title: MEMS Based RF Energy Harvester for Battery-Less Remote Control: A Review

abstract: The paper deals with the growing interest of energy harvesting systems due to great development in many new emerging technologies in electronics and telecommunication. The research focuses particularly about rectenna element comprises of antenna, impedance matching and rectifier. The rectenna is applied under Micro-Electromechanical-System (MEMS) technology design use in Radio Frequency (RF) energy harvester. MEMS is a technology of miniaturization that has been mostly adopted from integrated circuit industry together on a chip that are made using micro fabrication technique and applied for not only electrical systems. RF ambient source is considered over other ambient sources because RF can be broadcasted by various wireless systems in unlicensed frequency bands. However, the amount of energy captured from the ambient RF is extremely low which need improvements and more Direct Current (DC) voltage generated from RF energy harvester. For this motivation, a dual band MEMS rectenna is proposed for maximizing the efficiency. Power Management Unit (PMU) is interposed between MEMS rectenna and a load. The system is equipped with temporary energy storage and voltage regulator to produce optimum output voltage. The paper proposes a system that is designed and simulated using PSpice software and modeled in Mentor Graphic. The stated result from RF MEMS energy harvester is to provide functional conversion efficiency and reliable energy harvesting system to reach 1.5-3.0 V output voltage for operating frequency at 1.9 and 2.45 GHz from RF input power at -20 dBm with reveal approximately 100% improvement over other existing designs. The conceptual design can be the platform for innovative developments in recent technologies to achieve wireless transmission powered only by RF MEMS energy harvester.


title: Stacked patch array in LTCC for 28 GHz antenna-in-package applications

abstract: An antenna array is designed with 2×8 45-degree polarized stacked patches around 28 GHz in low temperature co-fired ceramic (LTCC) for the applications of the fifth-generation wireless communication (5G). As demonstrated by the measured reflection coefficient and realized gain response, the bandwidth is broadened when compared with single-patch array. The proposed antenna-in-package (AiP) can operate from 26.3 to 29.8 GHz, with the maximum realized gain of 15.1 dBi, the 3-dB beamwidth of 11° and the estimated efficiency of 55%. Reasonable agreement is achieved between the measured and simulated results. The AiP arrays on LTCC show a promising foreground for 5G high-frequency applications.


title: A novel five-phase pancake shaped switched reluctance motor for hybrid electric vehicles

abstract: Switched reluctance motors (SRMs) have been gaining increasing popularity and emerging as an attractive alternative to traditional electrical motors in hybrid vehicle applications due to their numerous advantages. However, large torque ripple and acoustic noise are its major disadvantages. This paper presents a novel five-phase 15/12 SRM which features higher power density, very low level of vibration with flexibility in controlling the torque ripple profile. This design is classified as an axial field SRM and hence it needs three-dimensional finite-element analysis model. However, an alternative two-dimensional model is presented and some design features and result are discussed in this paper.


title: Hydrogels in Cancer Drug Delivery Systems

abstract: Hydrogels have played a vital role in the development of controlled-release drug delivery systems. A hydrogel (also called an aquagel) is a three-dimensional (3-D) network of hydrophilic polymers swollen in water (1). The 3-D polymer network of a hydrogel is maintained in the form of elastic solid in the sense that there exists a remembered reference configuration to which the system returns even after being deformed for a very long time. By definition, hydrogels usually contain water at least 10% of the total weight. The term hydrogel implies that the material is already swollen in water. Dried hydrogels (or xerogels) absorb water to swell, and the size of the swollen gel depends on how much water is absorbed. A hydrogel swells for the same reason that an analogous linear polymer dissolves in water to form an ordinary polymer solution. The extent of swelling is usually measured by the swelling ratio, which is the volume (or weight) of the swollen gel divided by the volume (or weight) of the xerogel. If the weight of absorbed water exceeds 95% of the total weight, a hydrogel is often called a superabsorbent. Thus, 20g of fully swollen superabsorbent will have 1g or less of polymer network and 19g or more of water (i.e., the swelling ratio is more than 20). The swelling ratio of many hydrogels can easily reach greater than 100. Despite such a large quantity of water, highly swollen hydrogels still maintain solid forms.


title: An Ontology of Architectural Design Decisions in Software-Intensive Systems

abstract: Architectural design decisions deserve to be first class entities in the process of developing complex software-intensive systems. Preserving the graphs of decisions and all their interdependencies will support the evolution and maintenance of such systems. In this paper we present a possible ontology of architectural design decisions, their attributes and relationships, for complex, software-intensive systems.


title: Is Apache Spark scalable to seismic data analytics and computations?

abstract: High Performance Computing (HPC) has been a dominated technology used in seismic data processing at the petroleum industry. However, with the increasing data size and varieties, traditional HPC focusing on computation meets new challenges. Researchers are looking for new computing platforms with a balance of both performance and productivity, as well as featured with big data analytics capability. Apache Spark is a new big data analytics platform that supports more than map/reduce parallel execution mode with good scalability and fault tolerance. In this paper, we try to answer the question that if Apache Spark is scalable to process seismic data with its in-memory computation and data locality features. We use a few typical seismic data processing algorithms to study the performance and productivity. Our contributions include customized seismic data distributions in Spark, extraction of commonly used templates for seismic data processing algorithms, and performance analysis of several typical seismic processing algorithms.


title: Restoration and Enhancement of Underwater Image based on Wavelength Compensation and Image Dehazing Technique

abstract: Getting clear images in underwater environments is an important issue in ocean engineering . The quality of underwater images plays a important role in scientific world. Capturing images underwater is difficult, generally due to deflection and reflection of water particles, and color change due to light travelling in water with different wavelengths. Light dispersion and color transform result in contrast loss and color deviation in images acquired underwater. Restoration and Enhancement of an underwater object from an image distorted by moving water waves is a very challenging task. This paper proposes wavelength compensation and image dehazing technique to balance the color change and light scattering respectively. It also removes artificial light by using depth map technique. Water depth is estimated by background color. Color change compensation is done by residual energy ratio method. A new approach is presented in this paper. We make use of a special technique called wavelength compensation and dehazing technique along with the artificial light removal technique simultaneously to analyze the raw image sequences and recover the true object. We test our approach on both pretended and data of real world, separately. Such technique has wide applications to areas such.


title: Threat implications of the Internet of Things

abstract: There are currently more objects connected to the Internet than there are people in the world. This gap will continue to grow, as more objects gain the ability to directly interface with the Internet or become physical representations of data accessible via Internet systems. This trend toward greater independent object interaction in the Internet is collectively described as the Internet of Things (IoT). As with previous global technology trends, such as widespread mobile adoption and datacentre consolidation, the changing operating environment associated with the Internet of Things represents considerable impact to the attack surface and threat environment of the Internet and Internet-connected systems. The increase in Internet-connected systems and the accompanying, non-linear increase in Internet attack surface can be represented by several tiers of increased surface complexity. Users, or groups of users, are linked to a non-linear number of connected entities, which in turn are linked to a non-linear number of indirectly connected, trackable entities. At each tier of this model, the increasing population, complexity, heterogeneity, interoperability, mobility, and distribution of entities represents an expanding attack surface, measurable by additional channels, methods, and data items. Further, this expansion will necessarily increase the field of security stakeholders and introduce new manageability challenges. This document provides a framework for measurement and analysis of the security implications inherent in an Internet that is dominated by non-user endpoints, content in the form of objects, and content that is generated by objects without direct user involvement.


title: Eye Gaze Correction with a Single Webcam Based on Eye-Replacement

abstract: In traditional video conferencing systems, it is impossible for users to have eye contact when looking at the conversation partner’s face displayed on the screen, due to the disparity between the locations of the camera and the screen. In this work, we implemented a gaze correction system that can automatically maintain eye contact by replacing the eyes of the user with the direct looking eyes (looking directly into the camera) captured in the initialization stage. Our real-time system has good robustness against different lighting conditions and head poses, and it provides visually convincing and natural results while relying only on a single webcam that can be positioned almost anywhere around the screen.


title: SDN-based load balancing strategy for server cluster

abstract: Growing network traffic brings huge pressure to the server cluster. Using load balancing technology in server cluster becomes the choice of most enterprises. Because of many limitations, the development of the traditional load balancing technology has encountered bottlenecks. This has forced companies to find new load balancing method. Software Defined Network (SDN) provides a good method to solve the load balancing problem. In this paper, we implemented two load balancing algorithm that based on the latest SDN network architecture. The first one is a static scheduling algorithm and the second is a dynamic scheduling algorithm. Our experiments show that the performance of the dynamic algorithm is better than the static algorithm.


title: Dual-band modified complementary split ring resonator (MCSRR) based multi-resonator circuit for chipless RFID tag

abstract: As chipless RFID technology has potential to offer ultra-low cost and fully printable tags, recently, there has been considerable interest on implementing a compact chipless RFID tag with adequate data capacity. In this paper, a compact multiresonator circuit for spectral signature based chipless RFID tag is proposed. The multiresonator circuit utilizes a number of modified complementary split ring resonators (MCSRR) placed along the transmission line as data bit encoding element. A novel resonance detuning mechanism proposed here allows the use of an MCSRR to independently encode two data bits instead of one bit. Besides, symmetricity of the MCSRR with respect to the feed line causes the disappearance of the second harmonic and hence the total realizable bandwidth for data bit encoding is also enhanced. The multiresonator circuit is based on coplanar waveguide (CPW) configuration and can be realized on flexible substrate with single sided metallization. Once incorporated with cross polarized UWB tag antennas, the proposed multiresonator circuit can be used for a compact fully printable chipless RFID tag that may replace barcode in the long run.


