title: Partially-connected hybrid precoding in mm-wave systems with dynamic phase shifter networks

abstract: Hybrid precoding is a cost-effective approach to support directional transmissions for millimeter wave (mm-wave) communications, and its design challenge mainly lies in the analog component which consists of a network of phase shifters. The partially-connected structure employs a small number of phase shifters and therefore serves as an energy efficient solution for hybrid precoding. In this paper, we propose a double phase shifter (DPS) implementation for the phase shifter network in the partially-connected structure, which allows more tractable and flexible hybrid precoder design. In particular, the hybrid precoder design is identified as an eigenvalue problem. To further enhance the performance, dynamic mapping from radio frequency (RF) chains to antennas is proposed, for which a greedy algorithm and a modified K-means algorithm are developed. Simulation results demonstrate the performance gains of the proposed hybrid precoding algorithms with the DPS implementation over existing ones. Given its low hardware complexity and high spectral efficiency, the proposed structure is a promising candidate for 5G mm-wave systems.


title: Robust License Plate Detection Using Covariance Descriptor in a Neural Network Framework

abstract: We present a license plate detection algorithm that employs a novel image descriptor. Instead of using conventional gradient filters and intensity histograms, we compute a covariance matrix of low-level pixel-wise features within a given image window. Unlike the existing approaches, this matrix effectively captures both statistical and spatial properties within the window. We normalize the covariance matrix using local variance scores and restructure the unique coefficients into a feature vector form. Then, we feed these coefficients into a multi-layer neural network. Since no explicit similarity or distance computation is required in this framework, we are able to keep the computational load of the detection process low. To further accelerate the covariance matrix extraction process, we adapt an integral image based data propagation technique. Our extensive analysis shows that the detection process is robust against noise, illumination distortions, and rotation. In addition, the presented method does not require careful fine tuning of the decision boundaries.


title: Initial Capacitor Charging in Grid-Connected Flying Capacitor Multilevel Converters

abstract: This letter reports a method for the initial charging of capacitors in grid-connected flying capacitor (FC) multilevel converters. A resistor is inserted between each phase of the FC converter and the grid. A voltage balancing algorithm is activated from the beginning of the process and the FC converter generates proper output voltages to achieve balanced charging of both the dc-bus capacitor and the FCs. The proposed initial charging method achieves low voltage and current stress on the power devices and the passive components. The method is simple to implement and can be applied to an FC with any number of levels. Experimental results demonstrate the effectiveness of the proposed method.


title: Cluster based wireless sensor network routings using Artificial Bee Colony Algorithm

abstract: In this paper, we propose a novel hierarchical clustering approach for wireless sensor networks to maintain energy depletion of the network in minimum using Artificial Bee Colony Algorithm which is a new swarm based heuristic algorithm. We present a protocol using Artificial Bee Colony Algorithm, which tries to provide optimum cluster organization in order to minimize energy consumption. In cluster based networks, the selection of cluster heads and its members is an essential process which affects energy consumption. Simulation results demonstrate that the proposed approach provides promising solutions for the wireless sensor networks.


title: Memory-Augmented Dialogue Management for Task-Oriented Dialogue Systems

abstract: Dialogue management (DM) is responsible for predicting the next action of a dialogue system according to the current dialogue state and thus plays a central role in task-oriented dialogue systems. Since DM requires having access not only to local utterances but also to the global semantics of the entire dialogue session, modeling the long-range history information is a critical issue. To this end, we propose MAD, a novel memory-augmented dialogue management model that employs a memory controller and two additional memory structures (i.e., a slot-value memory and an external memory). The slot-value memory tracks the dialogue state by memorizing and updating the values of semantic slots (i.e., cuisine, price, and location), and the external memory augments the representation of hidden states of traditional recurrent neural networks by storing more context information. To update the dialogue state efficiently, we also propose slot-level attention on user utterances to extract specific semantic information for each slot. Experiments show that our model can obtain state-of-the-art performance and outperforms existing baselines.


title: Answer Sets and the Language of Answer Set Programming

abstract: Answer set programming is a declarative programming paradigm based on the answer set semantics of logic programs. This introductory article provides the mathematical background for the discussion of answer set programming in other contributions to this special issue.


title: Quad quadrant bidirectional DC/DC converter for electric vehicles with high gain voltage

abstract: To improve the energy quality, most of the renewable energy systems and DC motor drive systems include an energy storage element charged by the bidirectional Converter. This paper propose quad quadrants dc-dc converter with high gain voltage and has a smooth output current The low current ripple and high gain features result are helpful for devices. This paper propose bidirectional DC-DC converter employs two MOSFET switch, two capacitor, two couple inductor and one small inductor, in this converter it's not necessary increase turn ratio Transformer for obtain high gain. The simulation and Theoretical analysis results match with good agreement. Simulation using MATLAB/SIMULINK.


title: 24 GHz dielectric filled waveguide fed horn antenna using 3D-LDS MID technology

abstract: This paper presents the evaluation of dielectric tapered horn antennas that are fed by planar radio frequency (RF) transmission line to dielectric filled waveguide transitions. The antennas are developed to be efficiently manufactured with the laser direct structuring (LDS) method that allows for a 3d metalization of plastic surfaces. In doing so the flexibility of the antenna design process is increased. Furthermore the antennas can be directly combined with a circuitry if required. First of all, a fundamental evaluation is done by means of two different transitions from typical RF transmission lines to a rectangular dielectric filled waveguide at 24 GHz. Based on these findings a test antenna is designed and subsequently manufactured with the LDS method. To evaluate the manufacturability with the LDS method in general and additionally prove the concept, the configuration of this test antenna is kept relatively simple. The prototype is characterized in an anechoic chamber and the results are discussed. In the next step an additional example design that is based on an typical dielectric antenna is discussed. To conclude, the results obtained are summarized and discussed with respect to a use for the development of RF applications.


title: A home-based exercise program for children with congenital heart disease following interventional cardiac catheterization: study protocol for a randomized controlled trial

abstract: Cardiac catheterization has opened an innovative treatment field for cardiac disease; this treatment is becoming the most popular approach for pediatric congenital heart disease (CHD) and has led to a significant growth in the number of children with cardiac catheterization. Unfortunately, based on evidence, it has been demonstrated that the majority of children with CHD are at an increased risk of “non-cardiac” problems. Effective exercise therapy could improve their functional status significantly. As studies identifying the efficacy of exercise therapy are rare in this field, the aims of this study are to (1) identify the efficacy of a home-based exercise program to improve the motor function of children with CHD with cardiac catheterization, (2) reduce parental anxiety and parenting burden, and (3) improve the quality of life for parents whose children are diagnosed with CHD with cardiac catheterization through the program. A total of 300 children who will perform a cardiac catheterization will be randomly assigned to two groups: a home-based intervention group and a control group. The home-based intervention group will carry out a home-based exercise program, and the control group will receive only home-based exercise education. Assessments will be undertaken before catheterization and at 1, 3, and 6 months after catheterization. Motor ability quotients will be assessed as the primary outcomes. The modified Ross score, cardiac function, speed of sound at the tibia, functional independence of the children, anxiety, quality of life, and caregiver burden of their parents or the main caregivers will be the secondary outcome measurements. The proposed prospective randomized controlled trial will evaluate the efficiency of a home-based exercise program for children with CHD with cardiac catheterization. We anticipate that the home-based exercise program may represent a valuable and efficient intervention for children with CHD and their families. http://www.chictr.org.cn/ on: ChiCTR-IOR-16007762. Registered on 13 January 2016.


title: Optimization and application of Electron Beam Absorbed Current technique

abstract: Advanced microprocessors are aggressively scaled with process technology rapidly advancing to 14nm technology node. This presents a challenging task to uncover subtle physical defects resulting from resistive via/contact & shorted tight pitch metal interconnects. Electron Beam Absorbed Current (EBAC) is a promising technique that can help to identify the defective vias or metal shorts in non-invasive manner. This technique is based on scanning electron microscopy (SEM) and pizeo manipulators of tungsten tips. Metal lines are probed with tungsten probes in SEM and electrons beam current absorbed by the metal lines are collected and used to form a current or voltage contrast map of the area. Any abnormal metal EBAC image would indicate metal line defects and can be correlated with layout images.


title: A TIQ BASED CMOS FLASH A/D CONVERTER FOR SYSTEM-ON-CHIP APPLICATIONS

abstract: This thesis addresses a Threshold Inverter Quantization (TIQ) based CMOS flash analog-to-digital converter (ADC) for system-on-chip (SoC) applications. The TIQ technique, which uses two cascaded CMOS inverters as a voltage comparator, has been introduced by Ali Tangel in 1999. However, this TIQ technique must be developed to satisfy recent SoC trends, which force ADCs to be integrated on the chip with other digital circuits and focus on low-power and low-voltage implementations. Thus, this thesis proposes an optimal design method for the TIQ comparator, a new encoder, two low-power applications of the TIQ flash ADC, and a new voltage comparator for better implementation in SoC applications. ::: These four proposals contributed toward the achievement of high-speed conversion, low-power dissipation, and low-voltage operation in the TIQ flash ADC. First, an optimal design method called Systematic Size Variation (SSV) technique for the TIQ comparator reduced the impacts of the process, temperature, and power supply voltage variations. Therefore, we obtained a higher speed and resolution TIQ flash ADC. Second, a new encoder named the fat tree encoder replaced the ROM type encoder that was the speed bottleneck. Accordingly, we achieved significant improvement of speed of the TIQ flash ADC. Third, a Power and Resolution Adaptive flash ADC (PRA-ADC) and a power management method in the TIQ flash ADC reduced/managed power dissipation. By controlling the power dissipation in the comparator, we obtained a large power saving. Finally, a new Quantum Voltage (QV) comparator, which is a differential voltage comparator with the TIQ and the SSV techniques, for next generation deep submicron low-voltage CMOS flash ADC has reduced the noise susceptibility of the TIQ comparator. As a result of using the QV comparator, we can achieve very small voltage comparison for low-voltage operation. Also, the new comparator has a great advantage in power dissipation and noise rejection compared to the TIQ comparator. ::: In addition to the above four contributions, simulation and fabrication results of the TIQ flash ADC are discussed in this thesis. The results show that the TIQ flash ADC achieves high-speed conversion, and has a small size, low-power dissipation, and low-voltage operation compared to other flash ADCs.


