title: Aligning Knowledge and Text Embeddings by Entity Descriptions

abstract: We study the problem of jointly embedding a knowledge base and a text corpus. The key issue is the alignment model making sure the vectors of entities, relations and words are in the same space. Wang et al. (2014a) rely on Wikipedia anchors, making the applicable scope quite limited. In this paper we propose a new alignment model based on text descriptions of entities, without dependency on anchors. We require the embedding vector of an entity not only to fit the structured constraints in KBs but also to be equal to the embedding vector computed from the text description. Extensive experiments show that, the proposed approach consistently performs comparably or even better than the method of Wang et al. (2014a), which is encouraging as we do not use any anchor information.


title: Discovering learning processes using Inductive Miner: A case study with Learning Management Systems (LMSs)

abstract: BACKGROUND ::: Process mining with educational data has made use of various algorithms for model discovery, principally Alpha Miner, Heuristic Miner, and Evolutionary Tree Miner. In this study we propose the implementation of a new algorithm for educational data called Inductive Miner. ::: ::: ::: METHOD ::: We used data from the interactions of 101 university students in a course given over one semester on the Moodle 2.0 platform. Data was extracted from the platform's event logs; following preprocessing, the mining was carried out on 21,629 events to discover what models the various algorithms produced and to compare their fitness, precision, simplicity and generalization. ::: ::: ::: RESULTS ::: The Inductive Miner algorithm produced the best results in the tests on this dataset, especially for fitness, which is the most important criterion in terms of model discovery. In addition, when we weighted the various metrics according to their importance, Inductive Miner continued to produce the best results. ::: ::: ::: CONCLUSIONS ::: Inductive Miner is a new algorithm which, in addition to producing better results than other algorithms using our dataset, also provides valid models which can be interpreted in educational terms.


title: The spectrum of laser skin resurfacing: nonablative, fractional, and ablative laser resurfacing.

abstract: The drive to attain cosmetic facial enhancement with minimal risk and rapid recovery has inspired the field of nonsurgical skin rejuvenation. Laser resurfacing was introduced in the 1980s with continuous wave carbon dioxide (CO 2 ) lasers; however, because of a high rate of side effects, including scarring, short-pulse, high-peak power, and rapidly scanned, focused-beam CO 2 lasers and normal-mode erbium-doped yttrium aluminium garnet lasers were developed, which remove skin in a precisely controlled manner. The prolonged 2-week recovery time and small but significant complication risk prompted the development of non-ablative and, more recently, fractional resurfacing in order to minimize risk and shorten recovery times. Nonablative resurfacing produces dermal thermal injury to improve rhytides and photodamage while preserving the epidermis. Fractional resurfacing thermally ablates microscopic columns of epidermal and dermal tissue in regularly spaced arrays over a fraction of the skin surface. This intermediate approach increases efficacy as compared to nonablative resurfacing, but with faster recovery as compared to ablative resurfacing. Neither nonablative nor fractional resurfacing produces results comparable to ablative laser skin resurfacing, but both have become much more popular than the latter because the risks of treatment are limited in the face of acceptable improvement. Learning objectives At the completion of this learning activity, participants should be familiar with the spectrum of lasers and light technologies available for skin resurfacing, published studies of safety and efficacy, indications, methodologies, side effects, complications, and management.


title: An origami-inspired, self-locking robotic arm that can be folded flat

abstract: A foldable arm is one of the practical applications of folding. It can help mobile robots and unmanned aerial vehicles (UAVs) overcome access issues by allowing them to reach into confined spaces. The origami-inspired design enables a foldable structure to be lightweight, compact, and scalable while maintaining its kinematic behavior. However, the lack of structural stiffness has been a major limitation in the practical use of origami-inspired designs. Resolving this obstacle without losing the inherent advantages of origami is a challenge. We propose a solution by implementing a simple stiffening mechanism that uses an origami principle of perpendicular folding. The simplicity of the stiffening mechanism enables an actuation system to drive shape and stiffness changes with only a single electric motor. Our results show that this design was effective for a foldable arm and allowed a UAV to perform a variety of tasks in a confined space.


title: R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor Strategy

abstract: Object detection plays a vital role in natural scene and aerial scene and is full of challenges. Although many advanced algorithms have succeeded in the natural scene, the progress in the aerial scene has been slow due to the complexity of the aerial image and the large degree of freedom of remote sensing objects in scale, orientation, and density. In this paper, a novel multi-category rotation detector is proposed, which can efficiently detect small objects, arbitrary direction objects, and dense objects in complex remote sensing images. Specifically, the proposed model adopts a targeted feature fusion strategy called inception fusion network, which fully considers factors such as feature fusion, anchor sampling, and receptive field to improve the ability to handle small objects. Then we combine the pixel attention network and the channel attention network to weaken the noise information and highlight the objects feature. Finally, the rotational object detection algorithm is realized by redefining the rotating bounding box. Experiments on public datasets including DOTA, NWPU VHR-10 demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods. The code and models will be available at https://github.com/DetectionTeamUCAS/R2CNN-Plus-Plus_Tensorflow.


title: Meta Structure: Computing Relevance in Large Heterogeneous Information Networks

abstract: A heterogeneous information network (HIN) is a graph model in which objects and edges are annotated with types. Large and complex databases, such as YAGO and DBLP, can be modeled as HINs. A fundamental problem in HINs is the computation of closeness, or relevance, between two HIN objects. Relevance measures can be used in various applications, including entity resolution, recommendation, and information retrieval. Several studies have investigated the use of HIN information for relevance computation, however, most of them only utilize simple structure, such as path, to measure the similarity between objects. In this paper, we propose to use meta structure, which is a directed acyclic graph of object types with edge types connecting in between, to measure the proximity between objects. The strength of meta structure is that it can describe complex relationship between two HIN objects (e.g., two papers in DBLP share the same authors and topics). We develop three relevance measures based on meta structure. Due to the computational complexity of these measures, we further design an algorithm with data structures proposed to support their evaluation. Our extensive experiments on YAGO and DBLP show that meta structure-based relevance is more effective than state-of-the-art approaches, and can be efficiently computed.


title: Composable DevOps: Automated Ontology Based DevOps Maturity Analysis

abstract: In this era of the emerging digitized, mobilized, and cloudified enterprises, the concept of the "compos able business" is the most critical piece which ties everything together. The digital enterprise is here, and its prime characteristic is that is essentially detaches and segregates existing businesses and reassembles them according to market demands. Every industry, from transportation to eyewear is up for disruption, and developers are in the forefront of this movement. In turn, these developers are under intense pressure to accelerate time to market. The compos able enterprise approach requires a reconsideration of traditional models of the entire IT organization. These organization and their processes need to be broken up into components that follow certain key design principles such as The Minimal Functions with least Dependencies, portability, Shared Knowledge, Predictable Contracts and Maximized Human Value. The last three bullet points encapsulate the very definition of DevOps [3]. The concept of better integration between Development and Operations is a valuable objective. The goal is to foster measurable incremental cultural change to derive most overall value out of the union of people, process and technology. But the cultural issues, reward models, and risk allocation create obvious barriers in attaining those goals. The common industry belief is to use the compos able enterprise framework to build a platform using the right tools and you will have attained DevOps nirvana. In this paper we will explore valuable lessons learned from our mistakes in tool centric adoption of IT Infrastructure Library (ITIL) [8]. We will also show how we applied those lessons to develop a lightweight compos able/contextual DevOps framework that learns and measure itself to avoid those cultural pitfalls.


title: ByteSlice: Pushing the Envelop of Main Memory Data Processing with a New Storage Layout

abstract: Scan and lookup are two core operations in main memory column stores. A scan operation scans a column and returns a result bit vector that indicates which records satisfy a filter. Once a column scan is completed, the result bit vector is converted into a list of record numbers, which is then used to look up values from other columns of interest for a query. Recently there are several in-memory data layout proposals that aim to improve the performance of in-memory data processing. However, these solutions all stand at either end of a trade-off --- each is either good in lookup performance or good in scan performance, but not both. In this paper we present ByteSlice, a new main memory storage layout that supports both highly efficient scans and lookups. ByteSlice is a byte-level columnar layout that fully leverages SIMD data-parallelism. Micro-benchmark experiments show that ByteSlice achieves a data scan speed at less than 0.5 processor cycle per column value --- a new limit of main memory data scan, without sacrificing lookup performance. Our experiments on TPC-H data and real data show that ByteSlice offers significant performance improvement over all state-of-the-art approaches.


title: Visuospatial Skill Learning for Robots

abstract: A novel skill learning approach is proposed that allows a robot to acquire human-like visuospatial skills for object manipulation tasks. Visuospatial skills are attained by observing spatial relationships among objects through demonstrations. The proposed Visuospatial Skill Learning (VSL) is a goal-based approach that focuses on achieving a desired goal configuration of objects relative to one another while maintaining the sequence of operations. VSL is capable of learning and generalizing multi-operation skills from a single demonstration, while requiring minimum prior knowledge about the objects and the environment. In contrast to many existing approaches, VSL offers simplicity, efficiency and user-friendly human-robot interaction. We also show that VSL can be easily extended towards 3D object manipulation tasks, simply by employing point cloud processing techniques. In addition, a robot learning framework, VSL-SP, is proposed by integrating VSL, Imitation Learning, and a conventional planning method. In VSL-SP, the sequence of performed actions are learned using VSL, while the sensorimotor skills are learned using a conventional trajectory-based learning approach. such integration easily extends robot capabilities to novel situations, even by users without programming ability. In VSL-SP the internal planner of VSL is integrated with an existing action-level symbolic planner. Using the underlying constraints of the task and extracted symbolic predicates, identified by VSL, symbolic representation of the task is updated. Therefore the planner maintains a generalized representation of each skill as a reusable action, which can be used in planning and performed independently during the learning phase. The proposed approach is validated through several real-world experiments.


title: Best Practices in Running Collaborative GPU Hackathons: Advancing Scientific Applications with a Sustained Impact

abstract: This article highlights the Oak Ridge Leadership Compute Facilitys GPU Hackathon, presenting the training format used, trends observed, and reasons for teams successes and failures. It also summarizes participant outcomes and takeaways while demonstrating how educators could adopt this hackathon format for use in their respective institutions.