Detection of Brain Tumor Using K-Means Clustering


title: Dermoscopic patterns of acral melanocytic nevi and melanomas in a white population in central Italy.

abstract: OBJECTIVE ::: To investigate the dermoscopic features of acral melanocytic lesions in a white population in central Italy. ::: ::: ::: DESIGN ::: Retrospective review. ::: ::: ::: SETTING ::: University dermatology department. ::: ::: ::: PATIENTS ::: Six hundred fifty-one Italian subjects, ranging in age from 6 months to 78 years. ::: ::: ::: MAIN OUTCOME MEASURES ::: We retrospectively investigated all digital dermoscopic images of acral melanocytic lesions included in our database from January 1996 to May 2005. ::: ::: ::: RESULTS ::: We retrieved digital images of 723 benign acral melanocytic lesions in 641 patients (235 males and 406 females; mean age, 26.5 years) and of 10 acral melanomas in 10 patients (7 males and 3 females; mean age, 65 years). Individual lesions were located on the soles (n=520), fingers (n=146), and palms (n=67). Among acral nevi, the parallel furrow (42.1%) was the most common pattern, followed by the latticelike (14.9%), nontypical (13.7%), fibrillar (10.8%), homogeneous (9.3%), globular (5.4%), and reticular (2.1%) patterns. The frequency of distribution of the latticelike, nontypical, fibrillar, and homogeneous patterns significantly differed (P<.001, P=.03, P<.001, and P=.03, respectively) between anatomical sites. Also, 13 acral nevi (1.8%), mainly located on the fingers, showed a new combined pattern (transition pattern) consisting of a brownish black network associated with a parallel furrow or latticelike pattern. All 10 acral melanomas showed a multicomponent dermoscopic pattern. ::: ::: ::: CONCLUSIONS ::: In our series of acral nevi, we observed 8 dermoscopic patterns, with varying distribution by anatomical site. Identification of a specific pattern is highly suggestive of the benign or the malignant nature of any given acral melanocytic lesion.


title: Safety Evacuation Measures in Urban Subway Fire

abstract: The emergency incidents in urban subway system were analyzed. The main incident of emergency incidents was gained, then the reason of main emergency incident was analyzed. The different smoke control and evacuation modes were discussed, then the best smoke control and evacuation mode were obtained. Smoke spread at platform was simulated by FDS+EVAC. The relation of soot density, extinction coefficient and evacuation speed have liner relationship. The main influence factors of evacuation time were analyzed. The simulation results indicate that the existence of automatic fare gates of metro reduces the efficiency of evacuation. The evacuation time of no automatic fare gate is longer than evacuation time of automatic fare gate around 40%.


Using formal concept analysis and collaborative filtering to recommendation system in e-commerce


title: Emotive ontology: extracting fine-grained emotions from terse, informal messages

abstract: With the uptake of social media, such as Facebook and Twitter, there is now a vast amount of new user generated content on a daily basis, much of it in the form of short, informal free-form text. Businesses, institutions, governments and law enforcement organisations are now actively seeking ways to monitor and more generally analyse public response to various events, products and services. Our primary aim in this project was the development of an approach for capturing a wide and comprehensive range of emotions from sparse, text based messages in social-media, such as Twitter, to help monitor emotional responses to events. Prior work has focused mostly on negative / positive sentiment classification tasks, and although numerous approaches employ highly elaborate and effective techniques with some success, the sentiment or emotion granularity is generally limiting and arguably not always most appropriate for real-world problems. In this paper we employ an ontology engineering approach to the problem of fine-grained emotion detection in sparse messages. Messages are also processed using a custom NLP pipeline, which is appropriate for the sparse and informal nature of text encountered on micro-blogs. Our approach detects a range of eight high-level emotions; anger, confusion, disgust, fear, happiness, sadness, shame and surprise. We report f-measures (recall and precision) and compare our approach to two related approaches from recent literature. © 2013 IADIS.


title: Party Over Policy: The Dominating Impact of Group Influence on Political Beliefs.

abstract: Four studies demonstrated both the power of group influence in persuasion and people’s blindness to it. Even under conditions of effortful processing, attitudes toward a social policy depended almost exclusively upon the stated position of one’s political party. This effect overwhelmed the impact of both the policy’s objective content and participants’ ideological beliefs (Studies 1–3), and it was driven by a shift in the assumed factual qualities of the policy and in its perceived moral connotations (Study 4). Nevertheless, participants denied having been influenced by their political group, although they believed that other individuals, especially their ideological adversaries, would be so influenced. The underappreciated role of social identity in persuasion is discussed.


title: Personalized travel route recommendation with skyline query

abstract: Traveling has received much more attention with the rising living standard in recent years. Therefore, it is essential to make a good recommendation on how to choose a high-quality travel route for travelers. Besides, with the exponential increase of smart devices equipped with a GPS module and the advancement of social networks, Location-based social networks (LBSNs) have come into being. As a consequence, a great many check-in data are generated in human's routine life everyday, which can be used for future travel route recommendation. In this paper, we propose a personalized travel route recommendation scheme by mining the collected check-in data. Our method is based on applying skyline queries to recommend. Skyline is a set of points that are not dominated by other points on all dimensions. In our schemes, in order to search attractions to be recommended to users, we employ a variation of skyline queries which is called as K-dominating queries. It retrieves top-K points dominating the largest number of other points from the database. Then we choose top-K attractions by utilizing K-dominating queries and produce candidate routes. We also analyze the recommendation with considering individual preference. Finally, experiments are conducted to exhibit the performance of our schemes.


title: Problematic Internet use or Internet addiction?

abstract: The aim of this paper is to review the gradually evolving body of the literature on Internet addiction. Two schools of thought have emerged: those authors who believe that Internet addiction merits classification as a new or emerging psychiatric disorder in its own right, and those who define certain individuals as having problematic Internet use in relation to specific online activities, such as gambling, email or pornography. Despite a total lack of methodologically sound research, the evidence appears to support the second perspective. It appears that individuals who are premorbidly vulnerable, especially with a history of impulse control and addictive disorders, are especially at risk of using the Internet in a problematic way. Aside from the personal and social implications of this finding, this behavior has important implications for the workplace and may be resulting in substantial loss of productivity in companies who are not implementing Internet governance policies.


title: Dynamic Switching Networks

abstract: The concept of emergence is a powerful concept to explain very complex behaviour by simple underling rules. Existing approaches of producing emergent collective behaviour have many limitations making them unable to account for the complexity we see in the real world. In this paper we propose a new dynamic, non-local, and time independent approach that uses a network like structure to implement the laws or the rules, where the mathematical equations representing the rules are converted to a series of switching decisions carried out by the network on the particles moving in the network. The proposed approach is used to generate patterns with different types of symmetry.


title: Imaging cognitive anatomy

abstract: One of the major challenges in imaging neuroscience is the integration of cognitive science with the empiricism of neurophysiology. The cognitive architectures and principles offered by cognitive science have been essential in shaping experimental design and image analysis strategies from the outset. Now some of the cognitive models and their assumptions (for example, cognitive subtraction) are being re-evaluated in the light of how the brain actually implements putative components and processes. In this review we will consider experimental designs that go beyond cognitive subtraction and also consider how functional imaging can be used to assess the context-sensitivity of cognitive processing (using conjunction analyses), and the integration of different processes (in terms of interactions, using factorial designs) and how both these themes can be developed in the context of parametric designs. These new approaches reflect an ongoing discourse between cognitive science and the emerging principles of functional anatomy.


title: Working relations of technology production and use

abstract: This paper explores the relevance of recent feminist reconstructions of objectivity for the development of alternative visions of technology production and use. I take as my starting place the working relations that make up the design and use of technical systems. Working relations are understood as networks or webs of connections that sustain the visible and invisible work required to construct coherent technologies and put them into use. I outline the boundaries that characterize current relations of development and use, and the boundary crossings required to transform them. Three contrasting premises for design-the view from nowhere, detached engagement, and located accountability — are taken to represent incommensurate alternatives for a politics of professional design. From the position of located accountability, I close by sketching aspects of what a feminist politics and associated practices of system development could be.


title: Big Data analytics frameworks

abstract: Big Data concerns massive, heterogeneous, autonomous sources with distributed and decentralized control. These characteristics make it an extreme challenge for organizations using traditional data management mechanism to store and process these huge datasets. It is required to define a new paradigm and re-evaluate current system to manage and process Big Data. In this paper, the important characteristics, issues and challenges related to Big Data management has been explored. Various open source Big Data analytics frameworks that deal with Big Data analytics workloads have been discussed. Comparative study between the given frameworks and suitability of the same has been proposed.