title: Relationship between attachment styles and happiness in medical students

abstract: BACKGROUND ::: Attachment theory is one of the most important achievements of contemporary psychology. Role of medical students in the community health is important, so we need to know about the situation of happiness and attachment style in these students. ::: ::: ::: OBJECTIVES ::: This study was aimed to assess the relationship between medical students' attachment styles and demographic characteristics. ::: ::: ::: MATERIALS AND METHODS ::: This cross-sectional study was conducted on randomly selected students of Medical Sciences in Kurdistan University, in 2012. To collect data, Hazan and Shaver's attachment style measure and the Oxford Happiness Questionnaire were used. The results were analyzed using the SPSS software version 16 (IBM, Chicago IL, USA) and statistical analysis was performed via t-test, Chi-square test, and multiple regression tests. ::: ::: ::: RESULTS ::: Secure attachment style was the most common attachment style and the least common was ambivalent attachment style. Avoidant attachment style was more common among single persons than married people (P = 0.03). No significant relationship was observed between attachment style and gender and grade point average of the studied people. The mean happiness score of students was 62.71. In multivariate analysis, the variables of secure attachment style (P = 0.001), male gender (P = 0.005), and scholar achievement (P = 0.047) were associated with higher happiness score. ::: ::: ::: CONCLUSION ::: The most common attachment style was secure attachment style, which can be a positive prognostic factor in medical students, helping them to manage stress. Higher frequency of avoidant attachment style among single persons, compared with married people, is mainly due to their negative attitude toward others and failure to establish and maintain relationships with others.


title: GENETICS AND CELL BIOLOGY 5-HT1A Receptors in the Frontal Cortical Brain Areas in Cloninger Type 1 and 2 Alcoholics Measured

abstract: Aims: The Cloninger type 1 alcoholics are prone to anxiety, and in many cases patients have begun to use alcohol in order to relieve their anxiety. We have previously reported a decrease of the serotonin transporter density in the perigenual anterior cingulate cortex (pACC) in type 1 alcoholics. The 5-HT1A receptors are the binding sites for anxiolytic drug buspirone. We aimed to investigate the alteration in the density of 5-HT1A receptors, that may also alter the effect of serotonin in the pACC in alcoholics. Methods: The density of the serotonin receptor 5-HT1A among Cloninger type 1 and 2 alcoholics (nine and eight subjects, respectively) and 10 control subjects were determined by postmortem whole-hemisphere autoradiography with WAY-100635. Results: Substantially sparser 5-HT1A (by −31%, P = 0.010) density was observed in the pACC of alcoholic subjects in relation to non-alcoholic comparison subjects. In a secondary analysis for the difference between the alcoholic subtypes and controls, the 5-HT1A density was decreased significantly by −32% ( P = 0.015) in the upper level of pACC in type 1 alcoholics. Conclusions: The detected decrease of 5-HT1A receptor density on the pACC suggests further that the serotoninergic system is defected in the so-called affect region, especially in the type 1 alcoholics.


Arrhinencephaly associated with a deficiency involving chromosome 18.


title: Consensus-based clustering for document image segmentation

abstract: Segmentation of a document image plays an important role in automatic document processing. In this paper, we propose a consensus-based clustering approach for document image segmentation. In this method, the foreground regions of a document image are grouped into a set of primitive blocks, and a set of features is extracted from them. Similarities among the blocks are computed on each feature using a hypothesis test-based similarity measure. Based on the consensus of these similarities, clustering is performed on the primitive blocks. This clustering approach is used iteratively with a classifier to label each primitive block. Experimental results show the effectiveness of the proposed method. It is further shown in the experimental results that the dependency of classification performance on the training data is significantly reduced.


Trapped in the net: will internet addiction become a 21st-century epidemic?


title: Management of tumors of the parapharyngeal space.

abstract: Benign and malignant tumors can arise from any of the structures contained within the parapharyngeal space. Such tumors are very rare, however. Also, malignant tumors from adjacent areas (eg, the pharynx) can extend into the parapharyngeal space by direct growth, or distant tumors may metastasize to the lymphatics within the space. Although the history and physical examination can provide clues to the site of origin and nature of a parapharyngeal space tumor, imaging studies are more useful for defining the site of origin and extent of the mass, as well as its vascularity and relationship to the great vessels of the neck and other neurovascular structures. Surgery is the mainstay of treatment. The surgical approach chosen should facilitate complete tumor extirpation with minimal morbidity. Irradiation is administered as primary therapy in patients with unresectable tumors, poor surgical candidates, and selected other patients. Radiation therapy is also used after surgery for high-grade malignancies or when wide surgical margins cannot be achieved.


title: Deep Neural Network Approaches to Speaker and Language Recognition

abstract: The impressive gains in performance obtained using deep neural networks (DNNs) for automatic speech recognition (ASR) have motivated the application of DNNs to other speech technologies such as speaker recognition (SR) and language recognition (LR). Prior work has shown performance gains for separate SR and LR tasks using DNNs for direct classification or for feature extraction. In this work we present the application of single DNN for both SR and LR using the 2013 Domain Adaptation Challenge speaker recognition (DAC13) and the NIST 2011 language recognition evaluation (LRE11) benchmarks. Using a single DNN trained for ASR on Switchboard data we demonstrate large gains on performance in both benchmarks: a 55% reduction in EER for the DAC13 out-of-domain condition and a 48% reduction in ${C_{avg}}$ on the LRE11 30 s test condition. It is also shown that further gains are possible using score or feature fusion leading to the possibility of a single i-vector extractor producing state-of-the-art SR and LR performance


title: Coupling behaviors of quarter-wavelength impedance transformers for wideband CPW bandpass filters

abstract: Quarter-wavelength coplanar waveguide (/spl lambda//4-CPW) impedance transformers with low and high characteristic impedance are characterized via fullwave method of moments short-open calibration (MoM-SOC) approach and further utilized as alternative K- and J-inverter networks with enhanced coupling. Network parameters are obtained and compared with those of two traditional lumped-CPW elements, demonstrating tightened coupling behaviors. The two improved /spl lambda//4 CPW bandpass filters are then constructed and their performances are optimized via cascaded topology. Predicted results show us a few attractive features, i.e., wide passband and first-order harmonic suppression as confirmed by experiment.


title: Computer Aided Verification of Accountability in Electronic Payment Protocol with CryptoVerif

abstract: During the past few decades electronic payment protocols has been studied. A lot of electronic payment protocols, for example, 3KP, SET, have been proposed which claimed that have security properties. To our best knowledge, till now analysis of 3KP protocol has not with automatic tool in computational model. Recently owning to the contribution of Meng et al., 3KP protocol can be analyzed with automatic tool in computational model. In this study firstly the state-of-art of electronic payment protocol and the proof are presented. Then the term, process and correspondence assertion in Blanchet calculus are used to model accountability and 3KP protocol with Meng et al. mechanized framework of electronic payment protocols in computational model with active adversary. Finally, 3KP protocol is analyzed in Meng et al. framework with mechanized tool CryptoVerif. The result shows that 3KP protocol has money accountability and goods accountability, which is consistent with its claim. To our knowledge, we are conducting the first automatic analysis of 3KP protocol in computational model.


title: Credit Risk Analysis in Peer-to-Peer Lending System

abstract: This research paper aims to analyze the credit risk involved in peer-to-peer (P2P) lending system of “LendingClub” Company. The P2P system allows investors to get significantly higher return on investment as compared to bank deposit, but it comes with a risk of the loan and interest not being repaid. Ensemble machine learning algorithms and preprocessing techniques are used to explore, analyze and determine the factors which play crucial role in predicting the credit risk involved in “LendingClub” publicly available 2013–2015 loan applications dataset. A loan is considered “good” if it's repaid with interest and on time. The algorithms are optimized to favor the potential good loans whilst identifying defaults or risky credits.


title: Investigating e-learning system usage outcomes in the university context

abstract: This paper investigates the outcomes of e-learning systems adoption and use by conceptualizing three e-learning systems adoption outcome constructs namely perceived learning assistance, perceived community building assistance and perceived academic performance. Utilizing these constructs, the paper proposes a research model for assessing the possible outcomes of e-learning systems adoption and use. The study collected longitudinal survey data from 249 university students participating in hybrid courses using a popular learning management system, Moodle. Partial least squares (PLS) approach was then used to test the research model. The findings suggest that beliefs about perceived usefulness and perceived ease of use, and how an e-learning system is used influence students' perceived learning assistance and perceived community building assistance. In turn, perceived learning assistance and perceived community building assistance influence the students' perceived academic performance.


