workstations the teams of individual workers could use, the paired teams produced nearly as much code as the teams of individual workers at the same time. In addition, the code produced by the paired teams was easier to read and to understand. This facilitates finding errors and maintenance.


Single Event Effects in Static and Dynamic Registers in a 0.25m CMOS Technology


title: ISM 433-MHz Miniaturized Antenna Using the Shielding Box of Mobile Terminals

abstract: A miniaturized monopole antenna for ISM 433-MHz frequency band is designed, fabricated, and measured. The whole structure proposed in this letter consists of a metal shielding box for transceiver circuit, a rectangular ground for battery, and a meander-line monopole. The size of the structure is 51 × 82 mm2, and the area left of the antenna itself is just about 49 × 13 mm2. To make the antenna work in such a small area, and maintain good performance, the metal shielding box of the transceiver circuit in this work is connected to the meander-line monopole. Actually, the shielding box does not serve as a part of the ground, but a part of the antenna itself, which plays an important role in matching and increasing the antenna's electrical length. Meanwhile, no additional area is needed compared to the conventional design. The measured - 10-dB bandwidth is 5 MHz. The comparison between the traditional design and the proposed design as well as the critical parameter study is presented in this letter.


Borrow a Little from your Rich Cousin: Using Embeddings and Polarities of English Words for Multilingual Sentiment Classification


title: 3DCapture: 3D Reconstruction for a Smartphone

abstract: We propose a method of reconstruction of 3D representation (a mesh with a texture) of an object on a smartphone with a monocular camera. The reconstruction consists of two parts – real-time scanning around the object and postprocessing. At the scanning stage IMU sensors data are acquired along with tracks of features in video. A special care is taken to comply with 360 scan requirement. All these data are used to build a camera trajectory using bundle adjustment techniques after scanning is completed. This trajectory is used in calculation of depth maps, which then are used to construct a polygonal mesh with overlaid textures. The proposed method ensures tracking at 30 fps on a modern smartphone while the post-processing part is completed within 1 minute using an OpenCL compatible mobile GPU. In addition, we show that with a few modifications this algorithm can be adopted for human face reconstruction.


title: Entropic Graph-based Posterior Regularization

abstract: Graph smoothness objectives have achieved great success in semi-supervised learning but have not yet been applied extensively to unsupervised generative models. We define a new class of entropic graph-based posterior regularizers that augment a probabilistic model by encouraging pairs of nearby variables in a regularization graph to have similar posterior distributions. We present a three-way alternating optimization algorithm with closed-form updates for performing inference on this joint model and learning its parameters. This method admits updates linear in the degree of the regularization graph, exhibits monotone convergence, and is easily parallelizable. We are motivated by applications in computational biology in which temporal models such as hidden Markov models are used to learn a human-interpretable representation of genomic data. On a synthetic problem, we show that our method outperforms existing methods for graph-based regularization and a comparable strategy for incorporating long-range interactions using existing methods for approximate inference. Using genome-scale functional genomics data, we integrate genome 3D interaction data into existing models for genome annotation and demonstrate significant improvements in predicting genomic activity.


Combining Montague Semantics and Discourse Representation


title: Hilbert Huang transform based speech recognition

abstract: In today's world, to make man-machine interaction more effective speech recognition plays an important role in speech processing. This paper presents the application of Hilbert-Huang transform (HHT), a mathematical tool applied for feature extraction phase of the speech signal processing. These features are modeled and evaluated using Vector Quantization(VQ) and Fuzzy C Means(FCM) techniques. The proposed system highlights the importance of Discrete Cosine Transformation(DCT) applied for Hilbert Huang Transform to extract the better speech signal parameters. The features obtained from this process has better recognition accuracies. It also demonstrates the efficiency of DCT with HHT for FCM clustering technique over the VQ technique. The performance of HHT- FCM is discussed with the termination criteria ‘e’ and fuzzifier ‘m’ parameters of FCM.


title: Hierarchical Complex Activity Representation and Recognition Using Topic Model and Classifier Level Fusion

abstract: Human activity recognition is an important area of ubiquitous computing. Most current researches in activity recognition mainly focus on simple activities, e.g., sitting, running, walking, and standing. Compared with simple activities, complex activities are more complicated with high-level semantics, e.g., working, commuting, and having a meal. This paper presents a hierarchical model to recognize complex activities as mixtures of simple activities and multiple actions. We generate the components of complex activities using a clustering algorithm, represent and recognize complex activities by applying a topic model on these components. It is a data-driven method that can retain effective information for representing and recognizing complex activities. In addition, acceleration and physiological signals are fused in classifier level to ensure the overall performance of complex activity recognition. The results of experiments show that our method has ability to represent and recognize complex activities effectively.


title: Iterative Adaptive Approaches to MIMO Radar Imaging

abstract: Multiple-input multiple-output (MIMO) radar can achieve superior performance through waveform diversity over conventional phased-array radar systems. When a MIMO radar transmits orthogonal waveforms, the reflected signals from scatterers are linearly independent of each other. Therefore, adaptive receive filters, such as Capon and amplitude and phase estimation (APES) filters, can be directly employed in MIMO radar applications. High levels of noise and strong clutter, however, significantly worsen detection performance of the data-dependent beamformers due to a shortage of snapshots. The iterative adaptive approach (IAA), a nonparametric and user parameter-free weighted least-squares algorithm, was recently shown to offer improved resolution and interference rejection performance in several passive and active sensing applications. In this paper, we show how IAA can be extended to MIMO radar imaging, in both the negligible and nonnegligible intrapulse Doppler cases, and we also establish some theoretical convergence properties of IAA. In addition, we propose a regularized IAA algorithm, referred to as IAA-R, which can perform better than IAA by accounting for unrepresented additive noise terms in the signal model. Numerical examples are presented to demonstrate the superior performance of MIMO radar over single-input multiple-output (SIMO) radar, and further highlight the improved performance achieved with the proposed IAA-R method for target imaging.


title: Lock-free deques and doubly linked lists

abstract: We present a practical lock-free shared data structure that efficiently implements the operations of a concurrent deque as well as a general doubly linked list. The implementation supports parallelism for disjoint accesses and uses atomic primitives which are available in modern computer systems. Previously known lock-free algorithms of doubly linked lists are either based on non-available atomic synchronization primitives, only implement a subset of the functionality, or are not designed for disjoint accesses. Our algorithm only requires single-word compare-and-swap atomic primitives, supports fully dynamic list sizes, and allows traversal also through deleted nodes and thus avoids unnecessary operation retries. We have performed an empirical study of our new algorithm on two different multiprocessor platforms. Results of the experiments performed under high contention show that the performance of our implementation scales linearly with increasing number of processors. Considering deque implementations and systems with low concurrency, the algorithm by Michael shows the best performance. However, as our algorithm is designed for disjoint accesses, it performs significantly better on systems with high concurrency and non-uniform memory architecture.


