Query: Efficient Large-Scale Graph Processing on Hybrid CPU and GPU Systems

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Scalable Graph Exploration on Multicore Processors <=SEP=> Many important problems in computational sciences, social network analysis, security, and business analytics, are data-intensive and lend themselves to graph-theoretical analyses. In this paper we investigate the challenges involved in exploring very large graphs by designing a breadth-first search (BFS) algorithm for advanced multi-core processors that are likely to become the building blocks of future exascale systems. Our new methodology for large-scale graph analytics combines a highlevel algorithmic design that captures the machine-independent aspects, to guarantee portability with performance to future processors, with an implementation that embeds processorspecific optimizations. We present an experimental study that uses state-of-the-art Intel Nehalem EP and EX processors and up to 64 threads in a single system. Our performance on several benchmark problems representative of the power-law graphs found in real-world problems reaches processing rates that are competitive with supercomputing results in the recent literature. In the experimental evaluation we prove that our graph exploration algorithm running on a 4-socket Nehalem EX is (1) 2.4 times faster than a Cray XMT with 128 processors when exploring a random graph with 64 million vertices and 512 millions edges, (2) capable of processing 550 million edges per second with an R-MAT graph with 200 million vertices and 1 billion edges, comparable to the performance of a similar graph on a Cray MTA-2 with 40 processors and (3) 5 times faster than 256 BlueGene/L processors on a graph with average degree 50.

Thirteen Ways to Look at the Correlation Coefficient <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://dv1litvip.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Unicorn: A System for Searching the Social Graph <=SEP=> Unicorn is an online, in-memory social graph-aware indexing system designed to search trillions of edges between tens of billions of users and entities on thousands of commodity servers. Unicorn is based on standard concepts in information retrieval, but it includes features to promote results with good social proximity. It also supports queries that require multiple round-trips to leaves in order to retrieve objects that are more than one edge away from source nodes. Unicorn is designed to answer billions of queries per day at latencies in the hundreds of milliseconds, and it serves as an infrastructural building block for Facebook’s Graph Search product. In this paper, we describe the data model and query language supported by Unicorn. We also describe its evolution as it became the primary backend for Facebook’s search offerings.

The anatomy of a large-scale social search engine <=SEP=> We present Aardvark, a social search engine. With Aardvark, users ask a question, either by instant message, email, web input, text message, or voice. Aardvark then routes the question to the person in the user's extended social network most likely to be able to answer that question. As compared to a traditional web search engine, where the challenge lies in finding the right document to satisfy a user's information need, the challenge in a social search engine like Aardvark lies in finding the right person to satisfy a user's information need. Further, while trust in a traditional search engine is based on authority, in a social search engine like Aardvark, trust is based on intimacy. We describe how these considerations inform the architecture, algorithms, and user interface of Aardvark, and how they are reflected in the behavior of Aardvark users.

Bidirectional Expansion For Keyword Search on Graph Databases <=SEP=> Relational, XML and HTML data can be represented as graphs with entities as nodes and relationships as edges. Text is associated with nodes and possibly edges. Keyword search on such graphs has received much attention lately. A central problem in this scenario is to efficiently extract from the data graph a small number of the “best” answer trees. A Backward Expanding search, starting at nodes matching keywords and working up toward confluent roots, is commonly used for predominantly text-driven queries. But it can perform poorly if some keywords match many nodes, or some node has very large degree. In this paper we propose a new search algorithm, Bidirectional Search, which improves on Backward Expanding search by allowing forward search from potential roots towards leaves. To exploit this flexibility, we devise a novel search frontier prioritization technique based on spreading activation. We present a performance study on real data, establishing that Bidirectional Search significantly outperforms Backward Expanding search.