title: Operation Analysis of Fast Charging Stations With Energy Demand Control of Electric Vehicles

abstract: For dc fast charging stations (FCS), short charging time duration is its main advantage over other ac charging stations. However, due to the characteristics of internal resistance of electric vehicle (EV) batteries, if charging request of an EV is near the full state of charge (SoC), its charging time increases significantly. In this paper, considering the impact of requested SoC on charging time, we propose an operation analysis of FCS, where operators of FCS can set a limit on the EVs’ requested SoC to obtain maximized revenue. We model the FCS operation as an $M/G/S/K$ queue and incorporate the dc fast charging model into the queuing analysis, as well as the revenue model of FCS. Simulation results indicate that limiting the requested SoC in an overloaded condition at FCS can increase total charged energy and revenue of FCS, as well as decrease the probability of blocking arriving EVs.


title: Page Stream Segmentation with Convolutional Neural Nets Combining Textual and Visual Features

abstract: In recent years, (retro-)digitizing paper-based files became a major undertaking for private and public archives as well as an important task in electronic mailroom applications. As a first step, the workflow involves scanning and Optical Character Recognition (OCR) of documents. Preservation of document contexts of single page scans is a major requirement in this context. To facilitate workflows involving very large amounts of paper scans, page stream segmentation (PSS) is the task to automatically separate a stream of scanned images into multi-page documents. In a digitization project together with a German federal archive, we developed a novel approach based on convolutional neural networks (CNN) combining image and text features to achieve optimal document separation results. Evaluation shows that our PSS architecture achieves an accuracy up to 93 % which can be regarded as a new state-of-the-art for this task.


title: Reinforcement Learning for Spoken Dialogue Systems: Comparing Strengths and Weaknesses for Practical Deployment

abstract: In a spoken dialogue system, the function of a dialogue manager is to select actions based on observed events and inferred beliefs. To formalize and optimize the action selection process, researchers have turned to reinforcement learning methods which represent the dynamics of a spoken dialogue as a fully or partially observable Markov Decision Process. Once represented as such, optimal policies prescribing what actions the system should take in order to maximize a reward function can be learned from data. Formerly, this task was assigned to the application developer, who typically hand-crafted rules or heuristics. In this position paper, we assess to what extent the action selection process can be automated by current state-of-the-art reinforcement learning methods for dialogue management. In examining the strengths and weaknesses of these methods with respect to practical deployment, we discuss the challenges that need to be overcome before these methods can become commonplace in deployed systems.


title: Depth-Dependent Halos: Illustrative Rendering of Dense Line Data

abstract: We present a technique for the illustrative rendering of 3D line data at interactive frame rates. We create depth-dependent halos around lines to emphasize tight line bundles while less structured lines are de-emphasized. Moreover, the depth-dependent halos combined with depth cueing via line width attenuation increase depth perception, extending techniques from sparse line rendering to the illustrative visualization of dense line data. We demonstrate how the technique can be used, in particular, for illustrating DTI fiber tracts but also show examples from gas and fluid flow simulations and mathematics as well as describe how the technique extends to point data. We report on an informal evaluation of the illustrative DTI fiber tract visualizations with domain experts in neurosurgery and tractography who commented positively about the results and suggested a number of directions for future work.


title: Lip Reading Sentences in the Wild

abstract: The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio. Unlike previous works that have focussed on recognising a limited number of words or phrases, we tackle lip reading as an open-world problem – unconstrained natural language sentences, and in the wild videos. Our key contributions are: (1) a Watch, Listen, Attend and Spell (WLAS) network that learns to transcribe videos of mouth motion to characters, (2) a curriculum learning strategy to accelerate training and to reduce overfitting, (3) a Lip Reading Sentences (LRS) dataset for visual speech recognition, consisting of over 100,000 natural sentences from British television. The WLAS model trained on the LRS dataset surpasses the performance of all previous work on standard lip reading benchmark datasets, often by a significant margin. This lip reading performance beats a professional lip reader on videos from BBC television, and we also demonstrate that if audio is available, then visual information helps to improve speech recognition performance.


title: Ostro: Scalable Placement Optimization of Complex Application Topologies in Large-Scale Data Centers

abstract: A complex cloud application consists of virtual machines (VMs) running software such as web servers and load balancers, storage in the form of disk volumes, and network connections that enable communication between VMs and between VMs and disk volumes. The application is also associated with various requirements, including not only quantities such as the sizes of the VMs and disk volumes, but also quality of service (QoS) attributes such as throughput, latency, and reliability. This paper presents Ostro, an Open Stack-based scheduler that optimizes the utilization of data center resources, while satisfying the requirements of the cloud applications. The novelty of the approach realized by Ostro is that it makes holistic placement decisions, in which all the requirements of an application -- described using an application topology abstraction -- are considered jointly. Specific placement algorithms for application topologies are described including an estimate-based greedy algorithm and a time-bounded A algorithm. These algorithms can deal with complex topologies that have heterogeneous resource requirements, while still being scalable enough to handle the placement of hundreds of VMs and volumes across several thousands of host servers. The approach is evaluated using both extensive simulations and realistic experiments. These results show that Ostro significantly improves resource utilization when compared with naive approaches.


title: Cetus: A Source-to-Source Compiler Infrastructure for Multicores

abstract: The Cetus tool provides an infrastructure for research on multicore compiler optimizations that emphasizes automatic parallelization. The compiler infrastructure, which targets C programs, supports source-to-source transformations, is user-oriented and easy to handle, and provides the most important parallelization passes as well as the underlying enabling techniques.


title: Migrating from Virtualization to Dockerization in the Cloud: Simulation and Evaluation of Distributed Systems

abstract: Virtualization is the nucleus of the cloud computing for providing its services on-demand. Cloud-based distributed systems are predominantly developed using virtualization technology. However, the requirement of significant resources and issues of interoperability and deployment make it less adopt- able in the development of many types of distributed systems. Dockerization or Docker Container-based virtualization has been introduced in the last three years and gaining popularity in the software development community. Docker has recently introduced its distributed system development tool called Swarm, which extends the Docker Container-based system development process on multiple hosts in multiple clouds. Docker Swarm-based containerized distributed system is a brand new approach and needs to be compared with the virtualized distributed system. Therefore, this paper presents the simulation and evaluation of the development of a distributed system using virtualization and dockerization. This simulation is based on Docker Swarm, VirtualBox, Ubuntu, Mac OS X, nginx and redis. To simulate and evaluate the distributed system in the same environment, all Swarm Nodes and Virtual Machines are created using VirtualBox on the same Mac OS X host. For making this evaluation rational, almost similar system resources are allocated to both at the beginning. Subsequently, similar servers nginx and redis are installed on the Swarm Node and Virtual Machine. Finally, based on the experimental simulation results, it evaluates their required resources and operational overheads; thus, their performance and effectiveness for designing distributed systems.


title: An Integrated Inductor for Parallel Interleaved Three-Phase Voltage Source Converters

abstract: Three-phase voltage source converters (VSCs) are often connected in parallel to realize the high current output converter system. The harmonic quality of the resultant switched output voltage can be improved by interleaving the carrier signals of these parallel-connected VSCs. As a result, the line current filtering requirement can be reduced. However, an additional inductive filter is required to suppress the circulating current. The integrated inductive component, which combines the functionality of the line filter inductor and the circulating current inductor is presented in this paper. An analysis of the flux density distribution in the integrated inductor is presented and design procedure is described. The analysis has been also verified by performing finite-element analysis. The advantage offered by the use of the integrated inductor is demonstrated by comparing its volume with the volume of the state-of-the-art filtering solution. The performance of the integrated inductor is also verified by the experimental measurements.


title: Quantifying Uncertainties in Natural Language Processing Tasks

abstract: Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper, we propose novel methods to study the benefits of characterizing model and data uncertainties for natural language processing (NLP) tasks. With empirical experiments on sentiment analysis, named entity recognition, and language modeling using convolutional and recurrent neural network models, we show that explicitly modeling uncertainties is not only necessary to measure output confidence levels, but also useful at enhancing model performances in various NLP tasks.


title: A 19 nV/ $\surd$ Hz Noise 2- $\mu \text{V}$ Offset 75- $\mu \text{A}$ Capacitive-Gain Amplifier With Switched-Capacitor ADC Driving Capability

abstract: This paper describes a capacitive-gain amplifier (CGA) with common-mode (CM) sampling (CMS) and switched-capacitor driving capability. The CMS CGA defines the amplifier CM voltage in a single auto-zero phase that significantly reduces the CM settling time. A pre-charge technique and dynamic filtering are used to allow the CGA to drive a switched-cap analog-to-digital converter (ADC) directly that relaxes the speed requirement on CGA and reduces folded noise aliases due to ADC sampling. As a result, it achieves 19-nV/ $\surd$ Hz noise density while dissipating 75 $\mu \text{A}$ . The 5 ppm/full-scale-range integral nonlinearity is achieved with programmable gain range from 1 to 128 with 2.7 to 3.6 V supply. It has 2 $\mu \text{V}$ typical offset and 0.81 ppm/°C max gain error drift. The CGA was implemented in a 0.18- $\mu \text{m}$ CMOS 1.8/3.3 V ultra low leakage technology and occupies only 0.53 mm2 die area.