title: New Cryptographic Primitives

abstract: New communications paradigms such as wireless communications for handheld devices have increased requirements for operating speed of relevant security mechanisms, such as encryption for confidentiality, hashing for message integrity and combined hashing and public key encryption for digital signature. Existing secure services have traditionally been implemented by employing hybrid cryptographic protocols, i.e. combinations of symmetric and asymmetric (public key) algorithms to handle both effective key management and cryptographic performance. These solutions are in increasing degree becoming obsolete by the trend to connect end users with terminals with limited storage and processing power via wireless networks, sometimes established in a totally ad hoc fashion, with little or no centralized management to organize user authorization for access to secured services. New, lightweight protocols are in increasing demand, and the cryptographic primitives necessary for implementation of such protocols also need to be adapted to these new user scenarios.At NTNU/Q2S, research in this area has been ongoing for a period of time resulting in a number of new solutions adapted to the new paradigm encompassing mobile users of distributed communication services. In this talk, some of the general features of the new cryptographic primitives will be described. The designs are mainly based on new insight into the characteristics of quasigroups and quasi group string operations.


title: Novel architectures for efficient (m, n) parallel counters

abstract: Parallel counters are key elements in many arithmetic circuits, especially fast multipliers. In this paper, novel architectures and designs for high speed, low power (3,2), (7,3), (15,4) and (31,5) counters capable of operating at ultra-low voltages are presented. Based on these counters, a generalized architecture is derived for large (m, n) parallel counters. The proposed architecture lays emphasis on the use of multiplexers and a combination of CMOS and transmission gate logic in arithmetic circuits that result in high speed and efficient design. The proposed counter designs have been compared with existing designs and are shown to achieve an improvement of about 45% in delay and a reduction of about 25% in power consumption.


title: Success Factors for Agile Software Development - A Case Study from Sri Lanka

abstract: Agile software development methods provide great flexibility to adapt to changing requirements and rapidly market products. Sri Lankan software organizations too are embracing these methods to develop software products. Being an iterative an incremental software engineering methodology, agile philosophy promotes working software over comprehensive documentation and heavily relies on continuous customer collaboration throughout the life cycle of the product. Hence characteristics of the people involved with the project and their working environment plays an important role in the success of an agile project compared to any other software engineering methodology. This study investigated the factors that lead to the success of a project that adopts agile methodology in Sri Lanka. An online questionnaire was used to collect data to identify people and organizational factors that lead to project success. The sample consisted of Sri Lankan software professionals with several years of industry experience in developing projects using agile methods. According to the statistical data analysis, customer satisfaction, customer commitment, team size, corporate culture, technical competency, decision time, customer commitment and training and learning have a influence on the success of the project. General Terms Agile software development methods.


ScaleNet: Scale Invariant Network for Semantic Segmentation in Urban Driving Scenes


title: Topical web crawlers: Evaluating adaptive algorithms

abstract: Topical crawlers are increasingly seen as a way to address the scalability limitations of universal search engines, by distributing the crawling process across users, queries, or even client computers. The context available to such crawlers can guide the navigation of links with the goal of efficiently locating highly relevant target pages. We developed a framework to fairly evaluate topical crawling algorithms under a number of performance metrics. Such a framework is employed here to evaluate different algorithms that have proven highly competitive among those proposed in the literature and in our own previous research. In particular we focus on the tradeoff between exploration and exploitation of the cues available to a crawler, and on adaptive crawlers that use machine learning techniques to guide their search. We find that the best performance is achieved by a novel combination of explorative and exploitative bias, and introduce an evolutionary crawler that surpasses the performance of the best nonadaptive crawler after sufficiently long crawls. We also analyze the computational complexity of the various crawlers and discuss how performance and complexity scale with available resources. Evolutionary crawlers achieve high efficiency and scalability by distributing the work across concurrent agents, resulting in the best performance/cost ratio.


title: A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots

abstract: We present a robust deep learning based 6 degrees-of-freedom (DoF) localization system for endoscopic capsule robots. Our system mainly focuses on localization of endoscopic capsule robots inside the GI tract using only visual information captured by a mono camera integrated to the robot. The proposed system is a 23-layer deep convolutional neural network (CNN) that is capable to estimate the pose of the robot in real time using a standard CPU. The dataset for the evaluation of the system was recorded inside a surgical human stomach model with realistic surface texture, softness, and surface liquid properties so that the pre-trained CNN architecture can be transferred confidently into a real endoscopic scenario. An average error of 7:1% and 3:4% for translation and rotation has been obtained, respectively. The results accomplished from the experiments demonstrate that a CNN pre-trained with raw 2D endoscopic images performs accurately inside the GI tract and is robust to various challenges posed by reflection distortions, lens imperfections, vignetting, noise, motion blur, low resolution, and lack of unique landmarks to track.


title: Improving Block-level Efficiency with scsi-mq

abstract: Current generation solid-state storage devices are exposing a new bottlenecks in the SCSI and block layers of the Linux kernel, where IO throughput is limited by lock contention, inefficient interrupt handling, and poor memory locality. To address these limitations, the Linux kernel block layer underwent a major rewrite with the blk-mq project to move from a single request queue to a multi-queue model. The Linux SCSI subsystem rework to make use of this new model, known as scsi-mq, has been merged into the Linux kernel and work is underway for dm-multipath support in the upcoming Linux 4.0 kernel. These pieces were necessary to make use of the multi-queue block layer in a Lustre parallel filesystem with high availability requirements. We undertook adding support of the 3.18 kernel to Lustre with scsi-mq and dm-multipath patches to evaluate the potential of these efficiency improvements. In this paper we evaluate the block-level performance of scsi-mq with backing storage hardware representative of a HPC-targerted Lustre filesystem. Our findings show that SCSI write request latency is reduced by as much as 13.6%. Additionally, when profiling the CPU usage of our prototype Lustre filesystem, we found that CPU idle time increased by a factor of 7 with Linux 3.18 and blk-mq as compared to a standard 2.6.32 Linux kernel. Our findings demonstrate increased efficiency of the multi-queue block layer even with disk-based caching storage arrays used in existing parallel filesystems.


title: Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks

abstract: Motivation: Clinical data, such as patient history, laboratory analysis, ultrasound parameters—which are the basis of day-to-day clinical decision support—are often underused to guide the clinical management of cancer in the presence of microarray data. We propose a strategy based on Bayesian networks to treat clinical and microarray data on an equal footing. The main advantage of this probabilistic model is that it allows to integrate these data sources in several ways and that it allows to investigate and understand the model structure and parameters. Furthermore using the concept of a Markov Blanket we can identify all the variables that shield off the class variable from the influence of the remaining network. Therefore Bayesian networks automatically perform feature selection by identifying the (in)dependency relationships with the class variable. Results: We evaluated three methods for integrating clinical and microarray data: decision integration, partial integration and full integration and used them to classify publicly available data on breast cancer patients into a poor and a good prognosis group. The partial integration method is most promising and has an independent test set area under the ROC curve of 0.845. After choosing an operating point the classification performance is better than frequently used indices. Contact: olivier.gevaert@esat.kuleuven.be


title: Making Smart Contracts Smarter

abstract: Cryptocurrencies record transactions in a decentralized data structure called a blockchain. Two of the most popular cryptocurrencies, Bitcoin and Ethereum, support the feature to encode rules or scripts for processing transactions. This feature has evolved to give practical shape to the ideas of smart contracts, or full-fledged programs that are run on blockchains. Recently, Ethereum's smart contract system has seen steady adoption, supporting tens of thousands of contracts, holding millions dollars worth of virtual coins. In this paper, we investigate the security of running smart contracts based on Ethereum in an open distributed network like those of cryptocurrencies. We introduce several new security problems in which an adversary can manipulate smart contract execution to gain profit. These bugs suggest subtle gaps in the understanding of the distributed semantics of the underlying platform. As a refinement, we propose ways to enhance the operational semantics of Ethereum to make contracts less vulnerable. For developers writing contracts for the existing Ethereum system, we build a symbolic execution tool called Oyente to find potential security bugs. Among 19, 336 existing Ethereum contracts, Oyente flags 8, 833 of them as vulnerable, including the TheDAO bug which led to a 60 million US dollar loss in June 2016. We also discuss the severity of other attacks for several case studies which have source code available and confirm the attacks (which target only our accounts) in the main Ethereum network.


