Query: Accelerating aggregation using intra-cycle parallelism

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Multi-Core, Main-Memory Joins: Sort vs. Hash Revisited <=SEP=> In this paper we experimentally study the performance of main-memory, parallel, multi-core join algorithms, focusing on sort-merge and (radix-)hash join. The relative performance of these two join approaches have been a topic of discussion for a long time. With the advent of modern multicore architectures, it has been argued that sort-merge join is now a better choice than radix-hash join. This claim is justified based on the width of SIMD instructions (sort-merge outperforms radix-hash join once SIMD is sufficiently wide), and NUMA awareness (sort-merge is superior to hash join in NUMA architectures). We conduct extensive experiments on the original and optimized versions of these algorithms. The experiments show that, contrary to these claims, radixhash join is still clearly superior, and sort-merge approaches to performance of radix only when very large amounts of data are involved. The paper also provides the fastest implementations of these algorithms, and covers many aspects of modern hardware architectures relevant not only for joins but for any parallel data processing operator.

Implementing database operations using SIMD instructions <=SEP=> Modern CPUs have instructions that allow basic operations to be performed on several data elements in parallel. These instructions are called SIMD instructions, since they apply a single instruction to multiple data elements. SIMD technology was initially built into commodity processors in order to accelerate the performance of multimedia applications. SIMD instructions provide new opportunities for database engine design and implementation. We study various kinds of operations in a database context, and show how the inner loop of the operations can be accelerated using SIMD instructions. The use of SIMD instructions has two immediate performance benefits: It allows a degree of parallelism, so that many operands can be processed at once. It also often leads to the elimination of conditional branch instructions, reducing branch mispredictions.We consider the most important database operations, including sequential scans, aggregation, index operations, and joins. We present techniques for implementing these using SIMD instructions. We show that there are significant benefits in redesigning traditional query processing algorithms so that they can make better use of SIMD technology. Our study shows that using a SIMD parallelism of four, the CPU time for the new algorithms is from 10% to more than four times less than for the traditional algorithms. Superlinear speedups are obtained as a result of the elimination of branch misprediction effects.

Row-wise parallel predicate evaluation <=SEP=> Table scans have become more interesting recently due to greater use of ad-hoc queries and greater availability of multicore, vector-enabled hardware. Table scan performance is limited by value representation, table layout, and processing techniques. In this paper we propose a new layout and processing technique for efficient one-pass predicate evaluation. Starting with a set of rows with a fixed number of bits per column, we append columns to form a set of banks and then pad each bank to a supported machine word length, typically 16, 32, or 64 bits. We then evaluate partial predicates on the columns of each bank, using a novel evaluation strategy that evaluates column level equality, range tests, IN-list predicates, and conjuncts of these predicates, simultaneously on multiple columns within a bank, and on multiple rows within a machine register. This approach outperforms pure column stores, which must evaluate the partial predicates one column at a time. We evaluate and compare the performance and representation overhead of this new approach and several proposed alternatives.

The Vertica Analytic Database: C-Store 7 Years Later <=SEP=> This paper describes the system architecture of the Vertica Analytic Database (Vertica), a commercialization of the design of the C-Store research prototype. Vertica demonstrates a modern commercial RDBMS system that presents a classical relational interface while at the same time achieving the high performance expected from modern “web scale” analytic systems by making appropriate architectural choices. Vertica is also an instructive lesson in how academic systems research can be directly commercialized into a successful product.

MonetDB: Two Decades of Research in Column-oriented Database Architectures <=SEP=> MonetDB is a state-of-the-art open-source column-store database management system targeting applications in need for analytics over large collections of data. MonetDB is actively used nowadays in health care, in telecommunications as well as in scientific databases and in data management research, accumulating on average more than 10,000 downloads on a monthly basis. This paper gives a brief overview of the MonetDB technology as it developed over the past two decades and the main research highlights which drive the current MonetDB design and form the basis for its future evolution.

Calf stretching in correct alignment. An important consideration in plantar fasciopathies. <=SEP=> Stretching of the calf muscles is important in the treatment of plantar fasciopathy. In order to correctly stretch the calf muscles without strain on the plantar fascia the correct alignment of the lower limb should be maintained. A clinical method of achieving this is presented along with a practical guide to assisting the patient to become familiar with correct lower limb alignment.