Fast and Simple Agglomerative LBVH Construction


title: Expression of rice heat stress transcription factor OsHsfA2e enhances tolerance to environmental stresses in transgenic Arabidopsis

abstract: Plant growth and crop yields are limited by high-temperature stresses. In this study, we attempted to isolate the rice genes responsible for high-temperature stress tolerance using a transformed Arabidopsis population expressing a full-length cDNA library of rice. From approximately 20,000 lines of transgenic Arabidopsis, we isolated a thermotolerant line, R04333, that could survive transient heat stress at the cotyledon stage. The rice cDNA inserted in R04333 encodes OsHsfA2e, a member of the heat stress transcription factors. The thermotolerant phenotype was observed in newly constructed transgenic Arabidopsis plants expressing OsHsfA2e. Among 5 A2-type HSF genes encoded in the rice genome, four genes, including OsHsfA2e, are induced by high temperatures in rice seedlings. The OsHsfA2e protein was localized to the nuclear region and exhibited transcription activation activity in the C-terminal region. Microarray analysis demonstrated that under unstressed conditions transgenic Arabidopsis overexpressing OsHsfA2e highly expressed certain stress-associated genes, including several classes of heat-shock proteins. The thermotolerant phenotype was observed not only in the cotyledons but also in rosette leaves, inflorescence stems and seeds. In addition, transgenic Arabidopsis exhibited tolerance to high-salinity stress. These observations suggest that the OsHsfA2e may be useful in molecular breeding designed to improve the environmental stress tolerance of crops.


title: Hybrid Buck–Boost Feedforward and Reduced Average Inductor Current Techniques in Fast Line Transient and High-Efficiency Buck–Boost Converter

abstract: This paper presents a buck-boost converter with high efficiency and small output ripple to extend the battery life of portable devices. Besides, the hybrid buck-boost feedforward (HBBFF) technique is integrated in this converter to achieve fast line response. The new control topology minimizes the switching and conduction losses at the same time even when four switches are used. Therefore, over a wide input voltage range, the proposed buck-boost converter with minimum switching loss like the buck or boost converter can reduce the conduction loss through the use of the reduced average inductor current (RAIC) technique. Moreover, the HBBFF technique minimizes the voltage variation at the output of error amplifier. Consequently, a fast line transient response can be achieved with small dropout voltage at the output. Especially, the converter can offer good line and load regulations to ensure a regulated output voltage without being affected by the decreasing battery voltage. Experimental results show that the output voltage is regulated over a wide battery lifetime, and the output ripple is minimized during mode transition. The peak efficiency is 97% and the transient dropout voltage can be improved substantially.


title: A practical path loss model for indoor WiFi positioning enhancement

abstract: Positioning within a local area refers to technology whereby each node is self-aware of its position. Based on empirical study, this paper proposes an enhancement to the path loss model in the indoor environment for improved accuracy in the relationship between distance and received signal strength. We further demonstrate the potential of our model for the WiFi positioning system, where the mean errors in the distance estimation are 2.3 m and 2.9 m for line of sight and non line of sight environments, respectively.


title: Compact SIW leaky wave antenna

abstract: A compact Substrate Integrated Waveguide (SIW) Leaky-Wave Antenna (LWA) is proposed. Internal vias are inserted in the SIW in order to have narrow walls, and so reducing the size of the SIW-LWA, the new structure is called Slow Wave — Substrate Integrated Waveguide — Leaky Wave Antenna (SW-SIW-LWA), since inserting the vias induce the SW effect. After designing the antenna and simulating with HFSS a reduction of 30% of the transverse side of the antenna is attained while maintaining an acceptable gain. Other parameters like the radiation efficiency, Gain, directivity, and radiation pattern are analyzed. Finally a Comparison of our miniaturization technique with Half-Mode Substrate Integrated Waveguide (HMSIW) technique realized in recent articles is done, shows that SW-SIW-LWA technique could be a good candidate for SIW miniaturization.


title: 58.4: 10-Bit Column Driver with Split-DAC Architecture

abstract: An 8-bit coarse digital-to-analog converter (DAC), which adopts both array and tree-type decoders, is combined with a 2-bit fine interpolation DAC to reduce RC time delay and die area of a column driver for LCD-HDTV applications. Error amplifiers drive a pair of column lines in the output buffer to realize rail-to-rail voltage swing with a high slew rate. The design has been fabricated in 0.3 μm LV-HV CMOS technology.


title: Inhomogeneous Hypergraph Clustering with Applications

abstract: Hypergraph partitioning is an important problem in machine learning, computer vision and network analytics. A widely used method for hypergraph partitioning relies on minimizing a normalized sum of the costs of partitioning hyperedges across clusters. Algorithmic solutions based on this approach assume that different partitions of a hyperedge incur the same cost. However, this assumption fails to leverage the fact that different subsets of vertices within the same hyperedge may have different structural importance. We hence propose a new hypergraph clustering technique, termed inhomogeneous hypergraph partitioning, which assigns different costs to different hyperedge cuts. We prove that inhomogeneous partitioning produces a quadratic approximation to the optimal solution if the inhomogeneous costs satisfy submodularity constraints. Moreover, we demonstrate that inhomogenous partitioning offers significant performance improvements in applications such as structure learning of rankings, subspace segmentation and motif clustering.


title: Competing Memes Propagation on Networks: A Network Science Perspective

abstract: In this paper, we study the intertwined propagation of two competing "memes" (or data, rumors, etc.) in a composite network. Within the constraints of this scenario, we ask two key questions: (a) which meme will prevail? and (b) can one influence the outcome of the propagations? Our model is underpinned by two key concepts, a structural graph model (composite network) and a viral propagation model (SI1I2S). Using this framework, we formulate a non-linear dynamic system and perform an eigenvalue analysis to identify the tipping point of the epidemic behavior. Based on insights gained from this analysis, we demonstrate an effective and accurate prediction method to determine viral dominance, which we call the EigenPredictor. Next, using a combination of synthetic and real composite networks, we evaluate the effectiveness of various viral suppression techniques by either a) concurrently suppressing both memes or b) unilaterally suppressing a single meme while leaving the other relatively unaffected.


title: Invertebrates associated with aquatic plants bought from aquarium stores in Canada and New Zealand

abstract: Invertebrate species carried incidentally (i.e., ‘hitchhikers’) in the aquarium trade have gained increasing attention in recent years, but factors affecting the movement of species from stores to homes are poorly understood. We aimed to determine how macrophytes bought from stores act as vectors for transport of non-indigenous invertebrate species. We tested whether incidental invertebrate faunas carried on macrophytes vary internationally by comparing the New Zealand and Canadian trades, and if macrophyte species with different morphologies carry different risks. We recorded a large variety of invertebrate species associated with Vallisneria spp., Sword plants (Echinodorus spp.) and Elodea canadensis bought from stores, including species non-indigenous to both countries. Community composition of incidental fauna differed significantly between New Zealand (primarily domestically cultivated) and Canadian (primarily imported) bought macrophytes. Differences in composition between different macrophyte species were only statistically significant between wild-collected E. canadensis and the cultured species in New Zealand. Behaviours observed in stores, such as the amount of time macrophytes were removed from water before being placed in plastic bags for transport, did not affect the abundances or richness of incidental invertebrates transported, and thus did not appear to be effective in reducing invasion risk. We therefore recommend chemical treatment for removal of invertebrates from macrophytes at or pre-border, and from tanks containing plants at culture facilities and in stores. Such management will reduce the probability of introduction of hitchhikers to home aquaria, from which risk of release to natural waters is greatest.


title: Problematic Online Behaviors among Adolescents and Emerging Adults: Associations between Cyberbullying Perpetration, Problematic Social Media Use, and Psychosocial Factors

abstract: Over the past two decades, young people’s engagement in online activities has grown markedly. The aim of the present study was to examine the relationship between two specific online behaviors (i.e., cyberbullying perpetration, problematic social media use) and their relationships with social connectedness, belongingness, depression, and self-esteem among high school and university students. Data were collected from two different study groups via two questionnaires that included the Cyberbullying Offending Scale, Social Media Use Questionnaire, Social Connectedness Scale, General Belongingness Scale, Short Depression-Happiness Scale, and Single Item Self-Esteem Scale. Study 1 comprised 804 high school students (48% female; mean age 16.20 years). Study 2 comprised 760 university students (60% female; mean age 21.48 years). Results indicated that problematic social media use and cyberbullying perpetration (which was stronger among high school students) were directly associated with each other. Belongingness (directly) and social connectedness (indirectly) were both associated with cyberbullying perpetration and problematic social media use. Path analysis demonstrated that while age was a significant direct predictor of problematic social media use and cyberbullying perpetration among university students, it was not significant among high school students. In both samples, depression was a direct predictor of problematic social media use and an indirect predictor of cyberbullying perpetration. However, majority of these associations were relatively weak. The present study significantly adds to the emerging body of literature concerning the associations between problematic social media use and cyberbullying perpetration.


title: Embodying emergent masculinities: Men engaging with reproductive and sexual health technologies in the Middle East and Mexico

abstract: We employ Raymond Williams's concept of “emergence” to capture new forms of embodied masculinities in the Middle East and Mexico, two sites marked by powerful local stereotypes of manliness. Men there are enacting “emergent masculinities”—living out new ways of being men in attempts to counter forms of manhood that they see as harmfully hegemonic. They do this partly through engagements with emergent health technologies, including assisted reproductive technologies for male infertility and pharmaceutical technologies for erectile dysfunction that today are reshaping sociopolitical and intimate realities. We argue that masculinities research within anthropology must account for these ongoing, embodied changes in men's enactments of masculinities over time on both individual and societal levels. Furthermore, we heed R. W. Connell's advice that reformulations of hegemonic masculinity theory must consider new comparative geographies, forms of masculine embodiment, and social dynamics of masculinity around the globe.