title: The second-generation 600V RC-IGBT with optimized FWD

abstract: A Reverse Conducting IGBT (RC-IGBT) is a promising device to reduce a size and cost of the power module thanks to the integration of IGBT and FWD into a single chip. However, it is difficult to achieve well-balanced performance between IGBT and FWD. Indeed, the total inverter loss of the conventional RC-IGBT was not so small as the individual IGBT and FWD pair. To minimize the loss, the most important key is the improvement of reverse recovery characteristics of FWD. We carefully extracted five effective parameters to improve the FWD characteristics, and investigated the impact of these parameters by using simulation and experiments. Finally, optimizing these parameters, we succeeded in fabricating the second-generation 600V class RC-IGBT with a smaller FWD loss than the first-generation RC-IGBT.


title: Keep the PokerFace on! Thwarting cache side channel attacks by memory bus monitoring and cache obfuscation

abstract: Cloud instances are vulnerable to cross-core, cross-VM attacks against the shared, inclusive last-level cache. Automated cache template attacks, in particular, are very powerful as the vulnerabilities do not need to be manually identified. Such attacks can be devised using both the Prime+Probe and the Flush+Reload techniques. In this paper, we present PokerFace, a novel method to identify and mitigate such attacks. This approach allows us to identify suspicious cache accesses automatically, without prior knowledge about the system or access to hardware metrics. PokerFace consists of two components, Poker and Face. Poker executes a memory bus benchmark to measure the available bus bandwidth and derive information about cache accesses and possible side channel attacks. Our experiments with cache attacks show a reduction of up to 14% in the memory bandwidth during the attack. When an attack is detected, Poker triggers Face which performs cache obfuscation. We demonstrate the effectiveness of our approach against keypress logging attacks. We also test it against generic Prime+Probe and Flush+Reload attacks and show that it is practically useful against a variety of cache timing attacks. PokerFace incurs modest overheads (< 8%) and moreover, does not require support from the cloud provider or changes to the hypervisor. Unlike previously proposed techniques, it can be implemented by cloud subscribers.


title: Redroid: A Regression Test Selection Approach for Android Applications

abstract: As the mobile platform pervades human life, much research in recent years has focused on improving the reliability of mobile applications on this platform, for example by applying automatic testing. However, researchers have primarily consid- ered testing of single version of mobile applications. Although regression testing has been extensively studied for desktop appli- cations, and many efficient and effective approaches have been proposed, these approaches cannot be directly applied to mobile applications. We first present a bug study on real-world Android bugs to show the existence of regression bugs, which motivates the need for an efficient regression test selection technique for Android applications. Next, we introduce Redroid, a new ap- proach to regression test selection for Android applications. Our approach leverages the combination of static impact analysis and dynamic code coverage, and identifies a subset of test cases for re- execution on the modified application version. We implement our approach for Android applications, and demonstrate its efficacy through an extensive empirical study. as a runtime environment, which is significantly different from the Java Virtual Machine. This paper first presents a bug study based on 10 real- world Android apps from Google Code Repository (3). The study shows that there are regressions for Android apps during evolution, and an efficient and effective regression testing approach is highly needed for this area. This paper then intro- duces Redroid, a new approach to regression test selection for Android apps. Given a test suite that was performed on the original Android app, and the two versions involved in a change, Redroid identifies a subset of the tests that must be re-executed to test the new Android version. Leveraging the combination of static impact analysis with coverage informa- tion that is dynamically generated at runtime, our approach identifies a subset of tests to check the behaviors of the modified version that can potentially be different from the original version. We developed a prototype tool for Redroid, and conducted an evaluation based on two real-world Android apps, which shows that our approach can significantly reduce the number of tests for re-execution after an Android app is modified.


title: Effect of Capacitive Radiofrequency on the Fibrosis of Patients with Cellulite

abstract: Background. Cellulite is a type of lipodystrophy that develops primarily from an alteration in blood circulation or of the lymphatic system that causes structural changes in subcutaneous adipose tissue, collagen, and adjacent proteoglycans. The radiofrequency devices used for cutaneous applications have shown different physiological treatment effects, but there is controversy about the suitable parameters for this type of treatment. Objectives. The aim of this study was to evaluate the effects of low-temperature radiofrequency to confirm the thinning of the collagen tissue and interlobular septa and consequent improvement of cellulite. Methods. A sample of eight women was used to collect ultrasonographic data with a 12 MHz probe that measured collagen fiber thickness. The Vip Electromedicina (Argentina) device, frequency of 0.55 MHz and active electrode 3.5 cm in diameter (area = 9.61 cm(2)), was applied to a 10 cm(2) region of the gluteal region for 2 minutes per area of active electrode, during 10 biweekly sessions. Results. The Wilcoxon matched paired test was applied using GraphPad InStat 3.01 for Win95-NT software. Pre- and posttreatment mean collagen fiber thickness showed a 24.66% reduction from 1.01 to 0.67 mm. Statistical analysis using the Wilcoxon matched paired test obtained a significant two-tailed P value of 0.0391. Conclusion. It was concluded that the use of more comfortable temperatures favored a reduction in fibrous septum thickness and consequent cellulite improvement, evidenced by the lower degree of severity and decrease in interlobular septal thickness.


The syntactic construction of two non-active voices : passive and middle


title: Three-Dimensional Implicit Stratigraphic Model Building From Remote Sensing Data on Tetrahedral Meshes: Theory and Application to a Regional Model of La Popa Basin, NE Mexico

abstract: Remote sensing data provide significant information to constrain the geometry of geological structures at depth. However, the use of intraformational geomorphologic features such as flatirons and incised valleys often calls for tedious user interaction during 3-D model building. We propose a new method to generate 3-D models of stratigraphic formations, based primarily on remote sensing images and digital elevation models. This method is based on interpretations of the main relief markers and interpolation of a stratigraphic property on a tetahedral mesh covering the domain of study. The tetrahedral mesh provides a convenient way to integrate available data during the interpolation while accounting for discontinuities such as faults. Interpretive expert input may be provided through constrained interactive editing on arbitrary cross-sections, and additional surface or subsurface data may also be integrated in the modeling. We demonstrate this global workflow on a structurally complex basin in the Sierra Madre Oriental, Northeastern Mexico.


title: Differential Evolution for Efficient AUV Path Planning in Time Variant Uncertain Underwater Environment

abstract: The AUV three-dimension path planning in complex turbulent underwater environment is investigated in this research, in which static current map data and uncertain static-moving time variant obstacles are taken into account. Robustness of AUVs path planning to this strong variability is known as a complex NP-hard problem and is considered a critical issue to ensure vehicles safe deployment. Efficient evolutionary techniques have substantial potential of handling NP hard complexity of path planning problem as more powerful and fast algorithms among other approaches for mentioned problem. For the purpose of this research Differential Evolution (DE) technique is conducted to solve the AUV path planning problem in a realistic underwater environment. The path planners designed in this paper are capable of extracting feasible areas of a real map to determine the allowed spaces for deployment, where coastal area, islands, static/dynamic obstacles and ocean current is taken into account and provides the efficient path with a small computation time. The results obtained from analyze of experimental demonstrate the inherent robustness and drastic efficiency of the proposed scheme in enhancement of the vehicles path planning capability in coping undesired current, using useful current flow, and avoid colliding collision boundaries in a real-time manner. The proposed approach is also flexible and strictly respects to vehicle's kinematic constraints resisting current instabilities.


title: Filling missing data in point clouds by merging structured and unstructured point clouds

abstract: Point clouds arising from structured data, mainly as a result of CT scans, provides special properties on the distribution of points and the distances between those. Yet often, the amount of data provided can not compare to unstructured point clouds, i.e. data that arises from 3D light scans or laser scans. This article hereby proposes an approach to extend structured data and enhance the quality by inserting selected points from an unstructured point cloud. The resulting point cloud still has a partial structure that is called"half-structure". In this way, missing data that can not be optimally recovered through other surface reconstruction methods can be completed.