A REAL TIME RSSI BASED NOVEL ALGORITHM TO IMPROVE INDOOR LOCALIZATION ACCURACY FOR TARGET TRACKING IN WIRELESS SENSOR NETWORKS


title: Microstrip-fed circular substrate integrated waveguide (SIW) cavity resonator and antenna

abstract: Substrate integrated waveguide (SIW) cavity resonators are among the emerging group of SIW-based circuit components that are gaining popularity and increasingly employed in integrated microwave and mm-wave circuits. The SIW cavities offer a significantly enhanced performance in comparison to the previously available planar microwave resonators. The high quality factor of the waveguide-based cavity resonators enables designing microwave oscillators with very low phase noise as well as compact high-gain antennas [1–4]. The SIW-cavity-based antennas also show much promise for implementation of low-cost lightweight fully-integrated high-gain antennas that find application in ultra-light communication satellites, low payload spacecrafts, high-frequency radar, and sensors. In this paper, a circular SIW cavity resonator, which is fed by a microstrip and via probe, is presented. The microstrip feed is optimized to achieve a return loss of better than 20dB from both simulation and measurements. A resonance frequency of 16.79GHz and quality factor of 76.3 are determined from vector network analyzer (VNA) measurements. Due to the folded slot on the upper conductor layer, the resonator is an efficient cavity backed antenna. A maximum gain of over 7dB is measured in an anechoic chamber at the resonance frequency of 16.79 GHz.


title: The Lost Art of Discovery: The Case for Inductive Methods in Occupational Health Science and the Broader Organizational Sciences

abstract: This paper makes a case for the necessity of inductive and abductive approaches to research in occupational health science and the broader organizational sciences. Three forms of scientific inference are described: induction (exploratory research that generalizes from observations), abduction (deriving explanations for observations), and deduction (confirmatory research that tests theory-derived hypotheses). It is argued that the current deductive exclusiveness in the major journals of many fields has created unintended challenges to research integrity of confirmation bias, p-hacking, HARKing, and the chrysalis effect. Recommendations are given for writing and reviewing research papers that adopt an inductive and/or abductive approach.


title: TETRIS: Scalable and Efficient Neural Network Acceleration with 3D Memory

abstract: The high accuracy of deep neural networks (NNs) has led to the development of NN accelerators that improve performance by two orders of magnitude. However, scaling these accelerators for higher performance with increasingly larger NNs exacerbates the cost and energy overheads of their memory systems, including the on-chip SRAM buffers and the off-chip DRAM channels. This paper presents the hardware architecture and software scheduling and partitioning techniques for TETRIS, a scalable NN accelerator using 3D memory. First, we show that the high throughput and low energy characteristics of 3D memory allow us to rebalance the NN accelerator design, using more area for processing elements and less area for SRAM buffers. Second, we move portions of the NN computations close to the DRAM banks to decrease bandwidth pressure and increase performance and energy efficiency. Third, we show that despite the use of small SRAM buffers, the presence of 3D memory simplifies dataflow scheduling for NN computations. We present an analytical scheduling scheme that matches the efficiency of schedules derived through exhaustive search. Finally, we develop a hybrid partitioning scheme that parallelizes the NN computations over multiple accelerators. Overall, we show that TETRIS improves mthe performance by 4.1x and reduces the energy by 1.5x over NN accelerators with conventional, low-power DRAM memory systems.


title: Exploiting the Functional and Taxonomic Structure of Genomic Data by Probabilistic Topic Modeling

abstract: In this paper, we present a method that enable both homology-based approach and composition-based approach to further study the functional core (i.e., microbial core and gene core, correspondingly). In the proposed method, the identification of major functionality groups is achieved by generative topic modeling, which is able to extract useful information from unlabeled data. We first show that generative topic model can be used to model the taxon abundance information obtained by homology-based approach and study the microbial core. The model considers each sample as a "document,” which has a mixture of functional groups, while each functional group (also known as a "latent topic”) is a weight mixture of species. Therefore, estimating the generative topic model for taxon abundance data will uncover the distribution over latent functions (latent topic) in each sample. Second, we show that, generative topic model can also be used to study the genome-level composition of "N-mer” features (DNA subreads obtained by composition-based approaches). The model consider each genome as a mixture of latten genetic patterns (latent topics), while each functional pattern is a weighted mixture of the "N-mer” features, thus the existence of core genomes can be indicated by a set of common N-mer features. After studying the mutual information between latent topics and gene regions, we provide an explanation of the functional roles of uncovered latten genetic patterns. The experimental results demonstrate the effectiveness of proposed method.


title: Graph modeling and mining methods for brain images

abstract: Brain disease is a top cause of death. Currently, its main diagonosis is to take advantage of medical brain images to analyse patients' condition. In medical big data analysis field, it has been a research hotspot that how to effectively represent medical images and discover significant information hidden in them to further assist doctors to achieve a better diagnosis. Graphs, as one of the most general forms of data representation, can easily represent entities, their attributes and their relationships well. However, the existing medical image graph models do not exploit the specific relationships of brain images very well so that some essential information is lost. Therefore, aiming at brain images, we firstly construct a domain knowledge-oriented graph about the Topological Relationships among Ventricles and Lesions (TRVL) to represent a brain image, and give the algorithm of modeling a brain Image to a TRVL Graph (denoted as I2G). Then we propose a method named Frequent Approximate Subgraph Mining based on Graph Edit Distance (FASMGED) to exactly discover meaningful patterns hidden in brain images. This method employs a strong error-tolerant graph matching strategy which is accordant with ubiquitous noise in practice. Moreover, an approximate method of frequent approximate subgraph mining is proposed based on the greedy strategy. We have evaluated our algorithms on real and simulated data. Results show that I2G is computationally scalable, FASMGED can discover more significant patterns than other state-of-the-art frequent subgraph mining methods, and the approximate method of frequent approximate subgraph mining outperforms FASMGED.


title: Image Classification by Selective Regularized Subspace Learning

abstract: Feature learning is an intensively studied research topic in image classification. Although existing methods like sparse coding, locality-constrained linear coding, fisher vector encoding, etc., have shown their effectiveness in image representation, most of them overlook a phenomenon called thesmall sample size problem, where the number of training samples is relatively smaller than the dimensionality of the features, which may limit the predictive power of the classifier. Subspace learning is a strategy to mitigate this problem by reducing the dimensionality of the features. However, most conventional subspace learning methods attempt to learn a global subspace to discriminate all the classes, which proves to be difficult and ineffective in multi-class classification task. To this end, we propose to learn a local subspace for each sample instead of learning a global subspace for all samples. Our key observation is that, in multi-class image classification, the label of each testing sample is only confused by a few classes which have very similar visual appearance to it. Thus, in this work, we propose a coarse-to-fine strategy, which first picks out such classes, and then conducts a local subspace learning to discriminate them. As the subspace learning method is regularized and conducted within some selected classes, we term it selective regularized subspace learning (SRSL), and we term our classification pipeline selective regularized subspace learning based multi-class image classification (SRSL_MIC). Experimental results on four representative datasets (Caltech-101, Indoor-67, ORL Faces and AR Faces) demonstrate the effectiveness of the proposed method.


title: Understanding the mirai botnet

abstract: The Mirai botnet, composed primarily of embedded and IoT devices, took the Internet by storm in late 2016 when it overwhelmed several high-profile targets with massive distributed denial-of-service (DDoS) attacks. In this paper, we provide a seven-month retrospective analysis of Mirai's growth to a peak of 600k infections and a history of its DDoS victims. By combining a variety of measurement perspectives, we analyze how the botnet emerged, what classes of devices were affected, and how Mirai variants evolved and competed for vulnerable hosts. Our measurements serve as a lens into the fragile ecosystem of IoT devices. We argue that Mirai may represent a sea change in the evolutionary development of botnets--the simplicity through which devices were infected and its precipitous growth, demonstrate that novice malicious techniques can compromise enough low-end devices to threaten even some of the best-defended targets. To address this risk, we recommend technical and nontechnical interventions, as well as propose future research directions.


title: Energy-modelled climb and climb-dash - the Kaiser technique

abstract: F. Kaiser's germinal 1944 report on his ''resultant-height'' concept, now known as energy modelling, is reviewed. The data base for the Me. 262 jet fighter is recreated via spline-lattice representation of specific excess power. Minimum-time and ''distance''-climb trajectories are generated in an attempt to check Kaiser's results. Agreement is good for the minimum-time calculations but only qualitative agreement is obtained for the mysterious ''distance climbs,'' whose documentation is fragmentary. The character of optimal climb-dash trajectories in energy approximation is examined and illustrated.