title: A file-system-aware FTL design for flash-memory storage systems

abstract: As flash memory became popular over various platforms, there is a strong demand on the performance degradation problem, due to the special characteristics of flash memory. This research proposes the design of a file-system-aware flash translation layer, in which a filter mechanism is designed to separate the access requests of file-system metadata and file contents for better performance. A recovery scheme is then proposed to maintain the integrity of a file system. The proposed flash translation layer is implemented as a Linux device driver and evaluated with respect to ext2 and ext3 file systems. The experimental results show significant performance improvement over ext2 and ext3 file systems with limited system overheads.


title: Head mouse system based on gyro- and opto-sensors

abstract: We proposed the mouse to control a computer mouse with head rotations and eye blinks so that the severe disabled person can use a computer. We compared the performance of the proposed mouse to that of a general computer mouse and to that of alternative mouses such as Quick glance using eye movements and a Camera mouse using face movements. The mouse positions were estimated using gyro sensors that can measure the angular velocity of head rotations, and mouse events such as clicks/double clicks were detected using opto-sensors that can detect blinking of the eyes. We could eliminate the need to perform the cumbersome task of periodically removing the accumulated error. We facilitated intuitive control of the mouse by using a nonlinear relative coordinates system with dead zones. We added circuitry to opto-sensors for event detection in order to reduce the influence of the ambient light changes. As a result, we could increase the mouse movements and events accuracy. The proposed mouse showed superior performance about 21% in the experiment of the 20 times clicks, about 25% of Dasher, and about 37% of on-screen keyboard, respectively. The mouse is also better than Camera Mouse, which is used along with a web cam.


title: Statistical Approach to Shape from Shading: Reconstruction of Three-Dimensional Face Surfaces from Single Two-Dimensional Images

abstract: The human visual system is proficient in perceiving three-dimensional shape from the shading patterns in a two-dimensional image. How it does this is not well understood and continues to be a question of fundamental and practical interest. In this paper we present a new quantitative approach to shape-from-shading that may provide some answers. We suggest that the brain, through evolution or prior experience, has discovered that objects can be classified into lower-dimensional object-classes as to their shape. Extraction of shape from shading is then equivalent to the much simpler problem of parameter estimation in a low-dimensional space. We carry out this proposal for an important class of three-dimensional (3D) objects: human heads. From an ensemble of several hundred laser-scanned 3D heads, we use principal component analysis to derive a low-dimensional parameterization of head shape space. An algorithm for solving shape-from-shading using this representation is presented. It works well even on real images where it is able to recover the 3D surface for a given person, maintaining facial detail and identity, from a single 2D image of his face. This algorithm has applications in face recognition and animation.


title: Tiles: A Card-based Ideation Toolkit for the Internet of Things

abstract: The Internet of Things (IoT) offers new opportunities to invent technology-augmented things that are more useful, efficient or playful than their ordinary selves, yet only a few tools currently support ideation for the IoT. In this paper we present Tiles Cards, a set of 110 design cards and a workshop technique to involve non-experts in quick idea generation for augmented objects. Our tool aims to support exploring combinations of user interface metaphors, digital services, and physical objects. Then it supports creative thinking through provocative design goals inspired by human values and desires. Finally, it provides critical lenses through which analyze and judge design outcomes. We evaluated our tool in 9 ideation workshops with a total of 32 participants. Results show that the tool was useful in informing and guiding idea generation and was perceived as appealing and fun. Drawing on observations and participant feedbacks, we reflect on the strengths and limitations of this tool.


title: The diet and moderate exercise trial (DAMET): results after 24 weeks.

abstract: W.H.O. and U.S. Dept. of Health and Human Services advocate a fat-modified, fruits and vegetable-enriched diet in conjunction with mode-rate physical activity for fitness and health. The Diet and Moderate Exercise Trial (DAMET) is a randomized and controlled study and has provided scientific proof, possibly for the first time, to the above hypothesis (based on epidemiologic studies, short clinical trials and experimental studies) that the new approach can cause modulation of risk factors of coronary heart disease (CHD) as well as improve fitness. In the DAMET, 231 group A and 232 group B patients with risk factors of CHD were administered a prudent diet for 4 weeks, group A patients in addition were also given at least 400 g/day of fruits and vegetables that are rich in dietary fibre and antioxidants such as vitamins A, C, E, carotene and copper, selenium and magnesium. Fruits and vegetables were administered in a foods-to-eat approach by asking the patients to eat these foods before meals when they were hungry to allow better nutrient adequacy. After 4 weeks, group A patients also did moderate exercise such as brisk walking and spot running compared to no such advice to group B for another 20 weeks. After a follow-up period of 24 weeks, adding exercise to diet was associated with a significant decrease in blood total cholesterol (8.9%) and LDL-cholesterol (6.7%) and triglycerides (11.9%) and a marked increase in HDL-cholesterol (16.5%). Mean blood pressures, fasting blood glucose, body weight, body mass index, waist-to-hip ratio and subcutaneous fat also showed a significant decrease in group A, leading to a significant decrease in 12-year CHD risk. A long-term follow-up may be necessary to demonstrate the role of this new approach in decreasing cardiovascular morbidity and mortality.


title: COROLA: A Sequential Solution to Moving Object Detection Using Low-rank Approximation

abstract: Abstract Extracting moving objects from a video sequence and estimating the background of each individual image are fundamental issues in many practical applications such as visual surveillance, intelligent vehicle navigation, and traffic monitoring. Recently, some methods have been proposed to detect moving objects in a video via low-rank approximation and sparse outliers where the background is modeled with the computed low-rank component of the video and the foreground objects are detected as the sparse outliers in the low-rank approximation. Many of these existing methods work in a batch manner, preventing them from being applied in real time and long duration tasks. To address this issue, some online methods have been proposed; however, existing online methods fail to provide satisfactory results under challenging conditions such as dynamic background scene and noisy environments. In this paper, we present an online sequential framework, namely contiguous outliers representation via online low-rank approximation (COROLA), to detect moving objects and learn the background model at the same time. We also show that our model can detect moving objects with a moving camera. Our experimental evaluation uses simulated data and real public datasets to demonstrate the superior performance of COROLA to the existing batch and online methods in terms of both accuracy and efficiency.