title: Facial Expression-Aware Face Frontalization

abstract: Face frontalization is a rising technique for view-invariant face analysis. It enables a non-frontal facial image to recover its general facial appearances to frontal view. A few pioneering works have been proposed very recently. However, face frontalization with detailed facial expression recovering is still very challenging due to the non-linear relationships between head-pose and expression variations. In this paper, we propose a novel facial expression-aware face frontalization method aiming at reconstructing the frontal view while maintaining vivid appearances with regards to facial expressions. First of all, we design multiple face shape models as the reference templates in order to fit in with various shape of facial expressions. Each template describes a set of typical facial actions referred to Facial Action Coding System (FACS). Then a template matching strategy is applied by measuring a weighted Chi Square error such that the input image can be matched with the most approximate template. Finally, Robust Statistical face Frontalization (RSF) method is employed for the task of frontal view recovery. This method is validated on a spontaneous facial expression database and the experimental results show that the proposed method outperforms the state-of-the-art methods.


title: MUTLI-MATERIAL FOOD PRINTING WITH COMPLEX INTERNAL STRUCTURE SUITABLE FOR CONVENTIONAL POST-PROCESSING

abstract: Solid Freeform Fabrication (SFF) of food provides an exciting application for additive manufacturing technologies. A variety of materials has been used to demonstrate food printing. However, these materials were not suited for traditional food processing techniques (Baking, slow cooking, frying, etc) and thus eliminating the majority of today‟s consumed food. We demonstrated new materials suitable for baking, broiling and frying. Turkey, scallop, celery were processed and modified using transglutaminase to enable them to be slow cooked or deep-fried after printing. Mutli-material constructs of turkey meat and celery were successfully printed. A cookie recipe was modified to be printable while retaining shape during baking. By adding cocoa powder to the modified recipe a second, visually and differently tasting material was created. A complex shape of the cocoa modified material was printed within a block of the modified material. The complex internal geometry printed was fully preserved during baking.


title: The Discrete Probability Density Method for Emitter Geolocation

abstract: The Discrete Probability Density (DPD) method is a novel technique for fusing sensor data by numerically combining the probability density function (pdf) representing the error estimate of each measured value. This paper describes the sampling of multiple pdfs at common discrete intervals to calculate a joint discrete probability density distribution. This concept is extended into multi-dimensional space to combine sensor measurements taken from different locations by projecting each pdf onto an array of sample points. The joint product is calculated at each point to produce a discrete probability density distribution representing the fused result. The DPD method is applied to emitter geolocation using Line-Of-Bearing (LOB) measurements and compared to Stansfield's method and the Cramer-Rao Lower Bound using Monte-Carlo simulation. It is found that the DPD method performs better than Stansfield's method in the presence of large bias errors, especially for large number of LOBs.


title: A review on fingerprint orientation estimation

abstract: Fingerprint orientation plays important roles in fingerprint enhancement, fingerprint classification, and fingerprint recognition. This paper critically reviews the primary advances on fingerprint orientation estimation. Advantages and limitations of existing methods have been addressed. Issues on future development have been discussed. Copyright © 2010 John Wiley & Sons, Ltd.


title: IoT-Based Big Data: From Smart City towards Next Generation Super City Planning

abstract: Recently, a rapid growth in the population in urban regions demands the provision of services and infrastructure. These needs can be come up wit the use of Internet of Things IoT devices, such as sensors, actuators, smartphones and smart systems. This leans to building Smart City towards the next generation Super City planning. However, as thousands of IoT devices are interconnecting and communicating with each other over the Internet to establish smart systems, a huge amount of data, termed as Big Data, is being generated. It is a challenging task to integrate IoT services and to process Big Data in an efficient way when aimed at decision making for future Super City. Therefore, to meet such requirements, this paper presents an IoT-based system for next generation Super City planning using Big Data Analytics. Authors have proposed a complete system that includes various types of IoT-based smart systems like smart home, vehicular networking, weather and water system, smart parking, and surveillance objects, etc., for dada generation. An architecture is proposed that includes four tiers/layers i.e., 1 Bottom Tier-1, 2 Intermediate Tier-1, 3 Intermediate Tier 2, and 4 Top Tier that handle data generation and collections, communication, data administration and processing, and data interpretation, respectively. The system implementation model is presented from the generation and collection of data to the decision making. The proposed system is implemented using Hadoop ecosystem with MapReduce programming. The throughput and processing time results show that the proposed Super City planning system is more efficient and scalable.


A Descriptive Study of Science and Mathematics Teachers Pedagogy, ICT Use and Perceptions of How ICT Impacts Their Teaching.


title: Recursive X-Y cut using bounding boxes of connected components

abstract: A top-down page segmentation technique known as the recursive X-Y cut decomposes a document image recursively into a set of rectangular blocks. This paper proposes that the recursive X-Y cut be implemented using bounding boxes of connected components of black pixels instead of using image pixels. The advantage is that great improvement can be achieved in computation. In fact, once bounding boxes of connected components are obtained, the recursive X-Y cut is completed within an order of a second on Sparc-10 workstations for letter-sized document images scanned at 900 dpi resolution.


title: Information for cyber security issues related to aircraft systems REV-A

abstract: Existing FAA regulations, policy and guidance do not specifically address networks and data cyber security requirements for aircraft systems. This could result in security related certification criteria that are not standardized between FAA services and not harmonized between domestic and international regulatory agencies. FAA regulations, policy and guidance need to address unauthorized and potentially unsafe electronic access to aircraft related systems, networks, and software components from unauthorized users. Unauthorized access could result in the malicious use of networks, and loss or corruption of data (e.g., software applications, databases, configuration files, etc.) brought about by software worms, viruses or other malicious entities.


title: Estimating software maintenance effort: a neural network approach

abstract: Software maintenance forms an essential component of software development. Its planning includes estimation of maintenance effort, duration, personnel and costs. Adequate information regarding size, complexity and maintainability is however often unavailable. In the present work, a Neural Network (NN) based effort estimator is developed using Matlab. A feed forward back- propagation NN employing Bayesian regularization training is selected and trained for one dataset. Various categories of software maintenance cost drivers and their effect on maintenance effort have been analyzed using different combinations of number of hidden layers and hidden neurons etc. The NN is able to successfully model the maintenance effort as the obtained results are well within the previously published error limits


title: Effect of a Motor-Based Role-Play Intervention on the Social Behaviors of Adolescents With High-Functioning Autism: Multiple-Baseline Single-Subject Design

abstract: OBJECTIVE. We examined the effect of a motor-based role-play intervention on the social skills of adolescents with high-functioning autism. ::: ::: METHOD. An ABA multiple-baseline design with three 3-mo phases occurring over 12 mo was used with 7 participants. Frequency of targeted verbal and nonverbal behaviors was tallied in each phase. Frequency data were analyzed using repeated-measures analyses of variance with post hoc comparisons to examine differences in targeted behaviors over the three phases. ::: ::: RESULTS. Three participants completed all three study phases, 2 completed Phase 2, and 2 completed Phase 1. All participants (N = 7) demonstrated improved social skill use in Phase 1. Participants completing Phase 2 (n = 5) further improved social skill use. Additional improvements were observed among participants (n = 3) who completed Phase 3. ::: ::: CONCLUSION. The intervention helped participants improve targeted social skill use. Further testing with larger samples and intervention modifications is warranted


title: Secure JPEG scrambling enabling privacy in photo sharing

abstract: With the popularization of online social networks (OSNs) and smart mobile devices, photo sharing is becoming a part of people' daily life. An unprecedented number of photos are being uploaded and shared everyday through online social networks or photo hosting services, such as Facebook, Twitter, Instagram, and Flickr. However, such unrestrained online photo or multimedia sharing has raised serious privacy concerns, especially after reports of citizens surveillance by governmental agencies and scandalous leakage of private photos from prominent photo sharing sites or online cloud services. Popular OSNs typically offer privacy protection solutions only in response to the public demand and therefore are often rudimental, complex to use, and provide limited degree of control and protection. Most solutions allow users to control either who can access the shared photos or for how long they can be accessed. In contrast, in this paper, we take a structured privacy by design approach to the problem of online photo privacy protection. We propose a privacy-preserving photo sharing architecture based on a secure JPEG scrambling algorithm capable of protecting the privacy of multiple users involved in a photo. We demonstrate the proposed photo sharing architecture with a prototype application called ProShare that offers JPEG scrambling as the privacy protection tool for selected regions in a photo, secure access to the protected images, and secure photo sharing on Facebook.


title: A wall-climbing robot without any active suction mechanisms

abstract: This paper presents a wall-climbing robot that uses passive suction cups as the adhering components. Using a single motor, this robot can not only move on a wall but can also autonomously attach and detach suction cups to the wall. Passive suction cups do not consume additional energy to maintain adhesion. Therefore, the proposed robot can perform the wall-climbing motion with relatively low energy consumption. In order to prevent the robot from falling, a supporting tail was designed. Moreover, load distribution analysis showed that the tail can be used to enhance the distribution of the loads across the suction cups. In this paper, the optimal parameters of the robot are described and a robot is fabricated based on this analysis. We conducted experiments to verify the robot performance.


title: Using the wisdom of the crowds for keyword generation

abstract: In the sponsored search model, search engines are paid by businesses that are interested in displaying ads for their site alongside the search results. Businesses bid for keywords, and their ad is displayed when the keyword is queried to the search engine. An important problem in this process is 'keyword generation': given a business that is interested in launching a campaign, suggest keywords that are related to that campaign. We address this problem by making use of the query logs of the search engine. We identify queries related to a campaign by exploiting the associations between queries and URLs as they are captured by the user's clicks. These queries form good keyword suggestions since they capture the "wisdom of the crowd" as to what is related to a site. We formulate the problem as a semi-supervised learning problem, and propose algorithms within the Markov Random Field model. We perform experiments with real query logs, and we demonstrate that our algorithms scale to large query logs and produce meaningful results.