The Neuroarcheology of Childhood Maltreatment The Neurodevelopmental Costs of Adverse Childhood Events


title: Tailing RFID Tags for Clone Detection.

abstract: RFID (Radio-Frequency IDentification) is a key emerging technology for supply-chain monitoring and detection of counterfeit and grey-market goods. The most prevalent RFID tags are, however, simply “wireless barcodes,” themselves vulnerable to cloning and counterfeiting. While continuous monitoring can, in principle, detect cloning attacks, real-world supply chains often contain significant blind zones where tag readings are unavailable, allowing attackers to inject counterfeit goods with cloned tags into supply chains undetectably. This paper introduces tailing, a novel approach, both simple and practical, for detecting cloned RFID tags in supply chains. With tailing, RFID readers write random values to tags as they pass through a supply chain, creating in each tag a tail composed of random values. The tails of legitimate tags and cloned ones diverge over time, making cloning detectable by a centralized detector even across blind zones. We show that tailing works with existing barcode-type tags (e.g., EPC tags). The centralized detector is noninteractive, and requires no modification of existing supplychain data flows. We characterize the cloning-detection efficacy of tailing analytically and through supply-chain simulations, showing that tailing presents high detection rates and low false positive rates, as well as rate tradeoffs outperforming those of previous schemes.


title: Deep supervised learning for hyperspectral data classification through convolutional neural networks

abstract: Spectral observations along the spectrum in many narrow spectral bands through hyperspectral imaging provides valuable information towards material and object recognition, which can be consider as a classification task. Most of the existing studies and research efforts are following the conventional pattern recognition paradigm, which is based on the construction of complex handcrafted features. However, it is rarely known which features are important for the problem at hand. In contrast to these approaches, we propose a deep learning based classification method that hierarchically constructs high-level features in an automated way. Our method exploits a Convolutional Neural Network to encode pixels' spectral and spatial information and a Multi-Layer Perceptron to conduct the classification task. Experimental results and quantitative validation on widely used datasets showcasing the potential of the developed approach for accurate hyperspectral data classification.


title: Extensions of the Linear and Area Lighting Models

abstract: Techniques that aid the realistic rendering of lighting effects achieved from linear (1-D) and area (2-D) light sources are presented. They are based on a radiosity model that can be inserted into any traditional ray tracer. The approach is applied to both a 1-D light, analogous to a fluorescent tube, and to a 2-D light, analogous to a light set into the ceiling. >


title: Mixed Method Designs in Implementation Research

abstract: This paper describes the application of mixed method designs in implementation research in 22 mental health services research studies published in peer-reviewed journals over the last 5 years. Our analyses revealed 7 different structural arrangements of qualitative and quantitative methods, 5 different functions of mixed methods, and 3 different ways of linking quantitative and qualitative data together. Complexity of design was associated with number of aims or objectives, study context, and phase of implementation examined. The findings provide suggestions for the use of mixed method designs in implementation research.


title: Detecting gradual changes from data stream using MDL-change statistics

abstract: In this paper we propose a novel methodology of sequential change detection using the minimum description length (MDL)-change statistics. We first introduce the MDL-change statistics as the difference between the code-lengths with change and that without change. We give a theoretical justification for its use in the scenario of hypothesis testing. In it we evaluate the error probabilities for the MDL-change detection to relate them to the information-theoretic complexities of the probabilistic models and their discrepancy measure. We then convert the MDL-change statistics into the sequential change detection algorithm. It is designed to detect gradual changes as well as abrupt changes from big stream data. We empirically demonstrate the effectiveness of the proposed method by showing that it performs better than existing algorithms for synthetic data. We also show its validity through real problems such as SQL injection detection and failure symptom detection.


title: GEEC: Scalable, Efficient, and Consistent Consensus for Blockchains.

abstract: Blockchain technology has the potential to deploy broad decentralized applications, greatly improving their security and reliability. Unfortunately, despite much effort, no existing public blockchain protocol can ensure strong consistency with a high efficiency, while reliability of permissioned blockchain relies on consortium members. We present GEEC, a inital blockchain protocol and its runtime system by leveraging the Intel Software Guard eXtentions (SGX) hardware. GEECachieves privacy and security for permissioned blockchains and leveraging Intel SGX. GEEC can append a block with only one and a half network round-trips and two P2P broadcasts. We carry a proof sketch to show that GEEC is strongly consistent.


title: A planar path-following model predictive controller for fixed-wing Unmanned Aerial Vehicles

abstract: This paper addresses the design of a path-following using a sampled-data Model Predictive Control (MPC) approach for a fixed-wing Unmanned Aerial Vehicle (UAV). In path-following, the motion task consists in steering the vehicle towards a given desired geometric path and make it follow along the path with an assigned speed profile. To this end, we propose an MPC controller that will generate desired linear and angular velocities using a simplified kinematic model of a planar UAV. The output of the MPC is then fed to a low-level controller (the autopilot) that is tasked to track such reference signals. This combination allows the design of the MPC controller for a subset of the state variables, with the consequent reduction of computational burden, and to explicitly handle input constraints. Simulation results show the performance of the proposed scheme.


title: Dual-energy CT: radiation dose aspects.

abstract: OBJECTIVE ::: Various applications for dual-energy CT (DECT) have been investigated and have shown substantial clinical benefits. However, only limited data are available regarding the radiation dose associated with DECT imaging. The purpose of this article is to review the available literature regarding the radiation dose associated with DECT imaging applications in comparison with conventional single-energy CT techniques. ::: ::: ::: CONCLUSION ::: The rediscovery of DECT and the increasing availability of this technique on clinical CT systems have opened new dimensions for CT. The advanced spectral differentiation of materials within the human body as well as the selective visualization or subtraction of iodinated contrast material or xenon provides both advanced visualization of disease-specific molecular substrates as well as additional functional information within a single scan.


title: Development of Inspection Robots for Bridge Cables

abstract: This paper presents the bridge cable inspection robot developed in Korea. Two types of the cable inspection robots were developed for cable-suspension bridges and cable-stayed bridge. The design of the robot system and performance of the NDT techniques associated with the cable inspection robot are discussed. A review on recent advances in emerging robot-based inspection technologies for bridge cables and current bridge cable inspection methods is also presented.


title: The music of speech: Music training facilitates pitch processing in both music and language

abstract: The main aim of the present experiment was to determine whether extensive musical training facilitates pitch contour processing not only in music but also in language. We used a parametric manipulation of final notes’ or words’ fundamental frequency (F0), and we recorded behavioral and electrophysiological data to examine the precise time course of pitch processing. We compared professional musicians and nonmusicians. Results revealed that within both domains, musicians detected weak F0 manipulations better than nonmusicians. Moreover, F0 manipulations within both music and language elicited similar variations in brain electrical potentials, with overall shorter onset latency for musicians than for nonmusicians. Finally, the scalp distribution of an early negativity in the linguistic task varied with musical expertise, being largest over temporal sites bilaterally for musicians and largest centrally and over left temporal sites for nonmusicians. These results are taken as evidence that extensive musical training influences the perception of pitch contour in spoken language. Descriptors: Electroencephalogram, Event-related potentials, Prosody, Music, Pitch contour, Plasticity


title: Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit

abstract: The K-SVD algorithm is a highly efiective method of training overcomplete dictionaries for sparse signal representation. In this report we discuss an e‐cient implementation of this algorithm, which both accelerates it and reduces its memory consumption. The two basic components of our implementation are the replacement of the exact SVD computation with a much quicker approximation, and the use of the Batch-OMP method for performing the sparse-coding operations. Batch-OMP, which we also present in this report, is an implementation of the Orthogonal Matching Pursuit (OMP) algorithm which is speciflcally optimized for sparse-coding large sets of signals over the same dictionary. The Batch-OMP implementation is useful for a variety of sparsity-based techniques which involve coding large numbers of signals. In the report, we discuss the Batch-OMP and K-SVD implementations and analyze their complexities. The report is accompanied by Matlab r toolboxes which implement these techniques, and can be downloaded at http://www.cs.technion.ac.il/~ronrubin/software.html.


title: Enhancing My Keepon robot: A simple and low-cost solution for robot platform in Human-Robot Interaction studies

abstract: Many robots capable of performing social behaviors have recently been developed for Human-Robot Interaction (HRI) studies. These social robots are applied in various domains such as education, entertainment, medicine, and collaboration. Besides the undisputed advantages, a major difficulty in HRI studies with social robots is that the robot platforms are typically expensive and/or not open-source. It burdens researchers to broaden experiments to a larger scale or apply study results in practice. This paper describes a method to modify My Keepon, a toy version of Keepon robot, to be a programmable platform for HRI studies, especially for robot-assisted therapies. With an Arduino microcontroller board and an open-source Microsoft Visual C# software, users are able to fully control the sounds and motions of My Keepon, and configure the robot to the needs of their research. Peripherals can be added for advanced studies (e.g., mouse, keyboard, buttons, PlayStation2 console, Emotiv neuroheadset, Kinect). Our psychological experiment results show that My Keepon modification is a useful and low-cost platform for several HRI studies.


title: 4.6 A1.93TOPS/W scalable deep learning/inference processor with tetra-parallel MIMD architecture for big-data applications

abstract: Recently, deep learning (DL) has become a popular approach for big-data analysis in image retrieval with high accuracy [1]. As Fig. 4.6.1 shows, various applications, such as text, 2D image and motion recognition use DL due to its best-in-class recognition accuracy. There are 2 types of DL: supervised DL with labeled data and unsupervised DL with unlabeled data. With unsupervised DL, most of learning time is spent in massively iterative weight updates for a restricted Boltzmann machine [2]. For a −100MB training dataset, >100 TOP computational capability and ∼40GB/s IO and SRAM data bandwidth is required. So, a 3.4GHz CPU needs >10 hours learning time with a −100K input-vector dataset and takes ∼1 second for recognition, which is far from real-time processing. Thus, DL is typically done using cloud servers or high-performance GPU environments with learning-on-server capability. However, the wide use of smart portable devices, such as smartphones and tablets, results in many applications which need big-data processing with machine learning, such as tagging private photos in personal devices. A high-performance and energy-efficient DL/DI (deep inference) processor is required to realize user-centric pattern recognition in portable devices.