title: Network Centrality in the Human Functional Connectome

abstract: The network architecture of functional connectivity within the human brain connectome is poorly understood at the voxel level. Here, using resting state functional magnetic resonance imaging data from 1003 healthy adults, we investigate a broad array of network centrality measures to provide novel insights into connectivity within the whole-brain functional network (i.e., the functional connectome). We first assemble and visualize the voxel-wise (4 mm) functional connectome as a functional network. We then demonstrate that each centrality measure captures different aspects of connectivity, highlighting the importance of considering both global and local connectivity properties of the functional connectome. Beyond "detecting functional hubs," we treat centrality as measures of functional connectivity within the brain connectome and demonstrate their reliability and phenotypic correlates (i.e., age and sex). Specifically, our analyses reveal age-related decreases in degree centrality, but not eigenvector centrality, within precuneus and posterior cingulate regions. This implies that while local or (direct) connectivity decreases with age, connections with hub-like regions within the brain remain stable with age at a global level. In sum, these findings demonstrate the nonredundancy of various centrality measures and raise questions regarding their underlying physiological mechanisms that may be relevant to the study of neurodegenerative and psychiatric disorders.


title: Principles for Developing a Knowledge Graph of Interlinked Events from News Headlines on Twitter

abstract: The ever-growing datasets published on Linked Open Data mainly contain encyclopedic information. However, there is a lack of quality structured and semantically annotated datasets extracted from unstructured real-time sources. In this paper, we present principles for developing a knowledge graph of interlinked events using the case study of news headlines published on Twitter which is a real-time and eventful source of fresh information. We represent the essential pipeline containing the required tasks ranging from choosing background data model, event annotation (i.e., event recognition and classification), entity annotation and eventually interlinking events. The state-of-the-art is limited to domain-specific scenarios for recognizing and classifying events, whereas this paper plays the role of a domain-agnostic road-map for developing a knowledge graph of interlinked events.


title: COMPACT BANDPASS FILTER WITH WIDE UPPER-STOPBAND BASED ON SPIRAL-SHAPED RESONATORS AND SPUR-LINES

abstract: A novel compact bandpass fllter (BPF) with wide upper- stopband has been proposed in this paper. The structure is based on spiral-shaped resonators. Cross coupling is used to generate two transmission zeros at the lower and upper stopbands. Therefore, the out-of-band performance is improved. In addition, two spur-lines are adopted in the feed lines to reject the spurious response. The central frequency f0 of this fllter is at 2.45GHz with a minimum insertion loss of less than 1dB and a 3dB bandwidth of 12.5%. Four transmission zeros are located at 2GHz, 3GHz, 5.5GHz, and 8GHz. The attenuation is greater than 20dB in a wide upper stopband up to 9.8GHz.


title: Liveness Detection for Face Recognition in Biometrics: A Review

abstract: A Biometric system recognizes an individual by measuring or analyzing his physical or behavioral body traits. In recent times, Facial recognition is one of the most commonly used biometric technology and is rapidly integrated in various applications such as security login or access to the buildings or surveillance etc. Facial recognition is also user friendly and cost effective solution than other technologies. Beside this, Face recognition system is also prone to the spoofing attacks such as placing photographs,playing video clips in front of camera and dummy faces.In order to guard the system against such spoofing attacks,liveness detection can be integrated into the system. Liveness detection in face recognition tests whether the face presented to the system is real face or a fake face.This paper presents the state-of-the-art of various liveness detection techniques in the area of face recognition technology along with their strengths and limitations.


title: Applications of the FDTD method to lightning electromagnetic pulse and surge simulations

abstract: Electromagnetic computation methods (ECMs) have been widely used in analyzing lightning electromagnetic pulses (LEMPs) and lightning-caused surges in various systems. One of the advantages of ECMs, in comparison with circuit simulation methods, is that they allow a self-consistent full-wave solution for both the transient current distribution in a 3D conductor system and resultant electromagnetic fields, although they are computationally expensive. Among ECMs, the finite-difference time-domain (FDTD) method for solving Maxwell's equations has been most frequently used in LEMP and surge simulations. In this paper, we review applications of the FDTD method to LEMP and surge simulations, including (i) lightning electromagnetic fields at close and far distances; (ii) lightning surges on overhead power transmission line conductors and towers, (iii) lightning surges on overhead distribution and telecommunication lines; (iv) lightning electromagnetic environment in power substations; (v) lightning surges in wind-turbine-generator towers; (vi) lightning surges in photovoltaic (PV) arrays; (vii) lightning electromagnetic environment in electric vehicles (EVs); (viii) lightning electromagnetic environment in airborne vehicles; (ix) lightning surges and electromagnetic environment in buildings; and (x) surges on grounding electrodes.


title: Margin maximization for robust classification using deep learning

abstract: Deep neural networks have achieved significant success for image recognition problems. Despite the wide success, recent experiments demonstrated that neural networks are sensitive to small input perturbations, or adversarial noise. The lack of robustness is intuitively undesirable and limits neural networks applications in adversarial settings, and for image search and retrieval problems. Current approaches consider augmenting training dataset using adversarial examples to improve robustness. However, when using data augmentation, the model fails to anticipate changes in an adversary. In this paper, we consider maximizing the geometric margin of the classifier. Intuitively, a large margin relates to classifier robustness. We introduce novel margin maximization objective for deep neural networks. We theoretically show that the proposed objective is equivalent to the robust optimization problem for a neural network. Our work seamlessly generalizes SVM margin objective to deep neural networks. In the experiments, we extensively verify the effectiveness of the proposed margin maximization objective to improve neural network robustness and to reduce overfitting on MNIST and CIFAR-10 dataset.


title: Lumbar Muscle Fatigue and Chronic Lower Back Pain

abstract: There currently is a clinical need for an objective technique to assess muscle dysfunction associated with chronic lower back pain. A Back Analysis System for objectively measuring local fatigue in the back extensor muscles is presented. The reliability and validity of this technique was evaluated by testing chronic low-back pain patients and control subjects without back pain. Concurrent surface electromyograms (EMG) were detected from multiple back muscles during sustained isometric contractions at different force levels of trunk extension. Median frequency parameters of the EMG power density spectrum were monitored to quantify localized muscle fatigue. Results indicated: 1) high reliability estimates for repeated trials; 2) significant differences (P less than 0.05) in median frequency parameters between lower back pain patients and control subjects for specific combinations of contractile force level and muscle site tested; 3) Median Frequency parameters correctly classified lower back pain and control subjects using a two-group discriminant analysis procedure. The applicability of this technique as a treatment outcome measure and diagnostic screening method for lower back pain patients is discussed.


title: Population diversity and ecosystem services

abstract: Abstract The current rate of biodiversity loss threatens to disrupt greatly the functioning of ecosystems, with potentially significant consequences for humanity. The magnitude of the loss is generally measured with the use of species extinction rates, an approach that understates the severity of the problem and masks some of its most important consequences. Here, we propose a major expansion of this focus to include population diversity: considering changes in the size, number, distribution and genetic composition of populations and the implications of those changes for the functioning of ecosystems and the provision of ecosystem services. We also outline the key components of population diversity and describe a new approach to delineating a population unit that explicitly links it to the services that it provides


title: A SEMIOATIC ANALYSIS OF “CITRA” PRODUCT OF SKIN CARE ADVERTISEMENT

abstract: This study is about advertiemnet of the skin care for women, particularly in Indoensia. This study used semiotic theory of the advertisement of the women product od the skin care. So, this product sold out due to of this case. This study took adat from the product of the “cira” product of the skin care. So, this study only used semiotics study of the skin care of the citra product. This study can be concluded that the skin care/treatment for women with natural ingredient. This shows that the meaning of the sign of the women is skin. The skin is as measurement of the women performance and esteem. Keywords : Semiotics, Product and Advertisement


title: Small Moving Vehicle Detection in a Satellite Video of an Urban Area

abstract: Vehicle surveillance of a wide area allows us to learn much about the daily activities and traffic information. With the rapid development of remote sensing, satellite video has become an important data source for vehicle detection, which provides a broader field of surveillance. The achieved work generally focuses on aerial video with moderately-sized objects based on feature extraction. However, the moving vehicles in satellite video imagery range from just a few pixels to dozens of pixels and exhibit low contrast with respect to the background, which makes it hard to get available appearance or shape information. In this paper, we look into the problem of moving vehicle detection in satellite imagery. To the best of our knowledge, it is the first time to deal with moving vehicle detection from satellite videos. Our approach consists of two stages: first, through foreground motion segmentation and trajectory accumulation, the scene motion heat map is dynamically built. Following this, a novel saliency based background model which intensifies moving objects is presented to segment the vehicles in the hot regions. Qualitative and quantitative experiments on sequence from a recent Skybox satellite video dataset demonstrates that our approach achieves a high detection rate and low false alarm simultaneously.


title: The Ability-Motivation-Opportunity Framework for Behavior Research in IS

abstract: Some theoretical models used in IS research may lack explanatory power and consistency because of their failure to provide an adequate means of accounting for contextual and situational factors which influence behavior. The ability-motivation-opportunity (AMO) framework addresses these problems for important new areas of IS research by theorizing two of the most critical factors, ability and opportunity, as moderating influences on the link between motivation and behavior. The AMO framework also clarifies the function and meaning of motivation in theoretical models. In this paper, the origins and prior uses of the AMO framework are presented, its components and structure are explained, its place and purpose in the constellation of IS theories is discussed, theoretical and practical justifications for its use in IS behavioral research are presented, open theoretical issues and limitations regarding the AMO framework are identified, and recommendations for its operationalization in specific IS studies are offered


title: Leaky wave antenna integrated into gap waveguide technology

abstract: A novel leaky wave antenna, based on the gap waveguide technology, is here proposed. A groove gap-waveguide is used as feeding and it also acts as antenna at the same time. The proposed antenna provides an excellent performance while maintaining a simple design. To demonstrate the potential of this radiation mechanism, an antenna was designed to operate between 9 GHz and 11.5 GHz. The antenna has a gain around 12 dB and provides a radiation pattern that steers its radiation direction with the frequency.