Towards Multi-Stakeholder Utility Evaluation of Recommender Systems <=SEP=> A core value in recommender systems is personalization, the idea that the recommendations produced are those that match the user’s preferences. However, in many real-world recommendation contexts, the concerns of additional stakeholders may come into play, such as the producers of items or those of the system owner. Some researchers have examined special cases of such concerns, for example, in reciprocal recommendation. However, there has not been a comprehensive treatment of the integration of multiple stakeholders into recommendation calculations. The paper suggests a utility-based framework for representing stakeholder values in recommendation actions and calculating a multidimensional utility. We demonstrate how a standard algorithm performs in a simulation of a multi-stakeholder recommendation task requiring on-line optimization, and show that a simple greedy approach can lead to enhanced overall utility with minimal loss of accuracy for users.

SPARTan: Scalable PARAFAC2 for Large & Sparse Data <=SEP=> In exploratory tensor mining, a common problem is how to analyze a set of variables across a set of subjects whose observations do not align naturally. For example, when modeling medical features across a set of patients, the number and duration of treatments may vary widely in time, meaning there is no meaningful way to align their clinical records across time points for analysis purposes. To handle such data, the state-of-the-art tensor model is the so-called PARAFAC2, which yields interpretable and robust output and can naturally handle sparse data. However, its main limitation up to now has been the lack of efficient algorithms that can handle large-scale datasets.
 In this work, we fill this gap by developing a scalable method to compute the PARAFAC2 decomposition of large and sparse datasets, called SPARTan. Our method exploits special structure within PARAFAC2, leading to a novel algorithmic reformulation that is both faster (in absolute time) and more memory-efficient than prior work. We evaluate SPARTan on both synthetic and real datasets, showing 22X performance gains over the best previous implementation and also handling larger problem instances for which the baseline fails. Furthermore, we are able to apply SPARTan to the mining of temporally-evolving phenotypes on data taken from real and medically complex pediatric patients. The clinical meaningfulness of the phenotypes identified in this process, as well as their temporal evolution over time for several patients, have been endorsed by clinical experts.

mwetoolkit: a Framework for Multiword Expression Identification <=SEP=> This paper presents the Multiword Expression Toolkit (mwetoolkit), an environment for type and language-independent MWE identification from corpora. The mwetoolkit provides a targeted list of MWE candidates, extracted and filtered according to a number of user-defined criteria and a set of standard statistical association measures. For generating corpus counts, the toolkit provides both a corpus indexation facility and a tool for integration with web search engines, while for evaluation, it provides validation and annotation facilities. The mwetoolkit also allows easy integration with a machine learning tool for the creation and application of supervised MWE extraction models if annotated data is available. In our experiments, the mwetoolkit was tested and evaluated in the context of MWE extraction in the biomedical domain. Our preliminary results show that the toolkit performs better than other approaches, especially concerning recall. Moreover, this first version can be extended in several ways in order to improve the quality of the results.

Path planning algorithm development for autonomous vacuum cleaner robots <=SEP=> A vacuum cleaner robot, generally called a robovac, is an autonomous robot that is controlled by intelligent program. Autonomous vacuum cleaning robot will perform task like sweeping and vacuuming in a single pass. The DVR-1 vacuum cleaning robot consists of two DC motor operated wheels that allow 360 degree rotation, a castor wheel, side spinning brushes, a front bumper and a miniature vacuum pump. Sensors in the bumper are used for generating binary information of obstacle detection then they are processed by some controlling algorithms. These algorithms are used for path planning and navigation. The robot's bumper prevents them from bumping into walls and furniture by reversing or changing path accordingly.