title: A Comparison of Publicly Available Tools for Dynamic Buffer Overflow Prevention

abstract: This thesis contributes to three research areas in software security, namely security requirements and intrusion prevention via static analysis and runtime detection.We have investigated current practice in security requirements by doing a field study of eleven requirement specifications on IT systems. The conclusion is that security requirements are poorly specified due to three things: inconsistency in the selection of requirements, inconsistency in level of detail, and almost no requirements on standard security solutions. A follow-up interview study addressed the reasons for the inconsistencies and the impact of poor security requirements. It shows that the projects had relied heavily on in-house security competence and that mature producers of software compensate for poor requirements in general but not in the case of security and privacy requirements specific to the customer domain.Further, we have investigated the effectiveness of five publicly available static analysis tools for security. The test results show high rates of false positives for the tools building on lexical analysis and low rates of true positives for the tools building on syntactical and semantical analysis. As a first step toward a more effective and generic solution we propose decorated dependence graphs as a way of modeling and pattern matching security properties of code. The models can be used to characterize both good and bad programming practice as well as visually explain code properties to programmers. We have implemented a prototype tool that demonstrates how such models can be used to detect integer input validation flaws.Finally, we investigated the effectiveness of publicly available tools for runtime prevention of buffer overflow attacks. Our initial comparison showed that the best tool as of 2003 was effective against only 50 % of the attacks and there were six attack forms which none of the tools could handle. A follow-up study includes the release of a buffer overflow testbed which covers 850 attack forms. Our evaluation results show that the most popular, publicly available countermeasures cannot prevent all of these buffer overflow attack forms.


title: The effects of students' motivation, cognitive load and learning anxiety in gamification software engineering education: a structural equation modeling study

abstract: Past research has proven the significant effects of game-based learning on learning motivation and academic performance, and described the key factors in game-based design. Nonetheless, research on the correlations among learning motivation, cognitive load, learning anxiety and academic performance in gamified learning environments has been minimal. This study, therefore, aims to develop a Gamification Software Engineering Education Learning System (GSEELS) and evaluate the effects of gamification, learning motivation, cognitive load and learning anxiety on academic performance. By applying Structural Equation Modeling (SEM) to the empirical research, the questionnaire contains: 1. a Gamification Learning Scale; 2. a Learning Motivation Scale; 3. a Cognitive Load Scale; 4. a Learning Anxiety Scale; and 5. an Academic Performance Scale. A total of 107 undergraduates in two classes participated in this study. The Structural Equation Modeling (SEM) analysis includes the path directions and relationship between descriptive statistics, measurement model, structural model evaluation and five variables. The research results support all nine hypotheses, and the research findings also show the effects of cognitive load on learning anxiety, with strong learning motivation resulting from a low learning anxiety. As a result, it is further proven in this study that a well-designed GSEELS would affect student learning motivation and academic performance. Finally, the relationship model between gamification learning, learning motivation, cognitive load, learning anxiety and academic performance is elucidated, and four suggestions are proffered for instructors of software engineering education courses and for further research, so as to assist instructors in the application of favorable gamification teaching strategies.


title: Click model-based information retrieval metrics

abstract: In recent years many models have been proposed that are aimed at predicting clicks of web search users. In addition, some information retrieval evaluation metrics have been built on top of a user model. In this paper we bring these two directions together and propose a common approach to converting any click model into an evaluation metric. We then put the resulting model-based metrics as well as traditional metrics (like DCG or Precision) into a common evaluation framework and compare them along a number of dimensions. One of the dimensions we are particularly interested in is the agreement between offline and online experimental outcomes. It is widely believed, especially in an industrial setting, that online A/B-testing and interleaving experiments are generally better at capturing system quality than offline measurements. We show that offline metrics that are based on click models are more strongly correlated with online experimental outcomes than traditional offline metrics, especially in situations when we have incomplete relevance judgements.


title: The Aurora and Borealis Stream Processing Engines

abstract: Over the last several years, a great deal of progress has been made in the area of stream-processing engines (SPEs). Three basic tenets distinguish SPEs from current data processing engines. First, they must support primitives for streaming applications. Unlike Online Transaction Processing (OLTP), which processes messages in isolation, streaming applications entail time series operations on streams of messages. Second, streaming applications entail a real-time component. If one is content to see an answer later, then one can store incoming messages in a data warehouse and run a historical query on the warehouse to find information of interest. This tactic does not work if the answer must be constructed in real time. The need for real-time answers also dictates a fundamentally different storage architecture. DBMSs universally store and index data records before making them available for query activity. Such outbound processing, where data are stored before being processed, cannot deliver real-time latency, as required by SPEs. To meet more stringent latency requirements, SPEs must adopt an alternate model, which we refer to as “inbound processing”, where query processing is performed directly on incoming messages before (or instead of) storing them. Lastly, an SPE must have capabilities to gracefully deal with spikes in message load. Incoming traffic is usually bursty, and it is desirable to selectively degrade the performance of the applications running on an SPE. The Aurora stream-processing engine, motivated by these three tenets, is currently operational, has been used to build various application systems, and has been transferred to the commercial domain. Borealis is a distributed stream-processing system that inherits core stream-processing functionality from Aurora and enriches it with distribution functionality, in order to provide advanced capabilities that are commonly required by newly emerging stream-processing applications.


title: Digital Trust - Trusted Computing and Beyond: A Position Paper

abstract: Along with the invention of computers and interconnected networks, physical societal notions like security, trust, and privacy entered the digital environment. The concept of digital environments begins with the trust (established in the real world) in the organisation/individual that manages the digital resources. This concept evolved to deal with the rapid growth of the Internet, where it became impractical for entities to have prior offline (real world) trust. The evolution of digital trust took diverse approaches and now trust is defined and understood differently across heterogeneous domains. This paper looks at digital trust from the point of view of security and examines how valid trust approaches from other domains are now making their way into secure computing. The paper also revisits and analyses the Trusted Platform Module (TPM) along with associated technologies and their relevance in the changing landscape. We especially focus on the domains of cloud computing, mobile computing and cyber-physical systems. In addition, the paper also explores our proposals that are competing with and extending the traditional functionality of TPM specifications.


title: Skip N-grams and Ranking Functions for Predicting Script Events

abstract: In this paper, we extend current state-of-the-art research on unsupervised acquisition of scripts, that is, stereotypical and frequently observed sequences of events. We design, evaluate and compare different methods for constructing models for script event prediction: given a partial chain of events in a script, predict other events that are likely to belong to the script. Our work aims to answer key questions about how best to (1) identify representative event chains from a source text, (2) gather statistics from the event chains, and (3) choose ranking functions for predicting new script events. We make several contributions, introducing skip-grams for collecting event statistics, designing improved methods for ranking event predictions, defining a more reliable evaluation metric for measuring predictiveness, and providing a systematic analysis of the various event prediction models.


title: A Region Ensemble for 3-D Face Recognition

abstract: In this paper, we introduce a new system for 3D face recognition based on the fusion of results from a committee of regions that have been independently matched. Experimental results demonstrate that using 28 small regions on the face allow for the highest level of 3D face recognition. Score-based fusion is performed on the individual region match scores and experimental results show that the Borda count and consensus voting methods yield higher performance than the standard sum, product, and min fusion rules. In addition, results are reported that demonstrate the robustness of our algorithm by simulating large holes and artifacts in images. To our knowledge, no other work has been published that uses a large number of 3D face regions for high-performance face matching. Rank one recognition rates of 97.2% and verification rates of 93.2% at a 0.1% false accept rate are reported and compared to other methods published on the face recognition grand challenge v2 data set.


title: The Use of the Grounded Theory Methodology in Investigating Practitioners' Integration of COTS Components in Information Systems

abstract: This paper challenges the view that Grounded Theory is an unacceptable methodology for IS research lacking rigor and robustness. A demonstration is given of the systematic and rigorous use of the grounded theory data analysis techniques in researching the use of commercial IT components for developing and maintaining business IS. The data analysis processes of coding, conceptualizing and categorizing are shown in action and explained. The techniques of memoing, constant comparison and theoretical coding are shown step-by-step so that the grounded theory methodology can be seen as a systematic framework for IS data analysis. When the methodology is properly applied it provides the structure and rigor needed to reach meaningful conclusions from qualitative data. This research article contributes to the literature showing grounded theory as a powerful research methodology applicable to IS research.


title: Phonological features in discriminative classification of dysarthric speech

abstract: In an attempt to overcome problems associated with articulatory limitations and generative models, this work considers the use of phonological features in discriminative models for disabled speech. Specifically, we train feed-forward and recurrent neural networks, and radial basis and sequence-kernel support vector machines to abstractions of the vocal tract, and apply these models to phone recognition on dysarthric speech. The results show relative error reduction of between 1.5% and 10.9% with this approach against standard hidden Markov modeling, and increases in accuracy with speaker intelligibility across all classifiers. This work may be applied within components of assistive software for speakers with dysarthria.