title: Controlled Scalable Synthesis of Uniform, High-Quality Monolayer and Few-layer MoS2 Films

abstract: Two dimensional (2D) materials with a monolayer of atoms represent an ultimate control of material dimension in the vertical direction. Molybdenum sulfide (MoS2) monolayers, with a direct bandgap of 1.8 eV, offer an unprecedented prospect of miniaturizing semiconductor science and technology down to a truly atomic scale. Recent studies have indeed demonstrated the promise of 2D MoS2 in fields including field effect transistors, low power switches, optoelectronics, and spintronics. However, device development with 2D MoS2 has been delayed by the lack of capabilities to produce large-area, uniform, and high-quality MoS2 monolayers. Here we present a self-limiting approach that can grow high quality monolayer and few-layer MoS2 films over an area of centimeters with unprecedented uniformity and controllability. This approach is compatible with the standard fabrication process in semiconductor industry. It paves the way for the development of practical devices with 2D MoS2 and opens up new avenues for fundamental research.


title: Classifying racist texts using a support vector machine

abstract: In this poster we present an overview of the techniques we used to develop and evaluate a text categorisation system to automatically classify racist texts. Detecting racism is difficult because the presence of indicator words is insufficient to indicate racist texts, unlike some other text classification tasks. Support Vector Machines (SVM) are used to automatically categorise web pages based on whether or not they are racist. Different interpretations of what constitutes a term are taken, and in this poster we look at three representations of a web page within an SVM -- bag-of-words, bigrams and part-of-speech tags.


Removal of Selected Organochlorine Compounds by Ozone-based Processes


A Bumble Bees Mating Optimization algorithm for the Open Vehicle Routing Problem


title: Should your 8-year-old learn coding?

abstract: There has been considerable interest in teaching "coding" to primary school aged students, and many creative "Initial Learning Environments" (ILEs) have been released to encourage this. Announcements and commentaries about such developments can polarise opinions, with some calling for widespread teaching of coding, while others see it as too soon to have students learning industry-specific skills. It is not always clear what is meant by teaching coding (which is often used as a synonym for programming), and what the benefits and costs of this are. Here we explore the meaning and potential impact of learning coding/programming for younger students. We collect the arguments for and against learning coding at a young age, and review the initiatives that have been developed to achieve this (including new languages, school curricula, and teaching resources). This leads to a set of criteria around the value of teaching young people to code, to inform curriculum designers, teachers and parents. The age at which coding should be taught can depend on many factors, including the learning tools used, context, teacher training and confidence, culture, specific skills taught, how engaging an ILE is, how much it lets students explore concepts for themselves, and whether opportunities exist to continue learning after an early introduction.


title: An interface for virtual 3D sculpting via physical proxy

abstract: We explore the design space of using direct finger input in conjunction with a deformable physical prop for the creation and manipulation of conceptual 3D geometric models. The user sculpts virtual models by manipulating the space on, into, and around the physical prop, in an extension of the types of manipulations one would perform on traditional modeling media such as clay or foam. The prop acts as a proxy to the virtual model and hence as a frame of reference to the user's fingers. A prototype implementation uses camera-based motion tracking technology to track passive markers on the fingers and prop. The interface supports a variety of clay-like sculpting operations including deforming, smoothing, pasting, and extruding. All operations are performed using the unconstrained fingers, with command input enabled by a small set of finger gestures coupled with on-screen widgets.


title: Relating emotional abilities to social functioning: a comparison of performance and self-report measures of emotional intelligence

abstract: Three studies used J. D. Mayer and P. Salovey's (1997) theory of emotional intelligence (EI) as a framework to examine the role of emotional abilities (assessed with both self-report and performance measures) in social functioning. Self-ratings were assessed in ways that mapped onto the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT), a validated performance measure of El. In Study 1, self-ratings and MSCEIT scores were not strongly correlated. In Study 2, men's MSCEIT scores, but not self-ratings, correlated with perceived social competence after personality measures were held constant. In Study 3, only the MSCEIT predicted real-time social competence, again, just for men. Implications for analyzing how emotional abilities contribute to social behavior are discussed, as is the importance of incorporating gender into theoretical frameworks and study designs.


title: Runtime feedback in a meta-tracing JIT for efficient dynamic languages

abstract: Meta-tracing JIT compilers can be applied to a variety of different languages without explicitly encoding language semantics into the compiler. So far, they lacked a way to give the language implementor control over runtime feedback. This restricted their performance. In this paper we describe the mechanisms in PyPy's meta-tracing JIT that can be used to control runtime feedback in language-specific ways. These mechanisms are flexible enough to express classical VM techniques such as maps and runtime type feedback.


Investigating the relationship between : Smartphone Addiction , Social Anxiety , Self-Esteem , Age and Gender


title: Live-fly, large-scale field experimentation for large numbers of fixed-wing UAVs

abstract: In this paper, we present extensive advances in live-fly field experimentation capabilities of large numbers of fixed-wing aerial robots, and highlight both the enabling technologies as well as the challenges addressed in such largescale flight operations. We showcase results from recent field tests, including the autonomous launch, flight, and landing of 50 UAVs, which illuminate numerous operational lessons learned and generate rich multi-UAV datasets. We detail the design and open architecture of the testbed, which intentionally leverages low-cost and open-source components, aimed at promoting continued advances and alignment of multi-robot systems research and practice.


title: Network Transplanting

abstract: This paper focuses on a new task, i.e., transplanting a category-and-task-specific neural network to a generic, modular network without strong supervision. We design an functionally interpretable structure for the generic network. Like building LEGO blocks, we teach the generic network a new category by directly transplanting the module corresponding to the category from a pre-trained network with a few or even without sample annotations. Our method incrementally adds new categories to the generic network but does not affect representations of existing categories. In this way, our method breaks the typical bottleneck of learning a net for massive tasks and categories, i.e. the requirement of collecting samples for all tasks and categories at the same time before the learning begins. Thus, we use a new distillation algorithm, namely back-distillation, to overcome specific challenges of network transplanting. Our method without training samples even outperformed the baseline with 100 training samples.


title: All-optical machine learning using diffractive deep neural networks

abstract: We introduce an all-optical Diffractive Deep Neural Network (D2NN) architecture that can learn to implement various functions after deep learning-based design of passive diffractive layers that work collectively. We experimentally demonstrated the success of this framework by creating 3D-printed D2NNs that learned to implement handwritten digit classification and the function of an imaging lens at terahertz spectrum. With the existing plethora of 3D-printing and other lithographic fabrication methods as well as spatial-light-modulators, this all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D2NNs.


title: PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup

abstract: This paper introduces an automatic method for editing a portrait photo so that the subject appears to be wearing makeup in the style of another person in a reference photo. Our unsupervised learning approach relies on a new framework of cycle-consistent generative adversarial networks. Different from the image domain transfer problem, our style transfer problem involves two asymmetric functions: a forward function encodes example-based style transfer, whereas a backward function removes the style. We construct two coupled networks to implement these functions - one that transfers makeup style and a second that can remove makeup - such that the output of their successive application to an input photo will match the input. The learned style network can then quickly apply an arbitrary makeup style to an arbitrary photo. We demonstrate the effectiveness on a broad range of portraits and styles.


title: Analyzing and Characterizing User Intent in Information-seeking Conversations

abstract: Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems. In this paper, we introduce a new dataset designed for this purpose and use it to analyze information-seeking conversations by user intent distribution, co-occurrence, and flow patterns. The MSDialog dataset is a labeled dialog dataset of question answering (QA) interactions between information seekers and providers from an online forum on Microsoft products. The dataset contains more than 2,000 multi-turn QA dialogs with 10,000 utterances that are annotated with user intent on the utterance level. Annotations were done using crowdsourcing. With MSDialog, we find some highly recurring patterns in user intent during an information-seeking process. They could be useful for designing conversational search systems. We will make our dataset freely available to encourage exploration of information-seeking conversation models.


title: Development of an instrument measuring user satisfaction of the human-computer interface

abstract: This study is a part of a research effort to develop the Questionnaire for User Interface Satisfaction (QUIS). Participants, 150 PC user group members, rated familiar software products. Two pairs of software categories were compared: 1) software that was liked and disliked, and 2) a standard command line system (CLS) and a menu driven application (MDA). The reliability of the questionnaire was high, Cronbach's alpha=.94. The overall reaction ratings yielded significantly higher ratings for liked software and MDA over disliked software and a CLS, respectively. Frequent and sophisticated PC users rated MDA more satisfying, powerful and flexible than CLS. Future applications of the QUIS on computers are discussed.


title: MobileAppScrutinator: A Simple yet Efficient Dynamic Analysis Approach for Detecting Privacy Leaks across Mobile OSs

abstract: Smartphones, the devices we carry everywhere with us, are being heavily tracked and have undoubtedly become a major threat to our privacy. As " Tracking the trackers " has become a necessity, various static and dynamic analysis tools have been developed in the past. However, today, we still lack suitable tools to detect, measure and compare the ongoing tracking across mobile OSs. To this end, we propose MobileAppScrutinator, based on a simple yet efficient dynamic analysis approach, that works on both Android and iOS (the two most popular OSs today). To demonstrate the current trend in tracking, we select 140 most representative Apps available on both Android and iOS AppStores and test them with MobileAppScrutinator. In fact, choosing the same set of apps on both Android and iOS also enables us to compare the ongoing tracking on these two OSs. Finally, we also discuss the effectiveness of privacy safeguards available on Android and iOS. We show that neither Android nor iOS privacy safeguards in their present state are completely satisfying.