Pupillary Responses to Robotic and Human Emotions: The Uncanny Valley and Media Equation Confirmed <=SEP=> Physiological responses during human-robots interaction are useful alternatives to subjective measures of uncanny feelings for nearly humanlike robots (uncanny valley) and comparable emotional responses between humans and robots (media equation). However, no studies have employed the easily accessible measure of pupillometry to confirm the uncanny valley and media equation hypotheses, evidence in favor of the existence of these hypotheses in interaction with emotional robots is scarce, and previous studies have not controlled for low level image statistics across robot appearances. We therefore recorded pupil size of 40 participants that viewed and rated pictures of robotic and human faces that expressed a variety of basic emotions. The robotic faces varied along the dimension of human likeness from cartoonish to humanlike. We strictly controlled for confounding factors by removing backgrounds, hair, and color, and by equalizing low level image statistics. After the presentation phase, participants indicated to what extent the robots appeared uncanny and humanlike, and whether they could imagine social interaction with the robots in real life situations. The results show that robots rated as nearly humanlike scored higher on uncanniness, scored lower on imagined social interaction, evoked weaker pupil dilations, and their emotional expressions were more difficult to recognize. Pupils dilated most strongly to negative expressions and the pattern of pupil responses across emotions was highly similar between robot and human stimuli. These results highlight the usefulness of pupillometry in emotion studies and robot design by confirming the uncanny valley and media equation hypotheses.

A Miniaturized Hexagonal-Triangular Fractal Antenna for Wide-Band Applications [Antenna Applications Corner] <=SEP=> Defense and aerospace communication systems need compact wide-band antennas suitable for multiple-frequency-band operation. In this article, a miniaturized hexagonaltriangular fractal antenna is examined for wide-band applications. The iterations are made from hexagonal rings, where the sides of the rings are connected by triangular elements inside a hexagonal metal patch. Transmissionline-feed technique is used to feed the signal. A triangular slotted symmetrical defective ground structure (DGS) with a rectangular slit at the center is used to obtain a bandwidth ratio of 8.4:1 with an operating bandwidth ranging from 3 to 25.2 GHz. The percentage of impedance bandwidth and gain of the proposed antenna are much superior to the recently reported wide-band antennas, which makes it suitable for numerous wireless applications, such as wireless local area networks, Wi-Fi, ultrawideband (UWB), X band, and Ku band. The performance of the antenna is analyzed by various parametric studies and field and current distribution analysis. The corresponding design details and comparison of simulated and practical results are presented.

A model-based method for the computation of fingerprints' orientation field <=SEP=> As a global feature of fingerprints, the orientation field is very important for automatic fingerprint recognition. Many algorithms have been proposed for orientation field estimation, but their results are unsatisfactory, especially for poor quality fingerprint images. In this paper, a model-based method for the computation of orientation field is proposed. First a combination model is established for the representation of the orientation field by considering its smoothness except for several singular points, in which a polynomial model is used to describe the orientation field globally and a point-charge model is taken to improve the accuracy locally at each singular point. When the coarse field is computed by using the gradient-based algorithm, a further result can be gained by using the model for a weighted approximation. Due to the global approximation, this model-based orientation field estimation algorithm has a robust performance on different fingerprint images. A further experiment shows that the performance of a whole fingerprint recognition system can be improved by applying this algorithm instead of previous orientation estimation methods.

Skin Detection Using Color, Texture and Space Information <=SEP=> Among various skin detection methods, Skin Probability Map (SPM) method is an effective one. Though SPM method possesses high true acceptance rate (TAR), its false acceptance rate (FAR) is unacceptable in some cases. The reason is that SPM method only use pixel-level color information. This paper proposes an improved skin detection method that integrates color, texture and space information. After color filter, a texture filter is constructed based on texture features extracted form Gabor wavelet transform. Texture filter will further filter non-skin pixels, meanwhile, it may also filter some skin pixel. To compensate the loss, after texture filter, a marker driven watershed transform is then used to grow already obtained skin regions. Experimental results show that the proposed method can achieve better performance than that of SPM.

Information interaction: Providing a framework for information architecture <=SEP=> Information interaction is the process that people use in interacting with the content of an information system. Information architecture is a blueprint and navigational aid to the content of information-rich systems. As such information architecture performs an important supporting role in information interactivity. This article elaborates on a model of information interactivity that crosses the “no-man’s land” between user and computer articulating a model that includes user, content and system, illustrating the context for information architecture.