title: Secrecy Analysis in DF Relay Over Generalized- $K$ Fading Channels

abstract: In this paper, we analyze the secrecy performance of the decode-and-forward (DF) relay system in generalized- $K$ fading channels. In a typical four-node communications model, a source ( $S$ ) sends confidential information to a destination ( $D$ ) via a relay ( $R$ ) using DF strategy in two time slots, while an eavesdropper ( $E$ ) wants to overhear the information from $S$ to $D$ over generalized- $K$ fading channels. To be more realistic, we assume that $E$ can receive the signals of two time slots, and there is no direct link between $S$ and $D$ because of heavy fading. Based on those assumptions, we derive closed-form expressions for the secrecy outage probability (SOP) and ergodic secrecy capacity (ESC) by using a tight approximate probability density function of the generalized- $K$ model. Furthermore, asymptotic expressions for the SOP and ESC are also derived in the high signal-to-noise ratio region, not only because we can get some insights about SOP and ESC, but also because expressions for SOP and ESC can be simplified significantly. The single relay system is subsequently extended into a multi-relay system, where the asymptotic SOP analysis of three proposed relay selection strategies is investigated. Moreover, the security-reliability tradeoff analysis in the multi-relay system is also presented given that $S$ adopts a constant code rate. Finally, the Monte-Carlo simulation is used to demonstrate the accuracy of the derived closed-form expressions.


title: The location of trait emotional intelligence in personality factor space

abstract: The construct of trait emotional intelligence (trait EI or trait emotional self-efficacy) provides a comprehensive operationalization of emotion-related self-perceptions and dispositions. In the first part of the present study (N = 274, 92 males), we performed two joint factor analyses to determine the location of trait EI in Eysenckian and Big Five factor space. The results showed that trait EI is a compound personality construct located at the lower levels of the two taxonomies. In the second part of the study, we performed six two-step hierarchical regressions to investigate the incremental validity of trait EI in predicting, over and above the Giant Three and Big Five personality dimensions, six distinct criteria (life satisfaction, rumination, two adaptive and two maladaptive coping styles). Trait EI incrementally predicted four criteria over the Giant Three and five criteria over the Big Five. The discussion addresses common questions about the operationalization of emotional intelligence as a personality trait.


title: Design of an FMCW Radar Altimeter for Wide-Range and Low Measurement Error

abstract: This paper presents the design of a frequency-modulated continuous-wave radar altimeter with wide altitude range and low measurement error. A wide altitude range is attained by employing an optical delay in the path of transmission to reduce the dynamic range of the altitude being measured. The transmitter power and receiver gain are also controlled to facilitate a reduction in the dynamic range of the received power. In addition, low measurement error was obtained by improving the sweep linearity using a direct digital synthesizer and minimizing phase noise by employing a phase-locked loop with the offset frequency driven by a reference clock (Ref_CLK). The performance of the radar altimeter was experimentally verified using an optical-delay simulator, which provided a delay time corresponding to a 200-m radar distance. To assess the performance of the system in a realistic setting, the radar altimeter was tested using a crane setup. The crane test demonstrated that the design described in this paper resulted in a reduced measurement-error rate.


title: The Radar Cross Section of small propellers on Unmanned Aerial Vehicles

abstract: The radar cross section of small-sized unmanned aerial vehicles can be extremely low, making the perception of such objects impossible by standard radar techniques. However, the typical propeller size of these vehicles gives rise to a resonant behavior at some commonly used radar frequency bands. Around the resonant frequency, the radar cross section of the propeller highly depends on the incident wave polarization. Consequently, the rotation of the propeller causes a significant periodic fluctuation of the radar echo, moreover, the propeller length can also be estimated. This phenomenon is studied by means of anechoic chamber measurements and numerical simulations.


title: Australia's long-term electricity demand forecasting using deep neural networks

abstract: Accurate prediction of long-term electricity demand has a significant role in demand side management and electricity network planning and operation. Demand over-estimation results in over-investment in network assets, driving up the electricity prices, while demand under-estimation may lead to under-investment resulting in unreliable and insecure electricity. In this manuscript, we apply deep neural networks to predict Australia's long-term electricity demand. A stacked autoencoder is used in combination with multilayer perceptrons or cascade-forward multilayer perceptrons to predict the nation-wide electricity consumption rates for 1-24 months ahead of time. The experimental results show that the deep structures have better performance than classical neural networks, especially for 12-month to 24-month prediction horizon


title: Deep neural networks employing Multi-Task Learning and stacked bottleneck features for speech synthesis

abstract: Deep neural networks (DNNs) use a cascade of hidden representations to enable the learning of complex mappings from input to output features. They are able to learn the complex mapping from text-based linguistic features to speech acoustic features, and so perform text-to-speech synthesis. Recent results suggest that DNNs can produce more natural synthetic speech than conventional HMM-based statistical parametric systems. In this paper, we show that the hidden representation used within a DNN can be improved through the use of Multi-Task Learning, and that stacking multiple frames of hidden layer activations (stacked bottleneck features) also leads to improvements. Experimental results confirmed the effectiveness of the proposed methods, and in listening tests we find that stacked bottleneck features in particular offer a significant improvement over both a baseline DNN and a benchmark HMM system.


title: An artificial intelligence tool for heterogeneous team formation in the classroom

abstract: Abstract Nowadays, there is increasing interest in the development of teamwork skills in the educational context. This growing interest is motivated by its pedagogical effectiveness and the fact that, in labour contexts, enterprises organise their employees in teams to carry out complex projects. Despite its crucial importance in the classroom and industry, there is a lack of support for the team formation process. Not only do many factors influence team performance, but the problem becomes exponentially costly if teams are to be optimised. In this article, we propose a tool whose aim it is to cover such a gap. It combines artificial intelligence techniques such as coalition structure generation, Bayesian learning, and Belbin’s role theory to facilitate the generation of working groups in an educational context. This tool improves current state of the art proposals in three ways: i) it takes into account the feedback of other teammates in order to establish the most predominant role of a student instead of self-perception questionnaires; ii) it handles uncertainty with regard to each student’s predominant team role; iii) it is iterative since it considers information from several interactions in order to improve the estimation of role assignments. We tested the performance of the proposed tool in an experiment involving students that took part in three different team activities. The experiments suggest that the proposed tool is able to improve different teamwork aspects such as team dynamics and student satisfaction.


title: Scalable Discovery of Time-Series Shapelets

abstract: Time-series classification is an important problem for the data mining community due to the wide range of application domains involving time-series data. A recent paradigm, called shapelets, represents patterns that are highly predictive for the target variable. Shapelets are discovered by measuring the prediction accuracy of a set of potential (shapelet) candidates. The candidates typically consist of all the segments of a dataset, therefore, the discovery of shapelets is computationally expensive. This paper proposes a novel method that avoids measuring the prediction accuracy of similar candidates in Euclidean distance space, through an online clustering pruning technique. In addition, our algorithm incorporates a supervised shapelet selection that filters out only those candidates that improve classification accuracy. Empirical evidence on 45 datasets from the UCR collection demonstrate that our method is 3-4 orders of magnitudes faster than the fastest existing shapelet-discovery method, while providing better prediction accuracy.


title: High-resolution LIDAR-based depth mapping using bilateral filter

abstract: High resolution depth-maps, obtained by upsampling sparse range data from a 3D-LIDAR, find applications in many fields ranging from sensory perception to semantic segmentation and object detection. Upsampling is often based on combining data from a monocular camera to compensate the low-resolution of a LIDAR. This paper, on the other hand, introduces a novel framework to obtain dense depth-map solely from a single LIDAR point cloud; which is a research direction that has been barely explored. The formulation behind the proposed depth-mapping process relies on local spatial interpolation, using sliding-window (mask) technique, and on the Bilateral Filter (BF) where the variable of interest, the distance from the sensor, is considered in the interpolation problem. In particular, the BF is conveniently modified to perform depth-map upsampling such that the edges (foreground-background discontinuities) are better preserved by means of a proposed method which influences the range-based weighting term. Other methods for spatial upsampling are discussed, evaluated and compared in terms of different error measures. This paper also researches the role of the mask's size in the performance of the implemented methods. Quantitative and qualitative results from experiments on the KITTI Database, using LIDAR point clouds only, show very satisfactory performance of the approach introduced in this work.


title: Vehicle Tracking in Wide Area Motion Imagery via Stochastic Progressive Association Across Multiple Frames (SPAAM)

abstract: Vehicle tracking in Wide Area Motion Imagery (WAMI) relies on associating vehicle detections across multiple WAMI frames to form tracks corresponding to individual vehicles. The temporal window length, i.e., the number $M$ of sequential frames, over which associations are collectively estimated poses a trade-off between accuracy and computational complexity. A larger $M$ improves performance because the increased temporal context enables the use of motion models and allows occlusions and spurious detections to be handled better. The number of total hypotheses tracks, on the other hand, grows exponentially with increasing $M$, making larger values of $M$ computationally challenging to tackle. In this paper, we introduce SPAAM an iterative approach that progressively grows $M$ with each iteration to improve estimated tracks by exploiting the enlarged temporal context while keeping computation manageable through two novel approaches for pruning association hypotheses. First, guided by a road network, accurately co-registered to the WAMI frames, we disregard unlikely associations that do not agree with the road network. Second, as $M$ is progressively enlarged at each iteration, the related increase in association hypotheses is limited by revisiting only the subset of association possibilities rendered open by stochastically determined dis-associations for the previous iteration. The stochastic dis-association at each iteration maintains each estimated association according to an estimated probability for confidence, obtained via a probabilistic model. Associations at each iteration are then estimated globally over the $M$ frames by (approximately) solving a binary integer programming problem for selecting a set of compatible tracks. Vehicle tracking results obtained over test WAMI datasets indicate that our proposed approach provides significant performance improvements over 3 alternatives.