Assisted reproduction causes placental maldevelopment and dysfunction linked to reduced fetal weight in mice


title: Learning With $\ell ^{1}$-Graph for Image Analysis

abstract: The graph construction procedure essentially determines the potentials of those graph-oriented learning algorithms for image analysis. In this paper, we propose a process to build the so-called directed ¿1-graph, in which the vertices involve all the samples and the ingoing edge weights to each vertex describe its ¿1-norm driven reconstruction from the remaining samples and the noise. Then, a series of new algorithms for various machine learning tasks, e.g., data clustering, subspace learning, and semi-supervised learning, are derived upon the ¿1-graphs. Compared with the conventional k -nearest-neighbor graph and ¿-ball graph, the ¿1-graph possesses the advantages: (1) greater robustness to data noise, (2) automatic sparsity, and (3) adaptive neighborhood for individual datum. Extensive experiments on three real-world datasets show the consistent superiority of ¿1-graph over those classic graphs in data clustering, subspace learning, and semi-supervised learning tasks.


title: Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering

abstract: A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Existing methods usually extract answers from single passages independently. But some questions require a combination of evidence from across different sources to answer correctly. In this paper, we propose two models which make use of multiple passages to generate their answers. Both use an answer-reranking approach which reorders the answer candidates generated by an existing state-of-the-art QA model. We propose two methods, namely, strength-based re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from different passages to better determine the answer. Our models have achieved state-of-the-art results on three public open-domain QA datasets: Quasar-T, SearchQA and the open-domain version of TriviaQA, with about 8 percentage points of improvement over the former two datasets.


title: Reduced stability and intracellular transport of dsRNA contribute to poor RNAi response in lepidopteran insects

abstract: RNA interference (RNAi) has become a widely used reverse genetic tool to study gene function in eukaryotic organisms and is being developed as a technology for insect pest management. The efficiency of RNAi varies among organisms. Insects from different orders also display differential efficiency of RNAi, ranging from highly efficient (coleopterans) to very low efficient (lepidopterans). We investigated the reasons for varying RNAi efficiency between lepidopteran and coleopteran cell lines and also between the Colorado potato beetle, Leptinotarsa decemlineata and tobacco budworm, Heliothis virescens. The dsRNA either injected or fed was degraded faster in H. virescens than in L. decemlineata. Both lepidopteran and coleopteran cell lines and tissues efficiently took up the dsRNA. Interestingly, the dsRNA administered to coleopteran cell lines and tissues was taken up and processed to siRNA whereas the dsRNA was taken up by lepidopteran cell lines and tissues but no siRNA was detected in the total RNA isolated from these cell lines and tissues. The data included in this paper showed that the degradation and intracellular transport of dsRNA are the major factors responsible for reduced RNAi efficiency in lepidopteran insects.


OFDM SIMULATION in MATLAB


title: The mediating role of self-compassion in the relationship between victimization and psychological maladjustment in a sample of adolescents

abstract: The objectives of the present study were to analyze the relationship between victimization and psychological maladjustment in adolescents and the role of self-compassion as a mediator in this relationship. The sample was composed of 109 adolescents aged from 15 to 18 years old with poor school performance. The participants filled out a battery of questionnaires made up of: a socio-demographic data questionnaire; the Juvenile Victimization Questionnaire (JVQ); the Youth Self-Report (YSR); and the Self-Compassion Scale (SCS). RESULTS indicated that victimization was positively associated with psychological maladjustment. Moreover, adolescents reporting poly-victimization showed significantly higher level of psychological maladjustment and different types of victimization show different effects on adolescents' psychological maladjustment. Self-compassion partially mediated the relationship between victimization and psychological maladjustment and reduced negative consequences in adolescents who reported having been victimized. Adolescence is a time of development and search for identity in which strengthening personal protective factors could help overcome any traumas experienced. Therefore, developing self-compassion in adolescence could be a good way to help young people recover from bad experiences and protect themselves against future negative experiences. As self-compassion can be improved with practice it could be included in adolescent intervention and prevention programs. Language: en


title: Food Image Recognition Using Very Deep Convolutional Networks

abstract: We evaluated the effectiveness in classifying food images of a deep-learning approach based on the specifications of Google's image recognition architecture Inception. The architecture is a deep convolutional neural network (DCNN) having a depth of 54 layers. In this study, we fine-tuned this architecture for classifying food images from three well-known food image datasets: ETH Food-101, UEC FOOD 100, and UEC FOOD 256. On these datasets we achieved, respectively, 88.28%, 81.45%, and 76.17% as top-1 accuracy and 96.88%, 97.27%, and 92.58% as top-5 accuracy. To the best of our knowledge, these results significantly improve the best published results obtained on the same datasets, while requiring less computation power, since the number of parameters and the computational complexity are much smaller than the competitors?. Because of this, even if it is still rather large, the deep network based on this architecture appears to be at least closer to the requirements for mobile systems.


title: Missing the Point(er): On the Effectiveness of Code Pointer Integrity

abstract: Memory corruption attacks continue to be a major vector of attack for compromising modern systems. Numerous defenses have been proposed against memory corruption attacks, but they all have their limitations and weaknesses. Stronger defenses such as complete memory safety for legacy languages (C/C++) incur a large overhead, while weaker ones such as practical control flow integrity have been shown to be ineffective. A recent technique called code pointer integrity (CPI) promises to balance security and performance by focusing memory safety on code pointers thus preventing most control-hijacking attacks while maintaining low overhead. CPI protects access to code pointers by storing them in a safe region that is protected by instruction level isolation. On x86-32, this isolation is enforced by hardware, on x86-64 and ARM, isolation is enforced by information hiding. We show that, for architectures that do not support segmentation in which CPI relies on information hiding, CPI's safe region can be leaked and then maliciously modified by using data pointer overwrites. We implement a proof-of-concept exploit against Nginx and successfully bypass CPI implementations that rely on information hiding in 6 seconds with 13 observed crashes. We also present an attack that generates no crashes and is able to bypass CPI in 98 hours. Our attack demonstrates the importance of adequately protecting secrets in security mechanisms and the dangers of relying on difficulty of guessing without guaranteeing the absence of memory leaks.


title: From Databases to Big Data

abstract: There is a tremendous amount of buzz around the concept of "big data." In this article, the author discusses the origins of this trend, the relationship between big data and traditional databases and data processing platforms, and some of the new challenges that big data presents.


title: Approximation Algorithms for the Multiple Knapsack Problem with Assignment Restrictions

abstract: Motivated by a real world application, we study the multiple knapsack problem with assignment restrictions (MKAR). We are given a set of items, each with a positive real weight, and a set of knapsacks, each with a positive real capacity. In addition, for each item a set of knapsacks that can hold that item is specified. In a feasible assignment of items to knapsacks, each item is assigned to at most one knapsack, assignment restrictions are satisfied, and knapsack capacities are not exceeded. We consider the objectives of maximizing assigned weight and minimizing utilized capacity.We focus on obtaining approximate solutions in polynomial computational time. We show that simple greedy approaches yield 1/3-approximation algorithms for the objective of maximizing assigned weight. We give two different 1/2-approximation algorithms: the first one solves single knapsack problems successively and the second one is based on rounding the LP relaxation solution. For the bicriteria problem of minimizing utilized capacity subject to a minimum requirement on assigned weight, we give an (1/3,2)-approximation algorithm.


The Fast Bilateral Solver Supplement


title: A 60GHz wideband injection-locked frequency divider with adaptive-phase-enhancing technique

abstract: A 60-GHz injection-locked frequency divider (ILFD) with quadrature outputs is designed in 90-nm CMOS technology. An adaptive-coupling scheme is proposed to enlarge the phase shift for a wide-band locking range. The measured results exhibit an input locking range of 12.1GHz or 20.5%from 52.7 to 64.8GHz at an incident power of 0dBm. The core circuit consumes 8.6mW from a 1.2V power supply and occupies an area of 360µm×230µm2.


Wrapper Induction for Information Extraction


title: Smart mobile attendance system using voice recognition and fingerprint on smartphone

abstract: The attendance system has been known for a long time, ranging from manual system to automated and complicated system such as attendance system using biometrics. All those attendance system have several weakness, especially to control of employees who work outside the office. In this paper we introduced a attendance system that can be used to record the attendance of employees who are duty outside the building. Our system is also integrated with payroll system that make accounting department can easily calculate and report the salary included the overtime cost. The proposed attendance system used smartphone to verify the employee. The system provide two options for doing verification, such as fingerprint and voice recognition. In this research, we find that fingerprint verification has false positive 95% and the false negative of voice recognition is 5.88%.


title: POSFET devices based tactile sensing arrays

abstract: This work presents and experimentally evaluates novel POSFET (Piezoelectric Oxide Semiconductor Field Effect Transistor) devices based tactile sensing arrays. The arrays, primarily developed for the robotic applications, consist of 5 × 5 POSFET touch sensing devices or taxels. The POSFET touch sensing devices are developed by spin coating piezoelectric polymer P(VDF-TrFE) film on the gate area of MOS devices and polarizing the film in situ. To detect contact events, the taxels utilize the contact forces induced change in the polarization level (and hence change in the induced channel current) of piezoelectric polymer. Both, individual taxels and the array are designed to match spatio-temporal performance of the human fingertips. Experimental results demonstrate that the POSFET tactile sensing arrays presented here are able to detect complex dynamic contact events such as rolling of an object.