Thematic development for measuring cohesion and coherence between sentences in English paragraph <=SEP=> Writing is a skill of marking coherent words on a paper and composing a text. There are several criteria of good writing, two of them are cohesion and coherence. Research of cohesion and coherence writing has been conducted by using Centering Theory and Entity Transition Value method. However, the result can still be improved. Therefore, in this research, we tried to use Thematic Development approach which focused on the use of Theme and Rheme as a method to analyze coherence level of a paragraph, combined with CogNIAC rules and Centering Theory to analyze its cohesion. Besides improving the result of previous methods, this research aims to help users in evaluating and assessing their writing text. To achieve these objectives, the proposed method is compared to previous works as well as human judge. Based on the experiment, the proposed method yields average result of 91% which is nearly equivalent to human judge which is 92%. Thematic Development also yields better result compared to Centering Theory which is 29% and Entity Transition Value which is 0%, given the same data set of beginner and intermediate simple writing.

A brain-computer interface for high-level remote control of an autonomous, reinforcement-learning-based robotic system for reaching and grasping <=SEP=> We present an Internet-based brain-computer interface (BCI) for controlling an intelligent robotic device with autonomous reinforcement-learning. BCI control was achieved through dry-electrode electroencephalography (EEG) obtained during imaginary movements. Rather than using low-level direct motor control, we employed a high-level control scheme of the robot, acquired via reinforcement learning, to keep the users cognitive load low while allowing control a reaching-grasping task with multiple degrees of freedom. High-level commands were obtained by classification of EEG responses using an artificial neural network approach utilizing time-frequency features and conveyed through an intuitive user interface. The novel ombination of a rapidly operational dry electrode setup, autonomous control and Internet connectivity made it possible to conveniently interface subjects in an EEG laboratory with remote robotic devices in a closed-loop setup with online visual feedback of the robots actions to the subject. The same approach is also suitable to provide home-bound patients with the possibility to control state-of-the-art robotic devices currently confined to a research environment. Thereby, our BCI approach could help severely paralyzed patients by facilitating patient-centered research of new means of communication, mobility and independence.

The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity <=SEP=> We describe a joint probabilistic model for modeling the contents and inter-connectivity of document collections such as sets of web pages or research paper archives. The model is based on a probabilistic factor decomposition and allows identifying principal topics of the collection as well as authoritative documents within those topics. Furthermore, the relationships between topics is mapped out in order to build a predictive model of link content. Among the many applications of this approach are information retrieval and search, topic identification, query disambiguation, focused web crawling, web authoring, and bibliometric analysis.

An End-to-End Neural Network for Polyphonic Piano Music Transcription <=SEP=> We present a supervised neural network model for polyphonic piano music transcription. The architecture of the proposed model is analogous to speech recognition systems and comprises an acoustic model and a music language model. The acoustic model is a neural network used for estimating the probabilities of pitches in a frame of audio. The language model is a recurrent neural network that models the correlations between pitch combinations over time. The proposed model is general and can be used to transcribe polyphonic music without imposing any constraints on the polyphony. The acoustic and language model predictions are combined using a probabilistic graphical model. Inference over the output variables is performed using the beam search algorithm. We perform two sets of experiments. We investigate various neural network architectures for the acoustic models and also investigate the effect of combining acoustic and music language model predictions using the proposed architecture. We compare performance of the neural network-based acoustic models with two popular unsupervised acoustic models. Results show that convolutional neural network acoustic models yield the best performance across all evaluation metrics. We also observe improved performance with the application of the music language models. Finally, we present an efficient variant of beam search that improves performance and reduces run-times by an order of magnitude, making the model suitable for real-time applications.

A survey of code-based change impact analysis techniques <=SEP=> Software change impact analysis (CIA) is a technique for identifying the effects of a change, or estimating what needs to be modified to accomplish a change. Since the 1980s, there have been many investigations on CIA, especially for code-based CIA techniques. However, there have been very few surveys on this topic. This article tries to fill this gap. And 30 papers that provide empirical evaluation on 23 code-based CIA techniques are identified. Then, data was synthesized against four research questions. The study presents a comparative framework including seven properties, which characterize the CIA techniques, and identifies key applications of CIA techniques in software maintenance. In addition, the need for further research is also presented in the following areas: evaluating existing CIA techniques and proposing new CIA techniques under the proposed framework, developing more mature tools to support CIA, comparing current CIA techniques empirically with unified metrics and common benchmarks, and applying the CIA more extensively and effectively in the software maintenance phase. Copyright © 2012 John Wiley & Sons, Ltd.