title: Comprehensive biomechanical modeling and simulation of the upper body

abstract: We introduce a comprehensive biomechanical model of the human upper body. Our model confronts the combined challenge of modeling and controlling more or less all of the relevant articular bones and muscles, as well as simulating the physics-based deformations of the soft tissues. Its dynamic skeleton comprises 68 bones with 147 jointed degrees of freedom, including those of each vertebra and most of the ribs. To be properly actuated and controlled, the skeletal submodel requires comparable attention to detail with respect to muscle modeling. We incorporate 814 muscles, each of which is modeled as a piecewise uniaxial Hill-type force actuator. To simulate biomechanically-realistic flesh deformations, we also develop a coupled finite element model with the appropriate constitutive behavior, in which are embedded the detailed 3D anatomical geometries of the hard and soft tissues. Finally, we develop an associated physics-based animation controller that computes the muscle activation signals necessary to drive the elaborate musculoskeletal system in accordance with a sequence of target poses specified by an animator.


title: CEPM 3: lead time reduction via pre-positioning of inventory in an industrial construction supply chain

abstract: Lead time reduction is a key concern of many industrial buyers of capital facilities given current economic conditions. Supply chain initiatives in manufacturing settings have led owners to expect that dramatic reductions in lead time are possible in all phases of their business, including the delivery of capital materials. Further, narrowing product delivery windows and increasing pressure to be first-to-market create significant external pressure to reduce lead time. In this paper, a case study is presented in which an owner entered the construction supply chain to procure and position key long-lead materials. The materials were held at a position in the supply chain selected to allow some flexibility for continued customization, but dramatic reduction in the time-to-site. Simulation was used as a tool to consider time-to-site tradeoffs for multiple inventory locations so as to better match the needs of the construction effort.


title: Critical Factors That Affect The Success Of Business Intelligence Systems (BIS) Implementation In An Organization

abstract: As more organizations move towards intelligent IT infrastructure, nowadays, Business Intelligence Systems (BIS) become a more widely used IT solution. Even though it is a widely used IT solution, many BIS implementations are not successful because they are time consuming and expensive. The benefits of BIS can be achieved if the system is implemented successfully. This paper explores the critical success factors (CSFs) that effect BIS implementation both from the perspectives of management and technology. Also, based on literature and interviews, a conceptual framework of successful implementation of BIS is proposed. The framework summarizes CSFs of BIS based on project implementation life cycle (pre implementation stage, implementation stage and post-implementation stage) from managerial and technological perspectives. Finally, results, suggestions and directions for future researches are discussed.


title: Motion-Guided Cascaded Refinement Network for Video Object Segmentation

abstract: Deep CNNs have achieved superior performance in many tasks of computer vision and image understanding. However, it is still difficult to effectively apply deep CNNs to video object segmentation(VOS) since treating video frames as separate and static will lose the information hidden in motion. To tackle this problem, we propose a Motion-guided Cascaded Refinement Network for VOS. By assuming the object motion is normally different from the background motion, for a video frame we first apply an active contour model on optical flow to coarsely segment objects of interest. Then, the proposed Cascaded Refinement Network(CRN) takes the coarse segmentation as guidance to generate an accurate segmentation of full resolution. In this way, the motion information and the deep CNNs can well complement each other to accurately segment objects from video frames. Furthermore, in CRN we introduce a Single-channel Residual Attention Module to incorporate the coarse segmentation map as attention, making our network effective and efficient in both training and testing. We perform experiments on the popular benchmarks and the results show that our method achieves state-of-the-art performance at a much faster speed.


title: A Statistical Model For Multilingual Entity Detection And Tracking

abstract: Abstract : Entity detection and tracking is a relatively new addition to the repertoire of natural language tasks. In this paper, we present a statistical language-independent framework for identifying and tracking named, nominal and pronominal references to entities within unrestricted text documents, and chaining them into clusters corresponding to each logical entity present in the text. Both the mention detection model and the novel entity tracking model can use arbitrary feature types, being able to integrate a wide array of lexical, syntactic and semantic features. In addition, the mention detection model crucially uses feature streams derived from different named entity classifiers. The proposed framework is evaluated with several experiments run in Arabic, Chinese and English texts; a system based on the approach described here and submitted to the latest Automatic Content Extraction (ACE) evaluation achieved top-tier results in all three evaluation languages.


title: A Hybrid Recommendation Method Based on Feature for Offline Book Personalization

abstract: Recommendation system has been widely used in different areas. Collaborative filtering focuses on rating, ignoring the features of items itself. In order to effectively evaluate customers preferences on books, taking into consideration of the characteristics of offline book retail, we use LDA model to calculate customers preference on book topics and use word2vec to calculate customers preference on book types. When forecasting rating on books, we take two factors into consideration: similarity of customers and correlation between customers and books. The experiment shows that our hybrid recommendation method based on features performances better than single recommendation method in offline book retail data.


title: Νon-invasive screening for esophageal varices in patients with liver cirrhosis

abstract: Esophageal varices are one of the main complications of liver cirrhosis. Upper gastrointestinal endoscopy is the gold standard for the detection of esophageal varices. Many less invasive methods for screening of varices have been investigated and the most recent Baveno VI guidelines suggest that endoscopy is not necessary in patients with liver stiffness <20 kPa and platelets >150,000/μL. A critical review of the literature was performed concerning non-invasive or minimally invasive methods of screening for esophageal varices. Liver and spleen elastography, imaging methods including computed tomography, magnetic resonance imaging and ultrasound, laboratory tests and capsule endoscopy are discussed. The accuracy of each method, and its advantages and limitations compared to endoscopy are analyzed. There are data to support the Baveno VI guidelines, but there is still a lack of large prospective studies and low specificity has been reported for the liver stiffness and platelet count combination. Spleen elastography has shown promising results, as there are data to support its superiority to liver elastography, but it needs further assessment. Computed tomography has shown high diagnostic accuracy and can be part of the diagnostic work up of cirrhotic patients in the future, including screening for varices.


title: A survey of intrusion detection techniques in Cloud

abstract: In this paper, we survey different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. Proposals incorporating Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) in Cloud are examined. We recommend IDS/IPS positioning in Cloud environment to achieve desired security in the next generation networks.


title: Reusing Domain Ontologies in Linked Building Data: The Case of Building Automation and Control

abstract: Linked data and semantic web technologies are gaining impact and importance in the Architecture, Engineering, Construction and Facility Management (AEC/FM) industry. Whereas we have seen a strong technological shift with the emergence of Building Information Modeling (BIM) tools, this second technological shift to the exchange and management of building data over the web might be even stronger than the first one. In order to make this a success, the AEC/FM industry will need strong and appropriate ontologies, as they will allow industry practitioners to structure their data in a commonly agreed format and exchange the data. Herein, we look at the ontologies that are emerging in the area of Building Automation and Control Systems (BACS). We propose a BACS ontology in strong alignment with existing ontologies and evaluate how it can be used for capturing automation and control systems of a building by modeling a use case.


title: Empirical comparison of network sampling techniques

abstract: In the past few years, the storage and analysis of large-scale and fast evolving networks present a great challenge. Therefore, a number of different techniques have been proposed for sampling large networks. In general, network exploration techniques approximate the original networks more accurately than random node and link selection. Yet, link selection with additional subgraph induction step outperforms most other techniques. In this paper, we apply subgraph induction also to random walk and forest-fire sampling. We analyze different real-world networks and the changes of their properties introduced by sampling. We compare several sampling techniques based on the match between the original networks and their sampled variants. The results reveal that the techniques with subgraph induction underestimate the degree and clustering distribution, while overestimate average degree and density of the original networks. Techniques without subgraph induction step exhibit exactly the opposite behavior. Hence, the performance of the sampling techniques from random selection category compared to network exploration sampling does not differ significantly, while clear differences exist between the techniques with subgraph induction step and the ones without it.


title: A Reliability-Aware Network Service Chain Provisioning With Delay Guarantees in NFV-Enabled Enterprise Datacenter Networks

abstract: Traditionally, service-specific network functions (NFs) (e.g., Firewall, intrusion detection system, etc.) are executed by installation-and maintenance-costly hardware middleboxes that are deployed within a datacenter network following a strictly ordered chain. NF virtualization (NFV) virtualizes these NFs and transforms them into instances of plain software referred to as virtual NFs (VNFs) and executed by virtual machines, which, in turn, are hosted over one or multiple industry-standard physical machines. The failure (e.g., hardware or software) of any one of a service chain’s VNFs leads to breaking down the entire chain and causing significant data losses, delays, and resource wastage. This paper establishes a reliability-aware and delay-constrained (READ) routing optimization framework for NFV-enabled datacenter networks. READ encloses the formulation of a complex mixed integer linear program (MILP) whose resolution yields an optimal network service VNF placement and traffic routing policy that jointly maximizes the achieved respective reliabilities of supported network services and minimizes these services’ respective end-to-end delays. A heuristic algorithm dubbed Greedy- ${k}$ -shortest paths (GSP) is proposed for the purpose of overcoming the MILP’s complexity and develop an efficient routing scheme whose results are comparable to those of READ’s optimal counterparts. Thorough numerical analyses are conducted to evaluate the network’s performance under GSP, and hence, gauge its merit; particularly, when compared to existing schemes, GSP exhibits an improvement of 18.5% in terms of the average end-to-end delay as well as 7.4% to 14.8% in terms of reliability.


title: A general learning co-evolution method to generalize autonomous robot navigation behavior

abstract: A new coevolutive method, called Uniform Coevolution, is introduced, to learn weights for a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collision avoidance. The coevolutive method allows the evolution of the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with or without coevolution have been tested in a set of environments and the capability for generalization has been shown for each learned behavior. A simulator based on the mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to example-based problems.


title: An Industry 4.0-Enabled Low Cost Predictive Maintenance Approach for SMEs

abstract: This paper outlines the base concepts, materials and methods used to develop an Industry 4.0 architecture focused on predictive maintenance, while relying on low-cost principles to be affordable by Small Manufacturing Enterprises. The result of this research work was a low-cost, easy-to-develop cyber-physical system architecture that measures the temperature and vibration variables of a machining process in a Haas CNC turning centre, while storing such data in the cloud where Recursive Partitioning and Regression Tree model technique is run for predicting the rejection of machined parts based on a quality threshold. Machining quality is predicted based on temperature and/or vibration machining data and evaluated against average surface roughness of each machined part, demonstrating promising predictive accuracy.