RESPONSE OF LARGE SPAN STEEL FRAMES SUBJECTED TO HORIZONTAL AND VERTICAL SEISMIC MOTIONS


Improved Deep Embedded Clustering with Local Structure Preservation


title: Event-Centric Summary Generation

abstract: The Natural Language Processing Group at Microsoft Research participated in Task 2 of the Document Understanding Conference for the first time in 2004. Our primary interest is two-fold: 1) to explore an event-centric approach to summarization, 2) to explore a generation approach to summary realization.


title: Development of a Mobile GIS Property Mapping Application using Mobile Cloud Computing

abstract: This study presents the development of a mobile GIS Property mapping application for use by local authorities in developing countries. Attempts to develop property mapping applications especially in developing countries have mostly used GIS desktop productivity software tools that required the digitization of property maps by highly skilled GIS experts. In addition, these applications lacked real time capture of attribute, spatial and image data of properties. A survey was conducted in the Kafue local authority to gather systems requirements for the mobile application. After design and modeling, the developed application was trialed in the field and 10 properties were mapped successfully. The software tools used in this study included Android Studio, Leaflet mapping library, Apache2 web server, PostgresSQL with PostGIS Extensions and OpenstreetMaps and MapBox mobile cloud computing mapping services. The hardware tools used included a laptop computer and a mobile phone running android operating system. The study showed that mobile property mapping applications can be developed by tapping into the computing resources provided by mobile cloud computing. The benefits of this model include real time complete property data capture and the use of non GIS experts in mapping projects.


title: A New Multi-Sensor Fusion Scheme to Improve the Accuracy of Knee Flexion Kinematics for Functional Rehabilitation Movements

abstract: Exergames have been proposed as a potential tool to improve the current practice of musculoskeletal rehabilitation. Inertial or optical motion capture sensors are commonly used to track the subject’s movements. However, the use of these motion capture tools suffers from the lack of accuracy in estimating joint angles, which could lead to wrong data interpretation. In this study, we proposed a real time quaternion-based fusion scheme, based on the extended Kalman filter, between inertial and visual motion capture sensors, to improve the estimation accuracy of joint angles. The fusion outcome was compared to angles measured using a goniometer. The fusion output shows a better estimation, when compared to inertial measurement units and Kinect outputs. We noted a smaller error (3.96°) compared to the one obtained using inertial sensors (5.04°). The proposed multi-sensor fusion system is therefore accurate enough to be applied, in future works, to our serious game for musculoskeletal rehabilitation.


Ontologies for Supply Chain Management


title: Context Not Content: A Novel Approach to Real-Time User-Generated Video Composition

abstract: Instant sharing of user-generated video recordings has become a widely used service on platforms such as YouNow. Yet, it still poses technical challenges, as mobile upload speed and capacities are limited. One proposed solution to address these issues is video composition. It allows switching between multiple video streams–selecting the best source for a given time–for composing a live video of a better overall quality for viewers. Previous approaches require visual analysis of the video streams, usually limiting the scalability of the system. In contrast, our work allows the stream selection to be realized solely on context information, based on video-and service-quality aspects from sensor and network measurements. The implemented monitoring service for context-aware upload of video streams is evaluated in varying network conditions, with diverse user behavior, including camera shaking and user mobility. We show that a higher efficiency for video upload as well as QoE for viewers can be achieved.


title: On Uniquely List Colorable Graphs

abstract: Let G be a graph with n vertices and suppose that for each vertex v in G, there exists a list of k colors L(v), such that there is a unique proper coloring for G from this collection of lists, then G is called a uniquely k-list colorable graph. ::: Recently M. Mahdian and E.S. Mahmoodian characterized uniquely 2-list colorable graphs. Here we state some results which will pave the way in characterization of uniquely k-list colorable graphs. There is a relationship between this concept and defining sets in graph colorings and critical sets in latin squares.


title: An End-to-End Neural Network for Road Extraction From Remote Sensing Imagery by Multiple Feature Pyramid Network

abstract: Unlike single geospatial objects extraction from high-resolution remote sensing images, the task of road extraction faces more challenges, including its narrowness, sparsity, diversity, multiscale characteristics, and class imbalance. Focusing on these challenges, this paper proposes an end-to-end framework called the multiple feature pyramid network (MFPN). In MFPN, we design an effective feature pyramid and a tailored pyramid pooling module, taking advantage of multilevel semantic features of high-resolution remote sensing images. In the optimization stage, a weighted balance loss function is presented to solve the class imbalance problem caused by the sparseness of roads. The proposed novel loss function is more sensitive to the misclassified and the sparse real labeled pixels and helps to focus on the spare set of hard pixels in the training stage. Compared with the cross-entropy loss function, the weighted balance loss can reduce training time dramatically for the same precision. Experiments on two challenging datasets of high-resolution remote sensing images which illustrate the performance of the proposed algorithm have achieved significant improvements, especially for narrow rural roads.


title: PATTERNS OF NEURAL TUBE DEFECTS AT TWO TEACHING HOSPITALS IN ADDIS ABABA, ETHIOPIA A THREE YEARS RETROSPECTIVE STUDY.

abstract: BACKGROUND ::: Neural tube defects (NTDs), one of the most common congenital malformations, are potentially preventable cause of perinatal morbidity and mortality. ::: ::: ::: OBJECTIVES ::: To give baseline description of NTDs and their outcome at two teaching hospitals in Addis Ababa, Ethiopia. ::: ::: ::: MATERIALS AND METHODS ::: A retrospective cross sectional descriptive study conducted from September 2009 to August 2012. ::: ::: ::: RESULTS ::: During the study period out of 28,961 deliveries 177 cases of NTDs were identified, giving an overall NTD prevalence of 6.1/1000. Only 12% (21/177) were diagnosed before 28 weeks of gestation. The mean gestational age at diagnosis of NTDs was 33.8 weeks (±5.5). Majority, 93.2% (165/177), had antenatal care (ANC) follow-up. Most, 72% (127/177), were diagnosed by ultrasound before delivery while 28% (50/177) were identified at the time of delivery or expulsion. Majority, 85.3% (151/177), never received folic acid supplementation. Only less than 1% (2/177) of the mothers started taking folic acid supplementation pre-conceptionally. Only a third, 33.3% (59/177), of the fetuses were born alive while only 13.6% (24/177) were discharged alive. Myelomeningocele, identified in 51.4% (91/177), was the commonest NTD in this study. ::: ::: ::: CONCLUSION AND RECOMMENDATIONS ::: The proportion of NTDs in this study is among the highest globally reported rates. The practice of periconceptional folic acid supplementation is negligible. And although most had ANC follow-up the vast majority of NTDs were diagnosed late in the third trimester. It is, therefore, highly recommended to consider implementing national preventive strategies to reduce the prevalence of NTDs in Ethiopia.


title: A performance analysis of convolutional neural network models in SAR target recognition

abstract: In recent years, the deep learning method represented by Convolutional Neural Network (CNN) has made great progress in the field of image recognition. In this paper, the representative convolution neural network models such as AlexNet, VGGNet, GoogLeNet, ResNet, DenseNet, SENet and so on are applied to SAR image target recognition. According to the accuracy, parameter quantity, training time and other indicators, the performance of different CNN models are analyzed and compared on MSTAR data set, the superiority of CNN model in SAR image target recognition is verified, and it also provides a reference for the follow-up work in this field.


title: A New Framework for Synthetic Aperture Sonar Micronavigation

abstract: Synthetic aperture imaging systems achieve constant azimuth resolution by coherently summating the observations acquired along the aperture path. At this aim, their locations have to be known with subwavelength accuracy. In underwater Synthetic Aperture Sonar (SAS), the nature of propagation and navigation in water makes the retrieval of this information challenging. Inertial sensors have to be employed in combination with signal processing techniques, which are usually referred to as micronavigation. In this paper we propose a novel micronavigation approach based on the minimization of an error function between two contiguous pings having some mutual information. This error is obtained by comparing the vector space intersections between the pings orthogonal projectors. The effectiveness and generality of the proposed approach is demonstrated by means of simulations and by means of an experiment performed in a controlled environment.


New Inequalities and Uncertainty Relations on Linear Canonical Transform Revisit


title: The Middle East under Malware Attack Dissecting Cyber Weapons

abstract: The Middle East is currently the target of an unprecedented campaign of cyber attacks carried out by unknown parties. The energy industry is particularly targeted. The attacks are carried out by deploying extremely sophisticated malware. The campaign opened by the Stuxnet malware in 2010 and then continued through Duqu, Flame, Gauss, and Shamoon malware. This paper is a technical survey of the attacking vectors utilized by the three most famous malware, namely, Stuxnet, Flame, and Shamoon. We describe their main modules, their sophisticated spreading capabilities, and we discuss what it sets them apart from typical malware. The main purpose of the paper is to point out the recent trends infused by this new breed of malware into cyber attacks.