Color Correction of Underwater Images for Aquatic Robot Inspection <=SEP=> In this paper, we consider the problem of color restoration using statistical priors. This is applied to color recovery for underwater images, using an energy minimization formulation. Underwater images present a challenge when trying to correct the blue-green monochrome look to bring out the color we know marine life has. For aquatic robot tasks, the quality of the images is crucial and needed in real-time. Our method enhances the color of the images by using a Markov Random Field (MRF) to represent the relationship between color depleted and color images. The parameters of the MRF model are learned from the training data and then the most probable color assignment for each pixel in the given color depleted image is inferred by using belief propagation (BP). This allows the system to adapt the color restoration algorithm to the current environmental conditions and also to the task requirements. Experimental results on a variety of underwater scenes demonstrate the feasibility of our method.

Semantics-Based Online Malware Detection: Towards Efficient Real-Time Protection Against Malware <=SEP=> Recently, malware has increasingly become a critical threat to embedded systems, while the conventional software solutions, such as antivirus and patches, have not been so successful in defending the ever-evolving and advanced malicious programs. In this paper, we propose a hardware-enhanced architecture, GuardOL, to perform online malware detection. GuardOL is a combined approach using processor and field-programmable gate array (FPGA). Our approach aims to capture the malicious behavior (i.e., high-level semantics) of malware. To this end, we first propose the frequency-centric model for feature construction using system call patterns of known malware and benign samples. We then develop a machine learning approach (using multilayer perceptron) in FPGA to train classifier using these features. At runtime, the trained classifier is used to classify the unknown samples as malware or benign, with early prediction. The experimental results show that our solution can achieve high classification accuracy, fast detection, low power consumption, and flexibility for easy functionality upgrade to adapt to new malware samples. One of the main advantages of our design is the support of early prediction-detecting 46% of malware within first 30% of their execution, while 97% of the samples at 100% of their execution, with <;3% false positives.

A 14mW 6.25Gb/s Transceiver in 90nm CMOS for Serial Chip-to-Chip Communications <=SEP=> A power-efficient 6.25Gb/s transceiver in 90nm CMOS for chip-to-chip communication is presented, it dissipates 2.2mW/Gb/s operating at a BER of <10-15 over a channel with -15dB attenuation at 3.125GHz. A shared LC-PLL, resonant clock distribution, a low-swing voltage-mode transmitter, a low-power phase rotator, and a software-based CDR and an adaptive equalizer are used to reduce power

Hepta-Band Internal Antenna for Personal Communication Handsets <=SEP=> Modern personal communication handsets are shrinking in size and are required to operate at multiple frequency bands for enhanced functionality and performance. This poses an important challenge for antenna designers to build multiband antennas within the limited allowable space. In this paper, an internal antenna covering seven frequency bands is presented for personal communication handsets. The proposed antenna operates at GSM (880-960 MHz), DCS (1710-1880 MHz), PCS (1880-1990 MHz), UMTS (1900-2170 MHz), WiBro (2300-2390 MHz), Bluetooth (2.4-2.48 GHz), and WLAN (5.0-5.5 GHz) frequency bands. Measured input return loss of the antenna is better than dB at all the frequency bands with reasonable radiation performance. Antenna volume is 30 mm times15 mm times 4.0 mm (1.8 cm) that makes it attractive for modern multiband and multifunctional slim handsets.

An Introduction to Independent Component Analysis: InfoMax and FastICA algorithms <=SEP=> This paper presents an introduction to independent component analysis (ICA). Unlike principal component analysis, which is based on the assumptions of uncorrelatedness and normality, ICA is rooted in the assumption of statistical independence. Foundations and basic knowledge necessary to understand the technique are provided hereafter. Also included is a short tutorial illustrating the implementation of two ICA algorithms (FastICA and InfoMax) with the use of the Mathematica software.