title: The language of RNA: a formal grammar that includes pseudoknots

abstract: Motivation: In a previous paper, we presented a polynomial time dynamic programming algorithm for predicting optimal RNA secondary structure including pseudoknots. However, a formal grammatical representation for RNA secondary structure with pseudoknots was still lacking. Results: Here we show a one-to-one correspondence between that algorithm and a formal transformational grammar. This grammar class encompasses the contextfree grammars and goes beyond to generate pseudoknotted structures. The pseudoknot grammar avoids the use of general context-sensitive rules by introducing a small number of auxiliary symbols used to reorder the strings generated by an otherwise context-free grammar. This formal representation of the residue correlations in RNA structure is important because it means we can build full probabilistic models of RNA secondary structure, including pseudoknots, and use them to optimally parse sequences in polynomial time.


title: An efficient protocol for authenticated and secure message delivery in vehicular ad hoc networks

abstract: In Vehicular Ad hoc Networks (VANETs), anonymity of the nodes sending messages should be preserved, while at the same time the law enforcement agencies should be able to trace the messages to the senders when necessary. It is also necessary that the messages sent are authenticated and delivered to the vehicles in the relevant areas quickly. In this paper, we present an efficient protocol for fast dissemination of authenticated messages in VANETs. It ensures the anonymity of the senders and also provides mechanism for law enforcement agencies to trace the messages to their senders, when necessary.


title: Towards Knowledge Transfer in Deep Reinforcement Learning

abstract: Driven by recent developments in the area of Artificial Intelligence research, a promising new technology for building intelligent agents has evolved. The technology is termed Deep Reinforcement Learning (DRL) and combines the classic field of Reinforcement Learning (RL) with the representational power of modern Deep Learning approaches. DRL enables solutions for difficult and high dimensional tasks, such as Atari game playing, for which previously proposed RL methods were inadequate. However, these new solution approaches still take a long time to learn how to actuate in such domains and so far are mainly researched for single task scenarios. The ability to generalize gathered knowledge and transfer it to another task has been researched for classical RL, but remains an open problem for the DRL domain. Consequently, in this article we evaluate under which conditions the application of Transfer Learning (TL) to the DRL domain improves the learning of a new task. Our results indicate that TL can greatly accelerate DRL when transferring knowledge from similar tasks, and that the similarity between tasks plays a key role in the success or failure of knowledge transfer.


title: Wideband Circularly Polarized Substrate Integrated Cavity-Backed Antenna Array

abstract: In this letter, a 4 ×4 substrate integrated waveguide (SIW)-fed circularly polarized (CP) antenna array with a broad axial-ratio (AR) bandwidth is designed and fabricated by multilayer printed circuit board (PCB) technology. The antenna array consists of 16 sequentially rotated elliptical cavities fed by slots on the SIW acting as the radiating elements, four 1-to-4 SIW power dividers, and a transition from a coaxial cable to the SIW. The widened AR bandwidth of the antenna array is achieved by using an improved SIW power divider. The antenna prototype was fabricated and measured, and the discrepancies between simulations and measurements are carefully analyzed.


title: What the brain ‘Likes’: neural correlates of providing feedback on social media

abstract: Evidence increasingly suggests that neural structures that respond to primary and secondary rewards are also implicated in the processing of social rewards. The 'Like'-a popular feature on social media-shares features with both monetary and social rewards as a means of feedback that shapes reinforcement learning. Despite the ubiquity of the Like, little is known about the neural correlates of providing this feedback to others. In this study, we mapped the neural correlates of providing Likes to others on social media. Fifty-eight adolescents and young adults completed a task in the MRI scanner designed to mimic the social photo-sharing app Instagram. We examined neural responses when participants provided positive feedback to others. The experience of providing Likes to others on social media related to activation in brain circuity implicated in reward, including the striatum and ventral tegmental area, regions also implicated in the experience of receiving Likes from others. Providing Likes was also associated with activation in brain regions involved in salience processing and executive function. We discuss the implications of these findings for our understanding of the neural processing of social rewards, as well as the neural processes underlying social media use.


title: SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation

abstract: Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the STS Benchmark is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012-2017).


title: Augmented Reality with Tangible Auto-Fabricated Models for Molecular Biology Applications

abstract: The evolving technology of computer auto-fabrication ("3-D printing") now makes it possible to produce physical models for complex biological molecules and assemblies. We report on an application that demonstrates the use of auto-fabricated tangible models and augmented reality for research and education in molecular biology, and for enhancing the scientific environment for collaboration and exploration. We have adapted an augmented reality system to allows virtual 3-D representations (generated by the Python Molecular Viewer) to be overlaid onto a tangible molecular model. Users can easily change the overlaid information, switching between different representations of the molecule, displays of molecular properties such as electrostatics, or dynamic information. The physical model provides a powerful, intuitive interface for manipulating the computer models, streamlining the interface between human intent, the physical model, and the computational activity.


Low-Order Multi-Level Features for Speech Emotion Recognition


title: Fact, fiction, and feng shui: an exploratory study

abstract: Purpose – This exploratory study aims to determine whether a relationship is likely to exist between good feng shui and success.Design/methodology/approach – The feng shui of eight hotel foyers and entrances were evaluated against 20 criteria, and managers asked to comment on the success of their hotels. Results were examined for possible relationships between feng shui and their descriptions of success.Findings – Similarities between the reported success of hotel and feng shui evaluations were found in six out of the eight hotels in the study.Research limitations/implications – Responses on success were subjective and based on five criteria, which were insufficient to determine the existence of a firm relationship between actual success and feng shui. However, this was an exploratory study, and the relationships were sufficiently strong to warrant further research.Practical implications – Findings suggest that feng shui offers useful design principles, and hotels with good feng shui are described as bein...


title: Underwater Image Enhancement by Wavelength Compensation and Dehazing

abstract: Light scattering and color change are two major sources of distortion for underwater photography. Light scattering is caused by light incident on objects reflected and deflected multiple times by particles present in the water before reaching the camera. This in turn lowers the visibility and contrast of the image captured. Color change corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by a bluish tone. No existing underwater processing techniques can handle light scattering and color change distortions suffered by underwater images, and the possible presence of artificial lighting simultaneously. This paper proposes a novel systematic approach to enhance underwater images by a dehazing algorithm, to compensate the attenuation discrepancy along the propagation path, and to take the influence of the possible presence of an artifical light source into consideration. Once the depth map, i.e., distances between the objects and the camera, is estimated, the foreground and background within a scene are segmented. The light intensities of foreground and background are compared to determine whether an artificial light source is employed during the image capturing process. After compensating the effect of artifical light, the haze phenomenon and discrepancy in wavelength attenuation along the underwater propagation path to camera are corrected. Next, the water depth in the image scene is estimated according to the residual energy ratios of different color channels existing in the background light. Based on the amount of attenuation corresponding to each light wavelength, color change compensation is conducted to restore color balance. The performance of the proposed algorithm for wavelength compensation and image dehazing (WCID) is evaluated both objectively and subjectively by utilizing ground-truth color patches and video downloaded from the Youtube website. Both results demonstrate that images with significantly enhanced visibility and superior color fidelity are obtained by the WCID proposed.