title: SAMR: A Self-adaptive MapReduce Scheduling Algorithm in Heterogeneous Environment

abstract: Hadoop is seriously limited by its MapReduce scheduler which does not scale well in heterogeneous environment. Heterogenous environment is characterized by various devices which vary greatly with respect to the capacities of computation and communication, architectures, memorizes and power. As an important extension of Hadoop, LATE MapReduce scheduling algorithm takes heterogeneous environment into consideration. However, it falls short of solving the crucial problem – poor performance due to the static manner in which it computes progress of tasks. Consequently, neither Hadoop nor LATE schedulers are desirable in heterogeneous environment. To this end, we propose SAMR: a Self-Adaptive MapReduce scheduling algorithm, which calculates progress of tasks dynamically and adapts to the continuously varying environment automatically. When a job is committed, SAMR splits the job into lots of fine-grained map and reduce tasks, then assigns them to a series of nodes. Meanwhile, it reads historical information which stored on every node and updated after every execution. Then, SAMR adjusts time weight of each stage of map and reduce tasks according to the historical information respectively. Thus, it gets the progress of each task accurately and finds which tasks need backup tasks. What’s more, it identifies slow nodes and classifies them into the sets of slow nodes dynamically. According to the information of these slow nodes, SAMR will not launch backup tasks on them, ensuring the backup tasks will not be slow tasks any more. It gets the final results of the fine-grained tasks when either slow tasks or backup tasks finish first. The proposed algorithm is evaluated by extensive experiments over various heterogeneous environment. Experimental results show that SAMR significantly decreases the time of execution up to 25% compared with Hadoop’s scheduler and up to 14% compared with LATE scheduler.


title: Mandibular Rim Trilogy with Botulinum Toxin Injection: Reduction, Projection, and Lift

abstract: "Onabotulinum toxin A (Botox) revolution" has brought the fundamental change in the facial rejuvenation as well as the concept of microinjection. The aesthetic standard tends to be the "globalization"; however, Asians have different aesthetic cultures and unique facial features compared with Caucasians. A new rejuvenation concept is proposed during our practice; the Asian face should preserve the original facial identity during Botox treatments. The lower face is treated with botulinum toxin to achieve a harmonious facial profile. Twenty young females ranging in age from 30 to 45 years consented and received the three-pronged procedure from March 2014 to April 2015; photography at baseline and follow-up visit were taken and analyzed. After posttreatment for 2 months, significant improvement was observed compared with the baseline. And the reduced masseter prominence and prominent chin were obtained, showing a favorable facial contour and harmonious appearance during the follow-up. The novel three-pronged approach to lower facial rejuvenation was aimed at the Asian characteristic of hypertrophic masseter, chin retrusion, and the facial sagging during the aging process. Botox treatment was a quite effective and safe strategy to improve the appearance and contour of the lower face in Asian patients.


title: Recursive Support Vector Machines for Dimensionality Reduction

abstract: The usual dimensionality reduction technique in supervised learning is mainly based on linear discriminant analysis (LDA), but it suffers from singularity or undersampled problems. On the other hand, a regular support vector machine (SVM) separates the data only in terms of one single direction of maximum margin, and the classification accuracy may be not good enough. In this letter, a recursive SVM (RSVM) is presented, in which several orthogonal directions that best separate the data with the maximum margin are obtained. Theoretical analysis shows that a completely orthogonal basis can be derived in feature subspace spanned by the training samples and the margin is decreasing along the recursive components in linearly separable cases. As a result, a new dimensionality reduction technique based on multilevel maximum margin components and then a classifier with high accuracy are achieved. Experiments in synthetic and several real data sets show that RSVM using multilevel maximum margin features can do efficient dimensionality reduction and outperform regular SVM in binary classification problems.


title: An Ultra-Low-Power Long Range Battery/Passive RFID Tag for UHF and Microwave Bands With a Current Consumption of 700 nA at 1.5 V

abstract: We present for the first time, a fully integrated battery powered RFID integrated circuit (IC) for operation at ultrahigh frequency (UHF) and microwave bands. The battery powered RFID IC can also work as a passive RFID tag without a battery or when the battery has died (i.e., voltage has dropped below 1.3 V); this novel dual passive and battery operation allays one of the major drawbacks of currently available active tags, namely that the tag cannot be used once the battery has died. When powered by a battery, the current consumption is 700 nA at 1.5 V (400 nA if internal signals are not brought out on test pads). This ultra-low-power consumption permits the use of a very small capacity battery of 100 mA-hr for lifetimes exceeding ten years; as a result a battery tag that is very close to a passive tag both in form factor and cost is made possible. The chip is built on a 1-mum digital CMOS process with dual poly layers, EEPROM and Schottky diodes. The RF threshold power at 2.45 GHz is -19 dBm which is the lowest ever reported threshold power for RFID tags and has a range exceeding 3.5 m under FCC unlicensed operation at the 2.4-GHz microwave band. The low threshold is achieved with architectural choices and low-power circuit design techniques. At 915 MHz, based on the experimentally measured tag impedance (92-j837) and the threshold spec of the tag (200 mV), the theoretical minimum range is 24 m. The tag initially is in a "low-power" mode to conserve power and when issued the appropriate command, it operates in "full-power" mode. The chip has on-chip voltage regulators, clock and data recovery circuits, EEPROM and a digital state machine that implements the ISO 18000-4 B protocol in the "full-power" mode. We provide detailed explanation of the clock recovery circuits and the implementation of the binary sort algorithm, which includes a pseudorandom number generator. Other than the antenna board and a battery, no external components are used.


title: OpenStack Cloud Security Vulnerabilities from Inside and Outside

abstract: As usage of cloud computing increases, customers are mainly concerned about choosing cloud infrastructure with sufficient security. Concerns are greater in the multitenant environment on a public cloud. This paper addresses the security assessment of OpenStack open source cloud solution and virtual machine instances with different operating systems hosted in the cloud. The methodology and realized experiments target vulnerabilities from both inside and outside the cloud. We tested four different platforms and analyzed the security assessment. The main conclusions of the realized experiments show that multi-tenant environment raises new security challenges, there are more vulnerabilities from inside than outside and that Linux based Ubuntu, CentOS and Fedora are less vulnerable than Windows. We discuss details about these vulnerabilities and show how they can be solved by appropriate patches and other solutions. Keywords-Cloud Computing; Security Assessment; Virtualization.


Age-Dependent Changes in the Propofol-Induced Electroencephalogram in Children With Autism Spectrum Disorder


title: The dark side of information technology use

abstract: A large body of research has considered the positive aspects of information technology (IT) use. However, emerging research and practice commentary is beginning to focus on complex and often alarming ways in which use of IT affects organisational and social life. This Special Issue focuses on these ‘dark’ effects of IT use.


title: Google Scholar, Web of Science, and Scopus: a systematic comparison of citations in 252 subject categories

abstract: Despite citation counts from Google Scholar (GS), Web of Science (WoS), and Scopus being widely consulted by researchers and sometimes used in research evaluations, there is no recent or systematic evidence about the differences between them. In response, this paper investigates 2,448,055 citations to 2,299 English-language highly-cited documents from 252 GS subject categories published in 2006, comparing GS, the WoS Core Collection, and Scopus. GS consistently found the largest percentage of citations across all areas (93%-96%), far ahead of Scopus (35%-77%) and WoS (27%-73%). GS found nearly all the WoS (95%) and Scopus (92%) citations. Most citations found only by GS were from non-journal sources (48%-65%), including theses, books, conference papers, and unpublished materials. Many were non-English (19%-38%), and they tended to be much less cited than citing sources that were also in Scopus or WoS. Despite the many unique GS citing sources, Spearman correlations between citation counts in GS and WoS or Scopus are high (0.78-0.99). They are lower in the Humanities, and lower between GS and WoS than between GS and Scopus. The results suggest that in all areas GS citation data is essentially a superset of WoS and Scopus, with substantial extra coverage.


title: Anatomy of High-Performance Deep Learning Convolutions on SIMD Architectures

abstract: Convolution layers are prevalent in many classes of deep neural networks, including Convolutional Neural Networks (CNNs) which provide state-of-the-art results for tasks like image recognition, neural machine translation and speech recognition. The computationally expensive nature of a convolution operation has led to the proliferation of implementations including matrix-matrix multiplication formulation, and direct convolution primarily targeting GPUs. In this paper, we introduce direct convolution kernels for x86 architectures, in particular for Xeon and XeonPhi systems, which are implemented via a dynamic compilation approach. Our JIT-based implementation shows close to theoretical peak performance, depending on the setting and the CPU architecture at hand. We additionally demonstrate how these JIT-optimized kernels can be integrated into a lightweight multi-node graph execution model. This illustrates that single- and multi-node runs yield high efficiencies and high image-throughputs when executing state-of-the-art image recognition tasks on CPUs.


title: Estimating fractal dimension

abstract: Fractals arise from a variety of sources and have been observed in nature and on computer screens. One of the exceptional characteristics of fractals is that they can be described by a noninteger dimension. The geometry of fractals and the mathematics of fractal dimension have provided useful tools for a variety of scientific disciplines, among which is chaos. Chaotic dynamical systems exhibit trajectories in their phase space that converge to a strange attractor. The fractal dimension of this attractor counts the effective number of degrees of freedom in the dynamical system and thus quantifies its complexity. In recent years, numerical methods have been developed for estimating the dimension directly from the observed behavior of the physical system. The purpose of this paper is to survey briefly the kinds of fractals that appear in scientific research, to discuss the application of fractals to nonlinear dynamical systems, and finally to review more comprehensively the state of the art in numerical methods for estimating the fractal dimension of a strange attractor.