Colin B . Grant Uncertain Communications : Uncertain Social Systems


title: 3D Map Visualization for Real Time RSSI Indoor Location Tracking System on PDA

abstract: Processing of location sensing information and visualization of 3D graphics on PDA either in outdoor or indoor environment had been presented as one of research issues nowadays. Famous tracking system such as GPS had gained its trust as a feasible and effective outdoor tracking system. Traditional tracking system with 2D-image standard presents only few and dull information to users. This paper aim to develop a highly accuracy real-time indoor tracking system with 3D scenes integrated on PDA to provide more useful location sensing information to user. VRML, a powerful 3D modeling tool is used for designing 3D objects in indoor environments. In order to reduce PDApsilas computing time and increases 3D objects rendering time, culling algorithm is implemented in our system. Generic RSSI-based (received signal strength indicator) location tracking method is used for indoor location tracking due to its low-cost solutions. Moreover, a reliable RSSI smoothing algorithm had been proposed to cater the reflecting and attenuating of objects in surrounding environment for RSSI ranging. Experiments had revealed an ideal result with rendering time of 3D objects in less than 1 sec and high accuracy of real-time RSSI position estimation in our system.


title: Defining the brain systems of lust, romantic attraction, and attachment.

abstract: Mammals and birds have evolved three primary, discrete, interrelated emotion–motivation systems in the brain for mating, reproduction, and parenting: lust, attraction, and male–female attachment. Each emotion–motivation system is associated with a specific constellation of neural correlates and a distinct behavioral repertoire. Lust evolved to initiate the mating process with any appropriate partner; attraction evolved to enable individuals to choose among and prefer specific mating partners, thereby conserving their mating time and energy; male–female attachment evolved to enable individuals to cooperate with a reproductive mate until species-specific parental duties have been completed. The evolution of these three emotion–motivation systems contribute to contemporary patterns of marriage, adultery, divorce, remarriage, stalking, homicide and other crimes of passion, and clinical depression due to romantic rejection. This article defines these three emotion–motivation systems. Then it discusses an ongoing project using functional magnetic resonance imaging of the brain to investigate the neural circuits associated with one of these emotion–motivation systems, romantic attraction.


title: Relaxation oscillator calibration technique with comparator delay regulation

abstract: This paper presents an improved technique for the calibration of the relaxation oscillators with respect to the delay of the comparators. The drawbacks of the conventional topology for the relaxation oscillators are analyzed. Based on the analysis, the circuit modification which resolves the effects of the comparator delay in the trimming procedure is proposed. The simulations in ams 0.18μ CMOS technology exhibit more than 5x the improvement in the precision compared to the conventional topology, evaluated in the temperature range from −40 to 125 °C.


title: OpenVirteX: A Network Hypervisor.

abstract: Virtualized environments have been around for a long time, but until recently these have focused solely on compute virtualization and left the network behind. Network virtualization enables multiple tenants to share the same physical infrastructure by decoupling the physical network from the virtual network. In this paper, we introduce OpenVirteX, a Network Virtualization Platform which provides virtual Software Defined Networks (vSDNs). Each vSDN is customizable in terms of topology as well as addressing scheme. Introduction. Techniques for virtualizing network infrastructure have lagged behind that of compute resource virtualization. This has hampered the ability of operators to provide full virtualization that extends down to the network. In the ideal case, operators would be able to provide networks whose topologies, management schemes, and use cases are under the full control of their tenants. Realization of this scheme requires the ability to construct virtual networks that are not only logically isolated, but also have fully configurable topologies and properties akin to virtual machines, e.g., can be created, destroyed, snapshotted, and migrated on demand. Such a mechanism would provide operators with the ability to give a tenant the illusion that they have full control of topology, addressing, and management of the infrastructure despite sharing it with other tenants. Here we present OpenVirteX, a network hypervisor that enables operators to provide this form of network virtualization to their customers. We leverage the growing focus on Software Defined Networks (SDN) amongst infrastructure providers aiming to simplify and add flexibility to the process of provisioning networks. In specific, OpenVirteX builds on OpenFlow [1] as the SDN medium, and our experiences with FlowVisor [2] for design. Like FlowVisor, OpenVirteX functions as a proxy within the control channel, presenting OpenFlow networks to tenants, while controlling the underlying physical infrastructure via the southbound OpenFlow interface. Figure 1 depicts this architecture. By exposing OpenFlow networks, OpenVirteX allows tenants to use their own network OS (NOS) to control the network resources corresponding to their virtual network. In other words, OpenVirteX creates multiple virtual software defined networks out of one. Unlike FlowVisor, which simply slices the entire flowspace amongst the tenants, OpenVirteX provides each tenant with a fully virtualized network featuring a tenant-specified topology and a full header space. Motivation. OpenVirteX offers a pragmatic approach to meeting the requirements of both operators and tenants in increasingly common computing situations: • Cloud: OVX facilitates the migration of an enterprise to a Cloud Infrastructure. Each customer can request an exact copy of his/her physical network, with the same address scheme and topology of the current network. The level of abstraction offered by OpenVirteX creates the illusion that the Cloud physical infrastructure fits exactly the client requirements, in terms of isolation, topology and addressing scheme. • Provider: Similarly, in a WAN scenario, virtualization is essential, e.g. to allow multiple enterprises to connect their remote sites. With OVX, this could be done easily, spawning virtual networks that provide inherited traffic isolation, security, and flexibility. Moreover, with OpenVirteX, the tenant can still control how traffic is forwarded inside his network through a NOS and can apply policies (e.g. firewall, load balancer) that in a standard network are completely managed by the ISP. The remainder of this abstract describes the virtualization approach taken by OpenVirteX, and its current state. Topology Virtualization. OpenVirteX maintains internal representations of the physical and virtual networks as a collection of software switches, links, and end hosts. The physical topology representation is the basis upon which virtual networks are mapped. Tenants can request a topology by supplying a mapping between the elements in the physical topology and their desired virtual network. Virtual topologies can range from an exact copy or subset of the physical network, over a giant virtual switch that abstracts away all physical switches and links, to any custom topology as described by the tenant. The ability to control topology abstraction allows tenants to simplify their NOS, or operators to implement policies in the network. For example, a tenant may forego loop resolution in their NOS by requesting a loop-free topology, or an operator can require traffic to pass through chosen nodes by modifying the connecting path of a virtual link or between the ports of a giant switch. The mapping between virtual and physical elements are stored in a global map, but otherwise kept separate. This allows tenant networks to be reconfigured, halted, and re-initialized by simply modifying this mapping, i.e. pointing keys to different values. Finally, since virtual topologies need not correspond to the physical network, resilience can be enabled in the former Fig. 1: Network virtualization with OpenVirteX. Fig. 2: The address virtualization process. by allowing tenants to specify multiple backup paths for any virtual links between switches, or within giant switches, between ports. Address Virtualization. OpenVirteX grants tenants the ability to choose address assignments for their end hosts, allowing multiple, potentially-overlapping IP address blocks to exist in the same physical network. To differentiate hosts, OpenVirteX generates globally unique tenant IDs for each tenant, and for each host, a physical IP addresses that encodes the host’s membership using the tenant ID. Collisions of addresses are avoided by installing flow rules to rewrite addresses at the edge switches of the network, from tenant-assigned address to physical IP address at the ingress edge, and vice-versa at the egress edge. The translation process is illustrated in Figure 2. Importantly, this procedure is invisible to a tenant’s NOS or hosts, implying that a NOS used to control a virtual network created by OpenVirteX doesn’t need to be modified in any way to properly function. In addition to preventing address aliasing in a transparent way, the mapping created by the procedure is used by OpenVirteX to demultiplex northbound control messages so that they reach the correct tenant networks. Control Function Virtualization. Each virtual network may run its own NOS to program the virtual switches. OpenVirteX makes this possible by mapping various control functions for the virtual network on to the corresponding physical network resources. The translations can get complex, since an operation for one virtual switch may map onto multiple switches and links in the physical network. OpenVirteX leverages its position as proxy that allows it to intercept and manipulate the control packets before they reach their intended destination, which OpenVirteX also determines based on its virtual-physical mapping. Implementation. We have implemented a preliminary version of OpenVirteX in Java. This version currently supports OpenFlow 1.0, and supports the creation of custom topologies under overlapping IP address spaces, the ability to start and stop virtual networks and their components, and persistent storage using Mongo DB. Backup paths for resilient links and giant switches can be manually configured, or automatically generated with a simple SPF algorithm. A JSONRPC API is provided to enable configuration and monitoring of both tenant and infrastructure networks. This API has been used for an implementation of a GUI, as well as a topology embedder that generates virtual-physical mappings as configurations to OpenVirteX. Currently on-going work includes the addition of features such as virtual network snapshotting and migration, dynamic configuration of virtual networks, and external and inter-tenant connectivity. In the future topics such as HA and scalability may be explored.


title: Improving the State of Automotive Software Engineering

abstract: The automotive industry is fundamentally changing by becoming software intensive, rather than mechanically intensive. To stay ahead of the game, automakers must continuously improve their software engineering. For this article, the authors studied the existing literature on the subject and made practitioner-oriented recommendations.