Anticipative Interaction Primitives for Human-Robot Collaboration.


title: Face recognition by applying wavelet subband representation and kernel associative memory

abstract: In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.


title: Monte-Carlo Localization for Mobile Wireless Sensor Networks

abstract: Localization is crucial to many applications in wireless sensor networks. This article presents a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo Localization algorithm. We improve the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. Namely, we constrain the area from which samples are drawn by building a box that covers the region where anchors' radio ranges overlap. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73%, on average 30%, for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55%, on average 22%. Finally, the processing time is reduced by 93% for a similar localization accuracy.


title: Deep Head Pose: Gaze-Direction Estimation in Multimodal Video

abstract: In this paper we present a convolutional neural network (CNN)-based model for human head pose estimation in low-resolution multi-modal RGB-D data. We pose the problem as one of classification of human gazing direction. We further fine-tune a regressor based on the learned deep classifier. Next we combine the two models (classification and regression) to estimate approximate regression confidence. We present state-of-the-art results in datasets that span the range of high-resolution human robot interaction (close up faces plus depth information) data to challenging low resolution outdoor surveillance data. We build upon our robust head-pose estimation and further introduce a new visual attention model to recover interaction with the environment . Using this probabilistic model, we show that many higher level scene understanding like human-human/scene interaction detection can be achieved. Our solution runs in real-time on commercial hardware.


title: Absent multiple kernel learning

abstract: Multiple kernel learning (MKL) optimally combines the multiple channels of each sample to improve classification performance. However, existing MKL algorithms cannot effectively handle the situation where some channels are missing, which is common in practical applications. This paper proposes an absent MKL (AMKL) algorithm to address this issue. Different from existing approaches where missing channels are firstly imputed and then a standard MKL algorithm is deployed on the imputed data, our algorithm directly classifies each sample with its observed channels. In specific, we define a margin for each sample in its own relevant space, which corresponds to the observed channels of that sample. The proposed AMKL algorithm then maximizes the minimum of all sample-based margins, and this leads to a difficult optimization problem. We show that this problem can be reformulated as a convex one by applying the representer theorem. This makes it readily be solved via existing convex optimization packages. Extensive experiments are conducted on five MKL benchmark data sets to compare the proposed algorithm with existing imputation-based methods. As observed, our algorithm achieves superior performance and the improvement is more significant with the increasing missing ratio.


title: Path Problems in Temporal Graphs

abstract: Shortest path is a fundamental graph problem with numerous applications. However, the concept of classic shortest path is insufficient or even flawed in a temporal graph, as the temporal information determines the order of activities along any path. In this paper, we show the shortcomings of classic shortest path in a temporal graph, and study various concepts of "shortest" path for temporal graphs. Computing these temporal paths is challenging as subpaths of a "shortest" path may not be "shortest" in a temporal graph. We investigate properties of the temporal paths and propose efficient algorithms to compute them. We tested our algorithms on real world temporal graphs to verify their efficiency, and also show that temporal paths are essential for studying temporal graphs by comparing shortest paths in normal static graphs.


title: Cerebellum and Ocular Motor Control

abstract: An intact cerebellum is a prerequisite for optimal ocular motor performance. The cerebellum fine-tunes each of the subtypes of eye movements so they work together to bring and maintain images of objects of interest on the fovea. Here we review the major aspects of the contribution of the cerebellum to ocular motor control. The approach will be based on structural-functional correlation, combining the effects of lesions and the results from physiologic studies, with the emphasis on the cerebellar regions known to be most closely related to ocular motor function: 1) the flocculus/paraflocculus for high-frequency (brief) vestibular responses, sustained pursuit eye movements and gaze-holding, 2) the nodulus/ventral uvula for low-frequency (sustained) vestibular responses, and 3) the dorsal oculomotor vermis and its target in the posterior portion of the fastigial nucleus (the fastigial oculomotor region) for saccades and pursuit initiation.


title: Bit Cost Scalable (BiCS) technology for future ultra high density storage memories

abstract: We proposed Bit Cost Scalable (BiCS) technology in 2007 as a three-dimensional memory for the future ultra high density storage devices, which extremely reduce the bit costs by vertically stacking memory arrays with punch and plug process. We've applied it to just NAND flash, which is BiCS Flash memory, and established the mass production technology. Moreover, we can apply the BiCS technology to various memories. The critical scaling issues and the comparison among various 3D-Flash-type memories are to be discussed, as well.


title: TrueNorth: A High-Performance, Low-Power Neurosynaptic Processor for Multi-Sensory Perception, Action, and Cognition

abstract: Abstract : IBMs TrueNorth neurosynaptic processor is a radical departure from decades of traditional von Neumann computing. Containing 5.4 billion transistors and fabricated in a 28nm low-power CMOS process technology, TrueNorth contains 1 million neurons and 256 million synapses. With applications ranging from embedded and embodied intelligence to large-scale perceptual analysis of streaming multi-sensory data, this massively parallel processor consumes only 65mW typically.


title: Production of the antimalarial drug precursor artemisinic acid in engineered yeast

abstract: Through the bio-engineering of Saccharomyces cerevisiae high titres of artemisinic acid were produced using a novel cytochrome P450 monooxygenase. Optimization of this process on an industrial scale may significantly reduce the cost of artemisinin, which could then be used to combat malaria in resource-poor settings.


title: Manifesting Bugs in Machine Learning Code: An Explorative Study with Mutation Testing

abstract: Nowadays statistical machine learning is widely adopted in various domains such as data mining, image recognition and automated driving. However, software quality assurance for machine learning is still in its infancy. While recent efforts have been put into improving the quality of training data and trained models, this paper focuses on code-level bugs in the implementations of machine learning algorithms. In this explorative study we simulated program bugs by mutating Weka implementations of several classification algorithms. We observed that 8%-40% of the logically non-equivalent executable mutants were statistically indistinguishable from their golden versions. Moreover, other 15%-36% of the mutants were stubborn, as they performed not significantly worse than a reference classifier on at least one natural data set. We also experimented with several approaches to killing those stubborn mutants. Preliminary results indicate that bugs in machine learning code may have negative impacts on statistical properties such as robustness and learning curves, but they could be very difficult to detect, due to the lack of effective oracles.


title: Tracking a large number of objects from multiple views

abstract: We propose a multi-object multi-camera framework for tracking large numbers of tightly-spaced objects that rapidly move in three dimensions. We formulate the problem of finding correspondences across multiple views as a multidimensional assignment problem and use a greedy randomized adaptive search procedure to solve this NP-hard problem efficiently. To account for occlusions, we relax the one-to-one constraint that one measurement corresponds to one object and iteratively solve the relaxed assignment problem. After correspondences are established, object trajectories are estimated by stereoscopic reconstruction using an epipolar-neighborhood search. We embedded our method into a tracker-to-tracker multi-view fusion system that not only obtains the three-dimensional trajectories of closely-moving objects but also accurately settles track uncertainties that could not be resolved from single views due to occlusion. We conducted experiments to validate our greedy assignment procedure and our technique to recover from occlusions. We successfully track hundreds of flying bats and provide an analysis of their group behavior based on 150 reconstructed 3D trajectories.


title: An empirical study of automatic accent classification

abstract: This paper extends language identification (LID) techniques to a large scale accent classification task: 23-way classification of foreign-accented English. We find that a purely acoustic approach based on a combination of heteroscedastic linear discriminant analysis (HLDA) and maximum mutual information (MMI) training is very effective. In contrast to LID tasks, methods based on parallel language models prove much less effective. We focus on the Oregon Graduate Institute Foreign-Accented English dataset, and obtain a detection rate of 32%, which to our knowledge is the best reported result for 23-way accent classification.