Simulating Urban Growth Using the SLEUTH Model in a Coastal Peri-Urban District in China <=SEP=> China’s southeast coastal areas have witnessed rapid growth in the last two decades, owing mostly to their economic and social attractions. In this paper, we chose Jimei, a coastal peri-urban district of Xiamen city on the southeast coast of China, as a study area to explore the district’s growth dynamics, to predict future sprawl during the next decade and to provide a basis for urban planning. The SLEUTH urban growth model was calibrated against historical data derived from a series of Landsat TM 5 satellite images taken between 1992 and 2007. A Lee-Sallee value of 0.48 was calculated for the district, which is a satisfactory result compared with related studies. Five coefficients of urban growth, diffusion, spread, breed, slope resistance and road gravity had values of 25, 68, 86, 24 and 23, respectively, in 2007. The growth coefficients (i.e., urban character) can capture urban growth characteristics in Jimei district. The urban DNA revealed that, over the study period, urban growth in the district was dominated both by urbanization through establishment of new urban centers, and by expansion outward from existing urban centers. In contrast to interior cities, in which expansions are dramatically shaped by actual road patterns, urban expansion in the district was likely constrained by the nearby coastline. Future urban growth patterns were predicted to 2020 assuming three different development scenarios. The first scenario simulated a continuation of historical urban growth without changing current conditions. The second scenario projected managed growth in which OPEN ACCESS Sustainability 2014, 6 3900 urban growth is limited by a layer with areas excluded from urbanization, which is the future development plan for Jimei district and Xiamen city. The third scenario depicted a growth with maximum protection in which growth was allowed to continue, similar to the second scenario, but with lower diffusion and spread coefficients applied to the growth pattern. The third scenario demonstrated that valuable land could be saved, which is the most desirable outcome for Jimei urban development. The study showed that SLEUTH can be an extremely useful tool for coastal city managers to explore the likely outcomes of their city development plans.

Reuters Tracer: A Large Scale System of Detecting & Verifying Real-Time News Events from Twitter <=SEP=> News professionals are facing the challenge of discovering news from more diverse and unreliable information in the age of social media. More and more news events break on social media first and are picked up by news media subsequently. The recent Brussels attack is such an example. At Reuters, a global news agency, we have observed the necessity of providing a more effective tool that can help our journalists to quickly discover news on social media, verify them and then inform the public.
 In this paper, we describe Reuters Tracer, a system for sifting through all noise to detect news events on Twitter and assessing their veracity. We disclose the architecture of our system and discuss the various design strategies that facilitate the implementation of machine learning models for noise filtering and event detection. These techniques have been implemented at large scale and successfully discovered breaking news faster than traditional journalism

Facial Landmark Tracking and Facial Expression Recognition <=SEP=> In this paper, we address the challenging computer vision problem of obtaining a reliable facial expression analysis from a naturally interacting person. We propose a system that combines a 3D generic face model, 3D head tracking, and 2D tracker to track facial landmarks and recognize expressions. First, we extract facial landmarks from a neutral frontal face, and then we deform a 3D generic face to fit the input face. Next, we use our real-time 3D head tracking module to track a person’s head in 3D and predict facial landmark positions in 2D using the projection from the updated 3D face model. Finally, we use tracked 2D landmarks to update the 3D landmarks. This integrated tracking loop enables efficient tracking of the non-rigid parts of a face in the presence of large 3D head motion. We conducted experiments for facial expression recognition using both framebased and sequence-based approaches. Our method provides a 75.9% recognition rate in 8 subjects with 7 key expressions. Our approach provides a considerable step forward toward new applications including human-computer interactions, behavioral science, robotics, and game applications.

Increasing the breakdown capability of superjunction power MOSFETs at the edge of the active region <=SEP=> When superjunction power MOSFETs operate near the rim of the safe operating area, avalanche breakdown can occur in the transition region between the active cell array and the edge termination. Numerical device simulations confirmed this and revealed local charge imbalances, created by irregularities in the superjunction doping pattern, as major cause. Based on the simulation results, we proposed optimized transitions of the superjunction doping pattern from the active cell array to the edge termination. Numerical device simulations as well as experiments demonstrated the enhanced breakdown capability of these transition regions.

Implementation of virtual fitting room using image processing <=SEP=> There has been a great increase in interests towards online shopping. In case of purchase of products like apparels which always require a sense of knowledge on how cloths would fit upon a person. This is the major reason why less number of apparels are being shopped online. Hence, a virtual dressing room which would make people know how cloths personally fits in would be a great luxury for the online sellers which could give a wide choice for customers. For online marketers, this would be a great tool for enhancing its market.