title: Deep Learning for Image-to-Text Generation: A Technical Overview

abstract: Generating a natural language description from an image is an emerging interdisciplinary problem at the intersection of computer vision, natural language processing, and artificial intelligence (AI). This task, often referred to as image or visual captioning, forms the technical foundation of many important applications, such as semantic visual search, visual intelligence in chatting robots, photo and video sharing in social media, and aid for visually impaired people to perceive surrounding visual content. Thanks to the recent advances in deep learning, the AI research community has witnessed tremendous progress in visual captioning in recent years. In this article, we will first summarize this exciting emerging visual captioning area. We will then analyze the key development and the major progress the community has made, their impact in both research and industry deployment, and what lies ahead in future breakthroughs.


title: Aspen: a domain specific language for performance modeling

abstract: We present a new approach to analytical performance modeling using Aspen, a domain specific langauge. Aspen (Abstract Scalable Performance Engineering Notation) fills an important gap in existing performance modeling techniques and is designed to enable rapid exploration of new algorithms and architectures. It includes a formal specification of an application's performance behavior and an abstract machine model. We provide an overview of Aspen's features and demonstrate how it can be used to express a performance model for a three dimensional Fast Fourier Transform. We then demonstrate the composability and modularity of Aspen by importing and reusing the FFT model in a molecular dynamics model. We have also created a number of tools that allow scientists to balance application and system factors quickly and accurately.


title: Hypervisors vs. Lightweight Virtualization: A Performance Comparison

abstract: Virtualization of operating systems provides a common way to run different services in the cloud. Recently, the lightweight virtualization technologies claim to offer superior performance. In this paper, we present a detailed performance comparison of traditional hypervisor based virtualization and new lightweight solutions. In our measurements, we use several benchmarks tools in order to understand the strengths, weaknesses, and anomalies introduced by these different platforms in terms of processing, storage, memory and network. Our results show that containers achieve generally better performance when compared with traditional virtual machines and other recent solutions. Albeit containers offer clearly more dense deployment of virtual machines, the performance difference with other technologies is in many cases relatively small.


Quality Metrics for Information Visualization


title: High-Level Programming Abstractions for Distributed Graph Processing

abstract: Efficient processing of large-scale graphs in distributed environments has been an increasingly popular topic of research in recent years. Inter-connected data that can be modeled as graphs appear in application domains such as machine learning, recommendation, web search, and social network analysis. Writing distributed graph applications is inherently hard and requires programming models that can cover a diverse set of problems, including iterative refinement algorithms, graph transformations, graph aggregations, pattern matching, ego-network analysis, and graph traversals. Several high-level programming abstractions have been proposed and adopted by distributed graph processing systems and big data platforms. Even though significant work has been done to experimentally compare distributed graph processing frameworks, no qualitative study and comparison of graph programming abstractions has been conducted yet. In this survey, we review and analyze the most prevalent high-level programming models for distributed graph processing, in terms of their semantics and applicability. We review 34 distributed graph processing systems with respect to the graph processing models they implement and we survey applications that appear in recent distributed graph systems papers. Finally, we discuss trends and open research questions in the area of distributed graph processing.


Multicommodity flows in graphs II


title: Shuffling Cards for Blackjack, Bridge, and Other Card Games

abstract: This paper is about the following question: How many riffle shuffles mix a deck of card for games such as blackjack and bridge? An object that comes up in answering this question is the descent polynomial associated with pairs of decks, where the decks are allowed to have repeated cards. We prove that the problem of computing the descent polynomial given a pair of decks is #P-complete. We also prove that the coefficients of these polynomials can be approximated using the bell curve. However, as must be expected in view of the #P-completeness result, approximations using the bell curve are not good enough to answer our question. Some of our answers to the main question are supported by theorems, and others are based on experiments supported by heuristic arguments. In the introduction, we carefully discuss the validity of our answers.


title: Application of quick response (QR) codes in mobile tagging system for retrieving information about genetically modified food

abstract: The purpose of this paper is to introduce an integrated mobile tagging system, which can be used by consumers to retrieve product information about Genetically Modified Food (GMF) products in the market. In the proposed mobile tagging system, 2 dimensional Quick Response (QR) codes are adopted as the tag for identification purpose. Consumers can use the camera on their mobile phone to capture the image of the QR codes and send to the server for decoding. Subsequently, the server sends back the details of the GMF product in the form of Multimedia Message Service (MMS). This paper reviews and discusses various available tagging techniques, and thus justifies the appropriateness of QR codes to be used in the proposed system. Additionally, the paper presents the overall system architecture and system design of the proposed mobile tagging system.


title: CPOE workarounds, boundary objects, and assemblages

abstract: We conducted an ethnographically based study at a large teaching hospital to examine clinician workarounds engendered by the adoption of a Computerized Prescribe Order Entry (CPOE) system. Specifically, we investigated how adoption of computerized systems may alter medical practice, order management in particular, as manifested through the working-around behavior developed by doctors and nurses to accommodate the changes in their day-to-day work environment. In this paper, we focus on clinicians' workarounds, including those workarounds that gradually disappeared and those that have become routinized. Further, we extend the CSCW concept of boundary object (to "assemblage") in order to understand the workarounds created with CPOE system use and the changing nature of clinical practices that are increasingly computerized.


title: Design of an endoluminal NOTES robotic system

abstract: Natural orifice transluminal endoscopic surgery, or NOTES, allows for exceedingly minimally invasive surgery but has high requirements for the dexterity and force capabilities of the tools. An overview of the ViaCath System is presented. This system is a first generation teleoperated robot for endoluminal surgery and consists of a master console with haptic interfaces, slave drive mechanisms, and 6 degree-of-freedom, long-shafted flexible instruments that run alongside a standard gastroscope or colonoscope. The system was validated through animal studies. It was discovered that the devices were difficult to introduce into the GI tract and manipulation forces were insufficient. The design of a second generation system is outlined with improvements to the instrument articulation section and a steerable overtube. Results of basic evaluation tests performed on the tools are also presented.


title: Distributed Resource Allocation for Relay-Aided Device-to-Device Communication: A Message Passing Approach

abstract: Device-to-device (D2D) communication underlaying cellular wireless networks is a promising concept to improve user experience and resource utilization by allowing direct transmission between two cellular devices. In this paper, performance of network-assisted D2D communication is investigated where D2D traffic is carried through relay nodes. Considering a multi-user and multi-relay network, we propose a distributed solution for resource allocation with a view to maximizing network sum-rate. An optimization problem is formulated for radio resource allocation at the relays. The objective is to maximize end-to-end rate as well as satisfy the data rate requirements for cellular and D2D user equipments under total power constraint. Due to intractability of the resource allocation problem, we propose a solution approach using message passing technique where each user equipment sends and receives information messages to/from the relay node in an iterative manner with the goal of achieving an optimal allocation. Therefore, the computational effort is distributed among all the user equipments and the corresponding relay node. The convergence and optimality of the proposed scheme are proved and a possible distributed implementation of the scheme in practical LTE-Advanced networks is outlined. The numerical results show that there is a distance threshold beyond which relay-aided D2D communication significantly improves network performance with a small increase in end-to-end delay when compared to direct communication between D2D peers.


title: Multi-class AdaBoost

abstract: Boosting has been a very successful technique for solving the two-class classification problem. In going from two-class to multi-class classification, most algorithms have been restricted to reducing the multi-class classification problem to multiple two-class problems. In this paper, we propose a new algorithm that naturally extends the original AdaBoost algorithm to the multiclass case without reducing it to multiple two-class problems. Similar to AdaBoost in the twoclass case, this new algorithm combines weak classifiers and only requires the performance of each weak classifier be better than random guessing (rather than 1/2). We further provide a statistical justification for the new algorithm using a novel multi-class exponential loss function and forward stage-wise additive modeling. As shown in the paper, the new algorithm is extremely easy to implement and is highly competitive with the best currently available multi-class classification methods.


title: Wireless requirements and challenges in Industry 4.0

abstract: The next generation of industrial advancement which is referred as Industry 4.0 aims to inter-connect and computerize the traditional industrys such as manufacturing. The objective in Industry 4.0 is to make the factories smart enough in terms of improved adaptability, resource efficiency as well as the improved integration of supply and demand processes between the factories. Wireless communication will play a key role in enabling the Industry 4.0 systems and technologies. In this paper we focus the discussion on some of the key wireless communication challenges that will need to be met for the Industry 4.0 era. We look at how the 5th generation of communication standard may address these requirements. For machine to machine communication the three main design criterions that can be considered are latency, longevity and the reliability of communication. We take an example of WiFi communication, and benchmark it against the requirements, so as to emphasize the improvements required in wireless protocols.


title: Reuse in the wild: an empirical and ethnographic study of organizational content reuse

abstract: We present a large-scale study of content reuse networks in a large and highly hierarchical organization. In our study, we combine analysis of a collection of presentations produced by employees with interviews conducted throughout the organization and a survey to study presentation content reuse. Study results show a variety of information needs and behaviors related to content reuse as well as a need for a personalized and socially-integrated networking tool for enabling easy access to previously generated presentation material. In this paper we describe our findings and outline a set of requirements for an effective content reuse facility.