title: Generating Graphical Chain by Mutual Matching of Bayesian Network and Extracted Rules of Bayesian Network Using Genetic Algorithm

abstract: With the technology development, the need of analyze and extraction of useful information is increasing. Bayesian networks contain knowledge from data and experts that could be used for decision making processes But they are not easily understandable thus the rule extraction methods have been used but they have high computation costs. To overcome this problem we extract rules from Bayesian network using genetic algorithm. Then we generate the graphical chain by mutually matching the extracted rules and Bayesian network. This graphical chain could shows the sequence of events that lead to the target which could help the decision making process. The experimental results on small networks show that the proposed method has comparable results with brute force method which has a significantly higher computation cost.


title: Boosting in the Limit: Maximizing the Margin of Learned Ensembles

abstract: The "minimum margin" of an ensemble classifier on a given training set is, roughly speaking, the smallest vote it gives to any correct training label. Recent work has shown that the Adaboost algorithm is particularly effective at producing ensembles with large minimum margins, and theory suggests that this may account for its success at reducing generalization error. We note, however, that the problem of finding good margins is closely related to linear programming, and we use this connection to derive and test new "LPboosting" algorithms that achieve better minimum margins than Adaboost.However, these algorithms do not always yield better generalization performance. In fact, more often the opposite is true. We report on a series of controlled experiments which show that no simple version of the minimum-margin story can be complete. We conclude that the crucial question as to why boosting works so well in practice, and how to further improve upon it, remains mostly open.Some of our experiments are interesting for another reason: we show that Adaboost sometimes does overfit--eventually. This may take a very long time to occur, however, which is perhaps why this phenomenon has gone largely unnoticed.


title: Extracting search-focused key n-grams for relevance ranking in web search

abstract: In web search, relevance ranking of popular pages is relatively easy, because of the inclusion of strong signals such as anchor text and search log data. In contrast, with less popular pages, relevance ranking becomes very challenging due to a lack of information. In this paper the former is referred to as head pages, and the latter tail pages. We address the challenge by learning a model that can extract search-focused key n-grams from web pages, and using the key n-grams for searches of the pages, particularly, the tail pages. To the best of our knowledge, this problem has not been previously studied. Our approach has four characteristics. First, key n-grams are search-focused in the sense that they are defined as those which can compose "good queries" for searching the page. Second, key n-grams are learned in a relative sense using learning to rank techniques. Third, key n-grams are learned using search log data, such that the characteristics of key n-grams in the search log data, particularly in the heads; can be applied to the other data, particularly to the tails. Fourth, the extracted key n-grams are used as features of the relevance ranking model also trained with learning to rank techniques. Experiments validate the effectiveness of the proposed approach with large-scale web search datasets. The results show that our approach can significantly improve relevance ranking performance on both heads and tails; and particularly tails, compared with baseline approaches. Characteristics of our approach have also been fully investigated through comprehensive experiments.


title: The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing

abstract: Graph processing is becoming increasingly prevalent across many application domains. In spite of this prevalence, there is little research about how graphs are actually used in practice. We conducted an online survey aimed at understanding: (i) the types of graphs users have; (ii) the graph computations users run; (iii) the types of graph software users use; and (iv) the major challenges users face when processing their graphs. We describe the participants' responses to our questions highlighting common patterns and challenges. We further reviewed user feedback in the mailing lists, bug reports, and feature requests in the source repositories of a large suite of software products for processing graphs. Through our review, we were able to answer some new questions that were raised by participants' responses and identify specific challenges that users face when using different classes of graph software. The participants' responses and data we obtained revealed surprising facts about graph processing in practice. In particular, real-world graphs represent a very diverse range of entities and are often very large, and scalability and visualization are undeniably the most pressing challenges faced by participants. We hope these findings can guide future research.


title: Fuzzy topological spaces and fuzzy compactness

abstract: Abstract It is the purpose of this paper to go somewhat deeper into the structure of fuzzy topological spaces. In doing so we found we had to alter the definition of a fuzzy topology used up to now. We shall also introduce two functors \ gw and \ gi which will allow us to see more clearly the connection between fuzzy topological spaces and topological spaces. Finally we shall introduce the concept of fuzzy compactness as the generalization of compactness in topology. It will be shown in a following publication that contrary to the results obtained up to now, the Tychonoff-product theorem is safeguarded with fuzzy compactness.


title: A Particle Swarm Optimization-PID controller of a DC Servomotor for Multi-Fingered Robot Hand

abstract: In the construction of robot applications, controller is very important in producing good performance the robot system. This paper presents the modeling and simulation of multi fingered robot hand (MFRH) with the conventional and modern controllers where the output responses have been analyzed. A swarm algorithm known as Particle Swarm Optimization (PSO) has been used to find optimum solutions in a large search space. Simulation of modern controller (PSO-PID) has produced better results than the conventional controller in terms of system parameters such as rise time, settling time steady state error and maximum overshoot in the DC Servomotor speed control MFRH.


title: doi:10.1006/cviu.2002.0963 Geometry and Texture Recovery of Scenes of Large Scale

abstract: This paper presents a systematic approach to the problem of photorealistic 3-D model acquisition from the combination of range and image sensing. The input is a sequence of unregistered range scans of the scene and a sequence of unregistered 2-D photographs of the same scene. The output is a true texture-mapped geometric model of the scene. We believe that the developed modules are of vital importance for a flexible photorealistic 3-D model acquisition system. Segmentation algorithms simplify the dense datasets and provide stable features of interest which can be used for registration purposes. Solid modeling provides geometrically correct 3-D models. Finally, the automated range to an image registration algorithm can increase the flexibility of the system by decoupling the slow geometry recovery process from the image acquisition process; the camera does not have to be precalibrated and rigidly attached to the range sensor. The system is comprehensive in that it addresses all phases of the modeling problem with a particular emphasis on automating the entire process interaction.


title: Shift work and hypertension: Prevalence and analysis of disease pathways in a German car manufacturing company

abstract: Background ::: Hypertension and cardiovascular disease (CVD) may share a similar pathophysiology. Despite shift workers' CVD excess risk, studies on shift work and hypertension are inconclusive. ::: ::: Methods ::: Blood pressure and shift status for 25,343 autoworkers were obtained from medical check-ups and company registers. Cross-sectional associations modeling the total effect from shift work (day shifts, shift work without nights, rotating shift work with nights, and night shifts) on hypertension were assessed. By sequential adjustments, the influence of behavioral, psychosocial, and physiological factors on the total effect was examined, with subsequent mediation and moderation analyses. ::: ::: Results ::: Adjusted for confounders, shift work without nights (vs. day shifts) was significantly associated with hypertension (OR 1.15, 95%CI 1.02–1.30). The total effect was mediated by BMI, physical inactivity, and sleep disorders. No moderation of the total effect by behaviors was found. ::: ::: Conclusion ::: The association between shift work and hypertension seems mainly attributable to behavioral mechanisms. Am. J. Ind. Med. 58:549–560, 2015. © 2015 Wiley Periodicals, Inc.


title: Deep Projective 3D Semantic Segmentation

abstract: Semantic segmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionized the field of image semantic segmentation, its impact on point cloud data has been limited so far. Recent attempts, based on 3D deep learning approaches (3D-CNNs), have achieved below-expected results. Such methods require voxelizations of the underlying point cloud data, leading to decreased spatial resolution and increased memory consumption. Additionally, 3D-CNNs greatly suffer from the limited availability of annotated datasets.


title: Hairpin microstrip bandpass filter for millimeter-wave applications

abstract: A compact microstrip bandpass filter is proposed in this paper for millimeter-wave applications. The filter consists of three new non-uniform coupled-line modified hairpin resonators. The filter is designed to exhibit a fractional bandwidth of about 5.0 % at a center of frequency of approximately 34 GHz. The shapes of the hairpin resonators are modified to suppress the unwanted spurious harmonic response. As a result, the filter exhibits very wide stopband with a rejection level better than 10-dB. In addition, the filter shows a transmission zero close to upper edge of the desired passband which enhances the selectivity of the passband. The filter design is successfully realized on a RT/Duroid 6002 with a dielectric constant of 2.94 and a thickness of 127μm. The filter is simulated, and fabricated to demonstrate the proposed technique where excellent agreement is obtained.


title: Image classification by non-negative sparse coding, correlation constrained low-rank and sparse decomposition

abstract: We propose an image classification framework by leveraging the non-negative sparse coding, correlation constrained low rank and sparse matrix decomposition technique (CCLR-Sc+SPM). First, we propose a new non-negative sparse coding along with max pooling and spatial pyramid matching method (Sc+SPM) to extract local feature's information in order to represent images, where non-negative sparse coding is used to encode local features. Max pooling along with spatial pyramid matching (SPM) is then utilized to get the feature vectors to represent images. Second, we propose to leverage the correlation constrained low-rank and sparse matrix recovery technique to decompose the feature vectors of images into a low-rank matrix and a sparse error matrix by considering the correlations between images. To incorporate the common and specific attributes into the image representation, we still adopt the idea of sparse coding to recode the Sc+SPM representation of each image. In particular, we collect the columns of the both matrixes as the bases and use the coding parameters as the updated image representation by learning them through the locality-constrained linear coding (LLC). Finally, linear SVM classifier is trained for final classification. Experimental results show that the proposed method achieves or outperforms the state-of-the-art results on several benchmarks. (C) 2014 Elsevier Inc. All rights reserved.