A comparative study of game mechanics and control laws for an adaptive physiological game <=SEP=> We present an adaptive biofeedback game that aims to maintain the player’s arousal by modifying game difficulty in response to the player’s physiological state, as measured with wearable sensors. Our approach models the interaction between human physiology and game difficulty during gameplay as a control problem, where game difficulty is the system input and player arousal its output. We validate the approach on a car-racing game with real-time adaptive game mechanics. Specifically, we use (1) car speed, road visibility, and steering jitter as three mechanisms to manipulate game difficulty, (2) electrodermal activity as physiological correlate of arousal, and (3) two types of control law: proportional (P) control, and proportional-integral-derivative (PID) control. We also propose quantitative measures to characterize the effectiveness of these game adaptations and controllers in manipulating the player’s arousal. Experimental trials with 25 subjects in both open-loop (no feedback) and closed-loop (negative feedback) conditions show statistically significant differences in effectiveness among the three game mechanics and also between the two control laws. Specifically, manipulating car speed provides higher control of arousal levels than changing road visibility or vehicle steering. Our results also confirm that PID control leads to lower error and reduced oscillations in the closed-loop response compared to proportional-only control. Finally, we discuss the theoretical and practical implications of our approach.

A summary-statistic representation in peripheral vision explains visual crowding. <=SEP=> Peripheral vision provides a less faithful representation of the visual input than foveal vision. Nonetheless, we can gain a lot of information about the world from our peripheral vision, for example in order to plan eye movements. The phenomenon of crowding shows that the reduction of information available in the periphery is not merely the result of reduced resolution. Crowding refers to visual phenomena in which identification of a target stimulus is significantly impaired by the presence of nearby stimuli, or flankers. What information is available in the periphery? We propose that the visual system locally represents peripheral stimuli by the joint statistics of responses of cells sensitive to different position, phase, orientation, and scale. This "textural" representation by summary statistics predicts the subjective "jumble" of features often associated with crowding. We show that the difficulty of performing an identification task within a single pooling region using this representation of the stimuli is correlated with peripheral identification performance under conditions of crowding. Furthermore, for a simple stimulus with no flankers, this representation can be adequate to specify the stimulus with some position invariance. This provides evidence that a unified neuronal mechanism may underlie peripheral vision, ordinary pattern recognition in central vision, and texture perception. A key component of our methodology involves creating visualizations of the information available in the summary statistics of a stimulus. We call these visualizations "mongrels" and show that they are highly useful in examining how the early visual system represents the visual input. Mongrels enable one to study the "equivalence classes" of our model, i.e., the sets of stimuli that map to the same representation according to the model.

Enhancing SDN security for IoT-related deployments through blockchain <=SEP=> The majority of business activity of our integrated and connected world takes place in networks based on cloud computing infrastructure that cross national, geographic and jurisdictional boundaries. Such an efficient entity interconnection is made possible through an emerging networking paradigm, Software Defined Networking (SDN) that intends to vastly simplify policy enforcement and network reconfiguration in a dynamic manner. However, despite the obvious advantages this novel networking paradigm introduces, its increased attack surface compared to traditional networking deployments proved to be a thorny issue that creates skepticism when safety-critical applications are considered. Especially when SDN is used to support Internet-of-Things (IoT)-related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. The overall number of connected nodes makes the efficient monitoring of all entities a real challenge, that must be tackled to prevent system degradation and service outage. This position paper provides an overview of common security issues of SDN when linked to IoT clouds, describes the design principals of the recently introduced Blockchain paradigm and advocates the reasons that render Blockchain as a significant security factor for solutions where SDN and IoT are involved.

Deblurring Face Images with Exemplars <=SEP=> The human face is one of the most interesting subjects involved in numerous applications. Significant progress has been made towards the image deblurring problem, however, existing generic deblurring methods are not able to achieve satisfying results on blurry face images. The success of the state-of-the-art image deblurring methods stems mainly from implicit or explicit restoration of salient edges for kernel estimation. When there is not much texture in the blurry image (e.g., face images), existing methods are less effective as only few edges can be used for kernel estimation. Moreover, recent methods are usually jeopardized by selecting ambiguous edges, which are imaged from the same edge of the object after blur, for kernel estimation due to local edge selection strategies. In this paper, we address these problems of deblurring face images by exploiting facial structures. We propose a maximum a posteriori (MAP) deblurring algorithm based on an exemplar dataset, without using the coarse-to-fine strategy or ad-hoc edge selections. Extensive evaluations against state-of-the-art methods demonstrate the effectiveness of the proposed algorithm for deblurring face images. We also show the extendability of our method to other specific deblurring tasks.

Measuring Negative and Positive Thoughts in Children: An Adaptation of the Children’s Automatic Thoughts Scale (CATS) <=SEP=> The aim of this study is to describe the factor structure and psychometric properties of an extended version of the Children’s Automatic Thoughts Scale (CATS), the CATS-Negative/Positive (CATS-N/P). The CATS was originally designed to assess negative self-statements in children and adolescents. However, positive thoughts also play a major role in childhood disorders such as anxiety and depression. Therefore, positive self-statements were added to the CATS. The CATS-N/P was administered to a community sample of 554 children aged 8–18 years. The results of a confirmatory factor analysis revealed that the positive self-statements formed a separate and psychometrically sound factor. Internal and short-term test–retest reliability was good. Boys reported more hostile and positive thoughts than girls; and younger children reported more negative thoughts concerning physical threat, social threat, and failure than older children. In conclusion, the results of the current study support the use of the CATS-N/P for the measurement of positive and negative thoughts in children. The application of the CATS-N/P can facilitate further research on cognitive factors in different childhood disorders.

MODIFIED MULTILOOK CROSS CORRELATION ( MLCC ) ALGORITHM FOR DOPPLER CENTROID ESTIMATION IN SYNTHETIC APERTURE RADAR SIGNAL PROCESS - <=SEP=> The Multilook Cross Correlation (MLCC) is one of the most reliable algorithms used for Doppler ambiguity number estimation of the Doppler centroid parameter. However, the existing MLCC algorithm is only suitable for low contrast scenes. In high contrast scenes, the estimated result is not reliable, and the error is unacceptable. Besides, the Doppler centroid estimation processing time is long and can only be used in offline processing. In this paper, we introduce a modified MLCC algorithm that has better sensitivity which is suitable not only for low contrast scenes, but also for high contrast scenes. In addition, the modified MLCC algorithm can be implemented on parallel signal processing units for better time efficiency. Experiments with RADARSAT-1 data show that the modified algorithm works well in both high and low contrast scenes.

Author ' s personal copy A new hybrid arti fi cial neural networks for rainfall – runoff process modeling <=SEP=> This paper proposes a hybrid intelligent model for runoff prediction. The proposed model is a combination of data preprocessing methods, genetic algorithms and levenberg–marquardt (LM) algorithm for learning feed forward neural networks. Actually it evolves neural network initial weights for tuning with LM algorithm by using genetic algorithm. We also use data pre-processing methods such as data transformation, input variables selection and data clustering for improving the accuracy of the model. The capability of the proposed method is tested by applying it to predict runoff at the Aghchai watershed. The results show that this approach is able to predict runoff more accurately than Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models. & 2013 Elsevier B.V. All rights reserved.

Evaluating the Effect of Total Quality Management Practices on Business Performance : A Study of Manufacturing Firms of Pakistan <=SEP=> The present study has evaluated the effect of TQM practices on business performance of manufacturing firms in Pakistan. A sample of 65 managers working in the quality assurance department was taken from these manufacturing firms and their responses were collected using a structured questionnaire. Descriptive statistics were used to demonstrate the effect of different levels of TQM practices on business performance in terms of financial performance, product quality performance, customer satisfaction. The study had also considered organizational level variables like size of firm, year of existence, top management commitment as moderating variables, the results have shown that TQM was implemented only at the first three levels i.e. quality control ,quality assurance and continuous improvement in the selected firms and that top management commitment is the most important variable effecting on TQM implementation which then directly effect on business performance in these manufacturing firms.

Food Balance Estimation by Using Personal Dietary Tendencies in a Multimedia Food Log <=SEP=> We have investigated the “FoodLog” multimedia food-recording tool, whereby users upload photographs of their meals and a food diary is constructed using image-processing functions such as food-image detection and food-balance estimation. In this paper, following a brief introduction to FoodLog, we propose a Bayesian framework that makes use of personal dietary tendencies to improve both food-image detection and food-balance estimation. The Bayesian framework facilitates incremental learning. It incorporates three personal dietary tendencies that influence food analysis: likelihood, prior distribution, and mealtime category. In the evaluation of the proposed method using images uploaded to FoodLog, both food-image detection and food-balance estimation are improved. In particular, in the food-balance estimation, the mean absolute error is significantly reduced from 0.69 servings to 0.28 servings on average for two persons using more than 200 personal images, and 0.59 servings to 0.48 servings on average for four persons using 100 personal images. Among the works analyzing food images, this is the first to make use of statistical personal bias to improve the performance of the analysis.

IoT in Agriculture: Designing a Europe-Wide Large-Scale Pilot <=SEP=> The technologies associated with the Internet of Things have great potential for application in the domain of food and agriculture, especially in view of the societal and environmental challenges faced by this sector. From farm to fork, IoT technologies could transform the sector, contributing to food safety, and the reduction of agricultural inputs and food waste. A major step toward greater uptake of these technologies will be the execution of IoT-based large-scale pilots (LSPs) in the entire supply chain. This article outlines the challenges and constraints that an LSP deployment of IoT in this domain must consider. Sectoral and technological challenges are described in order to identify a set of technological and agrifood requirements. An architecture based on a system of systems approach is briefly presented, the importance of addressing the interoperability challenges faced by this sector is highlighted, and we elaborate on requirements for new business models, security, privacy, and data governance. A description of the technologies and solutions involved in designing pilots for four agrifood domains (dairy, fruit, arable, meat and vegetable supply chain) is eventually provided. In conclusion, it is noted that for IoT to be successful in this domain, a significant change of culture is needed.

Tapping the power of text mining <=SEP=> Sifting through vast collections of unstructured or semistructured data beyond the reach of data mining tools, text mining tracks information sources, links isolated concepts in distant documents, maps relationships between activities, and helps answer questions.

Designed Interdependence, Growing Social Capital and Knowledge Sharing in IS Project Teams <=SEP=> This study brings in the task interdependence as the antecedent for growing team social capital and increasing knowledge sharing among team members in IS project context in which heterogeneous members need to interact very closely. Data collected from 203 IS projects largely confirm the proposed research model. Between the task interdependence and team performance, the team social capital as well as the knowledge sharing among members fully mediates the relationship. Interestingly, direct path from task interdependence onto performance is found to be insignificant, rebutting the suggestions of the work design theory which says well designed interdependence directly increase performance in heterogeneous teams. Findings of our study signify the criticality of the mediating roles of social capital and knowledge sharing. Additionally, it is also confirmed that the social capital grows with time. Also, longer term projects revealed a bit different mechanism than shorter ones. Implications are discussed with limitations and further studies.

VOS-GAN: Adversarial Learning of Visual-Temporal Dynamics for Unsupervised Dense Prediction in Videos <=SEP=> Recent GAN-based video generation approaches model videos as the combination of a time-independent scene component and a time-varying motion component, thus factorizing the generation problem into generating background and foreground separately. One of the main limitations of current approaches is that both factors are learned by mapping one source latent space to videos, which complicates the generation task as a single data point must be informative of both background and foreground content. In this paper we propose a GAN framework for video generation that, instead, employs two latent spaces in order to structure the generative process in a more natural way: 1) a latent space to generate the static visual content of a scene (background), which remains the same for the whole video, and 2) a latent space where motion is encoded as a trajectory between sampled points and whose dynamics are modeled through an RNN encoder (jointly trained with the generator and the discriminator) and then mapped by the generator to visual objects’ motion. Additionally, we extend current video discrimination approaches by incorporating in the learning procedure motion estimation and, leveraging the peculiarity of the generation process, unsupervised pixel-wise dense predictions. Extensive performance evaluation showed that our approach is able to a) synthesize more realistic videos than state-of-the-art methods, b) learn effectively both local and global video dynamics, as demonstrated by the results achieved on a video action recognition task over the UCF-101 dataset, and c) accurately perform unsupervised video object segmentation on standard video benchmarks, such as DAVIS, SegTrack and F4K-Fish.

BAG: Managing GPU as Buffer Cache in Operating Systems <=SEP=> This paper presents the design, implementation and evaluation of BAG, a system that manages GPU as the buffer cache in operating systems. Unlike previous uses of GPUs, which have focused on the computational capabilities of GPUs, BAG is designed to explore a new dimension in managing GPUs in heterogeneous systems where the GPU memory is an exploitable but always ignored resource. With the carefully designed data structures and algorithms, such as concurrent hashtable, log-structured data store for the management of GPU memory, and highly-parallel GPU kernels for garbage collection, BAG achieves good performance under various workloads. In addition, leveraging the existing abstraction of the operating system not only makes the implementation of BAG non-intrusive, but also facilitates the system deployment.

Packaging a $W$ -Band Integrated Module With an Optimized Flip-Chip Interconnect on an Organic Substrate <=SEP=> This paper, for the first time, presents successful integration of a W-band antenna with an organically flip-chip packaged silicon-germanium (SiGe) low-noise amplifier (LNA). The successful integration requires an optimized flip-chip interconnect. The interconnect performance was optimized by modeling and characterizing the flip-chip transition on a low-loss liquid crystal polymer organic substrate. When the loss of coplanar waveguide (CPW) lines is included, an insertion loss of 0.6 dB per flip-chip-interconnect is measured. If the loss of CPW lines is de-embedded, 0.25 dB of insertion loss is observed. This kind of low-loss flip-chip interconnect is essential for good performance of W-band modules. The module, which we present in this paper, consists of an end-fire Yagi-Uda antenna integrated with an SiGe BiCMOS LNA. The module is 3 mm × 1.9 mm and consumes only 19.2 mW of dc power. We present passive and active E- and H-plane radiation pattern measurements at 87, 90, and 94 GHz. Passive and active antennas both showed a 10-dB bandwidth of 10 GHz. The peak gain of passive and active antennas was 5.2 dBi at 90 GHz and 21.2 dBi at 93 GHz, respectively. The measurements match well with the simulated results.

Human Activity Recognition from Sensor Data <=SEP=> Human Activity Recognition (HAR) refers to the problem of automatically identifying human activities by learning from data collected either from the individual or from the environment surrounding that individual. The form of the data might be inertial measurements, physiological signals from devices like wearable sensors or those such as image, audio and video data collected from environmental devices/sensors. HAR has been a well studied problem for its potential capability to mine human behavioral patterns and for monitoring human activity for security and health purposes.

End-to-End Joint Semantic Segmentation of Actors and Actions in Video <=SEP=> Traditional video understanding tasks include human action recognition and actor/object semantic segmentation. However, the combined task of providing semantic segmentation for different actor classes simultaneously with their action class remains a challenging but necessary task for many applications. In this work, we propose a new end-to-end architecture for tackling this task in videos. Our model effectively leverages multiple input modalities, contextual information, and multitask learning in the video to directly output semantic segmentations in a single unified framework. We train and benchmark our model on the Actor-Action Dataset (A2D) for joint actor-action semantic segmentation, and demonstrate state-of-the-art performance for both segmentation and detection. We also perform experiments verifying our approach improves performance for zero-shot recognition, indicating generalizability of our jointly learned feature space.

Comparison of Longley-Rice, ITM and ITWOM propagation models for DTV and FM broadcasting <=SEP=> With the rapid deployment of digital TV, there is an increasing need for accurate point-to-area prediction tools. There is a great deal of propagation models for coverage prediction of DTV. Some of them are pure empirical models, and others are mixed, empirical-analytical models, based on measurement campaigns and electromagnetic theory. The aim of this paper is to compare accurate measurements taken by a Rohde & Schwarz FSH-3 portable spectrum analyzer and precision antennas (biconical and log-periodic), with simulation results derived from coverage prediction models, like the NTIA-ITS Longley-Rice model, the ITM (Irregular Terrain Model) using the 3-arc-second SRTM (Satellite Radar Topography Mission) data that is available freely, and the newer ITWOM (Irregular Terrain with Obstructions Model) model which combines equations from ITU-R P.1546 model with Beer's law and Snell's law. Furthermore, measurements for analog FM broadcasting are compared to predictions from the above mentioned models.

iDocChip: A Configurable Hardware Architecture for Historical Document Image Processing: Percentile Based Binarization <=SEP=> End-to-end Optical Character Recognition (OCR) systems are heavily used to convert document images into machine-readable text. Commercial and open-source OCR systems (like Abbyy, OCRopus, Tesseract etc.) have traditionally been optimized for contemporary documents like books, letters, memos, and other end-user documents. However, these systems are difficult to use equally well for digitizing historical document images, which contain degradations like non-uniform shading, bleed-through, and irregular layout; such degradations usually do not exist in contemporary document images.
 The open-source anyOCR is an end-to-end OCR pipeline, which contains state-of-the-art techniques that are required for digitizing degraded historical archives with high accuracy. However, high accuracy comes at a cost of high computational complexity that results in 1) long runtime that limits digitization of big collection of historical archives and 2) high energy consumption that is the most critical limiting factor for portable devices with constrained energy budget. Therefore, we are targeting energy efficient and high throughput acceleration of the anyOCR pipeline. Generalpurpose computing platforms fail to meet these requirements that makes custom hardware design mandatory. In this paper, we are presenting a new concept named iDocChip. It is a portable hybrid hardware-software FPGA-based accelerator that is characterized by low footprint meaning small size, high power efficiency that will allow using it in portable devices, and high throughput that will make it possible to process big collection of historical archives in real time without effecting the accuracy.
 In this paper, we focus on binarization, which is the second most critical step in the anyOCR pipeline after text-line recognizer that we have already presented in our previous publication [21]. The anyOCR system makes use of a Percentile Based Binarization method that is suitable for overcoming degradations like non-uniform shading and bleed-through. To the best of our knowledge, we propose the first hardware architecture of the PBB technique. Based on the new architecture, we present a hybrid hardware-software FPGA-based accelerator that outperforms the existing anyOCR software implementation running on i7-4790T in terms of runtime by factor of 21, while achieving energy efficiency of 10 Images/J that is higher than that achieved by low power embedded processors with negligible loss of recognition accuracy.

Architecture Technical Debt: Understanding Causes and a Qualitative Model <=SEP=> A known problem in large software companies is to balance the prioritization of short-term with long-term responsiveness. Specifically, architecture violations (Architecture Technical Debt) taken to deliver fast might hinder future feature development, which would hinder agility. We conducted a multiple-case embedded case study in 7 sites at 5 large companies in order to shed light on the current causes for the accumulation of Architectural Technical Debt that causes effort. We provide a taxonomy of the factors and their influence in the accumulation of debt, and we provide a qualitative model of how the debt is accumulated and recovered over time.

E-S-QUAL A Multiple-Item Scale for Assessing Electronic Service Quality <=SEP=> Using the means-end framework as a theoretical foundation, this article conceptualizes, constructs, refines, and tests a multiple-item scale (E-S-QUAL) for measuring the service quality delivered by Web sites on which customers shop online. Two stages of empirical data collection revealed that two different scales were necessary for capturing electronic service quality. The basic E-S-QUAL scale developed in the research is a 22-item scale of four dimensions: efficiency, fulfillment, system availability, and privacy. The second scale, E-RecS-QUAL, is salient only to customers who had nonroutine encounters with the sites and contains 11 items in three dimensions: responsiveness, compensation, and contact. Both scales demonstrate good psychometric properties based on findings from a variety of reliability and validity tests and build on the research already conducted on the topic. Directions for further research on electronic service quality are offered. Managerial implications stemming from the empirical findings about E-S-QUAL are also discussed.

Visual Analysis of Large Graphs <=SEP=> The analysis of large graphs plays a prominent role in various fields of research and is relevant in many important application areas. Effective visual analysis of graphs requires appropriate visual presentations in combination with respective user interaction facilities and algorithmic graph analysis methods. How to design appropriate graph analysis systems depends on many factors, including the type of graph describing the data, the analytical task at hand, and the applicability of graph analysis methods. The most recent surveys of graph visualization and navigation techniques were presented by Herman et al. [HMM00] and Diaz [DPS02]. The first work surveyed the main techniques for visualization of hierarchies and graphs in general that had been introduced until 2000. The second work concentrated on graph layouts introduced until 2002. Recently, new techniques have been developed covering a broader range of graph types, such as time-varying graphs. Also, in accordance with ever growing amounts of graph-structured data becoming available, the inclusion of algorithmic graph analysis and interaction techniques becomes increasingly important. In this State-of-the-Art Report, we survey available techniques for the visual analysis of large graphs. Our review firstly considers graph visualization techniques according to the type of graphs supported. The visualization techniques form the basis for the presentation of interaction approaches suitable for visual graph exploration. As an important component of visual graph analysis, we discuss various graph algorithmic aspects useful for the different stages of the visual graph analysis process.

Internet Banking Adoption Among Young Intellectuals <=SEP=> The aim of this paper is to study technology acceptance of internet banking among undergraduate students in Malaysia. Thus, the theoretical framework of the paper is based on modified version of Technology Acceptance Model (TAM). This paper develops a technology acceptance model for internet banking, a conceptual framework to explain the factors influencing undergraduate students' acceptance of internet banking. The model employs perceived usefulness (PU), perceived ease of use (PEOU), perceived credibility (PC) and computer self-efficacy (CSE). The first two are two initial constructs for TAM model. The rest are new constructs to be included in the model in order to have an extension of TAM model that better reflects the students’ view. The results suggest that PU, PEOU and PC had a significant relationship with behavioral intention. Further, these measures are good determinant for undergraduate acceptance for internet banking. Results also suggest that PU and PEOU had a significant relationship with CSE. On the contrary, CSE did not associate with PC. Also, PEOU had JIBC December 2007, Vol. 12, No. 3 2 a significant relationship with PU and PC that indicate these scales are related to PEOU in explaining undergraduate preference. The paper is useful in providing the understanding of the TAM among undergraduate from Malaysians’ perspective.

Best Practices for Data Centers : Lessons Learned from Benchmarking 22 Data Centers <=SEP=> Over the past few years, the authors benchmarked 22 data center buildings. From this effort, we have determined that data centers can be over 40 times as energy intensive as conventional office buildings. Studying the more efficient of these facilities enabled us to compile a set of “best-practice” technologies for energy efficiency. These best practices include: improved air management, emphasizing control and isolation of hot and cold air streams; rightsizing central plants and ventilation systems to operate efficiently both at inception and as the data center load increases over time; optimized central chiller plants, designed and controlled to maximize overall cooling plant efficiency, central air-handling units, in lieu of distributed units; “free cooling” from either air-side or water-side economizers; alternative humidity control, including elimination of control conflicts and the use of direct evaporative cooling; improved uninterruptible power supplies; high-efficiency computer power supplies; on-site generation combined with special chillers for cooling using the waste heat; direct liquid cooling of racks or computers; and lowering the standby losses of standby generation systems. Other benchmarking findings include power densities from 5 to nearly 100 Watts per square foot; though lower than originally predicted, these densities are growing. A 5:1 variation in cooling effectiveness index (ratio of cooling power to computer power) was found, as well as large variations in power distribution efficiency and overall center performance (ratio of computer power to total building power). These observed variations indicate the potential of energy savings achievable through the implementation of best practices in the design and operation of data centers. The Data Center Challenge The energy used by a typical rack of state-of-the art servers, drawing 20 kilowatts of power at 10 cents per kWh, uses more that $17,000 per year in electricity. Given that data centers can hold hundreds of such racks, they constitute a very energy-intensive building type. Clearly, efforts to improve energy efficiency in data centers can pay big dividends. But, where to start? “You can’t manage what you don’t measure” is a mantra often recited by corporate leaders (Tschudi et al 2005). Similarly, the high-availability needs of data center facilities can be efficiently met using this philosophy. This paper presents a selected distillation of the extensive benchmarking in 22 data centers which formed the basis for the development of 10 best-practice guides for design and operation that summarized how better energy performance was obtained in these actual facilities (LBNL 2006). With typical annual energy costs per square foot 15 times (and in some cases over 40 times) that of typical office buildings, data centers are an important target for energy savings. They operate continuously, which means that their electricity demand is always contributing to 3-76 © 2006 ACEEE Summer Study on Energy Efficiency in Buildings peak utility system demands, an important fact given that utility pricing increasingly reflects time-dependent tariffs. Energy-efficiency best practices can realize significant energy and peakpower savings while maintaining or improving reliability, and yield other non-energy benefits. Data centers ideally are buildings built primarily for computer equipment: to house it and provide power and cooling to it. They can also be embedded in other buildings including highrise office buildings. In any event, a useful metric to help gauge the energy efficiency of the data center is the computer power consumption index, or the fraction of the total data center power (including computers, power conditioning, HVAC, lighting, and miscellaneous) to that used by the computers (higher is better; a realistic maximum is in the 0.8 to 0.9 range, depending on climate). As shown in Figure 1, this number varies by more than a factor of 2 from the worst centers to the best we’ve benchmarked to date. Even the best have room for improvement, suggesting that many centers have tremendous opportunities for greater energy efficiency. Figure 1. Computer Power Consumption Index Computer Power Consumption Index

Mesofluidic actuation for articulated finger and hand prosthetics <=SEP=> The loss of fingers and hands severely limits career and lifestyle options for the amputee. Unfortunately, while there have been strides made in advancements of upper arm and leg prosthetics, the state of the art in prosthetic hands is lagging far behind. Options are generally limited to claw like devices that provide limited gripping capacity. The overall objective of this paper is to demonstrate a path towards a low-cost prosthetic hand with multiple articulated fingers and a thumb that rivals the human hand in terms of weight, size, dexterity, range of motion, force carrying capacity and speed. We begin with a description of the functional requirements for a human hand. When comparing requirements with actuation technologies, the fluid power approach has the potential to realize a prosthetic hand that rivals a human hand in size, strength and dexterity. We introduce a new actuation technology, mesofluidics, that focuses on miniaturization of fluid power to the meso-scale (mm to cm). As a novel demonstration of the potential for this technology, we describe a proof-of-principle mesofluidic finger that has intrinsic actuation and control (actuators and control valves within the volume of the finger). This finger weighs 63 grams, is sized to the 50th percentile male finger, has a total of 25 mechanical parts and is capable of providing 10 kg (22 lbs) of pinch force.