title: Optimal Linear Cyber-Attack on Remote State Estimation

abstract: Recent years have witnessed the surge of interest of security issues in cyber-physical systems. In this paper, we consider malicious cyber attacks in a remote state estimation application where a smart sensor node transmits data to a remote estimator equipped with a false data detector. It is assumed that all the sensor data can be observed and modified by the malicious attacker and a residue-based detection algorithm is used at the remote side to detect data anomalies. We propose a linear deception attack strategy and present the corresponding feasibility constraint which guarantees that the attacker is able to successfully inject false data without being detected. The evolution of the estimation error covariance at the remote estimator is derived and the degradation of system performance under the proposed linear attack policy is analyzed. Furthermore, we obtain a closed-form expression of the optimal attack strategy among all linear attacks. Comparison of attack strategies through simulated examples are provided to illustrate the theoretical results.


title: Cerebral mechanisms of word masking and unconscious repetition priming

abstract: We used functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs) to visualize the cerebral processing of unseen masked words. Within the areas associated with conscious reading, masked words activated left extrastriate, fusiform and precentral areas. Furthermore, masked words reduced the amount of activation evoked by a subsequent conscious presentation of the same word. In the left fusiform gyrus, this repetition suppression phenomenon was independent of whether the prime and target shared the same case, indicating that case-independent information about letter strings was extracted unconsciously. In comparison to an unmasked situation, however, the activation evoked by masked words was drastically reduced and was undetectable in prefrontal and parietal areas, correlating with participants' inability to report the masked words.


title: Fiscal Federalism, Democracy and Trade Integration. The Case of Brazilian VAT

abstract: In a context of generalization of decentralization in the world going "hand in hand" with democratization and globalization, Brazil qualifies the universality of this process, at least in matter of fiscal federalism. In the paper, we firstly adopt a comparative perspective relative to an important component of fiscal revenue: VAT. The main differences between the two fiscal system –VAT in EU and ICMS in Brazil- is the "rule of origin" prominent in intra-Brazilian trade vs the "rule of destination" dominant in intra-European trade. We show how the Brazilian system induces distortions as well in trade between States as foreign trade. It can drive to fiscal wars between States. We confirm that if exports have no impact on VAT revenues, imports increase them. Even non-equalitarian, we show a trend in favor of convergence in VAT revenues between Brazilian states.


title: Understanding the Sources of Online Travel Information

abstract: Given that online travel media enable multilateral communication patterns in destination marketing, this study investigates technical attributes presenting the number and type of source-related visual cues. From a technological perspective, the sources of online travel information can be conceptualized in terms of specialization, endorsement, and other users’ star rating to reflect technological functions and psychological effects. An experiment with a 2 (specialization: a generalist website vs. a specialist website) × 2 (endorsement: absence vs. presence) × 3 (star rating: low vs. medium vs. high) factorial between-subjects design was conducted to test the relationships between source-related visual cues, cue-induced perceptions, information credibility, and destination images. This study found that each source-related visual cue produced distinctive psychological effects on a tourist’s perceptions. Furthermore, these cue-induced perceptions were influential to tourists’ judgment of information credibili...


title: The Social Psychology of False Confessions: Compliance, Internalization, and Confabulation

abstract: An experiment demonstrated that false incriminating evidence can lead people to accept guilt for a crime they did not commit Subjects in a fast- or slow-paced reaction time task were accused of damaging a computer by pressing the wrong key All were truly innocent and initially denied the charge A confederate then said she saw the subject hit the key or did not see the subject hit the key Compared with subjects in the slow-pacelno-witness group, those in the fast-pace/witness group were more likely to sign a confession, internalize guilt for the event, and confabulate details in memory consistent with that belief Both legal and conceptual implications are discussed


title: Balanced-to-Unbalanced Bandpass Filters and the Antenna Application

abstract: Novel balanced-to-unbalanced (balun) bandpass filters are presented and carefully examined. By properly converting a symmetric four-port balanced-to-balanced bandpass filter to a three-port device, with two ports forming a balanced output and one port remaining unbalanced, a novel three-port balun bandpass filter with both functions of balun and bandpass filter may be realized. Specifically, a balun bandpass filter is implemented with its center frequency at 0.99 GHz and a 3-dB fractional bandwidth (Bomega) of 10.1%. The implemented balun bandpass filter presents an excellent in-band balance performance with common-mode rejection ratio (Rcm) better than 41 dB (the corresponding amplitude and phase imbalances are within 0.003deg and 1deg) over the pass- band. In this study, a wideband balun bandpass filter is also implemented so as to improve the wideband performance of the quasi- Yagi antenna. Being integrated with the proposed wideband balun bandpass filter, the implemented quasi-Yagi antenna achieves a bandwidth of 48% for IS11 | < -10 dB, with front-to-back ratio better than 13 dB, cross-polarization smaller than -20 dB, and peak gain of 3-5 dBi within the wide operating bandwidth.


title: RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation

abstract: Indoor semantic segmentation has always been a difficult task in computer vision. In this paper, we propose an RGB-D residual encoder-decoder architecture, named RedNet, for indoor RGB-D semantic segmentation. In RedNet, the residual module is applied to both the encoder and decoder as the basic building block, and the skip-connection is used to bypass the spatial feature between the encoder and decoder. In order to incorporate the depth information of the scene, a fusion structure is constructed, which makes inference on RGB image and depth image separately, and fuses their features over several layers. In order to efficiently optimize the network's parameters, we propose a `pyramid supervision' training scheme, which applies supervised learning over different layers in the decoder, to cope with the problem of gradients vanishing. Experiment results show that the proposed RedNet(ResNet-50) achieves a state-of-the-art mIoU accuracy of 47.8% on the SUN RGB-D benchmark dataset.


title: Smart Augmentation Learning an Optimal Data Augmentation Strategy

abstract: A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks. There are many techniques to address this, including data augmentation, dropout, and transfer learning. In this paper, we introduce an additional method, which we call smart augmentation and we show how to use it to increase the accuracy and reduce over fitting on a target network. Smart augmentation works, by creating a network that learns how to generate augmented data during the training process of a target network in a way that reduces that networks loss. This allows us to learn augmentations that minimize the error of that network. Smart augmentation has shown the potential to increase accuracy by demonstrably significant measures on all data sets tested. In addition, it has shown potential to achieve similar or improved performance levels with significantly smaller network sizes in a number of tested cases.


title: Reading Text in the Wild with Convolutional Neural Networks

abstract: In this work we present an end-to-end system for text spotting--localising and recognising text in natural scene images--and text based image retrieval. This system is based on a region proposal mechanism for detection and deep convolutional neural networks for recognition. Our pipeline uses a novel combination of complementary proposal generation techniques to ensure high recall, and a fast subsequent filtering stage for improving precision. For the recognition and ranking of proposals, we train very large convolutional neural networks to perform word recognition on the whole proposal region at the same time, departing from the character classifier based systems of the past. These networks are trained solely on data produced by a synthetic text generation engine, requiring no human labelled data. Analysing the stages of our pipeline, we show state-of-the-art performance throughout. We perform rigorous experiments across a number of standard end-to-end text spotting benchmarks and text-based image retrieval datasets, showing a large improvement over all previous methods. Finally, we demonstrate a real-world application of our text spotting system to allow thousands of hours of news footage to be instantly searchable via a text query.