Dagiral and ' ' programmer-journalists ' ' in Chicago Data-driven journalism and the public good : ' ' Computer-assisted-reporters ' '


title: A note on the difference between profile and modified profile likelihood

abstract: The difference between profile likelihood and modified profile likelihood depends primarily on the expected value of a certain third order derivative of the log likelihood. It is shown that in exponential family problems this derivative vanishes if the parameter of interest is a mean parameter but in general not when it is a canonical parameter. As examples some matched pair problems for exponential family models are considered


title: 3D shape regression for real-time facial animation

abstract: We present a real-time performance-driven facial animation system based on 3D shape regression. In this system, the 3D positions of facial landmark points are inferred by a regressor from 2D video frames of an ordinary web camera. From these 3D points, the pose and expressions of the face are recovered by fitting a user-specific blendshape model to them. The main technical contribution of this work is the 3D regression algorithm that learns an accurate, user-specific face alignment model from an easily acquired set of training data, generated from images of the user performing a sequence of predefined facial poses and expressions. Experiments show that our system can accurately recover 3D face shapes even for fast motions, non-frontal faces, and exaggerated expressions. In addition, some capacity to handle partial occlusions and changing lighting conditions is demonstrated.


title: Reverse Training: An Efficient Approach for Image Set Classification

abstract: This paper introduces a new approach, called reverse training, to efficiently extend binary classifiers for the task of multi-class image set classification. Unlike existing binary to multi-class extension strategies, which require multiple binary classifiers, the proposed approach is very efficient since it trains a single binary classifier to optimally discriminate the class of the query image set from all others. For this purpose, the classifier is trained with the images of the query set (labelled positive) and a randomly sampled subset of the training data (labelled negative). The trained classifier is then evaluated on rest of the training images. The class of these images with their largest percentage classified as positive is predicted as the class of the query image set. The confidence level of the prediction is also computed and integrated into the proposed approach to further enhance its robustness and accuracy. Extensive experiments and comparisons with existing methods show that the proposed approach achieves state of the art performance for face and object recognition on a number of datasets.


title: Betweenness Centrality” as an Indicator of the “Interdisciplinarity” of Scientific Journals

abstract: In addition to science citation indicators of journals like impact and immediacy, social network analysis provides a set of centrality measures like degree, betweenness, and closeness centrality. These measures are first analyzed for the entire set of 7,379 journals included in the Journal Citation Reports of the Science Citation Index and the Social Sciences Citation Index 2004 (Thomson ISI, Philadelphia, PA), and then also in relation to local citation environments that can be considered as proxies of specialties and disciplines. Betweenness centrality is shown to be an indicator of the interdisciplinarity of journals, but only in local citation environments and after normalization; otherwise, the influence of degree centrality (size) overshadows the betweenness-centrality measure. The indicator is applied to a variety of citation environments, including policy-relevant ones like biotechnology and nanotechnology. The values of the indicator remain sensitive to the delineations of the set because of the indicator's local character. Maps showing interdisciplinarity of journals in terms of betweenness centrality can be drawn using information about journal citation environments, which is available online.


title: Knowledge Representation Concepts for Automated SLA Management

abstract: Outsourcing of complex IT infrastructure to IT service providers has increased substantially during the past years. IT service providers must be able to fulfil their service-quality commitments based upon predefined Service Level Agreements (SLAs) with the service customer. They need to manage, execute and maintain thousands of SLAs for different customers and different types of services, which needs new levels of flexibility and automation not available with the current technology. The complexity of contractual logic in SLAs requires new forms of knowledge representation to automatically draw inferences and execute contractual agreemen ts. A logic-based approach provides several advantages including automated rule chaining allowing for compact knowledge representation as well as flexibility to adapt to rapidly changing business requirements. We suggest logical formalisms for the representation and enforcement of SLA rules and describe a proof-of-concept implementation. The article describes selected formalisms of the ContractLog KR and their adequacy for automated SLA management and presents results of experiments and examples from common industry use cases to demonstrate the expressiveness of the language and the scalability of the approach.


title: Noise-Immune Embedded NAND-ROM Using a Dynamic Split Source-Line Scheme for VDDmin and Speed Improvements

abstract: Embedded NAND-type read-only-memory (NAND-ROM) provides large-capacity, high-reliability, on-chip non-volatile storage. However, NAND-ROM suffers from code-dependent read noises and cannot survive at low supply voltages (VDDs). These code-dependent read noises are primarily due to the charge-sharing effect, bitline leakage current, and crosstalk between bitlines, which become worse at lower VDD. This study proposes a dynamic split source-line (DSSL) scheme for NAND-ROM. The proposed scheme overcomes code-dependent read noises while improving the read access time and suppressing the active-mode gate leakage current, with only a 1% area penalty in the cell array. Experiments on a fabricated 256 Kb macro using a 90 nm industrial logic process demonstrate that the proposed DSSL scheme achieves 100% code-pattern coverage under a small sensing margin. Additionally, the DSSL NAND-ROM works with a wide range of supply voltages (1-0.31 V) with a 38%, 45.8%, and 37% improvement in speed, power, and standby current, respectively, at VDD = 1 V.


title: Cadaveric Assessment of Lip Injections: Locating the Serious Threats

abstract: BACKGROUND ::: Lip augmentation could be a possible cause of blindness following filler injections. This study evaluated the risk by simulating clinical scenarios of marginal injections to the upper and lower lips and then evaluated the risk of vascular injuries. ::: ::: ::: METHODS ::: A 22G cannula was inserted bilaterally along the wet-dry junction of the upper and lower lip margins in fifteen cadavers, and then both lips were dissected to verify possible injuries to the superior and inferior labial arteries. The position of the labial arteries in the vermilion zone was documented to determine the appropriate injection technique. ::: ::: ::: RESULTS ::: In the marginal injections to the lips, arterial injuries occurred at the medial segment of the vermilion zone of both the upper and lower lips, at the terminal part of the labial arteries or a distal branch. Considering arterial anatomy, the upper lip has a higher chance of arterial injury than the lower lip. The cannula should not be inserted in the submucosa as it is recommended to evert the vermilion because both the superior and inferior labial arteries are located in the submucosa of the medial and middle segments of the vermilion in all specimens. ::: ::: ::: CONCLUSION ::: Awareness of the possibility of vascular injury is necessary during injections of the medial segments of the vermilion of the lips. Vermilion border and marginal injections are recommended for safe and effective lip augmentation. Deep injection around the oral commissure and submucosal injection of the medial and middle segments of the vermilion zone are prohibited because of the high risk of arterial injury. ::: ::: ::: NO LEVEL ASSIGNED ::: This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.


title: Time Series Modeling of River Flow Using Wavelet Neural Networks

abstract: A new hybrid model which combines wavelets and Artificial Neural Network (ANN) called wavelet neural network (WNN) model was proposed in the current study and applied for time series modeling of river flow. The time series of daily river flow of the Malaprabha River basin (Karnataka state, India) were analyzed by the WNN model. The observed time series are decomposed into sub-series using discrete wavelet transform and then appropriate sub-series is used as inputs to the neural network for forecasting hydrological variables. The hybrid model (WNN) was compared with the standard ANN and AR models. The WNN model was able to provide a good fit with the observed data, especially the peak values during the testing period. The benchmark results from WNN model applications showed that the hybrid model produced better results in estimating the hydrograph properties than the latter models (ANN and AR).


title: Industrial ecosystems in the crude palm oil industry in Thailand

abstract: The crude palm oil industry plays an important role in Thai economic development and in enhancing the economic welfare of the population. Despite obvious benefits of this industrial development, it contributes to environmental degradation from both input and output sides of its activities. On the input side, crude palm oil mill uses much water in production process and consumes high energy. On the output side , manufacturing process generates large quantity of wastewater, solid waste/ by-product and air pollution. The current breakdowns of industrial wastes and recoverable materials are empty fruit brunch, fiber, shell kernel and ash. It is estimated that by year 2002, a total of 1.8 million ton of solid wastes/ byproducts and 2.5 million m of wastewater are generated. Industrial eco-systems are the environment friendly systems for industrial waste recycling, resembling the food chains, food webs and the nutrient cycles in natural environment. The crude palm oil mill has developed a number of ecosystems for its waste recycling. This paper describes the nature of these eco-systems, divided as in-plant, upstream industry and cross-industry of crude palm oil industrial in Thailand based on e n v i r o n m e n t a l l y b a l a n c e d i n d u s t r i a l c o m p l e x . 1. I n t r o d u c t i o n Industrial waste handling is the final and critical step for industrial pollution control. It is also an important issue to cleaner production and sustainable development. Industrial eco-systems are the environmental friendly systems for industrial waste recycling, resembling the food chains, food webs and the nutrient recycles in natural environment (liu and Shyng, 1999). Industrial ecosystems are much more environment friendly compared to other waste treatments such as incineration, solidification and landfill because: • It transforms the harmful component of waste into usable substance. • It slows down the depletion of primary resources in industrial production. There are three stages in the evolution of an industrial ecosystem. Type I industrial ecosystem, are characterized by linear, one-way flows of materials and energy where the production, use, and disposal of products occurred without reuse, or recovery, of energy or material. In Type II industrial ecosystem ,some internal cycling of raw materials occurs, but there is still a need for virgin material input, and wastes continue to be generated and disposed of outside the economic system. Hypothetical Type III industrial ecosystem would be characterized by completes internal cycling of materials. A mix of TypeI and Tpye II material flows can characterize current industrial ecosystem. The Type III industrial ecosystem model, material is highly conserved, no waste material is released, and heat escapes It is keeping with the limiting goal of the “zero discharge” adopted by several major companies (Allenby and Richards,1994). Chao (1999) reported that four type of industrial ecosystems are envisaged: