THE EVOLUTION OF ACCOUNTING INFORMATION SYSTEMS

Technological evolution becomes more and more a daily reality for businesses and individuals who use information systems as for supporting their operational activities. This article focuses on the way technological evolution changes the accounting practices, starting from the analysis of the traditional model and trying to determine future trends and arising challenges to face. From data input to consolidation and reporting, accountants’ function and operations are dissected in order to identify to what extent the development of new concepts, such as cloud computing, cloud accounting, real-time accounting or mobile accounting may affect the financial-accounting process, as well as the challenges that arise from the changing environment. SEA Practical Application of Science Volume III, Issue 1 (7) / 2015 92 Introduction Technology evolves rapidly as for responding to customers demand (Weiß and Leimeister, 2012). From a business perspective, more and more companies acknowledge the fact that technology may support process optimisation in terms of costs, lead time and involved resources. The actual market context is driving companies to continuously search for new ways to optimise their processes and increase their financial indicators (Christauskas and Miseviciene, 2012). The company’s efficiency is in direct relation with the objective and timely financial information provided by the accounting system, the main purpose of which is to collect and record information regarding transactions or events with economic impact upon the company, as well as to process and provide relevant, fair information to stakeholders (both internal and external) (Girdzijauskas, et al., 2008; Kundeliene, 2011). The accounting system is thus essential in providing reliable, relevant, significant and useful information for the users of financial data (Kalcinskaite, 2009). From the deployment of basic accounting program or the use of large ERP systems (Enterprise Resource Planning), to the creation of World-Wide Web and development of web-based communication, technology increased its development in speed and implications. Today, concepts such as cloud computing, cloud accounting or real-time reporting are more and more a daily reality among companies. But as technology evolves at an incredible speed, it is necessary for companies to adapt their processes and practices to the new trends. However, that could not be possible without the decision factors to completely acknowledge the implications of the new technology, how it can be used to better manage their businesses and what challenges it also brings along. The present study is based on the theoretical and empirical analysis of accounting process from the appearance of the first accounting information systems up to nowadays’ services and techniques available for supporting the accounting function of a company. The research comprised of analysing accounting operations, activities and practices as they followed the technological evolution for over than 20 years, focusing on a general level, applicable to companies of all sizes and business sectors. Although the geographic extent was limited to the European Union, the study may as well be considered globally applicable, considering the internationality of today’s technology (e.g. cloud computing services may be used by a company in Japan while the server is physically located in the USA). Traditional practices and future trends The accounting systems may be seen as aiming to support businesses in collecting, understanding and analysing the financial data (Chytilova et al., 2011). The evolution of software accounting generation may be split, according toPhillips (2012) into three major categories: ▪ 90’s era – marked by the apparition of the first accounting software programs under what is known as ‘the Windows age’; applications were solid, but only supporting basic accounting operations. ▪ 00’s era – ‘integration’ and ‘SaaS’ concepts took birth, bringing along more developed systems that would allow more complex accounting operations and data processing, as well as concurrent access to files and programs. ▪ 2010 – on-going – ‘Mobile’ accounting era, marked by real-time accounting, financial dashboards and other mobile applications supporting financial processing and reporting. The same author outlines the evolution of communication – if the traditional accounting model was based on e-mail or .ftp files communication, the technological evolution now allows sharing and concurrent access to data, through virtual platforms provided by cloud computing technology. Based on the types of accounting services available on the market, three major categories may be defined: ▪ On-premises accounting: a dedicated accounting software program is purchased by the company and installed using its own infrastructure. Investment in the software and hardware equipment is required for such programs. ▪ Hosted solutions: the logical access is remotely performed through the company’s installed programs; however the data centre is physically located in a different place, managed by a dedicated third party. Infrastructure costs are reduced for the company, as hardware is administered and maintained by the service provider. ▪ Cloud computing: the service could prove even more cost efficient for companies, as the data is managed through virtual platforms, and administered by a dedicated third party, allowing multi-tenancy of services in order to split fixed infrastructure costs between companies. Traditional accounting practices used to focus on bookkeeping and financial reporting, having as a final purpose the preparation and presentation of financial statements. The activities were driven by the need of financial information users (both internal and external) to gain a ‘fair view’ of the company. The objective was often SEA Practical Application of Science Volume III, Issue 1 (7) / 2015 93 reached through the use of small, atomic systems meant to support the reporting; nevertheless, collection of documents, processing of data and operations, posting of journal entries, as well as consolidation and final reporting operations were mostly manual, and manual controls (reconciliations, validations, etc.) were performed as systems did not communicate through automated interfaces. In early 1920s, the first outsourcing agreement was signed by British Petroleum with Accenture. Ever since, the accounting started changing its meaning within the companies, turning from the bookkeeping function to the management strategic and decision-making support function. The technological evolution gave birth in the late 80s to ERP (Enterprise Resource Planning) systems, used to incorporate and connect various organisational functions (accounting, asset management, operations, procurement, human resources, etc.) (Ziemba and Oblak, 2013). Ustasüleyman and Percin (2010) define the ERP systems as ‘software packages enabling the integration of business processes throughout an organisation’, while Salmeron andLopez (2010) see the ERP system as ‘a single software system allowing complete integration of information flow from all functional areas in companies by means of a single database, and accessible through a unified interface and communication channel’. The ERP systems became of common use among large companies who managed to reduce the process lead time and involved resources by automation of data transfer between ERP modules, processes within the ERP system, as well as validation and reconciliation controls. From an accounting perspective, the deployment of ERP systems represented a major change, offering support in bookkeeping (the operations performed through different modules would generate automated journal entries posting), processing and transfer of data (through automated interfaces between the ERP modules), as well as consolidation and reporting. This progress took the accountants one step away from the traditional accounting’s bookkeeping practices, and one step closer towards today’s role more close to management strategy and decision support. Further on, as a consequence of the financial-economic crisis started in 2008, the role of accountants within the company changed drastically from bookkeeping and reporting to playing an active and essential role in the management strategy and decision-making process. Thus, it was technology’s role to take in the traditional tasks and operations. More and more companies implemented automated controls for processing data, posting journal entries, consolidation and final reporting under the financial statements and other internal management reports. Soon after automating the accounting operations, technology evolved into also automating document collection and management, through development of concepts such as einvoicing, e-archiving, e-payments, etc. (Burinskiene&Burinskas, 2010). Technology proved once again responsive to the market’s demand, and thus accounting software easily customisable for each client’s particularities regarding the activity profile, accounting practices and chart of accounts, were built as for supporting the automation of accounting process. With the automation of the process, implementation of certain controls was also required as for ensuring the correctness and completeness of reported information. Technology also took into account the need for robust, transparent processes, and it was only a matter of time until cloud computing, cloud accounting or real-time reporting concepts became a daily reality among companies of all sizes, activity sectors or region/state. The access to financial information previously physically limited to the company’s premises (where the network and infrastructure would be located) was fairly improved through cloud computing to an extent where internet connection was the only condition users needed to respect in order to gain access to the financial programs and data. Cloud computing start


Interactive Topic Modeling for Exploring Asynchronous Online Conversations: Design and Evaluation of ConVisIT

Since the mid-2000s, there has been exponential growth of asynchronous online conversations, thanks to the rise of social media. Analyzing and gaining insights from such conversations can be quite challenging for a user, especially when the discussion becomes very long. A promising solution to this problem is topic modeling, since it may help the user to understand quickly what was discussed in a long conversation and to explore the comments of interest. However, the results of topic modeling can be noisy, and they may not match the user’s current information needs. To address this problem, we propose a novel topic modeling system for asynchronous conversations that revises the model on the fly on the basis of users’ feedback. We then integrate this system with interactive visualization techniques to support the user in exploring long conversations, as well as in revising the topic model when the current results are not adequate to fulfill the user’s information needs. Finally, we report on an evaluation with real users that compared the resulting system with both a traditional interface and an interactive visual interface that does not support human-in-the-loop topic modeling. Both the quantitative results and the subjective feedback from the participants illustrate the potential benefits of our interactive topic modeling approach for exploring conversations, relative to its counterparts.


ACCEPTANCE AND COMMITMENT THERAPY ( ACT ) IN PANIC DISORDER WITH AGORAPHOBIA : A CASE STUDY


Visual Categorization with Bags of Keypoints

We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors of image patches. We propose and compare two alternative implementations using different classifiers: Naïve Bayes and SVM. The main advantages of the method are that it is simple, computationally efficient and intrinsically invariant. We present results for simultaneously classifying seven semantic visual categories. These results clearly demonstrate that the method is robust to background clutter and produces good categorization accuracy even without exploiting geometric information.


CSIFT: A SIFT Descriptor with Color Invariant Characteristics

SIFT has been proven to be the most robust local invariant feature descriptor. SIFT is designed mainly for gray images. However, color provides valuable information in object description and matching tasks. Many objects can be misclassified if their color contents are ignored. This paper addresses this problem and proposes a novel colored local invariant feature descriptor. Instead of using the gray space to represent the input image, the proposed approach builds the SIFT descriptors in a color invariant space. The built Colored SIFT (CSIFT) is more robust than the conventional SIFT with respect to color and photometrical variations. The evaluation results support the potential of the proposed approach.


Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based weighted k-means algorithm is used to evaluate the similarity between the forecasting and historical days. The EMD method is employed to decompose the SD load to several intrinsic mode functions (IMFs) and residual. Separated LSTM neural networks were also employed to forecast each IMF and residual. Lastly, the forecasting values from each LSTM model were reconstructed. Numerical testing demonstrates that the SD-EMD-LSTM method can accurately forecast the electric load.


Short-term memory, working memory, and executive functioning in preschoolers: longitudinal predictors of mathematical achievement at age 7 years.

This study examined whether measures of short-term memory, working memory, and executive functioning in preschool children predict later proficiency in academic achievement at 7 years of age (third year of primary school). Children were tested in preschool (M age = 4 years, 6 months) on a battery of cognitive measures, and mathematics and reading outcomes (from standardized, norm-referenced school-based assessments) were taken on entry to primary school, and at the end of the first and third year of primary school. Growth curve analyses examined predictors of math and reading achievement across the duration of the study and revealed that better digit span and executive function skills provided children with an immediate head start in math and reading that they maintained throughout the first three years of primary school. Visual-spatial short-term memory span was found to be a predictor specifically of math ability. Correlational and regression analyses revealed that visual short-term and working memory were found to specifically predict math achievement at each time point, while executive function skills predicted learning in general rather than learning in one specific domain. The implications of the findings are discussed in relation to further understanding the role of cognitive skills in different mathematical tasks, and in relation to the impact of limited cognitive skills in the classroom environment.


Energy-Brushes: Interactive Tools for Illustrating Stylized Elemental Dynamics

Dynamic effects such as waves, splashes, fire, smoke, and explosions are an integral part of stylized animations. However, such dynamics are challenging to produce, as manually sketching key-frames requires significant effort and artistic expertise while physical simulation tools lack sufficient expressiveness and user control. We present an interactive interface for designing these elemental dynamics for animated illustrations. Users draw with coarse-scale energy brushes which serve as control gestures to drive detailed flow particles which represent local velocity fields. These fields can convey both realistic and artistic effects based on user specification. This painting metaphor for creating elemental dynamics simplifies the process, providing artistic control, and preserves the fluidity of sketching. Our system is fast, stable, and intuitive. An initial user evaluation shows that even novice users with no prior animation experience can create intriguing dynamics using our system.


The short-form McGill pain questionnaire

A short form of the McGill Pain Questionnaire (SF-MPQ) has been developed. The main component of the SF-MPQ consists of 15 descriptors (11 sensory; 4 affective) which are rated on an intensity scale as 0 = none, 1 = mild, 2 = moderate or 3 = severe. Three pain scores are derived from the sum of the intensity rank values of the words chosen for sensory, affective and total descriptors. The SF-MPQ also includes the Present Pain Intensity (PPI) index of the standard MPQ and a visual analogue scale (VAS). The SF-MPQ scores obtained from patients in post-surgical and obstetrical wards and physiotherapy and dental departments were compared to the scores obtained with the standard MPQ. The correlations were consistently high and significant. The SF-MPQ was also shown to be sufficiently sensitive to demonstrate differences due to treatment at statistical levels comparable to those obtained with the standard form. The SF-MPQ shows promise as a useful tool in situations in which the standard MPQ takes too long to administer, yet qualitative information is desired and the PPI and VAS are inadequate.


X10: an object-oriented approach to non-uniform cluster computing

It is now well established that the device scaling predicted by Moore's Law is no longer a viable option for increasing the clock frequency of future uniprocessor systems at the rate that had been sustained during the last two decades. As a result, future systems are rapidly moving from uniprocessor to multiprocessor configurations, so as to use parallelism instead of frequency scaling as the foundation for increased compute capacity. The dominant emerging multiprocessor structure for the future is a Non-Uniform Cluster Computing (NUCC) system with nodes that are built out of multi-core SMP chips with non-uniform memory hierarchies, and interconnected in horizontally scalable cluster configurations such as blade servers. Unlike previous generations of hardware evolution, this shift will have a major impact on existing software. Current OO language facilities for concurrent and distributed programming are inadequate for addressing the needs of NUCC systems because they do not support the notions of non-uniform data access within a node, or of tight coupling of distributed nodes.We have designed a modern object-oriented programming language, X10, for high performance, high productivity programming of NUCC systems. A member of the partitioned global address space family of languages, X10 highlights the explicit reification of locality in the form of places}; lightweight activities embodied in async, future, foreach, and ateach constructs; a construct for termination detection (finish); the use of lock-free synchronization (atomic blocks); and the manipulation of cluster-wide global data structures. We present an overview of the X10 programming model and language, experience with our reference implementation, and results from some initial productivity comparisons between the X10 and Java™ languages.


Musculoskeletal symptoms among mobile hand-held device users and their relationship to device use: A preliminary study in a Canadian university population.

The study aims were, in a population of university students, staff, and faculty (n = 140), to: 1) determine the distribution of seven measures of mobile device use; 2) determine the distribution of musculoskeletal symptoms of the upper extremity, upper back and neck; and 3) assess the relationship between device use and symptoms. 137 of 140 participants (98%) reported using a mobile device. Most participants (84%) reported pain in at least one body part. Right hand pain was most common at the base of the thumb. Significant associations found included time spent internet browsing and pain in the base of the right thumb (odds ratio 2.21, 95% confidence interval 1.02-4.78), and total time spent using a mobile device and pain in the right shoulder (2.55, 1.25-5.21) and neck (2.72, 1.24-5.96). Although this research is preliminary, the observed associations, together with the rising use of these devices, raise concern for heavy users.


The heroism of women and men.

Heroism consists of actions undertaken to help others, despite the possibility that they may result in the helper's death or injury. The authors examine heroism by women and men in 2 extremely dangerous settings: the emergency situations in which Carnegie medalists rescued others and the holocaust in which some non-Jews risked their lives to rescue Jews. The authors also consider 3 risky but less dangerous prosocial actions: living kidney donations, volunteering for the Peace Corps, and volunteering for Doctors of the World. Although the Carnegie medalists were disproportionately men, the other actions yielded representations of women that were at least equal to and in most cases higher than those of men. These findings have important implications for the psychology of heroism and of gender.


Ischiofemoral Impingement Syndrome

Ischiofemoral impingement syndrome is known as one of the causes of hip pain due to impingement of ischium and femur, and usually correlated with trauma or operation. We report a rare case of ischiofemoral impingement syndrome that has no history of trauma or surgery. A 48-year-old female patient was referred for 2 months history of the left hip pain, radiating to lower extremity with a hip snapping sensation. She had no history of trauma or surgery at or around the hip joint and femur. The magnetic resonance imaging (MRI) of the lumbar spine showed no abnormality, except diffuse bulging disc without cord compression at the lumbosacral area. Electrophysiologic study was normal, and there were no neurologic abnormalities compatible with the lumbosacral radiculopathy or spinal stenosis. Hip MRI revealed quadratus femoris muscle edema with concurrent narrowing of the ischiofemoral space. The distance of ischiofemoral space and quadratus femoris space were narrow. It was compatible with ischiofemoral impingement syndrome. After treatment with nonsteroidal anti-inflammatory drugs, physical therapy, and exercise program, the patient's pain was relieved and the snapping was improved. To our knowledge, this is the first reported case of a nontraumatic, noniatrogenic ischiofemoral impingement syndrome, and also the first case to be treated by a nonsurgical method in the Republic of Korea.


Statistical and Machine Learning approach in forex prediction based on empirical data

This study proposed a new insight in comparing common methods used in predicting based on data series i.e statistical method and machine learning. The corresponding techniques are use in predicting Forex (Foreign Exchange) rates. The Statistical method used in this paper is Adaptive Spline Threshold Autoregression (ASTAR), while for machine learning, Support Vector Machine (SVM) and hybrid form of Genetic Algorithm-Neural Network (GA-NN) are chosen. The comparison among the three methods accurate rate is measured in root mean squared error (RMSE). It is found that ASTAR and GA-NN method has advantages depend on the period time intervals.


Novel 2-D MMSE Subpixel-Based Image Down-Sampling

Subpixel-based down-sampling is a method that can potentially improve apparent resolution of a down-scaled image on LCD by controlling individual subpixels rather than pixels. However, the increased luminance resolution comes at price of chrominance distortion. A major challenge is to suppress color fringing artifacts while maintaining sharpness. We propose a new subpixel-based down-sampling pattern called diagonal direct subpixel-based down-sampling (DDSD) for which we design a 2-D image reconstruction model. Then, we formulate subpixel-based down-sampling as a MMSE problem and derive the optimal solution called minimum mean square error for subpixel-based down-sampling (MMSE-SD). Unfortunately, straightforward implementation of MMSE-SD is computational intensive. We thus prove that the solution is equivalent to a 2-D linear filter followed by DDSD, which is much simpler. We further reduce computational complexity using a small k × k filter to approximate the much larger MMSE-SD filter. To compare the performances of pixel and subpixel-based down-sampling methods, we propose two novel objective measures: normalized l1 high frequency energy for apparent luminance sharpness and PSNRU(V) for chrominance distortion. Simulation results show that both MMSE-SD and MMSE-SD(k) can give sharper images compared with conventional down-sampling methods, with little color fringing artifacts.


A Technique for Measuring Data Persistence Using the Ext4 File System Journal

In this paper, we propose a method of measuring data persistence using the Ext4 journal. Digital Forensic tools and techniques are commonly used to extract data from media. A great deal of research has been dedicated to the recovery of deleted data, however, there is a lack of information on quantifying the chance that an investigator will be successful in this endeavor. To that end, we suggest the file system journal be used as a source to gather empirical evidence of data persistence, which can later be used to formulate the probability of recovering deleted data under various conditions. Knowing this probability can help investigators decide where to best invest their resources. We have implemented a proof of concept system that interrogates the Ext4 file system journal and logs relevant data. We then detail how this information can be used to track the reuse of data blocks from the examination of file system metadata structures. This preliminary design contributes a novel method of tracking deleted data persistence that can be used to generate the information necessary to formulate probability models regarding the full and/or partial recovery of deleted data.


Joint Haar-like features for face detection

In this paper, we propose a new distinctive feature, called joint Haar-like feature, for detecting faces in images. This is based on co-occurrence of multiple Haar-like features. Feature co-occurrence, which captures the structural similarities within the face class, makes it possible to construct an effective classifier. The joint Haar-like feature can be calculated very fast and has robustness against addition of noise and change in illumination. A face detector is learned by stagewise selection of the joint Haar-like features using AdaBoost. A small number of distinctive features achieve both computational efficiency and accuracy. Experimental results with 5, 676 face images and 30,000 nonface images show that our detector yields higher classification performance than Viola and Jones' detector; which uses a single feature for each weak classifier. Given the same number of features, our method reduces the error by 37%. Our detector is 2.6 times as fast as Viola and Jones' detector to achieve the same performance


A sub-pW timer using gate leakage for ultra low-power sub-Hz monitoring systems

In this work, we present a novel ultra-low power timer designed using the gate leakage of MOS capacitors. The test chip was fabricated in a 0.13 mum CMOS technology and the total circuit area is 480 mum2. Measurement results show that the circuit functions correctly at a wide range of supply voltages from 300 mV to 1.2 V, making it particularly suitable for subthreshold systems. The temperature sensitivity is 0.16%/degC at 600 mV and 0.6%/degC at 300 mV. The power dissipation is less than 1pW running at 20degC and 300 mV.


A concise introduction to autonomic computing

School of Computing and Mathematics, Faculty of Engineering, University of Ulster at Jordanstown, Shore Road, Newtownabbey, County Antrim BT37 0QB, Northern Ireland, UK The Applied Software Systems Laboratory (TASSL), Electrical and Computer Engineering, Rutgers, The State University of New Jersey, 94 Brett Road, Piscataway, NJ 08854-8058, USA School of Computing and Mathematical Sciences, The SRIF/SHEFC Centre for Virtual Organization Technology Enabling Research (VOTER), Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK Institute for Computer Science and Business Information Systems (ICB), University of Duisburg-Essen, Schützenbahn 70, 45117 Essen, Germany


A 1.7 ps Equivalent Bin Size and 4.2 ps RMS FPGA TDC Based on Multichain Measurements Averaging Method

A high precision and high resolution time-to-digital converter (TDC) based on multichain measurements averaging method is implemented in a 40 nm fabrication process Virtex-6 FPGA. The results of the detailed theoretical analysis and the simulation with the MATLAB tool based on a complete TDC module show that the resolution limitation determined by the intrinsic cell delay of plain tapped-delay chain can be overcame, which results in an improvement on both resolution and precision without increasing the dead time. The test results agree with the simulation results quite well. In such a TDC, the input signal is connected to multiple tapped-delay chains simultaneously (the number of the chains is M), and each chain is just a plain TDC and generates a timestamp for a hit signal. Therefore, M timestamps should be obtained in total, which, after averaging, give the final timestamp. A TDC with 1.7 ps equivalent bin size, 1.5 ps averaged bin size and 4.2 ps RMS has been implemented with M being 16, which performs much better than the plain TDC constructed of a single tapped delay chain having 42.3 ps equivalent bin size, 24.0 ps averaged bin size resolution and 13.2 ps RMS precision. The comparisons of equivalent bin size and averaged bin size show that the nonlinearity is improved with a larger M. Due to the real time integral nonlinearity (INL) calibration and averaging calculation, the multichain TDC is almost insensitive to the process voltage and temperature (PVT) variations.


Recognition of handwritten Roman Numerals using Tesseract open source OCR engine

The objective of the paper is to recognize handwritten samples of Roman numerals using Tesseract open source Optical Character Recognition (OCR) engine. Tesseract is trained with data samples of different persons to generate one user-independent language model, representing the handwritten Roman digit-set. The system is trained with 1226 digit samples collected form the different users. The performance is tested on two different datasets, one consisting of samples collected from the known users (those who prepared the training data samples) and the other consisting of handwritten data samples of unknown users. The overall recognition accuracy is obtained as 92.1% and 86.59% on these test datasets respectively.


Design, Modeling, Control, and Evaluation of a Hybrid Hip Joint Miniature Climbing Robot

The subject of this paper is the design, control, and evaluation of a biped–climbing robot featuring a new hybrid hip joint. The hybrid hip provides both prismatic and revolute motion, discretely, to the robot, using a single actuator. This is intended to improve its adaptability in confined environments and its capability to maneuver over and around obstacles. Optimization of the hybrid hip joint relative to robot size, weight, and actuation limits is considered while maximizing range of motion. The mechanical structure of the robot is discussed, as well as forward and inverse kinematics for motion planning. Reachable robot workspace in both revolute and prismatic phases of the hybrid hip joint is analyzed. Different robot locomotion strides are developed and dynamic controller requirements are considered. Experimental evaluation of the robot walking and climbing on and between surfaces with different inclinations is conducted to evaluate performance and repeatability of the system. KEY WORDS—biped robot, climbing robot, robot design, kinematics, locomotion strides


A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis

Gene regulatory networks (GRNs) provide a representation of relationships between regulators and their target genes. Several methods for GRN inference, both unsupervised and supervised, have been developed to date. Because regulatory relationships consistently reprogram in diverse tissues or under different conditions, GRNs inferred without specific biological contexts are of limited applicability. In this report, a machine learning approach is presented to predict GRNs specific to developing Arabidopsis thaliana embryos. We developed the Beacon GRN inference tool to predict GRNs occurring during seed development in Arabidopsis based on a support vector machine (SVM) model. We developed both global and local inference models and compared their performance, demonstrating that local models are generally superior for our application. Using both the expression levels of the genes expressed in developing embryos and prior known regulatory relationships, GRNs were predicted for specific embryonic developmental stages. The targets that are strongly positively correlated with their regulators are mostly expressed at the beginning of seed development. Potential direct targets were identified based on a match between the promoter regions of these inferred targets and the cis elements recognized by specific regulators. Our analysis also provides evidence for previously unknown inhibitory effects of three positive regulators of gene expression. The Beacon GRN inference tool provides a valuable model system for context-specific GRN inference and is freely available at https://github.com/BeaconProjectAtVirginiaTech/beacon_network_inference.git.


Rotor Failures in Squirrel Cage Induction Motors

During the past two decades, tremendous improvement has been made in the design and manufacture of stator windings. This has been accomplished primarily through the development of improved insulation materials and treatment processes. As a result, the expected life from a thermal, dielectrical, mechanical, and environmental standpoint has been significantly increased. The rotor design and manufacturing remains basically unchanged. A survey of warranty data and service facilities suggests that rotor failures now account for a larger percentage of the total induction motor failures. The majority of these failures are caused by various stresses which act on the rotor assembly. These various stresses and how they affect the life of the motor and contribute to premature failure are discussed.


Annual Report Readability , Earnings , and Stock Returns ∗

This paper examines the relationship between annual report readability and firm performance. This is motivated by the Securities and Exchange Commission’s plain English disclosure regulations that attempt to make corporate disclosures easier to read for ordinary investors. I measure the readability of public company annual reports using the Fog Index and the Kincaid Index from computational linguistics. I find that the annual reports of firms with lower earnings are harder to read. Moreover, the positive earnings of firms with annual reports that are easier to read are more persistent in the next one to four years. This suggests that, consistent with the motivation behind the plain English disclosure regulation of the Securities and Exchange Commission, managers may be opportunistically choosing the readability of annual reports to hide adverse information from investors. However, I do not find any correlation between annual report readability and future stock returns, suggesting that the market does impound the implications of disclosure readability into stock prices.


Applying Business Intelligence Concepts to Medicaid Claim Fraud Detection

U.S. governmental agencies are striving to do more with less. Controlling the costs of delivering healthcare services such as Medicaid is especially critical at a time of increasing program enrollment and decreasing state budgets. Fraud is estimated to steal up to ten percent of the taxpayer dollars used to fund governmentally supported healthcare, making it critical for government authorities to find cost effective methods to detect fraudulent transactions. This paper explores the use of a business intelligence system relying on statistical methods to detect fraud in one state’s existing Medicaid claim payment data. This study shows that Medicaid claim transactions that have been collected for payment purposes can be reformatted and analyzed to detect fraud and provide input for decision makers charged with making the best use of available funding. The results illustrate the efficacy of using unsupervised statistical methods to detect fraud in healthcare-related data.


High-Resistivity SOI CMOS Cellular Antenna Switches

Results for cellular antenna switches using highresistivity silicon-on-insulator (SOI) CMOS technology are presented. The performance of SOI RF switch FETs is presented and compared to a production GaAs pHEMT technology. Data from prototype high-resistivity SOI RF switch designs is presented and compared to pHEMT based designs. Index Terms — RF CMOS, SOI, silicon-on-insulator, RF SOI, cellular switch, signal isolation


Controlling tensegrity robots through evolution

Tensegrity structures (built from interconnected rods and cables) have the potential to offer a revolutionary new robotic design that is light-weight, energy-efficient, robust to failures, capable of unique modes of locomotion, impact tolerant, and compliant (reducing damage between the robot and its environment). Unfortunately robots built from tensegrity structures are difficult to control with traditional methods due to their oscillatory nature, nonlinear coupling between components and overall complexity. Fortunately this formidable control challenge can be overcome through the use of evolutionary algorithms. In this paper we show that evolutionary algorithms can be used to efficiently control a ball shaped tensegrity robot. Experimental results performed with a variety of evolutionary algorithms in a detailed soft-body physics simulator show that a centralized evolutionary algorithm performs 400% better than a hand-coded solution, while the multiagent evolution performs 800% better. In addition, evolution is able to discover diverse control solutions (both crawling and rolling) that are robust against structural failures and can be adapted to a wide range of energy and actuation constraints. These successful controls will form the basis for building high-performance tensegrity robots in the near future.


A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models

The Programming by Demonstration (PbD) technique aims at teaching a robot to accomplish a task by learning from a human demonstration. In a manipulation context, recognizing the demonstrator's hand gestures, specifically when and how objects are grasped, plays a significant role. Here, a system is presented that uses both hand shape and contact-point information obtained from a data glove and tactile sensors to recognize continuous human-grasp sequences. The sensor fusion, grasp classification, and task segmentation are made by a hidden Markov model recognizer. Twelve different grasp types from a general, task-independent taxonomy are recognized. An accuracy of up to 95% could be achieved for a multiple-user system.


Dynamo: amazon's highly available key-value store

Reliability at massive scale is one of the biggest challenges we face at Amazon.com, one of the largest e-commerce operations in the world; even the slightest outage has significant financial consequences and impacts customer trust. The Amazon.com platform, which provides services for many web sites worldwide, is implemented on top of an infrastructure of tens of thousands of servers and network components located in many datacenters around the world. At this scale, small and large components fail continuously and the way persistent state is managed in the face of these failures drives the reliability and scalability of the software systems.
 This paper presents the design and implementation of Dynamo, a highly available key-value storage system that some of Amazon's core services use to provide an "always-on" experience. To achieve this level of availability, Dynamo sacrifices consistency under certain failure scenarios. It makes extensive use of object versioning and application-assisted conflict resolution in a manner that provides a novel interface for developers to use.


HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads

The production environment for analytical data management applications is rapidly changing. Many enterprises are shifting away from deploying their analytical databases on high-end proprietary machines, and moving towards cheaper, lower-end, commodity hardware, typically arranged in a shared-nothing MPP architecture, often in a virtualized environment inside public or private “clouds”. At the same time, the amount of data that needs to be analyzed is exploding, requiring hundreds to thousands of machines to work in parallel to perform the analysis. There tend to be two schools of thought regarding what technology to use for data analysis in such an environment. Proponents of parallel databases argue that the strong emphasis on performance and efficiency of parallel databases makes them wellsuited to perform such analysis. On the other hand, others argue that MapReduce-based systems are better suited due to their superior scalability, fault tolerance, and flexibility to handle unstructured data. In this paper, we explore the feasibility of building a hybrid system that takes the best features from both technologies; the prototype we built approaches parallel databases in performance and efficiency, yet still yields the scalability, fault tolerance, and flexibility of MapReduce-based systems.


C-Store: A Column-oriented DBMS

This paper presents the design of a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimized. Among the many differences in its design are: storage of data by column rather than by row, careful coding and packing of objects into storage including main memory during query processing, storing an overlapping collection of columnoriented projections, rather than the current fare of tables and indexes, a non-traditional implementation of transactions which includes high availability and snapshot isolation for read-only transactions, and the extensive use of bitmap indexes to complement B-tree structures. We present preliminary performance data on a subset of TPC-H and show that the system we are building, C-Store, is substantially faster than popular commercial products. Hence, the architecture looks very encouraging.


G2DeNet: Global Gaussian Distribution Embedding Network and Its Application to Visual Recognition

Recently, plugging trainable structural layers into deep convolutional neural networks (CNNs) as image representations has made promising progress. However, there has been little work on inserting parametric probability distributions, which can effectively model feature statistics, into deep CNNs in an end-to-end manner. This paper proposes a Global Gaussian Distribution embedding Network (G2DeNet) to take a step towards addressing this problem. The core of G2DeNet is a novel trainable layer of a global Gaussian as an image representation plugged into deep CNNs for end-to-end learning. The challenge is that the proposed layer involves Gaussian distributions whose space is not a linear space, which makes its forward and backward propagations be non-intuitive and non-trivial. To tackle this issue, we employ a Gaussian embedding strategy which respects the structures of both Riemannian manifold and smooth group of Gaussians. Based on this strategy, we construct the proposed global Gaussian embedding layer and decompose it into two sub-layers: the matrix partition sub-layer decoupling the mean vector and covariance matrix entangled in the embedding matrix, and the square-rooted, symmetric positive definite matrix sub-layer. In this way, we can derive the partial derivatives associated with the proposed structural layer and thus allow backpropagation of gradients. Experimental results on large scale region classification and fine-grained recognition tasks show that G2DeNet is superior to its counterparts, capable of achieving state-of-the-art performance.


A grounded theory analysis of modern web applications: knowledge, skills, and abilities for DevOps

Since 2009, DevOps, the combination of development and operation, has been adopted within organizations in industry, such as Netflix, Flickr, and Fotopedia. Configuration management tools have been used to support DevOps. However, in this paper we investigate which Knowledge, Skills, and Abilities (KSA) have been employed in developing and deploying modern web applications and how these KSAs support DevOps. By applying a qualitative analysis approach, namely grounded theory, to three web application development projects, we discover that the KSAs for both Software Development and IT Operator practitioners support the four perspectives of DevOps: collaboration culture, automation, measurement, and sharing.


Reducing restraint use for older adults in acute care.

Although no evidence shows that restraints are effective for maintaining safety, preventing disruption of treatment, or controlling behavior, they're still commonly used in acute care facilities (especially in critical care) in the United States, where the reported prevalence of their use ranges from 7.4% to 17%.[1–3] They may be used to protect patients from falls or to prevent them from inadvertently removing tubes and other devices.[4] Older adults are three times more likely to be restrained during an acute hospital admission than younger patients, even though this practice is associated with poor outcomes.[1] (See Adverse effects of physical restraints.)


The Impact of Information Communication and Technology on Students ' Academic Performance : Evidence from Indonesian EFL Classrooms

The present study examined the impact of Information Communication Technology [ICT] on a group of university students' academic performance in an Indonesian English as a Foreign Language (EFL) classroom. As the platform of the ICT usage in this current study, English learning websites was used as the independent variable. Academic performance (students' score on pre and post test) was used the dependent variable. The participants in the study were 60 students of the Department of Public Health at the State University of Gorontalo, Indonesia, i.e an experimental group of 30 students (n=30) and a control group of 30 students (n=30). They took English courses as one of the compulsory subjects in the university curriculum. This study used a mixed method of a quasi-experimental and a qualitative interview approaches. Based on the result of the quantitative method of data collection, ttests of this study indicated that students in the experiment group performed significantly better than the students in the control group. Test results also showed that there was a significant difference between students' score on the preand post-test. The students' score in the post test and post test in the control group, however, were not significantly different. As interview results showed, participants expressed their positive learning experience with technologies, growing motivation in English learning, and positive feeling and confidence on their language performance.


Evaluation of Features Detectors and Descriptors based on 3D Objects

We explore the performance of a number of popular feature detectors and descriptors in matching 3D object features across viewpoints and lighting conditions. To this end we design a method, based on intersecting epipolar constraints, for providing ground truth correspondence automatically. These correspondences are based purely on geometric information, and do not rely on the choice of a specific feature appearance descriptor. We test detector-descriptor combinations on a database of 100 objects viewed from 144 calibrated viewpoints under three different lighting conditions. We find that the combination of Hessian-affine feature finder and SIFT features is most robust to viewpoint change. Harris-affine combined with SIFT and Hessian-affine combined with shape context descriptors were best respectively for lighting change and change in camera focal length. We also find that no detector-descriptor combination performs well with viewpoint changes of more than 25–30∘.


Logic-Based Models for the Analysis of Cell Signaling Networks†

Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks.


Engineering design review of stance-control knee-ankle-foot orthoses.

Persons with quadriceps muscle weakness are often prescribed a knee-ankle-foot orthosis that locks the knee in full extension during both stance and swing phases of gait. Locking the knee results in abnormal gait patterns characterized by hip hiking and leg circumduction during swing. The stance-control knee-ankle-foot orthosis (SCKAFO), a new type of orthosis, has emerged that permits free knee motion during swing while resisting knee flexion during stance, thereby supporting the limb during weight-bearing. This article examines various SCKAFO designs, discuss the existing design limitations, and identify remaining design challenges. Several commercial SCKAFOs have been released that incorporate different locking mechanisms. Preliminary gait studies have shown some devices to be promising; however, an important functional limitation in some SCKAFOs is dependence on specific joint angles to switch between stance and swing modes. Important design factors such as size, weight, and noise must be considered in new orthosis designs to ensure wide consumer acceptance.


A Novel Cross Layer Intrusion Detection System in MANET

Intrusion detection System forms a vital component of internet security. To keep pace with the growing trends, there is a critical need to replace single layer detection technology with multi layer detection. Different types of Denial of Service (DoS) attacks thwart authorized users from gaining access to the networks and we tried to detect as well as alleviate some of those attacks. In this paper, we have proposed a novel cross layer intrusion detection architecture to discover the malicious nodes and different types of DoS attacks by exploiting the information available across different layers of protocol stack in order to improve the accuracy of detection. We have used cooperative anomaly intrusion detection with data mining technique to enhance the proposed architecture. We have implemented fixed width clustering algorithm for efficient detection of the anomalies in the MANET traffic and also generated different types of attacks in the network. The simulation of the proposed architecture is performed in OPNET simulator and we got the result as we expected.


Towards online anti-opinion spam: Spotting fake reviews from the review sequence

Detecting review spam is important for current e-commerce applications. However, the posted order of review has been neglected by the former work. In this paper, we explore the issue on fake review detection in review sequence, which is crucial for implementing online anti-opinion spam. We analyze the characteristics of fake reviews firstly. Based on review contents and reviewer behaviors, six time sensitive features are proposed to highlight the fake reviews. And then, we devise supervised solutions and a threshold-based solution to spot the fake reviews as early as possible. The experimental results show that our methods can identify the fake reviews orderly with high precision and recall.


Knowledge management strategy and its link to knowledge creation process

Knowledge has become to be considered as valuable strategic assets that can provide proprietary competitive advantages. It is more important for companies to distinguish themselves through knowledge management strategies. Without a constant creation of knowledge, a business is condemned to poor performance. However, it is still unclear how these strategies affect knowledge creation. Knowledge management strategies can be categorized as being either human or system oriented. This paper proposes a model to illustrate the link between the strategies and its creating process. The model is derived on the basis of samples from 58 Korean firms. The model depicts how companies should align the strategies with four knowledge creation modes such as socialization, externalization, combination, and internalization. It is found that human strategy is more likely to be effective for socialization while system strategy is more likely to be effective for combination. Furthermore, the survey result suggests that managers should adjust knowledge management strategies in view of the characteristics of their departments. q 2002 Elsevier Science Ltd. All rights reserved.


What makes a chair a chair?

Many object classes are primarily defined by their functions. However, this fact has been left largely unexploited by visual object categorization or detection systems. We propose a method to learn an affordance detector. It identifies locations in the 3d space which “support” the particular function. Our novel approach “imagines” an actor performing an action typical for the target object class, instead of relying purely on the visual object appearance. So, function is handled as a cue complementary to appearance, rather than being a consideration after appearance-based detection. Experimental results are given for the functional category “sitting”. Such affordance is tested on a 3d representation of the scene, as can be realistically obtained through SfM or depth cameras. In contrast to appearance-based object detectors, affordance detection requires only very few training examples and generalizes very well to other sittable objects like benches or sofas when trained on a few chairs.


High-Frequency Behavior of Graphene-Based Interconnects—Part II: Impedance Analysis and Implications for Inductor Design

This paper provides the first detailed insights into the ultrahigh-frequency behavior of graphene ribbons (GRs) and analyzes their consequences in designing interconnects and low-loss on-chip inductors. In the companion paper (part I), an accurate impedance modeling methodology has been developed based on the Boltzmann equation with the magnetic vector potential Green's function approach incorporating the dependency of current on the nonlocal electric field. Based on the developed methodology, this paper for the first time embarks on the rigorous investigation of the intricate processes occurring at high frequencies in GRs, such as anomalous skin effect (ASE), high-frequency resistance and inductance saturation, intercoupled relation between edge specularity and ASE, and the influence of the linear dimensions on impedance. A comparative study of the high-frequency response of GRs with that of carbon nanotubes (CNTs) and Cu is made to highlight the potential of GR interconnects for high-frequency applications. Subsequently, the high-frequency performance of GR inductors is analyzed, and it is shown that they can achieve 32% and 50% improvements in maximum Q-factor compared to Cu and single-walled CNT inductors with 1/3 metallic fraction, respectively.


Age estimation via face images: a survey

Facial aging adversely impacts performance of face recognition and face verification and authentication using facial features. This stochastic personalized inevitable process poses dynamic theoretical and practical challenge to the computer vision and pattern recognition community. Age estimation is labeling a face image with exact real age or age group. How do humans recognize faces across ages? Do they learn the pattern or use age-invariant features? What are these age-invariant features that uniquely identify one across ages? These questions and others have attracted significant interest in the computer vision and pattern recognition research community. In this paper, we present a thorough analysis of recent research in aging and age estimation. We discuss popular algorithms used in age estimation, existing models, and how they compare with each other; we compare performance of various systems and how they are evaluated, age estimation challenges, and insights for future research.


Machine Learning Based Missing Value Imputation Method for Clinical Datasets

Missing value imputation is one of the biggest tasks of data pre-processing when performing data mining. Most medical datasets are usually incomplete. Simply removing the incomplete cases from the original datasets can bring more problems than solutions. A suitable method for missing value imputation can help to produce good quality datasets for better analysing clinical trials. In this paper we explore the use of a machine learning technique as a missing value imputation method for incomplete cardiovascular data. Mean/mode imputation, fuzzy unordered rule induction algorithm imputation, decision tree imputation and other machine learning algorithms are used as missing value imputation and the final datasets are classified using decision tree, fuzzy unordered rule induction, KNN and K-Mean clustering. The experiment shows that final classifier performance is improved when the fuzzy unordered rule induction algorithm is used to predict missing attribute values for K-Mean clustering and in most cases, the machine learning techniques were found to perform better than the standard mean imputation technique.


Multi-terminal transport measurements of MoS2 using a van der Waals heterostructure device platform.

Atomically thin two-dimensional semiconductors such as MoS2 hold great promise for electrical, optical and mechanical devices and display novel physical phenomena. However, the electron mobility of mono- and few-layer MoS2 has so far been substantially below theoretically predicted limits, which has hampered efforts to observe its intrinsic quantum transport behaviours. Potential sources of disorder and scattering include defects such as sulphur vacancies in the MoS2 itself as well as extrinsic sources such as charged impurities and remote optical phonons from oxide dielectrics. To reduce extrinsic scattering, we have developed here a van der Waals heterostructure device platform where MoS2 layers are fully encapsulated within hexagonal boron nitride and electrically contacted in a multi-terminal geometry using gate-tunable graphene electrodes. Magneto-transport measurements show dramatic improvements in performance, including a record-high Hall mobility reaching 34,000 cm(2) V(-1) s(-1) for six-layer MoS2 at low temperature, confirming that low-temperature performance in previous studies was limited by extrinsic interfacial impurities rather than bulk defects in the MoS2. We also observed Shubnikov-de Haas oscillations in high-mobility monolayer and few-layer MoS2. Modelling of potential scattering sources and quantum lifetime analysis indicate that a combination of short-range and long-range interfacial scattering limits the low-temperature mobility of MoS2.


Test-potentiated learning: three independent replications, a disconfirmed hypothesis, and an unexpected boundary condition.

Arnold and McDermott [(2013). Test-potentiated learning: Distinguishing between direct and indirect effects of testing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 940-945] isolated the indirect effects of testing and concluded that encoding is enhanced to a greater extent following more versus fewer practice tests, referred to as test-potentiated learning. The current research provided further evidence for test-potentiated learning and evaluated the covert retrieval hypothesis as an alternative explanation for the observed effect. Learners initially studied foreign language word pairs and then completed either one or five practice tests before restudy occurred. Results of greatest interest concern performance on test trials following restudy for items that were not correctly recalled on the test trials that preceded restudy. Results replicate Arnold and McDermott (2013) by demonstrating that more versus fewer tests potentiate learning when trial time is limited. Results also provide strong evidence against the covert retrieval hypothesis concerning why the effect occurs (i.e., it does not reflect differential covert retrieval during pre-restudy trials). In addition, outcomes indicate that the magnitude of the test-potentiated learning effect decreases as trial length increases, revealing an unexpected boundary condition to test-potentiated learning.


An Improved Dijkstra Algorithm for Firefighting

In this paper, briefly the efforts going toward a mathematical calculation to validate a new-Dijkstra algorithm from the original normal Dijkstra algorithm [1] as an improvised Dijkstra. The result of this improvised Dijkstra was used to find the shortest path for firefighting unit to reach the location of the fire exactly. The idea depends on the influence of turns on the path, while there are two equal paths’, the more number of turns take more time to pass through it and the less number turns takes less time to pass through it. To apply this scenario practically we take a small real area in south Khartoum. The result gives strong justification that proves and verifies our methodology with a clear contribution in Improved Dijkstra algorithm for firefighting like Geo-Dijkstra. Furthermore, an evaluation of the above-mentioned algorithms has been done showing very promising and realistic results.


Computers and Creativity

The Painting Fool is software that we hope will one day be taken seriously as a creative artist in its own right. This aim is being pursued as an Artificial Intelligence (AI) project, with the hope that the technical difficulties overcome along the way will lead to new and improved generic AI techniques. It is also being pursued as a sociological project, where the effect of software which might be deemed as creative is tested in the art world and the wider public. In this chapter, we summarise our progress so far in The Painting Fool project. To do this, we first compare and contrast The Painting Fool with software of a similar nature arising from AI and graphics projects. We follow this with a discussion of the guiding principles from Computational Creativity research that we adhere to in building the software. We then describe five projects with The Painting Fool where our aim has been to produce increasingly interesting and culturally valuable pieces of art. We end by discussing the issues raised in building an automated painter, and describe further work and future prospects for the project. By studying both the technical difficulties and sociological issues involved in engineering software for creative purposes, we hope to help usher in a new era where computers routinely act as our creative collaborators, as well as independent and creative artists, musicians, writers, designers, engineers and scientists, and contribute in meaningful and interesting ways to human culture.


AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification

Aerial scene classification, which aims to automatically label an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote sensing imagery. In recent years, it has become an active task in the remote sensing area, and numerous algorithms have been proposed for this task, including many machine learning and data-driven approaches. However, the existing data sets for aerial scene classification, such as UC-Merced data set and WHU-RS19, contain relatively small sizes, and the results on them are already saturated. This largely limits the development of scene classification algorithms. This paper describes the Aerial Image data set (AID): a large-scale data set for aerial scene classification. The goal of AID is to advance the state of the arts in scene classification of remote sensing images. For creating AID, we collect and annotate more than 10000 aerial scene images. In addition, a comprehensive review of the existing aerial scene classification techniques as well as recent widely used deep learning methods is given. Finally, we provide a performance analysis of typical aerial scene classification and deep learning approaches on AID, which can be served as the baseline results on this benchmark.


300mm low k wafer dicing saw study

With the farther shrink of the IC dimension, the low k material has been widely used to replace the traditional SiO/sub 2/ ILD in order to reduce the interconnect delay. The introduction of low k material into silicon imposed challenges on dicing saw process, ILD and metal layers peeling and its penetration into the sealing ring of the die during dicing saw are the most common defects. In this paper, the low k material structure and its impact on wafer dicing were elaborated. A practical dicing quality inspection matrix was developed to assess the cutting process variation. A 300mm CMOS 90nm dual damascene low k wafer was chosen as a test vehicle to develop robust low k dicing saw process. The critical factors (dicing blade, index speed, spindle speed, cut in depth, test pattern in the saw street...) affecting cutting quality were studied and optimized. The selected C90 dual damascene low k device passed package reliability test with the optimized low k dicing saw recipe and process. The further improvement and solutions in eliminating the low k dicing saw peeling from both wafer fab and packaging assembly were also explored.


Evidence for perceptual deficits in associative visual (prosop)agnosia: a single-case study

Associative visual agnosia is classically defined as normal visual perception stripped of its meaning [Archiv für Psychiatrie und Nervenkrankheiten 21 (1890) 22/English translation: Cognitive Neuropsychol. 5 (1988) 155]: these patients cannot access to their stored visual memories to categorize the objects nonetheless perceived correctly. However, according to an influential theory of visual agnosia [Farah, Visual Agnosia: Disorders of Object Recognition and What They Tell Us about Normal Vision, MIT Press, Cambridge, MA, 1990], visual associative agnosics necessarily present perceptual deficits that are the cause of their impairment at object recognition Here we report a detailed investigation of a patient with bilateral occipito-temporal lesions strongly impaired at object and face recognition. NS presents normal drawing copy, and normal performance at object and face matching tasks as used in classical neuropsychological tests. However, when tested with several computer tasks using carefully controlled visual stimuli and taking both his accuracy rate and response times into account, NS was found to have abnormal performances at high-level visual processing of objects and faces. Albeit presenting a different pattern of deficits than previously described in integrative agnosic patients such as HJA and LH, his deficits were characterized by an inability to integrate individual parts into a whole percept, as suggested by his failure at processing structurally impossible three-dimensional (3D) objects, an absence of face inversion effects and an advantage at detecting and matching single parts. Taken together, these observations question the idea of separate visual representations for object/face perception and object/face knowledge derived from investigations of visual associative (prosop)agnosia, and they raise some methodological issues in the analysis of single-case studies of (prosop)agnosic patients.


An Infrastructure Approach to Context-Aware Computing

The Context Toolkit (Dey, Salber, and Abowd 2001 [this special issue]) is only one of many possible architectures for supporting context-aware applications. In this essay, we look at the trade-offs involved with a service infrastructure approach to context-aware computing. We describe the advantages that a service infrastructure for contextawareness has over other approaches, outline some of the core technical challenges that must be addressed before such an infrastructure can be built, and point out promising research directions for overcoming these challenges.


Intrusion Detection in Computer Networks based on Machine Learning Algorithms

Network security technology has become crucial in protecting government and industry computing infrastructure. Modern intrusion detection applications face complex requirements; they need to be reliable, extensible, easy to manage, and have low maintenance cost. In recent years, machine learning-based intrusion detection systems have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. This work aims to compare efficiency of machine learning methods in intrusion detection system, including artificial neural networks and support vector machine, with the hope of providing reference for establishing intrusion detection system in future. Compared with other related works in machine learning-based intrusion detectors, we propose to calculate the mean value via sampling different ratios of normal data for each measurement, which lead us to reach a better accuracy rate for observation data in real world. We compare the accuracy, detection rate, false alarm rate for 4 attack types. The extensive experimental results on the KDD-cup intrusion detection benchmark dataset demonstrate that the proposed approach produces higher performance than KDD Winner, especially for U2R and U2L type attacks.


BA-NET: DENSE BUNDLE ADJUSTMENT NETWORKS

This paper introduces a network architecture to solve the structure-from-motion (SfM) problem via feature-metric bundle adjustment (BA), which explicitly enforces multi-view geometry constraints in the form of feature-metric error. The whole pipeline is differentiable, so that the network can learn suitable features that make the BA problem more tractable. Furthermore, this work introduces a novel depth parameterization to recover dense per-pixel depth. The network first generates several basis depth maps according to the input image, and optimizes the final depth as a linear combination of these basis depth maps via feature-metric BA. The basis depth maps generator is also learned via end-to-end training. The whole system nicely combines domain knowledge (i.e. hard-coded multi-view geometry constraints) and deep learning (i.e. feature learning and basis depth maps learning) to address the challenging dense SfM problem. Experiments on large scale real data prove the success of the proposed method.


Elastic textures for additive fabrication

We introduce elastic textures: a set of parametric, tileable, printable, cubic patterns achieving a broad range of isotropic elastic material properties: the softest pattern is over a thousand times softer than the stiffest, and the Poisson's ratios range from below zero to nearly 0.5. Using a combinatorial search over topologies followed by shape optimization, we explore a wide space of truss-like, symmetric 3D patterns to obtain a small family. This pattern family can be printed without internal support structure on a single-material 3D printer and can be used to fabricate objects with prescribed mechanical behavior. The family can be extended easily to create anisotropic patterns with target orthotropic properties. We demonstrate that our elastic textures are able to achieve a user-supplied varying material property distribution. We also present a material optimization algorithm to choose material properties at each point within an object to best fit a target deformation under a prescribed scenario. We show that, by fabricating these spatially varying materials with elastic textures, the desired behavior is achieved.


Real Time Localization and 3D Reconstruction

In this paper we describe a method that estimates the motion of a calibrated camera (settled on an experimental vehicle) and the tridimensional geometry of the environment. The only data used is a video input. In fact, interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key-frames are selected and permit the features 3D reconstruction. The algorithm is particularly appropriate to the reconstruction of long images sequences thanks to the introduction of a fast and local bundle adjustment method that ensures both good accuracy and consistency of the estimated camera poses along the sequence. It also largely reduces computational complexity compared to a global bundle adjustment. Experiments on real data were carried out to evaluate speed and robustness of the method for a sequence of about one kilometer long. Results are also compared to the ground truth measured with a differential GPS.


Visual odometry for ground vehicle applications

We present a system that estimates the motion of a stereo head or a single moving camera based on video input. The system operates in real-time with low delay and the motion estimates are used for navigational purposes. The front end of the system is a feature tracker. Point features are matched between pairs of frames and linked into image trajectories at video rate. Robust estimates of the camera motion are then produced from the feature tracks using a geometric hypothesize-and-test architecture. This generates motion estimates from visual input alone. No prior knowledge of the scene nor the motion is necessary. The visual estimates can also be used in conjunction with information from other sources such as GPS, inertia sensors, wheel encoders, etc. The pose estimation method has been applied successfully to video from aerial, automotive and handheld platforms. We focus on results obtained with a stereo-head mounted on an autonomous ground vehicle. We give examples of camera trajectories estimated in real-time purely from images over previously unseen distances (600 meters) and periods of time .


Hybrid retinal image registration

This work studies retinal image registration in the context of the National Institutes of Health (NIH) Early Treatment Diabetic Retinopathy Study (ETDRS) standard. The ETDRS imaging protocol specifies seven fields of each retina and presents three major challenges for the image registration task. First, small overlaps between adjacent fields lead to inadequate landmark points for feature-based methods. Second, the non-uniform contrast/intensity distributions due to imperfect data acquisition will deteriorate the performance of area-based techniques. Third, high-resolution images contain large homogeneous nonvascular/texureless regions that weaken the capabilities of both feature-based and area-based techniques. In this work, we propose a hybrid retinal image registration approach for ETDRS images that effectively combines both area-based and feature-based methods. Four major steps are involved. First, the vascular tree is extracted by using an efficient local entropy-based thresholding technique. Next, zeroth-order translation is estimated by maximizing mutual information based on the binary image pair (area-based). Then image quality assessment regarding the ETDRS field definition is performed based on the translation model. If the image pair is accepted, higher-order transformations will be involved. Specifically, we use two types of features, landmark points and sampling points, for affine/quadratic model estimation. Three empirical conditions are derived experimentally to control the algorithm progress, so that we can achieve the lowest registration error and the highest success rate. Simulation results on 504 pairs of ETDRS images show the effectiveness and robustness of the proposed algorithm


Vehicle Localization along a Previously Driven Route Using Image Database

In most autonomous driving applications, such as parking and commuting, a vehicle follows a previously taken route, or almost the same route. In this paper, we propose a method to localize a vehicle along a previously driven route using images. The proposed method consists of two stages: offline creation of a database, and online localization. In the offline stage, a database is created from images that are captured when the vehicle drives a route for the first time. The database consists of images, 3D positions of feature points estimated by structure-from-motion, and a topological graph. In the online stage, the method first identifies the database image that is most similar to the current image by topometric localization, which considers topological information on a metric scale. The vehicle poses are then estimated from the 3D-2D correspondences of matching feature points between the current image and the identified database image. In an experiment, we estimated vehicle poses using images captured in an indoor parking lot.


Keyword extraction for social snippets

Today, a huge amount of text is being generated for social purposes on social networking services on the Web. Unlike traditional documents, such text is usually extremely short and tends to be informal. Analysis of such text benefit many applications such as advertising, search, and content filtering. In this work, we study one traditional text mining task on such new form of text, that is extraction of meaningful keywords. We propose several intuitive yet useful features and experiment with various classification models. Evaluation is conducted on Facebook data. Performances of various features and models are reported and compared.


UNIK: unsupervised social network spam detection

Social network spam increases explosively with the rapid development and wide usage of various social networks on the Internet. To timely detect spam in large social network sites, it is desirable to discover unsupervised schemes that can save the training cost of supervised schemes. In this work, we first show several limitations of existing unsupervised detection schemes. The main reason behind the limitations is that existing schemes heavily rely on spamming patterns that are constantly changing to avoid detection. Motivated by our observations, we first propose a sybil defense based spam detection scheme SD2 that remarkably outperforms existing schemes by taking the social network relationship into consideration. In order to make it highly robust in facing an increased level of spam attacks, we further design an unsupervised spam detection scheme, called UNIK. Instead of detecting spammers directly, UNIK works by deliberately removing non-spammers from the network, leveraging both the social graph and the user-link graph. The underpinning of UNIK is that while spammers constantly change their patterns to evade detection, non-spammers do not have to do so and thus have a relatively non-volatile pattern. UNIK has comparable performance to SD2 when it is applied to a large social network site, and outperforms SD2 significantly when the level of spam attacks increases. Based on detection results of UNIK, we further analyze several identified spam campaigns in this social network site. The result shows that different spammer clusters demonstrate distinct characteristics, implying the volatility of spamming patterns and the ability of UNIK to automatically extract spam signatures.


Efficient semantic image segmentation with superpixel pooling

In this work, we evaluate the use of superpixel pooling layers in deep network architectures for semantic segmentation. Superpixel pooling is a flexible and efficient replacement for other pooling strategies that incorporates spatial prior information. We propose a simple and efficient GPU-implementation of the layer and explore several designs for the integration of the layer into existing network architectures. We provide experimental results on the IBSR and Cityscapes dataset, demonstrating that superpixel pooling can be leveraged to consistently increase network accuracy with minimal computational overhead. Source code is available at https://github.com/bermanmaxim/superpixPool.


Collaborative matrix factorization mechanism for group recommendation in big data-based library systems

Purpose –Academic groups are designed specifically for researchers. A group recommendation procedure is essential to support scholars’ research-based social activities. However, group recommendation methods are rarely applied in online libraries and they often suffer from scalability problem in big data context. The purpose of this paper is to facilitate academic group activities in big data-based library systems by recommending satisfying articles for academic groups. Design/methodology/approach – The authors propose a collaborative matrix factorization (CoMF) mechanism and implement paralleled CoMF under Hadoop framework. Its rationale is collaboratively decomposing researcher-article interaction matrix and group-article interaction matrix. Furthermore, three extended models of CoMF are proposed. Findings – Empirical studies on CiteULike data set demonstrate that CoMF and three variants outperform baseline algorithms in terms of accuracy and robustness. The scalability evaluation of paralleled CoMF shows its potential value in scholarly big data environment. Research limitations/implications – The proposed methods fill the gap of group-article recommendation in online libraries domain. The proposed methods have enriched the group recommendation methods by considering the interaction effects between groups and members. The proposed methods are the first attempt to implement group recommendation methods in big data contexts. Practical implications – The proposed methods can improve group activity effectiveness and information shareability in academic groups, which are beneficial to membership retention and enhance the service quality of online library systems. Furthermore, the proposed methods are applicable to big data contexts and make library system services more efficient. Social implications – The proposed methods have potential value to improve scientific collaboration and research innovation. Originality/value – The proposed CoMF method is a novel group recommendation method based on the collaboratively decomposition of researcher-article matrix and group-article matrix. The process indirectly reflects the interaction between groups and members, which accords with actual library environments and provides an interpretable recommendation result.


High-Resolution Frequency-Wavenumber Spectrum Analysis

The output of an array bfoensors is considered to be a homogeneous random field. In this case there is a spectral representation for this field, similar to that for stationary random processes, which consists of a superposition of traveling waves. The frequencywavenumber power spectral density provides the mean-square value for the amplitudes of these waves and is of considerable importance in the analysis of propagating waves by means of an array of sensors. The conventional method of frequency-wavenumber power spectral density estimation uses a fixed wavenumber window and its resolution is determined esserrtially by& beam pattern of the array of sensors. A high-resolution method-of estimation is introduced which employs a wavenumber windoar whose shape changes and is a function of the wavenumber at which an estimate is obtained. It is shown that the wavenumber resolution of this method is considerably better than that of the conventional method. Application of these results is given to seismic data obtained from the large apenure seismic array located in eastern Montana. In addition, the application of the high-resolution method to other areas, such as radar, sonar, and radio astronomy, is indicated.


Handwritten Digit Recognition with a Back-Propagation Network

We present an application of back-propagation networks to handwritten digit recognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task. The input of the network consists of normalized images of isolated digits. The method has 1 % error rate and about a 9% reject rate on zipcode digits provided by the U.S. Postal Service.


An RIG-I-Like RNA Helicase Mediates Antiviral RNAi Downstream of Viral siRNA Biogenesis in Caenorhabditis elegans

Dicer ribonucleases of plants and invertebrate animals including Caenorhabditis elegans recognize and process a viral RNA trigger into virus-derived small interfering RNAs (siRNAs) to guide specific viral immunity by Argonaute-dependent RNA interference (RNAi). C. elegans also encodes three Dicer-related helicase (drh) genes closely related to the RIG-I-like RNA helicase receptors which initiate broad-spectrum innate immunity against RNA viruses in mammals. Here we developed a transgenic C. elegans strain that expressed intense green fluorescence from a chromosomally integrated flock house virus replicon only after knockdown or knockout of a gene required for antiviral RNAi. Use of the reporter nematode strain in a feeding RNAi screen identified drh-1 as an essential component of the antiviral RNAi pathway. However, RNAi induced by either exogenous dsRNA or the viral replicon was enhanced in drh-2 mutant nematodes, whereas exogenous RNAi was essentially unaltered in drh-1 mutant nematodes, indicating that exogenous and antiviral RNAi pathways are genetically distinct. Genetic epistatic analysis shows that drh-1 acts downstream of virus sensing and viral siRNA biogenesis to mediate specific antiviral RNAi. Notably, we found that two members of the substantially expanded subfamily of Argonautes specific to C. elegans control parallel antiviral RNAi pathways. These findings demonstrate both conserved and unique strategies of C. elegans in antiviral defense.


A Case Study on Implementing False Data Injection Attacks Against Nonlinear State Estimation

Smart grid aims to improve control and monitoring routines to ensure reliable and efficient supply of electricity. The rapid advancements in information and communication technologies of Supervisory Control And Data Acquisition (SCADA) networks, however, have resulted in complex cyber physical systems. This added complexity has broadened the attack surface of power-related applications, amplifying their susceptibility to cyber threats. A particular class of system integrity attacks against the smart grid is False Data Injection (FDI). In a successful FDI attack, an adversary compromises the readings of grid sensors in such a way that errors introduced into estimates of state variables remain undetected. This paper presents an end-to-end case study of how to instantiate real FDI attacks to the Alternating Current (AC) --nonlinear-- State Estimation (SE) process. The attack is realized through firmware modifications of the microprocessor-based remote terminal systems, falsifying the data transmitted to the SE routine, and proceeds regardless of perfect or imperfect knowledge of the current system state. The case study concludes with an investigation of an attack on the IEEE 14 bus system using load data from the New York Independent System Operator (NYISO).


A wall climbing robot for tank inspection. An autonomous prototype

This paper describes a solution to a mobile climbing robot on magnetic wheels, designed for inspecting exterior oil tank surfaces made of metal sheets. A mechanical design has been developed which presents a practical solution without an umbilical cord. The inspection system has been developed based on client/server architecture. The robot runs a client application and a remote PC executes the server functions. They will both allow any necessary inspections to be performed simultaneously by more than one robot. A sensorial system and a data fusion strategy to estimate the absolute robot position is proposed to allow the robot to navigate autonomously. The graphical monitoring of the robot position in the remote PC (server application) provides the operator with the possibility of controlling the robot, even in situations in which the operator visibility of an area tank is very low or inexistent. Previous experiments have demonstrated the mechanical system's robustness. These experiments consist of robot trajectory measurements and the comparison to a motion kinematic model.


Discovering Parallel Text from the World Wide Web

Parallel corpus is a rich linguistic resource for various multilingual text management tasks, including crosslingual text retrieval, multilingual computational linguistics and multilingual text mining. Constructing a parallel corpus requires effective alignment of parallel documents. In this paper, we develop a parallel page identification system for identifying and aligning parallel documents from the World Wide Web. The system crawls the Web to fetch potentially parallel multilingual Web documents using a Web spider. To determine the parallelism between potential document pairs, two modules are developed. First, a filename comparison module is used to check filename resemblance. Second, a content analysis module is used to measure the semantic similarity. The experiment conducted to a multilingual Web site shows the effectiveness of the system.


Racial Faces in-the-Wild: Reducing Racial Bias by Deep Unsupervised Domain Adaptation

Despite of the progress achieved by deep learning in face recognition (FR), more and more people find that racial bias explicitly degrades the performance in realistic FR systems. Facing the fact that existing training and testing databases consist of almost Caucasian subjects, there are still no independent testing databases to evaluate racial bias and even no training databases and methods to reduce it. To facilitate the research towards conquering those unfair issues, this paper contributes a new dataset called Racial Faces in-the-Wild (RFW) database with two important uses, 1) racial bias testing: four testing subsets, namely Caucasian, Asian, Indian and African, are constructed, and each contains about 3000 individuals with 6000 image pairs for face verification, 2) racial bias reducing: one labeled training subset with Caucasians and three unlabeled training subsets with Asians, Indians and Africans are offered to encourage FR algorithms to transfer recognition knowledge from Caucasians to other races. For we all know, RFW is the first database for measuring racial bias in FR algorithms. After proving the existence of domain gap among different races and the existence of racial bias in FR algorithms, we further propose a deep information maximization adaptation network (IMAN) to bridge the domain gap, and comprehensive experiments show that the racial bias could be narrowed-down by our algorithm.


PUSH: A Pipelined Reconstruction I/Of or Erasure-Coded Storage Clusters

A key design goal of erasure-coded storage clusters is to minimize reconstruction time, which in turn leads to high reliability by reducing vulnerability window size. PULL-Rep and PULL-Sur are two existing reconstruction schemes based on PULL-type transmission, where a rebuilding node initiates reconstruction by sending a set of read requests to surviving nodes to retrieve surviving blocks. To eliminate the transmission bottleneck of replacement nodes in PULL-Rep and mitigate the extra overhead caused by noncontiguous disk access in PULL-Sur, we incorporate PUSH-type transmissions to node reconstruction, where the reconstruction procedure is divided into multiple tasks accomplished by surviving nodes in a pipelining manner. We also propose two PUSH-based reconstruction schemes (i.e., PUSH-Rep and PUSH-Sur), which can not only exploit the I/O parallelism of PULL-Sur, but also maintain sequential I/O accesses inherited from PULL-Rep. We build four reconstruction-time models to study the reconstruction process and estimate the reconstruction time of the four schemes in large-scale storage clusters. We implement a proof-of-concept prototype where the four reconstruction schemes are deployed and quantitatively evaluated. Experimental results show that the PUSH-based reconstruction schemes outperform the PULL-based counterparts. In a real-world (9,6)RS-coded storage cluster, PUSH-Rep speeds up the reconstruction time by a factor of 5.76 compared with PULL-Rep; PUSH-Sur accelerates the reconstruction by a factor of 1.85 relative to PULL-Sur.


Writing a narrative biomedical review: considerations for authors, peer reviewers, and editors

Review articles comprehensively covering a specific topic are crucial for successful research and academic projects. Most editors consider review articles for special and regular issues of journals. Writing a review requires deep knowledge and understanding of a field. The aim of this review is to analyze the main steps in writing a narrative biomedical review and to consider points that may increase the chances of success. We performed a comprehensive search through MEDLINE, EMBASE, Scopus, and Web of Science using the following keywords: review of the literature, narrative review, title, abstract, authorship, ethics, peer review, research methods, medical writing, scientific writing, and writing standards. Opinions expressed in the review are also based on personal experience as authors, peer reviewers, and editors.


Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction

This paper addresses the tasks of automatic seed selection for bootstrapping relation extraction, and noise reduction for distantly supervised relation extraction. We first point out that these tasks are related. Then, inspired by ranking relation instances and patterns computed by the HITS algorithm, and selecting cluster centroids using the K-means, LSA, or NMF method, we propose methods for selecting the initial seeds from an existing resource, or reducing the level of noise in the distantly labeled data. Experiments show that our proposed methods achieve a better performance than the baseline systems in both tasks.


Biomedical question answering: A survey

OBJECTIVES
In this survey, we reviewed the current state of the art in biomedical QA (Question Answering), within a broader framework of semantic knowledge-based QA approaches, and projected directions for the future research development in this critical area of intersection between Artificial Intelligence, Information Retrieval, and Biomedical Informatics.

MATERIALS AND METHODS
We devised a conceptual framework within which to categorize current QA approaches. In particular, we used "semantic knowledge-based QA" as a category under which to subsume QA techniques and approaches, both corpus-based and knowledge base (KB)-based, that utilize semantic knowledge-informed techniques in the QA process, and we further classified those approaches into three subcategories: (1) semantics-based, (2) inference-based, and (3) logic-based. Based on the framework, we first conducted a survey of open-domain or non-biomedical-domain QA approaches that belong to each of the three subcategories. We then conducted an in-depth review of biomedical QA, by first noting the characteristics of, and resources available for, biomedical QA and then reviewing medical QA approaches and biological QA approaches, in turn. The research articles reviewed in this paper were found and selected through online searches.

RESULTS
Our review suggested the following tasks ahead for the future research development in this area: (1) Construction of domain-specific typology and taxonomy of questions (biological QA), (2) Development of more sophisticated techniques for natural language (NL) question analysis and classification, (3) Development of effective methods for answer generation from potentially conflicting evidences, (4) More extensive and integrated utilization of semantic knowledge throughout the QA process, and (5) Incorporation of logic and reasoning mechanisms for answer inference.

CONCLUSION
Corresponding to the growth of biomedical information, there is a growing need for QA systems that can help users better utilize the ever-accumulating information. Continued research toward development of more sophisticated techniques for processing NL text, for utilizing semantic knowledge, and for incorporating logic and reasoning mechanisms, will lead to more useful QA systems.


Automatic license plate recognition using extracted features

Automatic License Plate Recognition (ALPR) systems are employed for detection and recognition of license plate/number plate of vehicles. The performance of existing systems is well below the desired level. In this perspective, there is a definite need to propose a system to overcome the limitations of currently available systems. A new approach is being introduced in this paper for fast and efficient implementation of ALPR system. In this approach, the vertical edge detection algorithm is applied and removes unwanted edges by image normalization technique. The LP region is extracted by incorporating statistical and morphological image processing techniques. For character recognition, the template matching is employed for optical character recognition (OCR). The algorithm is tested on 500 real time images, which are acquired under different illumination conditions and from different scenes. Overall efficiency of the proposed method is 84.8% and the execution time is less than 0.5sec.