title: Mobile healthcare applications: system design review, critical issues and challenges

abstract: Mobile phones are becoming increasingly important in monitoring and delivery of healthcare interventions. They are often considered as pocket computers, due to their advanced computing features, enhanced preferences and diverse capabilities. Their sophisticated sensors and complex software applications make the mobile healthcare (m-health) based applications more feasible and innovative. In a number of scenarios user-friendliness, convenience and effectiveness of these systems have been acknowledged by both patients as well as healthcare providers. M-health technology employs advanced concepts and techniques from multidisciplinary fields of electrical engineering, computer science, biomedical engineering and medicine which benefit the innovations of these fields towards healthcare systems. This paper deals with two important aspects of current mobile phone based sensor applications in healthcare. Firstly, critical review of advanced applications such as; vital sign monitoring, blood glucose monitoring and in-built camera based smartphone sensor applications. Secondly, investigating challenges and critical issues related to the use of smartphones in healthcare including; reliability, efficiency, mobile phone platform variability, cost effectiveness, energy usage, user interface, quality of medical data, and security and privacy. It was found that the mobile based applications have been widely developed in recent years with fast growing deployment by healthcare professionals and patients. However, despite the advantages of smartphones in patient monitoring, education, and management there are some critical issues and challenges related to security and privacy of data, acceptability, reliability and cost that need to be addressed.


title: Contour-based feature extraction for image classification and retrieval

abstract: We propose a feature extraction scheme for application on image classification and retrieval that is based on shapes' contours, while discarding information within the boundaries such as colour and texture. The center of mass and opposite distances are calculated for every contour pixel and used to measure distances between pairs of images that are invariant to common transformations. We apply the k-nearest neighbours (k-NN) algorithm to classify/retrieve a query image according to the k closest images' classes. The resulting success rates were computed for the Kimia, MPEG-7 and Tari image data sets and compared with those of other techniques.


title: Expert support systems: integrating AI technologies

abstract: The computer's impact on business in recent years is nothing short of incredible. Both hardware and software technologies have been advancing at a very fast pace and computers have become a fixture in virtually every modern office. Sophisticated software has been developed that will assist managers in their decision- making processes and also enable computers to think like human experts. These software packages are respectively known as decision support systems (DSS) and expert systems (ES). A DSS is a computer-based system that can help decision makers address semistructured problems by allowing them to access and use data and analytic models


title: Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions

abstract: We describe tree edit models for representing sequences of tree transformations involving complex reordering phenomena and demonstrate that they offer a simple, intuitive, and effective method for modeling pairs of semantically related sentences. To efficiently extract sequences of edits, we employ a tree kernel as a heuristic in a greedy search routine. We describe a logistic regression model that uses 33 syntactic features of edit sequences to classify the sentence pairs. The approach leads to competitive performance in recognizing textual entailment, paraphrase identification, and answer selection for question answering.


title: Simplified Frame Rate Up-conversion algorithm with low computational complexity

abstract: This paper proposes the efficient Frame Rate Up-conversion (FRUC) that has low computational complexity. The proposed algorithm consists of motion vector (MV) smoothing, selective average based motion compensation (SAMC) and hole interpolation with different weights. The proposed MV smoothing constructs more smooth interpolated frames by correcting inaccurate MVs. The proposed SAMC and hole interpolation effectively deal with overlaps and holes, and thus, they can efficiently reduce the degradation of the interpolated frames by removing blocking artifacts and blurring. Experimental results show that the proposed algorithm improves the average PSNR of the interpolated frames by 4.15dB than the conventional algorithm using bilateral ME, while it shows the average 0.16dB less PSNR performance than the existing algorithm using unilateral ME. However, it can significantly reduce the computational complexity based on absolute difference by 89.3%.


title: Reverse-Engineering Visualizations: Recovering Visual Encodings from Chart Images

abstract: We investigate how to automatically recover visual encodings from a chart image, primarily using inferred text elements. We contribute an end-to-end pipeline which takes a bitmap image as input and returns a visual encoding specification as output. We present a text analysis pipeline which detects text elements in a chart, classifies their role e.g., chart title, x-axis label, y-axis title, etc., and recovers the text content using optical character recognition. We also train a Convolutional Neural Network for mark type classification. Using the identified text elements and graphical mark type, we can then infer the encoding specification of an input chart image. We evaluate our techniques on three chart corpora: a set of automatically labeled charts generated using Vega, charts from the Quartz news website, and charts extracted from academic papers. We demonstrate accurate automatic inference of text elements, mark types, and chart specifications across a variety of input chart types.


title: Mobile cloud computing: advantage, disadvantage and open challenge

abstract: With modern smart phones and powerful mobile devices, Mobile apps provide many advantages to the community but it has also grown the demand for online availability and accessibility. Cloud computing is provided to be widely adopted for several applications in mobile devices. However, there are many advantages and disadvantages of using mobile applications and cloud computing. This paper focuses in providing an overview of mobile cloud computing advantages, disadvantages. The paper discusses the importance of mobile cloud applications and highlights the mobile cloud computing open challenges


title: A Low Quiescent Current Asynchronous Digital-LDO With PLL-Modulated Fast-DVS Power Management in 40 nm SoC for MIPS Performance Improvement

abstract: A low quiescent current asynchronous digital- LDO (D-LDO) regulator integrated with a phase-locked loop (PLL)-modulated switching regulator (SWR) that achieves the near-optimum power management supply for core processor in system-on-chip (SoC). The parallel connection of the asynchronous D-LDO regulator and the ripple-based control SWR can accomplish fast-DVS (F-DVS) operation as well as high power conversion efficiency. The asynchronous D-LDO regulator controlled by bidirectional asynchronous wave pipeline realizes the F-DVS operation, which guarantees high million instructions per second (MIPS) performance of the core processor under distinct tasks. The use of a ripple-based control SWR operating with a leading phase amplifier ensures fast response and stable operation without the need for large equivalent-series-resistance, thus reducing the output voltage ripple for the enhancement of supply quality. The fabricated chip occupies 1.04 mm2 in 40 nm CMOS technology. Experimental results show that a 94% peak efficiency with a voltage tracking speed of 7.5 V/μs as well as the improved MIPS performance by 5.6 times was achieved.


title: Third generation biofuels from microalgae

abstract: IBB Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710057 Braga, Portugal Biofuel production from renewable sources is widely considered to be one of the most sustainable alternatives to petroleum sourced fuels and a viable means for environmental and economic sustainability. Microalgae are currently being promoted as an ideal third generation biofuel feedstock because of their rapid growth rate, CO


title: Semantic AutoSuggest for Electronic Health Records

abstract: In traditional Electronic Health Record (EHR) applications, medical information is provided in free text fields resulting in ambiguities in the use of medical vocabulary. This paper presents a semantic AutoSuggest service that supports consultants entering free-text medical information while at the same time normalizing the medical vocabulary used. For this, several medical ontologies are semantically integrated. Particular focus is on usability, optimizing the relevance of suggested terms as well as supporting diversity.


PRICING ASIAN OPTIONS IN FINANCIAL MARKETS USING


title: Analysis of stairs-climbing ability for a tracked reconfigurable modular robot

abstract: Stairs-climbing ability is the crucial performance of mobile robot for urban environment mission such as urban search and rescue or urban reconnaissance. The track type mobile mechanism has been widely applied for its advantages such as high stability, easy to control, low terrain pressure, and continuous drive. Stairs-climbing is a complicated process for a tracked mobile robot under kinematics and dynamics constraints. In this paper, the stairs-climbing process has been divided into riser climbing, riser crossing, and nose line climbing. During each climbing process, robot's mobility has been analyzed for its kinematics and dynamics factor. The track velocity and acceleration's influences on riser climbing have been analyzed. And the semiempirical design method of the track grouser and the module length has been provided in riser crossing and nose line climbing correspondingly. Finally, stairs-climbing experiments have been made on the two-module robot in line type, and three-module robot in line type and in triangle type respectively.


title: Topology Optimization of a Prosthetic Knee Joint Component

abstract: In order to simulate the damping effect of cartilage at human’s knee joint, the component of a prosthetic knee joint is topology optimized as a compliant mechanism. After optimization, the component not only has the desired damping effect, but also has a lightweight structure, which is still capable of withstanding the prescribed loads. The SIMP (Solid Isotropic Material with Penalization) interpolation scheme for topology optimization is used here with OC (Optimality Criteria) method as optimizer. The optimization is implemented by a MATLAB code. A proof test by ANSYS has shown that the optimized component with Polyethylene Terephthalate (PET) as material has a displacement about 1.65 mm within constraints of material volume and strength.


title: A Data-Driven Approach to Smooth Pitch Correction for Singing Voice in Pop Music

abstract: In this paper, we present a machine-learning approach to pitch correction for voice in a karaoke setting, where the vocals and accompaniment are on separate tracks and time-aligned. The network takes as input the time-frequency representation of the two tracks and predicts the amount of pitch-shifting in cents required to make the voice sound in-tune with the accompaniment. It is trained on examples of semi-professional singing. The proposed approach differs from existing real-time pitch correction methods by replacing pitch tracking and mapping to a discrete set of notes---for example, the twelve classes of the equal-tempered scale---with learning a correction that is continuous both in frequency and in time directly from the harmonics of the vocal and accompaniment tracks. A Recurrent Neural Network (RNN) model provides a correction that takes context into account, preserving expressive pitch bending and vibrato. This method can be extended into unsupervised pitch correction of a vocal performance---popularly referred to as autotuning.


RATIONAL DECISION-MAKING IN BUSINESS ORGANIZATIONS


title: Am I Done? Predicting Action Progress in Videos

abstract: In this paper we introduce the problem of predicting action progress in videos. We argue that this is an extremely important task because, on the one hand, it can be valuable for a wide range of applications and, on the other hand, it facilitates better action detection results. To this end we introduce a novel approach, named ProgressNet, capable of predicting when an action takes place in a video, where it is located within the frames, and how far it has progressed during its execution. Motivated by the recent success obtained from the interaction of Convolutional and Recurrent Neural Networks, our model is based on a combination of the Faster R-CNN framework, to make framewise predictions, and LSTM networks, to estimate action progress through time. After introducing two evaluation protocols for the task at hand, we demonstrate the capability of our model to effectively predict action progress on the UCF-101 and J-HMDB datasets. Additionally, we show that exploiting action progress it is also possible to improve spatio-temporal localization.


title: Narrowband Wireless Access for Low-Power Massive Internet of Things: A Bandwidth Perspective

abstract: LPWAN is a type of wireless telecommunication network designed to allow long range communications with relaxed requirements on data rate and latency between the core network and a high-volume of battery-operated devices. This article first reviews the leading LPWAN technologies on both unlicensed spectrum (SIGFOX, and LoRa) and licensed spectrum (LTE-M and NB-IoT). Although these technologies differ in many aspects, they do have one thing in common: they all utilize the narrow-band transmission mechanism as a leverage to achieve three fundamental goals, that is, high system capacity, long battery life, and wide coverage. This article introduces an effective bandwidth concept that ties these goals together with the transmission bandwidth, such that these contradicting goals are balanced for best overall system performance.


title: Plasma frequency selective surface for GNSS applications

abstract: We propose a low-cost and reconfigurable plasma frequency selective surface (PFSS) with fluorescent lamps for Global Navigation Satellite System (GNSS) applications. Although many antenna topologies exist for broadband operation, antennas with plasma frequency selective surfaces are limited and offer certain advantages. Especially for GNSS applications, antenna performance can be demanding given the limited space for the antenna. In almost all GNSS antennas, a perfect electric conductor (PEC) ground is utilized. In this study, we compare PEC and PFSS reflectors over entire GNSS bandwidth. We believe PFSS structure can not only provide the benefits of PEC ground but also lower antenna profile.


title: Software-Defined “Hardware” Infrastructures: A Survey on Enabling Technologies and Open Research Directions

abstract: This paper provides an overview of software-defined “hardware” infrastructures (SDHI). SDHI builds upon the concept of hardware (HW) resource disaggregation. HW resource disaggregation breaks today’s physical server-oriented model where the use of a physical resource (e.g., processor or memory) is constrained to a physical server’s chassis. SDHI extends the definition of of software-defined infrastructures (SDI) and brings greater modularity, flexibility, and extensibility to cloud infrastructures, thus allowing cloud operators to employ resources more efficiently and allowing applications not to be bounded by the physical infrastructure’s layout. This paper aims to be an initial introduction to SDHI and its associated technological advancements. This paper starts with an overview of the cloud domain and puts into perspective some of the most prominent efforts in the area. Then, it presents a set of differentiating use-cases that SDHI enables. Next, we state the fundamentals behind SDI and SDHI, and elaborate why SDHI is of great interest today. Moreover, it provides an overview of the functional architecture of a cloud built on SDHI, exploring how the impact of this transformation goes far beyond the cloud infrastructure level in its impact on platforms, execution environments, and applications. Finally, an in-depth assessment is made of the technologies behind SDHI, the impact of these technologies, and the associated challenges and potential future directions of SDHI.


title: State-space optimization of ETL workflows

abstract: Extraction-transformation-loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization, and insertion into a data warehouse. In this paper, we derive into the logical optimization of ETL processes, modeling it as a state-space search problem. We consider each ETL workflow as a state and fabricate the state space through a set of correct state transitions. Moreover, we provide an exhaustive and two heuristic algorithms toward the minimization of the execution cost of an ETL workflow. The heuristic algorithm with greedy characteristics significantly outperforms the other two algorithms for a large set of experimental cases.


title: Dual-band dual polarized D-shaped microstrip antenna

abstract: Design and analysis of novel dual-band dual polarized D-shaped microstrip antenna is proposed. The D-shaped microstrip antenna excites TM 10 and TM 01 modes which are orthogonal to each other, thereby realizing dual polarized antenna characteristics. Further, another novel configuration, a vertical slot cut D-shaped patch is proposed. The slot cut D-shaped patch demonstrates triple band dual polarized antenna response, with bandwidth of 1 to 2% at each of the frequencies in 1000 MHz frequency range. The proposed dual-polarized configuration yields broadside radiation pattern across all the resonant modes.


title: A Comparison of Usability Evaluation Methods: Heuristic Evaluation versus End-User Think-Aloud Protocol - An Example from a Web-based Communication Tool for Nurse Scheduling

abstract: We evaluated a web-based communication tool for nurse scheduling using two common usability evaluation methods, heuristic evaluation and end-user think aloud protocol. We found that heuristic evaluation performed by human-computer interaction (HCI) experts revealed more general interface design problems, while end-users’ think-aloud protocols identified more obstacles to task performance. To provide the most effective and thorough evaluation results, a combination of heuristic evaluation and end-user think-aloud protocol is recommended.


title: Seam-hiding for Looping Videos

abstract: The proposed algorithm creates a seamless looping video clip from a real world video of an almost-cyclic motion. For a video that has repeating motion, such as a person on a trampoline, the first and last video frames may not precisely line up, even though the content is very similar. Playing back the video in a looping fashion can cause the re-start transition to jump out and appear discontinuous, both spatially and in terms of object velocity. Most work on video looping has sought to find the best re-set point in a longer video's timeline, but we start there, and modify the frames to hide the jump point. Our approach essentially fits a curve to the (x, y) and RGB coordinates of points in the scene, and then smooths those curves using gradient domain optimisation. We address important qualitative factors, balancing smoothness against preservation of the original trajectories/curves. Our modular system also incorporates video stabilisation and inpainting, to cope with more dynamic videos. For most videos within our scope, we found that automatic seam-hiding is succesful. For the cases in which the proposed system cannot satisfactorily produce a seamless loop, we hope our framework can be modified with improved components to achieve better results in the future.


title: A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios

abstract: Retrieving specific persons with various types of queries, e.g., a set of attributes or a portrait photo has great application potential in large-scale intelligent surveillance systems. In this paper, we propose a richly annotated pedestrian (RAP) dataset which serves as a unified benchmark for both attribute-based and image-based person retrieval in real surveillance scenarios. Typically, previous datasets have three improvable aspects, including limited data scale and annotation types, heterogeneous data source, and controlled scenarios. Differently, RAP is a large-scale dataset which contains 84928 images with 72 types of attributes and additional tags of viewpoint, occlusion, body parts, and 2589 person identities. It is collected in the real uncontrolled scene and has complex visual variations in pedestrian samples due to the change of viewpoints, pedestrian postures, and cloth appearance. Towards a high-quality person retrieval benchmark, an amount of state-of-the-art algorithms on pedestrian attribute recognition and person re-identification (ReID), are performed for quantitative analysis with three evaluation tasks, i.e., attribute recognition, attribute-based and image-based person retrieval, where a new instance-based metric is proposed to measure the dependency of the prediction of multiple attributes. Finally, some interesting problems, e.g., the joint feature learning of attribute recognition and ReID, and the problem of cross-day person ReID, are explored to show the challenges and future directions in person retrieval.


title: Dynamic Memory Protection via Intel SGX-Supported Heap Allocation

abstract: There is an increased emphasis on attacks that utilize heap buffer overflow vulnerabilities to target critical metadata and sensitive data in programs. Mitigation solutions implemented within the same virtual memory space are vulnerable to attacks that leverage program metadata to gain further control of a user buffer or to manipulate the memory layout for the purpose of overflowing specific data. Solutions such as Intel's Software Guard Extensions (SGX) provide hardware-separated memory space using enclaves to protect critical data. We present METASAFE, a novel set of heap management APIs that leverages Intel's SGX to protect against tampering of heap metadata by securing the critical data within the enclaves. METASAFE's API suite is designed for ease of use so that the programmers can protect and manage critical data of user programs without significantly modifying the current programming model. METASAFE is evaluated on several open source projects, such as ngingx, nullhttpd, netcat and gzip. Finally, we present a use case in which METASAFE is used to secure the RSA private key in the OpenSSL Library. We evaluate the security of the key using a Metasploit module based on the famous Heartbleed exploit and show that in spite of a vulnerability, an attacker cannot access the key.


ESIM: an Open Event Camera Simulator


title: A Proposed Scalable Design and Simulation of Wireless Sensor Network-Based Long-Distance Water Pipeline Leakage Monitoring System

abstract: Anomalies such as leakage and bursts in water pipelines have severe consequences for the environment and the economy. To ensure the reliability of water pipelines, they must be monitored effectively. Wireless Sensor Networks (WSNs) have emerged as an effective technology for monitoring critical infrastructure such as water, oil and gas pipelines. In this paper, we present a scalable design and simulation of a water pipeline leakage monitoring system using Radio Frequency IDentification (RFID) and WSN technology. The proposed design targets long-distance aboveground water pipelines that have special considerations for maintenance, energy consumption and cost. The design is based on deploying a group of mobile wireless sensor nodes inside the pipeline and allowing them to work cooperatively according to a prescheduled order. Under this mechanism, only one node is active at a time, while the other nodes are sleeping. The node whose turn is next wakes up according to one of three wakeup techniques: location-based, time-based and interrupt-driven. In this paper, mathematical models are derived for each technique to estimate the corresponding energy consumption and memory size requirements. The proposed equations are analyzed and the results are validated using simulation.


title: Measuring the Adoption of DDoS Protection Services

abstract: Distributed Denial-of-Service (DDoS) attacks have steadily gained in popularity over the last decade, their intensity ranging from mere nuisance to severe. The increased number of attacks, combined with the loss of revenue for the targets, has given rise to a market for DDoS Protection Service (DPS) providers, to whom victims can outsource the cleansing of their traffic by using traffic diversion. In this paper, we investigate the adoption of cloud-based DPSs worldwide. We focus on nine leading providers. Our outlook on adoption is made on the basis of active DNS measurements. We introduce a methodology that allows us, for a given domain name, to determine if traffic diversion to a DPS is in effect. It also allows us to distinguish various methods of traffic diversion and protection. For our analysis we use a long-term, large-scale data set that covers well over 50\% of all names in the global domain namespace, in daily snapshots, over a period of 1.5 years. Our results show that DPS adoption has grown by 1.24x in our measurement period, a prominent trend compared to the overall expansion of the namespace. Our study also reveals that adoption is often lead by big players such as large Web hosters, which activate or deactivate DDoS protection for millions of domain names at once.


title: A full-reference color image quality measure in the DWT domain

abstract: In this paper, we present a new objective quality measure for color images. In any frequency domain transform, the coefficients in different frequency bands have different magnitudes. The 2-dimensional Discrete Wavelet Transform (DWT) separates a given image into four bands: LL, HL, LH, and HH. After applying the DWT to both the original and degraded images, we compute the absolute value of the difference of the magnitudes in each band, and obtain the standard deviation (SD) of these differences. The proposed measure is defined as the mean of four SD values. Correlation of the subjective ratings and objective scores gives the performance of the measure. A comparison with the peak signal-to-noise ratio (PSNR), and two state-of-the-art metrics, Q and MSSIM, shows that our results correlate better with the judgment of human observers.


title: Implementing A Data Mining Solution To Customer Segmentation For Decayable Products - A Case Study For A Textile Firm

abstract: In this paper is developed a model to cluster customers who need to production change continuously. Mere mathematical models to cluster customers lead to ignoring the environment factors in model. Therefore, for Adjusting model to reality, we should use environment factors in model building. The proposed model is flexible against changes of environment and it causes to resulted model can use for clustering the customers of decay able productions. Proposed model perform on customer's information of a textile firm.